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The Tangier law enforcement's response to the influx of new populations can best be described as ________.
A. Laissez-faire
B. Perfunctory
C. Authoritarian
D. Capricious
| One can't be too cautious about the people one meets in Tangier. They're all weirdies of one kind or another. Me? Oh, I'm A Stranger Here Myself By MACK REYNOLDS The Place de France is the town's hub. It marks the end of Boulevard Pasteur, the main drag of the westernized part of the city, and the beginning of Rue de la Liberté, which leads down to the Grand Socco and the medina. In a three-minute walk from the Place de France you can go from an ultra-modern, California-like resort to the Baghdad of Harun al-Rashid. It's quite a town, Tangier. King-size sidewalk cafes occupy three of the strategic corners on the Place de France. The Cafe de Paris serves the best draft beer in town, gets all the better custom, and has three shoeshine boys attached to the establishment. You can sit of a sunny morning and read the Paris edition of the New York Herald Tribune while getting your shoes done up like mirrors for thirty Moroccan francs which comes to about five cents at current exchange. You can sit there, after the paper's read, sip your expresso and watch the people go by. Tangier is possibly the most cosmopolitan city in the world. In native costume you'll see Berber and Rif, Arab and Blue Man, and occasionally a Senegalese from further south. In European dress you'll see Japs and Chinese, Hindus and Turks, Levantines and Filipinos, North Americans and South Americans, and, of course, even Europeans—from both sides of the Curtain. In Tangier you'll find some of the world's poorest and some of the richest. The poorest will try to sell you anything from a shoeshine to their not very lily-white bodies, and the richest will avoid your eyes, afraid you might try to sell them something. In spite of recent changes, the town still has its unique qualities. As a result of them the permanent population includes smugglers and black-marketeers, fugitives from justice and international con men, espionage and counter-espionage agents, homosexuals, nymphomaniacs, alcoholics, drug addicts, displaced persons, ex-royalty, and subversives of every flavor. Local law limits the activities of few of these. Like I said, it's quite a town. I looked up from my Herald Tribune and said, "Hello, Paul. Anything new cooking?" He sank into the chair opposite me and looked around for the waiter. The tables were all crowded and since mine was a face he recognized, he assumed he was welcome to intrude. It was more or less standard procedure at the Cafe de Paris. It wasn't a place to go if you wanted to be alone. Paul said, "How are you, Rupert? Haven't seen you for donkey's years." The waiter came along and Paul ordered a glass of beer. Paul was an easy-going, sallow-faced little man. I vaguely remembered somebody saying he was from Liverpool and in exports. "What's in the newspaper?" he said, disinterestedly. "Pogo and Albert are going to fight a duel," I told him, "and Lil Abner is becoming a rock'n'roll singer." He grunted. "Oh," I said, "the intellectual type." I scanned the front page. "The Russkies have put up another manned satellite." "They have, eh? How big?" "Several times bigger than anything we Americans have." The beer came and looked good, so I ordered a glass too. Paul said, "What ever happened to those poxy flying saucers?" "What flying saucers?" A French girl went by with a poodle so finely clipped as to look as though it'd been shaven. The girl was in the latest from Paris. Every pore in place. We both looked after her. "You know, what everybody was seeing a few years ago. It's too bad one of these bloody manned satellites wasn't up then. Maybe they would've seen one." "That's an idea," I said. We didn't say anything else for a while and I began to wonder if I could go back to my paper without rubbing him the wrong way. I didn't know Paul very well, but, for that matter, it's comparatively seldom you ever get to know anybody very well in Tangier. Largely, cards are played close to the chest. My beer came and a plate of tapas for us both. Tapas at the Cafe de Paris are apt to be potato salad, a few anchovies, olives, and possibly some cheese. Free lunch, they used to call it in the States. Just to say something, I said, "Where do you think they came from?" And when he looked blank, I added, "The Flying Saucers." He grinned. "From Mars or Venus, or someplace." "Ummmm," I said. "Too bad none of them ever crashed, or landed on the Yale football field and said Take me to your cheerleader , or something." Paul yawned and said, "That was always the trouble with those crackpot blokes' explanations of them. If they were aliens from space, then why not show themselves?" I ate one of the potato chips. It'd been cooked in rancid olive oil. I said, "Oh, there are various answers to that one. We could probably sit around here and think of two or three that made sense." Paul was mildly interested. "Like what?" "Well, hell, suppose for instance there's this big Galactic League of civilized planets. But it's restricted, see. You're not eligible for membership until you, well, say until you've developed space flight. Then you're invited into the club. Meanwhile, they send secret missions down from time to time to keep an eye on your progress." Paul grinned at me. "I see you read the same poxy stuff I do." A Moorish girl went by dressed in a neatly tailored gray jellaba, European style high-heeled shoes, and a pinkish silk veil so transparent that you could see she wore lipstick. Very provocative, dark eyes can be over a veil. We both looked after her. I said, "Or, here's another one. Suppose you have a very advanced civilization on, say, Mars." "Not Mars. No air, and too bloody dry to support life." "Don't interrupt, please," I said with mock severity. "This is a very old civilization and as the planet began to lose its water and air, it withdrew underground. Uses hydroponics and so forth, husbands its water and air. Isn't that what we'd do, in a few million years, if Earth lost its water and air?" "I suppose so," he said. "Anyway, what about them?" "Well, they observe how man is going through a scientific boom, an industrial boom, a population boom. A boom, period. Any day now he's going to have practical space ships. Meanwhile, he's also got the H-Bomb and the way he beats the drums on both sides of the Curtain, he's not against using it, if he could get away with it." Paul said, "I got it. So they're scared and are keeping an eye on us. That's an old one. I've read that a dozen times, dished up different." I shifted my shoulders. "Well, it's one possibility." "I got a better one. How's this. There's this alien life form that's way ahead of us. Their civilization is so old that they don't have any records of when it began and how it was in the early days. They've gone beyond things like wars and depressions and revolutions, and greed for power or any of these things giving us a bad time here on Earth. They're all like scholars, get it? And some of them are pretty jolly well taken by Earth, especially the way we are right now, with all the problems, get it? Things developing so fast we don't know where we're going or how we're going to get there." I finished my beer and clapped my hands for Mouley. "How do you mean, where we're going ?" "Well, take half the countries in the world today. They're trying to industrialize, modernize, catch up with the advanced countries. Look at Egypt, and Israel, and India and China, and Yugoslavia and Brazil, and all the rest. Trying to drag themselves up to the level of the advanced countries, and all using different methods of doing it. But look at the so-called advanced countries. Up to their bottoms in problems. Juvenile delinquents, climbing crime and suicide rates, the loony-bins full of the balmy, unemployed, threat of war, spending all their money on armaments instead of things like schools. All the bloody mess of it. Why, a man from Mars would be fascinated, like." Mouley came shuffling up in his babouche slippers and we both ordered another schooner of beer. Paul said seriously, "You know, there's only one big snag in this sort of talk. I've sorted the whole thing out before, and you always come up against this brick wall. Where are they, these observers, or scholars, or spies or whatever they are? Sooner or later we'd nab one of them. You know, Scotland Yard, or the F.B.I., or Russia's secret police, or the French Sûreté, or Interpol. This world is so deep in police, counter-espionage outfits and security agents that an alien would slip up in time, no matter how much he'd been trained. Sooner or later, he'd slip up, and they'd nab him." I shook my head. "Not necessarily. The first time I ever considered this possibility, it seemed to me that such an alien would base himself in London or New York. Somewhere where he could use the libraries for research, get the daily newspapers and the magazines. Be right in the center of things. But now I don't think so. I think he'd be right here in Tangier." "Why Tangier?" "It's the one town in the world where anything goes. Nobody gives a damn about you or your affairs. For instance, I've known you a year or more now, and I haven't the slightest idea of how you make your living." "That's right," Paul admitted. "In this town you seldom even ask a man where's he's from. He can be British, a White Russian, a Basque or a Sikh and nobody could care less. Where are you from, Rupert?" "California," I told him. "No, you're not," he grinned. I was taken aback. "What do you mean?" "I felt your mind probe back a few minutes ago when I was talking about Scotland Yard or the F.B.I. possibly flushing an alien. Telepathy is a sense not trained by the humanoids. If they had it, your job—and mine—would be considerably more difficult. Let's face it, in spite of these human bodies we're disguised in, neither of us is humanoid. Where are you really from, Rupert?" "Aldebaran," I said. "How about you?" "Deneb," he told me, shaking. We had a laugh and ordered another beer. "What're you doing here on Earth?" I asked him. "Researching for one of our meat trusts. We're protein eaters. Humanoid flesh is considered quite a delicacy. How about you?" "Scouting the place for thrill tourists. My job is to go around to these backward cultures and help stir up inter-tribal, or international, conflicts—all according to how advanced they are. Then our tourists come in—well shielded, of course—and get their kicks watching it." Paul frowned. "That sort of practice could spoil an awful lot of good meat." THE END Transcriber's Note: This etext was produced from Amazing Stories December 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. Laissez-faire |
Why does Si deliberate on how to spend his night?
A. He finally has the opportunity to let loose, and wants to revel in it.
B. He’s spent his money on “cheap” entertainment in the past, and wants to do better now.
C. He’s not used to this freedom and is unsure what to do.
D. He’s not used to living this way and is uncomfortable.
| SPACEMAN ON A SPREE BY MACK REYNOLDS Illustrated by Nodel [Transcriber's Note: This etext was produced from Worlds of Tomorrow June 1963 Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] What's more important—Man's conquest of space, or one spaceman's life? I They gave him a gold watch. It was meant to be symbolical, of course. In the old tradition. It was in the way of an antique, being one of the timepieces made generations past in the Alpine area of Eur-Asia. Its quaintness lay in the fact that it was wound, not electronically by power-radio, but by the actual physical movements of the bearer, a free swinging rotor keeping the mainspring at a constant tension. They also had a banquet for him, complete with speeches by such bigwigs of the Department of Space Exploration as Academician Lofting Gubelin and Doctor Hans Girard-Perregaux. There was also somebody from the government who spoke, but he was one of those who were pseudo-elected and didn't know much about the field of space travel nor the significance of Seymour Pond's retirement. Si didn't bother to remember his name. He only wondered vaguely why the cloddy had turned up at all. In common with recipients of gold watches of a score of generations before him, Si Pond would have preferred something a bit more tangible in the way of reward, such as a few shares of Variable Basic to add to his portfolio. But that, he supposed, was asking too much. The fact of the matter was, Si knew that his retiring had set them back. They hadn't figured he had enough shares of Basic to see him through decently. Well, possibly he didn't, given their standards. But Space Pilot Seymour Pond didn't have their standards. He'd had plenty of time to think it over. It was better to retire on a limited crediting, on a confoundedly limited crediting, than to take the two or three more trips in hopes of attaining a higher standard. He'd had plenty of time to figure it out, there alone in space on the Moon run, there on the Venus or Mars runs. There on the long, long haul to the Jupiter satellites, fearfully checking the symptoms of space cafard, the madness compounded of claustrophobia, monotony, boredom and free fall. Plenty of time. Time to decide that a one room mini-auto-apartment, complete with an autochair and built-in autobar, and with one wall a teevee screen, was all he needed to find contentment for a mighty long time. Possibly somebody like Doc Girard-Perregaux might be horrified at the idea of living in a mini-auto-apartment ... not realizing that to a pilot it was roomy beyond belief compared to the conning tower of a space craft. No. Even as Si listened to their speeches, accepted the watch and made a halting little talk of his own, he was grinning inwardly. There wasn't anything they could do. He had them now. He had enough Basic to keep him comfortably, by his standards, for the rest of his life. He was never going to subject himself to space cafard again. Just thinking about it, now, set the tic to going at the side of his mouth. They could count down and blast off, for all he gave a damn. The gold watch idea had been that of Lofting Gubelin, which was typical, he being in the way of a living anachronism himself. In fact, Academician Gubelin was possibly the only living man on North America who still wore spectacles. His explanation was that a phobia against having his eyes touched prohibited either surgery to remould his eyeballs and cure his myopia, or contact lenses. That was only an alibi so far as his closest associate, Hans Girard-Perregaux, was concerned. Doctor Girard-Perregaux was convinced Gubelin would have even worn facial hair, had he but a touch more courage. Gubelin longed for yesteryear, a seldom found phenomenon under the Ultrawelfare State. Slumped in an autochair in the escape room of his Floridian home, Lofting Gubelin scowled at his friend. He said, acidly, "Any more bright schemes, Hans? I presume you now acknowledge that appealing to the cloddy's patriotism, sentiment and desire for public acclaim have miserably failed." Girard-Perregaux said easily, "I wouldn't call Seymour Pond a cloddy. In his position, I am afraid I would do the same thing he has." "That's nonsense, Hans. Zoroaster! Either you or I would gladly take Pond's place were we capable of performing the duties for which he has been trained. There aren't two men on North America—there aren't two men in the world!—who better realize the urgency of continuing our delving into space." Gubelin snapped his fingers. "Like that, either of us would give our lives to prevent man from completely abandoning the road to his destiny." His friend said drily, "Either of us could have volunteered for pilot training forty years ago, Lofting. We didn't." "At that time there wasn't such a blistering percentage of funkers throughout this whole blistering Ultrawelfare State! Who could foresee that eventually our whole program would face ending due to lack of courageous young men willing to take chances, willing to face adventure, willing to react to the stimulus of danger in the manner our ancestors did?" Girard-Perregaux grunted his sarcasm and dialed a glass of iced tea and tequila. He said, "Nevertheless, both you and I conform with the present generation in finding it far more pleasant to follow one's way of life in the comfort of one's home than to be confronted with the unpleasantness of facing nature's dangers in more adventurous pastimes." Gubelin, half angry at his friend's argument, leaned forward to snap rebuttal, but the other was wagging a finger at him negatively. "Face reality, Lofting. Don't require or expect from Seymour Pond more than is to be found there. He is an average young man. Born in our Ultrawelfare State, he was guaranteed his fundamental womb-to-tomb security by being issued that minimum number of Basic shares in our society that allows him an income sufficient to secure the food, clothing, shelter, medical care and education to sustain a low level of subsistence. Percentages were against his ever being drafted into industry. Automation being what it is, only a fraction of the population is ever called up. But Pond was. His industrial aptitude dossier revealed him a possible candidate for space pilot, and it was you yourself who talked him into taking the training ... pointing out the more pragmatic advantages such as complete retirement after but six trips, added shares of Basic so that he could enjoy a more comfortable life than most and the fame that would accrue to him as one of the very few who still participate in travel to the planets. Very well. He was sold. Took his training, which, of course, required long years of drudgery to him. Then, performing his duties quite competently, he made his six trips. He is now legally eligible for retirement. He was drafted into the working force reserves, served his time, and is now free from toil for the balance of his life. Why should he listen to our pleas for a few more trips?" "But has he no spirit of adventure? Has he no feeling for...." Girard-Perregaux was wagging his finger again, a gesture that, seemingly mild though it was, had an astonishing ability to break off the conversation of one who debated with the easy-seeming, quiet spoken man. He said, "No, he hasn't. Few there are who have, nowadays. Man has always paid lip service to adventure, hardships and excitement, but in actuality his instincts, like those of any other animal, lead him to the least dangerous path. Today we've reached the point where no one need face danger—ever. There are few who don't take advantage of the fact. Including you and me, Lofting, and including Seymour Pond." His friend and colleague changed subjects abruptly, impatiently. "Let's leave this blistering jabber about Pond's motivation and get to the point. The man is the only trained space pilot in the world. It will take months, possibly more than a year, to bring another novitiate pilot to the point where he can safely be trusted to take our next explorer craft out. Appropriations for our expeditions have been increasingly hard to come by—even though in our minds, Hans, we are near important breakthroughs, breakthroughs which might possibly so spark the race that a new dream to push man out to the stars will take hold of us. If it is admitted that our organization has degenerated to the point that we haven't a single pilot, then it might well be that the Economic Planning Board, and especially those cloddies on Appropriations, will terminate the whole Department of Space Exploration." "So...." Girard-Perregaux said gently. "So some way we've got to bring Seymour Pond out of his retirement!" "Now we are getting to matters." Girard-Perregaux nodded his agreement. Looking over the rim of his glass, his eyes narrowed in thought as his face took on an expression of Machiavellianism. "And do not the ends justify the means?" Gubelin blinked at him. The other chuckled. "The trouble with you, Lofting, is that you have failed to bring history to bear on our problem. Haven't you ever read of the sailor and his way of life?" "Sailor? What in the name of the living Zoroaster has the sailor got to do with it?" "You must realize, my dear Lofting, that our Si Pond is nothing more than a latter-day sailor, with many of the problems and view-points, tendencies and weaknesses of the voyager of the past. Have you never heard of the seaman who dreamed of returning to the village of his birth and buying a chicken farm or some such? All the long months at sea—and sometimes the tramp freighters or whaling craft would be out for years at a stretch before returning to home port—he would talk of his retirement and his dream. And then? Then in port, it would be one short drink with the boys, before taking his accumulated pay and heading home. The one short drink would lead to another. And morning would find him, drunk, rolled, tattooed and possibly sleeping it off in jail. So back to sea he'd have to go." Gubelin grunted bitterly. "Unfortunately, our present-day sailor can't be separated from his money quite so easily. If he could, I'd personally be willing to lure him down some dark alley, knock him over the head and roll him myself. Just to bring him back to his job again." He brought his wallet from his pocket, and flicked it open to his universal credit card. "The ultimate means of exchange," he grunted. "Nobody can spend your money, but you, yourself. Nobody can steal it, nobody can, ah, con you out of it. Just how do you expect to sever our present-day sailor and his accumulated nest egg?" The other chuckled again. "It is simply a matter of finding more modern methods, my dear chap." II Si Pond was a great believer in the institution of the spree. Any excuse would do. Back when he had finished basic education at the age of twenty-five and was registered for the labor draft, there hadn't been a chance in a hundred that he'd have the bad luck to have his name pulled. But when it had been, Si had celebrated. When he had been informed that his physical and mental qualifications were such that he was eligible for the most dangerous occupation in the Ultrawelfare State and had been pressured into taking training for space pilot, he had celebrated once again. Twenty-two others had taken the training with him, and only he and Rod Cameroon had passed the finals. On this occasion, he and Rod had celebrated together. It had been quite a party. Two weeks later, Rod had burned on a faulty take-off on what should have been a routine Moon run. Each time Si returned from one of his own runs, he celebrated. A spree, a bust, a bat, a wing-ding, a night on the town. A commemoration of dangers met and passed. Now it was all over. At the age of thirty he was retired. Law prevented him from ever being called up for contributing to the country's labor needs again. And he most certainly wasn't going to volunteer. He had taken his schooling much as had his contemporaries. There wasn't any particular reason for trying to excell. You didn't want to get the reputation for being a wise guy, or a cloddy either. Just one of the fellas. You could do the same in life whether you really studied or not. You had your Inalienable Basic stock, didn't you? What else did you need? It had come as a surprise when he'd been drafted for the labor force. In the early days of the Ultrawelfare State, they had made a mistake in adapting to the automation of the second industrial revolution. They had attempted to give everyone work by reducing the number of working hours in the day, and the number of working days in the week. It finally became ludicrous when employees of industry were working but two days a week, two hours a day. In fact, it got chaotic. It became obvious that it was more practical to have one worker putting in thirty-five hours a week and getting to know his job well, than it was to have a score of employees, each working a few hours a week and none of them ever really becoming efficient. The only fair thing was to let the technologically unemployed remain unemployed, with their Inalienable Basic stock as the equivalent of unemployment insurance, while the few workers still needed put in a reasonable number of hours a day, a reasonable number of weeks a year and a reasonable number of years in a life time. When new employees were needed, a draft lottery was held. All persons registered in the labor force participated. If you were drawn, you must need serve. The dissatisfaction those chosen might feel at their poor luck was offset by the fact that they were granted additional Variable Basic shares, according to the tasks they fulfilled. Such shares could be added to their portfolios, the dividends becoming part of their current credit balance, or could be sold for a lump sum on the market. Yes, but now it was all over. He had his own little place, his own vacuum-tube vehicle and twice the amount of shares of Basic that most of his fellow citizens could boast. Si Pond had it made. A spree was obviously called for. He was going to do this one right. This was the big one. He'd accumulated a lot of dollars these past few months and he intended to blow them, or at least a sizeable number of them. His credit card was burning a hole in his pocket, as the expression went. However, he wasn't going to rush into things. This had to be done correctly. Too many a spree was played by ear. You started off with a few drinks, fell in with some second rate mopsy and usually wound up in a third rate groggery where you spent just as much as though you'd been in the classiest joint in town. Came morning and you had nothing to show for all the dollars that had been spent but a rum-head. Thus, Si was vaguely aware, it had always been down through the centuries since the Phoenecian sailor, back from his year-long trip to the tin mines of Cornwall, blew his hard earned share of the voyage's profits in a matter of days in the wine shops of Tyre. Nobody gets quite so little for his money as that loneliest of all workers, he who must leave his home for distant lands, returning only periodically and usually with the salary of lengthy, weary periods of time to be spent hurriedly in an attempt to achieve the pleasure and happiness so long denied him. Si was going to do it differently this time. Nothing but the best. Wine, women, song, food, entertainment. The works. But nothing but the best. To start off, he dressed with great care in the honorable retirement-rank suit he had so recently purchased. His space pin he attached carefully to the lapel. That was a good beginning, he decided. A bit of prestige didn't hurt you when you went out on the town. In the Ultrawelfare State hardly one person in a hundred actually ever performed anything of value to society. The efforts of most weren't needed. Those few who did contribute were awarded honors, decorations, titles. Attired satisfactorily, Si double-checked to see that his credit card was in his pocket. As an after-thought, he went over to the auto-apartment's teevee-phone, flicked it on, held the card to the screen and said, "Balance check, please." In a moment, the teevee-phone's robot voice reported, "Ten shares of Inalienable Basic. Twelve shares of Variable Basic, current value, four thousand, two hundred and thirty-three dollars and sixty-two cents apiece. Current cash credit, one thousand and eighty-four dollars." The screen went dead. One thousand and eighty-four dollars. That was plenty. He could safely spend as much as half of it, if the spree got as lively as he hoped it would. His monthly dividends were due in another week or so, and he wouldn't have to worry about current expenses. Yes, indeedy, Si Pond was as solvent as he had ever been in his thirty years. He opened the small, closet-like door which housed his vacuum-tube two-seater, and wedged himself into the small vehicle. He brought down the canopy, dropped the pressurizer and considered the dial. Only one place really made sense. The big city. He considered for a moment, decided against the boroughs of Baltimore and Boston, and selected Manhattan instead. He had the resources. He might as well do it up brown. He dialed Manhattan and felt the sinking sensation that presaged his car's dropping to tube level. While it was being taken up by the robot controls, being shuttled here and there preparatory to the shot to his destination, he dialed the vehicle's teevee-phone for information on the hotels of the island of the Hudson. He selected a swank hostelry he'd read about and seen on the teevee casts of society and celebrity gossip reporters, and dialed it on the car's destination dial. "Nothing too good for ex-Space Pilot Si Pond," he said aloud. The car hesitated for a moment, that brief hesitation before the shot, and Si took the involuntary breath from which only heroes could refrain. He sank back slowly into the seat. Moments passed, and the direction of the pressure was reversed. Manhattan. The shuttling began again, and one or two more traversing sub-shots. Finally, the dash threw a green light and Si opened the canopy and stepped into his hotel room. A voice said gently, "If the quarters are satisfactory, please present your credit card within ten minutes." Si took his time. Not that he really needed it. It was by far the most swank suite he had ever seen. One wall was a window of whatever size the guest might desire and Si touched the control that dilated it to the full. His view opened in such wise that he could see both the Empire State Building Museum and the Hudson. Beyond the river stretched the all but endless city which was Greater Metropolis. He didn't take the time to flick on the menu, next to the auto-dining table, nor to check the endless potables on the autobar list. All that, he well knew, would be superlative. Besides, he didn't plan to dine or do much drinking in his suite. He made a mock leer. Not unless he managed to acquire some feminine companionship, that was. He looked briefly into the swimming pool and bath, then flopped himself happily onto the bed. It wasn't up to the degree of softness he presently desired, and he dialed the thing to the ultimate in that direction so that with a laugh he sank almost out of sight into the mattress. He came back to his feet, gave his suit a quick patting so that it fell into press and, taking his credit card from his pocket, put it against the teevee-phone screen and pressed the hotel button so that registration could be completed. For a moment he stood in the center of the floor, in thought. Take it easy, Si Pond, take it all easy, this time. No throwing his dollars around in second-class groggeries, no eating in automated luncheterias. This time, be it the only time in his life, he was going to frolic in the grand manner. No cloddy was Si Pond. He decided a drink was in order to help him plan his strategy. A drink at the hotel's famous Kudos Room where celebrities were reputed to be a dime a dozen. He left the suite and stepped into one of the elevators. He said, "Kudos Room." The auto-elevator murmured politely, "Yes, sir, the Kudos Room." At the door to the famous rendezvous of the swankiest set, Si paused a moment and looked about. He'd never been in a place like this, either. However, he stifled his first instinct to wonder about what this was going to do to his current credit balance with an inner grin and made his way to the bar. There was actually a bartender. Si Pond suppressed his astonishment and said, offhand, attempting an air of easy sophistication, "Slivovitz Sour." "Yes, sir." The drinks in the Kudos Room might be concocted by hand, but Si noticed they had the routine teevee screens built into the bar for payment. He put his credit card on the screen immediately before him when the drink came, and had to quell his desire to dial for a balance check, so as to be able to figure out what the Sour had cost him. Well, this was something like it. This was the sort of thing he'd dreamed about, out there in the great alone, seated in the confining conning tower of his space craft. He sipped at the drink, finding it up to his highest expectations, and then swiveled slightly on his stool to take a look at the others present. To his disappointment, there were no recognizable celebrities. None that he placed, at least—top teevee stars, top politicians of the Ultrawelfare State or Sports personalities. He turned back to his drink and noticed, for the first time, the girl who occupied the stool two down from him. Si Pond blinked. He blinked and then swallowed. " Zo-ro-as-ter ," he breathed. She was done in the latest style from Shanghai, even to the point of having cosmetically duplicated the Mongolian fold at the corners of her eyes. Every pore, but every pore, was in place. She sat with the easy grace of the Orient, so seldom found in the West. His stare couldn't be ignored. She looked at him coldly, turned to the bartender and murmured, "A Far Out Cooler, please, Fredric." Then deliberately added, "I thought the Kudos Room was supposed to be exclusive." There was nothing the bartender could say to that, and he went about building the drink. Si cleared his throat. "Hey," he said, "how about letting this one be on me?" Her eyebrows, which had been plucked and penciled to carry out her Oriental motif, rose. "Really!" she said, drawing it out. The bartender said hurriedly, "I beg your pardon, sir...." The girl, her voice suddenly subtly changed, said, "Why, isn't that a space pin?" Si, disconcerted by the sudden reversal, said, "Yeah ... sure." "Good Heavens, you're a spaceman?" "Sure." He pointed at the lapel pin. "You can't wear one unless you been on at least a Moon run." She was obviously both taken back and impressed. "Why," she said, "you're Seymour Pond, the pilot. I tuned in on the banquet they gave you." Si, carrying his glass, moved over to the stool next to her. "Call me Si," he said. "Everybody calls me Si." She said, "I'm Natalie. Natalie Paskov. Just Natalie. Imagine meeting Seymour Pond. Just sitting down next to him at a bar. Just like that." "Si," Si said, gratified. Holy Zoroaster, he'd never seen anything like this rarified pulchritude. Maybe on teevee, of course, one of the current sex symbols, but never in person. "Call me Si," he said again. "I been called Si so long, I don't even know who somebody's talking to if they say Seymour." "I cried when they gave you that antique watch," she said, her tone such that it was obvious she hadn't quite adjusted as yet to having met him. Si Pond was surprised. "Cried?" he said. "Well, why? I was kind of bored with the whole thing. But old Doc Gubelin, I used to work under him in the Space Exploration department, he was hot for it." " Academician Gubelin?" she said. "You just call him Doc ?" Si was expansive. "Why, sure. In the Space Department we don't have much time for formality. Everybody's just Si, and Doc, and Jim. Like that. But how come you cried?" She looked down into the drink the bartender had placed before her, as though avoiding his face. "I ... I suppose it was that speech Doctor Girard-Perregaux made. There you stood, so fine and straight in your space-pilot uniform, the veteran of six exploration runs to the planets...." "Well," Si said modestly, "two of my runs were only to the Moon." "... and he said all those things about man's conquest of space. And the dream of the stars which man has held so long. And then the fact that you were the last of the space pilots. The last man in the whole world trained to pilot a space craft. And here you were, retiring." Si grunted. "Yeah. That's all part of the Doc's scheme to get me to take on another three runs. They're afraid the whole department'll be dropped by the Appropriations Committee on this here Economic Planning Board. Even if they can find some other patsy to train for the job, it'd take maybe a year before you could even send him on a Moon hop. So old man Gubelin, and Girard-Perregaux too, they're both trying to pressure me into more trips. Otherwise they got a Space Exploration Department, with all the expense and all, but nobody to pilot their ships. It's kind of funny, in a way. You know what one of those spaceships costs?" "Funny?" she said. "Why, I don't think it's funny at all." Si said, "Look, how about another drink?" Natalie Paskov said, "Oh, I'd love to have a drink with you, Mr...." "Si," Si said. He motioned to the bartender with a circular twist of the hand indicating their need for two more of the same. "How come you know so much about it? You don't meet many people who are interested in space any more. In fact, most people are almost contemptuous, like. Think it's kind of a big boondoggle deal to help use up a lot of materials and all and keep the economy going." Natalie said earnestly, "Why, I've been a space fan all my life. I've read all about it. Have always known the names of all the space pilots and everything about them, ever since I was a child. I suppose you'd say I have the dream that Doctor Girard-Perregaux spoke about." Si chuckled. "A real buff, eh? You know, it's kind of funny. I was never much interested in it. And I got a darn sight less interested after my first run and I found out what space cafard was." She frowned. "I don't believe I know much about that." Sitting in the Kudos Room with the most beautiful girl to whom he had ever talked, Si could be nonchalant about the subject. "Old Gubelin keeps that angle mostly hushed up and out of the magazine and newspaper articles. Says there's enough adverse publicity about space exploration already. But at this stage of the game when the whole ship's crammed tight with this automatic scientific apparatus and all, there's precious little room in the conning tower and you're the only man aboard. The Doc says later on when ships are bigger and there's a whole flock of people aboard, there won't be any such thing as space cafard, but...." Of a sudden the right side of Si Pond's mouth began to tic and he hurriedly took up his drink and knocked it back. | B. He’s spent his money on “cheap” entertainment in the past, and wants to do better now. |
Why did Davies attach himself to Infield outside of Infield & Morgan?
A. He was afraid of heights and falling over, so he had affixed himself to Infield in order to calm his fear.
B. He was attempting to catch Infield after he ran away from the fraternal club for the Cured.
C. He was afraid of the rain and lightning, and the cables helped him to feel more secure.
D. He had been aiming for George Price, whom he was trying to kill as punishment for knocking him to the ground earlier.
| Name Your Symptom By JIM HARMON Illustrated by WEISS [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Anybody who shunned a Cure needed his head examined—assuming he had one left! Henry Infield placed the insulated circlet on his head gently. The gleaming rod extended above his head about a foot, the wires from it leading down into his collar, along his spine and finally out his pants leg to a short metallic strap that dragged on the floor. Clyde Morgan regarded his partner. "Suppose—just suppose—you were serious about this, why not just the shoes?" Infield turned his soft blue eyes to the black and tan oxfords with the very thick rubber soles. "They might get soaked through." Morgan took his foot off the chair behind the desk and sat down. "Suppose they were soaked through and you were standing on a metal plate—steps or a manhole cover—what good would your lightning rod do you then?" Infield shrugged slightly. "I suppose a man must take some chances." Morgan said, "You can't do it, Henry. You're crossing the line. The people we treat are on one side of the line and we're on the other. If you cross that line, you won't be able to treat people again." The small man looked out the large window, blinking myopically at the brassy sunlight. "That's just it, Clyde. There is a line between us, a wall. How can we really understand the people who come to us, if we hide on our side of the wall?" Morgan shook his thick head, ruffling his thinning red hair. "I dunno, Henry, but staying on our side is a pretty good way to keep sane and that's quite an accomplishment these days." Infield whirled and stalked to the desk. "That's the answer! The whole world is going mad and we are just sitting back watching it hike along. Do you know that what we are doing is really the most primitive medicine in the world? We are treating the symptoms and not the disease. One cannibal walking another with sleeping sickness doesn't cure anything. Eventually the savage dies—just as all those sick savages out in the street will die unless we can cure the disease, not only the indications." Morgan shifted his ponderous weight uneasily. "Now, Henry, it's no good to talk like that. We psychiatrists can't turn back the clock. There just aren't enough of us or enough time to give that old-fashioned therapy to all the sick people." Infield leaned on the desk and glared. "I called myself a psychiatrist once. But now I know we're semi-mechanics, semi-engineers, semi-inventors, semi lots of other things, but certainly not even semi-psychiatrists. A psychiatrist wouldn't give a foetic gyro to a man with claustrophobia." His mind went back to the first gyro ball he had ever issued; the remembrance of his pride in the thing sickened him. Floating before him in memory was the vertical hoop and the horizontal hoop, both of shining steel-impervium alloy. Transfixed in the twin circles was the face of the patient, slack with smiles and sweat. But his memory was exaggerating the human element. The gyro actually passed over a man's shoulder, through his legs, under his arms. Any time he felt the walls creeping in to crush him, he could withdraw his head and limbs into the circle and feel safe. Steel-impervium alloy could resist even a nuclear explosion. The foetic gyro ball was worn day and night, for life. The sickness overcame him. He sat down on Morgan's desk. "That's just one thing, the gyro ball. There are so many others, so many." Morgan smiled. "You know, Henry, not all of our Cures are so—so—not all are like that. Those Cures for mother complexes aren't even obvious. If anybody does see that button in a patient's ear, it looks like a hearing aid. Yet for a nominal sum, the patient is equipped to hear the soothing recorded voice of his mother saying, 'It's all right, everything's all right, Mommy loves you, it's all right....'" "But is everything all right?" Infield asked intensely. "Suppose the patient is driving over one hundred on an icy road. He thinks about slowing down, but there's the voice in his ear. Or suppose he's walking down a railroad track and hears a train whistle—if he can hear anything over that verbal pablum gushing in his ear." Morgan's face stiffened. "You know as well as I do that those voices are nearly subsonic. They don't cut a sense efficiency more than 23 per cent." "At first, Clyde—only at first. But what about the severe case where we have to burn a three-dimensional smiling mother-image on the eyes of the patient with radiation? With that image over everything he sees and with that insidious voice drumming in his head night and day, do you mean to say that man's senses will only be impaired 23 per cent? Why, he'll turn violently schizophrenic sooner or later—and you know it. The only cure we have for that is still a strait jacket, a padded cell or one of those inhuman lobotomies." Morgan shrugged helplessly. "You're an idealist." "You're damned right!" Infield slammed the door behind him. The cool air of the street was a relief. Infield stepped into the main stream of human traffic and tried to adjust to the second change in the air. People didn't bathe very often these days. He walked along, buffeted by the crowd, carried along in this direction, shoved back in that direction. Most people in the crowd seemed to be Normals, but you couldn't tell. Many "Cures" were not readily apparent. A young man with black glasses and a radar headset (a photophobe) was unable to keep from being pushed against Infield. He sounded out the lightning rod, his face changing when he realized it must be some kind of Cure. "Pardon me," he said warmly. "Quite all right." It was the first time in years that anyone had apologized to Infield for anything. He had been one of those condemned Normals, more to be scorned than pitied. Perhaps he could really get to understand these people, now that he had taken down the wall. Suddenly something else was pushing against Infield, forcing the air from his lungs. He stared down at the magnetic suction dart clinging leechlike to his chest. Model Acrophobe 101-X, he catalogued immediately. Description: safety belt. But his emotions didn't behave so well. He was thoroughly terrified, heart racing, sweat glands pumping. The impervium cable undulated vulgarly. Some primitive fear of snake symbols? his mind wondered while panic crushed him. "Uncouple that cable!" the shout rang out. It was not his own. A clean-cut young man with mouse-colored hair was moving toward the stubble-chinned, heavy-shouldered man quivering in the center of a web of impervium cables stuck secure to the walls and windows of buildings facing the street, the sidewalk, a mailbox, the lamp post and Infield. Mouse-hair yelled hoarsely, "Uncouple it, Davies! Can't you see the guy's got a lightning rod? You're grounding him! "I can't," Davies groaned. "I'm scared!" Halfway down the twenty feet of cable, Mouse-hair grabbed on. "I'm holding it. Release it, you hear?" Davies fumbled for the broad belt around his thickening middle. He jabbed the button that sent a negative current through the cable. The magnetic suction dart dropped away from Infield like a thing that had been alive and now was killed. He felt an overwhelming sense of relief. After breathing deeply for a few moments, he looked up to see Davies releasing and drawing all his darts into his belt, making it resemble a Hydra-sized spiked dog collar. Mouse-hair stood by tensely as the crowd disassembled. "This isn't the first time you've pulled something like this, Davies," he said. "You weren't too scared to release that cable. You just don't care about other people's feelings. This is official ." Mouse-hair drove a fast, hard right into the soft blue flesh of Davies' chin. The big man fell silently. The other turned to Infield. "He was unconscious on his feet," he explained. "He never knew he fell." "What did you mean by that punch being official?" Infield asked while trying to arrange his feelings into the comfortable, familiar patterns. The young man's eyes almost seemed to narrow, although his face didn't move; he merely radiated narrowed eyes. "How long have you been Cured?" "Not—not long," Infield evaded. The other glanced around the street. He moistened his lips and spoke slowly. "Do you think you might be interested in joining a fraternal organization of the Cured?" Infield's pulse raced, trying to get ahead of his thoughts, and losing out. A chance to study a pseudo-culture of the "Cured" developed in isolation! "Yes, I think I might. I owe you a drink for helping me out. How about it?" The man's face paled so fast, Infield thought for an instant that he was going to faint. "All right. I'll risk it." He touched the side of his face away from the psychiatrist. Infield shifted around, trying to see that side of his benefactor, but couldn't manage it in good grace. He wondered if the fellow was sporting a Mom-voice hearing aid and was afraid of raising her ire. He cleared his throat, noticing the affectation of it. "My name's Infield." "Price," the other answered absently. "George Price. I suppose they have liquor at the Club. We can have a drink there, I guess." Price set the direction and Infield fell in at his side. "Look, if you don't drink, I'll buy you a cup of coffee. It was just a suggestion." Under the mousy hair, Price's strong features were beginning to gleam moistly. "You are lucky in one way, Mr. Infield. People take one look at your Cure and don't ask you to go walking in the rain. But even after seeing this , some people still ask me to have a drink." This was revealed, as he turned his head, to be a small metal cube above his left ear. Infield supposed it was a Cure, although he had never issued one like it. He didn't know if it would be good form to inquire what kind it was. "It's a cure for alcoholism," Price told him. "It runs a constant blood check to see that the alcohol level doesn't go over the sobriety limit." "What happens if you take one too many?" Price looked off as if at something not particularly interesting, but more interesting than what he was saying. "It drives a needle into my temple and kills me." The psychiatrist felt cold fury rising in him. The Cures were supposed to save lives, not endanger them. "What kind of irresponsible idiot could have issued such a device?" he demanded angrily. "I did," Price said. "I used to be a psychiatrist. I was always good in shop. This is a pretty effective mechanism, if I say so myself. It can't be removed without causing my death and it's indestructible. Impervium-shielded, you see." Price probably would never get crazed enough for liquor to kill himself, Infield knew. The threat of death would keep him constantly shocked sane. Men hide in the comforts of insanity, but when faced with death, they are often forced back to reality. A man can't move his legs; in a fire, though, he may run. His legs were definitely paralyzed before and may be again, but for one moment he would forget the moral defeat of his life and his withdrawal from life and live an enforced sanity. But sometimes the withdrawal was—or could become—too complete. "We're here." Infield looked up self-consciously and noticed that they had crossed two streets from his building and were standing in front of what appeared to be a small, dingy cafe. He followed Price through the screeching screen door. They seated themselves at a small table with a red-checked cloth. Infield wondered why cheap bars and restaurants always used red-checked cloths. Then he looked closer and discovered the reason. They did a remarkably good job of camouflaging the spots of grease and alcohol. A fat man who smelled of the grease and alcohol of the tablecloths shuffled up to them with a towel on his arm, staring ahead of him at some point in time rather than space. Price lit a cigarette with unsteady hands. "Reggie is studying biblical text. Cute gadget. His contact lenses are made up of a lot of layers of polarized glass. Every time he blinks, the amount of polarization changes and a new page appears. His father once told him that if he didn't study his Bible and pray for him, his old dad would die." The psychiatrist knew the threat on the father's part couldn't create such a fixation by itself. His eyebrows faintly inquired. Price nodded jerkily. "Twenty years ago, at least." "What'll you have, Georgie?" Reggie asked. The young man snubbed out his cigarette viciously. "Bourbon. Straight." Reggie smiled—a toothy, vacant, comedy-relief smile. "Fine. The Good Book says a little wine is good for a man, or something like that. I don't remember exactly." Of course he didn't, Infield knew. Why should he? It was useless to learn his Bible lessons to save his father, because it was obvious his father was dead. He would never succeed because there was no reason to succeed. But he had to try, didn't he, for his father's sake? He didn't hate his father for making him study. He didn't want him to die. He had to prove that. Infield sighed. At least this device kept the man on his feet, doing some kind of useful work instead of rotting in a padded cell with a probably imaginary Bible. A man could cut his wrists with the edge of a sheet of paper if he tried long enough, so of course the Bible would be imaginary. "But, Georgie," the waiter complained, "you know you won't drink it. You ask me to bring you drinks and then you just look at them. Boy, do you look funny when you're looking at drinks. Honest, Georgie, I want to laugh when I think of the way you look at a glass with a drink in it." He did laugh. Price fumbled with the cigarette stub in the black iron ashtray, examining it with the skill of scientific observation. "Mr. Infield is buying me the drink and that makes it different." Reggie went away. Price kept dissecting the tobacco and paper. Infield cleared his throat and again reminded himself against such obvious affectations. "You were telling me about some organization of the Cured," he said as a reminder. Price looked up, no longer interested in the relic of a cigarette. He was suddenly intensely interested and intensely observant of the rest of the cafe. "Was I? I was? Well, suppose you tell me something. What do you really think of the Incompletes?" The psychiatrist felt his face frown. "Who?" "I forgot. You haven't been one of us long. The Incompletes is a truer name for the so-called Normals. Have you ever thought of just how dangerous these people are, Mr. Infield?" "Frankly, no," Infield said, realizing it was not the right thing to say but tiring of constant pretense. "You don't understand. Everyone has some little phobia or fixation. Maybe everyone didn't have one once, but after being told they did have them for generations, everyone who didn't have one developed a defense mechanism and an aberration so they would be normal. If that phobia isn't brought to the surface and Cured, it may arise any time and endanger other people. The only safe, good sound citizens are Cured. Those lacking Cures—the Incompletes— must be dealt with ." Infield's throat went dry. "And you're the one to deal with them?" "It's my Destiny." Price quickly added, "And yours, too, of course." Infield nodded. Price was a demagogue, young, handsome, dynamic, likable, impassioned with his cause, and convinced that it was his divine destiny. He was a psychopathic egotist and a dangerous man. Doubly dangerous to Infield because, even though he was one of the few people who still read books from the old days of therapy to recognize Price for what he was, he nevertheless still liked the young man for the intelligence behind the egotism and the courage behind the fanaticism. "How are we going to deal with the Incompletes?" Infield asked. Price started to glance around the cafe, then half-shrugged, almost visibly thinking that he shouldn't run that routine into the ground. "We'll Cure them whether they want to be Cured or not—for their own good." Infield felt cold inside. After a time, he found that the roaring was not just in his head. It was thundering outside. He was getting sick. Price was the type of man who could spread his ideas throughout the ranks of the Cured—if indeed the plot was not already universal, imposed upon many ill minds. He could picture an entirely Cured world and he didn't like the view. Every Cure cut down on the mental and physical abilities of the patient as it was, whether Morgan and the others admitted it or not. But if everyone had a crutch to lean on for one phobia, he would develop secondary symptoms. People would start needing two Cures—perhaps a foetic gyro and a safety belt—then another and another. There would always be a crutch to lean on for one thing and then room enough to develop something else—until everyone would be loaded down with too many Cures to operate. A Cure was a last resort, dope for a malignancy case, euthanasia for the hopeless. Enforced Cures would be a curse for the individual and the race. But Infield let himself relax. How could anyone force a mechanical relief for neurotic or psychopathic symptoms on someone who didn't want or need it? "Perhaps you don't see how it could be done," Price said. "I'll explain." Reggie's heavy hand sat a straight bourbon down before Price and another before Infield. Price stared at the drink almost without comprehension of how it came to be. He started to sweat. "George, drink it." The voice belonged to a young woman, a blonde girl with pink skin and suave, draped clothes. In this den of the Cured, Infield thought half-humorously, it was surprising to see a Normal—an "Incomplete." But then he noticed something about the baby she carried. The Cure had been very simple. It wasn't even a mechanized half-human robot, just a rag doll. She sat down at the table. "George," she said, "drink it. One drink won't raise your alcohol index to the danger point. You've got to get over this fear of even the sight or smell of liquor." The girl turned to Infield. "You're one of us, but you're new, so you don't know about George. Maybe you can help if you do. It's all silly. He's not an alcoholic. He didn't need to put that Cure on his head. It's just an excuse for not drinking. All of this is just because a while back something happened to the baby here—" she adjusted the doll's blanket—"when he was drinking. Just drinking, not drunk. "I don't remember what happened to the baby—it wasn't important. But George has been brooding about it ever since. I guess he thinks something else bad will happen because of liquor. That's silly. Why don't you tell him it's silly?" "Maybe it is," Infield said softly. "You could take the shock if he downed that drink and the shock might do you good." Price laughed shortly. "I feel like doing something very melodramatic, like throwing my drink—and yours—across the room, but I haven't got the guts to touch those glasses. Do it for me, will you? Cauterizing the bite might do me good if I'd been bitten by a rabid dog, but I don't have the nerve to do it." Before Infield could move, Reggie came and set both drinks on a little circular tray. He moved away. "I knew it. That's all he did, just look at the drink. Makes me laugh." Price wiped the sweat off his palms. Infield sat and thought. Mrs. Price cooed to the rag doll, unmindful of either of them now. "You were explaining," the psychiatrist said. "You were going to tell me how you were going to Cure the Incompletes." "I said we were going to do it. Actually you will play a greater part than I, Doctor Infield." The psychiatrist sat rigidly. "You didn't think you could give me your right name in front of your own office building and that I wouldn't recognize you? I know some psychiatrists are sensitive about wearing Cures themselves, but it is a mark of honor of the completely sane man. You should be proud of your Cure and eager to Cure others. Very eager." "Just what do you mean?" He already suspected Price's meaning. Price leaned forward. "There is one phobia that is so wide-spread, a Cure is not even thought of—hypochondria. Hundreds of people come to your office for a Cure and you turn them away. Suppose you and the other Cured psychiatrists give everybody who comes to you a Cure?" Infield gestured vaguely. "A psychiatrist wouldn't hand out Cures unless they were absolutely necessary." "You'll feel differently after you've been Cured for a while yourself. Other psychiatrists have." Before Infield could speak, a stubble-faced, barrel-chested man moved past their table. He wore a safety belt. It was the man Price had called Davies, the one who had fastened one of his safety lines to Infield in the street. Davies went to the bar in the back. "Gimme a bottle," he demanded of a vacant-eyed Reggie. He came back toward them, carrying the bottle in one hand, brushing off rain drops with the other. He stopped beside Price and glared. Price leaned back. The chair creaked. Mrs. Price kept cooing to the doll. "You made me fall," Davies accused. Price shrugged. "You were unconscious. You never knew it." Sweat broke out on Davies' forehead. "You broke the Code. Don't you think I can imagine how it was to fall? You louse!" Suddenly, Davies triggered his safety belt. At close range, before the lines could fan out in a radius, all the lines in front attached themselves to Price, the ones at each side clung to their table and the floor, and all the others to the table behind Infield. Davies released all lines except those on Price, and then threw himself backward, dragging Price out of his chair and onto the floor. Davies didn't mind making others fall. They were always trying to make him fall just so they could laugh at him or pounce on him; why shouldn't he like to make them fall first? Expertly, Davies moved forward and looped the loose lines around Price's head and shoulders and then around his feet. He crouched beside Price and shoved the bottle into the gasping mouth and poured. Price twisted against the binding lines in blind terror, gagging and spouting whiskey. Davies laughed and tilted the bottle more. Mrs. Price screamed. "The Cure! If you get that much liquor in his system, it will kill him!" She rocked the rag doll in her arms, trying to soothe it, and stared in horror. Infield hit the big man behind the ear. He dropped the bottle and fell over sideways on the floor. Fear and hate mingled in his eyes as he looked up at Infield. Nonsense, Infield told himself. Eyes can't register emotion. Davies released his lines and drew them in. He got up precariously. "I'm going to kill you," he said, glaring at Infield. "You made me fall worse than Georgie did. I'm really going to kill you." Infield wasn't a large man, but he had pressed two hundred and fifty many times in gym. He grabbed Davies' belt with both hands and lifted him about six inches off the floor. "I could drop you," the psychiatrist said. "No!" Davies begged weakly. "Please!" "I'll do it if you cause more trouble." Infield sat down and rubbed his aching forearms. Davies backed off in terror, right into the arms of Reggie. The waiter closed his huge hands on the acrophobe's shoulders. " You broke the Code all the way," Reggie said. "The Good Book says 'Thou shouldn't kill' or something like that, and so does the Code." "Let him go, Reggie," Price choked out, getting to his feet. "I'm not dead." He wiped his hand across his mouth. "No. No, you aren't." Infield felt an excitement pounding through him, same as when he had diagnosed his first case. No, better than that. "That taste of liquor didn't kill you, Price. Nothing terrible happened. You could find some way to get rid of that Cure." Price stared at him as if he were a padded-cell case. "That's different. I'd be a hopeless drunk without the Cure. Besides, no one ever gets rid of a Cure." They were all looking at Infield. Somehow he felt this represented a critical point in history. It was up to him which turn the world took, the world as represented by these four Cured people. "I'm afraid I'm for less Cures instead of more, Price. Look, if I can show you that someone can discard a Cure, would you get rid of that—if I may use the word— monstrous thing on your head?" Price grinned. Infield didn't recognize its smugness at the time. "I'll show you." He took off the circlet with the lightning rod and yanked at the wire running down into his collar. The new-old excitement within was running high. He felt the wire snap and come up easily. He threw the Cure on the floor. "Now," he said, "I am going out in that rain storm. There's thunder and lightning out there. I'm afraid, but I can get along without a Cure and so can you." "You can't! Nobody can!" Price screamed after him. He turned to the others. "If he reveals us, the Cause is lost. We've got to stop him for good . We've got to go after him." "It's slippery," Davies whimpered. "I might fall." Mrs. Price cuddled her rag doll. "I can't leave the baby and she mustn't get wet." "Well, there's no liquor out there and you can study your text in the lightning flashes, Reggie. Come on." Running down the streets that were tunnels of shining tar, running into the knifing ice bristles of the rain, Henry Infield realized that he was very frightened of the lightning. There is no action without a reason, he knew from the old neglected books. He had had a latent fear of lightning when he chose the lightning rod Cure. He could have picked a safety belt or foetic gyro just as well. He sneezed. He was soaked through, but he kept on running. He didn't know what Price and Reggie planned to do when they caught him. He slipped and fell. He would soon find out what they wanted. The excitement was all gone now and it left an empty space into which fear rushed. Reggie said, "We shall make a sacrifice." Infield looked up and saw the lightning reflected on the blade of a thin knife. Infield reached toward it more in fascination than fear. He managed to get all his fingers around two of Reggie's. He jerked and the knife fell into Infield's palm. The psychiatrist pulled himself erect by holding to Reggie's arm. Staggering to his feet, he remembered what he must do and slashed at the waiter's head. A gash streaked across the man's brow and blood poured into his eyes. He screamed. "I can't see the words!" It was his problem. Infield usually solved other people's problems, but now he ran away—he couldn't even solve his own. Infield realized that he had gone mad as he held the thin blade high overhead, but he did need some kind of lightning rod. Price (who was right behind him, gaining) had been right. No one could discard a Cure. He watched the lightning play its light on the blade of his Cure and he knew that Price was going to kill him in the next moment. He was wrong. The lightning hit him first. Reggie squinted under the bandage at the lettering on the door that said INFIELD & MORGAN and opened the door. He ran across the room to the man sitting at the desk, reading by the swivel light. "Mr. Morgan, your partner, Mr. Infield, he—" "Just a moment." Morgan switched on the room lights. "What were you saying?" "Mr. Infield went out without his Cure in a storm and was struck by lightning. We took him to the morgue. He must have been crazy to go out without his Cure." Morgan stared into his bright desk light without blinking. "This is quite a shock to me. Would you mind leaving? I'll come over to your place and you can tell me about it later." Reggie went out. "Yes, sir. He was struck by lightning, struck dead. He must have been crazy to leave his Cure...." The door closed. Morgan exhaled. Poor Infield. But it wasn't the lightning that killed him, of course. Morgan adjusted the soundproofing plugs in his ears, thinking that you did have to have quite a bit of light to read lips. The thunder, naturally, was what had killed Infield. Loud noise—any noise—that would do it every time. Too bad Infield had never really stopped being one of the Incompletes. Dangerous people. He would have to deal with them. | A. He was afraid of heights and falling over, so he had affixed himself to Infield in order to calm his fear. |
What is the irony in Ben's contempt for a single action destroying "a man's life and his dream?"
A. If he had stayed and made the decision to confess, he wouldn't have ruined his life.
B. He'd just deliberately ended a man's life, and his running from what he's done.
C. It's against the morals of what he claims to stand by.
D. He'd just done the same to a man by striking him without thought, and is now running from his guilt.
| A Coffin for Jacob By EDWARD W. LUDWIG Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] With never a moment to rest, the pursuit through space felt like a game of hounds and hares ... or was it follow the leader? Ben Curtis eased his pale, gaunt body through the open doorway of the Blast Inn, the dead man following silently behind him. His fear-borne gaze traveled into the dimly illumined Venusian gin mill. The place was like an evil caldron steaming with a brew whose ingredients had been culled from the back corners of three planets. Most of the big room lay obscured behind a shimmering veil of tobacco smoke and the sweet, heavy fumes of Martian Devil's Egg. Here and there, Ben saw moving figures. He could not tell if they were Earthmen, Martians or Venusians. Someone tugged at his greasy coat. He jumped, thinking absurdly that it was the dead man's hand. " Coma esta, senor? " a small voice piped. " Speken die Deutsch? Desirez-vous d'amour? Da? Nyet? " Ben looked down. The speaker was an eager-eyed Martian boy of about ten. He was like a red-skinned marionette with pipestem arms and legs, clad in a torn skivvy shirt and faded blue dungarees. "I'm American," Ben muttered. "Ah, buena ! I speak English tres fine, senor . I have Martian friend, she tres pretty and tres fat. She weigh almost eighty pounds, monsieur . I take you to her, si ?" Ben shook his head. He thought, I don't want your Martian wench. I don't want your opium or your Devil's Egg or your Venusian kali. But if you had a drug that'd bring a dead man to life, I'd buy and pay with my soul. "It is deal, monsieur ? Five dollars or twenty keelis for visit Martian friend. Maybe you like House of Dreams. For House of Dreams—" "I'm not buying." The dirty-faced kid shrugged. "Then I show you to good table,— tres bien . I do not charge you, senor ." The boy grabbed his hand. Because Ben could think of no reason for resisting, he followed. They plunged into shifting layers of smoke and through the drone of alcohol-cracked voices. They passed the bar with its line of lean-featured, slit-eyed Earthmen—merchant spacemen. They wormed down a narrow aisle flanked by booths carved from Venusian marble that jutted up into the semi-darkness like fog-blanketed tombstones. Several times, Ben glimpsed the bulky figures of CO 2 -breathing Venusians, the first he'd ever seen. They were smoky gray, scaly, naked giants, toads in human shape. They stood solitary and motionless, aloof, their green-lidded eyes unblinking. They certainly didn't look like telepaths, as Ben had heard they were, but the thought sent a fresh rivulet of fear down his spine. Once he spied a white-uniformed officer of Hoover City's Security Police. The man was striding down an aisle, idly tapping his neuro-club against the stone booths. Keep walking , Ben told himself. You look the same as anyone else here. Keep walking. Look straight ahead. The officer passed. Ben breathed easier. "Here we are, monsieur ," piped the Martian boy. "A tres fine table. Close in the shadows." Ben winced. How did this kid know he wanted to sit in the shadows? Frowning, he sat down—he and the dead man. He listened to the lonely rhythms of the four-piece Martian orchestra. The Martians were fragile, doll-like creatures with heads too large for their spindly bodies. Their long fingers played upon the strings of their cirillas or crawled over the holes of their flutes like spider legs. Their tune was sad. Even when they played an Earth tune, it still seemed a song of old Mars, charged with echoes of lost voices and forgotten grandeur. For an instant, Ben's mind rose above the haunting vision of the dead man. He thought, What are they doing here, these Martians? Here, in a smoke-filled room under a metalite dome on a dust-covered world? Couldn't they have played their music on Mars? Or had they, like me, felt the challenge of new worlds? He sobered. It didn't matter. He ordered a whiskey from a Chinese waiter. He wet his lips but did not drink. His gaze wandered over the faces of the Inn's other occupants. You've got to find him , he thought. You've got to find the man with the red beard. It's the only way you can escape the dead man. The dead man was real. His name was Cobb. He was stout and flabby and about forty and he hated spacemen. His body was buried now—probably in the silent gray wastes outside Luna City. But he'd become a kind of invisible Siamese twin, as much a part of Ben as sight in his eyes. Sometimes the image would be shuffling drunkenly beside him, its lips spitting whiskey-slurred curses. Again, its face would be a pop-eyed mask of surprise as Ben's fist thudded into its jaw. More often, the face would be frozen in the whiteness of death. The large eyes would stare. Blood would trickle from a corner of the gaping mouth. You can forget a living man. You can defeat him or submit to him or ignore him, and the matter is over and done. You can't escape from a memory that has burned into your mind. It had begun a week ago in Luna City. The flight from White Sands had been successful. Ben, quietly and moderately, wanted to celebrate. He stopped alone in a rocketfront bar for a beer. The man named Cobb plopped his portly and unsteady posterior on the stool next to him. "Spacemen," he muttered, "are getting like flies. Everywhere, all you see's spacemen." He was a neatly dressed civilian. Ben smiled. "If it weren't for spacemen, you wouldn't be here." "The name's Cobb." The man hiccoughed. "Spacemen in their white monkey suits. They think they're little tin gods. Betcha you think you're a little tin god." He downed a shot of whiskey. Ben stiffened. He was twenty-four and dressed in the white, crimson-braided uniform of the Odyssey's junior astrogation officer. He was three months out of the Academy at White Sands and the shining uniform was like a key to all the mysteries of the Universe. He'd sought long for that key. At the age of five—perhaps in order to dull the memory of his parents' death in a recent strato-jet crash—he'd spent hours watching the night sky for streaking flame-tails of Moon rockets. At ten, he'd ground his first telescope. At fourteen, he'd converted an abandoned shed on the government boarding-school grounds to a retreat which housed his collection of astronomy and rocketry books. At sixteen, he'd spent every weekend holiday hitchhiking from Boys Town No. 5 in the Catskills to Long Island Spaceport. There, among the grizzled veterans of the old Moon Patrol, he'd found friends who understood his dream and who later recommended his appointment to the U. S. Academy for the Conquest of Space. And a month ago, he'd signed aboard the Odyssey —the first ship, it was rumored, equipped to venture as far as the asteroids and perhaps beyond. Cobb was persistent: "Damn fools shoulda known enough to stay on Earth. What the hell good is it, jumpin' from planet to planet?" The guy's drunk , Ben thought. He took his drink and moved three stools down the bar. Cobb followed. "You don't like the truth, eh, kid? You don't like people to call you a sucker." Ben rose and started to leave the bar, but Cobb grabbed his arm and held him there. "Thas what you are—a sucker. You're young now. Wait ten years. You'll be dyin' of radiation rot or a meteor'll get you. Wait and see, sucker!" Until this instant, Ben had suppressed his anger. Now, suddenly and without warning, it welled up into savage fury. His fist struck the man on the chin. Cobb's eyes gaped in shocked horror. He spun backward. His head cracked sickeningly on the edge of the bar. The sound was like a punctuation mark signaling the end of life. He sank to the floor, eyes glassy, blood tricking down his jaw. Ben knew that he was dead. Then, for a single absurd second, Ben was seized with terror—just as, a moment before, he'd been overwhelmed with anger. He ran. For some twenty minutes, he raced through a dizzying, nightmare world of dark rocketfront alleys and shouting voices and pursuing feet. At last, abruptly, he realized that he was alone and in silence. He saw that he was still on the rocketfront, but in the Tycho-ward side of the city. He huddled in a dark corner of a loading platform and lit a cigarette. A thousand stars—a thousand motionless balls of silver fire—shone above him through Luna City's transparent dome. He was sorry he'd hit Cobb, of course. He was not sorry he'd run. Escaping at least gave him a power of choice, of decision. You can do two things , he thought. You can give yourself up, and that's what a good officer would do. That would eliminate the escape charge. You'd get off with voluntary manslaughter. Under interplanetary law, that would mean ten years in prison and a dishonorable discharge. And then you'd be free. But you'd be through with rockets and space. They don't want new men over thirty-four for officers on rockets or even for third-class jet-men on beat-up freighters—they don't want convicted killers. You'd get the rest of the thrill of conquering space through video and by peeking through electric fences of spaceports. Or— There were old wives' tales of a group of renegade spacemen who operated from the Solar System's frontiers. The spacemen weren't outlaws. They were misfits, rejectees from the clearing houses on Earth. And whereas no legally recognized ship had ventured past Mars, the souped-up renegade rigs had supposedly hit the asteroids. Their headquarters was Venus. Their leader—a subject of popular and fantastic conjecture in the men's audiozines—was rumored to be a red-bearded giant. So , Ben reflected, you can take a beer-and-pretzels tale seriously. You can hide for a couple of days, get rid of your uniform, change your name. You can wait for a chance to get to Venus. To hell with your duty. You can try to stay in space, even if you exile yourself from Earth. After all, was it right for a single second, a single insignificant second, to destroy a man's life and his dream? He was lucky. He found a tramp freighter whose skipper was on his last flight before retirement. Discipline was lax, investigation of new personnel even more so. Ben Curtis made it to Venus. There was just one flaw in his decision. He hadn't realized that the memory of the dead man's face would haunt him, torment him, follow him as constantly as breath flowed into his lungs. But might not the rumble of atomic engines drown the murmuring dead voice? Might not the vision of alien worlds and infinite spaceways obscure the dead face? So now he sat searching for a perhaps nonexistent red-bearded giant, and hoping and doubting and fearing, all at once. "You look for someone, senor ?" He jumped. "Oh. You still here?" " Oui. " The Martian kid grinned, his mouth full of purple teeth. "I keep you company on your first night in Hoover City, n'est-ce-pas ?" "This isn't my first night here," Ben lied. "I've been around a while." "You are spacemen?" Ben threw a fifty-cent credit piece on the table. "Here. Take off, will you?" Spiderlike fingers swept down upon the coin. " Ich danke, senor. You know why city is called Hoover City?" Ben didn't answer. "They say it is because after women come, they want first thing a thousand vacuum cleaners for dust. What is vacuum cleaner, monsieur ?" Ben raised his hand as if to strike the boy. " Ai-yee , I go. You keep listen to good Martian music." The toothpick of a body melted into the semi-darkness. Minutes passed. There were two more whiskeys. A ceaseless parade of faces broke through the smoky veil that enclosed him—reddish balloon faces, scaly reptilian faces, white-skinned, slit-eyed faces, and occasionally a white, rouged, powdered face. But nowhere was there a face with a red beard. A sense of hopelessness gripped Ben Curtis. Hoover City was but one of a dozen cities of Venus. Each had twenty dives such as this. He needed help. But his picture must have been 'scoped to Venusian visiscreens. A reward must have been offered for his capture. Whom could he trust? The Martian kid, perhaps? Far down the darkened aisle nearest him, his eyes caught a flash of white. He tensed. Like the uniform of a Security Policeman, he thought. His gaze shifted to another aisle and another hint of whiteness. And then he saw another and another and another. Each whiteness became brighter and closer, like shrinking spokes of a wheel with Ben as their focal point. You idiot! The damned Martian kid! You should have known! Light showered the room in a dazzling explosion. Ben, half blinded, realized that a broad circle of unshaded globes in the ceiling had been turned on. The light washed away the room's strangeness and its air of brooding wickedness, revealing drab concrete walls and a debris-strewn floor. Eyes blinked and squinted. There were swift, frightened movements and a chorus of angry murmurs. The patrons of the Blast Inn were like tatter-clad occupants of a house whose walls have been ripped away. Ben Curtis twisted his lean body erect. His chair tumbled backward, falling. The white-clad men charged, neuro-clubs upraised. A woman screamed. The music ceased. The Martian orchestra slunk with feline stealth to a rear exit. Only the giant Venusians remained undisturbed. They stood unmoving, their staring eyes shifting lazily in Ben's direction. "Curtis!" one of the policemen yelled. "You're covered! Hold it!" Ben whirled away from the advancing police, made for the exit into which the musicians had disappeared. A hissing sound traveled past his left ear, a sound like compressed air escaping from a container. A dime-sized section of the concrete wall ahead of him crumbled. He stumbled forward. They were using deadly neuro-pistols now, not the mildly stunning neuro-clubs. Another hiss passed his cheek. He was about twelve feet from the exit. Another second , his brain screamed. Just another second— Or would the exits be guarded? He heard the hiss. It hit directly in the small of his back. There was no pain, just a slight pricking sensation, like the shallow jab of a needle. He froze as if yanked to a stop by a noose. His body seemed to be growing, swelling into balloon proportions. He knew that the tiny needle had imbedded itself deep in his flesh, knew that the paralyzing mortocain was spreading like icy fire into every fiber and muscle of his body. He staggered like a man of stone moving in slow motion. He'd have fifteen—maybe twenty—seconds before complete lethargy of mind and body overpowered him. In the dark world beyond his fading consciousness, he heard a voice yell, "Turn on the damn lights!" Then a pressure and a coldness were on his left hand. He realized that someone had seized it. A soft feminine voice spoke to him. "You're wounded? They hit you?" "Yes." His thick lips wouldn't let go of the word. "You want to escape—even now?" "Yes." "You may die if you don't give yourself up." "No, no." He tried to stumble toward the exit. "All right then. Not that way. Here, this way." Heavy footsteps thudded toward them. A few yards away, a flashlight flicked on. Hands were guiding him. He was aware of being pushed and pulled. A door closed behind him. The glare of the flashlight faded from his vision—if he still had vision. "You're sure?" the voice persisted. "I'm sure," Ben managed to say. "I have no antidote. You may die." His mind fought to comprehend. With the anti-paralysis injection, massage and rest, a man could recover from the effects of mortocain within half a day. Without treatment, the paralysis could spread to heart and lungs. It could become a paralysis of death. An effective weapon: the slightest wound compelled the average criminal to surrender at once. "Anti ... anti ..." The words were as heavy as blobs of mercury forced from his throat. "No ... I'm sure ... sure." He didn't hear the answer or anything else. Ben Curtis had no precise sensation of awakening. Return to consciousness was an intangible evolution from a world of black nothingness to a dream-like state of awareness. He felt the pressure of hands on his naked arms and shoulders, hands that massaged, manipulated, fought to restore circulation and sensitivity. He knew they were strong hands. Their strength seemed to transfer itself to his own body. For a long time, he tried to open his eyes. His lids felt welded shut. But after a while, they opened. His world of darkness gave way to a translucent cloak of mist. A round, featureless shape hovered constantly above him—a face, he supposed. He tried to talk. Although his lips moved slightly, the only sound was a deep, staccato grunting. But he heard someone say, "Don't try to talk." It was the same gentle voice he'd heard in the Blast Inn. "Don't talk. Just lie still and rest. Everything'll be all right." Everything all right , he thought dimly. There were long periods of lethargy when he was aware of nothing. There were periods of light and of darkness. Gradually he grew aware of things. He realized that the soft rubber mouth of a spaceman's oxygen mask was clamped over his nose. He felt the heat of electric blankets swathed about his body. Occasionally a tube would be in his mouth and he would taste liquid food and feel a pleasant warmth in his stomach. Always, it seemed, the face was above him, floating in the obscuring mist. Always, it seemed, the soft voice was echoing in his ears: "Swallow this now. That's it. You must have food." Or, "Close your eyes. Don't strain. It won't be long. You're getting better." Better , he'd think. Getting better.... At last, after one of the periods of lethargy, his eyes opened. The mist brightened, then dissolved. He beheld the cracked, unpainted ceiling of a small room, its colorless walls broken with a single, round window. He saw the footboard of his aluminite bed and the outlines of his feet beneath a faded blanket. Finally he saw the face and figure that stood at his side. "You are better?" the kind voice asked. The face was that of a girl probably somewhere between twenty-five and thirty. Her features, devoid of makeup, had an unhealthy-looking pallor, as if she hadn't used a sunlamp for many weeks. Yet, at the same time, her firm slim body suggested a solidity and a strength. Her straight brown hair was combed backward, tight upon her scalp, and drawn together in a knot at the nape of her neck. "I—I am better," he murmured. His words were still slow and thick. "I am going to live?" "You will live." He thought for a moment. "How long have I been here?" "Nine days." "You took care of me?" He noted the deep, dark circles beneath her sleep-robbed eyes. She nodded. "You're the one who carried me when I was shot?" "Yes." "Why?" Suddenly he began to cough. Breath came hard. She held the oxygen mask in readiness. He shook his head, not wanting it. "Why?" he asked again. "It would be a long story. Perhaps I'll tell you tomorrow." A new thought, cloaked in sudden fear, entered his murky consciousness. "Tell me, will—will I be well again? Will I be able to walk?" He lay back then, panting, exhausted. "You have nothing to worry about," the girl said softly. Her cool hand touched his hot forehead. "Rest. We'll talk later." His eyes closed and breath came easier. He slept. When he next awoke, his gaze turned first to the window. There was light outside, but he had no way of knowing if this was morning, noon or afternoon—or on what planet. He saw no white-domed buildings of Hoover City, no formal lines of green-treed parks, no streams of buzzing gyro-cars. There was only a translucent and infinite whiteness. It was as if the window were set on the edge of the Universe overlooking a solemn, silent and matterless void. The girl entered the room. "Hi," she said, smiling. The dark half-moons under her eyes were less prominent. Her face was relaxed. She increased the pressure in his rubberex pillows and helped him rise to a sitting position. "Where are we?" he asked. "Venus." "We're not in Hoover City?" "No." He looked at her, wondering. "You won't tell me?" "Not yet. Later, perhaps." "Then how did you get me here? How did we escape from the Inn?" She shrugged. "We have friends who can be bribed. A hiding place in the city, the use of a small desert-taxi, a pass to leave the city—these can be had for a price." "You'll tell me your name?" "Maggie." "Why did you save me?" Her eyes twinkled mischievously. "Because you're a good astrogator." His own eyes widened. "How did you know that?" She sat on a plain chair beside his bed. "I know everything about you, Lieutenant Curtis." "How did you learn my name? I destroyed all my papers—" "I know that you're twenty-four. Born July 10, 1971. Orphaned at four, you attended Boys Town in the Catskills till you were 19. You graduated from the Academy at White Sands last June with a major in Astrogation. Your rating for the five-year period was 3.8—the second highest in a class of fifty-seven. Your only low mark in the five years was a 3.2 in History of Martian Civilization. Want me to go on?" Fascinated, Ben nodded. "You were accepted as junior astrogation officer aboard the Odyssey . You did well on your flight from Roswell to Luna City. In a barroom fight in Luna City, you struck and killed a man named Arthur Cobb, a pre-fab salesman. You've been charged with second degree murder and escape. A reward of 5,000 credits has been offered for your capture. You came to Hoover City in the hope of finding a renegade group of spacemen who operate beyond Mars. You were looking for them in the Blast Inn." He gaped incredulously, struggling to rise from his pillows. "I—don't get it." "There are ways of finding out what we want to know. As I told you, we have many friends." He fell back into his pillows, breathing hard. She rose quickly. "I'm sorry," she said. "I shouldn't have told you yet. I felt so happy because you're alive. Rest now. We'll talk again soon." "Maggie, you—you said I'd live. You didn't say I'd be able to walk again." She lowered her gaze. "I hope you'll be able to." "But you don't think I will, do you?" "I don't know. We'll try walking tomorrow. Don't think about it now. Rest." He tried to relax, but his mind was a vortex of conjecture. "Just one more question," he almost whispered. "Yes?" "The man I killed—did he have a wife?" She hesitated. He thought, Damn it, of all the questions, why did I ask that? Finally she said, "He had a wife." "Children?" "Two. I don't know their ages." She left the room. He sank into the softness of his bed. As he turned over on his side, his gaze fell upon an object on a bureau in a far corner of the room. He sat straight up, his chest heaving. The object was a tri-dimensional photo of a rock-faced man in a merchant spaceman's uniform. He was a giant of a man with a neatly trimmed red beard ! Ben stared at the photo for a long time. At length, he slipped into restless sleep. Images of faces and echoes of words spun through his brain. The dead man returned to him. Bloodied lips cursed at him. Glassy eyes accused him. Somewhere were two lost children crying in the night. And towering above him was a red-bearded man whose great hands reached down and beckoned to him. Ben crawled through the night on hands and knees, his legs numb and useless. The crying of the children was a chilling wail in his ears. His head rose and turned to the red-bearded man. His pleading voice screamed out to him in a thick, harsh cackle. Yet even as he screamed, the giant disappeared, to be replaced by white-booted feet stomping relentlessly toward him. He awoke still screaming.... A night without darkness passed. Ben lay waiting for Maggie's return, a question already formed in his mind. She came and at once he asked, "Who is the man with the red beard?" She smiled. "I was right then when I gave you that thumbnail biog. You were looking for him, weren't you?" "Who is he?" She sat on the chair beside him. "My husband," she said softly. He began to understand. "And your husband needs an astrogator? That's why you saved me?" "We need all the good men we can get." "Where is he?" She cocked her head in mock suspicion. "Somewhere between Mercury and Pluto. He's building a new base for us—and a home for me. When his ship returns, I'll be going to him." "Why aren't you with him now?" "He said unexplored space is no place for a woman. So I've been studying criminal reports and photos from the Interplanetary Bureau of Investigation and trying to find recruits like yourself. You know how we operate?" He told her the tales he'd heard. She nodded. "There are quite a few of us now—about a thousand—and a dozen ships. Our base used to be here on Venus, down toward the Pole. The dome we're in now was designed and built by us a few years ago after we got pushed off Mars. We lost a few men in the construction, but with almost every advance in space, someone dies." "Venus is getting too civilized. We're moving out and this dome is only a temporary base when we have cases like yours. The new base—I might as well tell you it's going to be an asteroid. I won't say which one." "Don't get the idea that we're outlaws. Sure, about half our group is wanted by the Bureau, but we make honest livings. We're just people like yourself and Jacob." "Jacob? Your husband?" She laughed. "Makes you think of a Biblical character, doesn't it? Jacob's anything but that. And just plain 'Jake' reminds one of a grizzled old uranium prospector and he isn't like that, either." She lit a cigarette. "Anyway, the wanted ones stay out beyond the frontiers. Jacob and those like him can never return to Earth—not even to Hoover City—except dead. The others are physical or psycho rejects who couldn't get clearance if they went back to Earth. They know nothing but rocketing and won't give up. They bring in our ships to frontier ports like Hoover City to unload cargo and take on supplies." "Don't the authorities object?" "Not very strongly. The I. B. I. has too many problems right here to search the whole System for a few two-bit crooks. Besides, we carry cargoes of almost pure uranium and tungsten and all the stuff that's scarce on Earth and Mars and Venus. Nobody really cares whether it comes from the asteroids or Hades. If we want to risk our lives mining it, that's our business." She pursed her lips. "But if they guessed how strong we are or that we have friends planted in the I. B. I.—well, things might be different. There probably would be a crackdown." Ben scowled. "What happens if there is a crackdown? And what will you do when Space Corps ships officially reach the asteroids? They can't ignore you then." "Then we move on. We dream up new gimmicks for our crates and take them to Jupiter, Saturn, Uranus, Neptune, Pluto. In time, maybe, we'll be pushed out of the System itself. Maybe it won't be the white-suited boys who'll make that first hop to the stars. It could be us, you know—if we live long enough. But that Asteroid Belt is murder. You can't follow the text-book rules of astrogation out there. You make up your own." Ben stiffened. "And that's why you want me for an astrogator." Maggie rose, her eyes wistful. "If you want to come—and if you get well." She looked at him strangely. "Suppose—" He fought to find the right words. "Suppose I got well and decided not to join Jacob. What would happen to me? Would you let me go?" Her thin face was criss-crossed by emotion—alarm, then bewilderment, then fear. "I don't know. That would be up to Jacob." He lay biting his lip, staring at the photo of Jacob. She touched his hand and it seemed that sadness now dominated the flurry of emotion that had coursed through her. "The only thing that matters, really," she murmured, "is your walking again. We'll try this afternoon. Okay?" "Okay," he said. When she left, his eyes were still turned toward Jacob's photo. He was like two people, he thought. Half of him was an officer of the Space Corps. Perhaps one single starry-eyed boy out of ten thousand was lucky enough to reach that goal. He remembered a little picture book his mother had given him when she was alive. Under the bright pictures of spacemen were the captions: "A Space Officer Is Honest" "A Space Officer Is Loyal." "A Space Officer Is Dutiful." Honesty, loyalty, duty. Trite words, but without those concepts, mankind would never have broken away from the planet that held it prisoner for half a million years. Without them, Everson, after three failures and a hundred men dead, would never have landed on the Moon twenty-seven years ago. | D. He'd just done the same to a man by striking him without thought, and is now running from his guilt. |
What are major acquisitions that Ulta Beauty has done in FY2023 and FY2022? | Evidence 0:
Ulta Beauty, Inc.
Consolidated Statements of Cash Flows
Fiscal year ended
January 28,
January 29,
January 30,
(In thousands)
2023
2022
2021
Operating activities
Net income
$
1,242,408
$
985,837
$
175,835
Adjustments to reconcile net income to net cash provided by operating activities:
Depreciation and amortization
241,372
268,460
297,772
Non-cash lease expense
301,912
276,229
268,071
Long-lived asset impairment charge
72,533
Deferred income taxes
15,653
(25,666)
(24,008)
Stock-based compensation expense
43,044
47,259
27,583
Loss on disposal of property and equipment
6,688
5,358
6,827
Change in operating assets and liabilities:
Receivables
34,260
(40,573)
(53,772)
Merchandise inventories
(104,233)
(331,003)
125,486
Prepaid expenses and other current assets
(19,432)
(3,412)
(4,363)
Income taxes
(45,182)
(35,652)
58,916
Accounts payable
8,309
66,156
62,324
Accrued liabilities
48,249
58,598
58,599
Deferred revenue
41,098
79,196
36,848
Operating lease liabilities
(324,500)
(303,914)
(297,513)
Other assets and liabilities
(7,731)
12,392
(783)
Net cash provided by operating activities
1,481,915
1,059,265
810,355
Investing activities
Proceeds from short-term investments
110,000
Capital expenditures
(312,126)
(172,187)
(151,866)
Acquisitions, net of cash acquired
(1,220) | Ulta Beauty did not make any acquisitions in FY2023 and FY2022. |
In 2019, what treatment was continued for Mrs. Sanders despite gastrointestinal issues?
Choose the correct answer from the following options:
A. Antibiotic therapy
B. Oral corticosteroids
C. Subcutaneous immunoglobulin therapy
D. Intravenous iron supplementation
E. Antihypertensive medication
| ### Patient Report 0
**Dear colleague, **
We are writing to provide an update on the examination results of our
patient Mrs. Hilary Sanders, born on 08/24/1976, who presented to our
outpatient clinic on 10/09/2016.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy with
human immunoglobulin
**Medical History:** Mrs. Sanders presented with suspected previously
undiagnosed immunodeficiency. There were no reports of frequent
infections during childhood and adolescence. No increased herpes
infections. No history of pneumonia, meningitis, or other serious
infections.
**Current Presentation:** Mrs. Sanders has experienced recurrent
respiratory infections (bronchitis, pharyngitis) for about 3 years.
**Physical Examination:** She reported joint pain in the left knee and
numbness below the shoulder blade. A tendency to bruise easily. No
mucosal lesions, recurrent axillary lymph node swelling. No recurrent
fevers. No B-symptoms. No resting dyspnea, no subjective heart rhythm
disturbances, no syncope, no peripheral edema, or other signs of
cardiopulmonary decompensation.
**Immunological Diagnostics:**
- Immunoglobulins including subclasses: IgA, IgG, IgM, and all IgG
subclasses were reduced.
- Numerically unremarkable monocytes and granulocytes, lymphocytopenia
with reduced B- and NK-cells, normal CD4/CD8 ratio.
- B-lymphocyte subpopulation with numerically reduced B-cells.
- Monocytic HLA-DR expression (immune competence marker) within the
normal range.
- No evidence of acute or chronic T-cell activation.
- IL-6, LBP (Lipopolysaccharide-Binding Protein), and IL-8
post-erylysis were unremarkable, elevated s-IL-2.
- Monocytic TNF-alpha secretion after 4h LPS stimulation was
unremarkable.
- T-cell function after 24h polyvalent ConA stimulation: TNF-alpha,
IFN-gamma, IL-2, IL-4 unremarkable
**Assessment**: In the immunological diagnostics, as in previous
outpatient findings, a reduction in all major immunoglobulin classes and
subclasses was observed. Cellular immune status revealed lymphocytopenia
with reduced B- and natural killer-cells.
Further cellular immune status, including the complement system and
soluble mediators, showed no significant abnormalities except for an
elevated soluble IL-2 receptor. Given the unremarkable monocytic
TNF-alpha secretion after LPS stimulation, a significant Toll-like
Receptor 4 defect is unlikely. An antibody response to Tetanus Toxoid
was demonstrated in a vaccine titer test. Protective
pneumococcal-specific antibodies could not be detected. There were no
abnormalities in autoimmune diagnostics.
Immunofixation showed no evidence of monoclonal gammopathy.
Hypogammaglobulinemia due to enteral or renal protein loss is unlikely
in the presence of normal albumin.
Overall, the picture is consistent with Common Variable
Immunodeficiency. Formally, CVID is defined by a reduction in the major
immunoglobulin class IgG, with accompanying reduction in IgA and/or IgM,
in the absence of normal or impaired vaccine response. Due to very low
immunoglobulin levels and planned travel, determination of vaccine
response was currently omitted in the absence of therapeutic
consequence. After stable substitution, specific vaccine antibody levels
can be determined before or after vaccination, with the assumption that
stable antibody concentrations exist due to continuous immunoglobulin
substitution.
According to B-cell differentiation, it corresponds to Type Ib according
to the Freiburg Classification and Type B+smB-CD21lo according to the
Euro Classification. The classification is clinically relevant, as Type
Ia is associated with increased immunocytopenias (especially ITP and
AIH) and splenomegaly. In CVID with a high proportion (\>10%) of CD-21
low B-cells, increased granulomatous diseases and splenomegaly have also
been observed.
The indication for immunoglobulin substitution therapy exists because of
recurrent infections. The form of substitution therapy (intravenous. vs.
subcutaneous) is primarily based on patient preferences, but also on
medical conditions (concomitant diseases such as thrombocytopenia,
convenience, insurance, etc.).
**Current Recommendations:**
We propose to initiate immunoglobulin substitution therapy with Hizentra
20% (subcutaneous) at a dose of 200 ml once a week on Tuesdays. Further
information and training on subcutaneous immunoglobulin substitution
therapy will be provided by a home care nursing service.
Mrs. Sanders will remain under regular medical supervision with close
monitoring of clinical symptoms, laboratory parameters, and the
effectiveness of immunoglobulin substitution therapy. Any unexpected
side effects or changes in her condition should be reported immediately.
**Lab results:**
**Parameter** **Results** **Reference Range**
--------------------------------------- --------------- ---------------------
Sodium 141 mEq/L 132-146 mEq/L
Potassium 4.2 mEq/L 3.4-4.5 mEq/L
Calcium 2.41 mg/dL 2.15-2.50 mg/dL
Inorganic Phosphate 1.00 mg/dL 0.87-1.45 mg/dL
Selenium 0.79 µmol/L 0.60-1.50 µmol/L
Zinc 10.1 µmol/L 9.0-22.0 µmol/L
Creatinine 0.75 mg/dL 0.50-0.90 mg/dL
Estimated GFR (eGFR CKD-EPI) \>90 mL/min \>90 mL/min
Total Bilirubin 0.37 mg/dL \< 1.20 mg/dL
Albumin 4.55 g/dL 3.50-5.20 g/dL
Total Protein 6.3 g/dL 6.4-8.3 g/dL
Albumin Fraction 71.8% 55.8-66.1%
A1-Globulin 5.1% 2.9-4.9%
A2-Globulin in Serum 10.7% 7.1-11.8%
ß-Globulin in Serum 9.2% 8.4-13.1%
Gamma-Globulin in Serum 3.2% 11.1-18.8%
Immunoglobulin G 514 mg/dL 700-1600 mg/dL
Immunoglobulin A 14 mg/dL 70-400 mg/dL
Immunoglobulin M 19 mg/dL 40-230 mg/dL
Immunoglobulin E 90 kU/L 0.0-100.0 kU/L
IgG 1 299.5 mg/dL 280-800 mg/dL
IgG 2 162.7 mg/dL 115-570 mg/dL
IgG 3 49.1 mg/dL 24-125 mg/dL
IgG 4 4.0 mg/dL 5.2-125 mg/dL
Serum Immunofixation
CRP 4.8 mg/L \< 5.0 mg/L
C3 Complement 980 mg/L 900-1800 mg/L
C4 Complement 120 mg/L 100-400 mg/L
ß-2-Microglobulin 3.6 mg/L 0.8-2.2 mg/L
HBs Antigen Negative
HBc Antibody Negative
HBs Antibody Negative
Ferritin 56 µg/L 13-140 µg/L
ALT (GPT) 33 U/L \< 31 U/L
AST (GOT) 29 U/L \< 35 U/L
Alkaline Phosphatase 84 U/L 35-105 U/L
Creatine Kinase 90 U/L \< 167 U/L
CK-MB 8.3 U/L \< 24.0 U/L
Gamma-GT 40 U/L 5-36 U/L
LDH 204 U/L 135-214 U/L
Lipase 50 U/L 13-60 U/L
Cortisol 306.6 nmol/L 64.0-327.0 nmol/L
25-OH-Vitamin D3 65.3 nmol/L 50.0-150.0 nmol/L
1.25-OH-Vitamin D3 134 pmol/L 18.0-155.0 pmol/L
TSH 1.42 mU/L 0.27-4.20 mU/L
Vitamin B12 770 pg/mL 191-663 pg/mL
Folic Acid 14.6 ng/mL 4.6-18.7 ng/mL
Hemoglobin 13.9 g/dL 12.0-15.6 g/dL
Hematocrit 41.0% 35.5-45.5%
Erythrocytes 5.2 M/uL 3.9-5.2 M/uL
Leukocytes 4.13 K/uL 3.90-10.50 K/uL
Platelets 174 K/uL 150-370 K/uL
MCV 80.0 fL 80.0-99.0 fL
MCH 26.7 pg 27.0-33.5 pg
MCHC 33.6 g/dL 31.5-36.0 g/dL
RDW-CV 13.7% 11.5-15.0%
Absolute Neutrophils 2.87 K/uL 1.50-7.70 K/uL
Absolute Immature Granulocytes 0.010 K/uL \< 0.050 K/uL
Absolute Lymphocytes 0.71 K/uL 1.10-4.50 K/uL
Absolute Monocytes 0.42 K/uL 0.10-0.90 K/uL
Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL
Absolute Basophils 0.03 K/uL 0.00-0.20 K/uL
HbA1c 4.9% \< 6.0%
HbA1c (IFCC) 30.1 mmol/mol \< 42.0
HBV Serology Result Negative
HIV1/2 Antibodies, P24 Antigen Negative
Hepatitis C Virus Antibodies in Serum Negative
**Dear colleague,**
We report the examination results of Mrs. Hilary Sanders, born on
08/24/1976 who presented at our outpatient clinic on 03/04/2017.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
**Immunological Diagnostics:**
- Immunoglobulins including subclasses: IgA, IgG, IgM, and all
IgG-Subclasses were reduced.
- Numerically unremarkable monocytes and granulocytes, lymphocytopenia
with reduced B- and natural killer-cells, normal CD4/CD8 ratio.
- B-lymphocyte subpopulation with numerically reduced B cells.
- Monocytic HLA-DR expression within the normal range.
- No evidence of acute or chronic T-cell activation.
- IL-6, LBP (Lipopolysaccharide-Binding Protein), and IL-8
post-erylysis were unremarkable, elevated s-IL-2.
- Monocytic TNF-alpha secretion after 4h LPS stimulation was
unremarkable.
**Assessment**: In the immunological diagnostics, as in previous
outpatient findings, a reduction in all major immunoglobulin classes and
subclasses was observed. Cellular immune status revealed lymphocytopenia
with reduced B- and natural killer-cells. The further cellular immune
status, including the complement system and soluble mediators, showed no
significant abnormalities except for an elevated soluble IL-2 receptor.
**Current Presentation:** Mrs. Sanders was again provided with detailed
information about her condition and the planned course of action. We
scheduled an appointment to initiate regular subcutaneous immunoglobulin
therapy.
**Medical History:** Mrs. Sanders received her first dose of Hizentra
20% subcutaneously as immunoglobulin substitution therapy for CVID. The
administration was well-tolerated initially, with no evidence of
significant local or systemic side effects. Mrs. Sanders was once again
informed about possible risks (especially hypersensitivity reactions)
and advised to contact us immediately in case of questions,
uncertainties, or any abnormalities. The dosing for the first four weeks
was 3x20mL Hizentra 20% subcutaneously, and from the fifth week onward,
it was changed to either 1x40mL or 2x20mL Hizentra 20% subcutaneously
per week.
In the past days, Mrs. Sanders has been experiencing a cold: runny nose,
cough (green-yellow), difficulty clearing mucus, slight fever, sinus
inflammation, sore throat, difficulty speaking, and swallowing problems.
There was no improvement.
**Physical Examination:** Reddened throat, no exudates, non-swollen
cervical lymph nodes, lung examination showed bronchitis-like breathing
sounds, no rales.
**Therapy and Progression**: Today\'s CRP is not elevated. IgGs are
still below normal. We recommended increasing immunoglobulin
substitution during the infection. The patient had difficulty finding a
suitable injection site on her abdomen. However, she reported that the
secretions were gradually becoming lighter, so she decided to wait with
the antibiotic and only use it if there was no improvement.
The patient has been receiving 3x20mL Hizentra 20% per week since her
last visit. She complained of developing skin hardening at the injection
sites, so a slower infusion time was discussed. She has been
experiencing a strong cough for several weeks without fever. No rales or
signs of pleuritis were detected on auscultation. No abnormalities were
observed on the chest X-ray. Laboratory results now show normal IgG
levels, so the dose was reduced to 2x20mL per week. A CT scan of the
thorax and abdominal ultrasound were requested.
**Chest X-ray in two planes from 03/04/2017:**
[Findings/Assessment:]{.underline} No previous images are available for
comparison. Upper mediastinum and heart appear normal, with no central
congestion. No pneumothorax, effusions, confluent infiltrates, or
significant focal lesions.
**Abdominal ultrasound on 03/04/2017:**
Hepatosplenomegaly and retroperitoneal lymphadenopathy up to 26mm.
**CT Chest/Abdomen/ from 03/04/2017:**
[Methodology]{.underline}: Digital overview radiographs. After
intravenous injection of contrast agent a 16-row CT scan of the thorax
and entire abdomen was performed in the venous contrast phase, with
primary data set reconstruction at a thickness of 1.25 mm. Multiplanar
reconstructions were created.
[Findings]{.underline}: A conventional radiographic pre-image from
11/18/2014 is available for comparison.
[Thorax]{.underline}: Normal lung parenchyma with normal vascular
markings. Small, sometimes hazy, sometimes nodular densities measuring
up to 4mm in both lower lobes and the left upper lobe. Small
pleura-adjacent density in the right lower lobe. No evidence of
confluent infiltrates. No pleural effusion or pneumothorax. Normal heart
size and configuration. Normal diameter of the thoracic aorta and
pulmonary trunk. Increased number and enlarged retroclavicular lymph
nodes on the right and left, axillary on both sides measuring up to 30mm
in diameter. Trachea and esophagus displayed normally. No hiatus hernia.
Thyroid and neck soft tissues were unremarkable, as far as depicted.
Normal thoracic soft tissue mantle. No soft tissue emphysema.
[Abdomen]{.underline}: Hepatomegaly with morphologically normal liver
parenchyma. No portal vein thrombosis. Gallbladder is unremarkable with
no calculi. Intrahepatic and extrahepatic bile ducts are not dilated.
Pancreas is normally lobulated and structured, with no dilation of the
pancreatic duct. Splenomegaly. Accessory spleen measuring approximately
20 mm in diameter. Splenic parenchyma is homogeneously contrasted in the
venous phase. Kidneys are orthotopically positioned, normal size with no
side differences, and contrasted equally on both sides. Two regularly
configured hypodense lesions in the left kidney, suggestive of
uncomplicated renal cysts. No dilation of the urinary tract, and no
evidence of stones. Adrenal glands are not visualized. Increased and
enlarged mesenteric, pararaortic, parailiacal, and inguinal lymph nodes
up to 30 mm in size. Gastrointestinal tract is displayed normally, as
far as assessable. Normal representation of major abdominal vessels. No
free intraperitoneal fluid or air.
[Osseous structures:]{.underline} No evidence of suspicious osseous
destruction. Normal soft tissue mantle.
[Assessment:]{.underline} Intrapulmonary multifocal, sometimes hazy,
sometimes nodular densities, differential diagnosis includes atypical
pneumonia. Thoracoabdominal lymphadenopathy. Hepatosplenomegaly without
suspicious lesions.
**Current Recommendations:**
- Outpatient follow-up for discussion of findings
- Continue regular subcutaneous immunoglobulin administration with
current regimen of Hizentra 20% 2x20mL/week
- Lung function test
- Gastroscopy
- In case of acute infection: increase immunoglobulin administration
- Abdominal ultrasound: annually
- H. pylori testing, e.g., breath test or H. pylori antigen in stool:
annually Seasonal influenza vaccination: annually
**Lab results upon discharge:**
**Parameter** **Results** **Reference Range**
---------------------- ------------- ---------------------
Total Protein 6.3 g/dL 6.4-8.3 g/dL
Albumin Fraction 71.8% 55.8-66.1%
A1-Globulin 5.1% 2.9-4.9%
Gamma-Globulin 3.2% 11.1-18.8%
Immunoglobulin G 188 mg/dL 700-1600 mg/dL
Immunoglobulin A 11 mg/dL 70-400 mg/dL
Immunoglobulin M 12 mg/dL 40-230 mg/dL
IgG Subclass 1 113 mg/dL 280-800 mg/dL
IgG Subclass 2 49.1 mg/dL 115-570 mg/dL
IgG Subclass 4 \<0.0 mg/dL 5.2-125 mg/dL
aPCP-IgG 7.32 mg/dL 10.00-191.20 mg/dL
aPCP-IgG2 2.74 mg/dL 4.70-89.40 mg/dL
ß-2-Microglobulin 3.6 mg/L 0.8-2.2 mg/L
LDH 224 U/L 135-214 U/L
Vitamin B12 708 pg/mL 191-663 pg/mL
Erythrocytes 5.3 M/uL 3.9-5.2 M/uL
Platelets 129 K/uL 150-370 K/uL
MCV 78.0 fL 80.0-99.0 fL
MCH 25.1 pg 27.0-33.5 pg
Absolute Lymphocytes 0.91 K/uL 1.10-4.50 K/uL
### Patient Report 1
**Dear colleague, **
We are reporting on Mrs. Hilary Sanders, born on 08/24/1976, who
presented to our Immunodeficiency Clinic on 10/06/2017.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Leukopenia and lymphopenia
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Hepatosplenomegaly
- Thoracoabdominal, inguinal, and axillary lymphadenopathy
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
**Medical History:** Mrs. Sanders first presented herself to our clinic,
with suspected undiagnosed immunodeficiency. Regular subcutaneous
immunoglobulin therapy with Hizentra 20% (2x20mL/week) has been
well-tolerated. Initially, there were frequent upper respiratory tract
infections with sore throat and cough. In the absence of fever, a
one-time course of Cotrim was prescribed for 7 days due to sinusitis. We
discussed Mrs. Sanders' medical history in detail, including the recent
CT findings. She has been informed about the necessity of vigilance in
case of unclear and especially persistent lymph node swellings.
Regarding the inguinal and axillary lymph nodes measuring up to 30mm in
diameter found on CT, we recommend an observational approach with
regular sonographic monitoring. There have been no significant changes
in laboratory parameters, with good IgG levels during ongoing
substitution therapy and known moderate leukopenia and lymphopenia.
During the next appointment, an additional lung function test, including
diffusion measurement, will be conducted
**Current Recommendations:**
- Outpatient follow-up, including lung function test
- Continue regular subcutaneous immunoglobulin administration with
current regimen of Hizentra 20% (2x20mL/week).
- Current gastroscopy.
<!-- -->
- In case of acute infection: increase immunoglobulin administration.
- Administer targeted, sufficiently long, and high-dose antibiotic
therapy if bacterial infections require treatment.
- Ideally, obtain material for microbiological diagnostics.
- In case of increasing diarrhea, consider outpatient stool
examinations, including Giardia lamblia and Cryptosporidium.
- Abdominal ultrasound: annually.
- Lung function test, including diffusion measurement: annually.
- H. pylori testing, e.g., breath test or H. pylori antigen in stool:
annually.
- Gastroscopy: approximately every 2-3 years, depending on previous
findings or H. pylori testing
- Chest X-ray or CT thorax: if clinical symptoms or lung function
abnormalities are observed.
- Seasonal influenza vaccination: annually.
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
-------------------------------- ------------- ---------------------
Sodium 141 mEq/L 132-146 mEq/L
Potassium 4.1 mEq/L 3.4-4.5 mEq/L
Creatinine (Jaffé) 0.82 mg/dL 0.50-0.90 mg/dL
Estimated GFR (eGFR CKD-EPI) \>90 \-
Total Bilirubin 0.21 mg/dL \< 1.20 mg/dL
Albumin 4.09 g/dL 3.5-5.2 g/dL
Immunoglobulin G 1025 mg/dL 700-1600 mg/dL
Immunoglobulin A 16 mg/dL 70-400 mg/dL
Immunoglobulin M 28 mg/dL 40-230 mg/dL
Free Lambda Light Chains 5.86 5.70-26.30
Free Kappa Light Chains 6.05 3.30-19.40
Kappa/Lambda Ratio 1.03 0.26-1.65
IgG Subclass 1 580.9 mg/dL 280-800 mg/dL
IgG Subclass 2 340.7 mg/dL 115-570 mg/dL
IgG Subclass 3 50.9 mg/dL 24-125 mg/dL
IgG Subclass 4 5.7 mg/dL 5.2-125 mg/dL
CRP 7.3 mg/L \< 5.0 mg/L
Haptoglobin 108 mg/dL 30-200 mg/dL
Ferritin 24 µg/L 13-140 µg/L
ALT 24 U/L \< 31 U/L
AST 37 U/L \< 35 U/L
Gamma-GT 27 U/L 5-36 U/L
Lactate Dehydrogenase 244 U/L 135-214 U/L
25-OH-Vitamin D3 91.7 nmol/L 50.0-150.0 nmol/L
Hemoglobin 13.1 g/dL 12.0-15.6 g/dL
Hematocrit 40.0% 35.5-45.5%
Red Blood Cells 5.5 M/uL 3.9-5.2 M/uL
White Blood Cells 2.41 K/uL 3.90-10.50 K/uL
Platelets 142 K/uL 150-370 K/uL
MCV 73.0 fL 80.0-99.0 fL
MCH 23.9 pg 27.0-33.5 pg
MCHC 32.7 g/dL 31.5-36.0 g/dL
MPV 10.7 fL 7.0-12.0 fL
RDW-CV 14.8% 11.5-15.0%
Absolute Neutrophils 1.27 K/uL 1.50-7.70 K/uL
Absolute Immature Granulocytes 0.000 K/uL \< 0.050 K/uL
Absolute Lymphocytes 0.67 K/uL 1.10-4.50 K/uL
Absolute Monocytes 0.34 K/uL 0.10-0.90 K/uL
Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL
Absolute Basophils 0.04 K/uL 0.00-0.20 K/uL
Free Hemoglobin 5.00 mg/dL \< 20.00 mg/dL
**Abdominal Ultrasound on 10/06/2017:**
[Liver]{.underline}: Measures 19 cm in the MCL, homogeneous parenchyma,
no focal lesions.
[Gallbladder/Biliary Tract:]{.underline} No evidence of calculi, no
signs of inflammation, no congestion.
[Spleen]{.underline}: Measures 14 cm in diameter, homogeneous. Accessory
spleen measures 16 mm at the hilus.
[Pancreas]{.underline}: Morphologically unremarkable, as far as visible
due to intestinal gas overlay, no evidence of space-occupying processes.
Retroperitoneum: No signs of aneurysms. Enlarged retroperitoneal and
iliac lymph nodes, measuring up to approximately 2.5 cm in diameter.
[Kidneys]{.underline}: Both kidneys are of normal size (right 4.3 x 11.8
cm, left 4.6 cm x 11.9 cm). No congestion, no evidence of calculi
(stones), no evidence of space-occupying processes.
[Bladder]{.underline}: Smoothly defined and normally configured.
Minimally filled.
[Uterus]{.underline}: Size within the normal range, homogeneous.
No ascites.
[Assessment:]{.underline} Evidence of enlarged lymph nodes up to 2.5 cm
retroperitoneal and iliac. Compared to previous findings, a slight
decrease in splenomegaly.
### Patient Report 2
**Dear colleague, **
We are reporting on the examination results of our patient, Mrs. Hilary
Sanders, born on 08/24/1976, who presented herself in our
Immunodeficiency Clinic on 02/10/2018.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
**Medical History:** For a detailed medical history, please refer to our
previous medical records.
**Therapy and Progression:** Ongoing diarrhea in the morning, often
recurring in the afternoon. No melena, no fresh blood. Resolving
respiratory infection, positive influenza.
Currently, IgG levels remain within the target range. An increased need
for immunoglobulins is expected, especially in the third trimester of
pregnancy. Therefore, we recommend close monitoring with us during
pregnancy. Ferritin levels have further declined, indicating the need
for iron substitution. Anamnestically, there is an intolerance to oral
iron preparations.
**Recommendations:**
- Outpatient follow-up
- Early follow-up in case of infections or persistent diarrhea
- Continue regular subcutaneous immunoglobulin therapy, currently with
Hizentra 20% 2x20mL/week
- In case of increasing diarrhea, conduct outpatient stool
examinations, including testing for Giardia lamblia and
Cryptosporidium
- Pulmonary function tests including diffusion measurement: annually
- Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in
stool: annually
- Gastroscopy: approximately every 2-3 years, depending on previous
findings and HP testing
- Chest X-ray or chest CT: in case of abnormal clinical presentation
or pulmonary function
- Annual seasonal influenza vaccination
### Patient Report 3
**Dear colleague, **
We are writing to provide an update on Mrs. Hilary Sanders, born on
08/24/1976, who presented to our outpatient Immunodeficiency Clinic on
04/12/2018.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
- Suspected CVID Enteropathy
**Medical History:** For a detailed medical history, please refer to our
previous medical records.
**Therapy and Progression:** Respiratory infection with symptoms for 3-4
weeks. No antibiotics. No significant infections since then. Hizentra
3x20 mL with good tolerance.
IgG levels remain within the target range; therefore, we recommend
continuing the current treatment unchanged.
Since the last visit, mild upper respiratory tract infections. No fever
(except for one episode of sinusitis), no antibiotics. SCIG treatment
unchanged with 3x20mL/week of Hizentra ®.
Mrs. Sanders continues to experience watery diarrhea about 5-7 times
daily. No blood in stools, no pain, no vomiting, no nausea. There has
been no clear association with specific foods observed. Current weight:
69kg.
We discussed further diagnostic steps. Initially, outpatient endoscopic
diagnostics should be performed.
**Current Recommendations:**
- Outpatient follow-up in three months
- Continue SCIG treatment as is
- External upper gastrointestinal endoscopy and colonoscopy (please
return with findings)
- In case of increasing diarrhea, conduct outpatient stool
examinations, including testing for Giardia lamblia and
Cryptosporidium
- Abdominal ultrasound: annually
- Pulmonary function tests including diffusion measurement: annually
- Helicobacter pylori testing: e.g., breath test or Helicobacter
pylori antigen in stool: annually
- Gastroscopy: approximately every 2-3 years, depending on previous
findings and Helicobacter pylori testing
- Chest X-ray or chest CT: in case of abnormal clinical presentation
or pulmonary function
- Annual seasonal influenza vaccination
### Patient Report 4
**Dear colleague, **
We are writing to provide an update on Mrs. Hilary Sanders, born on
08/24/1976, who presented to our outpatient Immunodeficiency Clinic on
02/18/2019.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
- Suspected CVID Enteropathy
**Medical History:** For a detailed medical history, please refer to our
previous medical records.
**Therapy and Progression:** Respiratory infections with symptoms for 7
weeks. No antibiotics. No significant infections since then. Hizentra
3x20 mL with good tolerance. Continued diarrhea, approximately 6 times a
day, without weight loss. IgG levels remain within the target range;
therefore, we recommend continuing the current treatment unchanged.
We discussed further diagnostic steps. Initially, outpatient endoscopic
diagnostics should be performed.
**Current Recommendations:**
- Outpatient follow-up in three months
- Continue treatment as is
- External upper gastrointestinal endoscopy (and colonoscopy (please
return with findings)
- In case of increasing diarrhea, conduct outpatient stool
examinations, including testing for Giardia lamblia and
Cryptosporidium
- Abdominal ultrasound: annually
- Pulmonary function tests including diffusion measurement: annually
- Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in
stool: annually
- Gastroscopy: approximately every 2-3 years, depending on previous
findings and HP testing
- Chest X-ray or chest CT: in case of abnormal clinical presentation
or pulmonary function
- Annual seasonal influenza vaccination
### Patient Report 5
**Dear colleague, **
We are writing to provide summary on the clinical course of Mrs. Hilary
Sanders, born on 08/24/1976, who presented at our outpatient
Immunodeficiency Clinic.
**Diagnoses:**
- Common Variable Immunodeficiency Syndrome (CVID)
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Recurrent respiratory infections
- Idiopathic thombocytopenic purpura
- Arterial hypertension
- Initiation of subcutaneous immunoglobulin substitution therapy human
immunoglobulin
- Suspected CVID Enteropathy
- Iron-deficiency anemia
**Medical History:** For a detailed medical history, please refer to our
previous medical records.
**Therapy and Progression:** Overall stable condition. No longer
experiencing cough. Persistent fatigue. Upcoming appointment with the
Gastroenterology department next week. There is again an indication for
iron substitution.
**Update on 11/15/2019: Laboratory results from 11/15/2019:**
Transaminase elevation, Protein 18, markedly elevated BNP. However, IgA
is at 0.5 (otherwise not detectable), IgG subclasses within normal
range. Findings do not align. Patient informed by phone, returning for
further evaluation today; also screening for Hepatitis A, B, C, and E,
EBV, CMV, TSH, coagulation. No shortness of breath, no edema, no
abdominal enlargement, stable weight at 69 kg. In case of worsening
symptoms, shortness of breath, or fever, immediate referral to the
emergency department recommended.
**02/12/2020:** The patient is doing reasonably well. She has had a mild
cold for about 2 weeks, no fever, but nasal congestion and
yellowish-green sputum. No other infections. No antibiotics prescribed.
She has adapted to her gastrointestinal issues. An appointment with the
Gastroenterology department. She is currently working from home.
Medication: no new medications, only Cuvitru 20mL 3x weekly. Weight
remains stable at 67 kg. The last lung function test was in the summer
of this year and was within normal limits. Imaging has not been
performed recently. Gastroscopy and colonoscopy have not been conducted
for some time.
**04/14/2020:** Referral to Gastroenterology at is recommended for
persistent abdominal symptoms.
**10/24/2020:** The patient has mostly avoided social contacts due to
the pandemic. She continues to experience digestive problems (food
intolerances, diarrhea, flatulence). She has less stamina. Few
infections in the past year, at most a minor cold. No significant
infections. Hizentra injections remain unchanged at 20 mL 3 times a
week.
**03/22/2021:** Constant colds since December 2020. One-time antibiotic
treatment in October 2019. Subcutaneous Immunoglobulin therapy remains
unchanged at 20 mL 3 times weekly.
**09/19/2021:** She feels disoriented and very tired, more so than
usual. Difficulty maintaining a steady gaze. No steroid therapy was
administered. CT showed enlarged lymph nodes. Diarrhea, especially in
the morning, 3-4 times a day, additional bowel movements with meals,
sometimes watery. No fever, no infections. Hizentra injections continued
unchanged.
**Summary**: IgG levels are currently within the target range, so we
recommend continuing immunoglobulin substitution therapy without
changes. The antibody response (SARS-CoV-2 (S-Ag) IgG ELISA) to the
Covid-19 vaccination is, as expected, negative. However, there is a
positive detection of SARS-CoV-2 (N-Ag) IgG ELISA, as expected in the
case of viral contact (not vaccination). We consider this to be an
unspecific reaction and recommend further monitoring at the next
follow-up appointment. With a platelet count currently at 55 K/uL, we
recommend a short-term blood count check with us or your primary care
physician.
Due to the immunodeficiency, a lack of antibody response to vaccination
was expected. In the medium term, passive protection through
immunoglobulin substitution therapy will play a role. This is contingent
on a significant portion of plasma donors having antibodies against
SARS-CoV2. There is a multi-month delay from the time of donation to the
release of the preparations, so we anticipate that meaningful protection
through immunoglobulin products will not be expected. An exact prognosis
in this regard is not possible.
**Current Recommendations:**
- Outpatient follow-up in three months
- Consultation with Gastroenterology
- Continue SCIG treatment as is
- External upper gastrointestinal endoscopy and colonoscopy (please
return with findings)
- In case of increasing diarrhea, conduct outpatient stool
examinations, including testing for Giardia lamblia and
Cryptosporidium
- Abdominal ultrasound: annually
- Pulmonary function tests including diffusion measurement: annually
- Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in
stool: annually
- Gastroscopy: approximately every 2-3 years, depending on previous
findings and HP testing
- Chest X-ray or chest CT: in case of abnormal clinical presentation
or pulmonary function
- Annual seasonal influenza vaccination
### Patient Report 6
**Dear colleague, **
We are providing you with an update regarding our patient Mrs. Hilary
Sanders, born on 08/24/1976. She was under our inpatient care from
03/29/2023 to 04/05/2023.
**Diagnoses:**
- Suspected CVID-Associated enteropathy
- Known hepatosplenomegaly with a borderline enlarged portal vein, no
significant portocaval shunts. Multiple liver lesions, possibly
hemangiomas further evaluation if not already done.
- Known retroperitoneal and iliac lymphadenopathy, likely related to
the underlying condition.
- Known changes in the lower lung bases, likely associated with the
underlying condition, e.g., ILD. Refer to previous examinations.
- Capsule endoscopy: Incomplete capsule enteroscopy with no evidence
of inflammatory changes. Some hyperemia and blurry vascular pattern
observed in the visible colon.
- CVID-Associated Hepatopathy in the Form of Nodular Regenerative
Hyperplasia
**Other Diagnoses:** Common Variable Immunodeficiency Syndrome (CVID)
with:
- Complete IgG deficiency
- Complete IgM deficiency
- Complete IgA deficiency
- Leukopenia and lymphopenia
- Initiation of subcutaneous immunoglobulin substitution therapy with
Hizentra 20%
- Infectious manifestations: Frequent respiratory tract infections
- Non-Infectious manifestations:
- ITP (Immune Thrombocytopenia)
- Hepatosplenomegaly
- Lymphadenopathy in supraclavicular, infraclavicular,
thoracoabdominal, inguinal, and axillary regions
- Suspected Granulomatous-Lymphocytic Interstitial Lung Disease in
CVID
<!-- -->
- Iron-deficiency anemia
**Pysical Examination:** Patient in normal general condition and
nutritional status (175 cm, 65.8 kg. No resting dyspnea.
[Neuro (grossly orienting):]{.underline} awake, oriented to
time/place/person/situation, No evidence of focal neurological deficit.
No meningism.
[Head/neck]{.underline}: pharynx non-irritable. Moist, rosy mucous
membranes. Tongue occupied.
[Skin]{.underline}: intact, turgor normal, no icterus, no cyanosis.
[Thorax]{.underline}: normal configuration, no spinal palpitation, renal
bed clear.
[Lung]{.underline}: vesicular breath sound bds, no accessory sounds,
sonorous tapping sound bds.
[Cor]{.underline}: Cardiac action pure, rhythmic, no vitia typical
murmurs.
[Abdomen]{.underline}: regular bowel sounds, soft abdominal wall, no
tenderness, no resistances, no hepatosplenomegaly.
[Extremities]{.underline}: no edema. Feet warm. Dorsalis pedis +/+ and
posterior tibial artery +/+.
**Current Presentation:** The patient was admitted for further
evaluation of suspected CVID-associated enteropathy, as she had been
experiencing chronic diarrhea for the past three years. On admission,
the patient reported an overall good general and nutritional condition.
She described her current subjective well-being as good but mentioned
having chronic diarrhea for the past three years, with up to 7 bowel
movements per day. The stools were watery without any signs of blood.
There were no indications of infection, such as fever, chills, dysuria,
hematuria, cough, sputum, or dyspnea. She also experienced intermittent
left-sided upper abdominal pain, primarily postprandially. She had a
good appetite.
On the day of admission, an esophagogastroduodenoscopy was performed,
which revealed erythematous antral gastritis. Additionally, there was an
approximately 1 cm irregular mucosal area at the corpus-antrum junction
on the greater curvature side. A magnetic resonance imaging scan showed
no evidence of inflamed bowel loops, ruling out chronic inflammatory
bowel disease or celiac disease. To further investigate, a capsule
endoscopy was performed, with results pending at the time of discharge.
Hypovitaminosis B12 and folate deficiency were ruled out. However,
iron-deficiency anemia was confirmed, and the patient had already
scheduled an outpatient appointment for iron substitution. Serum levels
of vitamin B6 and zinc were pending at discharge.
Due to a moderate increase in transaminases and evidence of
hepatosplenomegaly, we decided, after detailed explanation and with the
patient\'s consent, to perform a sonographically guided liver biopsy in
addition to the planned endoscopy. The differential diagnosis included
CVID-associated hepatopathy. The biopsy was successfully conducted ,
without any post-interventional bleeding. Histology revealed mild acute
hepatitis and nodular regenerative hyperplasia.This finding could be
consistent with changes in CVID-associated hepatopathy. Granulomas were
not observed. With only slightly elevated liver values, a trial therapy
with budesonide was initiated, and clinical (diarrhea?) and laboratory
(transaminases?) follow-up will be performed in the outpatient setting.
We discharged Mrs. Sanders in a cardiopulmonarily stable condition.
[Current Recommendations:]{.underline}
- Follow-up in the gastroenterological outpatient clinic
**Esophagogastroduodenoscopy (EGD) on 04/01/2023:** Introduction of the
gastroscope in a left lateral position. Visualized up to the descending
part of the duodenum. Unremarkable upper esophageal sphincter. Normal
motility and mucosa in the upper, middle, and distal esophagus. The
Z-line is sharply demarcated in the hiatus. The cardia closes
sufficiently. The stomach expands normally in all parts under air
insufflation. Multiple glandular cysts \< 8 mm in size in the fundus and
corpus. Approximately 1 cm irregular mucosal area at the corpus-antrum
junction on the greater curvature side. Streaky redness of the mucosa in
the antrum. Unremarkable mucosa in the bulb. Unremarkable mucosa in the
descending part of the duodenum. Step biopsies performed.
[Summary]{.underline}: Erythematous antral gastritis. Approximately 1 cm
irregular mucosal area at the corpus-antrum junction on the greater
curvature side, suggestive of inflammation. Multiple glandular cysts
observed in the fundus and corpus.
[Abdominal MRI on 04/02/2023:]{.underline}
[Clinical information, questions, and justification for the
exam]{.underline}: Chronic diarrhea, suspected CVID-associated
enteropathy, differential diagnosis of celiac disease, and inflammatory
bowel disease (IBD). Assessment of malignancy.
Technique: After oral administration of mannitol solution and injection
of 40 mg Buscopan, a 3-Tesla abdominal MRI was performed.
[Findings]{.underline}: Multiple nodular consolidations and opacities
detected in the lower basal lung segments, measuring 7 x 4 mm, for
example, in the right lateral lower lobe (Series 18, Image 3).
Additionally, streaky-reticular changes observed. Left diaphragmatic
elevation. Liver globally enlarged and smooth-bordered with several
lesions showing mild to moderately hyperintense signals in T2-weighted
images and hypointense signals in T1-weighted images. These lesions
demonstrated increased enhancement in the early contrast phases,
especially those at the periphery, and more diffuse enhancement in the
late phases. For example, a lesion measuring 12 x 11 mm in Segment 2, a
lesion measuring 8 mm in Segment 8 and a lesion measuring 21 x 13 mm in
Segment 7. The portal vein measures borderline wide, up to 15 mm in
diameter. Gallbladder is unremarkable without evidence of stones. Intra-
and extrahepatic bile ducts are not dilated. Spleen significantly
enlarged, measuring 14 cm in pole-to-pole distance and 7.2 cm in
transverse diameter, homogeneous enhancement in native phases and late
contrast phase. Large accessory spleen located hilarly. Bilateral
adrenal glands appear slender. Pancreas displays typical appearance with
no ductal dilatation. Both kidneys are in orthotopic position, with
unremarkable cortical cysts on the right side. No signs of urinary
obstruction. The urinary bladder is moderately filled. No free fluid.
Adequate dilation of small bowel loops. No evidence of significant bowel
obstruction. No thickened bowel walls or increased post-contrast signal
in the bowel loops. Cystic lesion in the right ovary measuring 17 x 11
mm consistent with a corpus luteum cyst. Multiple enlarged
retroperitoneal lymph nodes observed, for example, paracaval node with a
short-axis diameter of 14 mm and right iliacoexternal node with a
short-axis diameter of 14.5 mm No evidence of enlarged mesenteric or
inguinal lymph nodes. | Subcutaneous immunoglobulin therapy |
Why do the Karna demand the conference begin in three days?
A. There is an immediate threat to the planet of Karn, and the Karna desperately need help from Earth.
B. The Karna are hoping to disrupt Earth's supply chain.
C. The Karna want to make Earth look bad in the eyes of the other planets.
D. The Karna are skilled negotiators and want to control the peace talks.
| IN CASE OF FIRE By RANDALL GARRETT There are times when a broken tool is better than a sound one, or a twisted personality more useful than a whole one. For instance, a whole beer bottle isn't half the weapon that half a beer bottle is ... Illustrated by Martinez In his office apartment, on the top floor of the Terran Embassy Building in Occeq City, Bertrand Malloy leafed casually through the dossiers of the four new men who had been assigned to him. They were typical of the kind of men who were sent to him, he thought. Which meant, as usual, that they were atypical. Every man in the Diplomatic Corps who developed a twitch or a quirk was shipped to Saarkkad IV to work under Bertrand Malloy, Permanent Terran Ambassador to His Utter Munificence, the Occeq of Saarkkad. Take this first one, for instance. Malloy ran his finger down the columns of complex symbolism that showed the complete psychological analysis of the man. Psychopathic paranoia. The man wasn't technically insane; he could be as lucid as the next man most of the time. But he was morbidly suspicious that every man's hand was turned against him. He trusted no one, and was perpetually on his guard against imaginary plots and persecutions. Number two suffered from some sort of emotional block that left him continually on the horns of one dilemma or another. He was psychologically incapable of making a decision if he were faced with two or more possible alternatives of any major importance. Number three ... Malloy sighed and pushed the dossiers away from him. No two men were alike, and yet there sometimes seemed to be an eternal sameness about all men. He considered himself an individual, for instance, but wasn't the basic similarity there, after all? He was—how old? He glanced at the Earth calendar dial that was automatically correlated with the Saarkkadic calendar just above it. Fifty-nine next week. Fifty-nine years old. And what did he have to show for it besides flabby muscles, sagging skin, a wrinkled face, and gray hair? Well, he had an excellent record in the Corps, if nothing else. One of the top men in his field. And he had his memories of Diane, dead these ten years, but still beautiful and alive in his recollections. And—he grinned softly to himself—he had Saarkkad. He glanced up at the ceiling, and mentally allowed his gaze to penetrate it to the blue sky beyond it. Out there was the terrible emptiness of interstellar space—a great, yawning, infinite chasm capable of swallowing men, ships, planets, suns, and whole galaxies without filling its insatiable void. Malloy closed his eyes. Somewhere out there, a war was raging. He didn't even like to think of that, but it was necessary to keep it in mind. Somewhere out there, the ships of Earth were ranged against the ships of the alien Karna in the most important war that Mankind had yet fought. And, Malloy knew, his own position was not unimportant in that war. He was not in the battle line, nor even in the major production line, but it was necessary to keep the drug supply lines flowing from Saarkkad, and that meant keeping on good terms with the Saarkkadic government. The Saarkkada themselves were humanoid in physical form—if one allowed the term to cover a wide range of differences—but their minds just didn't function along the same lines. For nine years, Bertrand Malloy had been Ambassador to Saarkkad, and for nine years, no Saarkkada had ever seen him. To have shown himself to one of them would have meant instant loss of prestige. To their way of thinking, an important official was aloof. The greater his importance, the greater must be his isolation. The Occeq of Saarkkad himself was never seen except by a handful of picked nobles, who, themselves, were never seen except by their underlings. It was a long, roundabout way of doing business, but it was the only way Saarkkad would do any business at all. To violate the rigid social setup of Saarkkad would mean the instant closing off of the supply of biochemical products that the Saarkkadic laboratories produced from native plants and animals—products that were vitally necessary to Earth's war, and which could be duplicated nowhere else in the known universe. It was Bertrand Malloy's job to keep the production output high and to keep the materiel flowing towards Earth and her allies and outposts. The job would have been a snap cinch in the right circumstances; the Saarkkada weren't difficult to get along with. A staff of top-grade men could have handled them without half trying. But Malloy didn't have top-grade men. They couldn't be spared from work that required their total capacity. It's inefficient to waste a man on a job that he can do without half trying where there are more important jobs that will tax his full output. So Malloy was stuck with the culls. Not the worst ones, of course; there were places in the galaxy that were less important than Saarkkad to the war effort. Malloy knew that, no matter what was wrong with a man, as long as he had the mental ability to dress himself and get himself to work, useful work could be found for him. Physical handicaps weren't at all difficult to deal with. A blind man can work very well in the total darkness of an infrared-film darkroom. Partial or total losses of limbs can be compensated for in one way or another. The mental disabilities were harder to deal with, but not totally impossible. On a world without liquor, a dipsomaniac could be channeled easily enough; and he'd better not try fermenting his own on Saarkkad unless he brought his own yeast—which was impossible, in view of the sterilization regulations. But Malloy didn't like to stop at merely thwarting mental quirks; he liked to find places where they were useful . The phone chimed. Malloy flipped it on with a practiced hand. "Malloy here." "Mr. Malloy?" said a careful voice. "A special communication for you has been teletyped in from Earth. Shall I bring it in?" "Bring it in, Miss Drayson." Miss Drayson was a case in point. She was uncommunicative. She liked to gather in information, but she found it difficult to give it up once it was in her possession. Malloy had made her his private secretary. Nothing—but nothing —got out of Malloy's office without his direct order. It had taken Malloy a long time to get it into Miss Drayson's head that it was perfectly all right—even desirable—for her to keep secrets from everyone except Malloy. She came in through the door, a rather handsome woman in her middle thirties, clutching a sheaf of papers in her right hand as though someone might at any instant snatch it from her before she could turn it over to Malloy. She laid them carefully on the desk. "If anything else comes in, I'll let you know immediately, sir," she said. "Will there be anything else?" Malloy let her stand there while he picked up the communique. She wanted to know what his reaction was going to be; it didn't matter because no one would ever find out from her what he had done unless she was ordered to tell someone. He read the first paragraph, and his eyes widened involuntarily. "Armistice," he said in a low whisper. "There's a chance that the war may be over." "Yes, sir," said Miss Drayson in a hushed voice. Malloy read the whole thing through, fighting to keep his emotions in check. Miss Drayson stood there calmly, her face a mask; her emotions were a secret. Finally, Malloy looked up. "I'll let you know as soon as I reach a decision, Miss Drayson. I think I hardly need say that no news of this is to leave this office." "Of course not, sir." Malloy watched her go out the door without actually seeing her. The war was over—at least for a while. He looked down at the papers again. The Karna, slowly being beaten back on every front, were suing for peace. They wanted an armistice conference—immediately. Earth was willing. Interstellar war is too costly to allow it to continue any longer than necessary, and this one had been going on for more than thirteen years now. Peace was necessary. But not peace at any price. The trouble was that the Karna had a reputation for losing wars and winning at the peace table. They were clever, persuasive talkers. They could twist a disadvantage to an advantage, and make their own strengths look like weaknesses. If they won the armistice, they'd be able to retrench and rearm, and the war would break out again within a few years. Now—at this point in time—they could be beaten. They could be forced to allow supervision of the production potential, forced to disarm, rendered impotent. But if the armistice went to their own advantage ... Already, they had taken the offensive in the matter of the peace talks. They had sent a full delegation to Saarkkad V, the next planet out from the Saarkkad sun, a chilly world inhabited only by low-intelligence animals. The Karna considered this to be fully neutral territory, and Earth couldn't argue the point very well. In addition, they demanded that the conference begin in three days, Terrestrial time. The trouble was that interstellar communication beams travel a devil of a lot faster than ships. It would take more than a week for the Earth government to get a vessel to Saarkkad V. Earth had been caught unprepared for an armistice. They objected. The Karna pointed out that the Saarkkad sun was just as far from Karn as it was from Earth, that it was only a few million miles from a planet which was allied with Earth, and that it was unfair for Earth to take so much time in preparing for an armistice. Why hadn't Earth been prepared? Did they intend to fight to the utter destruction of Karn? It wouldn't have been a problem at all if Earth and Karn had fostered the only two intelligent races in the galaxy. The sort of grandstanding the Karna were putting on had to be played to an audience. But there were other intelligent races throughout the galaxy, most of whom had remained as neutral as possible during the Earth-Karn war. They had no intention of sticking their figurative noses into a battle between the two most powerful races in the galaxy. But whoever won the armistice would find that some of the now-neutral races would come in on their side if war broke out again. If the Karna played their cards right, their side would be strong enough next time to win. So Earth had to get a delegation to meet with the Karna representatives within the three-day limit or lose what might be a vital point in the negotiations. And that was where Bertrand Malloy came in. He had been appointed Minister and Plenipotentiary Extraordinary to the Earth-Karn peace conference. He looked up at the ceiling again. "What can I do?" he said softly. On the second day after the arrival of the communique, Malloy made his decision. He flipped on his intercom and said: "Miss Drayson, get hold of James Nordon and Kylen Braynek. I want to see them both immediately. Send Nordon in first, and tell Braynek to wait." "Yes, sir." "And keep the recorder on. You can file the tape later." "Yes, sir." Malloy knew the woman would listen in on the intercom anyway, and it was better to give her permission to do so. James Nordon was tall, broad-shouldered, and thirty-eight. His hair was graying at the temples, and his handsome face looked cool and efficient. Malloy waved him to a seat. "Nordon, I have a job for you. It's probably one of the most important jobs you'll ever have in your life. It can mean big things for you—promotion and prestige if you do it well." Nordon nodded slowly. "Yes, sir." Malloy explained the problem of the Karna peace talks. "We need a man who can outthink them," Malloy finished, "and judging from your record, I think you're that man. It involves risk, of course. If you make the wrong decisions, your name will be mud back on Earth. But I don't think there's much chance of that, really. Do you want to handle small-time operations all your life? Of course not. "You'll be leaving within an hour for Saarkkad V." Nordon nodded again. "Yes, sir; certainly. Am I to go alone?" "No," said Malloy, "I'm sending an assistant with you—a man named Kylen Braynek. Ever heard of him?" Nordon shook his head. "Not that I recall, Mr. Malloy. Should I have?" "Not necessarily. He's a pretty shrewd operator, though. He knows a lot about interstellar law, and he's capable of spotting a trap a mile away. You'll be in charge, of course, but I want you to pay special attention to his advice." "I will, sir," Nordon said gratefully. "A man like that can be useful." "Right. Now, you go into the anteroom over there. I've prepared a summary of the situation, and you'll have to study it and get it into your head before the ship leaves. That isn't much time, but it's the Karna who are doing the pushing, not us." As soon as Nordon had left, Malloy said softly: "Send in Braynek, Miss Drayson." Kylen Braynek was a smallish man with mouse-brown hair that lay flat against his skull, and hard, penetrating, dark eyes that were shadowed by heavy, protruding brows. Malloy asked him to sit down. Again Malloy went through the explanation of the peace conference. "Naturally, they'll be trying to trick you every step of the way," Malloy went on. "They're shrewd and underhanded; we'll simply have to be more shrewd and more underhanded. Nordon's job is to sit quietly and evaluate the data; yours will be to find the loopholes they're laying out for themselves and plug them. Don't antagonize them, but don't baby them, either. If you see anything underhanded going on, let Nordon know immediately." "They won't get anything by me, Mr. Malloy." By the time the ship from Earth got there, the peace conference had been going on for four days. Bertrand Malloy had full reports on the whole parley, as relayed to him through the ship that had taken Nordon and Braynek to Saarkkad V. Secretary of State Blendwell stopped off at Saarkkad IV before going on to V to take charge of the conference. He was a tallish, lean man with a few strands of gray hair on the top of his otherwise bald scalp, and he wore a hearty, professional smile that didn't quite make it to his calculating eyes. He took Malloy's hand and shook it warmly. "How are you, Mr. Ambassador?" "Fine, Mr. Secretary. How's everything on Earth?" "Tense. They're waiting to see what is going to happen on Five. So am I, for that matter." His eyes were curious. "You decided not to go yourself, eh?" "I thought it better not to. I sent a good team, instead. Would you like to see the reports?" "I certainly would." Malloy handed them to the secretary, and as he read, Malloy watched him. Blendwell was a political appointee—a good man, Malloy had to admit, but he didn't know all the ins and outs of the Diplomatic Corps. When Blendwell looked up from the reports at last, he said: "Amazing! They've held off the Karna at every point! They've beaten them back! They've managed to cope with and outdo the finest team of negotiators the Karna could send." "I thought they would," said Malloy, trying to appear modest. The secretary's eyes narrowed. "I've heard of the work you've been doing here with ... ah ... sick men. Is this one of your ... ah ... successes?" Malloy nodded. "I think so. The Karna put us in a dilemma, so I threw a dilemma right back at them." "How do you mean?" "Nordon had a mental block against making decisions. If he took a girl out on a date, he'd have trouble making up his mind whether to kiss her or not until she made up his mind for him, one way or the other. He's that kind of guy. Until he's presented with one, single, clear decision which admits of no alternatives, he can't move at all. "As you can see, the Karna tried to give us several choices on each point, and they were all rigged. Until they backed down to a single point and proved that it wasn't rigged, Nordon couldn't possibly make up his mind. I drummed into him how important this was, and the more importance there is attached to his decisions, the more incapable he becomes of making them." The Secretary nodded slowly. "What about Braynek?" "Paranoid," said Malloy. "He thinks everyone is plotting against him. In this case, that's all to the good because the Karna are plotting against him. No matter what they put forth, Braynek is convinced that there's a trap in it somewhere, and he digs to find out what the trap is. Even if there isn't a trap, the Karna can't satisfy Braynek, because he's convinced that there has to be—somewhere. As a result, all his advice to Nordon, and all his questioning on the wildest possibilities, just serves to keep Nordon from getting unconfused. "These two men are honestly doing their best to win at the peace conference, and they've got the Karna reeling. The Karna can see that we're not trying to stall; our men are actually working at trying to reach a decision. But what the Karna don't see is that those men, as a team, are unbeatable because, in this situation, they're psychologically incapable of losing." Again the Secretary of State nodded his approval, but there was still a question in his mind. "Since you know all that, couldn't you have handled it yourself?" "Maybe, but I doubt it. They might have gotten around me someway by sneaking up on a blind spot. Nordon and Braynek have blind spots, but they're covered with armor. No, I'm glad I couldn't go; it's better this way." The Secretary of State raised an eyebrow. " Couldn't go, Mr. Ambassador?" Malloy looked at him. "Didn't you know? I wondered why you appointed me, in the first place. No, I couldn't go. The reason why I'm here, cooped up in this office, hiding from the Saarkkada the way a good Saarkkadic bigshot should, is because I like it that way. I suffer from agoraphobia and xenophobia. "I have to be drugged to be put on a spaceship because I can't take all that empty space, even if I'm protected from it by a steel shell." A look of revulsion came over his face. "And I can't stand aliens!" THE END Transcriber's Note: This etext was produced from Astounding Science Fiction March 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | D. The Karna are skilled negotiators and want to control the peace talks. |
What is Ahra referring to when she says "something has been taken?"
A. Gertrude's happiness.
B. Beamish's money.
C. The cansin male.
D. Jig and Shannon's safety.
| The Blue Behemoth By LEIGH BRACKETT Shannon's Imperial Circus was a jinxed space-carny leased for a mysterious tour of the inner worlds. It made a one-night pitch on a Venusian swamp-town—to find that death stalked it from the jungle in a tiny ball of flame. [Transcriber's Note: This etext was produced from Planet Stories May 1943. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Bucky Shannon leaned forward across the little hexagonal table. He knocked over the pitcher of thil , but it didn't matter. The pitcher was empty. He jabbed me in the breastbone with his forefinger, not very hard. Not hard enough to jar the ribs clean loose, just enough to spring them. "We," he said, "are broke. We are finished, through. Washed up and down the drain." He added, as an afterthought, "Destitute." I looked at him. I said sourly, "You're kidding!" "Kidding." Shannon put his elbows on the table and peered at me through a curtain of very blond hair that was trying hard to be red. "He says I'm kidding! With Shannon's Imperial Circus, the Greatest Show in Space, plastered so thick with attachments...." "It's no more plastered than you are." I was sore because he'd been a lot quicker grabbing the pitcher. "The Greatest Show in Space. Phooey! I've wet-nursed Shannon's Imperial Circus around the Triangle for eleven years, and I know. It's lousy, it's mangy, it's broken-down! Nothing works, from the ship to the roustabouts. In short, it stinks!" I must have had the pitcher oftener than I thought. Nobody insults Buckhalter Shannon's Imperial Circus to Buckhalter Shannon's face unless he's tired and wants a long rest in a comfy fracture-frame. Shannon got up. He got up slowly. I had plenty of time to see his grey-green eyes get sleepy, and hear the quarter-Earth-blood Martian girl wailing about love over by the battered piano, and watch the slanting cat-eyes of the little dark people at the tables swing round toward us, pleased and kind of hungry. I had plenty of time to think how I only weigh one-thirty-seven to Shannon's one-seventy-five, and how I'm not as young as I used to be. I said, "Bucky. Hold on, fella. I...." Somebody said, "Excuse me, gentlemen. Is one of you Mister Buckhalter Shannon?" Shannon put his hands down on his belt. He closed his eyes and smiled pleasantly and said, very gently: "Would you be collecting for the feed bill, or the fuel?" I shot a glance at the newcomer. He'd saved me from a beating, even if he was a lousy bill-collecter; and I felt sorry for him. Bucky Shannon settled his shoulders and hips like a dancer. The stranger was a little guy. He even made me look big. He was dressed in dark-green synthesilk, very conservative. There was a powdering of grey in his hair and his skin was pink, soft, and shaved painfully clean. He had the kind of a face that nice maiden-ladies will trust with their last dime. I looked for his strong-arm squad. There didn't seem to be any. The little guy looked at Shannon with pale blue eyes like a baby, and his voice was softer than Bucky's. He said, "I don't think you understand." I felt cold, suddenly, between the shoulders. Somebody scraped a chair back. It sounded like he'd ripped the floor open, it was so quiet. I got my brassies on, and my hands were sweating. Bucky Shannon sighed, and let his fist start traveling, a long, deceptive arc. Then I saw what the little guy was holding in his hand. I yelled and knocked the table over into Bucky. It made a lot of noise. It knocked him sideways and down, and the little dark men jumped up, quivering and showing their teeth. The Martian girl screamed. Bucky heaved the table off his lap and cursed me. "What's eating you, Jig? I'm not going to hurt him." "Shut up," I said. "Look what he's got there. Money!" The little guy looked at me. He hadn't turned a hair. "Yes," he said. "Money. Quite a lot of it. Would you gentlemen permit me to join you?" Bucky Shannon got up. He grinned his pleasantest grin. "Delighted. I'm Shannon. This is Jig Bentley, my business manager." He looked down at the table. "I'm sorry about that. Mistaken identity." The little guy smiled. He did it with his lips. The rest of his face stayed placid and babyish, almost transparent. I realized with a start that it wasn't transparent at all. It was the most complete dead-pan I ever met, and you couldn't see into those innocent blue eyes any more than you could see through sheet metal. I didn't like him. I didn't like him at all. But he had money. I said, "Howdy. Let's go find a booth. These Marshies make me nervous, looking like hungry cats at a mouse-hole." The little guy nodded. "Excellent idea. My name is Beamish. Simon Beamish. I wish to—ah—charter your circus." I looked at Bucky. He looked hungrier than the Marshies did. We didn't say anything until we got Beamish into a curtained booth with a fresh pitcher of thil on the table. Then I cleared my throat. "What exactly did you have in mind, Mr. Beamish?" Beamish sipped his drink, made a polite face, and put it down. "I have independent means, gentlemen. It has always been my desire to lighten the burden of life for those less fortunate...." Bucky got red around the ears. "Just a minute," he murmured, and started to get up. I kicked him under the table. "Shut up, you lug. Let Mister Beamish finish." He sat down, looking like a mean dog waiting for the postman. Beamish ignored him. He went on, quietly, "I have always held that entertainment, of the right sort, is the most valuable aid humanity can have in its search for the alleviation of toil and boredom...." I said, "Sure, sure. But what was your idea?" "There are many towns along the Venusian frontiers where no entertainment of the— proper sort has been available. I propose to remedy that. I propose to charter your circus, Mister Shannon, to make a tour of several settlements along the Tehara Belt." Bucky had relaxed. His grey-green eyes began to gleam. He started to speak, and I kicked him again. "That would be expensive, Mister Beamish," I said. "We'd have to cancel several engagements...." He looked at me. I was lying, and he knew it. But he said, "I quite understand that. I would be prepared...." The curtains were yanked back suddenly. Beamish shut up. Bucky and I glared at the head and shoulders poking in between the drapes. It was Gow, our zoo-man—a big, ugly son-of-a-gun from a Terran colony on Mercury. I was there once. Gow looks a lot like the scenery—scowling, unapproachable, and tough. His hands, holding the curtains apart, had thick black hair on them and were not much larger than the hams of a Venusian swamp-rhino. He said, "Boss, Gertrude's actin' up again." "Gertrude be blowed," growled Bucky. "Can't you see I'm busy?" Gow's black eyes were unpleasant. "I'm tellin' you, Boss, Gertrude ain't happy. She ain't had the right food. If something...." I said, "That'll all be taken care of, Gow. Run along now." He looked at me like he was thinking it wouldn't take much timber to fit me for a coffin. "Okay! But Gertrude's unhappy. She's lonesome, see? And if she don't get happier pretty soon I ain't sure your tin-pot ship'll hold her." He pulled the curtains to and departed. Bucky Shannon groaned. Beamish cleared his throat and said, rather stiffly, "Gertrude?" "Yeah. She's kind of temperamental." Bucky took a quick drink. I finished for him. "She's the star attraction of our show, Mr. Beamish. A real blue-swamp Venusian cansin . The only other one on the Triangle belongs to Savitt Brothers, and she's much smaller than Gertrude." She was also much younger, but I didn't go into that. Gertrude may be a little creaky, but she's still pretty impressive. I only hoped she wouldn't die on us, because without her we'd have a sicker-looking circus than even I could stand. Beamish looked impressed. "A cansin . Well, well! The mystery surrounding the origin and species of the cansin is a fascinating subject. The extreme rarity of the animal...." We were getting off the subject. I said tactfully, "We'd have to have at least a hundred U.C.'s." It was twice what we had any right to ask. I was prepared to dicker. Beamish looked at me with that innocent dead pan. For a fraction of a second I thought I saw something back of his round blue eyes, and my stomach jumped like it was shot. Beamish smiled sweetly. "I'm not much of a bargainer. One hundred Universal Credits will be agreeable to me." He dragged out a roll as big as my two fists, peeled off half a dozen credit slips, and laid them on the table. "By way of a retainer, gentleman. My attorney and I will call on you in the morning with a contract and itinerary. Good night." We said good night, trying not to drool. Beamish went away. Bucky made grab for the money, but I beat him to it. "Scram," I said. "There are guys waiting for this. Big guys with clubs. Here." I gave him a small-denomination slip I'd been holding out. "We can get lushed enough on this." Shannon has a good vocabulary. He used it. When he got his breath back he said suddenly, "Beamish is pulling some kind of a game." "Yeah." "It may be crooked." "Sure. And he may be screwball and on the level. For Pete's sake!" I yelled. "You want to sit here till we all dry up and blow away?" Shannon looked at me, kind of funny. He looked at the bulge in my tunic where the roll was. He raked back his thick light hair. "Yeah," he said. "I hope there'll be enough left to bribe the jury." He poked his head outside. "Hey, boy! More thildatum !" It was pretty late when we got back to the broken-down spaceport where Shannon's Imperial Circus was crouching beneath its attachments. Late as it was, they were waiting for us. About twenty of them, sitting around and smoking and looking very ugly. It was awfully lonesome out there, with the desert cold and restless under the two moons. There's a smell to Mars, like something dead and dried long past decay, but still waiting. An unhappy smell. The blown red dust gritted in my teeth. Bucky Shannon walked out into the glare of the light at the entrance to the roped-off space around the main lock. He was pretty steady on his feet. He waved and said, "Hiya, boys." They got up off the steps, and the packing cases, and came toward us. I grinned and got into my brassies. We felt we owed those boys a lot more than money. It grates on a man's pride to have to sneak in and out of his own property through the sewage lock. This was the first time in weeks we'd come in at the front door. I waved the money in their faces. That stopped them. Very solemnly, Bucky and I checked the bills, paid them, and pocketed the receipts. Bucky yawned and stretched sleepily. "Now?" he said. "Now," I said. We had a lot of fun. Some of the boys inside the ship came out to join in. We raised a lot of dust and nobody got killed, quite. We all went home happy. They had their money, and we had their blood. The news was all over the ship before we got inside. The freaks and the green girl from Tethys who could roll herself like a hoop, and Zurt the muscle man from Jupiter, and all the other assorted geeks and kinkers and joeys that make up the usual corny carnie were doing nip-ups in the passageways and drooling over the thought of steer and toppings. Bucky Shannon regarded them possessively, wiping blood from his nose. "They're good guys, Jig. Swell people. They stuck by me, and I've rewarded them." I said, "Sure," rather sourly. Bucky hiccoughed. "Let's go see Gertrude." I didn't want to see Gertrude. I never got over feeling funny going into the brute tank, especially at night or out in space. I'm a city guy, myself. The smell and sound of wildness gives me goose bumps. But Bucky was looking stubborn, so I shrugged. "Okay. But just for a minute. Then we go beddy-bye." "You're a pal, Jif. Bes' li'l' guy inna worl'...." The fight had just put the topper on him. I was afraid he'd fall down the ladder and break his neck. That's why I went along. If I hadn't.... Oh, well, what's a few nightmares among friends? It was dark down there in the tank. Way off at the other end, there was a dim glow. Gow was evidently holding Gertrude's hand. We started down the long passageway between the rows of cages and glassed-in tanks and compression units. Our footsteps sounded loud and empty on the iron floor. I wasn't near as happy as Shannon, and my skin began to crawl a little. It's the smell, I think; rank and sour and wild. And the sound of them, breathing and rustling in the dark, with the patient hatred walled around them as strong as the cage bars. Bucky Shannon lurched against me suddenly. I choked back a yell, and then wiped the sweat off my forehead and cursed. The scream came again. A high, ragged, whistling screech like nothing this side of hell, ripping through the musty darkness. Gertrude, on the wailing wall. It had been quiet. Now every brute in the place let go at the same time. My stomach turned clear over. I called Gertrude every name I could think of, and I couldn't hear myself doing it. Presently a great metallic clash nearly burst my eardrums, and the beasts shut up. Gow had them nicely conditioned to that gong. But they didn't quiet down. Not really. They were uneasy. You can feel them inside you when they're uneasy. I think that's why I'm scared of them. They make me feel like I'm not human as I thought—like I wanted to put my back-hair up and snarl. Yeah. They were uneasy that night, all of a sudden.... Gow glared at us as we came up into the lantern light. "She's gettin' worse," he said. "She's lonesome." "That's tough," said Bucky Shannon. His grey-green eyes looked like an owl's. He swayed slightly. "That's sure tough." He sniffled. I looked at Gertrude. Her cage is the biggest and strongest in the tank and even so she looked as though she could break it open just taking a deep breath. I don't know if you've ever seen a cansin . There's only two of them on the Triangle. If you haven't, nothing I can say will make much difference. They're what the brain gang calls an "end of evolution." Seems old Dame Nature had an idea that didn't jell. The cansins were pretty successful for a while, it seems, but something gummed up the works and now there's only a few left, way in the deep-swamp country, where even the Venusians hardly ever go. Living fossils. I wouldn't know, of course, but Gertrude looks to me like she got stuck some place between a dinosaur and a grizzly bear, with maybe a little bird blood thrown in. Anyway, she's big. I couldn't help feeling sorry for her. She was crouched in the cage with her hands—yeah, hands—hanging over her knees and her snaky head sunk into her shoulders, looking out. Just looking. Not at anything. Her eyes were way back in deep horny pits, like cold green fire. The lantern light was yellow on her blue-black skin, but it made the mane, or crest, of coarse wide scales that ran from between her eyes clear down to her flat, short tail, burn all colors. She looked like old Mother Misery herself, from way back before time began. Gow said softly, "She wants a mate. And somebody better get her one." Bucky Shannon sniffled again. I said irritably, "Be reasonable, Gow! Nobody's ever seen a male cansin . There may not even be any." Gertrude screamed again. She didn't move, not even to raise her head. The sadness just built up inside her until it had to come out. That close, the screech was deafening, and it turned me all limp and cold inside. The loneliness, the sheer stark, simple pain.... Bucky Shannon began to cry. I snarled, "You'll have to snap her out of this, Gow. She's driving the rest of 'em nuts." He hammered on his gong, and things quieted down again. Gow stood looking out over the tank, sniffing a little, like a hound. Then he turned to Gertrude. "I saved her life," he said. "When we bought her out of Hanak's wreck and everybody thought she was too hurt to live, I saved her. I know her. I can do things with her. But this time...." He shrugged. He was huge and tough and ugly, and his voice was like a woman's talking about a sick child. "This time," he said, "I ain't sure." "Well for Pete's sake, do what you can. We got a charter, and we need her." I took Shannon's arm. "Come to bed, Bucky darlin'." He draped himself over my shoulder and we went off. Gow didn't look at us. Bucky sobbed. "You were right, Jig," he mumbled. "Circus is no good. I know it. But it's all I got. I love it, Jig. Unnerstan' me? Like Gow there with Gertrude. She's ugly and no good, but he loves her. I love...." "Sure, sure," I told him. "Stop crying down my neck." We were a long way from the light, then. The cages and tanks loomed high and black over us. It was still. The secret, uneasy motion all around us and the scruffing of our feet only made it stiller. Bucky was almost asleep on me. I started to slap him. And then the mist rose up out of the darkness in little lazy coils, sparkling faintly with blue, cold fire. I yelled, "Gow! Gow, the Vapor snakes! Gow—for God's sake!" I started to run, back along the passageway. Bucky weighed on me, limp and heavy. The noise burst suddenly in a deafening hell of moans and roars and shrieks, packed in tight by the metal walls, and above it all I could hear Gertrude's lonely, whistling scream. I thought, " Somebody's down here. Somebody let 'em out. Somebody wants to kill us! " I tried to yell again. It strangled in my throat. I sobbed, and the sweat was thick and cold on me. One of Bucky's dragging, stumbling feet got between mine. We fell. I rolled on top of him, covering his face, and buried my own face in the hollow of his shoulder. The first snake touched me. It was like a live wire, sliding along the back of my neck. I screamed. It came down along my cheek, hunting my mouth. There were more of them, burning me through my clothes. Bucky moaned and kicked under me. I remember hanging on and thinking, "This is it. This is it, and oh God, I'm scared!" Then I went out. II Kanza the Martian croaker, was bending over me when I woke up. His little brown face was crinkled with laughter. He'd lost most of his teeth, and he gummed thak -weed. It smelt. "You pretty, Mis' Jig," he giggled. "You funny like hell." He slapped some cold greasy stuff on my face. It hurt. I cursed him and said, "Where's Shannon? How is he?" "Mis' Bucky okay. You save life. You big hero, Mis' Jig. Mis' Gow come nickuhtime get snakes. You hero. Haw! You funny like hell!" I said, "Yeah," and pushed him away and got up. I almost fell down a couple of times, but presently I made it to the mirror over the washstand—I was in my own cell—and I saw what Kanza meant. The damned snakes had done a good job. I looked like I was upholstered in Scotch plaid. I felt sick. Bucky Shannon opened the door. He looked white and grim, and there was a big burn across his neck. He said: "Beamish is here with his lawyer." I picked up my shirt. "Right with you." Kanza went out, still giggling. Bucky closed the door. "Jig," he said, "those vapor worms were all right when we went in. Somebody followed us down and let them out. On purpose." I hurt all over. I growled, "With that brain, son, you should go far. Nobody saw anything, of course?" Bucky shook his head. "Question is, Jig, who wants to kill us, and why?" "Beamish. He realizes he's been gypped." "One hundred U.C.'s," said Bucky softly, "for a few lousy swampedge mining camps. It stinks, Jig. You think we should back out?" I shrugged. "You're the boss man. I'm only the guy that beats off the creditors." "Yeah," Bucky said reflectively. "And I hear starvation isn't a comfortable death. Okay, Jig. Let's go sign." He put his hand on the latch and looked at my feet. "And—uh—Jig, I...." I said, "Skip it. The next time, just don't trip me up, that's all!" We had a nasty trip to Venus. Gertrude kept the brute tank on edge, and Gow, on the rare occasions he came up for air, went around looking like a disaster hoping to happen. To make it worse, Zurt the Jovian strong-man got hurt during the take-off, and the Mercurian cave-cat had kittens. Nobody would have minded that, only one of 'em had only four legs. It lived just long enough to scare that bunch of superstitious dopes out of their pants. Circus people are funny that way. Shannon and I did a little quiet sleuthing, but it was a waste of time. Anybody in the gang might have let those electric worms out on us. It didn't help any to know that somebody, maybe the guy next to you at dinner, was busy thinking ways to kill you. By the time we hit Venus, I was ready to do a Brodie out the refuse chute. Shannon set the crate down on the edge of Nahru, the first stop on our itinerary. I stood beside him, looking out the ports at the scenery. It was Venus, all right. Blue mud and thick green jungle and rain, and a bunch of ratty-looking plastic shacks huddling together in the middle of it. Men in slickers were coming out for a look. I saw Beamish's sleek yacht parked on a cradle over to the left, and our router's runabout beside it. Bucky Shannon groaned. "A blue one, Jig. A morgue if I ever saw one!" I snarled, "What do you want, with this lousy dog-and-pony show!" and went out. He followed. The gang was converging on the lock, but they weren't happy. You get so you can feel those things. The steamy Venus heat was already sneaking into the ship. While we passed the hatchway to the brute tank, I could hear Gertrude, screaming. The canvasmen were busy setting up the annex, slopping and cursing in the mud. The paste brigade was heading for the shacks. Shannon and I stood with the hot rain running off our slickers, looking. I heard a noise behind me and looked around. Ahra the Nahali woman was standing in the mud with her arms up and her head thrown back, and her triangular mouth open like a thirsty dog. She didn't have anything on but her blue-green, hard scaled hide, and she was chuckling. It didn't sound nice. You find a lot of Nahali people in side-shows, doing tricks with the electric power they carry in their own bodies. They're Venusian middle-swampers, they're not human, and they never forget it. Ahra opened her slitted red eyes and looked at me and laughed with white reptilian teeth. "Death," she whispered. "Death and trouble. The jungle tells me. I can smell it in the swamp wind." The hot rain sluiced over her. She shivered, and the pale skin under her jaw pulsed like a toad's, and her eyes were red. "The deep swamps are angry," she whispered. "Something has been taken. They are angry, and I smell death in the wind!" She turned away, laughing, and I cursed her, and my stomach was tight and cold. Bucky said, "Let's eat if they have a bar in this dump." We weren't half way across the mud puddle that passed as a landing field when a man came out of a shack on the edge of the settlement. We could see him plainly, because he was off to one side of the crowd. He fell on his knees in the mud, making noises. It took him three or four tries to get our names out clear enough to understand. Bucky said, "Jig—it's Sam Kapper." We started to run. The crowd, mostly big unshaken miners, wheeled around to see what was happening. People began to close in on the man who crawled and whimpered in the mud. Sam Kapper was a hunter, supplying animals to zoos and circuses and carnivals. He'd given us good deals a couple of times, when we weren't too broke, and we were pretty friendly. I hadn't seen him for three seasons. I remembered him as a bronzed, hard-bitten guy, lean and tough as a twist of tung wire. I felt sick, looking down at him. Bucky started to help him up. Kapper was crying, and he jerked all over like animals I've seen that were scared to death. Some guy leaned over and put a cigarette in his mouth and lighted it for him. I was thinking about Kapper, then, and I didn't pay much attention. I only caught a glimpse of the man's face as he straightened up. I didn't realize until later that he looked familiar. We got Kapper inside the shack. It turned out to be a cheap bar, with a couple of curtained booths at the back. We got him into one and pulled the curtain in a lot of curious faces. Kapper dragged hard on the cigarette. The man that gave it to him was gone. Bucky said gently, "Okay, Sam. Relax. What's the trouble?" Kapper tried to straighten up. He hadn't shaved. The lean hard lines of his face had gone slack and his eyes were bloodshot. He was covered with mud, and his mouth twitched like a sick old man's. He said thickly, "I found it. I said I'd do it, and I did. I found it and brought it out." The cigarette stub fell out of his mouth. He didn't notice it. "Help me," he said simply. "I'm scared." His mouth drooled. "I got it hidden. They want to find out, but I won't tell 'em. It's got to go back. Back where I found it. I tried to take it, but they wouldn't let me, and I was afraid they'd find it...." He reached suddenly and grabbed the edge of the table. "I don't know how they found out about it, but they did. I've got to get it back. I've got to...." Bucky looked at me. Kapper was blue around the mouth. I was scared, suddenly. I said, "Get what back where?" Bucky got up. "I'll get a doctor," he said. "Stick with him." Kapper grabbed his wrist. Kapper's nails were blue and the cords in his hands stood out like guy wires. "Don't leave me. Got to tell you—where it is. Got to take it back. Promise you'll take it back." He gasped and struggled over his breathing. "Sure," said Bucky. "Sure, well take it back. What is it?" Kapper's face was horrible. I felt sick, listening to him fight for air. I wanted to go for a doctor anyway, but somehow I knew it was no use. Kapper whispered, " Cansin . Male. Only one. You don't know...! Take him back." "Where is it, Sam?" I reached across Bucky suddenly and jerked the curtain back. Beamish was standing there. Beamish, bent over, with his ear cocked. Kapper made a harsh strangling noise and fell across the table. Beamish never changed expression. He didn't move while Bucky felt Kapper's pulse. Bucky didn't need to say anything. We knew. "Heart?" said Beamish finally. "Yeah," said Bucky. He looked as bad as I felt. "Poor Sam." I looked at the cigarette stub smoldering on the table. I looked at Beamish with his round dead baby face. I climbed over Shannon and pushed Beamish suddenly down into his lap. "Keep this guy here till I get back," I said. Shannon stared at me. Beamish started to get indignant. "Shut up," I told him. "We got a contract." I yanked the curtains shut and walked over to the bar. I began to notice something, then. There were quite a lot of men in the place. At first glance they looked okay—a hard-faced, muscular bunch of miners in dirty shirts and high boots. Then I looked at their hands. They were dirty enough. But they never did any work in a mine, on Venus or anywhere else. The place was awfully quiet, for that kind of a place. The bartender was a big pot-bellied swamp-edger with pale eyes and thick white hair coiled up on top of his bullet head. He was not happy. I leaned on the bar. " Lhak ," I said. He poured it, sullenly, out of a green bottle. I reached for it, casually. "That guy we brought in," I said. "He sure has a skinful. Passed out cold. What's he been spiking his drinks with?" " Selak ," said a voice in my ear. "As if you didn't know." I turned. The man who had given Kapper the cigarette was standing behind me. And I remembered him, then. | C. The cansin male. |
What is the narrator's ethnicity?
A. Irish
B. American
C. Leprechaun
D. Japanese
| Every writer must seek his own Flowery Kingdom in imagination's wide demesne, and if that search can begin and end on Earth his problem has been greatly simplified. In post-war Japan Walt Sheldon has found not only serenity, but complete freedom to write undisturbed about the things he treasures most. A one-time Air Force officer, he has turned to fantasy in his lighter moments, to bring us such brightly sparkling little gems as this. houlihan's equation by ... Walt Sheldon The tiny spaceship had been built for a journey to a star. But its small, mischievous pilots had a rendezvous with destiny—on Earth. I must admit that at first I wasn't sure I was hearing those noises. It was in a park near the nuclear propulsion center—a cool, green spot, with the leaves all telling each other to hush, be quiet, and the soft breeze stirring them up again. I had known precisely such a secluded little green sanctuary just over the hill from Mr. Riordan's farm when I was a boy. Now it was a place I came to when I had a problem to thrash out. That morning I had been trying to work out an equation to give the coefficient of discharge for the matter in combustion. You may call it gas, if you wish, for we treated it like gas at the center for convenience—as it came from the rocket tubes in our engine. Without this coefficient to give us control, we would have lacked a workable equation when we set about putting the first moon rocket around those extraordinary engines of ours, which were still in the undeveloped blueprint stage. I see I shall have to explain this, although I had hoped to get right along with my story. When you start from scratch, matter discharged from any orifice has a velocity directly proportional to the square root of the pressure-head driving it. But when you actually put things together, contractions or expansions in the gas, surface roughness and other factors make the velocity a bit smaller. At the terrible discharge speed of nuclear explosion—which is what the drive amounts to despite the fact that it is simply water in which nuclear salts have been previously dissolved—this small factor makes quite a difference. I had to figure everything into it—diameter of the nozzle, sharpness of the edge, the velocity of approach to the point of discharge, atomic weight and structure— Oh, there is so much of this that if you're not a nuclear engineer yourself it's certain to weary you. Perhaps you had better take my word for it that without this equation—correctly stated, mind you—mankind would be well advised not to make a first trip to the moon. And all this talk of coefficients and equations sits strangely, you might say, upon the tongue of a man named Kevin Francis Houlihan. But I am, after all, a scientist. If I had not been a specialist in my field I would hardly have found myself engaged in vital research at the center. Anyway, I heard these little noises in the park. They sounded like small working sounds, blending in eerily mysterious fashion with a chorus of small voices. I thought at first it might be children at play, but then at the time I was a bit absent-minded. I tiptoed to the edge of the trees, not wanting to deprive any small scalawags of their pleasure, and peered out between the branches. And what do you suppose I saw? Not children, but a group of little people, hard at work. There was a leader, an older one with a crank face. He was beating the air with his arms and piping: "Over here, now! All right, bring those electrical connections over here—and see you're not slow as treacle about it!" There were perhaps fifty of the little people. I was more than startled by it, too. I had not seen little people in—oh, close to thirty years. I had seen them first as a boy of eight, and then, very briefly again, on my tenth birthday. And I had become convinced they could never be seen here in America. I had never seen them so busy, either. They were building something in the middle of the glade. It was long and shiny and upright and a little over five feet in height. "Come along now, people!" said this crotchety one, looking straight at me. "Stop starin' and get to work! You'll not be needin' to mind that man standin' there! You know he can't see nor hear us!" Oh, it was good to hear the rich old tongue again. I smiled, and the foreman of the leprechauns—if that's what he was—saw me smile and became stiff and alert for a moment, as though suspecting that perhaps I actually could see him. Then he shrugged and turned away, clearly deeming such a thing impossible. I said, "Just a minute, friend, and I'll beg your pardon. It so happens I can see you." He whirled to face me again, staring open-mouthed. Then he said, "What? What's that, now?" "I can see you," I said. "Ohhh!" he said and put his palms to his cheekbones. "Saints be with us! He's a believer! Run everybody—run for your lives!" And they all began running, in as many directions as there were little souls. They began to scurry behind the trees and bushes, and a sloping embankment nearby. "No, wait!" I said. "Don't go away! I'll not be hurting you!" They continued to scurry. I knew what it was they feared. "I don't intend catching one of you!" I said. "Come back, you daft little creatures!" But the glade was silent, and they had all disappeared. They thought I wanted their crock of gold, of course. I'd be entitled to it if I could catch one and keep him. Or so the legends affirmed, though I've wondered often about the truth of them. But I was after no gold. I only wanted to hear the music of an Irish tongue. I was lonely here in America, even if I had latched on to a fine job of work for almost shamefully generous pay. You see, in a place as full of science as the nuclear propulsion center there is not much time for the old things. I very much wanted to talk to the little people. I walked over to the center of the glade where the curious shiny object was standing. It was as smooth as glass and shaped like a huge cigar. There were a pair of triangular fins down at the bottom, and stubby wings amidships. Of course it was a spaceship, or a miniature replica of one. I looked at it more closely. Everything seemed almost miraculously complete and workable. I shook my head in wonder, then stepped back from the spaceship and looked about the glade. I knew they were all hiding nearby, watching me apprehensively. I lifted my head to them. "Listen to me now, little people!" I called out. "My name's Houlihan of the Roscommon Houlihans. I am descended from King Niall himself—or so at least my father used to say! Come on out now, and pass the time o' day!" Then I waited, but they didn't answer. The little people always had been shy. Yet without reaching a decision in so many words I knew suddenly that I had to talk to them. I'd come to the glen to work out a knotty problem, and I was up against a blank wall. Simply because I was so lonely that my mind had become clogged. I knew that if I could just once hear the old tongue again, and talk about the old things, I might be able to think the problem through to a satisfactory conclusion. So I stepped back to the tiny spaceship, and this time I struck it a resounding blow with my fist. "Hear me now, little people! If you don't show yourselves and come out and talk to me, I'll wreck this spaceship from stem to stern!" I heard only the leaves rustling softly. "Do you understand? I'll give you until I count three to make an appearance! One!" The glade remained deathly silent. "Two!" I thought I heard a stirring somewhere, as if a small, brittle twig had snapped in the underbrush. " Three! " And with that the little people suddenly appeared. The leader—he seemed more wizened and bent than before—approached me slowly and warily as I stood there. The others all followed at a safe distance. I smiled to reassure them and then waved my arm in a friendly gesture of greeting. "Good morning," I said. "Good morning," the foreman said with some caution. "My name is Keech." "And mine's Houlihan, as I've told you. Are you convinced now that I have no intention of doing you any injury?" "Mr. Houlihan," said Keech, drawing a kind of peppered dignity up about himself, "in such matters I am never fully convinced. After living for many centuries I am all too acutely aware of the perversity of human nature." "Yes," I said. "Well, as you will quickly see, all I want to do is talk." I nodded as I spoke, and sat down cross-legged upon the grass. "Any Irishman wants to talk, Mr. Houlihan." "And often that's all he wants," I said. "Sit down with me now, and stop staring as if I were a snake returned to the Island." He shook his head and remained standing. "Have your say, Mr. Houlihan. And afterward we'll appreciate it if you'll go away and leave us to our work." "Well, now, your work," I said, and glanced at the spaceship. "That's exactly what's got me curious." The others had edged in a bit now and were standing in a circle, intently staring at me. I took out my pipe. "Why," I asked, "would a group of little people be building a spaceship here in America—out in this lonely place?" Keech stared back without much expression, and said, "I've been wondering how you guessed it was a spaceship. I was surprised enough when you told me you could see us but not overwhelmingly so. I've run into believers before who could see the little people. It happens every so often, though not as frequently as it did a century ago. But knowing a spaceship at first glance! Well, I must confess that does astonish me." "And why wouldn't I know a spaceship when I see one?" I said. "It just so happens I'm a doctor of science." "A doctor of science, now," said Keech. "Invited by the American government to work on the first moon rocket here at the nuclear propulsion center. Since it's no secret I can advise you of it." "A scientist, is it," said Keech. "Well, now, that's very interesting." "I'll make no apologies for it," I said. "Oh, there's no need for apology," said Keech. "Though in truth we prefer poets to scientists. But it has just now crossed my mind, Mr. Houlihan that you, being a scientist, might be of help to us." "How?" I asked. "Well, I might try starting at the beginning," he replied. "You might," I said. "A man usually does." Keech took out his own pipe—a clay dudeen—and looked hopeful. I gave him a pinch of tobacco from my pouch. "Well, now," he said, "first of all you're no doubt surprised to find us here in America." "I am surprised from time to time to find myself here," I said. "But continue." "We had to come here," said Keech, "to learn how to make a spaceship." "A spaceship, now," I said, unconsciously adopting some of the old manner. "Leprechauns are not really mechanically inclined," said Keech. "Their major passions are music and laughter and mischief, as anyone knows." "Myself included," I agreed. "Then why do you need a spaceship?" "Well, if I may use an old expression, we've had a feelin' lately that we're not long for this world. Or let me put it this way. We feel the world isn't long for itself." I scratched my cheek. "How would a man unravel a statement such as that?" "It's very simple. With all the super weapons you mortals have developed, there's the distinct possibility you might be blowin' us all up in the process of destroying yourselves." "There is that possibility," I said. "Well, then, as I say," said Keech, "the little people have decided to leave the planet in a spaceship. Which we're buildin' here and now. We've spied upon you and learned how to do it. Well—almost how to do it. We haven't learned yet how to control the power—" "Hold on, now," I said. "Leaving the planet, you say. And where would you be going?" "There's another committee working on that. 'Tis not our concern. I was inclined to suggest the constellation Orion, which sounds as though it has a good Irish name, but I was hooted down. Be that as it may, my own job was to go into your nuclear center, learn how to make the ship, and proceed with its construction. Naturally, we didn't understand all of your high-flyin' science, but some of our people are pretty clever at gettin' up replicas of things." "You mean you've been spying on us at the center all this time? Do you know, we often had the feeling we were being watched, but we thought it was by the Russians. There's one thing which puzzles me, though. If you've been constantly around us—and I'm still able to see the little people—why did I never see you before?" "It may be we never crossed your path. It may be you can only see us when you're thinkin' of us, and of course truly believin' in us. I don't know—'tis a thing of the mind, and not important at the moment. What's important is for us to get our first ship to workin' properly and then we'll be on our way." "You're determined to go." "Truly we are, Mr. Houlihan. Now—to business. Just during these last few minutes a certain matter has crossed my mind. That's why I'm wastin' all this time with you, sir. You say you are a scientist." "A nuclear engineer." "Well, then, it may be that you can help us—now that you know we're here." "Help you?" "The power control, Mr. Houlihan. As I understand it, 'tis necessary to know at any instant exactly how much thrust is bein' delivered through the little holes in back. And on paper it looks simple enough—the square of somethin' or other. I've got the figures jotted in a book when I need 'em. But when you get to doin' it it doesn't come out exactly as it does on paper." "You're referring to the necessity for a coefficient of discharge." "Whatever it might be named," said Keech, shrugging. "'Tis the one thing we lack. I suppose eventually you people will be gettin' around to it. But meanwhile we need it right now, if we're to make our ship move." "And you want me to help you with this?" "That is exactly what crossed my mind." I nodded and looked grave and kneaded my chin for a moment softly. "Well, now, Keech," I said finally, "why should I help you?" "Ha!" said Keech, grinning, but not with humor, "the avarice of humans! I knew it! Well, Mr. Houlihan, I'll give you reason enough. The pot o' gold, Mr. Houlihan!" "The one at the end of the rainbow?" "It's not at the end of the rainbow. That's a grandmother's tale. Nor is it actually in an earthen crock. But there's gold, all right, enough to make you rich for the rest of your life. And I'll make you a proposition." "Go ahead." "We'll not be needin' gold where we're goin'. It's yours if you show us how to make our ship work." "Well, now, that's quite an offer," I said. Keech had the goodness to be quiet while I sat and thought for a while. My pipe had gone out and I lit it again. I finally said, "Let's have a look at your ship's drive and see what we can see." "You accept the proposition then?" "Let's have a look," I said, and that was all. Well, we had a look, and then several looks, and before the morning was out we had half the spaceship apart, and were deep in argument about the whole project. It was a most fascinating session. I had often wished for a true working model at the center, but no allowance had been inserted in the budget for it. Keech brought me paper and pencil and I talked with the aid of diagrams, as engineers are wont to do. Although the pencils were small and I had to hold them between thumb and forefinger, as you would a needle, I was able to make many sensible observations and even a few innovations. I came back again the next day—and every day for the following two weeks. It rained several times, but Keech and his people made a canopy of boughs and leaves and I was comfortable enough. Every once in a while someone from the town or the center itself would pass by, and stop to watch me. But of course they wouldn't see the leprechauns or anything the leprechauns had made, not being believers. I would halt work, pass the time of day, and then, in subtle fashion, send the intruder on his way. Keech and the little people just stood by and grinned all the while. At the end of sixteen days I had the entire problem all but whipped. It is not difficult to understand why. The working model and the fact that the small people with their quick eyes and clever fingers could spot all sorts of minute shortcomings was a great help. And I was hearing the old tongue and talking of the old things every day, and truly that went far to take the clutter out of my mind. I was no longer so lonely that I couldn't think properly. On the sixteenth day I covered a piece of paper with tiny mathematical symbols and handed it to Keech. "Here is your equation," I said. "It will enable you to know your thrust at any given moment, under any circumstances, in or out of gravity, and under all conditions of friction and combustion." "Thank you, Mr. Houlihan," said Keech. All his people had gathered in a loose circle, as though attending a rite. They were all looking at me quietly. "Mr. Houlihan," said Keech, "you will not be forgotten by the leprechauns. If we ever meet again, upon another world perchance, you'll find our friendship always eager and ready." "Thank you," I said. "And now, Mr. Houlihan," said Keech, "I'll see that a quantity of gold is delivered to your rooms tonight, and so keep my part of the bargain." "I'll not be needing the gold," I said. Keech's eyebrows popped upward. "What's this now?" "I'll not be needing it," I repeated. "I don't feel it would be right to take it for a service of this sort." "Well," said Keech in surprise, and in some awe, too, "well, now, musha Lord help us! 'Tis the first time I ever heard such a speech from a mortal." He turned to his people. "We'll have three cheers now, do you hear, for Mr. Houlihan—friend of the little people as long as he shall live!" And they cheered. And little tears crept into the corners of some of their turned-up eyes. We shook hands, all of us, and I left. I walked through the park, and back to the nuclear propulsion center. It was another cool, green morning with the leaves making only soft noises as the breezes came along. It smelled exactly like a wood I had known in Roscommon. And I lit my pipe and smoked it slowly and chuckled to myself at how I had gotten the best of the little people. Surely it was not every mortal who could accomplish that. I had given them the wrong equation, of course. They would never get their spaceship to work now, and later, if they tried to spy out the right information I would take special measures to prevent it, for I had the advantage of being able to see them. As for our own rocket ship, it should be well on its way by next St. Patrick's Day. For I had indeed determined the true coefficient of discharge, which I never could have done so quickly without those sessions in the glade with Keech and his working model. It would go down in scientific literature now, I suppose, as Houlihan's Equation, and that was honor and glory enough for me. I could do without Keech's pot of gold, though it would have been pleasant to be truly rich for a change. There was no sense in cheating him out of the gold to boot, for leprechauns are most clever in matters of this sort and he would have had it back soon enough—or else made it a burden in some way. Indeed, I had done a piece of work greatly to my advantage, and also to the advantage of humankind, and when a man can do the first and include the second as a fortunate byproduct it is a most happy accident. For if I had shown the little people how to make a spaceship they would have left our world. And this world, as long as it lasts—what would it be in that event? I ask you now, wouldn't we be even more likely to blow ourselves to Kingdom Come without the little people here for us to believe in every now and then? Transcriber's Note: This etext was produced from Fantastic Universe September 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. Irish |
Which of the following sequences accurately represents the chronological order of key medical events and treatments for Mr. Bruno Hurley since his initial hospitalization in October 2019?
Choose the correct answer from the following options:
A. Hospitalization for neutropenic fever (10/2019) → Diagnosis of tuberculosis and initiation of four-drug anti-tuberculosis therapy (11/08/19) → First detection of acid-fast bacilli in sputum (01/02/20) → Secondary AML diagnosis (01/2021) → Start of Actimmune® injections (01/2021) → Allogeneic stem cell transplantation (07/2021).
B. Diagnosis of tuberculosis (12/06/19) → Initiation of Moxifloxacin (11/10/19) → Start of Ethambutol therapy (11/08/19) → Bone marrow biopsy showing 0.8% myeloid blasts (05/2021) → Start of Actimmune® injections (01/2021) → Hospitalization for acute pancreatitis (02/2020).
C. Hospitalization for fever during neutropenia (10/23/19) → Start of Pyrazinamide therapy (11/20/19) → Last cultural detection of Mycobacterium tuberculosis in sputum (01/02/20) → Start of Venetoclax/Vidaza (03/2021) → Allogeneic stem cell transplantation (07/29/2021) → Diagnosis of secondary AML (01/2021).
D. Start of Rifampicin and Isoniazid therapy (11/08/19) → Diagnosis of open lung tuberculosis (11/2019) → First sputum sample without acid-fast rods (03/12/21) → Diagnosis of Myelodysplastic Syndrome EB-2 (07/2010) → Hospitalization for acute pancreatitis (02/2022) → Start of Actimmune® therapy (01/2021).
E. Start of Levofloxacin therapy (02/11/20) → Diagnosis of secondary AML (01/2021) → Hospitalization for neutropenic fever (10/23/19) → First detection of acid-fast bacilli in sputum (01/02/20) → Allogeneic stem cell transplantation (07/29/2021) → Discontinuation of Actimmune® therapy (03/2021).
| ### Patient Report 0
**Dear colleague, **
We are reporting on Mr. Bruno Hurley, born on 12/24/1965, who has been
under our outpatient treatment since 02/11/2020.
**Diagnoses:**
- Refractory tuberculosis
- Manifestations: Open pulmonary tuberculosis, lymph node tuberculosis
(cervical, hilar, mediastinal), liver tuberculosis
**Imaging:**
- 11/01/19 Chest CT: Mediastinal lymph node conglomerate centrally
with poststenotic infiltrates on both sides. Splenomegaly.
- 11/04/19 Bronchoscopy: Large mediastinal and right hilar lymphomas.
Subcritical constriction of right segmental bronchi. EBUS-TBNA LK4R
and 10/11R.
**Microbiology:**
- 11/04/19 Tracheobronchial Secretions: Microscopic detection of
acid-fast rods, cultural detection of Mycobacterium tuberculosis,
phenotypically no evidence of resistance.
**Therapy:**
- Initial omission of pyrazinamide due to pancytopenia.
- Moxifloxacin: 11/10/19-11/20/19
- Pyrazinamide: 11/20/19-02/11/20
- Ethambutol: 11/08/19-02/11/20
- Rifampicin since: 11/08/19
- Isoniazid since: 11/08/19
- Levofloxacin since: 02/11/20
- Immunomodulatory therapy for low basal interferon / interferon
levels (ACTIMMUNE®)
**Microbiology:**
- 01/20/20 Sputum: Cultural detection of Mycobacterium tuberculosis:
Phenotypically no evidence of resistance.
- 01/02/20 Sputum: Last cultural detection of Mycobacterium
tuberculosis.
- 06/15/20 BAL: Occasional acid-fast rods, 16S-rRNA-PCR: M.
tuberculosis complex, no cultural evidence of Mycobacteria.
- 06/15/20 Lung biopsy: Occasional acid-fast rods, no cultural
evidence of Mycobacteria.
- 03/12/21 Sputum: first sputum without acid-fast rods, consistently
microscopically negative sputum samples since then.
**Histology:**
- 07/16/21: Mediastinal lymph node biopsy: Histologically no evidence
of malignancy/lymphoma.
**Other Diagnoses: **
- Secondary Acute Myeloid Leukemia with Myelodysplastic Syndrome
- Blood count at initial diagnosis: 15% blasts, erythrocyte
substitution required.
**Therapy:**
- 12/20-03/21 TB therapy
- 02/20-01/21 TB therapy: RMP + INH + FQ
- 01/21-04/21 RMP + INH + FQ + Actimmune® 04/22 CT: Regressive
findings of pulmonary TB changes, regressive cervical lymph nodes,
mediastinal LAP, and liver lesions size-stable; Sputum: No acid-fast
rods detected for the first time since 03/21.
- BM aspiration: Secondary AML.
**Current Presentation:** Admission for allogeneic stem cell
transplantation
Pathogen Location / Material of Detection or Infection Month/Year or
Last Detection
- HIV Serology: Negative - 11/19
- Mycobacterium tuberculosis Complex: Bronchoalveolar Lavage,
Tracheobronchial Secretion, Sputum - 11/19
**Medical History:** We took over Mr. Hurley for the continuation of TB
therapy on 11/02/20. His hospital admission took place at the end of
October 2019 due to neutropenic fever. The patient reported temperatures
up to 39°C for the past 3 days. Since 08/19, the patient has been
receiving hematological-oncological treatment for MDS. The colleagues
from hematology performed a repeat bone marrow aspiration before
transferring to Station 12. The blast percentage was significantly
reduced. HLA typing of the brother for allogeneic stem cell
transplantation planning had already been done in the summer of 2019.
After a chest CT revealed extensive mediastinal lymphomas with
compression of the bronchial tree bilaterally and post-stenotic
infiltrates, a bronchoscopy was performed. M. tuberculosis was cultured
from sputum and TBS. An EBUS-guided lymph node biopsy was histologically
processed, revealing granulomatous inflammation and molecular evidence
of the M. tuberculosis complex. On 11/08/19, a four-drug
anti-tuberculosis therapy was initiated, initially with Moxifloxacin
instead of Pyrazinamide due to pancytopenia. Moxifloxacin was replaced
by Pyrazinamide on 11/20/19. The four-drug therapy was continued for a
total of 3 months due to prolonged microscopic evidence of acid-fast
rods in follow-up sputum samples. Isoniazid dosage was adjusted after
peak level control (450 mg q24h), as was Rifampicin dose (900 mg q24h).
On 01/02/20, Mycobacterium tuberculosis was last cultured in a sputum
sample. Nevertheless, acid-fast rods continued to be detected in the
sputum. Due to the lack of culturability of mycobacteria, Mr. Hurley was
discharged to home care after consultation with the Tuberculosis Welfare
Office.
**Allergies**: None known. Toxic Substances: Smoking: Non-smoker;
Alcohol: No; Drugs: No
**Social History:** Originally from Brazil, has been living in the US
for 8 years. Lives with his partner.
**Current lab results:**
**Parameter** **Results** **Reference Range**
---------------------------------------- -------------- ---------------------
ConA-Induced Cytokines (Th1/Th2)
Interleukin 10 10 pg/mL \< 364 pg/mL
Interferon Gamma 265-6781 pg/mL
Interleukin 2 74 pg/mL 43-374 pg/mL
Interleukin 4 13 pg/mL \< 34 pg/mL
Interleukin 5 3 pg/mL \< 55 pg/mL
Naive CD45RA+CCR7+ (% of CD8+) 6.35 % 8.22-59.58 %
TEMRA CD45RA+CCR7- (% of CD8+) 50.44 % 7.32-55.99 %
Central Memory CD45RA-CCR7+ (% of CD) 2.60 % 1.67-5.84 %
Effector Memory CD45RA-CCR7- (% of CD) 40.60 % 22.52-62.25 %
Naive CD45RA+ (% of CD4+) 26.26 % 17.46-60.24 %
TEMRA CD45RA+ CCR7- (% of CD4+) 1.26 % 2.74-15.54 %
Central Memory CD45RA-CCR7+ (% of CD) 34.21 % 16.40-33.41 %
Effector Memory CD45RA-CCR7- (% of CD) 38.28 % 17.38-40.38 %
Granulocytes 0.60 abs./nL 3.00-6.50 abs./nL
Granulocytes (relative) 45 % 50-80 %
Lymphocytes 0.57 abs./nL 1.50-3.00 abs./nL
Lymphocytes (relative) 43 % 20-40 %
Monocytes 0.13 abs./nL \<0.50 abs./nL
Monocytes (relative) 10 % 2-10 %
NK Cells 0.16 abs./nL 0.10-0.40 abs./nL
NK Cells (% of Lymphocytes) 29 5-25
γ/δ TCR+ T-Cells (relative) 2 % \< 10 %
α/β TCR+ T-Cells (relative) 98 % \>90 %
CD19+ B-Cells (% of Lymphocytes) 3 % 5-25 %
CD4/CD8 Ratio 0.9 % 1.1-3.0 %
CD8-CD4-T-Cells (% of T-Cells) 5.86 % \< 15.00 %
CD8+CD4+-T-Cells (% of T-Cells) 0.74 % \< 10.00 %
CD3+ T-Cells 0.38 abs./nL 0.90-2.20 abs./nL
**Parameter** **Results** **Reference Range**
-------------------------------------------------- ---------------- ---------------------
Complete Blood Count (EDTA)
Hemoglobin 6.6 g/dL 13.5-17.0 g/dL
Hematocrit 19.0 % 39.5-50.5 %
Erythrocytes 2.3 x 10\^6/uL 4.3-5.8 x 10\^6/uL
Platelets 61 x 10\^3/uL 150-370 x 10\^3/uL
MCV (Mean Corpuscular Volume) 81.5 fL 80.0-99.0 fL
MCH (Mean Corpuscular Hemoglobin) 28.3 pg 27.0-33.5 pg
MCHC (Mean Corpuscular Hemoglobin Concentration) 34.7 g/dL 31.5-36.0 g/dL
MPV (Mean Platelet Volume) 10.4 fL 7.0-12.0 fL
RDW-CV (Red Cell Distribution Width-CV) 12.7 % 11.5-15.0 %
**Parameter** **Results** **Reference Range**
-------------------------- ------------- ---------------------
Other Investigations
QFT-TB Gold plus TB1 0.11 IU/mL \<0.35 IU/mL
QFT-TB Gold plus TB2 0.07 IU/mL \<0.35 IU/mL
QFT-TB Gold plus Mitogen 3.38 IU/mL \>0.50 IU/mL
QFT-TB Gold plus Result Negative
**Lung Aspiration from 06/15/20:** Examination Request: Acid-fast rods
(Microscopy + Culture) **Microscopic Findings:**
- Auramine stain: Occasionally, acid-fast rods Result: No growth of
Mycobacterium sp. after 12 weeks of incubation.
2. Forceps Biopsy Exophytic Trachea: One piece of tissue. Microscopy:
HE, PAS, Giemsa, Diagnosis:
3. Predominantly blood clot and necrotic material alongside sparsely
altered lymphatic tissue due to sampling (EBUS-TBNA LK 7 as
indicated).
4. Components of a granulation tissue polyp (Forceps Biopsy Exophytic
Trachea as indicated). Comment: The finding in 1. continues to be
suspicious of a mycobacterial infection. We are conducting molecular
pathological examinations in this regard and will report again.
> [Comment]{.underline}: Detection of mycobacterial DNA of the M.
> tuberculosis complex type. No evidence of atypical mycobacteria. No
> evidence of malignancy.
**Current Medication:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**\
**
### Patient Report 1
**Dear colleague, **
We report to you about our patient Mr. Bruno Hurley, born on 12/24/1965.
Who has received inpatient treatment from 07/17/2021 to 09/03/2021.
**Diagnoses**:
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
<!-- -->
- Myelodysplastic Syndrome EB-2, diagnosed in July 2010. Blood count
at initial diagnosis: 15% blasts, erythrocyte transfusion-dependent.
Cytogenetics: 46,XY \[1\]; 47,XY,+Y,i(21)(q10)\[15\];
47,XY,+Y,trp(21)(q11q22)\[4\]. Molecular genetics: Mutations in
RUNX1, SF3B1. IPSS-R: 7 (very high risk).
- In 08/2020, diagnosed with Myelodysplastic Syndrome with ring
sideroblasts.
- Received transfusions of 2 units of red blood cells every 3-4 weeks
to maintain hemoglobin between 4-6 g/dL.
- Bone marrow biopsy showed MDS-EB2 with 14.5% blasts.
- Initiated Azacitidine treatment (2x 75 mg subcutaneously, days 1-5 +
8-9 every 4 weeks) as an outpatient.
- 10/23/2019: Hospitalized for fever during neutropenia.
- 12/06/2019: Diagnosed with tuberculosis - positive Tbc-PCR in
tracheobronchial secretions, acid-fast bacilli in tracheobronchial
secretions, histological confirmation from EBUS biopsy of a
conglomerate of melted lymph nodes from 11/03/2019.
- 01/2021: Bone marrow biopsy showed secondary AML with 26% blasts.
- 03/2021: Started Venetoclax/Vidaza.
- 05/2021: Bone marrow biopsy showed 0.8% myeloid blasts coexpressing
CD117 and CD7. Cytology showed 6% blasts.
- 05/2021: Started the 4th cycle of Vidaza/Venetoclax.
- 06/17/2022: Started the 5th cycle of Vidaza/Venetoclax.
- 07/29/2021: Underwent allogeneic stem cell transplantation from a
HLA-identical unrelated donor (10/10 antigen match) for AML-MRC in
first complete remission (CR). Conditioning regimen included
Treosulfan 12g/m2, Fludarabin 5x 30 mg/m2, ATG 3x 10 mg/kg.
**Other Diagnoses:**
- Persistent tuberculosis with lymph node swelling since June 2020.
- Open lung tuberculosis diagnosed in November 2019.
- Location: CT of the chest showed central mediastinal lymph node
conglomerate with post-stenotic infiltrates bilaterally,
splenomegaly.
- Bronchoscopy on December 5, 2020, showed large mediastinal and
right hilar lymph nodes, subcritical narrowing of right
segmental bronchi. EBUS-TBNA
- CT Chest/Neck on 02/05/2020: Regression of pulmonary
infiltrates, enlargement of necrotic lymph nodes in the upper
mediastinum and infraclavicular on the right (compressing the
internal jugular vein/esophagus).
- Culture confirmation of Mycobacterium tuberculosis,
pansensitive: Tracheobronchial secretion
- Initiated antituberculous combination therapy
**Current Presentation:** Admission for allogeneic stem cell
transplantation from a HLA-identical unrelated donor (10/10 antigen
match) for AML-MRC in first complete remission.
**Medical History:** In 2019, Mr. Hurley was diagnosed with
Myelodysplastic Syndrome EB-2. Starting from September 2019, he received
Azacitidine therapy. In December 2020, he was diagnosed with open lung
tuberculosis, which was challenging to treat due to his dysfunctional
immune system. In January 2021, his MDS progressed to AML-MRC with 26%
blasts. After treatment with Venetoclax/Vidaza, he achieved remission in
May 2021. Tuberculosis remained largely under control.
Due to AML-MRC, he was recommended for allogeneic stem cell
transplantation from a HLA-identical unrelated donor. At the time of
admission for transplantation, he was largely asymptomatic. He
occasionally experienced mild dry cough but denied fever, night sweats,
or weight loss. The admission and counseling were conducted with
translation assistance from his life partner due to limited proficiency
in English.
**Allergies**: None
**Transfusion History**: Currently requires transfusions every 14 days.
Both red blood cell and platelet transfusions have been tolerated
without problems.
**Abdominal CT from 01/20/2021:**
**Findings**: Significant peripancreatic fluid accumulation in the upper
abdominal area with a somewhat indistinct border between the pancreatic
tissue, particularly in the pancreatic head region. Evidence of
inflammation affecting the stomach and duodenum. No presence of free air
or indications of hollow organ perforation. No conclusive signs of a
well-defined abscess. Moreover, the other parenchymal abdominal organs,
especially those lacking focal abnormalities suggestive of neoplastic or
inflammatory conditions, displayed normal appearances. The gallbladder
showed no notable issues, and there were no radiopaque concretions
observed. Both the intra- and extrahepatic bile ducts appeared
adequately dilated. Abdominal hollow organs exhibited unremarkable and
normal appearances without corresponding contrast and dilation. The
appendix appeared within normal parameters. Abdominal lymph nodes showed
no unusual findings. Some degree of aortic vasosclerosis was noted. The
depiction of the included lung portions revealed no abnormalities.
**Results**: Findings indicative of acute pancreatitis, most likely of
an exudative nature. No signs of hollow organ perforation were detected,
and there was no definitive evidence of an abscess (as far as could be
determined from native imaging).
**Summary**: The patient was admitted to our hospital through the
emergency department with the symptoms described above. With typical
upper abdominal pain and significantly elevated serum lipase levels, we
diagnosed acute pancreatitis. This diagnosis was corroborated by
peripancreatic fluid and ill-defined organ involvement in the abdominal
CT scan. There were no laboratory or anamnestic indications of a biliary
origin. The patient denied excessive alcohol consumption.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**Physical Examination:** General: Oriented in all qualities, in good
general condition with normal body weight (75 kg, 187 cm) Vital signs at
admission: Heart rate 63/min, Blood pressure 110/78 mmHg. Temperature at
admission 36.8 °C, Oxygen saturation 100% on room air. Skin and mucous
membranes: Dry skin, normal skin color, normal skin turgor. No scleral
icterus, non-irritated conjunctiva. Normal oral mucosa, moist tongue
without coating, no ulcers or thrush. Heart: Normal heart sounds,
rhythmic, regular rate, no pathological heart murmurs heard on
auscultation. Lungs: Resonant percussion sound, clear breath sounds
bilaterally, no wheezing, no prolonged expiration. Abdomen: Unremarkable
scar tissue, normal bowel sounds in all quadrants, soft, non-tender, no
guarding, liver and spleen not enlarged. Vascular: Central and
peripheral pulses palpable, no jugular vein distention, no peripheral
edema, extremities warm with no significant difference in size. Lymph
nodes: Palpable cervical swelling, inguinal and axillary lymph nodes
unremarkable. Neurology: Grossly neurologically unremarkable.
On 08/22/2021, a four-lumen central venous catheter was placed in the
right internal jugular vein without complications. During the
conditioning regimen, the patient received the following:
**Medication** **Dosage** **Frequency**
---------------------------------------------- ---------------------- -------------------------
Fludarabine (Fludara) 30 mg/m² (5x 57 mg) 07/23/2023 - 07/27/2023
Treosulfan (Ovastat) 12 g/m² (3x 22.9 g) 07/23/2023 - 07/25/2023
Anti-Thymocyte Globulin (ATG, Thymoglobulin) 10 mg/kg (3x 700 mg) 07/23/2023 - 07/28/2023
**Antiemetic Therapy:**
The antiemetic therapy included Ondansetron, Aprepitant, and
Dexamethasone, and the conditioning regimen was well tolerated.
**Prophylaxis of Graft-Versus-Host Disease (GvHD):**
**Substances** **Start Date** **Day -2** **Day 1**
---------------- ---------------- ------------ -----------
Cyclosporine 08/28/2022
Mycophenolate 07/30/2021
**Stem Cell Source** **Date** **CD34/kg KG** **CD45/kg KG** **CD3/kg KG** **Volume**
---------------------- ------------ ---------------- ---------------- --------------- ------------
PBSCT 07/29/2021 7.39 x10\^6 8.56 x10\^8 260.7 x10\^6 194 ml
**Summary:** Mr. Hurley was admitted for allogeneic stem cell
transplantation from a HLA-identical unrelated donor for AML-MRC. The
conditioning regimen with Treosulfan, Fludarabin, and ATG was well
tolerated, and the transplantation proceeded without complications.
**Toxicities:** There was an adverse event-related increase in bilirubin
levels, reaching a maximum of 2.68 mg/dL. Elevated ALT levels, up to a
maximum of 53 U/L, were observed.
**Acute Graft-Versus-Host Disease (GvHD):** Signs of GvHD were not
observed until the time of discharge.
**Medication upon Discharge:**Formularbeginn
**Medication ** **Dosage** **Frequency**
---------------------------------- ------------ ------------------------------------------------
Acyclovir (Zovirax) 500 mg 1-0-1-0
Entecavir (Baraclude) 0.5 mg 1-0-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 2-0-0-0
Levofloxacin (Levaquin) 500 mg 1-0-1-0
Mycophenolate Mofetil (CellCept) 500 mg 2-0-2-0
Folic Acid 5 mg 1-0-0-0
Magnesium \-- 3-3-3-0
Pantoprazole (Protonix) 40 mg 1-0-0-0 (before a meal)
Ursodeoxycholic Acid (Actigall) 250 mg 1-1-1-0
Cyclosporine (Sandimmune) 100 mg 100 mg 4-0-4-0
Cyclosporine (Sandimmune) 50 mg 50 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
Cyclosporine (Sandimmune) 10 mg 10 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
**Current Recommendations: **
1. Bone marrow puncture on Day +60, +120, and +360 post-transplantation
(including MRD and chimerism) and Day +180 depending on MRD and
chimerism progression.
2. Continuation of immunosuppressive therapy with ciclosporin adjusted
to achieve target levels of around 150 ng/ml, for a minimum of 3
months post-transplantation. Immunosuppression with mycophenolate
mofetil will be continued until Day +40.
3. Prophylaxis with Aciclovir must continue for 6 weeks after
discontinuation of immunosuppression at a dosage of 15-20 mg/kg/day
(divided into 2 doses). Dose adjustment based on renal function may
be necessary.
4. Pneumocystis pneumonia prophylaxis through monthly Pentamidine
inhalation or administration of Cotrim forte 960mg must continue at
least until immunosuppression is discontinued or until an absolute
CD4+ T-cell count exceeds \>200/µL in peripheral blood. Cotrim forte
960mg has not been started when leukocytes are \<2/nL.
5. Weekly monitoring of CMV and EBV viral loads through quantitative
PCR from EDTA blood.
6. Timing of antituberculous medication intake:
- Take Rifampicin and Isoniazid in the morning on an empty stomach, 30
minutes before breakfast.
- Take levofloxacin with a 2-hour gap from divalent cations (Mg2+,
strongly calcium-rich foods).
**Lab results upon Discharge:**
**Parameter** **Result** **Reference Range**
------------------------------ -------------------- ---------------------
Cyclosporine 127.00 ng/mL \--
Sodium 141 mEq/L 136-145 mEq/L
Potassium 4.1 mEq/L 3.5-4.5 mEq/L
Glucose 108 mg/dL 60-110 mg/dL
Creatinine 0.65 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 111 mL/min/1.73 m² \--
Urea 26 mg/dL 17-48 mg/dL
Total Bilirubin 0.35 mg/dL \<1.20 mg/dL
**Complete Blood Count **
**Parameter** **Results** **Reference Range**
--------------- ----------------- -----------------------
Hemoglobin 9.5 g/dL 13.5-17.0 g/dL
Hematocrit 28.2% 39.5-50.5%
Erythrocytes 3.2 x 10\^6/µL 4.3-5.8 x 10\^6/µL
Leukocytes 1.47 x 10\^3/µL 3.90-10.50 x 10\^3/µL
Platelets 193 x 10\^3/µL 150-370 x 10\^3/µL
MCV 88.7 fL 80.0-99.0 fL
MCH 29.9 pg 27.0-33.5 pg
MCHC 33.7 g/dL 31.5-36.0 g/dL
MPV 9.8 fL 7.0-12.0 fL
RDW-CV 18.9% 11.5-15.0%
### Patient Report 2
**Dear colleague, **
We report on Mr. Bruno Hurley, born on 12/24/1965, who was under our
inpatient care from 2/20/2022, to 02/24/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Medical History:** The patient presented via ambulance from his
workplace. The patient reported sudden onset upper abdominal pain,
mainly in the epigastric region, accompanied by nausea and vomiting. He
also experienced watery diarrhea once today. He had lunch around noon,
consisting noodles. There was no fever, cough, sputum production,
dyspnea, or urinary abnormalities. He has been taking daily
antitubercular combination therapy, including Rifampicin, for open
tuberculosis. The patient denied alcohol consumption and weight loss.
**Medication** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg 1-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 1-0-1
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed
Prednisolone 4 mg As needed
**Allergies:** None
**Physical Exam:**
Vital Signs: Blood Pressure 178/90 mmHg, Pulse 85/min, SpO2 89%,
Temperature 36.7°C, Respiratory Rate 20/min.
Clinical Status: Upon initial examination, a reduced general condition.
Cardiovascular: Heart sounds were normal, rhythm was regular, and no
murmurs were heard.
Respiratory: Vesicular breath sounds, sonorous percussion.
Abdominal: Sluggish peristalsis, soft abdominal walls, guarding and
tenderness in the epigastrium, liver and spleen not palpable, no free
fluid.
Extremities: Minimal edema.
**ECG Findings:** ECG on admission showed normal sinus rhythm (69/min),
normal ST intervals, R/S transition in V3/V4, and no significant
abnormalities.
´
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg PAUSED
Rifampin (Rifadin) 600 mg PAUSED
Isoniazid/Pyridoxine (Nydrazid) 300 mg PAUSED
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed (as needed)
Prednisolone 4 mg As needed (as needed)
Tramadol (Ultram) 50 mg 1 tablet, every 6 hours
**Parameter** **Results** **Reference Range**
------------------------- ----------------- -----------------------
White Blood Cells (WBC) 5.0 x 10\^9/L 3.7 - 9.9 x 10\^9/L
Hemoglobin 14.0 g/dL 13.6 - 17.5 g/dL
Hematocrit 40% 40 - 53%
Red Blood Cells (RBC) 4.00 x 10\^12/L 4.4 - 5.9 x 10\^12/L
MCV 99 fL 80 - 96 fL
MCH 32.8 pg 28.3 - 33.5 pg
MCHC 33.1 g/dL 31.5 - 34.5 g/dL
Platelets 161 x 10\^9/L 146 - 328 x 10\^9/L
Absolute Neutrophils 3.7 x 10\^9/L 1.8 - 6.2 x 10\^9/L
Absolute Monocytes 0.31 x 10\^9/L 0.25 - 0.85 x 10\^9/L
Absolute Eosinophils 0.03 x 10\^9/L 0.03 - 0.44 x 10\^9/L
Absolute Basophils 0.01 x 10\^9/L 0.01 - 0.08 x 10\^9/L
Absolute Lymphocytes 0.9 x 10\^9/L 1.1 - 3.2 x 10\^9/L
Immature Granulocytes 0.0 x 10\^9/L 0.0 x 10\^9/L
### Patient Report 3
**Dear colleague, **
We are writing to inform you on our patient, Mr. Hurley, who presented
to our outpatient clinic on 07/12/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Current Presentation:** Presented with a referral from outpatient
oncologist for suspected recurrent AML, with DD GvHD ITP in the setting
of progressive pancytopenia, primarily thrombocytopenia. The patient is
in good general condition, denying acute symptoms, particularly no rash,
diarrhea, dyspnea, or fever.
**Physical Examination:** Alert, oriented, no signs of respiratory
distress, heart sounds regular, abdomen soft, no tenderness, no skin
rashes, especially no signs of GvHD, no edema.
- Heart Rate (HR): 130/85
- Temperature (Temp): 36.7°C
- Oxygen Saturation (SpO2): 97
- Respiratory Rate (AF): 12
- Pupillary Response: 15
**Imaging (CT):**
- [11/04/19 CT Chest/Abdomen/Pelvis ]{.underline}
- [01/04/20 Chest CT:]{.underline} Marked necrotic lymph nodes hilar
right with bronchus and vascular stenosis. Significant increasing
pneumonic infiltrates predominantly on the right.
- [02/05/20 Neck/Chest CT]{.underline}: Regression of pulmonary
infiltrates, but increased size of necrotic lymph nodes, especially
in the upper mediastinum and right infraclavicular with slit-like
compression of the right internal jugular vein and the esophagus.
- [06/07/20 Neck/Chest CT]{.underline}: Size-stable necrotic lymph
node conglomerate infraclavicular right, dimensioned axially up to
about 6 x 2 cm, with ongoing slit-shaped compression of the right
internal jugular vein. Hypoplastic mastoid cells left, idem.
Progressive, partly new and large-volume consolidations with
adjacent ground glass infiltrates on the right in the anterior, less
posterior upper lobe and perihilar. Inhomogeneous, partially reduced
contrast of consolidated lung parenchyma, broncho pneumogram
preserved dorsally only.
- [10/02/20 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Size-regressive
consolidating infiltrate in the right upper lobe and adjacent
central lower lobe with increasing signs of liquefaction.
Progressive right pleural effusion and progressive signs of
pulmonary volume load. Regressive cervical, mediastinal, and right
hilar lymphadenopathy. Ongoing central hilar conglomerates that
compress the central hilar structures. Partly constant, partly
regressive presentation of known tuberculosis-suspected liver
lesions.
- [12/02/20 CT Chest/Abdomen/Pelvis]{.underline}.
- [01/20/2021 Abdominal CT.]{.underline}
- [02/23/21 Neck/Chest CT:]{.underline} Slightly regressive/nodular
fibrosing infiltrate in the right upper lobe and adjacent central
lower lobe with continuing significant residual findings. Within the
infiltrate, larger poorly perfused areas with cavitations and
scarred bronchiectasis. Increasing, partly patchy densities on the
left basal region, differential diagnoses include infiltrates and
ventilation disorders. Essentially constant cervical, mediastinal,
and hilar lymphadenopathy. Constant liver lesions in the upper
abdomen, differential diagnoses include TB manifestations and cystic
changes.
- [06/12/21 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Improved
ventilation with regressive necrotic TB manifestations perihilar,
now only subtotal lobar atelectasis. Essentially constant necrotic
lymph node manifestations cervical, mediastinal, and right hilar,
exemplarily suprasternal right or right paratracheal. Narrow right
pleural effusion. Medium-term constant hypodense liver lesions
(regressive).
**Patient History:** Known to have AML with myelodysplastic changes,
first diagnosed 01/2021, myelodysplastic syndrome EB-2, fist diagnoses
07/2019, and history of allogeneic stem cell transplantation.
**Treatment and Progression:** Patient is hemodynamically stable, vital
signs within normal limits, afebrile. In good general condition,
clinical examination unremarkable, especially no skin GvHD signs. Venous
blood gas: Acid-base status balanced, electrolytes within normal range.
Laboratory findings show pancytopenia, Hb 11.3 g/dL, thrombocytopenia
29/nL, leukopenia 1.8/nL, atypical lymphocytes described as \"resembling
CLL,\" no blasts noted. Consultation with Hemato/Oncology confirmed no
acute need for hospitalization. Follow-up in the Hemato-Oncological
Clinic in September.
**Imaging:**
**CT Chest/Abdomen/Pelvis on 11/04/19:**
**Assessment:** In comparison with 10/23/19: In today\'s
contrast-enhanced examination, a newly unmasked large tumor is noted in
the right pulmonary hilum with encasement of the conduits of the right
lung lobe. Differential diagnosis includes a lymph node conglomerate,
central bronchial carcinoma, or, less likely, an inflammatory lesion.
Multiple suspicious malignant enlarged mediastinal lymph nodes,
particularly on the right paratracheal and infracarinal regions.
Short-term progression of peribronchovascular consolidation in the right
upper lobe and multiple new subsolid micronodules bilaterally.
Differential diagnosis includes inflammatory lesions, especially in the
presence of known neutropenia, which could raise suspicion of fungal
infection.
Intraabdominally, there is an image suggestive of small bowel subileus
without a clearly defined mechanical obstruction.
Density-elevated and ill-defined cystic lesion in the left upper pole of
the kidney. Primary consideration is a hemorrhaged or thickened cyst,
but ultimately, the nature of the lesion remains uncertain.
**CT Chest on 01/04/20:** Significant necrotic hilar lymph nodes on the
right with bronchial and vascular stenosis. Marked progression of
pulmonary infiltrates, particularly on the right, still compatible with
superinfection in the context of known active tuberculosis
**CT Chest from 02/05/20**: Marked necrotic lymph nodes hilar right with
bronchial and vascular stenosis. Significantly increasing pneumonia-like
infiltrates, particularly on the right, still compatible with
superinfection in the context of known open tuberculosis.
**Neck/Chest CT from 06/07/20:** Size-stable necrotic lymph node
conglomerate infraclavicular right, dimensioned axially up to about 6 x
2 cm, with ongoing slit-shaped compression of the right internal jugular
vein. Hypoplastic mastoid cells left, idem. Progressive, partly new and
large-volume consolidations with adjacent ground glass infiltrates on
the right in the anterior, less posterior upper lobe and perihilar.
Inhomogeneous, partially reduced contrast of consolidated lung
parenchyma, broncho pneumogram preserved dorsally only.
**Neck Ultrasound from 08/14/2020:** Clinical History, Question,
Justifying Indication: Follow-up of cervical lymph nodes in
tuberculosis.
**Findings/Assessment:** Neck Lymph Node Ultrasound from 05/20/2020 for
comparison. As in the previous examination, evidence of two
significantly enlarged supraclavicular lymph nodes on the right, both
showing a decrease in size compared to the previous examination: The
more medial node measures 2.9 x 1.6 cm compared to the previous 3.7 x
1.7 cm, while the more laterally located lymph node measures 3.3 x 1.4
cm compared to the previous 4.2 x 1.5 cm. The more medial lymph node
appears centrally hypoechoic, indicative of partial liquefaction, while
the more lateral lymph node has a rather solid appearance. No other
pathologically enlarged lymph nodes detected in the cervical region.
**CT Neck/Chest/Abdomen/Pelvis from 10/02/2020:** Assessment: Compared
to the previous examination from 06/07/2020, there is evidence of
regression in findings: Size regression of consolidating infiltrate in
the right upper lobe and the adjacent central lower lobe, albeit with
increasing signs of cavitation. Progressive right pleural effusion and
progressive signs of pulmonary volume overload. Regression of cervical,
mediastinal, and right hilar lymphadenopathy. Persistent centrally
liquefying lymph node conglomerates in the right hilar region,
compressing central hilar structures. Some findings remain stable, while
others have regressed. No evidence of new manifestations.
**CT Chest/Abdomen/Pelvis from 12/02/20:** Assessment: Compared to
10/02/20: In today\'s contrast examination, a newly unmasked large tumor
is located right pulmonary hilar, encasing the conduits of the right
lung lobe; Differential diagnosis includes lymph node conglomerate,
central bronchial carcinoma, and a distant possibility of inflammatory
lesions. Multiple suspiciously enlarged mediastinal lymph nodes,
especially right paratracheal and infracarinal. In a short time,
progressive peribronchovascular consolidations in the right upper lobe
and multiple new subsolid micronodules bilaterally; Differential
diagnosis includes inflammatory lesions, potentially fungal in the
context of known neutropenia. Intra-abdominally, there is a picture of
small bowel subileus without discernible mechanical obstruction.
Corresponding symptoms? Densely elevated and ill-defined cystic lesion
in the upper pole of the left kidney; Differential diagnosis primarily
includes a hemorrhaged/thickened cyst, ultimately with uncertain
malignancy.
**Chest in two planes on 04/23/2021:** **Findings/Assessment:** In
comparison with the corresponding prior images, most recently on
08/14/2020. Also refer to CT Neck and Chest on 01/23/2021. The heart is
enlarged with a leftward emphasis, but there are no signs of acute
congestion. Extensive consolidation projecting onto the right mid-field,
with a long-term trend toward regression but still clearly demarcated.
No pneumothorax. No pleural effusion. Known lymph nodes in the
mediastinum/hilum. Degenerative spinal changes.
**Neck/Chest CT on 02/23/21:** Slightly regressive/nodular fibrosing
infiltrate in the right upper lobe and adjacent central lower lobe with
continuing significant residual findings. Within the infiltrate, larger
poorly perfused areas with cavitations and scarred bronchiectasis.
Increasing, partly patchy densities on the left basal region,
differential diagnoses include infiltrates and ventilation disorders.
Essentially constant cervical, mediastinal, and hilar lymphadenopathy.
Constant liver lesions in the upper abdomen, differential diagnoses
include TB manifestations and cystic changes.
**CT Neck/Chest/Abdomen/Pelvis from 06/12/2021**: CT from 02/23/2021
available for comparison. Neck/Chest: Improved right upper lobe (ROL)
ventilation with regressive necrotic TB manifestations peri-hilar, now
only with subtotal lobar atelectasis. Essentially stable necrotic lymph
node manifestations in the cervical, mediastinal, and right hilar
regions, for example, supraclavicular on the right (18 mm, previously
30.1 Im 21.2) or right paratracheal (18 mm, previously 30.1 Im 33.8).
Narrow right pleural effusion, same as before. No pneumothorax. Heart
size normal. No pericardial effusion. Abdomen: Mid-term stable hypodense
liver lesions (regressing since 07/2021).
**Treatment and Progression:** Due to the extensive findings and the
untreatable immunocompromising underlying condition, we decided to
switch from a four-drug TB therapy to a three-drug therapy after nearly
3 months. In addition to rifampicin and isoniazid, levofloxacin was
initiated. Despite very good therapy adherence, acid-fast bacilli
continued to be detected microscopically in sputum samples without
culture confirmation of mycobacteria, even after discharge. Furthermore,
the radiological findings worsened. In April 2020, liver lesions were
identified in the CT that had not been described up to that point, and
pulmonary and mediastinal changes increased. Clinically, right cervical
lymphadenopathy also progressed in size. Due to a possible immune
reaction, a therapy with prednisolone was attempted for several weeks,
which did not lead to improvement. In June 2020, Mr. Hurley was
readmitted for bronchoscopy with BAL and EBUS-guided biopsy to rule out
differential diagnoses. An NTM-NGS-PCR was performed on the BAL, which
did not detect DNA from nontuberculous mycobacteria. Histologically,
predominantly necrotic material was found in the lymph node tissue, and
molecular pathological analysis detected DNA from the M. tuberculosis
complex. There were no indications of malignancy. In addition,
whole-genome sequencing of the most recently cultured mycobacteria was
performed, and latent resistance genes were also ruled out. Other
pathogens, including fungi, were likewise not detected. Aspergillus
antigen in BAL and serum was also negative. We continued the three-drug
therapy with Rifampicin, Isoniazid, and Levofloxacin. Mr. Hurley
developed an increasing need for red blood cell transfusions due to
myelodysplastic syndrome and began receiving regular transfusions from
his outpatient hematologist-oncologist in the summer of 2020. In a
repeat CT control in October 2020, increasing necrotic breakdown of the
right upper and middle lobes was observed, as well as progressive
ipsilateral pleural effusion and persistent mediastinal lymphadenopathy
and liver lesions. Mr. Hurley was referred to the immunology colleagues
to discuss additional immunological treatment options. After extensive
immune deficiency assessment, a low basal interferon-gamma level was
noted in the setting of lymphopenia due to MDS. In an immunological
conference, the patient was thoroughly discussed, and a trial of
interferon-gamma therapy in addition to antituberculous therapy was
discussed due to a low basal interferon-gamma level and a negative
Quantiferon test. After approval of an off-label application, we began
Actimmune® injections in January 2021 after extensive patient education.
Mr. Hurley learned to self-administer the subcutaneous injections and
initially tolerated the treatment well. Due to continuous worsening of
the blood count, a bone marrow puncture was performed again on an
outpatient basis by the attending hematologist-oncologist, and secondary
AML was diagnosed. Since February 2021, Mr. Hurley has received
Azacitidine and regular red blood cell and platelet concentrates. After
3 months of Actimmune® therapy, sputum no longer showed acid-fast
bacilli in March 2021, and radiologically, the left pleural effusion had
completely regressed, and the infiltrates had decreased. Actimmune® was
discontinued after 3 months. Towards the end of Actimmune® therapy, Mr.
Hurley developed pronounced shoulder arthralgia and pain in the upper
thoracic spine. Fractures were ruled out. With pain therapy, the pain
became tolerable and gradually improved. Arthralgia and myalgia are
common side effects of interferon-gamma. Due to the demonstrable
therapeutic response, we presented Mr. Hurley, along with an
interpreter, at the Department of Hematology and Oncology to discuss
further therapeutic options for AML in the context of the hematological
and infectious disease situation. After extensive explanation of the
disease situation, the risks of aggressive AML therapy in the presence
of unresolved tuberculosis, and the consequences of palliative AML
therapy. Mr. Hurley agreed to allogeneic stem cell transplantation after
some consideration. On an outpatient basis, the cytostatic therapy with
Azacitidine was expanded to include Venetoclax. Antituberculous therapy
with rifampicin, isoniazid, and levofloxacin was continued. Regular
sputum checks remained consistently microscopically negative until
complete AML remission was achieved. Mr. will be admitted for allogeneic
stem cell transplantation in July 2021. A repeat CT in June 2021
confirmed continued regression of the tuberculosis findings.
Antituberculous therapy will be continued indefinitely.
**CT Neck/Chest/Abdomen/Pelvis on 06/12/2022:** CT for comparison.
Neck/Chest: Improved right lung upper lobe ventilation with regressing
necrotic tuberculosis manifestations, now with only subtotal lobar
atelectasis. Essentially constant necrotic lymph node manifestations in
the cervical, mediastinal, and right hilar regions, as exemplified by
the right supraclavicular (18 mm, SE 301 HU 212) or right paratracheal
(18 mm, SE 301 HU 338) nodes. Narrow pleural effusion on the right,
likewise. No pneumothorax. The heart is not enlarged. No pericardial
effusion. Abdomen: Medium-term constant hypodense liver lesions
(regressing)
**Current Recommendations:** Continue antituberculous therapy without a
defined endpoint. Sputum checks during allogeneic stem cell
transplantation every 1-2 weeks. In case of clinical signs of persistent
infection, perform early CT scans of the neck, chest, and abdomen.
Follow-up appointment in our infectious diseases outpatient clinic after
allogeneic stem cell transplantation. | Hospitalization for neutropenic fever (10/2019) → Diagnosis of tuberculosis and initiation of four-drug anti-tuberculosis therapy (11/08/19) → First detection of acid-fast bacilli in sputum (01/02/20) → Secondary AML diagnosis (01/2021) → Start of Actimmune® injections (01/2021) → Allogeneic stem cell transplantation (07/2021). |
Given all the nationalities present at the tournament and the information presented in the article, which nationality would be most likely to win?
A. Russian
B. Hungarian
C. French
D. American
| THE 64-SQUARE MADHOUSE by FRITZ LEIBER The machine was not perfect. It could be tricked. It could make mistakes. And—it could learn! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, May 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Silently, so as not to shock anyone with illusions about well dressed young women, Sandra Lea Grayling cursed the day she had persuaded the Chicago Space Mirror that there would be all sorts of human interest stories to be picked up at the first international grandmaster chess tournament in which an electronic computing machine was entered. Not that there weren't enough humans around, it was the interest that was in doubt. The large hall was crammed with energetic dark-suited men of whom a disproportionately large number were bald, wore glasses, were faintly untidy and indefinably shabby, had Slavic or Scandinavian features, and talked foreign languages. They yakked interminably. The only ones who didn't were scurrying individuals with the eager-zombie look of officials. Chess sets were everywhere—big ones on tables, still bigger diagram-type electric ones on walls, small peg-in sets dragged from side pockets and manipulated rapidly as part of the conversational ritual and still smaller folding sets in which the pieces were the tiny magnetized disks used for playing in free-fall. There were signs featuring largely mysterious combinations of letters: FIDE, WBM, USCF, USSF, USSR and UNESCO. Sandra felt fairly sure about the last three. The many clocks, bedside table size, would have struck a familiar note except that they had little red flags and wheels sprinkled over their faces and they were all in pairs, two clocks to a case. That Siamese-twin clocks should be essential to a chess tournament struck Sandra as a particularly maddening circumstance. Her last assignment had been to interview the pilot pair riding the first American manned circum-lunar satellite—and the five alternate pairs who hadn't made the flight. This tournament hall seemed to Sandra much further out of the world. Overheard scraps of conversation in reasonably intelligible English were not particularly helpful. Samples: "They say the Machine has been programmed to play nothing but pure Barcza System and Indian Defenses—and the Dragon Formation if anyone pushes the King Pawn." "Hah! In that case...." "The Russians have come with ten trunkfuls of prepared variations and they'll gang up on the Machine at adjournments. What can one New Jersey computer do against four Russian grandmasters?" "I heard the Russians have been programmed—with hypnotic cramming and somno-briefing. Votbinnik had a nervous breakdown." "Why, the Machine hasn't even a Haupturnier or an intercollegiate won. It'll over its head be playing." "Yes, but maybe like Capa at San Sebastian or Morphy or Willie Angler at New York. The Russians will look like potzers." "Have you studied the scores of the match between Moon Base and Circum-Terra?" "Not worth the trouble. The play was feeble. Barely Expert Rating." Sandra's chief difficulty was that she knew absolutely nothing about the game of chess—a point that she had slid over in conferring with the powers at the Space Mirror , but that now had begun to weigh on her. How wonderful it would be, she dreamed, to walk out this minute, find a quiet bar and get pie-eyed in an evil, ladylike way. "Perhaps mademoiselle would welcome a drink?" "You're durn tootin' she would!" Sandra replied in a rush, and then looked down apprehensively at the person who had read her thoughts. It was a small sprightly elderly man who looked like a somewhat thinned down Peter Lorre—there was that same impression of the happy Slavic elf. What was left of his white hair was cut very short, making a silvery nap. His pince-nez had quite thick lenses. But in sharp contrast to the somberly clad men around them, he was wearing a pearl-gray suit of almost exactly the same shade as Sandra's—a circumstance that created for her the illusion that they were fellow conspirators. "Hey, wait a minute," she protested just the same. He had already taken her arm and was piloting her toward the nearest flight of low wide stairs. "How did you know I wanted a drink?" "I could see that mademoiselle was having difficulty swallowing," he replied, keeping them moving. "Pardon me for feasting my eyes on your lovely throat." "I didn't suppose they'd serve drinks here." "But of course." They were already mounting the stairs. "What would chess be without coffee or schnapps?" "Okay, lead on," Sandra said. "You're the doctor." "Doctor?" He smiled widely. "You know, I like being called that." "Then the name is yours as long as you want it—Doc." Meanwhile the happy little man had edged them into the first of a small cluster of tables, where a dark-suited jabbering trio was just rising. He snapped his fingers and hissed through his teeth. A white-aproned waiter materialized. "For myself black coffee," he said. "For mademoiselle rhine wine and seltzer?" "That'd go fine." Sandra leaned back. "Confidentially, Doc, I was having trouble swallowing ... well, just about everything here." He nodded. "You are not the first to be shocked and horrified by chess," he assured her. "It is a curse of the intellect. It is a game for lunatics—or else it creates them. But what brings a sane and beautiful young lady to this 64-square madhouse?" Sandra briefly told him her story and her predicament. By the time they were served, Doc had absorbed the one and assessed the other. "You have one great advantage," he told her. "You know nothing whatsoever of chess—so you will be able to write about it understandably for your readers." He swallowed half his demitasse and smacked his lips. "As for the Machine—you do know, I suppose, that it is not a humanoid metal robot, walking about clanking and squeaking like a late medieval knight in armor?" "Yes, Doc, but...." Sandra found difficulty in phrasing the question. "Wait." He lifted a finger. "I think I know what you're going to ask. You want to know why, if the Machine works at all, it doesn't work perfectly, so that it always wins and there is no contest. Right?" Sandra grinned and nodded. Doc's ability to interpret her mind was as comforting as the bubbly, mildly astringent mixture she was sipping. He removed his pince-nez, massaged the bridge of his nose and replaced them. "If you had," he said, "a billion computers all as fast as the Machine, it would take them all the time there ever will be in the universe just to play through all the possible games of chess, not to mention the time needed to classify those games into branching families of wins for White, wins for Black and draws, and the additional time required to trace out chains of key-moves leading always to wins. So the Machine can't play chess like God. What the Machine can do is examine all the likely lines of play for about eight moves ahead—that is, four moves each for White and Black—and then decide which is the best move on the basis of capturing enemy pieces, working toward checkmate, establishing a powerful central position and so on." "That sounds like the way a man would play a game," Sandra observed. "Look ahead a little way and try to make a plan. You know, like getting out trumps in bridge or setting up a finesse." "Exactly!" Doc beamed at her approvingly. "The Machine is like a man. A rather peculiar and not exactly pleasant man. A man who always abides by sound principles, who is utterly incapable of flights of genius, but who never makes a mistake. You see, you are finding human interest already, even in the Machine." Sandra nodded. "Does a human chess player—a grandmaster, I mean—ever look eight moves ahead in a game?" "Most assuredly he does! In crucial situations, say where there's a chance of winning at once by trapping the enemy king, he examines many more moves ahead than that—thirty or forty even. The Machine is probably programmed to recognize such situations and do something of the same sort, though we can't be sure from the information World Business Machines has released. But in most chess positions the possibilities are so very nearly unlimited that even a grandmaster can only look a very few moves ahead and must rely on his judgment and experience and artistry. The equivalent of those in the Machine is the directions fed into it before it plays a game." "You mean the programming?" "Indeed yes! The programming is the crux of the problem of the chess-playing computer. The first practical model, reported by Bernstein and Roberts of IBM in 1958 and which looked four moves ahead, was programmed so that it had a greedy worried tendency to grab at enemy pieces and to retreat its own whenever they were attacked. It had a personality like that of a certain kind of chess-playing dub—a dull-brained woodpusher afraid to take the slightest risk of losing material—but a dub who could almost always beat an utter novice. The WBM machine here in the hall operates about a million times as fast. Don't ask me how, I'm no physicist, but it depends on the new transistors and something they call hypervelocity, which in turn depends on keeping parts of the Machine at a temperature near absolute zero. However, the result is that the Machine can see eight moves ahead and is capable of being programmed much more craftily." "A million times as fast as the first machine, you say, Doc? And yet it only sees twice as many moves ahead?" Sandra objected. "There is a geometrical progression involved there," he told her with a smile. "Believe me, eight moves ahead is a lot of moves when you remember that the Machine is errorlessly examining every one of thousands of variations. Flesh-and-blood chess masters have lost games by blunders they could have avoided by looking only one or two moves ahead. The Machine will make no such oversights. Once again, you see, you have the human factor, in this case working for the Machine." "Savilly, I have been looking allplace for you!" A stocky, bull-faced man with a great bristling shock of black, gray-flecked hair had halted abruptly by their table. He bent over Doc and began to whisper explosively in a guttural foreign tongue. Sandra's gaze traveled beyond the balustrade. Now that she could look down at it, the central hall seemed less confusedly crowded. In the middle, toward the far end, were five small tables spaced rather widely apart and with a chessboard and men and one of the Siamese clocks set out on each. To either side of the hall were tiers of temporary seats, about half of them occupied. There were at least as many more people still wandering about. On the far wall was a big electric scoreboard and also, above the corresponding tables, five large dully glassy chessboards, the White squares in light gray, the Black squares in dark. One of the five wall chessboards was considerably larger than the other four—the one above the Machine. Sandra looked with quickening interest at the console of the Machine—a bank of keys and some half-dozen panels of rows and rows of tiny telltale lights, all dark at the moment. A thick red velvet cord on little brass standards ran around the Machine at a distance of about ten feet. Inside the cord were only a few gray-smocked men. Two of them had just laid a black cable to the nearest chess table and were attaching it to the Siamese clock. Sandra tried to think of a being who always checked everything, but only within limits beyond which his thoughts never ventured, and who never made a mistake.... "Miss Grayling! May I present to you Igor Jandorf." She turned back quickly with a smile and a nod. "I should tell you, Igor," Doc continued, "that Miss Grayling represents a large and influential Midwestern newspaper. Perhaps you have a message for her readers." The shock-headed man's eyes flashed. "I most certainly do!" At that moment the waiter arrived with a second coffee and wine-and-seltzer. Jandorf seized Doc's new demitasse, drained it, set it back on the tray with a flourish and drew himself up. "Tell your readers, Miss Grayling," he proclaimed, fiercely arching his eyebrows at her and actually slapping his chest, "that I, Igor Jandorf, will defeat the Machine by the living force of my human personality! Already I have offered to play it an informal game blindfold—I, who have played 50 blindfold games simultaneously! Its owners refuse me. I have challenged it also to a few games of rapid-transit—an offer no true grandmaster would dare ignore. Again they refuse me. I predict that the Machine will play like a great oaf—at least against me . Repeat: I, Igor Jandorf, by the living force of my human personality, will defeat the Machine. Do you have that? You can remember it?" "Oh yes," Sandra assured him, "but there are some other questions I very much want to ask you, Mr. Jandorf." "I am sorry, Miss Grayling, but I must clear my mind now. In ten minutes they start the clocks." While Sandra arranged for an interview with Jandorf after the day's playing session, Doc reordered his coffee. "One expects it of Jandorf," he explained to Sandra with a philosophic shrug when the shock-headed man was gone. "At least he didn't take your wine-and-seltzer. Or did he? One tip I have for you: don't call a chess master Mister, call him Master. They all eat it up." "Gee, Doc, I don't know how to thank you for everything. I hope I haven't offended Mis—Master Jandorf so that he doesn't—" "Don't worry about that. Wild horses couldn't keep Jandorf away from a press interview. You know, his rapid-transit challenge was cunning. That's a minor variety of chess where each player gets only ten seconds to make a move. Which I don't suppose would give the Machine time to look three moves ahead. Chess players would say that the Machine has a very slow sight of the board. This tournament is being played at the usual international rate of 15 moves an hour, and—" "Is that why they've got all those crazy clocks?" Sandra interrupted. "Oh, yes. Chess clocks measure the time each player takes in making his moves. When a player makes a move he presses a button that shuts his clock off and turns his opponent's on. If a player uses too much time, he loses as surely as if he were checkmated. Now since the Machine will almost certainly be programmed to take an equal amount of time on successive moves, a rate of 15 moves an hour means it will have 4 minutes a move—and it will need every second of them! Incidentally it was typical Jandorf bravado to make a point of a blindfold challenge—just as if the Machine weren't playing blindfold itself. Or is the Machine blindfold? How do you think of it?" "Gosh, I don't know. Say, Doc, is it really true that Master Jandorf has played 50 games at once blindfolded? I can't believe that." "Of course not!" Doc assured her. "It was only 49 and he lost two of those and drew five. Jandorf always exaggerates. It's in his blood." "He's one of the Russians, isn't he?" Sandra asked. "Igor?" Doc chuckled. "Not exactly," he said gently. "He is originally a Pole and now he has Argentinian citizenship. You have a program, don't you?" Sandra started to hunt through her pocketbook, but just then two lists of names lit up on the big electric scoreboard. THE PLAYERS William Angler, USA Bela Grabo, Hungary Ivan Jal, USSR Igor Jandorf, Argentina Dr. S. Krakatower, France Vassily Lysmov, USSR The Machine, USA (programmed by Simon Great) Maxim Serek, USSR Moses Sherevsky, USA Mikhail Votbinnik, USSR Tournament Director : Dr. Jan Vanderhoef FIRST ROUND PAIRINGS Sherevsky vs. Serek Jal vs. Angler Jandorf vs. Votbinnik Lysmov vs. Krakatower Grabo vs. Machine "Cripes, Doc, they all sound like they were Russians," Sandra said after a bit. "Except this Willie Angler. Oh, he's the boy wonder, isn't he?" Doc nodded. "Not such a boy any longer, though. He's.... Well, speak of the Devil's children.... Miss Grayling, I have the honor of presenting to you the only grandmaster ever to have been ex-chess-champion of the United States while still technically a minor—Master William Augustus Angler." A tall, sharply-dressed young man with a hatchet face pressed the old man back into his chair. "How are you, Savvy, old boy old boy?" he demanded. "Still chasing the girls, I see." "Please, Willie, get off me." "Can't take it, huh?" Angler straightened up somewhat. "Hey waiter! Where's that chocolate malt? I don't want it next year. About that ex- , though. I was swindled, Savvy. I was robbed." "Willie!" Doc said with some asperity. "Miss Grayling is a journalist. She would like to have a statement from you as to how you will play against the Machine." Angler grinned and shook his head sadly. "Poor old Machine," he said. "I don't know why they take so much trouble polishing up that pile of tin just so that I can give it a hit in the head. I got a hatful of moves it'll burn out all its tubes trying to answer. And if it gets too fresh, how about you and me giving its low-temperature section the hotfoot, Savvy? The money WBM's putting up is okay, though. That first prize will just fit the big hole in my bank account." "I know you haven't the time now, Master Angler," Sandra said rapidly, "but if after the playing session you could grant me—" "Sorry, babe," Angler broke in with a wave of dismissal. "I'm dated up for two months in advance. Waiter! I'm here, not there!" And he went charging off. Doc and Sandra looked at each other and smiled. "Chess masters aren't exactly humble people, are they?" she said. Doc's smile became tinged with sad understanding. "You must excuse them, though," he said. "They really get so little recognition or recompense. This tournament is an exception. And it takes a great deal of ego to play greatly." "I suppose so. So World Business Machines is responsible for this tournament?" "Correct. Their advertising department is interested in the prestige. They want to score a point over their great rival." "But if the Machine plays badly it will be a black eye for them," Sandra pointed out. "True," Doc agreed thoughtfully. "WBM must feel very sure.... It's the prize money they've put up, of course, that's brought the world's greatest players here. Otherwise half of them would be holding off in the best temperamental-artist style. For chess players the prize money is fabulous—$35,000, with $15,000 for first place, and all expenses paid for all players. There's never been anything like it. Soviet Russia is the only country that has ever supported and rewarded her best chess players at all adequately. I think the Russian players are here because UNESCO and FIDE (that's Federation Internationale des Echecs —the international chess organization) are also backing the tournament. And perhaps because the Kremlin is hungry for a little prestige now that its space program is sagging." "But if a Russian doesn't take first place it will be a black eye for them." Doc frowned. "True, in a sense. They must feel very sure.... Here they are now." Four men were crossing the center of the hall, which was clearing, toward the tables at the other end. Doubtless they just happened to be going two by two in close formation, but it gave Sandra the feeling of a phalanx. "The first two are Lysmov and Votbinnik," Doc told her. "It isn't often that you see the current champion of the world—Votbinnik—and an ex-champion arm in arm. There are two other persons in the tournament who have held that honor—Jal and Vanderhoef the director, way back." "Will whoever wins this tournament become champion?" "Oh no. That's decided by two-player matches—a very long business—after elimination tournaments between leading contenders. This tournament is a round robin: each player plays one game with every other player. That means nine rounds." "Anyway there are an awful lot of Russians in the tournament," Sandra said, consulting her program. "Four out of ten have USSR after them. And Bela Grabo, Hungary—that's a satellite. And Sherevsky and Krakatower are Russian-sounding names." "The proportion of Soviet to American entries in the tournament represents pretty fairly the general difference in playing strength between the two countries," Doc said judiciously. "Chess mastery moves from land to land with the years. Way back it was the Moslems and the Hindus and Persians. Then Italy and Spain. A little over a hundred years ago it was France and England. Then Germany, Austria and the New World. Now it's Russia—including of course the Russians who have run away from Russia. But don't think there aren't a lot of good Anglo-Saxon types who are masters of the first water. In fact, there are a lot of them here around us, though perhaps you don't think so. It's just that if you play a lot of chess you get to looking Russian. Once it probably made you look Italian. Do you see that short bald-headed man?" "You mean the one facing the Machine and talking to Jandorf?" "Yes. Now that's one with a lot of human interest. Moses Sherevsky. Been champion of the United States many times. A very strict Orthodox Jew. Can't play chess on Fridays or on Saturdays before sundown." He chuckled. "Why, there's even a story going around that one rabbi told Sherevsky it would be unlawful for him to play against the Machine because it is technically a golem —the clay Frankenstein's monster of Hebrew legend." Sandra asked, "What about Grabo and Krakatower?" Doc gave a short scornful laugh. "Krakatower! Don't pay any attention to him . A senile has-been, it's a scandal he's been allowed to play in this tournament! He must have pulled all sorts of strings. Told them that his lifelong services to chess had won him the honor and that they had to have a member of the so-called Old Guard. Maybe he even got down on his knees and cried—and all the time his eyes on that expense money and the last-place consolation prize! Yet dreaming schizophrenically of beating them all! Please, don't get me started on Dirty Old Krakatower." "Take it easy, Doc. He sounds like he would make an interesting article? Can you point him out to me?" "You can tell him by his long white beard with coffee stains. I don't see it anywhere, though. Perhaps he's shaved it off for the occasion. It would be like that antique womanizer to develop senile delusions of youthfulness." "And Grabo?" Sandra pressed, suppressing a smile at the intensity of Doc's animosity. Doc's eyes grew thoughtful. "About Bela Grabo (why are three out of four Hungarians named Bela?) I will tell you only this: That he is a very brilliant player and that the Machine is very lucky to have drawn him as its first opponent." He would not amplify his statement. Sandra studied the Scoreboard again. "This Simon Great who's down as programming the Machine. He's a famous physicist, I suppose?" "By no means. That was the trouble with some of the early chess-playing machines—they were programmed by scientists. No, Simon Great is a psychologist who at one time was a leading contender for the world's chess championship. I think WBM was surprisingly shrewd to pick him for the programming job. Let me tell you—No, better yet—" Doc shot to his feet, stretched an arm on high and called out sharply, "Simon!" A man some four tables away waved back and a moment later came over. "What is it, Savilly?" he asked. "There's hardly any time, you know." The newcomer was of middle height, compact of figure and feature, with graying hair cut short and combed sharply back. Doc spoke his piece for Sandra. Simon Great smiled thinly. "Sorry," he said, "But I am making no predictions and we are giving out no advance information on the programming of the Machine. As you know, I have had to fight the Players' Committee tooth and nail on all sorts of points about that and they have won most of them. I am not permitted to re-program the Machine at adjournments—only between games (I did insist on that and get it!) And if the Machine breaks down during a game, its clock keeps running on it. My men are permitted to make repairs—if they can work fast enough." "That makes it very tough on you," Sandra put in. "The Machine isn't allowed any weaknesses." Great nodded soberly. "And now I must go. They've almost finished the count-down, as one of my technicians keeps on calling it. Very pleased to have met you, Miss Grayling—I'll check with our PR man on that interview. Be seeing you, Savvy." The tiers of seats were filled now and the central space almost clear. Officials were shooing off a few knots of lingerers. Several of the grandmasters, including all four Russians, were seated at their tables. Press and company cameras were flashing. The four smaller wallboards lit up with the pieces in the opening position—white for White and red for Black. Simon Great stepped over the red velvet cord and more flash bulbs went off. "You know, Doc," Sandra said, "I'm a dog to suggest this, but what if this whole thing were a big fake? What if Simon Great were really playing the Machine's moves? There would surely be some way for his electricians to rig—" Doc laughed happily—and so loudly that some people at the adjoining tables frowned. "Miss Grayling, that is a wonderful idea! I will probably steal it for a short story. I still manage to write and place a few in England. No, I do not think that is at all likely. WBM would never risk such a fraud. Great is completely out of practice for actual tournament play, though not for chess-thinking. The difference in style between a computer and a man would be evident to any expert. Great's own style is remembered and would be recognized—though, come to think of it, his style was often described as being machinelike...." For a moment Doc's eyes became thoughtful. Then he smiled again. "But no, the idea is impossible. Vanderhoef as Tournament Director has played two or three games with the Machine to assure himself that it operates legitimately and has grandmaster skill." "Did the Machine beat him?" Sandra asked. Doc shrugged. "The scores weren't released. It was very hush-hush. But about your idea, Miss Grayling—did you ever read about Maelzel's famous chess-playing automaton of the 19th Century? That one too was supposed to work by machinery (cogs and gears, not electricity) but actually it had a man hidden inside it—your Edgar Poe exposed the fraud in a famous article. In my story I think the chess robot will break down while it is being demonstrated to a millionaire purchaser and the young inventor will have to win its game for it to cover up and swing the deal. Only the millionaire's daughter, who is really a better player than either of them ... yes, yes! Your Ambrose Bierce too wrote a story about a chess-playing robot of the clickety-clank-grr kind who murdered his creator, crushing him like an iron grizzly bear when the man won a game from him. Tell me, Miss Grayling, do you find yourself imagining this Machine putting out angry tendrils to strangle its opponents, or beaming rays of death and hypnotism at them? I can imagine...." While Doc chattered happily on about chess-playing robots and chess stories, Sandra found herself thinking about him. A writer of some sort evidently and a terrific chess buff. Perhaps he was an actual medical doctor. She'd read something about two or three coming over with the Russian squad. But Doc certainly didn't sound like a Soviet citizen. He was older than she'd first assumed. She could see that now that she was listening to him less and looking at him more. Tired, too. Only his dark-circled eyes shone with unquenchable youth. A useful old guy, whoever he was. An hour ago she'd been sure she was going to muff this assignment completely and now she had it laid out cold. For the umpteenth time in her career Sandra shied away from the guilty thought that she wasn't a writer at all or even a reporter, she just used dime-a-dozen female attractiveness to rope a susceptible man (young, old, American, Russian) and pick his brain.... She realized suddenly that the whole hall had become very quiet. Doc was the only person still talking and people were again looking at them disapprovingly. All five wallboards were lit up and the changed position of a few pieces showed that opening moves had been made on four of them, including the Machine's. The central space between the tiers of seats was completely clear now, except for one man hurrying across it in their direction with the rapid yet quiet, almost tip-toe walk that seemed to mark all the officials. Like morticians' assistants , she thought. He rapidly mounted the stairs and halted at the top to look around searchingly. His gaze lighted on their table, his eyebrows went up, and he made a beeline for Doc. Sandra wondered if she should warn him that he was about to be shushed. The official laid a hand on Doc's shoulder. "Sir!" he said agitatedly. "Do you realize that they've started your clock, Dr. Krakatower?" | A. Russian |
Has Matilda changed the end of the story?
A. No. She still hasn’t found a husband, and will likely be Pen Pals again.
B. No. She’s still looking for fantasies, as evidence by her looking up at the shooting star.
C. Yes. She is like Mrs. Gorky no3, chasing after impossible theories.
D. Yes. She’s more grounded now, and less naive.
| PEN PAL Illustrated by DON SIBLEY By MILTON LESSER [Transcriber's Note: This etext was produced from Galaxy Science Fiction July 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] All she wanted was a mate and she had the gumption to go out and hunt one down. But that meant poaching in a strictly forbidden territory! The best that could be said for Matilda Penshaws was that she was something of a paradox. She was thirty-three years old, certainly not aged when you consider the fact that the female life expectancy is now up in the sixties, but the lines were beginning to etch their permanent paths across her face and now she needed certain remedial undergarments at which she would have scoffed ten or even five years ago. Matilda was also looking for a husband. This, in itself, was not unusual—but Matilda was so completely wrapped up in the romantic fallacy of her day that she sought a prince charming, a faithful Don Juan, a man who had been everywhere and tasted of every worldly pleasure and who now wanted to sit on a porch and talk about it all to Matilda. The fact that in all probability such a man did not exist disturbed Matilda not in the least. She had been known to say that there are over a billion men in the world, a goodly percentage of whom are eligible bachelors, and that the right one would come along simply because she had been waiting for him. Matilda, you see, had patience. She also had a fetish. Matilda had received her A.B. from exclusive Ursula Johns College and Radcliff had yielded her Masters degree, yet Matilda was an avid follower of the pen pal columns. She would read them carefully and then read them again, looking for the masculine names which, through a system known only to Matilda, had an affinity to her own. To the gentlemen upon whom these names were affixed, Matilda would write, and she often told her mother, the widow Penshaws, that it was in this way she would find her husband. The widow Penshaws impatiently told her to go out and get dates. That particular night, Matilda pulled her battered old sedan into the garage and walked up the walk to the porch. The widow Penshaws was rocking on the glider and Matilda said hello. The first thing the widow Penshaws did was to take Matilda's left hand in her own and examine the next-to-the-last finger. "I thought so," she said. "I knew this was coming when I saw that look in your eye at dinner. Where is Herman's engagement ring?" Matilda smiled. "It wouldn't have worked out, Ma. He was too darned stuffy. I gave him his ring and said thanks anyway and he smiled politely and said he wished I had told him sooner because his fifteenth college reunion was this weekend and he had already turned down the invitation." The widow Penshaws nodded regretfully. "That was thoughtful of Herman to hide his feelings." "Hogwash!" said her daughter. "He has no true feelings. He's sorry that he had to miss his college reunion. That's all he has to hide. A stuffy Victorian prude and even less of a man than the others." "But, Matilda, that's your fifth broken engagement in three years. It ain't that you ain't popular, but you just don't want to cooperate. You don't fall in love, Matilda—no one does. Love osmoses into you slowly, without you even knowing, and it keeps growing all the time." Matilda admired her mother's use of the word osmosis, but she found nothing which was not objectionable about being unaware of the impact of love. She said good-night and went upstairs, climbed out of her light summer dress and took a cold shower. She began to hum to herself. She had not yet seen the pen pal section of the current Literary Review , and because the subject matter of that magazine was somewhat highbrow and cosmopolitan, she could expect a gratifying selection of pen pals. She shut off the shower, brushed her teeth, gargled, patted herself dry with a towel, and jumped into bed, careful to lock the door of her bedroom. She dared not let the widow Penshaws know that she slept in the nude; the widow Penshaws would object to a girl sleeping in the nude, even if the nearest neighbor was three hundred yards away. Matilda switched her bed lamp on and dabbed some citronella on each ear lobe and a little droplet on her chin (how she hated insects!). Then she propped up her pillows—two pillows partially stopped her post-nasal drip; and took the latest issue of the Literary Review off the night table. She flipped through the pages and came to personals. Someone in Nebraska wanted to trade match books; someone in New York needed a midwestern pen pal, but it was a woman; an elderly man interested in ornithology wanted a young chick correspondent interested in the same subject; a young, personable man wanted an editorial position because he thought he had something to offer the editorial world; and— Matilda read the next one twice. Then she held it close to the light and read it again. The Literary Review was one of the few magazines which printed the name of the advertiser rather than a box number, and Matilda even liked the sound of the name. But mostly, she had to admit to herself, it was the flavor of the wording. This very well could be it . Or, that is, him . Intelligent, somewhat egotistical male who's really been around, whose universal experience can make the average cosmopolite look like a provincial hick, is in need of several female correspondents: must be intelligent, have gumption, be capable of listening to male who has a lot to say and wants to say it. All others need not apply. Wonderful opportunity cultural experience ... Haron Gorka, Cedar Falls, Ill. The man was egotistical, all right; Matilda could see that. But she had never minded an egotistical man, at least not when he had something about which he had a genuine reason to be egotistical. The man sounded as though he would have reason indeed. He only wanted the best because he was the best. Like calls to like. The name—Haron Gorka: its oddness was somehow beautiful to Matilda. Haron Gorka—the nationality could be anything. And that was it. He had no nationality for all intents and purposes; he was an international man, a figure among figures, a paragon.... Matilda sighed happily as she put out the light. The moon shone in through the window brightly, and at such times Matilda generally would get up, go to the cupboard, pull out a towel, take two hairpins from her powder drawer, pin the towel to the screen of her window, and hence keep the disturbing moonlight from her eyes. But this time it did not disturb her, and she would let it shine. Cedar Falls was a small town not fifty miles from her home, and she'd get there a hop, skip, and jump ahead of her competitors, simply by arriving in person instead of writing a letter. Matilda was not yet that far gone in years or appearance. Dressed properly, she could hope to make a favorable impression in person, and she felt it was important to beat the influx of mail to Cedar Falls. Matilda got out of bed at seven, tiptoed into the bathroom, showered with a merest wary trickle of water, tiptoed back into her bedroom, dressed in her very best cotton over the finest of uplifting and figure-moulding underthings, made sure her stocking seams were perfectly straight, brushed her suede shoes, admired herself in the mirror, read the ad again, wished for a moment she were a bit younger, and tiptoed downstairs. The widow Penshaws met her at the bottom of the stairwell. "Mother," gasped Matilda. Matilda always gasped when she saw something unexpected. "What on earth are you doing up?" The widow Penshaws smiled somewhat toothlessly, having neglected to put in both her uppers and lowers this early in the morning. "I'm fixing breakfast, of course...." Then the widow Penshaws told Matilda that she could never hope to sneak about the house without her mother knowing about it, and that even if she were going out in response to one of those foolish ads in the magazines, she would still need a good breakfast to start with like only mother could cook. Matilda moodily thanked the widow Penshaws. Driving the fifty miles to Cedar Falls in a little less than an hour, Matilda hummed Mendelssohn's Wedding March all the way. It was her favorite piece of music. Once, she told herself: Matilda Penshaws, you are being premature about the whole thing. But she laughed and thought that if she was, she was, and, meanwhile, she could only get to Cedar Falls and find out. And so she got there. The man in the wire cage at the Cedar Falls post office was a stereotype. Matilda always liked to think in terms of stereotypes. This man was small, roundish, florid of face, with a pair of eyeglasses which hung too far down on his nose. Matilda knew he would peer over his glasses and answer questions grudgingly. "Hello," said Matilda. The stereotype grunted and peered at her over his glasses. Matilda asked him where she could find Haron Gorka. "What?" "I said, where can I find Haron Gorka?" "Is that in the United States?" "It's not a that; it's a he. Where can I find him? Where does he live? What's the quickest way to get there?" The stereotype pushed up his glasses and looked at her squarely. "Now take it easy, ma'am. First place, I don't know any Haron Gorka—" Matilda kept the alarm from creeping into her voice. She muttered an oh under her breath and took out the ad. This she showed to the stereotype, and he scratched his bald head. Then he told Matilda almost happily that he was sorry he couldn't help her. He grudgingly suggested that if it really were important, she might check with the police. Matilda did, only they didn't know any Haron Gorka, either. It turned out that no one did: Matilda tried the general store, the fire department, the city hall, the high school, all three Cedar Falls gas stations, the livery stable, and half a dozen private dwellings at random. As far us the gentry of Cedar Falls was concerned, Haron Gorka did not exist. Matilda felt bad, but she had no intention of returning home this early. If she could not find Haron Gorka, that was one thing; but she knew that she'd rather not return home and face the widow Penshaws, at least not for a while yet. The widow Penshaws meant well, but she liked to analyze other people's mistakes, especially Matilda's. Accordingly, Matilda trudged wearily toward Cedar Falls' small and unimposing library. She could release some of her pent-up aggression by browsing through the dusty slacks. This she did, but it was unrewarding. Cedar Falls had what might be called a microscopic library, and Matilda thought that if this small building were filled with microfilm rather than books, the library still would be lacking. Hence she retraced her steps and nodded to the old librarian as she passed. Then Matilda frowned. Twenty years from now, this could be Matilda Penshaws—complete with plain gray dress, rimless spectacles, gray hair, suspicious eyes, and a broom-stick figure.... On the other hand—why not? Why couldn't the librarian help her? Why hadn't she thought of it before? Certainly a man as well-educated as Haron Gorka would be an avid reader, and unless he had a permanent residence here in Cedar Palls, one couldn't expect that he'd have his own library with him. This being the case, a third-rate collection of books was far better than no collection at all, and perhaps the librarian would know Mr. Haron Gorka. Matilda cleared her throat. "Pardon me," she began. "I'm looking for—" "Haron Gorka." The librarian nodded. "How on earth did you know?" "That's easy. You're the sixth young woman who came here inquiring about that man today. Six of you—five others in the morning, and now you in the afternoon. I never did trust this Mr. Gorka...." Matilda jumped as if she had been struck strategically from the rear. "You know him? You know Haron Gorka?" "Certainly. Of course I know him. He's our steadiest reader here at the library. Not a week goes by that he doesn't take out three, four books. Scholarly gentleman, but not without charm. If I were twenty years younger—" Matilda thought a little flattery might be effective. "Only ten," she assured the librarian. "Ten years would be more than sufficient, I'm sure." "Are you? Well. Well, well." The librarian did something with the back of her hair, but it looked the same as before. "Maybe you're right. Maybe you're right at that." Then she sighed. "But I guess a miss is as good as a mile." "What do you mean?" "I mean anyone would like to correspond with Haron Gorka. Or to know him well. To be considered his friend. Haron Gorka...." The librarian seemed about to soar off into the air someplace, and if five women had been here first, Matilda was now definitely in a hurry. "Um, where can I find Mr. Gorka?" "I'm not supposed to do this, you know. We're not permitted to give the addresses of any of our people. Against regulations, my dear." "What about the other five women?" "They convinced me that I ought to give them his address." Matilda reached into her pocket-book and withdrew a five dollar bill. "Was this the way?" she demanded. Matilda was not very good at this sort of thing. The librarian shook her head. Matilda nodded shrewdly and added a twin brother to the bill in her hand. "Then is this better?" "That's worse. I wouldn't take your money—" "Sorry. What then?" "If I can't enjoy an association with Haron Gorka directly, I still could get the vicarious pleasure of your contact with him. Report to me faithfully and you'll get his address. That's what the other five will do, and with half a dozen of you, I'll get an overall picture. Each one of you will tell me about Haron Gorka, sparing no details. You each have a distinct personality, of course, and it will color each picture considerably. But with six of you reporting, I should receive my share of vicarious enjoyment. Is it—ah—a deal?" Matilda assured her that it was, and, breathlessly, she wrote down the address. She thanked the librarian and then she went out to her car, whistling to herself. Haron Gorka lived in what could have been an agrarian estate, except that the land no longer was being tilled. The house itself had fallen to ruin. This surprised Matilda, but she did not let it keep her spirits in check. Haron Gorka, the man, was what counted, and the librarian's account of him certainly had been glowing enough. Perhaps he was too busy with his cultural pursuits to pay any real attention to his dwelling. That was it, of course: the conspicuous show of wealth or personal industry meant nothing at all to Haron Gorka. Matilda liked him all the more for it. There were five cars parked in the long driveway, and now Matilda's made the sixth. In spite of herself, she smiled. She had not been the only one with the idea to visit Haron Gorka in person. With half a dozen of them there, the laggards who resorted to posting letters would be left far behind. Matilda congratulated herself for what she thought had been her ingenuity, and which now turned out to be something which she had in common with five other women. You live and learn, thought Matilda. And then, quite annoyedly, she berated herself for not having been the first. Perhaps the other five all were satisfactory; perhaps she wouldn't be needed; perhaps she was too late.... As it turned out, she wasn't. Not only that, she was welcomed with open arms. Not by Haron Gorka; that she really might have liked. Instead, someone she could only regard as a menial met her, and when he asked had she come in response to the advertisement, she nodded eagerly. He told her that was fine and he ushered her straight into a room which evidently was to be her living quarters. It contained a small undersized bed, a table, and a chair, and, near a little slot in the wall, there was a button. "You want any food or drink," the servant told her, "and you just press that button. The results will surprise you." "What about Mr. Gorka?" "When he wants you, he will send for you. Meanwhile, make yourself to home, lady, and I will tell him you are here." A little doubtful now, Matilda thanked him and watched him leave. He closed the door softly behind his retreating feet, but Matilda's ears had not missed the ominous click. She ran to the door and tried to open it, but it would not budge. It was locked—from the outside. It must be said to Matilda's favor that she sobbed only once. After that she realized that what is done is done and here, past thirty, she wasn't going to be girlishly timid about it. Besides, it was not her fault if, in his unconcern, Haron Gorka had unwittingly hired a neurotic servant. For a time Matilda paced back and forth in her room, and of what was going on outside she could hear nothing. In that case, she would pretend that there was nothing outside the little room, and presently she lay down on the bed to take a nap. This didn't last long, however: she had a nightmare in which Haron Gorka appeared as a giant with two heads, but, upon awaking with a start, she immediately ascribed that to her overwrought nerves. At that point she remembered what the servant had said about food and she thought at once of the supreme justice she could do to a juicy beefsteak. Well, maybe they didn't have a beefsteak. In that case, she would take what they had, and, accordingly, she walked to the little slot in the wall and pressed the button. She heard the whir of machinery. A moment later there was a soft sliding sound. Through the slot first came a delicious aroma, followed almost instantly by a tray. On the tray were a bowl of turtle soup, mashed potatoes, green peas, bread, a strange cocktail, root-beer, a parfait—and a thick tenderloin sizzling in hot butter sauce. Matilda gasped once and felt about to gasp again—but by then her salivary glands were working overtime, and she ate her meal. The fact that it was precisely what she would have wanted could, of course, be attributed to coincidence, and the further fact that everything was extremely palatable made her forget all about Haron Gorka's neurotic servant. When she finished her meal a pleasant lethargy possessed her, and in a little while Matilda was asleep again. This time she did not dream at all. It was a deep sleep and a restful one, and when she awoke it was with the wonderful feeling that everything was all right. The feeling did not last long. Standing over her was Haron Gorka's servant, and he said, "Mr. Gorka will see you now." "Now?" "Now. That's what you're here for, isn't it?" He had a point there, but Matilda hardly even had time to fix her hair. She told the servant so. "Miss," he replied, "I assure you it will not matter in the least to Haron Gorka. You are here and he is ready to see you and that is all that matters." "You sure?" Matilda wanted to take no chances. "Yes. Come." She followed him out of the little room and across what should have been a spacious dining area, except that everything seemed covered with dust. Of the other women Matilda could see nothing, and she suddenly realized that each of them probably had a cubicle of a room like her own, and that each in her turn had already had her first visit with Haron Gorka. Well, then, she must see to it that she impressed him better than did all the rest, and, later, when she returned to tell the old librarian of her adventures, she could perhaps draw her out and compare notes. She would not admit even to herself that she was disappointed with Haron Gorka. It was not that he was homely and unimpressive; it was just that he was so ordinary -looking. She almost would have preferred the monster of her dreams. He wore a white linen suit and he had mousy hair, drab eyes, an almost-Roman nose, a petulant mouth with the slight arch of the egotist at each corner. He said, "Greetings. You have come—" "In response to your ad. How do you do, Mr. Gorka?" She hoped she wasn't being too formal. But, then, there was no sense in assuming that he would like informality. She could only wait and see and adjust her own actions to suit him. Meanwhile, it would be best to keep on the middle of the road. "I am fine. Are you ready?" "Ready?" "Certainly. You came in response to my ad. You want to hear me talk, do you not?" "I—do." Matilda had had visions of her prince charming sitting back and relaxing with her, telling her of the many things he had done and seen. But first she certainly would have liked to get to know the man. Well, Haron Gorka obviously had more experience along these lines than she did. He waited, however, as if wondering what to say, and Matilda, accustomed to social chatter, gave him a gambit. "I must admit I was surprised when I got exactly what I wanted for dinner," she told him brightly. "Eh? What say? Oh, yes, naturally. A combination of telepathy and teleportation. The synthetic cookery is attuned to your mind when you press the buzzer, and the strength of your psychic impulses determines how closely the meal will adjust to your desires. The fact that the adjustment here was near perfect is commendable. It means either that you have a high psi-quotient, or that you were very hungry." "Yes," said Matilda vaguely. Perhaps it might be better, after all, if Haron Gorka were to talk to her as he saw fit. "Ready?" "Uh—ready." "Well?" "Well, what, Mr. Gorka?" "What would you like me to talk about?" "Oh, anything." "Please. As the ad read, my universal experience—is universal. Literally. You'll have to be more specific." "Well, why don't you tell me about some of your far travels? Unfortunately, while I've done a lot of reading, I haven't been to all the places I would have liked—" "Good enough. You know, of course, how frigid Deneb VII is?" Matilda said, "Beg pardon?" "Well, there was the time our crew—before I had retired, of course—made a crash landing there. We could survive in the vac-suits, of course, but the thlomots were after us almost at once. They go mad over plastic. They will eat absolutely any sort of plastic. Our vac-suits—" "—were made of plastic," Matilda suggested. She did not understand a thing he was talking about, but she felt she had better act bright. "No, no. Must you interrupt? The air-hose and the water feed, these were plastic. Not the rest of the suit. The point is that half of us were destroyed before the rescue ship could come, and the remainder were near death. I owe my life to the mimicry of a flaak from Capella III. It assumed the properties of plastic and led the thlomots a merry chase across the frozen surface of D VII. You travel in the Deneb system now and Interstellar Ordinance makes it mandatory to carry flaaks with you. Excellent idea, really excellent." Almost at once, Matilda's educational background should have told her that Haron Gorka was mouthing gibberish. But on the other hand she wanted to believe in him and the result was that it took until now for her to realize it. "Stop making fun of me," she said. "So, naturally, you'll see flaaks all over that system—" "Stop!" "What's that? Making fun of you?" Haron Gorka's voice had been so eager as he spoke, high-pitched, almost like a child's, and now he seemed disappointed. He smiled, but it was a sad smile, a smile of resignation, and he said, "Very well. I'm wrong again. You are the sixth, and you're no better than the other five. Perhaps you are even more outspoken. When you see my wife, tell her to come back. Again she is right and I am wrong...." Haron Gorka turned his back. Matilda could do nothing but leave the room, walk back through the house, go outside and get into her car. She noticed not without surprise that the other five cars were now gone. She was the last of Haron Gorka's guests to depart. As she shifted into reverse and pulled out of the driveway, she saw the servant leaving, too. Far down the road, he was walking slowly. Then Haron Gorka had severed that relationship, too, and now he was all alone. As she drove back to town, the disappointment melted slowly away. There were, of course, two alternatives. Either Haron Gorka was an eccentric who enjoyed this sort of outlandish tomfoolery, or else he was plainly insane. She could still picture him ranting on aimlessly to no one in particular about places which had no existence outside of his mind, his voice high-pitched and eager. It was not until she had passed the small library building that she remembered what she had promised the librarian. In her own way, the aging woman would be as disappointed as Matilda, but a promise was a promise, and Matilda turned the car in a wide U-turn and parked it outside the library. The woman sat at her desk as Matilda had remembered her, gray, broom-stick figure, rigid. But now when she saw Matilda she perked up visibly. "Hello, my dear," she said. "Hi." "You're back a bit sooner than I expected. But, then, the other five have returned, too, and I imagine your story will be similar." "I don't know what they told you," Matilda said. "But this is what happened to me." She quickly then related everything which had happened, completely and in detail. She did this first because it was a promise, and second because she knew it would make her feel better. "So," she finished, "Haron Gorka is either extremely eccentric or insane. I'm sorry." "He's neither," the librarian contradicted. "Perhaps he is slightly eccentric by your standards, but really, my dear, he is neither." "What do you mean?" "Did he leave a message for his wife?" "Why, yes. Yes, he did. But how did you know? Oh, I suppose he told the five." "No. He didn't. But you were the last and I thought he would give you a message for his wife—" Matilda didn't understand. She didn't understand at all, but she told the little librarian what the message was. "He wanted her to return," she said. The librarian nodded, a happy smile on her lips. "You wouldn't believe me if I told you something." "What's that?" "I am Mrs. Gorka." The librarian stood up and came around the desk. She opened a drawer and took out her hat and perched it jauntily atop her gray hair. "You see, my dear, Haron expects too much. He expects entirely too much." Matilda did not say a word. One madman a day would be quite enough for anybody, but here she found herself confronted with two. "We've been tripping for centuries, visiting every habitable star system from our home near Canopus. But Haron is too demanding. He says I am a finicky traveler, that he could do much better alone, the accommodations have to be just right for me, and so forth. When he loses his temper, he tries to convince me that any number of females of the particular planet would be more than thrilled if they were given the opportunity just to listen to him. "But he's wrong. It's a hard life for a woman. Someday—five thousand, ten thousand years from now—I will convince him. And then we will settle down on Canopus XIV and cultivate torgas . That would be so nice—" "I'm sure." "Well, if Haron wants me back, then I have to go. Have a care, my dear. If you marry, choose a home-body. I've had the experience and you've seen my Haron for yourself." And then the woman was gone. Numbly, Matilda walked to the doorway and watched her angular figure disappear down the road. Of all the crazy things.... Deneb and Capella and Canopus, these were stars. Add a number and you might have a planet revolving about each star. Of all the insane— They were mad, all right, and now Matilda wondered if, actually, they were husband and wife. It could readily be; maybe the madness was catching. Maybe if you thought too much about such things, such travels, you could get that way. Of course, Herman represented the other extreme, and Herman was even worse in his own way—but hereafter Matilda would seek the happy medium. And, above all else, she had had enough of her pen pal columns. They were, she realized, for kids. She ate dinner in Cedar Falls and then she went out to her car again, preparing for the journey back home. The sun had set and it was a clear night, and overhead the great broad sweep of the Milky Way was a pale rainbow bridge in the sky. Matilda paused. Off in the distance there was a glow on the horizon, and that was the direction of Haron Gorka's place. The glow increased; soon it was a bright red pulse pounding on the horizon. It flickered. It flickered again, and finally it was gone. The stars were white and brilliant in the clear country air. That was why Matilda liked the country better than the city, particularly on a clear summer night when you could see the span of the Milky Way. But abruptly the stars and the Milky Way were paled by the brightest shooting star Matilda had ever seen. It flashed suddenly and it remained in view for a full second, searing a bright orange path across the night sky. Matilda gasped and ran into her car. She started the gears and pressed the accelerator to the floor, keeping it there all the way home. It was the first time she had ever seen a shooting star going up . | D. Yes. She’s more grounded now, and less naive. |
Of his fellow crew members, who does David seem to have the most concern for and why?
A. Karen, because she's a female crew member and because she has a bad reaction to being awoken.
B. John, because he relies on him to be his right-hand man.
C. John, because David first wakes him up with the apparatus and is unsure how safe the apparatus is to operate.
D. Karen, because she's his wife and he only remembers this with time.
| CAPTAIN CHAOS By D. ALLEN MORRISSEY Science equipped David Corbin with borrowed time; sent him winging out in a state of suspension to future centuries ... to a dark blue world whose only defense was to seal tight the prying minds of foolish interlopers. [Transcriber's Note: This etext was produced from Planet Stories November 1952. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I heard the voice as I opened my eyes. I was lying down, still not aware of where I was, waiting for the voice. "Your name is David Corbin. Do you understand?" I looked in the direction of the sound. Above my feet a bulkhead loomed. There were round dials set in a row above a speaker. Over the mesh-covered speaker, two knobs glowed red. I ran the words over in my sluggish mind, thinking about an answer. The muscles in my throat tightened up in reflex as I tried to bring some unity into the jumble of thoughts and ideas that kept forming. One word formed out of the rush of anxiety. "No." I shouted a protest against the strangeness of the room. I looked to the right, my eyes following the curving ceiling that started at the cot. The curve met another straight bulkhead on the left. I was in a small room, gray in color, like dull metal. Overhead a bright light burned into my vision. I wondered where in the universe I was. "Your name is David Corbin. If you understand, press button A on your right." I stared at the speaker in the wall. The mesh-covered hole and the two lights looked like a caricature of a face, set in a panel of dials. I twisted my head to look for the button. I pushed away from the close wall but I couldn't move. I reached down to the tightness that held my body, found the wide strap that held me and fumbled with the buckle. I threw it off and pushed myself up from the hard cot. I heard myself yell in surprise as I floated up towards the light overhead. I was weightless. How do you describe being weightless when you are born into a world bound by gravity. I twisted and shut my eyes in terror. There was no sensation of place, no feeling of up or down, no direction. My back bumped against the ceiling and I opened my eyes to stare at the cot and floor. I was concentrating too hard on remembering to be frightened for long. I pushed away from the warm metal and the floor moved up to meet me. "If you understand, press button A on your right." What should I understand? That I was floating in a room that had a curved wall ... that nothing was right in this hostile room? When I reached the cot I held it and drew myself down. I glanced at the planes of the room, trying to place it with other rooms I could see in my mind. Gray walls with a crazy curved ceiling ... a door to my left that appeared to be air tight. I stared at my familiar hands. I rubbed them across my face, feeling the solidity of flesh and bone, afraid to think too hard about myself. "My name ... my name is...." "Your name is David Corbin." I stared at the speaker. How long did this go on? The name meant nothing to me, but I thought about it, watching the relentless lights that shone below the dials. I stood up slowly and looked at myself. I was naked except for heavy shorts, and there was no clue to my name in the pockets. The room was warm and the air I had been breathing was good but it seemed wrong to be dressed like this. I didn't know why. I thought about insanity, and the room seemed to fit my thoughts. When the voice repeated the message again I had to act. Walking was like treading water that couldn't be seen or felt. I floated against the door, twisting the handle in fear that it wouldn't turn. The handle clanged as I pushed it down and I stared at the opposite wall of a narrow gray passageway. I pushed out into it and grasped the metal rail that ran along the wall. I reasoned it was there to propel yourself through the passageway in this weightless atmosphere. It was effortless to move. I turned on my side like a swimmer and went hand over hand, shooting down the corridor. I braced against forward motion and stopped against a door at the end. Behind me I could see the opened door I had left, and the thought of that questioning voice made me want to move. I swung the door open, catching a glimpse of a room crowded with equipment and.... I will always remember the scream of terror, the paralyzing fright of what I saw through the portholes in the wall of the room. I saw the blackest night, pierced by brilliance that blinded me. There was no depth to the searing brightness of countless stars. They seemed to press against the glass, blobs of fire against a black curtain burning into my eyes and brain. It was space. I looked out at deep space, star systems in clusters. I shut my eyes. When I looked again I knew where I was. Why the little room had been shaped like quarter round. Why I drifted weightlessly. Why I was.... David Corbin. I knew more of the puzzle. Something was wrong. After the first shock of looking out, I accepted the fact that I was in a space ship, yet I couldn't read the maps that were fastened to a table, nor understand the function or design of the compact machinery. WHY, Why, Why? The thought kept pounding at me. I was afraid to touch anything in the room. I pressed against the clear window, wondering if the stars were familiar. I had a brief vivid picture of a night sky on Earth. This was not the same sky. Back in the room where I had awakened, I touched the panel with the glowing eyes. It had asked me if I understood. Now it must tell me why I didn't. It had to help me, that flat metallic voice that repeated the same words. It must tell me.... "Your name is David Corbin. If you understand, press button A on your right." I pressed the button by the cot. The red lights blinked out as I stood in patient attention, trying to outguess the voice. I recalled a phrase ... some words about precaution. Precaution against forgetting. It was crazy, but I trusted the panel. It was the only thing I saw that could help me, guard me against another shock like seeing outside of the clear portholes. "It is assumed the experiment is a success," the voice said. What experiment? "You have been removed from suspension. Assume manual control of this ship." Control of a ship? Going where? "Do not begin operations until the others are removed from suspension." What others? Tell me what to do. "Rely on instructions for factoring when you check the coordinates. Your maximum deviation from schedule cannot exceed two degrees. Adopt emergency procedures as you see fit. Good luck." The voice snapped off and I laughed hysterically. None of it had made sense, and I cursed whatever madness had put me here. "Tell me what to do," I shouted wildly. I hammered the hard metal until the pain in my hands made me stop. "I can't remember what to do." I held my bruised hands to my mouth, and I knew that was all the message there was. In blind panic I pushed away from the panel. Something tripped me and I fell back in a graceless arc. I pushed away from the floor, barely feeling the pain in my leg, and went into the hall. Pain burned along my leg but I couldn't stop. In the first panic of waking up in strangeness I had missed the other doors in the passage. The first swung back to reveal a deep closet holding five bulky suits. The second room was like my own. A dark haired, deep chested man lay on the cot. His muscular body was secured by a wide belt. He was as still as death, motionless without warmth or breath as I hovered over him. I couldn't remember his face. The next room held another man. He was young and wiry, like an athlete cast in marble, dark haired and big jawed. A glassy eye stared up when I rolled back his eyelid. The eyelid remained open until I closed it and went on. Another room ... another man ... another stranger. This man was tall and raw boned, light of skin and hair, as dead as the others. A flat, illogical voice had instructed me to revive these men. I shivered in spite of the warmth of the room, studying the black box that squatted on a shelf by his head. My hand shook when I touched the metal. I dared not try to operate anything. Revive the others ... instructions without knowledge were useless to me. I stopped looking into the doors in the passageway and went back to the room with the portholes. Everything lay in readiness, fastened down star charts, instruments, glittering equipment. There was no feeling of disorder or use in the room. It waited for human hands to make it operate. Not mine. Not now. I went past the room into another, where the curves were more sharp. I could visualize the tapering hull leading to the nose of the ship. This room was filled with equipment that formed a room out of the bordered area I stood in. I sat in the deep chair facing the panel of dials and instruments, in easy reach. I ran my hands over the dials, the rows of smooth colored buttons, wondering. The ports on the side were shielded and I stared out at static energy, hung motionless in a world of searing light. There was no distortion, no movement outside and I glanced back at the dials. What speeds were they recording? What speeds and perhaps, what distance? It was useless to translate the markings. They stood for anything I might guess, and something kept pricking my mind, telling me I had no time to guess. I thought of time again. I was supposed to act according to ... plan. Did that mean ... in time ... in time. I went back down the passageway. The fourth small room was the same. Except for the woman. She lay on a cot, young and beautiful, even in the death-like immobility I had come to accept. Her beauty was graceful lines of face and her figure—smooth tapering legs, soft curves that were carved out of flesh colored stone. Yet not stone. I held her small hand, then put it back on the cot. Her attire was brief like the rest of us, shorts and a man's shirt. Golden hair curled up around her lovely face. I wondered if she would ever smile or move that graceful head. I rolled back her eyelid and looked at a deep blue eye that stared back in glassy surprise. Four people in all, depending on a blind helpless fool who didn't know their names or the reason for that dependence. I sat beside her on the cot until I could stand it no longer. Searching the ship made me forget my fear. I hoped I would find some answers. I went from the nose to the last bulkhead in a frenzy of floating motion, looking behind each door until I went as far as I could. There were two levels to the ship. They both ended in the lead shield that was set where the swell of the curve was biggest. It meant the engine or engines took up half the ship, cut off from the forward half by the instrument studded shield. I retraced my steps and took a rough estimate of size. The ship, as I called it, was at least four hundred feet long, fifty feet in diameter on the inside. The silence was a force in itself, pressing down from the metal walls, driving me back to the comforting smallness of the room where I had been reborn. I laughed bitterly, thinking about the aptness of that. I had literally been reborn in this room, equipped with half ideas, and no point to start from, no premise to seek. I sensed the place to start from was back in the room. I searched it carefully. Minutes later I realized the apparatus by the cot was different. It was the same type of black box, but out from it was a metal arm, bent in a funny angle. At the tip of the arm, a needle gleamed dully and I rubbed the deep gash on my leg. I bent the arm back until the angle looked right. It was then I realized the needle came to a spot where it could have hit my neck when I lay down. My shout of excitement rang out in the room, as I pictured the action of the extended arm. I lost my sudden elation in the cabin where the girl lay. The box behind her head was completely closed, and it didn't yield to the pressure I applied. It had a cover, but no other opening where an arm could extend. I ran my fingers over the unbroken surface, prying over the thin crack at the base helplessly. If some sort of antidote was to be administered manually I was lost. I had no knowledge of what to inject or where to look for it. The chamber of the needle that had awakened me was empty. That meant a measured amount. In the laboratory on the lower level I went over the rows of cans and tubes fastened to the shelves. There were earths and minerals, seeds and chemicals, testing equipment in compact drawers, but nothing marked for me. I wondered if I was an engineer or a pilot, or perhaps a doctor sent along to safeguard the others. Complete amnesia would have been terrible enough but this half knowledge, part awareness and association with the ship was a frightening force that seemed ready to break out of me. I went back to the cabin where the powerful man lay. I had to risk failure with one of them. I didn't want it to be the girl. I fought down the thought that he might be the key man, remembering the voice that had given the message. It was up to me, and soon. The metal in the box would have withstood a bullet. It couldn't be pried apart, and I searched again and again for a release mechanism. I found it. I swung the massive cover off and set it down. The equipment waited for the touch of a button and it went into operation. I stepped back as the tubes glowed to life and the arm swung down with the gleaming needle. The needle went into the corded neck of the man. The fluid chamber drained under pressure and the arm moved back. I stood by the man for long minutes. Finally it came. He stirred restlessly, closing his hands into fists. The deep chest rose and fell unevenly as he breathed. Finally the eyes opened and he looked at me. I watched him adjust to the room. It was in his eyes, wide at first, moving about the confines of the room back to me. "It looks like we made it," he said. "Yes." He unfastened the belt and sat up. I pushed him back as he floated up finding little humor in the comic expression on his face. "No gravity," he grunted and sat back. "You get used to it fast," I answered. I thought of what to say as he watched me. "How do you feel?" He shrugged at the question. "Fine, I guess. Funny, I can't remember." He saw it in my face, making him stop. "I can't remember dropping off to sleep," he finished. I held his hard arm. "What else? How much do you remember?" "I'm all right," he answered. "There aren't supposed to be any effects from this." "Who is in charge of this ship?" I asked. He tensed suddenly. "You are, sir. Why?" I moved away from the cot. "Listen, I can't remember. I don't know your name or anything about this ship." "What do you mean? What can't you remember?" he asked. He stood up slowly, edging around towards the door. I didn't want to fight him. I wanted him to understand. "Look, I'm in trouble. Nothing fits, except my name." "You don't know me?" "No." "Are you serious?" "Yes, yes. I don't know why but it's happened." He let his breath out in a whistle. "For God's sake. Any bump on your head?" "I feel all right physically. I just can't place enough." "The others. What about the others?" he blurted. "I don't know. You're the first besides myself. I don't know how I stumbled on the way to revive you." He shook his head, watching me like I was a freak. "Let's check the rest right away." "Yes. I've got to know if they are like me. I'm afraid to think they might be." "Maybe it's temporary. We can figure something out." II The second man, the dark haired one, opened his eyes and recognized us. He asked questions in rapid fire excitement. The third man, the tall Viking, was all right until he moved. The weightless sensation made him violently sick. We put him back on the cot, securing him again with the belt, but the sight of us floating made him shake. He was retching without results when we drifted out. I followed him to the girl's quarters. "What about her. Why is she here?" I asked my companion. He lifted the cover from the apparatus. "She's the chemist in the crew." "A girl?" "Dr. Thiesen is an expert, trained for this," he said. I looked at her. She looked anything but like a chemist. "There must be men who could have been sent. I've been wondering why a girl." "I don't know why, Captain. You tried to stop her before. Age and experience were all that mattered to the brass." "It's a bad thing to do." "I suppose. The mission stated one chemist." "What is the mission of this ship?" I asked. He held up his hand. "We'd better wait, sir. Everything was supposed to be all right on this end. First you, then Carl, sick to his stomach." "Okay. I'll hold the questions until we see about her." We were out of luck with the girl. She woke up and she was frightened. We questioned her and she was coherent but she couldn't remember. I tried to smile as I sat on the cot, wondering what she was thinking. "How do you feel?" I asked. Her face was a mask of wide-eyed fear as she shook her head. "Can you remember?" "I don't know." Blue eyes stared at me in fear. Her voice was low. "Do you know my name?" The question frightened her. "Should I? I feel so strange. Give me a minute to think." I let her sit up slowly. "Do you know your name?" She tightened up in my arms. "Yes. It's...." She looked at us for help, frightened by the lack of clothing we wore, by the bleak room. Her eyes circled the room. "I'm afraid," she cried. I held her and she shook uncontrollably. "What's happened to me?" she asked. The dark haired man came into the room, silent and watchful. My companion motioned to him. "Get Carl and meet us in Control." The man looked at me and I nodded. "We'll be there in a moment. I'm afraid we've got trouble." He nodded and pushed away from us. The girl screamed and covered her face with her hands. I turned to the other man. "What's your name?" "Croft. John Croft." "John, what are your duties if any?" "Automatic control. I helped to install it." "Can you run this ship? How about the other two?" He hit his hands together. "You fly it, sir. Can't you think?" "I'm trying. I know the ship is familiar, but I've looked it over. Maybe I'm trying too hard." "You flew her from earth until we went into suspension," he said. "I can't remember when," I said. I held the trembling girl against me, shaking my head. He glanced at the girl. "If the calculations are right it was more than a hundred years ago." We assembled in the control room for a council. We were all a little better for being together. John Croft named the others for me. I searched each face without recognition. The blond man was Carl Herrick, a metallurgist. His lean face was white from his spell but he was better. Paul Sample was a biologist, John said. He was lithe and restless, with dark eyes that studied the rest of us. I looked at the girl. She was staring out of the ports, her hands pressed against the transparent break in the smooth wall. Karen Thiesen was a chemist, now frightened and trying to remember. I wasn't in much better condition. "Look, if it comes too fast for me, for any of us, we'll stop. John, you can lead off." "You ask the questions," he said. I indicated the ship. "Where in creation are we going?" "We set out from Earth for a single star in the direction of the center of our Galaxy." "From Earth? How could we?" "Let's move slowly, sir," he said. "We're moving fast. I don't know if you can picture it, but we're going about one hundred thousand miles an hour." "Through space?" "Yes." "What direction?" Paul cut in. "It's a G type star, like our own sun in mass and luminosity. We hope to find a planetary system capable of supporting life." "I can't grasp it. How can we go very far in a lifetime?" "It can be done in two lifetimes," John said quietly. "You said I had flown this ship. You meant before this suspension." "Yes. That's why we can cross space to a near star." "How long ago was it?" "It was set at about a hundred years, sir. Doesn't that fit at all?" "I can't believe it's possible." Carl caught my eye. "Captain, we save this time without aging at all. It puts us near a calculated destination." "We've lost our lifetime." It was Karen. She had been crying silently while we talked. "Don't think about it," Paul said. "We can still pull this out all right if you don't lose your nerve." "What are we to do?" she asked. John answered for me. "First we've got to find out where we are. I know this ship but I can't fly it." "Can I?" I asked. We set up a temporary plan of action. Paul took Karen to the laboratory in an effort to help her remember her job. Carl went back to divide the rations. I was to study the charts and manuals. It was better than doing nothing, and I went into the navigation room and sat down. Earth was an infinitesimal point somewhere behind us on the galactic plane, and no one else was trained to navigate. The ship thundered to life as I sat there. The blast roared once ... twice, then settled into a muted crescendo of sound that hummed through the walls. I went into the control room and watched John at the panel. "I wish I knew what you were doing," I said savagely. "Give it time." "We can't spare any, can we?" I asked. "I wish we knew. What about her—Dr. Thiesen?" "She's in the lab. I don't think that will do much good. She's got to be shocked out of a mental state like that." "I guess you're right," he said slowly. "She's trained to administer the suspension on the return trip." I let my breath out slowly. "I didn't think about that." "We couldn't even get part way back in a lifetime," he said. "How old are you, John?" "Twenty-eight." "What about me?" "Thirty." He stared at the panel in thought for a minutes. "What about shock treatment? It sounds risky." "I know. It's the only thing I could think of. Why didn't everyone react the same?" "That had me wondering for a while. I don't know. Anyway how could you go about making her remember?" "Throw a crisis, some situation at her, I guess." He shrugged, letting his sure hands rest on the panel of dials. I headed back towards the lab. If I could help her I might help myself. I was past the rooms when the horn blasted through the corridor. I turned automatically with the sound, pushing against the rail, towards the control room. Deep in my mind I could see danger, and without questioning why I knew I had to be at Control when the sound knifed through the stillness. John was shouting as I thrust my way into the room. "Turn the ship. There's something dead ahead." I had a glimpse of his contorted face as I dove at the control board. My hands hit buttons, thumbed a switch and then a sudden force threw me to the right. I slammed into the panel on the right, as the pressure of the change dimmed my vision. Reflex made me look up at the radar control screen. It wasn't operating. John let go of the padded chair, grinning weakly. I was busy for a few seconds, feeding compensation into the gyros. Relief flooded through me like warm liquid. I hung on the intercom for support, drawing air into my heaving lungs. "What—made you—think of that," I asked weakly. "Shock treatment." "I must have acted on instinct." "You did. Even for a sick man that was pretty fast," he laughed. "I can think again, John. I know who I am," I shouted. I threw my arms around his massive shoulders. "You did it." "You gave me the idea, Mister, talking about Dr. Thiesen." "It worked. I'm okay," I said in giddy relief. "I don't have to tell you I was scared as hell. I wish you could have seen your face, the look in your eyes when I woke up." "I wouldn't want to wake up like that again." "You're all right now?" he asked. I grinned and nodded an answer. I saw John as he was at the base, big and competent, sweating in the blazing sun. I thought about the rest of the crew too. "We're heading right for a star...." "It's been dead ahead for hours," he grunted. I leaned over and threw the intercom to open. "This is control. Listen ... everyone. I'm over it. Disregard the warning siren ... we were testing the ship." The lab light blinked on as Paul cut in. "What was it ... hey, you said you're all right." "John did it. He hit the alarm figuring I would react. Listen, Paul. Is any one hurt?" "No. Carl is here too. His stomach flopped again but he's okay. What about food. We're supposed to be checked before we eat." "We'll have to go ahead without it. Any change?" "No, I put her to bed. Shall I bring food?" I glanced at John. He rubbed his stomach. "Yes," I answered. "Bring it when you can. I've got to find out where we are." We had to get off course before we ran into the yellow-white star that had been picked for us. Food was set down by me, grew cold and was carried away and I was still rechecking the figures. We were on a line ten degrees above the galactic plane. The parallactic baseline from Earth to the single star could be in error several degrees, or we could be right on the calculated position of the star. The radar confirmed my findings ... and my worst fears. When we set it for direction and distance, the screen glowed to life and recorded the star dead ahead. In all the distant star clusters, only this G type star was thought to have a planetary system like our own. We were out on a gamble to find a planet capable of supporting life. The idea had intrigued scientists before I had first looked up at the night sky. When I was sure the electronically recorded course was accurate for time, I checked direction and speed from the readings and plotted our position. If I was right we were much closer than we wanted to be. The bright pips on the screen gave us the distance and size of the star while we fed the figures into the calculator for our rate of approach. Spectroscopic tests were run on the sun and checked against the figures that had been calculated on Earth. We analyzed temperature, magnetic fields, radial motion, density and luminosity, checking against the standards the scientists had constructed. It was a G type star like our own. It had more density and temperature and suitable planets or not, we had to change course in a hurry. Carl analyzed the findings while we came to a decision. Somewhere along an orbit that might be two hundred miles across, our hypothetical planet circled this star. That distance was selected when the planets in Earth's solar system had proved to be barren. If the observations on this star were correct, we could expect to find a planet in a state of fertility ... if it existed ... if it were suitable for colonization ... if we could find it. | A. Karen, because she's a female crew member and because she has a bad reaction to being awoken. |
What was the diagnosis for Mrs. Anderson during her inpatient treatment from 07/20/2023 to 09/12/2023?
Choose the correct answer from the following options:
A. Seropneumothorax due to a clot at the port tip
B. Seropneumothorax due to lung metastasis from pancreatic carcinoma
C. Seropneumothorax secondary to punction of a malignant pleural effusion with progressive pulmonary metastasis of a pancreatic head carcinoma
D. Pneumothorax due to lung metastasis from pancreatic carcinoma
E. Seropneumothorax due to gemcitabine toxicity
| ### Patient Report 0
**Dear colleague, **
We report about your outpatient treatment on 09/01/2010.
Diagnoses: extensor tendon rupture D3 right foot
Anamnesis: The patient comes with a cut wound in the area of the MTP of
the D3 of the right foot to our surgical outpatient clinic. A large
shard of a broken vase had fallen on her toe with great force.
Findings: Right foot, D3:
Approximately 1cm long laceration in the area of the MTP. Tenderness to
pressure. Flexion
unrestricted, extension not possible.
X-ray: X-ray of the D3 of the right foot from 09/01/2010:
No evidence of bony lesion, regular joint position.
Therapy: inspection, clinical examination, radiographic control, primary
tendon suture and fitting of a dorsal splint.
Tetanus booster.
Medication: Mono-Embolex 3000IE s.c. (Certoparin).
Procedure: We recommend the patient to wear a dorsal splint until the
suture removal in 12-14 days. Afterwards further treatment with a vacuum
orthosis for another 4 weeks.
We ask for presentation in our accident surgery consultation on
September 14^th^, 2010.
In case of persistence or progression of complaints, we ask for an
immediate
our surgical clinic. If you have any questions, please do not hesitate
to contact us.
Best regards
### Patient Report 1
**Dear colleague, **
We report to you about our common patient, Mrs. Jill Anderson, born on
06/07/1975, who was in our outpatient treatment on 07/08/2014.
Diagnoses: Fracture tuberculum majus humeri
Luxation of the shoulder joint
Anamnesis: Fell on the left arm while falling down a hill during a hike.
No fall on the head.
Tetanus vaccination coverage is present according to the patient.
Findings: multiple abrasions: Left forearm, left pelvis and left tibia.
Dislocation of the shoulder. Motor function of forearm and hand not
limited. Peripheral circulation, motor function, and sensitivity intact.
X-ray: Shoulder left in two planes from 07/08/2014.
Anteroinferior shoulder dislocation with dislocated tuberculum majus
fracture and possible subcapital fracture line.
X-ray: Shoulder in 2 planes after reduction
Reduction of the shoulder joint. Still more than 3 mm dislocated
tuberculum majus
**Therapy**:
Reduction with **Midazolam** and **Fentanyl**.
**Medication**:
**Lovenox 40mg s.c.** daily
**Ibuprofen 400mg** 1-1-1
Pain management as needed.
**Procedure**:
Due to sedation, the patient was not able to be educated for surgery.
Surgery is planned for either tomorrow or today using a proximal humerus
internal locking system (PHILOS) or screw osteosynthesis. The patient is
to remain fasting.
**Other Notes**:
Inpatient admission.
### Patient Report 2
**Dear colleague, **
We report to you about our common patient, Mrs. Jill Anderson, who was
in our outpatient treatment on 02/01/2015.
Diagnoses: Ankle sprain on the right side.
Case history: patient presents to the surgical emergency department with
right ankle sprain after tripping on the stairs. The fall occurred
yesterday evening. Immediately thereafter cooled and
immobilized.
Findings: Right foot: Swelling and pressure pain over the fibulotalar
anterior ligament. No pressure pain over syndesmosis, outer ankle+fibula
head, Inner ankle, Achilles tendon, tarsus, or with midfoot compression.
Limited mobility due to pain. Toe mobility free, no pain over base of
fifth toe.
X-ray: X-ray of the right ankle in two planes dated 02/01/2015.
No evidence of fresh fracture
Procedure: The following procedure was discussed with the patient:
-Cooling, resting, elevation and immobilization in the splint for a
total of 6 weeks.
-Pain medication: Ibuprofen 400mg 1-1-1-1 under stomach protection with
Nexium 20mg 1-0-0
In case of persistence of symptoms, magnetic resonance imaging is
recommended.
Presentation with the findings to a resident orthopedist.
### Patient Report 3
**Dear colleague, **
we report on Mrs. Anderson, Jill, born 06/07/1975, who was in our
inpatient treatment from 09/28/2021 to 10/03/2021
Diagnosis:
Suspected pancreatic carcinoma
Other diseases and previous operations:
Status post thyroidectomy 2008
Fracture tuberculum majus humeri 2014
Current complaints:
The patient presented as an elective admission for ERCP and EUS puncture
for pancreatic head space involvement. She reported stool irregularities
with steatorrhea and acholic stool beginning in July 2021. Weight loss
of approximately 3kg. No bleeding stigmata. Micturition complaints are
denied. Urine color: dark yellow. The patient first noticed scleral and
cutaneous icterus in August 2021. No other hepatic skin signs. Patient
reported mild pain 1/10 in right upper quadrant.
CT of the chest and abdomen on 09/28/2021 showed a mass in the
pancreatic head with contact with the SMV (approximately 90 degrees) and
suspicion of lymph node metastasis dorsal adherent to the SMA.
Pronounced intra or extrahepatic cholestasis. Congested pancreatic duct.
Also showed suspicious locoregional lymph nodes, especially in the
interaortocaval space. No evidence of distant metastases.
Alcohol
Average consumption: 0.20L/day (wine)
Smoking status: Some days
Consumption: 0.20 packs/day
Smoking Years: 30.00; Pack Years: 6.00
Laboratory tests:
Blood group & Rhesus factor
Rh factor +
AB0 blood group: B
Family history
Patient's mother died of breast cancer
Occupational history: Consultant
Physical examination:
Fully oriented, neurologically unaffected. Normal general condition and
nutritional status
Heart: rhythmic, normofrequency, no heart murmurs.
Lungs: vesicular breath sounds bilaterally.
Abdomen: soft, vivid bowel sounds over all four quadrants. Negative
Murphy\'s sign.
Liver and spleen not enlarged palpable.
Lymph nodes: unremarkable
Scleral and cutaneous icterus. Mild skin itching. No other hepatic skin
signs.
### Patient Report 4
**Dear colleague, **
We report on Mrs. Jill Anderson, born born 06/07/1975, who was in our
inpatient treatment from 10/09/2021 to 10/30/2021.
**Diagnosis:**
High-grade suspicious for locally advanced pancreatic cancer.
**-CT of chest/abdomen/pelvis**: Mass in the head of the pancreas with
involvement of the SMV (approx. 90 degrees) and suspicious for lymph
node metastasis adjacent to the SMA. Prominent intra- or extrahepatic
bile duct dilation. Dilated pancreatic duct. Suspicious regional lymph
nodes, notably in the interaortocaval region. No evidence of distant
metastasis.
**-Endoscopic ultrasound-guided FNA (Fine Needle Aspiration)** on
09/29/21.
**-ERCP (Endoscopic Retrograde Cholangiopancreatography)** and metal
stent placement, 10 mm x 60 mm, on 09/29/21.
-Tumor board discussion on 09/30/21: Port placement recommended,
neoadjuvant chemotherapy with FOLFIRINOX proposed.
Medical history:
Mrs. Anderson was admitted to the hospital on 09/29/21 for ERCP and
endoscopic ultrasound-guided biopsy due to an unclear mass in the head
of the pancreas. She reported changes in bowel habits with fatty stools
and pale stools starting in July 2021, and has lost approximately 3 kg
since then. She denied any signs of bleeding. She had no urinary
symptoms but did note that her urine had been darker than usual. In
August 2021, she first noticed yellowing of the eyes and skin.
The CT scan of the chest and abdomen performed on 09/28/21 revealed a
mass in the pancreatic head in contact with the SMV (approx. 90 degrees)
and suspected lymph node metastasis close to the SMA. Additionally,
there was significant intra- or extrahepatic bile duct dilation and a
dilated pancreatic duct. Suspicious regional lymph nodes were also
noted, particularly in the area between the aorta and vena cava. No
distant metastases were found.
She was admitted to our gastroenterology ward for further evaluation of
the pancreatic mass. Upon admission, she reported only mild pain in the
right upper abdomen (pain scale 2/10).
Family history:
Her mother passed away from breast cancer.
Physical examination on admission:
Appearance: Alert and oriented, neurologically intact.
Heart: Regular rhythm, normal rate, no murmurs.
Lungs: Clear breath sounds in both lungs.
Abdomen: Soft, active bowel sounds in all quadrants. No tenderness.
Liver and spleen not palpable.
Lymph nodes: Not enlarged.
Skin: Jaundice present in the eyes and skin, slight itching. No other
liver-related skin changes.
Radiology
**Findings:**
**CT Chest/Abdomen/Pelvis with contrast on 09/28/21:**
Technique: After uneventful IV contrast injection, multi-slice spiral CT
was performed through the upper abdomen during arterial and parenchymal
phases and through the chest, abdomen, and pelvis during venous phase.
Oral contrast was also administered. Thin-section, coronal, and sagittal
reconstructions were done.
Thorax: The soft tissues of the neck appear symmetric. Heart and
mediastinum in midline position. No enlarged lymph nodes in mediastinum
or axilla. A calcified granuloma is seen in the right lower lung lobe;
no suspicious nodules or signs of inflammation. No fluid or air in the
pleural space.
Abdomen: A low-density mass is seen in the pancreatic head, measuring
about 37 x 26 mm. The mass is in contact with the superior mesenteric
artery (\<180°) and could represent lymph node metastasis. It is also in
contact with the superior mesenteric vein (\<180°) and the venous
confluence. There are some larger but not abnormally large lymph nodes
between the aorta and vena cava, as well as other suspicious regional
lymph nodes. Significant dilation of both intra- and extrahepatic bile
ducts is noted. The pancreatic duct is dilated to about 5 mm. The liver
appears normal without any suspicious lesions and shows signs of fatty
infiltration. The hepatic and portal veins appear normal. Spleen appears
normal; its vein is not involved. The left adrenal gland is slightly
enlarged while the right is normal. Kidneys show uniform contrast
uptake. No urinary retention. The contrast passes normally through the
small intestine after oral administration. Uterus and its appendages
appear normal. No free air or fluid inside the abdomen.
Bones: No signs of destructive lesions. Mild degenerative changes are
seen in the lower lumbar spine.
Assessment:
-Mass in the pancreatic head with contact to the SMV (approximately 90
degrees) and suspected lymph node metastasis near the SMA. There is
significant dilation of the intra- or extrahepatic bile ducts and the
pancreatic duct.
-Suspicious regional lymph nodes, especially between the aorta and vena
cava.
-No distant metastases.
**Ultrasound/Endoscopy:**
Endoscopic Ultrasound (EUS) on 09/29/21:
Procedure: Biopsy with a 22G needle was performed on an approximately 3
cm x 3 cm mass in the pancreatic head. No obvious bleeding was seen
post-procedure. Histopathological examination is pending.
Assessment: Biopsy of pancreatic head, awaiting histology results.
**ERCP on 09/29/21:**
Procedure: Fluoroscopy time: 17.7 minutes.
Indication: ERCP/Stenting.
The papilla was initially difficult to visualize due to a long mucosal
impression/swelling (possible tumor). Initially, only the pancreatic
duct was visualized with contrast. Afterward, the bile duct was probed
and dark bile was extracted for microbial testing. The contrast image
revealed a significant distal bile duct narrowing of about 2.8 cm length
with extrahepatic bile duct dilation. After an endoscopic papillotomy
(EPT) of 5 mm, a plastic stent with an inner diameter of 8.5 mm was
placed through the narrow passage, and the bile duct was emptied.
Assessment: Successful ERCP with stenting of bile duct. Clear signs of
tumor growth/narrowing in the distal bile duct. Awaiting microbial
results and histopathology results from the extracted bile.
Treatment:
Based on the initial findings, Mrs. Anderson was started on pain
management with acetaminophen and was scheduled for an ERCP and
endoscopic ultrasound-guided biopsy. The ERCP and stenting of the bile
duct were successful, and she is currently awaiting histopathological
examination results from the biopsy and microbial testing results from
the bile.
Gastrointestinal Tumor Board of 09/30/2021.
Meeting Occasion:
Pancreatic head carcinoma under evaluation.
CT:
Defined mass in the pancreatic head with contact to the SMV (approx. 90
degrees) and under evaluation for lymph node metastasis dorsally
adherent to the SMA. Pronounced intra- or extrahepatic bile duct
dilation. Dilated pancreatic duct.
-Suspected locoregional lymph nodes especially between aorta and vena
cava.
-No evidence of distant metastases.
MR liver (external):
-No liver metastases.
Previous therapy:
-ERCP/Stenting.
Question:
-Neoadjuvant chemo with FOLFIRINOX?
Consensus decision:
-CT: Pancreatic head tumor with contact to SMA \<180° and SMV, contact
to abdominal aorta, bile duct dilation.
MR: No liver metastases.
Pancreatic histology: -pending-.
Consensus:
-Surgical port placement,
-wait for final histology,
-intended neoadjuvant chemotherapy with FOLFIRINOX,
-Follow-up after 4 cycles.
Pathology findings as of 09/30/2021
Internal Pathology Report:
Clinical information/question:
FNA biopsy for pancreatic head carcinoma.
Macroscopic Description:
FNA: Fixed. Multiple fibrous tissue particles up to 2.2 cm in size.
Entirely embedded.
Processing: One block, H&E staining, PAS staining, serial sections.
Microscopic Description:
Histologically, multiple particles of columnar epithelium are present,
some with notable cribriform architecture. The nuclei within are
irregularly enlarged without discernible polarity. In the attached
fibrin/blood, individual cells with enlarged, irregular nuclei are also
observed. No clear stromal relationship is identified.
Critical Findings Report:
FNA: Segments of atypical glandular cell clusters, at least pancreatic
intraepithelial neoplasia with low-grade dysplasia. Corresponding
invasive growth can neither be confirmed nor ruled out with the current
sample.
For quality assurance, the case was reviewed by a pathology specialist.
Expected follow-up:
Mrs. Anderson is expected to follow up with her gastroenterologist and
the multidisciplinary team for her biopsy results, and the potential
treatment plan will be discussed after the results are available.
Depending on the biopsy results, she may need further imaging, surgery,
radiation, chemotherapy, or targeted therapies. Continuous monitoring of
her jaundice, abdominal pain, and bile duct function will be critical.
Based on this information, Mrs. Anderson has a mass in the pancreatic
head with suspected metastatic regional lymph nodes. The management and
prognosis for Mrs. Anderson will largely depend on the results of the
histopathological examination and staging of the tumor. If it is
pancreatic cancer, early diagnosis and treatment are crucial for a
better outcome. The multidisciplinary team will discuss the best course
of action for her treatment after the results are obtained.
### Patient Report 5
**Dear colleague, **
We are updating you on Mrs. Jill Anderson, who was under our outpatient
care on October 4th, 2021.
**Outpatient Treatment:**
**Diagnoses:**
Recommendation for neoadjuvant chemotherapy with FOLFIRINOX for advanced
pancreatic cancer (Dated 10/21)
Exocrine pancreatic dysfunction since around 07/21.
Prior occurrences on 02/21 and 2020.
**CT Scan of the chest, abdomen, and pelvis** on September 28, 2021:
**Thorax:** Symmetrical imaging of neck soft tissues. Cardiomediastinum
is centralized. There is no sign of mediastinal, hilar, or axillary
lymphadenopathy. Calcified granuloma noted in the right lower lobe, and
no concerning rounded objects or inflammatory infiltrates. No fluid in
the pleural cavity or pneumothorax.
**Abdomen:** Hypodense mass in the head of the pancreas measuring
approximately 34 x 28 mm. A secondary finding touching the superior
mesenteric artery (\< 180°). Possible lymph node metastasis. Contact
with the superior mesenteric vein (\<180°) and venous confluence.
Noticeable, yet not pathologically enlarged lymph nodes in the
interaortocaval space and other regional suspicious lymph nodes.
Significant intra- and extrahepatic bile duct blockage. The pancreatic
duct is dilated up to around 5 mm. The liver is consistent with no signs
of suspicious lesions and shows fatty infiltration. Liver and portal
veins are well perfused. The spleen appears normal with its vein not
infiltrated. The left adrenal gland appears enlarged, while the right is
slim. Kidney tissue displays even contrast. No urinary retention
observed. Post oral contrast, the contrast agent passed regularly
through the small intestine. Both the uterus and adnexa appear normal.
No free air or fluid present in the abdomen.
**Skeleton:** No osteodestructive lesions. Mild degenerative changes
with arthrosis of the facet joints in the lower back.
**Assessment:**
-Mass in the head of the pancreas touching the superior mesenteric vein
(approx 90 degrees) and possible lymph node metastasis adhering dorsally
to the superior mesenteric artery. Significant bile duct blockage.
Dilated pancreatic duct.
-Suspicious regional lymph nodes, especially interaortocaval.
-No distant metastases found.
**GI Tumor Board** on September 30, 2021:
**CT:** Tumor in the pancreatic head with contacts noted.
**MR:** No liver metastases.
**Pancreatic histology:** Pending.
**Consensus:**
Await final pathology.
Neoadjuvant-intended chemotherapy with FOLFIRINOX.
Review after 4 cycles.
**Summary:**
Mrs. Anderson was referred to us by her primary care physician following
the discovery of a tumor in the head of the pancreas through an
ultrasound. She has been experiencing unexplained diarrhea for
approximately 3 months, sometimes with an oily appearance. She exhibited
jaundice noticeable for about a week without any itching, and an MRI was
conducted.
Given the suspicion of a pancreatic head cancer, we proceeded with CT
staging. This identified an advanced pancreatic cancer with specific
contacts. MRI did not reveal liver metastases. The imaging did show bile
duct blockage consistent with her jaundice symptom.
She was admitted for an endosonographic biopsy of the pancreatic tumor
and ERCP/stenting. The biopsy identified dysplastic cells. No invasion
was observed due to the absence of a stromal component. A metal stent
was successfully inserted.
After reviewing the findings in our tumor board, we recommended
neoadjuvant chemotherapy with FOLFIRINOX. We scheduled her for a port
implant, and a DPD test is currently underway. Chemotherapy will begin
on October 14, with the first review scheduled after 4 cycles.
Please reach out if you have any questions. If her symptoms persist or
worsen, we advise an immediate revisit. For any emergencies outside
regular office hours, she can seek medical attention at our emergency
care unit.
Best regards,
### Patient Report 6
**Dear colleague, **
We are writing to update you on Ms. Jill Anderson, who visited our day
care center on December 22, 2021, for a partial inpatient treatment.
Diagnosis:
-Locally advanced pancreatic cancer recommended for neoadjuvant
chemotherapy with FOLFIRINOX.
-Exocrine pancreatic insufficiency since around July 2021.
-Previous incidents in February 2021 and 2020.
Past treatments:
-Diagnosis of locally advanced pancreatic head cancer in September 2021.
-4 cycles of FOLFIRINOX neoadjuvant were intended.
CT Scan:
GI Tumor Board Review:
Summary:
Mrs. Anderson had a CT follow-up while on FOLFIRINOX treatment. In case
her symptoms persist or worsen, we advise an immediate consultation. If
outside regular business hours, she can seek emergency care at our
emergency medical unit.
Best regards,
### Patient Report 7
**Dear colleague, **
Updating you about Mrs. Jill Anderson, who visited our surgical clinic
on December 25, 2021.
Diagnosis:
Potentially resectable pancreatic head cancer.
CT Scan:
-Progressive tumor growth with significant contact to the celiac trunk
and the superior mesenteric artery. Direct contact with the aorta
beneath.
-Progressive, suspicious lymph nodes around the aorta, but no clear
distant metastases.
-External MR for liver showed no liver metastases.
Medical History:
-ERCP/Stenting for bile duct blockage in 09/2021.
-4 cycles of FOLFIRINOX neoadjuvant from November to December 15, 2021.
-Encountered complications resulting in prolonged hospital stay.
-Received 3 Covid-19 vaccinations, last one in May 2021 and recovered
from the virus on August 14, 2021.
-Exocrine pancreatic insufficiency.
Physical stats: 65 kg (143 lbs), 176 cm (5\'9\").
CT consensus:
-Primary tumor has reduced in size with decreased contact with the
aorta. New tumor extension towards the celiac trunk. No distant
metastases found.
-MR showed no liver metastases.
-Tumor marker Ca19-9 levels: 525 U/mL (previously 575 U/mL in September
and 380 U/mL in November).
Recommendation:
Exploratory surgery and potential pancreatic head resection.
Procedure:
We discussed with the patient about undergoing an exploration with a
possible Whipple\'s procedure. The patient is scheduled to meet the
doctor today for lab work (Hemoglobin and white blood cell count). A
prescription for pantoprazole was provided.
Prehabilitation Recommendations:
-Individualized strength training and aerobic exercises.
-Lung function improvement exercises using Triflow, three times a day.
-Consider psycho-oncological support through primary care.
-Nutritional guidance, potential high-protein and calorie-dense diet,
supplemental nutrition through a port, and intake of creon and
pantoprazole.
The patient is scheduled for outpatient preoperative preparation on
January 13, 2022, at 10:00 AM. The surgical procedure is planned for
January 15th. Eliquis needs to be stopped 48 hours before the surgery.
Warm regards,
**Surgery Report:**
Diagnosis: Locally advanced pancreatic head cancer post 4 cycles of
FOLFIRINOX.
Procedure:
Exploratory laparotomy, adhesion removal, pancreatic head and vascular
visualization, biopsy of distal mesenteric root area, surgery halted due
to positive frozen section results, gallbladder removal, catheter
placement, and 2 drains.
Report:
Mrs. Anderson has a pancreatic head cancer and had received 4 cycles of
FOLFIRINOX neoadjuvant therapy. The surgery involved a detailed
abdominal exploration which did not reveal any liver metastases or
peritoneal cancer spread. However, a hard nodule was found away from the
head of the pancreas in the peripheral mesenteric root, from which a
biopsy was taken. Results showed adenocarcinoma infiltrates, leading to
the surgery\'s termination. An additional gallbladder removal was
performed due to its congested appearance. The surgical procedure
concluded with no complications.
**Histopathological Report:**
Further immunohistochemical tests were performed which indicate the
presence of a pancreatobiliary primary cancer. Other findings from the
gallbladder showed signs of chronic cholecystitis.
GI Tumor Board Review on January 9th, 2022:
Discussion focused on Mrs. Anderson's locally advanced pancreatic head
cancer, her exploratory laparotomy, and the halted surgery due to
positive frozen section results. The CT scan indicated the progression
of her tumor, but no distant metastases or liver metastases were found.
The question posed to the board concerns the best subsequent procedure
to follow.
### Patient Report 8
**Dear colleague, **
We are providing an update on Mrs. Jill Anderson, who was in our
outpatient care on 11/05/2022:
**Outpatient treatment**:
Diagnosis:
Progressive tumor disease under gemcitabine/nab-paclitaxel for
pancreatic head carcinoma (Date of onset 09/22).
01/17/22 Surgery: Exploratory laparotomy, adhesiolysis, visualization of
the pancreatic head and vascular structures, biopsy near the distal
mesenteric root. Surgery was stopped due to positive frozen section
results; gallbladder removal.
09/21 ERCP/Stenting: Metal stent insertion.
Diarrhea likely from exocrine pancreatic insufficiency since around
07/21.
Prior diagnosis: Locally advanced pancreatic head carcinoma as of 09/21.
Clinical presentation: Chronic diarrhea due to exocrine pancreatic
insufficiency.
CT: Pancreatic head carcinoma, borderline resectable.
MRI of liver: No liver metastases.
TM Ca19-9: 587 U/mL.
ERCP/Stenting: Metal stent in the bile duct.
EUS biopsy: PanIN with low-grade dysplasia.
GI tumor board: Proposed neoadjuvant chemotherapy.
From 10/21 to 12/21: 4 cycles of FOLFIRINOX (neoadjuvant).
Hospitalized for: Anemia, dehydration, and COVID.
12/21 CT: Mixed response, primary tumor site, lymph node metastasis.
GI tumor board: Recommendation for exploratory surgery/resection.
01/12/2021: Surgery: Evidence of adenocarcinoma near distal mesenteric
root. Surgery was discontinued.
GI tumor board: Chemotherapy change recommendation.
02/22 CT: Progression at the primary tumor site with increased contact
to the SMA; lymph node metastasis.
From 02/22 to 06/22: 4 cycles of gemcitabine/nab-paclitaxel.
05/22 TM Ca19-9: 224 U/mL.
1. Concomitant PRRT therapy:
02/22: 7.9 GBq Lutetium-177 FAP-3940.
04/22: 8.5 GBq Lutetium-177 FAP-3940.
06/22: 8.4 GBq Lutetium-177 FAP-3940.
07/22: CT: Progression of primary tumor with encasement of AMS;
suspected liver metastases.
TM: Ca19-9: 422 U/mL.
Recommendation: Switch to the NAPOLI regimen and perform diagnostic
panel sequencing.
**Summary**:
Mrs. Anderson visited with her sister and friend to discuss recent CT
results. With advanced pancreatic cancer and a prior surgery in 01/22,
she has been on gemcitabine/nab-paclitaxel and concurrent PRRT with
lutetium-177 FAP since 02/22. The latest CT indicates tumor progression
and potential liver metastases. We have recommended a change in
chemotherapy and continuation of PRRT. A follow-up CT in 3 months is
advised. Please contact us with any inquiries. If symptoms persist or
worsen, urgent consultation is advised. After hours, she can visit the
emergency room at our clinic.
**Operation report**:
Diagnosis: Infection of the right chest port.
Procedure: Removal of the port system and microbiological culture.
Anesthesia: Local.
**Procedure Details**:
Suspected infection of the right chest port. Elevated lab parameters
indicated a possible infection, prompting port removal. The patient was
informed and consented.
After local anesthesia, the previous incision site was reopened.
Yellowish discharge was observed. A sample was sent for microbiology.
The port was accessed, detached, and removed along with the associated
catheter. The vein was ligated. Infected tissue was excised and sent for
pathology. The site was cleaned with an antiseptic solution and sutured
closed. Sterile dressing applied.
Post-operative care followed standard protocols.
Warm regards,
### Patient Report 9
**Dear colleague, **
We report on Mrs. Jill Anderson, born 06/07/1975 who presented to our
outpatient clinic on12/01/2022.
Diagnosis:
Progressive tumor disease under gemcitabine/nab-paclitaxel for
pancreatic head carcinoma (Date of onset 09/22).
-low progressive lung lesions, possibly metastases
**CT pancreas, thorax, abdomen, pelvis dated 12/02/2022. **
**Findings:**
Chest:
Nodular goiter with low-density nodules in the left thyroid tissue. Port
placement in the right chest with the catheter tip located in the
superior vena cava. There are no suspicious pulmonary nodules. There is
also no increase in mediastinal or axillary lymph nodes. The dense
breast tissue on the right remains unchanged from the previous study.
Abdomen:
Fatty liver with uneven contrast in the liver tissue, possibly due to
uneven blood flow. As far as can be seen, no new liver lesions are
present. There is a small low-density area in the spleen, possibly a
splenic cyst. Two distinct low-density areas are noted in the right
kidney\'s tissue, likely cysts. Pancreatic tumor decreasing in site.
Local lymph nodes and nodules in the mesentery, with sizes up to about
9mm; some are near the intestines, also decreasing in size. There are
outpouchings (diverticula) in the left-sided colon. Hardening of the
abdominal vessels. An elongation of the right iliac artery is noted.
Spine:
There are degenerative changes, including a forward slip of the fifth
lumbar vertebra over the first sacral vertebra (grade 1-2
spondylolisthesis). There is also an indentation at the top of the tenth
thoracic vertebra.
Impression:
In the context of post-treatment chemotherapy following the surgical
removal of a pancreatic tumor, we note:
-Advanced pancreatic cancer, decreasing in size.
-Lymph nodes smaller than before.
-No other signs of metastatic spread.
**Summary:**
Mrs. Andersen completed neoadjuvant chemotherapy. Pancreatic head
resection can now be performed. For this we agreed on an appointment
next week. If you have any questions, please do not hesitate to contact
us. In case of persistence or worsening of the symptoms, we recommend an
immediate reappearance. Outside of regular office hours, this is also
possible in emergencies at our emergency unit.
Yours sincerely
### Patient Report 0
**Dear colleague, **
we report on Mrs. Jill Anderson, born 06/07/1975 who presented to our
outpatient clinic on 3/05/2023.
Diagnosis:
Progressive tumor disease under gemcitabine/nab-paclitaxel for
pancreatic head carcinoma after resection in 12/2022.
CT staging on 03/05/2023:
No local recurrence.
Intrapulmonary nodules of progressive size on both sides, suspicious for
pulmonary metastases.
Question:
Biopsy confirmation of suspicious lung foci?
Consensus decision:
VATS of a suspicious lung lesion (vs. CT-guided puncture).
### Patient Report 1
**Dear colleague, **
We report on your outpatient treatment on 04/01/2023.
Diagnoses:
Follow-up after completion of adjuvant chemotherapy with Gemcitabine
mono
to 03/23 (initial gemcitabine / 5-FU)
\- progressive lung lesions, possibly metastases -\> recommendation for
CT guided puncture
\- status post Whipple surgery for pancreatic cancer
CT staging: unexplained pulmonary lesions, possibly metastatic
**CT Chest/Abd./Pelvis with contrast dated 04/02/2023: **
Imaging method: Following complication-free bolus i.v. administration of
100 mL Ultravist 370, multi-detector spiral CT scan of the chest,
abdomen, and pelvis during arterial, late arterial, and venous phases of
contrast. Additionally, oral contrast was administered. Thin-slice
reconstructions, as well as coronal and sagittal secondary
reconstructions, were done.
Chest: Normal lung aeration, fully expanded to the chest wall. No
pneumothorax detected. Known metastatic lung nodules show increased size
in this study. For instance, the nodule in the apical segment of the
right lower lobe now measures 17 x 15 mm, previously around 8 x 10 mm.
Similarly, a solid nodule in the right posterior basal segment of the
lower lobe is now 12 mm (previously 8 mm) with adjacent atelectasis. No
signs of pneumonia. No pleural effusions. Homogeneous thyroid tissue
with a nodule on the left side. Solitary lymph nodes seen in the left
axillary region and previously smaller (now 9 mm, was 4mm) but with a
retained fatty hilum, suggesting an inflammatory origin. No other
evidence of abnormally enlarged or conspicuously shaped mediastinal or
hilar lymph nodes. A port catheter is inserted from the right, with its
tip in the superior vena cava; no signs of port tip thrombosis. Mild
coronary artery sclerosis.
Abdomen/Pelvis: Fatty liver changes visible with some areas of irregular
blood flow. No signs of lesions suspicious for cancer in the liver. A
small area of decreased density in segment II of the liver, seen
previously, hasn\'t grown in size. Portal and hepatic veins are patent.
History of pancreatic head resection with pancreatogastrostomy. The
remaining pancreas shows some dilated fluid-filled areas, consistent
with a prior scan from 06/26/20. No signs of cancer recurrence. Local
lymph nodes appear unchanged with no evidence of growth. More lymph
nodes than usual are seen in the mesentery and behind the peritoneum. No
signs of obstructions in the intestines. Mild abdominal artery
sclerosis, but no significant narrowing of major vessels. Both kidneys
appear normal with contrast, with some areas of dilated renal pelvis and
cortical cysts in both kidneys. Both adrenal glands are small. The rest
of the urinary system looks normal.
Skeleton: Known degenerative changes in the spine with calcification,
and a compression of the 10th thoracic vertebra, but no evidence of any
fractures. There are notable herniations between vertebral discs in the
lumbar spine and spondylolysis with spondylolisthesis at the L5/S1 level
(Meyerding grade I-II). No osteolytic or suspicious lesions found in the
skeleton.
Conclusion:
Oncologic follow-up post adjuvant chemotherapy and pancreatic cancer
resection:
-Lung nodules are increasing in size and number.
-No signs of local recurrence or regional lymph node spread.
-No new distant metastases detected
**Summary:**
Mrs. Anderson visited our outpatient department to discuss her CT scan
results, part of her ongoing pancreatic cancer follow-up. For a detailed
medical history, please refer to our previous notes. In brief, Mrs.
Anderson had advanced pancreatic head cancer for which she underwent a
pancreatic head resection after neoadjuvant therapy. She underwent three
cycles of adjuvant chemotherapy with gemcitabine/5-FU. The CT scan did
not show any local issues, and there was no evidence of local recurrence
or liver metastases. The previously known lung lesions have slightly
increased in size. We have considered a CT-guided biopsy. A follow-up
appointment has been set for 04/22/23. We are available for any
questions. If symptoms persist or worsen, we advise an immediate
revisit. Outside of regular hours, emergency care is available at our
clinic's department.
Dear Mrs. Anderson,
**Encounter Summary (05/01/2023):**
**Diagnosis:**
-Progressive lung metastasis during ongoing treatment break for
pancreatic adenocarcinoma
-CT scan 04/14-23: Uncertain progressive lung lesions -- differential
diagnoses include metastases and inflammation.
History of clot at the tip of the port.
**Previous Treatment:**
09/21: Diagnosed with pancreatic head cancer.
12/22: Surgery - pancreatic head removal-
3 months adjuvant chemo with gemcitabine/5-FU (outpatient).
**Summary:**
Recent CT results showed mainly progressive lung metastasis. Weight is
59 kg, slightly decreased over the past months, with ongoing diarrhea
(about 3 times daily). We have suggested adjusting the pancreatic enzyme
dose and if no improvement, trying loperamide. The CT indicated slight
size progression of individual lung metastases but no abdominal tumor
progression.
After discussing the potential for restarting treatment, considering her
diagnosis history and previous therapies, we believe there is a low
likelihood of a positive response to treatment, especially given
potential side effects. Given the minor tumor progression over the last
four months, we recommend continuing the treatment break. Mrs. Anderson
wants to discuss this with her partner. If she decides to continue the
break, we recommend another CT in 2-3 months.
**Upcoming Appointment:** Wednesday, 3/15/2023 at 11 a.m. (Arrive by
9:30 a.m. for the hospital\'s imaging center).
### Patient Report 2
**Dear colleague, **
we report on Mrs. Jill Anderson, who was in our inpatient treatment from
07/20/2023 to 09/12/2023.
**Diagnosis**
Seropneumothorax secondary to punction of a malignant pleural effusion
with progressive pulmonary metastasis of a pancreatic head carcinoma.
Previous therapy and course
-Status post Whipple surgery on 12/22
-3 months adjuvant CTx with gemcitabin/5-FU (out).
-\> discontinuation due to intolerance
1/23-3/23: 3 cycles gemcitabine mono
06/23 CT: progressive pulmonary lesions bipulmonary metastases.
06/23-07/23: 2 cycles gemcitabine / nab-paclitaxel
07/23 CT: progressive pulmonary metastases bilaterally, otherwise idem
Allergy: penicillin
**Medical History**
Mrs. Anderson came to our ER due to worsening shortness of breath. She
has a history of metastatic pancreatic cancer in her lungs. With
significant disease progression evident in the July 2023 CT scan and
worsening symptoms, she was advised to begin chemotherapy with 5-FU and
cisplatin (reduced dose) due to severe polyneuropathy in her lower
limbs. She has experienced worsening shortness of breath since July.
Three weeks ago, she developed a cough and consulted her primary care
physician, who prescribed cefuroxime for a suspected pneumonia. The
cough improved, but the shortness of breath worsened, leading her to
come to our ER with suspected pleural effusion. She denies fever and
systemic symptoms. Urinalysis was unremarkable, and stool is
well-regulated with Creon. She denies nausea and vomiting. For further
evaluation and treatment, she was admitted to our gastroenterology unit.
**Physical Examination at Admission**
48-year-old female, 176 cm, 59 kg. Alert and stable.
Skin: Warm, dry, no rashes.
Lungs: Diminished breath sounds on the right, normal on the left.
Cardiac: Regular rate and rhythm, no murmurs.
Abdomen: Soft, non-tender.
Extremities: Normal circulation, no edema.
Neuro: Alert, oriented x3. Neurological exam normal.
**Radiologic Findings**
07/20/2023 Chest X-ray: Evidence of right-sided pneumothorax with
pleural fluid, multiple lung metastases, port-a-cath in place with tip
at superior vena cava. Cardiomegaly observed.
08/02/2023 Chest X-ray: Pneumothorax on the right has increased. Fluid
still present.
08/06/2023 Chest X-ray after chest tube insertion: Improved lung
expansion, reduced fluid and pneumothorax.
08/17/2023 Chest X-ray: Chest tube on the right removed. Evidence of
right pleural effusion. No new pneumothorax.
07/12/2023 CT Chest/Abdomen/Pelvis with contrast: Progression of
pancreatic cancer with enlarged mediastinal and hilar lymph nodes
suggestive of metastasis. Increase in right pleural effusion. Right
adrenal mass noted, possibly adenoma.
**Consultations/Interventions**
06/07/2023 Surgery: Insertion of a 20Ch chest tube on the right side,
draining 500 mL of fluid immediately.
09/01/2023 Palliative Care: Discussed the progression of her disease,
current symptoms, and future care plans. Patient is waiting for the next
CT results but is leaning towards home care.
Patient advised about painkiller recall (burning in the upper abdomen,
central, radiating to the right; doctor\'s contact provided). Pain meds
distributed.
Patient reports increasing shortness of breath; according to on-call
physician, a consult for pleural condition is scheduled.
Patient denies pain and shortness of breath; overall, she is much
improved. Oxygen arranged by ward for home use.
-Home intake of pancreatic enzymes effective: 25,000 IU during main
meals and 10,000 IU for snacks.
-Patient notes constipation with excess pancreatic enzyme, insufficient
enzyme results in diarrhea/steatorrhea.
-Patient consumes Ensure Plus (400 kcal) once daily.
Assessment:
-Severe protein and calorie malnutrition with insufficient oral intake
-Current oral caloric intake: 700 kcal + 400 kcal drink supplement
-In the hospital, pancreatic enzyme intake is challenging because the
patient struggles to assess food fat content.
Recommendations:
Lab tests for malnutrition: Vitamin D, Vitamin B12, zinc, folic acid
Twice daily Ensure Plus or alternative product. Please record, possibly
order from pharmacy. After discharge, prescribe via primary care doctor.
-Pancreatic enzymes: 25,000 IU main meals, 10,000 IU snacks. Include in
the medical chart.
-Detailed discussion of pancreatic enzyme replacement (consumption of
enzymes with fatty meals, dosage based on fat content).
-Dietary guidelines for cancer patients (balanced nutrient-rich diet,
frequent small high-calorie, and protein-rich meals to maintain weight).
Psycho-oncology consult from 9/10/2023
Current status/medical history:
The patient is noticeably stressed due to her physical limitations in
the current scenario, leading to supply concerns. She is under added
strain because her insurance recently denied a care level. She dwells on
this and suffers from sleep disturbances. She also experiences pain but
is hesitant about \"imposing\" and requesting painkillers. The
palliative care service was consulted for both pain management and
exploration of potential additional outpatient support.
Mental assessment:
Alert, fully oriented. Engages openly and amicably. Thought processes
are orderly. Tends to ruminate. Worried about her care. No signs of
delusion or ego disorders. No anhedonia. Decreased drive and energy.
Appetite and sleep are significantly disrupted. No signs of suicidal
tendencies.
Coping with illness:
Patient\'s approach to illness appears passive. There is a notable
mental strain due to worries about living alone and managing daily life
independently.
Diagnosis: Adjustment disorder
Interventions:
A diagnostic and supportive discussion was held. We recommended
mirtazapine 7.5 mg at night, increasing to 15 mg after a week if
tolerated well. She was also encouraged to take pain medication with
Tylenol proactively or at fixed intervals if needed. A follow-up visit
at our outpatient clinic was scheduled for psycho-oncological care.
**Encounter Summary (07/24/2023):**
**Diagnosis:** Lung metastatic pancreatic cancer, seropneumothorax.
**Procedure:** Left-sided chest tube placement.
**Report: **
**INDICATION:**
Mrs. Anderson showed signs of a rapidly expanding seropneumothorax
following a procedure to drain a pleural effusion. Given the increase in
size and Mrs. Anderson\'s new requirement for supplemental oxygen, we
decided to place an emergency chest tube. After informing and obtaining
consent from Mrs. Anderson, the procedure was performed.
**PROCEDURE DETAILS:**
After pain management and patient positioning, a local anesthetic was
applied. An incision was made and the chest tube was inserted, which
immediately drained about 500 mL of fluid. The tube was then secured,
and the procedure was concluded. For the postoperative protocol, please
refer to the attached documentation.
**Pathology report (07/26/2023): **
Sample: Liquid material, 50 mL, yellow and cloudy.
Processing: Papanicolaou, Hemacolor, and HE staining.
Microscopic Findings:
Protein deposits, red blood cells, lymphocytes, many granulocytes,
eosinophils, histiocyte cell forms, mesothelium, and a lot of active
mesothelium. Granulocyte count is raised. There is a notable increase in
activated mesothelium. Additionally, atypical cells were found in
clusters with vacuolated cytoplasm and darkly stained nuclei.
Initial findings:
Presence of a malignant cell population in the samples, suggestive of
adenocarcinoma cells. A cell block was prepared from the residual liquid
for further categorization.
Follow-up findings from 8/04/2023:
Processing: Immunohistochemistry (BerEP4, CK7, CK20, CK19.9, CEA).
Microscopic Findings:
As mentioned, a cell block was created from the leftover liquid. HE
staining showed blood and clusters of plasma-rich cells, with contained
eosinophilia, mild to moderate vacuolization. Cell nuclei are darkly
stained, some are marginal. PAS test was negative. Immunohistochemical
reaction with antibodies against BerEP4, CK7, CK20, CK19.9, CEA were all
positive.
Final Findings:
After reviewing the leftover liquid in a cell block, the findings are:
Pleural puncture sample with evidence of atypical cells, both
cytopathologically and immunohistochemically, is consistent with cells
from a primary pancreatic-biliary cancer.
Diagnostic classification: Positive.
**Treatment and Progress:**
The patient was hospitalized with the mentioned medical history. Lab
results were inconclusive. During the physical exam, a notably weak
respiratory sound was noted on the right side; oxygen saturation was 97%
under 3L of O2. X-rays revealed a significant right-sided pleural
effusion, which was drained. After the procedure, the patient\'s
shortness of breath improved, with SpO2 at 95% under 2L of O2. However,
an x-ray follow-up displayed a seropneumothorax, which became more
evident over time, leading to the placement of a chest tube by the
thoracic surgery department. The pneumothorax decreased with suction and
remained stable without suction, allowing for tube removal. After the
pathological analysis of the fluid, atypical cells consistent with
pancreatic cancer were identified. A dietary consultation occurred; the
patient declined the recommended IV nutrition via port; proper
pancreatic enzyme intake was thoroughly explained. Given the cancer\'s
progression and the patient\'s deteriorating condition,
psycho-oncological care was initiated, and Mirtazapine 7.5 mg at night
was prescribed. An ultrasound follow-up at the bedside showed the
pleural effusion was slowly progressing (around 100-200mL/day), but no
draining was needed as vital signs were clinically stable. Our
palliative care colleagues arranged home care, including home oxygen
supply. The patient was discharged to her home on 9/28/2023 in stable
condition and without symptoms.
**Discharge Medications:**
Mirtazapine 7.5 mg at night
Paracetamol as required
Tylenol as required
Pancreatic enzymes: 25,000 IU main meals, 10,000 IU snacks.
Follow-up:
A follow-up visit was scheduled at our outpatient clinic for
psycho-oncological care. The patient is advised to get in touch
immediately if there are any concerns or if the pleural effusion
returns. | Seropneumothorax secondary to punction of a malignant pleural effusion with progressive pulmonary metastasis of a pancreatic head carcinoma |
What didn't Svan do to try to save his planet?
A. Blow up his own vehicle and friends
B. Spy on the people from Earth
C. Plant a bomb on the ship from Earth
D. Kill a Venusian guard
| DOUBLECROSS by JAMES Mac CREIGH Revolt was brewing on Venus, led by the descendant of the first Earthmen to land. Svan was the leader making the final plans—plotting them a bit too well. [Transcriber's Note: This etext was produced from Planet Stories Winter 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The Officer of the Deck was pleased as he returned to the main lock. There was no reason why everything shouldn't have been functioning perfectly, of course, but he was pleased to have it confirmed, all the same. The Executive Officer was moodily smoking a cigarette in the open lock, staring out over the dank Venusian terrain at the native town. He turned. "Everything shipshape, I take it!" he commented. The OD nodded. "I'll have a blank log if this keeps up," he said. "Every man accounted for except the delegation, cargo stowed, drivers ready to lift as soon as they come back." The Exec tossed away his cigarette. " If they come back." "Is there any question?" The Exec shrugged. "I don't know, Lowry," he said. "This is a funny place. I don't trust the natives." Lowry lifted his eyebrows. "Oh? But after all, they're human beings, just like us—" "Not any more. Four or five generations ago they were. Lord, they don't even look human any more. Those white, flabby skins—I don't like them." "Acclimation," Lowry said scientifically. "They had to acclimate themselves to Venus's climate. They're friendly enough." The Exec shrugged again. He stared at the wooden shacks that were the outskirts of the native city, dimly visible through the ever-present Venusian mist. The native guard of honor, posted a hundred yards from the Earth-ship, stood stolidly at attention with their old-fashioned proton-rifles slung over their backs. A few natives were gazing wonderingly at the great ship, but made no move to pass the line of guards. "Of course," Lowry said suddenly, "there's a minority who are afraid of us. I was in town yesterday, and I talked with some of the natives. They think there will be hordes of immigrants from Earth, now that we know Venus is habitable. And there's some sort of a paltry underground group that is spreading the word that the immigrants will drive the native Venusians—the descendants of the first expedition, that is—right down into the mud. Well—" he laughed—"maybe they will. After all, the fittest survive. That's a basic law of—" The annunciator over the open lock clanged vigorously, and a metallic voice rasped: "Officer of the Deck! Post Number One! Instruments reports a spy ray focused on the main lock!" Lowry, interrupted in the middle of a word, jerked his head back and stared unbelievingly at the tell-tale next to the annunciator. Sure enough, it was glowing red—might have been glowing for minutes. He snatched at the hand-phone dangling from the wall, shouted into it. "Set up a screen! Notify the delegation! Alert a landing party!" But even while he was giving orders, the warning light flickered suddenly and went out. Stricken, Lowry turned to the Exec. The Executive Officer nodded gloomily. He said, "You see!" "You see?" Svan clicked off the listening-machine and turned around. The five others in the room looked apprehensive. "You see?" Svan repeated. "From their own mouths you have heard it. The Council was right." The younger of the two women sighed. She might have been beautiful, in spite of her dead-white skin, if there had been a scrap of hair on her head. "Svan, I'm afraid," she said. "Who are we to decide if this is a good thing? Our parents came from Earth. Perhaps there will be trouble at first, if colonists come, but we are of the same blood." Svan laughed harshly. " They don't think so. You heard them. We are not human any more. The officer said it." The other woman spoke unexpectedly. "The Council was right," she agreed. "Svan, what must we do?" Svan raised his hand, thoughtfully. "One moment. Ingra, do you still object?" The younger woman shrank back before the glare in his eyes. She looked around at the others, found them reluctant and uneasy, but visibly convinced by Svan. "No," she said slowly. "I do not object." "And the rest of us? Does any of us object?" Svan eyed them, each in turn. There was a slow but unanimous gesture of assent. "Good," said Svan. "Then we must act. The Council has told us that we alone will decide our course of action. We have agreed that, if the Earth-ship returns, it means disaster for Venus. Therefore, it must not return." An old man shifted restlessly. "But they are strong, Svan," he complained. "They have weapons. We cannot force them to stay." Svan nodded. "No. They will leave. But they will never get back to Earth." "Never get back to Earth?" the old man gasped. "Has the Council authorized—murder?" Svan shrugged. "The Council did not know what we would face. The Councilmen could not come to the city and see what strength the Earth-ship has." He paused dangerously. "Toller," he said, "do you object?" Like the girl, the old man retreated before his eyes. His voice was dull. "What is your plan?" he asked. Svan smiled, and it was like a dark flame. He reached to a box at his feet, held up a shiny metal globe. "One of us will plant this in the ship. It will be set by means of this dial—" he touched a spot on the surface of the globe with a pallid finger—"to do nothing for forty hours. Then—it will explode. Atomite." He grinned triumphantly, looking from face to face. The grin faded uncertainly as he saw what was in their eyes—uncertainty, irresolution. Abruptly he set the bomb down, savagely ripped six leaves off a writing tablet on the table next him. He took a pencil and made a mark on one of them, held it up. "We will let chance decide who is to do the work," he said angrily. "Is there anyone here who is afraid? There will be danger, I think...." No answer. Svan jerked his head. "Good," he said. "Ingra, bring me that bowl." Silently the girl picked up an opaque glass bowl from the broad arm of her chair. It had held Venus-tobacco cigarettes; there were a few left. She shook them out and handed the bowl to Svan, who was rapidly creasing the six fatal slips. He dropped them in the bowl, stirred it with his hand, offered it to the girl. "You first, Ingra," he said. She reached in mechanically, her eyes intent on his, took out a slip and held it without opening it. The bowl went the rounds, till Svan himself took the last. All eyes were on him. No one had looked at their slips. Svan, too, had left his unopened. He sat at the table, facing them. "This is the plan," he said. "We will go, all six of us, in my ground car, to look at the Earth-ship. No one will suspect—the whole city has been to see it already. One will get out, at the best point we can find. It is almost dusk now. He can hide, surely, in the vegetation. The other five will start back. Something will go wrong with the car—perhaps it will run off the road, start to sink in the swamp. The guards will be called. There will be commotion—that is easy enough, after all; a hysterical woman, a few screams, that's all there is to it. And the sixth person will have his chance to steal to the side of the ship. The bomb is magnetic. It will not be noticed in the dark—they will take off before sunrise, because they must travel away from the sun to return—in forty hours the danger is removed." There was comprehension in their eyes, Svan saw ... but still that uncertainty. Impatiently, he crackled: "Look at the slips!" Though he had willed his eyes away from it, his fingers had rebelled. Instinctively they had opened the slip, turned it over and over, striving to detect if it was the fatal one. They had felt nothing.... And his eyes saw nothing. The slip was blank. He gave it but a second's glance, then looked up to see who had won the lethal game of chance. Almost he was disappointed. Each of the others had looked in that same second. And each was looking up now, around at his neighbors. Svan waited impatiently for the chosen one to announce it—a second, ten seconds.... Then gray understanding came to him. A traitor! his subconscious whispered. A coward! He stared at them in a new light, saw their indecision magnified, became opposition. Svan thought faster than ever before in his life. If there was a coward, it would do no good to unmask him. All were wavering, any might be the one who had drawn the fatal slip. He could insist on inspecting every one, but—suppose the coward, cornered, fought back? In fractions of a second, Svan had considered the evidence and reached his decision. Masked by the table, his hand, still holding the pencil, moved swiftly beneath the table, marked his own slip. In the palm of his hand, Svan held up the slip he had just marked in secret. His voice was very tired as he said, "I will plant the bomb." The six conspirators in Svan's old ground car moved slowly along the main street of the native town. Two Earth-ship sailors, unarmed except for deceptively flimsy-looking pistols at their hips, stood before the entrance to the town's Hall of Justice. "Good," said Svan, observing them. "The delegation is still here. We have ample time." He half turned in the broad front seat next to the driver, searching the faces of the others in the car. Which was the coward? he wondered. Ingra? Her aunt? One of the men? The right answer leaped up at him. They all are , he thought. Not one of them understands what this means. They're afraid. He clamped his lips. "Go faster, Ingra," he ordered the girl who was driving. "Let's get this done with." She looked at him, and he was surprised to find compassion in her eyes. Silently she nodded, advanced the fuel-handle so that the clumsy car jolted a trace more rapidly over the corduroy road. It was quite dark now. The car's driving light flared yellowishly in front of them, illuminating the narrow road and the pale, distorted vegetation of the jungle that surrounded them. Svan noticed it was raining a little. The present shower would deepen and intensify until midnight, then fall off again, to halt before morning. But before then they would be done. A proton-bolt lanced across the road in front of them. In the silence that followed its thunderous crash, a man's voice bellowed: "Halt!" The girl, Ingra, gasped something indistinguishable, slammed on the brakes. A Venusian in the trappings of the State Guard advanced on them from the side of the road, proton-rifle held ready to fire again. "Where are you going?" he growled. Svan spoke up. "We want to look at the Earth-ship," he said. He opened the door beside him and stepped out, careless of the drizzle. "We heard it was leaving tonight," he continued, "and we have not seen it. Is that not permitted?" The guard shook his head sourly. "No one is allowed near the ship. The order was just issued. It is thought there is danger." Svan stepped closer, his teeth bared in what passed for a smile. "It is urgent," he purred. His right hand flashed across his chest in a complicated gesture. "Do you understand?" Confusion furrowed the guard's hairless brows, then was replaced by a sudden flare of understanding—and fear. "The Council!" he roared. "By heaven, yes, I understand! You are the swine that caused this—" He strove instinctively to bring the clumsy rifle up, but Svan was faster. His gamble had failed; there was only one course remaining. He hurled his gross white bulk at the guard, bowled him over against the splintery logs of the road. The proton-rifle went flying, and Svan savagely tore at the throat of the guard. Knees, elbows and claw-like nails—Svan battered at the astonished man with every ounce of strength in his body. The guard was as big as Svan, but Svan had the initial advantage ... and it was only a matter of seconds before the guard lay unconscious, his skull a mass of gore at the back where Svan had ruthlessly pounded it against the road. Svan grunted as his fingers constricted brutally. Svan rose, panting, stared around. No one else was in sight, save the petrified five and the ground car. Svan glared at them contemptuously, then reached down and heaved on the senseless body of the guard. Over the shoulder of the road the body went, onto the damp swampland of the jungle. Even while Svan watched the body began to sink. There would be no trace. Svan strode back to the car. "Hurry up," he gasped to the girl. "Now there is danger for all of us, if they discover he is missing. And keep a watch for other guards." Venus has no moon, and no star can shine through its vast cloud layer. Ensign Lowry, staring anxiously out through the astro-dome in the bow of the Earth-ship, cursed the blackness. "Can't see a thing," he complained to the Exec, steadily writing away at the computer's table. "Look—are those lights over there?" The Exec looked up wearily. He shrugged. "Probably the guards. Of course, you can't tell. Might be a raiding party." Lowry, stung, looked to see if the Exec was smiling, but found no answer in his stolid face. "Don't joke about it," he said. "Suppose something happens to the delegation?" "Then we're in the soup," the Exec said philosophically. "I told you the natives were dangerous. Spy-rays! They've been prohibited for the last three hundred years." "It isn't all the natives," Lowry said. "Look how they've doubled the guard around us. The administration is co-operating every way they know how. You heard the delegation's report on the intercom. It's this secret group they call the Council." "And how do you know the guards themselves don't belong to it?" the Exec retorted. "They're all the same to me.... Look, your light's gone out now. Must have been the guard. They're on the wrong side to be coming from the town, anyhow...." Svan hesitated only a fraction of a second after the girl turned the lights out and stopped the car. Then he reached in the compartment under the seat. If he took a little longer than seemed necessary to get the atomite bomb out of the compartment, none of the others noticed. Certainly it did not occur to them that there had been two bombs in the compartment, though Svan's hand emerged with only one. He got out of the car, holding the sphere. "This will do for me," he said. "They won't be expecting anyone to come from behind the ship—we were wise to circle around. Now, you know what you must do?" Ingra nodded, while the others remained mute. "We must circle back again," she parroted. "We are to wait five minutes, then drive the car into the swamp. We will create a commotion, attract the guards." Svan, listening, thought: It's not much of a plan. The guards would not be drawn away. I am glad I can't trust these five any more. If they must be destroyed, it is good that their destruction will serve a purpose. Aloud, he said, "You understand. If I get through, I will return to the city on foot. No one will suspect anything if I am not caught, because the bomb will not explode until the ship is far out in space. Remember, you are in no danger from the guards." From the guards , his mind echoed. He smiled. At least, they would feel no pain, never know what happened. With the amount of atomite in that bomb in the compartment, they would merely be obliterated in a ground-shaking crash. Abruptly he swallowed, reminded of the bomb that was silently counting off the seconds. "Go ahead," he ordered. "I will wait here." "Svan." The girl, Ingra, leaned over to him. Impulsively she reached for him, kissed him. "Good luck to you, Svan," she said. "Good luck," repeated the others. Then silently the electric motor of the car took hold. Skilfully the girl backed it up, turned it around, sent it lumbering back down the road. Only after she had traveled a few hundred feet by the feel of the road did she turn the lights on again. Svan looked after them. The kiss had surprised him. What did it mean? Was it an error that the girl should die with the others? There was an instant of doubt in his steel-shackled mind, then it was driven away. Perhaps she was loyal, yet certainly she was weak. And since he could not know which was the one who had received the marked slip, and feared to admit it, it was better they all should die. He advanced along the midnight road to where the ground rose and the jungle plants thinned out. Ahead, on an elevation, were the rain-dimmed lights of the Earth-ship, set down in the center of a clearing made by its own fierce rockets. Svan's mist-trained eyes spotted the circling figures of sentries, and knew that these would be the ship's own. They would not be as easily overcome as the natives, not with those slim-shafted blasters they carried. Only deceit could get him to the side of the ship. Svan settled himself at the side of the road, waiting for his chance. He had perhaps three minutes to wait; he reckoned. His fingers went absently to the pouch in his wide belt, closed on the slip of paper. He turned it over without looking at it, wondering who had drawn the first cross, and been a coward. Ingra? One of the men? He became abruptly conscious of a commotion behind him. A ground car was racing along the road. He spun around and was caught in the glare of its blinding driving-light, as it bumped to a slithering stop. Paralyzed, he heard the girl's voice. "Svan! They're coming! They found the guard's rifle, and they're looking for us! Thirty Earthmen, Svan, with those frightful guns. They fired at us, but we got away and came for you. We must flee!" He stared unseeingly at the light. "Go away!" he croaked unbelievingly. Then his muscles jerked into action. The time was almost up—the bomb in the car— "Go away!" he shrieked, and turned to run. His fists clenched and swinging at his side, he made a dozen floundering steps before something immense pounded at him from behind. He felt himself lifted from the road, sailing, swooping, dropping with annihilating force onto the hard, charred earth of the clearing. Only then did he hear the sound of the explosion, and as the immense echoes died away he began to feel the pain seeping into him from his hideously racked body.... The Flight Surgeon rose from beside him. "He's still alive," he said callously to Lowry, who had just come up. "It won't last long, though. What've you got there?" Lowry, a bewildered expression on his beardless face, held out the two halves of a metallic sphere. Dangling ends of wires showed where a connection had been broken. "He had a bomb," he said. "A magnetic-type, delayed-action atomite bomb. There must have been another in the car, and it went off. They—they were planning to bomb us." "Amazing," the surgeon said dryly. "Well, they won't do any bombing now." Lowry was staring at the huddled, mutilated form of Svan. He shuddered. The surgeon, seeing the shudder, grasped his shoulder. "Better them than us," he said. "It's poetic justice if I ever saw it. They had it coming...." He paused thoughtfully, staring at a piece of paper between his fingers. "This is the only part I don't get," he said. "What's that?" Lowry craned his neck. "A piece of paper with a cross on it? What about it?" The surgeon shrugged. "He had it clenched in his hand," he said. "Had the devil of a time getting it loose from him." He turned it over slowly, displayed the other side. "Now what in the world would he be doing carrying a scrap of paper with a cross marked on both sides?" | C. Plant a bomb on the ship from Earth |
Why does Rupert like Tangier?
A. Tangier is full of criminals and subversives of various sorts.
B. Tangier is right in the center of things.
C. No one questions what he's doing in Tangier.
D. The current exchange rate makes Tangier a cheap place to live.
| One can't be too cautious about the people one meets in Tangier. They're all weirdies of one kind or another. Me? Oh, I'm A Stranger Here Myself By MACK REYNOLDS The Place de France is the town's hub. It marks the end of Boulevard Pasteur, the main drag of the westernized part of the city, and the beginning of Rue de la Liberté, which leads down to the Grand Socco and the medina. In a three-minute walk from the Place de France you can go from an ultra-modern, California-like resort to the Baghdad of Harun al-Rashid. It's quite a town, Tangier. King-size sidewalk cafes occupy three of the strategic corners on the Place de France. The Cafe de Paris serves the best draft beer in town, gets all the better custom, and has three shoeshine boys attached to the establishment. You can sit of a sunny morning and read the Paris edition of the New York Herald Tribune while getting your shoes done up like mirrors for thirty Moroccan francs which comes to about five cents at current exchange. You can sit there, after the paper's read, sip your expresso and watch the people go by. Tangier is possibly the most cosmopolitan city in the world. In native costume you'll see Berber and Rif, Arab and Blue Man, and occasionally a Senegalese from further south. In European dress you'll see Japs and Chinese, Hindus and Turks, Levantines and Filipinos, North Americans and South Americans, and, of course, even Europeans—from both sides of the Curtain. In Tangier you'll find some of the world's poorest and some of the richest. The poorest will try to sell you anything from a shoeshine to their not very lily-white bodies, and the richest will avoid your eyes, afraid you might try to sell them something. In spite of recent changes, the town still has its unique qualities. As a result of them the permanent population includes smugglers and black-marketeers, fugitives from justice and international con men, espionage and counter-espionage agents, homosexuals, nymphomaniacs, alcoholics, drug addicts, displaced persons, ex-royalty, and subversives of every flavor. Local law limits the activities of few of these. Like I said, it's quite a town. I looked up from my Herald Tribune and said, "Hello, Paul. Anything new cooking?" He sank into the chair opposite me and looked around for the waiter. The tables were all crowded and since mine was a face he recognized, he assumed he was welcome to intrude. It was more or less standard procedure at the Cafe de Paris. It wasn't a place to go if you wanted to be alone. Paul said, "How are you, Rupert? Haven't seen you for donkey's years." The waiter came along and Paul ordered a glass of beer. Paul was an easy-going, sallow-faced little man. I vaguely remembered somebody saying he was from Liverpool and in exports. "What's in the newspaper?" he said, disinterestedly. "Pogo and Albert are going to fight a duel," I told him, "and Lil Abner is becoming a rock'n'roll singer." He grunted. "Oh," I said, "the intellectual type." I scanned the front page. "The Russkies have put up another manned satellite." "They have, eh? How big?" "Several times bigger than anything we Americans have." The beer came and looked good, so I ordered a glass too. Paul said, "What ever happened to those poxy flying saucers?" "What flying saucers?" A French girl went by with a poodle so finely clipped as to look as though it'd been shaven. The girl was in the latest from Paris. Every pore in place. We both looked after her. "You know, what everybody was seeing a few years ago. It's too bad one of these bloody manned satellites wasn't up then. Maybe they would've seen one." "That's an idea," I said. We didn't say anything else for a while and I began to wonder if I could go back to my paper without rubbing him the wrong way. I didn't know Paul very well, but, for that matter, it's comparatively seldom you ever get to know anybody very well in Tangier. Largely, cards are played close to the chest. My beer came and a plate of tapas for us both. Tapas at the Cafe de Paris are apt to be potato salad, a few anchovies, olives, and possibly some cheese. Free lunch, they used to call it in the States. Just to say something, I said, "Where do you think they came from?" And when he looked blank, I added, "The Flying Saucers." He grinned. "From Mars or Venus, or someplace." "Ummmm," I said. "Too bad none of them ever crashed, or landed on the Yale football field and said Take me to your cheerleader , or something." Paul yawned and said, "That was always the trouble with those crackpot blokes' explanations of them. If they were aliens from space, then why not show themselves?" I ate one of the potato chips. It'd been cooked in rancid olive oil. I said, "Oh, there are various answers to that one. We could probably sit around here and think of two or three that made sense." Paul was mildly interested. "Like what?" "Well, hell, suppose for instance there's this big Galactic League of civilized planets. But it's restricted, see. You're not eligible for membership until you, well, say until you've developed space flight. Then you're invited into the club. Meanwhile, they send secret missions down from time to time to keep an eye on your progress." Paul grinned at me. "I see you read the same poxy stuff I do." A Moorish girl went by dressed in a neatly tailored gray jellaba, European style high-heeled shoes, and a pinkish silk veil so transparent that you could see she wore lipstick. Very provocative, dark eyes can be over a veil. We both looked after her. I said, "Or, here's another one. Suppose you have a very advanced civilization on, say, Mars." "Not Mars. No air, and too bloody dry to support life." "Don't interrupt, please," I said with mock severity. "This is a very old civilization and as the planet began to lose its water and air, it withdrew underground. Uses hydroponics and so forth, husbands its water and air. Isn't that what we'd do, in a few million years, if Earth lost its water and air?" "I suppose so," he said. "Anyway, what about them?" "Well, they observe how man is going through a scientific boom, an industrial boom, a population boom. A boom, period. Any day now he's going to have practical space ships. Meanwhile, he's also got the H-Bomb and the way he beats the drums on both sides of the Curtain, he's not against using it, if he could get away with it." Paul said, "I got it. So they're scared and are keeping an eye on us. That's an old one. I've read that a dozen times, dished up different." I shifted my shoulders. "Well, it's one possibility." "I got a better one. How's this. There's this alien life form that's way ahead of us. Their civilization is so old that they don't have any records of when it began and how it was in the early days. They've gone beyond things like wars and depressions and revolutions, and greed for power or any of these things giving us a bad time here on Earth. They're all like scholars, get it? And some of them are pretty jolly well taken by Earth, especially the way we are right now, with all the problems, get it? Things developing so fast we don't know where we're going or how we're going to get there." I finished my beer and clapped my hands for Mouley. "How do you mean, where we're going ?" "Well, take half the countries in the world today. They're trying to industrialize, modernize, catch up with the advanced countries. Look at Egypt, and Israel, and India and China, and Yugoslavia and Brazil, and all the rest. Trying to drag themselves up to the level of the advanced countries, and all using different methods of doing it. But look at the so-called advanced countries. Up to their bottoms in problems. Juvenile delinquents, climbing crime and suicide rates, the loony-bins full of the balmy, unemployed, threat of war, spending all their money on armaments instead of things like schools. All the bloody mess of it. Why, a man from Mars would be fascinated, like." Mouley came shuffling up in his babouche slippers and we both ordered another schooner of beer. Paul said seriously, "You know, there's only one big snag in this sort of talk. I've sorted the whole thing out before, and you always come up against this brick wall. Where are they, these observers, or scholars, or spies or whatever they are? Sooner or later we'd nab one of them. You know, Scotland Yard, or the F.B.I., or Russia's secret police, or the French Sûreté, or Interpol. This world is so deep in police, counter-espionage outfits and security agents that an alien would slip up in time, no matter how much he'd been trained. Sooner or later, he'd slip up, and they'd nab him." I shook my head. "Not necessarily. The first time I ever considered this possibility, it seemed to me that such an alien would base himself in London or New York. Somewhere where he could use the libraries for research, get the daily newspapers and the magazines. Be right in the center of things. But now I don't think so. I think he'd be right here in Tangier." "Why Tangier?" "It's the one town in the world where anything goes. Nobody gives a damn about you or your affairs. For instance, I've known you a year or more now, and I haven't the slightest idea of how you make your living." "That's right," Paul admitted. "In this town you seldom even ask a man where's he's from. He can be British, a White Russian, a Basque or a Sikh and nobody could care less. Where are you from, Rupert?" "California," I told him. "No, you're not," he grinned. I was taken aback. "What do you mean?" "I felt your mind probe back a few minutes ago when I was talking about Scotland Yard or the F.B.I. possibly flushing an alien. Telepathy is a sense not trained by the humanoids. If they had it, your job—and mine—would be considerably more difficult. Let's face it, in spite of these human bodies we're disguised in, neither of us is humanoid. Where are you really from, Rupert?" "Aldebaran," I said. "How about you?" "Deneb," he told me, shaking. We had a laugh and ordered another beer. "What're you doing here on Earth?" I asked him. "Researching for one of our meat trusts. We're protein eaters. Humanoid flesh is considered quite a delicacy. How about you?" "Scouting the place for thrill tourists. My job is to go around to these backward cultures and help stir up inter-tribal, or international, conflicts—all according to how advanced they are. Then our tourists come in—well shielded, of course—and get their kicks watching it." Paul frowned. "That sort of practice could spoil an awful lot of good meat." THE END Transcriber's Note: This etext was produced from Amazing Stories December 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | C. No one questions what he's doing in Tangier. |
What effects do the Green Flame rocks have?
A. It makes people lethargic and easily manipulated.
B. They spread radioactivity to people and make them ill.
C. They influence people to take power over other people.
D. They are electromagnetic and shock people.
| Doctor Universe By CARL JACOBI Grannie Annie, who wrote science fiction under the nom de plume of Annabella C. Flowers, had stumbled onto a murderous plot more hair-raising than any she had ever concocted. And the danger from the villain of the piece didn't worry her—I was the guy he was shooting at. [Transcriber's Note: This etext was produced from Planet Stories Fall 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I was killing an hour in the billiard room of the Spacemen's Club in Swamp City when the Venusian bellboy came and tapped me on the shoulder. "Beg pardon, thir," he said with his racial lisp, "thereth thome one to thee you in the main lounge." His eyes rolled as he added, "A lady!" A woman here...! The Spacemen's was a sanctuary, a rest club where in-coming pilots and crewmen could relax before leaving for another voyage. The rule that no females could pass its portals was strictly enforced. I followed the bellhop down the long corridor that led to the main lounge. At the threshold I jerked to a halt and stared incredulously. Grannie Annie! There she stood before a frantically gesticulating desk clerk, leaning on her faded green umbrella. A little wisp of a woman clad in a voluminous black dress with one of those doily-like caps on her head, tied by a ribbon under her chin. Her high-topped button shoes were planted firmly on the varpla carpet and her wrinkled face was set in calm defiance. I barged across the lounge and seized her hand. "Grannie Annie! I haven't seen you in two years." "Hi, Billy-boy," she greeted calmly. "Will you please tell this fish-face to shut up." The desk clerk went white. "Mithter Trenwith, if thith lady ith a friend of yourth, you'll have to take her away. It'th abtholutely againth the ruleth...." "Okay, okay," I grinned. "Look, we'll go into the grille. There's no one there at this hour." In the grille an equally astonished waiter served us—me a lime rickey and Grannie Annie her usual whisky sour—I waited until she had tossed the drink off at a gulp before I set off a chain of questions: "What the devil are you doing on Venus? Don't you know women aren't allowed in the Spacemen's ? What happened to the book you were writing?" "Hold it, Billy-boy." Laughingly she threw up both hands. "Sure, I knew this place had some antiquated laws. Pure fiddle-faddle, that's what they are. Anyway, I've been thrown out of better places." She hadn't changed. To her publishers and her readers she might be Annabella C. Flowers, author of a long list of science fiction novels. But to me she was still Grannie Annie, as old-fashioned as last year's hat, as modern as an atomic motor. She had probably written more drivel in the name of science fiction than anyone alive. But the public loved it. They ate up her stories, and they clamored for more. Her annual income totaled into six figures, and her publishers sat back and massaged their digits, watching their earnings mount. One thing you had to admit about her books. They may have been dime novels, but they weren't synthetic. If Annabella C. Flowers wrote a novel, and the locale was the desert of Mars, she packed her carpet bag and hopped a liner for Craterville. If she cooked up a feud between two expeditions on Callisto, she went to Callisto. She was the most completely delightful crackpot I had ever known. "What happened to Guns for Ganymede ?" I asked. "That was the title of your last, wasn't it?" Grannie spilled a few shreds of Martian tobacco onto a paper and deftly rolled herself a cigarette. "It wasn't Guns , it was Pistols ; and it wasn't Ganymede , it was Pluto ." I grinned. "All complete, I'll bet, with threats against the universe and beautiful Earth heroines dragged in by the hair." "What else is there in science fiction?" she demanded. "You can't have your hero fall in love with a bug-eyed monster." Up on the wall a clock chimed the hour. The old woman jerked to her feet. "I almost forgot, Billy-boy. I'm due at the Satellite Theater in ten minutes. Come on, you're going with me." Before I realized it, I was following her through the lounge and out to the jetty front. Grannie Annie hailed a hydrocar. Five minutes later we drew up before the big doors of the Satellite . They don't go in for style in Swamp City. A theater to the grizzled colonials on this side of the planet meant a shack on stilts over the muck, zilcon wood seats and dingy atobide lamps. But the place was packed with miners, freight-crew-men—all the tide and wash of humanity that made Swamp City the frontier post it is. In front was a big sign. It read: ONE NIGHT ONLY DOCTOR UNIVERSE AND HIS NINE GENIUSES THE QUESTION PROGRAM OF THE SYSTEM As we strode down the aisle a mangy-looking Venusian began to pound a tinpan piano in the pit. Grannie Annie pushed me into a seat in the front row. "Sit here," she said. "I'm sorry about all this rush, but I'm one of the players in this shindig. As soon as the show is over, we'll go somewhere and talk." She minced lightly down the aisle, climbed the stage steps and disappeared in the wings. "That damned fossilized dynamo," I muttered. "She'll be the death of me yet." The piano struck a chord in G, and the curtain went rattling up. On the stage four Earthmen, two Martians, two Venusians, and one Mercurian sat on an upraised dais. That is to say, eight of them sat. The Mercurian, a huge lump of granite-like flesh, sprawled there, palpably uncomfortable. On the right were nine visi sets, each with its new improved pantascope panel and switchboard. Before each set stood an Earthman operator. A tall man, clad in a claw-hammer coat, came out from the wings and advanced to the footlights. "People of Swamp City," he said, bowing, "permit me to introduce myself. I am Doctor Universe, and these are my nine experts." There was a roar of applause from the Satellite audience. When it had subsided, the man continued: "As most of you are familiar with our program, it will be unnecessary to give any advance explanation. I will only say that on this stage are nine visi sets, each tuned to one of the nine planets. At transmitting sets all over these planets listeners will appear and voice questions. These questions, my nine experts will endeavor to answer. For every question missed, the sender will receive a check for one thousand planetoles . "One thing more. As usual we have with us a guest star who will match her wits with the experts. May I present that renowned writer of science fiction, Annabella C. Flowers." From the left wing Grannie Annie appeared. She bowed and took her place on the dais. The Doctor's program began. The operator of the Earth visi twisted his dials and nodded. Blue light flickered on the pantascope panel to coalesce slowly into the face of a red-haired man. Sharp and dear his voice echoed through the theater: " Who was the first Earthman to titter the sunward side of Mercury? " Doctor Universe nodded and turned to Grannie Annie who had raised her hand. She said quietly: "Charles Zanner in the year 2012. In a specially constructed tracto-car." And so it went. Questions from Mars, from Earth, from Saturn flowed in the visi sets. Isolated miners on Jupiter, dancers in swank Plutonian cafes strove to stump the experts. With Doctor Universe offering bantering side play, the experts gave their answers. When they failed, or when the Truthicator flashed a red light, he announced the name of the winner. It grew a little tiresome after a while and I wondered why Grannie had brought me here. And then I began to notice things. The audience in the Satellite seemed to have lost much of its original fervor. They applauded as before but they did so only at the signal of Doctor Universe. The spell created by the man was complete. Pompous and erect, he strode back and forth across the stage like a general surveying his army. His black eyes gleamed, and his thin lips were turned in a smile of satisfaction. When the last question had been answered I joined the exit-moving crowd. It was outside under the street marquee that a strange incident occurred. A yellow-faced Kagor from the upper Martian desert country shuffled by, dragging his cumbersome third leg behind him. Kagors, of course, had an unpleasant history of persecution since the early colonization days of the Red Planet. But the thing that happened there was a throw back to an earlier era. Someone shouted, "Yah, yellow-face! Down with all Kagors!" As one man the crowd took up the cry and surged forward. The helpless Kagor was seized and flung to the pavement. A knife appeared from nowhere, snipped the Martian's single lock of hair. A booted foot bludgeoned into his mouth. Moments later an official hydrocar roared up and a dozen I.P. men rushed out and scattered the crowd. But a few stragglers lingered to shout derisive epithets. Grannie Annie came out from behind the box office then. She took my arm and led me around a corner and through a doorway under a sign that read THE JET. Inside was a deep room with booths along one wall. The place was all but deserted. In a booth well toward the rear the old lady surveyed me with sober eyes. "Billy-boy, did you see the way that crowd acted?" I nodded. "As disgraceful an exhibition as I've ever seen. The I.P. men ought to clamp down." "The I.P. men aren't strong enough." She said it quietly, but there was a glitter in her eyes and a harsh line about her usually smiling lips. "What do you mean?" For a moment the old lady sat there in silence; then she leaned back, closed her eyes, and I knew there was a story coming. "My last book, Death In The Atom , hit the stands last January," she began. "When it was finished I had planned to take a six months' vacation, but those fool publishers of mine insisted I do a sequel. Well, I'd used Mars and Pluto and Ganymede as settings for novels, so for this one I decided on Venus. I went to Venus City, and I spent six weeks in-country. I got some swell background material, and I met Ezra Karn...." "Who?" I interrupted. "An old prospector who lives out in the deep marsh on the outskirts of Varsoom country. To make a long story short, I got him talking about his adventures, and he told me plenty." The old woman paused. "Did you ever hear of the Green Flames?" she asked abruptly. I shook my head. "Some new kind of ..." "It's not a new kind of anything. The Green Flame is a radio-active rock once found on Mercury. The Alpha rays of this rock are similar to radium in that they consist of streams of material particles projected at high speed. But the character of the Gamma rays has never been completely analyzed. Like those set up by radium, they are electromagnetic pulsations, but they are also a strange combination of Beta or cathode rays with negatively charged electrons. "When any form of life is exposed to these Gamma rays from the Green Flame rock, they produce in the creature's brain a certain lassitude and lack of energy. As the period of exposure increases, this condition develops into a sense of impotence and a desire for leadership or guidance. Occasionally, as with the weak-willed, there is a spirit of intolerance. The Green Flames might be said to be an inorganic opiate, a thousand times more subtle and more powerful than any known drug." I was sitting up now, hanging on to the woman's every word. "Now in 2710, as you'd know if you studied your history, the three planets of Earth, Venus, and Mars were under governmental bondage. The cruel dictatorship of Vennox I was short-lived, but it lasted long enough to endanger all civilized life. "The archives tell us that one of the first acts of the overthrowing government was to cast out all Green Flames, two of which Vennox had ordered must be kept in each household. The effect on the people was immediate. Representative government, individual enterprise, freedom followed." Grannie Annie lit a cigarette and flipped the match to the floor. "To go back to my first trip to Venus. As I said, I met Ezra Karn, an old prospector there in the marsh. Karn told me that on one of his travels into the Varsoom district he had come upon the wreckage of an old space ship. The hold of that space ship was packed with Green Flames!" If Grannie expected me to show surprise at that, she was disappointed. I said, "So what?" "So everything, Billy-boy. Do you realize what such a thing would mean if it were true? Green Flames were supposedly destroyed on all planets after the Vennox regime crashed. If a quantity of the rock were in existence, and it fell into the wrong hands, there'd be trouble. "Of course, I regarded Karn's story as a wild dream, but it made corking good story material. I wrote it into a novel, and a week after it was completed, the manuscript was stolen from my study back on Earth." "I see," I said as she lapsed into silence. "And now you've come to the conclusion that the details of your story were true and that someone is attempting to put your plot into action." Grannie nodded. "Yes," she said. "That's exactly what I think." I got my pipe out of my pocket, tamped Martian tobacco into the bowl and laughed heartily. "The same old Flowers," I said. "Tell me, who's your thief ... Doctor Universe?" She regarded me evenly. "What makes you say that?" I shrugged. "The way the theater crowd acted. It all ties in." The old woman shook her head. "No, this is a lot bigger than a simple quiz program. The theater crowd was but a cross-section of what is happening all over the System. There have been riots on Earth and Mars, police officials murdered on Pluto and a demand that government by representation be abolished on Jupiter. The time is ripe for a military dictator to step in. "And you can lay it all to the Green Flames. It seems incredible that a single shipload of the ore could effect such a wide ranged area, but in my opinion someone has found a means of making that quantity a thousand times more potent and is transmiting it en masse ." If it had been anyone but Grannie Annie there before me, I would have called her a fool. And then all at once I got an odd feeling of approaching danger. "Let's get out of here," I said, getting up. Zinnng-whack! "All right!" On the mirror behind the bar a small circle with radiating cracks appeared. On the booth wall a scant inch above Grannie's head the fresco seemed to melt away suddenly. A heat ray! Grannie Annie leaped to her feet, grasped my arm and raced for the door. Outside a driverless hydrocar stood with idling motors. The old woman threw herself into the control seat, yanked me in after her and threw over the starting stud. An instant later we were plunging through the dark night. Six days after leaving Swamp City we reached Level Five, the last outpost of firm ground. Ahead lay the inner marsh, stretching as far as the eye could reach. Low islands projected at intervals from the thick water. Mold balls, two feet across, drifted down from the slate-gray sky like puffs of cotton. We had traveled this far by ganet , the tough little two headed pack animal of the Venus hinterland. Any form of plane or rocket would have had its motor instantly destroyed, of course, by the magnetic force belt that encircled the planet's equator. Now our drivers changed to boatmen, and we loaded our supplies into three clumsy jagua canoes. It was around the camp fire that night that Grannie took me into her confidence for the first time since we had left Swamp City. "We're heading directly for Varsoom country," she said. "If we find Ezra Karn so much the better. If we don't, we follow his directions to the lost space ship. Our job is to find that ore and destroy it. You see, I'm positive the Green Flames have never been removed from the ship." Sleep had never bothered me, yet that night I lay awake for hours tossing restlessly. The thousand sounds of the blue marsh droned steadily. And the news broadcast I had heard over the portable visi just before retiring still lingered in my mind. To a casual observer that broadcast would have meant little, a slight rebellion here, an isolated crime there. But viewed from the perspective Grannie had given me, everything dovetailed. The situation on Jupiter was swiftly coming to a head. Not only had the people on that planet demanded that representative government be abolished, but a forum was now being held to find a leader who might take complete dictatorial control. Outside a whisper-worm hissed softly. I got up and strode out of my tent. For some time I stood there, lost in thought. Could I believe Grannie's incredible story? Or was this another of her fantastic plots which she had skilfully blended into a novel? Abruptly I stiffened. The familiar drone of the marsh was gone. In its place a ringing silence blanketed everything. And then out in the gloom a darker shadow appeared, moving in undulating sweeps toward the center of the camp. Fascinated, I watched it advance and retreat, saw two hyalescent eyes swim out of the murk. It charged, and with but a split second to act, I threw myself flat. There was a rush of mighty wings as the thing swept over me. Sharp talons raked my clothing. Again it came, and again I rolled swiftly, missing the thing by the narrowest of margins. From the tent opposite a gaunt figure clad in a familiar dress appeared. Grannie gave a single warning: "Stand still!" The thing in the darkness turned like a cam on a rod and drove at us again. This time the old woman's heat gun clicked, and a tracery of purple flame shot outward. A horrible soul-chilling scream rent the air. A moment later something huge and heavy scrabbled across the ground and shot aloft. Grannie Annie fired with deliberate speed. I stood frozen as the diminuendo of its wild cries echoed back to me. "In heaven's name, what was it?" "Hunter-bird," Grannie said calmly. "A form of avian life found here in the swamp. Harmless in its wild state, but when captured, it can be trained to pursue a quarry until it kills. It has a single unit brain and follows with a relentless purpose." "Then that would mean...?" "That it was sent by our enemy, the same enemy that shot at us in the cafe in Swamp City. Exactly." Grannie Annie halted at the door of her tent and faced me with earnest eyes. "Billy-boy, our every move is being watched. From now on it's the survival of the fittest." The following day was our seventh in the swamp. The water here resembled a vast mosaic, striped and cross-striped with long winding ribbons of yellowish substance that floated a few inches below the surface. The mold balls coming into contact with the evonium water of the swamp had undergone a chemical change and evolved into a cohesive multi-celled marine life that lived and died within a space of hours. The Venusians paddled with extreme care. Had one of them dipped his hand into one of those yellow streaks, he would have been devoured in a matter of seconds. At high noon by my Earth watch I sighted a low white structure on one of the distant islands. Moments later we made a landing at a rude jetty, and Grannie Annie was introducing me to Ezra Karn. He was not as old a man as I had expected, but he was ragged and unkempt with iron gray hair falling almost to his shoulders. He was dressed in varpa cloth, the Venus equivalent of buckskin, and on his head was an enormous flop-brimmed hat. "Glad to meet you," he said, shaking my hand. "Any friend of Miss Flowers is a friend of mine." He ushered us down the catwalk into his hut. The place was a two room affair, small but comfortable. The latest type of visi set in one corner showed that Karn was not isolated from civilization entirely. Grannie Annie came to the point abruptly. When she had explained the object of our trip, the prospector became thoughtful. "Green Flames, eh?" he repeated slowly. "Well yes, I suppose I could find that space ship again. That is, if I wanted to." "What do you mean?" Grannie paused in the act of rolling herself a cigarette. "You know where it is, don't you?" "Ye-s," Karn nodded. "But like I told you before, that ship lies in Varsoom country, and that isn't exactly a summer vacation spot." "What are the Varsoom?" I asked. "A native tribe?" Karn shook his head. "They're a form of life that's never been seen by Earthmen. Strictly speaking, they're no more than a form of energy." "Dangerous?" "Yes and no. Only man I ever heard of who escaped their country outside of myself was the explorer, Darthier, three years ago. I got away because I was alone, and they didn't notice me, and Darthier escaped because he made 'em laugh." "Laugh?" A scowl crossed Grannie's face. "That's right," Karn said. "The Varsoom have a strange nervous reaction that's manifested by laughing. But just what it is that makes them laugh, I don't know." Food supplies and fresh drinking water were replenished at the hut. Several mold guns were borrowed from the prospector's supply to arm the Venusians. And then as we were about to leave, Karn suddenly turned. "The Doctor Universe program," he said. "I ain't missed one in months. You gotta wait 'til I hear it." Grannie frowned in annoyance, but the prospector was adamant. He flipped a stud, twisted a dial and a moment later was leaning back in a chair, listening with avid interest. It was the same show I had witnessed back in Swamp City. Once again I heard questions filter in from the far outposts of the System. Once again I saw the commanding figure of the quiz master as he strode back and forth across the stage. And as I sat there, looking into the visi screen, a curious numbing drowsiness seemed to steal over me and lead my thoughts far away. Half an hour later we headed into the unknown. The Venusian boatmen were ill-at-ease now and jabbered among themselves constantly. We camped that night on a miserable little island where insects swarmed about us in hordes. The next day an indefinable wave of weariness and despondency beset our entire party. I caught myself musing over the futility of the venture. Only the pleadings of Grannie Annie kept me from turning back. On the morrow I realized the truth in her warning, that all of us had been exposed to the insidious radiations. After that I lost track of time. Day after day of incessant rain ... of steaming swamp.... But at length we reached firm ground and began our advance on foot. It was Karn who first sighted the ship. Striding in the lead, he suddenly halted at the top of a hill and leveled his arm before him. There it lay, a huge cigar-shaped vessel of blackened arelium steel, half buried in the swamp soil. "What's that thing on top?" Karn demanded, puzzled. A rectangular metal envelope had been constructed over the stern quarters of the ship. Above this structure were three tall masts. And suspended between them was a network of copper wire studded with white insulators. Grannie gazed a long moment through binoculars. "Billy-boy, take three Venusians and head across the knoll," she ordered. "Ezra and I will circle in from the west. Fire a gun if you strike trouble." But we found no trouble. The scene before us lay steeped in silence. Moments later our two parties converged at the base of the great ship. A metal ladder extended from the envelope down the side of the vessel. Mid-way we could see a circular hatch-like door. "Up we go, Billy-boy." Heat gun in readiness, Grannie Annie began to climb slowly. The silence remained absolute. We reached the door and pulled it open. There was no sign of life. "Somebody's gone to a lot of trouble here," Ezra Karn observed. Somebody had. Before us stretched a narrow corridor, flanked on the left side by a wall of impenetrable stepto glass. The corridor was bare of furnishings. But beyond the glass, revealed to us in mocking clarity, was a high panel, studded with dials and gauges. Even as we looked, we could see liquid pulse in glass tubes, indicator needles swing slowly to and fro. Grannie nodded. "Some kind of a broadcasting unit. The Green Flames in the lower hold are probably exposed to a tholpane plate and their radiations stepped up by an electro-phosicalic process." Karn raised the butt of his pistol and brought it crashing against the glass wall. His arm jumped in recoil, but the glass remained intact. "You'll never do it that way," Grannie said. "Nothing short of an atomic blast will shatter that wall. It explains why there are no guards here. The mechanism is entirely self-operating. Let's see if the Green Flames are more accessible." In the lower hold disappointment again confronted us. Visible in the feeble shafts of daylight that filtered through cracks in the vessel's hull were tiers of rectangular ingots of green iridescent ore. Suspended by insulators from the ceiling over them was a thick metal plate. But between was a barrier. A wall of impenetrable stepto glass. Grannie stamped her foot. "It's maddening," she said. "Here we are at the crux of the whole matter, and we're powerless to make a single move." | A. It makes people lethargic and easily manipulated. |
What does the author think would have improved The Slums of Beverly Hills?
A. a more realistic plot
B. more episodes to explain the situation
C. a more experienced director
D. more attractive actors
| Dirty Laundry Now and then, a documentary film comes along that makes us re-examine the rules that unofficially govern the genre: Can there be a middle ground between fiction and fact? Can a documentary use scripted scenes and yet remain ontologically authentic? How much can you stylize material before you alter the reality that you're striving, at least in theory, to capture? Unmade Beds , Nicholas Barker's " 'real life' feature film," has proudly worn its mongrel status as a "directed" documentary of single life in the big city, employing, in the face of criticism, what amounts to a cackling-punk defiance. The movie tracks four aging New Yorkers--two men, two women--through their lonely dating rituals, in the process depicting a universe of lusty, coupled-up haves and downcast, excluded have-nots, all viewed Rear Window -style through rectangular openings in the massive apartment houses in which they reside. This is not cinema vérité , and nothing has been left to chance. The director selected his four subjects from many hundreds of potential candidates, followed them around for months, and then scripted their monologues and dialogues to reflect what he says he saw. Calling his own film "an exercise in mendacity," Barker goes on, "I'm quite happy to tell lies about my characters and even collude with their self-delusions if it enables me to communicate larger dramatic truths." Spurned by U.S. distributors, Unmade Beds opened two weeks ago in a small screening room in downtown Manhattan, where it proceeded to set box office records and generate lots of (largely favorable) press. In part due to smart publicity, which has bannered some of the bad reviews and commentary ("I have to tell you that this film upset me so much that I really don't want to have anything to do with it"--a New York publicist), it threatens to become a cause célèbre --and to be coming soon to a theater near you. It's always nice to see distributors proved wrong about the merits of "difficult" films, but in this case I think they did the decent thing. Unmade Beds isn't just bad--it's obnoxiously, noxiously bad, a freak show for the empathetically challenged. The outrage it has prompted isn't the Puritan kind; it's more like legitimate revulsion at watching a blowhard pervert people's lives in the name of "larger dramatic truths." Those truths are large, all right. Take Michael, the 40-year-old, 5 foot 4 inch lonely guy who has been looking for a wife for almost two decades. If you were to walk past him on the street, you might think that a man of his small stature might have some trouble getting dates and be rather bitter about it. The larger dramatic truth is that Michael has lots of trouble getting dates and is very bitter about it. Just in case you feel too sorry for him, however, Barker is careful to include a homophobic monologue in which Michael complains about young women who waste their lives hanging out with effeminate males. Michael turns out to be the film's most sympathetic subject--by a wide margin. At least he's not Mikey, a paunchy 54-year-old who writes but can't sell screenplays and who always flees blind dates, because the women he gets fixed up with are "mutts." Sounding like one of the low-level gangsters who posture like kingpins in Donnie Brasco , Mikey talks a lot about mutts. He also reminisces about that 24 hour period in the '70s when he managed to sleep with three different beautiful women, whose pictures he shows off. These days, all he meets are mutts. He comes off as a pathetic little loser--a mutt. Aimee, on the other hand, is a pathetic big loser, weighing in at 225 pounds. Determined to get married before she turns 30, she generally is filmed beside bags of groceries and assorted junk foods. She cries about her situation to her thin friend, Laurie, who, in one scene, gently mentions Aimee's weight. Clearly the scene is scripted, but Aimee does a good job acting taken aback. She has always been fat--and she's "OK with it," and a man just has to accept it. This is followed by more talk about how you attract men. Will they respect you if you call them back? If you express too much interest? "Or," the viewer thinks, "if you're 225 pounds?" The only natural performer here is Brenda, a garrulous exhibitionist who blossoms with the camera on her--she could have a career as a Penny Marshall-style character actress. Divorced and aging, Brenda needs money and is willing to charge for her sexual services. It shouldn't be too difficult, because men are always showing her their dicks ("I'm up to two dicks a day"). They meet her and, a few minutes later, they show her their dicks. Weird, huh? What Barker leaves out (it's in a New York Observer article) is that Brenda, a former lap dancer, works in marketing at a strip joint. Presumably, men standing next to her in line at McDonald's don't show her their dicks. Nor, presumably, does she show them her breasts--although she bares them for Barker's camera, jabbering about her body while she doffs her clothes and steps into the shower and soaps up. Barker might have crafted his subjects' monologues from their own words, but he has robbed them of their spontaneity--and, thus, of their essence. They aren't thinking or trying to come to grips with their situations in front of your eyes, because they already know what they're going to say: They've been fixed like butterflies on the ends of pins and held up for voyeuristic inspection. The scenes with friends and confidantes have a crude, programmatic purpose. You can imagine the director composing a shot (the shots are tightly composed and elaborately lighted) and reminding them, "In this scene she points out that you should lose weight and you get shocked and defensive. Ready ... Action." Call me square, but I find this antithetical to the documentary spirit. An Englishman who trained as an anthropologist before going to work for BBC Television, Barker clearly made up his mind about his material before his cameras began to roll--so it's no surprise that it feels prechewed and predigested. When reality interfered (Brenda apparently did not go through with a marriage to an immigrant in search of a green card for $10,000, as she does on-screen), Barker brushed the truth aside as immaterial, following her up the steps of City Hall in her wedding dress because it was "true to her character." But what separates documentary from fiction is that real people are often more complicated, and more conflicted, than finished characters--as Brenda proved to be more (or, at least, other) than the sum of her parts. That's the kind of truth that reveals itself to documentary filmmakers after the fact, when they go over footage and discover unexpected patterns, dissonances, glimmers of a universe that's richer and messier than the one they set out to portray. So what are Barker's "larger dramatic truths"? Single people in big cities can be desperate. Single people fear they're going to die alone--unloved and unloving. People are judged and, in turn, judge others by how they look. Big news. One could argue, charitably, that the movie is meant to be prescriptive, that Barker intends for us to regard the ways in which his subjects delude themselves and thereby learn to see through our own self-delusions. But Barker hasn't concocted a larger dramatic structure that would hold those larger dramatic truths together and help us comprehend where these people went wrong. He dramatizes right up to the point where a dramatist would be expected to provide some insight--and then, hey, he's a documentarian. Unmade Beds might make a good date movie. There's little to argue about in its subjects' personalities--both males and females will find them repulsive--and the picture the film paints of single life in the big city is so bleak that you'll probably want to jump into bed with whoever is sitting next to you. Anything to keep from turning into one of those people. The Slums of Beverly Hills also walks a line between two genres, in this case coming-of-age sex comedy and autobiographical monologue. Tamara Jenkins, the writer and first-time director, has an eye for absurd juxtapositions that was obviously sharpened by the pain of her nomadic upbringing. Her protagonist (Natasha Lyonne) spends her teen-age years being shuttled with her two brothers from one cheap dive to another in the 90210 ZIP code, all because her egregiously unsuccessful father (Alan Arkin) wants them to be educated in the best schools. ("Furniture's temporary; education is permanent.") It's a major omission, then, that we never see those schools or the kids' interaction with their stable, well-to-do Beverly Hills counterparts. We can't tell if the father is, on some weird level, justified in his fervor, or whether he's screwing up his children--subjecting them to humiliation and robbing them of a sense of permanence--for no reason. Jenkins hasn't quite figured out how to shape her narrative, which is full of episodes that are there because they actually happened but that don't have a payoff. I almost wish she'd included more voice-over narration, more commentary on the things that, as a filmmaker, she hasn't learned to bring out. The Slums of Beverly Hills never gels, but it has a likable spirit, and it's exceedingly easy on the eye, with lots of pretty girls and wry evocations of '70s fashions and decor. The father, to obtain financial support from his wealthy brother (Carl Reiner), volunteers to take in his vaguely schizzy, dipsomaniacal niece (Marisa Tomei). She and her cousin compare breasts, play with vibrators, and talk in pig Latinish gibberish, but Jenkins never lets the proceedings get too sentimental: The whimsy is always cut with an acidic awareness of the family's desperation. "Are we middle-class now?" ask the children, hopefully, before another crisis sends them back into their van, cruising past the movie stars' mansions, in the mean streets of Beverly Hills. Grading on the steep curve established by summer blockbuster seasons past, these have turned out to be a pretty good few months at the movies. Even the commercial swill ( Deep Impact , Armageddon , The Mask of Zorro , Small Soldiers , Snake Eyes , Halloween: H20 ) has been of a high grade, and Saving Private Ryan and Return to Paradise were Vitalis slaps in the kisser for people woozy from all the warm weather escapism. Out of Sight was tender and charming, as was, in its gross-out way, There's Something About Mary . And, on the indie front, The Opposite of Sex , Buffalo 66 , and Pi have proved that there's still commercial life after Sundance. Sure, we had stinkers, but even Godzilla was fun to jeer at. And there's something reassuring about the fact that The Avengers is so rotten: proof yet again that people with piles of money can hire wizard production designers but can't fake class. I don't know who the credited screenwriter, Don MacPherson, is, but it's unlikely that he has ever seen an episode of the old Avengers , let alone sussed out the source of its appeal. Opening with a slapstick sequence of agent John Steed (Ralph Fiennes) doing kung fu, the film shifts to a scene in which he meets Mrs. Peel (Uma Thurman) while sitting naked in a sauna with only a newspaper to cover his private parts. The series was erotic in a way only prim English humor can be: The Old Boy Steed was capable of throwing a punch and bonking someone with his bowler, but he left the karate kicking to his liberated, leather-suited distaff associate. Here their roles have been witlessly muddled, and MacPherson's idea of banter is to have the pair complete each other's clichés. Whereas the original Steed, Patrick Macnee, was to the English Men's Club born, Fiennes is an eternal caddie. The willowy Thurman looks great in her outfits, but it's ever more apparent that she isn't much of an actress--at least, not a trained one--and her attempts at insouciance are embarrassingly arch. As the eccentric master villain who controls the weather, even Sean Connery is flat-out terrible, acting high on the hog. To think Connery once found the Bond films so far beneath him! When he sputters lines like "Time to die!" one imagines Dr. No, Goldfinger, and Blofeld snickering in the wings. | C. a more experienced director |
What is White Sands?
A. A city in New Mexico.
B. A rocket base in New Mexico.
C. An obstetrics facility in New Mexico.
D. A mission control base in New Mexico.
| SPATIAL DELIVERY BY RANDALL GARRETT Women on space station assignments shouldn't get pregnant. But there's a first time for everything. Here's the story of such a time——and an historic situation. [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, October 1954. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] One thousand seventy-five miles above the wrinkled surface of Earth, a woman was in pain. There, high in the emptiness of space, Space Station One swung in its orbit. Once every two hours, the artificial satellite looped completely around the planet, watching what went on below. Outside its bright steel hull was the silence of the interplanetary vacuum; inside, in the hospital ward, Lieutenant Alice Britton clutched at the sheets of her bed in pain, then relaxed as it faded away. Major Banes looked at her and smiled a little. "How do you feel, Lieutenant?" She smiled back; she knew the pain wouldn't return for a few minutes yet. "Fine, doctor. It's no worse than I was expecting. How long will it before we can contact White Sands?" The major looked nervously at his wristwatch. "Nearly an hour. You'll be all right." "Certainly," she agreed, running a hand through her brown hair, "I'll be okay. Just you be on tap when I call." The major's grin broadened. "You don't think I'd miss a historical event like this, do you? You take it easy. We're over Eastern Europe now, but as soon as we get within radio range of New Mexico, I'll beam a call in." He paused, then repeated, "You just take it easy. Call the nurse if anything happens." Then he turned and walked out of the room. Alice Britton closed her eyes. Major Banes was all smiles and cheer now, but he hadn't been that way five months ago. She chuckled softly to herself as she thought of his blistering speech. "Lieutenant Britton, you're either careless or brainless; I don't know which! Your husband may be the finest rocket jockey in the Space Service, but that doesn't give him the right to come blasting up here on a supply rocket just to get you pregnant!" Alice had said: "I'm sure the thought never entered his mind, doctor. I know it never entered mine." "But that was two and a half months ago! Why didn't you come to me before this? Of all the tom-fool—" His voice had died off in suppressed anger. "I didn't know," she had said stolidly. "You know my medical record." "I know. I know." A puzzled frown had come over his face then, a frown which almost hid the green eyes that contrasted so startlingly with the flaming red of his hair. "The question is: what do we do next? We're not equipped for obstetrics up here." "Send me back down to Earth, of course." And he had looked up at her scathingly. "Lieutenant Britton, it is my personal opinion that you need your head examined, and not by a general practitioner, either! Why, I wouldn't let you get into an airplane, much less land on Earth in a rocket! If you think I'd permit you to subject yourself to eight gravities of acceleration in a rocket landing, you're daffy!" She hadn't thought of it before, but the major was right. The terrible pressure of a rocket landing would increase her effective body weight to nearly half a ton; an adult human being couldn't take that sort of punishment for long, much less the tiny life that was growing within her. So she had stayed on in the Space Station, doing her job as always. As Chief Radar Technician, she was important in the operation of the station. Her pregnancy had never made her uncomfortable; the slow rotation of the wheel-shaped station about its axis gave an effective gravity at the rim only half that of Earth's surface, and the closer to the hub she went, the less her weight became. According to the major, the baby was due sometime around the first of September. "Two hundred and eighty days," he had said. "Luckily, we can pinpoint it almost exactly. And at a maximum of half of Earth gravity, you shouldn't weigh more than seventy pounds then. You're to report to me at least once a week, Lieutenant." As the words went through her mind, another spasm of pain hit her, and she clenched her fists tightly on the sheets again. It went away, and she took a deep breath. Everything had been fine until today. And then, only half an hour ago, a meteor had hit the radar room. It had been only a tiny bit of rock, no bigger than a twenty-two bullet, and it hadn't been traveling more than ten miles per second, but it had managed to punch its way through the shielding of the station. The self-sealing walls had closed the tiny hole quickly, but even in that short time, a lot of air had gone whistling out into the vacuum of space. The depressurization hadn't hurt her too much, but the shock had been enough to start labor. The baby was going to come two months early. She relaxed a little more, waiting for the next pain. There was nothing to worry about; she had absolute faith in the red-haired major. The major himself was not so sure. He sat in his office, massaging his fingertips and looking worriedly at the clock on the wall. The Chief Nurse at a nearby desk took off her glasses and looked at him speculatively. "Something wrong, doctor?" "Incubator," he said, without taking his eyes off the clock. "I beg your pardon?" "Incubator. We can't deliver a seven-month preemie without an incubator." The nurse's eyes widened. "Good Lord! I never thought of that! What are you going to do?" "Right now, I can't do anything. I can't beam a radio message through to the Earth. But as soon as we get within radio range of White Sands, I'll ask them to send up an emergency rocket with an incubator. But—" "But what?" "Will we have time? The pains are coming pretty fast now. It will be at least three hours before they can get a ship up here. If they miss us on the next time around, it'll be five hours. She can't hold out that long." The Chief Nurse turned her eyes to the slowly moving second hand of the wall clock. She could feel a lump in her throat. Major Banes was in the Communications Center a full five minutes before the coastline of California appeared on the curved horizon of the globe beneath them. He had spent the hour typing out a complete report of what had happened to Alice Britton and a list of what he needed. He handed it to the teletype operator and paced the floor impatiently as he waited for the answer. When the receiver teletype began clacking softly, he leaned over the page, waiting anxiously for every word. WHITE SANDS ROCKET BASE 4 JULY 1984 0913 HRS URGENT TO: MAJ PETER BANES (MC) 0-266118 SS-1 MEDICAL OFFICER FROM: GEN DAVID BARRETT 0-199515 COMMANDING WSRB ROCKET. ORBIT NOW BEING COMPUTED FOR RENDEZVOUS WITH SS-1 AS OF NEXT PASSAGE ABOVE USA. CAPT. JAMES BRITTON PILOTING. MEDICS LOADING SHIP TWELVE WITH INCUBATOR AND OTHER SUPPLIES. BASE OBSTETRICIAN LT COL GATES ALSO COMING TO ASSIST IN DELIVERY. HANG ON. OVER. Banes nodded and turned to the operator. "I want a direct open telephone line to my office in case I have to get another message to the base before we get out of range again." He turned and left through the heavy door. Each room of the space station was protected by airtight doors and individual heating units; if some accident, such as a really large meteor hit, should release the air from one room, nearby rooms would be safe. Banes' next stop was the hospital ward. Alice Britton was resting quietly, but there were lines of strain around her eyes which hadn't been there an hour before. "How's it coming, Lieutenant?" She smiled, but another spasm hit her before she could answer. After a time, she said: "I'm doing fine, but you look as if you'd been through the mill. What's eating you?" He forced a nervous smile. "Nothing but the responsibility. You're going to be a very famous woman, you know. You'll be the mother of the first child born in space. And it's my job to see to it that you're both all right." She grinned. "Another Dr. Dafoe?" "Something on that order, I suppose. But it won't be all my glory. Colonel Gates, the O.B. man, was supposed to come up for the delivery in September, so when White Sands contacted us, they said he was coming immediately." He paused, and a genuine smile crossed his face. "Your husband is bringing him up." "Jim! Coming up here? Wonderful! But I'm afraid the colonel will be too late. This isn't going to last that long." Banes had to fight hard to keep his face smiling when she said that, but he managed an easy nod. "We'll see. Don't hurry it, though. Let nature take its course. I'm not such a glory hog that I'd not let Gates have part of it—or all of it, for that matter. Relax and take it easy." He went on talking, trying to keep the conversation light, but his eyes kept wandering to his wristwatch, timing Alice's pain intervals. They were coming too close together to suit him. There was a faint rap, and the heavy airtight door swung open to admit the Chief Nurse. "There's a message for you in your office, doctor. I'll send a nurse in to be with her." He nodded, then turned back to Alice. "Stiff uppah lip, and all that sort of rot," he said in a phony British accent. "Oh, raw ther , old chap," she grinned. Back in his office, Banes picked up the teletype flimsy. WHITE SANDS ROCKET BASE 4 JULY 1984 0928 HRS URGENT TO: MAJ PETER BANES (MC) 0-266118 SS-1 MEDICAL OFFICER FROM: GEN DAVID BARRETT 0-199515 COMMANDING WSRB ROCKET. ORBIT COMPUTED FOR RENDEZVOUS AT 1134 HRS MST. CAPT BRITTON SENDS PERSONAL TO LT BRITTON AS FOLLOWS: HOLD THE FORT, BABY, THE WHOLE WORLD IS PRAYING FOR YOU. OUT. Banes sat on the edge of his desk, pounding a fist into the palm of his left hand. "Two hours. It isn't soon enough. She'll never hold out that long. And we don't have an incubator." His voice was a clipped monotone, timed with the rhythmic slamming of his fist. The Chief Nurse said: "Can't we build something that will do until the rocket gets here?" Banes looked at her, his face expressionless. "What would we build it out of? There's not a spare piece of equipment in the station. It costs money to ship material up here, you know. Anything not essential is left on the ground." The phone rang. Banes picked it up and identified himself. The voice at the other end said: "This is Communications, Major. I tape recorded all the monitor pickups from the Earth radio stations, and it looks as though the Space Service has released the information to the public. Lieutenant Britton's husband was right when he said the whole world's praying for her. Do you want to hear the tapes?" "Not now, but thanks for the information." He hung up and looked into the Chief Nurse's eyes. "They've released the news to the public." She frowned. "That really puts you on the spot. If the baby dies, they'll blame you." Banes slammed his fist to the desk. "Do you think I give a tinker's dam about that? I'm interested in saving a life, not in worrying about what people may think!" "Yes, sir. I just thought—" "Well, think about something useful! Think about how we're going to save that baby!" He paused as he saw her eyes. "I'm sorry, Lieutenant. My nerves are all raw, I guess. But, dammit, my field is space medicine. I can handle depressurization, space sickness, and things like that, but I don't know anything about babies! I know what I read in medical school, and I watched a delivery once, but that's all I know. I don't even have any references up here; people aren't supposed to go around having babies on a space station!" "It's all right, doctor. Shall I prepare the delivery room?" His laugh was hard and short. "Delivery room! I wish to Heaven we had one! Prepare the ward room next to the one she's in now, I guess. It's the best we have. "So help me Hannah, I'm going to see some changes made in regulations! A situation like this won't happen again!" The nurse left quietly. She knew Banes wasn't really angry at the Brittons; it was simply his way of letting off steam to ease the tension within him. The slow, monotonous rotation of the second hand on the wall clock seemed to drag time grudgingly along with it. Banes wished he could smoke to calm his raw nerves, but it was strictly against regulations. Air was too precious to be used up by smoking. Every bit of air on board had had to be carried up in rockets when the station was built in space. The air purifiers in the hydroponics section could keep the air fresh enough for breathing, but fire of any kind would overtax the system, leaving too little oxygen in the atmosphere. It was a few minutes of ten when he decided he'd better get back to Alice Britton. She was trying to read a book between spasms, but she wasn't getting much read. She dropped it to the floor when he came in. "Am I glad to see you! It won't be long now." She looked at him analytically. "Say! Just what is eating you? You look more haggard than I do!" Again he tried to force a smile, but it didn't come off too well. "Nothing serious. I just want to make sure everything comes out all right." She smiled. "It will. You're all set. You ordered the instruments months ago. Or did you forget something?" That hit home, but he just grinned feebly. "I forgot to get somebody to boil water." "Whatever for?" "Coffee, of course. Didn't you know that? Papa always heats up the water; that keeps him out of the way, and the doctor has coffee afterwards." Alice's hands grasped the sheet again, and Banes glanced at his watch. Ninety seconds! It was long and hard. When the pain had ebbed away, he said: "We've got the delivery room all ready. It won't be much longer now." "I'll say it won't! How about the incubator?" There was a long pause. Finally, he said softly: "There isn't any incubator. I didn't take the possibility of a premature delivery into account. It's my fault. I've done what I could, though; the ship is bringing one up. I—I think we'll be able to keep the child alive until—" He stopped. Alice was bubbling up with laughter. "Lieutenant! Lieutenant Britton! Alice! This is no time to get hysterical! Stop it!" Her laughter slowed to a chuckle. " Me get hysterical! That's a good one! What about you? You're so nervous you couldn't sip water out of a bathtub without spilling it!" He blinked. "What do you mean?" Another pain came, and he had to wait until it was over before he got her answer. "Doctor," she said, "I thought you would have figured it out. Ask yourself just one question. Ask yourself, 'Why is a space station like an incubator?'" Space Ship Twelve docked at Space Station One at exactly eleven thirty-four, and two men in spacesuits pushed a large, bulky package through the airlock. Major Peter Banes, haggard but smiling, met Captain Britton in the corridor as he and the colonel entered the hospital ward. Banes nodded to Colonel Gates, then turned to Britton. "I don't know whether to congratulate you or take a poke at you, Captain, but I suppose congratulations come first. Your son, James Edward Britton II, is doing fine, thank you." "You mean— already ?" The colonel said nothing, but he raised an eyebrow. "Over an hour ago," said Banes. "But—but—the incubator—" Banes' grin widened. "We'll put the baby in it, now that we've got it, but it really isn't necessary. Your wife figured that one out. A space station is a kind of incubator itself, you see. It protects us poor, weak humans from the terrible conditions of space. So all we had to do was close up one of the airtight rooms, sterilize it, warm it up, and put in extra oxygen from the emergency tanks. Young James is perfectly comfortable." "Excellent, Major!" said the colonel. "Don't thank me. It was Captain Britton's wife who—" But Captain Britton wasn't listening any more. He was headed toward his wife's room at top speed. | B. A rocket base in New Mexico. |
How does Terry's mother's description of her son not match the reporter's preconceived image?
A. He is reserved and has difficulty making friends
B. He is an average American boy
C. He did not perform well in school
D. He preferred athletics over academics
| STAR MOTHER By ROBERT F. YOUNG A touching story of the most enduring love in all eternity. That night her son was the first star. She stood motionless in the garden, one hand pressed against her heart, watching him rise above the fields where he had played as a boy, where he had worked as a young man; and she wondered whether he was thinking of those fields now, whether he was thinking of her standing alone in the April night with her memories; whether he was thinking of the verandahed house behind her, with its empty rooms and silent halls, that once upon a time had been his birthplace. Higher still and higher he rose in the southern sky, and then, when he had reached his zenith, he dropped swiftly down past the dark edge of the Earth and disappeared from sight. A boy grown up too soon, riding round and round the world on a celestial carousel, encased in an airtight metal capsule in an airtight metal chariot ... Why don't they leave the stars alone? she thought. Why don't they leave the stars to God? The general's second telegram came early the next morning: Explorer XII doing splendidly. Expect to bring your son down sometime tomorrow . She went about her work as usual, collecting the eggs and allocating them in their cardboard boxes, then setting off in the station wagon on her Tuesday morning run. She had expected a deluge of questions from her customers. She was not disappointed. "Is Terry really way up there all alone, Martha?" "Aren't you scared , Martha?" "I do hope they can get him back down all right, Martha." She supposed it must have given them quite a turn to have their egg woman change into a star mother overnight. She hadn't expected the TV interview, though, and she would have avoided it if it had been politely possible. But what could she do when the line of cars and trucks pulled into the drive and the technicians got out and started setting up their equipment in the backyard? What could she say when the suave young man came up to her and said, "We want you to know that we're all very proud of your boy up there, ma'am, and we hope you'll do us the honor of answering a few questions." Most of the questions concerned Terry, as was fitting. From the way the suave young man asked them, though, she got the impression that he was trying to prove that her son was just like any other average American boy, and such just didn't happen to be the case. But whenever she opened her mouth to mention, say, how he used to study till all hours of the night, or how difficult it had been for him to make friends because of his shyness, or the fact that he had never gone out for football—whenever she started to mention any of these things, the suave young man was in great haste to interrupt her and to twist her words, by requestioning, into a different meaning altogether, till Terry's behavior pattern seemed to coincide with the behavior pattern which the suave young man apparently considered the norm, but which, if followed, Martha was sure, would produce not young men bent on exploring space but young men bent on exploring trivia. A few of the questions concerned herself: Was Terry her only child? ("Yes.") What had happened to her husband? ("He was killed in the Korean War.") What did she think of the new law granting star mothers top priority on any and all information relating to their sons? ("I think it's a fine law ... It's too bad they couldn't have shown similar humanity toward the war mothers of World War II.") It was late in the afternoon by the time the TV crew got everything repacked into their cars and trucks and made their departure. Martha fixed herself a light supper, then donned an old suede jacket of Terry's and went out into the garden to wait for the sun to go down. According to the time table the general had outlined in his first telegram, Terry's first Tuesday night passage wasn't due to occur till 9:05. But it seemed only right that she should be outside when the stars started to come out. Presently they did, and she watched them wink on, one by one, in the deepening darkness of the sky. She'd never been much of a one for the stars; most of her life she'd been much too busy on Earth to bother with things celestial. She could remember, when she was much younger and Bill was courting her, looking up at the moon sometimes; and once in a while, when a star fell, making a wish. But this was different. It was different because now she had a personal interest in the sky, a new affinity with its myriad inhabitants. And how bright they became when you kept looking at them! They seemed to come alive, almost, pulsing brilliantly down out of the blackness of the night ... And they were different colors, too, she noticed with a start. Some of them were blue and some were red, others were yellow ... green ... orange ... It grew cold in the April garden and she could see her breath. There was a strange crispness, a strange clarity about the night, that she had never known before ... She glanced at her watch, was astonished to see that the hands indicated two minutes after nine. Where had the time gone? Tremulously she faced the southern horizon ... and saw her Terry appear in his shining chariot, riding up the star-pebbled path of his orbit, a star in his own right, dropping swiftly now, down, down, and out of sight beyond the dark wheeling mass of the Earth ... She took a deep, proud breath, realized that she was wildly waving her hand and let it fall slowly to her side. Make a wish! she thought, like a little girl, and she wished him pleasant dreams and a safe return and wrapped the wish in all her love and cast it starward. Sometime tomorrow, the general's telegram had said— That meant sometime today! She rose with the sun and fed the chickens, fixed and ate her breakfast, collected the eggs and put them in their cardboard boxes, then started out on her Wednesday morning run. "My land, Martha, I don't see how you stand it with him way up there! Doesn't it get on your nerves ?" ("Yes ... Yes, it does.") "Martha, when are they bringing him back down?" ("Today ... Today !") "It must be wonderful being a star mother, Martha." ("Yes, it is—in a way.") Wonderful ... and terrible. If only he can last it out for a few more hours, she thought. If only they can bring him down safe and sound. Then the vigil will be over, and some other mother can take over the awesome responsibility of having a son become a star— If only ... The general's third telegram arrived that afternoon: Regret to inform you that meteorite impact on satellite hull severely damaged capsule-detachment mechanism, making ejection impossible. Will make every effort to find another means of accomplishing your son's return. Terry!— See the little boy playing beneath the maple tree, moving his tiny cars up and down the tiny streets of his make-believe village; the little boy, his fuzz of hair gold in the sunlight, his cherub-cheeks pink in the summer wind— Terry!— Up the lane the blue-denimed young man walks, swinging his thin tanned arms, his long legs making near-grownup strides over the sun-seared grass; the sky blue and bright behind him, the song of cicada rising and falling in the hazy September air— Terry ... —probably won't get a chance to write you again before take-off, but don't worry, Ma. The Explorer XII is the greatest bird they ever built. Nothing short of a direct meteorite hit can hurt it, and the odds are a million to one ... Why don't they leave the stars alone? Why don't they leave the stars to God? The afternoon shadows lengthened on the lawn and the sun grew red and swollen over the western hills. Martha fixed supper, tried to eat, and couldn't. After a while, when the light began to fade, she slipped into Terry's jacket and went outside. Slowly the sky darkened and the stars began to appear. At length her star appeared, but its swift passage blurred before her eyes. Tires crunched on the gravel then, and headlights washed the darkness from the drive. A car door slammed. Martha did not move. Please God , she thought, let it be Terry , even though she knew that it couldn't possibly be Terry. Footsteps sounded behind her, paused. Someone coughed softly. She turned then— "Good evening, ma'am." She saw the circlet of stars on the gray epaulet; she saw the stern handsome face; she saw the dark tired eyes. And she knew. Even before he spoke again, she knew— "The same meteorite that damaged the ejection mechanism, ma'am. It penetrated the capsule, too. We didn't find out till just a while ago—but there was nothing we could have done anyway ... Are you all right, ma'am?" "Yes. I'm all right." "I wanted to express my regrets personally. I know how you must feel." "It's all right." "We will, of course, make every effort to bring back his ... remains ... so that he can have a fitting burial on Earth." "No," she said. "I beg your pardon, ma'am?" She raised her eyes to the patch of sky where her son had passed in his shining metal sarcophagus. Sirius blossomed there, blue-white and beautiful. She raised her eyes still higher—and beheld the vast parterre of Orion with its central motif of vivid forget-me-nots, its far-flung blooms of Betelguese and Rigel, of Bellatrix and Saiph ... And higher yet—and there flamed the exquisite flower beds of Taurus and Gemini, there burgeoned the riotous wreath of the Crab; there lay the pulsing petals of the Pleiades ... And down the ecliptic garden path, wafted by a stellar breeze, drifted the ocher rose of Mars ... "No," she said again. The general had raised his eyes, too; now, slowly, he lowered them. "I think I understand, ma'am. And I'm glad that's the way you want it ... The stars are beautiful tonight, aren't they." "More beautiful than they've ever been," she said. After the general had gone, she looked up once more at the vast and variegated garden of the sky where her son lay buried, then she turned and walked slowly back to the memoried house. THE END Transcriber's Note: This etext was produced from Amazing Stories January 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. He is reserved and has difficulty making friends |
What model are the text features used in to provide predictions? | ### Introduction
Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obvious which political bias an author has. In other cases some expertise is required to judge the political bias of a text. When dealing with large amounts of text however there are simply not enough experts to examine all possible sources and publications. Assistive technology can help in this context to try and obtain a more unbiased sample of information. Ideally one would choose for each topic a sample of reports from the entire political spectrum in order to form an unbiased opinion. But ordering media content with respect to the political spectrum at scale requires automated prediction of political bias. The aim of this study is to provide empirical evidence indicating that leveraging open data sources of german texts, automated political bias prediction is possible with above chance accuracy. These experimental results confirm and extend previous findings BIBREF0 , BIBREF1 ; a novel contribution of this work is a proof of concept which applies this technology to sort news article recommendations according to their political bias. When human experts determine political bias of texts they will take responsibility for what they say about a text, and they can explain their decisions. This is a key difference to many statistical learning approaches. Not only is the responsibility question problematic, it can also be difficult to interpret some of the decisions. In order to validate and explain the predictions of the models three strategies that allow for better interpretations of the models are proposed. First the model misclassifications are related to changes in party policies. Second univariate measures of correlation between text features and party affiliation allow to relate the predictions to the kind of information that political experts use for interpreting texts. Third sentiment analysis is used to investigate whether this aspect of language has discriminatory power. In the following sec:related briefly surveys some related work, thereafter sec:data gives an overview of the data acquisition and preprocessing methods, sec:model presents the model, training and evaluation procedures; in sec:results the results are discussed and sec:conclusion concludes with some interpretations of the results and future research directions. ### Related Work
Throughout the last years automated content analyses for political texts have been conducted on a variety of text data sources (parliament data blogs, tweets, news articles, party manifestos) with a variety of methods, including sentiment analysis, stylistic analyses, standard bag-of-word (BOW) text feature classifiers and more advanced natural language processing tools. While a complete overview is beyond the scope of this work, the following paragraphs list similarities and differences between this study and previous work. For a more complete overview we refer the reader to BIBREF2 , BIBREF3 . A similar approach to the one presented here was taken in BIBREF0 . The authors extracted BOW feature vectors and applied linear classifiers to predict political party affiliation of US congress speeches. They used data from the two chambers of the US congress, House and Senat, in order to assess generalization performance of a classifier trained on data from one chamber and tested on data from another. They found that accuracies of the model when trained on one domain and tested on another were significantly decreased. Generalization was also affected by the time difference between the political speeches used for training and those used for testing. Other work has focused on developing dedicated methods for predicting political bias. Two popular methods are WordFish BIBREF4 and WordScores BIBREF5 , or improved versions thereof, see e.g. BIBREF6 . These approaches have been very valuable for a posteriori analysis of historical data but they do not seem to be used as much for analyses of new data in a predictive analytics setting. Moreover direct comparisons of the results obtained with these so called scaling methods with the results of the present study or those of studies as BIBREF0 are difficult, due to the different modeling and evaluation approaches: Validations of WordFish/WordScore based analyses often compare parameter estimates of the different models rather than predictions of these models on held-out data with respect to the same type of labels used to train the models. Finally Hirst et al conducted a large number of experiments on data from the Canadian parliament and the European parliament; these experiments can be directly compared to the present study both in terms of methodology but also with respect to their results BIBREF1 . The authors show that a linear classifier trained on parliament speeches uses language elements of defense and attack to classify speeches, rather than ideological vocabulary. The authors also argue that emotional content plays an important role in automatic analysis of political texts. Furthermore their results show a clear dependency between length of a political text and the accuracy with which it can be classified correctly. Taken together, there is a large body of literature in this expanding field in which scientists from quantitative empirical disciplines as well as political science experts collaborate on the challenging topic of automated analysis of political texts. Except for few exceptions most previous work has focused on binary classification or on assignment of a one dimensional policy position (mostly left vs right). Yet many applications require to take into account more subtle differences in political policies. This work focuses on more fine grained political view prediction: for one, the case of the german parliament is more diverse than two parliament systems, allowing for a distinction between more policies; second the political view labels considered are more fine grained than in previous studies. While previous studies used such labels only for partitioning training data BIBREF4 (which is not possible at test time in real-world applications where these labels are not known) the experiments presented in this study directly predict these labels. Another important contribution of this work is that many existing studies are primarily concerned with a posteriori analysis of historical data. This work aims at prediction of political bias on out-of-domain data with a focus on the practical application of the model on new data, for which a prototypical web application is provided. The experiments on out-of-domain generalization complement the work of BIBREF0 , BIBREF1 with results from data of the german parliament and novel sentiment analyses. ### Data Sets and Feature Extraction
All experiments were run on publicly available data sets of german political texts and standard libraries for processing the text. The following sections describe the details of data acquisition and feature extraction. ### Data
Annotated political text data was obtained from two sources: a) the discussions and speeches held in the german parliament (Bundestag) and b) all manifesto texts of parties running for election in the german parliament in the current 18th and the last, 17th, legislation period. Parliament texts are annotated with the respective party label, which we take here as a proxy for political bias. The texts of parliament protocols are available through the website of the german bundestag; an open source API was used to query the data in a cleaned and structured format. In total 22784 speeches were extracted for the 17th legislative period and 11317 speeches for the 18th period, queried until March 2016. For party manifestos another openly accessible API was used, provided by the Wissenschaftszentrum Berlin (WZB). The API is released as part of the Manifestoproject BIBREF7 . The data released in this project comprises the complete manifestos for each party that ran for election enriched with annotations by political experts. Each sentence (in some cases also parts of sentences) is annotated with one of 56 political labels. Examples of these labels are pro/contra protectionism, decentralism, centralism, pro/contra welfare; for a complete list and detailed explanations on how the annotators were instructed see BIBREF8 . The set of labels was developed by political scientists at the WZB and released for public use. All manifestos of parties that were running for election in this and the last legislative period were obtained. In total this resulted in 29451 political statements that had two types of labels: First the party affiliation of each political statement; this label was used to evaluate the party evaluation classifiers trained on the parliament speeches. For this purpose the data acquisition was constrained to only those parties that were elected into the parliament. Next to the party affiliation the political view labels were extracted. For the analyses based on political view labels all parties were considered, also those that did not make it into the parliament. The length of each annotated statement in the party manifestos was rather short. The longest statement was 522 characters long, the 25%/50%/75% percentiles were 63/95/135 characters. Measured in words the longest data point was 65 words and the 25%/50%/75% percentiles were 8/12/17 words, respectively. This can be considered as a very valuable property of the data set, because it allows a fine grained resolution of party manifestos. However for a classifier (as well as for humans) such short sentences can be rather difficult to classify. In order to obtain less 'noisy' data points from each party – for the party affiliation task only – all statements were aggregated into political topics using the manifesto code labels. Each political view label is a three digit code, the first digit represents the political domain. In total there were eight political domains (topics): External Relations, Freedom and Democracy, Political System, Economy, Welfare and Quality of Life, Fabric of Society, Social Groups and a topic undefined, for a complete list see also BIBREF8 . These 8 topics were used to aggregate all statements in each manifesto into topics. Most party manifestos covered all eight of them, some party manifestos in the 17th Bundestag only covered seven. ### Bag-of-Words Vectorization
First each data set was segmented into semantic units; in the case of parliament discussions this were the speeches, in the case of the party manifesto data semantic units were the sentences or sentence parts associated with one of the 56 political view labels. Parliament speeches were often interrupted; in this case each uninterrupted part of a speech was considered a semantic unit. Strings of each semantic unit were tokenised and transformed into bag-of-word vectors as implemented in scikit-learn BIBREF9 . The general idea of bag-of-words vectors is to simply count occurrences of words (or word sequences, also called n-grams) for each data point. A data point is usually a document, here it is the semantic units of parliament speeches and manifesto sentences, respectively. The text of each semantic unit is transformed into a vector INLINEFORM0 where INLINEFORM1 is the size of the dictionary; the INLINEFORM2 th entry of INLINEFORM3 contains the (normalized) count of the INLINEFORM4 th word (or sequence of words) in our dictionary. Several options for vectorizing the speeches were tried, including term-frequency-inverse-document-frequency normalisation, n-gram patterns up to size INLINEFORM5 and several cutoffs for discarding too frequent and too infrequent words. All of these hyperparameters were subjected to hyperparameter optimization as explained in sec:crossvalidation. ### Classification Model and Training Procedure
Bag-of-words feature vectors were used to train a multinomial logistic regression model. Let INLINEFORM0 be the true label, where INLINEFORM1 is the total number of labels and INLINEFORM2 is the concatenation of the weight vectors INLINEFORM3 associated with the INLINEFORM4 th party then DISPLAYFORM0 We estimated INLINEFORM0 using quasi-newton gradient descent. The optimization function was obtained by adding a penalization term to the negative log-likelihood of the multinomial logistic regression objective and the optimization hence found the INLINEFORM1 that minimized DISPLAYFORM0 Where INLINEFORM0 denotes the Frobenius Norm and INLINEFORM1 is a regularization parameter controlling the complexity of the model. The regularization parameter was optimized on a log-scaled grid from INLINEFORM2 . The performance of the model was optimized using the classification accuracy, but we also report all other standard measures, precision ( INLINEFORM3 ), recall ( INLINEFORM4 ) and f1-score ( INLINEFORM5 ). Three different classification problems were considered: Party affiliation is a five class problem for the 17th legislation period, and a four class problem for the 18th legislation period. Political view classification is based on the labels of the manifesto project, see sec:data and BIBREF8 . For each of first two problems, party affiliation and government membership prediction, classifiers were trained on the parliament speeches. For the third problem classifiers were trained only on the manifesto data for which political view labels were available. ### Optimisation of Model Parameters
The model pipeline contained a number of hyperparameters that were optimised using cross-validation. We first split the training data into a training data set that was used for optimisation of hyperparameters and an held-out test data set for evaluating how well the model performs on in-domain data; wherever possible the generalisation performance of the models was also evaluated on out-of domain data. Hyperparameters were optimised using grid search and 3-fold cross-validation within the training set only: A cross-validation split was made to obtain train/test data for the grid search and for each setting of hyperparameters the entire pipeline was trained and evaluated – no data from the in-domain evaluation data or the out-of-domain evaluation data were used for hyperparameter optimisation. For the best setting of all hyperparameters the pipeline was trained again on all training data and evaluated on the evaluation data sets. For party affiliation prediction and government membership prediction the training and test set were 90% and 10%, respectively, of all data in a given legislative period. Out-of-domain evaluation data were the texts from party manifestos. For the political view prediction setting there was no out-of-domain evaluation data, so all labeled manifesto sentences in both legislative periods were split into a training and evaluation set of 90% (train) and 10% (evaluation). ### Sentiment analysis
A publicly available key word list was used to extract sentiments BIBREF10 . A sentiment vector INLINEFORM0 was constructed from the sentiment polarity values in the sentiment dictionary. The sentiment index used for attributing positive or negative sentiment to a text was computed as the cosine similarity between BOW vectors INLINEFORM1 and INLINEFORM2 DISPLAYFORM0 ### Analysis of bag-of-words features
While interpretability of linear models is often propagated as one of their main advantages, doing so naively without modelling the noise covariances can lead to wrong conclusions, see e.g. BIBREF11 , BIBREF12 ; interpreting coefficients of linear models (independent of the regularizer used) implicitly assumes uncorrelated features; this assumption is violated by the text data used in this study. Thus direct interpretation of the model coefficients INLINEFORM0 is problematic. In order to allow for better interpretation of the predictions and to assess which features are discriminative correlation coefficients between each word and the party affiliation label were computed. The words corresponding to the top positive and negative correlations are shown in sec:wordpartycorrelations. ### Results
The following sections give an overview of the results for all political bias prediction tasks. Some interpretations of the results are highlighted and a web application of the models is presented at the end of the section. ### Predicting political party affiliation
The results for the political party affiliation prediction on held-out parliament data and on evaluation data are listed in tab:results17 for the 17th Bundestag and in tab:results18 for the 18th Bundestag, respectively. Shown are the evaluation results for in-domain data (held-out parliament speech texts) as well as the out-of-domain data; the party manifesto out-of-domain predictions were made on the sentence level. When predicting party affiliation on text data from the same domain that was used for training the model, average precision and recall values of above 0.6 are obtained. These results are comparable to those of BIBREF1 who report a classification accuracy of 0.61 on a five class problem of prediction party affiliation in the European parliament; the accuracy for the 17th Bundestag is 0.63, results of the 18th Bundestag are difficult to compare as the number of parties is four and the legislation period is not finished yet. For out-of domain data the models yield significantly lower precision and recall values between 0.3 and 0.4. This drop in out of domain prediction accuracy is in line with previous findings BIBREF0 . A main factor that made the prediction on the out-of-domain prediction task particularly difficult is the short length of the strings to be classified, see also sec:data. In order to investigate whether this low out-of-domain prediction performance was due the domain difference (parliament speech vs manifesto data) or due to the short length of the data points, the manifesto data was aggregated based on the topic. The manifesto code political topics labels were used to concatenate texts of each party to one of eight topics, see sec:data. The topic level results are shown in tab:resultstopic and tab:confusiontopic and demonstrate that when the texts to be classified are sufficiently long and the word count statistics are sufficiently dense the classification performance on out of domain data can achieve in the case of some parties reliably precision and recall values close to 1.0. This increase is in line with previous findings on the influence of text length on political bias prediction accuracy BIBREF1 . In order to investigate the errors the models made confusion matrices were extracted for the predictions on the out-of-domain evaluation data for sentence level predictions (see tab:confusion) as well as topic level predictions (see tab:confusiontopic). One example illustrates that the mistakes the model makes can be associated with changes in the party policy. The green party has been promoting policies for renewable energy and against nuclear energy in their manifestos prior to both legislative periods. Yet the statements of the green party are more often predicted to be from the government parties than from the party that originally promoted these green ideas, reflecting the trend that these legislative periods governing parties took over policies from the green party. This effect is even more pronounced in the topic level predictions: a model trained on data from the 18th Bundestag predicts all manifesto topics of the green party to be from one of the parties of the governing coalition, CDU/CSU or SPD. Next to the party affiliation labels also government membership labels were used to train models that predict whether or not a text is from a party that belonged to a governing coalition of the Bundestag. In tab:resultsbinary17 and tab:resultsbinary18 the results are shown for the 17th and the 18th Bundestag, respectively. While the in-domain evaluation precision and recall values reach values close to 0.9, the out-of-domain evaluation drops again to values between 0.6 and 0.7. This is in line with the results on binary classification of political bias in the Canadian parliament BIBREF0 . The authors report classification accuracies between 0.8 and 0.87, the accuracy in the 17th Bundestag was 0.85. While topic-level predictions were not performed in this binary setting, the party affiliation results in tab:resultstopic suggest that a similar increase in out-of-domain prediction accuracy could be achieved when aggregating texts to longer segments. ### Predicting political views
Parties change their policies and positions in the political spectrum. More reliable categories for political bias are party independent labels for political views, see sec:data. A separate suite of experiments was run to train and test the prediction performance of the text classifiers models described in sec:model. As there was no out-of-domain evaluation set available in this setting only evaluation error on in-domain data is reported. Note however that also in this experiment the evaluation data was never seen by any model during training time. In tab:resultsavgpoliticalview results for the best and worst classes, in terms of predictability, are listed along with the average performance metrics on all classes. Precision and recall values of close to 0.5 on average can be considered rather high considering the large number of labels. ### Correlations between words and parties
The 10 highest and lowest correlations between individual words and the party affiliation label are shown for each party in fig:partywordcorrelations. Correlations were computed on the data from the current, 18th, legislative period. Some unspecific stopwords are excluded. The following paragraphs highlight some examples of words that appear to be preferentially used or avoided by each respective party. Even though interpretations of these results are problematic in that they neglect the context in which these words were mentioned some interesting patterns can be found and related to the actual policies the parties are promoting. The left party mostly criticises measures that affect social welfare negatively, such as the Hartz IV program. Main actors that are blamed for decisions of the conservative governments by the left party are big companies (konzerne). Rarely the party addresses concerns related to security (sicherheit). The green party heavily criticised the secret negotiations about the TiSA agreement and insists in formal inquiries that the representatives of the green party put forward in this matter (fragen, anfragen). They also often ask questions related to army projects (Rüstungsprojekte, Wehrbericht) or the military development in east europe (Jalta). The social democrats often use words related to rights of the working class, as reflected by the heavy use of the International Labour Organisation (ILO) or rights of employes (Arbeitnehmerrechte). They rarely talk about competition (Wettbewerb) or climate change (klimapolitik). The conservative christian party often uses words related to a pro-economy attitude, such as competitiveness or (economic) development (Wettbewerbsfähigkeit, Entwicklung) and words related to security (Sicherheit). The latter could be related to the ongoing debates about whether or not the governments should be allowed to collect data and thus restrict fundamental civil rights in order to better secure the population. In contrast to the parties of the opposition, the conservatives rarely mention the word war (krieg) or related words. ### Speech sentiment correlates with political power
In order to investigate the features that give rise to the classifiers' performance the bag-of-words features were analysed with respect to their sentiment. The average sentiment of each political party is shown in fig:partysentiments. High values indicate more pronounced usage of positive words, whereas negative values indicate more pronounced usage of words associated with negative emotional content. The results show an interesting relationship between political power and sentiment. Political power was evaluated in two ways: a) in terms of the number of seats a party has and b) in terms of membership of the government. Correlating either of these two indicators of political power with the mean sentiment of a party shows a strong positive correlation between speech sentiment and political power. This pattern is evident from the data in fig:partysentiments and in tab:sentiments: In the current Bundestag, government membership correlates with positive sentiment with a correlation coefficient of 0.98 and the number of seats correlates with 0.89. Note that there is one party, the social democrats (SPD), which has many seats and switched from opposition to government with the 18th Bundestag: With its participation in the government the average sentiment of this party switched sign from negative to positive, suggesting that positive sentiment is a strong indicator of government membership. ### An example web application
To show an example use case of the above models a web application was implemented that downloads regularly all articles from some major german news paper websites and applies some simple topic modelling to them. For each news article topic, headlines of articles are plotted along with the predictions of the political view of an article and two labels derived deterministically from the 56 class output, a left right index and the political domain of a text, see BIBREF8 . Within each topic it is then possible to get an ordered (from left to right) overview of the articles on that topic. An example of one topic that emerged on March 31st is shown in fig:fipi. A preliminary demo is live at BIBREF13 and the code is available on github BIBREF14 . ### Conclusions, Limitations and Outlook
This study presents a simple approach for automated political bias prediction. The results of these experiments show that automated political bias prediction is possible with above chance accuracy in some cases. It is worth noting that even if the accuracies are not perfect, they are above chance and comparable with results of comparable studies BIBREF0 , BIBREF1 . While these results do not allow for usage in production systems for classification, it is well possible to use such a system as assistive technology for human annotators in an active learning setting. One of the main limiting factors of an automated political bias prediction system is the availability of training data. Most training data sets that are publicly available have an inherent bias as they are sampled from a different domain. This study tried to quantify the impact of this effect. For the cases in which evaluation data from two domains was available there was a pronounced drop in prediction accuracy between the in domain evaluation set and the out of domain evaluation set. This effect was reported previously for similar data, see e.g. BIBREF0 . Also the finding that shorter texts are more difficult to classify than longer texts is in line with previous studies BIBREF1 . When considering texts of sufficient length (for instance by aggregating all texts of a given political topic) classification performance improved and in some cases reliable predictions could be obtained even beyond the training text domain. Some aspects of these analyses could be interesting for social science researchers; three of these are highlighted here. First the misclassifications of a model can be related to the changes in policy of a party. Such analyses could be helpful to quantitatively investigate a change in policy. Second analysing the word-party correlations shows that some discriminative words can be related to the political views of a party; this allows for validation of the models by human experts. Third when correlating the sentiment of a speech with measures of political power there is a strong positive correlation between political power and positive sentiment. While such an insight in itself might seem not very surprising this quantifiable link between power and sentiment could be useful nonetheless: Sentiment analysis is a rather domain independent measure, it can be easily automated and scaled up to massive amounts of text data. Combining sentiment features with other measures of political bias could potentially help to alleviate some of the domain-adaptation problems encountered when applying models trained on parliament data to data from other domains. All data sets used in this study were publicly available, all code for experiments and the link to a live web application can be found online BIBREF14 . ### Acknowledgements
I would like to thank Friedrich Lindenberg for factoring out the https://github.com/bundestag/plpr-scraper from his bundestag project. Some backend configurations for the web application were taken from an earlier collaboration with Daniel Kirsch. Pola Lehmann and Michael Gaebler provided helpful feedback on an earlier version of the manuscript. Pola Lehman also helped with getting access to and documentation on the Manifestoproject data. Table 1. Classification performance on the party affiliation prediction problem for data from the 17th legislative period on test set and evaluation set, respectively. Predictions on the manifesto data was done on sentence level; N denotes number of data points in the evaluation set. Table 2. Classification performance on the party affiliation prediction problem for data from the 18th legislative period on test set and evaluation set, respectively. Predictions on the manifesto data was done on sentence level. Table 3. Topic level classification performance on the party affiliation prediction problem for data from the evaluation set (manifesto texts) of the 17th legislative period. In contrast to single sentence level predictions (see Table 1, Table 2, Table 4 for results and section 3 for topic definitions) the predictions made on topic level are reliable in many cases. Note that all manifesto topics of the green party in the 18th Bundestag are predicted to be from the parties of the governing coalition, CDU/CSU or SPD. Table 4. Confusion matrices (sentence level) for predictions on evaluation data (party manifestos); classifiers were trained on parliament speeches for the 17th legislative period (left) and 18th legislative period (right); the most prominent effect is the high likelihood for a party to be taken as the strongest, governing party, cdu/csu. This can be interpreted as a change in policies of the conservative party cdu/csu towards the policies of the green party. Table 5. Confusion matrices (topic level) for predictions on evaluation data (party manifestos) for classifiers trained on parliament speeches for the 17th legislative period (left) and 18th legislative period (right). Table 6. Classification performance on the binary prediction problem in the 17th legislative period, categorizing speeches into government (FDP/CDU/CSU) and opposition (Linke, Grüne, SPD). Table 8. Classification performance of 56 political views, see section 3. Table 7. Classification performance on the binary prediction problem in the 18th legislative period, categorizing speeches into government (SDP/CDU/CSU) and opposition (Linke, Grüne). Fig. 1. Correlations between words and party affiliation label for parliament speeches can help interpreting the features used by a predictive model. Shown are the top 10 positively and negatively correlated text features for the current Bundestag. For interpretations see subsection 5.3. Fig. 2. Speech sentiments computed for speeches of each party; parties are ordered according to the number of seats in the parliament. There is a trend for more positive speech content with more political power. Note that the SPD (red) switched from opposition to government in the 18th Bundestag: their seats in the parliament increased and the average sentiment of their speeches switched sign from negative to overall positive sentiment. Fig. 3. A screen shot of an example web application using the political view prediction combined with topic modelling to provide a heterogeneous overview of a topic. | multinomial logistic regression |
What do you think is most likely an accurate description of the rebellion?
A. It's probably on the right side of history, given the violence of the opposition.
B. It's only a disruption, stopping it is what will maximize the good in the world.
C. It's just as bad as what it's fighting, a peace treaty is the most likely and the best solution.
D. It's widely supported and few oppose it.
| MONOPOLY By Vic Phillips and Scott Roberts Sheer efficiency and good management can make a monopoly grow into being. And once it grows, someone with a tyrant mind is going to try to use it as a weapon if he can— [Transcriber's Note: This etext was produced from Astounding Science-Fiction April 1942. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] "That all, chief? Gonna quit now?" Brian Hanson looked disgustedly at Pete Brent, his lanky assistant. That was the first sign of animation he had displayed all day. "I am, but you're not," Hanson told him grimly. "Get your notes straightened up. Run those centrifuge tests and set up the still so we can get at that vitamin count early in the morning." "Tomorrow morning? Aw, for gosh sakes, chief, why don't you take a day off sometime, or better yet, a night off. It'd do you good to relax. Boy, I know a swell blonde you could go for. Wait a minute, I've got her radiophone number somewhere—just ask for Myrtle." Hanson shrugged himself out of his smock. "Never mind Myrtle, just have that equipment set up for the morning. Good night." He strode out of the huge laboratory, but his mind was still on the vitamin research they had been conducting, he barely heard the remarks that followed him. "One of these days the chief is going to have his glands catch up with him." "Not a chance," Pete Brent grunted. Brian Hanson wondered dispassionately for a moment how his assistants could fail to be as absorbed as he was by the work they were doing, then he let it go as he stepped outside the research building. He paused and let his eyes lift to the buildings that surrounded the compound. This was the administrative heart of Venus City. Out here, alone, he let his only known emotion sweep through him, pride. He had an important role in the building of this great new city. As head of the Venus Consolidated Research Organization, he was in large part responsible for the prosperity of this vigorous, young world. Venus Consolidated had built up this city and practically everything else that amounted to anything on this planet. True, there had been others, pioneers, before the company came, who objected to the expansion of the monopolistic control. But, if they could not realize that the company's regime served the best interests of the planet, they would just have to suffer the consequences of their own ignorance. There had been rumors of revolution among the disgruntled older families. He heard there had been killings, but that was nonsense. Venus Consolidated police had only powers of arrest. Anything involving executions had to be referred to the Interplanetary Council on Earth. He dismissed the whole business as he did everything else that did not directly influence his own department. He ignored the surface transport system and walked to his own apartment. This walk was part of a regular routine of physical exercise that kept his body hard and resilient in spite of long hours spent in the laboratory. As he opened the door of his apartment he heard the water running into his bath. Perfect timing. He was making that walk in precisely seven minutes, four and four-fifths seconds. He undressed and climbed into the tub, relaxing luxuriously in the exhilaration of irradiated water. He let all the problems of his work drift away, his mind was a peaceful blank. Then someone was hammering on his head. He struggled reluctantly awake. It was the door that was being attacked, not his head. The battering thunder continued persistently. He swore and sat up. "What do you want?" There was no answer; the hammering continued. "All right! All right! I'm coming!" He yelled, crawled out of the tub and reached for his bathrobe. It wasn't there. He swore some more and grabbed a towel, wrapping it inadequately around him; it didn't quite meet astern. He paddled wetly across the floor sounding like a flock of ducks on parade. Retaining the towel with one hand he inched the door cautiously open. "What the devil—" He stopped abruptly at the sight of a policeman's uniform. "Sorry, sir, but one of those rebels is loose in the Administration Center somewhere. We're making a check-up of all the apartments." "Well, you can check out; I haven't got any blasted rebels in here." The policeman's face hardened, then relaxed knowingly. "Oh, I see, sir. No rebels, of course. Sorry to have disturbed you. Have a good—Good night, sir," he saluted and left. Brian closed the door in puzzlement. What the devil had that flat-foot been smirking about? Well, maybe he could get his bath now. Hanson turned away from the door and froze in amazement. Through the open door of his bedroom he could see his bed neatly turned down as it should be, but the outline under the counterpane and the luxuriant mass of platinum-blond hair on the pillow was certainly no part of his regular routine. "Hello." The voice matched the calm alertness of a pair of deep-blue eyes. Brian just stared at her in numbed fascination. That was what the policeman had meant with his insinuating smirk. "Just ask for Myrtle." Pete Brent's joking words flashed back to him. Now he got it. This was probably the young fool's idea of a joke. He'd soon fix that. "All right, joke's over, you can beat it now." "Joke? I don't see anything funny, unless it's you and that suggestive towel. You should either abandon it or get one that goes all the way round." Brian slowly acquired a complexion suitable for painting fire plugs. "Shut up and throw me my dressing gown." He gritted. The girl swung her legs out of bed and Brian blinked; she was fully dressed. The snug, zippered overall suit she wore did nothing to conceal the fact that she was a female. He wrapped his bathrobe austerely around him. "Well, now what?" she asked and looked at him questioningly. "Well, what do you think?" he burst out angrily. "I'm going to finish my bath and I'd suggest you go down to the laboratory and hold hands with Pete. He'd appreciate it." He got the impression that the girl was struggling heroically to refrain from laughing and that didn't help his dignity any. He strode into the bathroom, slammed the door and climbed back into the bath. The door opened a little. "Well, good-by now." The girl said sweetly. "Remember me to the police force." "Get out of here!" he yelled and the door shut abruptly on a rippling burst of laughter. Damn women! It was getting so a man had to pack a gun with him or something. And Pete Brent. He thought with grim satisfaction of the unending extra work that was going to occur around the laboratory from now on. He sank back into the soothing liquid embrace of the bath and deliberately set his mind loose to wander in complete relaxation. A hammering thunder burst on the outer door. He sat up with a groan. "Lay off, you crazy apes!" he yelled furiously, but the pounding continued steadily. He struggled out of the bath, wrapped his damp bathrobe clammily around him and marched to the door with a seething fury of righteous anger burning within him. He flung the door wide, his mouth all set for a withering barrage, but he didn't get a chance. Four police constables and a sergeant swarmed into the room, shoving him away from the door. "Say! What the—" "Where is she?" the sergeant demanded. "Wherethehell's who?" "Quit stallin', bud. You know who. That female rebel who was in here." "Rebel? You're crazy! That was just ... Pete said ... rebel? Did you say rebel?" "Yeah, I said rebel, an' where is she?" "She ... why ... why ... she left, of course. You don't think I was going to have women running around in here, do you?" "She wuz in his bed when I seen her, sarge," one of the guards contributed. "But she ain't there now." "You don't think that I—" "Listen, bud, we don't do the thinkin' around here. You come on along and see the chief." Brian had had about enough. "I'm not going anywhere to see anybody. Maybe you don't know who I am. You can't arrest me." Brian Hanson, Chief of Research for Venus Consolidated, as dignified as possible in a damp bathrobe, glared out through the bars at a slightly bewildered Pete Brent. "What the devil do you want? Haven't you caused enough blasted trouble already?" "Me? For gosh sakes, chief—" "Yes, you! If sending that damn blonde to my apartment and getting me arrested is your idea of a joke—" "But, my gosh, I didn't send anybody, chief. And this is no joke. That wasn't Myrtle, that was Crystal James, old man James' daughter. They're about the oldest family on Venus. Police have been after her for months; she's a rebel and she's sure been raising plenty of hell around here. She got in and blew out the main communications control panel last night. Communications been tied up all day." Pete lowered his voice to an appreciative whisper, "Gosh, chief, I didn't know you had it in you. How long have you been in with that bunch? Is that girl as good-looking as they say she is?" "Now listen here, Brent. I don't know—" "Oh, it's all right, chief. You can trust me. I won't give you away." "There's nothing to give away, you fool!" Brian bellowed. "I don't know anything about any damn rebels. All I want is to get out of here—" "Gotcha, chief," Brent whispered understandingly. "I'll see if I can pass the word along." "Come here, you idiot!" Brian screamed after his erstwhile assistant. "Pipe down there, bud," a guard's voice cut in chillingly. Brian retired to his cell bunk and clutched his aching head in frustrated fury. For the nineteenth time Brian Hanson strode to the door of his cell and rattled the bars. "Listen here, guard, you've got to take a message to McHague. You can't hold me here indefinitely." "Shut up. Nobody ain't takin' no message to McHague. I don't care if you are—" Brian's eyes almost popped out as he saw a gloved hand reach around the guard's neck and jam a rag over his nose and mouth. Swift shadows moved expertly before his astonished gaze. Another guard was caught and silenced as he came around the end of the corridor. Someone was outside his cell door, a hooded figure which seemed, somehow, familiar. "Hello, pantless!" a voice breathed. He knew that voice! "What the devil are you doing here?" "Somebody by the name of Pete Brent tipped us off that you were in trouble because of me. But don't worry, we're going to get you out." "Damn that fool kid! Leave me alone. I don't want to get out of here that way!" he yelled wildly. "Guards! Help!" "Shut up! Do you want to get us shot?" "Sure I do. Guards! Guards!" Someone came running. "Guards are coming," a voice warned. He could hear the girl struggling with the lock. "Damn," she swore viciously. "This is the wrong key! Your goose is sure cooked now. Whether you like it or not, you'll hang with us when they find us trying to get you out of here." Brian felt as though something had kicked him in the stomach. She was right! He had to get out now. He wouldn't be able to explain this away. "Give me that key," he hissed and grabbed for it. He snapped two of the coigns off in the lock and went to work with the rest of the key. He had designed these escape-proof locks himself. In a few seconds the door swung open and they were fleeing silently down the jail corridor. The girl paused doubtfully at a crossing passage. "This way," he snarled and took the lead. He knew the ground plan of this jail perfectly. He had a moment of wonder at the crazy spectacle of himself, the fair-haired boy of Venus Consolidated, in his flapping bathrobe, leading a band of escaping rebels out of the company's best jail. They burst around a corner onto a startled guard. "They're just ahead of us," Brian yelled. "Come on!" "Right with you," the guard snapped and ran a few steps with them before a blackjack caught up with him and he folded into a corner. "Down this way, it's a short cut." Brian led the way to a heavily barred side door. The electric eye tripped a screaming alarm, but the broken key in Brian's hands opened the complicated lock in a matter of seconds. They were outside the jail on a side street, the door closed and the lock jammed immovably behind them. Sirens wailed. The alarm was out! The street suddenly burst into brilliance as the floodlights snapped on. Brian faltered to a stop and Crystal James pushed past him. "We've got reinforcements down here," she said, then skidded to a halt. Two guards barred the street ahead of them. Brian felt as though his stomach had fallen down around his ankles and was tying his feet up. He couldn't move. The door was jammed shut behind them, they'd have to surrender and there'd be no explaining this break. He started mentally cursing Pete Brent, when a projector beam slashed viciously by him. These guards weren't fooling! He heard a gasping grunt of pain as one of the rebels went down. They were shooting to kill. He saw a sudden, convulsive movement from the girl. A black object curved out against the lights. The sharp, ripping blast of an atomite bomb thundered along the street and slammed them to the ground. The glare left them blinded. He struggled to his feet. The guards had vanished, a shallow crater yawned in the road where they had been. "We've got to run!" the girl shouted. He started after her. Two surface transport vehicles waited around the corner. Brian and the rebels bundled into them and took away with a roar. The chase wasn't organized yet, and they soon lost themselves in the orderly rush of Venus City traffic. The two carloads of rebels cruised nonchalantly past the Administration Center and pulled into a private garage a little beyond. "What are we stopping here for?" Brian demanded. "We've got to get away." "That's just what we're doing," Crystal snapped. "Everybody out." The rebels piled out and the cars pulled away to become innocuous parts of the traffic stream. The rebels seemed to know where they were going and that gave them the edge on Brian. They followed Crystal down into the garage's repair pit. She fumbled in the darkness a moment, then a darker patch showed as a door swung open in the side of the pit. They filed into the solid blackness after her and the door thudded shut. The beam of a torch stabbed through the darkness and they clambered precariously down a steep, steel stairway. "Where the dickens are we?" Brian whispered hoarsely. "Oh, you don't have to whisper, we're safe enough here. This is one of the air shafts leading down to the old mines." "Old mines? What old mines?" "That's something you newcomers don't know anything about. This whole area was worked out long before Venus Consolidated came to the planet. These old tunnels run all under the city." They went five hundred feet down the air shaft before they reached a level tunnel. "What do we do? Hide here?" "I should say not. Serono Zeburzac, head of McHague's secret police will be after us now. We won't be safe anywhere near Venus City." "Don't be crazy. That Serono Zeburzac stuff is just a legend McHague keeps up to scare people with." "That's what you think," Crystal snapped. "McHague's legend got my father and he'll get all of us unless we run the whole company right off the planet." "Well, what the dickens does he look like?" Brian asked doubtfully. "I don't know, but his left hand is missing. Dad did some good shooting before he died," she said grimly. Brian was startled at the icy hardness of her voice. Two of the rebels pulled a screening tarpaulin aside and revealed one of the old-type ore cars that must have been used in the ancient mines. A brand-new atomic motor gleamed incongruously at one end. The rebels crowded into it and they went rumbling swiftly down the echoing passage. The lights of the car showed the old working, rotten and crumbling, fallen in in some places and signs of new work where the rebels had cleared away the debris of years. Brian struggled into a zippered overall suit as they followed a twisting, tortuous course for half an hour, switching from one tunnel to another repeatedly until he had lost all conception of direction. Crystal James, at the controls, seemed to know exactly where they were going. The tunnel emerged in a huge cavern that gloomed darkly away in every direction. The towering, massive remains of old machinery, eroded and rotten with age crouched like ancient, watching skeletons. "These were the old stamp mills," the girl said, and her voice seemed to be swallowed to a whisper in the vast, echoing darkness. Between two rows of sentinel ruins they came suddenly on two slim Venusian atmospheric ships. Dim light spilled over them from a ragged gash in the wall of the cavern. Brian followed Crystal into the smaller of the two ships and the rest of the rebels manned the other. "Wait a minute, how do we get out of here?" Brian demanded. "Through that hole up there," the girl said matter-of-factly. "You're crazy, you can't get through there." "Oh, yeah? Just watch this." The ship thundered to life beneath them and leaped off in a full-throttled take-off. "We're going to crash! That gap isn't wide enough!" The sides of the gap rushed in on the tips of the stubby wings. Brian braced himself for the crash, but it didn't come. At the last possible second, the ship rolled smoothly over. At the moment it flashed through the opening it was stood vertically on edge. Crystal held the ship in its roll and completed the maneuver outside the mountain while Brian struggled to get his internal economy back into some semblance of order. "That's some flying," he said as soon as he could speak. Crystal looked at him in surprise. "That's nothing. We Venusians fly almost as soon as we can walk." "Oh—I see," Brian said weakly and a few moments later he really did see. Two big, fast, green ships, carrying the insignia of the Venus Consolidated police, cruised suddenly out from a mountain air station. An aërial torpedo exploded in front of the rebel ship. Crystal's face set in grim lines as she pulled the ship up in a screaming climb. Brian got up off the floor. "You don't have to get excited like that," he complained. "They weren't trying to hit us." "That's what you think," Crystal muttered. "Those children don't play for peanuts." "But, girl, they're just Venus Consolidated police. They haven't got any authority to shoot anyone." "Authority doesn't make much difference to them," Crystal snapped bitterly. "They've been killing people all over the planet. What do you think this revolution is about?" "You must be mistak—" He slumped to the floor as Crystal threw the ship into a mad, rolling spin. A tremendous crash thundered close astern. "I guess that was a mistake!" Crystal yelled as she fought the controls. Brian almost got to his feet when another wild maneuver hurled him back to the floor. The police ship was right on their tail. The girl gunned her craft into a snap Immelmann and swept back on their pursuers, slicing in close over the ship. Brian's eyes bulged as he saw a long streak of paint and metal ripped off the wing of the police ship. He saw the crew battling their controls in startled terror. The ship slipped frantically away and fell into a spin. "That's them," Crystal said with satisfaction. "How are the others doing?" "Look! They're hit!" Brian felt sick. The slower rebel freight ship staggered drunkenly as a torpedo caught it and ripped away half a wing. It plunged down in flames with the white flowers of half a dozen parachutes blossoming around it. Brian watched in horror as the police ship came deliberately about. They heard its forward guns go into action. The bodies of the parachutists jerked and jumped like crazy marionettes as the bullets smashed into them. It was over in a few moments. The dead rebels drifted down into the mist-shrouded depths of the valley. "The dirty, murdering rats!" Brian's voice ripped out in a fury of outrage. "They didn't have a chance!" "Don't get excited," Crystal told him in a dead, flat voice. "That's just normal practice. If you'd stuck your nose out of your laboratory once in a while, you'd have heard of these things." "But why—" He ducked away instinctively as a flight of bullets spanged through the fuselage. "They're after us now!" Crystal's answer was to yank the ship into a rocketing climb. The police were watching for that. The big ship roared up after them. "Just follow along, suckers," Crystal invited grimly. She snapped the ship into a whip stall. For one nauseating moment they hung on nothing, then the ship fell over on its back and they screamed down in a terminal velocity dive, heading for the safety of the lower valley mists. The heavier police ship, with its higher wing-loading, could not match the maneuver. The rebel craft plunged down through the blinding fog. Half-seen, ghostly fingers of stone clutched up at them, talons of gray rock missed and fell away again as Crystal nursed the ship out of its dive. " Phew! " Brian gasped. "Well, we got away that time. How in thunder can you do it?" "Well, you don't do it on faith. Take a look at that fuel gauge! We may get as far as our headquarters—or we may not." For twenty long minutes they groped blindly through the fog, flying solely by instruments and dead reckoning. The needle of the fuel gauge flickered closer and closer to the danger point. They tore loose from the clinging fog as it swung firmly to "Empty." The drive sputtered and coughed and died. "That's figuring it nice and close," Crystal said in satisfaction. "We can glide in from here." "Into where?" Brian demanded. All he could see immediately ahead was the huge bulk of a mountain which blocked the entire width of the valley and soared sheer up to the high-cloud level. His eyes followed it up and up— "Look! Police ships. They've seen us!" "Maybe they haven't. Anyway, there's only one place we can land." The ship lunged straight for the mountain wall! "Are you crazy? Watch out—we'll crash!" "You leave the flying to me," Crystal snapped. She held the ship in its glide, aiming directly for the tangled foliage of the mountain face. Brian yelped and cowered instinctively back. The lush green of the mountainside swirled up to meet them. They ripped through the foliage—there was no crash. They burst through into a huge, brilliantly lighted cavern and settled to a perfect landing. Men came running. Crystal tumbled out of her ship. "Douse those lights," she shouted. "The police are outside." A tall, lean man with bulbous eyes and a face like a startled horse, rushed up to Crystal. "What do you mean by leading them here?" he yelled, waving his hands. "They jumped us when we had no fuel, and quit acting like an idiot." The man was shaking, his eyes looked wild. "They'll kill us. We've got to get out of here." "Wait, you fool. They may not even have seen us." But he was gone, running toward a group of ships lined up at the end of the cavern. "Who was that crazy coot and what is this place?" Brian demanded. "That was Gort Sterling, our leader," the girl said bitterly. "And this is our headquarters." One of the ships at the back of the cavern thundered to life, streaked across the floor and burst out through the opening Crystal's ship had left. "He hasn't got a chance! We'll be spotted for sure, now." The other rebels waited uncertainly, but not for long. There was the crescendoing roar of ships in a dive followed by the terrific crash of an explosion. "They got him!" Crystal's voice was a moan. "Oh, the fool, the fool!" "Sounded like more than one ship. They'll be after us, now. Is there any other way of getting out of this place?" "Not for ships. We'll have to walk and they'll follow us." "We've got to slow them down some way, then. I wonder how the devil they traced us? I thought we lost them in that fog." "It's that Serono Zeburzac, the traitor. He knows these mountains as well as we do." "How come?" "The Zeburzacs are one of the old families, but he sold out to McHague." "Well, what do we do now? Just stand here? It looks like everybody's leaving." "We might as well just wait," Crystal said hopelessly. "It won't do us any good to run out into the hills. Zeburzac and his men will follow." "We could slow them down some by swinging a couple of those ships around so their rocket exhausts sweep the entrance to the cavern," Brian suggested doubtfully. She looked at him steadily. "You sound like the only good rebel left. We can try it, anyway." They ran two ships out into the middle of the cavern, gunned them around and jockeyed them into position—not a moment too soon. Half a dozen police showed in brief silhouette as they slipped cautiously into the cavern, guns ready, expecting resistance. They met a dead silence. A score or more followed them without any attempt at concealment. Then Brian and Crystal cut loose with the drives of the two ships. Startled screams of agony burst from the crowded group of police as they were caught in the annihilating cross fire of roaring flame. They crisped and twisted, cooked to scorched horrors before they fell. A burst of thick, greasy smoke rushed out of the cavern. Two of the police, their clothes and flesh scorched and flaming, plunged as shrieking, living torches down the mountainside. Crystal was white and shaking, her face set in a mask of horror, as she climbed blindly from her ship. "Let's get away! I can smell them burning," she shuddered and covered her face with her hands. Brian grabbed her and shook her. "Snap out of it," he barked. "That's no worse than shooting helpless men in parachutes. We can't go, yet; we're not finished here." "Oh, let them shoot us! I can't go through that again!" "You don't have to. Wait here." He climbed back into one of the ships and cut the richness of the fuel mixture down till the exhaust was a lambent, shuddering stutter, verging on extinction. He dashed to the other ship and repeated the maneuver, fussing with the throttle till he had the fuel mixture adjusted to critical fineness. The beat of the stuttering exhaust seemed to catch up to the other and built to an aching pulsation. In a moment the whole mass of air in the cavern hit the frequency with a subtle, intangible thunder of vibration. Crystal screamed. "Brian! There's more police cutting in around the entrance." Brian clambered out of the ship and glanced at the glowing points in the rock where the police were cutting their way through outside the line of the exhaust flames. The pulsating thunder in the cavern crescendoed to an intolerable pitch. A huge mass of stalactites crashed to the floor. "It's time to check out," Brian shouted. Crystal led the way as they fled down the escape tunnel. The roaring crash of falling rock was a continuous, increasing avalanche of sound in the cavern behind them. They emerged from the tunnel on the face of the mountain, several hundred yards to the east of the cavern entrance. The ground shook and heaved beneath them. "The whole side of the mountain's sliding," Crystal screamed. "Run!" Brian shoved her and they plunged madly through the thick tangle of jungle away from the slide. Huge boulders leaped and smashed through the matted bush around them. Crystal went down as the ground slipped from under her. Brian grabbed her and a tree at the same time. The tree leaned and crashed down the slope, the whole jungle muttered and groaned and came to life as it joined the roaring rush of the slide. They were tumbled irresistibly downward, riding the edge of the slide for terrifying minutes till it stilled and left them bruised and shaken in a tangle of torn vegetation. The remains of two police ships, caught without warning in the rush as they attempted to land, stuck up grotesquely out of the foot of the slide. The dust was settling away. A flock of brilliant blue, gliding lizards barking in raucous terror, fled down the valley. Then they were gone and the primeval silence settled back into place. Brian and Crystal struggled painfully to solid ground. Crystal gazed with a feeling of awe at the devastated mountainside. "How did you do it?" "It's a matter of harmonics," Brian explained. "If you hit the right vibratory combination, you can shake anything down. But now that we've made a mess of the old homestead, what do we do?" "Walk," Crystal said laconically. She led the way as they started scrambling through the jungle up the mountainside. "Where are we heading for?" Brian grunted as he struggled along. "The headquarters of the Carlton family. They're the closest people we can depend on. They've kept out of the rebellion, but they're on our side. They've helped us before." | A. It's probably on the right side of history, given the violence of the opposition. |
What aspect of NLP research is examined? | ### Introduction
The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP) / Computational Linguistics (CL). It includes papers published in the family of ACL conferences as well as in other NLP conferences such as LREC and RANLP. AA is the largest single source of scientific literature on NLP. This project, which we call NLP Scholar, examines the literature as a whole to identify broad trends in productivity, focus, and impact. We will present the analyses in a sequence of questions and answers. The questions range from fairly mundane to oh-that-will-be-good-to-know. Our broader goal here is simply to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations. We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender). Target Audience: The analyses presented here are likely to be of interest to any NLP researcher. This might be particularly the case for those that are new to the field and wish to get a broad overview of the NLP publishing landscape. On the other hand, even seasoned NLP'ers have likely wondered about the questions raised here and might be interested in the empirical evidence. Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). Thus, all subsequent papers and citations are not included in the analysis. A fresh data collection is planned for January 2020. Interactive Visualizations: The visualizations we are developing for this work (using Tableau) are interactive—so one can hover, click to select and filter, move sliders, etc. Since this work is high in the number of visualizations, the main visualizations are presented as figures in the paper and some sets of visualizations are pointed to online. The interactive visualizations and data will be made available through the first author's website after peer review. Related Work: This work builds on past research, including that on Google Scholar BIBREF0, BIBREF1, BIBREF2, BIBREF3, on the analysis of NLP papers BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, on citation intent BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, and on measuring scholarly impact BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21. Caveats and Ethical Considerations: We list several caveats and limitations throughout the paper. A compilation of these is also available online in the About NLP Scholar page. The analyses presented here are also available as a series of blog posts. ### Size
Q. How big is the ACL Anthology (AA)? How is it changing with time? A. As of June 2019, AA had $\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. Figure FIGREF6 shows a graph of the number of papers published in each of the years from 1965 to 2018. Discussion: Observe that there was a spurt in the 1990s, but things really took off since the year 2000, and the growth continues. Also, note that the number of publications is considerably higher in alternate years. This is due to biennial conferences. Since 1998 the largest of such conferences has been LREC (In 2018 alone LREC had over 700 main conferences papers and additional papers from its 29 workshops). COLING, another biennial conference (also occurring in the even years) has about 45% of the number of main conference papers as LREC. Q. How many people publish in the ACL Anthology (NLP conferences)? A. Figure FIGREF7 shows a graph of the number of authors (of AA papers) over the years: Discussion: It is a good sign for the field to have a growing number of people join its ranks as researchers. A further interesting question would be: Q. How many people are actively publishing in NLP? A. It is hard to know the exact number, but we can determine the number of people who have published in AA in the last N years. #people who published at least one paper in 2017 and 2018 (2 years): $\sim $12k (11,957 to be precise) #people who published at least one paper 2015 through 2018 (4 years):$\sim $17.5k (17,457 to be precise) Of course, some number of researchers published NLP papers in non-AA venues, and some number are active NLP researchers who may not have published papers in the last few years. Q. How many journal papers exist in the AA? How many main conference papers? How many workshop papers? A. See Figure FIGREF8. Discussion: The number of journal papers is dwarfed by the number of conference and workshop papers. (This is common in computer science. Even though NLP is a broad interdisciplinary field, the influence of computer science practices on NLP is particularly strong.) Shared task and system demo papers are relatively new (introduced in the 2000s), but their numbers are already significant and growing. Creating a separate class for “Top-tier Conference” is somewhat arbitrary, but it helps make certain comparisons more meaningful (for example, when comparing the average number of citations, etc.). For this work, we consider ACL, EMNLP, NAACL, COLING, and EACL as top-tier conferences, but certainly other groupings are also reasonable. Q. How many papers have been published at ACL (main conference papers)? What are the other NLP venues and what is the distribution of the number of papers across various CL/NLP venues? A. # ACL (main conference papers) as of June 2018: 4,839 The same workshop can co-occur with different conferences in different years, so we grouped all workshop papers in their own class. We did the same for tutorials, system demonstration papers (demos), and student research papers. Figure FIGREF9 shows the number of main conference papers for various venues and paper types (workshop papers, demos, etc.). Discussion: Even though LREC is a relatively new conference that occurs only once in two years, it tends to have a high acceptance rate ($\sim $60%), and enjoys substantial participation. Thus, LREC is already the largest single source of NLP conference papers. SemEval, which started as SenseEval in 1998 and occurred once in two or three years, has now morphed into an annual two-day workshop—SemEval. It is the largest single source of NLP shared task papers. ### Demographics (focus of analysis: gender, age, and geographic diversity)
NLP, like most other areas of research, suffers from poor demographic diversity. There is very little to low representation from certain nationalities, race, gender, language, income, age, physical abilities, etc. This impacts the breadth of technologies we create, how useful they are, and whether they reach those that need it most. In this section, we analyze three specific attributes among many that deserve attention: gender (specifically, the number of women researchers in NLP), age (more precisely, the number of years of NLP paper publishing experience), and the amount of research in various languages (which loosely correlates with geographic diversity). ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Gender
The ACL Anthology does not record demographic information about the paper authors. (Until recently, ACL and other NLP conferences did not record demographic information of the authors.) However, many first names have strong associations with a male or female gender. We will use these names to estimate the percentage of female first authors in NLP. The US Social Security Administration publishes a database of names and genders of newborns. We use the dataset to identify 55,133 first names that are strongly associated with females (probability $\ge $99%) and 29,873 first names that are strongly associated with males (probability $\ge $99%). (As a side, it is interesting to note that there is markedly greater diversity in female names than in male names.) We identified 26,637 of the 44,896 AA papers ($\sim $60%) where the first authors have one of these names and determine the percentage of female first author papers across the years. We will refer to this subset of AA papers as AA*. Note the following caveats associated with this analysis: The names dataset used has a lower representation of names from nationalities other than the US. However, there is a large expatriate population living in the US. Chinese names (especially in the romanized form) are not good indicators of gender. Thus the method presented here disregards most Chinese names, and the results of the analysis apply to the group of researchers excluding those with Chinese names. The dataset only records names associated with two genders. The approach presented here is meant to be an approximation in the absence of true gender information. Q. What percent of the AA* papers have female first authors (FFA)? How has this percentage changed with time? A. Overall FFA%: 30.3%. Figure FIGREF16 shows how FFA% has changed with time. Common paper title words and FFA% of papers that have those words are shown in the bottom half of the image. Note that the slider at the bottom has been set to 400, i.e., only those title words that occur in 400 or more papers are shown. The legend on the bottom right shows that low FFA scores are shown in shades of blue, whereas relatively higher FFA scores are shown in shades of green. Discussion: Observe that as a community, we are far from obtaining male-female parity in terms of first authors. A further striking (and concerning) observation is that the female first author percentage has not improved since the years 1999 and 2000 when the FFA percentages were highest (32.9% and 32.8%, respectively). In fact there seems to even be a slight downward trend in recent years. The calculations shown above are for the percentage of papers that have female first authors. The percentage of female first authors is about the same ($\sim $31%). On average male authors had a slightly higher average number of publications than female authors. To put these numbers in context, the percentage of female scientists world wide (considering all areas of research) has been estimated to be around 30%. The reported percentages for many computer science sub-fields are much lower. (See Women in Science (2015).) The percentages are much higher for certain other fields such as psychology and linguistics. (See this study for psychology and this study for linguistics.) If we can identify ways to move the needle on the FFA percentage and get it closer to 50% (or more), NLP can be a beacon to many other fields, especially in the sciences. FFA percentages are particularly low for papers that have parsing, neural, and unsupervised in the title. There are some areas within NLP that enjoy a healthier female-male parity in terms of first authors of papers. Figure FIGREF20 shows FFA percentages for papers that have the word discourse in the title. There is burgeoning research on neural NLP in the last few years. Figure FIGREF21 shows FFA percentages for papers that have the word neural in the title. Figure FIGREF22 shows lists of terms with the highest and lowest FFA percentages, respectively, when considering terms that occur in at least 50 paper titles (instead of 400 in the analysis above). Observe that FFA percentages are relatively higher in non-English European language research such as papers on Russian, Portuguese, French, and Italian. FFA percentages are also relatively higher for certain areas of NLP such as work on prosody, readability, discourse, dialogue, paraphrasing, and individual parts of speech such as adjectives and verbs. FFA percentages are particularly low for papers on theoretical aspects of statistical modelling, and areas such as machine translation, parsing, and logic. The full lists of terms and FFA percentages will be made available with the rest of the data. ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Academic Age
While the actual age of NLP researchers might be an interesting aspect to explore, we do not have that information. Thus, instead, we can explore a slightly different (and perhaps more useful) attribute: NLP academic age. We can define NLP academic age as the number of years one has been publishing in AA. So if this is the first year one has published in AA, then their NLP academic age is 1. If one published their first AA paper in 2001 and their latest AA paper in 2018, then their academic age is 18. Q. How old are we? That is, what is the average NLP academic age of those who published papers in 2018? How has the average changed over the years? That is, have we been getting older or younger? What percentage of authors that published in 2018 were publishing their first AA paper? A. Average NLP Academic Age of people that published in 2018: 5.41 years Median NLP Academic Age of people that published in 2018: 2 years Percentage of 2018 authors that published their first AA paper in 2018: 44.9% Figure FIGREF24 shows how these numbers have changed over the years. Discussion: Observe that the Average academic age has been steadily increasing over the years until 2016 and 2017, when the trend has shifted and the average academic age has started to decrease. The median age was 1 year for most of the 1965 to 1990 period, 2 years for most of the 1991 to 2006 period, 3 years for most of the 2007 to 2015 period, and back to 2 years since then. The first-time AA author percentage decreased until about 1988, after which it sort of stayed steady at around 48% until 2004 with occasional bursts to $\sim $56%. Since 2005, the first-time author percentage has gone up and down every other year. It seems that the even years (which are also LREC years) have a higher first-time author percentage. Perhaps, this oscillation in first-time authors percentage is related to LREC’s high acceptance rate. Q. What is the distribution of authors in various academic age bins? For example, what percentage of authors that published in 2018 had an academic age of 2, 3, or 4? What percentage had an age between 5 and 9? And so on? A. See Figure FIGREF25. Discussion: Observe that about 65% of the authors that published in 2018 had an academic age of less than 5. This number has steadily reduced since 1965, was in the 60 to 70% range in 1990s, rose to the 70 to 72% range in early 2000s, then declined again until it reached the lowest value ($\sim $60%) in 2010, and has again steadily risen until 2018 (65%). Thus, even though it may sometimes seem at recent conferences that there is a large influx of new people into NLP (and that is true), proportionally speaking, the average NLP academic age is higher (more experienced) than what it has been in much of its history. ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Location (Languages)
Automatic systems with natural language abilities are growing to be increasingly pervasive in our lives. Not only are they sources of mere convenience, but are crucial in making sure large sections of society and the world are not left behind by the information divide. Thus, the limits of what automatic systems can do in a language, limit the world for the speakers of that language. We know that much of the research in NLP is on English or uses English datasets. Many reasons have been proffered, and we will not go into that here. Instead, we will focus on estimating how much research pertains to non-English languages. We will make use of the idea that often when work is done focusing on a non-English language, then the language is mentioned in the title. We collected a list of 122 languages indexed by Wiktionary and looked for the presence of these words in the titles of AA papers. (Of course there are hundreds of other lesser known languages as well, but here we wanted to see the representation of these more prominent languages in NLP literature.) Figure FIGREF27 is a treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green. Discussion: Even though the amount of work done on English is much larger than that on any other language, often the word English does not appear in the title, and this explains why English is not the first (but the second-most) common language name to appear in the titles. This is likely due to the fact that many papers fail to mention the language of study or the language of the datasets used if it is English. There is growing realization in the community that this is not quite right. However, the language of study can be named in other less prominent places than the title, for example the abstract, introduction, or when the datasets are introduced, depending on how central it is to the paper. We can see from the treemap that the most widely spoken Asian and Western European languages enjoy good representation in AA. These include: Chinese, Arabic, Korean, Japanese, and Hindi (Asian) as well as French, German, Swedish, Spanish, Portuguese, and Italian (European). This is followed by the relatively less widely spoken European languages (such as Russian, Polish, Norwegian, Romanian, Dutch, and Czech) and Asian languages (such as Turkish, Thai, and Urdu). Most of the well-represented languages are from the Indo-European language family. Yet, even in the limited landscape of the most common 122 languages, vast swathes are barren with inattention. Notable among these is the extremely low representation of languages from Africa, languages from non-Indo-European language families, and Indigenous languages from around the world. ### Areas of Research
Natural Language Processing addresses a wide range of research questions and tasks pertaining to language and computing. It encompasses many areas of research that have seen an ebb and flow of interest over the years. In this section, we examine the terms that have been used in the titles of ACL Anthology (AA) papers. The terms in a title are particularly informative because they are used to clearly and precisely convey what the paper is about. Some journals ask authors to separately include keywords in the paper or in the meta-information, but AA papers are largely devoid of this information. Thus titles are an especially useful source of keywords for papers—keywords that are often indicative of the area of research. Keywords could also be extracted from abstracts and papers; we leave that for future work. Further work is also planned on inferring areas of research using word embeddings, techniques from topic modelling, and clustering. There are clear benefits to performing analyses using that information. However, those approaches can be sensitive to the parameters used. Here, we keep things simple and explore counts of terms in paper titles. Thus the results are easily reproducible and verifiable. Caveat: Even though there is an association between title terms and areas of research, the association can be less strong for some terms and areas. We use the association as one (imperfect) source of information about areas of research. This information may be combined with other sources of information to draw more robust conclusions. Title Terms: The title has a privileged position in a paper. It serves many functions, and here are three key ones (from an article by Sneha Kulkarni): "A good research paper title: 1. Condenses the paper's content in a few words 2. Captures the readers' attention 3. Differentiates the paper from other papers of the same subject area". If we examine the titles of papers in the ACL Anthology, we would expect that because of Function 1 many of the most common terms will be associated with the dominant areas of research. Function 2 (or attempting to have a catchy title) on the other hand, arguably leads to more unique and less frequent title terms. Function 3 seems crucial to the effectiveness of a title; and while at first glance it may seem like this will lead to unique title terms, often one needs to establish a connection with something familiar in order to convey how the work being presented is new or different. It is also worth noting that a catchy term today, will likely not be catchy tomorrow. Similarly, a distinctive term today, may not be distinctive tomorrow. For example, early papers used neural in the title to distinguish themselves from non-nerual approaches, but these days neural is not particularly discriminative as far as NLP papers go. Thus, competing and complex interactions are involved in the making of titles. Nonetheless, an arguable hypothesis is that: broad trends in interest towards an area of research will be reflected, to some degree, in the frequencies of title terms associated with that area over time. However, even if one does not believe in that hypothesis, it is worth examining the terms in the titles of tens of thousands of papers in the ACL Anthology—spread across many decades. Q. What terms are used most commonly in the titles of the AA papers? How has that changed with time? A. Figure FIGREF28 shows the most common unigrams (single word) and bigrams (two-word sequences) in the titles of papers published from 1980 to 2019. (Ignoring function words.) The timeline graph at the bottom shows the percentage of occurrences of the unigrams over the years (the colors of the unigrams in the Timeline match those in the Title Unigram list). Note: For a given year, the timeline graph includes a point for a unigram if the sum of the frequency of the unigram in that year and the two years before it is at least ten. The period before 1980 is not included because of the small number of papers. Discussion: Appropriately enough, the most common term in the titles of NLP papers is language. Presence of high-ranking terms pertaining to machine translation suggest that it is the area of research that has received considerable attention. Other areas associated with the high-frequency title terms include lexical semantics, named entity recognition, question answering, word sense disambiguation, and sentiment analysis. In fact, the common bigrams in the titles often correspond to names of NLP research areas. Some of the bigrams like shared task and large scale are not areas of research, but rather mechanisms or trends of research that apply broadly to many areas of research. The unigrams, also provide additional insights, such as the interest of the community in Chinese language, and in areas such as speech and parsing. The Timeline graph is crowded in this view, but clicking on a term from the unigram list will filter out all other lines from the timeline. This is especially useful for determining whether the popularity of a term is growing or declining. (One can already see from above that neural has broken away from the pack in recent years.) Since there are many lines in the Timeline graph, Tableau labels only some (you can see neural and machine). However, hovering over a line, in the eventual interactive visualization, will display the corresponding term—as shown in the figure. Despite being busy, the graph sheds light on the relative dominance of the most frequent terms and how that has changed with time. The vocabulary of title words is smaller when considering papers from the 1980's than in recent years. (As would be expected since the number of papers then was also relatively fewer.) Further, dominant terms such as language and translation accounted for a higher percentage than in recent years where there is a much larger diversity of topics and the dominant research areas are not as dominant as they once were. Q. What are the most frequent unigrams and bigrams in the titles of recent papers? A. Figure FIGREF29 shows the most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection). Discussion: Some of the terms that have made notable gains in the top 20 unigrams and bigrams lists in recent years include: neural machine (presumably largely due to the phrase neural machine translation), neural network(s), word embeddings, recurrent neural, deep learning and the corresponding unigrams (neural, networks, etc.). We also see gains for terms related to shared tasks such as SemEval and task. The sets of most frequent unigrams and bigrams in the titles of AA papers from various time spans are available online. Apart from clicking on terms, one can also enter the query (say parsing) in the search box at the bottom. Apart from filtering the timeline graph (bottom), this action also filters the unigram list (top left) to provide information only about the search term. This is useful because the query term may not be one of the visible top unigrams. FigureFIGREF31 shows the timeline graph for parsing. Discussion: Parsing seems to have enjoyed considerable attention in the 1980s, began a period of steep decline in the early 1990s, and a period of gradual decline ever since. One can enter multiple terms in the search box or shift/command click multiple terms to show graphs for more than one term. FigureFIGREF32 shows the timelines for three bigrams statistical machine, neural machine, and machine translation: Discussion: The graph indicates that there was a spike in machine translation papers in 1996, but the number of papers dropped substantially after that. Yet, its numbers have been comparatively much higher than other terms. One can also see the rise of statistical machine translation in the early 2000s followed by its decline with the rise of neural machine translation. ### Impact
Research articles can have impact in a number of ways—pushing the state of the art, answering crucial questions, finding practical solutions that directly help people, making a new generation of potential-scientists excited about a field of study, and more. As scientists, it seems attractive to quantitatively measure scientific impact, and this is particularly appealing to governments and funding agencies; however, it should be noted that individual measures of research impact are limited in scope—they measure only some kinds of contributions. Citations The most commonly used metrics of research impact are derived from citations. A citation of a scholarly article is the explicit reference to that article. Citations serve many functions. However, a simplifying assumption is that regardless of the reason for citation, every citation counts as credit to the influence or impact of the cited work. Thus several citation-based metrics have emerged over the years including: number of citations, average citations, h-index, relative citation ratio, and impact factor. It is not always clear why some papers get lots of citations and others do not. One can argue that highly cited papers have captured the imagination of the field: perhaps because they were particularly creative, opened up a new area of research, pushed the state of the art by a substantial degree, tested compelling hypotheses, or produced useful datasets, among other things. Note however, that the number of citations is not always a reflection of the quality or importance of a piece of work. Note also that there are systematic biases that prevent certain kinds of papers from accruing citations, especially when the contributions of a piece of work are atypical, not easily quantified, or in an area where the number of scientific publications is low. Further, the citations process can be abused, for example, by egregious self-citations. Nonetheless, given the immense volume of scientific literature, the relative ease with which one can track citations using services such as Google Scholar and Semantic Scholar, and given the lack of other easily applicable and effective metrics, citation analysis is an imperfect but useful window into research impact. In this section, we examine citations of AA papers. We focus on two aspects: Most cited papers: We begin by looking at the most cited papers overall and in various time spans. We will then look at most cited papers by paper-type (long, short, demo, etc) and venue (ACL, LREC, etc.). Perhaps these make interesting reading lists. Perhaps they also lead to a qualitative understanding of the kinds of AA papers that have received lots of citations. Aggregate citation metrics by time span, paper type, and venue: Access to citation information allows us to calculate aggregate citation metrics such as average and median citations of papers published in different time periods, published in different venues, etc. These can help answer questions such as: on average, how well cited are papers published in the 1990s? on average, how many citations does a short paper get? how many citations does a long paper get? how many citations for a workshop paper? etc. Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). We extracted citation information from Google Scholar profiles of authors who had a Google Scholar Profile page and had published at least three papers in the ACL Anthology. This yielded citation information for about 75% of the papers (33,051 out of the 44,896 papers). We will refer to this subset of the ACL Anthology papers as AA’. All citation analysis below is on AA’. ### Impact ::: #Citations and Most Cited Papers
Q. How many citations have the AA’ papers received? How is that distributed among the papers published in various decades? A. $\sim $1.2 million citations (as of June 2019). Figure FIGREF36 shows a timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. Further, the bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Thus it is easy to spot the papers that received a large number of citations, and the years when the published papers received a large number of citations. Hovering over individual papers reveals an information box showing the paper title, authors, year of publication, publication venue, and #citations. Discussion: With time, not only have the number of papers grown, but also the number of high-citation papers. We see a marked jump in the 1990s over the previous decades, but the 2000s are the most notable in terms of the high number of citations. The 2010s papers will likely surpass the 2000s papers in the years to come. Q. What are the most cited papers in AA'? A. Figure FIGREF37 shoes the most cited papers in the AA'. Discussion: We see that the top-tier conference papers (green) are some of the most cited papers in AA’. There are a notable number of journal papers (dark green) in the most cited list as well, but very few demo (purple) and workshop (orange) papers. In the interactive visualizations (to be released later), one can click on the url to be to taken directly to the paper’s landing page in the ACL Anthology website. That page includes links to meta information, the pdf, and associated files such as videos and appendices. There will also be functionality to download the lists. Alas, copying the lists from the screenshots shown here is not easy. Q. What are the most cited AA' journal papers ? What are the most cited AA' workshop papers? What are the most cited AA' shared task papers? What are the most cited AA' demo papers? What are the most cited tutorials? A. The most cited AA’ journal papers, conference papers, workshop papers, system demo papers, shared task papers, and tutorials can be viewed online. The most cited papers from individual venues (ACL, CL journal, TACL, EMNLP, LREC, etc.) can also be viewed there. Discussion: Machine translation papers are well-represented in many of these lists, but especially in the system demo papers list. Toolkits such as MT evaluation ones, NLTK, Stanford Core NLP, WordNet Similarity, and OpenNMT have highly cited demo or workshop papers. The shared task papers list is dominated by task description papers (papers by task organizers describing the data and task), especially for sentiment analysis tasks. However, the list also includes papers by top-performing systems in these shared tasks, such as the NRC-Canada, HidelTime, and UKP papers. Q. What are the most cited AA' papers in the last decade? A. Figure FIGREF39 shows the most cited AA' papers in the 2010s. The most cited AA' papers from the earlier periods are available online. Discussion: The early period (1965–1989) list includes papers focused on grammar and linguistic structure. The 1990s list has papers addressing many different NLP problems with statistical approaches. Papers on MT and sentiment analysis are frequent in the 2000s list. The 2010s are dominated by papers on word embeddings and neural representations. ### Impact ::: Average Citations by Time Span
Q. How many citations did the papers published between 1990 and 1994 receive? What is the average number of citations that a paper published between 1990 and 1994 has received? What are the numbers for other time spans? A. Total citations for papers published between 1990 and 1994: $\sim $92k Average citations for papers published between 1990 and 1994: 94.3 Figure FIGREF41 shows the numbers for various time spans. Discussion: The early 1990s were an interesting period for NLP with the use of data from the World Wide Web and technologies from speech processing. This was the period with the highest average citations per paper, closely followed by the 1965–1969 and 1995–1999 periods. The 2000–2004 period is notable for: (1) a markedly larger number of citations than the previous decades; (2) third highest average number of citations. The drop off in the average citations for recent 5-year spans is largely because they have not had as much time to collect citations. ### Impact ::: Aggregate Citation Statistics, by Paper Type and Venue
Q. What are the average number of citations received by different types of papers: main conference papers, workshop papers, student research papers, shared task papers, and system demonstration papers? A. In this analysis, we include only those AA’ papers that were published in 2016 or earlier (to allow for at least 2.5 years to collect citations). There are 26,949 such papers. Figures FIGREF42 and FIGREF43 show the average citations by paper type when considering papers published 1965–2016 and 2010–2016, respectively. Figures FIGREF45 and FIGREF46 show the medians. Discussion: Journal papers have much higher average and median citations than other papers, but the gap between them and top-tier conferences is markedly reduced when considering papers published since 2010. System demo papers have the third highest average citations; however, shared task papers have the third highest median citations. The popularity of shared tasks and the general importance given to beating the state of the art (SOTA) seems to have grown in recent years—something that has come under criticism. It is interesting to note that in terms of citations, workshop papers are doing somewhat better than the conferences that are not top tier. Finally, the citation numbers for tutorials show that even though a small number of tutorials are well cited, a majority receive 1 or no citations. This is in contrast to system demo papers that have average and median citations that are higher or comparable to workshop papers. Throughout the analyses in this article, we see that median citation numbers are markedly lower than average citation numbers. This is particularly telling. It shows that while there are some very highly cited papers, a majority of the papers obtain much lower number of citations—and when considering papers other than journals and top-tier conferences, the number of citations is frequently lower than ten. Q. What are the average number of citations received by the long and short ACL main conference papers, respectively? A. Short papers were introduced at ACL in 2003. Since then ACL is by far the venue with the most number of short papers (compared to other venues). So we compare long and short papers published at ACL since 2003 to determine their average citations. Once again, we limit the papers to those published until 2016 to allow for the papers to have time to collect citations. Figure FIGREF47 shows the average and median citations for long and short papers. Discussion: On average, long papers get almost three times as many citations as short papers. However, the median for long papers is two-and-half times that of short papers. This difference might be because some very heavily cited long papers push the average up for long papers. Q. Which venue has publications with the highest average number of citations? What is the average number of citations for ACL and EMNLP papers? What is this average for other venues? What are the average citations for workshop papers, system demonstration papers, and shared task papers? A. CL journal has the highest average citations per paper. Figure FIGREF49 shows the average citations for AA’ papers published 1965–2016 and 2010–2016, respectively, grouped by venue and paper type. (Figure with median citations is available online.) Discussion: In terms of citations, TACL papers have not been as successful as EMNLP and ACL; however, CL journal (the more traditional journal paper venue) has the highest average and median paper citations (by a large margin). This gap has reduced in papers published since 2010. When considering papers published between 2010 and 2016, the system demonstration papers, the SemEval shared task papers, and non-SemEval shared task papers have notably high average (surpassing those of EACL and COLING); however their median citations are lower. This is likely because some heavily cited papers have pushed the average up. Nonetheless, it is interesting to note how, in terms of citations, demo and shared task papers have surpassed many conferences and even become competitive with some top-tier conferences such as EACL and COLING. Q. What percent of the AA’ papers that were published in 2016 or earlier are cited more than 1000 times? How many more than 10 times? How many papers are cited 0 times? A. Google Scholar invented the i-10 index as another measure of author research impact. It stands for the number of papers by an author that received ten or more citations. (Ten here is somewhat arbitrary, but reasonable.) Similar to that, one can look at the impact of AA’ as a whole and the impact of various subsets of AA’ through the number of papers in various citation bins. Figure FIGREF50 shows the percentage of AA’ papers in various citation bins. (The percentages of papers when considering papers from specific time spans are available online.) Discussion: About 56% of the papers are cited ten or more times. 6.4% of the papers are never cited. Note also that some portion of the 1–9 bin likely includes papers that only received self-citations. It is interesting that the percentage of papers with 0 citations is rather steady (between 7.4% and 8.7%) for the 1965–1989, 1990–1999, and 2010–2016 periods. The majority of the papers lie in the 10 to 99 citations bin, for all except the recent periods (2010–2016 and 2016Jan–2016Dec). With time, the recent period should also have the majority of the papers in the 10 to 99 citations bin. The numbers for the 2016Jan–2016Dec papers show that after 2.5 years, about 89% of the papers have at least one citation and about 33% of the papers have ten or more citations. Q. What are the citation bin percentages for individual venues and paper types? A. See Figure FIGREF51. Discussion: Observe that 70 to 80% of the papers in journals and top-tier conferences have ten or more citations. The percentages are markedly lower (between 30 and 70%) for the other conferences shown above, and even lower for some other conferences (not shown above). CL Journal is particularly notable for the largest percentage of papers with 100 or more citations. The somewhat high percentage of papers that are never cited (4.3%) are likely because some of the book reviews from earlier years are not explicitly marked in CL journal, and thus they were not removed from analysis. Also, letters to editors, which are more common in CL journal, tend to often obtain 0 citations. CL, EMNLP, and ACL have the best track record for accepting papers that have gone on to receive 1000 or more citations. *Sem, the semantics conference, seems to have notably lower percentage of high-citation papers, even though it has fairly competitive acceptance rates. Instead of percentage, if one considers raw numbers of papers that have at least ten citations (i-10 index), then LREC is particularly notable in terms of the large number of papers it accepts that have gone on to obtain ten or more citations ($\sim $1600). Thus, by producing a large number of moderate-to-high citation papers, and introducing many first-time authors, LREC is one of the notable (yet perhaps undervalued) engines of impact on NLP. About 50% of the SemEval shared task papers received 10 or more citations, and about 46% of the non-SemEval Shared Task Papers received 10 or more citations. About 47% of the workshop papers received ten or more citations. About 43% of the demo papers received 10 or more citations. ### Impact ::: Citations to Papers by Areas of Research
Q. What is the average number of citations of AA' papers that have machine translation in the title? What about papers that have the term sentiment analysis or word representations? A. Different areas of research within NLP enjoy varying amounts of attention. In Part II, we looked at the relative popularity of various areas over time—estimated through the number of paper titles that had corresponding terms. (You may also want to see the discussion on the use of paper title terms to sample papers from various, possibly overlapping, areas.) Figure FIGREF53 shows the top 50 title bigrams ordered by decreasing number of total citations. Only those bigrams that occur in at least 30 AA' papers (published between 1965 and 2016) are considered. (The papers from 2017 and later are not included, to allow for at least 2.5 years for the papers to accumulate citations.) Discussion: The graph shows that the bigram machine translation occurred in 1,659 papers that together accrued more than 93k citations. These papers have on average 68.8 citations and the median citations is 14. Not all machine translation (MT) papers have machine translation in the title. However, arguably, this set of 1,659 papers is a representative enough sample of machine translation papers; and thus, the average and median are estimates of MT in general. Second in the list are papers with statistical machine in the title—most commonly from the phrase statistical machine translation. One expects considerable overlap in the papers across the sets of papers with machine translation and statistical machine, but machine translation likely covers a broader range of research including work before statistical MT was introduced, neural MT, and MT evaluation. There are fewer papers with sentiment analysis in the title (356), but these have acquired citations at a higher average (104) than both machine translation and statistical machine. The bigram automatic evaluation jumps out because of its high average citations (337). Some of the neural-related bigrams have high median citations, for example, neural machine (49) and convolutional neural (40.5). Figure FIGREF54 shows the lists of top 25 bigrams ordered by average citations. Discussion: Observe the wide variety of topics covered by this list. In some ways that is reassuring for the health of the field as a whole; however, this list does not show which areas are not receiving sufficient attention. It is less clear to me how to highlight those, as simply showing the bottom 50 bigrams by average citations is not meaningful. Also note that this is not in any way an endorsement to write papers with these high-citation bigrams in the title. Doing so is of course no guarantee of receiving a large number of citations. ### Correlation of Age and Gender with Citations
In this section, we examine citations across two demographic dimensions: Academic age (number of years one has been publishing) and Gender. There are good reasons to study citations across each of these dimensions including, but not limited to, the following: Areas of research: To better understand research contributions in the context of the area where the contribution is made. Academic age: To better understand how the challenges faced by researchers at various stages of their career may impact the citations of their papers. For example, how well-cited are first-time NLP authors? On average, at what academic age do citations peak? etc. Gender: To better understand the extent to which systematic biases (explicit and implicit) pervasive in society and scientific publishing impact author citations. Some of these aspects of study may seem controversial. So it is worth addressing that first. The goal here is not to perpetuate stereotypes about age, gender, or even areas of research. The history of scientific discovery is awash with plenty of examples of bad science that has tried to erroneously show that one group of people is “better” than another, with devastating consequences. People are far more alike than different. However, different demographic groups have faced (and continue to face) various socio-cultural inequities and biases. Gender and race studies look at how demographic differences shape our experiences. They examine the roles of social institutions in maintaining the inequities and biases. This work is in support of those studies. Unless we measure differences in outcomes such as scientific productivity and impact across demographic groups, we will not fully know the extent to which these inequities and biases impact our scientific community; and we cannot track the effectiveness of measures to make our universities, research labs, and conferences more inclusive, equitable, and fair. ### Correlation of Age and Gender with Citations ::: Correlation of Academic Age with Citations
We introduced NLP academic age earlier in the paper, where we defined NLP academic age as the number of years one has been publishing in AA. Here we examine whether NLP academic age impacts citations. The analyses are done in terms of the academic age of the first author; however, similar analyses can be done for the last author and all authors. (There are limitations to each of these analyses though as discussed further below.) First author is a privileged position in the author list as it is usually reserved for the researcher that has done the most work and writing. The first author is also usually the main driver of the project; although, their mentor or advisor may also be a significant driver of the project. Sometimes multiple authors may be marked as first authors in the paper, but the current analysis simply takes the first author from the author list. In many academic communities, the last author position is reserved for the most senior or mentoring researcher. However, in non-university research labs and in large collaboration projects, the meaning of the last author position is less clear. (Personally, I prefer author names ordered by the amount of work done.) Examining all authors is slightly more tricky as one has to decide how to credit the citations to the possibly multiple authors. It might also not be a clear indicator of differences across gender as a large number of the papers in AA have both male and female authors. Q. How does the NLP academic age of the first author correlate with the amount of citations? Are first-year authors less cited than those with more experience? A. Figure FIGREF59 shows various aggregate citation statistics corresponding to academic age. To produce the graph we put each paper in a bin corresponding to the academic age of the first author when the paper was published. For example, if the first author of a paper had an academic age of 3 when that paper was published, then the paper goes in bin 3. We then calculate #papers, #citations, median citations, and average citations for each bin. For the figure below, We further group the bins 10 to 14, 15 to 19, 20 to 34, and 35 to 50. These groupings are done to avoid clutter, and also because many of the higher age bins have a low number of papers. Discussion: Observe that the number of papers where the first author has academic age 1 is much larger than the number of papers in any other bin. This is largely because a large number of authors in AA have written exactly one paper as first author. Also, about 60% of the authors in AA (17,874 out of the 29,941 authors) have written exactly one paper (regardless of author position). The curves for the average and median citations have a slight upside down U shape. The relatively lower average and median citations in year 1 (37.26 and 10, respectively) indicate that being new to the field has some negative impact on citations. The average increases steadily from year 1 to year 4, but the median is already at the highest point by year 2. One might say, that year 2 to year 14 are the period of steady and high citations. Year 15 onwards, there is a steady decline in the citations. It is probably wise to not draw too many conclusions from the averages of the 35 to 50 bin, because of the small number of papers. There seems to be a peak in average citations at age 7. However, there is not a corresponding peak in the median. Thus the peak in average might be due to an increase in the number of very highly cited papers. Citations to Papers by First Author Gender As noted in Part I, neither ACL nor the ACL Anthology have recorded demographic information for the vast majority of the authors. Thus we use the same setup discussed earlier in the section on demographics, to determine gender using the United States Social Security Administration database of names and genders of newborns to identify 55,133 first names that are strongly associated with females (probability $\ge $99%) and 29,873 first names that are strongly associated with males (probability $\ge $99%). Q. On average, are women cited less than men? A. Yes, on average, female first author papers have received markedly fewer citations than male first author papers (36.4 compared to 52.4). The difference in median is smaller (11 compared to 13). See Figure FIGREF60. Discussion: The large difference in averages and smaller difference in medians suggests that there are markedly more very heavily cited male first-author papers than female first-author papers. The gender-unknown category, which here largely consist of authors with Chinese origin names and names that are less strongly associated with one gender have a slightly higher average, but the same median citations, as authors with female-associated first names. The differences in citations, or citation gap, across genders may: (1) vary by period of time; (2) vary due to confounding factors such as academic age and areas of research. We explore these next. Q. How has the citation gap across genders changed over the years? A. Figure FIGREF61 (left side) shows the citation statistics across four time periods. Discussion: Observe that female first authors have always been a minority in the history of ACL; however, on average, their papers from the early years (1965 to 1989) received a markedly higher number of citations than those of male first authors from the same period. We can see from the graph that this changed in the 1990s where male first-author papers obtained markedly more citations on average. The citation gap reduced considerably in the 2000s, and the 2010–2016 period saw a further slight reduction in the citation gap. It is also interesting to note that the gender-unknown category has almost bridged the gap with the males in this most recent time period. Further, the proportion of the gender-unknown authors has increased over the years—arguably, an indication of better representations of authors from around the world in recent years. (Nonetheless, as indicated in Part I, there is still plenty to be done to promote greater inclusion of authors from Africa and South America.) Q. How have citations varied by gender and academic age? Are women less cited because of a greater proportion of new-to-NLP female first authors than new-to-NLP male first authors? A. Figure FIGREF61 (right side) shows citation statistics broken down by gender and academic age. (This figure is similar to the academic age graph seen earlier, except that it shows separate average and median lines for female, male, and unknown gender first authors.) Discussion: The graphs show that female first authors consistently receive fewer citations than male authors for the first fifteen years. The trend is inverted with a small citation gap in the 15th to 34th years period. Q. Is the citation gap common across the vast majority of areas of research within NLP? Is the gap simply because more women work in areas that receive low numbers of citations (regardless of gender)? A. Figure FIGREF64 shows the most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. (This figure is similar to the areas of research graph seen earlier, except that it shows separate citation statistics for the genders.) Note that the figure includes rows for only those bigram and gender pairs with at least 30 AA’ papers (published between 1965 and 2016). Thus for some of the bigrams certain gender entries are not shown. Discussion: Numbers for an additional 32 areas are available online. Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP. ### Conclusions
This work examined the ACL Anthology to identify broad trends in productivity, focus, and impact. We examined several questions such as: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? Particular attention was paid to the demographics and inclusiveness of the NLP community. Notably, we showed that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also showed that, on average, female first authors are cited less than male first authors, even when controlling for academic age. We hope that recording citation and participation gaps across demographic groups will encourage our university, industry, and government research labs to be more inclusive and fair. Several additional aspects of the AA will be explored in future work (see the bottom of the blog posts). Acknowledgments This work was possible due to the helpful discussion and encouragement from a number of awesome people, including: Dan Jurafsky, Tara Small, Michael Strube, Cyril Goutte, Eric Joanis, Matt Post, Patrick Littell, Torsten Zesch, Ellen Riloff, Norm Vinson, Iryna Gurevych, Rebecca Knowles, Isar Nejadgholi, and Peter Turney. Also, a big thanks to the ACL Anthology team for creating and maintaining a wonderful resource. Figure 1 The number of AA papers published in each of the years from 1965 to 2018. Figure 2 The number of authors of AA papers from 1965 to 2018. Figure 3 Number of AA papers by type. Figure 4 The number of main conference papers for various venues and paper types (workshop papers, demos, etc.). Figure 5 Female first author (FFA) percentage over the years. Figure 6 FFA percentages for papers that have the word discourse in the title. Figure 7 FFA percentages for papers that have the word neural in the title. Figure 8 Lists of terms with the highest and lowest FFA percentages, respectively. Figure 9 Graphs showing average academic age, median academic age, and percentage of first-time publishers in AA over time. Figure 10 The distribution of authors in academic age bins for papers published 2011–2018. Figure 11 A treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green. Figure 12 The most common unigrams and bigrams in the titles of AA papers published 1980–2019. Figure 13 The most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection). Figure 14 The timeline graph for parsing. Figure 15 The timelines for three bigrams statistical machine, neural machine, and machine translation. Figure 16 A timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. The bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Figure 17 The most cited papers in AA’. Figure 18 The most cited AA’ papers in the 2010s. Figure 19 Left-side graph: Total number of citations received by AAâĂŹ papers in various 5-year time spans. Right-side graph 2: Average citations per paper from various time spans. Figure 20 Average citations by paper type when considering papers published 1965âĂŞ2016. Figure 21 Average citations by paper type when considering papers published 2010–2016. Figure 22 Median citations by paper type when considering papers published 1965–2016 Figure 23 Median citations by paper type when considering papers published 2010–2016. Figure 24 Average and median citations for long and short papers. Figure 25 Average citations for papers published 1965–2016 (left side) and 2010–2016 (right side), grouped by venue and paper type. Figure 26 The percentage of AAâĂŹ papers in various citation bins. Figure 27 The citation bin percentages for individual venues and paper types. Figure 28 The top 50 title bigrams ordered by decreasing number of total citations. Figure 29 The lists of top 25 bigrams ordered by average citations. Figure 30 Aggregate citation statistics by academic age. Figure 31 Average citations received by female and male first authors. Figure 32 Citation gap across genders for papers: (a) published in different time spans, (b) by academic age. Figure 33 The most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. | size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender) |
What is the author's strongest critique of Barker's directorial style?
A. The drafted nature of Barker's characters' speech is inconsistent with his claims of the film being categorized as a documentary
B. The film does not include enough monologues from each of the four characters to be considered a documentary, and instead relies predominantly on voice-over narration
C. Barker attempts to capitalize on western society's simultaneous intrigue and revulsion with vile characters who live at the margins
D. Barker's juxtaposition of the sympathetic with the distasteful does not match up with the actual lived realities of the four main characters featured in the film
| Dirty Laundry Now and then, a documentary film comes along that makes us re-examine the rules that unofficially govern the genre: Can there be a middle ground between fiction and fact? Can a documentary use scripted scenes and yet remain ontologically authentic? How much can you stylize material before you alter the reality that you're striving, at least in theory, to capture? Unmade Beds , Nicholas Barker's " 'real life' feature film," has proudly worn its mongrel status as a "directed" documentary of single life in the big city, employing, in the face of criticism, what amounts to a cackling-punk defiance. The movie tracks four aging New Yorkers--two men, two women--through their lonely dating rituals, in the process depicting a universe of lusty, coupled-up haves and downcast, excluded have-nots, all viewed Rear Window -style through rectangular openings in the massive apartment houses in which they reside. This is not cinema vérité , and nothing has been left to chance. The director selected his four subjects from many hundreds of potential candidates, followed them around for months, and then scripted their monologues and dialogues to reflect what he says he saw. Calling his own film "an exercise in mendacity," Barker goes on, "I'm quite happy to tell lies about my characters and even collude with their self-delusions if it enables me to communicate larger dramatic truths." Spurned by U.S. distributors, Unmade Beds opened two weeks ago in a small screening room in downtown Manhattan, where it proceeded to set box office records and generate lots of (largely favorable) press. In part due to smart publicity, which has bannered some of the bad reviews and commentary ("I have to tell you that this film upset me so much that I really don't want to have anything to do with it"--a New York publicist), it threatens to become a cause célèbre --and to be coming soon to a theater near you. It's always nice to see distributors proved wrong about the merits of "difficult" films, but in this case I think they did the decent thing. Unmade Beds isn't just bad--it's obnoxiously, noxiously bad, a freak show for the empathetically challenged. The outrage it has prompted isn't the Puritan kind; it's more like legitimate revulsion at watching a blowhard pervert people's lives in the name of "larger dramatic truths." Those truths are large, all right. Take Michael, the 40-year-old, 5 foot 4 inch lonely guy who has been looking for a wife for almost two decades. If you were to walk past him on the street, you might think that a man of his small stature might have some trouble getting dates and be rather bitter about it. The larger dramatic truth is that Michael has lots of trouble getting dates and is very bitter about it. Just in case you feel too sorry for him, however, Barker is careful to include a homophobic monologue in which Michael complains about young women who waste their lives hanging out with effeminate males. Michael turns out to be the film's most sympathetic subject--by a wide margin. At least he's not Mikey, a paunchy 54-year-old who writes but can't sell screenplays and who always flees blind dates, because the women he gets fixed up with are "mutts." Sounding like one of the low-level gangsters who posture like kingpins in Donnie Brasco , Mikey talks a lot about mutts. He also reminisces about that 24 hour period in the '70s when he managed to sleep with three different beautiful women, whose pictures he shows off. These days, all he meets are mutts. He comes off as a pathetic little loser--a mutt. Aimee, on the other hand, is a pathetic big loser, weighing in at 225 pounds. Determined to get married before she turns 30, she generally is filmed beside bags of groceries and assorted junk foods. She cries about her situation to her thin friend, Laurie, who, in one scene, gently mentions Aimee's weight. Clearly the scene is scripted, but Aimee does a good job acting taken aback. She has always been fat--and she's "OK with it," and a man just has to accept it. This is followed by more talk about how you attract men. Will they respect you if you call them back? If you express too much interest? "Or," the viewer thinks, "if you're 225 pounds?" The only natural performer here is Brenda, a garrulous exhibitionist who blossoms with the camera on her--she could have a career as a Penny Marshall-style character actress. Divorced and aging, Brenda needs money and is willing to charge for her sexual services. It shouldn't be too difficult, because men are always showing her their dicks ("I'm up to two dicks a day"). They meet her and, a few minutes later, they show her their dicks. Weird, huh? What Barker leaves out (it's in a New York Observer article) is that Brenda, a former lap dancer, works in marketing at a strip joint. Presumably, men standing next to her in line at McDonald's don't show her their dicks. Nor, presumably, does she show them her breasts--although she bares them for Barker's camera, jabbering about her body while she doffs her clothes and steps into the shower and soaps up. Barker might have crafted his subjects' monologues from their own words, but he has robbed them of their spontaneity--and, thus, of their essence. They aren't thinking or trying to come to grips with their situations in front of your eyes, because they already know what they're going to say: They've been fixed like butterflies on the ends of pins and held up for voyeuristic inspection. The scenes with friends and confidantes have a crude, programmatic purpose. You can imagine the director composing a shot (the shots are tightly composed and elaborately lighted) and reminding them, "In this scene she points out that you should lose weight and you get shocked and defensive. Ready ... Action." Call me square, but I find this antithetical to the documentary spirit. An Englishman who trained as an anthropologist before going to work for BBC Television, Barker clearly made up his mind about his material before his cameras began to roll--so it's no surprise that it feels prechewed and predigested. When reality interfered (Brenda apparently did not go through with a marriage to an immigrant in search of a green card for $10,000, as she does on-screen), Barker brushed the truth aside as immaterial, following her up the steps of City Hall in her wedding dress because it was "true to her character." But what separates documentary from fiction is that real people are often more complicated, and more conflicted, than finished characters--as Brenda proved to be more (or, at least, other) than the sum of her parts. That's the kind of truth that reveals itself to documentary filmmakers after the fact, when they go over footage and discover unexpected patterns, dissonances, glimmers of a universe that's richer and messier than the one they set out to portray. So what are Barker's "larger dramatic truths"? Single people in big cities can be desperate. Single people fear they're going to die alone--unloved and unloving. People are judged and, in turn, judge others by how they look. Big news. One could argue, charitably, that the movie is meant to be prescriptive, that Barker intends for us to regard the ways in which his subjects delude themselves and thereby learn to see through our own self-delusions. But Barker hasn't concocted a larger dramatic structure that would hold those larger dramatic truths together and help us comprehend where these people went wrong. He dramatizes right up to the point where a dramatist would be expected to provide some insight--and then, hey, he's a documentarian. Unmade Beds might make a good date movie. There's little to argue about in its subjects' personalities--both males and females will find them repulsive--and the picture the film paints of single life in the big city is so bleak that you'll probably want to jump into bed with whoever is sitting next to you. Anything to keep from turning into one of those people. The Slums of Beverly Hills also walks a line between two genres, in this case coming-of-age sex comedy and autobiographical monologue. Tamara Jenkins, the writer and first-time director, has an eye for absurd juxtapositions that was obviously sharpened by the pain of her nomadic upbringing. Her protagonist (Natasha Lyonne) spends her teen-age years being shuttled with her two brothers from one cheap dive to another in the 90210 ZIP code, all because her egregiously unsuccessful father (Alan Arkin) wants them to be educated in the best schools. ("Furniture's temporary; education is permanent.") It's a major omission, then, that we never see those schools or the kids' interaction with their stable, well-to-do Beverly Hills counterparts. We can't tell if the father is, on some weird level, justified in his fervor, or whether he's screwing up his children--subjecting them to humiliation and robbing them of a sense of permanence--for no reason. Jenkins hasn't quite figured out how to shape her narrative, which is full of episodes that are there because they actually happened but that don't have a payoff. I almost wish she'd included more voice-over narration, more commentary on the things that, as a filmmaker, she hasn't learned to bring out. The Slums of Beverly Hills never gels, but it has a likable spirit, and it's exceedingly easy on the eye, with lots of pretty girls and wry evocations of '70s fashions and decor. The father, to obtain financial support from his wealthy brother (Carl Reiner), volunteers to take in his vaguely schizzy, dipsomaniacal niece (Marisa Tomei). She and her cousin compare breasts, play with vibrators, and talk in pig Latinish gibberish, but Jenkins never lets the proceedings get too sentimental: The whimsy is always cut with an acidic awareness of the family's desperation. "Are we middle-class now?" ask the children, hopefully, before another crisis sends them back into their van, cruising past the movie stars' mansions, in the mean streets of Beverly Hills. Grading on the steep curve established by summer blockbuster seasons past, these have turned out to be a pretty good few months at the movies. Even the commercial swill ( Deep Impact , Armageddon , The Mask of Zorro , Small Soldiers , Snake Eyes , Halloween: H20 ) has been of a high grade, and Saving Private Ryan and Return to Paradise were Vitalis slaps in the kisser for people woozy from all the warm weather escapism. Out of Sight was tender and charming, as was, in its gross-out way, There's Something About Mary . And, on the indie front, The Opposite of Sex , Buffalo 66 , and Pi have proved that there's still commercial life after Sundance. Sure, we had stinkers, but even Godzilla was fun to jeer at. And there's something reassuring about the fact that The Avengers is so rotten: proof yet again that people with piles of money can hire wizard production designers but can't fake class. I don't know who the credited screenwriter, Don MacPherson, is, but it's unlikely that he has ever seen an episode of the old Avengers , let alone sussed out the source of its appeal. Opening with a slapstick sequence of agent John Steed (Ralph Fiennes) doing kung fu, the film shifts to a scene in which he meets Mrs. Peel (Uma Thurman) while sitting naked in a sauna with only a newspaper to cover his private parts. The series was erotic in a way only prim English humor can be: The Old Boy Steed was capable of throwing a punch and bonking someone with his bowler, but he left the karate kicking to his liberated, leather-suited distaff associate. Here their roles have been witlessly muddled, and MacPherson's idea of banter is to have the pair complete each other's clichés. Whereas the original Steed, Patrick Macnee, was to the English Men's Club born, Fiennes is an eternal caddie. The willowy Thurman looks great in her outfits, but it's ever more apparent that she isn't much of an actress--at least, not a trained one--and her attempts at insouciance are embarrassingly arch. As the eccentric master villain who controls the weather, even Sean Connery is flat-out terrible, acting high on the hog. To think Connery once found the Bond films so far beneath him! When he sputters lines like "Time to die!" one imagines Dr. No, Goldfinger, and Blofeld snickering in the wings. | A. The drafted nature of Barker's characters' speech is inconsistent with his claims of the film being categorized as a documentary |
What didn't William get accused of as a young boy?
A. lying to his parents
B. wetting the bed
C. calling his mother names
D. stealing from his parents
| Charity Case By JIM HARMON Illustrated by DICK FRANCIS [Transcriber's Note: This etext was produced from Galaxy Science Fiction December 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Certainly I see things that aren't there and don't say what my voice says—but how can I prove that I don't have my health? When he began his talk with "You got your health, don't you?" it touched those spots inside me. That was when I did it. Why couldn't what he said have been "The best things in life are free, buddy" or "Every dog has his day, fellow" or "If at first you don't succeed, man"? No, he had to use that one line. You wouldn't blame me. Not if you believe me. The first thing I can remember, the start of all this, was when I was four or five somebody was soiling my bed for me. I absolutely was not doing it. I took long naps morning and evening so I could lie awake all night to see that it wouldn't happen. It couldn't happen. But in the morning the bed would sit there dispassionately soiled and convict me on circumstantial evidence. My punishment was as sure as the tide. Dad was a compact man, small eyes, small mouth, tight clothes. He was narrow but not mean. For punishment, he locked me in a windowless room and told me to sit still until he came back. It wasn't so bad a punishment, except that when Dad closed the door, the light turned off and I was left there in the dark. Being four or five, I didn't know any better, so I thought Dad made it dark to add to my punishment. But I learned he didn't know the light went out. It came back on when he unlocked the door. Every time I told him about the light as soon as I could talk again, but he said I was lying. One day, to prove me a liar, he opened and closed the door a few times from outside. The light winked off and on, off and on, always shining when Dad stuck his head inside. He tried using the door from the inside, and the light stayed on, no matter how hard he slammed the door. I stayed in the dark longer for lying about the light. Alone in the dark, I wouldn't have had it so bad if it wasn't for the things that came to me. They were real to me. They never touched me, but they had a little boy. He looked the way I did in the mirror. They did unpleasant things to him. Because they were real, I talked about them as if they were real, and I almost earned a bunk in the home for retarded children until I got smart enough to keep the beasts to myself. My mother hated me. I loved her, of course. I remember her smell mixed up with flowers and cookies and winter fires. I remember she hugged me on my ninth birthday. The trouble came from the notes written in my awkward hand that she found, calling her names I didn't understand. Sometimes there were drawings. I didn't write those notes or make those drawings. My mother and father must have been glad when I was sent away to reform school after my thirteenth birthday party, the one no one came to. The reform school was nicer. There were others there who'd had it about like me. We got along. I didn't watch their shifty eyes too much, or ask them what they shifted to see. They didn't talk about my screams at night. It was home. My trouble there was that I was always being framed for stealing. I didn't take any of those things they located in my bunk. Stealing wasn't in my line. If you believe any of this at all, you'll see why it couldn't be me who did the stealing. There was reason for me to steal, if I could have got away with it. The others got money from home to buy the things they needed—razor blades, candy, sticks of tea. I got a letter from Mom or Dad every now and then before they were killed, saying they had sent money or that it was enclosed, but somehow I never got a dime of it. When I was expelled from reform school, I left with just one idea in mind—to get all the money I could ever use for the things I needed and the things I wanted. It was two or three years later that I skulked into Brother Partridge's mission on Durbin Street. The preacher and half a dozen men were singing Onward Christian Soldiers in the meeting room. It was a drafty hall with varnished camp chairs. I shuffled in at the back with my suitcoat collar turned up around my stubbled jaw. I made my hand shaky as I ran it through my knotted hair. Partridge was supposed to think I was just a bum. As an inspiration, I hugged my chest to make him think I was some wino nursing a flask full of Sneaky Pete. All I had there was a piece of copper alloy tubing inside a slice of plastic hose for taking care of myself, rolling sailors and the like. Who had the price of a bottle? Partridge didn't seem to notice me, but I knew that was an act. I knew people were always watching every move I made. He braced his red-furred hands on the sides of his auctioneer's stand and leaned his splotched eagle beak toward us. "Brothers, this being Thanksgiving, I pray the good Lord that we all are truly thankful for all that we have received. Amen." Some skin-and-bones character I didn't know struggled out of his seat, amening. I could see he had a lot to be thankful for—somewhere he had received a fix. "Brothers," Partridge went on after enjoying the interruption with a beaming smile, "you shall all be entitled to a bowl of turkey soup prepared by Sister Partridge, a generous supply of sweet rolls and dinner rolls contributed by the Early Morning Bakery of this city, and all the coffee you can drink. Let us march out to The Stars and Stripes Forever , John Philip Sousa's grand old patriotic song." I had to laugh at all those bums clattering the chairs in front of me, scampering after water soup and stale bread. As soon as I got cleaned up, I was going to have dinner in a good restaurant, and I was going to order such expensive food and leave such a large tip for the waiter and send one to the chef that they were going to think I was rich, and some executive with some brokerage firm would see me and say to himself, "Hmm, executive material. Just the type we need. I beg your pardon, sir—" just like the razor-blade comic-strip ads in the old magazines that Frankie the Pig sells three for a quarter. I was marching. Man, was I ever marching, but the secret of it was I was only marking time the way we did in fire drills at the school. They passed me, every one of them, and marched out of the meeting room into the kitchen. Even Partridge made his way down from the auctioneer's stand like a vulture with a busted wing and darted through his private door. I was alone, marking time behind the closed half of double doors. One good breath and I raced past the open door and flattened myself to the wall. Crockery was ringing and men were slurping inside. No one had paid any attention to me. That was pretty odd. People usually watch my every move, but a man's luck has to change sometime, doesn't it? Following the wallboard, I went down the side of the room and behind the last row of chairs, closer, closer, and halfway up the room again to the entrance—the entrance and the little wooden box fastened to the wall beside it. The box was old and made out of some varnished wood. There was a slot in the top. There wasn't any sign anywhere around it, but you knew it wasn't a mailbox. My hand went flat on the top of the box. One finger at a time drew up and slipped into the slot. Index, fore, third, little. I put my thumb in my palm and shoved. My hand went in. There were coins inside. I scooped them up with two fingers and held them fast with the other two. Once I dropped a dime—not a penny, milled edge—and I started to reach for it. No, don't be greedy. I knew I would probably lose my hold on all the coins if I tried for that one. I had all the rest. It felt like about two dollars, or close to it. Then I found the bill. A neatly folded bill in the box. Somehow I knew all along it would be there. I tried to read the numbers on the bill with my fingertips, but I couldn't. It had to be a one. Who drops anything but a one into a Skid Row collection box? But still there were tourists, slummers. They might leave a fifty or even a hundred. A hundred! Yes, it felt new, crisp. It had to be a hundred. A single would be creased or worn. I pulled my hand out of the box. I tried to pull my hand out of the box. I knew what the trouble was, of course. I was in a monkey trap. The monkey reaches through the hole for the bait, and when he gets it in his hot little fist, he can't get his hand out. He's too greedy to let go, so he stays there, caught as securely as if he were caged. I was a man, not a monkey. I knew why I couldn't get my hand out. But I couldn't lose that money, especially that century bill. Calm, I ordered myself. Calm. The box was fastened to the vertical tongue-and-groove laths of the woodwork, not the wall. It was old lumber, stiffened by a hundred layers of paint since 1908. The paint was as thick and strong as the boards. The box was fastened fast. Six-inch spike nails, I guessed. Calmly, I flung my whole weight away from the wall. My wrist almost cracked, but there wasn't even a bend in the box. Carefully, I tried to jerk my fist straight up, to pry off the top of the box. It was as if the box had been carved out of one solid piece of timber. It wouldn't go up, down, left or right. But I kept trying. While keeping a lookout for Partridge and somebody stepping out of the kitchen for a pull on a bottle, I spotted the clock for the first time, a Western Union clock high up at the back of the hall. Just as I seen it for the first time, the electricity wound the spring motor inside like a chicken having its neck wrung. The next time I glanced at the clock, it said ten minutes had gone by. My hand still wasn't free and I hadn't budged the box. "This," Brother Partridge said, "is one of the most profound experiences of my life." My head hinged until it lined my eyes up with Brother Partridge. The pipe hung heavy in my pocket, but he was too far from me. "A vision of you at the box projected itself on the crest of my soup," the preacher explained in wonderment. I nodded. "Swimming right in there with the dead duck." "Cold turkey," he corrected. "Are you scoffing at a miracle?" "People are always watching me, Brother," I said. "So now they do it even when they aren't around. I should have known it would come to that." The pipe was suddenly a weight I wanted off me. I would try robbing a collection box, knowing positively that I would get caught, but I wasn't dumb enough to murder. Somebody, somewhere, would be a witness to it. I had never got away with anything in my life. I was too smart to even try anything but the little things. "I may be able to help you," Brother Partridge said, "if you have faith and a conscience." "I've got something better than a conscience," I told him. Brother Partridge regarded me solemnly. "There must be something special about you, for your apprehension to come through miraculous intervention. But I can't imagine what." "I always get apprehended somehow, Brother," I said. "I'm pretty special." "Your name?" "William Hagle." No sense lying. I had been booked and printed before. Partridge prodded me with his bony fingers as if making sure I was substantial. "Come. Let's sit down, if you can remove your fist from the money box." I opened up my fingers and let the coins ring inside the box and I drew out my hand. The bill stuck to the sweat on my fingers and slid out along with the digits. A one, I decided. I had got into trouble for a grubby single. It wasn't any century. I had been kidding myself. I unfolded the note. Sure enough, it wasn't a hundred-dollar bill, but it was a twenty, and that was almost the same thing to me. I creased it and put it back into the slot. As long as it stalled off the cops, I'd talk to Partridge. We took a couple of camp chairs and I told him the story of my life, or most of it. It was hard work on an empty stomach; I wished I'd had some of that turkey soup. Then again I was glad I hadn't. Something always happened to me when I thought back over my life. The same thing. The men filed out of the kitchen, wiping their chins, and I went right on talking. After some time Sister Partridge bustled in and snapped on the overhead lights and I kept talking. The brother still hadn't used the phone to call the cops. "Remarkable," Partridge finally said when I got so hoarse I had to take a break. "One is almost— almost —reminded of Job. William, you are being punished for some great sin. Of that, I'm sure." "Punished for a sin? But, Brother, I've always had it like this, as long as I can remember. What kind of a sin could I have committed when I was fresh out of my crib?" "William, all I can tell you is that time means nothing in Heaven. Do you deny the transmigration of souls?" "Well," I said, "I've had no personal experience—" "Of course you have, William! Say you don't remember. Say you don't want to remember. But don't say you have no personal experience!" "And you think I'm being punished for something I did in a previous life?" He looked at me in disbelief. "What else could it be?" "I don't know," I confessed. "I certainly haven't done anything that bad in this life." "William, if you atone for this sin, perhaps the horde of locusts will lift from you." It wasn't much of a chance, but I was unused to having any at all. I shook off the dizziness of it. "By the Lord Harry, Brother, I'm going to give it a try!" I cried. "I believe you," Partridge said, surprised at himself. He ambled over to the money box on the wall. He tapped the bottom lightly and a box with no top slid out of the slightly larger box. He reached in, fished out the bill and presented it to me. "Perhaps this will help in your atonement," he said. I crumpled it into my pocket fast. Not meaning to sound ungrateful, I'm pretty sure he hadn't noticed it was a twenty. And then the bill seemed to lie there, heavy, a lead weight. It would have been different if I had managed to get it out of the box myself. You know how it is. Money you haven't earned doesn't seem real to you. There was something I forgot to mention so far. During the year between when I got out of the reformatory and the one when I tried to steal Brother Partridge's money, I killed a man. It was all an accident, but killing somebody is reason enough to get punished. It didn't have to be a sin in some previous life, you see. I had gotten my first job in too long, stacking boxes at the freight door of Baysinger's. The drivers unloaded the stuff, but they just dumped it off the truck. An empty rear end was all they wanted. The freight boss told me to stack the boxes inside, neat and not too close together. I stacked boxes the first day. I stacked more the second. The third day I went outside with my baloney and crackers. It was warm enough even for November. Two of them, dressed like Harvard seniors, caps and striped duffer jackets, came up to the crate I was dining off. "Work inside, Jack?" the taller one asked. "Yeah," I said, chewing. "What do you do, Jack?" the fatter one asked. "Stack boxes." "Got a union card?" I shook my head. "Application?" "No," I said. "I'm just helping out during Christmas." "You're a scab, buddy," Long-legs said. "Don't you read the papers?" "I don't like comic strips," I said. They sighed. I think they hated to do it, but I was bucking the system. Fats hit me high. Long-legs hit me low. I blew cracker crumbs into their faces. After that, I just let them go. I know how to take a beating. That's one thing I knew. Then lying there, bleeding to myself, I heard them talking. I heard noises like make an example of him and do something permanent and I squirmed away across the rubbish like a polite mouse. I made it around a corner of brick and stood up, hurting my knee on a piece of brown-splotched pipe. There were noises on the other angle of the corner and so I tested if the pipe was loose and it was. I closed my eyes and brought the pipe up and then down. It felt as if I connected, but I was so numb, I wasn't sure until I unscrewed my eyes. There was a big man in a heavy wool overcoat and gray homburg spread on a damp centerfold from the News . There was a pick-up slip from the warehouse under the fingers of one hand, and somebody had beaten his brains out. The police figured it was part of some labor dispute, I guess, and they never got to me. I suppose I was to blame anyway. If I hadn't been alive, if I hadn't been there to get beaten up, it wouldn't have happened. I could see the point in making me suffer for it. There was a lot to be said for looking at it like that. But there was nothing to be said for telling Brother Partridge about the accident, or murder, or whatever had happened that day. Searching myself after I left Brother Partridge, I finally found a strip of gray adhesive tape on my side, out of the fuzzy area. Making the twenty the size of a thick postage stamp, I peeled back the tape and put the folded bill on the white skin and smoothed the tape back. There was only one place for me to go now. I headed for the public library. It was only about twenty blocks, but not having had anything to eat since the day before, it enervated me. The downstairs washroom was where I went first. There was nobody there but an old guy talking urgently to a kid with thick glasses, and somebody building a fix in one of the booths. I could see charred matches dropping down on the floor next to his tennis shoes, and even a few grains of white stuff. But he managed to hold still enough to keep from spilling more from the spoon. I washed my hands and face, smoothed my hair down, combing it with my fingers. Going over my suit with damp toweling got off a lot of the dirt. I put my collar on the outside of my jacket and creased the wings with my thumbnail so it would look more like a sports shirt. It didn't really. I still looked like a bum, but sort of a neat, non-objectionable bum. The librarian at the main desk looked sympathetically hostile, or hostilely sympathetic. "I'd like to get into the stacks, miss," I said, "and see some of the old newspapers." "Which newspapers?" the old girl asked stiffly. I thought back. I couldn't remember the exact date. "Ones for the first week in November last year." "We have the Times microfilmed. I would have to project them for you." "I didn't want to see the Times ," I said, fast. "Don't you have any newspapers on paper?" I didn't want her to see what I wanted to read up on. "We have the News , bound, for last year." I nodded. "That's the one I wanted to see." She sniffed and told me to follow her. I didn't rate a cart to my table, I guess, or else the bound papers weren't supposed to come out of the stacks. The cases of books, row after row, smelled good. Like old leather and good pipe tobacco. I had been here before. In this world, it's the man with education who makes the money. I had been reading the Funk & Wagnalls Encyclopedia. So far I knew a lot about Mark Antony, Atomic Energy, Boron, Brussels, Catapults, Demons, and Divans. I guess I had stopped to look around at some of the titles, because the busy librarian said sharply, "Follow me." I heard my voice say, "A pleasure. What about after work?" I didn't say it, but I was used to my voice independently saying things. Her neck got to flaming, but she walked stiffly ahead. She didn't say anything. She must be awful mad, I decided. But then I got the idea she was flushed with pleasure. I'm pretty ugly and I looked like a bum, but I was young. You had to grant me that. She waved a hand at the rows of bound News and left me alone with them. I wasn't sure if I was allowed to hunt up a table to lay the books on or not, so I took the volume for last year and laid it on the floor. That was the cleanest floor I ever saw. It didn't take me long to find the story. The victim was a big man, because the story was on the second page of the Nov. 4 edition. I started to tear the page out, then only memorized the name and home address. Somebody was sure to see me and I couldn't risk trouble just now. I stuck the book back in line and left by the side door. I went to a dry-cleaner, not the cheapest place I knew, because I wouldn't be safe with the change from a twenty in that neighborhood. My suit was cleaned while I waited. I paid a little extra and had it mended. Funny thing about a suit—it's almost never completely shot unless you just have it ripped off you or burned up. It wasn't exactly in style, but some rich executives wore suits out of style that they had paid a lot of money for. I remembered Fredric March's double-breasted in Executive Suite while Walter Pidgeon and the rest wore Ivy Leagues. Maybe I would look like an eccentric executive. I bought a new shirt, a good used pair of shoes, and a dime pack of single-edged razor blades. I didn't have a razor, but anybody with nerve can shave with a single-edge blade and soap and water. The clerk took my two bucks in advance and I went up to my room. I washed out my socks and underwear, took a bath, shaved and trimmed my hair and nails with the razor blade. With some soap on my finger, I scrubbed my teeth. Finally I got dressed. Everything was all right except that I didn't have a tie. They had them, a quarter a piece, where I got the shoes. It was only six blocks—I could go back. But I didn't want to wait. I wanted to complete the picture. The razor blade sliced through the pink bath towel evenly. I cut out a nice modern-style tie, narrow, with some horizontal stripes down at the bottom. I made a tight, thin knot. It looked pretty good. I was ready to leave, so I started for the door. I went back. I had almost forgotten my luggage. The box still had three unwrapped blades in it. I pocketed it. I hefted the used blade, dulled by all the work it had done. You can run being economical into stinginess. I tossed it into the wastebasket. I had five hamburgers and five cups of coffee. I couldn't finish all of the French fries. "Mac," I said to the fat counterman, who looked like all fat countermen, "give me a Milwaukee beer." He stopped polishing the counter in front of his friend. "Milwaukee, Wisconsin, or Milwaukee, Oregon?" "Wisconsin." He didn't argue. It was cold and bitter. All beer is bitter, no matter what they say on TV. I like beer. I like the bitterness of it. It felt like another, but I checked myself. I needed a clear head. I thought about going back to the hotel for some sleep; I still had the key in my pocket (I wasn't trusting it to any clerk). No, I had had sleep on Thanksgiving, bracing up for trying the lift at Brother Partridge's. Let's see, it was daylight outside again, so this was the day after Thanksgiving. But it had only been sixteen or twenty hours since I had slept. That was enough. I left the money on the counter for the hamburgers and coffee and the beer. There was $7.68 left. As I passed the counterman's friend on his stool, my voice said, "I think you're yellow." He turned slowly, his jaw moving further away from his brain. I winked. "It was just a bet for me to say that to you. I won two bucks. Half of it is yours." I held out the bill to him. His paw closed over the money and punched me on the biceps. Too hard. He winked back. "It's okay." I rubbed my shoulder, marching off fast, and I counted my money. With my luck, I might have given the counterman's friend the five instead of one of the singles. But I hadn't. I now had $6.68 left. "I still think you're yellow," my voice said. It was my voice, but it didn't come from me. There were no words, no feeling of words in my throat. It just came out of the air the way it always did. I ran. Harold R. Thompkins, 49, vice-president of Baysinger's, was found dead behind the store last night. His skull had been crushed by a vicious beating with a heavy implement, Coroner McClain announced in preliminary verdict. Tompkins, who resided at 1467 Claremont, Edgeway, had been active in seeking labor-management peace in the recent difficulties.... I had read that a year before. The car cards on the clanking subway and the rumbling bus didn't seem nearly so interesting to me. Outside the van, a tasteful sign announced the limits of the village of Edgeway, and back inside, the monsters of my boyhood went bloomp at me. I hadn't seen anything like them in years. The slimy, scaly beasts were slithering over the newspaper holders, the ad card readers, the girl watchers as the neat little carbon-copy modern homes breezed past the windows. I ignored the devils and concentrated on reading the withered, washed-out political posters on the telephone poles. My neck ached from holding it so stiff, staring out through the glass. More than that, I could feel the jabberwocks staring at me. You know how it is. You can feel a stare with the back of your neck and between your eyes. They got one brush of a gaze out of me. The things abruptly started their business, trying to act casually as if they hadn't been waiting for me to look at them at all. They had a little human being of some sort. It was the size of a small boy, like the small boy who looked like me that they used to destroy when I was locked up with them in the dark. Except this was a man, scaled down to child's size. He had sort of an ugly, worried, tired, stupid look and he wore a shiny suit with a piece of a welcome mat or something for a necktie. Yeah, it was me. I really knew it all the time. They began doing things to the midget me. I didn't even lift an eyebrow. They couldn't do anything worse to the small man than they had done to the young boy. It was sort of nostalgic watching them, but I really got bored with all that violence and killing and killing the same kill over and over. Like watching the Saturday night string of westerns in a bar. The sunlight through the window was yellow and hot. After a time, I began to dose. The shrieks woke me up. For the first time, I could hear the shrieks of the monster's victim and listen to their obscene droolings. For the very first time in my life. Always before it had been all pantomime, like Charlie Chaplin. Now I heard the sounds of it all. They say it's a bad sign when you start hearing voices. I nearly panicked, but I held myself in the seat and forced myself to be rational about it. My own voice was always saying things everybody could hear but which I didn't say. It wasn't any worse to be the only one who could hear other things I never said. I was as sane as I ever was. There was no doubt about that. But a new thought suddenly impressed itself on me. Whatever was punishing me for my sin was determined that I turn back before reaching 1467 Claremont. | D. stealing from his parents |
What is a likely reason that the narrator chooses to go with what the citizens of Dondromogon believe about him?
A. He thinks that going with what the citizens of Dondromogon believe will be his key to escape.
B. The people of Dondromogon are harmless, so he perceives no danger in remaining on the planet.
C. He does not remember anything, is confused, and cannot back himself up on who he truly is.
D. He figures that he will eventually be returned to Earth just as mysteriously as he left.
| Warrior of Two Worlds By MANLY WADE WELLMAN He was the man of two planets, drawn through the blackness of space to save a nation from ruthless invaders. He was Yandro, the Stranger of the Prophecy—and he found that he was destined to fight both sides. [Transcriber's Note: This etext was produced from Planet Stories Summer 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] My senses came to me slowly and somehow shyly, as if not sure of their way or welcome. I felt first—pressure on my brow and chest, as if I lay face downward; then the tug and buffet of a strong, probing wind, insistent but not cold, upon my naked skin. Closing my hands, I felt them dig into coarse dirt. I turned my face downwind and opened my eyes. There was little to see, so thick was the dust cloud around me. Words formed themselves on my thick tongue, words that must have been spoken by so many reviving unfortunates through the ages: "Where am I?" And at once there was an answer: " You lie upon the world Dondromogon. " I knew the language of that answer, but where it came from—above, beneath, or indeed within me—I could not say. I lifted a hand, and knuckled dust from my eyes. "How did I get here?" I demanded of the speaker. "It was ordered—by the Masters of the Worlds—that you should be brought from your own home planet, called Earth in the System of the star called Sun. Do you remember Earth?" And I did not know whether I remembered or not. Vague matters stirred deep in me, but I could not for certain say they were memories. I asked yet again: "Who am I?" The voice had a note of triumph. "You do not know that. It is as well, for this will be a birth and beginning of your destined leadership on Dondromogon." "Destined—leadership—" I began to repeat, and fell silent. I had need to think. The voice was telling me that I had been snatched from worlds away, for a specified purpose here on whatever windswept planet Dondromogon might be. "Birth and beginning—destined leadership—" Fantastic! And yet, for all I could say to the contrary, unvarnishedly true. "Dondromogon?" I mumbled. "The name is strange to me." "It is a world the size of your native one," came words of information. "Around a star it spins, light-years away from the world of your birth. One face of Dondromogon ever looks to the light and heat, wherefore its metals run in glowing seas. The other face is ever away in cold darkness, with its air freezing into solid chunks. But because Dondromogon wavers on its axis, there are two lunes of its surface which from time to time shift from night to day. These are habitable." My eyes were tight shut against the dust, but they saw in imagination such a planet—one-half incandescent, one-half pitchy black. From pole to pole on opposite sides ran the two twilight zones, widest at the equators like the outer rind of two slices of melon. Of course, such areas, between the hot and cold hemispheres, would be buffeted by mighty gales ... the voice was to be heard again: "War is fought between the two strips of habitable ground. War, unceasing, bitter, with no quarter asked, given or expected. Dondromogon was found and settled long ago, by adventurers from afar. Now come invaders, to reap the benefits of discovery and toil." A pause. "You find that thought unpleasant? You wish to right that wrong?" "Anyone would wish that," I replied. "But how—" "You are going to ask how you were brought here. That is the mystery of the Masters ." The voice became grand. "Suffice it that you were needed, and that the time was ripe. There is a proper time, like a proper place, for each thing and each happening. Now, go to your destiny." I rose on my knees, shielding my face from the buffeting wind by lifting a forearm. Somewhere through the murky clouds showed a dim blocky silhouette, a building of sorts. The voice spoke no more. I had not the time to wonder about it. I got to my feet, bent double to keep from being blown over, and staggered toward the promised haven. I reached it, groped along until I found a door. There was no latch, handle or entry button, and I pounded heavily on the massive panels. The door opened from within, and I was blown inside, to fall sprawling. I struck my forehead upon a floor of stone or concrete, and so was half-stunned, but still I could distinguish something like the sound of agitated voices. Then I felt myself grasped, by both shoulders, and drawn roughly erect. The touch restored my senses, and I wrenched myself violently free. What had seized me? That was my first wonder. On this strange world called Dondromogon, what manner of intelligent life bade defiance to heat and cold and storm, and built these stout structures, and now laid hands—were they hands indeed?—upon me? I swung around, setting my back to a solid wall. My first glance showed me that my companions were creatures like myself—two-legged, fair-skinned men, shorter and slighter than I, but clad in metal-faced garments and wearing weapons in their girdles. I saw that each bore a swordlike device with a curved guard, set in a narrow sheath as long as my arm. Each also had a shorter weapon, with a curved stock to fit the palm of the hand, borne snugly in a holster. With such arms I had a faint sense of familiarity. "Who are you, and where are you from?" said one of the two, a broad-faced middle-aged fellow. "Don't lie any more than you can help." I felt a stirring of the hair on my neck, but kept my voice mild and level: "Why should I lie? Especially as I don't know who I am, or where I'm from, or anything that has happened longer ago than just a moment. I woke up out there in the dust storm, and I managed to come here for shelter." "He's a Newcomer spy," quoth the other. "Let's put him under arrest." "And leave this gate unguarded?" demanded the other. "Sound the signal," and he jerked his head toward a system of levers and gauges on the wall beside the door-jamb. "There's a bigger reward for capture than for warning," objected his friend in turn, "and whoever comes to take this man will claim 'capture.' I'll guard here, and you take him in, then we'll divide—" "No. Yours is the idea. I'll guard and you take him in." The second man studied me apprehensively. "He's big, and looks strong, even without weapons." "Don't be afraid," I urged. "I'll make no resistance, if you'll only conduct me to your commander. I can show him that I'm no spy or enemy." Both stared narrowly. "No spy? No enemy?" asked the broad-faced one who had first spoken. Then, to his comrade: "No reward, then." "I think there'll be a reward," was the rejoinder, and the second man's hand stole to the sword-weapon. With a whispering rasp it cleared from its scabbard. "If he's dead, we get pay for both warning and capture—" His thumb touched a button at the pommel of the hilt. The dull blade suddenly glowed like heated iron, and from it crackled and pulsed little rainbow rays. There was no time to think or plan or ponder. I moved in, with a knowing speed that surprised me as much as the two guards. Catching the fellow's weapon wrist, I clamped it firmly and bent it back and around. He whimpered and swore, and his glowing sword dropped. Its radiant blade almost fell on my naked foot. Before the clang of its fall was through echoing, I had caught it up, and set the point within inches of its owner's unprotected face. "Quiet, or I'll roast you," I told him. The other had drawn a weapon of his own, a pistol-form arrangement. I turned on him, but too late. He pressed the trigger, and from the muzzle came—not a projectile but a flying, spouting filament of cord that seemed to spring on me like a long thin snake and to fasten coil after coil around my body. The stuff that gushed from the gun-muzzle seemed plastic in form, but hardened so quickly upon contact with the air, it bound me like wire. Half a dozen adroit motions of the fellow's gun hand, and my arms were caught to my body. I dropped my sword to prevent it burning me, and tried to break away, but my bonds were too much for me. "Let me out of this," I growled, and kicked at the man with my still unbound foot. He snapped a half-hitch on my ankle, and threw me heavily. Triumphant laughter came from both adversaries. Then: "What's this?" The challenge was clear, rich, authoritative. Someone else had come, from a rearward door into the stone-walled vestibule where the encounter was taking place. A woman this time, not of great height, and robust but not heavy. She was dressed for vigorous action in dark slacks with buskins to make them snug around ankles and calves, a jerkin of stout material that was faced with metal armor plates and left bare her round, strong arms. A gold-worked fillet bound her tawny hair back from a rosy, bold-featured face—a nose that was positively regal, a mouth short and firm but not hard, and blue eyes that just now burned and questioned. She wore a holstered pistol, and a cross-belt supported several instruments of a kind I could not remember seeing before. A crimson cloak gave color and dignity to her costume, and plainly she was someone of position, for both the men stiffened to attention. "A spy," one ventured. "He pushed in, claimed he was no enemy, then tried to attack—" "They lie," I broke in, very conscious of my naked helplessness before her regard. "They wanted to kill me and be rewarded for a false story of vigilance. I only defended myself." "Get him on his feet," the young woman said, and the two guards obeyed. Then her eyes studied me again. "Gods! What a mountain of a man!" she exclaimed. "Can you walk, stranger?" "Barely, with these bonds." "Then manage to do so." She flung off her cloak and draped it over my nakedness. "Walk along beside me. No tricks, and I promise you fair hearing." We went through the door by which she had entered, into a corridor beyond. It was lighted by small, brilliant bulbs at regular intervals. Beyond, it gave into several passages. She chose one of them and conducted me along. "You are surely not of us," she commented. "Men I have seen who are heavier than you, but none taller. Whence came you?" I remembered the strange voice that had instructed me. "I am from a far world," I replied. "It is called—yes, Earth. Beyond that, I know nothing. Memory left me." "The story is a strange one," she commented. "And your name?" "I do not know that, either. Who are you?" "Doriza—a gentlewoman of the guard. My inspection tour brought me by chance to where you fought my outposts. But it is not for you to ask questions. Enter here." We passed through another door, and I found myself in an office. A man in richly-embossed armor platings sat there. He had a fringe of pale beard, and his eyes were bluer than the gentlewoman Doriza's. She made a gesture of salute, hand at shoulder height, and reported the matter. He nodded for her to fall back to a corner. "Stranger," he said to me, "can you think of no better tale to tell than you now offer?" "I tell the truth," was my reply, not very gracious. "You will have to prove that," he admonished me. "What proof have I?" I demanded. "On this world of yours—Dondromogon, isn't it called?—I'm no more than an hour old. Accident or shock has taken my memory. Let me have a medical examination. A scientist probably can tell what happened to put me in such a condition." "I am a scientist," offered Doriza, and came forward. Her eyes met mine, suddenly flickered and lowered. "His gaze," she muttered. The officer at the table was touching a button. An attendant appeared, received an order, and vanished again. In a few moments two other men came—one a heavily armed officer of rank, the other an elderly, bearded fellow in a voluminous robe that enfolded him in most dignified manner. This latter man opened wide his clear old eyes at sight of me. "The stranger of the prophecy!" he cried, in a voice that made us all jump. The officer rose from behind the table. "Are you totally mad, Sporr? You mystic doctors are too apt to become fuddled—" "But it is, it is!" The graybeard flourished a thin hand at me. "Look at him, you of little faith! Your mind dwells so much on material strength that you lose touch with the spiritual—" He broke off, and wheeled on the attendant who had led him in. "To my study," he commanded. "On the shelf behind my desk, bring the great gold-bound book that is third from the right." Then he turned back, and bowed toward me. "Surely you are Yandro, the Conquering Stranger," he said, intoning as if in formal prayer. "Pardon these short-sighted ones—deign to save us from our enemies—" The girl Doriza spoke to the officer: "If Sporr speaks truth, and he generally does, you have committed a blasphemy." The other made a little grimace. "This may be Yandro, though I'm a plain soldier and follow the classics very little. The First Comers are souls to worship, not to study. If indeed he is Yandro," and he was most respectful, "he will appreciate, like a good military mind, my caution against possible impostors." "Who might Yandro be?" I demanded, very uncomfortable in my bonds and loose draperies. Old Sporr almost crowed. "You see? If he was a true imposter, he would come equipped with all plausible knowledge. As it is—" "As it is, he may remember that the Conquering Stranger is foretold to come with no memory of anything," supplied the officer. "Score one against you, Sporr. You should have been able to instruct me, not I you." The attendant reentered, with a big book in his hands. It looked old and well-thumbed, with dim gold traceries on its binding. Sporr snatched it, and turned to a brightly colored picture. He looked once, his beard gaped, and he dropped to his knees. "Happy, happy the day," he jabbered, "that I was spared to see our great champion come among us in the flesh, as was foretold of ancient time by the First Comers!" Doriza and the officer crossed to his side, snatching the book. Their bright heads bent above it. Doriza was first to speak. "It is very like," she half-stammered. The officer faced me, with a sort of baffled respect. "I still say you will understand my caution," he addressed me, with real respect and shyness this time. "If you are Yandro himself, you can prove it. The prophecy even sketches a thumb-print—" And he held the book toward me. It contained a full-page likeness, in color, of myself wrapped in a scarlet robe. Under this was considerable printed description, and to one side a thumb-print, or a drawing of one, in black. "Behold," Doriza was saying, "matters which even expert identification men take into thought. The ears in the picture are like the ears of the real man—" "That could be plastic surgery," rejoined the officer. "Such things are artfully done by the Newcomers, and the red mantle he wears more easily assumed." Doriza shook her head. "That happens to be my cloak. I gave it to him because he was naked, and not for any treasonable masquerade. But the thumb-print—" "Oh, yes, the thumb-print," I repeated wearily. "By all means, study my thumbs, if you'll first take these bonds off of me." "Bonds," mumbled old Sporr. He got creakily up from his knees and bustled to me. From under his robe he produced a pouch, and took out a pencil-sized rod. Gingerly opening the red mantle, he touched my tether in several places with the glowing end of the rod. The coils dropped away from my grateful body and limbs. I thrust out my hands. "Thumb-prints?" I offered. Sporr had produced something else, a little vial of dark pigment. He carefully anointed one of my thumbs, and pressed it to the page. All three gazed. "The same," said Doriza. And they were all on their knees before me. "Forgive me, great Yandro," said the officer thickly. "I did not know." "Get up," I bade them. "I want to hear why I was first bound, and now worshipped." II They rose, but stood off respectfully. The officer spoke first. "I am Rohbar, field commander of this defense position," he said with crisp respect. "Sporr is a mystic doctor, full of godly wisdom. Doriza, a junior officer and chief of the guard. And you—how could you know?—are sent by the First Comers to save us from our enemies." "Enemies?" I repeated. "The Newcomers," supplemented Doriza. "They have taken the "Other Side" of Dondromogon, and would take our side as well. We defend ourselves at the poles. Now," and her voice rang joyously, "you will lead us to defeat and crush them utterly!" "Not naked like this," I said, and laughed. I must have sounded foolish, but it had its effect. "Follow me, deign to follow me," Sporr said. "Your clothing, your quarters, your destiny, all await you." We went out by the door at the rear, and Sporr respectfully gestured me upon a metal-plated platform. Standing beside me, he tinkered with a lever. We dropped smoothly away into a dark corridor, past level after level of light and sound. "Our cities are below ground," he quavered. "Whipped by winds above, we must scrabble in the depths for life's necessities—chemicals to transmute into food, to weave into clothing, to weld into tools and weapons—" The mention of food brought to me the thought that I was hungry. I said as much, even as our elevator platform came to the lowest level and stopped. "I have arranged for that," Sporr began, then fell silent, fingers combing his beard in embarrassment. "Arranged food for me?" I prompted sharply. "As if you know I had come? What—" "Pardon, great Yandro," babbled Sporr. "I was saying that I arranged food, as always, for whatever guest should come. Please follow." We entered a new small chamber, where a table was set with dishes of porcelain-like plastic. Sporr held a chair for me, and waited on me with the utmost gingerly respect. The food was a pungent and filling jelly, a little bundle of transparent leaves or scraps like cellophane and tasting of spice, and a tumbler of pink juice. I felt refreshed and satisfied, and thanked Sporr, who led me on to the next room. "Behold!" he said, with a dramatic gesture. "Your garments, even as they have been preserved against your coming!" It was a sleeping chamber, with a cot made fast to the wall, a metal locker or cupboard, with a glass door through which showed the garments of which Sporr spoke. The door closed softly behind me—I was left alone. Knowing that it was expected of me, I went to the locker and opened the door. The garments inside were old, I could see, but well kept and serviceable. I studied their type, and my hands, if not my mind, seemed familiar with them. There was a kiltlike item, belted at the waist and falling to mid-thigh. A resilient band at the top, with a series of belt-holes, made it adaptable to my own body or to any other. Then came an upper garment, a long strip of soft, close-woven fabric that spiralled around the torso from hip to armpit, the end looping over the left shoulder and giving full play to the arms. A gold-worked fillet bound the brows and swept back my longish hair, knotting at the nape of the neck. The only fitted articles were a pair of shoes, metal-soled and soft-uppered, that went on well enough and ran cross-garters up to below the knee, like buskins. The case also held a platinum chain for the neck, a belt-bag, and a handsome sword, with clips to fasten them in place. These things, too, I donned, and closed the glass door. The light struck it at such an angle as to make it serve for a full-length mirror. With some curiosity I gazed at my image. The close-fitting costume was rich and dark, with bright colors only for edgings and minor accessories. I myself—and it was as if I saw my body for the first time—towered rather bluffly, with great breadth of chest and shoulder, and legs robust enough to carry such bulk. The face was square but haggard, as if from some toil or pain which was now wiped from my recollection. That nose had been even bigger than it was now, but a fracture had shortened it somewhat. The eyes were deep set and dark and moody—small wonder!—the chin heavy, the mouth made grim by a scar at one corner. Black, shaggy hair hung down like brackets. All told, I looked like a proper person for physical labor, or even fierce fighting—but surely no inspirational leader or savior of a distressed people. I took the military cloak which Doriza had lent me and slung it over my shoulders. Turning, I clanked out on my metal-soled shoes. Sporr was waiting in the room where I had eaten. His eyes widened at sight of me, something like a grin of triumph flashed through his beard. Then he bowed, supple and humble, his palms together. "It is indeed Yandro, our great chief," he mumbled. Then he turned and crossed the room. A sort of mouthpiece sprouted from the wall. "I announce," he intoned into it. "I announce, I, Sporr, the reader and fore-teller of wisdom. Yandro is with us, he awaits his partners and friends. Let them meet him in the audience hall." Facing me again, he motioned most respectfully toward the door to the hall. I moved to open it, and he followed, muttering. Outside stood Doriza. Her blue eyes met mine, and her lips moved to frame a word. Then, suddenly, she was on her knee, catching my hand and kissing it. "I serve Yandro," she vowed tremulously. "Now and forever—and happy that I was fated to live when he returned for the rescue of all Dondromogon." "Please get up," I bade her, trying not to sound as embarrassed as I felt. "Come with me. There is still much that I do not understand." "I am Yandro's orderly and helper," she said. Rising, she ranged herself at my left hand. "Will Yandro come this way? He will be awaited in the audience hall." It seemed to me then that the corridors were vast and mixed as a labyrinth, but Doriza guided me without the slightest hesitation past one tangled crossway after another. My questions she answered with a mixture of awe and brightness. "It is necessary that we live like this," she explained. "The hot air of Dondromogon's sunlit face is ever rising, and the cold air from the dark side comes rushing under to fill the vacuum. Naturally, our strip of twilight country is never free of winds too high and fierce to fight. No crops can grow outside, no domestic animals flourish. We must pen ourselves away from the sky and soil, with stout walls and heavy sunken parapets. Our deep mines afford every element for necessities of life." I looked at my garments, and hers. There were various kinds of fabric, which I now saw plainly to be synthetic. "The other side, where those you call the Newcomers dwell and fight," I reminded. "Is it also windswept? Why can two people not join forces and face toil and nature together? They should fight, not each other, but the elements." Doriza had no answer that time, but Sporr spoke up behind us: "Great Yandro is wise as well as powerful. But the Newcomers do not want to help, not even to conquer. They want to obliterate us. There is nothing to do—not for lifetimes—but to fight them back at the two poles." We came to a main corridor. It had a line of armed guards, but no pedestrians or vehicles, though I thought I caught a murmur of far-off traffic. Doriza paused before a great portal, closed by a curtainlike sheet of dull metal. She spoke into a mouthpiece: "Doriza, gentlewoman of the guard, conducts Yandro, the Conquering Stranger, to greet his lieutenants!" I have said that the portal was closed by a curtainlike metal sheet; and like a curtain it lifted, letting us through into the auditorium. That spacious chamber had rows of benches, with galleries above, that might have seated a thousand. However, only a dozen or so were present, on metal chairs ranged across the stage upon which we entered. They were all men but two, and wore robes of black, plum-purple or red. At sight of me, they rose together, most respectfully. They looked at me, and I looked at them. My first thought was, that if these were people of authority and trust in the nation I seemed destined to save, my work was cut out for me. Not that they really seemed stupid—none had the look, or the subsequent action, of stupidity. But they were not pleasant. Their dozen pairs of eyes fixed me with some steadiness, but with no frankness anywhere. One man had a round, greedy-seeming face. Another was too narrow and cunning to look it. Of the women, one was nearly as tall as I and nobly proportioned, with hair of a red that would be inspiring were it not so blatantly dyed. The other was a little wisp of a brunette, with teeth too big for her scarlet mouth and bright eyes like some sort of a rodent. They all wore jewelry. Too much jewelry. My mind flew back to the two scrubby, venial guardsmen who had first welcomed me; to stuffy Rohbar, the commander; to Sporr, spry and clever enough, but somehow unwholesome; Doriza—no, she was not like these others, who may have lived too long in their earth-buried shelters. And Doriza now spoke to the gathering: "Yandro, folk of the Council! He deigns to give you audience." " Yandro! " They all spoke the name in chorus, and bowed toward me. Silence then, a silence which evidently I must break. I broke it: "Friends, I am among you with no more memory or knowledge than an infant. I hear wonderful things, of which I seem to be the center. Are they true?" "The tenth part of the wonders which concern mighty Yandro have not been told," intoned Sporr, ducking his bearded head in a bow, but fixing me with his wise old eyes. One of the group, called Council by Doriza, now moved a pace forward. He was the greedy-faced man, short but plump, and very conscious of the dignified folds of his purple robe. One carefully-tended hand brushed back his ginger-brown hair, then toyed with a little moustache. "I am Gederr, senior of this Council," he purred. "If Yandro permits, I will speak simply. Our hopes have been raised by Yandro's return—the return presaged of old by those who could see the future, and more recently by the death in battle of the Newcomer champion, called Barak." "Barak!" I repeated. "I—I—" And I paused. When I had to learn my own name, how could it be that I sensed memory of another's name? "Barak was a brute—mighty, but a brute." Thus Gederr continued. "Weapons in his hands were the instruments of fate. His hands alone caused fear and ruin. But it pleased our fortune-bringing stars to encompass his destruction." He grinned, and licked his full lips. "Now, even as they are without their battle-leader, so we have ours." "You honor me," I told him. "Yet I still know little. It seems that I am expected to aid and lead and save the people of this world called Dondromogon. But I must know them before I can help." Gederr turned his eyes upon the woman with the red hair, and gestured to her "Tell him, Elonie." Then he faced me. "Have we Yandro's permission to sit?" "By all means," I granted, a little impatiently, and sat down myself. The others followed suit—the Council on their range of chairs, Doriza on a bench near me, Sporr somewhere behind. The woman called Elonie remained upon her sandalled feet, great eyes the color of deep green water fixed upon me. | C. He does not remember anything, is confused, and cannot back himself up on who he truly is. |
What is an example of a prefixing language? | ### Introduction
A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains unknown. For instance, it is unclear whether it is constraints imposed by the phonological or by the morphosyntactic attributes of the L1 that are more important during the process of learning an L2's morphosyntax. Within the area of natural language processing (NLP) research, experimenting on neural network models just as if they were human subjects has recently been gaining popularity BIBREF3, BIBREF4, BIBREF5. Often, so-called probing tasks are used, which require a specific subset of linguistic knowledge and can, thus, be leveraged for qualitative evaluation. The goal is to answer the question: What do neural networks learn that helps them to succeed in a given task? Neural network models, and specifically sequence-to-sequence models, have pushed the state of the art for morphological inflection – the task of learning a mapping from lemmata to their inflected forms – in the last years BIBREF6. Thus, in this work, we experiment on such models, asking not what they learn, but, motivated by the respective research on human subjects, the related question of how what they learn depends on their prior knowledge. We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. We aim at finding answers to two main questions: (i) Do errors systematically differ between source languages? (ii) Do these differences seem explainable, given the properties of the source and target languages? In other words, we are interested in exploring if and how L2 acquisition of morphological inflection depends on the L1, i.e., the "native language", in neural network models. To this goal, we select a diverse set of eight source languages from different language families – Basque, French, German, Hungarian, Italian, Navajo, Turkish, and Quechua – and three target languages – English, Spanish and Zulu. We pretrain a neural sequence-to-sequence architecture on each of the source languages and then fine-tune the resulting models on small datasets in each of the target languages. Analyzing the errors made by the systems, we find that (i) source and target language being closely related simplifies the successful learning of inflection in the target language, (ii) the task is harder to learn in a prefixing language if the source language is suffixing – as well as the other way around, and (iii) a source language which exhibits an agglutinative morphology simplifies learning of a second language's inflectional morphology. ### Task
Many of the world's languages exhibit rich inflectional morphology: the surface form of an individual lexical entry changes in order to express properties such as person, grammatical gender, or case. The citation form of a lexical entry is referred to as the lemma. The set of all possible surface forms or inflections of a lemma is called its paradigm. Each inflection within a paradigm can be associated with a tag, i.e., 3rdSgPres is the morphological tag associated with the inflection dances of the English lemma dance. We display the paradigms of dance and eat in Table TABREF1. The presence of rich inflectional morphology is problematic for NLP systems as it increases word form sparsity. For instance, while English verbs can have up to 5 inflected forms, Archi verbs have thousands BIBREF7, even by a conservative count. Thus, an important task in the area of morphology is morphological inflection BIBREF8, BIBREF9, which consists of mapping a lemma to an indicated inflected form. An (irregular) English example would be with PAST being the target tag, denoting the past tense form. Additionally, a rich inflectional morphology is also challenging for L2 language learners, since both rules and their exceptions need to be memorized. In NLP, morphological inflection has recently frequently been cast as a sequence-to-sequence problem, where the sequence of target (sub-)tags together with the sequence of input characters constitute the input sequence, and the characters of the inflected word form the output. Neural models define the state of the art for the task and obtain high accuracy if an abundance of training data is available. Here, we focus on learning of inflection from limited data if information about another language's morphology is already known. We, thus, loosely simulate an L2 learning setting. ### Task ::: Formal definition.
Let ${\cal M}$ be the paradigm slots which are being expressed in a language, and $w$ a lemma in that language. We then define the paradigm $\pi $ of $w$ as: $f_k[w]$ denotes an inflected form corresponding to tag $t_{k}$, and $w$ and $f_k[w]$ are strings consisting of letters from an alphabet $\Sigma $. The task of morphological inflection consists of predicting a missing form $f_i[w]$ from a paradigm, given the lemma $w$ together with the tag $t_i$. ### Model ::: Pointer–Generator Network
The models we experiment with are based on a pointer–generator network architecture BIBREF10, BIBREF11, i.e., a recurrent neural network (RNN)-based sequence-to-sequence network with attention and a copy mechanism. A standard sequence-to-sequence model BIBREF12 has been shown to perform well for morphological inflection BIBREF13 and has, thus, been subject to cognitively motivated experiments BIBREF14 before. Here, however, we choose the pointer–generator variant of sharma-katrapati-sharma:2018:K18-30, since it performs better in low-resource settings, which we will assume for our target languages. We will explain the model shortly in the following and refer the reader to the original paper for more details. ### Model ::: Pointer–Generator Network ::: Encoders.
Our architecture employs two separate encoders, which are both bi-directional long short-term memory (LSTM) networks BIBREF15: The first processes the morphological tags which describe the desired target form one by one. The second encodes the sequence of characters of the input word. ### Model ::: Pointer–Generator Network ::: Attention.
Two separate attention mechanisms are used: one per encoder LSTM. Taking all respective encoder hidden states as well as the current decoder hidden state as input, each of them outputs a so-called context vector, which is a weighted sum of all encoder hidden states. The concatenation of the two individual context vectors results in the final context vector $c_t$, which is the input to the decoder at time step $t$. ### Model ::: Pointer–Generator Network ::: Decoder.
Our decoder consists of a uni-directional LSTM. Unlike a standard sequence-to-sequence model, a pointer–generator network is not limited to generating characters from the vocabulary to produce the output. Instead, the model gives certain probability to copying elements from the input over to the output. The probability of a character $y_t$ at time step $t$ is computed as a sum of the probability of $y_t$ given by the decoder and the probability of copying $y_t$, weighted by the probabilities of generating and copying: $p_{\textrm {dec}}(y_t)$ is calculated as an LSTM update and a projection of the decoder state to the vocabulary, followed by a softmax function. $p_{\textrm {copy}}(y_t)$ corresponds to the attention weights for each input character. The model computes the probability $\alpha $ with which it generates a new output character as for context vector $c_t$, decoder state $s_t$, embedding of the last output $y_{t-1}$, weights $w_c$, $w_s$, $w_y$, and bias vector $b$. It has been shown empirically that the copy mechanism of the pointer–generator network architecture is beneficial for morphological generation in the low-resource setting BIBREF16. ### Model ::: Pretraining and Finetuning
Pretraining and successive fine-tuning of neural network models is a common approach for handling of low-resource settings in NLP. The idea is that certain properties of language can be learned either from raw text, related tasks, or related languages. Technically, pretraining consists of estimating some or all model parameters on examples which do not necessarily belong to the final target task. Fine-tuning refers to continuing training of such a model on a target task, whose data is often limited. While the sizes of the pretrained model parameters usually remain the same between the two phases, the learning rate or other details of the training regime, e.g., dropout, might differ. Pretraining can be seen as finding a suitable initialization of model parameters, before training on limited amounts of task- or language-specific examples. In the context of morphological generation, pretraining in combination with fine-tuning has been used by kann-schutze-2018-neural, which proposes to pretrain a model on general inflection data and fine-tune on examples from a specific paradigm whose remaining forms should be automatically generated. Famous examples for pretraining in the wider area of NLP include BERT BIBREF17 or GPT-2 BIBREF18: there, general properties of language are learned using large unlabeled corpora. Here, we are interested in pretraining as a simulation of familiarity with a native language. By investigating a fine-tuned model we ask the question: How does extensive knowledge of one language influence the acquisition of another? ### Experimental Design ::: Target Languages
We choose three target languages. English (ENG) is a morphologically impoverished language, as far as inflectional morphology is concerned. Its verbal paradigm only consists of up to 5 different forms and its nominal paradigm of only up to 2. However, it is one of the most frequently spoken and taught languages in the world, making its acquisition a crucial research topic. Spanish (SPA), in contrast, is morphologically rich, and disposes of much larger verbal paradigms than English. Like English, it is a suffixing language, and it additionally makes use of internal stem changes (e.g., o $\rightarrow $ ue). Since English and Spanish are both Indo-European languages, and, thus, relatively similar, we further add a third, unrelated target language. We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing. ### Experimental Design ::: Source Languages
For pretraining, we choose languages with different degrees of relatedness and varying morphological similarity to English, Spanish, and Zulu. We limit our experiments to languages which are written in Latin script. As an estimate for morphological similarity we look at the features from the Morphology category mentioned in The World Atlas of Language Structures (WALS). An overview of the available features as well as the respective values for our set of languages is shown in Table TABREF13. We decide on Basque (EUS), French (FRA), German (DEU), Hungarian (HUN), Italian (ITA), Navajo (NAV), Turkish (TUR), and Quechua (QVH) as source languages. Basque is a language isolate. Its inflectional morphology makes similarly frequent use of prefixes and suffixes, with suffixes mostly being attached to nouns, while prefixes and suffixes can both be employed for verbal inflection. French and Italian are Romance languages, and thus belong to the same family as the target language Spanish. Both are suffixing and fusional languages. German, like English, belongs to the Germanic language family. It is a fusional, predominantly suffixing language and, similarly to Spanish, makes use of stem changes. Hungarian, a Finno-Ugric language, and Turkish, a Turkic language, both exhibit an agglutinative morphology, and are predominantly suffixing. They further have vowel harmony systems. Navajo is an Athabaskan language and the only source language which is strongly prefixing. It further exhibits consonant harmony among its sibilants BIBREF19, BIBREF20. Finally, Quechua, a Quechuan language spoken in South America, is again predominantly suffixing and unrelated to all of our target languages. ### Experimental Design ::: Hyperparameters and Data
We mostly use the default hyperparameters by sharma-katrapati-sharma:2018:K18-30. In particular, all RNNs have one hidden layer of size 100, and all input and output embeddings are 300-dimensional. For optimization, we use ADAM BIBREF21. Pretraining on the source language is done for exactly 50 epochs. To obtain our final models, we then fine-tune different copies of each pretrained model for 300 additional epochs for each target language. We employ dropout BIBREF22 with a coefficient of 0.3 for pretraining and, since that dataset is smaller, with a coefficient of 0.5 for fine-tuning. We make use of the datasets from the CoNLL–SIGMORPHON 2018 shared task BIBREF9. The organizers provided a low, medium, and high setting for each language, with 100, 1000, and 10000 examples, respectively. For all L1 languages, we train our models on the high-resource datasets with 10000 examples. For fine-tuning, we use the low-resource datasets. ### Quantitative Results
In Table TABREF18, we show the final test accuracy for all models and languages. Pretraining on EUS and NAV results in the weakest target language inflection models for ENG, which might be explained by those two languages being unrelated to ENG and making at least partial use of prefixing, while ENG is a suffixing language (cf. Table TABREF13). In contrast, HUN and ITA yield the best final models for ENG. This is surprising, since DEU is the language in our experiments which is closest related to ENG. For SPA, again HUN performs best, followed closely by ITA. While the good performance of HUN as a source language is still unexpected, ITA is closely related to SPA, which could explain the high accuracy of the final model. As for ENG, pretraining on EUS and NAV yields the worst final models – importantly, accuracy is over $15\%$ lower than for QVH, which is also an unrelated language. This again suggests that the prefixing morphology of EUS and NAV might play a role. Lastly, for ZUL, all models perform rather poorly, with a minimum accuracy of 10.7 and 10.8 for the source languages QVH and EUS, respectively, and a maximum accuracy of 24.9 for a model pretrained on Turkish. The latter result hints at the fact that a regular and agglutinative morphology might be beneficial in a source language – something which could also account for the performance of models pretrained on HUN. ### Qualitative Results
For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. As we can see, the results are similar to the test set results for all language combinations. We manually annotate the outputs for the first 75 development examples for each source–target language combination. All found errors are categorized as belonging to one of the following categories. ### Qualitative Results ::: Stem Errors
SUB(X): This error consists of a wrong substitution of one character with another. SUB(V) and SUB(C) denote this happening with a vowel or a consonant, respectively. Letters that differ from each other by an accent count as different vowels. Example: decultared instead of decultured DEL(X): This happens when the system ommits a letter from the output. DEL(V) and DEL(C) refer to a missing vowel or consonant, respectively. Example: firte instead of firtle NO_CHG(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (NO_CHG(V)) or a consonant (NO_CHG(C)), but this is missing in the predicted form. Example: verto instead of vierto MULT: This describes cases where two or more errors occur in the stem. Errors concerning the affix are counted for separately. Example: aconcoonaste instead of acondicionaste ADD(X): This error occurs when a letter is mistakenly added to the inflected form. ADD(V) refers to an unnecessary vowel, ADD(C) refers to an unnecessary consonant. Example: compillan instead of compilan CHG2E(X): This error occurs when inflecting the lemma to the gold form requires a change of either a vowel (CHG2E(V)) or a consonant (CHG2E(C)), and this is done, but the resulting vowel or consonant is incorrect. Example: propace instead of propague ### Qualitative Results ::: Affix Errors
AFF: This error refers to a wrong affix. This can be either a prefix or a suffix, depending on the correct target form. Example: ezoJulayi instead of esikaJulayi CUT: This consists of cutting too much of the lemma's prefix or suffix before attaching the inflected form's prefix or suffix, respectively. Example: irradiseis instead of irradiaseis ### Qualitative Results ::: Miscellaneous Errors
REFL: This happens when a reflective pronoun is missing in the generated form. Example: doliéramos instead of nos doliéramos REFL_LOC: This error occurs if the reflective pronouns appears at an unexpected position within the generated form. Example: taparsebais instead of os tapabais OVERREG: Overregularization errors occur when the model predicts a form which would be correct if the lemma's inflections were regular but they are not. Example: underteach instead of undertaught ### Qualitative Results ::: Error Analysis: English
Table TABREF35 displays the errors found in the 75 first ENG development examples, for each source language. From Table TABREF19, we know that HUN $>$ ITA $>$ TUR $>$ DEU $>$ FRA $>$ QVH $>$ NAV $>$ EUS, and we get a similar picture when analyzing the first examples. Thus, especially keeping HUN and TUR in mind, we cautiously propose a first conclusion: familiarity with languages which exhibit an agglutinative morphology simplifies learning of a new language's morphology. Looking at the types of errors, we find that EUS and NAV make the most stem errors. For QVH we find less, but still over 10 more than for the remaining languages. This makes it seem that models pretrained on prefixing or partly prefixing languages indeed have a harder time to learn ENG inflectional morphology, and, in particular, to copy the stem correctly. Thus, our second hypotheses is that familiarity with a prefixing language might lead to suspicion of needed changes to the part of the stem which should remain unaltered in a suffixing language. DEL(X) and ADD(X) errors are particularly frequent for EUS and NAV, which further suggests this conclusion. Next, the relatively large amount of stem errors for QVH leads to our second hypothesis: language relatedness does play a role when trying to produce a correct stem of an inflected form. This is also implied by the number of MULT errors for EUS, NAV and QVH, as compared to the other languages. Considering errors related to the affixes which have to be generated, we find that DEU, HUN and ITA make the fewest. This further suggests the conclusion that, especially since DEU is the language which is closest related to ENG, language relatedness plays a role for producing suffixes of inflected forms as well. Our last observation is that many errors are not found at all in our data sample, e.g., CHG2E(X) or NO_CHG(C). This can be explained by ENG having a relatively poor inflectional morphology, which does not leave much room for mistakes. ### Qualitative Results ::: Error Analysis: Spanish
The errors committed for SPA are shown in Table TABREF37, again listed by source language. Together with Table TABREF19 it gets clear that SPA inflectional morphology is more complex than that of ENG: systems for all source languages perform worse. Similarly to ENG, however, we find that most stem errors happen for the source languages EUS and NAV, which is further evidence for our previous hypothesis that familiarity with prefixing languages impedes acquisition of a suffixing one. Especially MULT errors are much more frequent for EUS and NAV than for all other languages. ADD(X) happens a lot for EUS, while ADD(C) is also frequent for NAV. Models pretrained on either language have difficulties with vowel changes, which reflects in NO_CHG(V). Thus, we conclude that this phenomenon is generally hard to learn. Analyzing next the errors concerning affixes, we find that models pretrained on HUN, ITA, DEU, and FRA (in that order) commit the fewest errors. This supports two of our previous hypotheses: First, given that ITA and FRA are both from the same language family as SPA, relatedness seems to be benficial for learning of the second language. Second, the system pretrained on HUN performing well suggests again that a source language with an agglutinative, as opposed to a fusional, morphology seems to be beneficial as well. ### Qualitative Results ::: Error Analysis: Zulu
In Table TABREF39, the errors for Zulu are shown, and Table TABREF19 reveals the relative performance for different source languages: TUR $>$ HUN $>$ DEU $>$ ITA $>$ FRA $>$ NAV $>$ EUS $>$ QVH. Again, TUR and HUN obtain high accuracy, which is an additional indicator for our hypothesis that a source language with an agglutinative morphology facilitates learning of inflection in another language. Besides that, results differ from those for ENG and SPA. First of all, more mistakes are made for all source languages. However, there are also several finer differences. For ZUL, the model pretrained on QVH makes the most stem errors, in particular 4 more than the EUS model, which comes second. Given that ZUL is a prefixing language and QVH is suffixing, this relative order seems important. QVH also committs the highest number of MULT errors. The next big difference between the results for ZUL and those for ENG and SPA is that DEL(X) and ADD(X) errors, which previously have mostly been found for the prefixing or partially prefixing languages EUS and NAV, are now most present in the outputs of suffixing languages. Namely, DEL(C) occurs most for FRA and ITA, DEL(V) for FRA and QVH, and ADD(C) and ADD(V) for HUN. While some deletion and insertion errors are subsumed in MULT, this does not fully explain this difference. For instance, QVH has both the second most DEL(V) and the most MULT errors. The overall number of errors related to the affix seems comparable between models with different source languages. This weakly supports the hypothesis that relatedness reduces affix-related errors, since none of the pretraining languages in our experiments is particularly close to ZUL. However, we do find more CUT errors for HUN and TUR: again, these are suffixing, while CUT for the target language SPA mostly happened for the prefixing languages EUS and NAV. ### Qualitative Results ::: Limitations
A limitation of our work is that we only include languages that are written in Latin script. An interesting question for future work might, thus, regard the effect of disjoint L1 and L2 alphabets. Furthermore, none of the languages included in our study exhibits a templatic morphology. We make this choice because data for templatic languages is currently mostly available in non-Latin alphabets. Future work could investigate languages with templatic morphology as source or target languages, if needed by mapping the language's alphabet to Latin characters. Finally, while we intend to choose a diverse set of languages for this study, our overall number of languages is still rather small. This affects the generalizability of the results, and future work might want to look at larger samples of languages. ### Related Work ::: Neural network models for inflection.
Most research on inflectional morphology in NLP within the last years has been related to the SIGMORPHON and CoNLL–SIGMORPHON shared tasks on morphological inflection, which have been organized yearly since 2016 BIBREF6. Traditionally being focused on individual languages, the 2019 edition BIBREF23 contained a task which asked for transfer learning from a high-resource to a low-resource language. However, source–target pairs were predefined, and the question of how the source language influences learning besides the final accuracy score was not considered. Similarly to us, kyle performed a manual error analysis of morphological inflection systems for multiple languages. However, they did not investigate transfer learning, but focused on monolingual models. Outside the scope of the shared tasks, kann-etal-2017-one investigated cross-lingual transfer for morphological inflection, but was limited to a quantitative analysis. Furthermore, that work experimented with a standard sequence-to-sequence model BIBREF12 in a multi-task training fashion BIBREF24, while we pretrain and fine-tune pointer–generator networks. jin-kann-2017-exploring also investigated cross-lingual transfer in neural sequence-to-sequence models for morphological inflection. However, their experimental setup mimicked kann-etal-2017-one, and the main research questions were different: While jin-kann-2017-exploring asked how cross-lingual knowledge transfer works during multi-task training of neural sequence-to-sequence models on two languages, we investigate if neural inflection models demonstrate interesting differences in production errors depending on the pretraining language. Besides that, we differ in the artificial neural network architecture and language pairs we investigate. ### Related Work ::: Cross-lingual transfer in NLP.
Cross-lingual transfer learning has been used for a large variety NLP of tasks, e.g., automatic speech recognition BIBREF25, entity recognition BIBREF26, language modeling BIBREF27, or parsing BIBREF28, BIBREF29, BIBREF30. Machine translation has been no exception BIBREF31, BIBREF32, BIBREF33. Recent research asked how to automatically select a suitable source language for a given target language BIBREF34. This is similar to our work in that our findings could potentially be leveraged to find good source languages. ### Related Work ::: Acquisition of morphological inflection.
Finally, a lot of research has focused on human L1 and L2 acquisition of inflectional morphology BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39, BIBREF40. To name some specific examples, marques2011study investigated the effect of a stay abroad on Spanish L2 acquisition, including learning of its verbal morphology in English speakers. jia2003acquisition studied how Mandarin Chinese-speaking children learned the English plural morpheme. nicoladis2012young studied the English past tense acquisition in Chinese–English and French–English bilingual children. They found that, while both groups showed similar production accuracy, they differed slightly in the type of errors they made. Also considering the effect of the native language explicitly, yang2004impact investigated the acquisition of the tense-aspect system in an L2 for speakers of a native language which does not mark tense explicitly. Finally, our work has been weakly motivated by bliss2006l2. There, the author asked a question for human subjects which is similar to the one we ask for neural models: How does the native language influence L2 acquisition of inflectional morphology? ### Conclusion and Future Work
Motivated by the fact that, in humans, learning of a second language is influenced by a learner's native language, we investigated a similar question in artificial neural network models for morphological inflection: How does pretraining on different languages influence a model's learning of inflection in a target language? We performed experiments on eight different source languages and three different target languages. An extensive error analysis of all final models showed that (i) for closely related source and target languages, acquisition of target language inflection gets easier; (ii) knowledge of a prefixing language makes learning of inflection in a suffixing language more challenging, as well as the other way around; and (iii) languages which exhibit an agglutinative morphology facilitate learning of inflection in a second language. Future work might leverage those findings to improve neural network models for morphological inflection in low-resource languages, by choosing suitable source languages for pretraining. Another interesting next step would be to investigate how the errors made by our models compare to those by human L2 learners with different native languages. If the exhibited patterns resemble each other, computational models could be used to predict errors a person will make, which, in turn, could be leveraged for further research or the development of educational material. ### Acknowledgments
I would like to thank Samuel R. Bowman and Kyle Gorman for helpful discussions and suggestions. This work has benefited from the support of Samsung Research under the project Improving Deep Learning using Latent Structure and from the donation of a Titan V GPU by NVIDIA Corporation. Table 1: Paradigms of the English lemmas dance and eat. dance has 4 distinct inflected forms; eat has 5. Table 2: WALS features from the Morphology category. 20A: 0=Exclusively concatenative, 1=N/A. 21A: 0=No case, 1=Monoexponential case, 2=Case+number, 3=N/A. 21B: 0=monoexponential TAM, 1=TAM+agreement, 2=N/A. 22A: 0=2-3 categories per word, 1=4-5 categories per word, 2=N/A, 3=6-7 categories per word, 4=8-9 categories per word. 23A: 0=Dependent marking, 1=Double marking, 2=Head marking, 3=No marking, 4=N/A. 24A: 0=Dependent marking, 1=N/A, 2=Double marking. 25A: 0=Dependent-marking, 1=Inconsistent or other, 2=N/A. 25B: 0=Non-zero marking, 1=N/A. 26A: 0=Strongly suffixing, 1=Strong prefixing, 2=Equal prefixing and suffixing. 27A: 0=No productive reduplication, 1=Full reduplication only, 2=Productive full and partial reduplication. 28A: 0=Core cases only, 1=Core and non-core, 2=No case marking, 3=No syncretism, 4=N/A. 29A: 0=Syncretic, 1=Not syncretic, 2=N/A. Table 3: Test accuracy. Table 4: Validation accuracy. Table 5: Error analysis for ENG as the model’s L2. Table 7: Error analysis for ZUL as the model’s L2. Table 6: Error analysis for SPA as the model’s L2. | Zulu |
Why does the Sand God keep the webfoots around?
A. It amuses the Sand God to watch the webfoots evolution.
B. It amuses the Sand God to play with the webfoots.
C. The webfoots worship him like a God even though he is not one.
D. The webfoots fear of the Sand God amuses him.
| THE GOD NEXT DOOR By BILL DOEDE Illustrated by IVIE [Transcriber's Note: This etext was produced from Galaxy Magazine August 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The sand-thing was powerful, lonely and strange. No doubt it was a god—but who wasn't? Stinson lay still in the sand where he fell, gloating over the success of his arrival. He touched the pencil-line scar behind his ear where the cylinder was buried, marveling at the power stored there, power to fling him from earth to this fourth planet of the Centaurian system in an instant. It had happened so fast that he could almost feel the warm, humid Missouri air, though he was light years from Missouri. He got up. A gray, funnel-shaped cloud of dust stood off to his left. This became disturbing, since there was scarcely enough wind to move his hair. He watched it, trying to recall what he might know about cyclones. But he knew little. Weather control made cyclones and other climatic phenomena on earth practically non-existent. The cloud did not move, though, except to spin on its axis rapidly, emitting a high-pitched, scarcely audible whine, like a high speed motor. He judged it harmless. He stood on a wide valley floor between two mountain ranges. Dark clouds capped one peak of the mountains on his left. The sky was deep blue. He tested the gravity by jumping up and down. Same as Earth gravity. The sun—no, not the sun. Not Sol. What should he call it, Alpha or Centaurus? Well, perhaps neither. He was here and Earth was somewhere up there. This was the sun of this particular solar system. He was right the first time. The sun burned fiercely, although he would have said it was about four o'clock in the afternoon, if this had been Earth. Not a tree, nor a bush, nor even a wisp of dry grass was in sight. Everywhere was desert. The funnel of sand had moved closer and while he watched it, it seemed to drift in the wind—although there was no wind. Stinson backed away. It stopped. It was about ten feet tall by three feet in diameter at the base. Then Stinson backed away again. It was changing. Now it became a blue rectangle, then a red cube, a violet sphere. He wanted to run. He wished Benjamin were here. Ben might have an explanation. "What am I afraid of?" he said aloud, "a few grains of sand blowing in the wind? A wind devil?" He turned his back and walked away. When he looked up the wind devil was there before him. He looked back. Only one. It had moved. The sun shone obliquely, throwing Stinson's shadow upon the sand. The wind devil also had a shadow, although the sun shone through it and the shadow was faint. But it moved when the funnel moved. This was no illusion. Again Stinson felt the urge to run, or to use the cylinder to project himself somewhere else, but he said, "No!" very firmly to himself. He was here to investigate, to determine if this planet was capable of supporting life. Life? Intelligence? He examined the wind devil as closely as he dared, but it was composed only of grains of sand. There was no core, no central place you could point to and say, here is the brain, or the nervous system. But then, how could a group of loosely spaced grains of sand possibly have a nervous system? It was again going through its paces. Triangle, cube, rectangle, sphere. He watched, and when it became a triangle again, he smoothed a place in the sand and drew a triangle with his forefinger. When it changed to a cube he drew a square, a circle for a sphere, and so on. When the symbols were repeated he pointed to each in turn, excitement mounting. He became so absorbed in doing this that he failed to notice how the wind devil drew closer and closer, but when he inhaled the first grains of sand, the realization of what was happening dawned with a flash of fear. Instantly he projected himself a thousand miles away. Now he was in an area of profuse vegetation. It was twilight. As he stood beside a small creek, a chill wind blew from the northwest. He wanted to cover himself with the long leaves he found, but they were dry and brittle, for here autumn had turned the leaves. Night would be cold. He was not a woodsman. He doubted if he could build a fire without matches. So he followed the creek to where it flowed between two great hills. Steam vapors rose from a crevice. A cave was nearby and warm air flowed from its mouth. He went inside. At first he thought the cave was small, but found instead that he was in a long narrow passageway. The current of warm air flowed toward him and he followed it, cautiously, stepping carefully and slowly. Then it was not quite so dark. Soon he stepped out of the narrow passageway into a great cavern with a high-vaulted ceiling. The light source was a mystery. He left no shadow on the floor. A great crystal sphere hung from the ceiling, and he was curious about its purpose, but a great pool of steaming water in the center of the cavern drew his attention. He went close, to warm himself. A stone wall surrounding the pool was inscribed with intricate art work and indecipherable symbols. Life. Intelligence. The planet was inhabited. Should he give up and return to earth? Or was there room here for his people? Warming his hands there over the great steaming pool he thought of Benjamin, and Straus, and Jamieson—all those to whom he had given cylinders, and who were now struggling for life against those who desired them. He decided it would not be just, to give up so easily. The wide plaza between the pool and cavern wall was smooth as polished glass. Statues lined the wall. He examined them. The unknown artist had been clever. From one angle they were animals, from another birds, from a third they were vaguely humanoid creatures, glowering at him with primitive ferocity. The fourth view was so shocking he had to turn away quickly. No definable form or sculptured line was visible, yet he felt, or saw—he did not know which senses told him—the immeasurable gulf of a million years of painful evolution. Then nothing. It was not a curtain drawn to prevent him from seeing more. There was no more. He stumbled toward the pool's wall and clutched for support, but his knees buckled. His hand slid down the wall, over the ancient inscriptions. He sank to the floor. Before he lost consciousness he wondered, fleetingly, if a lethal instrument was in the statue. He woke with a ringing in his ears, feeling drugged and sluggish. Sounds came to him. He opened his eyes. The cavern was crowded. These creatures were not only humanoid, but definitely human, although more slight of build than earth people. The only difference he could see at first sight was that they had webbed feet. All were dressed from the waist down only, in a shimmering skirt that sparkled as they moved. They walked with the grace of ballet dancers, moving about the plaza, conversing in a musical language with no meaning for Stinson. The men were dark-skinned, the women somewhat lighter, with long flowing hair, wide lips and a beauty that was utterly sensual. He was in chains! They were small chains, light weight, of a metal that looked like aluminum. But all his strength could not break them. They saw him struggling. Two of the men came over and spoke to him in the musical language. "My name is Stinson," he said, pointing to himself. "I'm from the planet Earth." They looked at each other and jabbered some more. "Look," he said, "Earth. E-A-R-T-H, Earth." He pointed upward, described a large circle, then another smaller, and showed how Earth revolved around the sun. One of the men poked him with a stick, or tube of some kind. It did not hurt, but angered him. He left the chains by his own method of travel, and reappeared behind the two men. They stared at the place where he had been. The chains tinkled musically. He grasped the shoulder of the offender, spun him around and slapped his face. A cry of consternation rose from the group, echoing in the high ceilinged cavern. "SBTL!" it said, "ZBTL ... XBTL ... zbtl." The men instantly prostrated themselves before him. The one who had poked Stinson with the stick rose, and handed it to him. Still angered, Stinson grasped it firmly, with half a notion to break it over his head. As he did so, a flash of blue fire sprang from it. The man disappeared. A small cloud of dust settled slowly to the floor. Disintegrated! Stinson's face drained pale, and suddenly, unaccountably, he was ashamed because he had no clothes. "I didn't mean to kill him!" he cried. "I was angry, and...." Useless. They could not understand. For all he knew, they might think he was threatening them. The object he had thought of as a stick was in reality a long metal tube, precisely machined, with a small button near one end. This weapon was completely out of place in a culture such as this. Or was it? What did he know of these people? Very little. They were humanoid. They had exhibited human emotions of anger, fear and, that most human of all characteristics, curiosity. But up to now the tube and the chain was the only evidence of an advanced technology, unless the ancient inscriptions in the stone wall of the pool, and the statues lining the wall were evidences. There was a stirring among the crowd. An object like a pallet was brought, carried by four of the women. They laid it at his feet, and gestured for him to sit. He touched it cautiously, then sat. Instantly he sprang to his feet. There, at the cavern entrance, the wind devil writhed and undulated in a brilliant harmony of colors. It remained in one spot, though, and he relaxed somewhat. One of the women came toward him, long golden hair flowing, firm breasts dipping slightly at each step. Her eyes held a language all their own, universal. She pressed her body against him and bore him to the pallet, her kisses fire on his face. Incongruously, he thought of Benjamin back on earth, and all the others with cylinders, who might be fighting for their lives at this moment. He pushed her roughly aside. She spoke, and he understood! Her words were still the same gibberish, but now he knew their meaning. Somehow he knew also that the wind devil was responsible for his understanding. "You do not want me?" she said sadly. "Then kill me." "Why should I kill you?" She shrugged her beautiful shoulders. "It is the way of the Gods," she said. "If you do not, then the others will." He took the tube-weapon in his hands, careful not to touch the button. "Don't be afraid. I didn't mean to kill the man. It was an accident. I will protect you." She shook her head. "One day they will find me alone, and they'll kill me." "Why?" She shrugged. "I have not pleased you." "On the contrary, you have. There is a time and place for everything, though." Suddenly a great voice sounded in the cavern, a voice with no direction. It came from the ceiling, the floor, the walls, the steaming pool. It was in the language of the web-footed people; it was in his own tongue. "No harm must come to this woman. The God with fingers on his feet has decreed this." Those in the cavern looked at the woman with fear and respect. She kissed Stinson's feet. Two of the men came and gave her a brilliant new skirt. She smiled at him, and he thought he had never seen a more beautiful face. The great, bodiless voice sounded again, but those in the cavern went about their activities. They did not hear. "Who are you?" Stinson looked at the wind devil, since it could be no one else speaking, and pointed to himself. "Me?" "Yes." "I am Stinson, of the planet Earth." "Yes, I see it in your mind, now. You want to live here, on this planet." "Then you must know where I came from, and how." "I do not understand how. You have a body, a physical body composed of atoms. It is impossible to move a physical body from one place to another by a mere thought and a tiny instrument, yet you have done so. You deserted me out in the desert." "I deserted you?" Stinson cried angrily, "You tried to kill me!" "I was attempting communication. Why should I kill you?" He was silent a moment, looking at the people in the cavern. "Perhaps because you feared I would become the God of these people in your place." Stinson felt a mental shrug. "It is of no importance. When they arrived on this planet I attempted to explain that I was not a God, but the primitive is not deeply buried in them. They soon resorted to emotion rather than reason. It is of no importance." "I'd hardly call them primitive, with such weapons." "The tube is not of their technology. That is, they did not make it directly. These are the undesirables, the incorrigibles, the nonconformists from the sixth planet. I permit them here because it occupies my time, to watch them evolve." "You should live so long." "Live?" the wind devil said. "Oh, I see your meaning. I'd almost forgotten. You are a strange entity. You travel by a means even I cannot fully understand, yet you speak of time as if some event were about to take place. I believe you think of death. I see your physical body has deteriorated since yesterday. Your body will cease to exist, almost as soon as those of the sixth planet peoples. I am most interested in you. You will bring your people, and live here." "I haven't decided. There are these web-footed people, who were hostile until they thought I was a God. They have destructive weapons. Also, I don't understand you. I see you as a cone of sand which keeps changing color and configuration. Is it your body? Where do you come from? Is this planet populated with your kind?" The wind devil hesitated. "Where do I originate? It seems I have always been. You see this cavern, the heated pool, the statues, the inscriptions. Half a million years ago my people were as you. That is, they lived in physical bodies. Our technology surpassed any you have seen. The tube these webfoots use is a toy by comparison. Our scientists found the ultimate nature of physical law. They learned to separate the mind from the body. Then my people set a date. Our entire race was determined to free itself from the confines of the body. The date came." "What happened?" "I do not know. I alone exist. I have searched all the levels of time and matter from the very beginning. My people are gone. Sometimes it almost comes to me, why they are gone. And this is contrary to the greatest law of all—that an entity, once in existence, can never cease to exist." Stinson was silent, thinking of the endless years of searching through the great gulf of time. His eyes caught sight of the woman, reclining now on the pallet. The men had left her and stood in groups, talking, glancing at him, apparently free of their awe and fear already. The woman looked at him, and she was not smiling. "Please ask the Sand God," she said, "to speak to my people again. Their fear of him does not last. When He is gone they will probably kill us." "As for the webfoots," the wind devil, or Sand God, said, "I will destroy them. You and your people will have the entire planet." "Destroy them?" Stinson asked, incredulously, "all these people? They have a right to live like any one else." "Right? What is it—'right?' They are entities. They exist, therefore they always will. My people are the only entities who ever died. To kill the body is unimportant." "No. You misunderstand. Listen, you spoke of the greatest law. Your law is a scientific hypothesis. It has to do with what comes after physical existence, not with existence itself. The greatest law is this, that an entity, once existing, must not be harmed in any way. To do so changes the most basic structure of nature." The Sand God did not reply. The great bodiless, directionless voice was silent, and Stinson felt as if he had been taken from some high place and set down in a dark canyon. The cone of sand was the color of wood ashes. It pulsed erratically, like a great heart missing a beat now and then. The web-footed people milled about restlessly. The woman's eyes pleaded. When he looked back, the Sand God was gone. Instantly a new note rose in the cavern. The murmur of unmistakable mob fury ran over the webfoots. Several of the men approached the woman with hatred in their voices. He could not understand the words now. But he understood her. "They'll kill me!" she cried. Stinson pointed the disintegrating weapon at them and yelled. They dropped back. "We'll have to get outside," he told her. "This mob will soon get out of hand. Then the tube won't stop them. They will rush in. I can't kill them all at once, even if I wanted to. And I don't." Together they edged toward the cavern entrance, ran quickly up the inclined passageway, and came out into crisp, cold air. The morning sun was reflected from a million tiny mirrors on the rocks, the trees and grass. A silver thaw during the night had covered the whole area with a coating of ice. Stinson shivered. The woman handed him a skirt she had thoughtfully brought along from the cavern. He took it, and they ran down the slippery path leading away from the entrance. From the hiding place behind a large rock they watched, as several web-footed men emerged into the sunlight. They blinked, covered their eyes, and jabbered musically among themselves. One slipped and fell on the ice. They re-entered the cave. Stinson donned the shimmering skirt, smiling as he did so. The others should see him now. Benjamin and Straus and Jamieson. They would laugh. And Ben's wife, Lisa, she would give her little-girl laugh, and probably help him fasten the skirt. It had a string, like a tobacco pouch, which was tied around the waist. It helped keep him warm. He turned to the woman. "I don't know what I'll do with you, but now that we're in trouble together, we may as well introduce ourselves. My name is Stinson." "I am Sybtl," she said. "Syb-tl." He tried to imitate her musical pronunciation. "A very nice name." She smiled, then pointed to the cavern. "When the ice is gone, they will come out and follow us." "We'd better make tracks." "No," she said, "we must run, and make no tracks." "Okay, Sis," he said. "Sis?" "That means, sister." "I am not your sister. I am your wife." " What? " "Yes. When a man protects a woman from harm, it is a sign to all that she is his chosen. Otherwise, why not let her die? You are a strange God." "Listen, Sybtl," he said desperately, "I am not a God and you are not my wife. Let's get that straight." "But...." "No buts. Right now we'd better get out of here." He took her hand and they ran, slid, fell, picked themselves up again, and ran. He doubted the wisdom of keeping her with him. Alone, the webfoots were no match for him. He could travel instantly to any spot he chose. But with Sybtl it was another matter; he was no better than any other man, perhaps not so good as some because he was forty, and never had been an athlete. How was he to decide if this planet was suitable for his people, hampered by a woman, slinking through a frozen wilderness like an Indian? But the woman's hand was soft. He felt strong knowing she depended on him. Anyway, he decided, pursuit was impossible. They left no tracks on the ice. They were safe, unless the webfoots possessed talents unknown to him. So they followed the path leading down from the rocks, along the creek with its tumbling water. Frozen, leafless willows clawed at their bodies. The sun shone fiercely in a cloudless sky. Already water ran in tiny rivulets over the ice. The woman steered him to the right, away from the creek. Stinson's bare feet were numb from walking on ice. Christ, he thought, what am I doing here, anyway? He glanced down at Sybtl and remembered the webfoots. He stopped, tempted to use his cylinder and move to a warmer, less dangerous spot. The woman pulled on his arm. "We must hurry!" He clutched the tube-weapon. "How many shots in this thing?" "Shots?" "How often can I use it?" "As often as you like. It is good for fifty years. Kaatr—he is the one you destroyed—brought it from the ship when we came. Many times he has used it unwisely." "When did you come?" "Ten years ago. I was a child." "I thought only criminals were brought here." She nodded. "Criminals, and their children." "When will your people come again?" She shook her head. "Never. They are no longer my people. They have disowned us." "And because of me even those in the cavern have disowned you." Suddenly she stiffened beside him. There, directly in their path, stood the Sand God. It was blood red now. It pulsed violently. The great voice burst forth. "Leave the woman!" it demanded angrily. "The webfoots are nearing your position." "I cannot leave her. She is helpless against them." "What form of primitive stupidity are you practicing now? Leave, or they will kill you." Stinson shook his head. The Sand God pulsed more violently than before. Ice melted in a wide area around it. Brown, frozen grass burned to ashes. "You will allow them to kill you, just to defend her life? What business is it of yours if she lives or dies? My race discarded such primitive logic long before it reached your level of development." "Yes," Stinson said, "and your race no longer exists." The Sand God became a sphere of blue flame. A wave of intense heat drove them backward. "Earthman," the great voice said, "go back to your Earth. Take your inconsistencies with you. Do not come here again to infect my planet with your primitive ideas. The webfoots are not as intelligent as you, but they are sane. If you bring your people here, I shall destroy you all." The sphere of blue fire screamed away across the frozen wilderness, and the thunder of its passing shook the ground and echoed among the lonely hills. Sybtl shivered against his arm. "The Sand God is angry," she said. "My people tell how he was angry once before, when we first came here. He killed half of us and burned the ship that brought us. That is how Kaatr got the tube-weapon. It was the only thing the Sand God didn't burn, that and the skirts. Then, when he had burned the ship, the Sand God went to the sixth planet and burned two of the largest cities, as a warning that no more of us must come here." Well, Stinson said to himself, that does it. We are better off on Earth. We can't fight a monster like him. Sybtl touched his arm. "Why did the Sand God come? He did not speak." "He spoke to me." "I did not hear." "Yes, I know now. His voice sounds like thunder in the sky, but it is a voice that speaks only in the mind. He said I must leave this planet." She glanced at him with suddenly awakened eyes, as if thinking of it for the first time. "Where is your ship?" "I have no ship." "Then he will kill you." She touched her fingers on his face. "I am sorry. It was all for me." "Don't worry. The Sand God travels without a ship, why shouldn't I?" "Now?" "As soon as you are safe. Come." Steam rose from the burned area, charred like a rocket launching pit. They stepped around it carefully. Stinson felt warm air, but there was no time, now, to warm cold feet or dwell on the vagaries of Sand Gods. Together they crossed the narrow valley. Sybtl led him toward a tall mound of rock. Here they came to the creek again, which flowed into a small canyon. They climbed the canyon wall. Far away, small figures moved. The webfoots were on their trail. She drew him into a small cave. It was heated, like the great cavern, but held no walled pool nor mysterious lighting. But it was warm, and the small entrance made an excellent vantage point for warding off attack. "They will not find us...." A high-pitched keening burst suddenly around them. Stinson knew they had heard, or felt the sound for some time, that now its frequency was in an audible range. "The Sand God," Sybtl said. "Sometimes he plays among the clouds. He makes it rain in a dry summer, or sometimes warms the whole world for days at a time in winter, so the snow melts and the grass begins to green. Then he tires and lets winter come back again. He is the loneliest God in the universe." "What makes you think he's lonely?" She shrugged her shoulders. "I just know. But he's an angry God now. See those clouds piling in the East? Soon they will hide the sun. Then he will make them churn and boil, like river whirlpools in spring. At least he does this when he plays. Who knows what he will do when he's angry?" "The Sand God isn't doing this," Stinson said. "It's only a storm." She covered his lips with her fingers. "Don't say that. He may hear you and be more angry." "But it is, don't you see? You give him powers he does not possess." Sybtl shook her head and stroked his face with her long, slim fingers. "Poor little God-with-fingers-on-his-feet," she said. "You do not understand. The Sand God is terrible, even when he plays. See the lightning? It is blue. The lightning of a storm that comes by itself is not blue. He is running around the world on feet like the rockets of space ships, and when he strikes the clouds, blue fire shoots away." The clouds continued to build on one another. Soon the blue flashes of lightning extended across the sky from horizon to horizon. The earth trembled. Sybtl moved closer, trembling also. "He never did this before," she said. "He never made the earth shake before." Great boulders crashed down the canyon walls and dropped into the creek. They dared not move from the cave, although death seemed certain if they stayed. "I'll leave for a moment," he said. "I'll be back soon." "You're leaving?" There was panic in her voice. "Only for a moment." "And you won't come back. You will go to your world." "No. I'll be back." "Promise? No, don't promise. The promises of Gods often are forgotten before the sounds die away." "I'll be back." He disappeared at once, giving her no chance to object again, and went to the desert of sand, where he had first arrived on the planet. He wanted to see if the storm were world-wide. Stinson had never been in a sand storm before, even on Earth. He could not breathe. He could not see. Bullets of sand stung his skin. Bullets of sand shot into his eyes. Clouds of sand howled around him. He fell, and the wind rolled him over and over in the sand like a tumbleweed. The skirt flew up around his face. He could not get up again. He returned to the cave. Soon after, while they sat huddled together, watching the chaos of tumbling rocks, lightning, and driving rain, the high-pitched keening came again. A sphere of blue fire appeared in the east. Its brilliance put the lightning to shame. It bore down on the cave swiftly, purposefully. Stinson prepared himself to leave. In spite of his desire to protect Sybtl, it was useless to get himself killed when he was powerless to help her. But at the last moment it veered off. "Fiend!" Stinson screamed the word, vaguely marvelling at his own fury. The blue sphere turned and came back. "Monster!" Again. "Murderer!" "Adolescent!" This time it kept going. The rain and wind ceased. Lightning stopped. Thunder rumbled distantly. Clouds disappeared. Stinson and Sybtl emerged from the cave. There was no longer a question of attack from the webfoots, the storm had taken care of that. The fierce sun began its work of drying rocks and throwing shadows and coaxing life out into the open again. Down in the canyon a bird sang, a lonely, cheerful twitter. "The Sand God is tired," Sybtl said. "He is not angry now. I'm glad. Perhaps he will let you stay." "No. Even if he allowed it, I couldn't stay. My people could never live here with a God who is half devil." The cone of sand suddenly appeared. It stood in the canyon, its base on a level with the cave. It was quiet. It was dull gray in color. It exuded impressions of death, of hopeful words solemnly spoken over lowered coffins, of cold earth and cold space, of dank, wet catacombs, of creeping, crawling nether things. The bird's twitter stopped abruptly. "Earthman," the Sand God said, as if he were about to make a statement. Stinson ignored him. He glanced down at Sybtl, who sensed that this was a time for good-bys. He thought, perhaps I can stay here alone with her. The webfoots might find us, or the Sand God might destroy us in one of his fits, but it might be worth it. "Don't go," she said. "Not yet." "Earthman, hear me." "I hear you." "Why does your mind shrink backward?" "I've decided not to bring my people here." " You decided?" "Certainly," Stinson said boldly. "Call it rationalization, if you wish. You ordered us away; and I have several good reasons for not coming here if the door was open." "I've changed my mind. You will be welcomed." "Listen to that, will you?" Stinson said angrily. "Just listen! You set yourself up as a God for the webfoots. You get them eating out of your hand. Then what do you do? You throw a fit. Yes, a fit! Like an adolescent. Worse." "Earthman, wait...." "No!" Stinson shot back. "You've owned this planet for a million years. You have brooded here alone since before my people discovered fire, and in all those ages you never learned self-control. I can't subject my people to the whims of an entity who throws a planetary fit when it pleases him." Stinson relaxed. He'd had his say. Sybtl trembled beside him. A small mammal, round, furry, hopped by, sniffing inquisitively. Sybtl said, "Is the Sand God happy?" She shook her head. "No, he is not happy. He is old, old, old. I can feel it. My people say that when one gets too old it is well to die. But Gods never die, do they? I would not like to be a God." "Stinson," the Sand God said. "You said I was adolescent. You are correct. Do you remember I told you how my people, the entire race, left their bodies at the same time? Do you imagine all of us were adults?" "I suppose not. Sounds reasonable. How old were you?" "Chronologically, by our standards, I was nine years old." "But you continued to develop after...." "No." Stinson tried to imagine it. At first there must have been a single voice crying into a monstrous emptiness, "Mother, where are you? MOTHER! Where is everyone ?" A frenzied searching of the planet, the solar system, the galaxy. Then a returning to the planet. Empty.... Change. Buildings, roads, bridges weathering slowly. Such a race would have built of durable metal. Durable? Centuries, eons passed. Buildings crumbled to dust, dust blew away. Bridges eroded, fell, decomposed into basic elements. The shape of constellations changed. All trace of civilization passed except in the cavern of the heated pool. Constellations disappeared, new patterns formed in the night sky. The unutterably total void of time—FIVE HUNDRED THOUSAND YEARS! And a nine-year-old child brooding over an empty world. "I don't understand why your development stopped," Stinson said. "Nor do I. But perhaps ... well, I sense that I would continue, if you brought your people here. You have already taught me the value of life. There is a oneness, a bond that ties each living thing to every other living thing. It is a lesson my people never knew. Select any portion of this planet that suits you. Take the web-footed woman for your wife. Have children. I promise never to harm you in any way." "The webfoots?" "You and they shall share the planet." The Sand God disappeared. Sybtl said; "Is the Sand God angry again?" "No, he is not angry." "I'm glad. You will leave now?" "No. This is my home." She laughed softly. "You are a strange God." "Listen," he said, "I am not a God. Get that through your head." She drew him into the cave. Her lips were cool and sweet. The cave was pleasantly warm. | A. It amuses the Sand God to watch the webfoots evolution. |
What is the effectiveness plan generation? | ### Introduction
In the data-to-text generation task (D2T), the input is data encoding facts (e.g., a table, a set of tuples, or a small knowledge graph), and the output is a natural language text representing those facts. In neural D2T, the common approaches train a neural end-to-end encoder-decoder system that encodes the input data and decodes an output text. In recent work BIBREF0 we proposed to adopt ideas from “traditional” language generation approaches (i.e. BIBREF1, BIBREF2, BIBREF3) that separate the generation into a planning stage that determines the order and structure of the expressed facts, and a realization stage that maps the plan to natural language text. We show that by breaking the task this way, one can achieve the same fluency of neural generation systems while being able to better control the form of the generated text and to improve its correctness by reducing missing facts and “hallucinations”, common in neural systems. In this work we adopt the step-by-step framework of BIBREF0 and propose four independent extensions that improve aspects of our original system: we suggest a new plan generation mechanism, based on a trainable-yet-verifiable neural decoder, that is orders of magnitude faster than the original one (§SECREF3); we use knowledge of the plan structure to add typing information to plan elements. This improves the system's performance on unseen relations and entities (§SECREF4); the separation of planning from realizations allows the incorporation of a simple output verification heuristic that drastically improves the correctness of the output (§SECREF5); and finally we incorporate a post-processing referring expression generation (REG) component, as proposed but not implemented in our previous work, to improve the naturalness of the resulting output (§SECREF6). ### Step-by-step Generation
We provide a brief overview of the step-by-step system. See BIBREF0 for further details. The system works in two stages. The first stage (planning) maps the input facts (encoded as a directed, labeled graph, where nodes represent entities and edges represent relations) to text plans, while the second stage (realization) maps the text plans to natural language text. The text plans are a sequence of sentence plans—each of which is a tree— representing the ordering of facts and entities within the sentence. In other words, the plans determine the separation of facts into sentences, the ordering of sentences, and the ordering of facts and entities within each sentence. This stage is completely verifiable: the text plans are guaranteed to faithfully encode all and only the facts from the input. The realization stage then translates the plans into natural language sentences, using a neural sequence-to-sequence system, resulting in fluent output. ### Fast and Verifiable Planner
The data-to-plan component in BIBREF0 exhaustively generates all possible plans, scores them using a heuristic, and chooses the highest scoring one for realization. While this is feasible with the small input graphs in the WebNLG challenge BIBREF4, it is also very computationally intensive, growing exponentially with the input size. We propose an alternative planner which works in linear time in the size of the graph and remains verifiable: generated plans are guaranteed to represent the input faithfully. The original planner works by first enumerating over all possible splits into sentences (sub-graphs), and for each sub-graph enumerating over all possible undirected, unordered, Depth First Search (DFS) traversals, where each traversal corresponds to a sentence plan. Our planner combines these into a single process. It works by performing a series of what we call random truncated DFS traversals. In a DFS traversal, a node is visited, then its children are visited recursively in order. Once all children are visited, the node “pops” back to the parent. In a random truncated traversal, the choice of which children to visit next, as well as whether to go to the next children or to “pop”, is non-deterministic (in practice, our planner decides by using a neural-network controller). Popping at a node before visiting all its children truncates the DFS: further descendants of that node will not be visited in this traversal. It behaves as a DFS on a graph where edges to these descendants do not exist. Popping the starting node terminates the traversal. Our planner works by choosing a node with a non-zero degree and performing a truncated DFS traversal from that node. Then, all edges visited in the traversal are removed from the input graph, and the process repeats (performing another truncated DFS) until no more edges remain. Each truncated DFS traversal corresponds to a sentence plan, following the DFS-to-plan procedure of BIBREF0: the linearized plan is generated incrementally at each step of the traversal. This process is linear in the number of edges in the graph. At training time, we use the plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller, choosing which action to perform at each step. At test time, we use the controller to guide the truncated DFS process. This mechanism is inspired by transition based parsing BIBREF5. The action set at each stage is dynamic. During traversal, it includes the available children at each stage and pop. Before traversals, it includes a choose-i action for each available node $n_i$. We assign a score to each action, normalize with softmax, and train to choose the desired one using cross-entropy loss. At test time, we either greedily choose the best action, or we can sample plans by sampling actions according to their assigned probabilities. Feature Representation and action scoring. Each graph node $n_i$ corresponds to an entity $x_{n_i}$, and has an associated embedding vector $\mathbf {x_{n_i}}$. Each relation $r_i$ is associated with an embedding vector $\mathbf {r_i}$. Each labeled input graph edge $e_k = (n_i, r_\ell , n_j)$ is represented as a projected concatenated vector $\mathbf {e_k}=\mathbf {E}(\mathbf {x_{n_i}};\mathbf {r_\ell };\mathbf {x_{n_j}})$, where $\mathbf {E}$ is a projection matrix. Finally, each node $n_i$ is then represented as a vector $\mathbf {n_i} = \mathbf {V}[\mathbf {x_{n_i}};\sum _{e_j\in \pi (i)}\mathbf {e_j};\sum _{e_j\in \pi ^{-1}(i)}\mathbf {e_j}]$, where $\pi (i)$ and $\pi ^{-1}(i)$ are the incoming and outgoing edges from node $n_i$. The traverse-to-child-via-edge-$e_j$ action is represented as $\mathbf {e_j}$, choose-node-i is represented as $\mathbf {n_i}$ and pop-to-node-i is represented as $\mathbf {n_i}+\mathbf {p}$ where $\mathbf {p}$ is a learned vector. The score for an action $a$ at time $t$ is calculated as a dot-product between the action representation and the LSTM state over the symbols generated in the plan so far. Thus, each decision takes into account the immediate surrounding of the node in the graph, and the plan structure generated so far. Speed On a 7 edges graph, the planner of BIBREF0 takes an average of 250 seconds to generate a plan, while our planner takes 0.0025 seconds, 5 orders of magnitude faster. ### Incorporating typing information for unseen entities and relations
In BIBREF0, the sentence plan trees were linearized into strings that were then fed to a neural machine translation decoder (OpenNMT) BIBREF6 with a copy mechanism. This linearization process is lossy, in the sense that the linearized strings do not explicitly distinguish between symbols that represent entities (e.g., BARACK_OBAMA) and symbols that represent relations (e.g., works-for). While this information can be deduced from the position of the symbol within the structure, there is a benefit in making it more explicit. In particular, the decoder needs to act differently when decoding relations and entities: entities are copied, while relations need to be verbalized. By making the typing information explicit to the decoder, we make it easier for it to generalize this behavior distinction and apply it also for unseen entities and relations. We thus expect the typing information to be especially useful for the unseen part of the evaluation set. We incorporate typing information by concatenating to the embedding vector of each input symbol one of three embedding vectors, S, E or R, where S is concatenated to structural elements (opening and closing brackets), E to entity symbols and R to relation symbols. ### Output verification
While the plan generation stage is guaranteed to be faithful to the input, the translation process from plans to text is based on a neural seq2seq model and may suffer from known issues with such models: hallucinating facts that do not exist in the input, repeating facts, or dropping facts. While the clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors of these kinds. ### Output verification ::: Existing approaches: soft encouragement via neural modules.
Recent work in neural text generation and summarization attempt to address these issues by trying to map the textual outputs back to structured predicates, and comparing these predicates to the input data. BIBREF7 uses a neural checklist model to avoid the repetition of facts and improve coverage. BIBREF8 generate $k$-best output candidates with beam search, and then try to map each candidate output back to the input structure using a reverse seq2seq model trained on the same data. They then select the highest scoring output candidate that best translates back to the input. BIBREF9 reconstructs the input in training time, by jointly learning a back-translation model and enforcing the back-translation to reconstruct the input. Both of these approaches are “soft” in the sense that they crucially rely on the internal dynamics or on the output of a neural network module that may or may not be correct. ### Output verification ::: Our proposal: explicit verification.
The separation between planning and realization provided by the step-by-step framework allows incorporating a robust and straightforward verification step, that does not rely on brittle information extraction procedures or trust neural network models. The plan-to-text generation handles each sentence individually and translates entities as copy operations. We thus have complete knowledge of the generated entities and their locations. We can then assess the correctness of an output sentence by comparing its sequence of entities to the entity sequence in the corresponding sentence plan, which is guaranteed to be complete. We then decode $k$-best outputs and rerank them based on their correctness scores, tie-breaking using model scores. We found empirically that, with a beam of size 5 we find at least one candidate with an exact match to the plan's entity sequence in 99.82% of the cases for seen entities and relations compared to 98.48% at 1-best, and 72.3% for cases of unseen entities and relations compared to 58.06% at 1-best. In the remaining cases, we set the system to continue searching by trying other plans, by going down the list of plans (when using the exhaustive planner of BIBREF0) or by sampling a new plan (when using the linear time planner suggested in this paper). ### Referring Expressions
The step-by-step system generates entities by first generating an indexed entity symbols, and then lexicalizing each symbol to the string associated with this entity in the input structure (i.e., all occurrences of the entity 11TH MISSISSIPPI INFANTRY MONUMENT will be lexicalized with the full name rather than “it” or “the monument”). This results in correct but somewhat unnatural structures. In contrast, end-to-end neural generation systems are trained on text that includes referring expressions, and generate them naturally as part of the decoding process, resulting in natural looking text. However, the generated referring expressions are sometimes incorrect. BIBREF0 suggests the possibility of handling this with a post-processing referring-expression generation step (REG). Here, we propose a concrete REG module and demonstrate its effectiveness. One option is to use a supervised REG module BIBREF11, that is trained to lexicalize in-context mentions. Such an approach is sub-optimal for our setup as it is restricted to the entities and contexts it seen in training, and is prone to error on unseen entities and contexts. Our REG solution lexicalizes the first mention of each entity as its associated string and attempts to generate referring expressions to subsequent mentions. The generated referring expressions can take the form “Pron”, “X” or “the X” where Pron is a pronoun, and X is a word appearing in the entity's string (allowing, e.g., John, or the monument). We also allow referring to its entity with its entire associated string. We restrict the set of allowed pronouns for each entity according to its type (male, female, plural-animate, unknown-animate, inanimate). We then take, for each entity mention individually, the referring expression that receives the best language model score in context, using a strong unsupervised neural LM (BERT BIBREF12). The system is guaranteed to be correct in the sense that it will not generate wrong pronouns. It also has failure modes: it is possible for the system to generate ambiguous referring expressions (e.g., John is Bob's father. He works as a nurse.), and may lexicalize Boston University as Boston. We find that the second kind of mistake is rare as it is handled well by the language model. It can also be controlled by manually restricting the set of possible referring expression to each entity. Similarly, it is easy to extend the system to support other lexicalizations of entities by extending the sets of allowed lexicalizations (for example, supporting abbreviations, initials or nicknames) either as user-supplied inputs or using heuristics. ### Evaluation and Results
We evaluate each of the introduced components separately. Tables listing their interactions are available in the appendix. The appendix also lists some qualitative outputs. The main trends that we observe are: The new planner causes a small drop in BLEU, but is orders of magnitude faster (§SECREF12). Typing information causes a negligible drop in BLEU overall, but improves results substantially for the unseen portion of the dataset (§SECREF13). The verification step is effective at improving the faithfulness of the output, practically eliminating omitted and overgenerated facts, reducing the number of wrong facts, and increasing the number of correctly expressed facts. This is based on both manual and automatic evaluations. (§SECREF14). The referring expression module is effective, with an intrinsic correctness of 92.2%. It substantially improves BLEU scores. (§SECREF16). ### Evaluation and Results ::: Setup
We evaluate on the WebNLG dataset BIBREF4, comparing to the step-by-step systems described in BIBREF0, which are state of the art. Due to randomness inherent in neural training, our reported automatic evaluation measures are based on an average of 5 training runs of each system (neural planner and neural realizer), each run with a different random seed. ### Evaluation and Results ::: Neural Planner vs Exhaustive Planner
We compare the exhaustive planner from BIBREF0 to our neural planner, by replacing the planner component in the BIBREF0 system. Moving to the neural planner exhibits a small drop in BLEU (46.882 dropped to 46.506). However, figure indicates 5 orders of magnitude (100,000x) speedup for graphs with 7 edges, and a linear growth in time for number of edges compared to exponential time for the exhaustive planner. ### Evaluation and Results ::: Effect of Type Information
We repeat the coverage experiment in BIBREF0, counting the number of output texts that contain all the entities in the input graph, and, of these text, counting the ones in which the entities appear in the exact same order as the plan. Incorporating typing information reduced the number of texts not containing all entities by 18% for the seen part of the test set, and 16% for the unseen part. Moreover, for the text containing all entities, the number of texts that did not follow the plan's entity order is reduced by 46% for the seen part of the test set, and by 35% for the unseen part. We also observe a small drop in BLEU scores, which we attribute to some relations being verbalized more freely (though correctly). ### Evaluation and Results ::: Effect of Output Verification
The addition of output verification resulted in negligible changes in BLEU, reinforcing that automatic metrics are not sensitive enough to output accuracy. We thus performed manual analysis, following the procedure in BIBREF0. We manually inspect 148 samples from the seen part of the test set, containing 440 relations, counting expressed, omitted, wrong and over-generated (hallucinated) facts. We compare to the StrongNeural and BestPlan systems from BIBREF0. Results in Table indicate that the effectiveness of the verification process in ensuring correct output, reducing the already small number of ommited and overgenerated facts to 0 (with the exhaustive planner) and keeping it small (with the fast neural planner). ### Evaluation and Results ::: Referring Expression Module ::: Intrinsic evaluation of the REG module.
We manually reviewed 1,177 pairs of entities and referring expressions generated by the system. We find that 92.2% of the generated referring expressions refer to the correct entity. From the generated expressions, 325 (27.6%) were pronouns, 192 (16.3%) are repeating a one-token entity as is, and 505 (42.9%) are generating correct shortening of a long entity. In 63 (5.6%) of the cases the system did not find a good substitute and kept the entire entity intact. Finally, 92 (7.82%) are wrong referrals. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions. ### Evaluation and Results ::: Referring Expression Module ::: Effect on BLEU scores.
As can be seen in Table , using the REG module increases BLEU scores for both the exhaustive and the neural planner. ### Conclusions
We adopt the planning-based neural generation framework of BIBREF0 and extend it to be orders of magnitude faster and produce more correct and more fluent text. We conclude that these extensions not only improve the system of BIBREF0 but also highlight the flexibility and advantages of the step-by-step framework for text generation. ### Acknowledgements
This work was supported in part by the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1) and by a grant from Reverso and Theo Hoffenberg. We report some additional results. ### Interaction of different components
We introduced 4 components: neural planner instead of exhaustive one, adding type information, adding output verification stage, and incorporating a referring expression generation (REG). In Table we report BLEU scores BIBREF13 for all 16 combinations of components. The numbers are averages of 5 runs with different random seeds. ### REG Error Analysis
We perform further analysis of the errors of the unsupervised LM based REG module. We categorise all entities into 3 groups: (1) names of people; (2) locations (cities / counties / countries); and (3) places and objects. For person names, the module did not produce any errors, selecting either a correct pronoun, or either the first or last name of a person, all valid refferences. For location names, we observe two distinct error types, both relating to our module's restriction to predict a single MASK token. The first type is in cases like “city, country” or “county, country”, where the more specific location is not in the LM vocabulary, and cannot be predicted with a single token. For example, in “Punjab, Pakistan”, Punjab is not contained in the vocabulary as a single token, causing the model to select “Pakistan”, which we consider a mistake. The second type is when a city name is longer than a single token, as in “New York”. While it is common to refer to “New Jersey” as “Jersey”, it is wrong to refer to “New York” as either “New” or “York”, and as BERT can only fill in one MASK token, it chooses only one (in this case “York”). Finally, for places and objects, we also identify to mistake types. The first occurs for multi-token entities. While for some cases it is possible to select the correct one (i.e., “Agra Airport” $\rightarrow $ “The Airport” or “Boston University” $\rightarrow $ “The University”), in other cases it is not possible (i.e., “Baked Alaska”, where choosing either word does not produce a useful reference). The second type occurs with names of objects, like books titles. For example, for the entity “A Severed Wasp” we would like the model to predict “The Book”. However, as we only allow either pronouns or words from the original entity, the model cannot produce “The book”, producing the erroneous “The Wasp” instead. ### Output Examples
The following output examples demonstrate the kinds of texts produces by the final system. The following outputs are correct, expressing all and only the facts from their input graphs. We enumerate them as number of facts: The leader of Azerbaijan is Artur Rasizade. Baked Alaska, containing Sponge Cake, is from France. Above The Veil, written by Garth Nix, is available in Hardcover and has 248 pages. The Akita Museum Of Art is located in Japan where the Brazilians In Japan are an ethnic group. The Museum is located in Akita, Akita which is part of Akita Prefecture . The AWH Engineering College in Kuttikkattoor, Kerala has Mahé, India to its northwest . The College was established in 2001 and has a staff of 250. An example where the system failed, producing a wrong lexicalization of a fact is: “The AWH Engineering College is located in the state of Kerala, Kochi, in India. The largest city in India is Mumbai and the river is the Ganges”. In this example, the input entity Kochi refers to the leader of Kerala, and not tpo the location (although there is also a location by that name). The text lexicalizes this fact such that Kerala and Kochi are related, but with a relation of part-of, implying Kerala is in Kochi. Figure 1: Average (+std) planning time (seconds) for different graph sizes, for exhaustive vs neural planner. Table 1: Manual correctness analysis comparing our systems with the ones from Moryossef et al. (2019). Table 2: Effect of the REG component on BLEU score Table 3: Average BLEU score for every combination of methods (avg of 5 independent runs). | clear mapping between plans and text helps to reduce these issues greatly, the system in BIBREF0 still has 2% errors, work in neural text generation and summarization attempt to address these issues |
Who are the four to blame for the Comerford’s incident?
A. Nelson, Androka, Brandt, Bradford
B. Curtis, Androka, Brandt, Bradford
C. Bradford, Nelson, Androka, Curtis
D. Androka, Curtis, the radioman, Bradford
| SILENCE IS—DEADLY By Bertrand L. Shurtleff Radio is an absolute necessity in modern organization—and particularly in modern naval organization. If you could silence all radio—silence of that sort would be deadly! [Transcriber's Note: This etext was produced from Astounding Science-Fiction April 1942. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The hurried rat-a-tat of knuckles hammered on the cabin door. Commander Bob Curtis roused himself from his doze, got up from his chair, stretched himself to his full, lanky height and yawned. That would be Nelson, his navigating officer. Nelson always knocked that way—like a man in an external state of jitters over nothing at all. Curtis didn't hurry. It pleased him to let Nelson wait. He moved slowly to the door, paused there, and flung a backward glance at the man in the cabin with him—Zukor Androka, the elderly Czech scientist, a guest of the United States navy, here aboard the cruiser Comerford . The wizened face of the older man was molded in intent lines of concentration, as his bushy gray head bent over his drawing board. Curtis got a glimpse of the design on which he was working, and his lips relaxed in a faint smile. Androka had arrived on board the Comerford the day before she sailed from Norfolk. With him came a boatload of scientific apparatus and equipment, including a number of things that looked like oxygen tanks, which were now stored in the forward hold. Androka had watched over his treasures with the jealous care of a mother hen, and spent hours daily in the room in the superstructure that had been assigned as his laboratory. Sometimes, Curtis thought old Androka was a bit wacky—a scientist whose mind had been turned by the horror that had come to his country under the domination of the Nazi gestapo . At other times, the man seemed a genius. Perhaps that was the answer—a mad genius! Curtis opened the door and looked out. Rain whipped against his face like a stinging wet lash. Overhead, the sky was a storm-racked mass of clouds, broken in one spot by a tiny patch of starlit blue. His eyes rested inquiringly on the face of the man who stood before him. It was Nelson, his shaggy blond brows drawn scowlingly down over his pale eyes; his thin face a mass of tense lines; his big hands fumbling at the neck of his slicker. Rain was coursing down his white cheeks, streaking them with glistening furrows. The fellow was a headache to Curtis. He was overfriendly with a black-browed bos'n's mate named Joe Bradford—the worst trouble maker on board. But there was no question of his ability. He was a good navigating officer—dependable, accurate, conscientious. Nevertheless, his taut face, restless, searching eyes, and eternally nervous manner got Curtis' goat. "Come in, Nelson!" he said. Nelson shouldered his way inside, and stood there in his dripping oilskins, blinking his eyes against the yellow light. Curtis closed the door and nodded toward the bent form of Zukor Androka, with a quizzical grin. "Old Czech-and-Double-Czech is working hard on his latest invention to pull Hitler's teeth and re-establish the Czech Republic!" Nelson had no answering smile, although there had been a great deal of good-natured joking aboard the Comerford ever since the navy department had sent the scientist on board the cruiser to carry on his experiments. "I'm worried, sir!" Nelson said. "I'm not sure about my dead reckoning. This storm—" Curtis threw his arm around Nelson's dripping shoulders. "Forget it! Don't let a little error get you down!" "But this storm, sir!" Nelson avoided Curtis' friendly eyes and slipped out from under his arm. "It's got me worried. Quartering wind of undetermined force, variable and gusty. There's a chop to the sea—as if from unestimated currents among the islets. No chance to check by observation, and now there is a chance—look at me!" He held out his hands. They were shaking as if he had the chills. "You say there is a chance?" Curtis asked. "Stars out?" "As if by providence, sir, there's a clear patch. I'm wondering—" His voice trailed off, but his eyes swung toward the gleaming sextant on the rack. Commander Curtis shrugged good-naturedly and reached for the instrument. "Not that I've lost confidence in you, Nels, but just because you asked for it!" Curtis donned his slicker and went outside, sextant in hand. In a few minutes he returned and handed Nelson a sheet of paper with figures underlined heavily. "Here's what I make it," the commander told his navigating officer. "Bet you're not off appreciably." Nelson stared at the computations with shaking head. Then he mutely held up his own. Curtis stared, frowned, grabbed his own sheet again. "Any time I'm that far off old Figure-'em Nelson's estimate, I'm checking back," he declared, frowning at the two papers and hastily rechecking his own figures. "Call up to the bridge to stop her," he told Nelson. "We can't afford to move in these waters with such a possibility of error!" Nelson complied, and the throbbing drive of the engines lessened at once. Nelson said: "I've been wondering, sir, if it wouldn't be advisable to try getting a radio cross-bearing. With all these rocks and islets—" "Radio?" repeated the little Czech, thrusting his face between the other two, in his independent fashion that ignored ship's discipline. "You're using your radio?" He broke into a knowing chuckle, his keen old eyes twinkling behind their thick lenses. "Go ahead and try it. See how much you can get! It will be no more than Hitler can get when Zukor Androka decrees silence over the German airways! Try it! Try it, I say!" Bob Curtis stared at him, as if questioning his sanity. Then he hastened to the radio room, with Nelson at his heels, and the Czech trotting along behind. The door burst open as they neared it. A frightened operator came out, still wearing his earphones, and stood staring upward incredulously at the aërial. "Get us a radio cross-bearing for location at once," Curtis said sharply, for the operator seemed in a daze. "Bearing, sir?" The man brought his eyes down with difficulty, as if still dissatisfied. "I'm sorry, sir, but the outfit's dead. Went out on me about five minutes ago. I was taking the weather report when the set conked. I was trying to see if something's wrong." The Czech inventor giggled. Curtis gave him another curious look and thrust himself into the radio room. "Try again!" he told the operator. "See what you can get!" The radio man leaped to his seat and tried frantically. Again and again, he sent off a request for a cross-bearing from shore stations that had recently been established to insure safety to naval vessels, but there was no answer on any of the bands—not even the blare of a high-powered commercial program in the higher reach, nor the chatter of ships or amateurs on the shorter. "Dead!" Androka muttered, with a bitter laugh. "Yet not dead, gentlemen! The set is uninjured. The waves are what have been upset. I have shattered them around your ship, just as I can eventually shatter them all over Central Europe! For the next two hours, no radio messages can enter or leave my zone of radio silence—of refracted radio waves, set up by my little station on one of the neighboring islets!" There was a long pause, while commander and navigator stared at him. Curtis was the first to speak. "Your secrecy might well cost the United States navy one of its best light cruisers—and us our lives!" he said angrily. "We need that check by radio at once! If you're not talking nonsense, call off your dogs till we learn just where we are!" Androka held out his palms helplessly. "I can do nothing. I have given orders to my assistant that he must keep two hours of radio silence! I can get no message to him, for our radio is dead!" As if to mock him, the ship's radio began to answer: "Station 297 calling U. S. Cruiser Comerford . Station 297 calling U. S. Cruiser Comerford —" "U. S. Cruiser Comerford calling Station 297!" the operator intoned, winking at the two officers over Androka's discomfiture, and asked for the bearings. The answer came back: "Bearings north east by a quarter east, U. S. Cruiser Comerford !" Curtis sighed with relief. He saw that Nelson was staring fiercely at the radio operator, as the man went on calling: "U. S. Cruiser Comerford calling Station 364. U. S. Cruiser Comerford calling Station 364—" Then the instrument rasped again: "Station 364 calling U. S. Cruiser Comerford . Bearings north west by three west. Bearings north west by three west, U. S. Cruiser Comerford from Cay 364." Commander and navigator had both scribbled verifications of the numbers. Ignoring the gibbering Androka, who was wailing his disappointment that messages had penetrated his veil of silence, they raced for the chart room. Quickly the parallels stepped off the bearing from the designated points. Light intersecting lines proclaimed a check on their position. Curtis frowned and shook his head. Slowly he forced a reluctant grin as he stuck out his hand. "Shake, Nels," he said. "It's my turn to eat crow. You and the radio must be right. Continue as you were!" "I'm relieved, sir, just the same," Nelson admitted, "to have the radio bearings. We'd have piled up sure if you'd been right." They went on through the night. The starlit gap in the clouds had closed. The sky was again a blanket of darkness pouring sheets of rain at them. Nelson went back to the bridge, and Androka returned to the commander's cabin. Curtis lingered in the wireless room with the radio operator. "It's a funny thing," the latter said, still dialing and grousing, "how I got that cross-bearing through and can't get another squeak out of her. I'm wondering if that old goat really has done something to the ether. The set seems O. K." He lingered over the apparatus, checking and rechecking. Tubes lighted; wires were alive to the touch and set him to shaking his head at the tingle they sent through his inquiring fingers. Curtis left him at it, and went to rejoin Androka in the cabin. He found the little inventor pacing up and down, shaking his fists in the air; pausing every now and then to run his bony fingers through his tangled mop of gray hair, or to claw nervously at his beard. "You have seen a miracle, commander!" he shouted at Curtis. " My miracle! My invention has shattered the ether waves hereabouts hopelessly." "Seems to me," Curtis said dryly, "this invention can harm your friends as much as your enemies." The scientist drew himself up to his full height—which was only a little over five feet. His voice grew shrill. "Wait! Just wait! There are other inventions to supplement this one. Put them together, and they will defeat the Nazi hordes which have ravaged my country!" Curtis was a little shocked by the hatred that gleamed in Androka's eyes, under their bushy brows. There was something of the wild animal in the man's expression, as his lips drew back from his yellowed teeth. "Those tanks you have below," Curtis said, "have they some connection with this radio silence?" A far-away look came into Androka's eyes. He did not seem to hear the question. He lowered his voice: "My daughter is still in Prague. So are my sister and her husband, and their two daughters. If the gestapo knew what I am doing, all of them would be better dead. You understand—better dead?" Curtis said: "I understand." "And if the Nazi agents in America knew of the islet from which my zone of silence is projected—" Androka paused, his head tilted to one side, as if he were listening to something— On deck, there was shouting and commotion. Curtis rushed out, pulling on his slicker as he went. The shout from the watch forward had been picked up, and was being relayed all over the ship. The words struck on Curtis' ears with a note of impending tragedy. "Breakers ahead!" He was beside Navigating Officer Nelson on the bridge, and saw the helmsman climbing the rapidly spinning wheel like a monkey as he put it hard aport. Then the ship struck. Everything movable shot ahead until it brought up at the end of a swing or smacked against something solid. Curtis felt Nelson's hand grip his shoulder, as he put his lips close to his ear and shouted: "You must have been right, sir, and the radio bearings and my reckoning wrong. We've hit that reef a terrific smack. I'm afraid we're gored!" "Get out the collision mat!" Curtis ordered. "We ought to be able to keep her up!" And then he became aware of a deadly stillness. A vast wall of silence enveloped the entire cruiser. Looking over the side, he could no longer see the waves that a few minutes before had beaten savagely against the ship. The Comerford was shrouded in a huge pall of yellowish-gray mist, and more of it was coming up from below—from ventilators and hatchways and skylights—as if the whole ship were flooded with some evil vapor. Somehow, Curtis' mind flashed to the stories he'd heard of the forts of the Maginot Line, and of other forts in Holland and Belgium that had fallen before the early Nazi blitzkrieg, when their defenders found themselves struck numb and helpless by a gas that had been flooded into the inner compartments of their strongholds. There were those who said it was the work of sappers who had tunneled under the foundations, while others laid the induction of the gas to Fifth Column traitors. There were a hundred more or less plausible explanations— The vapor clouds that enveloped the Comerford were becoming thicker. All about the deck lay the forms of unconscious seamen, suddenly stricken helpless. And then Curtis saw other forms flitting about the deck—forms that looked like creatures from another world, but he recognized them for what they were—men wearing gas masks. Nelson was nowhere in sight. The steersman lay in a limp heap beside the swinging wheel. Then a gas-masked figure appeared through the shroud of mist and steadied it, so that the cruiser would not be completely at the mercy of the wind and the waves. Curtis heard the anchor let down, as if by invisible hands, the chain screaming and flailing its clanking way through the hawse hole. Then he was completely walled in by the yellowish-gray mist. He felt his senses swimming. Voices droned all around him in mumbling confusion—guttural voices that ebbed and flowed in a tide of excited talk. He caught a word of English now and then, mixed in with a flood of Teuton phonetics. Two words, in particular, registered clearly on his mind. One was " Carethusia "; the other was "convoy." But gradually his eardrums began to throb, as if someone were pounding on them from the inside. He couldn't get his breath; a cloud seemed to be mounting within him until it swept over his brain— He felt something strike the side of his head, and realized that he had fallen in a heap on the bridge. And after that, he wasn't conscious of anything— The rain had abated to a foggy drizzle. The wash of the surf swung the Comerford in a lazy, rolling motion, as she lay with her bow nosing into the sandbar at the entrance of the inlet. From her bridge, Navigating Officer Nelson watched the gas-masked figures moving about the decks, descending companionways—like goblins from an ancient fairy tale or a modern horror story. Nelson looked like a goblin himself, with his face covered by a respirator. At his side, stood his fellow conspirator Bos'n's Mate Joe Bradford, also wearing a gas mask. Nelson spoke in a low tone, his lips close to Bradford's ear. "It worked, Joe!" "Yeah!" Bradford agreed. "It worked—fine!" The limp bodies of the Comerford's crew were being carried to the lowered accommodation ladder and transferred into waiting lifeboats. Nelson swore under his breath. "Reckon it'll take a couple of hours before the ship's rid of that damn gas!" Bradford shook his head in disagreement. "The old geezer claims he's got a neutralizing chemical in one of them tanks of his that'll clear everything up inside half an hour." "I'd rather get along without Androka, if we could!" Nelson muttered. "He's nothing but a crackpot!" "It was a crackpot who invented the gas we used to break up the Maginot Line," Bradford reminded him. "It saved a lot of lives for the Fuehrer —lives that'd have been lost if the forts had to be taken by our storm troopers!" Nelson grunted and turned away. A short, thick-set figure in the uniform of a German naval commander had ascended the accommodation ladder and was mounting to the bridge. He, too, was equipped with a respirator. He came up to Nelson, saluted, and held out his hand, introducing himself as Herr Kommander Brandt. He began to speak in German, but Nelson stopped him. "I don't speak any German," he explained. "I was born and educated in the United States—of German parents, who had been ruined in the First World War. My mother committed suicide when she learned that we were penniless. My father—" He paused and cleared his throat. " Ja! Your father?" the German officer prompted, dropping into accented English. "Your father?" "My father dedicated me to a career of revenge—to wipe out his wrongs," Nelson continued. "If America hadn't gone into the First World War, he wouldn't have lost his business; my mother would still be living. When he joined the Nazi party, the way became clear to use me—to educate me in a military prep school, then send me to Annapolis, for a career in the United States navy—and no one suspected me. No one—" "Sometimes," Bradford put in, "I think Curtis suspected you." "Maybe Curtis'll find out his suspicions were justified," Nelson said bitterly. "But it won't do Curtis any good—a commander who's lost his ship." He turned to Brandt. "You have plenty of men to work the Comerford ?" Brandt nodded his square head. "We have a full crew—two hundred men—officers, seamen, mechanics, radio men, technical experts, all German naval reservists living in the United States, who've been sent here secretly, a few at a time, during the past six weeks!" The three—Brandt, Nelson and Bradford—stood on the bridge and talked, while the efficient stretcher-bearers worked industriously to remove the limp bodies of the Comerford's unconscious crew and row them ashore. And when that task was completed, lifeboats began to come alongside with strange-looking radio equipment, and more gas tanks like those Androka had brought aboard the Comerford with him, and dynamos and batteries that looked like something out of a scientific nightmare. And bustling all over the place, barking excited commands in German, pushing and pulling and pointing to emphasize his directions, was the strange figure of Professor Zukor Androka! "The professor's in his glory!" Nelson remarked to Kommander Brandt. "Funny thing about him," Bradford put in, "is that his inventions work. That zone of silence cut us off completely." Kommander Brandt nodded. "Goodt! But you got your message giving your bearings—the wrong ones?" "Yes," Nelson said. "That came through all right. And won't Curtis have a time explaining it!" "Hereafter," Brandt said solemnly, "the zone of silence vill be projected from the Comerford ; and ve have another invention of Androka's vich vill be even more useful vhen ve come to cut the Carethusia out of her convoy." "The Carethusia ?" Nelson asked, in a puzzled tone. Brandt said: "She's a freighter in a convoy out of St. Johns—twelve thousand tons. The orders are to take her; not sink her." "What's the idea?" "Her cargo," Brandt explained. "It iss more precious than rubies. It includes a large shipment of boarts." "Boarts?" Nelson repeated. "What are they?" "Boarts," Brandt told him, "are industrial diamonds—black, imperfectly crystallized stones, but far more valuable to us than flawless diamonds from Tiffany's on Fift' Avenue. They are needed for making machine tools. They come from northern Brazil—and our supply is low." "I should think we could get a shipment of these boarts direct from Brazil—through the blockade," Nelson said, "without taking the risk of capturing a United States navy cruiser." "There are other things Germany needs desperately on board the Carethusia ," Brandt explained. "Vanadium and nickel and hundreds of barrels of lard oil for machine-tool lubrication. Our agents have been watching the convoys closely for weeks for just such a cargo as the Carethusia is taking over." "Can we trust Androka?" Nelson asked, with a sudden note of suspicion in his voice. "Yes," Brandt assured him. "Of all men—we can trust Androka!" "But he's a Czech," Nelson argued. "The gestapo takes care of Czechs and Poles and Frenchmen and other foreigners whom it chooses as its agents," Brandt pointed out. "Androka has a daughter and other relations in Prague. He knows that if anything misfires, if there is the slightest suspicion of treachery on his part, his daughter and the others will suffer. Androka's loyalty is assured!" Nelson turned to watch the forward fighting top of the Comerford . The masked German seamen were installing some sort of apparatus up there—a strange-looking object that looked something like an old-fashioned trench mortar, and which connected with cables to the room that served as Androka's laboratory and workshop. Another crew was installing radio apparatus in the mizzentop turret. Descending a companionway to see what was going on below, Nelson found that portholes were being opened, and men were spraying chemical around to rid the below-decks atmosphere of the lethal gas that had overcome the Comerford's American crew. Returning to the bridge, he found that the tide in the inlet had risen considerably, and that the cruiser was riding more easily at her anchor. Then, at Brandt's orders, the anchor was hauled in, and lifeboats and a motor launch were used as tugs to work the vessel entirely free of the sand bar. This was accomplished without difficulty. Brandt came over to where Nelson was standing on the bridge and held out his hand. "Congratulations, Herr Kommander Nelson!" he said. "Ve have stolen one of the United States navy's newest and fastest cruisers!" He made a gesture as if raising a beer stein to drink a toast. " Prosit! " he added. " Prosit! " Nelson repeated, and the two grinned at each other. Stars were twinkling in a patch of black-blue sky, and broken mountains of gray cloud were skudding before the east wind. Commander Bob Curtis found himself lying in wet sand, on a beach, somewhere, with the rain—now a light, driving mist—beating on his face. He was chilled; his limbs were stiff and numb. His nose and throat felt parched inside, as if a wave of searing heat had scorched them. According to his last calculations, the Comerford had been cruising off the Maine coast. This probably was one of the islets of that region, or it might be the mainland. It was hard work getting to his feet, and when he did manage to stand, he could only plant his heels in the sand and sway to and fro for fully a minute, like a child learning to walk. All around him in the nearly total darkness, he could make out the dim forms of men sprawled on the beach; and of other men moving about, exploring. He heard the murmur of voices and saw the glow of lighted cigarettes. A man with a flashlight was approaching him. Its white glare shone for a moment in Curtis' face, and the familiar voice of Ensign Jack Dillon spoke: "Commander Curtis! Are you O. K., sir?" "I think so!" Curtis' heart warmed at the eager expression in Dillon's face; at the heartfelt concern in his friendly brown eyes. The young ensign was red-headed, impetuous, thoroughly genuine in his emotions. "How about yourself, Jack?" Curtis added. "A bit of a headache from the gas, but that's all. Any orders, sir?" Curtis thought for a moment. "Muster the crew, as best you can. We'll try to make a roll call. Is there any sign of the ship?" There was a solemn note in Dillon's voice. "No, sir. She's been worked off the sandbar and put to sea!" The words struck Curtis with the numbing shock of a blow on some nerve center. For the first time, he realized fully the tragedy that had swept down on him. He had lost his ship—one of the United States navy's fastest and newest small light cruisers—under circumstances which smelled strongly of treachery and sabotage. As he thought back, he realized that he might have prevented the loss, if he had been more alert, more suspicious. For it was clear to him now that the Comerford had been deliberately steered to this place; that the men who had seized her had been waiting here for that very purpose. The pieces of the picture fitted together like a jigsaw puzzle—Androka's zone of silence; the bearings given by radio; Navigating Officer Nelson's queer conduct. They were all part of a carefully laid plan! All the suspicious circumstances surrounding Nelson came flooding into Curtis' mind. He had never liked the man; never trusted him. Nelson always acted as if he had some secret, something to hide. Curtis recalled that Nelson and Androka had long conversations together—conversations which they would end abruptly when anyone else came within earshot. And Nelson had always been chummy with the worst trouble maker in the crew—Bos'n's Mate Bradford. Curtis went around, finding the officers, issuing orders. There were still some unconscious men to be revived. In a sheltered cove among the rocks, an exploring group had found enough dry driftwood to make a fire— In another hour, the skies had cleared, and white moonlight flooded the scene with a ghostly radiance. The men of the Comerford had all regained consciousness and were drying out in front of the big driftwood bonfires in the cove. Curtis ordered a beacon kept burning on a high promontory. Then he got the men lined up, according to their respective classifications, for a check-up on the missing. When this was completed, it was found that the Comerford's entire complement of two hundred and twenty men were present—except Navigating Officer Nelson, and Bos'n's Mate Bradford! And Zukor Androka was also missing! With the coming of dawn, a little exploration revealed that the Comerford's crew was marooned on an islet, about a square mile in area; that they had been put ashore without food or extra clothing or equipment of any kind, and that no boats had been left for them. One searching party reported finding the remains of what had been a radio station on a high promontory on the north shore of the islet. Another had found the remains of tents and log cabins, recently demolished, in a small, timbered hollow—a well-hidden spot invisible from the air, unless one were flying very low; a place where two hundred or more men could have camped. There was a good water supply—a small creek fed by springs—but nothing in the way of food. Evidently food was a precious commodity which the recent inhabitants of the islet couldn't afford to leave behind. Curtis was studying the wreckage of the wireless station, wondering if this might have been the source of Androka's zone of silence, when Ensign Jack Dillon came up to him. "There's a coast-guard cutter heading for the island, sir," he announced. | A. Nelson, Androka, Brandt, Bradford |
Roughly how many times has AES Corporation sold its inventory in FY2022? Calculate inventory turnover ratio for the FY2022; if conventional inventory management is not meaningful for the company then state that and explain why. | Evidence 0:
Consolidated Balance Sheets
December 31, 2022 and 2021
2022
2021
(in millions, except share and per share data)
ASSETS
CURRENT ASSETS
Cash and cash equivalents
$
1,374
$
943
Restricted cash
536
304
Short-term investments
730
232
Accounts receivable, net of allowance for doubtful accounts of $5 and $5, respectively
1,799
1,418
Inventory
1,055
604
Evidence 1:
Consolidated Statements of Operations
Years ended December 31, 2022, 2021, and 2020
2022
2021
2020
(in millions, except per share amounts)
Revenue:
Regulated
$
3,538
$
2,868
$
2,661
Non-Regulated
9,079
8,273
6,999
Total revenue
12,617
11,141
9,660
Cost of Sales:
Regulated
(3,162)
(2,448)
(2,235)
Non-Regulated
(6,907)
(5,982)
(4,732)
Total cost of sales
(10,069)
(8,430)
(6,967) | AES has converted inventory 9.5 times in FY 2022. |
Why can't the crew radio the Earth for help?
A. Kroger broke the radio.
B. Jones broke the radio.
C. Lloyd broke the radio.
D. Pat broke the radio.
| THE DOPE on Mars By JACK SHARKEY Somebody had to get the human angle on this trip ... but what was humane about sending me? Illustrated by WOOD My agent was the one who got me the job of going along to write up the first trip to Mars. He was always getting me things like that—appearances on TV shows, or mentions in writers' magazines. If he didn't sell much of my stuff, at least he sold me . "It'll be the biggest break a writer ever got," he told me, two days before blastoff. "Oh, sure there'll be scientific reports on the trip, but the public doesn't want them; they want the human slant on things." "But, Louie," I said weakly, "I'll probably be locked up for the whole trip. If there are fights or accidents, they won't tell me about them." "Nonsense," said Louie, sipping carefully at a paper cup of scalding coffee. "It'll be just like the public going along vicariously. They'll identify with you." "But, Louie," I said, wiping the dampness from my palms on the knees of my trousers as I sat there, "how'll I go about it? A story? An article? A you-are-there type of report? What?" Louie shrugged. "So keep a diary. It'll be more intimate, like." "But what if nothing happens?" I insisted hopelessly. Louie smiled. "So you fake it." I got up from the chair in his office and stepped to the door. "That's dishonest," I pointed out. "Creative is the word," Louie said. So I went on the first trip to Mars. And I kept a diary. This is it. And it is honest. Honest it is. October 1, 1960 They picked the launching date from the March, 1959, New York Times , which stated that this was the most likely time for launching. Trip time is supposed to take 260 days (that's one way), so we're aimed toward where Mars will be (had better be, or else). There are five of us on board. A pilot, co-pilot, navigator and biochemist. And, of course, me. I've met all but the pilot (he's very busy today), and they seem friendly enough. Dwight Kroger, the biochemist, is rather old to take the "rigors of the journey," as he puts it, but the government had a choice between sending a green scientist who could stand the trip or an accomplished man who would probably not survive, so they picked Kroger. We've blasted off, though, and he's still with us. He looks a damn sight better than I feel. He's kind of balding, and very iron-gray-haired and skinny, but his skin is tan as an Indian's, and right now he's telling jokes in the washroom with the co-pilot. Jones (that's the co-pilot; I didn't quite catch his first name) is scarlet-faced, barrel-chested and gives the general appearance of belonging under the spreading chestnut tree, not in a metal bullet flinging itself out into airless space. Come to think of it, who does belong where we are? The navigator's name is Lloyd Streeter, but I haven't seen his face yet. He has a little cubicle behind the pilot's compartment, with all kinds of maps and rulers and things. He keeps bent low over a welded-to-the-wall (they call it the bulkhead, for some reason or other) table, scratching away with a ballpoint pen on the maps, and now and then calling numbers over a microphone to the pilot. His hair is red and curly, and he looks as though he'd be tall if he ever gets to stand up. There are freckles on the backs of his hands, so I think he's probably got them on his face, too. So far, all he's said is, "Scram, I'm busy." Kroger tells me that the pilot's name is Patrick Desmond, but that I can call him Pat when I get to know him better. So far, he's still Captain Desmond to me. I haven't the vaguest idea what he looks like. He was already on board when I got here, with my typewriter and ream of paper, so we didn't meet. My compartment is small but clean. I mean clean now. It wasn't during blastoff. The inertial gravities didn't bother me so much as the gyroscopic spin they put on the ship so we have a sort of artificial gravity to hold us against the curved floor. It's that constant whirly feeling that gets me. I get sick on merry-go-rounds, too. They're having pork for dinner today. Not me. October 2, 1960 Feeling much better today. Kroger gave me a box of Dramamine pills. He says they'll help my stomach. So far, so good. Lloyd came by, also. "You play chess?" he asked. "A little," I admitted. "How about a game sometime?" "Sure," I said. "Do you have a board?" He didn't. Lloyd went away then, but the interview wasn't wasted. I learned that he is tall and does have a freckled face. Maybe we can build a chessboard. With my paper and his ballpoint pen and ruler, it should be easy. Don't know what we'll use for pieces, though. Jones (I still haven't learned his first name) has been up with the pilot all day. He passed my room on the way to the galley (the kitchen) for a cup of dark brown coffee (they like it thick) and told me that we were almost past the Moon. I asked to look, but he said not yet; the instrument panel is Top Secret. They'd have to cover it so I could look out the viewing screen, and they still need it for steering or something. I still haven't met the pilot. October 3, 1960 Well, I've met the pilot. He is kind of squat, with a vulturish neck and close-set jet-black eyes that make him look rather mean, but he was pleasant enough, and said I could call him Pat. I still don't know Jones' first name, though Pat spoke to him, and it sounded like Flants. That can't be right. Also, I am one of the first five men in the history of the world to see the opposite side of the Moon, with a bluish blurred crescent beyond it that Pat said was the Earth. The back of the Moon isn't much different from the front. As to the space in front of the ship, well, it's all black with white dots in it, and none of the dots move, except in a circle that Pat says is a "torque" result from the gyroscopic spin we're in. Actually, he explained to me, the screen is supposed to keep the image of space locked into place no matter how much we spin. But there's some kind of a "drag." I told him I hoped it didn't mean we'd land on Mars upside down. He just stared at me. I can't say I was too impressed with that 16 x 19 view of outer space. It's been done much better in the movies. There's just no awesomeness to it, no sense of depth or immensity. It's as impressive as a piece of velvet with salt sprinkled on it. Lloyd and I made a chessboard out of a carton. Right now we're using buttons for men. He's one of these fast players who don't stop and think out their moves. And so far I haven't won a game. It looks like a long trip. October 4, 1960 I won a game. Lloyd mistook my queen-button for my bishop-button and left his king in jeopardy, and I checkmated him next move. He said chess was a waste of time and he had important work to do and he went away. I went to the galley for coffee and had a talk about moss with Kroger. He said there was a good chance of lichen on Mars, and I misunderstood and said, "A good chance of liking what on Mars?" and Kroger finished his coffee and went up front. When I got back to my compartment, Lloyd had taken away the chessboard and all his buttons. He told me later he needed it to back up a star map. Pat slept mostly all day in his compartment, and Jones sat and watched the screen revolve. There wasn't much to do, so I wrote a poem, sort of. Mary, Mary, quite contrary, How does your garden grow? With Martian rime, Venusian slime, And a radioactive hoe. I showed it to Kroger. He says it may prove to be environmentally accurate, but that I should stick to prose. October 5, 1960 Learned Jones' first name. He wrote something in the ship's log, and I saw his signature. His name is Fleance, like in "Macbeth." He prefers to be called Jones. Pat uses his first name as a gag. Some fun. And only 255 days to go. April 1, 1961 I've skipped over the last 177 days or so, because there's nothing much new. I brought some books with me on the trip, books that I'd always meant to read and never had the time. So now I know all about Vanity Fair , Pride and Prejudice , War and Peace , Gone with the Wind , and Babbitt . They didn't take as long as I thought they would, except for Vanity Fair . It must have been a riot when it first came out. I mean, all those sly digs at the aristocracy, with copious interpolations by Mr. Thackeray in case you didn't get it when he'd pulled a particularly good gag. Some fun. And only 78 days to go. June 1, 1961 Only 17 days to go. I saw Mars on the screen today. It seems to be descending from overhead, but Pat says that that's the "torque" doing it. Actually, it's we who are coming in sideways. We've all grown beards, too. Pat said it was against regulations, but what the hell. We have a contest. Longest whiskers on landing gets a prize. I asked Pat what the prize was and he told me to go to hell. June 18, 1961 Mars has the whole screen filled. Looks like Death Valley. No sign of canals, but Pat says that's because of the dust storm down below. It's nice to have a "down below" again. We're going to land, so I have to go to my bunk. It's all foam rubber, nylon braid supports and magnesium tubing. Might as well be cement for all the good it did me at takeoff. Earth seems awfully far away. June 19, 1961 Well, we're down. We have to wear gas masks with oxygen hook-ups. Kroger says the air is breathable, but thin, and it has too much dust in it to be any fun to inhale. He's all for going out and looking for lichen, but Pat says he's got to set up camp, then get instructions from Earth. So we just have to wait. The air is very cold, but the Sun is hot as hell when it hits you. The sky is a blinding pink, or maybe more of a pale fuchsia. Kroger says it's the dust. The sand underfoot is kind of rose-colored, and not really gritty. The particles are round and smooth. No lichen so far. Kroger says maybe in the canals, if there are any canals. Lloyd wants to play chess again. Jones won the beard contest. Pat gave him a cigar he'd smuggled on board (no smoking was allowed on the ship), and Jones threw it away. He doesn't smoke. June 20, 1961 Got lost today. Pat told me not to go too far from camp, so, when I took a stroll, I made sure every so often that I could still see the rocket behind me. Walked for maybe an hour; then the oxygen gauge got past the halfway mark, so I started back toward the rocket. After maybe ten steps, the rocket disappeared. One minute it was standing there, tall and silvery, the next instant it was gone. Turned on my radio pack and got hold of Pat. Told him what happened, and he told Kroger. Kroger said I had been following a mirage, to step back a bit. I did, and I could see the ship again. Kroger said to try and walk toward where the ship seemed to be, even when it wasn't in view, and meantime they'd come out after me in the jeep, following my footprints. Started walking back, and the ship vanished again. It reappeared, disappeared, but I kept going. Finally saw the real ship, and Lloyd and Jones waving their arms at me. They were shouting through their masks, but I couldn't hear them. The air is too thin to carry sound well. All at once, something gleamed in their hands, and they started shooting at me with their rifles. That's when I heard the noise behind me. I was too scared to turn around, but finally Jones and Lloyd came running over, and I got up enough nerve to look. There was nothing there, but on the sand, paralleling mine, were footprints. At least I think they were footprints. Twice as long as mine, and three times as wide, but kind of featureless because the sand's loose and dry. They doubled back on themselves, spaced considerably farther apart. "What was it?" I asked Lloyd when he got to me. "Damned if I know," he said. "It was red and scaly, and I think it had a tail. It was two heads taller than you." He shuddered. "Ran off when we fired." "Where," said Jones, "are Pat and Kroger?" I didn't know. I hadn't seen them, nor the jeep, on my trip back. So we followed the wheel tracks for a while, and they veered off from my trail and followed another, very much like the one that had been paralleling mine when Jones and Lloyd had taken a shot at the scaly thing. "We'd better get them on the radio," said Jones, turning back toward the ship. There wasn't anything on the radio but static. Pat and Kroger haven't come back yet, either. June 21, 1961 We're not alone here. More of the scaly things have come toward the camp, but a few rifle shots send them away. They hop like kangaroos when they're startled. Their attitudes aren't menacing, but their appearance is. And Jones says, "Who knows what's 'menacing' in an alien?" We're going to look for Kroger and Pat today. Jones says we'd better before another windstorm blows away the jeep tracks. Fortunately, the jeep has a leaky oil pan, so we always have the smears to follow, unless they get covered up, too. We're taking extra oxygen, shells, and rifles. Food, too, of course. And we're locking up the ship. It's later , now. We found the jeep, but no Kroger or Pat. Lots of those big tracks nearby. We're taking the jeep to follow the aliens' tracks. There's some moss around here, on reddish brown rocks that stick up through the sand, just on the shady side, though. Kroger must be happy to have found his lichen. The trail ended at the brink of a deep crevice in the ground. Seems to be an earthquake-type split in solid rock, with the sand sifting over this and the far edge like pink silk cataracts. The bottom is in the shade and can't be seen. The crack seems to extend to our left and right as far as we can look. There looks like a trail down the inside of the crevice, but the Sun's setting, so we're waiting till tomorrow to go down. Going down was Jones' idea, not mine. June 22, 1961 Well, we're at the bottom, and there's water here, a shallow stream about thirty feet wide that runs along the center of the canal (we've decided we're in a canal). No sign of Pat or Kroger yet, but the sand here is hard-packed and damp, and there are normal-size footprints mingled with the alien ones, sharp and clear. The aliens seem to have six or seven toes. It varies from print to print. And they're barefoot, too, or else they have the damnedest-looking shoes in creation. The constant shower of sand near the cliff walls is annoying, but it's sandless (shower-wise) near the stream, so we're following the footprints along the bank. Also, the air's better down here. Still thin, but not so bad as on the surface. We're going without masks to save oxygen for the return trip (Jones assures me there'll be a return trip), and the air's only a little bit sandy, but handkerchiefs over nose and mouth solve this. We look like desperadoes, what with the rifles and covered faces. I said as much to Lloyd and he told me to shut up. Moss all over the cliff walls. Swell luck for Kroger. We've found Kroger and Pat, with the help of the aliens. Or maybe I should call them the Martians. Either way, it's better than what Jones calls them. They took away our rifles and brought us right to Kroger and Pat, without our even asking. Jones is mad at the way they got the rifles so easily. When we came upon them (a group of maybe ten, huddling behind a boulder in ambush), he fired, but the shots either bounced off their scales or stuck in their thick hides. Anyway, they took the rifles away and threw them into the stream, and picked us all up and took us into a hole in the cliff wall. The hole went on practically forever, but it didn't get dark. Kroger tells me that there are phosphorescent bacteria living in the mold on the walls. The air has a fresh-dug-grave smell, but it's richer in oxygen than even at the stream. We're in a small cave that is just off a bigger cave where lots of tunnels come together. I can't remember which one we came in through, and neither can anyone else. Jones asked me what the hell I kept writing in the diary for, did I want to make it a gift to Martian archeologists? But I said where there's life there's hope, and now he won't talk to me. I congratulated Kroger on the lichen I'd seen, but he just said a short and unscientific word and went to sleep. There's a Martian guarding the entrance to our cave. I don't know what they intend to do with us. Feed us, I hope. So far, they've just left us here, and we're out of rations. Kroger tried talking to the guard once, but he (or it) made a whistling kind of sound and flashed a mouthful of teeth. Kroger says the teeth are in multiple rows, like a tiger shark's. I'd rather he hadn't told me. June 23, 1961, I think We're either in a docket or a zoo. I can't tell which. There's a rather square platform surrounded on all four sides by running water, maybe twenty feet across, and we're on it. Martians keep coming to the far edge of the water and looking at us and whistling at each other. A little Martian came near the edge of the water and a larger Martian whistled like crazy and dragged it away. "Water must be dangerous to them," said Kroger. "We shoulda brought water pistols," Jones muttered. Pat said maybe we can swim to safety. Kroger told Pat he was crazy, that the little island we're on here underground is bordered by a fast river that goes into the planet. We'd end up drowned in some grotto in the heart of the planet, says Kroger. "What the hell," says Pat, "it's better than starving." It is not. June 24, 1961, probably I'm hungry . So is everybody else. Right now I could eat a dinner raw, in a centrifuge, and keep it down. A Martian threw a stone at Jones today, and Jones threw one back at him and broke off a couple of scales. The Martian whistled furiously and went away. When the crowd thinned out, same as it did yesterday (must be some sort of sleeping cycle here), Kroger talked Lloyd into swimming across the river and getting the red scales. Lloyd started at the upstream part of the current, and was about a hundred yards below this underground island before he made the far side. Sure is a swift current. But he got the scales, walked very far upstream of us, and swam back with them. The stream sides are steep, like in a fjord, and we had to lift him out of the swirling cold water, with the scales gripped in his fist. Or what was left of the scales. They had melted down in the water and left his hand all sticky. Kroger took the gummy things, studied them in the uncertain light, then tasted them and grinned. The Martians are made of sugar. Later, same day . Kroger said that the Martian metabolism must be like Terran (Earth-type) metabolism, only with no pancreas to make insulin. They store their energy on the outside of their bodies, in the form of scales. He's watched them more closely and seen that they have long rubbery tubes for tongues, and that they now and then suck up water from the stream while they're watching us, being careful not to get their lips (all sugar, of course) wet. He guesses that their "blood" must be almost pure water, and that it washes away (from the inside, of course) the sugar they need for energy. I asked him where the sugar came from, and he said probably their bodies isolated carbon from something (he thought it might be the moss) and combined it with the hydrogen and oxygen in the water (even I knew the formula for water) to make sugar, a common carbohydrate. Like plants, on Earth, he said. Except, instead of using special cells on leaves to form carbohydrates with the help of sunpower, as Earth plants do in photosynthesis (Kroger spelled that word for me), they used the shape of the scales like prisms, to isolate the spectra (another Kroger word) necessary to form the sugar. "I don't get it," I said politely, when he'd finished his spiel. "Simple," he said, as though he were addressing me by name. "They have a twofold reason to fear water. One: by complete solvency in that medium, they lose all energy and die. Two: even partial sprinkling alters the shape of the scales, and they are unable to use sunpower to form more sugar, and still die, if a bit slower." "Oh," I said, taking it down verbatim. "So now what do we do?" "We remove our boots," said Kroger, sitting on the ground and doing so, "and then we cross this stream, fill the boots with water, and spray our way to freedom." "Which tunnel do we take?" asked Pat, his eyes aglow at the thought of escape. Kroger shrugged. "We'll have to chance taking any that seem to slope upward. In any event, we can always follow it back and start again." "I dunno," said Jones. "Remember those teeth of theirs. They must be for biting something more substantial than moss, Kroger." "We'll risk it," said Pat. "It's better to go down fighting than to die of starvation." The hell it is. June 24, 1961, for sure The Martians have coal mines. That's what they use those teeth for. We passed through one and surprised a lot of them chewing gritty hunks of anthracite out of the walls. They came running at us, whistling with those tubelike tongues, and drooling dry coal dust, but Pat swung one of his boots in an arc that splashed all over the ground in front of them, and they turned tail (literally) and clattered off down another tunnel, sounding like a locomotive whistle gone berserk. We made the surface in another hour, back in the canal, and were lucky enough to find our own trail to follow toward the place above which the jeep still waited. Jones got the rifles out of the stream (the Martians had probably thought they were beyond recovery there) and we found the jeep. It was nearly buried in sand, but we got it cleaned off and running, and got back to the ship quickly. First thing we did on arriving was to break out the stores and have a celebration feast just outside the door of the ship. It was pork again, and I got sick. June 25, 1961 We're going back . Pat says that a week is all we were allowed to stay and that it's urgent to return and tell what we've learned about Mars (we know there are Martians, and they're made of sugar). "Why," I said, "can't we just tell it on the radio?" "Because," said Pat, "if we tell them now, by the time we get back we'll be yesterday's news. This way we may be lucky and get a parade." "Maybe even money," said Kroger, whose mind wasn't always on science. "But they'll ask why we didn't radio the info, sir," said Jones uneasily. "The radio," said Pat, nodding to Lloyd, "was unfortunately broken shortly after landing." Lloyd blinked, then nodded back and walked around the rocket. I heard a crunching sound and the shattering of glass, not unlike the noise made when one drives a rifle butt through a radio. Well, it's time for takeoff. This time it wasn't so bad. I thought I was getting my space-legs, but Pat says there's less gravity on Mars, so escape velocity didn't have to be so fast, hence a smoother (relatively) trip on our shock-absorbing bunks. Lloyd wants to play chess again. I'll be careful not to win this time. However, if I don't win, maybe this time I'll be the one to quit. Kroger is busy in his cramped lab space trying to classify the little moss he was able to gather, and Jones and Pat are up front watching the white specks revolve on that black velvet again. Guess I'll take a nap. June 26, 1961 Hell's bells . Kroger says there are two baby Martians loose on board ship. Pat told him he was nuts, but there are certain signs he's right. Like the missing charcoal in the air-filtration-and-reclaiming (AFAR) system. And the water gauges are going down. But the clincher is those two sugar crystals Lloyd had grabbed up when we were in that zoo. They're gone. Pat has declared a state of emergency. Quick thinking, that's Pat. Lloyd, before he remembered and turned scarlet, suggested we radio Earth for instructions. We can't. Here we are, somewhere in a void headed for Earth, with enough air and water left for maybe three days—if the Martians don't take any more. Kroger is thrilled that he is learning something, maybe, about Martian reproductive processes. When he told Pat, Pat put it to a vote whether or not to jettison Kroger through the airlock. However, it was decided that responsibility was pretty well divided. Lloyd had gotten the crystals, Kroger had only studied them, and Jones had brought them aboard. So Kroger stays, but meanwhile the air is getting worse. Pat suggested Kroger put us all into a state of suspended animation till landing time, eight months away. Kroger said, "How?" June 27, 1961 Air is foul and I'm very thirsty. Kroger says that at least—when the Martians get bigger—they'll have to show themselves. Pat says what do we do then ? We can't afford the water we need to melt them down. Besides, the melted crystals might all turn into little Martians. Jones says he'll go down spitting. Pat says why not dismantle interior of rocket to find out where they're holing up? Fine idea. How do you dismantle riveted metal plates? June 28, 1961 The AFAR system is no more and the water gauges are still dropping. Kroger suggests baking bread, then slicing it, then toasting it till it turns to carbon, and we can use the carbon in the AFAR system. We'll have to try it, I guess. The Martians ate the bread. Jones came forward to tell us the loaves were cooling, and when he got back they were gone. However, he did find a few of the red crystals on the galley deck (floor). They're good-sized crystals, too. Which means so are the Martians. Kroger says the Martians must be intelligent, otherwise they couldn't have guessed at the carbohydrates present in the bread after a lifelong diet of anthracite. Pat says let's jettison Kroger. This time the vote went against Kroger, but he got a last-minute reprieve by suggesting the crystals be pulverized and mixed with sulphuric acid. He says this'll produce carbon. I certainly hope so. So does Kroger. Brief reprieve for us. The acid-sugar combination not only produces carbon but water vapor, and the gauge has gone up a notch. That means that we have a quart of water in the tanks for drinking. However, the air's a bit better, and we voted to let Kroger stay inside the rocket. Meantime, we have to catch those Martians. June 29, 1961 Worse and worse . Lloyd caught one of the Martians in the firing chamber. We had to flood the chamber with acid to subdue the creature, which carbonized nicely. So now we have plenty of air and water again, but besides having another Martian still on the loose, we now don't have enough acid left in the fuel tanks to make a landing. Pat says at least our vector will carry us to Earth and we can die on our home planet, which is better than perishing in space. The hell it is. March 3, 1962 Earth in sight . The other Martian is still with us. He's where we can't get at him without blow-torches, but he can't get at the carbon in the AFAR system, either, which is a help. However, his tail is prehensile, and now and then it snakes out through an air duct and yanks food right off the table from under our noses. Kroger says watch out. We are made of carbohydrates, too. I'd rather not have known. March 4, 1962 Earth fills the screen in the control room. Pat says if we're lucky, he might be able to use the bit of fuel we have left to set us in a descending spiral into one of the oceans. The rocket is tighter than a submarine, he insists, and it will float till we're rescued, if the plates don't crack under the impact. We all agreed to try it. Not that we thought it had a good chance of working, but none of us had a better idea. I guess you know the rest of the story, about how that destroyer spotted us and got us and my diary aboard, and towed the rocket to San Francisco. News of the "captured Martian" leaked out, and we all became nine-day wonders until the dismantling of the rocket. Kroger says he must have dissolved in the water, and wonders what that would do. There are about a thousand of those crystal-scales on a Martian. So last week we found out, when those red-scaled things began clambering out of the sea on every coastal region on Earth. Kroger tried to explain to me about salinity osmosis and hydrostatic pressure and crystalline life, but in no time at all he lost me. The point is, bullets won't stop these things, and wherever a crystal falls, a new Martian springs up in a few weeks. It looks like the five of us have abetted an invasion from Mars. Needless to say, we're no longer heroes. I haven't heard from Pat or Lloyd for a week. Jones was picked up attacking a candy factory yesterday, and Kroger and I were allowed to sign on for the flight to Venus scheduled within the next few days—because of our experience. Kroger says there's only enough fuel for a one-way trip. I don't care. I've always wanted to travel with the President. —JACK SHARKEY Transcriber's Note: This etext was produced from Galaxy Magazine June 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | C. Lloyd broke the radio. |
What dataset is used for training? | ### Introduction
Tonal languages use pitch to distinguish different words, for example, yi in Mandarin may mean `one', `to move', `already', or `art', depending on the pitch contour. Of over 6000 languages in the world, it is estimated that as many as 60-70% are tonal BIBREF0, BIBREF1. A few of these are national languages (e.g., Mandarin Chinese, Vietnamese, and Thai), but many tonal languages have a small number of speakers and are scarcely documented. There is a limited availability of trained linguists to perform language documentation before these languages become extinct, hence the need for better tools to assist linguists in these tasks. One of the first tasks during the description of an unfamiliar language is determining its phonemic inventory: what are the consonants, vowels, and tones of the language, and which pairs of phonemes are contrastive? Tone presents a unique challenge because unlike consonants and vowels, which can be identified in isolation, tones do not have a fixed pitch, and vary by speaker and situation. Since tone data is subject to interpretation, different linguists may produce different descriptions of the tone system of the same language BIBREF1. In this work, we present a model to automatically infer phonemic tone categories of a tonal language. We use an unsupervised representation learning and clustering approach, which requires only a set of spoken words in the target language, and produces clusters of syllables that probably have the same tone. We apply our method on Mandarin Chinese and Cantonese datasets, for which the ground truth annotation is used for evaluation. Our method does not make any language-specific assumptions, so it may be applied to low-resource languages whose phonemic inventories are not already established. ### Introduction ::: Tone in Mandarin and Cantonese
Mandarin Chinese (1.1 billion speakers) and Cantonese (74 million speakers) are two tonal languages in the Sinitic family BIBREF0. Mandarin has four lexical tones: high (55), rising (25), low-dipping (214), and falling (51). The third tone sometimes undergoes sandhi, addressed in section SECREF3. We exclude a fifth, neutral tone, which can only occur in word-final positions and has no fixed pitch. Cantonese has six lexical tones: high-level (55), mid-rising (25), mid-level (33), low-falling (21), low-rising (23), and low-level (22). Some descriptions of Cantonese include nine tones, of which three are checked tones that are flat, shorter in duration, and only occur on syllables ending in /p/, /t/, or /k/. Since each one of the checked tones are in complementary distribution with an unchecked tone, we adopt the simpler six tone model that treats the checked tones as variants of the high, mid, and low level tones. Contours for the lexical tones in both languages are shown in Figure FIGREF2. ### Related Work
Many low-resource languages lack sufficient transcribed data for supervised speech processing, thus unsupervised models for speech processing is an emerging area of research. The Zerospeech 2015 and 2017 challenges featured unsupervised learning of contrasting phonemes in English and Xitsonga, evaluated by an ABX phoneme discrimination task BIBREF3. One successful approach used denoising and correspondence autoencoders to learn a representation that avoided capturing noise and irrelevant inter-speaker variation BIBREF4. Deep LSTMs for segmenting and clustering phonemes in speech have also been explored in BIBREF5 and BIBREF6. In Mandarin Chinese, deep neural networks have been successful for tone classification in isolated syllables BIBREF7 as well as in continuous speech BIBREF8, BIBREF9. Both of these models found that Mel-frequency cepstral coefficients (MFCCs) outperformed pitch contour features, despite the fact that MFCC features do not contain pitch information. In Cantonese, support vector machines (SVMs) have been applied to classify tones in continuous speech, using pitch contours as input BIBREF10. Unsupervised learning of tones remains largely unexplored. Levow BIBREF11 performed unsupervised and semi-supervised tone clustering in Mandarin, using average pitch and slope as features, and $k$-means and asymmetric $k$-lines for clustering. Graph-based community detection techniques have been applied to group $n$-grams of contiguous contours into clusters in Mandarin BIBREF12. Our work appears to be the first model to use unsupervised deep neural networks for phonemic tone clustering. ### Data and Preprocessing
We use data from Mandarin Chinese and Cantonese. For each language, the data consists of a list of spoken words, recorded by the same speaker. The Mandarin dataset is from a female speaker and is provided by Shtooka, and the Cantonese dataset is from a male speaker and is downloaded from Forvo, an online crowd-sourced pronunciation dictionary. We require all samples within each language to be from the same speaker to avoid the difficulties associated with channel effects and inter-speaker variation. We randomly sample 400 words from each language, which are mostly between 2 and 4 syllables; to reduce the prosody effects with longer utterances, we exclude words longer than 4 syllables. We extract ground-truth tones for evaluation purposes. In Mandarin, the tones are extracted from the pinyin transcription; in Cantonese, we reference the character entries on Wiktionary to retrieve the romanized pronunciation and tones. For Mandarin, we correct for third-tone sandhi (a phonological rule where a pair of consecutive third-tones is always realized as a second-tone followed by a third-tone). We also exclude the neutral tone, which has no fixed pitch and is sometimes thought of as a lack of tone. ### Data and Preprocessing ::: Pitch extraction and syllable segmentation
We use Praat's autocorrelation-based pitch estimation algorithm to extract the fundamental frequency (F0) contour for each sample, using a minimum frequency of 75Hz and a maximum frequency of 500Hz BIBREF13. The interface between Python and Praat is handled using Parselmouth BIBREF14. We normalize the contour to be between 0 and 1, based on the speaker's pitch range. Next, we segment each speech sample into syllables, which is necessary because syllable boundaries are not provided in our datasets. This is done using a simple heuristic that detects continuously voiced segments, and manual annotation where the heuristic fails. To obtain a constant length pitch contour as input to our model, we sample the pitch at 40 equally spaced points. Note that by sampling a variable length contour to a constant length, information about syllable length is lost; this is acceptable because we consider tones which differ on length as variations of the same tone. ### Model ::: Convolutional autoencoder
We use a convolutional autoencoder (Figure FIGREF4) to learn a two-dimensional latent vector for each syllable. Convolutional layers are widely used in computer vision and speech processing to learn spatially local features that are invariant of position. We use a low dimensional latent space so that the model learns to generate a representation that only captures the most important aspects of the input contour, and also because clustering algorithms tend to perform poorly in high dimensional spaces. Our encoder consists of three layers. The first layer applies 2 convolutional filters (kernel size 4, stride 1) followed by max pooling (kernel size 2) and a tanh activation. The second layer applies 4 convolutional filters (kernel size 4, stride 1), again with max pooling (kernel size 2) and a tanh activation. The third layer is a fully connected layer with two dimensional output. Our decoder is the encoder in reverse, consisting of one fully connected layer and two deconvolution layers, with the same layer shapes as the encoder. We train the autoencoder using PyTorch BIBREF15, for 500 epochs, with a batch size of 60. The model is optimized using Adam BIBREF16 with a learning rate of 5e-4 to minimize the mean squared error between the input and output contours. ### Model ::: Mean shift clustering
We run the encoder on each syllable's pitch contour to get their latent representations; we apply principal component analysis (PCA) to remove any correlation between the two dimensions. Then, we run mean shift clustering BIBREF17, BIBREF18, estimating a probability density function in the latent space. The procedure performs gradient ascent on all the points until they converge to a set of stationary points, which are local maxima of the density function. These stationary points are taken to be cluster centers, and points that converge to the same stationary point belong to the same cluster. Unlike $k$-means clustering, the mean shift procedure does not require the number of clusters to be specified, only a bandwidth parameter (set to 0.6 for our experiments). The cluster centers are always in regions of high density, so they can be viewed as prototypes that represent their respective clusters. Another advantage is that unlike $k$-means, mean shift clustering is robust to outliers. Although the mean shift procedure technically assigns every point to a cluster, not all such clusters are linguistically plausible as phonemic tones, because they contain very few points. Thus, we take only clusters larger than a threshold, determined empirically from the distribution of cluster sizes; the rest are considered spurious clusters and we treat them as unclustered. Finally, we feed the remaining cluster centers into the decoder to generate a prototype pitch contour for each cluster. ### Results
Figure FIGREF9 shows the latent space learned by the autoencoders and the clustering output. Our model found 4 tone clusters in Mandarin, matching the number of phonemic tones (Table TABREF12) and 5 in Cantonese, which is one fewer than the number of phonemic tones (Table TABREF13). In Mandarin, the 4 clusters correspond very well with the the 4 phonemic tone categories, and the generated contours closely match the ground truth in Figure FIGREF2. There is some overlap between tones 3 and 4; this is because tone 3 is sometimes realized a low-falling tone without the final rise, a process known as half T3 sandhi BIBREF19, thus, it may overlap with tone 4 (falling tone). In Cantonese, the 5 clusters A-E correspond to low-falling, mid-level, high-level, mid-rising, and low-rising tones. Tone clustering in Cantonese is expected to be more difficult than in Mandarin because of 6 contrastive tones, rather than 4. The model is more effective at clustering the higher tones (1, 2, 3), and less effective at clustering the lower tones (4, 5, 6), particularly tone 4 (low-falling) and tone 6 (low-level). This confirms the difficulties in prior work, which reported worse classification accuracy on the lower-pitched tones because the lower region of the Cantonese tone space is more crowded than the upper region BIBREF10. Two other sources of error are carry-over and declination effects. A carry-over effect is when the pitch contour of a tone undergoes contextual variation depending on the preceding tone; strong carry-over effects have been observed in Mandarin BIBREF20. Prior work BIBREF11 avoided carry-over effects by using only the second half of every syllable, but we do not consider language-specific heuristics in our model. Declination is a phenomenon in which the pitch declines over an utterance BIBREF1, BIBREF10. This is especially a problem in Cantonese, which has tones that differ only on pitch level and not contour: for example, a mid-level tone near the end of a phrase may have the same absolute pitch as a low-level tone at the start of a phrase. To test this hypothesis, we evaluate the model on only the first syllable of every word, which eliminates carry-over and declination effects (Table TABREF14). In both Mandarin and Cantonese, the clustering is more accurate when using only the first syllables, compared to using all of the syllables. ### Conclusions and future work
We propose a model for unsupervised clustering and discovery of phonemic tones in tonal languages, using spoken words as input. Our model extracts the F0 pitch contour, trains a convolutional autoencoder to learn a low-dimensional representation for each contour, and applies mean shift clustering to the resulting latent space. We obtain promising results with both Mandarin Chinese and Cantonese, using only 400 spoken words from each language. Cantonese presents more difficulties because of its larger number of tones, especially at the lower half of the pitch range, and also due to multiple contrastive level tones. Finally, we briefly explore the influence of contextual variation on our model. A limitation of this study is that our model only considers pitch, which is only one aspect of tone. In reality, pitch is determined not only by tone, but by a complex mixture of intonation, stress, and other prosody effects. Tone is not a purely phonetic property – it is impossible to determine on a phonetic basis whether two pitch contours have distinct underlying tones, or are variants of the same underlying tone (perhaps in complementary distribution). Instead, two phonemic tones can be shown to be contrastive only by providing a minimal pair, where two semantically different lexical items are identical in every respect other than their tones. The last problem is not unique to tone: similar difficulties have been noted when attempting to identify consonant and vowel phonemes automatically BIBREF21. In future work, we plan to further explore these issues and develop more nuanced models to learn tone from speech. ### Acknowledgments
We thank Prof Gerald Penn for his help suggestions during this project. Rudzicz is a CIFAR Chair in AI. Fig. 1. Pitch contours for the four Mandarin tones and six Cantonese tones in isolation, produced by native speakers. Figure adapted from [3]. Fig. 2. Diagram of our model architecture, consisting of a convolutional autoencoder to learn a latent representation for each pitch contour, and mean shift clustering to identify groups of similar tones. Fig. 3. Latent space generated by autoencoder and the results of mean shift clustering for Mandarin and Cantonese. Each cluster center is fed through the decoder to generate the corresponding pitch contour. The clusters within each language are ordered by size, from largest to smallest. Table 3. Normalized mutual information (NMI) between cluster assignments and ground truth tones, considering only the first syllable of each word, or all syllables. Table 1. Cluster and tone frequencies for Mandarin. Table 2. Cluster and tone frequencies for Cantonese. | Mandarin dataset, Cantonese dataset |
Why was Altha away from the other Hairy People of her kind?
A. The outlaws had turned the others against her.
B. She had left their group in fear of attacks.
C. The outlaws had stolen her.
D. She had been lost from their group and never reconnected.
| THE HAIRY ONES by BASIL WELLS Marooned on a world within a world, aided by a slim girl and an old warrior, Patrolman Sisko Rolf was fighting his greatest battle—to bring life to dying Mars. [Transcriber's Note: This etext was produced from Planet Stories Winter 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] "The outlaw ships are attacking!" Old Garmon Nash's harsh voice snapped like a thunderclap in the cramped rocket flyer's cabin. "Five or six of them. Cut the searchlights!" Sisko Rolf's stocky body was a blur of motion as he cut the rocket jets, doused the twin searchlights, and switched over to the audio beams that served so well on the surface when blind flying was in order. But here in the cavern world, thirty-seventh in the linked series of vast caves that underlie the waterless wastes of Mars, the reflected waves of sound were of little value. Distances were far too cramped—disaster might loom but a few hundred feet away. "Trapped us neatly," Rolf said through clenched teeth. "Tolled into their underground hideout by that water-runner we tried to capture. We can't escape, that's certain. They know these caverns better than.... We'll down some of them, though." "Right!" That was old Garmon Nash, his fellow patrolman aboard the Planet Patrol ship as he swung the deadly slimness of his rocket blast's barrel around to center on the fiery jets that betrayed the approaching outlaw flyers. Three times he fired the gun, the rocket projectiles blasting off with their invisible preliminary jets of gas, and three times an enemy craft flared up into an intolerable torch of flame before they realized the patrol ship had fired upon them. Then a barrage of enemy rocket shells exploded into life above and before them. Rolf swung the lax controls over hard as the bursts of fire revealed a looming barrier of stone dead ahead, and then he felt the tough skin of the flyer crumple inward. The cabin seemed to telescope about him. In a slow sort of wonder Rolf felt the scrape of rock against metal, and then the screeching of air through the myriad rents in the cabin's meralloy walls grew to a mad whining wail. Down plunged the battered ship, downward ever downward. Somehow Rolf found the strength to wrap his fingers around the control levers and snap on a quick burst from the landing rockets. Their mad speed checked momentarily, but the nose of the vertically plunging ship dissolved into an inferno of flame. The ship struck; split open like a rotten squash, and Rolf felt himself being flung far outward through thick blackness. For an eternity it seemed he hung in the darkness before something smashed the breath and feeling from his nerveless body. With a last glimmer of sanity he knew that he lay crushed against a rocky wall. Much later Rolf groaned with the pain of bruised muscles and tried to rise. To his amazement he could move all his limbs. Carefully he came to his knees and so to his feet. Not a bone was broken, unless the sharp breathlessness that strained at his chest meant cracked ribs. There was light in the narrow pit in which he found himself, light and heat from the yet-glowing debris of the rocket flyer. The outlaws had blasted the crashed ship, his practiced eyes told him, and Garmon Nash must have died in the wreckage. He was alone in the waterless trap of a deep crevice. In the fading glow of the super-heated metal the vertical walls above mocked him. There could be no ascent from this natural prison-pit, and even if there were he could never hope to reach the surface forty miles and more overhead. The floors of the thirty-seven caves through which they had so carefully jetted were a splintered, creviced series of canyon-like wastes, and as he ascended the rarefied atmosphere of the higher levels would spell death. Rolf laughed. Without a pressure mask on the surface of Mars an Earthman was licked. Without water and food certain death grinned in his face, for beyond the sand-buried entrance to these lost equatorial caves there were no pressure domes for hundreds of miles. Here at least the air was thick enough to support life, and somewhere nearby the outlaws who smuggled their precious contraband water into the water-starved domes of North Mars lay hidden. The young patrolman unzippered his jacket pocket and felt for the emergency concentrate bars that were standard equipment. Half of the oval bar he crushed between his teeth, and when the concentrated energy flooded into his muscles he set off around the irregular wall of the pit. He found the opening less than ten paces from the starting point, an empty cavity higher than a man and half as wide. The glow from the gutted ship was failing and he felt for the solar torch that hugged flatly against his hip. He uncapped the torch and the miniature sun glowed redly from its lensed prison to reveal the rocky corridor stretching out ahead. Light! How many hours later it was when the first faint glow of white light reached his eyes Rolf did not know—it had seemed an eternity of endless plodding along that smooth-floored descending tunnel. Rolf capped the solar torch. No use wasting the captive energy needlessly he reasoned. And he loosened the expoder in its holster as he moved carefully forward. The outlaw headquarters might be close ahead, headquarters where renegade Frogs, Venusians from the southern sunken marshes of Mars, and Earthmen from dusty North Mars, concealed their precious hoard of water from the thirsty colonists of North Mars. "They may have found the sunken seas of Mars," thought Rolf as he moved alertly forward, "water that would give the mining domes new life." His fists clenched dryly. "Water that should be free!" Then the light brightened before him as he rounded a shouldering wall of smoothly trimmed stone, and the floor fell away beneath his feet! He found himself shooting downward into a vast void that glowed softly with a mysterious all-pervading radiance. His eyes went searching out, out into undreamed distance. For miles below him there was nothing but emptiness, and for miles before him there was that same glowing vacancy. Above the cavern's roof soared majestically upward; he could see the narrow dark slit through which his feet had betrayed him, and he realized that he had fallen through the vaulted rocky dome of this fantastic abyss. It was then, even as he snapped the release of his spinner and the nested blades spun free overhead, that he saw the slowly turning bulk of the cloud-swathed world, a tiny five mile green ball of a planet! The weird globe was divided equally into hemispheres, and as the tiny world turned between its confining columns a green, lake-dotted half alternated with a blasted, splintered black waste of rocky desert. As the spinner dropped him slowly down into the vast emptiness of the great shining gulf, Rolf could see that a broad band of stone divided the green fertile plains and forests from the desolate desert wastes of the other half. Toward this barrier the spinner bore him, and Rolf was content to let it move in that direction—from the heights of the wall he could scout out the country beyond. The wall expanded as he came nearer to the pygmy planet. The spinner had slowed its speed; it seemed to Rolf that he must be falling free in space for a time, but the feeble gravity of the tiny world tugged at him more strongly as he neared the wall. And the barrier became a jumbled mass of roughly-dressed stone slabs, from whose earth-filled crevices sprouted green life. So slowly was the spinner dropping that the blackened desolation of the other hemisphere came sliding up beneath his boots. He looked down into great gashes in the blackness of the desert and saw there the green of sunken oases and watered canyons. He drifted slowly toward the opposite loom of the mysterious wall with a swift wind off the desert behind him. A hundred yards from the base of the rocky wall his feet scraped through black dust, and he came to a stop. Deftly Rolf nested the spinners again in their pack before he set out toward the heaped-up mass of stone blocks that was the wall. Ten steps he took before an excited voice called out shrilly from the rocks ahead. Rolf's slitted gray eyes narrowed yet more and his hand dropped to the compact expoder machine-gun holstered at his hip. There was the movement of a dark shape behind the screen of vines and ragged bushes. "Down, Altha," a deeper voice rumbled from above, "it's one of the Enemy." The voice had spoken in English! Rolf took a step forward eagerly and then doubt made his feet falter. There were Earthmen as well as Frogs among the outlaws. This mysterious world that floated above the cavern floor might be their headquarters. "But, Mark," the voice that was now unmistakably feminine argued, "he wears the uniform of a patrolman." "May be a trick." The deep voice was doubtful. "You know their leader, Cannon, wanted you. This may be a trick to join the Outcasts and kidnap you." The girl's voice was merry. "Come on Spider-legs," she said. Rolf found himself staring, open-mouthed, at the sleek-limbed vision that parted the bushes and came toward him. A beautiful woman she was, with the long burnished copper of her hair down around her waist, but beneath the meager shortness of the skin tunic he saw that her firm flesh was covered with a fine reddish coat of hair. Even her face was sleek and gleaming with its coppery covering of down. "Hello, patrol-a-man," she said shyly. An elongated pencil-ray of a man bounced nervously out to her side. "Altha," he scolded, scrubbing at his reddened bald skull with a long-fingered hand, "why do you never listen to me? I promised your father I'd look after you." He hitched at his tattered skin robe. The girl laughed, a low liquid sound that made Rolf's heart pump faster. "This Mark Tanner of mine," she explained to the patrolman, "is always afraid for me. He does not remember that I can see into the minds of others." She smiled again as Rolf's face slowly reddened. "Do not be ashamed," she said. "I am not angry that you think I am—well, not too unattractive." Rolf threw up the mental block that was the inheritance from his grueling years of training on Earth Base. His instructors there had known that a few gifted mortals possess the power of a limited telepathy, and the secrets of the Planet Patrol must be guarded. "That is better, perhaps." The girl's face was demure. "And now perhaps you will visit us in the safety of the vaults of ancient Aryk." "Sorry," said the tall man as Rolf sprang easily from the ground to their side. "I'm always forgetting the mind-reading abilities of the Hairy People." "She one of them?" Rolf's voice was low, but he saw Altha's lip twitch. "Mother was." Mark Tanner's voice was louder. "Father was Wayne Stark. Famous explorer you know. I was his assistant." "Sure." Rolf nodded. "Lost in equatorial wastelands—uh, about twenty years ago—2053, I believe." "Only we were not lost on the surface," explained Tanner, his booming voice much too powerful for his reedy body, "Wayne Stark was searching for the lost seas of Mars. Traced them underground. Found them too." He paused to look nervously out across the blasted wasteland. "We ran out of fuel here on Lomihi," he finished, "with the vanished surface waters of Mars less than four miles beneath us." Rolf followed the direction of the other's pale blue eyes. Overhead now hung the bottom of the cavern. An almost circular island of pale yellow lifted above the restless dark waters of a vast sea. Rolf realized with a wrench of sudden fear that they actually hung head downward like flies walking across a ceiling. "There," roared Tanner's voice, "is one of the seas of Mars." "One," repeated Rolf slowly. "You mean there are more?" "Dozens of them," the older man's voice throbbed with helpless rage. "Enough to make the face of Mars green again. Cavern after cavern lies beyond this first one, their floors flooded with water." Rolf felt new strength pump into his tired bruised muscles. Here lay the salvation of Earth's thirsting colonies almost within reach. Once he could lead the scientists of North Mars to this treasure trove of water.... "Mark!" The girl's voice was tense. Rolf felt her arm tug at his sleeve and he dropped beside her in the shelter of a clump of coarse-leaved gray bushes. "The Furry Women attack!" A hundred paces away Rolf made the dark shapes of armed warriors as they filed downward from the Barrier into the blackened desolation of the desert half of Lomihi. "Enemies?" he whispered to Mark Tanner hoarsely. "Right." The older man was slipping the stout bowstring into its notched recess on the upper end of his long bow. "They cross the Barrier from the fertile plains of Nyd to raid the Hairy People. They take them for slaves." "I must warn them." Altha's lips thinned and her brown-flecked eyes flamed. "The outlaws may capture," warned Tanner. "They have taken over the canyons of Gur and Norpar, remember." "I will take the glider." Altha was on her feet, her body crouched over to take advantage of the sheltering shrubs. She threaded her way swiftly back along a rocky corridor in the face of the Barrier toward the ruins of ancient Aryk. Tanner shrugged his shoulders. "What can I do? Altha has the blood of the Hairy People in her veins. She will warn them even though the outlaws have turned her people against her." Rolf watched the column of barbarically clad warriors file out upon the barren desert and swing to the right along the base of the Barrier. Spear tips and bared swords glinted dully. "They will pass within a few feet!" he hissed. "Right." Tanner's fingers bit into Rolf's arm. "Pray that the wind does not shift, their nostrils are sensitive as those of the weasels they resemble." Rolf's eyes slitted. There was something vaguely unhuman about those gracefully marching figures. He wondered what Tanner had meant by calling them weasels, wondered until they came closer. Then he knew. Above half naked feminine bodies, sinuous and supple as the undulating coils of a serpent, rose the snaky ditigrade head of a weasel-brute! Their necks were long and wide, merging into the gray-furred muscles of their narrow bodies until they seemed utterly shoulderless, and beneath their furry pelts the ripples of smooth-flowing muscles played rhythmically. There was a stench, a musky penetrating scent that made the flesh of his body crawl. "See!" Tanner's voice was muted. "Giffa, Queen of the Furry Ones!" Borne on a carved and polished litter of ebon-hued wood and yellowed bone lolled the hideous queen of that advancing horde. Gaunt of body she was, her scarred gray-furred hide hanging loose upon her breastless frame. One eye was gone but the other gleamed, black and beady, from her narrow earless skull. And the skulls of rodents and men alike linked together into ghastly festoons about her heavy, short-legged litter. Men bore the litter, eight broad-shouldered red-haired men whose arms had been cut off at the shoulders and whose naked backs bore the weals of countless lashes. Their bodies, like that of Altha, were covered with a silky coat of reddish hair. Rolf raised his expoder, red anger clouding his eyes as he saw these maimed beasts of burden, but the hand of Mark Tanner pressed down firmly across his arm. The older man shook his head. "Not yet," he said. "When Altha has warned the Hairy People we can cut off their retreat. After they have passed I will arouse the Outcasts who live here upon the Barrier. Though their blood is that of the two races mingled they hate the Furry Ones." A shadow passed over their hiding place. The Furry Amazons too saw the indistinct darkness and looked up. High overhead drifted the narrow winged shape of a glider, and the warrior women shrieked their hatred. Gone now was their chance for a surprise attack on the isolated canyons of the Hairy People. They halted, clustered about their leader. Giffa snarled quick orders at them, her chisel-teeth clicking savagely. The column swung out into the wasteland toward the nearest sunken valleys of the Hairy People. Rolf and Mark Tanner came to their feet. Abruptly, then, the wind veered. From behind the two Earthmen it came, bearing the scent of their bodies out to the sensitive nostrils of the beast-women. Again the column turned. They glimpsed the two men and a hideous scrawling battle-cry burst from their throats. Rolf's expoder rattled briefly like a high-speed sewing machine as he flicked its muzzle back and forth along the ranks of attacking Furry Ones. Dozens of the hideous weasel creatures fell as the needles of explosive blasted them but hundreds more were swarming over their fallen sisters. Mark Tanner's bow twanged again and again as he drove arrows at the bloodthirsty warrior women. But the Furry Ones ran fearlessly into that rain of death. The expoder hammered in Rolf's heavy fist. Tanner smashed an elbow into Rolf's side. "Retreat!" he gasped. The Furry Amazons swarmed up over the lower terraces of rocks, their snaky heads thrust forward and their swords slashing. The two Earthmen bounded up and backward to the next jumbled layer of giant blocks behind them, their powerful earthly muscles negating Lomihi's feeble gravity. Spears showered thick about them and then they dropped behind the sheltering bulk of a rough square boulder. "Now where?" Rolf snapped another burst of expoder needles at the furry attackers as he asked. "To the vaults beneath the Forbidden City," Mark Tanner cried. "None but the Outcasts and we two have entered the streets of deserted Aryk." The bald scientist slung his bow over his head and one shoulder and went bounding away along a shadowy crevice that plunged raggedly into the heart of the Barrier. Rolf blasted another spurt of explosive needles at the Furry Ones and followed. Darkness thickened as they penetrated into the maze of the Barrier's shattered heart. An unseen furry shape sprang upon Rolf's shoulders and as he sank to his knees he felt hot saliva drip like acid upon his neck. His fist sent the attacker's bulk smashing against the rocky floor before fangs or claws could rip at his tender flesh, and he heard a choked snarl that ended convulsively in silence. Bat-winged blobs of life dragged wet leathery hide across his face, and beneath his feet slimy wriggling things crushed into quivering pulp. Then there was faint light again, and the high-vaulted roof of a rock dungeon rose above him. Mark Tanner was peering out a slitted embrasure that overlooked the desolate land of the Hairy People. Tanner's finger pointed. "Altha!" Rolf saw the graceful wings of the glider riding the thermals back toward the Barrier. "She had warned the Hairy People, and now she returns." "The weasel heads won't follow us here?" asked Rolf. Tanner laughed. "Hardly. They fear the spirits of the Ancients too much for that. They believe the invisible powers will drink their souls." "Then how about telling me about this hanging world?" "Simply the whim of an ancient Martian ruler. As I have learned from the inscriptions and metal tablets here in Aryk he could not conquer all of Mars so he created a world that would be all his own." Rolf laughed. "Like the pleasure globes of the wealthy on Earth." "Right." Tanner kept his eyes on the enlarging winged shape of Altha's flyer as he spoke. "Later, when the nations of Mars began draining off the seas and hoarding them in their underground caverns, Lomihi became a fortress for the few thousand aristocrats and slaves who escaped the surface wars. "The Hairy People were the rulers," he went on, "and the Furry Ones were their slaves. In the revolt that eventually split Lomihi into two warring races this city, Aryk, was destroyed by a strange vegetable blight and the ancient knowledge was lost to both races." "But," Rolf frowned thoughtfully, "what keeps Lomihi from crashing into the island? Surely the two columns at either end cannot support it?" "The island is the answer," said Tanner. "Somehow it blocks the force of gravity—shields Lomihi from...." He caught his breath suddenly. "The outlaws!" he cried. "They're after Altha." Rolf caught a glimpse of a sleek rocket flyer diving upon Altha's frail wing. He saw the girl go gliding steeply down toward a ragged jumble of volcanic spurs and pits and disappear from view. He turned to see the old man pushing another crudely constructed glider toward the outer wall of the rock chamber. Tanner tugged at a silvery metal bar inset into the stone wall. A section of the wall swung slowly inward. Rolf sprang to his side. "Let me follow," he said. "I can fly a glider, and I have my expoder." The older man's eyes were hot. He jerked at Rolf's hands and then suddenly thought better of it. "You're right," he agreed. "Help her if you can. Your weapon is our only hope now." Rolf pushed up and outward with all the strength of his weary muscles. The glider knifed forward with that first swift impetus, and drove out over the Barrier. The Furry Ones were struggling insect shapes below him, and he saw with a thrill that larger bodied warriors, whose bodies glinted with a dull bronze, were attacking them from the burnt-out wastelands. The Hairy People had come to battle the invaders. He guided the frail wing toward the shattered badlands where the girl had taken shelter, noting as he did so that the rocket flyer had landed near its center in a narrow strip of rocky gulch. A sudden thought made him grin. He drove directly toward the grounded ship. With this rocket flyer he could escape from Lomihi, return through the thirty-seven caverns to the upper world, and give to thirsty Mars the gift of limitless water again. A man stood on guard just outside the flyer's oval door. Rolf lined up his expoder and his jaw tensed. He guided the tiny soarer closer with one hand. If he could crash the glider into the guard, well and good. There would be no explosion of expoder needles to warn the fellow's comrades. But if the outlaw saw him Rolf knew that he would be the first to fire—his was the element of surprise. A score of feet lay between them, and suddenly the outlaw whirled about. Rolf pressed the firing button; the expoder clicked over once and the trimmer key jammed, and the doughy-faced Venusian swung up his own long-barreled expoder! Rolf snapped his weapon overhand at the Frog's hairless skull. The fish-bellied alien ducked but his expoder swung off the target momentarily. In that instant Rolf launched himself from the open framework of the slowly diving glider, full upon the Venusian. They went down, Rolf swinging his fist like a hammer. He felt the Frog go limp and he loosed a relieved whistle. Now with a rocket flyer and the guard's rifle expoder in his grasp the problem of escape from the inner caverns was solved. He would rescue the girl, stop at the Forbidden City for Mark Tanner, and blast off for the upper crust forty miles and more overhead. He knelt over the prostrate Venusian, using his belt and a strip torn from his greenish tunic to bind the unconscious man. The knots were not too tight, the man could free himself in the course of a few hours. He shrugged his shoulders wearily and started to get up. A foot scraped on stone behind him. He spun on bent knees and flung himself fifty feet to the further side of the narrow gulch with the same movement. Expoder needles splintered the rocks about him as he dropped behind a sheltering rocky ledge, and he caught a glimpse of two green-clad men dragging the bronze-haired body of the girl he had come to save into the shelter of the flyer. A green bulge showed around the polished fuselage and Rolf pressed his captured weapon's firing button. A roar of pain came from the wounded man, and he saw an outflung arm upon the rocky ground that clenched tightly twice and relaxed to move no more. The outlaw weapon must have been loaded with a drum of poisoned needles, the expoder needles had not blasted a vital spot in the man's body. The odds were evening, he thought triumphantly. There might be another outlaw somewhere out there in the badlands, but no more than that. The flyer was built to accommodate no more than five passengers and four was the usual number. He shifted his expoder to cover the opposite end of the ship's squatty fuselage. And something that felt like a mountain smashed into his back. He was crushed downward, breathless, his eyes glimpsing briefly the soiled greenish trousers of his attacker as they locked on either side of his neck, and then blackness engulfed him as a mighty sledge battered endlessly at his skull. This sledge was hammering relentlessly as Rolf sensed his first glimmer of returning light. There were two sledges, one of them that he identified as the hammering of blood in his throbbing temples, and the other the measured blasting pulse of rocket jets. He opened his eyes slowly to find himself staring at the fine-crusted metal plates of a flyer's deck. His nose was grinding into the oily muck that only undisciplined men would have permitted to accumulate. Cautiously his head twisted until he could look forward toward the controls. The bound body of Altha Stark faced him, and he saw her lips twist into a brief smile of recognition. She shook her head and frowned as he moved his arm. But Rolf had learned that his limbs were not bound—apparently the outlaws had considered him out of the blasting for the moment. By degrees Rolf worked his arm down to his belt where his solar torch was hooked. His fingers made careful adjustments within the inset base of the torch, pushing a lever here and adjusting a tension screw there. The ship bumped gently as it landed and the thrum of rockets ceased. The cabin shifted with the weight of bodies moving from their seats. Rolf heard voices from a distance and the answering triumphant bawling of his two captors. The moment had come. He turned the cap of the solar torch away from his body and freed it. Heat blasted at his body as the stepped-up output of the torch made the oily floor flame. He lay unmoving while the thick smoke rolled over him. "Fire!" There was panic in the outlaw's voice. Rolf came to his knees in the blanketing fog and looked forward. One of the men flung himself out the door, but the other reached for the extinguisher close at hand. His thoughts were on the oily smoke; not on the prisoners, and so the impact of Rolf's horizontally propelled body drove the breath from his lungs before his hand could drop to his belted expoder. The outlaw was game. His fists slammed back at Rolf, and his knees jolted upward toward the patrolman's vulnerable middle. But Rolf bored in, his own knotted hands pumping, and his trained body weaving instinctively aside from the crippling blows aimed at his body. For a moment they fought, coughing and choking from the thickening pall of smoke, and then the fingers of the outlaw clamped around Rolf's throat and squeezed hard. The patrolman was weary; the wreck in the upper cavern and the long trek afterward through the dark tunnels had sapped his strength, and now he felt victory slipping from his grasp. He felt something soft bump against his legs, legs so far below that he could hardly realize that they were his, and then he was falling with the relentless fingers still about his throat. As from a great distant he heard a cry of pain and the blessed air gulped into his raw throat. His eyes cleared. He saw Altha's bound body and head. Her jaws were clamped upon the arm of the outlaw and even as he fought for more of the reeking smoky air of the cabin he saw the man's clenched fist batter at her face. Rolf swung, all the weight of his stocky body behind the blow, and the outlaw thudded limply against the opposite wall of the little cabin. No time to ask the girl if she were injured. The patrolman flung himself into the spongy control chair's cushions and sent the ship rocketing skyward. Behind him the thin film of surface oil no longer burned and the conditioning unit was clearing the air. "Patrolman," the girl's voice was beside him. "We're safe!" "Everything bongo?" Rolf wanted to know. "Of course," she smiled crookedly. "Glad of that." Rolf felt the warmth of her body so close beside him. A sudden strange restlessness came with the near contact. Altha smiled shyly and winced with pain. "Do you know," she said, "even yet I do not know your name." Rolf grinned up at her. "Need to?" he asked. The girl's eyes widened. A responsive spark blazed in them. "Handier than calling you Shorty all the time," she quipped. Then they were over the Barrier and Rolf saw the last of the beaten Furry Ones racing back across the great wall toward the Plains of Nyd. He nosed the captured ship down toward the ruined plaza of the Forbidden City. Once Mark Tanner was aboard they would blast surfaceward with their thrilling news that all Mars could have water in plenty again. Rolf snorted. "Shorty," he said disgustedly as they landed, but his arm went out toward the girl's red-haired slimness, and curved around it. | A. The outlaws had turned the others against her. |
What were the evaluation metrics used? | ### Introduction
In this work, we investigate the problem of task-oriented dialogue in mixed-domain settings. Our work is related to two lines of research in Spoken Dialogue System (SDS), namely task-oriented dialogue system and multi-domain dialogue system. We briefly review the recent literature related to these topics as follows. Task-oriented dialogue systems are computer programs which can assist users to complete tasks in specific domains by understanding user requests and generating appropriate responses within several dialogue turns. Such systems are useful in domain-specific chatbot applications which help users find a restaurant or book a hotel. Conventional approach for building a task-oriented dialogue system is concerned with building a quite complex pipeline of many connected components. These components are usually independently developed which include at least four crucial modules: a natural language understanding module, a dialogue state tracking module, a dialogue policy learning module, and a answer generation module. Since these systems components are usually trained independently, their optimization targets may not fully align with the overall system evaluation criteria BIBREF0. In addition, such a pipeline system often suffers from error propagation where error made by upstream modules are accumuated and got amplified to the downstream ones. To overcome the above limitations of pipeline task-oriented dialogue systems, much research has focused recently in designing end-to-end learning systems with neural network-based models. One key property of task-oriented dialogue model is that it is required to reason and plan over multiple dialogue turns by aggregating useful information during the conversation. Therefore, sequence-to-sequence models such as the encoder-decoder based neural network models are proven to be suitable for both task-oriented and non-task-oriented systems. Serban et al. proposed to build end-to-end dialogue systems using generative hierarchical recurrent encoder-decoder neural network BIBREF1. Li et al. presented persona-based models which incorporate background information and speaking style of interlocutors into LSTM-based seq2seq network so as to improve the modeling of human-like behavior BIBREF2. Wen et al. designed an end-to-end trainable neural dialogue model with modularly connected components BIBREF3. Bordes et al. BIBREF4 proposed a task-oriented dialogue model using end-to-end memory networks. At the same time, many works explored different kinds of networks to model the dialogue state, such as copy-augmented networks BIBREF5, gated memory networks BIBREF6, query-regression networks BIBREF7. These systems do not perform slot-filling or user goal tracking; they rank and select a response from a set of response candidates which are conditioned on the dialogue history. One of the significant effort in developing end-to-end task-oriented systems is the recent Sequicity framework BIBREF8. This framework also relies on the sequence-to-sequence model and can be optimized with supervised or reinforcement learning. The Sequicity framework introduces the concept of belief span (bspan), which is a text span that tracks the dialogue states at each turn. In this framework, the task-oriented dialogue problem is decomposed into two stages: bspan generation and response generation. This framework has been shown to significantly outperform state-of-the-art pipeline-based methods. The second line of work in SDS that is related to this work is concerned with multi-domain dialogue systems. As presented above, one of the key components of a dialogue system is dialogue state tracking, or belief tracking, which maintains the states of conversation. A state is usually composed of user's goals, evidences and information which is accumulated along the sequence of dialogue turns. While the user's goal and evidences are extracted from user's utterances, the useful information is usually aggregated from external resources such as knowledge bases or dialogue ontologies. Such knowledge bases contain slot type and slot value entries in one or several predefined domains. Most approaches have difficulty scaling up with multiple domains due to the dependency of their model parameters on the underlying knowledge bases. Recently, Ramadan et al. BIBREF9 has introduced a novel approach which utilizes semantic similarity between dialogue utterances and knowledge base terms, allowing the information to be shared across domains. This method has been shown not only to scale well to multi-domain dialogues, but also outperform existing state-of-the-art models in single-domain tracking tasks. The problem that we are interested in this work is task-oriented dialogue in mixed-domain settings. This is different from the multi-domain dialogue problem above in several aspects, as follows: First, we investigate the phenomenon of alternating between different dialogue domains in subsequent dialogue turns, where each turn is defined as a pair of user question and machine answer. That is, the domains are mixed between turns. For example, in the first turn, the user requests some information of a restaurant; then in the second turn, he switches to the a different domain, for example, he asks about the weather at a specific location. In a next turn, he would either switch to a new domain or come back to ask about some other property of the suggested restaurant. This is a realistic scenario which usually happens in practical chatbot applications in our observations. We prefer calling this problem mixed-domain dialogue rather than multiple-domain dialogue. Second, we study the effect of the mixed-domain setting in the context of multi-domain dialogue approaches to see how they perform in different experimental scenarios. The main findings of this work include: A specialized state tracking component in multiple domains still plays an important role and gives better results than a state-of-the-art end-to-end task-oriented dialogue system. A combination of specialized state tracking system and an end-to-end task-oriented dialogue system is beneficial in mix-domain dialogue systems. Our hybrid system is able to improve the belief tracking accuracy of about 28% of average absolute point on a standard multi-domain dialogue dataset. These experimental results give some useful insights on data preparation and acquisition in the development of the chatbot platform FPT.AI, which is currently deployed for many practical chatbot applications. The remainder of this paper is structured as follows. First, Section SECREF2 discusses briefly the two methods in building dialogue systems that our method relies on. Next, Section SECREF3 presents experimental settings and results. Finally, Section SECREF4 concludes the paper and gives some directions for future work. ### Methodology
In this section, we present briefly two methods that we use in our experiments which have been mentioned in the previous section. The first method is the Sequicity framework and the second one is the state-of-the-art multi-domain dialogue state tracking approach. ### Methodology ::: Sequicity
Figure FIGREF1 shows the architecture of the Sequicity framework as described in BIBREF8. In essence, in each turn, the Sequicity model first takes a bspan ($B_1$) and a response ($R_1$) which are determined in the previous step, and the current human question ($U_2$) to generate the current bspan. This bspan is then used together with a knowledge base to generate the corresponding machine answer ($R_2$), as shown in the right part of Figure FIGREF1. The left part of that figure shows an example dialogue in a mixed-domain setting (which will be explained in Section SECREF3). ### Methodology ::: Multi-domain Dialogue State Tracking
Figure FIGREF8 shows the architecture of the multi-domain belief tracking with knowledge sharing as described in BIBREF9. This is the state-of-the-art belief tracker for multi-domain dialogue. This system encodes system responses with 3 bidirectional LSTM network and encodes user utterances with 3+1 bidirectional LSTM network. There are in total 7 independent LSTMs. For tracking domain, slot and value, it uses 3 corresponding LSTMs, either for system response or user utterance. There is one special LSTM to track the user affirmation. The semantic similarity between the utterances and ontology terms are learned and shared between domains through their embeddings in the same semantic space. ### Experiments
In this section, we present experimental settings, different scenarios and results. We first present the datasets, then implementation settings, and finally obtained results. ### Experiments ::: Datasets
We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset is created by the Wizard-of-Oz method BIBREF10 on Amazon Mechanical Turk platform. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12. In this original dataset, each dialogue is of a single domain where all of its turns are on that domain. Each turn is composed of a sentence pair, one sentence is a user utterance, the other sentence is the corresponding machine response. A dialogue is a sequence of turns. To create mix-domain dialogues for our experiments, we make some changes in this dataset as follows: We keep the dialogues in the calendar domain as they are. We take a half of dialogues in the weather domain and a half of dialogues in the POI domain and mix their turns together, resulting in a dataset of mixed weather-POI dialogues. In this mixed-domain dialogue, there is a turn in the weather domain, followed by a turn in POI domain or vice versa. We call this dataset the sequential turn dataset. Since the start turn of a dialogue has a special role in triggering the learning systems, we decide to create another and different mixed-domain dataset with the following mixing method: The first turn and the last turn of each dialogue are kept as in their original. The internal turns are mixed randomly. We call this dataset the random turn dataset. Some statistics of these mixed-domain datasets are shown in the lower half of the Table TABREF12. ### Experiments ::: Experimental Settings
For the task-oriented Sequicity model, we keep the best parameter settings as reported in the original framework, on the same KVRET dataset BIBREF8. In particular, the hidden size of GRU unit is set to 50; the learning rate of Adam optimizer is 0.003. In addition to the original GRU unit, we also re-run this framework with simple RNN unit to compare the performance of different recurrent network types. The Sequicity tool is freely available for download. For the multi-domain belief tracker model, we set the hidden size of LSTM units to 50 as in the original model; word embedding size is 300 and number of training epochs is 100. The corresponding tool is also freely available for download. ### Experiments ::: Results
Our experimental results are shown in Table TABREF21. The first half of the table contains results for task-oriented dialogue with the Sequicity framework with two scenarios for training data preparation. For each experiment, we run our models for 3 times and their scores are averaged as the final score. The mixed training scenario performs the mixing of both the training data, development data and the test data as described in the previous subsection. The non-mixed training scenario performs the mixing only on the development and test data, keeps the training data unmixed as in the original KVRET dataset. As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score. Entity match rate evaluates task completion, it determines if a system can generate all correct constraints to search the indicated entities of the user. BLEU score evaluates the language quality of generated responses. Success F1 balances the recall and precision rates of slot answers. For further details on these metrics, please refer to BIBREF8. In the first series of experiments, we evaluate the Sequicity framework on different mixing scenarios and different recurrent units (GRU or RNN), on two mixing methods (sequential turn or random turn), as described previously. We see that when the training data is kept unmixed, the match rates are better than those of the mixed training data. It is interesting to note that the GRU unit is much more sensitive with mixed data than the simple RNN unit with the corresponding absolute point drop of about 10%, compared to about 3.5%. However, the entity match rate is less important than the Success F1 score, where the GRU unit outperforms RNN in both sequential turn and random turn by a large margin. It is logical that if the test data are mixed but the training data are unmixed, we get lower scores than when both the training data and test data are mixed. The GRU unit is also better than the RNN unit on response generation in terms of BLEU scores. We also see that the task-oriented dialogue system has difficulty running on mixed-domain dataset; it achieves only about 75.62% of Success F1 in comparison to about 81.1% (as reported in the Sequicity paper, not shown in our table). Appendix SECREF5 shows some example dialogues generated automatically by our implemented system. In the second series of experiments, we evaluate the belief tracking components of two systems, the specialized multi-domain belief tracker and the Sequicity bspan component. As shown in the lower half of the Table TABREF21, Sequicity capability of belief tracking is much worse than that of the multi-domain belief tracker. The slot accuracy gap between the tools is about 21.6%, the value accuracy gap is about 34.4%; that is a large average gap of 28% of accuracy. This result suggests a future work on combining a specialized belief tracking module with an end-to-end task-oriented dialogue system to improve further the performance of the overall dialogue system. ### Experiments ::: Error Analysis
In this subsection, we present an example of erroneous mixed dialogue with multple turns. Table TABREF23 shows a dialogue in the test set where wrong generated responses of the Sequicity system are marked in bold font. In the first turn, the system predicts incorrectly the bspan, thus generates wrong slot values (heavy traffic and Pizza Hut). The word Pizza Hut is an arbitrary value selected by the system when it cannot capture the correct value home in the bspan. In the second turn, the machine is not able to capture the value this_week. This failure does not manifest immediately at this turn but it is accumulated to make a wrong answer at the third turn (monday instead of this_week). The third turn is of domain weather and the fourth turn is switched to domain POI. The bspan value cleveland is retained through cross domain, resulting in an error in the fourth turn, where cleveland is shown instead of home. This example demonstrates a weakness of the system when being trained on a mixed-domain dataset. In the fifth turn, since the system does not recognize the value fastest in the bspan, it generates a random and wrong value moderate traffic. Note that the generated answer of the sixth turn is correct despite of the wrong predicted bspan; however, it is likely that if the dialogue continues, this wrong bspan may result in more answer mistakes. In such situations, multi-domain belief tracker usually performs better at bspan prediction. ### Conclusion
We have presented the problem of mixed-domain task-oriented dialogue and its empirical results on two datasets. We employ two state-of-the-art, publicly available tools, one is the Sequicity framework for task-oriented dialogue, and another is the multi-domain belief tracking system. The belief tracking capability of the specialized system is much better than that of the end-to-end system. We also show the difficulty of task-oriented dialogue systems on mixed-domain datasets through two series of experiments. These results give some useful insights in combining the approaches to improve the performance of a commercial chatbot platform which is under active development in our company. We plan to extend this current research and integrate its fruitful results into a future version of the platform. ### Example Dialogues
The following is three example dialogues generated by our system. The first dialogue is in single-domain. The next two dialogues are in mixed-domains. Fig. 1. Sequicity architecture. Fig. 2. Multi-domain belief tracking with knowledge sharing. TABLE I SOME STATISTICS OF THE DATASETS USED IN OUR EXPERIMENTS. THE ORIGINAL KVRET DATASET IS SHOWN IN THE UPPER HALF OF THE TABLE. THE MIXED DATASET IS SHOWN IN THE LOWER HALF OF THE TABLE. TABLE II OUR EXPERIMENTAL RESULTS.MATCH. AND SUCC. F1 ARE ENTITY MATCH RATE AND SUCCESS F1. THE UPPER HALF OF THE TABLE SHOWS RESULTS OF TASK-ORIENTED DIALOGUE WITH THE SEQUICITY FRAMEWORK. THE LOWER HALF OF THE TABLE SHOWS RESULTS OF MULTI-DOMAIN BELIEF TRACKER. TABLE III A MIXED DIALOGUE EXAMPLE IN THE TEST SET WITH ERRONEOUS GENERATED RESPONSES. THE LAST TWO COLUMNS SHOW RESPECTIVELY THE SYSTEM’S GENERATED BSPAN AND THE GOLD BSPAN OR BELIEF TRACKER. | entity match rate, BLEU score, Success F1 score |
What word best describes Tynan's reputation in Britain?
A. Tynan was understood to be a fraudulent and sociopathic manipulator
B. Tynan was viewed as an attention-seeking, irksome personality
C. Tynan was well-regarded as an outspoken person who tells it like it is
D. Tynan was looked upon with condescension as a vulgar, rude figure
| Maledict oratory The high costs of low language. Sunday, Jan. 14, 1996: A day that will live in--well, not infamy, exactly. Blasphemy would be closer to it. Early that afternoon, the Pittsburgh Steelers defeated the Indianapolis Colts to win the American Football Conference championship. Linebacker Greg Lloyd, accepting the trophy in front of a national television audience, responded with enthusiasm. "Let's see if we can bring this damn thing back here next year," he said, "along with the [expletive] Super Bowl." A few hours later, Michael Irvin of the Dallas Cowboys offered this spirited defense of his coach on TV after his team won the National Football Conference title: "Nobody deserves it more than Barry Switzer. He took all of this [expletive] ." Iwatched those episodes, and, incongruous as it may sound, I thought of Kenneth Tynan. Britain's great postwar drama critic was no fan of American football, but he was a fan of swearing. Thirty years earlier, almost to the week, Tynan was interviewed on BBC television in his capacity as literary director of Britain's National Theater and asked if he would allow the theater to present a play in which sex took place on stage. "Certainly," he replied. "I think there are very few rational people in this world to whom the word '[expletive]' is particularly diabolical or revolting or totally forbidden." It turned out there were a few more than Tynan thought. Within 24 hours, resolutions had been introduced in the House of Commons calling for his prosecution on charges of obscenity, for his removal as a theater official, and for censure of the network for allowing an obscene word to go out on the airwaves. Tynan escaped punishment, but he acquired a public reputation for tastelessness that he carried for the rest his life. To much of ordinary Britain, he became the man who had said "[expletive]" on the BBC. Neither Greg Lloyd nor Michael Irvin was so stigmatized. "It's live television," NBC Vice President Ed Markey said, rationalizing the outbursts. "It's an emotional moment. These things happen." Irvin wasn't about to let that stand. "I knew exactly what I was saying," he insisted later. "Those of you who can't believe I said it--believe it." Swearing isn't the only public act that Western civilization condones today but didn't 30 years ago. But it is one of the most interesting. It is everywhere, impossible to avoid or tune out. I am sitting in a meeting at the office, talking with a colleague about a business circumstance that may possibly go against us. "In that case, we're [expletive] ," he says. Five years ago, he would have said "screwed." Twenty years ago, he would have said, "We're in big trouble." Societal tolerance of profanity requires us to increase our dosage as time goes on. I am walking along a suburban street, trailing a class of pre-schoolers who are linked to each other by a rope. A pair of teen-agers passes us in the other direction. By the time they have reached the end of the line of children, they have tossed off a whole catalog of obscenities I did not even hear until I was well into adolescence, let alone use in casual conversation on a public street. I am talking to a distinguished professor of public policy about a foundation grant. I tell her something she wasn't aware of before. In 1965, the appropriate response was "no kidding." In 1996, you do not say "no kidding." It is limp and ineffectual. If you are surprised at all, you say what she says: "No shit." What word is taboo in middle-class America in 1996? There are a couple of credible candidates: The four-letter word for "vagina" remains off-limits in polite conversation (although that has more to do with feminism than with profanity), and the slang expression for those who engage in oral sex with males is not yet acceptable by the standards of office-meeting etiquette. But aside from a few exceptions, the supply of genuinely offensive language has dwindled almost to nothing as the 20th century comes to an end; the currency of swearing has been inflated to the brink of worthlessness. When almost anything can be said in public, profanity ceases to exist in any meaningful way at all. That most of the forbidden words of the 1950s are no longer forbidden will come as news to nobody: The steady debasement of the common language is only one of many social strictures that have loosened from the previous generation to the current. What is important is that profanity served a variety of purposes for a long time in Western culture. It does not serve those purposes any more. What purposes? There are a couple of plausible answers. One of them is emotional release. Robert Graves, who wrote a book in the 1920s called The Future of Swearing , thought that profanity was the adult replacement for childhood tears. There comes a point in life, he wrote, when "wailing is rightly discouraged, and groans are also considered a signal of extreme weakness. Silence under suffering is usually impossible." So one reaches back for a word one does not normally use, and utters it without undue embarrassment or guilt. And one feels better--even stimulated. The anthropologist Ashley Montagu, whose Anatomy of Swearing , published in 1967, is the definitive modern take on the subject, saw profanity as a safety valve rather than a stimulant, a verbal substitute for physical aggression. When someone swears, Montagu wrote, "potentially noxious energy is converted into a form that renders it comparatively innocuous." One could point out, in arguing against the safety-valve theory, that as America has grown more profane in the past 30 years, it has also grown more violent, not less. But this is too simple. It isn't just the supply of dirty words that matters, it's their emotive power. If they have lost that power through overuse, it's perfectly plausible to say that their capacity to deter aggressive behavior has weakened as well. But there is something else important to say about swearing--that it represents the invocation of those ideas a society considers powerful, awesome, and a little scary. I'm not sure there is an easy way to convey to anybody under 30, for example, the sheer emotive force that the word "[expletive]" possessed in the urban childhood culture of 40 years ago. It was the verbal link to a secret act none of us understood but that was known to carry enormous consequences in the adult world. It was the embodiment of both pleasure and danger. It was not a word or an idea to mess with. When it was used, it was used, as Ashley Montagu said, "sotto voce , like a smuggler cautiously making his way across a forbidden frontier." In that culture, the word "[expletive]" was not only obscene, it was profane, in the original sense: It took an important idea in vain. Profanity can be an act of religious defiance, but it doesn't have to be. The Greeks tempted fate by invoking the names of their superiors on Mount Olympus; they also swore upon everyday objects whose properties they respected but did not fully understand. "By the Cabbage!" Socrates is supposed to have said in moments of stress, and that was for good reason. He believed that cabbage cured hangovers, and as such, carried sufficient power and mystery to invest any moment with the requisite emotional charge. These days, none of us believes in cabbage in the way Socrates did, or in the gods in the way most Athenians did. Most Americans tell poll-takers that they believe in God, but few of them in a way that would make it impossible to take His name in vain: That requires an Old Testament piety that disappeared from American middle-class life a long time ago. Nor do we believe in sex any more the way most American children and millions of adults believed in it a generation ago: as an act of profound mystery and importance that one did not engage in, or discuss, or even invoke, without a certain amount of excitement and risk. We have trivialized and routinized sex to the point where it just doesn't carry the emotional freight it carried in the schoolyards and bedrooms of the 1950s. Many enlightened people consider this to be a great improvement over a society in which sex generated not only emotion and power, but fear. For the moment, I wish to insist only on this one point: When sexuality loses its power to awe, it loses its power to create genuine swearing. When we convert it into a casual form of recreation, we shouldn't be surprised to hear linebackers using the word "[expletive]" on national television. To profane something, in other words, one must believe in it. The cheapening of profanity in modern America represents, more than anything else, the crumbling of belief. There are very few ideas left at this point that are awesome or frightening enough for us to enforce a taboo against them. The instinctive response of most educated people to the disappearance of any taboo is to applaud it, but this is wrong. Healthy societies need a decent supply of verbal taboos and prohibitions, if only as yardsticks by which ordinary people can measure and define themselves. By violating these taboos over and over, some succeed in defining themselves as rebels. Others violate them on special occasions to derive an emotional release. Forbidden language is one of the ways we remind children that there are rules to everyday life, and consequences for breaking them. When we forget this principle, or cease to accept it, it is not just our language that begins to fray at the edges. What do we do about it? Well, we could pass a law against swearing. Mussolini actually did that. He decreed that trains and buses, in addition to running on time, had to carry signs that read "Non bestemmiare per l'onore d'Italia." ("Do not swear for the honor of Italy.") The commuters of Rome reacted to those signs exactly as you would expect: They cursed them. What Mussolini could not do, I am reasonably sure that American governments of the 1990s cannot do, nor would I wish it. I merely predict that sometime in the coming generation, profanity will return in a meaningful way. It served too many purposes for too many years of American life to disappear on a permanent basis. We need it. And so I am reasonably sure that when my children have children, there will once again be words so awesome that they cannot be uttered without important consequences. This will not only represent a new stage of linguistic evolution, it will be a token of moral revival. What the dirty words will be, God only knows. | D. Tynan was looked upon with condescension as a vulgar, rude figure |
Why is the lack of hotel space important for Simon's story?
A. It made him cut his trip short without finding any time travelers.
B. Simon would have to learn how to time travel in order to keep his bag from being stolen.
C. It set the stage for him to encounter an alien's home for himself.
D. It meant he would find a number of unsavory characters as he tried to find somewhere to sleep.
| UNBORN TOMORROW BY MACK REYNOLDS Unfortunately , there was only one thing he could bring back from the wonderful future ... and though he didn't want to ... nevertheless he did.... Illustrated by Freas Betty looked up from her magazine. She said mildly, "You're late." "Don't yell at me, I feel awful," Simon told her. He sat down at his desk, passed his tongue over his teeth in distaste, groaned, fumbled in a drawer for the aspirin bottle. He looked over at Betty and said, almost as though reciting, "What I need is a vacation." "What," Betty said, "are you going to use for money?" "Providence," Simon told her whilst fiddling with the aspirin bottle, "will provide." "Hm-m-m. But before providing vacations it'd be nice if Providence turned up a missing jewel deal, say. Something where you could deduce that actually the ruby ring had gone down the drain and was caught in the elbow. Something that would net about fifty dollars." Simon said, mournful of tone, "Fifty dollars? Why not make it five hundred?" "I'm not selfish," Betty said. "All I want is enough to pay me this week's salary." "Money," Simon said. "When you took this job you said it was the romance that appealed to you." "Hm-m-m. I didn't know most sleuthing amounted to snooping around department stores to check on the clerks knocking down." Simon said, enigmatically, "Now it comes." There was a knock. Betty bounced up with Olympic agility and had the door swinging wide before the knocking was quite completed. He was old, little and had bug eyes behind pince-nez glasses. His suit was cut in the style of yesteryear but when a suit costs two or three hundred dollars you still retain caste whatever the styling. Simon said unenthusiastically, "Good morning, Mr. Oyster." He indicated the client's chair. "Sit down, sir." The client fussed himself with Betty's assistance into the seat, bug-eyed Simon, said finally, "You know my name, that's pretty good. Never saw you before in my life. Stop fussing with me, young lady. Your ad in the phone book says you'll investigate anything." "Anything," Simon said. "Only one exception." "Excellent. Do you believe in time travel?" Simon said nothing. Across the room, where she had resumed her seat, Betty cleared her throat. When Simon continued to say nothing she ventured, "Time travel is impossible." "Why?" "Why?" "Yes, why?" Betty looked to her boss for assistance. None was forthcoming. There ought to be some very quick, positive, definite answer. She said, "Well, for one thing, paradox. Suppose you had a time machine and traveled back a hundred years or so and killed your own great-grandfather. Then how could you ever be born?" "Confound it if I know," the little fellow growled. "How?" Simon said, "Let's get to the point, what you wanted to see me about." "I want to hire you to hunt me up some time travelers," the old boy said. Betty was too far in now to maintain her proper role of silent secretary. "Time travelers," she said, not very intelligently. The potential client sat more erect, obviously with intent to hold the floor for a time. He removed the pince-nez glasses and pointed them at Betty. He said, "Have you read much science fiction, Miss?" "Some," Betty admitted. "Then you'll realize that there are a dozen explanations of the paradoxes of time travel. Every writer in the field worth his salt has explained them away. But to get on. It's my contention that within a century or so man will have solved the problems of immortality and eternal youth, and it's also my suspicion that he will eventually be able to travel in time. So convinced am I of these possibilities that I am willing to gamble a portion of my fortune to investigate the presence in our era of such time travelers." Simon seemed incapable of carrying the ball this morning, so Betty said, "But ... Mr. Oyster, if the future has developed time travel why don't we ever meet such travelers?" Simon put in a word. "The usual explanation, Betty, is that they can't afford to allow the space-time continuum track to be altered. If, say, a time traveler returned to a period of twenty-five years ago and shot Hitler, then all subsequent history would be changed. In that case, the time traveler himself might never be born. They have to tread mighty carefully." Mr. Oyster was pleased. "I didn't expect you to be so well informed on the subject, young man." Simon shrugged and fumbled again with the aspirin bottle. Mr. Oyster went on. "I've been considering the matter for some time and—" Simon held up a hand. "There's no use prolonging this. As I understand it, you're an elderly gentleman with a considerable fortune and you realize that thus far nobody has succeeded in taking it with him." Mr. Oyster returned his glasses to their perch, bug-eyed Simon, but then nodded. Simon said, "You want to hire me to find a time traveler and in some manner or other—any manner will do—exhort from him the secret of eternal life and youth, which you figure the future will have discovered. You're willing to pony up a part of this fortune of yours, if I can deliver a bona fide time traveler." "Right!" Betty had been looking from one to the other. Now she said, plaintively, "But where are you going to find one of these characters—especially if they're interested in keeping hid?" The old boy was the center again. "I told you I'd been considering it for some time. The Oktoberfest , that's where they'd be!" He seemed elated. Betty and Simon waited. "The Oktoberfest ," he repeated. "The greatest festival the world has ever seen, the carnival, feria , fiesta to beat them all. Every year it's held in Munich. Makes the New Orleans Mardi gras look like a quilting party." He began to swing into the spirit of his description. "It originally started in celebration of the wedding of some local prince a century and a half ago and the Bavarians had such a bang-up time they've been holding it every year since. The Munich breweries do up a special beer, Marzenbräu they call it, and each brewery opens a tremendous tent on the fair grounds which will hold five thousand customers apiece. Millions of liters of beer are put away, hundreds of thousands of barbecued chickens, a small herd of oxen are roasted whole over spits, millions of pair of weisswurst , a very special sausage, millions upon millions of pretzels—" "All right," Simon said. "We'll accept it. The Oktoberfest is one whale of a wingding." "Well," the old boy pursued, into his subject now, "that's where they'd be, places like the Oktoberfest . For one thing, a time traveler wouldn't be conspicuous. At a festival like this somebody with a strange accent, or who didn't know exactly how to wear his clothes correctly, or was off the ordinary in any of a dozen other ways, wouldn't be noticed. You could be a four-armed space traveler from Mars, and you still wouldn't be conspicuous at the Oktoberfest . People would figure they had D.T.'s." "But why would a time traveler want to go to a—" Betty began. "Why not! What better opportunity to study a people than when they are in their cups? If you could go back a few thousand years, the things you would wish to see would be a Roman Triumph, perhaps the Rites of Dionysus, or one of Alexander's orgies. You wouldn't want to wander up and down the streets of, say, Athens while nothing was going on, particularly when you might be revealed as a suspicious character not being able to speak the language, not knowing how to wear the clothes and not familiar with the city's layout." He took a deep breath. "No ma'am, you'd have to stick to some great event, both for the sake of actual interest and for protection against being unmasked." The old boy wound it up. "Well, that's the story. What are your rates? The Oktoberfest starts on Friday and continues for sixteen days. You can take the plane to Munich, spend a week there and—" Simon was shaking his head. "Not interested." As soon as Betty had got her jaw back into place, she glared unbelievingly at him. Mr. Oyster was taken aback himself. "See here, young man, I realize this isn't an ordinary assignment, however, as I said, I am willing to risk a considerable portion of my fortune—" "Sorry," Simon said. "Can't be done." "A hundred dollars a day plus expenses," Mr. Oyster said quietly. "I like the fact that you already seem to have some interest and knowledge of the matter. I liked the way you knew my name when I walked in the door; my picture doesn't appear often in the papers." "No go," Simon said, a sad quality in his voice. "A fifty thousand dollar bonus if you bring me a time traveler." "Out of the question," Simon said. "But why ?" Betty wailed. "Just for laughs," Simon told the two of them sourly, "suppose I tell you a funny story. It goes like this:" I got a thousand dollars from Mr. Oyster (Simon began) in the way of an advance, and leaving him with Betty who was making out a receipt, I hustled back to the apartment and packed a bag. Hell, I'd wanted a vacation anyway, this was a natural. On the way to Idlewild I stopped off at the Germany Information Offices for some tourist literature. It takes roughly three and a half hours to get to Gander from Idlewild. I spent the time planning the fun I was going to have. It takes roughly seven and a half hours from Gander to Shannon and I spent that time dreaming up material I could put into my reports to Mr. Oyster. I was going to have to give him some kind of report for his money. Time travel yet! What a laugh! Between Shannon and Munich a faint suspicion began to simmer in my mind. These statistics I read on the Oktoberfest in the Munich tourist pamphlets. Five million people attended annually. Where did five million people come from to attend an overgrown festival in comparatively remote Southern Germany? The tourist season is over before September 21st, first day of the gigantic beer bust. Nor could the Germans account for any such number. Munich itself has a population of less than a million, counting children. And those millions of gallons of beer, the hundreds of thousands of chickens, the herds of oxen. Who ponied up all the money for such expenditures? How could the average German, with his twenty-five dollars a week salary? In Munich there was no hotel space available. I went to the Bahnhof where they have a hotel service and applied. They put my name down, pocketed the husky bribe, showed me where I could check my bag, told me they'd do what they could, and to report back in a few hours. I had another suspicious twinge. If five million people attended this beer bout, how were they accommodated? The Theresienwiese , the fair ground, was only a few blocks away. I was stiff from the plane ride so I walked. There are seven major brewers in the Munich area, each of them represented by one of the circuslike tents that Mr. Oyster mentioned. Each tent contained benches and tables for about five thousand persons and from six to ten thousands pack themselves in, competing for room. In the center is a tremendous bandstand, the musicians all lederhosen clad, the music as Bavarian as any to be found in a Bavarian beer hall. Hundreds of peasant garbed fräuleins darted about the tables with quart sized earthenware mugs, platters of chicken, sausage, kraut and pretzels. I found a place finally at a table which had space for twenty-odd beer bibbers. Odd is right. As weird an assortment of Germans and foreign tourists as could have been dreamed up, ranging from a seventy- or eighty-year-old couple in Bavarian costume, to the bald-headed drunk across the table from me. A desperate waitress bearing six mugs of beer in each hand scurried past. They call them masses , by the way, not mugs. The bald-headed character and I both held up a finger and she slid two of the masses over to us and then hustled on. "Down the hatch," the other said, holding up his mass in toast. "To the ladies," I told him. Before sipping, I said, "You know, the tourist pamphlets say this stuff is eighteen per cent. That's nonsense. No beer is that strong." I took a long pull. He looked at me, waiting. I came up. "Mistaken," I admitted. A mass or two apiece later he looked carefully at the name engraved on his earthenware mug. "Löwenbräu," he said. He took a small notebook from his pocket and a pencil, noted down the word and returned the things. "That's a queer looking pencil you have there," I told him. "German?" "Venusian," he said. "Oops, sorry. Shouldn't have said that." I had never heard of the brand so I skipped it. "Next is the Hofbräu," he said. "Next what?" Baldy's conversation didn't seem to hang together very well. "My pilgrimage," he told me. "All my life I've been wanting to go back to an Oktoberfest and sample every one of the seven brands of the best beer the world has ever known. I'm only as far as Löwenbräu. I'm afraid I'll never make it." I finished my mass . "I'll help you," I told him. "Very noble endeavor. Name is Simon." "Arth," he said. "How could you help?" "I'm still fresh—comparatively. I'll navigate you around. There are seven beer tents. How many have you got through, so far?" "Two, counting this one," Arth said. I looked at him. "It's going to be a chore," I said. "You've already got a nice edge on." Outside, as we made our way to the next tent, the fair looked like every big State-Fair ever seen, except it was bigger. Games, souvenir stands, sausage stands, rides, side shows, and people, people, people. The Hofbräu tent was as overflowing as the last but we managed to find two seats. The band was blaring, and five thousand half-swacked voices were roaring accompaniment. In Muenchen steht ein Hofbräuhaus! Eins, Zwei, G'sufa! At the G'sufa everybody upped with the mugs and drank each other's health. "This is what I call a real beer bust," I said approvingly. Arth was waving to a waitress. As in the Löwenbräu tent, a full quart was the smallest amount obtainable. A beer later I said, "I don't know if you'll make it or not, Arth." "Make what?" "All seven tents." "Oh." A waitress was on her way by, mugs foaming over their rims. I gestured to her for refills. "Where are you from, Arth?" I asked him, in the way of making conversation. "2183." "2183 where?" He looked at me, closing one eye to focus better. "Oh," he said. "Well, 2183 South Street, ah, New Albuquerque." "New Albuquerque? Where's that?" Arth thought about it. Took another long pull at the beer. "Right across the way from old Albuquerque," he said finally. "Maybe we ought to be getting on to the Pschorrbräu tent." "Maybe we ought to eat something first," I said. "I'm beginning to feel this. We could get some of that barbecued ox." Arth closed his eyes in pain. "Vegetarian," he said. "Couldn't possibly eat meat. Barbarous. Ugh." "Well, we need some nourishment," I said. "There's supposed to be considerable nourishment in beer." That made sense. I yelled, " Fräulein! Zwei neu bier! " Somewhere along in here the fog rolled in. When it rolled out again, I found myself closing one eye the better to read the lettering on my earthenware mug. It read Augustinerbräu. Somehow we'd evidently navigated from one tent to another. Arth was saying, "Where's your hotel?" That seemed like a good question. I thought about it for a while. Finally I said, "Haven't got one. Town's jam packed. Left my bag at the Bahnhof. I don't think we'll ever make it, Arth. How many we got to go?" "Lost track," Arth said. "You can come home with me." We drank to that and the fog rolled in again. When the fog rolled out, it was daylight. Bright, glaring, awful daylight. I was sprawled, complete with clothes, on one of twin beds. On the other bed, also completely clothed, was Arth. That sun was too much. I stumbled up from the bed, staggered to the window and fumbled around for a blind or curtain. There was none. Behind me a voice said in horror, "Who ... how ... oh, Wodo , where'd you come from?" I got a quick impression, looking out the window, that the Germans were certainly the most modern, futuristic people in the world. But I couldn't stand the light. "Where's the shade," I moaned. Arth did something and the window went opaque. "That's quite a gadget," I groaned. "If I didn't feel so lousy, I'd appreciate it." Arth was sitting on the edge of the bed holding his bald head in his hands. "I remember now," he sorrowed. "You didn't have a hotel. What a stupidity. I'll be phased. Phased all the way down." "You haven't got a handful of aspirin, have you?" I asked him. "Just a minute," Arth said, staggering erect and heading for what undoubtedly was a bathroom. "Stay where you are. Don't move. Don't touch anything." "All right," I told him plaintively. "I'm clean. I won't mess up the place. All I've got is a hangover, not lice." Arth was gone. He came back in two or three minutes, box of pills in hand. "Here, take one of these." I took the pill, followed it with a glass of water. And went out like a light. Arth was shaking my arm. "Want another mass ?" The band was blaring, and five thousand half-swacked voices were roaring accompaniment. In Muenchen steht ein Hofbräuhaus! Eins, Zwei, G'sufa! At the G'sufa everybody upped with their king-size mugs and drank each other's health. My head was killing me. "This is where I came in, or something," I groaned. Arth said, "That was last night." He looked at me over the rim of his beer mug. Something, somewhere, was wrong. But I didn't care. I finished my mass and then remembered. "I've got to get my bag. Oh, my head. Where did we spend last night?" Arth said, and his voice sounded cautious, "At my hotel, don't you remember?" "Not very well," I admitted. "I feel lousy. I must have dimmed out. I've got to go to the Bahnhof and get my luggage." Arth didn't put up an argument on that. We said good-by and I could feel him watching after me as I pushed through the tables on the way out. At the Bahnhof they could do me no good. There were no hotel rooms available in Munich. The head was getting worse by the minute. The fact that they'd somehow managed to lose my bag didn't help. I worked on that project for at least a couple of hours. Not only wasn't the bag at the luggage checking station, but the attendant there evidently couldn't make heads nor tails of the check receipt. He didn't speak English and my high school German was inadequate, especially accompanied by a blockbusting hangover. I didn't get anywhere tearing my hair and complaining from one end of the Bahnhof to the other. I drew a blank on the bag. And the head was getting worse by the minute. I was bleeding to death through the eyes and instead of butterflies I had bats in my stomach. Believe me, nobody should drink a gallon or more of Marzenbräu. I decided the hell with it. I took a cab to the airport, presented my return ticket, told them I wanted to leave on the first obtainable plane to New York. I'd spent two days at the Oktoberfest , and I'd had it. I got more guff there. Something was wrong with the ticket, wrong date or some such. But they fixed that up. I never was clear on what was fouled up, some clerk's error, evidently. The trip back was as uninteresting as the one over. As the hangover began to wear off—a little—I was almost sorry I hadn't been able to stay. If I'd only been able to get a room I would have stayed, I told myself. From Idlewild, I came directly to the office rather than going to my apartment. I figured I might as well check in with Betty. I opened the door and there I found Mr. Oyster sitting in the chair he had been occupying four—or was it five—days before when I'd left. I'd lost track of the time. I said to him, "Glad you're here, sir. I can report. Ah, what was it you came for? Impatient to hear if I'd had any results?" My mind was spinning like a whirling dervish in a revolving door. I'd spent a wad of his money and had nothing I could think of to show for it; nothing but the last stages of a grand-daddy hangover. "Came for?" Mr. Oyster snorted. "I'm merely waiting for your girl to make out my receipt. I thought you had already left." "You'll miss your plane," Betty said. There was suddenly a double dip of ice cream in my stomach. I walked over to my desk and looked down at the calendar. Mr. Oyster was saying something to the effect that if I didn't leave today, it would have to be tomorrow, that he hadn't ponied up that thousand dollars advance for anything less than immediate service. Stuffing his receipt in his wallet, he fussed his way out the door. I said to Betty hopefully, "I suppose you haven't changed this calendar since I left." Betty said, "What's the matter with you? You look funny. How did your clothes get so mussed? You tore the top sheet off that calendar yourself, not half an hour ago, just before this marble-missing client came in." She added, irrelevantly, "Time travelers yet." I tried just once more. "Uh, when did you first see this Mr. Oyster?" "Never saw him before in my life," she said. "Not until he came in this morning." "This morning," I said weakly. While Betty stared at me as though it was me that needed candling by a head shrinker preparatory to being sent off to a pressure cooker, I fished in my pocket for my wallet, counted the contents and winced at the pathetic remains of the thousand. I said pleadingly, "Betty, listen, how long ago did I go out that door—on the way to the airport?" "You've been acting sick all morning. You went out that door about ten minutes ago, were gone about three minutes, and then came back." "See here," Mr. Oyster said (interrupting Simon's story), "did you say this was supposed to be amusing, young man? I don't find it so. In fact, I believe I am being ridiculed." Simon shrugged, put one hand to his forehead and said, "That's only the first chapter. There are two more." "I'm not interested in more," Mr. Oyster said. "I suppose your point was to show me how ridiculous the whole idea actually is. Very well, you've done it. Confound it. However, I suppose your time, even when spent in this manner, has some value. Here is fifty dollars. And good day, sir!" He slammed the door after him as he left. Simon winced at the noise, took the aspirin bottle from its drawer, took two, washed them down with water from the desk carafe. Betty looked at him admiringly. Came to her feet, crossed over and took up the fifty dollars. "Week's wages," she said. "I suppose that's one way of taking care of a crackpot. But I'm surprised you didn't take his money and enjoy that vacation you've been yearning about." "I did," Simon groaned. "Three times." Betty stared at him. "You mean—" Simon nodded, miserably. She said, "But Simon . Fifty thousand dollars bonus. If that story was true, you should have gone back again to Munich. If there was one time traveler, there might have been—" "I keep telling you," Simon said bitterly, "I went back there three times. There were hundreds of them. Probably thousands." He took a deep breath. "Listen, we're just going to have to forget about it. They're not going to stand for the space-time continuum track being altered. If something comes up that looks like it might result in the track being changed, they set you right back at the beginning and let things start—for you—all over again. They just can't allow anything to come back from the future and change the past." "You mean," Betty was suddenly furious at him, "you've given up! Why this is the biggest thing— Why the fifty thousand dollars is nothing. The future! Just think!" Simon said wearily, "There's just one thing you can bring back with you from the future, a hangover compounded of a gallon or so of Marzenbräu. What's more you can pile one on top of the other, and another on top of that!" He shuddered. "If you think I'm going to take another crack at this merry-go-round and pile a fourth hangover on the three I'm already nursing, all at once, you can think again." THE END Transcriber's Note: This etext was produced from Astounding Science Fiction June 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | C. It set the stage for him to encounter an alien's home for himself. |
How do they evaluate the sentence representations? | ### Introduction
Learning sentence representations from unlabelled data is becoming increasingly prevalent in both the machine learning and natural language processing research communities, as it efficiently and cheaply allows knowledge extraction that can successfully transfer to downstream tasks. Methods built upon the distributional hypothesis BIBREF0 and distributional similarity BIBREF1 can be roughly categorised into two types: Word-prediction Objective: The objective pushes the system to make better predictions of words in a given sentence. As the nature of the objective is to predict words, these are also called generative models. In one of the two classes of models of this type, an encoder-decoder model is learnt using a corpus of contiguous sentences BIBREF2 , BIBREF3 , BIBREF4 to make predictions of the words in the next sentence given the words in the current one. After training, the decoder is usually discarded as it is only needed during training and is not designed to produce sentence representations. In the other class of models of this type, a large language model is learnt BIBREF5 , BIBREF6 , BIBREF7 on unlabelled corpora, which could be an autoregressive model or a masked language model, which gives extremely powerful language encoders but requires massive computing resources and training time. Similarity-based Objective: The objective here relies on a predefined similarity function to enforce the model to produce more similar representations for adjacent sentences than those that are not BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . Therefore, the inductive biases introduced by the two key components, the differential similarity function and the context window, in the objective crucially determine the quality of learnt representations and what information of sentences can be encoded in them. To avoid tuning the inductive biases in the similarity-based objective, we follow the word-prediction objective with an encoder and a decoder, and we are particularly interested in exploiting invertible decoding functions, which can then be used as additional encoders during testing. The contribution of our work is summarised as follows: ### Related Work
Learning vector representations for words with a word embedding matrix as the encoder and a context word embedding matrix as the decoder BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 can be considered as a word-level example of our approach, as the models learn to predict the surrounding words in the context given the current word, and the context word embeddings can also be utilised to augment the word embeddings BIBREF14 , BIBREF16 . We are thus motivated to explore the use of sentence decoders after learning instead of ignoring them as most sentence encoder-decoder models do. Our approach is to invert the decoding function in order to use it as another encoder to assist the original encoder. In order to make computation of the inverse function well-posed and tractable, careful design of the decoder is needed. A simple instance of an invertible decoder is a linear projection with an orthonormal square matrix, whose transpose is its inverse. A family of bijective transformations with non-linear functions BIBREF17 , BIBREF18 , BIBREF19 can also be considered as it empowers the decoder to learn a complex data distribution. In our paper, we exploit two types of plausible decoding functions, including linear projection and bijective functions with neural networks BIBREF17 , and with proper design, the inverse of each of the decoding functions can be derived without expensive inverse calculation after learning. Thus, the decoder function can be utilised along with the encoder for building sentence representations. We show that the ensemble of the encoder and the inverse of the decoder outperforms each of them. ### Model Design
Our model has similar structure to that of skip-thought BIBREF2 and, given the neighbourhood hypothesis BIBREF20 , learns to decode the next sentence given the current one instead of predicting both the previous sentence and the next one at the same time. ### Training Objective
Given the finding BIBREF4 that neither an autoregressive nor an RNN decoder is necessary for learning sentence representations that excel on downstream tasks as the autoregressive decoders are slow to train and the quality of the generated sequences is not highly correlated with that of the representations of the sentences, our model only learns to predict words in the next sentence in a non-autoregressive fashion. Suppose that the $i$ -th sentence $S_i=\lbrace w_1,w_2,...,w_{N_i}\rbrace $ has $N_i$ words, and $S_{i+1}$ has $N_{i+1}$ words. The learning objective is to maximise the averaged log-likelihood for all sentence pairs: $$\ell _{S_{i+i}|S_i}(\phi ,\theta )=\frac{1}{N_{i+1}}\sum _{w_j\in S_{i+1}}\log P(w_j|S_i) \nonumber $$ (Eq. 5) where $\theta $ and $\phi $ contain the parameters in the encoder $f_\text{en}(S_i;\theta )$ and the decoder $f_\text{de}(_i;\phi )$ respectively. The forward computation of our model for a given sentence pair $\lbrace S_i, S_{i+1}\rbrace $ , in which the words in $S_i$ are the input to the learning system and the words in $S_{i+1}$ are targets is defined as: $$_i &= f_\text{en}(S_i;\theta ) \nonumber \\
_i &= f_\text{de}(_i;\phi ) \nonumber $$ (Eq. 6) where $_i$ is the vector representation of $S_i$ , and $_i$ is the vector output of the decoder which will be compared with the vector representations of words in the next sentence $S_{i+1}$ . Since calculating the likelihood of generating each word involves a computationally demanding softmax function, the negative sampling method BIBREF12 is applied to replace the softmax, and $\log P(w_j|s_i)$ is calculated as: $$\log \sigma (_i^\top _{w_j}) + \sum _{k=1}^{K}\mathbb {E}_{w_k\sim P_e(w)}\log \sigma (-_i^\top _{w_k}) \nonumber $$ (Eq. 7) where $_{w_k}\in ^{d_}$ is the pretrained vector representation for $w_k$ , the empirical distribution $P_e(w)$ is the unigram distribution of words in the training corpus raised to power 0.75 as suggested in the prior work BIBREF21 , and $K$ is the number of negative samples. In this case, we enforce the output of the decoder $_i$ to have the same dimensionality as the pretrained word vectors $_{w_j}$ . The loss function is summed over all contiguous sentence pairs in the training corpus. For simplicity, we omit the subscription for indexing the sentences in the following sections. ### Encoder
The encoder $f_\text{en}(S;\theta )$ is a bi-directional Gated Recurrent Unit BIBREF22 with $d$ -dimensions in each direction. It processes word vectors in an input sentence $\lbrace _{w_1},_{w_2},...,_{w_{N}}\rbrace $ sequentially according to the temporal order of the words, and generates a sequence of hidden states. During learning, in order to reduce the computation load, only the last hidden state serves as the sentence representation $\in ^{d_}$ , where $d_=2d$ . ### Decoder
As the goal is to reuse the decoding function $f_{\text{de}}()$ as another plausible encoder for building sentence representations after learning rather than ignoring it, one possible solution is to find the inverse function of the decoder function during testing, which is noted as $f^{-1}_{\text{de}}()$ . In order to reduce the complexity and the running time during both training and testing, the decoding function $f_{\text{de}}()$ needs to be easily invertible. Here, two types of decoding functions are considered and explored. In this case, the decoding function is a linear projection, which is $= f_{\text{de}}()=+ $ , where $\in ^{d_\times d_}$ is a trainable weight matrix and $\in ^{d_\times 1}$ is the bias term. As $f_\text{de}$ is a linear projection, the simplest situation is when $$ is an orthogonal matrix and its inverse is equal to its transpose. Often, as the dimensionality of vector $$ doesn't necessarily need to match that of word vectors $$ , $$ is not a square matrix . To enforce invertibility on $$ , a row-wise orthonormal regularisation on $$ is applied during learning, which leads to $^\top =$ , where $$0 is the identity matrix, thus the inverse function is simply $$1 , which is easily computed. The regularisation formula is $$2 , where $$3 is the Frobenius norm. Specifically, the update rule BIBREF23 for the regularisation is: $$:=(1+\beta )-\beta (^\top )\nonumber $$ (Eq. 12) The usage of the decoder during training and testing is defined as follows: $$\text{Training:} \hspace{2.84544pt} & = f_{\text{de}}()=+ \nonumber \\
\text{Testing:} \hspace{2.84544pt} & = f_\text{de}^{-1}()=^\top (- ) \nonumber $$ (Eq. 13) Therefore, the decoder is also utilised after learning to serve as a linear encoder in addition to the RNN encoder. A general case is to use a bijective function as the decoder, as the bijective functions are naturally invertible. However, the inverse of a bijective function could be hard to find and its calculation could also be computationally intense. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D\rightarrow ^D$ and its inverse $f^{-1}$ is defined as: $$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1) \nonumber \\
h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1) \nonumber $$ (Eq. 15) where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d\rightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an `additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the `affine coupling layer' BIBREF24 : $$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot \text{exp}(s(_1)) + t(_1) \nonumber \\
h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot \text{exp}(-s(_1)) \nonumber $$ (Eq. 16) where $s:^d\rightarrow ^{D-d}$ and $t:^d\rightarrow ^{D-d}$ are both neural networks with linear output units. The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly. In our case, the output $\in ^{d_}$ of the decoding function $f_{\text{de}}$ has lower dimensionality than the input $\in ^{d_}$ does. Our solution is to add an orthonormal regularised linear projection before the bijective function to transform the vector representation of a sentence to the desired dimension. The usage of the decoder that is composed of a bijective function and a regularised linear projection during training and testing is defined as: $$\text{Training:} \hspace{2.84544pt} & = f_{\text{de}}() = h(+ ) \nonumber \\
\text{Testing:} \hspace{2.84544pt} & = f_\text{de}^{-1}() = ^\top (h^{-1}() - ) \nonumber $$ (Eq. 17) ### Using Decoder in the Test Phase
As the decoder is easily invertible, it is also used to produce vector representations. The post-processing step BIBREF25 that removes the top principal component is applied on the representations from $f_\text{en}$ and $f^{-1}_\text{de}$ individually. In the following sections, $_\text{en}$ denotes the post-processed representation from $f_\text{en}$ , and $_\text{de}$ from $f^{-1}_\text{de}$ . Since $f_\text{en}$ and $f^{-1}_\text{de}$ naturally process sentences in distinctive ways, it is reasonable to expect that the ensemble of $_\text{en}$ and $_\text{de}$ will outperform each of them. ### Experimental Design
Experiments are conducted in PyTorch BIBREF26 , with evaluation using the SentEval package BIBREF27 with modifications to include the post-processing step. Word vectors $_{w_j}$ are initialised with FastText BIBREF15 , and fixed during learning. ### Unlabelled Corpora
Two unlabelled corpora, including BookCorpus BIBREF28 and UMBC News Corpus BIBREF29 , are used to train models with invertible decoders. These corpora are referred as B, and U in Table 3 and 5 . The UMBC News Corpus is roughly twice as large as the BookCorpus, and the details are shown in Table 1 . ### Unsupervised Evaluation
The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 . The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset. ### Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 . In these tasks, MR, CR, SST, SUBJ, MPQA and MRPC are binary classification tasks, TREC is a multi-class classification task. SICK and MRPC require the same feature engineering method BIBREF44 in order to compose a vector from vector representations of two sentences to indicate the difference between them. ### Hyperparameter Tuning
The hyperparameters are tuned on the averaged scores on STS14 of the model trained on BookCorpus, thus it is marked with a $^\star $ in tables to indicate potential overfitting. The hyperparameter setting for our model is summarised as follows: the batch size $N=512$ , the dimension of sentence vectors $d_=2048$ , the dimension of word vectors $d_{_{w_j}}=300$ , the number of negative samples $K=5$ , and the initial learning rate is $5\times 10^{-4}$ which is kept fixed during learning. The Adam optimiser BIBREF45 with gradient clipping BIBREF46 is applied for stable learning. Each model in our experiment is only trained for one epoch on the given training corpus. $\beta $ in the invertible constraint of the linear projection is set to be $0.01$ , and after learning, all 300 eigenvalues are close to 1. For the bijective transformation, in order to make sure that each output unit is influenced by all input units, we stack four affine coupling layers in the bijective transformation BIBREF17 . The non-linear mappings $s$ and $t$ are both neural networks with one hidden layer with the rectified linear activation function. ### Representation Pooling
Various pooling functions are applied to produce vector representations for input sentences. For unsupervised evaluation tasks, as recommended in previous studies BIBREF14 , BIBREF50 , BIBREF51 , a global mean-pooling function is applied on both the output of the RNN encoder $f_\text{en}$ to produce a vector representation $_\text{en}$ and the inverse of the decoder $f_\text{de}^{-1}$ to produce $_\text{de}$ . For supervised evaluation tasks, three pooling functions, including global max-, min-, and mean-pooling, are applied on top of the encoder and the outputs from three pooling functions are concatenated to serve as a vector representation for a given sentence. The same representation pooling strategy is applied on the inverse of the decoder. The reason for applying different representation pooling strategies for two categories of tasks is: (1) cosine similarity of two vector representations is directly calculated in unsupervised evaluation tasks to determine the textual similarity of two sentences, and it suffers from the curse-of-dimensionality BIBREF52 , which leads to more equidistantly distributed representations for higher dimensional vector representations decreasing the difference among similarity scores. (2) given Cover's theorem BIBREF53 and the blessings-of-dimensionality property, it is more likely for the data points to be linearly separable when they are presented in high dimensional space, and in the supervised evaluation tasks, high dimensional vector representations are preferred as a linear classifier will be learnt to evaluate how likely the produced sentence representations are linearly separable; (3) in our case, both the encoder and the inverse of the decoder are capable of producing a vector representation per time step in a given sentence, although during training, only the last one is regarded as the sentence representation for the fast training speed, it is more reasonable to make use of all representations at all time steps with various pooling functions to compute a vector representations to produce high-quality sentence representations that excel the downstream tasks. ### Discussion
It is worth discussing the motivation of the model design and the observations in our experiments. As mentioned as one of the take-away messages BIBREF54 , to demonstrate the effectiveness of the invertible constraint, the comparison of our model with the constraint and its own variants use the same word embeddings from FastText BIBREF15 and have the same dimensionaility of sentence representations during learning, and use the same classifier on top of the produced representations with the same hyperparameter settings. Overall, given the performance of the inverse of each decoder presented in Table 3 and 5 , it is reasonable to state that the inverse of the decoder provides high-quality sentence representations as well as the encoder does. However, there is no significant difference between the two decoders in terms of the performance on the downstream tasks. In this section, observations and thoughts are presented based on the analyses of our model with the invertible constraint. ### Effect of Invertible Constraint
The motivation of enforcing the invertible constraint on the decoder during learning is to make it usable and potentially helpful during testing in terms of boosting the performance of the lone RNN encoder in the encoder-decoder models (instead of ignoring the decoder part after learning). Therefore, it is important to check the necessity of the invertible constraint on the decoders. A model with the same hyperparameter settings but without the invertible constraint is trained as the baseline model, and macro-averaged results that summarise the same type of tasks are presented in Table 2 . As noted in the prior work BIBREF55 , there exists significant inconsistency between the group of unsupervised tasks and the group of supervised ones, it is possible for a model to excel on one group of tasks but fail on the other one. As presented in our table, the inverse of the decoder tends to perform better than the encoder on unsupervised tasks, and the situation reverses when it comes to the supervised ones. In our model, the invertible constraint helps the RNN encoder $f_\text{en}$ to perform better on the unsupervised evaluation tasks, and helps the inverse of the decoder $f_\text{de}^{-1}$ to provide better results on single sentence classification tasks. An interesting observation is that, by enforcing the invertible constraint, the model learns to sacrifice the performance of $f_\text{de}^{-1}$ and improve the performance of $f_\text{en}$ on unsupervised tasks to mitigate the gap between the two encoding functions, which leads to more aligned vector representations between $f_\text{en}$ and $f_\text{de}^{-1}$ . ### Effect on Ensemble
Although encouraging the invertible constraint leads to slightly poorer performance of $f_\text{de}^{-1}$ on unsupervised tasks, it generally leads to better sentence representations when the ensemble of the encoder $f_\text{en}$ and the inverse of the decoder $f_\text{de}^{-1}$ is considered. Specifically, for unsupervised tasks, the ensemble is an average of two vector representations produced from two encoding functions during the testing time, and for supervised tasks, the concatenation of two representations is regarded as the representation of a given sentence. The ensemble method is recommended in prior work BIBREF14 , BIBREF16 , BIBREF51 , BIBREF56 , BIBREF4 , BIBREF54 . As presented in Table 2 , on unsupervised evaluation tasks (STS12-16 and SICK14), the ensemble of two encoding functions is averaging, which benefits from aligning representations from $f_\text{en}$ and $f_\text{de}^{-1}$ by enforcing the invertible constraint. While in the learning system without the invertible constraint, the ensemble of two encoding functions provides worse performance than $f_\text{de}^{-1}$ . On supervised evaluation tasks, as the ensemble method is concatenation and a linear model is applied on top of the concatenated representations, as long as the two encoding functions process sentences distinctively, the linear classifier is capable of picking relevant feature dimensions from both encoding functions to make good predictions, thus there is no significant difference between our model with and without invertible constraint. ### Effect of Learning
Recent research BIBREF54 showed that the improvement on the supervised evaluation tasks led by learning from labelled or unlabelled corpora is rather insignificant compared to random initialised projections on top of pretrained word vectors. Another interesting direction of research that utilises probabilistic random walk models on the unit sphere BIBREF57 , BIBREF25 , BIBREF58 derived several simple yet effective post-processing methods that operate on pretrained word vectors and are able to boost the performance of the averaged word vectors as the sentence representation on unsupervised tasks. While these papers reveal interesting aspects of the downstream tasks and question the need for optimising a learning objective, our results show that learning on unlabelled corpora helps. On unsupervised evaluation tasks, in order to show that learning from an unlabelled corpus helps, the performance of our learnt representations should be directly compared with the pretrained word vectors, FastText in our system, at the same dimensionality with the same post-processing BIBREF25 . The word vectors are scattered in the 300-dimensional space, and our model has a decoder that is learnt to project a sentence representation $\in ^{d_}$ to $=f_\text{de}(;\phi )\in ^{300}$ . The results of our learnt representations and averaged word vectors with the same postprocessing are presented in Table 4 . As shown in the Table 4 , the performance of our learnt system is better than FastText at the same dimensionality. It is worth mentioning that, in our system, the final representation is an average of postprocessed word vectors and the learnt representations $$ , and the invertible constraint guarantees that the ensemble of both gives better performance. Otherwise, as discussed in the previous section, an ensemble of postprocessed word vectors and some random encoders won't necessarily lead to stronger results. Table 3 also provides evidence for the effectiveness of learning on the unsupervised evaluation tasks. On supervised evaluation tasks, we agree that higher dimensional vector representations give better results on the downstream tasks. Compared to random projections with $4096\times 6$ output dimensions, learning from unlabelled corpora leverages the distributional similarity BIBREF1 at the sentence-level into the learnt representations and potentially helps capture the meaning of a sentence. In our system, the raw representations are in 2400-dimensional space, and the use of various pooling functions expands it to $2048\times 6$ dimensions, which is half as large as the random projection dimension and still yields better performance. Both our models and random projections with no training are presented in Table 5 . The evidence from both sets of downstream tasks support our argument that learning from unlabelled corpora helps the representations capture meaning of sentences. However, current ways of incorporating the distributional hypothesis only utilise it as a weak and noisy supervision, which might limit the quality of the learnt sentence representations. ### Conclusion
Two types of decoders, including an orthonormal regularised linear projection and a bijective transformation, whose inverses can be derived effortlessly, are presented in order to utilise the decoder as another encoder in the testing phase. The experiments and comparisons are conducted on two large unlabelled corpora, and the performance on the downstream tasks shows the high usability and generalisation ability of the decoders in testing. Analyses show that the invertible constraint enforced on the decoder encourages each one to learn from the other one during learning, and provides improved encoding functions after learning. Ensemble of the encoder and the inverse of the decoder gives even better performance when the invertible constraint is applied on the decoder side. Furthermore, by comparing with prior work, we argue that learning from unlabelled corpora indeed helps to improve the sentence representations, although the current way of utilising corpora might not be optimal. We view this as unifying the generative and discriminative objectives for unsupervised sentence representation learning, as it is trained with a generative objective which when inverted can be seen as creating a discriminative target. Our proposed method in our implementation doesn't provide extremely good performance on the downstream tasks, but we see our method as an opportunity to fuse all possible components in a model, even a usually discarded decoder, to produce sentence representations. Future work could potentially expand our work into end-to-end invertible model that is able to produce high-quality representations by omnidirectional computations. ### Acknowledgements
Many Thanks to Andrew Ying for helpful clarifications on several concepts. Table 1: Summary statistics of the two corpora used. For simplicity, the two corpora are referred to as B and U in the following tables respectively. Table 2: The effect of the invertible constraint on linear projection. The arrow and its associated value of a representation is the relative performance gain or loss compared to its comparison partner with the invertible constraint. As shown, the invertible constraint does help improve each representation, and ensures the ensemble of two encoding functions gives better performance. Better view in colour. Table 3: Results on unsupervised evaluation tasks (Pearson’s r × 100) . Bold numbers are the best results among unsupervised transfer models, and underlined numbers are the best ones among all models. ‘WR’ refers to the post-processing step that removes the top principal component. Table 4: Comparison of the learnt representations in our system with the same dimensionality as the average of the same pretrained word vectors on unsupervised evaluation tasks. The encoding function that is learnt to compose a sentence representation from pretrained word vectors outperforms averaging the same word vectors, which supports our argument that learning helps to produce higher-quality sentence representations. Table 5: Results on supervised evaluation tasks. Bold numbers are the best results among unsupervised transfer models with ordered sentences, and underlined numbers are the best ones among all models. | The unsupervised tasks include five tasks from SemEval Semantic Textual Similarity (STS) in 2012-2016 BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 and the SemEval2014 Semantic Relatedness task (SICK-R) BIBREF35 .
The cosine similarity between vector representations of two sentences determines the textual similarity of two sentences, and the performance is reported in Pearson's correlation score between human-annotated labels and the model predictions on each dataset., Supervised Evaluation
It includes Semantic relatedness (SICK) BIBREF35 , SemEval (STS-B) BIBREF36 , paraphrase detection (MRPC) BIBREF37 , question-type classification (TREC) BIBREF38 , movie review sentiment (MR) BIBREF39 , Stanford Sentiment Treebank (SST) BIBREF40 , customer product reviews (CR) BIBREF41 , subjectivity/objectivity classification (SUBJ) BIBREF42 , opinion polarity (MPQA) BIBREF43 . |
As of May 26, 2023, what is the total amount Pepsico may borrow under its unsecured revolving credit agreements? | Evidence 0:
Item 8.01.
Other Events.
Effective May 26, 2023, PepsiCo, Inc. (PepsiCo) terminated the $3,800,000,000 364 day unsecured revolving credit agreement, dated as of
May 27, 2022, among PepsiCo, as borrower, the lenders party thereto, and Citibank, N.A., as administrative agent (the 2022 364 Day Credit
Agreement). There were no outstanding borrowings under the 2022 364 Day Credit Agreement at the time of its termination.
On May 26, 2023, PepsiCo entered into a new $4,200,000,000 364 day unsecured revolving credit agreement (the 2023 364 Day Credit
Agreement) among PepsiCo, as borrower, the lenders party thereto, and Citibank, N.A., as administrative agent. The 2023 364 Day Credit Agreement
enables PepsiCo and its borrowing subsidiaries to borrow up to $4,200,000,000 in U.S. Dollars and/or Euros, subject to customary terms and conditions,
and expires on May 24, 2024. PepsiCo may also, upon the agreement of either the then existing lenders or of additional banks not currently party to the
2023 364 Day Credit Agreement, increase the commitments under the 2023 364 Day Credit Agreement to up to $4,950,000,000 in U.S. Dollars and/or
Euros. PepsiCo may request renewal of the 2023 364 Day Credit Agreement for an additional 364 day period or convert any amounts outstanding into a
term loan for a period of up to one year, which term loan would mature no later than the anniversary of the then effective termination date. Subject to
certain conditions stated in the 2023 364 Day Credit Agreement, PepsiCo and its borrowing subsidiaries may borrow, prepay and reborrow amounts under
the 2023 364 Day Credit Agreement at any time during the term of the 2023 364 Day Credit Agreement. Funds borrowed under the 2023 364 Day Credit
Agreement may be used for general corporate purposes of PepsiCo and its subsidiaries. The 2023 364 Day Credit Agreement contains customary
representations and warranties and events of default. In the ordinary course of their respective businesses, the lenders under the 2023 364 Day Credit
Agreement and their affiliates have engaged, and may in the future engage, in commercial banking and/or investment banking transactions with PepsiCo
and its affiliates.
Effective May 26, 2023, PepsiCo terminated the $3,800,000,000 five year unsecured revolving credit agreement, dated as of May 27, 2022, among
PepsiCo, as borrower, the lenders party thereto, and Citibank, N.A., as administrative agent (the 2022 Five Year Credit Agreement). There were no
outstanding borrowings under the 2022 Five Year Credit Agreement at the time of its termination.
On May 26, 2023, PepsiCo entered into a new $4,200,000,000 five year unsecured revolving credit agreement (the 2023 Five Year Credit
Agreement) among PepsiCo, as borrower, the lenders party thereto, and Citibank, N.A., as administrative agent. The 2023 Five Year Credit Agreement
enables PepsiCo and its borrowing subsidiaries to borrow up to $4,200,000,000 in U.S. Dollars and/or Euros, including a $750,000,000 swing line
subfacility for Euro-denominated borrowings permitted to be borrowed on a same day basis, subject to customary terms and conditions, and expires on
May 26, 2028. PepsiCo may also, upon the agreement of either the then existing lenders or of additional banks not currently party to the 2023 Five Year
Credit Agreement, increase the commitments under the 2023 Five Year Credit Agreement to up to $4,950,000,000 in U.S. Dollars and/or Euros. PepsiCo
may, once a year, request renewal of the 2023 Five Year Credit Agreement for an additional one year period. Subject to certain conditions stated in the 2023
Five Year Credit Agreement, PepsiCo and its borrowing subsidiaries may borrow, prepay and reborrow amounts under the 2023 Five Year Credit
Agreement at any time during the term of the 2023 Five Year Credit Agreement. Funds borrowed under the 2023 Five Year Credit Agreement may be used
for general corporate purposes of PepsiCo and its subsidiaries. The 2023 Five Year Credit Agreement contains customary representations and warranties
and events of default. In the ordinary course of their respective businesses, the lenders under the 2023 Five Year Credit Agreement and their affiliates have
engaged, and may in the future engage, in commercial banking and/or investment banking transactions with PepsiCo and its affiliates. | Total amount Pepsico may borrow under unsecured revolving credit agreements = $8,400,000,000. |
What do they learn jointly? | ### Introduction
The problem of knowledge base population (KBP) – constructing a knowledge base (KB) of facts gleaned from a large corpus of unstructured data – poses several challenges for the NLP community. Commonly, this relation extraction task is decomposed into two subtasks – entity linking, in which entities are linked to already identified identities within the document or to entities in the existing KB, and slot filling, which identifies certain attributes about a target entity. We present our work-in-progress for KBP slot filling based on our probabilistic logic formalisms and present the different components of the system. Specifically, we employ Relational Dependency Networks BIBREF0 , a formalism that has been successfully used for joint learning and inference from stochastic, noisy, relational data. We consider our RDN system against the current state-of-the-art for KBP to demonstrate the effectiveness of our probabilistic relational framework. Additionally, we show how RDNs can effectively incorporate many popular approaches in relation extraction such as joint learning, weak supervision, word2vec features, and human advice, among others. We provide a comprehensive comparison of settings such as joint learning vs learning of individual relations, use of weak supervision vs gold standard labels, using expert advice vs only learning from data, etc. These questions are extremely interesting from a general machine learning perspective, but also critical to the NLP community. As we show empirically, some of the results such as human advice being useful in many relations and joint learning being beneficial in the cases where the relations are correlated among themselves are on the expected lines. However, some surprising observations include the fact that weak supervision is not as useful as expected and word2vec features are not as predictive as the other domain-specific features. We first present the proposed pipeline with all the different components of the learning system. Next we present the set of 14 relations that we learn on before presenting the experimental results. We finally discuss the results of these comparisons before concluding by presenting directions for future research. ### Proposed Pipeline
We present the different aspects of our pipeline, depicted in Figure FIGREF1 . We will first describe our approach to generating features and training examples from the KBP corpus, before describing the core of our framework – the RDN Boost algorithm. ### Feature Generation
Given a training corpus of raw text documents, our learning algorithm first converts these documents into a set of facts (i.e., features) that are encoded in first order logic (FOL). Raw text is processed using the Stanford CoreNLP Toolkit BIBREF1 to extract parts-of-speech, word lemmas, etc. as well as generate parse trees, dependency graphs and named-entity recognition information. The full set of extracted features is listed in Table TABREF3 . These are then converted into features in prolog (i.e., FOL) format and are given as input to the system. In addition to the structured features from the output of Stanford toolkit, we also use deeper features based on word2vec BIBREF2 as input to our learning system. Standard NLP features tend to treat words as individual objects, ignoring links between words that occur with similar meanings or, importantly, similar contexts (e.g., city-country pairs such as Paris – France and Rome – Italy occur in similar contexts). word2vec provide a continuous-space vector embedding of words that, in practice, capture many of these relationships BIBREF2 , BIBREF3 . We use word vectors from Stanford and Google along with a few specific words that, experts believe, are related to the relations learned. For example, we include words such as “father” and “mother” (inspired by the INLINEFORM0 relation) or “devout”,“convert”, and “follow” ( INLINEFORM1 relation). We generated features from word vectors by finding words with high similarity in the embedded space. That is, we used word vectors by considering relations of the following form: INLINEFORM2 , where INLINEFORM3 is the cosine similarity score between the words. Only the top cosine similarity scores for a word are utilized. ### Weak Supervision
One difficulty with the KBP task is that very few documents come labeled as gold standard labels, and further annotation is prohibitively expensive beyond a few hundred documents. This is problematic for discriminative learning algorithms, like the RDN learning algorithm, which excel when given a large supervised training corpus. To overcome this obstacle, we employ weak supervision – the use of external knowledge (e.g., a database) to heuristically label examples. Following our work in Soni et al. akbc16, we employ two approaches for generating weakly supervised examples – distant supervision and knowledge-based weak supervision. Distant supervision entails the use of external knowledge (e.g., a database) to heuristically label examples. Following standard procedure, we use three data sources – Never Ending Language Learner (NELL) BIBREF4 , Wikipedia Infoboxes and Freebase. For a given target relation, we identify relevant database(s), where the entries in the database form entity pairs (e.g., an entry of INLINEFORM0 for a parent database) that will serve as a seed for positive training examples. These pairs must then be mapped to mentions in our corpus – that is, we must find sentences in our corpus that contain both entities together BIBREF5 . This process is done heuristically and is fraught with potential errors and noise BIBREF6 . An alternative approach, knowledge-based weak supervision is based on previous work BIBREF7 , BIBREF8 with the following insight: labels are typically created by “domain experts” who annotate the labels carefully, and who typically employ some inherent rules in their mind to create examples. For example, when identifying family relationship, we may have an inductive bias towards believing two persons in a sentence with the same last name are related, or that the words “son” or “daughter” are strong indicators of a parent relation. We call this world knowledge as it describes the domain (or the world) of the target relation. To this effect, we encode the domain expert's knowledge in the form of first-order logic rules with accompanying weights to indicate the expert's confidence. We use the probabilistic logic formalism Markov Logic Networks BIBREF9 to perform inference on unlabeled text (e.g., the TAC KBP corpus). Potential entity pairs from the corpus are queried to the MLN, yielding (weakly-supervised) positive examples. We choose MLNs as they permit domain experts to easily write rules while providing a probabilistic framework that can handle noise, uncertainty, and preferences while simultaneously ranking positive examples. We use the Tuffy system BIBREF10 to perform inference. The inference algorithm implemented inside Tuffy appears to be robust and scales well to millions of documents. For the KBP task, some rules that we used are shown in Table TABREF8 . For example, the first rule identifies any number following a person's name and separated by a comma is likely to be the person's age (e.g., “Sharon, 42”). The third and fourth rule provide examples of rules that utilize more textual features; these rules state the appearance of the lemma “mother” or “father” between two persons is indicative of a parent relationship (e.g.,“Malia's father, Barack, introduced her...”). To answer Q1, we generated positive training examples using the weak supervision techniques specified earlier. Specifically, we evaluated 10 relations as show in Table TABREF20 . Based on experiments from BIBREF8 , we utilized our knowledge-based weak supervision approach to provide positive examples in all but two of our relations. A range of 4 to 8 rules are derived for each relation. Examples for the organization relations INLINEFORM0 and INLINEFORM1 were generated using standard distant supervision techniques – Freebase databases were mapped to INLINEFORM2 while Wikipedia Infoboxes provides entity pairs for INLINEFORM3 . Lastly, only 150 weakly supervised examples were utilized in our experiments (all gold standard examples were utilized). Performing larger runs is part of work in progress. The results are presented in Table TABREF20 . We compared our standard pipeline (individually learned relations with only standard features) learned on gold standard examples only versus our system learned with weak and gold examples combined. Surprisingly, weak supervision does not seem to help learn better models for inferring relations in most cases. Only two relations – INLINEFORM0 , INLINEFORM1 – see substantial improvements in AUC ROC, while F1 shows improvements for INLINEFORM2 and, INLINEFORM3 , and INLINEFORM4 . We hypothesize that generating more examples will help (some relations produced thousands of examples), but nonetheless find the lack of improved models from even a modest number of examples a surprising result. Alternatively, the number of gold standard examples provided may be sufficient to learn RDN models. Thus Q1 is answered equivocally, but in the negative. ### Learning Relational Dependency Networks
Previous research BIBREF11 has demonstrated that joint inferences of the relations are more effective than considering each relation individually. Consequently, we have considered a formalism that has been successfully used for joint learning and inference from stochastic, noisy, relational data called Relational Dependency Networks (RDNs) BIBREF0 , BIBREF12 . RDNs extend dependency networks (DN) BIBREF13 to the relational setting. The key idea in a DN is to approximate the joint distribution over a set of random variables as a product of their marginal distributions, i.e., INLINEFORM0 INLINEFORM1 INLINEFORM2 . It has been shown that employing Gibbs sampling in the presence of a large amount of data allows this approximation to be particularly effective. Note that, one does not have to explicitly check for acyclicity making these DNs particularly easy to be learned. In an RDN, typically, each distribution is represented by a relational probability tree (RPT) BIBREF14 . However, following previous work BIBREF12 , we replace the RPT of each distribution with a set of relational regression trees BIBREF15 built in a sequential manner i.e., replace a single tree with a set of gradient boosted trees. This approach has been shown to have state-of-the-art results in learning RDNs and we adapted boosting to learn for relation extraction. Since this method requires negative examples, we created negative examples by considering all possible combinations of entities that are not present in positive example set and sampled twice as many negatives as positive examples. ### Incorporating Human Advice
While most relational learning methods restrict the human to merely annotating the data, we go beyond and request the human for advice. The intuition is that we as humans read certain patterns and use them to deduce the nature of the relation between two entities present in the text. The goal of our work is to capture such mental patterns of the humans as advice to the learning algorithm. We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients. This allows the system to trade-off between data and advice throughout the learning phase, rather than only consider advice in initial iterations. Advice, in particular, become influential in the presence of noisy or less amout of data. A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . Note that some of the rules are “soft" rules in that they are not true in many situations. Odom et al. odomAAAI15 weigh the effect of the rules against the data and hence allow for partially correct rules. ### Experiments and Results
We now present our experimental evaluation. We considered 14 specific relations from two categories, person and organization from the TAC KBP competition. The relations considered are listed in the left column of Table TABREF13 . We utilize documents from KBP 2014 for training while utilizing documents from the 2015 corpus for testing. All results presented are obtained from 5 different runs of the train and test sets to provide more robust estimates of accuracy. We consider three standard metrics – area under the ROC curve, F-1 score and the recall at a certain precision. We chose the precision as INLINEFORM0 since the fraction of positive examples to negatives is 1:2 (we sub-sampled the negative examples for the different training sets). Negative examples are re-sampled for each training run. It must be mentioned that not all relations had the same number of hand-annotated (gold standard) examples because the 781 documents that we annotated had different number of instances for these relations. The train/test gold-standard sizes are provided in the table, including weakly supervised examples, if available. Lastly, to control for other factors, the default setting for our experiments is individual learning, standard features, with gold standard examples only (i.e., no weak supervision, word2vec, advice, or advice). Since our system had different components, we aimed to answer the following questions: ### Joint learning
To address our next question, we assessed our pipeline when learning relations independently (i.e., individually) versus learning relations jointly within the RDN, displayed in Table TABREF22 . Recall and F1 are omitted for conciseness – the conclusions are the same across all metrics. Joint learning appears to help in about half of the relations (8/14). Particularly, in person category, joint learning with gold standard outperforms their individual learning counterparts. This is due to the fact that some relations such as parents, spouse, siblings etc. are inter-related and learning them jointly indeed improves performance. Hence Q2 can be answered affirmatively for half the relations. ### word2vec
Table TABREF24 shows the results of experiments comparing the RDN framework with and without word2vec features. word2vec appears to largely have no impact, boosting results in just 4 relations. We hypothesize that this may be due to a limitation in the depth of trees learned. Learning more and/or deeper trees may improve use of word2vec features, and additional work can be done to generate deep features from word vectors. Q3 is answered cautiously in the negative, although future work could lead to improvements. ### Advice
Table TABREF26 shows the results of experiments that test the use of advice within the joint learning setting. The use of advice improves or matches the performance of using only joint learning. The key impact of advice can be mostly seen in the improvement of recall in several relations. This clearly shows that using human advice patterns allows us to extract more relations effectively making up for noisy or less number of training examples. This is in-line with previously published machine learning literature BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 in that humans can be more than mere labelers by providing useful advice to learning algorithms that can improve their performance. Thus Q4 can be answered affirmatively. ### RDN Boost vs Relation Factory
Relation factory (RF) BIBREF16 is an efficient, open source system for performing relation extraction based on distantly supervised classifiers. It was the top system in the TAC KBP 2013 competition BIBREF21 and thus serves as a suitable baseline for our method. RF is very conservative in its responses, making it very difficult to adjust the precision levels. To be most generous to RF, we present recall for all returned results (i.e., score INLINEFORM0 ). The AUC ROC, recall, and F1 scores of our system against RF are presented in Table TABREF28 . Our system performs comparably, and often better than the state-of-the-art Relation Factory system. In particular, our method outperforms Relation Factory in AUC ROC across all relations. Recall provides a more mixed picture with both approaches showing some improvements – RDN outperforms in 6 relations while Relation Factory does so in 8. Note that in the instances where RDN provides superior recall, it does so with dramatic improvements (RF often returns 0 positives in these relations). F1 also shows RDN's superior performance, outperforming RF in most relations. Thus, the conclusion for Q5 is that our RDN framework performas comparably, if not better, across all metrics against the state-of-the-art. ### Conclusion
We presented our fully relational system utilizing Relational Dependency Networks for the Knowledge Base Population task. We demonstrated RDN's ability to effectively learn the relation extraction task, performing comparably (and often better) than the state-of-art Relation Factory system. Furthermore, we demonstrated the ability of RDNs to incorporate various concepts in a relational framework, including word2vec, human advice, joint learning, and weak supervision. Some surprising results are that weak supervision and word2vec did not significantly improve performance. However, advice is extremely useful thus validating the long-standing results inside the Artificial Intelligence community for the relation extraction task as well. Possible future directions include considering a larger number of relations, deeper features and finally, comparisons with more systems. We believe further work on developing word2vec features and utilizing more weak supervision examples may reveal further insights into how to effectively utilize such features in RDNs. Fig. 1. Pipeline Full RDN relation extraction pipeline Table 1. Standard NLP Features Features derived from the training corpus used by our learning system. POS - part of speech. NE - Named Entity. DPR - root of dependency path tree. Table 2. Rules for KB Weak Supervision A sample of knowledge-based rules for weak supervision. The first value defines a weight, or confidence in the accuracy of the rule. The target relation appears at the end of each clause. “PER”, “ORG”, “NUM” represent entities that are persons, organizations, and numbers, respectively. Table 3. Advice Rules Sample advice rules used for relation extraction. We employed a total of 72 such rules for our 14 relations. Fig. 2. Example regression tree for the siblings relation. This tree states that the weight for the relation being true is higher if either “husband” or “wife” appear between the entities. Table 4. Relations The relations considered from TAC KBP. Columns indicate the number of training examples utilized – both human annotated (Gold) and weakly supervised (WS), when available – from TAC KBP 2014 and number of test examples from TAC KBP 2015. 10 relations describe person entities (per) while the last 4 describe organizations (org). Table 5. Weak Supervision Results comparing models trained with gold standard examples only (G) and models trained with gold standard and weakly supervised examples combined (G+WS). Table 6. Joint Learning Results comparing relation models learned individually (IL) and jointly (JL). Table 8. Advice Results comparing models trained without (-Adv) and with advice (+Adv). Table 9. RelationFactory (RF) vs RDN Values in bold indicate superiour performance against the alternative approach. | relations |
How higher are F1 scores compared to previous work? | ### Introduction
Distantly-supervised information extraction systems extract relation tuples with a set of pre-defined relations from text. Traditionally, researchers BIBREF0, BIBREF1, BIBREF2 use pipeline approaches where a named entity recognition (NER) system is used to identify the entities in a sentence and then a classifier is used to find the relation (or no relation) between them. However, due to the complete separation of entity detection and relation classification, these models miss the interaction between multiple relation tuples present in a sentence. Recently, several neural network-based models BIBREF3, BIBREF4 were proposed to jointly extract entities and relations from a sentence. These models used a parameter-sharing mechanism to extract the entities and relations in the same network. But they still find the relations after identifying all the entities and do not fully capture the interaction among multiple tuples. BIBREF5 (BIBREF5) proposed a joint extraction model based on neural sequence tagging scheme. But their model could not extract tuples with overlapping entities in a sentence as it could not assign more than one tag to a word. BIBREF6 (BIBREF6) proposed a neural encoder-decoder model for extracting relation tuples with overlapping entities. However, they used a copy mechanism to copy only the last token of the entities, thus this model could not extract the full entity names. Also, their best performing model used a separate decoder to extract each tuple which limited the power of their model. This model was trained with a fixed number of decoders and could not extract tuples beyond that number during inference. Encoder-decoder models are powerful models and they are successful in many NLP tasks such as machine translation, sentence generation from structured data, and open information extraction. In this paper, we explore how encoder-decoder models can be used effectively for extracting relation tuples from sentences. There are three major challenges in this task: (i) The model should be able to extract entities and relations together. (ii) It should be able to extract multiple tuples with overlapping entities. (iii) It should be able to extract exactly two entities of a tuple with their full names. To address these challenges, we propose two novel approaches using encoder-decoder architecture. We first propose a new representation scheme for relation tuples (Table TABREF1) such that it can represent multiple tuples with overlapping entities and different lengths of entities in a simple way. We employ an encoder-decoder model where the decoder extracts one word at a time like machine translation models. At the end of sequence generation, due to the unique representation of the tuples, we can extract the tuples from the sequence of words. Although this model performs quite well, generating one word at a time is somewhat unnatural for this task. Each tuple has exactly two entities and one relation, and each entity appears as a continuous text span in a sentence. The most effective way to identify them is to find their start and end location in the sentence. Each relation tuple can then be represented using five items: start and end location of the two entities and the relation between them (see Table TABREF1). Keeping this in mind, we propose a pointer network-based decoding framework. This decoder consists of two pointer networks which find the start and end location of the two entities in a sentence, and a classification network which identifies the relation between them. At every time step of the decoding, this decoder extracts an entire relation tuple, not just a word. Experiments on the New York Times (NYT) datasets show that our approaches work effectively for this task and achieve state-of-the-art performance. To summarize, the contributions of this paper are as follows: (1) We propose a new representation scheme for relation tuples such that an encoder-decoder model, which extracts one word at each time step, can still find multiple tuples with overlapping entities and tuples with multi-token entities from sentences. We also propose a masking-based copy mechanism to extract the entities from the source sentence only. (2) We propose a modification in the decoding framework with pointer networks to make the encoder-decoder model more suitable for this task. At every time step, this decoder extracts an entire relation tuple, not just a word. This new decoding framework helps in speeding up the training process and uses less resources (GPU memory). This will be an important factor when we move from sentence-level tuple extraction to document-level extraction. (3) Experiments on the NYT datasets show that our approaches outperform all the previous state-of-the-art models significantly and set a new benchmark on these datasets. ### Task Description
A relation tuple consists of two entities and a relation. Such tuples can be found in sentences where an entity is a text span in a sentence and a relation comes from a pre-defined set $R$. These tuples may share one or both entities among them. Based on this, we divide the sentences into three classes: (i) No Entity Overlap (NEO): A sentence in this class has one or more tuples, but they do not share any entities. (ii) Entity Pair Overlap (EPO): A sentence in this class has more than one tuple, and at least two tuples share both the entities in the same or reverse order. (iii) Single Entity Overlap (SEO): A sentence in this class has more than one tuple and at least two tuples share exactly one entity. It should be noted that a sentence can belong to both EPO and SEO classes. Our task is to extract all relation tuples present in a sentence. ### Encoder-Decoder Architecture
In this task, input to the system is a sequence of words, and output is a set of relation tuples. In our first approach, we represent each tuple as entity1 ; entity2 ; relation. We use `;' as a separator token to separate the tuple components. Multiple tuples are separated using the `$\vert $' token. We have included one example of such representation in Table TABREF1. Multiple relation tuples with overlapping entities and different lengths of entities can be represented in a simple way using these special tokens (; and $\vert $). During inference, after the end of sequence generation, relation tuples can be extracted easily using these special tokens. Due to this uniform representation scheme, where entity tokens, relation tokens, and special tokens are treated similarly, we use a shared vocabulary between the encoder and decoder which includes all of these tokens. The input sentence contains clue words for every relation which can help generate the relation tokens. We use two special tokens so that the model can distinguish between the beginning of a relation tuple and the beginning of a tuple component. To extract the relation tuples from a sentence using the encoder-decoder model, the model has to generate the entity tokens, find relation clue words and map them to the relation tokens, and generate the special tokens at appropriate time. Our experiments show that the encoder-decoder models can achieve this quite effectively. ### Encoder-Decoder Architecture ::: Embedding Layer & Encoder
We create a single vocabulary $V$ consisting of the source sentence tokens, relation names from relation set $R$, special separator tokens (`;', `$\vert $'), start-of-target-sequence token (SOS), end-of-target-sequence token (EOS), and unknown word token (UNK). Word-level embeddings are formed by two components: (1) pre-trained word vectors (2) character embedding-based feature vectors. We use a word embedding layer $\mathbf {E}_w \in \mathbb {R}^{\vert V \vert \times d_w}$ and a character embedding layer $\mathbf {E}_c \in \mathbb {R}^{\vert A \vert \times d_c}$, where $d_w$ is the dimension of word vectors, $A$ is the character alphabet of input sentence tokens, and $d_c$ is the dimension of character embedding vectors. Following BIBREF7 (BIBREF7), we use a convolutional neural network with max-pooling to extract a feature vector of size $d_f$ for every word. Word embeddings and character embedding-based feature vectors are concatenated ($\Vert $) to obtain the representation of the input tokens. A source sentence $\mathbf {S}$ is represented by vectors of its tokens $\mathbf {x}_1, \mathbf {x}_2,....,\mathbf {x}_n$, where $\mathbf {x}_i \in \mathbb {R}^{(d_w+d_f)}$ is the vector representation of the $i$th word and $n$ is the length of $\mathbf {S}$. These vectors $\mathbf {x}_i$ are passed to a bi-directional LSTM BIBREF8 (Bi-LSTM) to obtain the hidden representation $\mathbf {h}_i^E$. We set the hidden dimension of the forward and backward LSTM of the Bi-LSTM to be $d_h/2$ to obtain $\mathbf {h}_i^E \in \mathbb {R}^{d_h}$, where $d_h$ is the hidden dimension of the sequence generator LSTM of the decoder described below. ### Encoder-Decoder Architecture ::: Word-level Decoder & Copy Mechanism
A target sequence $\mathbf {T}$ is represented by only word embedding vectors of its tokens $\mathbf {y}_0, \mathbf {y}_1,....,\mathbf {y}_m$ where $\mathbf {y}_i \in \mathbb {R}^{d_w}$ is the embedding vector of the $i$th token and $m$ is the length of the target sequence. $\mathbf {y}_0$ and $\mathbf {y}_m$ represent the embedding vector of the SOS and EOS token respectively. The decoder generates one token at a time and stops when EOS is generated. We use an LSTM as the decoder and at time step $t$, the decoder takes the source sentence encoding ($\mathbf {e}_t \in \mathbb {R}^{d_h}$) and the previous target word embedding ($\mathbf {y}_{t-1}$) as the input and generates the hidden representation of the current token ($\mathbf {h}_t^D \in \mathbb {R}^{d_h}$). The sentence encoding vector $\mathbf {e}_t$ can be obtained using attention mechanism. $\mathbf {h}_t^D$ is projected to the vocabulary $V$ using a linear layer with weight matrix $\mathbf {W}_v \in \mathbb {R}^{\vert V \vert \times d_h}$ and bias vector $\mathbf {b}_v \in \mathbb {R}^{\vert V \vert }$ (projection layer). $\mathbf {o}_t$ represents the normalized scores of all the words in the embedding vocabulary at time step $t$. $\mathbf {h}_{t-1}^D$ is the previous hidden state of the LSTM. The projection layer of the decoder maps the decoder output to the entire vocabulary. During training, we use the gold label target tokens directly. However, during inference, the decoder may predict a token from the vocabulary which is not present in the current sentence or the set of relations or the special tokens. To prevent this, we use a masking technique while applying the softmax operation at the projection layer. We mask (exclude) all words of the vocabulary except the current source sentence tokens, relation tokens, separator tokens (`;', `$\vert $'), UNK, and EOS tokens in the softmax operation. To mask (exclude) some word from softmax, we set the corresponding value in $\hat{\mathbf {o}}_t$ at $-\infty $ and the corresponding softmax score will be zero. This ensures the copying of entities from the source sentence only. We include the UNK token in the softmax operation to make sure that the model generates new entities during inference. If the decoder predicts an UNK token, we replace it with the corresponding source word which has the highest attention score. During inference, after decoding is finished, we extract all tuples based on the special tokens, remove duplicate tuples and tuples in which both entities are the same or tuples where the relation token is not from the relation set. This model is referred to as WordDecoding (WDec) henceforth. ### Encoder-Decoder Architecture ::: Pointer Network-Based Decoder
In the second approach, we identify the entities in the sentence using their start and end locations. We remove the special tokens and relation names from the word vocabulary and word embeddings are used only at the encoder side along with character embeddings. We use an additional relation embedding matrix $\mathbf {E}_r \in \mathbb {R}^{\vert R \vert \times d_r}$ at the decoder side of our model, where $R$ is the set of relations and $d_r$ is the dimension of relation vectors. The relation set $R$ includes a special relation token EOS which indicates the end of the sequence. Relation tuples are represented as a sequence $T=y_0, y_1,....,y_m$, where $y_t$ is a tuple consisting of four indexes in the source sentence indicating the start and end location of the two entities and a relation between them (see Table TABREF1). $y_0$ is a dummy tuple that represents the start tuple of the sequence and $y_m$ functions as the end tuple of the sequence which has EOS as the relation (entities are ignored for this tuple). The decoder consists of an LSTM with hidden dimension $d_h$ to generate the sequence of tuples, two pointer networks to find the two entities, and a classification network to find the relation of a tuple. At time step $t$, the decoder takes the source sentence encoding ($\mathbf {e}_t \in \mathbb {R}^{d_h}$) and the representation of all previously generated tuples ($\mathbf {y}_{prev}=\sum _{j=0}^{t-1}\mathbf {y}_{j}$) as the input and generates the hidden representation of the current tuple, $\mathbf {h}_t^D \in \mathbb {R}^{d_h}$. The sentence encoding vector $\mathbf {e}_t$ is obtained using an attention mechanism as explained later. Relation tuples are a set and to prevent the decoder from generating the same tuple again, we pass the information about all previously generated tuples at each time step of decoding. $\mathbf {y}_j$ is the vector representation of the tuple predicted at time step $j < t$ and we use the zero vector ($\mathbf {y}_0=\overrightarrow{0}$) to represent the dummy tuple $y_0$. $\mathbf {h}_{t-1}^D$ is the hidden state of the LSTM at time step $t-1$. ### Encoder-Decoder Architecture ::: Relation Tuple Extraction
After obtaining the hidden representation of the current tuple $\mathbf {h}_t^D$, we first find the start and end pointers of the two entities in the source sentence. We concatenate the vector $\mathbf {h}_t^D$ with the hidden vectors $\mathbf {h}_i^E$ of the encoder and pass them to a Bi-LSTM layer with hidden dimension $d_p$ for forward and backward LSTM. The hidden vectors of this Bi-LSTM layer $\mathbf {h}_i^k \in \mathbb {R}^{2d_p}$ are passed to two feed-forward networks (FFN) with softmax to convert each hidden vector into two scalar values between 0 and 1. Softmax operation is applied across all the words in the input sentence. These two scalar values represent the probability of the corresponding source sentence token to be the start and end location of the first entity. This Bi-LSTM layer with the two feed-forward layers is the first pointer network which identifies the first entity of the current relation tuple. where $\mathbf {W}_s^1 \in \mathbb {R}^{1 \times 2d_p}$, $\mathbf {W}_e^1 \in \mathbb {R}^{1 \times 2d_p}$, ${b}_s^1$, and ${b}_e^1$ are the weights and bias parameters of the feed-forward layers. ${s}_i^1$, ${e}_i^1$ represent the normalized probabilities of the $i$th source word being the start and end token of the first entity of the predicted tuple. We use another pointer network to extract the second entity of the tuple. We concatenate the hidden vectors $\mathbf {h}_i^k$ with $\mathbf {h}_t^D$ and $\mathbf {h}_i^E$ and pass them to the second pointer network to obtain ${s}_i^2$ and ${e}_i^2$, which represent the normalized probabilities of the $i$th source word being the start and end of the second entity. These normalized probabilities are used to find the vector representation of the two entities, $\mathbf {a}_t^1$ and $\mathbf {a}_t^2$. We concatenate the entity vector representations $\mathbf {a}_t^1$ and $\mathbf {a}_t^2$ with $\mathbf {h}_t^D$ and pass it to a feed-forward network (FFN) with softmax to find the relation. This feed-forward layer has a weight matrix $\mathbf {W}_r \in \mathbb {R}^{\vert R \vert \times (8d_p + d_h)}$ and a bias vector $\mathbf {b}_r \in \mathbb {R}^{\vert R \vert }$. $\mathbf {r}_t$ represents the normalized probabilities of the relation at time step $t$. The relation embedding vector $\mathbf {z}_t$ is obtained using $\mathrm {argmax}$ of $\mathbf {r}_t$ and $\mathbf {E}_r$. $\mathbf {y}_t \in \mathbb {R}^{(8d_p + d_r)}$ is the vector representation of the tuple predicted at time step $t$. During training, we pass the embedding vector of the gold label relation in place of the predicted relation. So the $\mathrm {argmax}$ function does not affect the back-propagation during training. The decoder stops the sequence generation process when the predicted relation is EOS. This is the classification network of the decoder. During inference, we select the start and end location of the two entities such that the product of the four pointer probabilities is maximized keeping the constraints that the two entities do not overlap with each other and $1 \le b \le e \le n$ where $b$ and $e$ are the start and end location of the corresponding entities. We first choose the start and end location of entity 1 based on the maximum product of the corresponding start and end pointer probabilities. Then we find entity 2 in a similar way excluding the span of entity 1 to avoid overlap. The same procedure is repeated but this time we first find entity 2 followed by entity 1. We choose that pair of entities which gives the higher product of four pointer probabilities between these two choices. This model is referred to as PtrNetDecoding (PNDec) henceforth. ### Encoder-Decoder Architecture ::: Attention Modeling
We experimented with three different attention mechanisms for our word-level decoding model to obtain the source context vector $\mathbf {e}_t$: (1) Avg.: The context vector is obtained by averaging the hidden vectors of the encoder: $\mathbf {e}_t=\frac{1}{n}\sum _{i=1}^n \mathbf {h}_i^E$ (2) N-gram: The context vector is obtained by the N-gram attention mechanism of BIBREF9 (BIBREF9) with N=3. $\textnormal {a}_i^g=(\mathbf {h}_n^{E})^T \mathbf {V}^g \mathbf {w}_i^g$, $\alpha ^g = \mathrm {softmax}(\mathbf {a}^g)$ $\mathbf {e}_t=[\mathbf {h}_n^E \Vert \sum _{g=1}^N \mathbf {W}^g (\sum _{i=1}^{\vert G^g \vert } \alpha _i^g \mathbf {w}_i^g)$] Here, $\mathbf {h}_n^E$ is the last hidden state of the encoder, $g \in \lbrace 1, 2, 3\rbrace $ refers to the word gram combination, $G^g$ is the sequence of g-gram word representations for the input sentence, $\mathbf {w}_i^g$ is the $i$th g-gram vector (2-gram and 3-gram representations are obtained by average pooling), $\alpha _i^g$ is the normalized attention score for the $i$th g-gram vector, $\mathbf {W} \in \mathbb {R}^{d_h \times d_h}$ and $\mathbf {V} \in \mathbb {R}^{d_h \times d_h}$ are trainable parameters. (3) Single: The context vector is obtained by the attention mechanism proposed by BIBREF10 (BIBREF10). This attention mechanism gives the best performance with the word-level decoding model. $\mathbf {u}_t^i = \mathbf {W}_{u} \mathbf {h}_i^E, \quad \mathbf {q}_t^i = \mathbf {W}_{q} \mathbf {h}_{t-1}^D + \mathbf {b}_{q}$, $\textnormal {a}_t^i = \mathbf {v}_a \tanh (\mathbf {q}_t^i + \mathbf {u}_t^i), \quad \alpha _t = \mathrm {softmax}(\mathbf {a}_t)$, $\mathbf {e}_t = \sum _{i=1}^n \alpha _t^i \mathbf {h}_i^E$ where $\mathbf {W}_u \in \mathbb {R}^{d_h \times d_h}$, $\mathbf {W}_q \in \mathbb {R}^{d_h \times d_h}$, and $\mathbf {v}_a \in \mathbb {R}^{d_h}$ are all trainable attention parameters and $\mathbf {b}_q \in \mathbb {R}^{d_h}$ is a bias vector. $\alpha _t^i$ is the normalized attention score of the $i$th source word at the decoding time step $t$. For our pointer network-based decoding model, we use three variants of the single attention model. First, we use $\mathbf {h}_{t-1}^D$ to calculate $\mathbf {q}_t^i$ in the attention mechanism. Next, we use $\mathbf {y}_{prev}$ to calculate $\mathbf {q}_t^i$, where $\mathbf {W}_q \in \mathbb {R}^{(8d_p + d_r) \times d_h}$. In the final variant, we obtain the attentive context vector by concatenating the two attentive vectors obtained using $\mathbf {h}_{t-1}^D$ and $\mathbf {y}_{prev}$. This gives the best performance with the pointer network-based decoding model. These variants are referred to as $\mathrm {dec_{hid}}$, $\mathrm {tup_{prev}}$, and $\mathrm {combo}$ in Table TABREF17. ### Encoder-Decoder Architecture ::: Loss Function
We minimize the negative log-likelihood loss of the generated words for word-level decoding ($\mathcal {L}_{word}$) and minimize the sum of negative log-likelihood loss of relation classification and the four pointer locations for pointer network-based decoding ($\mathcal {L}_{ptr}$). $v_t^b$ is the softmax score of the target word at time step $t$ for the word-level decoding model. $r$, $s$, and $e$ are the softmax score of the corresponding true relation label, true start and end pointer location of an entity. $b$, $t$, and $c$ refer to the $b$th training instance, $t$th time step of decoding, and the two entities of a tuple respectively. $B$ and $T$ are the batch size and maximum time step of the decoder respectively. ### Experiments ::: Datasets
We focus on the task of extracting multiple tuples with overlapping entities from sentences. We choose the New York Times (NYT) corpus for our experiments. This corpus has multiple versions, and we choose the following two versions as their test dataset has significantly larger number of instances of multiple relation tuples with overlapping entities. (i) The first version is used by BIBREF6 (BIBREF6) (mentioned as NYT in their paper) and has 24 relations. We name this version as NYT24. (ii) The second version is used by BIBREF11 (BIBREF11) (mentioned as NYT10 in their paper) and has 29 relations. We name this version as NYT29. We select 10% of the original training data and use it as the validation dataset. The remaining 90% is used for training. We include statistics of the training and test datasets in Table TABREF11. ### Experiments ::: Parameter Settings
We run the Word2Vec BIBREF12 tool on the NYT corpus to initialize the word embeddings. The character embeddings and relation embeddings are initialized randomly. All embeddings are updated during training. We set the word embedding dimension $d_w=300$, relation embedding dimension $d_r=300$, character embedding dimension $d_c=50$, and character-based word feature dimension $d_f=50$. To extract the character-based word feature vector, we set the CNN filter width at 3 and the maximum length of a word at 10. The hidden dimension $d_h$ of the decoder LSTM cell is set at 300 and the hidden dimension of the forward and the backward LSTM of the encoder is set at 150. The hidden dimension of the forward and backward LSTM of the pointer networks is set at $d_p=300$. The model is trained with mini-batch size of 32 and the network parameters are optimized using Adam BIBREF13. Dropout layers with a dropout rate fixed at $0.3$ are used in our network to avoid overfitting. ### Experiments ::: Baselines and Evaluation Metrics
We compare our model with the following state-of-the-art joint entity and relation extraction models: (1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. Sequence LSTM is used to identify all the entities first and then Tree LSTM is used to find the relation between all pairs of entities. (2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. They used a Cartesian product of entity tags and relation tags to encode the entity and relation information together. This model does not work when tuples have overlapping entities. (3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. It copies only the last token of an entity from the source sentence. Their best performing multi-decoder model is trained with a fixed number of decoders where each decoder extracts one tuple. (4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. A high-level RL finds the relation and a low-level RL identifies the two entities using a sequence tagging approach. This sequence tagging approach cannot always ensure extraction of exactly two entities. (5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. They use graph convolution network (GCN) to predict the relations of every edge and then filter out some of the relations. (6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data. The encoder uses the source tokens as its vocabulary and the decoder uses the entire Wikidata BIBREF15 entity IDs and relation IDs as its vocabulary. The encoder takes the source sentence as input and the decoder outputs the two entity IDs and relation ID for every tuple. During training, it uses the mapping of entity names and their Wikidata IDs of the entire Wikidata for proper alignment. Our task of extracting relation tuples with the raw entity names from a sentence is more challenging since entity names are not of fixed length. Our more generic approach is also helpful for extracting new entities which are not present in the existing knowledge bases such as Wikidata. We use their N-gram attention mechanism in our model to compare its performance with other attention models (Table TABREF17). We use the same evaluation method used by BIBREF11 (BIBREF11) in their experiments. We consider the extracted tuples as a set and remove the duplicate tuples. An extracted tuple is considered as correct if the corresponding full entity names are correct and the relation is also correct. We report precision, recall, and F1 score for comparison. ### Experiments ::: Experimental Results
Among the baselines, HRL achieves significantly higher F1 scores on the two datasets. We run their model and our models five times and report the median results in Table TABREF15. Scores of other baselines in Table TABREF15 are taken from previous published papers BIBREF6, BIBREF11, BIBREF14. Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. We perform a statistical significance test (t-test) under a bootstrap pairing between HRL and our models and see that the higher F1 scores achieved by our models are statistically significant ($p < 0.001$). Next, we combine the outputs of five runs of our models and five runs of HRL to build ensemble models. For a test instance, we include those tuples which are extracted in the majority ($\ge 3$) of the five runs. This ensemble mechanism increases the precision significantly on both datasets with a small improvement in recall as well. In the ensemble scenario, compared to HRL, WDec achieves $4.2\%$ and $3.5\%$ higher F1 scores and PNDec achieves $4.2\%$ and $2.9\%$ higher F1 scores on the NYT29 and NYT24 datasets respectively. ### Analysis and Discussion ::: Ablation Studies
We include the performance of different attention mechanisms with our WordDecoding model, effects of our masking-based copy mechanism, and ablation results of three variants of the single attention mechanism with our PtrNetDecoding model in Table TABREF17. WordDecoding with single attention achieves the highest F1 score on both datasets. We also see that our copy mechanism improves F1 scores by around 4–7% in each attention mechanism with both datasets. PtrNetDecoding achieves the highest F1 scores when we combine the two attention mechanisms with respect to the previous hidden vector of the decoder LSTM ($\mathbf {h}_{t-1}^D$) and representation of all previously extracted tuples ($\mathbf {y}_{prev}$). ### Analysis and Discussion ::: Performance Analysis
From Table TABREF15, we see that CopyR, HRL, and our models achieve significantly higher F1 scores on the NYT24 dataset than the NYT29 dataset. Both datasets have a similar set of relations and similar texts (NYT). So task-wise both datasets should pose a similar challenge. However, the F1 scores suggest that the NYT24 dataset is easier than NYT29. The reason is that NYT24 has around 72.0% of overlapping tuples between the training and test data (% of test tuples that appear in the training data with different source sentences). In contrast, NYT29 has only 41.7% of overlapping tuples. Due to the memorization power of deep neural networks, it can achieve much higher F1 score on NYT24. The difference between the F1 scores of WordDecoding and PtrNetDecoding on NYT24 is marginally higher than NYT29, since WordDecoding has more trainable parameters (about 27 million) than PtrNetDecoding (about 24.5 million) and NYT24 has very high tuple overlap. However, their ensemble versions achieve closer F1 scores on both datasets. Despite achieving marginally lower F1 scores, the pointer network-based model can be considered more intuitive and suitable for this task. WordDecoding may not extract the special tokens and relation tokens at the right time steps, which is critical for finding the tuples from the generated sequence of words. PtrNetDecoding always extracts two entities of varying length and a relation for every tuple. We also observe that PtrNetDecoding is more than two times faster and takes one-third of the GPU memory of WordDecoding during training and inference. This speedup and smaller memory consumption are achieved due to the fewer number of decoding steps of PtrNetDecoding compared to WordDecoding. PtrNetDecoding extracts an entire tuple at each time step, whereas WordDecoding extracts just one word at each time step and so requires eight time steps on average to extract a tuple (assuming that the average length of an entity is two). The softmax operation at the projection layer of WordDecoding is applied across the entire vocabulary and the vocabulary size can be large (more than 40,000 for our datasets). In case of PtrNetDecoding, the softmax operation is applied across the sentence length (maximum of 100 in our experiments) and across the relation set (24 and 29 for our datasets). The costly softmax operation and the higher number of decoding time steps significantly increase the training and inference time for WordDecoding. The encoder-decoder model proposed by BIBREF9 (BIBREF9) faces a similar softmax-related problem as their target vocabulary contains the entire Wikidata entity IDs and relation IDs which is in the millions. HRL, which uses a deep reinforcement learning algorithm, takes around 8x more time to train than PtrNetDecoding with a similar GPU configuration. The speedup and smaller memory consumption will be useful when we move from sentence-level extraction to document-level extraction, since document length is much higher than sentence length and a document contains a higher number of tuples. ### Analysis and Discussion ::: Error Analysis
The relation tuples extracted by a joint model can be erroneous for multiple reasons such as: (i) extracted entities are wrong; (ii) extracted relations are wrong; (iii) pairings of entities with relations are wrong. To see the effects of the first two reasons, we analyze the performance of HRL and our models on entity generation and relation generation separately. For entity generation, we only consider those entities which are part of some tuple. For relation generation, we only consider the relations of the tuples. We include the performance of our two models and HRL on entity generation and relation generation in Table TABREF20. Our proposed models perform better than HRL on both tasks. Comparing our two models, PtrNetDecoding performs better than WordDecoding on both tasks, although WordDecoding achieves higher F1 scores in tuple extraction. This suggests that PtrNetDecoding makes more errors while pairing the entities with relations. We further analyze the outputs of our models and HRL to determine the errors due to ordering of entities (Order), mismatch of the first entity (Ent1), and mismatch of the second entity (Ent2) in Table TABREF21. WordDecoding generates fewer errors than the other two models in all the categories and thus achieves the highest F1 scores on both datasets. ### Related Work
Traditionally, researchers BIBREF0, BIBREF1, BIBREF2, BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25 used a pipeline approach for relation tuple extraction where relations were identified using a classification network after all entities were detected. BIBREF26 (BIBREF26) used an encoder-decoder model to extract multiple relations present between two given entities. Recently, some researchers BIBREF3, BIBREF4, BIBREF27, BIBREF28 tried to bring these two tasks closer together by sharing their parameters and optimizing them together. BIBREF5 (BIBREF5) used a sequence tagging scheme to jointly extract the entities and relations. BIBREF6 (BIBREF6) proposed an encoder-decoder model with copy mechanism to extract relation tuples with overlapping entities. BIBREF11 (BIBREF11) proposed a joint extraction model based on reinforcement learning (RL). BIBREF14 (BIBREF14) used a graph convolution network (GCN) where they treated each token in a sentence as a node in a graph and edges were considered as relations. BIBREF9 (BIBREF9) used an N-gram attention mechanism with an encoder-decoder model for completion of knowledge bases using distant supervised data. Encoder-decoder models have been used for many NLP applications such as neural machine translation BIBREF29, BIBREF10, BIBREF30, sentence generation from structured data BIBREF31, BIBREF32, and open information extraction BIBREF33, BIBREF34. Pointer networks BIBREF35 have been used to extract a text span from text for tasks such as question answering BIBREF36, BIBREF37. For the first time, we use pointer networks with an encoder-decoder model to extract relation tuples from sentences. ### Conclusion
Extracting relation tuples from sentences is a challenging task due to different length of entities, the presence of multiple tuples, and overlapping of entities among tuples. In this paper, we propose two novel approaches using encoder-decoder architecture to address this task. Experiments on the New York Times (NYT) corpus show that our proposed models achieve significantly improved new state-of-the-art F1 scores. As future work, we would like to explore our proposed models for a document-level tuple extraction task. ### Acknowledgments
We would like to thank the anonymous reviewers for their valuable and constructive comments on this paper. Table 1: Relation tuple representation for encoder-decoder models. Figure 1: The architecture of an encoder-decoder model (left) and a pointer network-based decoder block (right). Table 2: Statistics of train/test split of the two datasets. Table 4: Ablation of attention mechanisms with WordDecoding (WDec) and PtrNetDecoding (PNDec) model. Table 3: Performance comparison on the two datasets. Table 5: Comparison on entity and relation generation tasks. Table 6: % errors for wrong ordering and entity mismatch. | Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively, In the ensemble scenario, compared to HRL, WDec achieves $4.2\%$ and $3.5\%$ higher F1 scores |
Why does Edith want Hank to go out on the town?
A. Edith promised Hank's mother that she would make an effort to return to normalcy, as death had not parted them after all.
B. Edith is making an effort to return to normalcy, even though she is scared. She loves Hank.
C. Edith promised General Carlisle that she would make an effort to return to normalcy. She was aware of the new return-to-life policy before Hank left on the mission.
D. Edith wants to get Hank out of the house so Ralphie can have his friends over. Ralphie's friends don't want to visit while Hank is at the house.
| THE FIRST ONE By HERBERT D. KASTLE Illustrated by von Dongen [Transcriber's Note: This etext was produced from Analog July 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The first man to return from beyond the Great Frontier may be welcomed ... but will it be as a curiosity, rather than as a hero...? There was the usual welcoming crowd for a celebrity, and the usual speeches by the usual politicians who met him at the airport which had once been twenty miles outside of Croton, but which the growing city had since engulfed and placed well within its boundaries. But everything wasn't usual. The crowd was quiet, and the mayor didn't seem quite as at-ease as he'd been on his last big welcoming—for Corporal Berringer, one of the crew of the spaceship Washington , first to set Americans upon Mars. His Honor's handclasp was somewhat moist and cold. His Honor's eyes held a trace of remoteness. Still, he was the honored home-comer, the successful returnee, the hometown boy who had made good in a big way, and they took the triumphal tour up Main Street to the new square and the grandstand. There he sat between the mayor and a nervous young coed chosen as homecoming queen, and looked out at the police and fire department bands, the National Guard, the boy scouts and girl scouts, the Elks and Masons. Several of the churches in town had shown indecision as to how to instruct their parishioners to treat him. But they had all come around. The tremendous national interest, the fact that he was the First One, had made them come around. It was obvious by now that they would have to adjust as they'd adjusted to all the other firsts taking place in these—as the newspapers had dubbed the start of the Twenty-first Century—the Galloping Twenties. He was glad when the official greeting was over. He was a very tired man and he had come farther, traveled longer and over darker country, than any man who'd ever lived before. He wanted a meal at his own table, a kiss from his wife, a word from his son, and later to see some old friends and a relative or two. He didn't want to talk about the journey. He wanted to forget the immediacy, the urgency, the terror; then perhaps he would talk. Or would he? For he had very little to tell. He had traveled and he had returned and his voyage was very much like the voyages of the great mariners, from Columbus onward—long, dull periods of time passing, passing, and then the arrival. The house had changed. He saw that as soon as the official car let him off at 45 Roosevelt Street. The change was, he knew, for the better. They had put a porch in front. They had rehabilitated, spruced up, almost rebuilt the entire outside and grounds. But he was sorry. He had wanted it to be as before. The head of the American Legion and the chief of police, who had escorted him on this trip from the square, didn't ask to go in with him. He was glad. He'd had enough of strangers. Not that he was through with strangers. There were dozens of them up and down the street, standing beside parked cars, looking at him. But when he looked back at them, their eyes dropped, they turned away, they began moving off. He was still too much the First One to have his gaze met. He walked up what had once been a concrete path and was now an ornate flagstone path. He climbed the new porch and raised the ornamental knocker on the new door and heard the soft music sound within. He was surprised that he'd had to do this. He'd thought Edith would be watching at a window. And perhaps she had been watching ... but she hadn't opened the door. The door opened; he looked at her. It hadn't been too long and she hadn't changed at all. She was still the small, slender girl he'd loved in high school, the small, slender woman he'd married twelve years ago. Ralphie was with her. They held onto each other as if seeking mutual support, the thirty-three-year old woman and ten-year-old boy. They looked at him, and then both moved forward, still together. He said, "It's good to be home!" Edith nodded and, still holding to Ralphie with one hand, put the other arm around him. He kissed her—her neck, her cheek—and all the old jokes came to mind, the jokes of travel-weary, battle-weary men, the and- then -I'll-put-my-pack-aside jokes that spoke of terrible hunger. She was trembling, and even as her lips came up to touch his he felt the difference, and because of this difference he turned with urgency to Ralphie and picked him up and hugged him and said, because he could think of nothing else to say, "What a big fella, what a big fella." Ralphie stood in his arms as if his feet were still planted on the floor, and he didn't look at his father but somewhere beyond him. "I didn't grow much while you were gone, Dad, Mom says I don't eat enough." So he put him down and told himself that it would all change, that everything would loosen up just as his commanding officer, General Carlisle, had said it would early this morning before he left Washington. "Give it some time," Carlisle had said. "You need the time; they need the time. And for the love of heaven, don't be sensitive." Edith was leading him into the living room, her hand lying still in his, a cool, dead bird lying still in his. He sat down on the couch, she sat down beside him—but she had hesitated. He wasn't being sensitive; she had hesitated. His wife had hesitated before sitting down beside him. Carlisle had said his position was analogous to Columbus', to Vasco De Gama's, to Preshoff's when the Russian returned from the Moon—but more so. Carlisle had said lots of things, but even Carlisle who had worked with him all the way, who had engineered the entire fantastic journey—even Carlisle the Nobel prize winner, the multi-degreed genius in uniform, had not actually spoken to him as one man to another. The eyes. It always showed in their eyes. He looked across the room at Ralphie, standing in the doorway, a boy already tall, already widening in the shoulders, already large of feature. It was like looking into the mirror and seeing himself twenty-five years ago. But Ralphie's face was drawn, was worried in a way that few ten-year-old faces are. "How's it going in school?" he asked. "Gee, Dad, it's the second month of summer vacation." "Well, then, before summer vacation?" "Pretty good." Edith said, "He made top forum the six-month period before vacation, and he made top forum the six-month period you went away, Hank." He nodded, remembering that, remembering everything, remembering the warmth of her farewell, the warmth of Ralphie's farewell, their tears as he left for the experimental flight station in the Aleutians. They had feared for him, having read of the many launchings gone wrong even in continent-to-continent experimental flight. They had been right to worry. He had suffered much after that blow-up. But now they should be rejoicing, because he had survived and made the long journey. Ralphie suddenly said, "I got to go, Dad. I promised Walt and the others I'd pitch. It's Inter-Town Little League, you know. It's Harmon, you know. I got to keep my word." Without waiting for an answer, he waved his hand—it shook; a ten-year-old boy's hand that shook—and ran from the room and from the house. He and Edith sat beside each other, and he wanted badly to take her in his arms, and yet he didn't want to oppress her. He stood up. "I'm very tired. I'd like to lie down a while." Which wasn't true, because he'd been lying down all the months of the way back. She said, "Of course. How stupid of me, expecting you to sit around and make small talk and pick up just where you left off." He nodded. But that was exactly what he wanted to do—make small talk and pick up just where he'd left off. But they didn't expect it of him; they wouldn't let him; they felt he had changed too much. She led him upstairs and along the foyer past Ralphie's room and past the small guest room to their bedroom. This, too, had changed. It was newly painted and it had new furniture. He saw twin beds separated by an ornate little table with an ornate little lamp, and this looked more ominous a barrier to him than the twelve-foot concrete-and-barbed-wire fence around the experimental station. "Which one is mine," he asked, and tried to smile. She also tried to smile. "The one near the window. You always liked the fresh air, the sunshine in the morning. You always said it helped you to get up on time when you were stationed at the base outside of town. You always said it reminded you—being able to see the sky—that you were going to go up in it, and that you were going to come down from it to this bed again." "Not this bed," he murmured, and was a little sorry afterward. "No, not this bed," she said quickly. "Your lodge donated the bedroom set and I really didn't know—" She waved her hand, her face white. He was sure then that she had known, and that the beds and the barrier between them were her own choice, if only an unconscious choice. He went to the bed near the window, stripped off his Air Force blue jacket, began to take off his shirt, but then remembered that some arm scars still showed. He waited for her to leave the room. She said, "Well then, rest up, dear," and went out. He took off his shirt and saw himself in the mirror on the opposite wall; and then took off his under-shirt. The body scars were faint, the scars running in long lines, one dissecting his chest, the other slicing diagonally across his upper abdomen to disappear under his trousers. There were several more on his back, and one on his right thigh. They'd been treated properly and would soon disappear. But she had never seen them. Perhaps she never would. Perhaps pajamas and robes and dark rooms would keep them from her until they were gone. Which was not what he'd considered at all important on leaving Walter Reed Hospital early this morning; which was something he found distasteful, something he felt beneath them both. And, at the same time, he began to understand that there would be many things, previously beneath them both, which would have to be considered. She had changed; Ralphie had changed; all the people he knew had probably changed—because they thought he had changed. He was tired of thinking. He lay down and closed his eyes. He let himself taste bitterness, unhappiness, a loneliness he had never known before. But sometime later, as he was dozing off, a sense of reassurance began filtering into his mind. After all, he was still Henry Devers, the same man who had left home eleven months ago, with a love for family and friends which was, if anything, stronger than before. Once he could communicate this, the strangeness would disappear and the First One would again become good old Hank. It was little enough to ask for—a return to old values, old relationships, the normalcies of the backwash instead of the freneticisms of the lime-light. It would certainly be granted to him. He slept. Dinner was at seven p.m. His mother came; his Uncle Joe and Aunt Lucille came. Together with Edith, Ralphie and himself, they made six, and ate in the dining room at the big table. Before he'd become the First One, it would have been a noisy affair. His family had never been noted for a lack of ebullience, a lack of talkativeness, and Ralphie had always chosen mealtimes—especially with company present—to describe everything and anything that had happened to him during the day. And Edith herself had always chatted, especially with his mother, though they didn't agree about much. Still, it had been good-natured; the general tone of their lives had been good-natured. This wasn't good-natured. Exactly what it was he wasn't sure. "Stiff" was perhaps the word. They began with grapefruit, Edith and Mother serving quickly, efficiently from the kitchen, then sitting down at the table. He looked at Mother as he raised his first spoonful of chilled fruit, and said, "Younger than ever." It was nothing new; he'd said it many many times before, but his mother had always reacted with a bright smile and a quip something like, "Young for the Golden Age Center, you mean." This time she burst into tears. It shocked him. But what shocked him even more was the fact that no one looked up, commented, made any attempt to comfort her; no one indicated in any way that a woman was sobbing at the table. He was sitting directly across from Mother, and reached out and touched her left hand which lay limply beside the silverware. She didn't move it—she hadn't touched him once beyond that first, quick, strangely-cool embrace at the door—then a few seconds later she withdrew it and let it drop out of sight. So there he was, Henry Devers, at home with the family. So there he was, the hero returned, waiting to be treated as a human being. The grapefruit shells were cleaned away and the soup served. Uncle Joe began to talk. "The greatest little development of circular uniform houses you ever did see," he boomed in his powerful salesman's voice. "Still going like sixty. We'll sell out before—" At that point he looked at Hank, and Hank nodded encouragement, desperately interested in this normalcy, and Joe's voice died away. He looked down at his plate, mumbled, "Soup's getting cold," and began to eat. His hand shook a little; his ruddy face was not quite as ruddy as Hank remembered it. Aunt Lucille made a few quavering statements about the Ladies' Tuesday Garden Club, and Hank looked across the table to where she sat between Joe and Mother—his wife and son bracketed him, and yet he felt alone—and said, "I've missed fooling around with the lawn and the rose bushes. Here it is August and I haven't had my hand to a mower or trowel." Aunt Lucille smiled, if you could call it that—a pitiful twitching of the lips—and nodded. She threw her eyes in his direction, and past him, and then down to her plate. Mother, who was still sniffling, said, "I have a dismal headache. I'm going to lie down in the guest room a while." She touched his shoulder in passing—his affectionate, effusive mother who would kiss stray dogs and strange children, who had often irritated him with an excess of physical and verbal caresses—she barely touched his shoulder and fled. So now five of them sat at the table. The meat was served—thin, rare slices of beef, the pink blood-juice oozing warmly from the center. He cut into it and raised a forkful to his mouth, then glanced at Ralphie and said, "Looks fresh enough to have been killed in the back yard." Ralphie said, "Yeah, Dad." Aunt Lucille put down her knife and fork and murmured something to her husband. Joe cleared his throat and said Lucille was rapidly becoming a vegetarian and he guessed she was going into the living room for a while. "She'll be back for dessert, of course," he said, his laugh sounding forced. Hank looked at Edith; Edith was busy with her plate. Hank looked at Ralphie; Ralphie was busy with his plate. Hank looked at Joe; Joe was chewing, gazing out over their heads to the kitchen. Hank looked at Lucille; she was disappearing into the living room. He brought his fist down on the table. The settings jumped; a glass overturned, spilling water. He brought it down again and again. They were all standing now. He sat there and pounded the table with his big right fist—Henry Devers, who would never have thought of making such a scene before, but who was now so sick and tired of being treated as the First One, of being stood back from, looked at in awe of, felt in fear of, that he could have smashed more than a table. Edith said, "Hank!" He said, voice hoarse, "Shut up. Go away. Let me eat alone. I'm sick of the lot of you." Mother and Joe returned a few minutes later where he sat forcing food down his throat. Mother said, "Henry dear—" He didn't answer. She began to cry, and he was glad she left the house then. He had never said anything really bad to his mother. He was afraid this would have been the time. Joe merely cleared his throat and mumbled something about getting together again soon and "drop out and see the new development" and he, too, was gone. Lucille never did manage to speak to him. He finished his beef and waited. Soon Edith came in with the special dessert she'd been preparing half the day—a magnificent English trifle. She served him, and spooned out a portion for herself and Ralphie. She hesitated near his chair, and when he made no comment she called the boy. Then the three of them were sitting, facing the empty side of the table. They ate the trifle. Ralphie finished first and got up and said, "Hey, I promised—" "You promised the boys you'd play baseball or football or handball or something; anything to get away from your father." Ralphie's head dropped and he muttered, "Aw, no, Dad." Edith said, "He'll stay home, Hank. We'll spend an evening together—talking, watching TV, playing Monopoly." Ralphie said, "Gee, sure, Dad, if you want to." Hank stood up. "The question is not whether I want to. You both know I want to. The question is whether you want to." They answered together that of course they wanted to. But their eyes—his wife's and son's eyes—could not meet his, and so he said he was going to his room because he was, after all, very tired and would in all probability continue to be very tired for a long, long time and that they shouldn't count on him for normal social life. He fell asleep quickly, lying there in his clothes. But he didn't sleep long. Edith shook him and he opened his eyes to a lighted room. "Phil and Rhona are here." He blinked at her. She smiled, and it seemed her old smile. "They're so anxious to see you, Hank. I could barely keep Phil from coming up and waking you himself. They want to go out and do the town. Please, Hank, say you will." He sat up. "Phil," he muttered. "Phil and Rhona." They'd had wonderful times together, from grammar school on. Phil and Rhona, their oldest and closest friends. Perhaps this would begin his real homecoming. Do the town? They'd paint it and then tear it down! It didn't turn out that way. He was disappointed; but then again, he'd also expected it. This entire first day at home had conditioned him to expect nothing good. They went to the bowling alleys, and Phil sounded very much the way he always had—soft spoken and full of laughter and full of jokes. He patted Edith on the head the way he always had, and clapped Hank on the shoulder (but not the way he always had—so much more gently, almost remotely), and insisted they all drink more than was good for them as he always had. And for once, Hank was ready to go along on the drinking. For once, he matched Phil shot for shot, beer for beer. They didn't bowl very long. At ten o'clock they crossed the road to Manfred's Tavern, where Phil and the girls ordered sandwiches and coffee and Hank went right on drinking. Edith said something to him, but he merely smiled and waved his hand and gulped another ounce of nirvana. There was dancing to a juke box in Manfred's Tavern. He'd been there many times before, and he was sure several of the couples recognized him. But except for a few abortive glances in his direction, it was as if he were a stranger in a city halfway around the world. At midnight, he was still drinking. The others wanted to leave, but he said, "I haven't danced with my girl Rhona." His tongue was thick, his mind was blurred, and yet he could read the strange expression on her face—pretty Rhona, who'd always flirted with him, who'd made a ritual of flirting with him. Pretty Rhona, who now looked as if she were going to be sick. "So let's rock," he said and stood up. They were on the dance floor. He held her close, and hummed and chatted. And through the alcoholic haze saw she was a stiff-smiled, stiff-bodied, mechanical dancing doll. The number finished; they walked back to the booth. Phil said, "Beddy-bye time." Hank said, "First one dance with my loving wife." He and Edith danced. He didn't hold her close as he had Rhona. He waited for her to come close on her own, and she did, and yet she didn't. Because while she put herself against him, there was something in her face—no, in her eyes; it always showed in the eyes—that made him know she was trying to be the old Edith and not succeeding. This time when the music ended, he was ready to go home. They rode back to town along Route Nine, he and Edith in the rear of Phil's car, Rhona driving because Phil had drunk just a little too much, Phil singing and telling an occasional bad joke, and somehow not his old self. No one was his old self. No one would ever be his old self with the First One. They turned left, to take the short cut along Hallowed Hill Road, and Phil finished a story about a Martian and a Hollywood sex queen and looked at his wife and then past her at the long, cast-iron fence paralleling the road. "Hey," he said, pointing, "do you know why that's the most popular place on earth?" Rhona glanced to the left, and so did Hank and Edith. Rhona made a little sound, and Edith seemed to stop breathing, but Phil went on a while longer, not yet aware of his supposed faux pas . "You know why?" he repeated, turning to the back seat, the laughter rumbling up from his chest. "You know why, folks?" Rhona said, "Did you notice Carl Braken and his wife at—" Hank said, "No, Phil, why is it the most popular place on earth?" Phil said, "Because people are—" And then he caught himself and waved his hand and muttered, "I forgot the punch line." "Because people are dying to get in," Hank said, and looked through the window, past the iron fence, into the large cemetery at the fleeting tombstones. The car was filled with horrified silence when there should have been nothing but laughter, or irritation at a too-old joke. "Maybe you should let me out right here," Hank said. "I'm home—or that's what everyone seems to think. Maybe I should lie down in an open grave. Maybe that would satisfy people. Maybe that's the only way to act, like Dracula or another monster from the movies." Edith said, "Oh, Hank, don't, don't!" The car raced along the road, crossed a macadam highway, went four blocks and pulled to a stop. He didn't bother saying good night. He didn't wait for Edith. He just got out and walked up the flagstone path and entered the house. "Hank," Edith whispered from the guest room doorway, "I'm so sorry—" "There's nothing to be sorry about. It's just a matter of time. It'll all work out in time." "Yes," she said quickly, "that's it. I need a little time. We all need a little time. Because it's so strange, Hank. Because it's so frightening. I should have told you that the moment you walked in. I think I've hurt you terribly, we've all hurt you terribly, by trying to hide that we're frightened." "I'm going to stay in the guest room," he said, "for as long as necessary. For good if need be." "How could it be for good? How, Hank?" That question was perhaps the first firm basis for hope he'd had since returning. And there was something else; what Carlisle had told him, even as Carlisle himself had reacted as all men did. "There are others coming, Edith. Eight that I know of in the tanks right now. My superior, Captain Davidson, who died at the same moment I did—seven months ago next Wednesday—he's going to be next. He was smashed up worse than I was, so it took a little longer, but he's almost ready. And there'll be many more, Edith. The government is going to save all they possibly can from now on. Every time a young and healthy man loses his life by accident, by violence, and his body can be recovered, he'll go into the tanks and they'll start the regenerative brain and organ process—the process that made it all possible. So people have to get used to us. And the old stories, the old terrors, the ugly old superstitions have to die, because in time each place will have some of us; because in time it'll be an ordinary thing." Edith said, "Yes, and I'm so grateful that you're here, Hank. Please believe that. Please be patient with me and Ralphie and—" She paused. "There's one question." He knew what the question was. It had been the first asked him by everyone from the president of the United States on down. "I saw nothing," he said. "It was as if I slept those six and a half months—slept without dreaming." She came to him and touched his face with her lips, and he was satisfied. Later, half asleep, he heard a dog howling, and remembered stories of how they announced death and the presence of monsters. He shivered and pulled the covers closer to him and luxuriated in being safe in his own home. THE END | B. Edith is making an effort to return to normalcy, even though she is scared. She loves Hank. |
After the initial MRI, what was the next step taken for Mr. Havers' treatment?
Choose the correct answer from the following options:
A. En bloc tumor excision.
B. Incisional biopsy.
C. MRI follow-up.
D. Pulmonary metastasis treatment.
E. Chemotherapy.
| ### Patient Report 0
**Dear colleague, **
We are reporting on our patient, John Havers, born on 05/29/1953, who
received an MRI of the right proximal thigh for further clarification of
a potential tumor.
**MRI of the right thigh, plain and with contrast agent, on
02/19/2017:**
[Technique]{.underline}: Surface coil, localization scan, coronal T1 SE,
transverse, coronal, sagittal T2 TSE with fat suppression. After
intravenous contrast administration, T1-TSE transverse and T1-TSE FS
(coronal, T2 TSE FS coronal as an additional fat-saturated sequence in
the same section level for exploring relevant edema).
[Findings]{.underline}: Normal bone marrow signal consistent with age.
No signs of fractures. Coexistence of moderate degenerative changes in
the hip joints, more pronounced on the right than on the left. Mild
activation of the muscles in the left proximal adductor region. Ventral
to the gracilis muscle and dorsal to the sartorius muscle at the level
of the middle third of the right thigh is a subfascial intermuscular
oval mass lesion with a high-signal appearance on T2-weighted images and
a low-signal appearance on T1-weighted images. It is partially septated,
well-demarcated, and shows strong contrast enhancement. No evidence of
blood degradation products. Dimensions are 35 x 45 x 40 mm. No evidence
of suspiciously enlarged lymph nodes. Other assessed soft tissues are
unremarkable for the patient\'s age.
[Assessment]{.underline}: Overall, a high suspicion of a mucinous mass
lesion in the region of the right adductor compartment. Differential
Diagnosis: Mucinous liposarcoma. Further histological evaluation is
strongly recommended.
**Current Recommendations:** Presentation at the clinic for surgery for
further differential diagnostic clarification.
### Patient Report 1
**Dear colleague, **
We are reporting on our patient, John Havers, born on 05/29/1953. He was
under our inpatient care from 03/10/2017 to 03/12/2017.
**Diagnosis:** Soft tissue tumor of the right proximal thigh
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus
- Coronary artery disease with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Allergies**: Hay fever
**Treatment**: Incisional biopsy on 03/10/2017
**Histology:**
[Microscopy]{.underline}: (Hematoxylin and Eosin staining):
Histologically, an infiltrate of a mesenchymal neoplasm is evident in a
section prepared by us and stained with HE. There are areas with an
estimated tumor percentage of approximately 90% that were selected and
labeled for molecular pathology analysis.
[Molecular Pathology]{.underline}: After macrodissection of labeled
tumor areas from unstained consecutive sections, RNA was extracted and
analyzed using focused next-generation sequencing technology. The
analysis was performed using FusioPlex Sarcoma v2 assays, allowing
detection of fusions in 63 genes.
**Medical History:** We may kindly assume that you are familiar with Mr.
Havers's medical history. The patient presented to our surgery clinic
due to a mass in the right proximal thigh. The swelling was first
noticed approximately 3 months ago and has shown significant enlargement
since. The patient subsequently consulted a general surgeon, who
referred him to our center after performing an MRI, suspecting an
intramuscular liposarcoma. After presenting the case to our
interdisciplinary tumor board, the decision was made to perform an
incisional biopsy. The patient was admitted for the above procedure on
03/10/2017.
**Physical Examination:** On clinical examination, a patient in slightly
reduced general and nutritional status was observed. Approximately 6 x 7
x 4 cm-sized tumor in the right proximal thigh, well mobile,
intramuscular. Numbness in both legs at L5/S1.
No change in skin color. No fluctuation or redness. The rest of the
clinical examination was unremarkable.
**Treatment and Progression:** Following routine preoperative
preparations and informed consent, the above-mentioned procedure was
performed under general anesthesia on 03/10/2017. The intraoperative and
postoperative courses were uncomplicated.
Initial mild swelling regressed over time. The inserted drainage was
removed on the second postoperative day. The patient mobilized
independently on the ward. Pain management was provided as needed.
With the patient\'s subjective well-being and inconspicuous wound
conditions, we were able to discharge Mr. Havers on 03/12/2017 for
outpatient follow-up.
**Current Recommendations:**
- Suture material to be shortened on the 14th postoperative day.
- Follow-up appointments in our outpatient clinic
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
-------------------------------------- ------------ ---------------
Empagliflozin (Jardiance) 10 mg 1-0-0-0
Metformin Hydrochloride (Glucophage) 1000 mg 1-0-1-0
Atorvastatin Calcium (Lipitor) 21.7 mg 0-0-1-0
Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0
Aspirin 100 mg 1-0-0-0
Pantoprazole Sodium (Protonix) 22.6 mg 1-0-0-0
**Lab results upon Discharge: **
**Parameter** **Results** **Reference Range**
---------------------- ------------- ---------------------
Sodium 138 mEq/L 136-145 mEq/L
Potassium 4.9 mEq/L 3.5-4.5 mEq/L
Creatinine 0.81 mg/dL 0.70-1.20 mg/dL
Estimated GFR \- \-
Urea 38 mg/dL 17-48 mg/dL
C-Reactive Protein 2.6 mg/dL \< 5.0 mg/dL
Complete Blood Count \- \-
Hemoglobin 16.7 g/dL 13.5-17.0 g/dL
Hematocrit 49.5% 39.5-50.5%
Erythrocytes 5.2 M/µL 4.3-5.8 M/µL
Leukocytes 10.07 K/µL 3.90-10.50 K/µL
Platelets 167 K/µL 150-370 K/µL
MCV 95.4 fL 80.0-99.0 fL
MCH 32.2 pg 27.0-33.5 pg
MCHC 33.7 g/dL 31.5-36.0 g/dL
MPV 11.7 fL 7.0-12.0 fL
RDW-CV 12.6% 11.5-15.0%
Prothrombin Time 120% 78-123%
INR 0.94 0.90-1.25
aPTT 30.1 sec 25.0-38.0 sec
**Addition: Histology Report:**
[Microscopy:]{.underline} (Hematoxylin and Eosin staining):
Histologically, infiltrates of a mesenchymal neoplasm can be seen in a
section we prepared. Below this are areas estimated to contain 90%
tumor, which have been selected and labeled for molecular pathological
analysis.
[Molecular Pathology:]{.underline} After macrodissection of the marked
tumor areas from unstained consecutive sections, RNA was extracted and
analyzed using focused Next-Generation Sequencing technology. The
examination was performed using the FusioPlex Sarcoma v2 Assays, that
allows for the detection of fusions involving 63 genes.
[Diagnosis:]{.underline}
1. Incisional biopsy from a myxoid liposarcoma, Grade 1 according to
FNCLCC (Sum score 2 + 0 + 1 = 3), with the detection of a FUS: DDIT3
fusion transcript (right adductor compartment).
2. Predominantly mature fatty tissue as well as fascial tissue.
- In addition to previous reports, myxoid neoplasm is characterized by
minimal cell density/round cell areas, here less than 25%, according
to FNCLCC (=2 points for tumor differentiation).
- No evidence of necrosis (=0 points).
- 2 mitotic figures in 10 high-power fields (=1 point).
- Total score is 2 + 0 + 1 = 3, corresponding to Grade 1 according to
FNCLCC.
[Diagnosis]{.underline}
1. Incisional biopsy from a myxoid liposarcoma (right adductor
compartment).
2. Predominantly mature fatty tissue as well as fascial tissue
(subcutaneous).
[Comment]{.underline}: The present biopsy material corresponds to Grade
1 according to FNCLCC. A supplementary report follows.
**Supplementary Report from: 03/29/2017:**
[Clinical Information:]{.underline} Suspected liposarcoma of the right
proximal thigh. Encapsulated subfascial tumor, palpably indurated.
Adipose tissue adjacent to the tumor, macroscopically lighter and finer
than the subcutaneous adipose tissue towards the skin.
[Material]{.underline}: Microscopy and Molecular Pathology Interphase
FISH analysis using a two-color break-apart probe to examine a
chromosomal break in the FUS gene (chromosome 16p11.2) and in the DDIT3
gene (chromosome 12q13.3-q14.1).
Interphase FISH analysis reveals a specific break event in the FUS gene
(FUS-FISH positive). This indicates the presence of a FUS translocation.
Similarly, in interphase FISH analysis, a specific break event is
detectable in the DDIT3 gene (DDIT3-FISH positive), indicating the
presence of a DDIT3 translocation.
[Diagnosis:]{.underline} Incisional biopsy from a myxoid liposarcoma of
the right adductor compartment.
Predominantly mature fatty tissue as well as fascial tissue.
[Comment]{.underline}: The cytogenetic findings are indicative of a
myxoid liposarcoma. Technical validation by RNA sequencing will be
provided in a supplemental report. This does not affect the above
diagnosis.
**Supplementary Report from: 03/18/2017:**
[Microscopy: MDM2, S100:]{.underline} Partial weak expression of S100
protein by the lesional cells, occasionally including pre-existing
adipocytes. No abnormal expression of MDM2. No abnormal expression of
MDM2 in mature adipose tissue.
[Diagnosis:]{.underline} Incisional biopsy from a myxoid liposarcoma of
the right adductor compartment.
Predominantly mature fatty tissue as well as fascial tissue.
**Main Report from: 03/18/2017**
[Clinical Information:]{.underline} Suspected liposarcoma of the right
proximal thigh, as per MRI 02/19/2017. Encapsulated subfascial tumor,
palpably indurated located in the right adductor compartment. Adipose
tissue adjacent to the tumor, macroscopically lighter and finer than the
subcutaneous adipose tissue towards the skin.
[Macroscopy:]{.underline}
Tumor: Brown, nodular piece of tissue, 20 x 14 x 10 mm, with smooth and
rough surface. Cut surface shiny and mottled, sometimes gray, sometimes
brown.
Subcutaneous adipose tissue: A piece of adipose tissue, 25 x 20 x 5 mm.
[Microscopy:]{.underline}
Moderately cell dense mesenchymal proliferation with a myxoid matrix.
Predominantly round nuclei, moderately dense nuclear chromatin, slight
pleomorphism. Occasional adipocytic cells with univacuolar cytoplasm.
Partially dense, ribbon-like connective tissue as well as mature
univacuolar adipose tissue.
[Diagnosis:]{.underline}
Incisional biopsy suspected of a myxoid liposarcoma. Predominantly
mature fatty tissue as well as fascial tissue.
### Patient Report 2
**Dear colleague, **
We would like to inform you about our patient Mr. John Havers, born on
05/29/1953, who was admitted to our hospital from 03/29/2017 to
04/05/2017.
**Diagnoses**:
- Myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1
R0, Stage IB
- Incisional biopsy on 03/10/2017
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus
- Coronary artery disease with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Allergies**: Hay fever
**Current Presentation**: Neoplasm of uncertain or unknown behavior.
**Treatment**: On 04/01/2017, en bloc tumor excision with removal of the
old biopsy scar, partial resection of the M. gracilis, fibers of the M.
sartorius and M. adductor longus, and ligation of the V. saphena magna
was performed.
**Histology from 04/11/2017**
Clinical Information: Myxoid liposarcoma, localized in the right thigh.
[Macroscopy Tumor, right thigh]{.underline}: A triple surgical resection
was performed, removing skin and subcutaneous tissue and the underlying
soft tissue and muscle. The size of the excised skin spindle was 130 x
45 mm with a resection depth of up to 48 mm. A wound 25 mm long and 6 mm
wide was noted on the skin surface. The muscle attached
laterally/dorsally measured 75 x 25 x 6 mm. Two nodules were noted on
the cut surface. The larger nodule, located in the subcutaneous tissue,
measured 33 mm (proximal/distal) x 36 mm (anterior/dorsal) x 30 mm. Its
distance from the proximal preparation cap was 26 mm, from the distal
preparation cap more than 60 mm, from the ventral soft tissue 20 mm, and
from the dorsal soft tissue 3 mm, with less than 1 mm basal extension.
Superficially, it was surrounded by a delicate capsule. A separate
nodule measuring 20 mm (proximal/distal) x 24 mm (ventral/dorsal) x 20
mm was found immediately ventro-distal to the first nodule. This nodule
was located more than 40 mm from the proximal preparation cap, more than
50 mm from the distal preparation cap, 12 mm ventrally, 11 mm dorsally,
and 5 mm basally. Consequently, the maximum size of the tumor from
proximal to distal was 53 mm. No macroscopic necrotic areas were evident
on the cut surface of the nodule. However, partial necrosis of the
subcutaneous fatty tissue in the vicinity of the described wound was
observed.
[Microscopy HE, PAS:]{.underline} Histomorphologically, there is a
moderately cell-dense proliferation with a significant myxoid matrix in
the area of the two confluent nodules, with a maximum diameter of 53 mm.
There are also areas with relative cell poverty. Within the myxoid
matrix, there are blood vessels with a distinct growth pattern referred
to as the \"chicken wire pattern.\" No clear tumor necroses are evident.
The tumor cell nuclei have a round configuration with moderately dense
chromatin. Apoptotic figures are increased. The number of mitoses is
low.The lesion was completely removed with a minimal margin of 0.5 mm
from the posterior resection edge. In the superficial subcutaneous
tissue, there is a band-like necrosis directly related to superficial
granulation tissue. The included skin spindle shows regular epidermal
covering and a largely unremarkable dermis.
[Diagnosis]{.underline}: Skin/subcutaneous excision with a maximum 53 mm
myxoid liposarcoma that was completely removed (minimum distance to
posterior cutoff plane 0.5 mm).
[Comment]{.underline}: In view of the present morphology and knowledge
of the molecular pathological examination results with proven break
events in the FUS gene and DDIT3 gene as part of interphase FISH
analysis, the diagnosed condition is myxoid liposarcoma.
According to the FNCLCC grading scheme, this corresponds to grade 1:
Histological type: 2 points + mitotic index 1 point + necrosis index 0
points = 3 points.
ICD-O-3 tumor classification: Myxoid liposarcoma TNM (8th edition): pT2
pNX L0 V0 Pn0 G1 R0
**Medical History:** We assume that you are familiar with Mr. Havers's
medical history, and we refer to our previous correspondence.
**Physical Examination:** Patient in good general condition. Oriented in
all aspects. No cyanosis. No edema. Warm and dry skin. Normal nasal and
pharyngeal findings. Pupils round, equal, and react promptly to light
bilaterally. Moist tongue. Pharynx and buccal mucosa unremarkable. No
jugular vein distension. No carotid bruits heard. Palpation of lymph
nodes unremarkable. Palpation of the thyroid gland unremarkable, freely
movable. Lungs: Normal chest shape, moderately mobile, vesicular breath
sounds.
Heart: Regular heart action, normal rate; heart sounds clear, no
pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all
quadrants; soft abdomen, no tenderness, no palpable masses, liver and
spleen not palpable due to limited access, non-tender kidneys.
Normal peripheral pulses; joints freely movable. Strength, motor
function, and sensation are unremarkable.
**Therapy and Progression**: The patient presented to our surgical
clinic because of a mass in the right proximal thigh. The swelling was
first noticed about 3 months ago and has increased significantly in size
since then. MRI findings raised suspicion of a liposarcoma. After
consultation in the interdisciplinary tumor board, the indication for
incisional biopsy was performed on 03/10/2017. The histopathological
examination confirmed the presence of a myxoid liposarcoma, leading to
the decision for en bloc excision. The patient was extensively informed
about the procedure and the risks and gave his consent. The patient was
admitted for the procedure on 03/29/2017.
Upon clinical examination, a patient in good general and nutritional
condition was noted. Other general clinical findings were unremarkable.
A wound healing disorder of 2 cm was observed in the area of the wound
after incisional biopsy.
**Sarcoma Tumor Board Recommendation dated 03/11/2017:** R0 G1 finding,
standard sarcoma follow-up.
**Procedure**: Following standard preoperative preparations and informed
consent, the aforementioned procedure was performed on 03/01/2017 under
general anesthesia. The intraoperative and postoperative course was
uneventful.
On the first postoperative day, there was slight swelling in the
affected area, which gradually subsided. Analgesia was sufficient with
Acetaminophen as needed. Thrombosis prophylaxis was administered with
subcutaneous Enoxaparin 0.4 mL. The patient mobilized independently on
the ward. The inserted drainage could not be removed so far due to
excessive drainage output. During the hospital stay, a staging CT of the
chest and abdomen was performed. No thoracoabdominal metastases were
detected.
**Summary**: With a good subjective well-being and unremarkable wound
conditions, Mr. Havers was discharged on 04/05/2017 for further
outpatient care. Clinical examination reveals slight swelling of the
wound area. The wound is not dehiscent and shows no signs of irritation.
The patient is mobilizing independently.
**CT Chest/Abdomen/Pelvis from 04/01/2017: **
[Clinical Information, Question, Justification]{.underline}: Liposarcoma
of the thigh. Staging.
[Technique]{.underline}: Digital overview radiographs. Following
intravenous contrast agent administration (100 ml Xenetix), CT of the
chest and entire abdomen in the venous contrast phase. Reconstruction of
the primary dataset with a slice thickness of 0.625 mm. Multiplanar
reconstruction. Total DLP: 885 mGy\*cm.
[Findings]{.underline}: There are no prior images available for
comparison.
[Chest]{.underline}: Lungs are evenly ventilated and normally developed
bilaterally. No pneumothorax on either side. Minimal right-sided pleural
effusion. Mild basilar hypoventilation, particularly in the right lower
lobe. Calcified granuloma in the apical right lower lobe. No suspicious
pulmonary nodules.
Heart shows enlargement of the left ventricle and left atrium. Coronary
artery sclerosis. Atherosclerosis of the aortic arch. No pericardial
effusion. Aorta and pulmonary trunk have normal diameters. No central
pulmonary artery embolism. No pathologically enlarged mediastinal or
hilar lymph nodes. Symmetric appearance of the neck soft tissues.
Thyroid gland without focal lesions. Axillary lymph nodes are of normal
size.
[Abdomen]{.underline}: Liver is of normal size and has a smooth contour.
No signs of cholestasis. No portal vein thrombosis. No suspicious
intrahepatic lesions. Gallbladder appears normal. Common bile duct is
not dilated. Spleen is not enlarged. Pancreas shows regular lobulation,
and there is no dilatation of the pancreatic duct. Both kidneys are free
from urinary tract obstruction. No solid intrarenal masses. Few renal
cysts. Adrenal glands appear unremarkable. Urinary bladder shows no
focal wall thickening. Prostate is not enlarged. Advanced
atherosclerosis of the abdominal aorta and pelvic vessels. History of
stenting of the left external iliac artery with no reocclusion.
Mesenteric, para-aortic, and parailiac lymph nodes are not
pathologically enlarged. No free intraperitoneal fluid or air is
detected. Osseous Structures: Degenerative changes in the spine. No
evidence of suspicious osseous destruction suggestive of tumors. Soft
tissue mantle appears unremarkable.
**Assessment**: No thoracoabdominal metastases.
**Current Recommendations**:
- Regular wound inspections and dressing changes.
- Documentation of drainage output and removal if the output is \<20
ml/24 hours, expected removal on 04/23/2017 at our outpatient
clinic.
- Removal of sutures is not required for absorbable sutures.
- According to the tumor board decision dated 04/11/2017, we recommend
regular follow-up according to the schedule.
**Sarcoma Follow-up Schedule Stage I**
- Local Follow-up:
1. MRI right thigh: Years 1-5: every 6 months
2. Years 6-10: every 12 months
- Pulmonary Follow-up:
3. Chest X-ray, CT chest with contrast agent Years 1-5: every 6
months in alternation
4. Years 6-10: every 12 months in alternation
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
-------------------------------------- ------------ -------------------
Aspirin 100 mg 1-0-0-0
Atorvastatin (Lipitor) 20 mg 0-0-1-0
Enoxaparin (Lovenox) Variable 0-0-1-0
Empagliflozin (Jardiance) 10 mg 1-0-0-0
Metformin Hydrochloride (Glucophage) 1000 mg 1-0-1-0
Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0
Acetaminophen (Tylenol) 500 mg 2-2-2-2 if needed
Pantoprazole (Protonix) 20 mg 1-0-0-0
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
------------------------------------------- ------------- ---------------------
Sodium 137 mEq/L 136-145 mEq/L
Potassium 4.4 mEq/L 3.5-4.5 mEq/L
Creatinine 0.74 mg/dL 0.70-1.20 mg/dL
Blood Urea Nitrogen 33 mg/dL 17-48 mg/dL
C-Reactive Protein 1.7 mg/dL \< 5.0 mg/dL
Thyroid-Stimulating Hormone 3.58 mIU/L 0.27-4.20 mIU/L
Hemoglobin 16.5 g/dL 13.5-17.0 g/dL
Hematocrit 49.3% 39.5-50.5%
Red Blood Cells 5.2 M/µL 4.3-5.8 M/µL
White Blood Cells 9.63 K/µL 3.90-10.50 K/µL
Platelets 301 K/µL 150-370 K/µL
Mean Corpuscular Volume 95.7 fL 80.0-99.0 fL
Mean Corpuscular Hemoglobin 32.0 pg 27.0-33.5 pg
Mean Corpuscular Hemoglobin Concentration 33.5 g/dL 31.5-36.0 g/dL
Mean Platelet Volume 10.4 fL 7.0-12.0 fL
Red Cell Distribution Width 12.1% 11.6-14.4%
Activated Partial Thromboplastin Time 32.4 sec 25.0-38.0 sec
### Patient Report 3
**Dear colleague, **
We are writing to provide an update on our patient Mr. John Havers, born
on 05/29/1953, who presented to our outpatient surgery clinic on
04/23/2017.
**Diagnosis**: Myxoid liposarcoma, right medial thigh, pT2 pNX L0 V0 Pn0
G1 R0, Stage IB
- Following incisional biopsy
- After en bloc tumor excision with removal of the previous biopsy
scar, partial resection of the gracilis, sartorius and adductor
longus muscles and ligation of the great saphenous vein.
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus
- Coronary artery disease with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Allergies**: Hay fever
**Medical History:** We kindly assume that you are familiar with the
patient\'s detailed medical history and refer to our previous discharge
letter.
**Current Presentation:** The patient presented today for a follow-up
visit in our clinic. He reported no complaints. The Redon drain has not
produced any secretions in the last 2 days.
Clinical examination revealed uneventful wound conditions with applied
Steri-strips. There is no evidence of infection. The Redon drain
contains serous wound secretions.
Procedure: The Redon drain is being removed today. With nearly fully
healed wound conditions, we recommend initiating scar massage with fatty
topical products in the near future.
**MRI of the Right Thigh on** 04/23/2017**:**
[Clinical Background, Question, Justification:]{.underline} Sarcoma
follow-up for myxoid liposarcoma on the right medial thigh, pT2 pNX L0
V0 Pn0 G1 R0, Stage IB. Recurrence? Regional behavior? Lymph nodes?
[Technique]{.underline}: 3 Tesla MRI of the right thigh, both plain and
after the administration of 8 ml of Gadovist intravenously. Supine
position, surface coil.
Sequences: TIRM coronal and axial, T2-TSE coronal and axial, T1 VIBE
Dixon axial, EPI-DWI with ADC map axial, T1-Starvibe vascular images
plain and post-contrast axial with subtraction images, T1-TSE FS
post-contrast coronal.
[Findings]{.underline}: Minor FLAIR hyperintense streaky signal
alteration in the surgical area, most likely scar-related, with slight
diffusion restriction and streaky contrast enhancement. No evidence of a
recurrent suspicious substrate. No nodular contrast enhancement.
Slightly accentuated inguinal lymph nodes on the right, most likely
reactive. Unremarkable visualization of the remaining soft tissue.
Normal bone marrow signal. Bladder filled. Unremarkable representation
of the imaged pelvic organs.
[Assessment]{.underline}: Following the resection of a myxoid
liposarcoma on the right medial thigh, there is a regular postoperative
finding. No indication of local recurrence.
**Chest X-ray in Two Planes on 04/23/2017: **
[Clinical Background, Question, Justification]{.underline}: Myxoid
liposarcoma of the right thigh, initial diagnosis in 2022. Follow-up.
Metastases?
[Findings]{.underline}: No corresponding prior images for comparison.
The upper mediastinum is centrally located and not widened. Hila are
free. No acute congestion. No confluent pneumonic infiltrate. No
evidence of larger intrapulmonary lesions. A 7 mm spot shadow is noted
right suprahilar, primarily representing a vascular structure. No
effusions. No pneumothorax.
**Current Recommendations:** The patient would like to continue
follow-up care with us, so we scheduled an MRI control appointment to
assess the possibility of local recurrence. On this day, a two-view
chest X-ray is also required.
**We recommend the following follow-up schedule:**
- Local Follow-up:
5. MRI right thigh: Years 1-5: every 6 months
6. Years 6-10: every 12 months
- Pulmonary Follow-up:
7. Chest X-ray, CT chest with contrast agent Years 1-5: every 6
months in alternation
8. Years 6-10: every 12 months in alternation
### Patient Report 4
**Dear colleague, **
We are writing to provide an update on our patient Mr. John Havers, born
on 05/29/1953, who presented for tumor follow-up on 02/10/2018, in our
outpatient surgery clinic for a discussion of findings.
**Diagnosis**: Myxoid liposarcoma on the right medial thigh, pT2 pNX L0
V0 Pn0 G1 R0, Stage IB
- Following incisional biopsy
- After en bloc tumor excision with removal of the previous biopsy
scar, partial resection of the gracilis, sartorius and adductor
longus muscles and ligation of the great saphenous vein.
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus
- Coronary artery disease with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Summary**: Clinically, there is a regular postoperative finding on the
right thigh.
The control MRI with contrast of the right thigh on 04/23/2017 revealed
morphologically:
- No evidence of a local-regional recurrence.
- In pulmonary follow-up using conventional chest X-ray on 04/23/2017,
no signs of pulmonary metastasis were detected.
**Current Recommendations:** Sarcoma Follow-up Schedule Stage I
- Local Follow-up:
9. MRI right thigh: Years 1-5: every 6 months
10. Years 6-10: every 12 months
- Pulmonary Follow-up:
11. Chest X-ray, CT chest with contrast agent Years 1-5: every 6
months in alternation
12. Years 6-10: every 12 months in alternation
### Patient Report 5
**Dear colleague, **
We are reporting to you on our patient Mr. John Havers, born on
05/29/1953, who presented himself on **08/01/2018** at our outpatient
surgery clinic for a discussion of findings as part of tumor follow-up.
**Diagnosis**: Myxoid liposarcoma, right medial thigh, pT2 pNX L0 V0 Pn0
G1 R0, Stage IB
- Post-incision biopsy
- After en bloc tumor excision with removal of the previous biopsy
scar, partial resection of the gracilis, sartorius and adductor
longus muscles and ligation of the great saphenous vein.
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus (NIDDM)
- Coronary artery disease (CAD) with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement (THR)
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Summary**: Clinically, there is a normal postoperative condition in
the right thigh.
**MRI of the Right Thigh on 08/01/2018:**
[Clinical Background, Question, Justification:]{.underline} Sarcoma
follow-up for myxoid liposarcoma on the right medial thigh. Progress
assessment.
[Method]{.underline}: 1.5 Tesla. Localization sequences. TIRM and T2 TSE
coronal. TIRM, T2 TSE, VIBE DIXON, and RESOLVE-DWI axial. StarVIBE FS
before and after contrast + subtraction. T1 TSE FS coronal after
contrast.
[Findings]{.underline}: Comparison with MRI from 04/23/2017.
Post-resection of a myxoid liposarcoma in the proximal medial right
thigh soft tissue. In the surgical area, there is no evidence of a
suspicious nodular, contrast-affine lesion, and no evidence of
malignancy-suspected diffusion restriction. Slight scar-related changes
in the access path. Otherwise, unremarkable presentation of soft tissues
and included bony structures. No inguinal lymphadenopathy. Assessment:
For myxoid liposarcoma, there has been consistent evidence since
02/2018:
**Chest CT on 08/01/2018**:
[Clinical Background, Question, Justification: Liposarcoma on the thigh.
Staging.]{.underline} After risk history assessment, oral and written
explanation of contrast agent application and examination procedure, as
well as potential risks of the examination (see also informed consent
form). Written patient consent.
[Method]{.underline}: Digital overview radiographs. After intravenous
contrast agent administration (80 ml of Imeron), CT of the chest in
venous contrast phase, reconstruction of the primary dataset with a
slice thickness of 0.625 mm. Total DLP 185 mGy\*cm.
[Findings]{.underline}: For comparison, there is a CT of the
chest/abdomen/pelvis from 04/01/2018. No evidence of suspicious
pulmonary nodules. Several partly calcified micronodules bipulmonary,
especially in the right lower lobe (ex. S303/IMA179). Partial
underventilation bipulmonary. No pleural effusion. No evidence of
pathologically enlarged lymph nodes. Constant calcified right hilar
lymph nodes. Calcifying aortic sclerosis along with coronary sclerosis.
Hepatic steatosis. Individual renal cysts. Slightly shrunken left
adrenal gland. Degenerative changes of the axial skeleton without
evidence of a malignancy-suspected osseous lesion.
[Assessment]{.underline}: No evidence of a new thoracic tumor
manifestation.
**Recommendations:** Sarcoma Follow-up
- Local Follow-up:
13. MRI right thigh: Years 1-5: every 6 months
14. Years 6-10: every 12 months
- Pulmonary Follow-up:
15. Chest X-ray, CT chest with contrast agent Years 1-5: every 6
months in alternation
16. Years 6-10: every 12 months in alternation
**Lab results upon Discharge: **
**Parameter** **Results** **Reference Range**
---------------------------------------------- ------------- ---------------------
Sodium 138 mEq/L 136-145 mEq/L
Potassium 4.9 mEq/L 3.5-4.5 mEq/L
Creatinine 0.81 mg/dL 0.70-1.20 mg/dL
Estimated GFR \- \-
Blood Urea Nitrogen 38 mg/dL 17-48 mg/dL
C-Reactive Protein 2.6 mg/dL \< 5.0 mg/dL
Hemoglobin 16.7 g/dL 13.5-17.0 g/dL
Hematocrit 49.5% 39.5-50.5%
RBC 5.2 M/µL 4.3-5.8 M/µL
WBC 10.07 K/µL 3.90-10.50 K/µL
Platelets 167 K/µL 150-370 K/µL
MCV 95.4 fL 80.0-99.0 fL
MCH 32.2 pg 27.0-33.5 pg
MCHC 33.7 g/dL 31.5-36.0 g/dL
MPV 11.7 fL 7.0-12.0 fL
RDW-CV 12.6% 11.5-15.0%
Prothrombin Time 120% 78-123%
International Normalized Ratio (INR) 0.94 0.90-1.25
Activated Partial Thromboplastin Time (aPTT) 30.1 sec 25.0-38.0 sec
### Patient Report 6
**Dear colleague, **
We are writing to provide an update on our patient Mr. John Havers, born
on 05/29/1953, who was admitted to our clinic from 08/14/2023 to
09/02/2023.
**Diagnosis:** Pulmonary Metastasis from Myxoid Liposarcoma
- Myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1
R0, Stage IB
<!-- -->
- Post-incision biopsy
- After en bloc tumor excision with removal of the previous biopsy
scar, partial resection of the gracilis, sartorius and adductor
longus muscles and ligation of the great saphenous vein.
**Other Diagnoses:**
- Arterial hypertension
- Non-insulin-dependent diabetes mellitus (NIDDM)
- Coronary artery disease (CAD) with stent placement
- Nicotine abuse (80-100 pack-years)
- Arterial hypertension
- Status post apoplexy
- Status post cataract surgery
- Status post right hip total hip replacement (THR)
- Status post Polypectomy for polyposis coli (minimal dysplasia)
- Status post appendectomy
- M. Meniere
**Medical History:** Mr. Havers has been under our care for myxoid
liposarcoma, which was previously excised from his right medial thigh.
He had a stable postoperative course and was scheduled for regular
follow-up to monitor for any potential recurrence or metastasis.
**Current Presentation:** During a follow-up appointment on 08/14/2023,
Mr. Havers complained of mild shortness of breath, occasional coughing,
and intermittent chest discomfort. He reported no significant weight
loss but noted a decrease in his overall energy levels. Physical
examination revealed decreased breath sounds in the right lung base.
**Physical Examination:** Patient in adequate general condition.
Oriented in all aspects. No cyanosis. No edema. Warm and dry skin.
Normal nasal and pharyngeal findings. Pupils round, equal, and react
promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa
unremarkable. No jugular vein distension. No carotid bruits heard.
Palpation of lymph nodes unremarkable. Palpation of the thyroid gland
unremarkable, freely movable. Lungs: Normal chest shape, moderately
mobile, decreased breath sounds in the right lung base. Heart: Regular
heart action, normal rate; heart sounds clear, no pathological sounds.
Abdomen: Peristalsis and bowel sounds normal in all quadrants; soft
abdomen, markedly obese, no tenderness, no palpable masses, liver and
spleen not palpable due to limited access, non-tender kidneys. Normal
peripheral pulses; joints freely movable. Strength, motor function, and
sensation is unremarkable.
**Chest X-ray (08/14/2023):** A chest X-ray was performed, which
revealed a suspicious opacity in the right lower lung field.
**CT Chest (08/16/2023):** In light of the chest X-ray findings, a
contrast-enhanced CT scan of the chest was conducted to obtain more
detailed information. The CT imaging demonstrated a well-defined,
irregularly shaped lesion in the right lower lobe of the lung, measuring
approximately 2.5 cm in diameter. The lesion exhibited characteristics
highly suggestive of a metastatic deposit. There were no other
significant abnormalities noted in the chest.
**Histology (08/21/2023):** Based on the CT findings, a CT-guided core
needle biopsy of the pulmonary lesion was performed to confirm the
nature of the lesion. Histopathological examination of the biopsy
specimen confirmed the presence of myxoid liposarcoma cells in the
pulmonary lesion. Immunohistochemical staining for MDM2 and CDK4
supported the diagnosis of metastatic myxoid liposarcoma.
**Treatment Discussion:** Given the diagnosis of a pulmonary metastasis
from myxoid liposarcoma, the case was reviewed in the interdisciplinary
tumor board. The consensus decision was to pursue surgical resection of
the pulmonary metastasis, as it remained localized and resectable. The
patient and his family were informed of the treatment options and
associated risks, and they provided informed consent for the procedure.
**Surgery Report (08/29/2023):** Mr. Havers underwent a right lower
lobectomy with lymph node dissection to remove the pulmonary metastasis.
The procedure was performed by our thoracic surgery team and was
completed without any immediate complications. Intraoperative frozen
section analysis confirmed the presence of metastatic myxoid liposarcoma
in the resected lung tissue.
**Postoperative Course:** Mr. Havers postoperative course was
uneventful, and he demonstrated good respiratory recovery. He was
managed with adequate pain control and underwent chest physiotherapy to
prevent postoperative complications. Pathological examination of the
resected lung tissue confirmed the presence of metastatic myxoid
liposarcoma, with clear surgical margins.
**Current Recommendations:**
1. **Follow-up:** A strict follow-up plan should be established for Mr.
Havers to monitor for any potential recurrence or new metastatic
lesions. This should include regular clinical assessments, chest
imaging, and other relevant investigations.
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
--------------------------------- ------------ ---------------
Empagliflozin (Jardiance) 10 mg 1-0-0-0
Metformin (Glucophage) 1000 mg 1-0-1-0
Atorvastatin (Lipitor) 20 mg 0-0-1-0
Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0
Aspirin 100 mg 1-0-0-0
Pantoprazole (Protonix) 20 mg 1-0-0-0
**Lab results upon Discharge: **
**Parameter** **Results** **Reference Range**
--------------------- ------------- ---------------------
Sodium 135 mEq/L 136-145 mEq/L
Potassium 4.4 mEq/L 3.5-4.5 mEq/L
Creatinine 0.82 mg/dL 0.70-1.20 mg/dL
Estimated GFR \- \-
Blood Urea Nitrogen 39 mg/dL 17-48 mg/dL
C-Reactive Protein 2.5 mg/dL \< 5.0 mg/dL
Hemoglobin 16.6 g/dL 13.5-17.0 g/dL
Hematocrit 49.4 % 39.5-50.5 %
RBC 5.1 M/µL 4.3-5.8 M/µL
WBC 10.04 K/µL 3.90-10.50 K/µL
Platelets 166 K/µL 150-370 K/µL
MCV 95.2 fL 80.0-99.0 fL
MCH 32.6 pg 27.0-33.5 pg
MCHC 33.2 g/dL 31.5-36.0 g/dL
MPV 11.4 fL 7.0-12.0 fL
RDW-CV 12.5 % 11.5-15.0 %
Prothrombin Time 122 % 78-123 %
INR 0.99 0.90-1.25
aPTT 30.1 sec 25.0-38.0 sec | Incisional biopsy. |
Why did the Department hope that Si would continue for three more space missions?
A. He didn't complain about the explorations and enjoyed his time in space.
B. His required compensation was lower than the other pilots.
C. It would take too long to train a new pilot to complete the explorations.
D. He was the best of the best in the space exploration team.
| SPACEMAN ON A SPREE BY MACK REYNOLDS Illustrated by Nodel [Transcriber's Note: This etext was produced from Worlds of Tomorrow June 1963 Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] What's more important—Man's conquest of space, or one spaceman's life? I They gave him a gold watch. It was meant to be symbolical, of course. In the old tradition. It was in the way of an antique, being one of the timepieces made generations past in the Alpine area of Eur-Asia. Its quaintness lay in the fact that it was wound, not electronically by power-radio, but by the actual physical movements of the bearer, a free swinging rotor keeping the mainspring at a constant tension. They also had a banquet for him, complete with speeches by such bigwigs of the Department of Space Exploration as Academician Lofting Gubelin and Doctor Hans Girard-Perregaux. There was also somebody from the government who spoke, but he was one of those who were pseudo-elected and didn't know much about the field of space travel nor the significance of Seymour Pond's retirement. Si didn't bother to remember his name. He only wondered vaguely why the cloddy had turned up at all. In common with recipients of gold watches of a score of generations before him, Si Pond would have preferred something a bit more tangible in the way of reward, such as a few shares of Variable Basic to add to his portfolio. But that, he supposed, was asking too much. The fact of the matter was, Si knew that his retiring had set them back. They hadn't figured he had enough shares of Basic to see him through decently. Well, possibly he didn't, given their standards. But Space Pilot Seymour Pond didn't have their standards. He'd had plenty of time to think it over. It was better to retire on a limited crediting, on a confoundedly limited crediting, than to take the two or three more trips in hopes of attaining a higher standard. He'd had plenty of time to figure it out, there alone in space on the Moon run, there on the Venus or Mars runs. There on the long, long haul to the Jupiter satellites, fearfully checking the symptoms of space cafard, the madness compounded of claustrophobia, monotony, boredom and free fall. Plenty of time. Time to decide that a one room mini-auto-apartment, complete with an autochair and built-in autobar, and with one wall a teevee screen, was all he needed to find contentment for a mighty long time. Possibly somebody like Doc Girard-Perregaux might be horrified at the idea of living in a mini-auto-apartment ... not realizing that to a pilot it was roomy beyond belief compared to the conning tower of a space craft. No. Even as Si listened to their speeches, accepted the watch and made a halting little talk of his own, he was grinning inwardly. There wasn't anything they could do. He had them now. He had enough Basic to keep him comfortably, by his standards, for the rest of his life. He was never going to subject himself to space cafard again. Just thinking about it, now, set the tic to going at the side of his mouth. They could count down and blast off, for all he gave a damn. The gold watch idea had been that of Lofting Gubelin, which was typical, he being in the way of a living anachronism himself. In fact, Academician Gubelin was possibly the only living man on North America who still wore spectacles. His explanation was that a phobia against having his eyes touched prohibited either surgery to remould his eyeballs and cure his myopia, or contact lenses. That was only an alibi so far as his closest associate, Hans Girard-Perregaux, was concerned. Doctor Girard-Perregaux was convinced Gubelin would have even worn facial hair, had he but a touch more courage. Gubelin longed for yesteryear, a seldom found phenomenon under the Ultrawelfare State. Slumped in an autochair in the escape room of his Floridian home, Lofting Gubelin scowled at his friend. He said, acidly, "Any more bright schemes, Hans? I presume you now acknowledge that appealing to the cloddy's patriotism, sentiment and desire for public acclaim have miserably failed." Girard-Perregaux said easily, "I wouldn't call Seymour Pond a cloddy. In his position, I am afraid I would do the same thing he has." "That's nonsense, Hans. Zoroaster! Either you or I would gladly take Pond's place were we capable of performing the duties for which he has been trained. There aren't two men on North America—there aren't two men in the world!—who better realize the urgency of continuing our delving into space." Gubelin snapped his fingers. "Like that, either of us would give our lives to prevent man from completely abandoning the road to his destiny." His friend said drily, "Either of us could have volunteered for pilot training forty years ago, Lofting. We didn't." "At that time there wasn't such a blistering percentage of funkers throughout this whole blistering Ultrawelfare State! Who could foresee that eventually our whole program would face ending due to lack of courageous young men willing to take chances, willing to face adventure, willing to react to the stimulus of danger in the manner our ancestors did?" Girard-Perregaux grunted his sarcasm and dialed a glass of iced tea and tequila. He said, "Nevertheless, both you and I conform with the present generation in finding it far more pleasant to follow one's way of life in the comfort of one's home than to be confronted with the unpleasantness of facing nature's dangers in more adventurous pastimes." Gubelin, half angry at his friend's argument, leaned forward to snap rebuttal, but the other was wagging a finger at him negatively. "Face reality, Lofting. Don't require or expect from Seymour Pond more than is to be found there. He is an average young man. Born in our Ultrawelfare State, he was guaranteed his fundamental womb-to-tomb security by being issued that minimum number of Basic shares in our society that allows him an income sufficient to secure the food, clothing, shelter, medical care and education to sustain a low level of subsistence. Percentages were against his ever being drafted into industry. Automation being what it is, only a fraction of the population is ever called up. But Pond was. His industrial aptitude dossier revealed him a possible candidate for space pilot, and it was you yourself who talked him into taking the training ... pointing out the more pragmatic advantages such as complete retirement after but six trips, added shares of Basic so that he could enjoy a more comfortable life than most and the fame that would accrue to him as one of the very few who still participate in travel to the planets. Very well. He was sold. Took his training, which, of course, required long years of drudgery to him. Then, performing his duties quite competently, he made his six trips. He is now legally eligible for retirement. He was drafted into the working force reserves, served his time, and is now free from toil for the balance of his life. Why should he listen to our pleas for a few more trips?" "But has he no spirit of adventure? Has he no feeling for...." Girard-Perregaux was wagging his finger again, a gesture that, seemingly mild though it was, had an astonishing ability to break off the conversation of one who debated with the easy-seeming, quiet spoken man. He said, "No, he hasn't. Few there are who have, nowadays. Man has always paid lip service to adventure, hardships and excitement, but in actuality his instincts, like those of any other animal, lead him to the least dangerous path. Today we've reached the point where no one need face danger—ever. There are few who don't take advantage of the fact. Including you and me, Lofting, and including Seymour Pond." His friend and colleague changed subjects abruptly, impatiently. "Let's leave this blistering jabber about Pond's motivation and get to the point. The man is the only trained space pilot in the world. It will take months, possibly more than a year, to bring another novitiate pilot to the point where he can safely be trusted to take our next explorer craft out. Appropriations for our expeditions have been increasingly hard to come by—even though in our minds, Hans, we are near important breakthroughs, breakthroughs which might possibly so spark the race that a new dream to push man out to the stars will take hold of us. If it is admitted that our organization has degenerated to the point that we haven't a single pilot, then it might well be that the Economic Planning Board, and especially those cloddies on Appropriations, will terminate the whole Department of Space Exploration." "So...." Girard-Perregaux said gently. "So some way we've got to bring Seymour Pond out of his retirement!" "Now we are getting to matters." Girard-Perregaux nodded his agreement. Looking over the rim of his glass, his eyes narrowed in thought as his face took on an expression of Machiavellianism. "And do not the ends justify the means?" Gubelin blinked at him. The other chuckled. "The trouble with you, Lofting, is that you have failed to bring history to bear on our problem. Haven't you ever read of the sailor and his way of life?" "Sailor? What in the name of the living Zoroaster has the sailor got to do with it?" "You must realize, my dear Lofting, that our Si Pond is nothing more than a latter-day sailor, with many of the problems and view-points, tendencies and weaknesses of the voyager of the past. Have you never heard of the seaman who dreamed of returning to the village of his birth and buying a chicken farm or some such? All the long months at sea—and sometimes the tramp freighters or whaling craft would be out for years at a stretch before returning to home port—he would talk of his retirement and his dream. And then? Then in port, it would be one short drink with the boys, before taking his accumulated pay and heading home. The one short drink would lead to another. And morning would find him, drunk, rolled, tattooed and possibly sleeping it off in jail. So back to sea he'd have to go." Gubelin grunted bitterly. "Unfortunately, our present-day sailor can't be separated from his money quite so easily. If he could, I'd personally be willing to lure him down some dark alley, knock him over the head and roll him myself. Just to bring him back to his job again." He brought his wallet from his pocket, and flicked it open to his universal credit card. "The ultimate means of exchange," he grunted. "Nobody can spend your money, but you, yourself. Nobody can steal it, nobody can, ah, con you out of it. Just how do you expect to sever our present-day sailor and his accumulated nest egg?" The other chuckled again. "It is simply a matter of finding more modern methods, my dear chap." II Si Pond was a great believer in the institution of the spree. Any excuse would do. Back when he had finished basic education at the age of twenty-five and was registered for the labor draft, there hadn't been a chance in a hundred that he'd have the bad luck to have his name pulled. But when it had been, Si had celebrated. When he had been informed that his physical and mental qualifications were such that he was eligible for the most dangerous occupation in the Ultrawelfare State and had been pressured into taking training for space pilot, he had celebrated once again. Twenty-two others had taken the training with him, and only he and Rod Cameroon had passed the finals. On this occasion, he and Rod had celebrated together. It had been quite a party. Two weeks later, Rod had burned on a faulty take-off on what should have been a routine Moon run. Each time Si returned from one of his own runs, he celebrated. A spree, a bust, a bat, a wing-ding, a night on the town. A commemoration of dangers met and passed. Now it was all over. At the age of thirty he was retired. Law prevented him from ever being called up for contributing to the country's labor needs again. And he most certainly wasn't going to volunteer. He had taken his schooling much as had his contemporaries. There wasn't any particular reason for trying to excell. You didn't want to get the reputation for being a wise guy, or a cloddy either. Just one of the fellas. You could do the same in life whether you really studied or not. You had your Inalienable Basic stock, didn't you? What else did you need? It had come as a surprise when he'd been drafted for the labor force. In the early days of the Ultrawelfare State, they had made a mistake in adapting to the automation of the second industrial revolution. They had attempted to give everyone work by reducing the number of working hours in the day, and the number of working days in the week. It finally became ludicrous when employees of industry were working but two days a week, two hours a day. In fact, it got chaotic. It became obvious that it was more practical to have one worker putting in thirty-five hours a week and getting to know his job well, than it was to have a score of employees, each working a few hours a week and none of them ever really becoming efficient. The only fair thing was to let the technologically unemployed remain unemployed, with their Inalienable Basic stock as the equivalent of unemployment insurance, while the few workers still needed put in a reasonable number of hours a day, a reasonable number of weeks a year and a reasonable number of years in a life time. When new employees were needed, a draft lottery was held. All persons registered in the labor force participated. If you were drawn, you must need serve. The dissatisfaction those chosen might feel at their poor luck was offset by the fact that they were granted additional Variable Basic shares, according to the tasks they fulfilled. Such shares could be added to their portfolios, the dividends becoming part of their current credit balance, or could be sold for a lump sum on the market. Yes, but now it was all over. He had his own little place, his own vacuum-tube vehicle and twice the amount of shares of Basic that most of his fellow citizens could boast. Si Pond had it made. A spree was obviously called for. He was going to do this one right. This was the big one. He'd accumulated a lot of dollars these past few months and he intended to blow them, or at least a sizeable number of them. His credit card was burning a hole in his pocket, as the expression went. However, he wasn't going to rush into things. This had to be done correctly. Too many a spree was played by ear. You started off with a few drinks, fell in with some second rate mopsy and usually wound up in a third rate groggery where you spent just as much as though you'd been in the classiest joint in town. Came morning and you had nothing to show for all the dollars that had been spent but a rum-head. Thus, Si was vaguely aware, it had always been down through the centuries since the Phoenecian sailor, back from his year-long trip to the tin mines of Cornwall, blew his hard earned share of the voyage's profits in a matter of days in the wine shops of Tyre. Nobody gets quite so little for his money as that loneliest of all workers, he who must leave his home for distant lands, returning only periodically and usually with the salary of lengthy, weary periods of time to be spent hurriedly in an attempt to achieve the pleasure and happiness so long denied him. Si was going to do it differently this time. Nothing but the best. Wine, women, song, food, entertainment. The works. But nothing but the best. To start off, he dressed with great care in the honorable retirement-rank suit he had so recently purchased. His space pin he attached carefully to the lapel. That was a good beginning, he decided. A bit of prestige didn't hurt you when you went out on the town. In the Ultrawelfare State hardly one person in a hundred actually ever performed anything of value to society. The efforts of most weren't needed. Those few who did contribute were awarded honors, decorations, titles. Attired satisfactorily, Si double-checked to see that his credit card was in his pocket. As an after-thought, he went over to the auto-apartment's teevee-phone, flicked it on, held the card to the screen and said, "Balance check, please." In a moment, the teevee-phone's robot voice reported, "Ten shares of Inalienable Basic. Twelve shares of Variable Basic, current value, four thousand, two hundred and thirty-three dollars and sixty-two cents apiece. Current cash credit, one thousand and eighty-four dollars." The screen went dead. One thousand and eighty-four dollars. That was plenty. He could safely spend as much as half of it, if the spree got as lively as he hoped it would. His monthly dividends were due in another week or so, and he wouldn't have to worry about current expenses. Yes, indeedy, Si Pond was as solvent as he had ever been in his thirty years. He opened the small, closet-like door which housed his vacuum-tube two-seater, and wedged himself into the small vehicle. He brought down the canopy, dropped the pressurizer and considered the dial. Only one place really made sense. The big city. He considered for a moment, decided against the boroughs of Baltimore and Boston, and selected Manhattan instead. He had the resources. He might as well do it up brown. He dialed Manhattan and felt the sinking sensation that presaged his car's dropping to tube level. While it was being taken up by the robot controls, being shuttled here and there preparatory to the shot to his destination, he dialed the vehicle's teevee-phone for information on the hotels of the island of the Hudson. He selected a swank hostelry he'd read about and seen on the teevee casts of society and celebrity gossip reporters, and dialed it on the car's destination dial. "Nothing too good for ex-Space Pilot Si Pond," he said aloud. The car hesitated for a moment, that brief hesitation before the shot, and Si took the involuntary breath from which only heroes could refrain. He sank back slowly into the seat. Moments passed, and the direction of the pressure was reversed. Manhattan. The shuttling began again, and one or two more traversing sub-shots. Finally, the dash threw a green light and Si opened the canopy and stepped into his hotel room. A voice said gently, "If the quarters are satisfactory, please present your credit card within ten minutes." Si took his time. Not that he really needed it. It was by far the most swank suite he had ever seen. One wall was a window of whatever size the guest might desire and Si touched the control that dilated it to the full. His view opened in such wise that he could see both the Empire State Building Museum and the Hudson. Beyond the river stretched the all but endless city which was Greater Metropolis. He didn't take the time to flick on the menu, next to the auto-dining table, nor to check the endless potables on the autobar list. All that, he well knew, would be superlative. Besides, he didn't plan to dine or do much drinking in his suite. He made a mock leer. Not unless he managed to acquire some feminine companionship, that was. He looked briefly into the swimming pool and bath, then flopped himself happily onto the bed. It wasn't up to the degree of softness he presently desired, and he dialed the thing to the ultimate in that direction so that with a laugh he sank almost out of sight into the mattress. He came back to his feet, gave his suit a quick patting so that it fell into press and, taking his credit card from his pocket, put it against the teevee-phone screen and pressed the hotel button so that registration could be completed. For a moment he stood in the center of the floor, in thought. Take it easy, Si Pond, take it all easy, this time. No throwing his dollars around in second-class groggeries, no eating in automated luncheterias. This time, be it the only time in his life, he was going to frolic in the grand manner. No cloddy was Si Pond. He decided a drink was in order to help him plan his strategy. A drink at the hotel's famous Kudos Room where celebrities were reputed to be a dime a dozen. He left the suite and stepped into one of the elevators. He said, "Kudos Room." The auto-elevator murmured politely, "Yes, sir, the Kudos Room." At the door to the famous rendezvous of the swankiest set, Si paused a moment and looked about. He'd never been in a place like this, either. However, he stifled his first instinct to wonder about what this was going to do to his current credit balance with an inner grin and made his way to the bar. There was actually a bartender. Si Pond suppressed his astonishment and said, offhand, attempting an air of easy sophistication, "Slivovitz Sour." "Yes, sir." The drinks in the Kudos Room might be concocted by hand, but Si noticed they had the routine teevee screens built into the bar for payment. He put his credit card on the screen immediately before him when the drink came, and had to quell his desire to dial for a balance check, so as to be able to figure out what the Sour had cost him. Well, this was something like it. This was the sort of thing he'd dreamed about, out there in the great alone, seated in the confining conning tower of his space craft. He sipped at the drink, finding it up to his highest expectations, and then swiveled slightly on his stool to take a look at the others present. To his disappointment, there were no recognizable celebrities. None that he placed, at least—top teevee stars, top politicians of the Ultrawelfare State or Sports personalities. He turned back to his drink and noticed, for the first time, the girl who occupied the stool two down from him. Si Pond blinked. He blinked and then swallowed. " Zo-ro-as-ter ," he breathed. She was done in the latest style from Shanghai, even to the point of having cosmetically duplicated the Mongolian fold at the corners of her eyes. Every pore, but every pore, was in place. She sat with the easy grace of the Orient, so seldom found in the West. His stare couldn't be ignored. She looked at him coldly, turned to the bartender and murmured, "A Far Out Cooler, please, Fredric." Then deliberately added, "I thought the Kudos Room was supposed to be exclusive." There was nothing the bartender could say to that, and he went about building the drink. Si cleared his throat. "Hey," he said, "how about letting this one be on me?" Her eyebrows, which had been plucked and penciled to carry out her Oriental motif, rose. "Really!" she said, drawing it out. The bartender said hurriedly, "I beg your pardon, sir...." The girl, her voice suddenly subtly changed, said, "Why, isn't that a space pin?" Si, disconcerted by the sudden reversal, said, "Yeah ... sure." "Good Heavens, you're a spaceman?" "Sure." He pointed at the lapel pin. "You can't wear one unless you been on at least a Moon run." She was obviously both taken back and impressed. "Why," she said, "you're Seymour Pond, the pilot. I tuned in on the banquet they gave you." Si, carrying his glass, moved over to the stool next to her. "Call me Si," he said. "Everybody calls me Si." She said, "I'm Natalie. Natalie Paskov. Just Natalie. Imagine meeting Seymour Pond. Just sitting down next to him at a bar. Just like that." "Si," Si said, gratified. Holy Zoroaster, he'd never seen anything like this rarified pulchritude. Maybe on teevee, of course, one of the current sex symbols, but never in person. "Call me Si," he said again. "I been called Si so long, I don't even know who somebody's talking to if they say Seymour." "I cried when they gave you that antique watch," she said, her tone such that it was obvious she hadn't quite adjusted as yet to having met him. Si Pond was surprised. "Cried?" he said. "Well, why? I was kind of bored with the whole thing. But old Doc Gubelin, I used to work under him in the Space Exploration department, he was hot for it." " Academician Gubelin?" she said. "You just call him Doc ?" Si was expansive. "Why, sure. In the Space Department we don't have much time for formality. Everybody's just Si, and Doc, and Jim. Like that. But how come you cried?" She looked down into the drink the bartender had placed before her, as though avoiding his face. "I ... I suppose it was that speech Doctor Girard-Perregaux made. There you stood, so fine and straight in your space-pilot uniform, the veteran of six exploration runs to the planets...." "Well," Si said modestly, "two of my runs were only to the Moon." "... and he said all those things about man's conquest of space. And the dream of the stars which man has held so long. And then the fact that you were the last of the space pilots. The last man in the whole world trained to pilot a space craft. And here you were, retiring." Si grunted. "Yeah. That's all part of the Doc's scheme to get me to take on another three runs. They're afraid the whole department'll be dropped by the Appropriations Committee on this here Economic Planning Board. Even if they can find some other patsy to train for the job, it'd take maybe a year before you could even send him on a Moon hop. So old man Gubelin, and Girard-Perregaux too, they're both trying to pressure me into more trips. Otherwise they got a Space Exploration Department, with all the expense and all, but nobody to pilot their ships. It's kind of funny, in a way. You know what one of those spaceships costs?" "Funny?" she said. "Why, I don't think it's funny at all." Si said, "Look, how about another drink?" Natalie Paskov said, "Oh, I'd love to have a drink with you, Mr...." "Si," Si said. He motioned to the bartender with a circular twist of the hand indicating their need for two more of the same. "How come you know so much about it? You don't meet many people who are interested in space any more. In fact, most people are almost contemptuous, like. Think it's kind of a big boondoggle deal to help use up a lot of materials and all and keep the economy going." Natalie said earnestly, "Why, I've been a space fan all my life. I've read all about it. Have always known the names of all the space pilots and everything about them, ever since I was a child. I suppose you'd say I have the dream that Doctor Girard-Perregaux spoke about." Si chuckled. "A real buff, eh? You know, it's kind of funny. I was never much interested in it. And I got a darn sight less interested after my first run and I found out what space cafard was." She frowned. "I don't believe I know much about that." Sitting in the Kudos Room with the most beautiful girl to whom he had ever talked, Si could be nonchalant about the subject. "Old Gubelin keeps that angle mostly hushed up and out of the magazine and newspaper articles. Says there's enough adverse publicity about space exploration already. But at this stage of the game when the whole ship's crammed tight with this automatic scientific apparatus and all, there's precious little room in the conning tower and you're the only man aboard. The Doc says later on when ships are bigger and there's a whole flock of people aboard, there won't be any such thing as space cafard, but...." Of a sudden the right side of Si Pond's mouth began to tic and he hurriedly took up his drink and knocked it back. | C. It would take too long to train a new pilot to complete the explorations. |
How much is performance difference of existing model between original and corrected corpus? | ### Introduction
Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (premise image, textual hypothesis) a label among entailment, neutral, and contradiction. In Figure FIGREF3, the label for the first image-sentence pair is entailment, because the hypothesis states that “a bunch of people display different flags”, which can be clearly derived from the image. On the contrary, the second image-sentence pair is labelled as contradiction, because the hypothesis stating that “people [are] running a marathon” contradicts the image with static people. Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\sim }31\%$ errors in this class, and ${\sim }1\%$ for the contradiction and entailment classes. Xie BIBREF1 introduced the VTE task under the name of “visual entailment”, which could imply recognizing entailment between images only. This paper prefers to follow Suzuki BIBREF4 and call it “visual-textual entailment” instead, as it involves reasoning on image-sentence pairs. In this work, we first focus on decreasing the error in the neutral class by collecting new labels for the neutral pairs in the validation and test sets of SNLI-VE, using Amazon Mechanical Turk (MTurk). To ensure high quality annotations, we used a series of quality control measures, such as in-browser checks, inserting trusted examples, and collecting three annotations per instance. Secondly, we re-evaluate current image-text understanding systems, such as the bottom-up top-down attention network (BUTD) BIBREF5 on VTE using our corrected dataset, which we call SNLI-VE-2.0. Thirdly, we introduce the e-SNLI-VE-2.0 corpus, which we form by appending human-written natural language explanations to SNLI-VE-2.0. These explanations were collected in e-SNLI BIBREF6 to support textual entailment for SNLI. For the same reasons as above, we re-annotate the explanations for the neutral pairs in the validation and test sets, while keeping the explanations from e-SNLI for all the rest. Finally, we extend a current VTE model with the capacity of learning from these explanations at training time and outputting an explanation for each predicted label at testing time. ### SNLI-VE-2.0
The goal of VTE is to determine if a textual hypothesis $H_{text}$ can be concluded, given the information in a premise image $P_{image}$ BIBREF1. There are three possible labels: Entailment: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is true. Contradiction: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is false. Neutral: if neither of the earlier two are true. The SNLI-VE dataset proposed by Xie BIBREF1 is the combination of Flickr30k, a popular image dataset for image captioning BIBREF2 and SNLI, an influential dataset for natural language inference BIBREF0. Textual premises from SNLI are replaced with images from Flickr30k, which is possible, as these premises were originally collected as captions of these images (see Figure FIGREF3). However, in practice, a sensible proportion of labels are wrong due to the additional information contained in images. This mostly affects neutral pairs, since images may contain the necessary information to ground a hypothesis for which a simple premise caption was not sufficient. An example is shown in Figure FIGREF3. Vu BIBREF3 report that the label is wrong for ${\sim }31\%$ of neutral examples, based on a random subset of 171 neutral points from the test set. We also annotated 150 random neutral examples from the test set and found a similar percentage of 30.6% errors. Our annotations are available at https://github.com/virginie-do/e-SNLI-VE/tree/master/annotations/gt_labels.csv ### SNLI-VE-2.0 ::: Re-annotation details
In this work, we only collect new labels for the neutral pairs in the validation and test sets of SNLI-VE. While the procedure of re-annotation is generic, we limit our re-annotation to these splits as a first step to verify the difference in performance that current models have when evaluated on the corrected test set as well as the effect of model selection on the corrected validation set. We leave for future work re-annotation of the training set, which would likely lead to training better VTE models. We also chose not to re-annotate entailment and contradiction classes, as their error rates are much lower ($<$1% as reported by Vu BIBREF3). The main question that we want our dataset to answer is: “What is the relationship between the image premise and the sentence hypothesis?”. We provide workers with the definitions of entailment, neutral, and contradiction for image-sentence pairs and one example for each label. As shown in Figure FIGREF8, for each image-sentence pair, workers are required to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using at least half of the words that they highlighted. The collected explanations will be presented in more detail in Section SECREF20, as we focus here on the label correction. We point out that it is likely that requiring an explanation at the same time as requiring a label has a positive effect on the correctness of the label, since having to justify in writing the picked label may make workers pay an increased attention. Moreover, we implemented additional quality control measures for crowdsourced annotations, such as (a) collecting three annotations for every input, (b) injecting trusted annotations into the task for verification BIBREF7, and (c) restricting to workers with at least 90% previous approval rate. First, we noticed that some instances in SNLI-VE are ambiguous. We show some examples in Figure FIGREF3 and in Appendix SECREF43. In order to have a better sense of this ambiguity, three authors of this paper independently annotated 100 random examples. All three authors agreed on 54% of the examples, exactly two authors agreed on 45%, and there was only one example on which all three authors disagreed. We identified the following three major sources of ambiguity: mapping an emotion in the hypothesis to a facial expression in the image premise, e.g., “people enjoy talking”, “angry people”, “sad woman”. Even when the face is seen, it may be subjective to infer an emotion from a static image (see Figure FIGREF44 in Appendix SECREF43). personal taste, e.g., “the sign is ugly”. lack of consensus on terms such as “many people” or “crowded”. To account for the ambiguity that the neutral labels seem to present, we considered that an image-sentence pair is too ambiguous and not suitable for a well-defined visual-textual entailment task when three different labels were assigned by the three workers. Hence, we removed these examples from the validation (5.2%) and test (5.5%) sets. To ensure that our workers are correctly performing the task, we randomly inserted trusted pairs, i.e., pairs among the 54% on which all three authors agreed on the label. For each set of 10 pairs presented to a worker, one trusted pair was introduced at a random location, so that the worker, while being told that there is such a test pair, cannot figure out which one it is. Via an in-browser check, we only allow workers to submit their answers for each set of 10 instances only if the trusted pair was correctly labelled. Other in-browser checks were done for the collection of explanations, as we will describe in Section SECREF20. More details about the participants and design of the Mechanical Turk task can be found in Appendix SECREF41. After collecting new labels for the neutral instances in the validation and testing sets, we randomly select and annotate 150 instances from the validation set that were neutral in SNLI-VE. Based on this sample, the error rate went down from 31% to 12% in SNLI-VE-2.0. Looking at the 18 instances where we disagreed with the label assigned by MTurk workers, we noticed that 12 were due to ambiguity in the examples, and 6 were due to workers' errors. Further investigation into potentially eliminating ambiguous instances would likely be beneficial. However, we leave it as future work, and we proceed in this work with using our corrected labels, since our error rate is significantly lower than that of the original SNLI-VE. Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class. ### SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment
Since we decreased the error rate of labels in the validation and test set, we are interested in the performance of a VTE model when using the corrected sets. ### SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.
To tackle SNLI-VE, Xie BIBREF1 used EVE (for “Explainable Visual Entailment”), a modified version of the BUTD architecture, the winner of the Visual Question Answering (VQA) challenge in 2017 BIBREF5. Since the EVE implementation is not available at the time of this work, we used the original BUTD architecture, with the same hyperparameters as reported in BIBREF1. BUTD contains an image processing module and a text processing module. The image processing module encodes each image region proposed by FasterRCNN BIBREF8 into a feature vector using a bottom-up attention mechanism. In the text processing module, the text hypothesis is encoded into a fixed-length vector, which is the last output of a recurrent neural network with 512-GRU units BIBREF9. To input each token into the recurrent network, we use the pretrained GloVe vectors BIBREF10. Finally, a top-down attention mechanism is used between the hypothesis vector and each of the image region vectors to obtain an attention weight for each region. The weighted sum of these image region vectors is then fused with the text hypothesis vector. The multimodal fusion is fed to a multilayer percetron (MLP) with tanh activations and a final softmax layer to classify the image-sentence relation as entailment, contradiction, or neutral. Using the implementation from https://github.com/claudiogreco/coling18-gte. We use the original training set from SNLI-VE. To see the impact of correcting the validation and test sets, we do the following three experiments: model selection as well as testing are done on the original uncorrected SNLI-VE. model selection is done on the uncorrected SNLI-VE validation set, while testing is done on the corrected SNLI-VE-2.0 test set. model selection as well as testing are done on the corrected SNLI-VE-2.0. Models are trained with cross-entropy loss optimized by the Adam optimizer BIBREF11 with batch size 64. The maximum number of training epochs is set to 100, with early stopping when no improvement is observed on validation accuracy for 3 epochs. The final model checkpoint selected for testing is the one with the highest validation accuracy. ### SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Results.
The results of the three experiments enumerated above are reported in Table TABREF18. Surprisingly, we obtained an accuracy of 73.02% on SNLI-VE using BUTD, which is better than the 71.16% reported by Xie BIBREF1 for the EVE system which meant to be an improvement over BUTD. It is also better than their reproduction of BUTD, which gave 68.90%. The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant. Finally, we recall that the training set has not been re-annotated, and hence approximately 31% image-sentence pairs are wrongly labelled as neutral, which likely affects the performance of the model. ### Visual-Textual Entailment with Natural Language Explanations
In this work, we also introduce e-SNLI-VE-2.0, a dataset combining SNLI-VE-2.0 with human-written explanations from e-SNLI BIBREF6, which were originally collected to support textual entailment. We replace the explanations for the neutral pairs in the validation and test sets with new ones collected at the same time as the new labels. We extend a current VTE model with an explanation module able to learn from these explanations at training time and generate an explanation for each predicted label at testing time. ### Visual-Textual Entailment with Natural Language Explanations ::: e-SNLI-VE-2.0
e-SNLI BIBREF6 is an extension of the SNLI corpus with human-annotated natural language explanations for the ground-truth labels. The authors use the explanations to train models to also generate natural language justifications for their predictions. They collected one explanation for each instance in the training set of SNLI and three explanations for each instance in the validation and testing sets. We randomly selected 100 image-sentence pairs in the validation set of SNLI-VE and their corresponding explanations in e-SNLI and examined how relevant these explanations are for the VTE task. More precisely, we say that an explanation is relevant if it brings information that justifies the relationship between the image and the sentence. We restricted the count to correctly labelled inputs and found that 57% explanations were relevant. For example, the explanation for entailment in Figure FIGREF21 (“Cooking in his apartment is cooking”) was counted as irrelevant in our statistics, because it would not be the best explanation for an image-sentence pair, even though it is coherent with the textual pair. We investigate whether these explanations improve a VTE model when enhanced with a component that can process explanations at train time and output them at test time. To form e-SNLI-VE-2.0, we append to SNLI-VE-2.0 the explanations from e-SNLI for all except the neutral pairs in the validation and test sets of SNLI-VE, which we replace with newly crowdsourced explanations collected at the same time as the labels for these splits (see Figure FIGREF21). Statistics of e-SNLI-VE-2.0 are shown in Appendix SECREF39, Table TABREF40. ### Visual-Textual Entailment with Natural Language Explanations ::: Collecting Explanations
As mentioned before, in order to submit the annotation of an image-sentence pair, three steps must be completed: workers must choose a label, highlight words in the hypothesis, and use at least half of the highlighted words to write an explanation for their decision. The last two steps thus follow the quality control of crowd-sourced explanations introduced by Camburu BIBREF6. We also ensured that workers do not simply use a copy of the given hypothesis as explanation. We ensured all the above via in-browser checks before workers' submission. An example of collected explanations is given in Figure FIGREF21. To check the success of our crowdsourcing, we manually assessed the relevance of explanations among a random subset of 100 examples. A marking scale between 0 and 1 was used, assigning a score of $k$/$n$ when $k$ required attributes were given in an explanation out of $n$. We report an 83.5% relevance of explanations from workers. We note that, since our explanations are VTE-specific, they were phrased differently from the ones in e-SNLI, with more specific mentions to the images (e.g., “There is no labcoat in the picture, just a man wearing a blue shirt.”, “There are no apples or oranges shown in the picture, only bananas.”). Therefore, it would likely be beneficial to collect new explanations for all SNLI-VE-2.0 (not only for the neutral pairs in the validation and test sets) such that models can learn to output convincing explanations for the task at hand. However, we leave this as future work, and we show in this work the results that one obtains when using the explanations from e-SNLI-VE-2.0. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations
This section presents two VTE models that generate natural language explanations for their own decisions. We name them PaE-BUTD-VE and EtP-BUTD-VE, where PaE (resp. EtP) is for PredictAndExplain (resp. ExplainThenPredict), two models with similar principles introduced by Camburu BIBREF6. The first system learns to generate an explanation conditioned on the image premise, textual hypothesis, and predicted label. In contrast, the second system learns to first generate an explanation conditioned on the image premise and textual hypothesis, and subsequently makes a prediction solely based on the explanation. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain
PaE-BUTD-VE is a system for solving VTE and generating natural language explanations for the predicted labels. The explanations are conditioned on the image premise, the text hypothesis, and the predicted label (ground-truth label at train time), as shown in Figure FIGREF24. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model.
As described in Section SECREF12, in the BUTD model, the hypothesis vector and the image vector were fused in a fixed-size feature vector f. The vector f was then given as input to an MLP which outputs a probability distribution over the three labels. In PaE-BUTD-VE, in addition to the classification layer, we add a 512-LSTM BIBREF12 decoder to generate an explanation. The decoder takes the feature vector f as initial state. Following Camburu BIBREF6, we prepend the label as a token at the beginning of the explanation to condition the explanation on the label. The ground truth label is provided at training time, whereas the predicted label is given at test time. At test time, we use beam search with a beam width of 3 to decode explanations. For memory and time reduction, we replaced words that appeared less than 15 times among explanations with “#UNK#”. This strategy reduces the output vocabulary size to approximately 8.6k words. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Loss.
The training loss is a weighted combination of the classification loss and the explanation loss, both computed using softmax cross entropy: $\mathcal {L} = \alpha \mathcal {L}_{label} + (1-\alpha ) \mathcal {L}_{explanation} \; \textrm {;} \; \alpha \in [0,1]$. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model selection.
In this experiment, we are first interested in examining if a neural network can generate explanations at no cost for label accuracy. Therefore, only balanced accuracy on label is used for the model selection criterion. However, future work can investigate other selection criteria involving a combination between the label and explanation performances. We performed hyperparameter search on $\alpha $, considering values between 0.2 and 0.8 with a step of 0.2. We found $\alpha =0.4$ to produce the best validation balanced accuracy of 72.81%, while BUTD trained without explanations yielded a similar 72.58% validation balanced accuracy. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Results.
As summarised in Table TABREF30, we obtain a test balanced accuracy for PaE-BUTD-VE of 73%, while the same model trained without explanations obtains 72.52%. This is encouraging, since it shows that one can obtain additional natural language explanations without sacrificing performance (and eventually even improving the label performance, however, future work is needed to conclude whether the difference $0.48\%$ improvement in performance is statistically significant). Camburu BIBREF6 mentioned that the BLEU score was not an appropriate measure for the quality of explanations and suggested human evaluation instead. We therefore manually scored the relevance of 100 explanations that were generated when the model predicted correct labels. We found that only 20% of explanations were relevant. We highlight that the relevance of explanations is in terms of whether the explanation reflects ground-truth reasons supporting the correct label. This is not to be confused with whether an explanation is correctly illustrating the inner working of the model, which is left as future work. It is also important to note that on a similar experimental setting, Camburu report as low as 34.68% correct explanations, training with explanations that were actually collected for their task. Lastly, the model selection criterion at validation time was the prediction balanced accuracy, which may contribute to the low quality of explanations. While we show that adding an explanation module does not harm prediction performance, more work is necessary to get models that output trustable explanations. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict
When assigning a label, an explanation is naturally part of the decision-making process. This motivates the design of a system that explains itself before deciding on a label, called EtP-BUTD-VE. For this system, a first neural network is trained to generate an explanation given an image-sentence input. Separately, a second neural network, called ExplToLabel-VE, is trained to predict a label from an explanation (see Figure FIGREF32). ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model.
For the first network, we set $\alpha =0$ in the training loss of the PaE-BUTD-VE model to obtain a system that only learns to generate an explanation from the image-sentence input, without label prediction. Hence, in this setting, no label is prepended before the explanation. For the ExplToLabel-VE model, we use a 512-LSTM followed by an MLP with three 512-layers and ReLU activation, and softmax activation to classify the explanation between entailment, contradiction, and neutral. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model selection.
For ExplToLabel-VE, the best model is selected on balanced accuracy at validation time. For EtP-BUTD-VE, perplexity is used to select the best model parameters at validation time. It is computed between the explanations produced by the LSTM and ground truth explanations from the validation set. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Results.
When we train ExplToLabel-VE on e-SNLI-VE-2.0, we obtain a balanced accuracy of 90.55% on the test set. As reported in Table TABREF30, the overall PaE-BUTD-VE system achieves 69.40% balanced accuracy on the test set of e-SNLI-VE-2.0, which is a 3% decrease from the non-explanatory BUTD counterpart (72.52%). However, by setting $\alpha $ to zero and selecting the model that gives the best perplexity per word at validation, the quality of explanation significantly increased, with 35% relevance, based on manual evaluation. Thus, in our model, generating better explanations involves a small sacrifice in label prediction accuracy, implying a trade-off between explanation generation and accuracy. We note that there is room for improvement in our explanation generation method. For example, one can implement an attention mechanism similar to Xu BIBREF13, so that each generated word relates to a relevant part of the multimodal feature representation. ### Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Qualitative Analysis of Generated Explanations
We complement our quantitative results with a qualitative analysis of the explanations generated by our enhanced VTE systems. In Figures FIGREF36 and FIGREF37, we present examples of the predicted labels and generated explanations. Figure FIGREF36 shows an example where the EtP-BUTD-VE model produces both a correct label and a relevant explanation. The label is contradiction, because in the image, the students are playing with a soccer ball and not a basketball, thus contradicting the text hypothesis. Given the composition of the generated sentence (“Students cannot be playing soccer and baseball at the same time.”), ExplToLabel-VE was able to detect a contradiction in the image-sentence input. In comparison, the explanation from e-SNLI-VE-2.0 is not correct, even if it was valid for e-SNLI when the text premise was given. This emphasizes the difficulty that we are facing with generating proper explanations when training on a noisy dataset. Even when the generated explanations are irrelevant, we noticed that they are on-topic and that most of the time the mistakes come from repetitions of certain sub-phrases. For example, in Figure FIGREF37, PaE-BUTD-VE predicts the label neutral, which is correct, but the explanation contains an erroneous repetition of the n-gram “are in a car”. However, it appears that the system learns to generate a sentence in the form “Just because ...doesn't mean ...”, which is frequently found for the justification of neutral pairs in the training set. The explanation generated by EtP-BUTD-VE adopts the same structure, and the ExplToLabel-VE component correctly classifies the instance as neutral. However, even if the explanation is semantically correct, it is not relevant for the input and fails to explain the classification. ### Conclusion
In this paper, we first presented SNLI-VE-2.0, which corrects the neutral instances in the validation and test sets of SNLI-VE. Secondly, we re-evaluated an existing model on the corrected sets in order to update the estimate of its performance on this task. Thirdly, we introduced e-SNLI-VE-2.0, a dataset which extends SNLI-VE-2.0 with natural language explanations. Finally, we trained two types of models that learn from these explanations at training time, and output such explanations at test time, as a stepping stone in explainable artificial intelligence. Our work is a jumping-off point for both the identification and correction of SNLI-VE, as well as in the extension to explainable VTE. We hope that the community will build on our findings to create more robust as well as explainable multimodal systems. ### Conclusion ::: Acknowledgements.
This work was supported by the Oxford Internet Institute, a JP Morgan PhD Fellowship 2019-2020, an Oxford-DeepMind Graduate Scholarship, the Alan Turing Institute under the EPSRC grant EP/N510129/1, and the AXA Research Fund, as well as DFG-EXC-Nummer 2064/1-Projektnummer 390727645 and the ERC under the Horizon 2020 program (grant agreement No. 853489). ### Appendix ::: Statistics of e-SNLI-VE-2.0
e-SNLI-VE-2.0 is the combination of SNLI-VE-2.0 with explanations from either e-SNLI or our crowdsourced annotations where applicable. The statistics of e-SNLI-VE-2.0 are shown in Table TABREF40. Including text hypotheses and explanations. ### Appendix ::: Details of the Mechanical Turk Task
We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location. Each assignment consisted of a set of 10 image-sentence pairs. For each pair, the participant was asked to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using a subset of the words that they highlighted. The instructions are shown in Figure FIGREF42. Workers were also guided with three annotated examples, one for each label. For each assignment of 10 questions, one trusted annotation with gold standard label was inserted at a random position, as a measure to control the quality of label annotation. Each assignment was completed by three different workers. An example of question is shown in Figure FIGREF8 in the core paper. ### Appendix ::: Ambiguous Examples from SNLI-VE
Some examples in SNLI-VE were ambiguous and could find correct justifications for incompatible labels, as shown in Figures FIGREF44, FIGREF45, and FIGREF46. Figure 1. Examples from SNLI-VE-2.0. (a) In red, the neutral label from SNLI-VE is wrong, since the picture clearly shows that the crowd is outdoors. We corrected it to entailment in SNLIVE-2.0. (b) In green, an ambiguous instance. There is indeed an American flag in the background but it is very hard to see, hence the ambiguity between neutral and entailment, and even contradiction if one cannot spot it. Further, it is not clear whether “they” implies the whole group or the people visible in the image. Figure 2. MTurk annotation screen. (a) The label contradiction is chosen, (b) the evidence words “man”, “violin”, and “crowd” are highlighted, and (c) an explanation is written with these words. Table 1. Accuracies obtained with BUTD on SNLI-VE (valoriginal, test-original) and SNLI-VE-2.0 (val-corrected, testcorrected). Figure 3. Two image-sentence pairs from e-SNLI-VE-2.0 with (a) at the top, an uninformative explanation from e-SNLI, (b) at the bottom, an explanation collected from our crowdsourcing. We only collected new explanations for the neutral class (along with new labels). The SNLI premise is not included in e-SNLI-VE-2.0. Figure 4. PAE-BUTD-VE. The generation of explanation is conditioned on the image premise, textual hypothesis, and predicted label. Table 2. Label balanced accuracies and explanation relevance rates of our two explanatory systems on e-SNLI-VE-2.0. Comparison with their counterparts in e-SNLI [3]. Without the explanation component, the balanced accuracy on SNLI-VE-2.0 is 72.52% Figure 5. Architecture of ETP-BUTD-VE. Firstly, an explanation is generated, secondly the label is predicted from the explanation. The two models (in separate dashed rectangles) are not trained jointly. Figure 6. Both systems PAE-BUTD-VE and ETP-BUTD-VE predict the correct label, but only ETP-BUTD-VE generates a relevant explanation. Figure 7. Both systems PAE-BUTD-VE and ETP-BUTD-VE predict the correct label, but generate irrelevant explanations. Figure 8. Instructions given to workers on Mechanical Turk Table 3. Summary of e-SNLI-VE-2.0 (= SNLI-VE-2.0 + explanations). Image-sentence pairs labelled as neutral in the training set have not been corrected. Figure 9. Ambiguous SNLI-VE instance. Some may argue that the woman’s face betrays sadness, but the image is not quite clear. Secondly, even with better resolution, facial expression may not be a strong enough evidence to support the hypothesis about the woman’s emotional state. Figure 10. Ambiguous SNLI-VE instance. The lack of consensus is on whether the man is “leering” at the woman. While it is likely the case, this interpretation in favour of entailment is subjective, and a cautious annotator would prefer to label the instance as neutral. Figure 11. Ambiguous SNLI-VE instance. Some may argue that it is impossible to certify from the image that the children are kindergarten students, and label the instance as neutral. On the other hand, the furniture may be considered as typical of kindergarten, which would be sufficient evidence for entailment. | 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set |
Why is Sara upset when her dad asks her to read the article about solar panels?
A. She is embarassed to admit she hasn't read up on the solar panels
B. There is an implication that she's not informed about the job she does every day
C. She was trying to avoid having phones out at the dinner table
D. She doesn't want it to come up that she blocked him on facebook
| Divided we stand Sara lets the Lyft park itself in the drive, lets out a sigh, and tweets wish me luck plus some emojis before slipping her phone into a hoody pocket. Curtains twitch, and before she can get her bag out of the back Mom is there, right there next to her, their hands touching on the handle as they compete for control. "It's OK Mom, I got it." "You should have let us come pick you up." "It's fine, there was no need. I didn't want to put any-" "But you shouldn't be wasting money, not with how much rent you pay and-" Jesus. Not this already. "Mom. I can afford a cab ride. I'm not that much of a failure." Mom sighs, shoulders falling, looks at Sara directly. "I'm sorry honey." She looks old, Sara thinks, watching a resigned tiredness flicker across her face in a way she'd not noticed before. Like she's exhausted by conflict, surrendered to it. "Now, don't I get a hug?" Sara smiles. They hold each other for a few long seconds, rubbing and squeezing each other as the Lyft silently backs itself out of the driveway. When they part it's Mom's hand that's on the bag's handle. Inside she unwraps herself from scarves and layers, the heat in the house almost a shock after the cold air. Michigan in February. Mom is already halfway up the stairs, bag in tow, headed for her room. "Mom, just leave that and I'll…" "Your father's in the front room," she says, just before she disappears from view. "Go say hi." For a few seconds Sara is alone in the hallway, the smell of cooking meat coming from one doorway, the sound of rolling news from another. She shakes her head, kicks off shoes, tucks hair behind her ears. Braces herself. He's sat in the living room, reclining in the Lazy Boy. He doesn't hear her enter - her socked feet silent on the pile carpet floor, his attention lost in the screen that fills most of the wall. Fox News. She braces herself again. "Hey Dad." His head jerks to look at her. "Hey! When did you get here?" He starts to push himself up. "Don't get up Dad, it's fine. Really." She takes a seat on the couch. "I just got here, like two minutes ago." "Good flight?" "Yeah. Fine. Y'know. Same as always." He smiles back at her, nods knowingly. Their first words in nearly a year. Fine. So far. She relaxes. Of course it is. How bad could it be? "I thought I was gonna come pick you up from the airport?" "Ah, no. I got a cab. I didn't want to bother you." "Bother me? You think I'm too old and infirm to pick my own daughter up from the airport?" "No Dad, of course not." The war spills out of Fox News, casualty figures scrolling across monochrome drone footage, attack helicopters circling over Caracas apartment blocks, pundits with bronzed skin and immaculate blond hair smiling from four-way split screens. "So you just got a cab?" "Yeah." "How much did that cost?" "Not much. Really. I can afford-" "Cabs are expensive. You shouldn't be wasting your money." "It wasn't expensive. It wasn't a cab, it was a Lyft." "One of those driverless things?" "Yeah." Ad break. An elderly couple ride a tandem bicycle through a park, laughing and smiling in Instagram-perfect sunshine, as a calm, relaxing voice lists the potentially lethal side effects of a diabetes drug. Dad shakes his head. "I don't know how you can use those things. I don't trust them." "Dad, they're perfectly safe." "That's not what I mean. They're stealing people's jobs." There's a brief second, a fleeting moment, where Sara can bite her lip, let it go. She misses it. "But I thought it was immigrants that are stealing people's jobs?" "You might think it's funny little lady, but let me tell you - you remember Kyle and Max, Bill Cooper's boys? Live up off Lafayette, past the Checkers?" "Nope." "Well let me tell you," He shifts in the recliner, with some obvious pain and effort, to face her. "Both of 'em lost their jobs just this last year. Both of 'em were truckers. Both of 'em been driving trucks since high school. Now the damn trucks are driving themselves and they're both out of work. And they got families to support. Kids." "Well I'm sure they'll be fine." She regrets the sarcasm as soon as she hears it in her own voice, but she still can't stop herself, like it's expected, like it's part of the routine. Part of their schtick. "They just got to get themselves out there, huh Dad? Pull themselves up by their bootstraps. That's the American way, right?" "I'm glad you think this is funny, I really do. But what you New York types need to realise is-" "Ed!" Mom had appeared in the doorway. "Please! Both of you. No fighting today, please." "Sheryl-" "No. I don't want to hear you two as much as disagreeing about anything today, unless it's about the game. And even then you'd better keep it civil. Otherwise you can both go hungry. Understand?" Awkward pause. "Fine." "Sorry Mom." Sara turns back to the TV, to watching the war, to trying to work out which one it is. It had always been this way, ever since she was about thirteen. Up until then it just seemed like constant warmth, as though she didn't have any childhood concept of Dad apart from him getting home from work, then her sitting on his knee, eating cookies and watching football highlights until Mom came in and scolded them both for ruining their appetites before dinner. And then everything changed. Suddenly there was rap music and nose rings, sneaking out of the house to see her friends and not wanting to go to church. Suddenly he was no longer this lovable bear-man that ruffled her hair and gave her candy and explained defensive plays to her, but this huge obelisk of injustice that just wanted to crush her high school life into dust. It was constant warfare; every opinion she had became a battle, every decision she made a conflict. Getting away to college gave her escape, but bred resentment too; he hated that she went to New York, even though NYU was a good school, and her decision to stay there after she finished made things even worse. And then politics got all crazy, weirder then ever, and it became impossible for them to talk without it erupting into fights almost instantly. It was bad enough when the smart, young guy she liked was president and Dad constantly spewed his hate for him at her, but somehow it got even worse when the old, racist, women hating war-starter he liked won. Twice. So they didn't talk much now, barely online, never on the phone. Since her second year of school he'd never been to NYC to visit her. She came back when she could face it; sometimes for birthdays, sometimes for Thanksgiving. Maybe for Christmas. But somehow always, like now, for the Super Bowl. Like football was the one thing they still had, that one thing they could still sit in the same room together for. Shouting at players, screaming at the ref, laughing at the ads. Dad is in the bathroom, and Sara has had enough of Fox and whichever war this is. She reaches over and grabs the remote from the arm of his chair, and tries to find something else to watch. The government had scrapped all the rules about how the internet worked, and for most people like her parents it had suddenly gotten a lot cheaper to get their TV through Facebook, so all she can find is Fox, Breitbart News, Family Values TV, Info Wars, The Rebel, Glenn Beck, The Voice of America, America First, The Bible Today and lots of hunting and sports channels she doesn't even recognise. It's signed in to her Dad's FB account, and the last thing she wants is to try and log in on hers before he gets back from the john. Yeah. There was no way that would end up with them keeping it civil. In her pocket her phone vibrates, purrs against her skin, reminding her it's there, making sure she's not forgotten where her real friends are, that there's a world outside, beyond Dad and his TV. She takes it out and cradles it in her hands, the dark screen fleetingly reflecting back her face before it jumps awake at her very touch, opening up to bathe her in blue light, in comfort and warmth and the familiar. For the first time since she got home she feels herself relax. Dinner is Mom's meatloaf, with gravy and mashed potatoes. Cornbread and broccoli. Every mouthful tastes like nostalgia, and Sara can feel herself being encompassed by a bubble, this barrier of warm air and long forgotten simplicity enveloping her body, protecting her from the confusion of the world outside. "How's work, honey?" Mom asks. "Yeah, going OK." Sara works for a non-profit in Brooklyn that helps big organisations to transition to renewable energy. The pay is lousy but it feels important. "We just got the last few schools in the city to agree to put solar panels on their roofs. Big deal for us. I've been working on them for the last two years." Mom says nothing, just looks down at her plate. Dad finishes chewing his mouthful, swallows, wipes his beard with a napkin. Sighs, barely controlled anger simmering behind his face. "Solar panels cause cancer." Sara laughs, covering her mouth as she nearly chokes on chewed food. "What? No they don't Dad." "They do. The material they use to coat them reacts to sunlight, and produces an airborne carcinogen. It's based on a particular kind of rare earth. It's a bit like teflon. The Chinese have known about this for decades but have kept it covered up, because they-" "Dad, no. Just no. Trust me." "-because they are the world's largest manufacturers of solar panels. But the research has been done. The scientific evidence is out there. Look it up." "Look it up?" Sara shakes her head, not knowing where to even start. "Dad, who is telling you this stuff?" "No one is telling me it, Sara. I read it. It's in the news. I mean, really, I'm surprised you've not seen it. It was all over Facebook." "Maybe on yours, but it's not all over my Facebook." She doesn't have the heart to tell him she muted him six months ago. "Well, I don't read the news and I don't know any science," says Mom, "But I do know this: after they opened that solar farm up near Mary, within just a few years her and two of her neighbours had cancer. I mean I don't know anything for sure honey, but given the risk are you sure it's safe to be putting these panels on top of schools?" "There's no risk, Mom. None at all. Dad, I wish you'd stop believing everything you see on Facebook." "Well, maybe you should read things yourself before passing judgement on them." He pushes himself up from his seat, steps away from the table. Sara sighs, thinking she's upset him that much that he's actually abandoning his dinner, but he stops to grab something off a nearby shelf. His iPad. He heads back and takes his seat again. Oh, here we fucking go she thinks to herself. He stabs at the screen, looks for a while, stabs again. Flips it over and hands it to her. "Here. Read." Reluctantly, she takes it. His Facebook feed. Somewhere in the middle of it is the article, a very to the point CHINESE SOLAR PANELS CAUSE CANCER headline. But she can't even focus on it, because the rest of the screen is filled with distractions, looping videos and animated gifs, all adverts, and all for guns. Or security systems. Panic rooms. Back up power generators. Emergency rations. More guns. "Jesus Christ Dad, these ads!" "No blasphemy at the dinner table, please honey" says Mom. "What about them?" "Just… just look at them. They're terrifying. They're like… like adverts for the end of the world! You know they show you this stuff just to make you scared, right? Just to keep you paranoid." "They show me this stuff because they've got products to sell. That's how the economy works. That's how we create jobs. Godammit Sara, are you telling me you hate advertising now? Do you just hate everything about America?" Sara looks over to Mom, who looks like she's on the brink of tears. Suddenly she finds she's also lost the will to fight. Gently she closes the iPad and puts it down on the table, next to her plate. "No, of course not Dad. Maybe I'll read this later, after the game." After dinner she helps Mom clean-up, the two of them loading the dishwasher in near silence. She's leaning against the counter, scrolling through Twitter on her phone, when Mom finally speaks. "You should go easy on your father, you know. He's worried about a lot of things." "What things? Solar panel cancer?" "Don't joke Sara, I'm serious. There's a lot that bothers him. The state of the world. The future. All these damn wars." "We're all worried about all that, Mom." "He's worried about his health. I'm worried about his health. Probably more than he is." Sara looks up from her phone, genuine concern. "Is he OK?" "I don't know. He won't go to the doctor. Hasn't been in months. He's worried about his insurance." "I had no idea-" "Yeah, well you know your father. Doesn't like to talk about it. Doesn't want to burden other people with his problems. Hates pity." She pauses, looks out the window into the yard. When she turns back to Sara her eyes are damp. "This is why I was so excited about you coming back. Why he was so excited! I thought it'd take his mind of all this. He was so excited to see you. You know he loves watching the game with you, Sara." "I know. I'm sorry I-" "And the ads! The Super Bowl ads! You know how much he loves watching the new ads with you. It's a stupid thing, sure, but he loves it. Talks about it all the time. It's like a tradition to him. That's why he got so upset over dinner when you got angry at his ads. It's something special he has with you, he doesn't want to lose it." Sara slips her phone into her pocket, genuine guilt. Feels like a spoiled kid. "I didn't realise. I'm sorry." Mom smiles, walks over and kisses her on the forehead. "It's OK honey. Don't feel bad. Just go. Just go sit in there with him and watch some TV. Please." It's the second down on the Falcon's 60 yard line with 30 yards to cover, and the Lions need one touchdown to equalise. Sara and her Dad are sat in the front room, working their way through a family sized pack of Oreos, when the ad break starts. Dawn. Red skies over the desert. A Chevrolet truck pulls up next to a large, trailer. Low shot next to the front tire, as a cowboy booted foot drops down from the door, disturbing dust. Cut to: internal shot of the trailer, darkness split by morning light through the opening door. The figure enters, flicks on lights. The room is full of equipment, computers. The figure takes a seat, puts on a headset, thumbs on screens. Rests their hands on two large joysticks on the desk. Cut to: airfield, the desert. The distinctive silhouette of a Predator drone taxis across the screen, rising heat shimmering the air around it. Cut to: interior of the trailer. The faceless figure works controls, the joysticks, touch screens. Voiceover: They say you need to get up pretty early to get past America's finest. But the truth is we never sleep. Cut to: a uniformed guard on top of the border wall. He looks up and gives a salute to the drone as it soars above him, out and across the desert. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. "Fuck this," says Sara, getting up from her seat. "Sara!" says Mom. "No I'm sorry, I can't. I can't sit here and watch this… this bullshit. This propaganda." She storms out of the room. "Sara!" Mom makes to get up. "No, just leave her," says Dad, gently, his eyes still fixed on the screen. "Just let her go." Out in the kitchen Sara sits at the table and wants to scream. She's angry, mainly with herself. She should never have fucking come here. She should have known better. There was never any fucking way anything good was going to come from this. As much as Mom wants to romanticise things, to make them sound cute and adorable, the truth is shit with Dad has never been right since she was a teenager. Too much resentment, too much bad blood, too much control and rebellion. They hadn't agreed on anything - they hadn't managed to have a simple conversation that didn't descend into fighting - in 15 goddamn years, and no amount of eating cookies and watching fucking Super Bowl ads on the TV was going to fix that. She sighs, wipes a tear from her cheek. On autopilot she takes her phone from her pocket, feels its reassuring warmth in her hand, and swipes open Twitter. Everybody seems to be talking about the same thing. omg im crying holy shit that chevrolet ad /fire emoji that was sooooo beautiful who knew chevrolet were so woke i can't believe they did that, so amazing Hang on, are they taking about the same ad? Hastily she opens her FB TV app, pulls up the game. The ad is just finishing. She hits the 10-second rewind icon a couple of times, then leans the phone on its side against a ketchup bottle. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. Cut to: on the ground, in the desert. The group of figures are revealed to be a Mexican family, maybe two. Men, women, children. They look tired, hungry. They stop to rest, sipping the little water they have left from tattered plastic bottles. A little way away from the main group sits a small child, a girl. Maybe 8 years old. She is drawing shapes in the dust with a stick. She's drawn quite a bit it looks like, but from our angle we can't see what. Cut to: drone footage. The pilot is watching the group. As he tracks away from the main party to where the girl is sat, the camera reveals what she has drawn. A large, child's rendition of the American flag. Underneath it, it childlike handwriting, some words. 'I have a dream' Text flashes across the screen. ALERT CANCELLED. ALL PATROLS: STAND DOWN Cut to: the drone, banking and turning, flying away. Cut to: exterior shot of the trailer. The still anonymous pilot exits, walks back towards his jeep. Voiceover: Keeping America safe means never sleeping, but keeping America great means never forgetting who we are, and how we got here. The jeep starts up, pulls away from the camera in a cloud of dust. Fade to black. Chevrolet logo. White text against black. 'We know what really makes America great' Sara finds herself in the front room, sobbing. "Honey?" Dad pauses the TV, looks up at her. It looks like he's been crying too. "Sara?" "Did you - did you watch it?" "The Chevrolet ad?" "Yeah." "Yeah, we did." Embarrassed, he wipes a tear from his cheek. "It was… it was very moving." She falls on him, wrapping her arms around his neck, burying her face in his chest. "I'm sorry Dad. I'm so sorry. I didn't mean to be so mean-" "It's OK, honey. It really is." "No, no it's not. We always fight. And I know that's mainly my fault-" 'Well, now, c'mon-" "No, it is. It's my fault. I got myself into thinking we can never agree on anything, that we can never see eye to eye. That we've got nothing in common anymore." She lifts her head to look up at him. "But I know that's wrong. That I shouldn't assume things about you. That there's still things that can bring us together." He grins back at her. "Like Super Bowl ads?" She laughs. "I guess. But you know what I mean, really." "I know honey. And I'm sorry too. I didn't mean what I said earlier. I know you don't really hate this country." He gestures to the couch next to him. "Why don't you sit down, huh? We can watch the rest of the game together." She straightens herself up, wipes her eyes. Suddenly feels a little self conscious. "Sure. Let me just go freshen up first." "Of course honey." Mom and Dad watch Sara leave the room, and then look at each other. "Well." "Well indeed." "What did I tell you? You two just needed to spend some time together. Some quality time." "I guess so. What did I ever do to deserve a woman as hot and as smart as you, huh Sheryl?" Mom stands up and makes to leave the room, leaning down to kiss him as she passes. "I ask myself that question every day." Alone, seen only by the TV, Dad smiles to himself. He picks up the remote, but instead of hitting play, he finds himself hitting rewind. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. Cut to: on the ground, in the desert. The group of figures are all men. Dirty, scruffy, furtive. Like they mean business.They carry guns, pistols, and assault riffles. Bad hombres. One of them pulls open a bag, looks inside. Cut to: close up of the inside of the bag. Inside are packets of white powder. Suddenly, one of the party looks up, shouts something in Spanish. They all go to grab their guns. But it's too late. From three different directions, three different Chevrolet jeeps appear, screeching to a halt, kicking up dust. From them jump Border Patrol agents and Minutemen militia, guns drawn and ready. The gang of men don't even put up a fight. They know they're surrounded, they drop their weapons and pathetically raise their hands. All except one. The guy with the bag full of drugs. He's got nothing to lose. He reaches for his rifle. Cut to: Border Patrol agents, opening fire. Text flashes across the screen. ALERT CANCELLED. THREAT NEUTRALISED. Cut to: the drone, banking and turning, flying away. Cut to: exterior shot of the trailer. The still anonymous pilot exits, walks back towards his jeep. Voiceover: Keeping America safe means never sleeping, but keeping America great means never forgetting who we are, and what keeps us strong. The jeep starts up, pulls away from the camera in a cloud of dust. Fade to black. Chevrolet logo. White text against black. 'We know what really makes America great' Dad wipes another team from his eye. "I think we're going to be OK," he says to himself. "I think we're going to be just fine." This article was originally published on TheLong+Short. Read the original article. | B. There is an implication that she's not informed about the job she does every day |
What event or experience had the strongest impact on Williamson's literary style?
A. Reading books by some of the most illustrious science fiction authors as a child and adolescent
B. Watching his father make sacrifices to provide for him, his mother, and younger siblings
C. Growing up with little protection from exposure to the suffering from the elements
D. Not having the same access to innovative, life-saving technology in his formative years
| Transcriber's Note: This etext was produced from Amazing Stories December 1961 and was first published in Amazing Stories November 1930. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. A Classic Reprint from AMAZING STORIES, November, 1930 Copyright 1931, by Experimenter Publications Inc. The Cosmic Express By JACK WILLIAMSON Introduction by Sam Moskowitz The year 1928 was a great year of discovery for AMAZING STORIES . They were uncovering new talent at such a great rate, (Harl Vincent, David H. Keller, E. E. Smith, Philip Francis Nowlan, Fletcher Pratt and Miles J. Breuer), that Jack Williamson barely managed to become one of a distinguished group of discoveries by stealing the cover of the December issue for his first story The Metal Man. A disciple of A. Merritt, he attempted to imitate in style, mood and subject the magic of that late lamented master of fantasy. The imitation found great favor from the readership and almost instantly Jack Williamson became an important name on the contents page of AMAZING STORIES . He followed his initial success with two short novels , The Green Girl in AMAZING STORIES and The Alien Intelligence in SCIENCE WONDER STORIES , another Gernsback publication. Both of these stories were close copies of A. Merritt, whose style and method Jack Williamson parlayed into popularity for eight years. Yet the strange thing about it was that Jack Williamson was one of the most versatile science fiction authors ever to sit down at the typewriter. When the vogue for science-fantasy altered to super science, he created the memorable super lock-picker Giles Habilula as the major attraction in a rousing trio of space operas , The Legion of Space, The Cometeers and One Against the Legion. When grim realism was the order of the day, he produced Crucible of Power and when they wanted extrapolated theory in present tense, he assumed the disguise of Will Stewart and popularized the concept of contra terrene matter in science fiction with Seetee Ship and Seetee Shock. Finally, when only psychological studies of the future would do, he produced "With Folded Hands ..." "... And Searching Mind." The Cosmic Express is of special interest because it was written during Williamson's A. Merritt "kick," when he was writing little else but, and it gave the earliest indication of a more general capability. The lightness of the handling is especially modern, barely avoiding the farcical by the validity of the notion that wireless transmission of matter is the next big transportation frontier to be conquered. It is especially important because it stylistically forecast a later trend to accept the background for granted, regardless of the quantity of wonders, and proceed with the story. With only a few thousand scanning-disk television sets in existence at the time of the writing, the surmise that this media would be a natural for westerns was particularly astute. Jack Williamson was born in 1908 in the Arizona territory when covered wagons were the primary form of transportation and apaches still raided the settlers. His father was a cattle man, but for young Jack, the ranch was anything but glamorous. "My days were filled," he remembers, "with monotonous rounds of what seemed an endless, heart-breaking war with drought and frost and dust-storms, poison-weeds and hail, for the sake of survival on the Llano Estacado." The discovery of AMAZING STORIES was the escape he sought and his goal was to be a science fiction writer. He labored to this end and the first he knew that a story of his had been accepted was when he bought the December, 1929 issue of AMAZING STORIES . Since then, he has written millions of words of science fiction and has gone on record as follows: "I feel that science-fiction is the folklore of the new world of science, and the expression of man's reaction to a technological environment. By which I mean that it is the most interesting and stimulating form of literature today." Mr. Eric Stokes-Harding tumbled out of the rumpled bed-clothing, a striking slender figure in purple-striped pajamas. He smiled fondly across to the other of the twin beds, where Nada, his pretty bride, lay quiet beneath light silk covers. With a groan, he stood up and began a series of fantastic bending exercises. But after a few half-hearted movements, he gave it up, and walked through an open door into a small bright room, its walls covered with bookcases and also with scientific appliances that would have been strange to the man of four or five centuries before, when the Age of Aviation was beginning. Suddenly there was a sharp tingling sensation where they touched the polished surface. Yawning, Mr. Eric Stokes-Harding stood before the great open window, staring out. Below him was a wide, park-like space, green with emerald lawns, and bright with flowering plants. Two hundred yards across it rose an immense pyramidal building—an artistic structure, gleaming with white marble and bright metal, striped with the verdure of terraced roof-gardens, its slender peak rising to help support the gray, steel-ribbed glass roof above. Beyond, the park stretched away in illimitable vistas, broken with the graceful columned buildings that held up the great glass roof. Above the glass, over this New York of 2432 A. D., a freezing blizzard was sweeping. But small concern was that to the lightly clad man at the window, who was inhaling deeply the fragrant air from the plants below—air kept, winter and summer, exactly at 20° C. With another yawn, Mr. Eric Stokes-Harding turned back to the room, which was bright with the rich golden light that poured in from the suspended globes of the cold ato-light that illuminated the snow-covered city. With a distasteful grimace, he seated himself before a broad, paper-littered desk, sat a few minutes leaning back, with his hands clasped behind his head. At last he straightened reluctantly, slid a small typewriter out of its drawer, and began pecking at it impatiently. For Mr. Eric Stokes-Harding was an author. There was a whole shelf of his books on the wall, in bright jackets, red and blue and green, that brought a thrill of pleasure to the young novelist's heart when he looked up from his clattering machine. He wrote "thrilling action romances," as his enthusiastic publishers and television directors said, "of ages past, when men were men. Red-blooded heroes responding vigorously to the stirring passions of primordial life!" He was impartial as to the source of his thrills—provided they were distant enough from modern civilization. His hero was likely to be an ape-man roaring through the jungle, with a bloody rock in one hand and a beautiful girl in the other. Or a cowboy, "hard-riding, hard-shooting," the vanishing hero of the ancient ranches. Or a man marooned with a lovely woman on a desert South Sea island. His heroes were invariably strong, fearless, resourceful fellows, who could handle a club on equal terms with a cave-man, or call science to aid them in defending a beautiful mate from the terrors of a desolate wilderness. And a hundred million read Eric's novels, and watched the dramatization of them on the television screens. They thrilled at the simple, romantic lives his heroes led, paid him handsome royalties, and subconsciously shared his opinion that civilization had taken all the best from the life of man. Eric had settled down to the artistic satisfaction of describing the sensuous delight of his hero in the roasted marrow-bones of a dead mammoth, when the pretty woman in the other room stirred, and presently came tripping into the study, gay and vivacious, and—as her husband of a few months most justly thought—altogether beautiful in a bright silk dressing gown. Recklessly, he slammed the machine back into its place, and resolved to forget that his next "red-blooded action thriller" was due in the publisher's office at the end of the month. He sprang up to kiss his wife, held her embraced for a long happy moment. And then they went hand in hand, to the side of the room and punched a series of buttons on a panel—a simple way of ordering breakfast sent up the automatic shaft from the kitchens below. Nada Stokes-Harding was also an author. She wrote poems—"back to nature stuff"—simple lyrics of the sea, of sunsets, of bird songs, of bright flowers and warm winds, of thrilling communion with Nature, and growing things. Men read her poems and called her a genius. Even though the whole world had grown up into a city, the birds were extinct, there were no wild flowers, and no one had time to bother about sunsets. "Eric, darling," she said, "isn't it terrible to be cooped up here in this little flat, away from the things we both love?" "Yes, dear. Civilization has ruined the world. If we could only have lived a thousand years ago, when life was simple and natural, when men hunted and killed their meat, instead of drinking synthetic stuff, when men still had the joys of conflict, instead of living under glass, like hot-house flowers." "If we could only go somewhere—" "There isn't anywhere to go. I write about the West, Africa, South Sea Islands. But they were all filled up two hundred years ago. Pleasure resorts, sanatoriums, cities, factories." "If only we lived on Venus! I was listening to a lecture on the television, last night. The speaker said that the Planet Venus is younger than the Earth, that it has not cooled so much. It has a thick, cloudy atmosphere, and low, rainy forests. There's simple, elemental life there—like Earth had before civilization ruined it." "Yes, Kinsley, with his new infra-red ray telescope, that penetrates the cloud layers of the planet, proved that Venus rotates in about the same period as Earth; and it must be much like Earth was a million years ago." "Eric, I wonder if we could go there! It would be so thrilling to begin life like the characters in your stories, to get away from this hateful civilization, and live natural lives. Maybe a rocket—" The young author's eyes were glowing. He skipped across the floor, seized Nada, kissed her ecstatically. "Splendid! Think of hunting in the virgin forest, and bringing the game home to you! But I'm afraid there is no way.—Wait! The Cosmic Express." "The Cosmic Express?" "A new invention. Just perfected a few weeks ago, I understand. By Ludwig Von der Valls, the German physicist." "I've quit bothering about science. It has ruined nature, filled the world with silly, artificial people, doing silly, artificial things." "But this is quite remarkable, dear. A new way to travel—by ether!" "By ether!" "Yes. You know of course that energy and matter are interchangeable terms; both are simply etheric vibration, of different sorts." "Of course. That's elementary." She smiled proudly. "I can give you examples, even of the change. The disintegration of the radium atom, making helium and lead and energy . And Millikan's old proof that his Cosmic Ray is generated when particles of electricity are united to form an atom." "Fine! I thought you said you weren't a scientist." He glowed with pride. "But the method, in the new Cosmic Express, is simply to convert the matter to be carried into power, send it out as a radiant beam and focus the beam to convert it back into atoms at the destination." "But the amount of energy must be terrific—" "It is. You know short waves carry more energy than long ones. The Express Ray is an electromagnetic vibration of frequency far higher than that of even the Cosmic Ray, and correspondingly more powerful and more penetrating." The girl frowned, running slim fingers through golden-brown hair. "But I don't see how they get any recognizable object, not even how they get the radiation turned back into matter." "The beam is focused, just like the light that passes through a camera lens. The photographic lens, using light rays, picks up a picture and reproduces it again on the plate—just the same as the Express Ray picks up an object and sets it down on the other side of the world. "An analogy from television might help. You know that by means of the scanning disc, the picture is transformed into mere rapid fluctuations in the brightness of a beam of light. In a parallel manner, the focal plane of the Express Ray moves slowly through the object, progressively, dissolving layers of the thickness of a single atom, which are accurately reproduced at the other focus of the instrument—which might be in Venus! "But the analogy of the lens is the better of the two. For no receiving instrument is required, as in television. The object is built up of an infinite series of plane layers, at the focus of the ray, no matter where that may be. Such a thing would be impossible with radio apparatus because even with the best beam transmission, all but a tiny fraction of the power is lost, and power is required to rebuild the atoms. Do you understand, dear?" "Not altogether. But I should worry! Here comes breakfast. Let me butter your toast." A bell had rung at the shaft. She ran to it, and returned with a great silver tray, laden with dainty dishes, which she set on a little side table. They sat down opposite each other, and ate, getting as much satisfaction from contemplation of each other's faces as from the excellent food. When they had finished, she carried the tray to the shaft, slid it in a slot, and touched a button—thus disposing of the culinary cares of the morning. She ran back to Eric, who was once more staring distastefully at his typewriter. "Oh, darling! I'm thrilled to death about the Cosmic Express! If we could go to Venus, to a new life on a new world, and get away from all this hateful conventional society—" "We can go to their office—it's only five minutes. The chap that operates the machine for the company is a pal of mine. He's not supposed to take passengers except between the offices they have scattered about the world. But I know his weak point—" Eric laughed, fumbled with a hidden spring under his desk. A small polished object, gleaming silvery, slid down into his hand. "Old friendship, plus this, would make him—like spinach." Five minutes later Mr. Eric Stokes-Harding and his pretty wife were in street clothes, light silk tunics of loose, flowing lines—little clothing being required in the artificially warmed city. They entered an elevator and dropped thirty stories to the ground floor of the great building. There they entered a cylindrical car, with rows of seats down the sides. Not greatly different from an ancient subway car, except that it was air-tight, and was hurled by magnetic attraction and repulsion through a tube exhausted of air, at a speed that would have made an old subway rider gasp with amazement. In five more minutes their car had whipped up to the base of another building, in the business section, where there was no room for parks between the mighty structures that held the unbroken glass roofs two hundred stories above the concrete pavement. An elevator brought them up a hundred and fifty stories. Eric led Nada down a long, carpeted corridor to a wide glass door, which bore the words: COSMIC EXPRESS stenciled in gold capitals across it. As they approached, a lean man, carrying a black bag, darted out of an elevator shaft opposite the door, ran across the corridor, and entered. They pushed in after him. They were in a little room, cut in two by a high brass grill. In front of it was a long bench against the wall, that reminded one of the waiting room in an old railroad depot. In the grill was a little window, with a lazy, brown-eyed youth leaning on the shelf behind it. Beyond him was a great, glittering piece of mechanism, half hidden by the brass. A little door gave access to the machine from the space before the grill. The thin man in black, whom Eric now recognized as a prominent French heart-specialist, was dancing before the window, waving his bag frantically, raving at the sleepy boy. "Queek! I have tell you zee truth! I have zee most urgent necessity to go queekly. A patient I have in Paree, zat ees in zee most creetical condition!" "Hold your horses just a minute, Mister. We got a client in the machine now. Russian diplomat from Moscow to Rio de Janeiro.... Two hundred seventy dollars and eighty cents, please.... Your turn next. Remember this is just an experimental service. Regular installations all over the world in a year.... Ready now. Come on in." The youth took the money, pressed a button. The door sprang open in the grill, and the frantic physician leaped through it. "Lie down on the crystal, face up," the young man ordered. "Hands at your sides, don't breathe. Ready!" He manipulated his dials and switches, and pressed another button. "Why, hello, Eric, old man!" he cried. "That's the lady you were telling me about? Congratulations!" A bell jangled before him on the panel. "Just a minute. I've got a call." He punched the board again. Little bulbs lit and glowed for a second. The youth turned toward the half-hidden machine, spoke courteously. "All right, madam. Walk out. Hope you found the transit pleasant." "But my Violet! My precious Violet!" a shrill female voice came from the machine. "Sir, what have you done with my darling Violet?" "I'm sure I don't know, madam. You lost it off your hat?" "None of your impertinence, sir! I want my dog." "Ah, a dog. Must have jumped off the crystal. You can have him sent on for three hundred and—" "Young man, if any harm comes to my Violet—I'll—I'll—I'll appeal to the Society for the Prevention of Cruelty to Animals!" "Very good, madam. We appreciate your patronage." The door flew open again. A very fat woman, puffing angrily, face highly colored, clothing shimmering with artificial gems, waddled pompously out of the door through which the frantic French doctor had so recently vanished. She rolled heavily across the room, and out into the corridor. Shrill words floated back: "I'm going to see my lawyer! My precious Violet—" The sallow youth winked. "And now what can I do for you, Eric?" "We want to go to Venus, if that ray of yours can put us there." "To Venus? Impossible. My orders are to use the Express merely between the sixteen designated stations, at New York, San Francisco, Tokyo, London, Paris—" "See here, Charley," with a cautious glance toward the door, Eric held up the silver flask. "For old time's sake, and for this—" The boy seemed dazed at sight of the bright flask. Then, with a single swift motion, he snatched it out of Eric's hand, and bent to conceal it below his instrument panel. "Sure, old boy. I'd send you to heaven for that, if you'd give me the micrometer readings to set the ray with. But I tell you, this is dangerous. I've got a sort of television attachment, for focusing the ray. I can turn that on Venus—I've been amusing myself, watching the life there, already. Terrible place. Savage. I can pick a place on high land to set you down. But I can't be responsible for what happens afterward." "Simple, primitive life is what we're looking for. And now what do I owe you—" "Oh, that's all right. Between friends. Provided that stuff's genuine! Walk in and lie down on the crystal block. Hands at your sides. Don't move." The little door had swung open again, and Eric led Nada through. They stepped into a little cell, completely surrounded with mirrors and vast prisms and lenses and electron tubes. In the center was a slab of transparent crystal, eight feet square and two inches thick, with an intricate mass of machinery below it. Eric helped Nada to a place on the crystal, lay down at her side. "I think the Express Ray is focused just at the surface of the crystal, from below," he said. "It dissolves our substance, to be transmitted by the beam. It would look as if we were melting into the crystal." "Ready," called the youth. "Think I've got it for you. Sort of a high island in the jungle. Nothing bad in sight now. But, I say—how're you coming back? I haven't got time to watch you." "Go ahead. We aren't coming back." "Gee! What is it? Elopement? I thought you were married already. Or is it business difficulties? The Bears did make an awful raid last night. But you better let me set you down in Hong Kong." A bell jangled. "So long," the youth called. Nada and Eric felt themselves enveloped in fire. Sheets of white flame seemed to lap up about them from the crystal block. Suddenly there was a sharp tingling sensation where they touched the polished surface. Then blackness, blankness. The next thing they knew, the fires were gone from about them. They were lying in something extremely soft and fluid; and warm rain was beating in their faces. Eric sat up, found himself in a mud-puddle. Beside him was Nada, opening her eyes and struggling up, her bright garments stained with black mud. All about rose a thick jungle, dark and gloomy—and very wet. Palm-like, the gigantic trees were, or fern-like, flinging clouds of feathery green foliage high against a somber sky of unbroken gloom. They stood up, triumphant. "At last!" Nada cried. "We're free! Free of that hateful old civilization! We're back to Nature!" "Yes, we're on our feet now, not parasites on the machines." "It's wonderful to have a fine, strong man like you to trust in, Eric. You're just like one of the heroes in your books!" "You're the perfect companion, Nada.... But now we must be practical. We must build a fire, find weapons, set up a shelter of some kind. I guess it will be night, pretty soon. And Charley said something about savage animals he had seen in the television. "We'll find a nice dry cave, and have a fire in front of the door. And skins of animals to sleep on. And pottery vessels to cook in. And you will find seeds and grown grain." "But first we must find a flint-bed. We need flint for tools, and to strike sparks to make a fire with. We will probably come across a chunk of virgin copper, too—it's found native." Presently they set off through the jungle. The mud seemed to be very abundant, and of a most sticky consistence. They sank into it ankle deep at every step, and vast masses of it clung to their feet. A mile they struggled on, without finding where a provident nature had left them even a single fragment of quartz, to say nothing of a mass of pure copper. "A darned shame," Eric grumbled, "to come forty million miles, and meet such a reception as this!" Nada stopped. "Eric," she said, "I'm tired. And I don't believe there's any rock here, anyway. You'll have to use wooden tools, sharpened in the fire." "Probably you're right. This soil seemed to be of alluvial origin. Shouldn't be surprised if the native rock is some hundreds of feet underground. Your idea is better." "You can make a fire by rubbing sticks together, can't you?" "It can be done, I'm sure. I've never tried it, myself. We need some dry sticks, first." They resumed the weary march, with a good fraction of the new planet adhering to their feet. Rain was still falling from the dark heavens in a steady, warm downpour. Dry wood seemed scarce as the proverbial hen's teeth. "You didn't bring any matches, dear?" "Matches! Of course not! We're going back to Nature." "I hope we get a fire pretty soon." "If dry wood were gold dust, we couldn't buy a hot dog." "Eric, that reminds me that I'm hungry." He confessed to a few pangs of his own. They turned their attention to looking for banana trees, and coconut palms, but they did not seem to abound in the Venerian jungle. Even small animals that might have been slain with a broken branch had contrary ideas about the matter. At last, from sheer weariness, they stopped, and gathered branches to make a sloping shelter by a vast fallen tree-trunk. "This will keep out the rain—maybe—" Eric said hopefully. "And tomorrow, when it has quit raining—I'm sure we'll do better." They crept in, as gloomy night fell without. They lay in each other's arms, the body warmth oddly comforting. Nada cried a little. "Buck up," Eric advised her. "We're back to nature—where we've always wanted to be." With the darkness, the temperature fell somewhat, and a high wind rose, whipping cold rain into the little shelter, and threatening to demolish it. Swarms of mosquito-like insects, seemingly not inconvenienced in the least by the inclement elements, swarmed about them in clouds. Then came a sound from the dismal stormy night, a hoarse, bellowing roar, raucous, terrifying. Nada clung against Eric. "What is it, dear?" she chattered. "Must be a reptile. Dinosaur, or something of the sort. This world seems to be in about the same state as the Earth when they flourished there.... But maybe it won't find us." The roar was repeated, nearer. The earth trembled beneath a mighty tread. "Eric," a thin voice trembled. "Don't you think—it might have been better— You know the old life was not so bad, after all." "I was just thinking of our rooms, nice and warm and bright, with hot foods coming up the shaft whenever we pushed the button, and the gay crowds in the park, and my old typewriter." "Eric?" she called softly. "Yes, dear." "Don't you wish—we had known better?" "I do." If he winced at the "we" the girl did not notice. The roaring outside was closer. And suddenly it was answered by another raucous bellow, at considerable distance, that echoed strangely through the forest. The fearful sounds were repeated, alternately. And always the more distant seemed nearer, until the two sounds were together. And then an infernal din broke out in the darkness. Bellows. Screams. Deafening shrieks. Mighty splashes, as if struggling Titans had upset oceans. Thunderous crashes, as if they were demolishing forests. Eric and Nada clung to each other, in doubt whether to stay or to fly through the storm. Gradually the sound of the conflict came nearer, until the earth shook beneath them, and they were afraid to move. Suddenly the great fallen tree against which they had erected the flimsy shelter was rolled back, evidently by a chance blow from the invisible monsters. The pitiful roof collapsed on the bedraggled humans. Nada burst into tears. "Oh, if only—if only—" Suddenly flame lapped up about them, the same white fire they had seen as they lay on the crystal block. Dizziness, insensibility overcame them. A few moments later, they were lying on the transparent table in the Cosmic Express office, with all those great mirrors and prisms and lenses about them. A bustling, red-faced official appeared through the door in the grill, fairly bubbling apologies. "So sorry—an accident—inconceivable. I can't see how he got it! We got you back as soon as we could find a focus. I sincerely hope you haven't been injured." "Why—what—what—" "Why I happened in, found our operator drunk. I've no idea where he got the stuff. He muttered something about Venus. I consulted the auto-register, and found two more passengers registered here than had been recorded at our other stations. I looked up the duplicate beam coordinates, and found that it had been set on Venus. I got men on the television at once, and we happened to find you. "I can't imagine how it happened. I've had the fellow locked up, and the 'dry-laws' are on the job. I hope you won't hold us for excessive damages." "No, I ask nothing except that you don't press charges against the boy. I don't want him to suffer for it in any way. My wife and I will be perfectly satisfied to get back to our apartment." "I don't wonder. You look like you've been through—I don't know what. But I'll have you there in five minutes. My private car—" Mr. Eric Stokes-Harding, noted author of primitive life and love, ate a hearty meal with his pretty spouse, after they had washed off the grime of another planet. He spent the next twelve hours in bed. At the end of the month he delivered his promised story to his publishers, a thrilling tale of a man marooned on Venus, with a beautiful girl. The hero made stone tools, erected a dwelling for himself and his mate, hunted food for her, defended her from the mammoth saurian monsters of the Venerian jungles. The book was a huge success. THE END | A. Reading books by some of the most illustrious science fiction authors as a child and adolescent |
How did Wikipedia eclipse all commercial encyclopedias except Britannica?
A. They engaged the human and social.
B. They built an open and inviting system that lets people learn together.
C. They hired the smartest guys in the room.
D. They issued a large IPO.
| COMPLEXITY AND HUMANITY We have all seen the images. Volunteers pitching in. People working day and night; coming up with the most ingenious, improvised solutions to everything from food and shelter to communications and security. Working together; patching up the fabric that is rent. Disaster, natural or otherwise, is a breakdown of systems. For a time, chaos reigns. For a time, what will happen in the next five minutes, five hours, and five days is unknown. All we have to rely on are our wits, fortitude, and common humanity Contemporary life is not chaotic, in the colloquial sense we apply to disaster zones. It is, however, complex and rapidly changing; much more so than life was in the past; even the very near past. Life, of course, was never simple. But the fact that day-to-day behaviors in Shenzhen and Bangalore have direct and immediate effects on people from Wichita to Strasbourg, from Rio de Janeiro to Sydney, or that unscrupulous lenders and careless borrowers in the United States can upend economic expectations everywhere else in the world, no matter how carefully others have planned, means that there are many more moving parts that affect each other. And from this scale of practical effects, complexity emerges. New things too were ever under the sun; but the systematic application of knowledge to the creation of new knowledge, innovation to innovation, and information to making more information has become pervasive; and with it the knowledge that next year will be very different than this. The Web, after all, is less than a generation old. These two features−the global scale of interdependence of human action, and the systematic acceleration of innovation, make contemporary life a bit like a slow motion disaster, in one important respect. Its very unpredictability makes it unwise to build systems that take too much away from what human beings do best: look, think, innovate, adapt, discuss, learn, and repeat. That is why we have seen many more systems take on a loose, human centric model in the last decade and a half: from the radical divergence of Toyota’s production system from the highly structured model put in place by Henry Ford, to the Internet’s radical departure from the AT&T system that preceded it, and on to the way Wikipedia constructs human knowledge on the fly, incrementally, in ways that would have been seen, until recently, as too chaotic ever to work (and are still seen so be many). But it is time we acknowledge that systems work best by making work human. Modern Times Modern times were hard enough. Trains and planes, telegraph and telephone, all brought many people into the same causal space. The solution to this increased complexity in the late 19th, early 20th century was to increase the role of structure and improve its design. During the first two-thirds of the twentieth century, this type of rationalization took the form of ever-more complex managed systems, with crisp specification of roles, lines of authority, communication and control. In business, this rationalization was typified by Fredrick Taylor’s Scientific Management, later embodied in Henry Ford’s assembly line. The ambition of these approaches was to specify everything that needed doing in minute detail, to enforce it through monitoring and rewards, and later to build it into the very technology of work−the assembly line. The idea was to eliminate human error and variability in the face of change by removing thinking to the system, and thus neutralizing the variability of the human beings who worked it. Few images captured that time, and what it did to humanity, more vividly than Charlie Chaplin’s assembly line worker in Modern Times. At the same time, government experienced the rise of bureaucratization and the administrative state. Nowhere was this done more brutally than in the totalitarian states of mid-century. But the impulse to build fully-specified systems, designed by experts, monitored and controlled so as to limit human greed and error and to manage uncertainty, was basic and widespread. It underlay the development of the enormously successful state bureaucracies that responded to the Great Depression with the New Deal. It took shape in the Marshall Plan to pull Europe out of the material abyss into which it had been plunged by World War II, and shepherded Japan’s industrial regeneration from it. In technical systems too, we saw in mid-century marvels like the AT&T telephone system and the IBM mainframe. For a moment in history, these large scale managed systems were achieving efficiencies that seemed to overwhelm competing models: from the Tennessee Valley Authority to Sputnik, from Watson’s IBM to General Motors. Yet, to list these paragons from today’s perspective is already to presage the demise of the belief in their inevitable victory. The increasing recognition of the limits of command-and-control systems led to a new approach; but it turned out to be a retrenchment, not an abandonment, of the goal of perfect rationalization of systems design, which assumed much of the human away. What replaced planning and control in these systems was the myth of perfect markets. This was achieved through a hyper-simplification of human nature, wedded to mathematical modeling of what hyper-simplified selfish rational actors, looking only to their own interests, would do under diverse conditions. This approach was widespread and influential; it still is. And yet it led to such unforgettable gems as trying to understand why people do, or do not, use condoms by writing sentences like: “The expected utility (EU) of unsafe sex for m and for f is equal to the benefits (B) of unsafe sex minus its expected costs, and is given by EUm = B - C(1-Pm)(Pf) and EUf = B - C(1-Pf)(Pm),” and believing that you will learn anything useful about lust and desire, recklessness and helplessness, or how to slow down the transmission of AIDS. Only by concocting such a thin model of humanity−no more than the economists’ utility curve−and neglecting any complexities of social interactions that could not be conveyed through prices, could the appearance of rationalization be maintained. Like bureaucratic rationalization, perfect-market rationalization also had successes. But, like its predecessor, its limits as an approach to human systems design are becoming cleare Work, Trust and Play Pricing perfectly requires perfect information. And perfect information, while always an illusion, has become an ever receding dream in a world of constant, rapid change and complex global interactions. What we are seeing instead is the rise of human systems that increasingly shy away from either control or perfect pricing. Not that there isn’t control. Not that there aren’t markets. And not that either of these approaches to coordinating human action will disappear. But these managed systems are becoming increasingly interlaced with looser structures, which invite and enable more engaged human action by drawing on intrinsic motivations and social relations. Dress codes and a culture of play in the workplace in Silicon Valley, like the one day per week that Google employees can use to play at whatever ideas they like, do not exist to make the most innovative region in the United States a Ludic paradise, gratifying employees at the expense of productivity, but rather to engage the human and social in the pursuit of what is, in the long term, the only core business competency−innovation. Wikipedia has eclipsed all the commercial encyclopedias except Britannica not by issuing a large IPO and hiring the smartest guys in the room, but by building an open and inviting system that lets people learn together and pursue their passion for knowledge, and each other’s company. The set of human systems necessary for action in this complex, unpredictable set of conditions, combining rationalization with human agency, learning and adaptation, is as different from managed systems and perfect markets as the new Toyota is from the old General Motors, or as the Internet now is from AT&T then. The hallmarks of these newer systems are: (a) location of authority and practical capacity to act at the edges of the system, where potentialities for sensing the environment, identifying opportunities and challenges to action and acting upon them, are located; (b) an emphasis on the human: on trust, cooperation, judgment and insight; (c) communication over the lifetime of the interaction; and (d) loosely-coupled systems: systems in which the regularities and dependencies among objects and processes are less strictly associated with each other; where actions and interactions can occur through multiple systems simultaneously, have room to fail, maneuver, and be reoriented to fit changing conditions and new learning, or shift from one system to another to achieve a solution. Consider first of all the triumph of Toyota over the programs of Taylor and Ford. Taylorism was typified by the ambition to measure and specify all human and material elements of the production system. The ambition of scientific management was to offer a single, integrated system where all human variance (the source of slothful shirking and inept error) could be isolated and controlled. Fordism took that ambition and embedded the managerial knowledge in the technological platform of the assembly line, guided by a multitude of rigid task specifications and routines. Toyota Production System, by comparison, has a substantially smaller number of roles that are also more loosely defined, with a reliance on small teams where each team member can perform all tasks, and who are encouraged to experiment, improve, fail, adapt, but above all communicate. The system is built on trust and a cooperative dynamic. The enterprise functions through a managerial control system, but also through social cooperation mechanisms built around teamwork and trust. However, even Toyota might be bested in this respect by the even more loosely coupled networks of innovation and supply represented by Taiwanese original-design manufacturers. But let us also consider the system in question that has made this work possible, the Internet, and compare it to the design principles of the AT&T network in its heyday. Unlike the Internet, AT&T’s network was fully managed. Mid-century, the company even retained ownership of the phones at the endpoints, arguing that it needed to prohibit customers from connecting unlicensed phones to the system (ostensibly to ensure proper functioning of the networking and monitoring of customer behavior, although it didn’t hurt either that this policy effectively excluded competitors). This generated profit, but any substantial technical innovations required the approval of management and a re-engineering of the entire network. The Internet, on the other hand, was designed to be as general as possible. The network hardware merely delivers packets of data using standardized addressing information. The hard processing work−manipulating a humanly-meaningful communication (a letter or a song, a video or a software package) and breaking it up into a stream of packets−was to be done by its edge devices, in this case computers owned by users. This system allowed the breathtaking rate of innovation that we have seen, while also creating certain vulnerabilities in online security. These vulnerabilities have led some to argue that a new system to manage the Internet is needed. We see first of all that doubts about trust and security on the Internet arise precisely because the network was originally designed for people who could more-or-less trust each other, and offloaded security from the network to the edges. As the network grew and users diversified, trust (the practical belief that other human agents in the system were competent and benign, or at least sincere) declined. This decline was met with arguments in favor of building security into the technical system, both at its core, in the network elements themselves, and at its periphery, through “trusted computing.” A “trusted computer” will, for example, not run a program or document that its owner wants to run, unless it has received authorization from some other locus: be it the copyright owner, the virus protection company, or the employer. This is thought to be the most completely effective means of preventing copyright infringement or system failure, and preserving corporate security (these are the main reasons offered for implementing such systems). Trusted computing in this form is the ultimate reversal of the human-centric, loosely-coupled design approach of the Internet. Instead of locating authority and capacity to act at the endpoints, where human beings are located and can make decisions about what is worthwhile, it implements the belief that machines−technical systems−are trustworthy, while their human users are malevolent, incompetent, or both. Reintroducing the Human Taylorism, the Bell system and trusted computing are all efforts to remove human agency from action and replace it with well-designed, tightly-bound systems. That is, the specifications and regularities of the system are such that they control or direct action and learning over time. Human agency, learning, communication and adaptation are minimized in managed systems, if not eliminated, and the knowledge in the system comes from the outside, from the designer, in the initial design over time, and through observation of the system’s performance by someone standing outside its constraints−a manager or systems designer. By contrast, loosely-coupled systems affirmatively eschew this level of control, and build in room for human agency, experimentation, failure, communication, learning and adaptation. Loose-coupling is central to the new systems. It is a feature of system design that leaves room for human agency over time, only imperfectly constraining and enabling any given action by the system itself. By creating such domains of human agency, system designers are accepting the limitations of design and foresight, and building in the possibilities of learning over time through action in the system, by agents acting within To deal with the new complexity of contemporary life we need to re-introduce the human into the design of systems. We must put the soul back into the system. If years of work on artificial intelligence have taught us anything, it is that what makes for human insight is extremely difficult to replicate or systematize. At the center of these new systems, then, sits a human being who has a capacity to make judgments, experiment, learn and adapt. But enabling human agency also provides scope of action for human frailty. Although this idea is most alien to the mainstream of system design in the twentieth century, we must now turn our attention to building systems that support human sociality−our ability to think of others and their needs, and to choose for ourselves goals consistent with a broader social concern than merely our own self-interest. The challenge of the near future is to build systems that will allow us to be largely free to inquire, experiment, learn and communicate, that will encourage us to cooperate, and that will avoid the worst of what human beings are capable of, and elicit what is best. Free software, Wikipedia, Creative Commons and the thousands of emerging human practices of productive social cooperation in the networked information economy give us real existence proofs that human-centric systems can not merely exist, but thrive, as can the human beings and social relations that make them. | B. They built an open and inviting system that lets people learn together. |
What was likely the biggest impact of the lawsuits against the UFC?
A. Being in a legal battle doesn't look good, and it made the fans distrust the organizations promoting UFC
B. The cost of the lawsuits drained the resources of the promoters so they didn't have the money for ads, fighters, and venues
C. The lawsuits took up so much time that fights were delayed long past when the fans were willing to wait until
D. The UFC's lawyers were tied up in TV network disputes, and were too busy to guarantee good contracts for the fighters
| Fight Clubbed Fight Club , a movie about a fictional organization of men who strip down and beat each other to pulp, has provoked more than its share of media hand-wringing, particularly diatribes about Hollywood's infatuation with violence and Faludi-esque ruminations about the emasculated American male. Fight Club , however, has not sparked an iota of interest in a real organization of men who strip down and beat each other to pulp: the Ultimate Fighting Championship. UFC's flameout from national sensation to total irrelevance is a tragedy of American sports, a cautionary tale of prudishness, heavy-handed politics, and cultural myopia. UFC began in 1993 as a locker-room fantasy. What would happen if a kickboxer fought a wrestler? A karate champion fought a sumo champion? Promoters built an octagonal chain-link cage, invited eight top martial artists, and set them loose in no-holds-barred, bare-knuckles fights. "There are no rules!" bragged an early press release. Contestants would fight till "knockout, submission, doctor's intervention, or death." UFC allowed, even promoted, all notions of bad sportsmanship: kicking a man when he's down, hitting him in the groin, choking. Four-hundred-pound men were sent into the Octagon to maul guys half their size. Only biting and eye-gouging were forbidden. The gimmick entranced thousands of people (well, men). What happens when a 620-pound sumo champion fights a 200-pound kickboxer? Answer: The kickboxer knocks him silly in 35 seconds. They tuned in for bloodshed--"the damage," as fans like to call it. UFC fights could be horrifying. Tank Abbott, an ill-tempered, 270-pound street fighter, knocks out hapless opponent John Matua in 15 seconds. Then, before the ref can intervene, Abbott belts the unconscious Matua in the head, sending him into a fit, limbs quivering uncontrollably, blood spurting from his mouth. Abbott, naturally, became a cult hero and won a guest spot on Friends . (Matua walked out of the ring.) Soon, UFC was selling out huge arenas and drawing 300,000 pay-per-view subscribers for its quarterly competitions. But a subtle sport was emerging from the gimmicks and carnage. My passion for ultimate fighting (which is also called "extreme" or "no-holds-barred" fighting) began when I saw the finals of UFC IV. Royce Gracie, a 180-pound Brazilian jujitsu specialist, was matched against a 275-pound beast named Dan Severn, one of the top heavyweight wrestlers in the world and a national champion many times over. In 30 seconds, Severn had grabbed Gracie, flung him to the canvas, and mounted him. For the next 15 minutes, Severn pummeled and elbowed and head-butted the smaller man. Gracie's face grew drawn, and he squirmed wildly to avoid Severn's bombardment. Then, all of sudden, Gracie, still lying on his back, saw an opening, wrapped his arms and legs around Severn like a python and choked the giant into submission. UFC's caged matches revolutionized the idea of fighting. Nursed on boxing and Hollywood, Americans imagine fights as choreography, a dance of elegant combinations, roundhouse kicks, clean knockouts. The UFC punctured this. Boxers floundered. Experts in striking martial arts such as karate and tae kwon do, who fancied themselves the world's greatest fighters, found themselves pretzeled by jujitsu masters, who pulled them to the ground and slowly choked or leg-locked them. "UFC immediately debunked a lot of myths of fighting, of boxing, karate, kung fu. It showed the reality of what works in an actual fight," says Dave Meltzer, editor of Wrestling Observer . Instead of being carnivals of gore, UFC fights looked strangely like ... sex. Almost all fights ended on the ground, one man mounting the other in missionary position, the pair of them wiggling mysteriously along the canvas for five, 10, even 30 minutes. There were few spectacular knockouts. The referee--yes, there was always a referee--stopped many bouts, and in most others, fighters "tapped out," surrendering to mild-looking but agonizing chokes and joint locks. It was not barbarism. It was science. The UFC spawned a new breed of "mixed martial artists." World-class wrestlers learned to kickbox. Champion kickboxers learned to grapple. (The karate experts learned to stay home.) They became, without doubt, the best fighters in the world. (Click for more about the fighters.) Mike Tyson wouldn't last 30 seconds in an ultimate fighting match. When Olympic gold medal wrestler Kevin Jackson came to the UFC, a fighter named Frank Shamrock KO'd him with a submission hold in 16 seconds. Ultimate fighting schools began sprouting up all over the country, replacing the stylized gestures of the Eastern martial arts with techniques that actually work. UFC's promoters predicted that it would supplant boxing as America's martial art. Instead, it fell apart. The collapse began in 1996, when Sen. John McCain, R-Ariz., saw a UFC tape. McCain, a lifelong boxing fan, was horrified at the ground fighting, kicks, and head butts. It was "barbaric," he said. It was "not a sport." He sent letters to all 50 governors asking them to ban ultimate fighting. The outcry against "human cockfighting" became a crusade, and like many crusades, it was founded on misunderstanding. UFC fell victim to cultural determinism about what a fight is. In countries such as Brazil and Japan, where no-holds-barred fighting has a long history, it is popular and uncontroversial. But Americans adhere to the Marquis of Queensbury rules. A fight consists of an exchange of upper-body blows that halts when one fighter falls. Any blood sport can be barbaric, whether it's boxing or wrestling or ultimate fighting. It is impossible to draw a bright line between ultimate fighting and boxing. If anything, ultimate fighting is safer and less cruel than America's blood sport. For example, critics pilloried ultimate fighting because competitors fought with bare knuckles: To a nation accustomed to boxing gloves, this seemed revolting, an invitation to brain damage. But it's just the reverse: The purpose of boxing gloves is not to cushion the head but to shield the knuckles. Without gloves, a boxer would break his hands after a couple of punches to the skull. That's why ultimate fighters won't throw multiple skull punches. As a result, they avoid the concussive head wounds that kill boxers--and the long-term neurological damage that cripples them. Similarly, the chain-link fence surrounding the octagon looks grotesque. Critics have demanded that UFC install ropes instead. But ropes are a major cause of death and injury in boxing: Fighters hyperextend their necks when they are punched against the ropes, because nothing stops their heads from snapping back. The chain-link fence prevents hyperextension. When I tell people I'm an ultimate fighting fan, they invariably respond: "Don't people get killed all the time doing that?" But no one has ever been killed at the UFC--though boxers are killed every year. No one has even been seriously injured at the UFC. On the rare occasions when a bout has ended with a bloody knockout, the loser has always walked out of the ring. But this does not impress boxing fans, who are the most vigorous opponents of extreme fighting. McCain sat ringside at a boxing match where a fighter was killed. When I asked him to explain the moral distinction between boxing and ultimate fighting, he exploded at me, "If you can't see the moral distinction, then we have nothing to talk about!" Then he cut our interview short and stormed out of his office. But logic has not served the UFC well. Where McCain led, a prudish nation followed. George Will opined against UFC. The American Medical Association recommended a ban. New York state outlawed ultimate fighting, as did other states. The Nevada Athletic Commission refused to sanction UFC bouts, barring the UFC from the lucrative casino market. (One public TV station refused a UFC sponsorship ad. The only other organization the station ever rejected was the Ku Klux Klan.) Lawsuits blocked or delayed UFC events all over the country, forcing the promoters to spend millions in legal fees. The UFC was exiled from mega-arenas to ever-smaller venues in ever more out-of-the-way states: Louisiana, Iowa, and Alabama. The match I attended in October 1997 was held in the parking lot of a small Mississippi casino. The cable TV industry struck the fatal blow. In early 1997, McCain became chairman of the commerce committee, which oversees the cable industry. In April 1997, the president of the National Cable Television Association warned that UFC broadcasts could jeopardize the cable industry's influence in Washington. Time Warner, TCI, Request, Cablevision Systems, Viewer's Choice, and other major operators stopped airing UFC events, saying they were too violent for children. Never mind that 1) UFC only aired on pay-per-view, so children could not see it unless their parents paid for it; and 2) the same cable outfits carried boxing matches, R and NC-17 movies, and professional wrestling shows far more violent than UFC. The UFC's "addressable audience"--the potential number of PPV subscribers--shrank from 35 million at its peak to 7.5 million today. "It was a very cheap way for the cable companies to portray themselves as anti-violence. It did not cost them much and it made them look good in Washington," says Carol Klenfner, spokeswoman for UFC's parent company, SEG. The ultimate fighting industry did little to help its own cause. The UFC promoted itself less as a serious sport than as a circus of carnage. Its early ads emphasized extreme fighting's potential for death. UFC folks accused McCain, without any evidence, of opposing the sport as a favor to campaign contributors. Extreme fighting was tarnished when fighters from the other ultimate fighting operation, the now-defunct Battlecade, were arrested for violating Canadian prizefighting laws when they fought on an Indian reservation outside Montreal. In the past two years, an increasingly desperate UFC has been trying to assuage its critics. The competition, which had been gradually adding safety rules since the first fight, imposed even more. It institued rounds and a "10-point must" scoring system. It banned head butts and groin strikes. You can no longer kick a downed man or elbow someone in the back of the head. Fighters are required to wear thin martial arts gloves (a purely cosmetic change). The UFC imposed weight classes, ending the David-and-Goliath mismatches that made early fights so compelling. None of this soothed the cable operators, who have kept UFC off the air. The pay-per-view audience has plunged from 300,000 per show to 15,000. UFC can no longer afford its best fighters: Some are fighting overseas. Others, notably Ken Shamrock (Frank's brother), have become pro wrestlers. Fights have deteriorated. UFC is limping along, but it has been reduced to scheduling events in Japan and Brazil. "Sports fans want to grow with the sport," says former UFC fighter David Beneteau. "They want to recognize the athletes. They want to see the same fighters come back. When you compare UFC now to what it was, the fighters are not the same, the rules are not the same. The fans have no story to follow." Even as it disappears from public view, ultimate fighting is returning to its roots. Away from the scrutiny of the major media, state legislators, and McCain, kids are still learning mixed martial-arts techniques, and small-time promoters are quietly staging events. You can see Kage Kombat competitions at Dancing Waters nightclub in San Pedro, Calif. You can watch the Warrior's Challenge at a small Indian casino outside Sacramento. Texans compete in Houston's Dungal All Styles Fighting Championship. Tribal casinos in Northern Idaho are hosting small Pankration tournaments. The Extreme Fighting Challenge is popular in Iowa. The money is low; the crowds are small; and there's not a TV camera in sight. Ultimate fighting should have become boxing. Instead it has gone underground. It has become Fight Club. | B. The cost of the lawsuits drained the resources of the promoters so they didn't have the money for ads, fighters, and venues |
What is NOT a recommendation they make in future experiments?
A. If you're going to test a certain type of beer, they recommended specific brands to try and one to avoid
B. Give the test subjects a palette cleanser (they didn't and it would make the data a lot cleaner in future studies)
C. Provide the test subjects with different information
D. If you're running the experiment, you can't participate as well
| More Booze You Can Use When we last heard from them, the members of the Slate beer-testing team were coping with lagers and trying to see if they could taste the 3-to-1 price difference between the most- and least-expensive brands. (Click for a wrap-up of the first round of beer tasting.) The answer was: They found one beer they really liked, Samuel Adams Boston Lager , and one they really hated, imported Grolsch from Holland. Both were expensive beers--Grolsch was the most expensive in the test--and otherwise the testers had a hard time telling beers apart. The members of the team, as noted in the original article, all hold day jobs at Microsoft, mainly as designers, managers, and coders for Microsoft Word. The point of the second test was not to find the difference between cheap and expensive beers but instead to compare a variety of top-of-the-line beers. Was there one kind the tasters preferred consistently? Could they detect any of the subtleties of brewing style and provenance that microbrew customers pay such attention to when choosing some Doppelbock over a cream ale? Since the tasting panel had left the first round grumbling that cheap lagers were not a fair test of their abilities, this second round of testing was advertised to the panel as a reward. Every beer in Round 2 would be a fancy beer. A microbrew. A "craft beer." A prestigious import. These were the kinds of beer the panel members said they liked--and the ones they said they were most familiar with. One aspect of the reward was that they would presumably enjoy the actual testing more--fewer rueful beer descriptions along the lines of "urine" or "get it away!" were expected than in the first round. The other aspect of anticipated reward was the panelists' unspoken but obvious assumption that this time they would "do better" on the test. Intellectual vanity being what it is, people who had fought for and won jobs at Microsoft and who still must fight every six months for primacy on the employee-ranking scale (which determines--gasp!--how many new stock options they receive) would assume that their skill as tasters was on trial, just as much as the beer was. Of course they were right, which is what made this round as amusing to administer as the first one had been. Here is what happened and what it meant: 1. Procedure. This was similar in most ways to the experimental approach of Round 1. The nine testers who showed up were a subset of the original 12. The missing three dropped out with excuses of "my wife is sick" (one person) and "meeting is running long" (two). As before, each tester found before him on a table 10 red plastic cups, labeled A through J. Each cup held 3 ounces of one of the beers. The A-to-J labeling scheme was the same for all testers. Instead of saltines for palate-cleansing, this time we had popcorn and nuts. As they began, the tasters were given these and only these clues: that the flight included one "holdover" beer from the previous round (Sam Adams); that it included at least one import (Bass); that it included at least one macrobrew , specifically, a member of the vast Anheuser-Busch family (Michelob Hefeweizen). After sampling all beers, the tasters rated them as follows: Overall quality points, from zero to 100, reflecting their personal, subjective fondness for the beer. Descriptions of and comments about each beer's taste--"smooth and nutty," "too strong," etc. If the first ranking was a measure of how good each beer was, this was an attempt to explain what made it good. Best and Worst , one of each from the group. Name that beer! The tasters were told that some of the drinks were Hefeweizens, some might be IPAs (India pale ales), some might be bitters, and so on. They were asked to put each beer in its proper category--and to name a specific brewery and brand if they could. The idea here was to test the veteran beer drinkers' claim to recognize the distinctive tastes of famous brands. (To see all the grids for all the beers, click .) 2. Philosophy. The first round of testing was All Lager. This second round was All Fancy, and Mainly Not Lager. As several correspondents (for instance, the of Best American Beers ) have helpfully pointed out, the definition of lager provided last time was not exactly "accurate." If you want to stay within the realm of textbook definitions, a lager is a beer brewed a particular way--slowly, at cool temperatures, with yeast that settles on the bottom of the vat. This is in contrast with an ale, which is brewed faster, warmer, and with the yeast on top. By this same reasoning, lagers don't have to be light-colored, weak-flavored, and watery, as mainstream American lagers are. In principle, lagers can be dark, fierce, manly. Therefore, the correspondents suggest, it was wrong to impugn Sam Adams or Pete's Wicked for deceptive labeling, in presenting their tawnier, more flavorful beers as lagers too. To this the beer scientist must say: Book-learning is fine in its place. But let's be realistic. Actual drinking experience teaches the American beer consumer that a) all cheap beers are lagers; and b) most lagers are light-colored and weak. The first test was designed to evaluate low-end beers and therefore had to be lager-centric. This one is designed to test fancy beers--but in the spirit of open-mindedness and technical accuracy, it includes a few "strong" lagers too. 3. Materials. The 10 test beers were chosen with several goals in mind: To cover at least a modest range of fancy beer types--extra special bitter, India pale ale, Hefeweizen, and so on. To include both imported and domestic beers. Among the domestic microbrews, there's an obvious skew toward beers from the Pacific Northwest. But as Microsoft would put it, that's a feature not a bug. These beers all came from the Safeway nearest the Redmond, Wash., "main campus" of Microsoft, and microbrews are supposed to be local. To include one holdover from the previous test, as a scientific control on our tasters' preferences. This was Sam Adams , runaway winner of Round 1. To include one fancy product from a monster-scale U.S. mass brewery, to see if the tasters liked it better or worse than the cute little microbrews. This was Michelob Hefeweizen , from the pride of St. Louis, Anheuser-Busch. Click for pricing information and pre-quaffing evaluations. The beers tasted were: 4. Data Analysis. a) Best and Worst. Compared to the lager test, we would expect the range of "best" choices to be more varied, since all the tested beers were supposed to be good. This expectation was most dramatically borne out in the "Best and Worst" rankings. The nine tasters cast a total of nine Worst votes and 11.5 Best votes. (Tester No. 1 turned in a sheet with three Best selections, or two more than his theoretical quota. Tester No. 4 listed a Best and a Best-minus, which counted as half a vote.) The results were clearest at the bottom: three Worsts for Pyramid Hefeweizen , even though most comments about the beer were more or less respectful. ("Bitter, drinkable.") But at the top and middle the situation was muddier: There were three Bests for Full Sail ESB , which most of the tasters later said they weren't familiar with, and 2.5 for Redhook IPA , which all the tasters knew. But each of these also got a Worst vote, and most of the other beers had a mixed reading. So far, the tasters are meeting expectations, finding something to like in nearly all these fancy beers. b) Overall preference points. Here the complications increase. The loser was again apparent: Pyramid Hefeweizen came in last on rating points, as it had in the Best/Worst derby. But the amazing dark horse winner was Michelob Hefeweizen . The three elements of surprise here, in ascending order of unexpectedness, are: This best-liked beer belonged to the same category, Hefeweizen, as the least-liked product, from Pyramid. This was also the only outright Anheuser-Busch product in the contest (the Redhooks are 75 percent A-B free). It is safe to say that all tasters would have said beforehand that they would rank an American macrobrew last, and Anheuser-Busch last of all. Although it clearly won on overall preference points, Michelob Hefeweizen was the only beer not to have received a single "Best" vote. The first two anomalies can be written off as testament to the power of a blind taste test. The third suggests an important difference in concepts of "bestness." Sometimes a product seems to be the best of a group simply because it's the most unusual or distinctive. This is why very high Wine Spectator ratings often go to wines that mainly taste odd. But another kind of bestness involves an unobtrusive, day-in day-out acceptability. That seems to be Michelob Hefe 's achievement here: no one's first choice, but high on everyone's list. Let's go to the charts: This table shows how the beers performed on "raw score"--that is, without the advanced statistical adjustment of throwing out the highest and lowest score each beer received. Next, we have "corrected average preference points," throwing out the high and low marks for each beer. The result is basically the same: It is worth noting the fate of Sam Adams on these charts. Here it ends up with a score of less than 61. These were the numbers awarded by the very same tasters who gave it a corrected preference rating of 83.33 the last time around--and 10 "Best" votes, vs. one Best (and one Worst) this time. The shift in Bests is understandable and demonstrates the importance of picking your competition. The severe drop in preference points illustrates more acutely the ancient principle of being a big fish in a small pond. These same tasters thought that Sam Adams was objectively much better when it was surrounded by Busch and Schmidt's. c) Value rankings. Last time this calculation led to what the colorful French would call a bouleversement. One of the cheapest beers, Busch, which had been in the lower ranks on overall preference points, came out at the top on value-for-money ratings, because it was so cheap. The big surprise now is that the highest-rated beer was also the cheapest one, Michelob Hefe , so the value calculation turned into a rout: Pyramid Hefeweizen was expensive on top of being unpopular, so its position at the bottom was hammered home--but not as painfully as that of Bass Ale . Bass had been in the respectable lower middle class of the preference rankings, so its disappointing Val-u-meter showing mainly reflects the fact that it was the only beer not on "sale" and therefore by far the costliest entry in the experiment. d) Taster skill. As members of the tasting panel began to suspect, they themselves were being judged while they judged the beer. One of the tasters, No. 7, decided to live dangerously and give specific brands and breweries for Samples A through J. This man was the only panel member whose job does not involve designing Microsoft Word--and the only one to identify two or more of the beers accurately and specifically. (He spotted Redhook IPA and Redhook ESB.) The fact that the beers correctly identified were the two most popular microbrews in the Seattle area suggests that familiarity is the main ingredient in knowing your beer. Many others were simply lost. Barely half the tasters, five of nine, recognized that Michelob Hefeweizen was a Hefeweizen. Before the test, nine of nine would have said that picking out a Hefe was easy, because of its cloudy look and wheaty flavor. Three tasters thought Sam Adams was an IPA ; two thought Redhook's IPA was a Hefeweizen. In fairness, six of nine testers identified Pyramid Hefeweizen as a Hefe, and six recognized Full Sail ESB as a bitter. Much in the fashion of blind men describing an elephant, here is a how the testers handled Sam Adams Boston Lager : 5. Implications and Directions for Future Research. Science does not always answer questions; often, it raises many new ones. This excursion into beer science mainly raises the question: What kind of people are we? If we are Gradgrind-like empiricists, living our life for "welfare maximization" as described in introductory econ. courses, the conclusion is obvious. We learned from the first experiment to buy either Sam Adams (when we wanted maximum lager enjoyment per bottle) or Busch (for maximum taste and snob appeal per dollar). From this second round we see an even more efficient possibility: Buy Michelob Hefeweizen and nothing else, since on the basis of this test it's the best liked and the cheapest beer. By the way, if there is a single company whose achievements the testing panel honored, it would be Anheuser-Busch . From its brewing tanks came two of the double-crown winners of the taste tests: plain old Busch , the Taste-o-meter and Snob-o-meter victor of Round 1, and Michelob Hefeweizen , the preference-point and Val-u-meter winner this time. But, of course, there is another possibility: that what is excluded in a blind taste test is in fact what we want, and are happy to pay for, when we sit down with a beer. The complicated label, the fancy bottle, the exotic concept that this beer has traveled from some far-off corner of Bohemia or even the Yakima Valley--all this may be cheap at the $1.25-per-pint cost difference between the cheapest and the most expensive beers. In elementary school, we all endured a standard science experiment: If you shut your eyes and pinch your nose closed, can you tell any difference in the taste of a slice of apple, of carrot, of pear? You can't--but that doesn't mean that from then on you should close your eyes, hold your nose, and chew a cheap carrot when you feel like having some fruit. There is a time and place for carrots, but also for juicy pears. There is a time for Busch, but also for Full Sail "Equinox." For scientists who want to continue this work at home, here are a few suggestions for further research: Tell the testers ahead of time what beers they will be drinking. Ask them to rank the beers, 1 through 10, based on how well they like them. Then compare the list with the "revealed preferences" that come from the blind test. As a variation, show them the list ahead of time and ask them to pick out the beer they know they love and the one they know they hate. Then compare this with the "after" list. If you're going to test imported lagers, try Foster's or Corona rather than Grolsch. Remember to stay strictly in the scientist's role. Don't take the test yourself. | B. Give the test subjects a palette cleanser (they didn't and it would make the data a lot cleaner in future studies) |
Why is Doc struggling with a man at the beginning?
A. The man had insulted the narrator, and he couldn't stand for that, so he attacked him
B. Doc was trying to get information from the man that he was refusing to share
C. Doc was in the throes of withdrawal and was easily upset, latching on to the closest person he saw
D. The other man was argumentative and didn't think Doc knew the truth about the story he was telling
| Confidence Game By JIM HARMON Illustrated by EPSTEIN [Transcriber's Note: This etext was produced from Galaxy Science Fiction June 1957. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I admit it: I didn't know if I was coming or going—but I know that if I stuck to the old man, I was a comer ... even if he was a goner! Doc had this solemn human by the throat when I caught up with him. "Tonight," Doc was saying in his old voice that was as crackled and important as parchment, "tonight Man will reach the Moon. The golden Moon and the silver ship, symbols of greed. Tonight is the night when this is to happen." "Sure," the man agreed severely, prying a little worriedly at Doc's arthritic fingers that were clamped on his collar. "No argument. Sure, up we go. But leave me go or, so help me, I'll fetch you one in the teeth!" I came alongside and carefully started to lever the old man loose, one finger at a time. It had to be done this way. I had learned that during all these weeks and months. His hands looked old and crippled, but I felt they were the strongest in the world. If a half dozen winos in Seattle hadn't helped me get them loose, Doc and I would have been wanted for the murder of a North American Mountie. It was easier this night and that made me afraid. Doc's thin frame, layered with lumpy fat, was beginning to muscle-dance against my side. One of his times was coming on him. Then at last he was free of the greasy collar of the human. "I hope you'll forgive him, sir," I said, not meeting the man's eyes. "He's my father and very old, as you can see." I laughed inside at the absurd, easy lie. "Old events seem recent to him." The human nodded, Adam's apple jerking in the angry neon twilight. "'Memory Jump,' you mean. All my great-grandfathers have it. But Great-great-grandmother Lupos, funny thing, is like a schoolgirl. Sharp, you know. I.... Say, the poor old guy looks sick. Want any help?" I told the human no, thanks, and walked Doc toward the flophouse three doors down. I hoped we would make it. I didn't know what would happen if we didn't. Doc was liable to say something that might nova Sol, for all I knew. Martians approaching the corner were sensing at Doc and me. They were just cheap tourists slumming down on Skid Row. I hated tourists and especially I hated Martian tourists because I especially hated Martians. They were aliens . They weren't men like Doc and me. Then I realized what was about to happen. It was foolish and awful and true. I was going to have one of mine at the same time Doc was having his. That was bad. It had happened a few times right after I first found him, but now it was worse. For some undefinable reason, I felt we kept getting closer each of the times. I tried not to think about it and helped Doc through the fly-specked flophouse doors. The tubercular clerk looked up from the gaudy comics sections of one of those little tabloids that have the funnies a week in advance. "Fifteen cents a bed," he said mechanically. "We'll use one bed," I told him. "I'll give you twenty cents." I felt the round hard quarter in my pocket, sweaty hand against sticky lining. "Fifteen cents a bed," he played it back for me. Doc was quivering against me, his legs boneless. "We can always make it over to the mission," I lied. The clerk turned his upper lip as if he were going to spit. "Awright, since we ain't full up. In ad vance." I placed the quarter on the desk. "Give me a nickel." The clerk's hand fell on the coin and slid it off into the unknown before I could move, what with holding up Doc. "You've got your nerve," he said at me with a fine mist of dew. "Had a quarter all along and yet you Martian me down to twenty cents." He saw the look on my face. "I'll give you a room for the two bits. That's better'n a bed for twenty." I knew I was going to need that nickel. Desperately. I reached across the desk with my free hand and hauled the scrawny human up against the register hard. I'm not as strong in my hands as Doc, but I managed. "Give me a nickel," I said. "What nickel?" His eyes were big, but they kept looking right at me. "You don't have any nickel. You don't have any quarter, not if I say so. Want I should call a cop and tell him you were flexing a muscle?" I let go of him. He didn't scare me, but Doc was beginning to mumble and that did scare me. I had to get him alone. "Where's the room?" I asked. The room was six feet in all directions and the walls were five feet high. The other foot was finished in chicken wire. There was a wino singing on the left, a wino praying on the right, and the door didn't have any lock on it. At last, Doc and I were alone. I laid Doc out on the gray-brown cot and put his forearm over his face to shield it some from the glare of the light bulb. I swept off all the bedbugs in sight and stepped on them heavily. Then I dropped down into the painted stool chair and let my burning eyes rest on the obscene wall drawings just to focus them. I was so dirty, I could feel the grime grinding together all over me. My shaggy scalp still smarted from the alcohol I had stolen from a convertible's gas tank to get rid of Doc's and my cooties. Lucky that I never needed to shave and that my face was so dirty, no one would even notice that I didn't need to. The cramp hit me and I folded out of the chair onto the littered, uncovered floor. It stopped hurting, but I knew it would begin if I moved. I stared at a jagged cut-out nude curled against a lump of dust and lint, giving it an unreal distortion. Doc began to mumble louder. I knew I had to move. I waited just a moment, savoring the painless peace. Then, finally, I moved. I was bent double, but I got from the floor to the chair and found my notebook and orb-point in my hands. I found I couldn't focus both my mind and my eyes through the electric flashes of agony, so I concentrated on Doc's voice and trusted my hands would follow their habit pattern and construct the symbols for his words. They were suddenly distinguishable. " Outsider ... Thoth ... Dyzan ... Seven ... Hsan ... Beyond Six, Seven, Eight ... Two boxes ... Ralston ... Richard Wentworth ... Jimmy Christopher ... Kent Allard ... Ayem ... Oh, are ... see ...." His voice rose to a meaningless wail that stretched into non-existence. The pen slid across the scribbled face of the notebook and both dropped from my numb hands. But I knew. Somehow, inside me, I knew that these words were what I had been waiting for. They told everything I needed to know to become the most powerful man in the Solar Federation. That wasn't just an addict's dream. I knew who Doc was. When I got to thinking it was just a dream and that I was dragging this old man around North America for nothing, I remembered who he was. I remembered that he was somebody very important whose name and work I had once known, even if now I knew him only as Doc. Pain was a pendulum within me, swinging from low throbbing bass to high screaming tenor. I had to get out and get some. But I didn't have a nickel. Still, I had to get some. I crawled to the door and raised myself by the knob, slick with greasy dirt. The door opened and shut—there was no lock. I shouldn't leave Doc alone, but I had to. He was starting to cry. He didn't always do that. I listened to him for a moment, then tested and tasted the craving that crawled through my veins. I got back inside somehow. Doc was twisting on the cot, tears washing white streaks across his face. I shoved Doc's face up against my chest. I held onto him and let him bellow. I soothed the lanks of soiled white hair back over his lumpy skull. He shut up at last and I laid him down again and put his arm back across his face. (You can't turn the light off and on in places like that. The old wiring will blow the bulb half the time.) I don't remember how I got out onto the street. She was pink and clean and her platinum hair was pulled straight back, drawing her cheek-bones tighter, straightening her wide, appealing mouth, drawing her lean, athletic, feminine body erect. She was wearing a powder-blue dress that covered all of her breasts and hips and the upper half of her legs. The most wonderful thing about her was her perfume. Then I realized it wasn't perfume, only the scent of soap. Finally, I knew it wasn't that. It was just healthy, fresh-scrubbed skin. I went to her at the bus stop, forcing my legs not to stagger. Nobody would help a drunk. I don't know why, but nobody will help you if they think you are blotto. "Ma'am, could you help a man who's not had work?" I kept my eyes down. I couldn't look a human in the eye and ask for help. "Just a dime for a cup of coffee." I knew where I could get it for three cents, maybe two and a half. I felt her looking at me. She spoke in an educated voice, one she used, perhaps, as a teacher or supervising telephone operator. "Do you want it for coffee, or to apply, or a glass or hypo of something else?" I cringed and whined. She would expect it of me. I suddenly realized that anybody as clean as she was had to be a tourist here. I hate tourists. "Just coffee, ma'am." She was younger than I was, so I didn't have to call her that. "A little more for food, if you could spare it." I hadn't eaten in a day and a half, but I didn't care much. "I'll buy you a dinner," she said carefully, "provided I can go with you and see for myself that you actually eat it." I felt my face flushing red. "You wouldn't want to be seen with a bum like me, ma'am." "I'll be seen with you if you really want to eat." It was certainly unfair and probably immoral. But I had no choice whatever. "Okay," I said, tasting bitterness over the craving. The coffee was in a thick white cup before me on the counter. It was pale, grayish brown and steaming faintly. I picked it up in both hands to feel its warmth. Out of the corner of my eye, I could see the woman sitting on the stool beside me. She had no right to intrude. This moment should be mine, but there she sat, marring it for me, a contemptible tourist . I gulped down the thick, dark liquid brutally. It was all I could do. The cramp flowed out of my diaphragm. I took another swallow and was able to think straight again. A third swallow and I felt—good. Not abnormally stimulated, but strong, alert, poised on the brink of exhilaration. That was what coffee did for me. I was a caffeine addict. Earth-norm humans sometimes have the addiction to a slight extent, but I knew that as a Centurian I had it infinitely worse. Caffeine affected my metabolism like a pure alkaloid. The immediate effects weren't the same, but the need ran as deep. I finished the cup. I didn't order another because I wasn't a pure sensualist. I just needed release. Sometimes, when I didn't have the price of a cup, I would look around in alleys and find cola bottles with a few drops left in them. They have a little caffeine in them—not enough, never enough, but better than nothing. "Now what do you want to eat?" the woman asked. I didn't look at her. She didn't know. She thought I was a human—an Earth human. I was a man , of course, not an alien like a Martian. Earthmen ran the whole Solar Federation, but I was just as good as an Earthman. With my suntan and short mane, I could pass, couldn't I? That proved it, didn't it? "Hamburger," I said. "Well done." I knew that would probably be all they had fit to eat at a place like this. It might be horse meat, but then I didn't have the local prejudices. I didn't look at the woman. I couldn't. But I kept remembering how clean she looked and I was aware of how clean she smelled. I was so dirty, so very dirty that I could never get clean if I bathed every hour for the rest of my life. The hamburger was engulfed by five black-crowned, broken fingernails and raised to two rows of yellow ivory. I surrounded it like an ameba, almost in a single movement of my jaws. Several other hamburgers followed the first. I lost count. I drank a glass of milk. I didn't want to black out on coffee with Doc waiting for me. "Could I have a few to take with me, miss?" I pleaded. She smiled. I caught that out of the edge of my vision, but mostly I just felt it. "That's the first time you've called me anything but 'ma'am'," she said. "I'm not an old-maid schoolteacher, you know." That probably meant she was a schoolteacher, though. "No, miss," I said. "It's Miss Casey—Vivian Casey," she corrected. She was a schoolteacher, all right. No other girl would introduce herself as Miss Last Name. Then there was something in her voice.... "What's your name?" she said to me. I choked a little on a bite of stale bun. I had a name, of course . Everybody has a name, and I knew if I went off somewhere quiet and thought about it, mine would come to me. Meanwhile, I would tell the girl that my name was ... Kevin O'Malley. Abruptly I realized that that was my name. "Kevin," I told her. "John Kevin." "Mister Kevin," she said, her words dancing with bright absurdity like waterhose mist on a summer afternoon, "I wonder if you could help me ." "Happy to, miss," I mumbled. She pushed a white rectangle in front of me on the painted maroon bar. "What do you think of this?" I looked at the piece of paper. It was a coupon from a magazine. Dear Acolyte R. I. S. : Please send me FREE of obligation, in sealed wrapper, "The Scarlet Book" revealing to me how I may gain Secret Mastery of the Universe. Name : ........................ Address : ..................... The world disoriented itself and I was on the floor of the somber diner and Miss Vivian Casey was out of sight and scent. There was a five dollar bill tight in my fist. The counterman was trying to pull it out. I looked up at his stubbled face. "I had half a dozen hamburgers, a cup of coffee and a glass of milk. I want four more 'burgers to go and a pint of coffee. By your prices, that will be one sixty-five—if the lady didn't pay you." "She didn't," he stammered. "Why do you think I was trying to get that bill out of your hand?" I didn't say anything, just got up off the floor. After the counterman put down my change, I spread out the five dollar bill on the vacant bar, smoothing it. I scooped up my change and walked out the door. There was no one on the sidewalk, only in the doorways. First I opened the door on an amber world, then an azure one. Neon light was coming from the chickenwire border of the room, from a window somewhere beyond. The wino on one side of the room was singing and the one on the other side was praying, same as before. Only they had changed around—prayer came from the left, song from the right. Doc sat on the floor in the half-darkness and he had made a thing . My heart hammered at my lungs. I knew this last time had been different. Whatever it was was getting closer. This was the first time Doc had ever made anything. It didn't look like much, but it was a start. He had broken the light bulb and used the filament and screw bottom. His strong hands had unraveled some of the bed "springs"—metal webbing—and fashioned them to his needs. My orb-point pen had dissolved under his touch. All of them, useless parts, were made into a meaningful whole. I knew the thing had meaning, but when I tried to follow its design, I became lost. I put the paper container of warm coffee and the greasy bag of hamburgers on the wooden chair, hoping the odor wouldn't bring any hungry rats out of the walls. I knelt beside Doc. "An order, my boy, an order," he whispered. I didn't know what he meant. Was he suddenly trying to give me orders? He held something out to me. It was my notebook. He had used my pen, before dismantling it, to write something. I tilted the notebook against the neon light, now red wine, now fresh grape. I read it. "Concentrate," Doc said hoarsely. "Concentrate...." I wondered what the words meant. Wondering takes a kind of concentration. The words "First Edition" were what I was thinking about most. The heavy-set man in the ornate armchair was saying, "The bullet struck me as I was pulling on my boot...." I was kneeling on the floor of a Victorian living room. I'm quite familiar with Earth history and I recognized the period immediately. Then I realized what I had been trying to get from Doc all these months—time travel. A thin, sickly man was sprawled in the other chair in a rumpled dressing gown. My eyes held to his face, his pinpoint pupils and whitened nose. He was a condemned snowbird! If there was anything I hated or held in more contempt than tourists or Martians, it was a snowbird. "My clients have occasioned singular methods of entry into these rooms," the thin man remarked, "but never before have they used instantaneous materialization." The heavier man was half choking, half laughing. "I say—I say, I would like to see you explain this, my dear fellow." "I have no data," the thin man answered coolly. "In such instance, one begins to twist theories into fact, or facts into theories. I must ask this unemployed, former professional man who has gone through a serious illness and is suffering a more serious addiction to tell me the place and time from which he comes." The surprise stung. "How did you know?" I asked. He gestured with a pale hand. "To maintain a logical approach, I must reject the supernatural. Your arrival, unless hallucinatory—and despite my voluntary use of one drug and my involuntary experiences recently with another, I must accept the evidence of my senses or retire from my profession—your arrival was then super-normal. I might say super-scientific, of a science not of my or the good doctor's time, clearly. Time travel is a familiar folk legend and I have been reading an article by the entertaining Mr. Wells. Perhaps he will expand it into one of his novels of scientific romance." I knew who these two men were, with a tormenting doubt. "But the other—" "Your hands, though unclean, have never seen physical labor. Your cranial construction is of a superior type, or even if you reject my theories, concentration does set the facial features. I judge you have suffered an illness because of the inhibition of your beard growth. Your over-fondness for rum or opium, perhaps, is self-evident. You are at too resilient an age to be so sunk by even an amour. Why else then would you let yourself fall into such an underfed and unsanitary state?" He was so smug and so sure, this snowbird. I hated him. Because I couldn't trust to my own senses as he did. "You don't exist," I said slowly, painfully. "You are fictional creations." The doctor flushed darkly. "You give my literary agent too much credit for the addition of professional polish to my works." The other man was filling a large, curved pipe from something that looked vaguely like an ice-skate. "Interesting. Perhaps if our visitor would tell us something of his age with special reference to the theory and practice of temporal transference, Doctor, we would be better equipped to judge whether we exist." There was no theory or practice of time travel. I told them all I had ever heard theorized from Hindu yoga through Extra-sensory Perception to Relativity and the positron and negatron. "Interesting." He breathed out suffocating black clouds of smoke. "Presume that the people of your time by their 'Extra-sensory Perception' have altered the past to make it as they suppose it to be. The great historical figures are made the larger than life-size that we know them. The great literary creations assume reality." I thought of Cleopatra and Helen of Troy and wondered if they would be the goddesses of love that people imagined or the scrawny, big-nosed redhead and fading old woman of scholarship. Then I noticed the detective's hand that had been resting idly on a round brass weight of unknown sort to me. His tapered fingertips had indented the metal. His bright eyes followed mine and he smiled faintly. "Withdrawal symptoms." The admiration and affection for this man that had been slowly building up behind my hatred unbrinked. I remembered now that he had stopped. He was not really a snowbird. After a time, I asked the doctor a question. "Why, yes. I'm flattered. This is the first manuscript. Considering my professional handwriting, I recopied it more laboriously." Accepting the sheaf of papers and not looking back at these two great and good men, I concentrated on my own time and Doc. Nothing happened. My heart raced, but I saw something dancing before me like a dust mote in sunlight and stepped toward it.... ... into the effective range of Miss Casey's tiny gun. She inclined the lethal silver toy. "Let me see those papers, Kevin." I handed her the doctor's manuscript. Her breath escaped slowly and loudly. "It's all right. It's all right. It exists. It's real. Not even one of the unwritten ones. I've read this myself." Doc was lying on the cot, half his face twisted into horror. "Don't move, Kevin," she said. "I'll have to shoot you—maybe not to kill, but painfully." I watched her face flash blue, red, blue and knew she meant it. But I had known too much in too short a time. I had to help Doc, but there was something else. "I just want a drink of coffee from that container on the chair," I told her. She shook her head. "I don't know what you think it does to you." It was getting hard for me to think. "Who are you?" She showed me a card from her wrist purse. Vivian Casey, Constable, North American Mounted Police. I had to help Doc. I had to have some coffee. "What do you want?" "Listen, Kevin. Listen carefully to what I am saying. Doc found a method of time travel. It was almost a purely mathematical, topographical way divorced from modern physical sciences. He kept it secret and he wanted to make money with it. He was an idealist—he had his crusades. How can you make money with time travel?" I didn't know whether she was asking me, but I didn't know. All I knew was that I had to help Doc and get some coffee. "It takes money—money Doc didn't have—to make money," Miss Casey said, "even if you know what horse will come in and what stock will prosper. Besides, horse-racing and the stock market weren't a part of Doc's character. He was a scholar." Why did she keep using the past tense in reference to Doc? It scared me. He was lying so still with the left side of his face so twisted. I needed some coffee. "He became a book finder. He got rare editions of books and magazines for his clients in absolutely mint condition. That was all right—until he started obtaining books that did not exist ." I didn't know what all that was supposed to mean. I got to the chair, snatched up the coffee container, tore it open and gulped down the soothing liquid. I turned toward her and threw the rest of the coffee into her face. The coffee splashed out over her platinum hair and powder-blue dress that looked white when the neon was azure, purple when it was amber. The coffee stained and soiled and ruined, and I was fiercely glad, unreasonably happy. I tore the gun away from her by the short barrel, not letting my filthy hands touch her scrubbed pink ones. I pointed the gun generally at her and backed around the thing on the floor to the cot. Doc had a pulse, but it was irregular. I checked for a fever and there wasn't one. After that, I didn't know what to do. I looked up finally and saw a Martian in or about the doorway. "Call me Andre," the Martian said. "A common name but foreign. It should serve as a point of reference." I had always wondered how a thing like a Martian could talk. Sometimes I wondered if they really could. "You won't need the gun," Andre said conversationally. "I'll keep it, thanks. What do you want?" "I'll begin as Miss Casey did—by telling you things. Hundreds of people disappeared from North America a few months ago." "They always do," I told him. "They ceased to exist—as human beings—shortly after they received a book from Doc," the Martian said. Something seemed to strike me in the back of the neck. I staggered, but managed to hold onto the gun and stand up. "Use one of those sneaky Martian weapons again," I warned him, "and I'll kill the girl." Martians were supposed to be against the destruction of any life-form, I had read someplace. I doubted it, but it was worth a try. "Kevin," Andre said, "why don't you take a bath?" The Martian weapon staggered me again. I tried to say something. I tried to explain that I was so dirty that I could never get clean no matter how often I bathed. No words formed. "But, Kevin," Andre said, "you aren't that dirty." The blow shook the gun from my fingers. It almost fell into the thing on the floor, but at the last moment seemed to change direction and miss it. I knew something. "I don't wash because I drink coffee." "It's all right to drink coffee, isn't it?" he asked. "Of course," I said, and added absurdly, "That's why I don't wash." "You mean," Andre said slowly, ploddingly, "that if you bathed, you would be admitting that drinking coffee was in the same class as any other solitary vice that makes people wash frequently." I was knocked to my knees. "Kevin," the Martian said, "drinking coffee represents a major vice only in Centurian humanoids, not Earth-norm human beings. Which are you? " Nothing came out of my gabbling mouth. " What is Doc's full name? " I almost fell in, but at the last instant I caught myself and said, "Doctor Kevin O'Malley, Senior." From the bed, Doc said a word. "Son." Then he disappeared. I looked at that which he had made. I wondered where he had gone, in search of what. "He didn't use that," Andre said. So I was an Earthman, Doc's son. So my addiction to coffee was all in my mind. That didn't change anything. They say sex is all in your mind. I didn't want to be cured. I wouldn't be. Doc was gone. That was all I had now. That and the thing he left. "The rest is simple," Andre said. "Doc O'Malley bought up all the stock in a certain ancient metaphysical order and started supplying members with certain books. Can you imagine the effect of the Book of Dyzan or the Book of Thoth or the Seven Cryptical Books of Hsan or the Necronomican itself on human beings?" "But they don't exist," I said wearily. "Exactly, Kevin, exactly. They have never existed any more than your Victorian detective friend. But the unconscious racial mind has reached back into time and created them. And that unconscious mind, deeper than psychology terms the subconscious, has always known about the powers of ESP, telepathy, telekinesis, precognition. Through these books, the human race can tell itself how to achieve a state of pure logic, without food, without sex, without conflict—just as Doc has achieved such a state—a little late, true. He had a powerful guilt complex, even stronger than your withdrawal, over releasing this blessing on the inhabited universe, but reason finally prevailed. He had reached a state of pure thought." "The North American government has to have this secret, Kevin," the girl said. "You can't let it fall into the hands of the Martians." Andre did not deny that he wanted it to fall into his hands. I knew I could not let Doc's—Dad's—time travel thing fall into anyone's hands. I remembered that all the copies of the books had disappeared with their readers now. There must not be any more, I knew. Miss Casey did her duty and tried to stop me with a judo hold, but I don't think her heart was in it, because I reversed and broke it. I kicked the thing to pieces and stomped on the pieces. Maybe you can't stop the progress of science, but I knew it might be millenniums before Doc's genes and creative environment were recreated and time travel was rediscovered. Maybe we would be ready for it then. I knew we weren't now. Miss Casey leaned against my dirty chest and cried into it. I didn't mind her touching me. "I'm glad," she said. Andre flowed out of the doorway with a sigh. Of relief? I would never know. I supposed I had destroyed it because I didn't want the human race to become a thing of pure reason without purpose, direction or love, but I would never know for sure. I thought I could kick the habit—perhaps with Miss Casey's help—but I wasn't really confident. Maybe I had destroyed the time machine because a world without material needs would not grow and roast coffee. | C. Doc was in the throes of withdrawal and was easily upset, latching on to the closest person he saw |
Which debt securities are registered to trade on a national securities exchange under Ulta Beauty's name as of FY2023? | Evidence 0:
UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
Washington, DC 20549
FORM 10-K
Annual Report Pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934
For the fiscal year ended January 28, 2023
or
Transition Report Pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934
For the transition period from _____________ to _____________
Commission File Number: 001-33764
ULTA BEAUTY, INC.
(Exact name of registrant as specified in its charter)
Delaware
(State or other jurisdiction of
incorporation or organization)
38-4022268
(I.R.S. Employer
Identification No.)
1000 Remington Blvd., Suite 120
Bolingbrook, Illinois
(Address of principal executive offices)
60440
(Zip code)
Registrants telephone number, including area code: (630) 410-4800
Securities registered pursuant to Section 12(b) of the Act:
Title of each class
Trading symbol
Name of each exchange on which registered
Common stock, par value $0.01 per share
ULTA
The NASDAQ Global Select Market
Securities registered pursuant to Section 12(g) of the Act: None | There are none |
Why does Jack stop going to meetings for the terminally ill?
A. His apartment explodes, and he must move out of the meeting area.
B. He dies from a terminal illness.
C. Bob, from the testicular cancer group, has become too clingy.
D. A woman, Marla, starts coming to the same meetings. Marla is not terminally ill.
| Boys Do Bleed Fight Club is silly stuff, sensationalism that mistakes itself for satire, but it's also a brash and transporting piece of moviemaking, like Raging Bull on acid. The film opens with--literally--a surge of adrenalin, which travels through the bloodstream and into the brain of its protagonist, Jack (Edward Norton), who's viewed, as the camera pulls out of his insides, with a gun stuck in his mouth. How'd he get into this pickle? He's going to tell you, breezily, and the director, David Fincher, is going to illustrate his narrative--violently. Fincher ( Seven , 1995; The Game , 1997) is out to bombard you with so much feverish imagery that you have no choice but to succumb to the movie's reeling, punch-drunk worldview. By the end, you might feel as if you, too, have a mouthful of blood. Not to mention a hole in your head. Fight Club careers from one resonant satirical idea to the next without quite deciding whether its characters are full of crap or are Gen X prophets. It always gives you a rush, though. At first, it goofs on the absurd feminization of an absurdly macho culture. An increasingly desperate insomniac, Jack finds relief (and release) only at meetings for the terminally ill. At a testicular cancer group, he's enfolded in the ample arms of Bob (the singer Meat Loaf Aday), a former bodybuilder who ruined his health with steroids and now has "bitch tits." Jack and Bob subscribe to a new form of male bonding: They cling to each other and sob. But Jack's idyll is rudely disrupted by--wouldn't you know it?--a woman. A dark-eyed, sepulchral head case named Marla Singer (Helena Bonham Carter) begins showing up at all the same disparate meetings for essentially the same voyeuristic ends, and the presence of this "tourist" makes it impossible for Jack to emote. Jack finds another outlet, though. On a plane, he meets Tyler Durden (Brad Pitt), a cryptic hipster with a penchant for subversive acts both large (he makes high-priced soaps from liposuctioned human fat) and small (he splices frames from porn flicks into kiddie movies). When Jack's apartment mysteriously explodes--along with his carefully chosen IKEA furniture--he moves into Tyler's squalid warehouse and helps to found a new religion: Fight Club, in which young males gather after hours in the basement of a nightclub to pound one another (and be pounded) to a bloody pulp. That last parenthesis isn't so parenthetical. In some ways, it's the longing to be beaten into oblivion that's the strongest. "Self-improvement," explains Tyler, "is masturbation"; self-destruction is the new way. Tyler's manifesto calls for an end to consumerism ("Things you own end up owning you"), and since society is going down ("Martha Stewart is polishing brass on the Titanic "), the only creative outlet left is annihilation. "It's only after we've lost everything that we're free to do anything," he says. Fincher and his screenwriter, Jim Uhls, seem to think they've broken new ground in Fight Club , that their metaphor for our discontents hits harder than anyone else's. Certainly it produces more bloody splatter. But 20 years ago, the same impulse was called punk and, as Greil Marcus documents in Lipstick Traces , it was other things before that. Yes, the mixture of Johnny Rotten, Jake La Motta, and Jesus is unique; and the Faludi-esque emasculation themes are more explicit. But there's something deeply movie-ish about the whole conceit, as if the novelist and director were weaned on Martin Scorsese pictures and never stopped dreaming of recapturing that first masochistic rush. The novel, the first by Chuck Palahniuk (the surname sounds like Eskimo for "palooka"--which somehow fits), walks a line between the straight and ironic--it isn't always clear if its glib sociological pronouncements are meant to be taken straight or as the ravings of a delusional mama's boy. But onscreen, when Pitt announces to the assembled fighters that they are the "middle children of history" with "no purpose and no place"--emasculated on one hand by the lack of a unifying crisis (a world war or depression) and on the other by lack of material wealth as promised by television--he seems meant to be intoning gospel. "We are a generation of men raised by women," Tyler announces, and adds, "If our fathers bail, what does that tell you about God?" (I give up: What?) F ight Club could use a few different perspectives: a woman's, obviously, but also an African-American's--someone who'd have a different take on the "healing" properties of violence. It's also unclear just what has emasculated Jack: Is it that he's a materialist or that the materials themselves (i.e., IKEA's lacquered particle boards) don't measure up to his fantasies of opulence? Is he motivated by spiritual hunger or envy? Tyler's subsequent idea of confining his group's mayhem to franchise coffee bars and corporate-subsidized art is a witty one--it's like a parody of neo-Nazism as re-enacted by yuppies. It might have been a howl if performed by, say, the troupe of artsy German nihilists in Joel and Ethan Coen's The Big Lebowski (1998). Somehow Brad Pitt doesn't have the same piquancy. Actually, Pitt isn't as terrible as usual: He's playing not a character but a conceit, and he can bask in his movie-idol arrogance, which seems to be the most authentic emotion he has. But the film belongs to Norton. As a ferocious skinhead in last year's American History X , Norton was taut and ropy, his long torso curled into a sneer; here, he's skinny and wilting, a quivering pansy. Even when he fights he doesn't transform--he's a raging wimp. The performance is marvelous, and it makes poetic sense in light of the movie's climactic twist. But that twist will annoy more people than it will delight, if only because it shifts the drama from the realm of the sociological to that of the psychoanalytic. The finale, scored with the Pixies' great "Where Is My Mind?" comes off facetiously--as if Fincher is throwing the movie away. Until then, however, he has done a fabulous job of keeping it spinning. The most thrilling thing about Fight Club isn't what it says but how Uhls and Fincher pull you into its narrator's head and simulate his adrenalin rushes. A veteran of rock videos, Fincher is one of those filmmakers who helps make the case that MTV--along with digital editing--has transformed cinema for better as well as worse. The syntax has become more intricate. Voice-over narration, once considered uncinematic, is back in style, along with novelistic asides, digressions, fantasies, and flashbacks. To make a point, you can jazzily interject anything--even, as in Three Kings , a shot of a bullet slicing through internal organs. Films like Fight Club might not gel, but they have a breathless, free-associational quality that points to new possibilities in storytelling. Or maybe old possibilities: The language of movies hasn't seemed this unfettered since the pre-sound days of Sergei Eisenstein and Abel Gance. An actress named Hilary Swank gives one of the most rapturous performances I've ever seen as the cross-dressing Brandon Teena (a k a Teena Brandon) in Kimberly Peirce's stark and astonishingly beautiful debut feature, Boys Don't Cry . The movie opens with Teena being shorn of her hated female tresses and becoming "Brandon," who swaggers around in tight jeans and leather jackets. The joy is in watching the actor transform, and I don't just mean Swank: I mean Teena Brandon playing Brandon Teena--the role she has been longing for her whole life. In a redneck Nebraska bar, Brandon throws back a shot of whiskey and the gesture--a macho cliché--becomes an act of self-discovery. Every gesture does. "You're gonna have a shiner in the morning," someone tells Brandon after a barroom brawl, and he takes the news with a glee that's almost mystical: "I am????? Oh, shit!!!" he cries, grinning. That might be my favorite moment in the picture, because Swank's ecstatic expression carries us through the next hour, as Brandon acts out his urban-cowboy fantasies--"surfing" from the bumper of a pickup truck, rolling in the mud, and straddling a barstool with one hand on a brewski and the other on the shoulder of a gorgeous babe. That the people with whom Brandon feels most at home would kill him if they knew his true gender is the movie's most tragic irony--and the one that lifts it out of the realm of gay-martyr hagiography and into something more complex and irreducible: a meditation on the irrelevance of gender. Peirce's triumph is to make these scenes at once exuberant (occasionally hilarious) and foreboding, so that all the seeds of Brandon's killing are right there on the screen. John (Peter Sarsgaard), one of his future rapists and murderers, calls him "little buddy" and seems almost attracted to him; Sarsgaard's performance is a finely chiseled study of how unresolved emotion can suddenly resolve itself into violence. Though harrowing, the second half of Boys Don't Cry isn't as great as the first. The early scenes evoke elation and dread simultaneously, the later ones just dread; and the last half-hour is unrelieved torture. What keeps the movie tantalizing is Chloë Sevigny's Lana, who might or might not know that Brandon is a girl but who's entranced by him anyway. With her lank hair, hooded eyes, and air of sleepy sensuality, Sevigny--maybe even more than Swank--embodies the mystery of sex that's at the core of Boys Don't Cry . Everything she does is deliberate, ironic, slightly unreadable--and unyielding. She's could be saying, "I'm in this world but not of it. ... You'd never dream what's underneath." I n brief: If a friend tells you you'll love Happy Texas , rethink the friendship. This clunky mistaken-identity comedy about escaped cons who impersonate gay pageant directors doesn't even make sense on its own low farcical terms; it's mostly one lame homo joke after another. The only bright spot is Steve Zahn, who could be the offspring of Michael J. Fox and Crispin Glover if they'd mated on the set of Back to the Future (1985). It's hard to make a serious case for Lawrence Kasdan's Mumford , which has apparently flopped but which you can still catch at second- and third-tier theaters. It looks peculiar--a Norman Rockwell painting with noir shadows. And its tale of a small town healed by a depressive (Loren Dean) posing as a psychologist is full of doddering misconceptions about psychotherapy. I almost don't know why I loved it, but the relaxed pacing and the witty turns by Martin Short, Ted Danson, David Paymer, and Mary McDonnell surely helped. I can't decide if the weirdly affectless Dean is inspired or inept, but my indecision suggests why he works in the role. There's no doubt, however, about his even more depressive love object, Hope Davis, who posseses the cinema's most expressive honking-nasal voice and who slumps through the movie like the world's most lyrical anti-ballerina. Even her puffy cheeks are eloquent: They made me think of Mumford as the home of the psychological mumps. | D. A woman, Marla, starts coming to the same meetings. Marla is not terminally ill. |
Why is supporting fact supervision necessary for DMN? | ### Introduction
Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neural networks. For instance, memory networks BIBREF2 are able to reason over several facts written in natural language or (subject, relation, object) triplets. Attention mechanisms have been successful components in both machine translation BIBREF3 , BIBREF4 and image captioning models BIBREF5 . The dynamic memory network BIBREF6 (DMN) is one example of a neural network model that has both a memory component and an attention mechanism. The DMN yields state of the art results on question answering with supporting facts marked during training, sentiment analysis, and part-of-speech tagging. We analyze the DMN components, specifically the input module and memory module, to improve question answering. We propose a new input module which uses a two level encoder with a sentence reader and input fusion layer to allow for information flow between sentences. For the memory, we propose a modification to gated recurrent units (GRU) BIBREF7 . The new GRU formulation incorporates attention gates that are computed using global knowledge over the facts. Unlike before, the new DMN+ model does not require that supporting facts (i.e. the facts that are relevant for answering a particular question) are labeled during training. The model learns to select the important facts from a larger set. In addition, we introduce a new input module to represent images. This module is compatible with the rest of the DMN architecture and its output is fed into the memory module. We show that the changes in the memory module that improved textual question answering also improve visual question answering. Both tasks are illustrated in Fig. 1 . ### Dynamic Memory Networks
We begin by outlining the DMN for question answering and the modules as presented in BIBREF6 . The DMN is a general architecture for question answering (QA). It is composed of modules that allow different aspects such as input representations or memory components to be analyzed and improved independently. The modules, depicted in Fig. 1 , are as follows: Input Module: This module processes the input data about which a question is being asked into a set of vectors termed facts, represented as $F=[f_1,\hdots ,f_N]$ , where $N$ is the total number of facts. These vectors are ordered, resulting in additional information that can be used by later components. For text QA in BIBREF6 , the module consists of a GRU over the input words. As the GRU is used in many components of the DMN, it is useful to provide the full definition. For each time step $i$ with input $x_i$ and previous hidden state $h_{i-1}$ , we compute the updated hidden state $h_i = GRU(x_i,h_{i-1})$ by $$u_i &=& \sigma \left(W^{(u)}x_{i} + U^{(u)} h_{i-1} + b^{(u)} \right)\\
r_i &=& \sigma \left(W^{(r)}x_{i} + U^{(r)} h_{i-1} + b^{(r)} \right)\\
\tilde{h}_i &=& \tanh \left(Wx_{i} + r_i \circ U h_{i-1} + b^{(h)}\right)\\
h_i &=& u_i\circ \tilde{h}_i + (1-u_i) \circ h_{i-1}$$ (Eq. 2) where $\sigma $ is the sigmoid activation function, $\circ $ is an element-wise product, $W^{(z)}, W^{(r)}, W \in \mathbb {R}^{n_H \times n_I}$ , $U^{(z)}, U^{(r)}, U \in \mathbb {R}^{n_H \times n_H}$ , $n_H$ is the hidden size, and $n_I$ is the input size. Question Module: This module computes a vector representation $q$ of the question, where $q \in \mathbb {R}^{n_H}$ is the final hidden state of a GRU over the words in the question. Episodic Memory Module: Episode memory aims to retrieve the information required to answer the question $q$ from the input facts. To improve our understanding of both the question and input, especially if questions require transitive reasoning, the episode memory module may pass over the input multiple times, updating episode memory after each pass. We refer to the episode memory on the $t^{th}$ pass over the inputs as $m^t$ , where $m^t \in \mathbb {R}^{n_H}$ , the initial memory vector is set to the question vector: $m^0 = q$ . The episodic memory module consists of two separate components: the attention mechanism and the memory update mechanism. The attention mechanism is responsible for producing a contextual vector $c^t$ , where $c^t \in \mathbb {R}^{n_H}$ is a summary of relevant input for pass $t$ , with relevance inferred by the question $q$ and previous episode memory $m^{t-1}$ . The memory update mechanism is responsible for generating the episode memory $m^t$ based upon the contextual vector $c^t$ and previous episode memory $m^{t-1}$ . By the final pass $T$ , the episodic memory $m^T$ should contain all the information required to answer the question $c^t \in \mathbb {R}^{n_H}$0 . Answer Module: The answer module receives both $q$ and $m^T$ to generate the model's predicted answer. For simple answers, such as a single word, a linear layer with softmax activation may be used. For tasks requiring a sequence output, an RNN may be used to decode $a = [q ; m^T]$ , the concatenation of vectors $q$ and $m^T$ , to an ordered set of tokens. The cross entropy error on the answers is used for training and backpropagated through the entire network. ### Improved Dynamic Memory Networks: DMN+
We propose and compare several modeling choices for two crucial components: input representation, attention mechanism and memory update. The final DMN+ model obtains the highest accuracy on the bAbI-10k dataset without supporting facts and the VQA dataset BIBREF8 . Several design choices are motivated by intuition and accuracy improvements on that dataset. ### Input Module for Text QA
In the DMN specified in BIBREF6 , a single GRU is used to process all the words in the story, extracting sentence representations by storing the hidden states produced at the end of sentence markers. The GRU also provides a temporal component by allowing a sentence to know the content of the sentences that came before them. Whilst this input module worked well for bAbI-1k with supporting facts, as reported in BIBREF6 , it did not perform well on bAbI-10k without supporting facts (Sec. "Model Analysis" ). We speculate that there are two main reasons for this performance disparity, all exacerbated by the removal of supporting facts. First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU. Input Fusion Layer For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction. Fig. 2 shows an illustration of an input module, where a positional encoder is used for the sentence reader and a bi-directional GRU is adopted for the input fusion layer. Each sentence encoding $f_i$ is the output of an encoding scheme taking the word tokens $[w^i_1, \hdots , w^i_{M_i}]$ , where $M_i$ is the length of the sentence. The sentence reader could be based on any variety of encoding schemes. We selected positional encoding described in BIBREF10 to allow for a comparison to their work. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used. For the positional encoding scheme, the sentence representation is produced by $f_i = \sum ^{j=1}_M l_j \circ w^i_j$ , where $\circ $ is element-wise multiplication and $l_j$ is a column vector with structure $l_{jd} = (1 - j / M) - (d / D) (1 - 2j / M)$ , where $d$ is the embedding index and $D$ is the dimension of the embedding. The input fusion layer takes these input facts and enables an information exchange between them by applying a bi-directional GRU. $$\overrightarrow{f_i} = GRU_{fwd}(f_i, \overrightarrow{f_{i-1}}) \\
\overleftarrow{f_{i}} = GRU_{bwd}(f_{i}, \overleftarrow{f_{i+1}}) \\
\overleftrightarrow{f_i} = \overleftarrow{f_i} + \overrightarrow{f_i}$$ (Eq. 5) where $f_i$ is the input fact at timestep $i$ , $ \overrightarrow{f_i}$ is the hidden state of the forward GRU at timestep $i$ , and $\overleftarrow{f_i}$ is the hidden state of the backward GRU at timestep $i$ . This allows contextual information from both future and past facts to impact $\overleftrightarrow{f_i}$ . We explored a variety of encoding schemes for the sentence reader, including GRUs, LSTMs, and the positional encoding scheme described in BIBREF10 . For simplicity and speed, we selected the positional encoding scheme. GRUs and LSTMs were also considered but required more computational resources and were prone to overfitting if auxiliary tasks, such as reconstructing the original sentence, were not used. ### Input Module for VQA
To apply the DMN to visual question answering, we introduce a new input module for images. The module splits an image into small local regions and considers each region equivalent to a sentence in the input module for text. The input module for VQA is composed of three parts, illustrated in Fig. 3 : local region feature extraction, visual feature embedding, and the input fusion layer introduced in Sec. "Input Module for Text QA" . Local region feature extraction: To extract features from the image, we use a convolutional neural network BIBREF0 based upon the VGG-19 model BIBREF11 . We first rescale the input image to $448 \times 448$ and take the output from the last pooling layer which has dimensionality $d = 512 \times 14 \times 14$ . The pooling layer divides the image into a grid of $14 \times 14$ , resulting in 196 local regional vectors of $d = 512$ . Visual feature embedding: As the VQA task involves both image features and text features, we add a linear layer with tanh activation to project the local regional vectors to the textual feature space used by the question vector $q$ . Input fusion layer: The local regional vectors extracted from above do not yet have global information available to them. Without global information, their representational power is quite limited, with simple issues like object scaling or locational variance causing accuracy problems. To solve this, we add an input fusion layer similar to that of the textual input module described in Sec. "Input Module for Text QA" . First, to produce the input facts $F$ , we traverse the image in a snake like fashion, as seen in Figure 3 . We then apply a bi-directional GRU over these input facts $F$ to produce the globally aware input facts $\overleftrightarrow{F}$ . The bi-directional GRU allows for information propagation from neighboring image patches, capturing spatial information. ### The Episodic Memory Module
The episodic memory module, as depicted in Fig. 4 , retrieves information from the input facts $\overleftrightarrow{F} = [\overleftrightarrow{f_1}, \hdots , \overleftrightarrow{f_N}]$ provided to it by focusing attention on a subset of these facts. We implement this attention by associating a single scalar value, the attention gate $g^t_i$ , with each fact $\overleftrightarrow{f}_i$ during pass $t$ . This is computed by allowing interactions between the fact and both the question representation and the episode memory state. $$z^t_i &=& [\overleftrightarrow{f_i} \circ q; \overleftrightarrow{f_i} \circ m^{t-1}; \vert \overleftrightarrow{f_i} - q \vert ; \vert \overleftrightarrow{f_i} - m^{t-1} \vert ] \\
Z^t_i &=& W^{(2)} \tanh \left(W^{(1)}z^t_i + b^{(1)} \right)+ b^{(2)} \\
g^t_i &=& \frac{\exp (Z^t_i)}{\sum _{k=1}^{M_i} \exp (Z^t_k)} $$ (Eq. 10) where $\overleftrightarrow{f_i}$ is the $i^{th}$ fact, $m^{t-1}$ is the previous episode memory, $q$ is the original question, $\circ $ is the element-wise product, $|\cdot |$ is the element-wise absolute value, and $;$ represents concatenation of the vectors. The DMN implemented in BIBREF6 involved a more complex set of interactions within $z$ , containing the additional terms $[f; m^{t-1}; q; f^T W^{(b)} q; f^T W^{(b)} m^{t-1}]$ . After an initial analysis, we found these additional terms were not required. Attention Mechanism Once we have the attention gate $g^t_i$ we use an attention mechanism to extract a contextual vector $c^t$ based upon the current focus. We focus on two types of attention: soft attention and a new attention based GRU. The latter improves performance and is hence the final modeling choice for the DMN+. Soft attention: Soft attention produces a contextual vector $c^t$ through a weighted summation of the sorted list of vectors $\overleftrightarrow{F}$ and corresponding attention gates $g_i^t$ : $c^t = \sum _{i=1}^N g^t_i \overleftrightarrow{f}_i$ This method has two advantages. First, it is easy to compute. Second, if the softmax activation is spiky it can approximate a hard attention function by selecting only a single fact for the contextual vector whilst still being differentiable. However the main disadvantage to soft attention is that the summation process loses both positional and ordering information. Whilst multiple attention passes can retrieve some of this information, this is inefficient. Attention based GRU: For more complex queries, we would like for the attention mechanism to be sensitive to both the position and ordering of the input facts $\overleftrightarrow{F}$ . An RNN would be advantageous in this situation except they cannot make use of the attention gate from Equation . We propose a modification to the GRU architecture by embedding information from the attention mechanism. The update gate $u_i$ in Equation 2 decides how much of each dimension of the hidden state to retain and how much should be updated with the transformed input $x_i$ from the current timestep. As $u_i$ is computed using only the current input and the hidden state from previous timesteps, it lacks any knowledge from the question or previous episode memory. By replacing the update gate $u_i$ in the GRU (Equation 2 ) with the output of the attention gate $g^t_i$ (Equation ) in Equation , the GRU can now use the attention gate for updating its internal state. This change is depicted in Fig 5 . $$h_i &=& g^t_i \circ \tilde{h}_i + (1-g^t_i) \circ h_{i-1}$$ (Eq. 12) An important consideration is that $g^t_i$ is a scalar, generated using a softmax activation, as opposed to the vector $u_i \in \mathbb {R}^{n_H}$ , generated using a sigmoid activation. This allows us to easily visualize how the attention gates activate over the input, later shown for visual QA in Fig. 6 . Though not explored, replacing the softmax activation in Equation with a sigmoid activation would result in $g^t_i \in \mathbb {R}^{n_H}$ . To produce the contextual vector $c^t$ used for updating the episodic memory state $m^t$ , we use the final hidden state of the attention based GRU. Episode Memory Updates After each pass through the attention mechanism, we wish to update the episode memory $m^{t-1}$ with the newly constructed contextual vector $c^t$ , producing $m^t$ . In the DMN, a GRU with the initial hidden state set to the question vector $q$ is used for this purpose. The episodic memory for pass $t$ is computed by $$m^t = GRU(c^t, m^{t-1})$$ (Eq. 13) The work of BIBREF10 suggests that using different weights for each pass through the episodic memory may be advantageous. When the model contains only one set of weights for all episodic passes over the input, it is referred to as a tied model, as in the “Mem Weights” row in Table 1 . Following the memory update component used in BIBREF10 and BIBREF12 we experiment with using a ReLU layer for the memory update, calculating the new episode memory state by $$m^t = ReLU\left(W^t [m^{t-1} ; c^t ; q] + b\right)$$ (Eq. 14) where $;$ is the concatenation operator, $W^t \in \mathbb {R}^{n_H \times n_H}$ , $b \in \mathbb {R}^{n_H}$ , and $n_H$ is the hidden size. The untying of weights and using this ReLU formulation for the memory update improves accuracy by another 0.5% as shown in Table 1 in the last column. The final output of the memory network is passed to the answer module as in the original DMN. ### Related Work
The DMN is related to two major lines of recent work: memory and attention mechanisms. We work on both visual and textual question answering which have, until now, been developed in separate communities. Neural Memory Models The earliest recent work with a memory component that is applied to language processing is that of memory networks BIBREF2 which adds a memory component for question answering over simple facts. They are similar to DMNs in that they also have input, scoring, attention and response mechanisms. However, unlike the DMN their input module computes sentence representations independently and hence cannot easily be used for other tasks such as sequence labeling. Like the original DMN, this memory network requires that supporting facts are labeled during QA training. End-to-end memory networks BIBREF10 do not have this limitation. In contrast to previous memory models with a variety of different functions for memory attention retrieval and representations, DMNs BIBREF6 have shown that neural sequence models can be used for input representation, attention and response mechanisms. Sequence models naturally capture position and temporality of both the inputs and transitive reasoning steps. Neural Attention Mechanisms Attention mechanisms allow neural network models to use a question to selectively pay attention to specific inputs. They can benefit image classification BIBREF13 , generating captions for images BIBREF5 , among others mentioned below, and machine translation BIBREF14 , BIBREF3 , BIBREF4 . Other recent neural architectures with memory or attention which have proposed include neural Turing machines BIBREF15 , neural GPUs BIBREF16 and stack-augmented RNNs BIBREF17 . Question Answering in NLP Question answering involving natural language can be solved in a variety of ways to which we cannot all do justice. If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction BIBREF18 . Neural approaches can include reasoning over knowledge bases, BIBREF19 , BIBREF20 or directly via sentences for trivia competitions BIBREF21 . Visual Question Answering (VQA) In comparison to QA in NLP, VQA is still a relatively young task that is feasible only now that objects can be identified with high accuracy. The first large scale database with unconstrained questions about images was introduced by BIBREF8 . While VQA datasets existed before they did not include open-ended, free-form questions about general images BIBREF22 . Others are were too small to be viable for a deep learning approach BIBREF23 . The only VQA model which also has an attention component is the stacked attention network BIBREF24 . Their work also uses CNN based features. However, unlike our input fusion layer, they use a single layer neural network to map the features of each patch to the dimensionality of the question vector. Hence, the model cannot easily incorporate adjacency of local information in its hidden state. A model that also uses neural modules, albeit logically inspired ones, is that by BIBREF25 who evaluate on knowledgebase reasoning and visual question answering. We compare directly to their method on the latter task and dataset. Related to visual question answering is the task of describing images with sentences BIBREF26 . BIBREF27 used deep learning methods to map images and sentences into the same space in order to describe images with sentences and to find images that best visualize a sentence. This was the first work to map both modalities into a joint space with deep learning methods, but it could only select an existing sentence to describe an image. Shortly thereafter, recurrent neural networks were used to generate often novel sentences based on images BIBREF28 , BIBREF29 , BIBREF30 , BIBREF5 . ### Datasets
To analyze our proposed model changes and compare our performance with other architectures, we use three datasets. ### bAbI-10k
For evaluating the DMN on textual question answering, we use bAbI-10k English BIBREF31 , a synthetic dataset which features 20 different tasks. Each example is composed of a set of facts, a question, the answer, and the supporting facts that lead to the answer. The dataset comes in two sizes, referring to the number of training examples each task has: bAbI-1k and bAbI-10k. The experiments in BIBREF10 found that their lowest error rates on the smaller bAbI-1k dataset were on average three times higher than on bAbI-10k. ### DAQUAR-ALL visual dataset
The DAtaset for QUestion Answering on Real-world images (DAQUAR) BIBREF23 consists of 795 training images and 654 test images. Based upon these images, 6,795 training questions and 5,673 test questions were generated. Following the previously defined experimental method, we exclude multiple word answers BIBREF32 , BIBREF33 . The resulting dataset covers 90% of the original data. The evaluation method uses classification accuracy over the single words. We use this as a development dataset for model analysis (Sec. "Model Analysis" ). ### Visual Question Answering
The Visual Question Answering (VQA) dataset was constructed using the Microsoft COCO dataset BIBREF34 which contained 123,287 training/validation images and 81,434 test images. Each image has several related questions with each question answered by multiple people. This dataset contains 248,349 training questions, 121,512 validation questions, and 244,302 for testing. The testing data was split into test-development, test-standard and test-challenge in BIBREF8 . Evaluation on both test-standard and test-challenge are implemented via a submission system. test-standard may only be evaluated 5 times and test-challenge is only evaluated at the end of the competition. To the best of our knowledge, VQA is the largest and most complex image dataset for the visual question answering task. ### Model Analysis
To understand the impact of the proposed module changes, we analyze the performance of a variety of DMN models on textual and visual question answering datasets. The original DMN (ODMN) is the architecture presented in BIBREF6 without any modifications. DMN2 only replaces the input module with the input fusion layer (Sec. "Input Module for Text QA" ). DMN3, based upon DMN2, replaces the soft attention mechanism with the attention based GRU proposed in Sec. "The Episodic Memory Module" . Finally, DMN+, based upon DMN3, is an untied model, using a unique set of weights for each pass and a linear layer with a ReLU activation to compute the memory update. We report the performance of the model variations in Table 1 . A large improvement to accuracy on both the bAbI-10k textual and DAQUAR visual datasets results from updating the input module, seen when comparing ODMN to DMN2. On both datasets, the input fusion layer improves interaction between distant facts. In the visual dataset, this improvement is purely from providing contextual information from neighboring image patches, allowing it to handle objects of varying scale or questions with a locality aspect. For the textual dataset, the improved interaction between sentences likely helps the path finding required for logical reasoning when multiple transitive steps are required. The addition of the attention GRU in DMN3 helps answer questions where complex positional or ordering information may be required. This change impacts the textual dataset the most as few questions in the visual dataset are likely to require this form of logical reasoning. Finally, the untied model in the DMN+ overfits on some tasks compared to DMN3, but on average the error rate decreases. From these experimental results, we find that the combination of all the proposed model changes results, culminating in DMN+, achieves the highest performance across both the visual and textual datasets. ### Comparison to state of the art using bAbI-10k
We trained our models using the Adam optimizer BIBREF35 with a learning rate of 0.001 and batch size of 128. Training runs for up to 256 epochs with early stopping if the validation loss had not improved within the last 20 epochs. The model from the epoch with the lowest validation loss was then selected. Xavier initialization was used for all weights except for the word embeddings, which used random uniform initialization with range $[-\sqrt{3}, \sqrt{3}]$ . Both the embedding and hidden dimensions were of size $d = 80$ . We used $\ell _2$ regularization on all weights except bias and used dropout on the initial sentence encodings and the answer module, keeping the input with probability $p=0.9$ . The last 10% of the training data on each task was chosen as the validation set. For all tasks, three passes were used for the episodic memory module, allowing direct comparison to other state of the art methods. Finally, we limited the input to the last 70 sentences for all tasks except QA3 for which we limited input to the last 130 sentences, similar to BIBREF10 . On some tasks, the accuracy was not stable across multiple runs. This was particularly problematic on QA3, QA17, and QA18. To solve this, we repeated training 10 times using random initializations and evaluated the model that achieved the lowest validation set loss. Text QA Results We compare our best performing approach, DMN+, to two state of the art question answering architectures: the end to end memory network (E2E) BIBREF10 and the neural reasoner framework (NR) BIBREF12 . Neither approach use supporting facts for training. The end-to-end memory network is a form of memory network BIBREF2 tested on both textual question answering and language modeling. The model features both explicit memory and a recurrent attention mechanism. We select the model from the paper that achieves the lowest mean error over the bAbI-10k dataset. This model utilizes positional encoding for input, RNN-style tied weights for the episode module, and a ReLU non-linearity for the memory update component. The neural reasoner framework is an end-to-end trainable model which features a deep architecture for logical reasoning and an interaction-pooling mechanism for allowing interaction over multiple facts. While the neural reasoner framework was only tested on QA17 and QA19, these were two of the most challenging question types at the time. In Table 2 we compare the accuracy of these question answering architectures, both as mean error and error on individual tasks. The DMN+ model reduces mean error by 1.4% compared to the the end-to-end memory network, achieving a new state of the art for the bAbI-10k dataset. One notable deficiency in our model is that of QA16: Basic Induction. In BIBREF10 , an untied model using only summation for memory updates was able to achieve a near perfect error rate of $0.4$ . When the memory update was replaced with a linear layer with ReLU activation, the end-to-end memory network's overall mean error decreased but the error for QA16 rose sharply. Our model experiences the same difficulties, suggesting that the more complex memory update component may prevent convergence on certain simpler tasks. The neural reasoner model outperforms both the DMN and end-to-end memory network on QA17: Positional Reasoning. This is likely as the positional reasoning task only involves minimal supervision - two sentences for input, yes/no answers for supervision, and only 5,812 unique examples after removing duplicates from the initial 10,000 training examples. BIBREF12 add an auxiliary task of reconstructing both the original sentences and question from their representations. This auxiliary task likely improves performance by preventing overfitting. ### Comparison to state of the art using VQA
For the VQA dataset, each question is answered by multiple people and the answers may not be the same, the generated answers are evaluated using human consensus. For each predicted answer $a_i$ for the $i_{th}$ question with target answer set $T^{i}$ , the accuracy of VQA: $Acc_{VQA} = \frac{1}{N}\sum _{i=1}^Nmin(\frac{\sum _{t\in T^i}{1}_{(a_i==t)}}{3},1)$ where ${1}_{(\cdot )}$ is the indicator function. Simply put, the answer $a_i$ is only 100 $\%$ accurate if at least 3 people provide that exact answer. Training Details We use the Adam optimizer BIBREF35 with a learning rate of 0.003 and batch size of 100. Training runs for up to 256 epochs with early stopping if the validation loss has not improved in the last 10 epochs. For weight initialization, we sampled from a random uniform distribution with range $[-0.08, 0.08]$ . Both the word embedding and hidden layers were vectors of size $d=512$ . We apply dropout on the initial image output from the VGG convolutional neural network BIBREF11 as well as the input to the answer module, keeping input with probability $p=0.5$ . Results and Analysis The VQA dataset is composed of three question domains: Yes/No, Number, and Other. This enables us to analyze the performance of the models on various tasks that require different reasoning abilities. The comparison models are separated into two broad classes: those that utilize a full connected image feature for classification and those that perform reasoning over multiple small image patches. Only the SAN and DMN approach use small image patches, while the rest use the fully-connected whole image feature approach. Here, we show the quantitative and qualitative results in Table 3 and Fig. 6 , respectively. The images in Fig. 6 illustrate how the attention gate $g^t_i$ selectively activates over relevant portions of the image according to the query. In Table 3 , our method outperforms baseline and other state-of-the-art methods across all question domains (All) in both test-dev and test-std, and especially for Other questions, achieves a wide margin compared to the other architectures, which is likely as the small image patches allow for finely detailed reasoning over the image. However, the granularity offered by small image patches does not always offer an advantage. The Number questions may be not solvable for both the SAN and DMN architectures, potentially as counting objects is not a simple task when an object crosses image patch boundaries. ### Conclusion
We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains. Figure 1. Question Answering over text and images using a Dynamic Memory Network. Figure 2. The input module with a “fusion layer”, where the sentence reader encodes the sentence and the bi-directional GRU allows information to flow between sentences. Figure 3. VQA input module to represent images for the DMN. Figure 5. (a) The traditional GRU model, and (b) the proposed attention-based GRU model Figure 4. The episodic memory module of the DMN+ when using two passes. The ←→ F is the output of the input module. Table 2. Test error rates of various model architectures on tasks from the the bAbI English 10k dataset. E2E = End-To-End Memory Network results from Sukhbaatar et al. (2015). NR = Neural Reasoner with original auxiliary task from Peng et al. (2015). DMN+ and E2E achieve an error of 0 on bAbI question sets (1,4,10,12,13,15,20). Table 1. Test error rates of various model architectures on the bAbI-10k dataset, and accuracy performance on the DAQUAR-ALL visual dataset. The skipped bAbI questions (1,4,11,12,13,15,19) achieved 0 error across all models. Table 3. Performance of various architectures and approaches on VQA test-dev and test-standard data. Baseline only uses the spatial mean of the last pooling layer without input fusion and episoidic memory; VQA numbers are from Antol et al. (2015); ACK Wu et al. (2015); iBOWIMG -Zhou et al. (2015); DPPnet - Noh et al. (2015); D-NMN - Andreas et al. (2016); SMem-VQA -Xu & Saenko (2015); SAN -Yang et al. (2015) Figure 6. Examples of qualitative results of attention for VQA. The original images are shown on the left. On the right we show how the attention gate gti activates given one pass over the image and query. White regions are the most active. Answers are given by the DMN+. | First, the GRU only allows sentences to have context from sentences before them, but not after them. This prevents information propagation from future sentences. Second, the supporting sentences may be too far away from each other on a word level to allow for these distant sentences to interact through the word level GRU. |
What is the significance of the lifting-off of the Washington Monument at the story's conclusion?
A. Mental 'illness' could and should, in many cases, be viewed as an asset, rather than a deficit
B. Society is too quick to dismiss the thoughts and behaviors of people living with mental illness as irrational or absurd
C. People living with mental illness pose risks and/or threats to society and should be entrusted to government care
D. People living with mental illness(es) may possess abilities not understood by humans living without mental illness
| Transcriber's Note: This etext was produced from Astounding Science Fiction November 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. A FILBERT IS A NUT BY RICK RAPHAEL That the gentleman in question was a nut was beyond question. He was an institutionalized psychotic. He was nutty enough to think he could make an atom bomb out of modeling clay! Illustrated by Freas Miss Abercrombie, the manual therapist patted the old man on the shoulder. "You're doing just fine, Mr. Lieberman. Show it to me when you have finished." The oldster in the stained convalescent suit gave her a quick, shy smile and went back to his aimless smearing in the finger paints. Miss Abercrombie smoothed her smock down over trim hips and surveyed the other patients working at the long tables in the hospital's arts and crafts shop. Two muscular and bored attendants in spotless whites, lounged beside the locked door and chatted idly about the Dodgers' prospects for the pennant. Through the barred windows of the workshop, rolling green hills were seen, their tree-studded flanks making a pleasant setting for the mental institution. The crafts building was a good mile away from the main buildings of the hospital and the hills blocked the view of the austere complex of buildings that housed the main wards. The therapist strolled down the line of tables, pausing to give a word of advice here, and a suggestion there. She stopped behind a frowning, intense patient, rapidly shaping blobs of clay into odd-sized strips and forms. As he finished each piece, he carefully placed it into a hollow shell hemisphere of clay. "And what are we making today, Mr. Funston?" Miss Abercrombie asked. The flying fingers continued to whip out the bits of shaped clay as the patient ignored the question. He hunched closer to his table as if to draw away from the woman. "We mustn't be antisocial, Mr. Funston," Miss Abercrombie said lightly, but firmly. "You've been coming along famously and you must remember to answer when someone talks to you. Now what are you making? It looks very complicated." She stared professionally at the maze of clay parts. Thaddeus Funston continued to mold the clay bits and put them in place. Without looking up from his bench he muttered a reply. "Atom bomb." A puzzled look crossed the therapist's face. "Pardon me, Mr. Funston. I thought you said an 'atom bomb.'" "Did," Funston murmured. Safely behind the patient's back, Miss Abercrombie smiled ever so slightly. "Why that's very good, Mr. Funston. That shows real creative thought. I'm very pleased." She patted him on the shoulder and moved down the line of patients. A few minutes later, one of the attendants glanced at his watch, stood up and stretched. "All right, fellows," he called out, "time to go back. Put up your things." There was a rustle of paint boxes and papers being shuffled and chairs being moved back. A tall, blond patient with a flowing mustache, put one more dab of paint on his canvas and stood back to survey the meaningless smears. He sighed happily and laid down his palette. At the clay table, Funston feverishly fabricated the last odd-shaped bit of clay and slapped it into place. With a furtive glance around him, he clapped the other half of the clay sphere over the filled hemisphere and then stood up. The patients lined up at the door, waiting for the walk back across the green hills to the main hospital. The attendants made a quick count and then unlocked the door. The group shuffled out into the warm, afternoon sunlight and the door closed behind them. Miss Abercrombie gazed around the cluttered room and picked up her chart book of patient progress. Moving slowly down the line of benches, she made short, precise notes on the day's work accomplished by each patient. At the clay table, she carefully lifted the top half of the clay ball and stared thoughtfully at the jumbled maze of clay strips laced through the lower hemisphere. She placed the lid back in place and jotted lengthily in her chart book. When she had completed her rounds, she slipped out of the smock, tucked the chart book under her arm and left the crafts building for the day. The late afternoon sun felt warm and comfortable as she walked the mile to the main administration building where her car was parked. As she drove out of the hospital grounds, Thaddeus Funston stood at the barred window of his locked ward and stared vacantly over the hills towards the craft shop. He stood there unmoving until a ward attendant came and took his arm an hour later to lead him off to the patients' mess hall. The sun set, darkness fell over the stilled hospital grounds and the ward lights winked out at nine o'clock, leaving just a single light burning in each ward office. A quiet wind sighed over the still-warm hills. At 3:01 a.m., Thaddeus Funston stirred in his sleep and awakened. He sat up in bed and looked around the dark ward. The quiet breathing and occasional snores of thirty other sleeping patients filled the room. Funston turned to the window and stared out across the black hills that sheltered the deserted crafts building. He gave a quick cry, shut his eyes and clapped his hands over his face. The brilliance of a hundred suns glared in the night and threw stark shadows on the walls of the suddenly-illuminated ward. An instant later, the shattering roar and blast of the explosion struck the hospital buildings in a wave of force and the bursting crash of a thousand windows was lost in the fury of the explosion and the wild screams of the frightened and demented patients. It was over in an instant, and a stunned moment later, recessed ceiling lights began flashing on throughout the big institution. Beyond the again-silent hills, a great pillar of smoke, topped by a small mushroom-shaped cloud, rose above the gaping hole that had been the arts and crafts building. Thaddeus Funston took his hands from his face and lay back in his bed with a small, secret smile on his lips. Attendants and nurses scurried through the hospital, seeing how many had been injured in the explosion. None had. The hills had absorbed most of the shock and apart from a welter of broken glass, the damage had been surprisingly slight. The roar and flash of the explosion had lighted and rocked the surrounding countryside. Soon firemen and civil defense disaster units from a half-dozen neighboring communities had gathered at the still-smoking hole that marked the site of the vanished crafts building. Within fifteen minutes, the disaster-trained crews had detected heavy radiation emanating from the crater and there was a scurry of men and equipment back to a safe distance, a few hundred yards away. At 5:30 a.m., a plane landed at a nearby airfield and a platoon of Atomic Energy Commission experts, military intelligence men, four FBI agents and an Army full colonel disembarked. At 5:45 a.m. a cordon was thrown around both the hospital and the blast crater. In Ward 4-C, Thaddeus Funston slept peacefully and happily. "It's impossible and unbelievable," Colonel Thomas Thurgood said for the fifteenth time, later that morning, as he looked around the group of experts gathered in the tent erected on the hill overlooking the crater. "How can an atom bomb go off in a nut house?" "It apparently was a very small bomb, colonel," one of the haggard AEC men offered timidly. "Not over three kilotons." "I don't care if it was the size of a peanut," Thurgood screamed. "How did it get here?" A military intelligence agent spoke up. "If we knew, sir, we wouldn't be standing around here. We don't know, but the fact remains that it WAS an atomic explosion." Thurgood turned wearily to the small, white-haired man at his side. "Let's go over it once more, Dr. Crane. Are you sure you knew everything that was in that building?" Thurgood swept his hand in the general direction of the blast crater. "Colonel, I've told you a dozen times," the hospital administrator said with exasperation, "this was our manual therapy room. We gave our patients art work. It was a means of getting out of their systems, through the use of their hands, some of the frustrations and problems that led them to this hospital. They worked with oil and water paints and clay. If you can make an atomic bomb from vermillion pigments, then Madame Curie was a misguided scrubwoman." "All I know is that you say this was a crafts building. O.K. So it was," Thurgood sighed. "I also know that an atomic explosion at 3:02 this morning blew it to hell and gone. "And I've got to find out how it happened." Thurgood slumped into a field chair and gazed tiredly up at the little doctor. "Where's that girl you said was in charge of this place?" "We've already called for Miss Abercrombie and she's on her way here now," the doctor snapped. Outside the tent, a small army of military men and AEC technicians moved around the perimeter of the crater, scintillators in hand, examining every tiny scrap that might have been a part of the building at one time. A jeep raced down the road from the hospital and drew up in front of the tent. An armed MP helped Miss Abercrombie from the vehicle. She walked to the edge of the hill and looked down with a stunned expression. "He did make an atom bomb," she cried. Colonel Thurgood, who had snapped from his chair at her words, leaped forward to catch her as she collapsed in a faint. At 4:00 p.m., the argument was still raging in the long, narrow staff room of the hospital administration building. Colonel Thurgood, looking more like a patient every minute, sat on the edge of his chair at the head of a long table and pounded with his fist on the wooden surface, making Miss Abercrombie's chart book bounce with every beat. "It's ridiculous," Thurgood roared. "We'll all be the laughingstocks of the world if this ever gets out. An atomic bomb made out of clay. You are all nuts. You're in the right place, but count me out." At his left, Miss Abercrombie cringed deeper into her chair at the broadside. Down both sides of the long table, psychiatrists, physicists, strategists and radiologists sat in various stages of nerve-shattered weariness. "Miss Abercrombie," one of the physicists spoke up gently, "you say that after the patients had departed the building, you looked again at Funston's work?" The therapist nodded unhappily. "And you say that, to the best of your knowledge," the physicist continued, "there was nothing inside the ball but other pieces of clay." "I'm positive that's all there was in it," Miss Abercrombie cried. There was a renewed buzz of conversation at the table and the senior AEC man present got heads together with the senior intelligence man. They conferred briefly and then the intelligence officer spoke. "That seems to settle it, colonel. We've got to give this Funston another chance to repeat his bomb. But this time under our supervision." Thurgood leaped to his feet, his face purpling. "Are you crazy?" he screamed. "You want to get us all thrown into this filbert factory? Do you know what the newspapers would do to us if they ever got wind of the fact, that for one, tiny fraction of a second, anyone of us here entertained the notion that a paranoidal idiot with the IQ of an ape could make an atomic bomb out of kid's modeling clay? "They'd crucify us, that's what they'd do!" At 8:30 that night, Thaddeus Funston, swathed in an Army officer's greatcoat that concealed the strait jacket binding him and with an officer's cap jammed far down over his face, was hustled out of a small side door of the hospital and into a waiting staff car. A few minutes later, the car pulled into the flying field at the nearby community and drove directly to the military transport plane that stood at the end of the runway with propellers turning. Two military policemen and a brace of staff psychiatrists sworn to secrecy under the National Atomic Secrets Act, bundled Thaddeus aboard the plane. They plopped him into a seat directly in front of Miss Abercrombie and with a roar, the plane raced down the runway and into the night skies. The plane landed the next morning at the AEC's atomic testing grounds in the Nevada desert and two hours later, in a small hot, wooden shack miles up the barren desert wastelands, a cluster of scientists and military men huddled around a small wooden table. There was nothing on the table but a bowl of water and a great lump of modeling clay. While the psychiatrists were taking the strait jacket off Thaddeus in the staff car outside, Colonel Thurgood spoke to the weary Miss Abercrombie. "Now you're positive this is just about the same amount and the same kind of clay he used before?" "I brought it along from the same batch we had in the store room at the hospital," she replied, "and it's the same amount." Thurgood signaled to the doctors and they entered the shack with Thaddeus Funston between them. The colonel nudged Miss Abercrombie. She smiled at Funston. "Now isn't this nice, Mr. Funston," she said. "These nice men have brought us way out here just to see you make another atom bomb like the one you made for me yesterday." A flicker of interest lightened Thaddeus' face. He looked around the shack and then spotted the clay on the table. Without hesitation, he walked to the table and sat down. His fingers began working the damp clay, making first the hollow, half-round shell while the nation's top atomic scientists watched in fascination. His busy fingers flew through the clay, shaping odd, flat bits and clay parts that were dropped almost aimlessly into the open hemisphere in front of him. Miss Abercrombie stood at his shoulder as Thaddeus hunched over the table just as he had done the previous day. From time to time she glanced at her watch. The maze of clay strips grew and as Funston finished shaping the other half hemisphere of clay, she broke the tense silence. "Time to go back now, Mr. Funston. You can work some more tomorrow." She looked at the men and nodded her head. The two psychiatrists went to Thaddeus' side as he put the upper lid of clay carefully in place. Funston stood up and the doctors escorted him from the shack. There was a moment of hushed silence and then pandemonium burst. The experts converged on the clay ball, instruments blossoming from nowhere and cameras clicking. For two hours they studied and gently probed the mass of child's clay and photographed it from every angle. Then they left for the concrete observatory bunker, several miles down range where Thaddeus and the psychiatrists waited inside a ring of stony-faced military policemen. "I told you this whole thing was asinine," Thurgood snarled as the scientific teams trooped into the bunker. Thaddeus Funston stared out over the heads of the MPs through the open door, looking uprange over the heat-shimmering desert. He gave a sudden cry, shut his eyes and clapped his hands over his face. A brilliance a hundred times brighter than the glaring Nevada sun lit the dim interior of the bunker and the pneumatically-operated door slammed shut just before the wave of the blast hit the structure. Six hours and a jet plane trip later, Thaddeus, once again in his strait jacket, sat between his armed escorts in a small room in the Pentagon. Through the window he could see the hurried bustle of traffic over the Potomac and beyond, the domed roof of the Capitol. In the conference room next door, the joint chiefs of staff were closeted with a gray-faced and bone-weary Colonel Thurgood and his baker's dozen of AEC brains. Scraps of the hot and scornful talk drifted across a half-opened transom into the room where Thaddeus Funston sat in a neatly-tied bundle. In the conference room, a red-faced, four-star general cast a chilling glance at the rumpled figure of Colonel Thurgood. "I've listened to some silly stories in my life, colonel," the general said coldly, "but this takes the cake. You come in here with an insane asylum inmate in a strait jacket and you have the colossal gall to sit there and tell me that this poor soul has made not one, but two atomic devices out of modeling clay and then has detonated them." The general paused. "Why don't you just tell me, colonel, that he can also make spaceships out of sponge rubber?" the general added bitingly. In the next room, Thaddeus Funston stared out over the sweeping panorama of the Washington landscape. He stared hard. In the distance, a white cloud began billowing up from the base of the Washington Monument, and with an ear-shattering, glass-splintering roar, the great shaft rose majestically from its base and vanished into space on a tail of flame. THE END | B. Society is too quick to dismiss the thoughts and behaviors of people living with mental illness as irrational or absurd |
Regarding Mr. Hurley, what was the source of stem cells for the allogeneic transplantation performed in July 2021?
Choose the correct answer from the following options:
A. Sibling donor
B. Autologous stem cells
C. HLA-identical unrelated donor
D. Cord blood
E. HLA-mismatched unrelated donor
| ### Patient Report 0
**Dear colleague, **
We are reporting on Mr. Bruno Hurley, born on 12/24/1965, who has been
under our outpatient treatment since 02/11/2020.
**Diagnoses:**
- Refractory tuberculosis
- Manifestations: Open pulmonary tuberculosis, lymph node tuberculosis
(cervical, hilar, mediastinal), liver tuberculosis
**Imaging:**
- 11/01/19 Chest CT: Mediastinal lymph node conglomerate centrally
with poststenotic infiltrates on both sides. Splenomegaly.
- 11/04/19 Bronchoscopy: Large mediastinal and right hilar lymphomas.
Subcritical constriction of right segmental bronchi. EBUS-TBNA LK4R
and 10/11R.
**Microbiology:**
- 11/04/19 Tracheobronchial Secretions: Microscopic detection of
acid-fast rods, cultural detection of Mycobacterium tuberculosis,
phenotypically no evidence of resistance.
**Therapy:**
- Initial omission of pyrazinamide due to pancytopenia.
- Moxifloxacin: 11/10/19-11/20/19
- Pyrazinamide: 11/20/19-02/11/20
- Ethambutol: 11/08/19-02/11/20
- Rifampicin since: 11/08/19
- Isoniazid since: 11/08/19
- Levofloxacin since: 02/11/20
- Immunomodulatory therapy for low basal interferon / interferon
levels (ACTIMMUNE®)
**Microbiology:**
- 01/20/20 Sputum: Cultural detection of Mycobacterium tuberculosis:
Phenotypically no evidence of resistance.
- 01/02/20 Sputum: Last cultural detection of Mycobacterium
tuberculosis.
- 06/15/20 BAL: Occasional acid-fast rods, 16S-rRNA-PCR: M.
tuberculosis complex, no cultural evidence of Mycobacteria.
- 06/15/20 Lung biopsy: Occasional acid-fast rods, no cultural
evidence of Mycobacteria.
- 03/12/21 Sputum: first sputum without acid-fast rods, consistently
microscopically negative sputum samples since then.
**Histology:**
- 07/16/21: Mediastinal lymph node biopsy: Histologically no evidence
of malignancy/lymphoma.
**Other Diagnoses: **
- Secondary Acute Myeloid Leukemia with Myelodysplastic Syndrome
- Blood count at initial diagnosis: 15% blasts, erythrocyte
substitution required.
**Therapy:**
- 12/20-03/21 TB therapy
- 02/20-01/21 TB therapy: RMP + INH + FQ
- 01/21-04/21 RMP + INH + FQ + Actimmune® 04/22 CT: Regressive
findings of pulmonary TB changes, regressive cervical lymph nodes,
mediastinal LAP, and liver lesions size-stable; Sputum: No acid-fast
rods detected for the first time since 03/21.
- BM aspiration: Secondary AML.
**Current Presentation:** Admission for allogeneic stem cell
transplantation
Pathogen Location / Material of Detection or Infection Month/Year or
Last Detection
- HIV Serology: Negative - 11/19
- Mycobacterium tuberculosis Complex: Bronchoalveolar Lavage,
Tracheobronchial Secretion, Sputum - 11/19
**Medical History:** We took over Mr. Hurley for the continuation of TB
therapy on 11/02/20. His hospital admission took place at the end of
October 2019 due to neutropenic fever. The patient reported temperatures
up to 39°C for the past 3 days. Since 08/19, the patient has been
receiving hematological-oncological treatment for MDS. The colleagues
from hematology performed a repeat bone marrow aspiration before
transferring to Station 12. The blast percentage was significantly
reduced. HLA typing of the brother for allogeneic stem cell
transplantation planning had already been done in the summer of 2019.
After a chest CT revealed extensive mediastinal lymphomas with
compression of the bronchial tree bilaterally and post-stenotic
infiltrates, a bronchoscopy was performed. M. tuberculosis was cultured
from sputum and TBS. An EBUS-guided lymph node biopsy was histologically
processed, revealing granulomatous inflammation and molecular evidence
of the M. tuberculosis complex. On 11/08/19, a four-drug
anti-tuberculosis therapy was initiated, initially with Moxifloxacin
instead of Pyrazinamide due to pancytopenia. Moxifloxacin was replaced
by Pyrazinamide on 11/20/19. The four-drug therapy was continued for a
total of 3 months due to prolonged microscopic evidence of acid-fast
rods in follow-up sputum samples. Isoniazid dosage was adjusted after
peak level control (450 mg q24h), as was Rifampicin dose (900 mg q24h).
On 01/02/20, Mycobacterium tuberculosis was last cultured in a sputum
sample. Nevertheless, acid-fast rods continued to be detected in the
sputum. Due to the lack of culturability of mycobacteria, Mr. Hurley was
discharged to home care after consultation with the Tuberculosis Welfare
Office.
**Allergies**: None known. Toxic Substances: Smoking: Non-smoker;
Alcohol: No; Drugs: No
**Social History:** Originally from Brazil, has been living in the US
for 8 years. Lives with his partner.
**Current lab results:**
**Parameter** **Results** **Reference Range**
---------------------------------------- -------------- ---------------------
ConA-Induced Cytokines (Th1/Th2)
Interleukin 10 10 pg/mL \< 364 pg/mL
Interferon Gamma 265-6781 pg/mL
Interleukin 2 74 pg/mL 43-374 pg/mL
Interleukin 4 13 pg/mL \< 34 pg/mL
Interleukin 5 3 pg/mL \< 55 pg/mL
Naive CD45RA+CCR7+ (% of CD8+) 6.35 % 8.22-59.58 %
TEMRA CD45RA+CCR7- (% of CD8+) 50.44 % 7.32-55.99 %
Central Memory CD45RA-CCR7+ (% of CD) 2.60 % 1.67-5.84 %
Effector Memory CD45RA-CCR7- (% of CD) 40.60 % 22.52-62.25 %
Naive CD45RA+ (% of CD4+) 26.26 % 17.46-60.24 %
TEMRA CD45RA+ CCR7- (% of CD4+) 1.26 % 2.74-15.54 %
Central Memory CD45RA-CCR7+ (% of CD) 34.21 % 16.40-33.41 %
Effector Memory CD45RA-CCR7- (% of CD) 38.28 % 17.38-40.38 %
Granulocytes 0.60 abs./nL 3.00-6.50 abs./nL
Granulocytes (relative) 45 % 50-80 %
Lymphocytes 0.57 abs./nL 1.50-3.00 abs./nL
Lymphocytes (relative) 43 % 20-40 %
Monocytes 0.13 abs./nL \<0.50 abs./nL
Monocytes (relative) 10 % 2-10 %
NK Cells 0.16 abs./nL 0.10-0.40 abs./nL
NK Cells (% of Lymphocytes) 29 5-25
γ/δ TCR+ T-Cells (relative) 2 % \< 10 %
α/β TCR+ T-Cells (relative) 98 % \>90 %
CD19+ B-Cells (% of Lymphocytes) 3 % 5-25 %
CD4/CD8 Ratio 0.9 % 1.1-3.0 %
CD8-CD4-T-Cells (% of T-Cells) 5.86 % \< 15.00 %
CD8+CD4+-T-Cells (% of T-Cells) 0.74 % \< 10.00 %
CD3+ T-Cells 0.38 abs./nL 0.90-2.20 abs./nL
**Parameter** **Results** **Reference Range**
-------------------------------------------------- ---------------- ---------------------
Complete Blood Count (EDTA)
Hemoglobin 6.6 g/dL 13.5-17.0 g/dL
Hematocrit 19.0 % 39.5-50.5 %
Erythrocytes 2.3 x 10\^6/uL 4.3-5.8 x 10\^6/uL
Platelets 61 x 10\^3/uL 150-370 x 10\^3/uL
MCV (Mean Corpuscular Volume) 81.5 fL 80.0-99.0 fL
MCH (Mean Corpuscular Hemoglobin) 28.3 pg 27.0-33.5 pg
MCHC (Mean Corpuscular Hemoglobin Concentration) 34.7 g/dL 31.5-36.0 g/dL
MPV (Mean Platelet Volume) 10.4 fL 7.0-12.0 fL
RDW-CV (Red Cell Distribution Width-CV) 12.7 % 11.5-15.0 %
**Parameter** **Results** **Reference Range**
-------------------------- ------------- ---------------------
Other Investigations
QFT-TB Gold plus TB1 0.11 IU/mL \<0.35 IU/mL
QFT-TB Gold plus TB2 0.07 IU/mL \<0.35 IU/mL
QFT-TB Gold plus Mitogen 3.38 IU/mL \>0.50 IU/mL
QFT-TB Gold plus Result Negative
**Lung Aspiration from 06/15/20:** Examination Request: Acid-fast rods
(Microscopy + Culture) **Microscopic Findings:**
- Auramine stain: Occasionally, acid-fast rods Result: No growth of
Mycobacterium sp. after 12 weeks of incubation.
2. Forceps Biopsy Exophytic Trachea: One piece of tissue. Microscopy:
HE, PAS, Giemsa, Diagnosis:
3. Predominantly blood clot and necrotic material alongside sparsely
altered lymphatic tissue due to sampling (EBUS-TBNA LK 7 as
indicated).
4. Components of a granulation tissue polyp (Forceps Biopsy Exophytic
Trachea as indicated). Comment: The finding in 1. continues to be
suspicious of a mycobacterial infection. We are conducting molecular
pathological examinations in this regard and will report again.
> [Comment]{.underline}: Detection of mycobacterial DNA of the M.
> tuberculosis complex type. No evidence of atypical mycobacteria. No
> evidence of malignancy.
**Current Medication:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**\
**
### Patient Report 1
**Dear colleague, **
We report to you about our patient Mr. Bruno Hurley, born on 12/24/1965.
Who has received inpatient treatment from 07/17/2021 to 09/03/2021.
**Diagnoses**:
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
<!-- -->
- Myelodysplastic Syndrome EB-2, diagnosed in July 2010. Blood count
at initial diagnosis: 15% blasts, erythrocyte transfusion-dependent.
Cytogenetics: 46,XY \[1\]; 47,XY,+Y,i(21)(q10)\[15\];
47,XY,+Y,trp(21)(q11q22)\[4\]. Molecular genetics: Mutations in
RUNX1, SF3B1. IPSS-R: 7 (very high risk).
- In 08/2020, diagnosed with Myelodysplastic Syndrome with ring
sideroblasts.
- Received transfusions of 2 units of red blood cells every 3-4 weeks
to maintain hemoglobin between 4-6 g/dL.
- Bone marrow biopsy showed MDS-EB2 with 14.5% blasts.
- Initiated Azacitidine treatment (2x 75 mg subcutaneously, days 1-5 +
8-9 every 4 weeks) as an outpatient.
- 10/23/2019: Hospitalized for fever during neutropenia.
- 12/06/2019: Diagnosed with tuberculosis - positive Tbc-PCR in
tracheobronchial secretions, acid-fast bacilli in tracheobronchial
secretions, histological confirmation from EBUS biopsy of a
conglomerate of melted lymph nodes from 11/03/2019.
- 01/2021: Bone marrow biopsy showed secondary AML with 26% blasts.
- 03/2021: Started Venetoclax/Vidaza.
- 05/2021: Bone marrow biopsy showed 0.8% myeloid blasts coexpressing
CD117 and CD7. Cytology showed 6% blasts.
- 05/2021: Started the 4th cycle of Vidaza/Venetoclax.
- 06/17/2022: Started the 5th cycle of Vidaza/Venetoclax.
- 07/29/2021: Underwent allogeneic stem cell transplantation from a
HLA-identical unrelated donor (10/10 antigen match) for AML-MRC in
first complete remission (CR). Conditioning regimen included
Treosulfan 12g/m2, Fludarabin 5x 30 mg/m2, ATG 3x 10 mg/kg.
**Other Diagnoses:**
- Persistent tuberculosis with lymph node swelling since June 2020.
- Open lung tuberculosis diagnosed in November 2019.
- Location: CT of the chest showed central mediastinal lymph node
conglomerate with post-stenotic infiltrates bilaterally,
splenomegaly.
- Bronchoscopy on December 5, 2020, showed large mediastinal and
right hilar lymph nodes, subcritical narrowing of right
segmental bronchi. EBUS-TBNA
- CT Chest/Neck on 02/05/2020: Regression of pulmonary
infiltrates, enlargement of necrotic lymph nodes in the upper
mediastinum and infraclavicular on the right (compressing the
internal jugular vein/esophagus).
- Culture confirmation of Mycobacterium tuberculosis,
pansensitive: Tracheobronchial secretion
- Initiated antituberculous combination therapy
**Current Presentation:** Admission for allogeneic stem cell
transplantation from a HLA-identical unrelated donor (10/10 antigen
match) for AML-MRC in first complete remission.
**Medical History:** In 2019, Mr. Hurley was diagnosed with
Myelodysplastic Syndrome EB-2. Starting from September 2019, he received
Azacitidine therapy. In December 2020, he was diagnosed with open lung
tuberculosis, which was challenging to treat due to his dysfunctional
immune system. In January 2021, his MDS progressed to AML-MRC with 26%
blasts. After treatment with Venetoclax/Vidaza, he achieved remission in
May 2021. Tuberculosis remained largely under control.
Due to AML-MRC, he was recommended for allogeneic stem cell
transplantation from a HLA-identical unrelated donor. At the time of
admission for transplantation, he was largely asymptomatic. He
occasionally experienced mild dry cough but denied fever, night sweats,
or weight loss. The admission and counseling were conducted with
translation assistance from his life partner due to limited proficiency
in English.
**Allergies**: None
**Transfusion History**: Currently requires transfusions every 14 days.
Both red blood cell and platelet transfusions have been tolerated
without problems.
**Abdominal CT from 01/20/2021:**
**Findings**: Significant peripancreatic fluid accumulation in the upper
abdominal area with a somewhat indistinct border between the pancreatic
tissue, particularly in the pancreatic head region. Evidence of
inflammation affecting the stomach and duodenum. No presence of free air
or indications of hollow organ perforation. No conclusive signs of a
well-defined abscess. Moreover, the other parenchymal abdominal organs,
especially those lacking focal abnormalities suggestive of neoplastic or
inflammatory conditions, displayed normal appearances. The gallbladder
showed no notable issues, and there were no radiopaque concretions
observed. Both the intra- and extrahepatic bile ducts appeared
adequately dilated. Abdominal hollow organs exhibited unremarkable and
normal appearances without corresponding contrast and dilation. The
appendix appeared within normal parameters. Abdominal lymph nodes showed
no unusual findings. Some degree of aortic vasosclerosis was noted. The
depiction of the included lung portions revealed no abnormalities.
**Results**: Findings indicative of acute pancreatitis, most likely of
an exudative nature. No signs of hollow organ perforation were detected,
and there was no definitive evidence of an abscess (as far as could be
determined from native imaging).
**Summary**: The patient was admitted to our hospital through the
emergency department with the symptoms described above. With typical
upper abdominal pain and significantly elevated serum lipase levels, we
diagnosed acute pancreatitis. This diagnosis was corroborated by
peripancreatic fluid and ill-defined organ involvement in the abdominal
CT scan. There were no laboratory or anamnestic indications of a biliary
origin. The patient denied excessive alcohol consumption.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**Physical Examination:** General: Oriented in all qualities, in good
general condition with normal body weight (75 kg, 187 cm) Vital signs at
admission: Heart rate 63/min, Blood pressure 110/78 mmHg. Temperature at
admission 36.8 °C, Oxygen saturation 100% on room air. Skin and mucous
membranes: Dry skin, normal skin color, normal skin turgor. No scleral
icterus, non-irritated conjunctiva. Normal oral mucosa, moist tongue
without coating, no ulcers or thrush. Heart: Normal heart sounds,
rhythmic, regular rate, no pathological heart murmurs heard on
auscultation. Lungs: Resonant percussion sound, clear breath sounds
bilaterally, no wheezing, no prolonged expiration. Abdomen: Unremarkable
scar tissue, normal bowel sounds in all quadrants, soft, non-tender, no
guarding, liver and spleen not enlarged. Vascular: Central and
peripheral pulses palpable, no jugular vein distention, no peripheral
edema, extremities warm with no significant difference in size. Lymph
nodes: Palpable cervical swelling, inguinal and axillary lymph nodes
unremarkable. Neurology: Grossly neurologically unremarkable.
On 08/22/2021, a four-lumen central venous catheter was placed in the
right internal jugular vein without complications. During the
conditioning regimen, the patient received the following:
**Medication** **Dosage** **Frequency**
---------------------------------------------- ---------------------- -------------------------
Fludarabine (Fludara) 30 mg/m² (5x 57 mg) 07/23/2023 - 07/27/2023
Treosulfan (Ovastat) 12 g/m² (3x 22.9 g) 07/23/2023 - 07/25/2023
Anti-Thymocyte Globulin (ATG, Thymoglobulin) 10 mg/kg (3x 700 mg) 07/23/2023 - 07/28/2023
**Antiemetic Therapy:**
The antiemetic therapy included Ondansetron, Aprepitant, and
Dexamethasone, and the conditioning regimen was well tolerated.
**Prophylaxis of Graft-Versus-Host Disease (GvHD):**
**Substances** **Start Date** **Day -2** **Day 1**
---------------- ---------------- ------------ -----------
Cyclosporine 08/28/2022
Mycophenolate 07/30/2021
**Stem Cell Source** **Date** **CD34/kg KG** **CD45/kg KG** **CD3/kg KG** **Volume**
---------------------- ------------ ---------------- ---------------- --------------- ------------
PBSCT 07/29/2021 7.39 x10\^6 8.56 x10\^8 260.7 x10\^6 194 ml
**Summary:** Mr. Hurley was admitted for allogeneic stem cell
transplantation from a HLA-identical unrelated donor for AML-MRC. The
conditioning regimen with Treosulfan, Fludarabin, and ATG was well
tolerated, and the transplantation proceeded without complications.
**Toxicities:** There was an adverse event-related increase in bilirubin
levels, reaching a maximum of 2.68 mg/dL. Elevated ALT levels, up to a
maximum of 53 U/L, were observed.
**Acute Graft-Versus-Host Disease (GvHD):** Signs of GvHD were not
observed until the time of discharge.
**Medication upon Discharge:**Formularbeginn
**Medication ** **Dosage** **Frequency**
---------------------------------- ------------ ------------------------------------------------
Acyclovir (Zovirax) 500 mg 1-0-1-0
Entecavir (Baraclude) 0.5 mg 1-0-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 2-0-0-0
Levofloxacin (Levaquin) 500 mg 1-0-1-0
Mycophenolate Mofetil (CellCept) 500 mg 2-0-2-0
Folic Acid 5 mg 1-0-0-0
Magnesium \-- 3-3-3-0
Pantoprazole (Protonix) 40 mg 1-0-0-0 (before a meal)
Ursodeoxycholic Acid (Actigall) 250 mg 1-1-1-0
Cyclosporine (Sandimmune) 100 mg 100 mg 4-0-4-0
Cyclosporine (Sandimmune) 50 mg 50 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
Cyclosporine (Sandimmune) 10 mg 10 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
**Current Recommendations: **
1. Bone marrow puncture on Day +60, +120, and +360 post-transplantation
(including MRD and chimerism) and Day +180 depending on MRD and
chimerism progression.
2. Continuation of immunosuppressive therapy with ciclosporin adjusted
to achieve target levels of around 150 ng/ml, for a minimum of 3
months post-transplantation. Immunosuppression with mycophenolate
mofetil will be continued until Day +40.
3. Prophylaxis with Aciclovir must continue for 6 weeks after
discontinuation of immunosuppression at a dosage of 15-20 mg/kg/day
(divided into 2 doses). Dose adjustment based on renal function may
be necessary.
4. Pneumocystis pneumonia prophylaxis through monthly Pentamidine
inhalation or administration of Cotrim forte 960mg must continue at
least until immunosuppression is discontinued or until an absolute
CD4+ T-cell count exceeds \>200/µL in peripheral blood. Cotrim forte
960mg has not been started when leukocytes are \<2/nL.
5. Weekly monitoring of CMV and EBV viral loads through quantitative
PCR from EDTA blood.
6. Timing of antituberculous medication intake:
- Take Rifampicin and Isoniazid in the morning on an empty stomach, 30
minutes before breakfast.
- Take levofloxacin with a 2-hour gap from divalent cations (Mg2+,
strongly calcium-rich foods).
**Lab results upon Discharge:**
**Parameter** **Result** **Reference Range**
------------------------------ -------------------- ---------------------
Cyclosporine 127.00 ng/mL \--
Sodium 141 mEq/L 136-145 mEq/L
Potassium 4.1 mEq/L 3.5-4.5 mEq/L
Glucose 108 mg/dL 60-110 mg/dL
Creatinine 0.65 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 111 mL/min/1.73 m² \--
Urea 26 mg/dL 17-48 mg/dL
Total Bilirubin 0.35 mg/dL \<1.20 mg/dL
**Complete Blood Count **
**Parameter** **Results** **Reference Range**
--------------- ----------------- -----------------------
Hemoglobin 9.5 g/dL 13.5-17.0 g/dL
Hematocrit 28.2% 39.5-50.5%
Erythrocytes 3.2 x 10\^6/µL 4.3-5.8 x 10\^6/µL
Leukocytes 1.47 x 10\^3/µL 3.90-10.50 x 10\^3/µL
Platelets 193 x 10\^3/µL 150-370 x 10\^3/µL
MCV 88.7 fL 80.0-99.0 fL
MCH 29.9 pg 27.0-33.5 pg
MCHC 33.7 g/dL 31.5-36.0 g/dL
MPV 9.8 fL 7.0-12.0 fL
RDW-CV 18.9% 11.5-15.0%
### Patient Report 2
**Dear colleague, **
We report on Mr. Bruno Hurley, born on 12/24/1965, who was under our
inpatient care from 2/20/2022, to 02/24/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Medical History:** The patient presented via ambulance from his
workplace. The patient reported sudden onset upper abdominal pain,
mainly in the epigastric region, accompanied by nausea and vomiting. He
also experienced watery diarrhea once today. He had lunch around noon,
consisting noodles. There was no fever, cough, sputum production,
dyspnea, or urinary abnormalities. He has been taking daily
antitubercular combination therapy, including Rifampicin, for open
tuberculosis. The patient denied alcohol consumption and weight loss.
**Medication** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg 1-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 1-0-1
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed
Prednisolone 4 mg As needed
**Allergies:** None
**Physical Exam:**
Vital Signs: Blood Pressure 178/90 mmHg, Pulse 85/min, SpO2 89%,
Temperature 36.7°C, Respiratory Rate 20/min.
Clinical Status: Upon initial examination, a reduced general condition.
Cardiovascular: Heart sounds were normal, rhythm was regular, and no
murmurs were heard.
Respiratory: Vesicular breath sounds, sonorous percussion.
Abdominal: Sluggish peristalsis, soft abdominal walls, guarding and
tenderness in the epigastrium, liver and spleen not palpable, no free
fluid.
Extremities: Minimal edema.
**ECG Findings:** ECG on admission showed normal sinus rhythm (69/min),
normal ST intervals, R/S transition in V3/V4, and no significant
abnormalities.
´
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg PAUSED
Rifampin (Rifadin) 600 mg PAUSED
Isoniazid/Pyridoxine (Nydrazid) 300 mg PAUSED
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed (as needed)
Prednisolone 4 mg As needed (as needed)
Tramadol (Ultram) 50 mg 1 tablet, every 6 hours
**Parameter** **Results** **Reference Range**
------------------------- ----------------- -----------------------
White Blood Cells (WBC) 5.0 x 10\^9/L 3.7 - 9.9 x 10\^9/L
Hemoglobin 14.0 g/dL 13.6 - 17.5 g/dL
Hematocrit 40% 40 - 53%
Red Blood Cells (RBC) 4.00 x 10\^12/L 4.4 - 5.9 x 10\^12/L
MCV 99 fL 80 - 96 fL
MCH 32.8 pg 28.3 - 33.5 pg
MCHC 33.1 g/dL 31.5 - 34.5 g/dL
Platelets 161 x 10\^9/L 146 - 328 x 10\^9/L
Absolute Neutrophils 3.7 x 10\^9/L 1.8 - 6.2 x 10\^9/L
Absolute Monocytes 0.31 x 10\^9/L 0.25 - 0.85 x 10\^9/L
Absolute Eosinophils 0.03 x 10\^9/L 0.03 - 0.44 x 10\^9/L
Absolute Basophils 0.01 x 10\^9/L 0.01 - 0.08 x 10\^9/L
Absolute Lymphocytes 0.9 x 10\^9/L 1.1 - 3.2 x 10\^9/L
Immature Granulocytes 0.0 x 10\^9/L 0.0 x 10\^9/L
### Patient Report 3
**Dear colleague, **
We are writing to inform you on our patient, Mr. Hurley, who presented
to our outpatient clinic on 07/12/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Current Presentation:** Presented with a referral from outpatient
oncologist for suspected recurrent AML, with DD GvHD ITP in the setting
of progressive pancytopenia, primarily thrombocytopenia. The patient is
in good general condition, denying acute symptoms, particularly no rash,
diarrhea, dyspnea, or fever.
**Physical Examination:** Alert, oriented, no signs of respiratory
distress, heart sounds regular, abdomen soft, no tenderness, no skin
rashes, especially no signs of GvHD, no edema.
- Heart Rate (HR): 130/85
- Temperature (Temp): 36.7°C
- Oxygen Saturation (SpO2): 97
- Respiratory Rate (AF): 12
- Pupillary Response: 15
**Imaging (CT):**
- [11/04/19 CT Chest/Abdomen/Pelvis ]{.underline}
- [01/04/20 Chest CT:]{.underline} Marked necrotic lymph nodes hilar
right with bronchus and vascular stenosis. Significant increasing
pneumonic infiltrates predominantly on the right.
- [02/05/20 Neck/Chest CT]{.underline}: Regression of pulmonary
infiltrates, but increased size of necrotic lymph nodes, especially
in the upper mediastinum and right infraclavicular with slit-like
compression of the right internal jugular vein and the esophagus.
- [06/07/20 Neck/Chest CT]{.underline}: Size-stable necrotic lymph
node conglomerate infraclavicular right, dimensioned axially up to
about 6 x 2 cm, with ongoing slit-shaped compression of the right
internal jugular vein. Hypoplastic mastoid cells left, idem.
Progressive, partly new and large-volume consolidations with
adjacent ground glass infiltrates on the right in the anterior, less
posterior upper lobe and perihilar. Inhomogeneous, partially reduced
contrast of consolidated lung parenchyma, broncho pneumogram
preserved dorsally only.
- [10/02/20 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Size-regressive
consolidating infiltrate in the right upper lobe and adjacent
central lower lobe with increasing signs of liquefaction.
Progressive right pleural effusion and progressive signs of
pulmonary volume load. Regressive cervical, mediastinal, and right
hilar lymphadenopathy. Ongoing central hilar conglomerates that
compress the central hilar structures. Partly constant, partly
regressive presentation of known tuberculosis-suspected liver
lesions.
- [12/02/20 CT Chest/Abdomen/Pelvis]{.underline}.
- [01/20/2021 Abdominal CT.]{.underline}
- [02/23/21 Neck/Chest CT:]{.underline} Slightly regressive/nodular
fibrosing infiltrate in the right upper lobe and adjacent central
lower lobe with continuing significant residual findings. Within the
infiltrate, larger poorly perfused areas with cavitations and
scarred bronchiectasis. Increasing, partly patchy densities on the
left basal region, differential diagnoses include infiltrates and
ventilation disorders. Essentially constant cervical, mediastinal,
and hilar lymphadenopathy. Constant liver lesions in the upper
abdomen, differential diagnoses include TB manifestations and cystic
changes.
- [06/12/21 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Improved
ventilation with regressive necrotic TB manifestations perihilar,
now only subtotal lobar atelectasis. Essentially constant necrotic
lymph node manifestations cervical, mediastinal, and right hilar,
exemplarily suprasternal right or right paratracheal. Narrow right
pleural effusion. Medium-term constant hypodense liver lesions
(regressive).
**Patient History:** Known to have AML with myelodysplastic changes,
first diagnosed 01/2021, myelodysplastic syndrome EB-2, fist diagnoses
07/2019, and history of allogeneic stem cell transplantation.
**Treatment and Progression:** Patient is hemodynamically stable, vital
signs within normal limits, afebrile. In good general condition,
clinical examination unremarkable, especially no skin GvHD signs. Venous
blood gas: Acid-base status balanced, electrolytes within normal range.
Laboratory findings show pancytopenia, Hb 11.3 g/dL, thrombocytopenia
29/nL, leukopenia 1.8/nL, atypical lymphocytes described as \"resembling
CLL,\" no blasts noted. Consultation with Hemato/Oncology confirmed no
acute need for hospitalization. Follow-up in the Hemato-Oncological
Clinic in September.
**Imaging:**
**CT Chest/Abdomen/Pelvis on 11/04/19:**
**Assessment:** In comparison with 10/23/19: In today\'s
contrast-enhanced examination, a newly unmasked large tumor is noted in
the right pulmonary hilum with encasement of the conduits of the right
lung lobe. Differential diagnosis includes a lymph node conglomerate,
central bronchial carcinoma, or, less likely, an inflammatory lesion.
Multiple suspicious malignant enlarged mediastinal lymph nodes,
particularly on the right paratracheal and infracarinal regions.
Short-term progression of peribronchovascular consolidation in the right
upper lobe and multiple new subsolid micronodules bilaterally.
Differential diagnosis includes inflammatory lesions, especially in the
presence of known neutropenia, which could raise suspicion of fungal
infection.
Intraabdominally, there is an image suggestive of small bowel subileus
without a clearly defined mechanical obstruction.
Density-elevated and ill-defined cystic lesion in the left upper pole of
the kidney. Primary consideration is a hemorrhaged or thickened cyst,
but ultimately, the nature of the lesion remains uncertain.
**CT Chest on 01/04/20:** Significant necrotic hilar lymph nodes on the
right with bronchial and vascular stenosis. Marked progression of
pulmonary infiltrates, particularly on the right, still compatible with
superinfection in the context of known active tuberculosis
**CT Chest from 02/05/20**: Marked necrotic lymph nodes hilar right with
bronchial and vascular stenosis. Significantly increasing pneumonia-like
infiltrates, particularly on the right, still compatible with
superinfection in the context of known open tuberculosis.
**Neck/Chest CT from 06/07/20:** Size-stable necrotic lymph node
conglomerate infraclavicular right, dimensioned axially up to about 6 x
2 cm, with ongoing slit-shaped compression of the right internal jugular
vein. Hypoplastic mastoid cells left, idem. Progressive, partly new and
large-volume consolidations with adjacent ground glass infiltrates on
the right in the anterior, less posterior upper lobe and perihilar.
Inhomogeneous, partially reduced contrast of consolidated lung
parenchyma, broncho pneumogram preserved dorsally only.
**Neck Ultrasound from 08/14/2020:** Clinical History, Question,
Justifying Indication: Follow-up of cervical lymph nodes in
tuberculosis.
**Findings/Assessment:** Neck Lymph Node Ultrasound from 05/20/2020 for
comparison. As in the previous examination, evidence of two
significantly enlarged supraclavicular lymph nodes on the right, both
showing a decrease in size compared to the previous examination: The
more medial node measures 2.9 x 1.6 cm compared to the previous 3.7 x
1.7 cm, while the more laterally located lymph node measures 3.3 x 1.4
cm compared to the previous 4.2 x 1.5 cm. The more medial lymph node
appears centrally hypoechoic, indicative of partial liquefaction, while
the more lateral lymph node has a rather solid appearance. No other
pathologically enlarged lymph nodes detected in the cervical region.
**CT Neck/Chest/Abdomen/Pelvis from 10/02/2020:** Assessment: Compared
to the previous examination from 06/07/2020, there is evidence of
regression in findings: Size regression of consolidating infiltrate in
the right upper lobe and the adjacent central lower lobe, albeit with
increasing signs of cavitation. Progressive right pleural effusion and
progressive signs of pulmonary volume overload. Regression of cervical,
mediastinal, and right hilar lymphadenopathy. Persistent centrally
liquefying lymph node conglomerates in the right hilar region,
compressing central hilar structures. Some findings remain stable, while
others have regressed. No evidence of new manifestations.
**CT Chest/Abdomen/Pelvis from 12/02/20:** Assessment: Compared to
10/02/20: In today\'s contrast examination, a newly unmasked large tumor
is located right pulmonary hilar, encasing the conduits of the right
lung lobe; Differential diagnosis includes lymph node conglomerate,
central bronchial carcinoma, and a distant possibility of inflammatory
lesions. Multiple suspiciously enlarged mediastinal lymph nodes,
especially right paratracheal and infracarinal. In a short time,
progressive peribronchovascular consolidations in the right upper lobe
and multiple new subsolid micronodules bilaterally; Differential
diagnosis includes inflammatory lesions, potentially fungal in the
context of known neutropenia. Intra-abdominally, there is a picture of
small bowel subileus without discernible mechanical obstruction.
Corresponding symptoms? Densely elevated and ill-defined cystic lesion
in the upper pole of the left kidney; Differential diagnosis primarily
includes a hemorrhaged/thickened cyst, ultimately with uncertain
malignancy.
**Chest in two planes on 04/23/2021:** **Findings/Assessment:** In
comparison with the corresponding prior images, most recently on
08/14/2020. Also refer to CT Neck and Chest on 01/23/2021. The heart is
enlarged with a leftward emphasis, but there are no signs of acute
congestion. Extensive consolidation projecting onto the right mid-field,
with a long-term trend toward regression but still clearly demarcated.
No pneumothorax. No pleural effusion. Known lymph nodes in the
mediastinum/hilum. Degenerative spinal changes.
**Neck/Chest CT on 02/23/21:** Slightly regressive/nodular fibrosing
infiltrate in the right upper lobe and adjacent central lower lobe with
continuing significant residual findings. Within the infiltrate, larger
poorly perfused areas with cavitations and scarred bronchiectasis.
Increasing, partly patchy densities on the left basal region,
differential diagnoses include infiltrates and ventilation disorders.
Essentially constant cervical, mediastinal, and hilar lymphadenopathy.
Constant liver lesions in the upper abdomen, differential diagnoses
include TB manifestations and cystic changes.
**CT Neck/Chest/Abdomen/Pelvis from 06/12/2021**: CT from 02/23/2021
available for comparison. Neck/Chest: Improved right upper lobe (ROL)
ventilation with regressive necrotic TB manifestations peri-hilar, now
only with subtotal lobar atelectasis. Essentially stable necrotic lymph
node manifestations in the cervical, mediastinal, and right hilar
regions, for example, supraclavicular on the right (18 mm, previously
30.1 Im 21.2) or right paratracheal (18 mm, previously 30.1 Im 33.8).
Narrow right pleural effusion, same as before. No pneumothorax. Heart
size normal. No pericardial effusion. Abdomen: Mid-term stable hypodense
liver lesions (regressing since 07/2021).
**Treatment and Progression:** Due to the extensive findings and the
untreatable immunocompromising underlying condition, we decided to
switch from a four-drug TB therapy to a three-drug therapy after nearly
3 months. In addition to rifampicin and isoniazid, levofloxacin was
initiated. Despite very good therapy adherence, acid-fast bacilli
continued to be detected microscopically in sputum samples without
culture confirmation of mycobacteria, even after discharge. Furthermore,
the radiological findings worsened. In April 2020, liver lesions were
identified in the CT that had not been described up to that point, and
pulmonary and mediastinal changes increased. Clinically, right cervical
lymphadenopathy also progressed in size. Due to a possible immune
reaction, a therapy with prednisolone was attempted for several weeks,
which did not lead to improvement. In June 2020, Mr. Hurley was
readmitted for bronchoscopy with BAL and EBUS-guided biopsy to rule out
differential diagnoses. An NTM-NGS-PCR was performed on the BAL, which
did not detect DNA from nontuberculous mycobacteria. Histologically,
predominantly necrotic material was found in the lymph node tissue, and
molecular pathological analysis detected DNA from the M. tuberculosis
complex. There were no indications of malignancy. In addition,
whole-genome sequencing of the most recently cultured mycobacteria was
performed, and latent resistance genes were also ruled out. Other
pathogens, including fungi, were likewise not detected. Aspergillus
antigen in BAL and serum was also negative. We continued the three-drug
therapy with Rifampicin, Isoniazid, and Levofloxacin. Mr. Hurley
developed an increasing need for red blood cell transfusions due to
myelodysplastic syndrome and began receiving regular transfusions from
his outpatient hematologist-oncologist in the summer of 2020. In a
repeat CT control in October 2020, increasing necrotic breakdown of the
right upper and middle lobes was observed, as well as progressive
ipsilateral pleural effusion and persistent mediastinal lymphadenopathy
and liver lesions. Mr. Hurley was referred to the immunology colleagues
to discuss additional immunological treatment options. After extensive
immune deficiency assessment, a low basal interferon-gamma level was
noted in the setting of lymphopenia due to MDS. In an immunological
conference, the patient was thoroughly discussed, and a trial of
interferon-gamma therapy in addition to antituberculous therapy was
discussed due to a low basal interferon-gamma level and a negative
Quantiferon test. After approval of an off-label application, we began
Actimmune® injections in January 2021 after extensive patient education.
Mr. Hurley learned to self-administer the subcutaneous injections and
initially tolerated the treatment well. Due to continuous worsening of
the blood count, a bone marrow puncture was performed again on an
outpatient basis by the attending hematologist-oncologist, and secondary
AML was diagnosed. Since February 2021, Mr. Hurley has received
Azacitidine and regular red blood cell and platelet concentrates. After
3 months of Actimmune® therapy, sputum no longer showed acid-fast
bacilli in March 2021, and radiologically, the left pleural effusion had
completely regressed, and the infiltrates had decreased. Actimmune® was
discontinued after 3 months. Towards the end of Actimmune® therapy, Mr.
Hurley developed pronounced shoulder arthralgia and pain in the upper
thoracic spine. Fractures were ruled out. With pain therapy, the pain
became tolerable and gradually improved. Arthralgia and myalgia are
common side effects of interferon-gamma. Due to the demonstrable
therapeutic response, we presented Mr. Hurley, along with an
interpreter, at the Department of Hematology and Oncology to discuss
further therapeutic options for AML in the context of the hematological
and infectious disease situation. After extensive explanation of the
disease situation, the risks of aggressive AML therapy in the presence
of unresolved tuberculosis, and the consequences of palliative AML
therapy. Mr. Hurley agreed to allogeneic stem cell transplantation after
some consideration. On an outpatient basis, the cytostatic therapy with
Azacitidine was expanded to include Venetoclax. Antituberculous therapy
with rifampicin, isoniazid, and levofloxacin was continued. Regular
sputum checks remained consistently microscopically negative until
complete AML remission was achieved. Mr. will be admitted for allogeneic
stem cell transplantation in July 2021. A repeat CT in June 2021
confirmed continued regression of the tuberculosis findings.
Antituberculous therapy will be continued indefinitely.
**CT Neck/Chest/Abdomen/Pelvis on 06/12/2022:** CT for comparison.
Neck/Chest: Improved right lung upper lobe ventilation with regressing
necrotic tuberculosis manifestations, now with only subtotal lobar
atelectasis. Essentially constant necrotic lymph node manifestations in
the cervical, mediastinal, and right hilar regions, as exemplified by
the right supraclavicular (18 mm, SE 301 HU 212) or right paratracheal
(18 mm, SE 301 HU 338) nodes. Narrow pleural effusion on the right,
likewise. No pneumothorax. The heart is not enlarged. No pericardial
effusion. Abdomen: Medium-term constant hypodense liver lesions
(regressing)
**Current Recommendations:** Continue antituberculous therapy without a
defined endpoint. Sputum checks during allogeneic stem cell
transplantation every 1-2 weeks. In case of clinical signs of persistent
infection, perform early CT scans of the neck, chest, and abdomen.
Follow-up appointment in our infectious diseases outpatient clinic after
allogeneic stem cell transplantation. | HLA-identical unrelated donor |
Why were the Venus women transfixed by the Earthmen?
A. They felt abandoned by their own men who had obsessions with war and little time for them.
B. The Earthmen were much more attractive and had real facial hair.
C. The women of Venus liked to break the rules.
D. Venus was solely occupied by women, leaving them no other option.
| IMAGE OF SPLENDOR By LU KELLA From Venus to Earth, and all the way between, it was a hell of a world for men ... and Apprentice Burnerman O'Rielly particularly. [Transcriber's Note: This etext was produced from Planet Stories Summer 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The intercom roared fit to blow O'Rielly back to Venus. "Burner Four!" "On my way, sir!" At the first flash of red on the bank of meters Apprentice Burnerman O'Rielly had slammed the safety helmet on his head; he was already throwing open the lock to the burner room. The hot, throbbing rumble whipped around him and near crushed his breath away. Power! Power of the universe trapped here and ready to destroy its captors given one chance! Swiftly O'Rielly unlocked the controls and reset them. The throbbing rumble changed tone. Old Callahan's voice crackled now through the helmet's ear contact. "Well, Mr. O'Rielly?" "Fusion control two points low, sir." O'Rielly wondered had Callahan passed out, was so long before the old Burner Chief demanded hoarsely, "Didn't you lock them controls before blast-off?" "If every control hadn't been locked in correct setting," O'Rielly answered from his own angry bewilderment, "the error would have registered before blast-off—wouldn't it, sir?" "So a control reset itself in flight, hey?" "I don't know yet, sir." "Well, Mr. O'Rielly, you better know before we orbit Earth!" The icy knot in O'Rielly's stomach jerked tighter. A dozen burners on this ship; why did something crazy have to happen to O'Rielly's? In a hundred years, so the instructors—brisk females all—had told O'Rielly in pre-flight school, no control had ever been known to slip. But one had moved here. Not enough to cause serious trouble this far out from Earth. On blast-down, though, with one jet below peak, the uneven thrust could throw the ship, crash it, the whole lovely thing and all aboard gone in a churning cloud. Sweat pouring off him, O'Rielly prowled around his burner. Design of the thing had been bossed by dames of course; what on Earth wasn't any more? Anyway, nobody could get to a burner except through its watch room. Anyone entered or left there, a bell clanged, lights flashed and a meter registered beside the Burnerman's bunk and on the Burner Chief's console up in the flight room full of beautifully efficient officers. Ever since Venus blast-off O'Rielly had been in Four's watch room. Nobody had passed through. O'Rielly knew it. Callahan knew it. By now the Old Woman herself, Captain Millicent Hatwoody, had probably inquired what was in charge of Burner Four. Well, ma'am, O'Rielly searched every cranny where even a three-tailed mouse of Venus could have stowed away. His first flight, and O'Rielly saw himself washed out, busted to sweeper on the blast-off stands of some God-forsaken satellite. He staggered back into his watch room. And his brain was suddenly taken apart and slapped together again. Felt that way. She was sitting on his bunk. No three-tailed mouse. No Old Woman either. Oh, she was a female human, though, this creature at which O'Rielly stood gaping. Yes, ma'am! "I was in your burner room." Her voice matched the rest of her, a blend of loveliness unlike anything outside a guy's most secret dreams. "I couldn't stand the heat any longer and I couldn't open that big door. So I moved one of your controls a tiny bit. All the noise in there, naturally you couldn't hear me walk out while your back was turned resetting the control." O'Rielly suddenly felt like turning her over his knee and whaling her until she couldn't sit for a year. This, mind you, he felt in an age where no Earth guy for a thousand years had dared raise so much as a breath against woman's supremacy in all matters. That male character trait, however, did not seem to be the overpowering reason why O'Rielly, instead of laying violent hands upon this one's person, heard himself saying in sympathetic outrage, "A shame you had to go to all that bother to get out here!" "You're so kind. But I'm afraid I became rather sticky and smelly in there." "They ought to cool the air in there with perfume! I'll drop a suggestion in the Old Woman's box first chance I get." "You're so thoughtful. And do you have bathing facilities?" "That door right there. Oh, let me open it for you!" "You're so sweet." Her big dark eyes glowed with such pure innocence that O'Rielly could have torn down the universe and rebuilt it just for her. Yes, ma'am, O'Rielly was floating on a pink cloud with heavenly music in his head. Never felt so fine before. Except on the Venus layover when he'd been roped into a dice game with a bunch of Venus lads who had a jug to cheer one's parting with one's money. A bell suddenly clanged fit to wake the dead while the overhead lights flashed wildly. Only the watch room door. Only Callahan here now. Old buzzard had a drooped nose like a pick, chin like a shovel. When he talked he was like digging a hole in front of himself. "Well, what about that control?" "What control?" "Your fusion control that got itself two points low!" "Oh, that little thing." Callahan said something through his teeth, then studied O'Rielly sharply. "Hey, you been wetting your whistle on that Venus vino again? Lemme smell your breath! Bah. Loaded yourself full of chlorophyll again probably. All right, stand aside whilst I see your burner." "Charmed to, Burner Chief Callahan, sir," O'Rielly said while bowing gracefully. "Higher than a swacked skunk's tail again," Callahan muttered, then snapped back over his shoulder, "Use your shower!" O'Rielly stood considering his shower door. Somehow he doubted that Burner Chief Terrence Callahan's mood, or Captain Millicent Hatwoody's, would be improved by knowledge of she who was in O'Rielly's shower now. Not that the dear stowaway was less than charming. Quite the contrary. Oh, very quite! "You rockhead!" Only Callahan back from the burner. "Didn't I tell you to shower the stink off yourself? Old Woman's taking a Venus bigwig on tour the ship. Old Woman catches you like you been rassling skunks she'll peel both our hides off. Not to mention what she'll do anyway about your fusion control!" "Burner Chief Callahan, sir," O'Rielly responded courteously, "I have been thinking." "With what? Never mind, just keep on trying whilst I have a shower for myself here." Wherewith Callahan reached hand for O'Rielly's shower door. "Venus dames," O'Rielly said dreamily, "don't boss anything, do they?" Callahan yelped like he'd been bit in the pants by a big Jupiter ant. "O'Rielly! You trying to get both of us condemned to a Uranus moon?" Callahan also shot a wild look to the intercom switch. It was in OFF position; the flight room full of fancy gold-lace petticoats could not have overheard from here. Nevertheless Callahan's eyes rolled like the devil was behind him with the fork ready. "O'Rielly, open your big ears whilst for your own good and mine I speak of certain matters. "Thousand years ago, it was, the first flight reached Venus. Guys got one look at them dames. Had to bring some home or bust. So then everybody on Earth got a look, mostly by TV only of course. That did it. Every guy on Earth began blowing his fuse over them dames. Give up the shirt off his back, last buck in the bank, his own Earth dame or family—everything. "Well, that's when Earth dames took over like armies of wild cats with knots in their tails. Before the guys who'd brought the Venus dames to Earth could say anything they was taken apart too small to pick up with a blotter. Earth dames wound up by flying the Venus ones back where they come from and serving notice if one ever set foot on Earth again there wouldn't be enough left of Venus to find with an electron microscope. "Venus boys rared up and served notice that if Earth ever got any funny notions, right away there wouldn't be enough Earth left to hide in an atom's eyebrow. Touchy as hornets on a hot griddle, them Venus guys. Crazier than bed bugs about war. Could smell a loose dollar a million light years away too. Finagled around until they finally cooked up a deal. "No Venus dames allowed within fifty miles of their port. Earth guys stay inside the high-voltage fence. Any dame caught trying to leave Venus thrown to the tigers for supper. Same for any Earth guy caught around a Venus dame. In return, Earth could buy practically everything at bargain basement prices." "Oh, I was shown the history films in pre-flight," O'Rielly said, still dreamily. "But not a peek of any Venus dame." "Pray heaven you'll never lay eyes on one nor have one get within ten foot of you! Even though you'd know she'd be your damnation wouldn't make a whit difference—you'd still act sappier than thirty-seven angels flying on vino." Callahan suddenly stared at O'Rielly. "Holy hollering saints!" "Now, now, Burner Chief Callahan, sir," O'Rielly responded with an airy laugh. "No Earth guy for a hundred twenty-five years been near one and lived to tell it, has he?" "So the whispers run," Callahan murmured with a queer flame dancing into his eyes. "So the old whispers still run." "Never a name, though. Never how it was done." O'Rielly snorted. "Probably just a goofy tale set loose by some old space bum." "Oh?" Callahan bristled up like a bad name had been bandied about. "Seen them ditty bags Venus bigwigs have, ain't you? Some big enough to stuff a cow in. Notice how nobody ever dares question a bigwig's bags, even through customs? Just run 'em through the big Geiger that tells whether there's any fusionable junk inside. Well, our boy got himself one of them bags, stuffed himself inside and joined a bigwig's pile of 'em. "Didn't pull it whilst on the Venus port during a layover either, when a crew check would of turned him up missing. Pulled it on vacation. Started on the Earth end. Made himself a pair of beards to paste on his ears of course. Wove Jupiter wiggle worms in to keep the beards moving. Wasn't like the real thing, but good enough to flimflam Venus guys." With suddenly enlivened interest O'Rielly looked at Callahan. "Hey, how come you know so much?" "Hah? What?" Callahan blinked like waking from a trance; even groaned to himself, something that sounded like, "Blabbering like I'd had a nip myself—or one of them dillies was radiating nearby." Then Callahan glared fit to drill holes in O'Rielly's head. "Look! I was a full Burnerman before you was born. Been flying the spaces hundred twenty-five years now. Had more chances to hear more—just hear more, you hear! Only tried to clear your mind about Venus dames so you could put your brain on your control mess. So now put it! If you ain't high on vino and ain't been made nuts by a Venus dame, what answer do we feed the Old Woman?" "Search me," Apprentice Burnerman O'Rielly responded cheerfully. "Of all the loony apprentices I ever had to answer the Old Woman for! Awp, lemme out where I can think of something to save me own neck at least!" Was all O'Rielly could do to keep from rolling on the deck with glee. Old Callahan had been flimflammed for fair! The dear little stowaway was saved! And O'Rielly would now think of grand ways to save her lovely neck and his own forever. O'Rielly's shower door, however, opened abruptly. O'Rielly had not opened it. O'Rielly, however, suffered a cruel stab of dismay. Surely his dear stowaway had been listening through the door. Why didn't she have brains enough to stay hid until Callahan was gone! At sight of her, of course, Callahan's eyes near popped from his old head. "Berta!" "Oh, I'm Trillium," she assured Callahan sweetly. "But Grandmamma's name is Berta and people say I'm just like she was a hundred and twenty-five years ago." "Hah? What?" Callahan blinked like his brain had been taken apart and was being slapped together again. "O'Rielly! Awp, you angel-faced pirate, couldn't you hide her somewheres better than that? Shut up, you don't have to explain to me, but God help the whole universe if we don't flimflam the Old Woman!" With which ominous remark, rendered in a zesty devil-may-care manner, however, Callahan threw himself into O'Rielly's shower. O'Rielly stood looking thoughtfully at lovely, womanly, exquisite Trillium. Just like that, O'Rielly felt as sparkling of mind as a spiral nebula. "My locker!" he crowed with inspiration and yanked open the doors under his bunk. He glimpsed a black ditty bag, also the cap and coverall uniform of a baggage boy. "I threw them in there before you came on duty before blast-off," Trillium explained. "I knew the burner room would be warm." Trillium—with her shape—passing as a boy hustling bags through this ship. O'Rielly chortled as he tucked her under his bunk. "Now don't you worry about another thing!" "Oh, I'm not," she assured him happily. "Everything is going just the way Grandmamma knew it would!" O'Rielly's shower opened and Callahan, glowing like a young bucko, bounced onto the bunk. "Well, did you hide her good this time? No, don't tell me! I want to be surprised if the Old Woman ever finds her." "If what old woman finds whom?" a voice like thin ice crackling wanted to know. The watch room's door had opened. Wouldn't think the Old Woman was a day over seventy-five, let alone near two hundred. Cut of her uniform probably lent a helping hand or three to the young snap of her figure. Frosty blue of fancy hair-do, she was, though, and icy of eye as she looked at O'Rielly and Callahan still lolling on the bunk. Her voice was an iceberg exploding. "At attention!" Never in his right mind would any crewman dare fail to come stiffly erect the instant the Old Woman appeared. Behind her stood a colorfully robed specimen of Venus man. Handsome as the devil himself. Fit to snap lesser men in two with his highly bejeweled hands. Fuzzy beards trailed from his ears and kept twitching lazily as he sneered at the spectacle of two men meekly acknowledging the superiority of a woman. She was fit to put frost on a hydrogen burner. "Mr. Callahan, I asked you a question, did I not?" "Believe you did, ma'am," Callahan responded cheerfully. "And the answer is, ma'am, that Apprentice Burnerman O'Rielly and me was discussing—ah—matrimony, ma'am. Mr. Apprentice Burnerman O'Rielly here is considering it, ma'am." Wasn't too bad a fib. The more O'Rielly thought of Trillium, the more ideas he got of doing things he'd never dreamt of before in his life. Yes, ma'am! "Wasting your time talking nonsense!" Old Woman's look was fit to freeze O'Rielly's brain, then she gave Callahan the look. "I sent you down here to find the answer to that fusion control slippage!" "Oh, you'll have the best answer you ever heard of before long, ma'am!" Callahan assured her heartily. "The subject of nonsense—I mean, women—merely chanced to arise whilst we was scientifically analyzing the control phenomenon, ma'am. Naturally I offered this innocent young Burnerman the benefit of me long years of experience. Why," Callahan said with a jaunty laugh, "dames mean nothing to me. Indeed 'twouldn't bother me none if there wasn't one of the things left in the world! Present company excepted, of course," Callahan hastened to say with a courtly bow. "Stay at attention!" Old Woman sniffed the air near Callahan's face, then in O'Rielly's vicinity. "Smothered it with chlorophyll probably," she muttered through her teeth, "if it is that vino." Something horrible as a plague flickered in her eyes, then the old ice was there again. "Apprentice Burnerman, don't you know what your shower is for? Then use it! Mr. Callahan, remain at attention while I inspect this burner!" She tendered a cool glance at the Venus bigwig. "Care to join me, Your Excellency?" "May as well." His Excellency glanced at O'Rielly and Callahan much as he might at a couple of worms. Could bet your last old sox no female ever told any Venus man what to do. The shower units were equipped so no Burnerman need be more than two steps from his responsibility. To keep the Old Woman from possibly blowing her gaskets completely, O'Rielly simply stepped in, shut the door, flipped a switch and tingled as he was electronically cleansed of person and clothes. By time he finished, the Old Woman and His Excellency were already coming out of the burner room, dripping with sweat. Old Woman opened the shower with her customary commanding air. "You first, Your Excellency." "My dear Captain," His Excellency replied like a smoothly drawn dagger, "always the lesser gender enjoys precedence." No Earth dame ever admitted any guy was even equal to any female. Old Woman, a prime symbol of her gender's superiority, whipped a razor edge onto her own words. "Facilities of the Captain's quarters are more satisfactory." "No more so than those of the Ambassadorial Suite." Seeming to grind her teeth, the Old O Woman turned abruptly to leave O'Rielly's watch room. Was all O'Rielly could do to keep from busting out laughing for joy. Old Woman had been flimflammed for fair! Dear Trillium was saved! And betwixt O'Rielly's grand brain and Callahan's great experience she'd be happy forever. A fine loud "thump," however, was now heard. Old Woman whirled back and yanked open the doors under O'Rielly's bunk. "Of all the sappy hiding places!" Callahan yelped, in surprise of course. "Trillium?" His Excellency bellowed as if stung by one of the sabre-tailed hornets of his native planet. "Trillium!" "Trillium," O'Rielly pleaded in loving anguish, "why do you have to keep coming out of hiding just when nobody's going to find you?" Her eyes merely became deep pools in which O'Rielly would have gladly drowned himself if he could. "There are rewards," the Old Woman said with the deadly coldness of outer space, "for Earthmen found in a Venus woman's company, and for her leaving her planet." "Shut up!" His Excellency's ear beards were standing straight out sideways. "I'll handle this!" "May I remind His Excellency," the Old Woman snapped, "that I represent Earth and her dominion of space gained by right of original flight!" "May I remind the Captain," His Excellency declared fit to be heard back to his planet, "that I am the Personal Ambassador of the President of Venus and this thing can mean war!" "Yes! War in which people will actually die!" As His Excellency paled at that grisly remark, the Old Woman spoke through her teeth at O'Rielly, Callahan and Trillium. "All right, come along!" O'Rielly joined the death march gladly. He felt the way Callahan looked: ready to wrap his arms around Trillium's brave loveliness and protect it to his last breath of life. Old Woman led the way to her office. Jabbed some buttons on her desk. Panels on opposite walls lit up. "Presidents of Earth and Venus, please," the Old Woman stated evenly. "Interplanetary emergency." Highly groomed flunkies appeared on the panels and were impersonally pleasant. "Madame President's office. She is in a Cabinet meeting." "Mr. President's office. He is in personal command of our glorious war efforts." Old Woman sighed through her teeth. "Venus woman aboard this ship. Stowaway. Rattle that around your belfries." The flunkies' faces went slack with shock, then were replaced by a blizzard of scrambled faces and torrents of incoherent voices. Finally on the Earth panel appeared the famous classic features. "The facts, if you please, Captain Hatwoody." The Venus panel finally held steady on universally notorious features, that were as fierce as an eagle's, in a fancy war helmet. "Trillium! My own granddaughter? Impossible! Dimdooly," Mr. President roared at his Excellency, "what's this nonsense?" "Some loud creature is interfering," Madame President snapped with annoyance. "Blasted fools still have the circuits crossed," Mr. President swore. "Some silly female cackling now!" The parties in the panels saw each other now. Each one's left hand on a desk moved toward a big red button marked, ROCKETS. "So," Mr. President said evenly. "Another violation by your Earthmen." "By your granddaughter, at least," Madame President replied coolly. "An innocent child," Mr. President snapped, "obviously kidnapped by those two idiotic Earthmen there!" "Oh, no, Grandpapa," Trillium said swiftly; "I stole away all by myself, and Mr. O'Rielly and Callahan have been very helpful." "Impossible!" Grandpapa President's ear beards stood near straight up as he roared, "You couldn't have stolen away by yourself! Trillium, tell the truth!" "Very well. Grandmamma told me how." "Obviously Trillium's poor little brain has been drugged," His Excellency Dimdooly declared. "Grandmamma Berta wouldn't know the first thing about such things!" "Impossible!" Grandpapa President agreed. "I've been married to her for a hundred and twenty-four and a half years and she's the finest rattle-brain I ever knew!" "She learned," Trillium stated emphatically, "a hundred and twenty-five years ago." "Hundred twenty-five," Grandpapa president growled like a boiling volcano. "The year some Earthman.... Never did catch the devil.... Berta? Impossible!" Madame President's shapely finger now rested full on the button that could launch the fleets of war rockets that had been pre-aimed for a thousand years. "I'm afraid your Ambassador is unwelcome now," Madame President stated coolly. "Your granddaughter's actions have every mark of an invasion tactic by your government." "What do you mean, her actions?" Grandpapa President's finger now lay poised on the button that had been waiting a thousand years to blow Earth out of the universe. "My grandchild was kidnapped by men under your official command! Weren't you, Trillium dear?" "No. One of us stowing away was the only way we Venus women could bring our cause to the attention of Earth's President. If Earth will only stop buying from Venus, you won't have any money to squander on your wars any longer no matter what happens to we revolutionaries!" "Revolutionaries? Such claptrap! And what's wrong with my wars? People have to have something to keep their minds off their troubles! Nobody around here gets hurt. Oh, maybe a few scratches here and there. But nobody on Venus dies from the things any more." "But Venus men are so excited all the time about going to war they haven't time for us women. That's why we always radiated such a fatal attraction for Earthmen. We want to be loved! We want our own men home doing useful work!" "Well, they do come home and do useful work! Couple weeks every ten months. Proven to be a highly efficient arrangement." "More boys to run off to your old wars and more girls to stay home and be lonely!" "Now you just listen to me, Trillium!" Grandpapa President was all Venus manhood laying down the law. "That's the way things have been on Venus for ten thousand years and all the women in the universe can't change it!" "I have been in constant contact with my Cabinet during these conversations," Madame President said crisply. "Earth is terminating all trade agreements with Venus as of this instant." "What?" Grandpapa's beards near pulled his ears off. "It's not legal! You can't get away with this!" "Take your finger off that trigger, boy!" a heavenly voice similar to Trillium's advised from the Venus panel. Whereupon Grandpapa glared to one side. "Berta! What are you doing here? I am deciding matters of the gravest interplanetary nature!" "Were." Features more beautifully mature than Trillium's crowded onto the panel too. "From now on I'm doing the deciding." "Nonsense! You're only my wife!" "And new President of Venus, elected by unanimous vote of all women." "Impossible! The men run Venus! Nobody's turning this planet into another Earth where a man can't even sneeze unless some woman says so!" "Take him away, girls," Berta ordered coolly, whereupon her spouse was yanked from view. His bellows, however, could be heard yet. "Unhand me, you fool creatures! Guards! Guards!" "Save your breath," Berta advised him. "And while you're in the cooler, enjoy this latest batch of surrender communiques. We women are in control everywhere now." "Dimmy," Trillium was saying firmly to His Excellency, "you have beat around the bush with me long enough. Now say it!" Dimdooly—the mighty, the lordly, who had sneered at the sight of mere Earthmen kowtowing to a mere woman—swelled up fit to blow his gaskets, then all the gas went out of him. His ear beards, however, still had enough zip left to flutter like butterflies. "Yes, Trillium dear. I love only you. Please marry me at your earliest convenience." "Well, Grandmamma," Trillium said with a highly self-satisfied air, "it works. And just like you said, Earthmen meant nothing once I knew we Venus women had our own men in our power." "Those crewmen there," Grandmamma President said, "seem to be proof enough that we Venus women no longer radiate any threat to Earth's tranquility." Yes, ma'am, O'Rielly sure felt like proof of something all of a sudden. Worse than the hangover from that crap game with Venus vino. He looked away from Trillium and took a look at Callahan. Old guy looked away from Grandmamma President like he was packing the second biggest headache in history. "Hmmmm, yes," Madame President of Earth observed. "Reactions agree perfectly with the psychoanalytical research project we have been conducting on the subject of the Venus female influence. Madame President of Venus, congratulations on your victory! "Long may the superior sex reign on Venus too! We shall be delighted to receive an Ambassadoress to discuss a new trade treaty at your earliest convenience." "Thank you for cancelling the old trade agreements at the psychological moment," Grandmamma President said cordially. "What with the communications mixup, we managed to have the scenes on these panels broadcast throughout all Venus. When the rug went out from under the top man, the tide really turned in our favor. Now, Trillium, you take over Dimmy's credentials." "The Ambassadorial Suite, too," Madame President of Earth said graciously. "Anything else now, Berta?" "I should like," Grandmamma President Berta said charmingly, "that Mr. O'Rielly and Mr. Callahan be suitably rewarded for assisting our revolution better than they knew." "Of course," Madame President of Earth was delighted to oblige. "No doubt Captain Hatwoody knows what reward would satisfy their needs best." The Madame Presidents switched to a private circuit, Trillium dragged Dimdooly off somewhere and the Old Woman eyed O'Rielly and Callahan. Especially she eyed Callahan, like running chilled drills through his old conniving brain. "I award the pair of you five minutes leisure before returning to your stations." "Oh, well," O'Rielly muttered, once he and Callahan were safely beyond earshot, "could have been rewarded worse, I suppose." "What you expect for being flimflammed by a foreign dame, the rings of Saturn? Lucky we ain't programmed to be hung, shot and thrown to the crows for breakfast." Callahan's old pick-and-shovel face wore a little grin like the cat that nobody could prove ate the canary. "You—I mean, that Earth guy a hundred twenty-five years ago," O'Rielly said in sudden thought. "If Venus dames wanted to be loved so bad, why did Trillium's Grandmamma let him go?" "Venus guys wasn't so busy playing war all the time," Callahan mumbled, like to himself, "they'd of found out the answer centuries ago. Yep, guess our boy was the only guy on Earth or Venus to find out and live. Dames bossing both planets now, though, his old secret won't be one much longer. Venus dames could of let it out centuries ago themselves but didn't, just to spite Earth probably. Later, was part of organizing to take over Venus, I guess." O'Rielly still had memories of the way he had felt about Trillium before her revolution. "All right, Callahan, why did 'our boy' leave Grandmamma?" "Yes, ma'am," Callahan sighed like he hadn't heard a word O'Rielly said, "you could sweet-talk 'em, kiss 'em and hold 'em tighter'n Billy-be-damned. And that's all." "I'm not sure," O'Rielly said, "what you mean by, 'that's all.'" "Anybody ever seen anybody but a Venus guy come built with ear beards? Course not." "But I thought our boy was wearing the best fakes ever." "Ain't nothing can match the natural growed-on variety, no, ma'am. Venus guy kisses a Venus dame, his beards grabs her roundst the ears." "So what?" "Tickles 'em, boy, tickles 'em!" | A. They felt abandoned by their own men who had obsessions with war and little time for them. |
Which type of debt received the largest investment among the short term investments for MGM in H1 FY2023? | Evidence 0:
Fair value level
June 30, 2023
December 31, 2022
(In thousands)
Cash and cash equivalents:
Money market funds
Level 1
$
2,195
$
12,009
Commercial paper and certificates of deposit
Level 2
5,992
Cash and cash equivalents
2,195
18,001
Short-term investments:
U.S. government securities
Level 1
57,696
56,835
U.S. agency securities
Level 2
29,049
9,530
Commercial paper and certificates of deposit
Level 2
4,561
4,466
Corporate bonds
Level 2
416,420
213,875
Short-term investments
507,726
284,706
Total debt investments
$
509,921
$
302,707 | the biggest short term investment is in corporate bonds (almost 82% of the total investment) |
Which lexicon-based models did they compare with? | ### Introduction
Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural language. Therefore, machine commenting is an important problem faced in building an intelligent and interactive agent. Machine commenting is also useful in improving the activeness of communities, including online forums and news websites. Article comments can provide extended information and external opinions for the readers to have a more comprehensive understanding of the article. Therefore, an article with more informative and interesting comments will attract more attention from readers. Moreover, machine commenting can kick off the discussion about an article or a topic, which helps increase user engagement and interaction between the readers and authors. Because of the advantage and importance described above, more recent studies have focused on building a machine commenting system with neural models BIBREF0 . One bottleneck of neural machine commenting models is the requirement of a large parallel dataset. However, the naturally paired commenting dataset is loosely paired. Qin et al. QinEA2018 were the first to propose the article commenting task and an article-comment dataset. The dataset is crawled from a news website, and they sample 1,610 article-comment pairs to annotate the relevance score between articles and comments. The relevance score ranges from 1 to 5, and we find that only 6.8% of the pairs have an average score greater than 4. It indicates that the naturally paired article-comment dataset contains a lot of loose pairs, which is a potential harm to the supervised models. Besides, most articles and comments are unpaired on the Internet. For example, a lot of articles do not have the corresponding comments on the news websites, and the comments regarding the news are more likely to appear on social media like Twitter. Since comments on social media are more various and recent, it is important to exploit these unpaired data. Another issue is that there is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comment does not always tell the same thing as the corresponding article. Table TABREF1 shows an example of an article and several corresponding comments. The comments do not directly tell what happened in the news, but talk about the underlying topics (e.g. NBA Christmas Day games, LeBron James). However, existing methods for machine commenting do not model the topics of articles, which is a potential harm to the generated comments. To this end, we propose an unsupervised neural topic model to address both problems. For the first problem, we completely remove the need of parallel data and propose a novel unsupervised approach to train a machine commenting system, relying on nothing but unpaired articles and comments. For the second issue, we bridge the articles and comments with their topics. Our model is based on a retrieval-based commenting framework, which uses the news as the query to retrieve the comments by the similarity of their topics. The topic is represented with a variational topic, which is trained in an unsupervised manner. The contributions of this work are as follows: ### Machine Commenting
In this section, we highlight the research challenges of machine commenting, and provide some solutions to deal with these challenges. ### Challenges
Here, we first introduce the challenges of building a well-performed machine commenting system. The generative model, such as the popular sequence-to-sequence model, is a direct choice for supervised machine commenting. One can use the title or the content of the article as the encoder input, and the comments as the decoder output. However, we find that the mode collapse problem is severed with the sequence-to-sequence model. Despite the input articles being various, the outputs of the model are very similar. The reason mainly comes from the contradiction between the complex pattern of generating comments and the limited parallel data. In other natural language generation tasks, such as machine translation and text summarization, the target output of these tasks is strongly related to the input, and most of the required information is involved in the input text. However, the comments are often weakly related to the input articles, and part of the information in the comments is external. Therefore, it requires much more paired data for the supervised model to alleviate the mode collapse problem. One article can have multiple correct comments, and these comments can be very semantically different from each other. However, in the training set, there is only a part of the correct comments, so the other correct comments will be falsely regarded as the negative samples by the supervised model. Therefore, many interesting and informative comments will be discouraged or neglected, because they are not paired with the articles in the training set. There is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comments often have some external information, or even tell an opposite opinion from the articles. Therefore, it is difficult to automatically mine the relationship between articles and comments. ### Solutions
Facing the above challenges, we provide three solutions to the problems. Given a large set of candidate comments, the retrieval model can select some comments by matching articles with comments. Compared with the generative model, the retrieval model can achieve more promising performance. First, the retrieval model is less likely to suffer from the mode collapse problem. Second, the generated comments are more predictable and controllable (by changing the candidate set). Third, the retrieval model can be combined with the generative model to produce new comments (by adding the outputs of generative models to the candidate set). The unsupervised learning method is also important for machine commenting to alleviate the problems descried above. Unsupervised learning allows the model to exploit more data, which helps the model to learn more complex patterns of commenting and improves the generalization of the model. Many comments provide some unique opinions, but they do not have paired articles. For example, many interesting comments on social media (e.g. Twitter) are about recent news, but require redundant work to match these comments with the corresponding news articles. With the help of the unsupervised learning method, the model can also learn to generate these interesting comments. Additionally, the unsupervised learning method does not require negative samples in the training stage, so that it can alleviate the negative sampling bias. Although there is semantic gap between the articles and the comments, we find that most articles and comments share the same topics. Therefore, it is possible to bridge the semantic gap by modeling the topics of both articles and comments. It is also similar to how humans generate comments. Humans do not need to go through the whole article but are capable of making a comment after capturing the general topics. ### Proposed Approach
We now introduce our proposed approach as an implementation of the solutions above. We first give the definition and the denotation of the problem. Then, we introduce the retrieval-based commenting framework. After that, a neural variational topic model is introduced to model the topics of the comments and the articles. Finally, semi-supervised training is used to combine the advantage of both supervised and unsupervised learning. ### Retrieval-based Commenting
Given an article, the retrieval-based method aims to retrieve a comment from a large pool of candidate comments. The article consists of a title INLINEFORM0 and a body INLINEFORM1 . The comment pool is formed from a large scale of candidate comments INLINEFORM2 , where INLINEFORM3 is the number of the unique comments in the pool. In this work, we have 4.5 million human comments in the candidate set, and the comments are various, covering different topics from pets to sports. The retrieval-based model should score the matching between the upcoming article and each comments, and return the comments which is matched with the articles the most. Therefore, there are two main challenges in retrieval-based commenting. One is how to evaluate the matching of the articles and comments. The other is how to efficiently compute the matching scores because the number of comments in the pool is large. To address both problems, we select the “dot-product” operation to compute matching scores. More specifically, the model first computes the representations of the article INLINEFORM0 and the comments INLINEFORM1 . Then the score between article INLINEFORM2 and comment INLINEFORM3 is computed with the “dot-product” operation: DISPLAYFORM0 The dot-product scoring method has proven a successful in a matching model BIBREF1 . The problem of finding datapoints with the largest dot-product values is called Maximum Inner Product Search (MIPS), and there are lots of solutions to improve the efficiency of solving this problem. Therefore, even when the number of candidate comments is very large, the model can still find comments with the highest efficiency. However, the study of the MIPS is out of the discussion in this work. We refer the readers to relevant articles for more details about the MIPS BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Another advantage of the dot-product scoring method is that it does not require any extra parameters, so it is more suitable as a part of the unsupervised model. ### Neural Variational Topic Model
We obtain the representations of articles INLINEFORM0 and comments INLINEFORM1 with a neural variational topic model. The neural variational topic model is based on the variational autoencoder framework, so it can be trained in an unsupervised manner. The model encodes the source text into a representation, from which it reconstructs the text. We concatenate the title and the body to represent the article. In our model, the representations of the article and the comment are obtained in the same way. For simplicity, we denote both the article and the comment as “document”. Since the articles are often very long (more than 200 words), we represent the documents into bag-of-words, for saving both the time and memory cost. We denote the bag-of-words representation as INLINEFORM0 , where INLINEFORM1 is the one-hot representation of the word at INLINEFORM2 position, and INLINEFORM3 is the number of words in the vocabulary. The encoder INLINEFORM4 compresses the bag-of-words representations INLINEFORM5 into topic representations INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , and INLINEFORM3 are the trainable parameters. Then the decoder INLINEFORM4 reconstructs the documents by independently generating each words in the bag-of-words: DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 is the number of words in the bag-of-words, and INLINEFORM1 is a trainable matrix to map the topic representation into the word distribution. In order to model the topic information, we use a Dirichlet prior rather than the standard Gaussian prior. However, it is difficult to develop an effective reparameterization function for the Dirichlet prior to train VAE. Therefore, following BIBREF6 , we use the Laplace approximation BIBREF7 to Dirichlet prior INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 denotes the logistic normal distribution, INLINEFORM1 is the number of topics, and INLINEFORM2 is a parameter vector. Then, the variational lower bound is written as: DISPLAYFORM0 where the first term is the KL-divergence loss and the second term is the reconstruction loss. The mean INLINEFORM0 and the variance INLINEFORM1 are computed as follows: DISPLAYFORM0 DISPLAYFORM1 We use the INLINEFORM0 and INLINEFORM1 to generate the samples INLINEFORM2 by sampling INLINEFORM3 , from which we reconstruct the input INLINEFORM4 . At the training stage, we train the neural variational topic model with the Eq. EQREF22 . At the testing stage, we use INLINEFORM0 to compute the topic representations of the article INLINEFORM1 and the comment INLINEFORM2 . ### Training
In addition to the unsupervised training, we explore a semi-supervised training framework to combine the proposed unsupervised model and the supervised model. In this scenario we have a paired dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1 . The supervised model is trained on INLINEFORM2 so that we can learn the matching or mapping between articles and comments. By sharing the encoder of the supervised model and the unsupervised model, we can jointly train both the models with a joint objective function: DISPLAYFORM0 where INLINEFORM0 is the loss function of the unsupervised learning (Eq. refloss), INLINEFORM1 is the loss function of the supervised learning (e.g. the cross-entropy loss of Seq2Seq model), and INLINEFORM2 is a hyper-parameter to balance two parts of the loss function. Hence, the model is trained on both unpaired data INLINEFORM3 , and paired data INLINEFORM4 . ### Datasets
We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model. The dataset is collected from Tencent News, which is one of the most popular Chinese websites for news and opinion articles. The dataset consists of 198,112 news articles. Each piece of news contains a title, the content of the article, and a list of the users' comments. Following the previous work BIBREF0 , we tokenize all text with the popular python package Jieba, and filter out short articles with less than 30 words in content and those with less than 20 comments. The dataset is split into training/validation/test sets, and they contain 191,502/5,000/1,610 pieces of news, respectively. The whole dataset has a vocabulary size of 1,858,452. The average lengths of the article titles and content are 15 and 554 Chinese words. The average comment length is 17 words. ### Implementation Details
The hidden size of the model is 512, and the batch size is 64. The number of topics INLINEFORM0 is 100. The weight INLINEFORM1 in Eq. EQREF26 is 1.0 under the semi-supervised setting. We prune the vocabulary, and only leave 30,000 most frequent words in the vocabulary. We train the model for 20 epochs with the Adam optimizing algorithms BIBREF8 . In order to alleviate the KL vanishing problem, we set the initial learning to INLINEFORM2 , and use batch normalization BIBREF9 in each layer. We also gradually increase the KL term from 0 to 1 after each epoch. ### Baselines
We compare our model with several unsupervised models and supervised models. Unsupervised baseline models are as follows: TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. We use the concatenation of the title and the body as the query to retrieve the candidate comment set by means of the similarity of the tf-idf value. The model is trained on unpaired articles and comments, which is the same as our proposed model. LDA (Topic, Non-Neural) is a popular unsupervised topic model, which discovers the abstract "topics" that occur in a collection of documents. We train the LDA with the articles and comments in the training set. The model retrieves the comments by the similarity of the topic representations. NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic. The supervised baseline models are: S2S (Generative) BIBREF11 is a supervised generative model based on the sequence-to-sequence network with the attention mechanism BIBREF12 . The model uses the titles and the bodies of the articles as the encoder input, and generates the comments with the decoder. IR (Retrieval) BIBREF0 is a supervised retrieval-based model, which trains a convolutional neural network (CNN) to take the articles and a comment as inputs, and output the relevance score. The positive instances for training are the pairs in the training set, and the negative instances are randomly sampled using the negative sampling technique BIBREF13 . ### Retrieval Evaluation
For text generation, automatically evaluate the quality of the generated text is an open problem. In particular, the comment of a piece of news can be various, so it is intractable to find out all the possible references to be compared with the model outputs. Inspired by the evaluation methods of dialogue models, we formulate the evaluation as a ranking problem. Given a piece of news and a set of candidate comments, the comment model should return the rank of the candidate comments. The candidate comment set consists of the following parts: Correct: The ground-truth comments of the corresponding news provided by the human. Plausible: The 50 most similar comments to the news. We use the news as the query to retrieve the comments that appear in the training set based on the cosine similarity of their tf-idf values. We select the top 50 comments that are not the correct comments as the plausible comments. Popular: The 50 most popular comments from the dataset. We count the frequency of each comments in the training set, and select the 50 most frequent comments to form the popular comment set. The popular comments are the general and meaningless comments, such as “Yes”, “Great”, “That's right', and “Make Sense”. These comments are dull and do not carry any information, so they are regarded as incorrect comments. Random: After selecting the correct, plausible, and popular comments, we fill the candidate set with randomly selected comments from the training set so that there are 200 unique comments in the candidate set. Following previous work, we measure the rank in terms of the following metrics: Recall@k: The proportion of human comments found in the top-k recommendations. Mean Rank (MR): The mean rank of the human comments. Mean Reciprocal Rank (MRR): The mean reciprocal rank of the human comments. The evaluation protocol is compatible with both retrieval models and generative models. The retrieval model can directly rank the comments by assigning a score for each comment, while the generative model can rank the candidates by the model's log-likelihood score. Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information. We also evaluate two popular supervised models, i.e. seq2seq and IR. Since the articles are very long, we find either RNN-based or CNN-based encoders cannot hold all the words in the articles, so it requires limiting the length of the input articles. Therefore, we use an MLP-based encoder, which is the same as our model, to encode the full length of articles. In our preliminary experiments, the MLP-based encoder with full length articles achieves better scores than the RNN/CNN-based encoder with limited length articles. It shows that the seq2seq model gets low scores on all relevant metrics, mainly because of the mode collapse problem as described in Section Challenges. Unlike seq2seq, IR is based on a retrieval framework, so it achieves much better performance. ### Generative Evaluation
Following previous work BIBREF0 , we evaluate the models under the generative evaluation setting. The retrieval-based models generate the comments by selecting a comment from the candidate set. The candidate set contains the comments in the training set. Unlike the retrieval evaluation, the reference comments may not appear in the candidate set, which is closer to real-world settings. Generative-based models directly generate comments without a candidate set. We compare the generated comments of either the retrieval-based models or the generative models with the five reference comments. We select four popular metrics in text generation to compare the model outputs with the references: BLEU BIBREF14 , METEOR BIBREF15 , ROUGE BIBREF16 , CIDEr BIBREF17 . Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation. Still, the supervised IR achieves better scores than the seq2seq model. With the help of our proposed model, both IR and S2S achieve an improvement under the semi-supervised scenarios. ### Analysis and Discussion
We analyze the performance of the proposed method under the semi-supervised setting. We train the supervised IR model with different numbers of paired data. Figure FIGREF39 shows the curve (blue) of the recall1 score. As expected, the performance grows as the paired dataset becomes larger. We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M). It shows that IR+Proposed can outperform the supervised IR model given the same paired dataset. It concludes that the proposed model can exploit the unpaired data to further improve the performance of the supervised model. Although our proposed model can achieve better performance than previous models, there are still remaining two questions: why our model can outperform them, and how to further improve the performance. To address these queries, we perform error analysis to analyze the error types of our model and the baseline models. We select TF-IDF, S2S, and IR as the representative baseline models. We provide 200 unique comments as the candidate sets, which consists of four types of comments as described in the above retrieval evaluation setting: Correct, Plausible, Popular, and Random. We rank the candidate comment set with four models (TF-IDF, S2S, IR, and Proposed+IR), and record the types of top-1 comments. Figure FIGREF40 shows the percentage of different types of top-1 comments generated by each model. It shows that TF-IDF prefers to rank the plausible comments as the top-1 comments, mainly because it matches articles with the comments based on the similarity of the lexicon. Therefore, the plausible comments, which are more similar in the lexicon, are more likely to achieve higher scores than the correct comments. It also shows that the S2S model is more likely to rank popular comments as the top-1 comments. The reason is the S2S model suffers from the mode collapse problem and data sparsity, so it prefers short and general comments like “Great” or “That's right”, which appear frequently in the training set. The correct comments often contain new information and different language models from the training set, so they do not obtain a high score from S2S. IR achieves better performance than TF-IDF and S2S. However, it still suffers from the discrimination between the plausible comments and correct comments. This is mainly because IR does not explicitly model the underlying topics. Therefore, the correct comments which are more relevant in topic with the articles get lower scores than the plausible comments which are more literally relevant with the articles. With the help of our proposed model, proposed+IR achieves the best performance, and achieves a better accuracy to discriminate the plausible comments and the correct comments. Our proposed model incorporates the topic information, so the correct comments which are more similar to the articles in topic obtain higher scores than the other types of comments. According to the analysis of the error types of our model, we still need to focus on avoiding predicting the plausible comments. ### Article Comment
There are few studies regarding machine commenting. Qin et al. QinEA2018 is the first to propose the article commenting task and a dataset, which is used to evaluate our model in this work. More studies about the comments aim to automatically evaluate the quality of the comments. Park et al. ParkSDE16 propose a system called CommentIQ, which assist the comment moderators in identifying high quality comments. Napoles et al. NapolesTPRP17 propose to discriminating engaging, respectful, and informative conversations. They present a Yahoo news comment threads dataset and annotation scheme for the new task of identifying “good” online conversations. More recently, Kolhaatkar and Taboada KolhatkarT17 propose a model to classify the comments into constructive comments and non-constructive comments. In this work, we are also inspired by the recent related work of natural language generation models BIBREF18 , BIBREF19 . ### Topic Model and Variational Auto-Encoder
Topic models BIBREF20 are among the most widely used models for learning unsupervised representations of text. One of the most popular approaches for modeling the topics of the documents is the Latent Dirichlet Allocation BIBREF21 , which assumes a discrete mixture distribution over topics is sampled from a Dirichlet prior shared by all documents. In order to explore the space of different modeling assumptions, some black-box inference methods BIBREF22 , BIBREF23 are proposed and applied to the topic models. Kingma and Welling vae propose the Variational Auto-Encoder (VAE) where the generative model and the variational posterior are based on neural networks. VAE has recently been applied to modeling the representation and the topic of the documents. Miao et al. NVDM model the representation of the document with a VAE-based approach called the Neural Variational Document Model (NVDM). However, the representation of NVDM is a vector generated from a Gaussian distribution, so it is not very interpretable unlike the multinomial mixture in the standard LDA model. To address this issue, Srivastava and Sutton nvlda propose the NVLDA model that replaces the Gaussian prior with the Logistic Normal distribution to approximate the Dirichlet prior and bring the document vector into the multinomial space. More recently, Nallapati et al. sengen present a variational auto-encoder approach which models the posterior over the topic assignments to sentences using an RNN. ### Conclusion
We explore a novel way to train a machine commenting model in an unsupervised manner. According to the properties of the task, we propose using the topics to bridge the semantic gap between articles and comments. We introduce a variation topic model to represent the topics, and match the articles and comments by the similarity of their topics. Experiments show that our topic-based approach significantly outperforms previous lexicon-based models. The model can also profit from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios. Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.) Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.) Figure 1: The performance of the supervised model and the semi-supervised model trained on different paired data size. Figure 2: Error types of comments generated by different models. | TF-IDF, NVDM |
What news comment dataset was used? | ### Introduction
Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural language. Therefore, machine commenting is an important problem faced in building an intelligent and interactive agent. Machine commenting is also useful in improving the activeness of communities, including online forums and news websites. Article comments can provide extended information and external opinions for the readers to have a more comprehensive understanding of the article. Therefore, an article with more informative and interesting comments will attract more attention from readers. Moreover, machine commenting can kick off the discussion about an article or a topic, which helps increase user engagement and interaction between the readers and authors. Because of the advantage and importance described above, more recent studies have focused on building a machine commenting system with neural models BIBREF0 . One bottleneck of neural machine commenting models is the requirement of a large parallel dataset. However, the naturally paired commenting dataset is loosely paired. Qin et al. QinEA2018 were the first to propose the article commenting task and an article-comment dataset. The dataset is crawled from a news website, and they sample 1,610 article-comment pairs to annotate the relevance score between articles and comments. The relevance score ranges from 1 to 5, and we find that only 6.8% of the pairs have an average score greater than 4. It indicates that the naturally paired article-comment dataset contains a lot of loose pairs, which is a potential harm to the supervised models. Besides, most articles and comments are unpaired on the Internet. For example, a lot of articles do not have the corresponding comments on the news websites, and the comments regarding the news are more likely to appear on social media like Twitter. Since comments on social media are more various and recent, it is important to exploit these unpaired data. Another issue is that there is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comment does not always tell the same thing as the corresponding article. Table TABREF1 shows an example of an article and several corresponding comments. The comments do not directly tell what happened in the news, but talk about the underlying topics (e.g. NBA Christmas Day games, LeBron James). However, existing methods for machine commenting do not model the topics of articles, which is a potential harm to the generated comments. To this end, we propose an unsupervised neural topic model to address both problems. For the first problem, we completely remove the need of parallel data and propose a novel unsupervised approach to train a machine commenting system, relying on nothing but unpaired articles and comments. For the second issue, we bridge the articles and comments with their topics. Our model is based on a retrieval-based commenting framework, which uses the news as the query to retrieve the comments by the similarity of their topics. The topic is represented with a variational topic, which is trained in an unsupervised manner. The contributions of this work are as follows: ### Machine Commenting
In this section, we highlight the research challenges of machine commenting, and provide some solutions to deal with these challenges. ### Challenges
Here, we first introduce the challenges of building a well-performed machine commenting system. The generative model, such as the popular sequence-to-sequence model, is a direct choice for supervised machine commenting. One can use the title or the content of the article as the encoder input, and the comments as the decoder output. However, we find that the mode collapse problem is severed with the sequence-to-sequence model. Despite the input articles being various, the outputs of the model are very similar. The reason mainly comes from the contradiction between the complex pattern of generating comments and the limited parallel data. In other natural language generation tasks, such as machine translation and text summarization, the target output of these tasks is strongly related to the input, and most of the required information is involved in the input text. However, the comments are often weakly related to the input articles, and part of the information in the comments is external. Therefore, it requires much more paired data for the supervised model to alleviate the mode collapse problem. One article can have multiple correct comments, and these comments can be very semantically different from each other. However, in the training set, there is only a part of the correct comments, so the other correct comments will be falsely regarded as the negative samples by the supervised model. Therefore, many interesting and informative comments will be discouraged or neglected, because they are not paired with the articles in the training set. There is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comments often have some external information, or even tell an opposite opinion from the articles. Therefore, it is difficult to automatically mine the relationship between articles and comments. ### Solutions
Facing the above challenges, we provide three solutions to the problems. Given a large set of candidate comments, the retrieval model can select some comments by matching articles with comments. Compared with the generative model, the retrieval model can achieve more promising performance. First, the retrieval model is less likely to suffer from the mode collapse problem. Second, the generated comments are more predictable and controllable (by changing the candidate set). Third, the retrieval model can be combined with the generative model to produce new comments (by adding the outputs of generative models to the candidate set). The unsupervised learning method is also important for machine commenting to alleviate the problems descried above. Unsupervised learning allows the model to exploit more data, which helps the model to learn more complex patterns of commenting and improves the generalization of the model. Many comments provide some unique opinions, but they do not have paired articles. For example, many interesting comments on social media (e.g. Twitter) are about recent news, but require redundant work to match these comments with the corresponding news articles. With the help of the unsupervised learning method, the model can also learn to generate these interesting comments. Additionally, the unsupervised learning method does not require negative samples in the training stage, so that it can alleviate the negative sampling bias. Although there is semantic gap between the articles and the comments, we find that most articles and comments share the same topics. Therefore, it is possible to bridge the semantic gap by modeling the topics of both articles and comments. It is also similar to how humans generate comments. Humans do not need to go through the whole article but are capable of making a comment after capturing the general topics. ### Proposed Approach
We now introduce our proposed approach as an implementation of the solutions above. We first give the definition and the denotation of the problem. Then, we introduce the retrieval-based commenting framework. After that, a neural variational topic model is introduced to model the topics of the comments and the articles. Finally, semi-supervised training is used to combine the advantage of both supervised and unsupervised learning. ### Retrieval-based Commenting
Given an article, the retrieval-based method aims to retrieve a comment from a large pool of candidate comments. The article consists of a title INLINEFORM0 and a body INLINEFORM1 . The comment pool is formed from a large scale of candidate comments INLINEFORM2 , where INLINEFORM3 is the number of the unique comments in the pool. In this work, we have 4.5 million human comments in the candidate set, and the comments are various, covering different topics from pets to sports. The retrieval-based model should score the matching between the upcoming article and each comments, and return the comments which is matched with the articles the most. Therefore, there are two main challenges in retrieval-based commenting. One is how to evaluate the matching of the articles and comments. The other is how to efficiently compute the matching scores because the number of comments in the pool is large. To address both problems, we select the “dot-product” operation to compute matching scores. More specifically, the model first computes the representations of the article INLINEFORM0 and the comments INLINEFORM1 . Then the score between article INLINEFORM2 and comment INLINEFORM3 is computed with the “dot-product” operation: DISPLAYFORM0 The dot-product scoring method has proven a successful in a matching model BIBREF1 . The problem of finding datapoints with the largest dot-product values is called Maximum Inner Product Search (MIPS), and there are lots of solutions to improve the efficiency of solving this problem. Therefore, even when the number of candidate comments is very large, the model can still find comments with the highest efficiency. However, the study of the MIPS is out of the discussion in this work. We refer the readers to relevant articles for more details about the MIPS BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Another advantage of the dot-product scoring method is that it does not require any extra parameters, so it is more suitable as a part of the unsupervised model. ### Neural Variational Topic Model
We obtain the representations of articles INLINEFORM0 and comments INLINEFORM1 with a neural variational topic model. The neural variational topic model is based on the variational autoencoder framework, so it can be trained in an unsupervised manner. The model encodes the source text into a representation, from which it reconstructs the text. We concatenate the title and the body to represent the article. In our model, the representations of the article and the comment are obtained in the same way. For simplicity, we denote both the article and the comment as “document”. Since the articles are often very long (more than 200 words), we represent the documents into bag-of-words, for saving both the time and memory cost. We denote the bag-of-words representation as INLINEFORM0 , where INLINEFORM1 is the one-hot representation of the word at INLINEFORM2 position, and INLINEFORM3 is the number of words in the vocabulary. The encoder INLINEFORM4 compresses the bag-of-words representations INLINEFORM5 into topic representations INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , and INLINEFORM3 are the trainable parameters. Then the decoder INLINEFORM4 reconstructs the documents by independently generating each words in the bag-of-words: DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 is the number of words in the bag-of-words, and INLINEFORM1 is a trainable matrix to map the topic representation into the word distribution. In order to model the topic information, we use a Dirichlet prior rather than the standard Gaussian prior. However, it is difficult to develop an effective reparameterization function for the Dirichlet prior to train VAE. Therefore, following BIBREF6 , we use the Laplace approximation BIBREF7 to Dirichlet prior INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 denotes the logistic normal distribution, INLINEFORM1 is the number of topics, and INLINEFORM2 is a parameter vector. Then, the variational lower bound is written as: DISPLAYFORM0 where the first term is the KL-divergence loss and the second term is the reconstruction loss. The mean INLINEFORM0 and the variance INLINEFORM1 are computed as follows: DISPLAYFORM0 DISPLAYFORM1 We use the INLINEFORM0 and INLINEFORM1 to generate the samples INLINEFORM2 by sampling INLINEFORM3 , from which we reconstruct the input INLINEFORM4 . At the training stage, we train the neural variational topic model with the Eq. EQREF22 . At the testing stage, we use INLINEFORM0 to compute the topic representations of the article INLINEFORM1 and the comment INLINEFORM2 . ### Training
In addition to the unsupervised training, we explore a semi-supervised training framework to combine the proposed unsupervised model and the supervised model. In this scenario we have a paired dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1 . The supervised model is trained on INLINEFORM2 so that we can learn the matching or mapping between articles and comments. By sharing the encoder of the supervised model and the unsupervised model, we can jointly train both the models with a joint objective function: DISPLAYFORM0 where INLINEFORM0 is the loss function of the unsupervised learning (Eq. refloss), INLINEFORM1 is the loss function of the supervised learning (e.g. the cross-entropy loss of Seq2Seq model), and INLINEFORM2 is a hyper-parameter to balance two parts of the loss function. Hence, the model is trained on both unpaired data INLINEFORM3 , and paired data INLINEFORM4 . ### Datasets
We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model. The dataset is collected from Tencent News, which is one of the most popular Chinese websites for news and opinion articles. The dataset consists of 198,112 news articles. Each piece of news contains a title, the content of the article, and a list of the users' comments. Following the previous work BIBREF0 , we tokenize all text with the popular python package Jieba, and filter out short articles with less than 30 words in content and those with less than 20 comments. The dataset is split into training/validation/test sets, and they contain 191,502/5,000/1,610 pieces of news, respectively. The whole dataset has a vocabulary size of 1,858,452. The average lengths of the article titles and content are 15 and 554 Chinese words. The average comment length is 17 words. ### Implementation Details
The hidden size of the model is 512, and the batch size is 64. The number of topics INLINEFORM0 is 100. The weight INLINEFORM1 in Eq. EQREF26 is 1.0 under the semi-supervised setting. We prune the vocabulary, and only leave 30,000 most frequent words in the vocabulary. We train the model for 20 epochs with the Adam optimizing algorithms BIBREF8 . In order to alleviate the KL vanishing problem, we set the initial learning to INLINEFORM2 , and use batch normalization BIBREF9 in each layer. We also gradually increase the KL term from 0 to 1 after each epoch. ### Baselines
We compare our model with several unsupervised models and supervised models. Unsupervised baseline models are as follows: TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. We use the concatenation of the title and the body as the query to retrieve the candidate comment set by means of the similarity of the tf-idf value. The model is trained on unpaired articles and comments, which is the same as our proposed model. LDA (Topic, Non-Neural) is a popular unsupervised topic model, which discovers the abstract "topics" that occur in a collection of documents. We train the LDA with the articles and comments in the training set. The model retrieves the comments by the similarity of the topic representations. NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic. The supervised baseline models are: S2S (Generative) BIBREF11 is a supervised generative model based on the sequence-to-sequence network with the attention mechanism BIBREF12 . The model uses the titles and the bodies of the articles as the encoder input, and generates the comments with the decoder. IR (Retrieval) BIBREF0 is a supervised retrieval-based model, which trains a convolutional neural network (CNN) to take the articles and a comment as inputs, and output the relevance score. The positive instances for training are the pairs in the training set, and the negative instances are randomly sampled using the negative sampling technique BIBREF13 . ### Retrieval Evaluation
For text generation, automatically evaluate the quality of the generated text is an open problem. In particular, the comment of a piece of news can be various, so it is intractable to find out all the possible references to be compared with the model outputs. Inspired by the evaluation methods of dialogue models, we formulate the evaluation as a ranking problem. Given a piece of news and a set of candidate comments, the comment model should return the rank of the candidate comments. The candidate comment set consists of the following parts: Correct: The ground-truth comments of the corresponding news provided by the human. Plausible: The 50 most similar comments to the news. We use the news as the query to retrieve the comments that appear in the training set based on the cosine similarity of their tf-idf values. We select the top 50 comments that are not the correct comments as the plausible comments. Popular: The 50 most popular comments from the dataset. We count the frequency of each comments in the training set, and select the 50 most frequent comments to form the popular comment set. The popular comments are the general and meaningless comments, such as “Yes”, “Great”, “That's right', and “Make Sense”. These comments are dull and do not carry any information, so they are regarded as incorrect comments. Random: After selecting the correct, plausible, and popular comments, we fill the candidate set with randomly selected comments from the training set so that there are 200 unique comments in the candidate set. Following previous work, we measure the rank in terms of the following metrics: Recall@k: The proportion of human comments found in the top-k recommendations. Mean Rank (MR): The mean rank of the human comments. Mean Reciprocal Rank (MRR): The mean reciprocal rank of the human comments. The evaluation protocol is compatible with both retrieval models and generative models. The retrieval model can directly rank the comments by assigning a score for each comment, while the generative model can rank the candidates by the model's log-likelihood score. Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information. We also evaluate two popular supervised models, i.e. seq2seq and IR. Since the articles are very long, we find either RNN-based or CNN-based encoders cannot hold all the words in the articles, so it requires limiting the length of the input articles. Therefore, we use an MLP-based encoder, which is the same as our model, to encode the full length of articles. In our preliminary experiments, the MLP-based encoder with full length articles achieves better scores than the RNN/CNN-based encoder with limited length articles. It shows that the seq2seq model gets low scores on all relevant metrics, mainly because of the mode collapse problem as described in Section Challenges. Unlike seq2seq, IR is based on a retrieval framework, so it achieves much better performance. ### Generative Evaluation
Following previous work BIBREF0 , we evaluate the models under the generative evaluation setting. The retrieval-based models generate the comments by selecting a comment from the candidate set. The candidate set contains the comments in the training set. Unlike the retrieval evaluation, the reference comments may not appear in the candidate set, which is closer to real-world settings. Generative-based models directly generate comments without a candidate set. We compare the generated comments of either the retrieval-based models or the generative models with the five reference comments. We select four popular metrics in text generation to compare the model outputs with the references: BLEU BIBREF14 , METEOR BIBREF15 , ROUGE BIBREF16 , CIDEr BIBREF17 . Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation. Still, the supervised IR achieves better scores than the seq2seq model. With the help of our proposed model, both IR and S2S achieve an improvement under the semi-supervised scenarios. ### Analysis and Discussion
We analyze the performance of the proposed method under the semi-supervised setting. We train the supervised IR model with different numbers of paired data. Figure FIGREF39 shows the curve (blue) of the recall1 score. As expected, the performance grows as the paired dataset becomes larger. We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M). It shows that IR+Proposed can outperform the supervised IR model given the same paired dataset. It concludes that the proposed model can exploit the unpaired data to further improve the performance of the supervised model. Although our proposed model can achieve better performance than previous models, there are still remaining two questions: why our model can outperform them, and how to further improve the performance. To address these queries, we perform error analysis to analyze the error types of our model and the baseline models. We select TF-IDF, S2S, and IR as the representative baseline models. We provide 200 unique comments as the candidate sets, which consists of four types of comments as described in the above retrieval evaluation setting: Correct, Plausible, Popular, and Random. We rank the candidate comment set with four models (TF-IDF, S2S, IR, and Proposed+IR), and record the types of top-1 comments. Figure FIGREF40 shows the percentage of different types of top-1 comments generated by each model. It shows that TF-IDF prefers to rank the plausible comments as the top-1 comments, mainly because it matches articles with the comments based on the similarity of the lexicon. Therefore, the plausible comments, which are more similar in the lexicon, are more likely to achieve higher scores than the correct comments. It also shows that the S2S model is more likely to rank popular comments as the top-1 comments. The reason is the S2S model suffers from the mode collapse problem and data sparsity, so it prefers short and general comments like “Great” or “That's right”, which appear frequently in the training set. The correct comments often contain new information and different language models from the training set, so they do not obtain a high score from S2S. IR achieves better performance than TF-IDF and S2S. However, it still suffers from the discrimination between the plausible comments and correct comments. This is mainly because IR does not explicitly model the underlying topics. Therefore, the correct comments which are more relevant in topic with the articles get lower scores than the plausible comments which are more literally relevant with the articles. With the help of our proposed model, proposed+IR achieves the best performance, and achieves a better accuracy to discriminate the plausible comments and the correct comments. Our proposed model incorporates the topic information, so the correct comments which are more similar to the articles in topic obtain higher scores than the other types of comments. According to the analysis of the error types of our model, we still need to focus on avoiding predicting the plausible comments. ### Article Comment
There are few studies regarding machine commenting. Qin et al. QinEA2018 is the first to propose the article commenting task and a dataset, which is used to evaluate our model in this work. More studies about the comments aim to automatically evaluate the quality of the comments. Park et al. ParkSDE16 propose a system called CommentIQ, which assist the comment moderators in identifying high quality comments. Napoles et al. NapolesTPRP17 propose to discriminating engaging, respectful, and informative conversations. They present a Yahoo news comment threads dataset and annotation scheme for the new task of identifying “good” online conversations. More recently, Kolhaatkar and Taboada KolhatkarT17 propose a model to classify the comments into constructive comments and non-constructive comments. In this work, we are also inspired by the recent related work of natural language generation models BIBREF18 , BIBREF19 . ### Topic Model and Variational Auto-Encoder
Topic models BIBREF20 are among the most widely used models for learning unsupervised representations of text. One of the most popular approaches for modeling the topics of the documents is the Latent Dirichlet Allocation BIBREF21 , which assumes a discrete mixture distribution over topics is sampled from a Dirichlet prior shared by all documents. In order to explore the space of different modeling assumptions, some black-box inference methods BIBREF22 , BIBREF23 are proposed and applied to the topic models. Kingma and Welling vae propose the Variational Auto-Encoder (VAE) where the generative model and the variational posterior are based on neural networks. VAE has recently been applied to modeling the representation and the topic of the documents. Miao et al. NVDM model the representation of the document with a VAE-based approach called the Neural Variational Document Model (NVDM). However, the representation of NVDM is a vector generated from a Gaussian distribution, so it is not very interpretable unlike the multinomial mixture in the standard LDA model. To address this issue, Srivastava and Sutton nvlda propose the NVLDA model that replaces the Gaussian prior with the Logistic Normal distribution to approximate the Dirichlet prior and bring the document vector into the multinomial space. More recently, Nallapati et al. sengen present a variational auto-encoder approach which models the posterior over the topic assignments to sentences using an RNN. ### Conclusion
We explore a novel way to train a machine commenting model in an unsupervised manner. According to the properties of the task, we propose using the topics to bridge the semantic gap between articles and comments. We introduce a variation topic model to represent the topics, and match the articles and comments by the similarity of their topics. Experiments show that our topic-based approach significantly outperforms previous lexicon-based models. The model can also profit from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios. Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.) Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.) Figure 1: The performance of the supervised model and the semi-supervised model trained on different paired data size. Figure 2: Error types of comments generated by different models. | Chinese dataset BIBREF0 |
How can we interpret Mr. Schwartzberg was feeling from his theory not being taken seriously?
A. Frustrated because his evidentiary support showed it was logical
B. Happy that he might be incorrect and it was only dust
C. Disappointed that he had missed his opportunity for scientific acknowledgement.
D. Excited that it could likely be something more exciting
| THE GREAT NEBRASKA SEA By ALLAN DANZIG Illustrated by WOOD [Transcriber's Note: This etext was produced from Galaxy Magazine August 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] It has happened a hundred times in the long history of Earth—and, sooner or later, will happen again! Everyone—all the geologists, at any rate—had known about the Kiowa Fault for years. That was before there was anything very interesting to know about it. The first survey of Colorado traced its course north and south in the narrow valley of Kiowa Creek about twenty miles east of Denver; it extended south to the Arkansas River. And that was about all even the professionals were interested in knowing. There was never so much as a landslide to bring the Fault to the attention of the general public. It was still a matter of academic interest when in the late '40s geologists speculated on the relationship between the Kiowa Fault and the Conchas Fault farther south, in New Mexico, and which followed the Pecos as far south as Texas. Nor was there much in the papers a few years later when it was suggested that the Niobrara Fault (just inside and roughly parallel to the eastern border of Wyoming) was a northerly extension of the Kiowa. By the mid sixties it was definitely established that the three Faults were in fact a single line of fissure in the essential rock, stretching almost from the Canadian border well south of the New Mexico-Texas line. It is not really surprising that it took so long to figure out the connection. The population of the states affected was in places as low as five people per square mile! The land was so dry it seemed impossible that it could ever be used except for sheep-farming. It strikes us today as ironic that from the late '50s there was grave concern about the level of the water table throughout the entire area. The even more ironic solution to the problem began in the summer of 1973. It had been a particularly hot and dry August, and the Forestry Service was keeping an anxious eye out for the fires it knew it could expect. Dense smoke was reported rising above a virtually uninhabited area along Black Squirrel Creek, and a plane was sent out for a report. The report was—no fire at all. The rising cloud was not smoke, but dust. Thousands of cubic feet of dry earth rising lazily on the summer air. Rock slides, they guessed; certainly no fire. The Forestry Service had other worries at the moment, and filed the report. But after a week had gone by, the town of Edison, a good twenty miles away from the slides, was still complaining of the dust. Springs was going dry, too, apparently from underground disturbances. Not even in the Rockies could anyone remember a series of rock slides as bad as this. Newspapers in the mountain states gave it a few inches on the front page; anything is news in late August. And the geologists became interested. Seismologists were reporting unusual activity in the area, tremors too severe to be rock slides. Volcanic activity? Specifically, a dust volcano? Unusual, they knew, but right on the Kiowa Fault—could be. Labor Day crowds read the scientific conjectures with late summer lassitude. Sunday supplements ran four-color artists' conceptions of the possible volcano. "Only Active Volcano in U. S.?" demanded the headlines, and some papers even left off the question mark. It may seem odd that the simplest explanation was practically not mentioned. Only Joseph Schwartzberg, head geographer of the Department of the Interior, wondered if the disturbance might not be a settling of the Kiowa Fault. His suggestion was mentioned on page nine or ten of the Monday newspapers (page 27 of the New York Times ). The idea was not nearly so exciting as a volcano, even a lava-less one, and you couldn't draw a very dramatic picture of it. To excuse the other geologists, it must be said that the Kiowa Fault had never acted up before. It never sidestepped, never jiggled, never, never produced the regular shows of its little sister out in California, which almost daily bounced San Francisco or Los Angeles, or some place in between. The dust volcano was on the face of it a more plausible theory. Still, it was only a theory. It had to be proved. As the tremors grew bigger, along with the affected area, as several towns including Edison were shaken to pieces by incredible earthquakes, whole bus- and plane-loads of geologists set out for Colorado, without even waiting for their university and government department to approve budgets. They found, of course, that Schwartzberg had been perfectly correct. They found themselves on the scene of what was fast becoming the most violent and widespread earthquake North America—probably the world—has ever seen in historic times. To describe it in the simplest terms, land east of the Fault was settling, and at a precipitous rate. Rock scraped rock with a whining roar. Shuddery as a squeaky piece of chalk raked across a blackboard, the noise was deafening. The surfaces of the land east and west of the Fault seemed no longer to have any relation to each other. To the west, tortured rock reared into cliffs. East, where sharp reports and muffled wheezes told of continued buckling and dropping, the earth trembled downward. Atop the new cliffs, which seemed to grow by sudden inches from heaving rubble, dry earth fissured and trembled, sliding acres at a time to fall, smoking, into the bucking, heaving bottom of the depression. There the devastation was even more thorough, if less spectacular. Dry earth churned like mud, and rock shards weighing tons bumped and rolled about like pebbles as they shivered and cracked into pebbles themselves. "It looks like sand dancing in a child's sieve," said the normally impassive Schwartzberg in a nationwide broadcast from the scene of disaster. "No one here has ever seen anything like it." And the landslip was growing, north and south along the Fault. "Get out while you can," Schwartzberg urged the population of the affected area. "When it's over you can come back and pick up the pieces." But the band of scientists who had rallied to his leadership privately wondered if there would be any pieces. The Arkansas River, at Avondale and North Avondale, was sluggishly backing north into the deepening trough. At the rate things were going, there might be a new lake the entire length of El Paso and Pueblo Counties. And, warned Schwartzberg, this might only be the beginning. By 16 September the landslip had crept down the Huerfano River past Cedarwood. Avondale, North Avondale and Boone had totally disappeared. Land west of the Fault was holding firm, though Denver had recorded several small tremors; everywhere east of the Fault, to almost twenty miles away, the now-familiar lurch and steady fall had already sent several thousand Coloradans scurrying for safety. All mountain climbing was prohibited on the Eastern Slope because of the danger of rock slides from minor quakes. The geologists went home to wait. There wasn't much to wait for. The news got worse and worse. The Platte River, now, was creating a vast mud puddle where the town of Orchard had been. Just below Masters, Colorado, the river leaped 70-foot cliffs to add to the heaving chaos below. And the cliffs were higher every day as the land beneath them groaned downward in mile-square gulps. As the Fault moved north and south, new areas quivered into unwelcome life. Fields and whole mountainsides moved with deceptive sloth down, down. They danced "like sand in a sieve"; dry, they boiled into rubble. Telephone lines, railroad tracks, roads snapped and simply disappeared. Virtually all east-west land communication was suspended and the President declared a national emergency. By 23 September the Fault was active well into Wyoming on the north, and rapidly approaching the border of New Mexico to the south. Trinchera and Branson were totally evacuated, but even so the over-all death toll had risen above 1,000. Away to the east the situation was quiet but even more ominous. Tremendous fissures opened up perpendicular to the Fault, and a general subsidence of the land was noticeable well into Kansas and Nebraska. The western borders of these states, and soon of the Dakotas and Oklahoma as well, were slowly sinking. On the actual scene of the disaster (or the scenes ; it is impossible to speak of anything this size in the singular) there was a horrifying confusion. Prairie and hill cracked open under intolerable strains as the land shuddered downward in gasps and leaps. Springs burst to the surface in hot geysers and explosions of steam. The downtown section of North Platte, Nebraska, dropped eight feet, just like that, on the afternoon of 4 October. "We must remain calm," declared the Governor of Nebraska. "We must sit this thing out. Be assured that everything possible is being done." But what could be done, with his state dropping straight down at a mean rate of a foot a day? The Fault nicked off the south-east corner of Montana. It worked its way north along the Little Missouri. South, it ripped past Roswell, New Mexico, and tore down the Pecos toward Texas. All the upper reaches of the Missouri were standing puddles by now, and the Red River west of Paris, Texas, had begun to run backward. Soon the Missouri began slowly slipping away westward over the slowly churning land. Abandoning its bed, the river spread uncertainly across farmland and prairie, becoming a sea of mud beneath the sharp new cliffs which rose in rending line, ever taller as the land continued to sink, almost from Canada to the Mexican border. There were virtually no floods, in the usual sense. The water moved too slowly, spread itself with no real direction or force. But the vast sheets of sluggish water and jelly-like mud formed death-traps for the countless refugees now streaming east. Perhaps the North Platte disaster had been more than anyone could take. 193 people had died in that one cave-in. Certainly by 7 October it had to be officially admitted that there was an exodus of epic proportion. Nearly two million people were on the move, and the U. S. was faced with a gigantic wave of refugees. Rails, roads and air-lanes were jammed with terrified hordes who had left everything behind to crowd eastward. All through October hollow-eyed motorists flocked into Tulsa, Topeka, Omaha, Sioux Falls and Fargo. St. Louis was made distributing center for emergency squads which flew everywhere with milk for babies and dog food for evacuating pets. Gasoline trucks boomed west to meet the demand for gas, but once inside the "zone of terror," as the newspapers now called it, they found their route blocked by eastbound cars on the wrong side of the road. Shops left by their fleeing owners were looted by refugees from further west; an American Airlines plane was wrecked by a mob of would-be passengers in Bismarck, North Dakota. Federal and State troops were called out, but moving two million people was not to be done in an orderly way. And still the landslip grew larger. The new cliffs gleamed in the autumn sunshine, growing higher as the land beneath them continued its inexorable descent. On 21 October, at Lubbock, Texas, there was a noise variously described as a hollow roar, a shriek and a deep musical vibration like a church bell. It was simply the tortured rock of the substrata giving way. The second phase of the national disaster was beginning. The noise traveled due east at better than 85 miles per hour. In its wake the earth to the north "just seemed to collapse on itself like a punctured balloon," read one newspaper report. "Like a cake that's failed," said a Texarkana housewife who fortunately lived a block south of Thayer Street, where the fissure raced through. There was a sigh and a great cloud of dust, and Oklahoma subsided at the astounding rate of about six feet per hour. At Biloxi, on the Gulf, there had been uneasy shufflings under foot all day. "Not tremors, exactly," said the captain of a fishing boat which was somehow to ride out the coming flood, "but like as if the land wanted to be somewhere else." Everyone in doomed Biloxi would have done well to have been somewhere else that evening. At approximately 8:30 p.m. the town shuddered, seemed to rise a little like the edge of a hall carpet caught in a draft, and sank. So did the entire Mississippi and Alabama coast, at about the same moment. The tidal wave which was to gouge the center from the U. S. marched on the land. From the north shore of Lake Ponchartrain to the Appalachicola River in Florida, the Gulf coast simply disappeared. Gulfport, Biloxi, Mobile, Pensacola, Panama City: 200 miles of shoreline vanished, with over two and a half million people. An hour later a wall of water had swept over every town from Dothan, Alabama, to Bogalusa on the Louisiana-Mississippi border. "We must keep panic from our minds," said the Governor of Alabama in a radio message delivered from a hastily arranged all-station hookup. "We of the gallant southland have faced and withstood invasion before." Then, as ominous creakings and groanings of the earth announced the approach of the tidal wave, he flew out of Montgomery half an hour before the town disappeared forever. One head of the wave plunged north, eventually to spend itself in the hills south of Birmingham. The main sweep followed the lowest land. Reaching west, it swallowed Vicksburg and nicked the corner of Louisiana. The whole of East Carroll Parish was scoured from the map. The Mississippi River now ended at about Eudora, Arkansas, and minute by minute the advancing flood bit away miles of river bed, swelling north. Chicot, Jennie, Lake Village, Arkansas City, Snow Lake, Elaine, Helena and Memphis felt the tremors. The tormented city shuddered through the night. The earth continued its descent, eventually tipping 2-1/2 degrees down to the west. The "Memphis Tilt" is today one of the unique and charming characteristics of the gracious Old Town, but during the night of panic Memphis residents were sure they were doomed. South and west the waters carved deeply into Arkansas and Oklahoma. By morning it was plain that all of Arkansas was going under. Waves advanced on Little Rock at almost 100 miles an hour, new crests forming, overtopping the wave's leading edge as towns, hills and the thirst of the soil temporarily broke the furious charge. Washington announced the official hope that the Ozarks would stop the wild gallop of the unleashed Gulf, for in northwest Arkansas the land rose to over 2,000 feet. But nothing could save Oklahoma. By noon the water reached clutching fingers around Mt. Scott and Elk Mountain, deluging Hobart and almost all of Greer County. Despite hopeful announcements that the wave was slowing, had virtually stopped after inundating Oklahoma City, was being swallowed up in the desert near Amarillo, the wall of water continued its advance. For the land was still sinking, and the floods were constantly replenished from the Gulf. Schwartzberg and his geologists advised the utmost haste in evacuating the entire area between Colorado and Missouri, from Texas to North Dakota. Lubbock, Texas, went under. On a curling reflex the tidal wave blotted out Sweetwater and Big Spring. The Texas panhandle disappeared in one great swirl. Whirlpools opened. A great welter of smashed wood and human debris was sucked under, vomited up and pounded to pieces. Gulf-water crashed on the cliffs of New Mexico and fell back on itself in foam. Would-be rescuers on the cliffs along what had been the west bank of the Pecos River afterwards recalled the hiss and scream like tearing silk as the water broke furiously on the newly exposed rock. It was the most terrible sound they had ever heard. "We couldn't hear any shouts, of course, not that far away and with all the noise," said Dan Weaver, Mayor of Carlsbad. "But we knew there were people down there. When the water hit the cliffs, it was like a collision between two solid bodies. We couldn't see for over an hour, because of the spray." Salt spray. The ocean had come to New Mexico. The cliffs proved to be the only effective barrier against the westward march of the water, which turned north, gouging out lumps of rock and tumbling down blocks of earth onto its own back. In places scoops of granite came out like ice cream. The present fishing town of Rockport, Colorado, is built on a harbor created in such a way. The water had found its farthest westering. But still it poured north along the line of the original Fault. Irresistible fingers closed on Sterling, Colorado, on Sidney, Nebraska, on Hot Springs, South Dakota. The entire tier of states settled, from south to north, down to its eventual place of stability one thousand feet below the level of the new sea. Memphis was by now a seaport. The Ozarks, islands in a mad sea, formed precarious havens for half-drowned humanity. Waves bit off a corner of Missouri, flung themselves on Wichita. Topeka, Lawrence and Belleville were the last Kansas towns to disappear. The Governor of Kansas went down with his State. Daniel Bernd of Lincoln, Nebraska, was washed up half-drowned in a cove of the Wyoming cliffs, having been sucked from one end of vanished Nebraska to the other. Similar hair-breadth escapes were recounted on radio and television. Virtually the only people saved out of the entire population of Pierre, South Dakota were the six members of the Creeth family. Plucky Timothy Creeth carried and dragged his aged parents to the loft of their barn on the outskirts of town. His brother Geoffrey brought along the younger children and what provisions they could find—"Mostly a ham and about half a ton of vanilla cookies," he explained to his eventual rescuers. The barn, luckily collapsing in the vibrations as the waves bore down on them, became an ark in which they rode out the disaster. "We must of played cards for four days straight," recalled genial Mrs. Creeth when she afterwards appeared on a popular television spectacular. Her rural good-humor undamaged by an ordeal few women can ever have been called on to face, she added, "We sure wondered why flushes never came out right. Jimanettly, we'd left the king of hearts behind, in the rush!" But such lightheartedness and such happy endings were by no means typical. The world could only watch aghast as the water raced north under the shadow of the cliffs which occasionally crumbled, roaring, into the roaring waves. Day by day the relentless rush swallowed what had been dusty farmland, cities and towns. Some people were saved by the helicopters which flew mercy missions just ahead of the advancing waters. Some found safety in the peaks of western Nebraska and the Dakotas. But when the waters came to rest along what is roughly the present shoreline of our inland sea, it was estimated that over fourteen million people had lost their lives. No one could even estimate the damage to property; almost the entirety of eight states, and portions of twelve others, had simply vanished from the heart of the North American continent forever. It was in such a cataclysmic birth that the now-peaceful Nebraska Sea came to America. Today, nearly one hundred years after the unprecedented—and happily unrepeated—disaster, it is hard to remember the terror and despair of those weeks in October and November, 1973. It is inconceivable to think of the United States without its beautiful and economically essential curve of interior ocean. Two-thirds as long as the Mediterranean, it graduates from the warm waters of the Gulf of Mexico through the equally blue waves of the Mississippi Bight, becoming cooler and greener north and west of the pleasant fishing isles of the Ozark Archipelago, finally shading into the gray-green chop of the Gulf of Dakota. What would the United States have become without the 5600-mile coastline of our inland sea? It is only within the last twenty years that any but the topmost layer of water has cleared sufficiently to permit a really extensive fishing industry. Mud still held in suspension by the restless waves will not precipitate fully even in our lifetimes. Even so, the commercial fisheries of Missouri and Wyoming contribute no small part to the nation's economy. Who can imagine what the middle west must have been like before the amelioration of climate brought about by the proximity of a warm sea? The now-temperate state of Minnesota (to say nothing of the submerged Dakotas) must have been Siberian. From contemporary accounts Missouri, our second California, was unbelievably muggy, almost uninhabitable during the summer months. Our climate today, from Ohio and North Carolina to the rich fields of New Mexico and the orchards of Montana, is directly ameliorated by the marine heart of the continent. Who today could imagine the United States without the majestic sea-cliffs in stately parade from New Mexico to Montana? The beaches of Wyoming, the American Riviera, where fruit trees grow almost to the water's edge? Or incredible Colorado, where the morning skier is the afternoon bather, thanks to the monorail connecting the highest peaks with the glistening white beaches? Of course there have been losses to balance slightly these strong gains. The Mississippi was, before 1973, one of the great rivers of the world. Taken together with its main tributary, the Missouri, it vied favorably with such giant systems as the Amazon and the Ganges. Now, ending as it does at Memphis and drawing its water chiefly from the Appalachian Mountains, it is only a slight remnant of what it was. And though the Nebraska Sea today carries many times the tonnage of shipping in its ceaseless traffic, we have lost the old romance of river shipping. We may only guess what it was like when we look upon the Ohio and the truncated Mississippi. And transcontinental shipping is somewhat more difficult, with trucks and the freight-railroads obliged to take the sea-ferries across the Nebraska Sea. We shall never know what the United States was like with its numerous coast-to-coast highways busy with trucks and private cars. Still, the ferry ride is certainly a welcome break after days of driving, and for those who wish a glimpse of what it must have been like, there is always the Cross-Canada Throughway and the magnificent U. S. Highway 73 looping north through Minnesota and passing through the giant port of Alexis, North Dakota, shipping center for the wheat of Manitoba and crossroad of a nation. The political situation has long been a thorny problem. Only tattered remnants of the eight submerged states remained after the flood, but none of them wanted to surrender its autonomy. The tiny fringe of Kansas seemed, for a time, ready to merge with contiguous Missouri, but following the lead of the Arkansas Forever faction, the remaining population decided to retain political integrity. This has resulted in the continuing anomaly of the seven "fringe States" represented in Congress by the usual two Senators each, though the largest of them is barely the size of Connecticut and all are economically indistinguishable from their neighboring states. Fortunately it was decided some years ago that Oklahoma, only one of the eight to have completely disappeared, could not in any sense be considered to have a continuing political existence. So, though there are still families who proudly call themselves Oklahomans, and the Oklahoma Oil Company continues to pump oil from its submerged real estate, the state has in fact disappeared from the American political scene. But this is by now no more than a petty annoyance, to raise a smile when the talk gets around to the question of State's Rights. Not even the tremendous price the country paid for its new sea—fourteen million dead, untold property destroyed—really offsets the asset we enjoy today. The heart of the continent, now open to the shipping of the world, was once dry and land-locked, cut off from the bustle of trade and the ferment of world culture. It would indeed seem odd to an American of the '50s or '60s of the last century to imagine sailors from the merchant fleets of every nation walking the streets of Denver, fresh ashore at Newport, only fifteen miles away. Or to imagine Lincoln, Fargo, Kansas City and Dallas as world ports and great manufacturing centers. Utterly beyond their ken would be Roswell, New Mexico; Benton, Wyoming; Westport, Missouri, and the other new ports of over a million inhabitants each which have developed on the new harbors of the inland sea. Unimaginable too would have been the general growth of population in the states surrounding the new sea. As the water tables rose and manufacturing and trade moved in to take advantage of the just-created axis of world communication, a population explosion was touched off of which we are only now seeing the diminution. This new westering is to be ranked with the first surge of pioneers which created the American west. But what a difference! Vacation paradises bloom, a new fishing industry thrives; her water road is America's main artery of trade, and fleets of all the world sail ... where once the prairie schooner made its laborious and dusty way west! | A. Frustrated because his evidentiary support showed it was logical |
Is it likely for William to have a normal life in the future?
A. yes - he knows how to take care of himself
B. no - he will probably waste all of his money
C. yes, if he ignores the jabberwocks
D. no - he seems to have a lot of demons that will impact his life
| Charity Case By JIM HARMON Illustrated by DICK FRANCIS [Transcriber's Note: This etext was produced from Galaxy Science Fiction December 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Certainly I see things that aren't there and don't say what my voice says—but how can I prove that I don't have my health? When he began his talk with "You got your health, don't you?" it touched those spots inside me. That was when I did it. Why couldn't what he said have been "The best things in life are free, buddy" or "Every dog has his day, fellow" or "If at first you don't succeed, man"? No, he had to use that one line. You wouldn't blame me. Not if you believe me. The first thing I can remember, the start of all this, was when I was four or five somebody was soiling my bed for me. I absolutely was not doing it. I took long naps morning and evening so I could lie awake all night to see that it wouldn't happen. It couldn't happen. But in the morning the bed would sit there dispassionately soiled and convict me on circumstantial evidence. My punishment was as sure as the tide. Dad was a compact man, small eyes, small mouth, tight clothes. He was narrow but not mean. For punishment, he locked me in a windowless room and told me to sit still until he came back. It wasn't so bad a punishment, except that when Dad closed the door, the light turned off and I was left there in the dark. Being four or five, I didn't know any better, so I thought Dad made it dark to add to my punishment. But I learned he didn't know the light went out. It came back on when he unlocked the door. Every time I told him about the light as soon as I could talk again, but he said I was lying. One day, to prove me a liar, he opened and closed the door a few times from outside. The light winked off and on, off and on, always shining when Dad stuck his head inside. He tried using the door from the inside, and the light stayed on, no matter how hard he slammed the door. I stayed in the dark longer for lying about the light. Alone in the dark, I wouldn't have had it so bad if it wasn't for the things that came to me. They were real to me. They never touched me, but they had a little boy. He looked the way I did in the mirror. They did unpleasant things to him. Because they were real, I talked about them as if they were real, and I almost earned a bunk in the home for retarded children until I got smart enough to keep the beasts to myself. My mother hated me. I loved her, of course. I remember her smell mixed up with flowers and cookies and winter fires. I remember she hugged me on my ninth birthday. The trouble came from the notes written in my awkward hand that she found, calling her names I didn't understand. Sometimes there were drawings. I didn't write those notes or make those drawings. My mother and father must have been glad when I was sent away to reform school after my thirteenth birthday party, the one no one came to. The reform school was nicer. There were others there who'd had it about like me. We got along. I didn't watch their shifty eyes too much, or ask them what they shifted to see. They didn't talk about my screams at night. It was home. My trouble there was that I was always being framed for stealing. I didn't take any of those things they located in my bunk. Stealing wasn't in my line. If you believe any of this at all, you'll see why it couldn't be me who did the stealing. There was reason for me to steal, if I could have got away with it. The others got money from home to buy the things they needed—razor blades, candy, sticks of tea. I got a letter from Mom or Dad every now and then before they were killed, saying they had sent money or that it was enclosed, but somehow I never got a dime of it. When I was expelled from reform school, I left with just one idea in mind—to get all the money I could ever use for the things I needed and the things I wanted. It was two or three years later that I skulked into Brother Partridge's mission on Durbin Street. The preacher and half a dozen men were singing Onward Christian Soldiers in the meeting room. It was a drafty hall with varnished camp chairs. I shuffled in at the back with my suitcoat collar turned up around my stubbled jaw. I made my hand shaky as I ran it through my knotted hair. Partridge was supposed to think I was just a bum. As an inspiration, I hugged my chest to make him think I was some wino nursing a flask full of Sneaky Pete. All I had there was a piece of copper alloy tubing inside a slice of plastic hose for taking care of myself, rolling sailors and the like. Who had the price of a bottle? Partridge didn't seem to notice me, but I knew that was an act. I knew people were always watching every move I made. He braced his red-furred hands on the sides of his auctioneer's stand and leaned his splotched eagle beak toward us. "Brothers, this being Thanksgiving, I pray the good Lord that we all are truly thankful for all that we have received. Amen." Some skin-and-bones character I didn't know struggled out of his seat, amening. I could see he had a lot to be thankful for—somewhere he had received a fix. "Brothers," Partridge went on after enjoying the interruption with a beaming smile, "you shall all be entitled to a bowl of turkey soup prepared by Sister Partridge, a generous supply of sweet rolls and dinner rolls contributed by the Early Morning Bakery of this city, and all the coffee you can drink. Let us march out to The Stars and Stripes Forever , John Philip Sousa's grand old patriotic song." I had to laugh at all those bums clattering the chairs in front of me, scampering after water soup and stale bread. As soon as I got cleaned up, I was going to have dinner in a good restaurant, and I was going to order such expensive food and leave such a large tip for the waiter and send one to the chef that they were going to think I was rich, and some executive with some brokerage firm would see me and say to himself, "Hmm, executive material. Just the type we need. I beg your pardon, sir—" just like the razor-blade comic-strip ads in the old magazines that Frankie the Pig sells three for a quarter. I was marching. Man, was I ever marching, but the secret of it was I was only marking time the way we did in fire drills at the school. They passed me, every one of them, and marched out of the meeting room into the kitchen. Even Partridge made his way down from the auctioneer's stand like a vulture with a busted wing and darted through his private door. I was alone, marking time behind the closed half of double doors. One good breath and I raced past the open door and flattened myself to the wall. Crockery was ringing and men were slurping inside. No one had paid any attention to me. That was pretty odd. People usually watch my every move, but a man's luck has to change sometime, doesn't it? Following the wallboard, I went down the side of the room and behind the last row of chairs, closer, closer, and halfway up the room again to the entrance—the entrance and the little wooden box fastened to the wall beside it. The box was old and made out of some varnished wood. There was a slot in the top. There wasn't any sign anywhere around it, but you knew it wasn't a mailbox. My hand went flat on the top of the box. One finger at a time drew up and slipped into the slot. Index, fore, third, little. I put my thumb in my palm and shoved. My hand went in. There were coins inside. I scooped them up with two fingers and held them fast with the other two. Once I dropped a dime—not a penny, milled edge—and I started to reach for it. No, don't be greedy. I knew I would probably lose my hold on all the coins if I tried for that one. I had all the rest. It felt like about two dollars, or close to it. Then I found the bill. A neatly folded bill in the box. Somehow I knew all along it would be there. I tried to read the numbers on the bill with my fingertips, but I couldn't. It had to be a one. Who drops anything but a one into a Skid Row collection box? But still there were tourists, slummers. They might leave a fifty or even a hundred. A hundred! Yes, it felt new, crisp. It had to be a hundred. A single would be creased or worn. I pulled my hand out of the box. I tried to pull my hand out of the box. I knew what the trouble was, of course. I was in a monkey trap. The monkey reaches through the hole for the bait, and when he gets it in his hot little fist, he can't get his hand out. He's too greedy to let go, so he stays there, caught as securely as if he were caged. I was a man, not a monkey. I knew why I couldn't get my hand out. But I couldn't lose that money, especially that century bill. Calm, I ordered myself. Calm. The box was fastened to the vertical tongue-and-groove laths of the woodwork, not the wall. It was old lumber, stiffened by a hundred layers of paint since 1908. The paint was as thick and strong as the boards. The box was fastened fast. Six-inch spike nails, I guessed. Calmly, I flung my whole weight away from the wall. My wrist almost cracked, but there wasn't even a bend in the box. Carefully, I tried to jerk my fist straight up, to pry off the top of the box. It was as if the box had been carved out of one solid piece of timber. It wouldn't go up, down, left or right. But I kept trying. While keeping a lookout for Partridge and somebody stepping out of the kitchen for a pull on a bottle, I spotted the clock for the first time, a Western Union clock high up at the back of the hall. Just as I seen it for the first time, the electricity wound the spring motor inside like a chicken having its neck wrung. The next time I glanced at the clock, it said ten minutes had gone by. My hand still wasn't free and I hadn't budged the box. "This," Brother Partridge said, "is one of the most profound experiences of my life." My head hinged until it lined my eyes up with Brother Partridge. The pipe hung heavy in my pocket, but he was too far from me. "A vision of you at the box projected itself on the crest of my soup," the preacher explained in wonderment. I nodded. "Swimming right in there with the dead duck." "Cold turkey," he corrected. "Are you scoffing at a miracle?" "People are always watching me, Brother," I said. "So now they do it even when they aren't around. I should have known it would come to that." The pipe was suddenly a weight I wanted off me. I would try robbing a collection box, knowing positively that I would get caught, but I wasn't dumb enough to murder. Somebody, somewhere, would be a witness to it. I had never got away with anything in my life. I was too smart to even try anything but the little things. "I may be able to help you," Brother Partridge said, "if you have faith and a conscience." "I've got something better than a conscience," I told him. Brother Partridge regarded me solemnly. "There must be something special about you, for your apprehension to come through miraculous intervention. But I can't imagine what." "I always get apprehended somehow, Brother," I said. "I'm pretty special." "Your name?" "William Hagle." No sense lying. I had been booked and printed before. Partridge prodded me with his bony fingers as if making sure I was substantial. "Come. Let's sit down, if you can remove your fist from the money box." I opened up my fingers and let the coins ring inside the box and I drew out my hand. The bill stuck to the sweat on my fingers and slid out along with the digits. A one, I decided. I had got into trouble for a grubby single. It wasn't any century. I had been kidding myself. I unfolded the note. Sure enough, it wasn't a hundred-dollar bill, but it was a twenty, and that was almost the same thing to me. I creased it and put it back into the slot. As long as it stalled off the cops, I'd talk to Partridge. We took a couple of camp chairs and I told him the story of my life, or most of it. It was hard work on an empty stomach; I wished I'd had some of that turkey soup. Then again I was glad I hadn't. Something always happened to me when I thought back over my life. The same thing. The men filed out of the kitchen, wiping their chins, and I went right on talking. After some time Sister Partridge bustled in and snapped on the overhead lights and I kept talking. The brother still hadn't used the phone to call the cops. "Remarkable," Partridge finally said when I got so hoarse I had to take a break. "One is almost— almost —reminded of Job. William, you are being punished for some great sin. Of that, I'm sure." "Punished for a sin? But, Brother, I've always had it like this, as long as I can remember. What kind of a sin could I have committed when I was fresh out of my crib?" "William, all I can tell you is that time means nothing in Heaven. Do you deny the transmigration of souls?" "Well," I said, "I've had no personal experience—" "Of course you have, William! Say you don't remember. Say you don't want to remember. But don't say you have no personal experience!" "And you think I'm being punished for something I did in a previous life?" He looked at me in disbelief. "What else could it be?" "I don't know," I confessed. "I certainly haven't done anything that bad in this life." "William, if you atone for this sin, perhaps the horde of locusts will lift from you." It wasn't much of a chance, but I was unused to having any at all. I shook off the dizziness of it. "By the Lord Harry, Brother, I'm going to give it a try!" I cried. "I believe you," Partridge said, surprised at himself. He ambled over to the money box on the wall. He tapped the bottom lightly and a box with no top slid out of the slightly larger box. He reached in, fished out the bill and presented it to me. "Perhaps this will help in your atonement," he said. I crumpled it into my pocket fast. Not meaning to sound ungrateful, I'm pretty sure he hadn't noticed it was a twenty. And then the bill seemed to lie there, heavy, a lead weight. It would have been different if I had managed to get it out of the box myself. You know how it is. Money you haven't earned doesn't seem real to you. There was something I forgot to mention so far. During the year between when I got out of the reformatory and the one when I tried to steal Brother Partridge's money, I killed a man. It was all an accident, but killing somebody is reason enough to get punished. It didn't have to be a sin in some previous life, you see. I had gotten my first job in too long, stacking boxes at the freight door of Baysinger's. The drivers unloaded the stuff, but they just dumped it off the truck. An empty rear end was all they wanted. The freight boss told me to stack the boxes inside, neat and not too close together. I stacked boxes the first day. I stacked more the second. The third day I went outside with my baloney and crackers. It was warm enough even for November. Two of them, dressed like Harvard seniors, caps and striped duffer jackets, came up to the crate I was dining off. "Work inside, Jack?" the taller one asked. "Yeah," I said, chewing. "What do you do, Jack?" the fatter one asked. "Stack boxes." "Got a union card?" I shook my head. "Application?" "No," I said. "I'm just helping out during Christmas." "You're a scab, buddy," Long-legs said. "Don't you read the papers?" "I don't like comic strips," I said. They sighed. I think they hated to do it, but I was bucking the system. Fats hit me high. Long-legs hit me low. I blew cracker crumbs into their faces. After that, I just let them go. I know how to take a beating. That's one thing I knew. Then lying there, bleeding to myself, I heard them talking. I heard noises like make an example of him and do something permanent and I squirmed away across the rubbish like a polite mouse. I made it around a corner of brick and stood up, hurting my knee on a piece of brown-splotched pipe. There were noises on the other angle of the corner and so I tested if the pipe was loose and it was. I closed my eyes and brought the pipe up and then down. It felt as if I connected, but I was so numb, I wasn't sure until I unscrewed my eyes. There was a big man in a heavy wool overcoat and gray homburg spread on a damp centerfold from the News . There was a pick-up slip from the warehouse under the fingers of one hand, and somebody had beaten his brains out. The police figured it was part of some labor dispute, I guess, and they never got to me. I suppose I was to blame anyway. If I hadn't been alive, if I hadn't been there to get beaten up, it wouldn't have happened. I could see the point in making me suffer for it. There was a lot to be said for looking at it like that. But there was nothing to be said for telling Brother Partridge about the accident, or murder, or whatever had happened that day. Searching myself after I left Brother Partridge, I finally found a strip of gray adhesive tape on my side, out of the fuzzy area. Making the twenty the size of a thick postage stamp, I peeled back the tape and put the folded bill on the white skin and smoothed the tape back. There was only one place for me to go now. I headed for the public library. It was only about twenty blocks, but not having had anything to eat since the day before, it enervated me. The downstairs washroom was where I went first. There was nobody there but an old guy talking urgently to a kid with thick glasses, and somebody building a fix in one of the booths. I could see charred matches dropping down on the floor next to his tennis shoes, and even a few grains of white stuff. But he managed to hold still enough to keep from spilling more from the spoon. I washed my hands and face, smoothed my hair down, combing it with my fingers. Going over my suit with damp toweling got off a lot of the dirt. I put my collar on the outside of my jacket and creased the wings with my thumbnail so it would look more like a sports shirt. It didn't really. I still looked like a bum, but sort of a neat, non-objectionable bum. The librarian at the main desk looked sympathetically hostile, or hostilely sympathetic. "I'd like to get into the stacks, miss," I said, "and see some of the old newspapers." "Which newspapers?" the old girl asked stiffly. I thought back. I couldn't remember the exact date. "Ones for the first week in November last year." "We have the Times microfilmed. I would have to project them for you." "I didn't want to see the Times ," I said, fast. "Don't you have any newspapers on paper?" I didn't want her to see what I wanted to read up on. "We have the News , bound, for last year." I nodded. "That's the one I wanted to see." She sniffed and told me to follow her. I didn't rate a cart to my table, I guess, or else the bound papers weren't supposed to come out of the stacks. The cases of books, row after row, smelled good. Like old leather and good pipe tobacco. I had been here before. In this world, it's the man with education who makes the money. I had been reading the Funk & Wagnalls Encyclopedia. So far I knew a lot about Mark Antony, Atomic Energy, Boron, Brussels, Catapults, Demons, and Divans. I guess I had stopped to look around at some of the titles, because the busy librarian said sharply, "Follow me." I heard my voice say, "A pleasure. What about after work?" I didn't say it, but I was used to my voice independently saying things. Her neck got to flaming, but she walked stiffly ahead. She didn't say anything. She must be awful mad, I decided. But then I got the idea she was flushed with pleasure. I'm pretty ugly and I looked like a bum, but I was young. You had to grant me that. She waved a hand at the rows of bound News and left me alone with them. I wasn't sure if I was allowed to hunt up a table to lay the books on or not, so I took the volume for last year and laid it on the floor. That was the cleanest floor I ever saw. It didn't take me long to find the story. The victim was a big man, because the story was on the second page of the Nov. 4 edition. I started to tear the page out, then only memorized the name and home address. Somebody was sure to see me and I couldn't risk trouble just now. I stuck the book back in line and left by the side door. I went to a dry-cleaner, not the cheapest place I knew, because I wouldn't be safe with the change from a twenty in that neighborhood. My suit was cleaned while I waited. I paid a little extra and had it mended. Funny thing about a suit—it's almost never completely shot unless you just have it ripped off you or burned up. It wasn't exactly in style, but some rich executives wore suits out of style that they had paid a lot of money for. I remembered Fredric March's double-breasted in Executive Suite while Walter Pidgeon and the rest wore Ivy Leagues. Maybe I would look like an eccentric executive. I bought a new shirt, a good used pair of shoes, and a dime pack of single-edged razor blades. I didn't have a razor, but anybody with nerve can shave with a single-edge blade and soap and water. The clerk took my two bucks in advance and I went up to my room. I washed out my socks and underwear, took a bath, shaved and trimmed my hair and nails with the razor blade. With some soap on my finger, I scrubbed my teeth. Finally I got dressed. Everything was all right except that I didn't have a tie. They had them, a quarter a piece, where I got the shoes. It was only six blocks—I could go back. But I didn't want to wait. I wanted to complete the picture. The razor blade sliced through the pink bath towel evenly. I cut out a nice modern-style tie, narrow, with some horizontal stripes down at the bottom. I made a tight, thin knot. It looked pretty good. I was ready to leave, so I started for the door. I went back. I had almost forgotten my luggage. The box still had three unwrapped blades in it. I pocketed it. I hefted the used blade, dulled by all the work it had done. You can run being economical into stinginess. I tossed it into the wastebasket. I had five hamburgers and five cups of coffee. I couldn't finish all of the French fries. "Mac," I said to the fat counterman, who looked like all fat countermen, "give me a Milwaukee beer." He stopped polishing the counter in front of his friend. "Milwaukee, Wisconsin, or Milwaukee, Oregon?" "Wisconsin." He didn't argue. It was cold and bitter. All beer is bitter, no matter what they say on TV. I like beer. I like the bitterness of it. It felt like another, but I checked myself. I needed a clear head. I thought about going back to the hotel for some sleep; I still had the key in my pocket (I wasn't trusting it to any clerk). No, I had had sleep on Thanksgiving, bracing up for trying the lift at Brother Partridge's. Let's see, it was daylight outside again, so this was the day after Thanksgiving. But it had only been sixteen or twenty hours since I had slept. That was enough. I left the money on the counter for the hamburgers and coffee and the beer. There was $7.68 left. As I passed the counterman's friend on his stool, my voice said, "I think you're yellow." He turned slowly, his jaw moving further away from his brain. I winked. "It was just a bet for me to say that to you. I won two bucks. Half of it is yours." I held out the bill to him. His paw closed over the money and punched me on the biceps. Too hard. He winked back. "It's okay." I rubbed my shoulder, marching off fast, and I counted my money. With my luck, I might have given the counterman's friend the five instead of one of the singles. But I hadn't. I now had $6.68 left. "I still think you're yellow," my voice said. It was my voice, but it didn't come from me. There were no words, no feeling of words in my throat. It just came out of the air the way it always did. I ran. Harold R. Thompkins, 49, vice-president of Baysinger's, was found dead behind the store last night. His skull had been crushed by a vicious beating with a heavy implement, Coroner McClain announced in preliminary verdict. Tompkins, who resided at 1467 Claremont, Edgeway, had been active in seeking labor-management peace in the recent difficulties.... I had read that a year before. The car cards on the clanking subway and the rumbling bus didn't seem nearly so interesting to me. Outside the van, a tasteful sign announced the limits of the village of Edgeway, and back inside, the monsters of my boyhood went bloomp at me. I hadn't seen anything like them in years. The slimy, scaly beasts were slithering over the newspaper holders, the ad card readers, the girl watchers as the neat little carbon-copy modern homes breezed past the windows. I ignored the devils and concentrated on reading the withered, washed-out political posters on the telephone poles. My neck ached from holding it so stiff, staring out through the glass. More than that, I could feel the jabberwocks staring at me. You know how it is. You can feel a stare with the back of your neck and between your eyes. They got one brush of a gaze out of me. The things abruptly started their business, trying to act casually as if they hadn't been waiting for me to look at them at all. They had a little human being of some sort. It was the size of a small boy, like the small boy who looked like me that they used to destroy when I was locked up with them in the dark. Except this was a man, scaled down to child's size. He had sort of an ugly, worried, tired, stupid look and he wore a shiny suit with a piece of a welcome mat or something for a necktie. Yeah, it was me. I really knew it all the time. They began doing things to the midget me. I didn't even lift an eyebrow. They couldn't do anything worse to the small man than they had done to the young boy. It was sort of nostalgic watching them, but I really got bored with all that violence and killing and killing the same kill over and over. Like watching the Saturday night string of westerns in a bar. The sunlight through the window was yellow and hot. After a time, I began to dose. The shrieks woke me up. For the first time, I could hear the shrieks of the monster's victim and listen to their obscene droolings. For the very first time in my life. Always before it had been all pantomime, like Charlie Chaplin. Now I heard the sounds of it all. They say it's a bad sign when you start hearing voices. I nearly panicked, but I held myself in the seat and forced myself to be rational about it. My own voice was always saying things everybody could hear but which I didn't say. It wasn't any worse to be the only one who could hear other things I never said. I was as sane as I ever was. There was no doubt about that. But a new thought suddenly impressed itself on me. Whatever was punishing me for my sin was determined that I turn back before reaching 1467 Claremont. | D. no - he seems to have a lot of demons that will impact his life |
What was Judy's grandmother delivering on the day that they took their wagon ride?
A. Pies that she had baked.
B. Magic carpets
C. Old magazines that she had collected for years.
D. Hooked rugs
| The Haunted Fountain CHAPTER I An Unsolved Mystery “Tell Judy about it,” begged Lois. “Please, Lorraine, it can’t be as bad as it appears. There isn’t anything that Judy can’t solve.” Lorraine tilted her head disdainfully. “We’re sisters now. We’re both Farringdon-Petts and should be loyal to each other. But you always did take Judy’s part. She was the one who nearly spoiled our double wedding trying to solve a mystery. I don’t believe she’d understand—understand any better than I do. Everyone has problems, and I’m sure Judy is no exception.” “You’re right, Lorraine,” announced Judy, coming in to serve dessert to the two friends she had invited for lunch at Peter’s suggestion. “I do have problems, and there are plenty of mysteries I can’t solve.” “Name one,” charged Lois. “Just mention one single spooky thing you couldn’t explain, and I’ll believe you. I’ve seen you in action, Judy Bolton—” “Judy Dobbs, remember?” “Well, you were Judy Bolton when you solved all those mysteries. I met you when the whole valley below the big Roulsville dam was threatened by flood and you solved that—” “That,” declared Judy, “was my brother Horace, not me. He was the hero without even meaning to be. He was the one who rode through town and warned people that the flood was coming. I was off chasing a shadow.” “A vanishing shadow,” Lois said with a sigh. “What you did wasn’t easy, Judy.” “It didn’t need to be as hard as it was,” Judy confessed. “I know now that keeping that promise not to talk about the dam was a great big mistake and could have cost lives. I should have told Arthur.” “Please,” Lorraine said, a pained expression clouding her pretty face, “let’s not talk about him now.” “Very well,” Judy agreed. “What shall we talk about?” “You,” Lois said, “and all the mysteries you’ve solved. Maybe you were mistaken about a thing or two before the flood, but what about the haunted house you moved into? You were the one who tracked down the ghosts in the attic and the cellar and goodness knows where all. You’ve been chasing ghosts ever since I met you, and not one of them did you fail to explain in some sensible, logical fashion.” “Before I met you,” Judy said, thinking back, “there were plenty of them I couldn’t explain. There was one I used to call the spirit of the fountain, but what she was or how she spoke to me is more than I know. If my grandparents knew, they weren’t telling. And now they’re both dead and I can’t ask them. They left me a lot of unsolved mysteries along with this house. Maybe I’ll find the answers to some of them when I finish sorting Grandma’s things. They’re stored in one end of the attic.” “Another haunted attic? How thrilling!” exclaimed Lois. “Why don’t you have another ghost party and show up the spooks?” “I didn’t say the attic was haunted.” Judy was almost sorry she had mentioned it. She wasn’t in the mood for digging up old mysteries, but Lois and Lorraine insisted. It all began, she finally told them, the summer before they met. Horace had just started working on the paper. Judy remembered that it was Lorraine’s father, Richard Thornton Lee, who gave him his job with the Farringdon Daily Herald . He had turned in some interesting church news, convincing Mr. Lee that he had in him the makings of a good reporter. And so it was that he spent the summer Judy was remembering in Farringdon where the Farringdon-Petts had their turreted mansion, while she had to suffer the heat and loneliness of Dry Brook Hollow. Her thoughts were what had made it so hard, she confessed now as she reviewed everything that had happened. She just couldn’t help resenting the fact that her parents left her every summer while they went off on a vacation by themselves. What did they think she would do? “You’ll have plenty to read,” her father had told her. “I bought you six new books in that mystery series you like. When they’re finished there are plenty of short stories around. Your grandmother never throws anything away. She has magazines she’s saved since your mother was a girl. If you ask for them she’ll let you have the whole stack. I know how you love to read.” “I do, Dad, but if the magazines are that old—” Judy had stopped. She had seen her father’s tired eyes and had realized that a busy doctor needed a vacation much more than a schoolgirl who had too little to do. He and Judy’s mother usually went to the beach hotel where they had honeymooned. It was a precious memory. Every summer Dr. Bolton and his wife relived it. And every summer Judy went to stay with her grandmother Smeed, who scolded and fussed and tried to pretend she wasn’t glad to have her. “You here again?” she had greeted her that summer, and Judy hadn’t noticed her old eyes twinkling behind her glasses. “What do you propose to do with yourself this time?” “Read,” Judy had told her. “Mom and Dad say you have a whole stack of old magazines—” “In the attic. Go up and look them over if you can stand the heat.” Judy went, not to look over the old magazines so much as to escape to a place where she could have a good cry. It was the summer before her fifteenth birthday. In another year she would have outgrown her childish resentment of her parents’ vacation or be grown up enough to ask them to let her have a vacation of her own. In another year she would be summering among the beautiful Thousand Islands and solving a mystery to be known as the Ghost Parade . “A whole parade of ghosts,” Lois would be telling her, “and you solved everything.” But then she didn’t even know Lois. She had no idea so many thrilling adventures awaited her. There seemed to be nothing—nothing—and so the tears came and spilled over on one of the magazines. As Judy wiped it away she noticed that it had fallen on a picture of a fountain. “A fountain with tears for water. How strange!” she remembered saying aloud. Judy had never seen a real fountain. The thrill of walking up to the door of the palatial Farringdon-Pett mansion was still ahead of her. On the lawn a fountain still caught and held rainbows like those she was to see on her honeymoon at Niagara Falls. But all that was in the future. If anyone had told the freckled-faced, pigtailed girl that she would one day marry Peter Dobbs, she would have laughed in their faces. “That tease!” For then she knew Peter only as an older boy who used to tease her and call her carrot-top until one day she yelled back at him, “Carrot-tops are green and so are you!” Peter was to win Judy’s heart when he gave her a kitten and suggested the name Blackberry for him. The kitten was now a dignified family cat. But the summer Judy found the picture of a fountain and spilled tears on it she had no kitten. She had nothing, she confessed, not even a friend. It had helped to pretend the fountain in the picture was filled with all the tears lonely girls like herself had ever cried. “But that would make it enchanted!” she had suddenly exclaimed. “If I could find it I’d wish—” A step had sounded on the stairs. Judy remembered it distinctly. She had turned to see her grandmother and to hear her say in her usual abrupt fashion, “Enchanted fountain, indeed! If you let people know your wishes instead of muttering them to yourself, most of them aren’t so impossible.” “Were they?” asked Lois. She and Lorraine had listened to this much of what Judy was telling them without interruption. “That’s the unsolved mystery,” Judy replied. “There weren’t any of them impossible.” And she went on to tell them how, the very next day, her grandparents had taken her to a fountain exactly like the one in the picture. It was in the center of a deep, circular pool with steps leading up to it. Beside the steps were smaller fountains with the water spurting from the mouths of stone lions. Judy had stared at them a moment and then climbed the steps to the pool. “Am I dreaming?” she remembered saying aloud. “Is this beautiful fountain real?” A voice had answered, although she could see no one. “Make your wishes, Judy. Wish wisely. If you shed a tear in the fountain your wishes will surely come true.” “A tear?” Judy had asked. “How can I shed a tear when I’m happy? This is a wonderful place.” “Shed a tear in the fountain and your wishes will surely come true,” the voice had repeated. “But what is there to cry about?” “You found plenty to cry about back at your grandmother’s house,” the mysterious voice had reminded her. “Weren’t you crying on my picture up there in the attic?” “Then you—you are the fountain!” Judy remembered exclaiming. “But a fountain doesn’t speak. It doesn’t have a voice.” “Wish wisely,” the voice from the fountain had said in a mysterious whisper. CHAPTER II If Wishes Came True “Did you?” Lois interrupted the story to ask excitedly. “Oh, Judy! Don’t keep us in suspense any longer. What did you wish?” “Patience,” Judy said with a smile. “I’m coming to that.” First, she told her friends, she had to think of a wise wish. There had been so much she wanted in those early days before the flood. Dora Scott had been her best friend in Roulsville, but she had moved away. “You see,” she explained, “I made the mistake of having just one best friend. There wasn’t anybody in Dry Brook Hollow. I remember thinking of how lonely I was and how I wished for a friend or a sister, and suddenly a tear splashed in the water. It made little ripples. I thought I had to wish quickly before they vanished, and so I began naming the things I wanted as fast as I could. I’m not sure they were wise wishes. They seem rather selfish to me, now. I wasn’t thinking of anybody but me, Judy Bolton, and what I wanted. It wasn’t until after I began to think of others that my wishes started to come true.” “But what were they?” Lois insisted. Lorraine seemed unusually quiet and thoughtful. Judy did not notice the fear in her eyes as she replied airily, “Oh, didn’t I tell you? I wished for lots of friends and a sister, and I wished I could marry a G-man and solve a lot of mysteries and that’s as far as I got when the ripples vanished. I thought the spell was broken and so I didn’t wish for anything more.” “Wasn’t there anything more you wanted?” Lois asked. “Of course,” replied Judy. “There were lots more things. I wanted to go places, of course, and keep pets, and have a nice home, and—” “And your wishes all came true!” “Every one of them,” Judy agreed, “even the one about the sister. You see, it wasn’t a baby sister I wanted. It was a sister near my own age. That seemed impossible at the time, but the future did hold a sister for me.” “It held one for me, too,” Lois said, squeezing Lorraine’s hand under the table. “Don’t you think sisters should tell each other their problems, Judy?” “Honey and I always do,” she replied “but then it was different. I didn’t know I would marry Peter or that he would become a G-man, and he didn’t know he had a sister. It is strange, isn’t it? But the strangest thing of all was the fountain itself.” “Why?” asked Lorraine. “Do you still think it was enchanted?” Lois laughed at this, but Judy was serious as she answered, “I was still little girl enough to think so at the time. I wandered around, growing very drowsy. Then I found a hammock and climbed into it. I must have gone to sleep, because I remember waking up and wondering if the voice in the fountain had been a dream.” “A hammock?” Lois questioned. “Are you sure it wasn’t a flying carpet?” “No, it was a hammock all right,” Judy assured her, laughing. “It was hung between two trees in a beautiful garden all enclosed in rose trellises thick with roses. Did I tell you it was June?” “All the year around?” Again Lois laughed. But Lorraine said abruptly, “Let’s not talk about rose gardens in June. It’s a long way from June to December.” “Do you mean a garden changes? I know,” Judy said, “but I think this one would be beautiful at any time of the year. There were rhododendrons, too, and I don’t know how many different kinds of evergreens. I explored the garden all around the fountain.” “And then what happened?” Lorraine urged her. “Yes, yes. Go on,” entreated Lois. “I didn’t dream you’d kept anything that exciting a secret. Why didn’t you try to solve the mystery?” “I think I would have tried,” Judy admitted, “if I had been older or more experienced. I really should have investigated it more thoroughly and learned the secret of the fountain. But after the ripples went away it didn’t speak to me any more, and I didn’t really think it had heard my wishes. I was still wishing for a friend when I met you, Lois. It did seem impossible for us to be friends at first, didn’t it? Lorraine was your friend.” “I did make trouble for you,” Lorraine remembered. “It was all because of my foolish jealousy.” “It was nothing compared to the trouble caused by the Roulsville flood,” declared Judy. “After that things started happening so fast that I completely forgot about the fountain. Honestly, Lois, I don’t believe I thought about it again until after we moved to Farringdon and I walked up to your door and saw the fountain on your lawn.” “The Farringdon-Pett puddle, I always called it,” Lois said with a giggle. “I’ve seen lots nicer fountains.” “You have?” asked Judy. “Then maybe you’ve seen the one I’ve been telling you about. I think the picture of it is still in the attic. Come on up and I’ll show you.” Lois and Lorraine had finished their dessert while Judy was telling them the story of the fountain. Somehow, she wasn’t hungry for hers. She had tasted it too often while she was making it. “I’ll leave it for Blackberry,” she decided. Lois watched in amusement as the cat lapped up the chocolate pudding after Judy had mixed it generously with cream. “Sometimes,” Judy said fondly, “Blackberry thinks he’s a person. He eats everything we eat, including lettuce. Do you mind if he comes with us, Lorraine? He wants to explore the attic, too.” “He’ll remember he’s a cat fast enough if there are any mice up there,” Lois said with a giggle. Leaving the table, they all started upstairs with the cat bounding ahead of them. In modernizing her grandparents’ house to suit her own and Peter’s tastes, Judy had seen to it that the old stair door was removed. But there was still a door closing off the narrower stairs that led to the attic. Blackberry reached it first and yowled for Judy to open it. “He can read my mind. He always knows where I’m going,” Judy said as the door creaked open and the cat shot through it. A moment later a weird rolling noise came from the floor above. “Come on. There’s nothing up here to be afraid of,” Judy urged her friends. “Maybe not, but I’m beginning to get the shivers,” confessed Lois as she followed Judy to the sewing room at the top of the last flight of stairs. “So am I,” Lorraine admitted. “I’m not superstitious about black cats, but they are creepy. Does Blackberry have to roll spools across the floor?” “Now he thinks he’s a kitten,” laughed Judy. Pausing at still another door that led to the darker part of the attic, she turned and said mysteriously, “Up here we can all turn back the clock. Does anybody care to explore the past?” The exploration began enthusiastically with Judy relating still more of what she remembered about the fountain. “When I told Grandma about it she laughed and said I must have dreamed it. She said if wishes came true that easily she’d be living in a castle. But would she?” Judy wondered. “When I first remember this house she was still burning kerosene lamps like those you see on that high shelf by the window. I think she and Grandpa like the way they lived without any modern conveniences or anything.” “I think so, too,” Lois agreed, looking around the old attic with a shiver. “It is strange they both died the same winter, isn’t it?” “Maybe they wanted it that way. Maybe they wished neither of them would outlive the other. If they did wish in the fountain,” Judy went on more thoughtfully, “I’m sure that was one of their wishes. Another could have been to keep the good old days, as Grandma used to call them. That one came true in a way. They did manage to keep a little of the past when they kept all these old things. That’s what I meant about turning back the clock.” “If wishes came true I’d like to turn it back a little myself,” Lorraine began. “It would be nice if things were the way they used to be when I trusted Arthur—” “Don’t you trust him now?” Judy asked. Afterwards she was sorry for the interruption. Lois and Judy both questioned Lorraine, but that was all she would say. Judy wondered, as they searched through the old magazines, what was wrong. Lorraine was of a jealous disposition. Was the green-eyed monster coming between her and her handsome husband, Arthur Farringdon-Pett? Until now they had seemed blissfully happy. But there was no happiness in Lorraine’s face as she gazed at a picture of one of the fountains and then said in a tight little voice, “It is. It’s the very same one.” “But that’s the picture I’ve been searching for!” Judy said eagerly. “Do you know where it is?” “I can’t be sure. But if it ever was enchanted, I’m sure it isn’t now. Let’s go,” Lorraine said suddenly to Lois. Judy knew she was suggesting a fast trip home. But, apparently, Lois did not understand it that way. If she did, she pretended not to. “Where?” she asked. “To the fountain? I’d love to, wouldn’t you, Judy?” “I certainly would,” Judy replied enthusiastically. “Do you recognize it, too?” “I think so,” Lois answered after studying a little more closely the picture they had found. “It looks like the fountain on the Brandt estate.” “The department store Brandts?” Judy questioned. “Then my grandparents must have driven old Fanny all the way to Farringdon.” “Not quite all the way,” Lorraine objected. “The Brandts own that stretch of woods just before you come into the city. You’ve passed it lots of times.” “Of course,” agreed Judy. She put the magazine back in its place under the eaves and turned eagerly to her friends. “I do remember a road turning off into the woods and going on uphill,” she told them. “I never thought it led to a house, though. There isn’t even a gate. Could that be the road my grandparents took?” “Why don’t we take it ourselves and find out?” Lois suggested. CHAPTER III A Strange Encounter Lorraine was not too enthusiastic about the proposed trip to the Brandt estate. Finally she agreed to it under one condition. They were not to drive all the way to the house which, she said, was just over the hilltop. They were to park the car where no one would see it and follow the path to the fountain. “But suppose we can’t find the path?” asked Judy. “You’ll remember it, won’t you?” Judy thought she would, but she wasn’t too sure. She and Lois both argued that it would be better to inquire at the house. Lois knew Helen Brandt slightly. “She’d be glad to show us around. This way it looks as if we’re planning a crime,” Lois said as they started off in the blue car she was driving. It was a neat little car, not too conspicuous, and easy to park in out-of-the-way places. Judy laughed and said if they did find the fountain she thought she’d wish for one exactly like it. “Well, you know what your grandmother said about wishes, don’t you?” Lorraine asked. “If you let people know about them instead of muttering them to yourself most of them aren’t so impossible.” “Quite true,” Judy agreed. “I’ll let Peter know about this one. He’s my Santa Claus, and it will soon be Christmas. Maybe I should have worn the fur coat he gave me last year.” “Your reversible’s better in case it rains. It’s too warm for snow. We picked a perfect day for this trip,” Lois continued, guiding the car around curves as it climbed the steep hill beyond Dry Brook Hollow. The trip was a short one. In twenty minutes they had covered the distance that had seemed such a long way to Judy when she was riding in her grandfather’s wagon. “I’ve been thinking about it,” she said, “and I’ve just about figured out how it happened. I didn’t think my grandparents knew the Brandts well enough to pay them a visit, though. We must have looked queer driving up to a beautiful estate in Grandpa’s old farm wagon. I do remember that Grandma had some hooked rugs to deliver. But that still doesn’t explain what happened afterwards. When I woke up in the hammock I was alone in the garden. Horse, wagon, grandparents—all had disappeared.” “How could they?” asked Lois. “Anyway,” Lorraine began, “you had a chance to see how beautiful everything was before—” Again she broke off as if there were something she wanted to tell but didn’t quite dare. “Before what?” questioned Judy. “Oh, nothing. Forget I said anything about it. You were telling us how you woke up in the hammock, but you never did explain how you got back home,” Lorraine reminded her. “Didn’t I?” asked Judy. “I’d forgotten a lot of it, but it’s beginning to come back now. I do remember driving home along this road. You see, I thought my grandparents had left me in the garden for a surprise and would return for me. I told you I was all alone. There wasn’t a house in sight.” “The Brandt house is just over the top of this next hill,” Lois put in. “I know. You told me that. Now I know why I couldn’t see it. All I could see was a windowless old tower and a path leading in that direction. Naturally, I followed it. There’s something about a path in the woods that always tempts me.” “We know that, Judy. Honey told us all about your latest mystery. You followed a trail or something.” “Well, this trail led out of the rose garden where the hammock was and then through an archway,” Judy continued. “All sorts of little cupids and gnomes peered out at me from unexpected places. I was actually scared by the time I reached the old tower. There wasn’t time to explore it. Just then I heard the rumble of my grandfather’s wagon and knew he was driving off without me.” “He was!” Judy’s friends both chorused in surprise, and Lois asked, “Why would he do a thing like that?” “I think now it was just to tease me. He did stop and wait for me after a while,” Judy remembered. “The rugs were gone. Grandma must have delivered them, but I didn’t ask where. If she made them for Mrs. Brandt they may still be there.” “I wouldn’t depend on it,” Lorraine said as they turned up the narrow road to the Brandt estate. “Watch out!” Judy suddenly exclaimed. “There’s another car coming.” As Lois swerved to avoid the oncoming car, Lorraine ducked her head. She kept herself hidden behind Judy until the car had passed. The man driving it was a stranger to Judy, but she would remember his hypnotic, dark eyes and swarthy complexion for a long time. The soft brown hat he was wearing covered most of his hair. “What’s the matter with you two?” asked Lois when the car had passed. “Aren’t you a little old for playing hide and seek?” “I wasn’t—playing. Let’s not go up there,” Lorraine begged. “I don’t think the Brandts live there any more.” “Maybe not, but we can pretend we think they do, can’t we?” Judy replied a little uncertainly. She was beginning to suspect that Lorraine knew more about the Brandt estate than she was telling. Lois kept on driving along the narrow, gravelly road. Soon there were more evergreens and a hedge of rhododendrons to be seen. They looked very green next to the leafless trees in the woods beyond. The sky was gray with white clouds being driven across it by the wind. “There’s the tower!” Lorraine exclaimed. “I can see it over to the left. It looks like something out of Grimm’s Fairy Tales, doesn’t it?” “It looks grim all right,” agreed Judy. “I wonder what it is.” “I suppose it’s nothing but an old water tower. It would be fun to explore it, though,” Lois said. “But if there are new people living here they’ll never give us permission.” “We might explore it without permission,” Judy suggested daringly. “Come on!” she urged her friends as Lois parked the car in a cleared place beside the road. “Who’s going to stop us? And who wants to explore a gloomy old tower, anyway? Let’s look for the fountain.” “Do you think we should?” Lorraine asked. “It won’t be enchanted. I told you—” “You told us very little,” Lois reminded her. “If you know anything about the people who live here now, I think you ought to let us know. Otherwise, I’m afraid we won’t be very welcome.” “I don’t think they’ll welcome us, anyway. I do know who they are,” Lorraine admitted. “You remember Roger Banning from school, don’t you? I’ve seen him around here. His family must have acquired sudden wealth, or else he’s just working on the estate.” “Then you’ve been here lately? Why didn’t you tell me?” asked Lois. “We always used to go places together.” “It wasn’t important,” Lorraine replied evasively. “I was just out for a drive.” “You plutocrats!” laughed Judy. “Each with a car of your own. You’re not interested in Roger Banning, are you, Lois? I’m sure you can do better than that. I did know him slightly, but not from school. The boys and girls were separated and went to different high schools by the time we moved to Farringdon. I remember his pal, Dick Hartwell, a lot better. He was in our young people’s group at church.” “Sh!” Lois cautioned her. “Nice people no longer mention Dick Hartwell’s name. He’s doing time.” “For what?” asked Judy. Like Peter, her FBI husband, she preferred facts to gossip. “Forgery, I guess. He stole some checkbooks from his father’s desk and forged the names of a lot of important business people. I think he forged some legal documents, too. Anyway, he went to the Federal Penitentiary. It was all in the papers,” Lorraine told her. Now Judy did remember. It was something she would have preferred to forget. She liked to think she was a good judge of character, and she had taken Dick Hartwell for a quiet, refined boy who would never stoop to crime. “I don’t see what all this has to do with the fountain,” Lois said impatiently. “Are we going to look for it, or aren’t we?” “Of course we are. That’s what we came for. I just like to know what a tiger looks like before he springs at me,” Judy explained. “You seem to think there’s danger in this expedition of ours, don’t you?” asked Lorraine. “I don’t know what to think. You’re the one who seems to know the answers, but you’re not telling. Hiding your face back there gave you away. You’ve seen that character who drove down this road and, for some reason, you were afraid he would see you. Why, Lorraine? Why didn’t you want to be recognized?” Lorraine hesitated a moment and then replied evasively, “People don’t generally enter private estates without an invitation. That’s all.” “I’d better turn the car around,” Lois decided, “in case we have to leave in a hurry. I don’t expect we’ll encounter any tigers, but we may be accused of trespassing.” “I’m sure we will be,” announced Judy as two dark-coated figures strode down the road toward them. “You drove right by a NO TRESPASSING sign, and this isn’t a welcoming committee coming to meet us!” | D. Hooked rugs |
What kind of relationship does the third mate have with his wife?
A. He is extremely obsessed with her and has no intent of letting her change husbands
B. He is torn between his relationship with her and his relationship with Wanda, but wants to be loyal
C. He doesn't feel strongly and is mostly using her as a pawn to trade for the wife he really wants
D. He wants what is best for her, and is dedicated to supporting her in everything she asks for
| VOYAGE TO FAR N'JURD By KRIS NEVILLE Illustrated by MACK [Transcriber's Note: This etext was produced from Galaxy Magazine April 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] They would never live to see the trip's end. So they made a few changes in their way of life—and many in their way of death! I "I don't see why we have to be here," a crewman said. "He ain't liable to say anything." "He shore better," the man in front of him said loudly. "Be still," his wife said. "People's lookin' at ya." "I don't care a smidgen," he said, "if en they ayre." "Please," she said. "Joanne Marie," he said, "you know that when I aims ta do somethin', I'm jest natcher'lly bound to do hit. An' iffen I aims ta talk...." "Here comes the priest. Now, be still." The man looked up. "So he do; an' I'll tell ya, hit shore is time he's a-gittin' hyere. I ain't got no all night fer ta sit." The crewman to his left bent over and whispered, "I'll bet he's gonna tell us it's gonna be another postponement." "Iffen he does, I'm jest a-gonna stand up an' yell right out that I ain't gonna stand fer hit no longer." "Now, dear," said Joanne Marie, "the captain can hear ya, if you're gonna talk so loud." "I hope he does; I jest hope he does. He's th' one that's a-keepin' us all from our Reward, an' I jest hope he does heyar me, so he'll know I'm a-gittin' mighty tyird uv waitin'." "You tell 'im!" someone said from two rows behind him. The captain, in the officer's section, sat very straight and tall. He was studiously ignoring the crew. This confined his field of vision to the left half of the recreation area. While the priest stood before the speaker's rostrum waiting for silence, the captain reached back with great dignity and scratched his right shoulder blade. Nestir, the priest, was dressed out in the full ceremonial costume of office. His high, strapless boots glistened with polish. His fez perched jauntily on his shiny, shaven head. The baldness was symbolic of diligent mental application to abstruse points of doctrine. Cotian exentiati pablum re overum est : "Grass grows not in the middle of a busy thoroughfare." The baldness was the result of the diligent application of an effective depilatory. His blood-red cloak had been freshly cleaned for the occasion, and it rustled around him in silky sibilants. "Men," he said. And then, more loudly, "Men!" The hiss and sputter of conversation guttered away. "Men," he said. "The other evening," he said, "—Gelday it was, to be exact—one of the crew came to me with a complaint." "Well, I'll be damned," Joanne Marie's husband said loudly. Nestir cleared his throat. "It was about the Casting Off. That's why I called you all together today." He stared away, at a point over the head and to the rear of the audience. "It puts me in mind of the parable of the six Vergios." Joanne Marie's husband sighed deeply. "Three, you will recall, were wise. When Prophet was at Meizque, they came to him and said, 'Prophet, we are afflicted. We have great sores upon our bodies.' The Prophet looked at them and did see that it was true. Then he blessed them and took out His knife and lay open their sores. For which the three wise Vergios were passing grateful. And within the last week, they were dead of infection. But three were foolish and hid their sores; and these three did live." The captain rubbed his nose. " Calex i pundendem hoy , my children. 'Secrecy makes for a long life,' as it says in the Jarcon ." Nestir tugged behind him at his cloak. "I want you all to remember that little story. I want you all to take it away from here with you and think about it, tonight, in the privacy of your cabins. "And like the three wise Vergios who went to the Prophet, one of the crewmen came to me. He came to me, and he said: 'Father, I am weary of sailing.' "Yes, he said, 'I am weary of sailing.' "Now, don't you think I don't know that. Every one of you—every blessed one of you—is weary of sailing. I know that as well as I know my own name, yes. "But because he came to me and said, 'Father, I am weary of sailing,' I went to the captain, and I said, 'Captain, the men are weary of sailing.' "And then the captain said: 'All right, Father,' he said, 'I will set the day for the Festival of the Casting Off!'" The little fellow was pleased by the rustle of approval from the audience. "God damn, hit's about time!" Joanne Marie's husband said. Nestir cleared his throat again. "Hummm. Uh. And the day is not very far distant," said Nestir. "I knowed there was a catch to hit," Joanne Marie's husband said. "I know you will have many questions; yes, I know you will have—ah, ah—well, many questions. You are thinking: 'What kind of a Festival can we have here on this ship?' You are thinking: 'What a fine thing—ah, what a good thing, that is—ah, how nice it would be to have the Casting Off at home, among friends.'" Nestir waved his hands. "Well, I just want to tell you: I come from Koltah. And you know that Koltah never let any city state outdo her in a Festival, uh-huh. "The arena in Koltah is the greatest arena in the whole system. We have as many as sixty thousand accepted applicants. All of them together in the arena is a—uh, uh, well—a sight to behold. People come from all over to behold it. I never will forget the Festival at which my father was accepted. He.... "Well, the point I want to make is this: I just wanted to tell you that I know what a Festival should be, and the captain and I will do everything in our power to make our Casting Off as wonderful as any anywhere. "And I want to tell you that if you'll come to me with your suggestions, I'll do all I can to see that we do this thing just the way you want it done. I want you to be proud of this Casting Off Festival, so you can look back on it and say, uh, uh—this day was the real high point of your whole life!" Everyone but Joanne Marie's husband cheered. He sat glumly muttering to himself. Nestir bobbed his shiny head at them and beamed his cherubic smile. And noticed that there was a little blonde, one of the crewmen's wives, in the front row that had very cute ankles. While they were still cheering and stomping and otherwise expressing their enthusiasm and approval, Nestir walked off the speaker's platform and into the officer's corridor. He wiped his forehead indecorously on the hem of his cloak and felt quite relieved that the announcement was over with and the public speaking done. II Dinner that evening was a gala occasion aboard the ship. The steward ordered the holiday feast prepared in celebration of Nestir's announcement. And, for the officers, he broke out of the special cellar the last case allotment for Crew One of the delicate Colta Barauche ('94). He ordered the messman to put a bottle of it to the right of each plate. The captain came down from his stateroom after the meal had begun. He nodded curtly to the officers when he entered the mess hall, walked directly to his place at the head of the table, sat down and morosely began to work the cork out of his wine bottle with his teeth. "You'll spoil the flavor, shaking it that way," the third mate cautioned. He was particularly fond of that year. The captain twisted the bottle savagely, and the cork came free with a little pop. He removed the cork from between his teeth, placed it very carefully beside his fork, and poured himself a full glass of the wine. "Very probably," he said sadly. "I don't think hit'll do hit," the first mate said. "He hain't shook hard enough to matter." The captain picked up the glass, brought it toward his lips—then, suddenly having thought of something, he put it back down and turned to Nestir. "I say. Have you decided on this Carstar thing yet, Father?" The little priest looked up. He laid his knife across the rim of his plate. "It has ramifications," he said. When the third mate saw that his opinion on the wine was not immediately to be justified, he settled back in his chair with a little sigh of disapproval. "Well, what do you think your decision will be, Father?" the steward asked. Nestir picked up his knife and fork and cut off a piece of meat. "Hummmm," he said. "It's hard to say. The whole issue involves, as a core point, the principle of casta cum mae stotiti ." The first mate nodded sagely. "The intent, of course, could actually be—ah— sub mailloux ; and in that event, naturally, the decision would be even more difficult. I wish I could talk to higher authority about it; but of course I haven't the time. I'll have to decide something." "He had a very pretty wife," the third mate said. "Yes, very." Nestir agreed. "But as I was saying, if it could be proven that the culstem fell due to no negligence on his part, either consciously or subconsciously, then the obvious conclusion would be that no stigma would be attached." He speared his meat and chewed it thoughtfully. "But it wasn't at all bloody," the wife of the second mate said. "I scarcely think he felt it at all. It happened too fast." Nestir swallowed the mouthful of food and washed it down with a gulp of wine. "The problem, my dear Helen," he said, "is one of intent. To raise the issue of concomitant agonies is to confuse the whole matter. For instance. Take Wilson, in my home state of Koltah. Certainly he died as miserable a death as anyone could desire." "Yes," said the second mate's wife. "I remember that. I read about it in the newspapers." "But it was a case of obvious intent ," continued Nestir, "and therefore constituted a clear out attempt to avoid his duty by hastening to his Reward." Upon hearing the word duty, the captain brightened. "That," he said to Nestir, "my dear Father, is the cardinal point of the whole game, y'know." He scratched the back of his left hand. "Duty. And I must say, I think you're being quite short-sighted about the Casting Off date. After all, it's not only a question of how we go, but also a question of leaving only after having done our duty. And that's equally important." "The Synod of Cathau—" Nestir began. "Plague take it, Father! Really, now, I must say. The Synod of Cathau! Certainly you've misinterpreted that. Anticipation can be a joy, y'know: almost equal to the very Reward. Anticipation should spur man in duty. It's all noble and self sacrificing." He scratched the back of his right hand. The second mate had been trying to get a word in edgewise for several minutes; he finally succeeded by utilizing the temporary silence following the captain's outburst. "You don't need to worry about your Casting Off, Captain. You can leave that to me. I assure you, I have in mind a most ingenious method." The captain was not visibly cheered; he was still brooding about the sad absence of a sense of duty on the part of Nestir. "I will welcome it," he said, "at the proper time, sir. And I certainly hope—" His eyes swept the table. "I certainly hope to be Cast Off by an officer. It would be very humiliating, y'know, to have a crew member do it." "Oh, very," said the steward. "I don't know," the second mate's wife said, "whether you better count on my husband or not. I have my own plans for him." "This problem of Carstar interests me," the third mate said. "Did I ever tell you about my wife? She strangled our second baby." "He was a very annoying child," his wife said. "He probably wouldn't have lived, anyway," the third mate said. "Puny baby." "That," said Nestir, "is not at all like the Carstar case. Not at all. Yours is a question of saliex y cuminzund ." The first mate nodded. "It seems to me that the whole thing would depend on the intent of the strangler." "Captain," the steward said, "you really must let me give you some of that salve." "That's very kind of you, but I...." "No bother at all," the steward said. "As I see it," Nestir said, "if the intent was the natural maternal instinct of the mother to release her child from its duty, then...." "Oh, not at all," the third mate's wife said. "I did it to make him stop crying." "Well, in that case, I see no reason why he shouldn't get his Reward." "I certainly hope so," the third mate said. "Jane worries about it all the time." "I do not," Jane contradicted. "Now, honey, you know you do so." At that moment, he lost interest in his wife and leaned across the table toward the captain, "Well?" he asked. The captain rolled the wine over his tongue. "You were right, of course." The third mate turned triumphantly to the first mate. "There, I told you so." The first mate shrugged. "I never do say nothin' right," he said. "I hain't got no luck. I've spent more years un all ya, carpenterin' up a duty log that's better un even th' captain's. An' hit's Martha an' me that gotta wait an' help th' next crew. Lord above knows how long time hit'll be afore we uns'll got ta have a Festival." "Oh, really, now. Now. Duty, duty," the captain reprimanded him mildly. "Duty! Duty! Duty! You all ur in a conspiracy. You all want me ta die uv old age." "Nonsense," said the steward. "We don't want anything of the sort. After all, someone has to orient the new crew." "Quite right," said the captain. "You ought to be proud." The first mate slammed his napkin in the middle of his food and stalked out of the mess hall. "Quite touchy today," Nestir observed. "By the way," the third mate said. "Wanda gave me a petition to give to you, Father." "Wanda?" "Yes. She's sixteen, now." "Wanda who?" the steward asked. "Wanda Miller, the bosun's daughter." "I know her," Helen said. "She's the oldest child on the ship, and she wants you to sign her adult petition so she can be in the Festival, Father." "She's so young...." "Sixteen, Father." "After all, one must have done some duty," the captain said. "He wants you to sign it so he can take her in the Changing of the Wives," Jane said. Nestir fidgeted uncomfortably. "Well, I'll look at her record," he said. "It's an idea," the second mate said. "Otherwise, we'll be short one woman." "There wouldn't be one short if he had brought a wife," the first mate's wife said, looking squarely at the captain. "Now, Martha. I place duty above pleasure. You're just angry, y'know, because you have to stay with your husband." "All right, so I am. But it's true. And if Carstar hadn't been killed, there would have been two short." She shot a wicked glance at Nestir. "Why don't you and him share a woman—" "Martha!" "Although the Prophet knows what woman in her right mind would consent to...." "Well," said Nestir hesitantly. "Listen," the third mate said, "the second's right. If you don't sign it, someone will have to do without a woman." Nestir blushed. "I'll look it over very carefully, but you must realize that the priestcraft...." "Actually, in a way, it would be her duty to, you see. Think of it like that: as her way to do her duty." "She's too young for you, dear," Jane said to her husband. "Oh, I don't know," the steward said. "Sometimes they're the best, I hear." III The third mate, whose name was Harry, stood before the mirror combing his hair. He had been combing his hair for the last fifteen minutes. "I suppose the crew is celebrating?" his wife said. "I suppose." She stood up and walked over to the dresser. Absently she began to finger the articles on it. "You really shouldn't have told them about little Glenn tonight." "Pish-tush." "No, Harry. I mean it. Helen looked at me strangely all through dinner. She has three children, you know." "You're imagining things." "But she does have three children." "I mean about her looking at you." "Oh." Harry fiddled with his tie without speaking. "I mean, as much as to say: 'Well, I raised all of mine.'" "But honey, about little Glenn. That was an accident, almost. You didn't really mean to choke him that hard." "But still ... it ... I mean, there was Helen, looking at me like I wasn't doing my duty. You know." "No," he said. "That's nonsense, Jane. Sheer nonsense. You know what the priest said." He polished one of his brass buttons with the sleeve of his coat. "Harry?" "Yes?" "I don't think all that is necessary just to go on duty." "Probably not." She walked to the bed and sat down. "Harry?" "Yes, dear?" "Don't you really think she's awful young?" "Huh-uh." "I mean, why don't you pick someone else? Like Mary? She's awful sweet. I'll bet she'd be better." "Probably." "She's a lot of fun." He brushed at his hair again. "Who do you want, Jane?" "Oh, I don't know." She looked down at her legs, raised them up from the floor and held them out in front of her. "I think I'd kind of like Nestir. With his funny bald head. I hope he asks me." "I'll mention it to him." "Would you really, Harry? That would be sweet." "Sure, honey." He looked down at his watch. "Harry? Are you going to meet Wanda in the control room?" "Uh-huh." "I thought so. Well, remember this, dear: It isn't the day of the Changing of the Wives yet. Don't forget." "Honey! You don't think for a minute that...." "No, dear. I know you wouldn't. But just don't , I mean." He walked over and kissed her forehead and patted her cheek. "Course not," he said, comfortingly. He left her sitting on the bed and strolled down the officers' corridor, whistling. He made a mental note to have the bosun send some of the crew in tomorrow to wash down these bulkheads. They needed it. In one corner a spider spun its silver web. He jogged up the companionway, turned left and felt the air as fresh as spring when he stepped under the great ventilator. And beneath it lay one of the crew. He kicked the man several times in the ribs until he came to consciousness. "Can't sleep here, my man," Harry explained. "Awww. Go way an' le' me 'lone, huh?" "Here. Here." He pulled the fellow erect and slapped him in the face briskly. "This is the officers' corridor." "Oh? Ish it? Schorry. Shore schorry, shir. So schorry." Harry assisted him to the crew's corridor where he sank to the floor and relapsed once more into a profound slumber. Harry continued on to the control room. When he entered it, the second mate was yawning. "Hi, John. Sleepy?" "Uh-huh. You're early." "Don't mind, do you?" "No ... Quiet tonight. Had to cut the motors an hour ago. Control technician passed out." "Oh?" The second mate took out a cigarette and lit it. "Can't blow the ship up, you know. Look like hell on the record. Hope the captain don't find out about it, though. He'll figure the man was neglecting his duty." He blew a smoke ring. "Might even bar him from the Festival." "Yeah," said Harry, "the captain's funny that way." The second mate blew another smoke ring. "Well," Harry said. "Uh. Harry? Are you really going to take that Wanda girl?" "If Nestir lets me." "Say. Harry. Do you suppose your wife would...?" Harry crossed to the second mate and put a hand on his shoulder. "Sorry, old fellow. She's got it in her head to take Nestir." He shrugged. "I don't exactly approve, of course, but ... I'm sure if he doesn't want her, she'd be glad to hear your offer." "Aw, that's all right," John said. "Don't really matter. Say. By the way. Have I told you what I intend to do to the captain? I've got it all thought out. You know that saber I picked up on Queglat? Well...." "Look. How about telling me another time?" "Uh, Sure. If you say so. Uh?" "I'm kind of expecting Wanda." "Oh. Sure. I should have known you weren't here early for nothing. In that case, I better be shoving off. Luck." "Thanks. See you at breakfast." "Right-o." After the second mate left, Harry walked over to the control panel. The jet lights were dead. He picked up the intercom and switched over the engine call bell. "'Lo," he said into the microphone. "This is the bridge.... Oh, hi, Barney. Harry.... Have you got a sober control technician down there yet...? Fine. We'll start the jets again. If the captain comes in now—well, you know how he is.... Okay, thanks. Night." He replaced the microphone. He reached over and threw the forward firing lever. The jet lights came on and the ship began to brake acceleration again. Having done that, he switched on the space viewer. The steady buzz of the equipment warming sounded in his ears. Wanda would be sure to want to look at the stars. She was simple minded. "Hello." He swiveled around. "Oh, hello, Wanda, honey." "Hello, Haireee. Are you glad little ol' me could come, huh?" "Sure am." "Me, too. Can I look at the—oh. It's already on." "Uh-huh. Look. Wanda." "Hum?" "I talked to Nestir today." "Goody. What did he say, huh? I can be an adult and get to play in the Festival, can I?" "I don't know, yet. He's thinking about it. That's why I want to see you. He's going to check your record. And Wanda?" "Them stars shore are purty." "Wanda, listen to me." "I'm a-listenin', Haireee." "You're simply going to have to stop carrying that doll around with you if you want to be an adult." In Nestir's cabin the next morning, the captain and the priest held a conference. "No, Captain. I'm afraid I can't agree to that," Nestir said. The captain said, "Oh, don't be unreasonable, Father. After all, this is a ship, y'know. And I am, after all, the captain." Nestir shook his head. "The crew and the officers will participate together in the Festival. I will not put the officers' corridor off limits, and—Oh! Yes? Come in!" The door opened. "Father?" "Yes, my son? Come in." "Thank you, Father. Good morning, Captain, sir." "Sit down, my son. Now, Captain, as I was saying: no segregation. It's contrary to the spirit, if not the wording, of the Jarcon ." "But Father! A crewman! In the officers' corridor! Think!" "Before the Prophet, we are all equal. I'm sorry, Captain. Now on Koltah, we practiced it with very good results, and...." "I say, really—" "Father?" said the crewman who had just entered. "Yes, my son. In one moment. Now, Captain. As I have been explaining: The arena method has advantages. In Koltah we always used it. But here—due to the—ah—exigencies of deep space—I feel convinced that a departure from normal procedure is warranted. It is not without precedent. Such things were fairly common, in astoli tavoro , up until centralization, three hundred years before Allth. Indeed, in my home city—Koltah—in the year of the seventh plague, a most unusual expedient was adopted. It seems...." "You're perfectly correct, of course," the captain said. "That's just what I wanted to see you about, Father," the crewman said. "Now, in my city state of Ni, for the Festivals, we...." "Shut up," said the captain softly. "Yes, sir." "Now, as I was saying, Captain, when the methods used in...." "If you'll excuse me, Father, I really should return to duty," said the crewman. "Quite all right, my son. Close the door after you." "I must say, fellow, your sense of duty is commendable." "Well, uh, thank you, sir. And thank you, Father, for your time." "Quite all right, my son. That's what I'm here for. Come in as often as you like." The crewman closed the door after him. He had been gone only a moment, scarcely time for Nestir to get properly launched on his account, when Harry, the third mate, knocked on the door and was admitted. "Oh? Good morning, Captain. I didn't know you were here." Then, to the priest: "I'll come back later, Father." "Nonsense," said the captain. "Come in." "Well, I had hoped to see the Father for a minute on ... private business." "I have to be toddling along," said the captain. "But Captain! I haven't finished telling you about...." "I'll just go down and get a cup of coffee," the captain said. "I'll call you when I'm through," said Harry. The captain left the room. "It's about Wanda, Father," said the third mate. The priest studied the table top. He rearranged some papers. "Ah, yes. The young girl." "Well, I mean, it's not only about Wanda," said Harry. "You see, my wife, Jane, that is...." "Yes?" said the priest. He took his pen out of the holder. "I think, with the proper ... ah ... you know. What I mean is, I think she might look with favor on you in the Changing of the Wives, if I said a few well chosen words in your behalf." "That is very flattering, my son." He returned the pen to the holder. "Such bounty, as it says in the Jarcon , is cull tensio ." "And with your permission, Father...." "Ah...." "She's a very pretty woman." "Ah.... Quite so." "Well, about Wanda. I really shouldn't mention this. But Father, if we are short one woman...." "Hummmm." "I mean, the girls might think a man gets rusty." "I see what you mean." Nestir blinked his eyes. "It wouldn't be fair, all things considered." He stood up. "I may tell you, my son, that, in thinking this matter over last night, I decided that Wanda—ah—Miller, yes, has had sufficient duty to merit participation in the Festival." "Justice is a priestly virtue," Harry said. "And you really think your wife would...?" "Oh, yes, Father." "Well, ahem. But...." "Yes, Father?" " Ad dulce verboten. " "Uh?" "That is to say, in order for a woman to join in the ritual of the Changing of the Wives, she must, ahem, be married." "I never thought of that," said the third mate disconsolately. "I think that can be arranged, however," said Nestir. "If you go by the mess hall on your way out, please tell the captain we can continue our discussion at his pleasure." IV "Sit down, Captain," said Nestir, when the captain entered. "No. Over there, in the comfortable chair. There. Are you comfortable, Captain?" "Of course I am." "Good. I have a question to ask you, Captain." "I say?" Nestir rubbed his bald head. "Sir," he said by way of preamble, "I know you have the greatest sensibility in questions of duty." "That's quite so, y'know. I pride myself upon it, if I do say so." "Exactly. Argot y calpex. No sacrifice is too great." "True; true." "Well, then, say the first day of Wenslaus, that would be—ah, a Zentahday—I may depend upon you to wed Wanda Miller, the bosun's daughter, yes?" "No," said the captain. "Come now, sir. I realize she is the daughter of a crewman, but—" "Father," said the captain, "did I ever tell you about the time I led an expeditionary force against Zelthalta?" "I don't believe you have." "Then I will tell you. Came about this way. I was given command of fifty-three thousand Barains. Savage devils. Uncivilized, but fine fighters. I was to march them ninety-seven miles across the desert that...." "Captain! I fear I must be very severe with you. I will be forced to announce in the mess hall this evening that you have refused to do your duty when it was plainly and properly called to your attention." "Very well, Father," the captain said after several minutes. "I will do it." He was trembling slightly. That morning was to be the time of the captain's wedding. He had insisted that it be done in privacy. For the ceremony, he refused to make the slightest change in his everyday uniform; nor would he consent to Nestir's suggestion that he carry a nosegay of hydroponic flowers. He had intended, after the ceremony, to go about his duty as if nothing out of the ordinary had happened; but after it was done with, the vast indignity of it came home to him even more poignantly than he had imagined it would. Without a word, he left the priest's stateroom and walked slowly, ponderously, with great dignity, to his own. It was a very fine stateroom. The finest, but for Nestir's, in the whole ship. The velvet and gold drapes (his single esthetic joy) were scented with exotic perfume. The carpet was an inch and a half thick. He walked through his office without breaking his stride. The bed was large and fluffy. An unbroken expanse of white coverlette jutting out from the far bulkhead. It looked as soft as feather down. Without even a sigh, he threw himself upon the bed and lay very, very quiet. His left leg was suspended in the air, intersecting, at the thigh, the plane of the coverlet at forty-five degrees; the number of degrees remained stiffly, unrelaxingly forty-five. Only after a long, long time did he roll over on his back and then it was merely to stare fixedly at the ceiling. It is entirely possible that he would have lain there until Doomsday had not his introspection been, around noon, interrupted by an apologetic tap on the door. "Come in," he whispered, hoping she would not hear him and go away. But she heard him. "Husband," Wanda said simply. She closed the door behind her and stood staring at him. "Madam," he said, "I hope you will have the kindness not to refer to me by that indecent appelation a second time." "Gee. You say the cutest things. I'm awful glad you had to marry me, huh." The captain stood up, adjusted his coat and his shoulders, and walked across the room to the dressing table. He opened the left-hand drawer, removed a bottle, poured himself half a water-glass full and drank it off. "Ah," he said. He returned to the bed and sat down. "Can'tcha even say hello ta little ol' me, huh?" she asked. "Hello," he said. "Madam, sit down. I intend to give you an instructive lecture in the natural order of...." "Huh?" "Ah," he said. "Quite true, of course." She walked over to the chair and sat down. "I don't like them," she said. "Them cloth things over there." "Those, Madam," he said, "are priceless drapes I had imported from the province of San Xalthan. They have a long, strange history. "About three thousand years ago, a family by the name of Soong was forced to flee from the city of Xan because the eldest son of the family had become involved in a conspiracy against the illustrious King Fod. As the Soong family was traveling...." "I don't like 'em anyway," said Wanda. "Madam," said the captain, "kindly bring me that." "This?" "Yes. Thank you." He took the doll from her. He got up again, walked to the chest of drawers, searched around for a penknife. Finally he located it under a stack of socks. | C. He doesn't feel strongly and is mostly using her as a pawn to trade for the wife he really wants |
What feedback labels are used? | ### Introduction
Voice-controlled virtual assistants (VVA) such as Siri and Alexa have experienced an exponential growth in terms of number of users and provided capabilities. They are used by millions for a variety of tasks including shopping, playing music, and even telling jokes. Arguably, their success is due in part to the emotional and personalized experience they provide. One important aspect of this emotional interaction is humor, a fundamental element of communication. Not only can it create in the user a sense of personality, but also be used as fallback technique for out-of-domain queries BIBREF0. Usually, a VVA's humorous responses are invoked by users with the phrase "Tell me a joke". In order to improve the joke experience and overall user satisfaction with a VVA, we propose to personalize the response to each request. To achieve this, a method should be able to recognize and evaluate humor, a challenging task that has been the focus of extensive work. Some authors have applied traditional NLP techniques BIBREF1, while others deep learning models BIBREF2. Moreover, BIBREF3 follows a semantic-based approach, while BIBREF4 and BIBREF5 tackle the challenge from a cognitive and linguistic perspective respectively. To this end, we have developed two methods. The first one is based on traditional NLP techniques. Although relatively simple, it is robust, scalable, and has low latency, a fundamental property for real-time VVA systems. The other approaches combine multi-task learning BIBREF6 and self-attentional networks BIBREF7 to obtain better results, at the cost of added complexity. Both BERT BIBREF8 and an adapted transformer BIBREF7 architecture are considered. This choice of architecture was motivated by the advantages it presents over traditional RNN and CNN models, including better performance BIBREF9, faster training/inference (important for real-time systems), and better sense disambiguation BIBREF10 (an important component of computational humor BIBREF3). The proposed models use binary classifiers to perform point-wise ranking, and therefore require a labelled dataset. To generate it, we explore two implicit user-feedback labelling strategies: five-minute reuse and one-day return. Online A/B testing is used to determine if these labelling strategies are suited to optimize the desired user-satisfaction metrics, and offline data to evaluated and compared the system's performance. ### Method ::: Labelling Strategies
Generating labels for this VVA skill is challenging. Label generation through explicit user feedback is unavailable since asking users for feedback creates friction and degrade the user experience. In addition, available humor datasets such as BIBREF3, BIBREF11 only contain jokes and corresponding labels, but not the additional features we need to personalize the jokes. To overcome this difficulty, it is common to resort to implicit feedback. In particular, many VVA applications use interruptions as negative labels, the rationale being that unhappy users will stop the VVA. This strategy, however, is not suitable for our use-case since responses are short and users need to hear the entire joke to decide if it is funny. Instead, we explore two other implicit feedback labelling strategies: five-minute reuse and 1-day return. Five-minute reuse labels an instance positive if it was followed by a new joke request within five-minutes. Conversely, 1-day return marks as positive all joke requests that were followed by a new one within the following 1 to 25-hour interval. Both strategies assume that if a user returns, he is happy with the jokes. This is clearly an approximation, since a returning user might be overall satisfied with the experience, but not with all the jokes. The same is true for the implied negatives; the user might have been satisfied with some or all of the jokes. Therefore, these labels are noisy and only provide weak supervision to the models. Table TABREF2 shows an example of the labels' values for a set of joke requests from one user. ### Method ::: Features
All models have access to the same raw features, which we conceptually separate into user, item and contextual features. Examples of features in each of these categories are shown in Table TABREF4. Some of these are used directly by the models, while others need to be pre-processed. The manner in which each model consumes them is explained next. ### Method ::: NLP-based: LR-Model
To favor simplicity over accuracy, a logistic regression (LR) model is first proposed. Significant effort was put into finding expressive features. Categorical features are one-hot encoded and numerical ones are normalized. The raw Joke Text and Timestamp features require special treatment. The Joke Text is tokenized and the stop-words are removed. We can then compute computational humor features on the clean text such as sense combination BIBREF3 and ambiguity BIBREF12. In addition, since many jokes in our corpus are related to specific events (Christmas, etc), we check for keywords that relate the jokes to them. For example, if "Santa" is included, we infer it is a Christmas joke. Finally, pre-computed word embeddings with sub-word information are used to represent jokes by taking the average and maximum vectors over the token representations. Sub-word information is important when encoding jokes since many can contain out-of-vocabulary tokens. The joke's vector representations are also used to compute a summarized view of the user's past liked and disliked jokes. We consider that a user liked a joke when the assigned label is 1, an approximation given the noisy nature of the labels. The user's liked/disliked joke vectors are also combined with the candidate joke vector by taking the cosine similarity between them. For the raw Timestamp feature, we first extract simple time/date features such as month, day and isWeekend. We then compute binary features that mark if the timestamp occurred near one of the special events mentioned before. Some of these events occur the same day every year, while others change (for example, the Super Bowl). In addition, many events are country dependent. The timestamp's event features are combined with the joke's event features to allow the model to capture if an event-related joke occurs at the right time of the year. The LR classifier is trained on the processed features and one of the labels. The model's posterior probability is used to sort the candidates, which are chosen randomly from a pool of unheard jokes. Although useful (see Validation section), this model has several shortcomings. In particular, many of the used features require significant feature engineering and/or are country/language dependent, limiting the extensibility of the model. ### Method ::: Deep-Learning-based: DL-Models
To overcome the LR-model's limitations, we propose the following model (see Figure FIGREF7). In the input layer, features are separated into context, item and user features. Unlike the LR-model, time and text features do not require extensive feature engineering. Instead, simple features (day, month and year) are extracted from the timestamp. After tokenization and stop-word removal, text features are passed through a pre-trained word embeding layer, and later, input into the joke encoder block. The basis of the joke encoder is a modified transformer. Firstly, only the encoder is needed. Moreover, since studies suggest that humor is subjective and conditioned on the user's context BIBREF13, we add an additional sub-layer in the transformer encoder that performs attention over the user's features. This sub-layer, inserted between the two typical transformer sub-layers at certain depths of the network, allows the encoder to adapt the representations of the jokes to different user contexts. Thus, the same joke can be encoded differently depending on the user's features. In practice, this additional sub-layer works like the normal self-attention sub-layer, except it creates its query matrix Q from the sub-layer below, and its K and V matrices from the user features. As an alternative, we also test encoding the jokes using a pre-trained BERT model. Regardless of the used encoder, we average the token representations to obtain a global encoding of the jokes. The same encoder is used to represent the item's (the joke to rank) and the user's (liked and disliked jokes) textual features through weight sharing, and the cosine similarity between both representations are computed. The processed features are then concatenated and passed through a final block of fully connected layers that contains the output layers. Since experiments determined (see Validation section) that both labeling strategies can improve the desired business metrics, instead of optimizing for only one of them, we take a multi-task learning approach. Thus, we have two softmax outputs. Finally, we use a loss function that considers label uncertainty, class imbalance and the different labeling functions. We start from the traditional cross-entropy loss for one labelling function. We then apply uniform label smoothing BIBREF14, which converts the one-hot-encoded label vectors into smoothed label vectors towards $0.5$: with $\epsilon $ a hyper-parameter. Label smoothing provides a way of considering the uncertainty on the labels by encouraging the model to be less confident. We have also experimented with other alternatives, including specialized losses such as BIBREF15. However, they did not produce a significant increase in performance in our tests. To further model the possible uncertainty in the feedback, we apply sample weights calculated using an exponential decay function on the time difference between the current and the following training instance of the same customer: where $w_i$ is the weight of sample $i$, $t_i$ is the time difference between instances $i$ and $i+1$ for the same user, and $a,b$ are hyper-parameters such that $a>0$ and $0<b<1$. The rationale behind these weights is the following. If for example, we consider labeling function 1, and a user asks for consecutive jokes, first within 10 seconds and later within 4.9 minutes, both instances are labeled as positive. However, we hypothesize that there is a lower chance that in the second case the user requested an additional joke because he liked the first one. In addition, class weights are applied to each sample to account for the natural class imbalance of the dataset. Finally, the total loss to be optimized is the weighted sum of the losses for each of the considered labeling functions: where $w_{l}$ are manually set weights for each label and $\mathcal {L}_{l}$ are the losses corresponding to each label, which include all the weights mentioned before. ### Validation
A two-step validation was conducted for English-speaking customers. An initial A/B testing for the LR model in a production setting was performed to compare the labelling strategies. A second offline comparison of the models was conducted on historical data and a selected labelling strategy. One month of data and a subset of the customers was used (approx. eighty thousand). The sampled dataset presents a fraction of positive labels of approximately 0.5 for reuse and 0.2 for one-day return. Importantly, since this evaluation is done on a subset of users, the dataset characteristic's do not necessarily represent real production traffic. The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. As a benchmark, we also consider two additional methods: a non-personalized popularity model and one that follows BIBREF16, replacing the transformer joke encoder with a CNN network (the specialized loss and other characteristics of the DL model are kept). Hyperparameters were optimized using grid-search for the LR-Model. Due to computational constraints, random search was instead used for the DL-Model. In both cases, hyperparameters are selected to optimize the AUC-ROC on the validation set. Table TABREF11 lists some of the considered hyperparameter values and ranges for both models. The actual optimal values are sample specific. ### Validation ::: Online Results: A/B Testing
Two treatment groups are considered, one per label. Users in the control group are presented jokes at random, without repetition. Several user-satisfaction metrics such as user interruption rate, reuse of this and other VVA skills, and number of active dialogs are monitored during the tests. The relative improvement/decline of these metrics is compared between the treatments and control, and between the treatments themselves. The statistical significance is measured when determining differences between the groups. Results show that the LR-based model consistently outperforms the heuristic method for both labeling strategies, significantly improving retention, dialogs and interruptions. These results suggest that models trained using either label can improve the VVA's joke experience. ### Validation ::: Offline Results
One-day return was selected for the offline evaluation because models trained on it have a better AUC-ROC, and both labeling strategies were successful in the online validation. All results are expressed as relative change with respect to the popularity model. We start by evaluating the models using AUC-ROC. As seen in Table TABREF14, the transformer-based models, and in particular our custom architecture, outperform all other approaches. Similar conclusions can be reached regarding overall accuracy. However, given the class imbalance, accuracy is not necessarily the best metric to consider. In addition, to better understand the effect to the original transformer architecture, we present the performance of the model with and without the modified loss and special attention sub-layer (see Table TABREF14). Results suggest both modifications have a positive impact on the performance. Finally, to further evaluate the ranking capabilities of the proposed methods, we use top-1 accuracy. Additional positions in the ranking are not considered because only the top ranked joke is presented to the customer. Results show that the DL based models outperform the other systems, with a relative change in top-1 accuracy of 1.4 for DL-BERT and 0.43 for DL-T, compared with 0.14 for the LR method. Results show that the proposed methods provide different compromises between accuracy, scalability and robustness. On one hand, the relatively good performance of the LR model with engineered features provides a strong baseline both in terms of accuracy and training/inference performance, at the cost of being difficult to extend to new countries and languages. On the other hand, DL based methods give a significant accuracy gain and require no feature engineering, which facilitates the expansion of the joke experience to new markets and languages. This comes at a cost of added complexity if deployed in production. In addition, given the size of the BERT model (340M parameters), real-time inference using DL-BERT becomes problematic due to latency constraints. In this regard, the DL-T model could be a good compromise since its complexity can be adapted, and it provides good overall accuracy. ### Conclusions and Future Work
This paper describes systems to personalize a VVA's joke experience using NLP and deep-learning techniques that provide different compromises between accuracy, scalability and robustness. Implicit feedback signals are used to generate weak labels and provide supervision to the ranking models. Results on production data show that models trained on any of the considered labels present a positive real-world impact on user satisfaction, and that the deep learning approaches can potentially improve the joke skill with respect to the other considered methods. In the future, we would like to compare all methods in A/B testing, and to extend the models to other languages. Table 1: Example of labelling strategies: five-minute reuse (label 1) and 1-day return (label 2) Table 2: Examples of features within each category Figure 1: Architecture of the transformer-based model Table 3: Hyperparameter values tuned over, LR (top) and DL models (bottom) Table 4: Relative change w.r.t popularity model of AUCROC and Overall Accuracy: transformer model (DL-T), BERT model (DL-BERT), transformer without special context-aware attention (DL-T-noAtt) and without both special attention and modified loss (DL-T-basic), CNN model (DL-CNN) and LR model (LR). | five-minute reuse and one-day return |
Which terms best describe the narrator's tone?
A. authoritative and oblivious
B. manipulative and meticulous
C. congenial and self-aware
D. hostile and condescending
| Every writer must seek his own Flowery Kingdom in imagination's wide demesne, and if that search can begin and end on Earth his problem has been greatly simplified. In post-war Japan Walt Sheldon has found not only serenity, but complete freedom to write undisturbed about the things he treasures most. A one-time Air Force officer, he has turned to fantasy in his lighter moments, to bring us such brightly sparkling little gems as this. houlihan's equation by ... Walt Sheldon The tiny spaceship had been built for a journey to a star. But its small, mischievous pilots had a rendezvous with destiny—on Earth. I must admit that at first I wasn't sure I was hearing those noises. It was in a park near the nuclear propulsion center—a cool, green spot, with the leaves all telling each other to hush, be quiet, and the soft breeze stirring them up again. I had known precisely such a secluded little green sanctuary just over the hill from Mr. Riordan's farm when I was a boy. Now it was a place I came to when I had a problem to thrash out. That morning I had been trying to work out an equation to give the coefficient of discharge for the matter in combustion. You may call it gas, if you wish, for we treated it like gas at the center for convenience—as it came from the rocket tubes in our engine. Without this coefficient to give us control, we would have lacked a workable equation when we set about putting the first moon rocket around those extraordinary engines of ours, which were still in the undeveloped blueprint stage. I see I shall have to explain this, although I had hoped to get right along with my story. When you start from scratch, matter discharged from any orifice has a velocity directly proportional to the square root of the pressure-head driving it. But when you actually put things together, contractions or expansions in the gas, surface roughness and other factors make the velocity a bit smaller. At the terrible discharge speed of nuclear explosion—which is what the drive amounts to despite the fact that it is simply water in which nuclear salts have been previously dissolved—this small factor makes quite a difference. I had to figure everything into it—diameter of the nozzle, sharpness of the edge, the velocity of approach to the point of discharge, atomic weight and structure— Oh, there is so much of this that if you're not a nuclear engineer yourself it's certain to weary you. Perhaps you had better take my word for it that without this equation—correctly stated, mind you—mankind would be well advised not to make a first trip to the moon. And all this talk of coefficients and equations sits strangely, you might say, upon the tongue of a man named Kevin Francis Houlihan. But I am, after all, a scientist. If I had not been a specialist in my field I would hardly have found myself engaged in vital research at the center. Anyway, I heard these little noises in the park. They sounded like small working sounds, blending in eerily mysterious fashion with a chorus of small voices. I thought at first it might be children at play, but then at the time I was a bit absent-minded. I tiptoed to the edge of the trees, not wanting to deprive any small scalawags of their pleasure, and peered out between the branches. And what do you suppose I saw? Not children, but a group of little people, hard at work. There was a leader, an older one with a crank face. He was beating the air with his arms and piping: "Over here, now! All right, bring those electrical connections over here—and see you're not slow as treacle about it!" There were perhaps fifty of the little people. I was more than startled by it, too. I had not seen little people in—oh, close to thirty years. I had seen them first as a boy of eight, and then, very briefly again, on my tenth birthday. And I had become convinced they could never be seen here in America. I had never seen them so busy, either. They were building something in the middle of the glade. It was long and shiny and upright and a little over five feet in height. "Come along now, people!" said this crotchety one, looking straight at me. "Stop starin' and get to work! You'll not be needin' to mind that man standin' there! You know he can't see nor hear us!" Oh, it was good to hear the rich old tongue again. I smiled, and the foreman of the leprechauns—if that's what he was—saw me smile and became stiff and alert for a moment, as though suspecting that perhaps I actually could see him. Then he shrugged and turned away, clearly deeming such a thing impossible. I said, "Just a minute, friend, and I'll beg your pardon. It so happens I can see you." He whirled to face me again, staring open-mouthed. Then he said, "What? What's that, now?" "I can see you," I said. "Ohhh!" he said and put his palms to his cheekbones. "Saints be with us! He's a believer! Run everybody—run for your lives!" And they all began running, in as many directions as there were little souls. They began to scurry behind the trees and bushes, and a sloping embankment nearby. "No, wait!" I said. "Don't go away! I'll not be hurting you!" They continued to scurry. I knew what it was they feared. "I don't intend catching one of you!" I said. "Come back, you daft little creatures!" But the glade was silent, and they had all disappeared. They thought I wanted their crock of gold, of course. I'd be entitled to it if I could catch one and keep him. Or so the legends affirmed, though I've wondered often about the truth of them. But I was after no gold. I only wanted to hear the music of an Irish tongue. I was lonely here in America, even if I had latched on to a fine job of work for almost shamefully generous pay. You see, in a place as full of science as the nuclear propulsion center there is not much time for the old things. I very much wanted to talk to the little people. I walked over to the center of the glade where the curious shiny object was standing. It was as smooth as glass and shaped like a huge cigar. There were a pair of triangular fins down at the bottom, and stubby wings amidships. Of course it was a spaceship, or a miniature replica of one. I looked at it more closely. Everything seemed almost miraculously complete and workable. I shook my head in wonder, then stepped back from the spaceship and looked about the glade. I knew they were all hiding nearby, watching me apprehensively. I lifted my head to them. "Listen to me now, little people!" I called out. "My name's Houlihan of the Roscommon Houlihans. I am descended from King Niall himself—or so at least my father used to say! Come on out now, and pass the time o' day!" Then I waited, but they didn't answer. The little people always had been shy. Yet without reaching a decision in so many words I knew suddenly that I had to talk to them. I'd come to the glen to work out a knotty problem, and I was up against a blank wall. Simply because I was so lonely that my mind had become clogged. I knew that if I could just once hear the old tongue again, and talk about the old things, I might be able to think the problem through to a satisfactory conclusion. So I stepped back to the tiny spaceship, and this time I struck it a resounding blow with my fist. "Hear me now, little people! If you don't show yourselves and come out and talk to me, I'll wreck this spaceship from stem to stern!" I heard only the leaves rustling softly. "Do you understand? I'll give you until I count three to make an appearance! One!" The glade remained deathly silent. "Two!" I thought I heard a stirring somewhere, as if a small, brittle twig had snapped in the underbrush. " Three! " And with that the little people suddenly appeared. The leader—he seemed more wizened and bent than before—approached me slowly and warily as I stood there. The others all followed at a safe distance. I smiled to reassure them and then waved my arm in a friendly gesture of greeting. "Good morning," I said. "Good morning," the foreman said with some caution. "My name is Keech." "And mine's Houlihan, as I've told you. Are you convinced now that I have no intention of doing you any injury?" "Mr. Houlihan," said Keech, drawing a kind of peppered dignity up about himself, "in such matters I am never fully convinced. After living for many centuries I am all too acutely aware of the perversity of human nature." "Yes," I said. "Well, as you will quickly see, all I want to do is talk." I nodded as I spoke, and sat down cross-legged upon the grass. "Any Irishman wants to talk, Mr. Houlihan." "And often that's all he wants," I said. "Sit down with me now, and stop staring as if I were a snake returned to the Island." He shook his head and remained standing. "Have your say, Mr. Houlihan. And afterward we'll appreciate it if you'll go away and leave us to our work." "Well, now, your work," I said, and glanced at the spaceship. "That's exactly what's got me curious." The others had edged in a bit now and were standing in a circle, intently staring at me. I took out my pipe. "Why," I asked, "would a group of little people be building a spaceship here in America—out in this lonely place?" Keech stared back without much expression, and said, "I've been wondering how you guessed it was a spaceship. I was surprised enough when you told me you could see us but not overwhelmingly so. I've run into believers before who could see the little people. It happens every so often, though not as frequently as it did a century ago. But knowing a spaceship at first glance! Well, I must confess that does astonish me." "And why wouldn't I know a spaceship when I see one?" I said. "It just so happens I'm a doctor of science." "A doctor of science, now," said Keech. "Invited by the American government to work on the first moon rocket here at the nuclear propulsion center. Since it's no secret I can advise you of it." "A scientist, is it," said Keech. "Well, now, that's very interesting." "I'll make no apologies for it," I said. "Oh, there's no need for apology," said Keech. "Though in truth we prefer poets to scientists. But it has just now crossed my mind, Mr. Houlihan that you, being a scientist, might be of help to us." "How?" I asked. "Well, I might try starting at the beginning," he replied. "You might," I said. "A man usually does." Keech took out his own pipe—a clay dudeen—and looked hopeful. I gave him a pinch of tobacco from my pouch. "Well, now," he said, "first of all you're no doubt surprised to find us here in America." "I am surprised from time to time to find myself here," I said. "But continue." "We had to come here," said Keech, "to learn how to make a spaceship." "A spaceship, now," I said, unconsciously adopting some of the old manner. "Leprechauns are not really mechanically inclined," said Keech. "Their major passions are music and laughter and mischief, as anyone knows." "Myself included," I agreed. "Then why do you need a spaceship?" "Well, if I may use an old expression, we've had a feelin' lately that we're not long for this world. Or let me put it this way. We feel the world isn't long for itself." I scratched my cheek. "How would a man unravel a statement such as that?" "It's very simple. With all the super weapons you mortals have developed, there's the distinct possibility you might be blowin' us all up in the process of destroying yourselves." "There is that possibility," I said. "Well, then, as I say," said Keech, "the little people have decided to leave the planet in a spaceship. Which we're buildin' here and now. We've spied upon you and learned how to do it. Well—almost how to do it. We haven't learned yet how to control the power—" "Hold on, now," I said. "Leaving the planet, you say. And where would you be going?" "There's another committee working on that. 'Tis not our concern. I was inclined to suggest the constellation Orion, which sounds as though it has a good Irish name, but I was hooted down. Be that as it may, my own job was to go into your nuclear center, learn how to make the ship, and proceed with its construction. Naturally, we didn't understand all of your high-flyin' science, but some of our people are pretty clever at gettin' up replicas of things." "You mean you've been spying on us at the center all this time? Do you know, we often had the feeling we were being watched, but we thought it was by the Russians. There's one thing which puzzles me, though. If you've been constantly around us—and I'm still able to see the little people—why did I never see you before?" "It may be we never crossed your path. It may be you can only see us when you're thinkin' of us, and of course truly believin' in us. I don't know—'tis a thing of the mind, and not important at the moment. What's important is for us to get our first ship to workin' properly and then we'll be on our way." "You're determined to go." "Truly we are, Mr. Houlihan. Now—to business. Just during these last few minutes a certain matter has crossed my mind. That's why I'm wastin' all this time with you, sir. You say you are a scientist." "A nuclear engineer." "Well, then, it may be that you can help us—now that you know we're here." "Help you?" "The power control, Mr. Houlihan. As I understand it, 'tis necessary to know at any instant exactly how much thrust is bein' delivered through the little holes in back. And on paper it looks simple enough—the square of somethin' or other. I've got the figures jotted in a book when I need 'em. But when you get to doin' it it doesn't come out exactly as it does on paper." "You're referring to the necessity for a coefficient of discharge." "Whatever it might be named," said Keech, shrugging. "'Tis the one thing we lack. I suppose eventually you people will be gettin' around to it. But meanwhile we need it right now, if we're to make our ship move." "And you want me to help you with this?" "That is exactly what crossed my mind." I nodded and looked grave and kneaded my chin for a moment softly. "Well, now, Keech," I said finally, "why should I help you?" "Ha!" said Keech, grinning, but not with humor, "the avarice of humans! I knew it! Well, Mr. Houlihan, I'll give you reason enough. The pot o' gold, Mr. Houlihan!" "The one at the end of the rainbow?" "It's not at the end of the rainbow. That's a grandmother's tale. Nor is it actually in an earthen crock. But there's gold, all right, enough to make you rich for the rest of your life. And I'll make you a proposition." "Go ahead." "We'll not be needin' gold where we're goin'. It's yours if you show us how to make our ship work." "Well, now, that's quite an offer," I said. Keech had the goodness to be quiet while I sat and thought for a while. My pipe had gone out and I lit it again. I finally said, "Let's have a look at your ship's drive and see what we can see." "You accept the proposition then?" "Let's have a look," I said, and that was all. Well, we had a look, and then several looks, and before the morning was out we had half the spaceship apart, and were deep in argument about the whole project. It was a most fascinating session. I had often wished for a true working model at the center, but no allowance had been inserted in the budget for it. Keech brought me paper and pencil and I talked with the aid of diagrams, as engineers are wont to do. Although the pencils were small and I had to hold them between thumb and forefinger, as you would a needle, I was able to make many sensible observations and even a few innovations. I came back again the next day—and every day for the following two weeks. It rained several times, but Keech and his people made a canopy of boughs and leaves and I was comfortable enough. Every once in a while someone from the town or the center itself would pass by, and stop to watch me. But of course they wouldn't see the leprechauns or anything the leprechauns had made, not being believers. I would halt work, pass the time of day, and then, in subtle fashion, send the intruder on his way. Keech and the little people just stood by and grinned all the while. At the end of sixteen days I had the entire problem all but whipped. It is not difficult to understand why. The working model and the fact that the small people with their quick eyes and clever fingers could spot all sorts of minute shortcomings was a great help. And I was hearing the old tongue and talking of the old things every day, and truly that went far to take the clutter out of my mind. I was no longer so lonely that I couldn't think properly. On the sixteenth day I covered a piece of paper with tiny mathematical symbols and handed it to Keech. "Here is your equation," I said. "It will enable you to know your thrust at any given moment, under any circumstances, in or out of gravity, and under all conditions of friction and combustion." "Thank you, Mr. Houlihan," said Keech. All his people had gathered in a loose circle, as though attending a rite. They were all looking at me quietly. "Mr. Houlihan," said Keech, "you will not be forgotten by the leprechauns. If we ever meet again, upon another world perchance, you'll find our friendship always eager and ready." "Thank you," I said. "And now, Mr. Houlihan," said Keech, "I'll see that a quantity of gold is delivered to your rooms tonight, and so keep my part of the bargain." "I'll not be needing the gold," I said. Keech's eyebrows popped upward. "What's this now?" "I'll not be needing it," I repeated. "I don't feel it would be right to take it for a service of this sort." "Well," said Keech in surprise, and in some awe, too, "well, now, musha Lord help us! 'Tis the first time I ever heard such a speech from a mortal." He turned to his people. "We'll have three cheers now, do you hear, for Mr. Houlihan—friend of the little people as long as he shall live!" And they cheered. And little tears crept into the corners of some of their turned-up eyes. We shook hands, all of us, and I left. I walked through the park, and back to the nuclear propulsion center. It was another cool, green morning with the leaves making only soft noises as the breezes came along. It smelled exactly like a wood I had known in Roscommon. And I lit my pipe and smoked it slowly and chuckled to myself at how I had gotten the best of the little people. Surely it was not every mortal who could accomplish that. I had given them the wrong equation, of course. They would never get their spaceship to work now, and later, if they tried to spy out the right information I would take special measures to prevent it, for I had the advantage of being able to see them. As for our own rocket ship, it should be well on its way by next St. Patrick's Day. For I had indeed determined the true coefficient of discharge, which I never could have done so quickly without those sessions in the glade with Keech and his working model. It would go down in scientific literature now, I suppose, as Houlihan's Equation, and that was honor and glory enough for me. I could do without Keech's pot of gold, though it would have been pleasant to be truly rich for a change. There was no sense in cheating him out of the gold to boot, for leprechauns are most clever in matters of this sort and he would have had it back soon enough—or else made it a burden in some way. Indeed, I had done a piece of work greatly to my advantage, and also to the advantage of humankind, and when a man can do the first and include the second as a fortunate byproduct it is a most happy accident. For if I had shown the little people how to make a spaceship they would have left our world. And this world, as long as it lasts—what would it be in that event? I ask you now, wouldn't we be even more likely to blow ourselves to Kingdom Come without the little people here for us to believe in every now and then? Transcriber's Note: This etext was produced from Fantastic Universe September 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | C. congenial and self-aware |
Which medication was Mr. Carter prescribed upon his last recorded discharge that was not part of his initial medication upon admission?
Choose the correct answer from the following options:
A. Trexall
B. Toprol XL
C. Atacand
D. Singulair
E. Clarinex
| ### Patient Report 0
**Dear colleague, **
We are reporting on our mutual patient, Mr. Brian Carter, born on
04/24/1956, who was under our inpatient care from 09/28/2021 to
09/30/2021.
**Diagnosis**: ARDS in the context of a COVID-19 infection
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Current Presentation:** Mr. Carter presented to our facility on foot
on 09/28/2021 with a five-day history of slowly progressive dyspnea, dry
cough, and non-apoplectic, holocentral headache. His initial room air
saturation was 78%, which improved to 86% with 10 liters of oxygen.
Arterial blood gas analysis revealed an oxygenation disorder with a paO2
of 50 mmHg, prompting the initiation of NIV therapy, under which Mr.
Carter remained hemodynamically stable. CT imaging showed bilateral
interstitial pneumonia with COVID-typical infiltrates. Both a rapid test
in the initial care unit and one from his primary care physician were
negative for COVID-19. Therefore, we admitted Mr. Carter to our
intensive care unit for further evaluation.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
-------------------------- ------------ ---------------
Prednisone (Deltasone) 5 mg 1-0-0
Methotrexate (Trexall) 25 mg 1-0-0
Candesartan (Atacand) 4 mg 1-0-0
Quetiapine (Seroquel) 300 mg 0-0-1
Amitriptyline (Elavil) 25 mg 0-0-1
Citalopram (Celexa) 40 mg 1-0-0
Montelukast (Singulair) 10 mg 1-0-0
Desloratadine (Clarinex) 5 mg 1-0-0
**Physical Examination:**
[Neurology]{.underline}: Alert and cooperative
[Cardiovascular/Abdominal Examination]{.underline}: Severely impaired
oxygenation improved with NIV; Sinus rhythm at 80 beats per minute
[Abdomen]{.underline}: Surgical abdomen
[Renal System:]{.underline} Urination initially scant, then polyuria
Others.
**Therapy and Progression:** Upon admission, Mr. Carter was alert,
cooperative, and hemodynamically stable despite significant oxygenation
impairment. Temporary improvement was achieved with differentiated NIV
mask ventilation. On 09/30, there was a further deterioration in
oxygenation with an increase in respiratory rate and escalation of
ventilator settings, leading to the decision to intubate. A tolerable
ventilation situation was achieved with an oxygenation index of 125. Due
to radiological suspicion of atypical pneumonia, we initiated empirical
anti-infective therapy with Piperacillin/Tazobactam, Clarithromycin, and
Cotrimoxazole. Microbiological test results were pending at the time of
transfer. We also initiated mucolytic therapy with Ambroxol. The
pre-existing immunosuppressive therapy with Prednisolone was
discontinued and switched to Dexamethasone 10 mg. At the time of
transfer, Mr. Carter was hemodynamically stable with low catecholamine
doses (0.07 µg/kg/min). A central venous catheter was placed, and
enteral or parenteral nutrition had not yet been initiated. Diuresis was
sufficient after a single dose of 20 mg furosemide, with retention
parameters within the normal range. Prophylactic anticoagulation with
heparin 500 U/h was initiated.
**Status at Transfer**:
[Neurology]{.underline}: RASS -5 under Propofol and Sufentanil sedation
[Cardiovascular]{.underline}: Normal sinus rhythm, noradrenaline (NA)
0.07; Hemoglobin 12.8 g/dL
[Lungs]{.underline}: Adequate decarboxylation with borderline
oxygenation: paO2 87.6 under FiO2 0.7; PEEP 16; PEAK 27
[Abdomen]{.underline}: Soft abdomen, no nutrition initiated
[Renal System]{.underline}: Normal urine output without stimulation.
Retention values within normal range. Clear urine.
[Access]{.underline}: CVC placed on 09/30, left radial artery catheter
placed on 09/30.
### Patient Report 1
**Dear colleague, **
We are reporting on our patient, Mr. Brian Carter, born on 04/24/1956,
who was under our inpatient care from 09/30/2021 to 10/13/2021.
**Diagnosis:** ARDS due to COVID-19 pneumonia with superinfection by
Aspergillus fumigatus
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** The patient was admitted from the emergency
department, presenting with dyspnea and confirmed SARS-CoV-2 infection.
After initial management in the intensive care unit, a non-invasive
ventilation (NIV) trial was attempted, followed by successful
intubation. The patient was then transferred to the Intensive Care Unit.
**Therapy and Progression:** Upon admission, the patient was sedated,
intubated, and controlled on mechanical ventilation with mild
catecholamine support. Due to oxygenation impairment despite
lung-protective ventilation and inhaled supportive NO therapy,
conservative ARDS therapy was initiated, including positioning therapy
(a total of 9 prone positions). After stabilization of gas exchange with
positioning therapy, sedation and ventilation weaning were performed.
Gas exchange and oxygenation are currently stable under BIPAP
ventilation (PiP 25 mbar, PEEP 13 mbar, breathing rate 18/min). The
patient, under reduced analgosedation with Sufentanil and Clonidine,
exhibits a sufficient awakening response, is adequately responsive, and
follows commands with reduced muscle strength.
The home medication of Methotrexate and Prednisolone for uveitis was
discontinued upon admission. The patient received Dexamethasone for 10
days initially and, starting from 11/10, prednisolone with prophylactic
Cotrimoxazole therapy.
Upon detection of Aspergillus in tracheobronchial secretions, antifungal
therapy with Voriconazole and Caspofungin (until target Voriconazole
levels were achieved) was initiated. The initially started antimicrobial
therapy with Piperacillin + Tazobactam was escalated to Meropenem on
10/05/2021 due to worsening infection parameters and progression of
infiltrates on X-ray. Infection parameters have been fluctuating, and
fever is not currently observed. Diuresis is qualitatively and
quantitatively within normal limits, and retention parameters are within
the normal range.
Anticoagulation was administered in therapeutic doses using
low-molecular-weight heparin.
Enteral nutrition is provided through a nasogastric tube, and the
patient has regular bowel movements.
**Physical Examination:**
[Neurology]{.underline}: Analgosedated, GCS 10, pupils equal and
reactive, limb movement prompt, follows commands with reduced strength
[Lungs]{.underline}: Intubated with BIPAP 25/13, FiO2 0.4
[Cardiovascular]{.underline}: Normal sinus rhythm, noradrenaline 0.05
[Abdomen]{.underline}: Obese, no tenderness, abdomen soft, oral intake
via a nasogastric tube, regular bowel movements
[Diuresis]{.underline}: Normal urine output, retention parameters within
normal limits
Skin/Wounds: Some pressure sores from positioning (see nursing handover
sheet)
[Mobilization]{.underline}: Not conducted
**Imaging:**
**Bedside Chest X-ray from 10/11/2021:**
[Clinical information, question, justifying indication:]{.underline}
COVID pneumonia, insertion of a central venous catheter (CVC)
**Assessment**: Comparison with 10/05/21: Endotracheal tube identical,
gastric tube seen extending well into the abdomen, left CVC currently
positioned in the brachiocephalic vein region, right CVC via internal
jugular vein with tip in superior vena cava. No pneumothorax, no
effusions, increasing consolidation of infiltrates in the right lower
lobe and retrocardially on the left without significant cavitation as
far as can be assessed. Left heart without significant central
congestion.
### Patient Report 2
**Dear colleague, **
We are reporting on our patient, Mr. Brian Carter, born on 04/24/1956,
who was under intensive care treatment from 09/28/2021 to 10/12/2021 and
in our intensive care unit from 10/13/2021 to 10/21/2021.
**Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8
- Intubation on 09/30, initial extubation on 10/13
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** The initial hospital admission of the patient was
through our emergency department due to severe respiratory insufficiency
in the context of COVID-19 pneumonia.
**Current Medication:**
**Medication** **Dosage** **Frequency**
-------------------------- ------------ ---------------
Prednisone (Deltasone) 5 mg 1-0-0
Methotrexate (Trexall) 25 mg 1-0-0
Candesartan (Atacand) 4 mg 1-0-0
Quetiapine (Seroquel) 300 mg 0-0-1
Amitriptyline (Elavil) 25 mg 0-0-1
Citalopram (Celexa) 40 mg 1-0-0
Montelukast (Singulair) 10 mg 1-0-0
Desloratadine (Clarinex) 5 mg 1-0-0
**Physical Examination:**
[Skin/Mucous Membranes]{.underline}: Warm, Skin Perfusion: Good
perfusion, Edema: Lower legs
[Head]{.underline}: Mobility: Active and passive free movement, Tongue:
Moist
[Thorax]{.underline}: Auscultation: Clear bilaterally
[Abdomen]{.underline}: Soft, no guarding, Bowel Sounds: Sparse
peristalsis, Tenderness: None
[Neurology]{.underline}: Pupil Shape: Round, Pupil Size: Moderate, Light
Reaction: Both sides +++
Alertness: Awake
**ECG on admission:** Tachycardic sinus rhythm with 107/min, Left type,
P-wave normally configured, normal PQ interval, no pathological Q as in
Pardee-Q, narrow QRS, regular R progression, R/S transition in V3/4, no
S persistence, no ST segment changes, no discordant T-negatives.
**Therapy and Progression:** Despite intensified oxygen therapy with
nasal high-flow and mask CPAP, adequate oxygenation could not be
achieved, and the patient was intubated on 09/29/21. Leading oxygenation
impairment led to lung-protective ventilation with inhaled supportive NO
therapy and conservative ARDS therapy, including positioning therapy (a
total of 9 prone positions, 16 hours each, from 09/29/21 to 10/8/21).
Due to elevated procalcitonin, the patient received empirical antibiotic
treatment with Piperacillin/Tazobactam starting from 10/2/21, which was
escalated to Meropenem on 10/5/21 and continued until 10/14/21.
After the detection of Aspergillus in tracheobronchial secretions and
BAL, the patient received Voriconazole since 10/7/2021 (treatment
duration formally 4-6 weeks). Most recently, the level was
subtherapeutic, so the dose was adjusted to 2 x 400 mg daily.
The immunosuppressive therapy with Methotrexate and Prednisolone for
rheumatoid arthritis was switched to Dexamethasone (09/29 to 10/8) and,
since 10/09, Prednisolone monotherapy. After controlling the fungal
infection, a rheumatology re-consultation was planned. Furthermore,
subtherapeutic anticoagulation with Fraxiparine was initiated for the
prevention of thrombotic complications in the context of COVID-19.
Under this treatment regimen, gas exchange continuously improved, and on
10/12/21, the patient was transferred with low catecholamine
requirements for ventilation and sedation weaning. Mr. Carter was
extubated on 12/13/21 and now maintains good oxygenation with less than
3L oxygen via nasal cannula. Delirium symptoms after extubation
completely regressed within a few days.
Severe dysphagia was observed after invasive ventilation, leading to a
speech therapy consultation. Oral feeding is currently not possible, so
Mr. Carter is receiving parenteral nutrition. As a result, there was a
paravasate in the upper right extremity with painful erythema. Adequate
pain control was achieved with local cooling and Piritramide as needed.
Due to continued dietary restrictions, a central venous catheter was
placed on 10/20/2021 for parenteral nutrition.
We request continued speech therapy treatment.
On 10/21/21, Staphylococcus aureus was detected in blood culture, so we
initiated the administration of Flucloxacillin. The MRSA rapid test was
negative.
We are transferring Mr. Carter on 10/21/21 in stable condition, awake,
and appropriately responsive for further treatment. We appreciate the
transfer of our patient and are available for any further questions.
**Current Recommendations:**
- Continuation of antifungal therapy for a total of at least 4-6 weeks
- Voriconazole level measurement
- Speech therapy consultation
- Rheumatology re-consultation
- Follow-up blood cultures upon detection of Staph. aureus
### Patient Report 3
**Dear colleague, **
We are reporting on Mr. Brian Carter, born on 04/24/1956, who was under
our inpatient care from 10/21/2021 to 11/08/2021.
**Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8 and NO therapy
- Intubation on 09/30, initial extubation on 10/13
- Reporting to the health department by the referring physician
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Current Presentation:** Transfer for continuation of antimicrobial
therapy for MSSA bacteremia. Transesophageal echocardiogram planned for
tomorrow. Cleared for full diet by speech therapy today. Patient
mobilized to standing position for the first time today. Overall,
mobility is significantly limited, but the patient can mobilize to the
edge of the bed independently. No pain, no fever, mild cough without
sputum. No shortness of breath. Mood is significantly depressed, but
this is a known issue. Before COVID-19, he was heavily affected by
rheumatoid arthritis.
**Medical History:** The patient was transferred to our COVID ward after
a positive SARS-CoV-2 RNA PCR test in the naso-oropharyngeal swab and
respiratory failure. On physical examination, he had a reduced general
condition. Respiratory rate was 24/min, and oxygen saturation was 97% on
4 L/min of O2 via nasal cannula. Oxygen supply of 4 L via nasal cannula
could not be reduced during the course. A chest X-ray performed on 11/21
showed increasingly loosened infiltrates in the left basal region and a
minimal effusion at the base.
A SARS-CoV-2 RNA PCR test from 11/10/2020 was negative, so Mr. Carter
was no longer in isolation.
Due to the detection of Aspergillus fumigatus in bronchoalveolar lavage,
intravenous Voriconazole therapy initiated on 10/07/2021 was continued
and was planned to be adjusted according to drug level monitoring.
Additionally, Staph. aureus was identified in a blood culture, and
Staph. epidermidis. Antibiotic therapy with Cefazolin was started on
10/22 and was to be continued for a total of 14 days after the first
negative blood culture. The central venous catheter, likely the source
of infection, was removed on 10/22, and microbiological examination of
the catheter tip indicated suspicion of Staphylococci. To rule out
endocarditis, a transesophageal echocardiogram was scheduled for 10/24.
Mr. Carter has already been informed about this intervention, and
Fraxiparine was to be paused on the evening of 10/24 and the morning of
10/27, with the patient kept fasting.
There is also a known history of rheumatoid arthritis, which was treated
on an outpatient basis with Methotrexate and Prednisolone. Due to the
current infection, Methotrexate was paused, and after consultation with
the rheumatologists, it was decided to continue with prednisolone 5mg.
After complete pulmonary recovery, a rheumatology re-consultation was
planned, and the resumption of methotrexate was considered.
Upon admission, the patient had significant dysphagia, which improved
during the course. A flexible endoscopic swallowing examination
performed on 10/24/2021 by speech therapists and phoniatrics revealed a
normal swallow reflex. Mr. Carter can now resume a regular diet.
**Physical Examination:** Weight: 83 kg, Height: 182 cm. Temperature:
36.5°C, Heart rate: 80/min, Respiratory rate: 25/min, Blood pressure:
130/80 mmHg, Oxygen saturation: 98% with 2 L/min O2
[Skin/mucous membranes:]{.underline} No edema, no skin abnormalities.
Central venous catheter exit site on the neck is unremarkable.
[Head/neck:]{.underline} Own teeth, intact mucous membranes
[Heart]{.underline}: Rhythmic, tachycardic up to 100/min, clear heart
sounds, no murmurs
[Lungs]{.underline}: Bilateral vesicular breath sounds, no adventitious
sounds
[Abdomen]{.underline}: Soft, active bowel sounds, no tenderness, no
resistance
[Lymph nodes:]{.underline} Cervical, axillary nodes not palpable
[Vessels]{.underline}: Foot pulses palpable
[Musculoskeletal:]{.underline} Muscle strength reduced due to CIP/CIM.
Can mobilize to the bedside independently
[Basic neurological examination:]{.underline} Alert, oriented, friendly
[Psychological state]{.underline}: Depressed mood
**Therapy and Progression:** The emergency presentation of Mr. Carter
was on 09/28/2021 due to severe dyspnea and respiratory insufficiency.
After direct transfer to Intensive Care Unit, despite intensified oxygen
therapy with nasal high flow and mask CPAP, adequate oxygenation could
not be achieved, leading to intubation on 10/29/21.
Lung-protective ventilation was initiated due to leading oxygenation
impairment, with inhalational supportive NO therapy and conservative
ARDS therapy, including positional changes (a total of 9 sessions of 16
hours each from 09/29/21 to 10/08/21). Due to elevated PCT levels, the
patient received empiric antibiotic therapy with
Piperacillin/Tazobactam, escalated to Meropenem. Voriconazole was
initiated on 10/07/2021 after the detection of Aspergillus in
tracheobronchial secretions and BAL (intended treatment duration 4-6
weeks).
Subtherapeutic anticoagulation with Fraxiparine was administered for the
prevention of thrombotic complications in the context of COVID-19. Under
this treatment regimen, gas exchange steadily improved, and on 10/12/21,
the patient was transferred with low catecholamine requirements for
weaning from mechanical ventilation and sedation. There, he was
extubated on 10/13/21.
After extubation, severe dysphagia was observed, and speech therapy was
consulted. Oral diet is currently not possible, so Mr. Carter is on
parenteral nutrition. This led to a paravasate in the right upper
extremity with painful erythema. Adequate pain control was achieved with
local cooling and subcutaneous Piritramide, as needed.
**Lab results:**
**Parameter** **Result** **Reference Range**
--------------------------------------- ------------ ---------------------
Absolute Reticulocytes 0.01/nL \< 0.01/nL
Sodium 138 mEq/L 136-145 mEq/L
Potassium 4.3 mEq/L 3.5-4.5 mEq/L
Creatinine 0.61 mg/dL 0.70-1.20 mg/dL
Estimated GFR \>90 \>90
BUN 23 mg/dL 17-48 mg/dL
Total Bilirubin 0.18 mg/dL \< 1.20 mg/dL
C-Reactive Protein 4.1 mg/L \< 5.0 mg/L
Troponin-T 6.1 ng/L \< 14 ng/L
ALT 50 U/L \< 41 U/L
AST 40 U/L \< 50 U/L
Alkaline Phosphatase 111 U/L 40-130 U/L
Gamma-GT 200 U/L 8-61 U/L
Free Triiodothyronine (T3) 2.3 ng/L 2.00-4.40 ng/L
Free Thyroxine (T4) 14.2 ng/L 9.30-17.00 ng/L
Thyroid Stimulating Hormone (TSH) 4.1 mIU/L 0.27-4.20 mIU/L
Hemoglobin 11.6 g/dL 13.5-17.0 g/dL
Hematocrit 34.5% 39.5-50.5%
Red Blood Cells 3.7 M/µL 4.3-5.8 M/µL
White Blood Cells 9.56 K/µL 3.90-10.50 K/µL
Platelets 280 K/µL 150-370 K/µL
MCV 92.5 fL 80.0-99.0 fL
MCH 31.1 pg 27.0-33.5 pg
MCHC 33.6 g/dL 31.5-36.0 g/dL
MPV 8.9 fL 7.0-12.0 fL
RDW-CV 14.0% 11.5-15.0%
Prothrombin Time 89% 78-123%
INR 1.09 0.90-1.25
Activated Partial Thromboplastin Time 25.3 sec. 22.0-29.0 sec.
**Imaging:**
**Chest X-ray bedside on 09/29/2021:** CT scan of the chest from
9/28/2021 is available for comparison. Tracheal tube tip supracarinal.
Central venous catheter (CVC) via right internal jugular vein, tip in
the confluence of veins. Gastric tube tip infradiaphragmatic. Patchy
confluent bilateral lung infiltrates, mainly perihilar, left and right
upper and lower fields. No significant changes compared to the previous
day. Small bilateral pleural effusions. No pneumothorax in the lying
position. Left-sided heart prominence with mild stasis/capillary leak.
**Chest X-ray bedside on 10/3/2021:**
[Findings]{.underline}: Compared to 09/29/2021. Tracheal tube with tip
approximately 4 cm above the carina. Gastric tube slightly retracted,
tip located just below the diaphragm. Central venous catheter via the
right internal jugular vein, currently with the tip in the superior vena
cava. Regression and loosening of infiltrates (mainly in the lower
fields on both sides). No significant effusion or pneumothorax. No
substantial volume overload.
**Chest X-ray bedside on 10/6/2021:**
[Findings]{.underline}: Compared to the previous examination on
11/4/2020. New central venous catheter (CVC) from the left internal
jugular vein with tip in the confluence. No pneumothorax in the lying
position, no large pleural effusions. Progressive infiltrates in the
right lower field, perihilar regions on both sides. No significant
central stasis. Heart not enlarged, mediastinum slim.
**Chest X-ray bedside on 10/11/2021:**
[Findings]{.underline}: Compared to 10/5/2021. Tracheal tube and gastric
tube as before. Left CVC with the tip currently in the region of the
brachiocephalic vein, right CVC via the internal jugular vein with the
tip in the superior vena cava. No pneumothorax, no effusions, increasing
consolidation of infiltrates in the right lower field and retrocardial
left with no significant cavitation. Left-biased heart without
significant central congestion.
**Chest X-ray bedside on 10/16/2021:**
[Findings]{.underline}: Compared to previous examinations on 10/11/2021.
Heart borderline enlarged. Mediastinum, as far as can be assessed from
slightly rotated images, appears central and slim. Increasing
consolidation in the right lower lobe and left lower lobe, which is well
compatible with pneumonic infiltrates. At most, a small pleural effusion
on the left. No pneumothorax in the lying position. No signs of
significant congestion. Right jugular catheter projecting into the
superior vena cava. Tracheal tube and left jugular catheter have been
removed since the last examination.
**Chest X-ray bedside on 10/20/2021:**
[Findings]{.underline}: Compared to the examination on 10/16/2021. In
the course of known COVID pneumonia, there is an increasingly loosened
appearance of infiltrates in the left basal region. A small effusion
continues to drain basally. Otherwise, no significant changes in the
short-term follow-up. Right jugular catheter projecting into the
superior vena cava, as before.
**EKG on 10/27/2021:** Normal sinus rhythm, 86/min. Indeterminate axis.
PQ interval: 108ms QRS duration: 108ms. QTc interval: 484ms. Peripheral
low voltage. Delayed R progression up to and including V3. RS transition
in V4. No significant ST-T wave changes.
**Ultrasound Abdomen on 11/01/2021:**
[Reason for referral:]{.underline} History of COVID, Aspergillosis
[Liver]{.underline}: Vertical diameter in the midclavicular line on the
right is 120 mm.
[Biliary tract]{.underline}: Well visualized. No abnormalities in the
intrahepatic and extrahepatic bile ducts. Maximum width of the common
bile duct is 3 mm.
[Gallbladder]{.underline}: Well visualized. Normal findings.
[Pancreas]{.underline}: Maximum diameters - Head: 17 mm, Body: 12 mm,
Tail: 15 mm. Well visualized. Normal findings.
[Spleen]{.underline}: Normal size, normal homogeneous internal echo
pattern, no focal changes, hilum is free. Organ size: 120 mm x 38 mm.
[Right kidney:]{.underline} Partially assessable, as far as
recognizable, parenchymal edge is age-appropriate, smooth organ contour,
no urinary obstruction, no stones. Size: 120 mm x 45 mm, parenchymal
thickness 21 mm.
[Left kidney:]{.underline} Partially assessable, as far as recognizable,
parenchymal edge is age-appropriate, smooth organ contour, no urinary
obstruction, no stones. Size: 115 mm x 61 mm, parenchymal thickness 19
mm.
[Bladder:]{.underline} Well visualized, orthotopically located, normal
wall proportions, no pathological echo structures in the lumen, normal
organ size.
[Abdominal vessels:]{.underline} Normal findings.
[Abdominal lymph nodes:]{.underline} No evidence of enlarged lymph nodes
in the subphrenic region.
[Peritoneum]{.underline}: No free fluid.
[Color duplex sonography of the portal vein:]{.underline} Orthograde
flow, no evidence of thrombosis.
[Assessment]{.underline}: In the right lower lobe cranial-lateral
(segment VII), there is an entirely echo-free cystic structure with a
slightly lobulated contour. There is no \"double wall,\" and there are
no features suggestive of Echinococcus. This is most likely a congenital
cyst. The overall structure, architecture, and texture of the liver are
normal, with no other focal abnormalities. In the rest of the abdomen,
there are no other pathological findings.
**Cardiology Consultation on 10/29/2021:**
**Medical History:** The patient reports thoracic complaints following
the intensive care unit stay post-COVID. These pains have been noticed
with mild exertion and are described as retrosternal with radiation to
the left chest. This last occurred on Sunday afternoon, lasting for
approximately 1 hour and then spontaneously resolving at rest. This pain
cannot be induced by a change in position, coughing, or deep
inspiration. Dyspnea is continuously present, and the patient still
requires oxygen. Dyspnea worsens when lying down.
**Cardiovascular risk factors**: Mildly elevated blood pressure
(hypertension) since May of this year, managed with half a tablet
according to self-measurements (averaging 120/80 mmHg, rarely in the
130s). Lipid profile checked by the general practitioner earlier this
year, presumably with good results. No known diabetes. Former smoker
until 2007, but it is difficult to estimate the pack-years, as smoking
occurred on occasions and during stressful times, less than 15
pack-years. No family history of cardiovascular diseases.
Uveitis/scleritis/episcleritis managed with 10mg MTX per week and 5 mg p
Prednisolone orally daily, well-controlled without recurrence.
**Physical Examination**: Lungs with moist rales bilaterally. Cardiac
examination with faint heart sounds. Regular heart rate of 80/min. No
pericardial rub. Pale-gray skin color. Respiratory rate of 15/min while
sitting. Radial pulses palpable bilaterally. Groin pulses not examined.
Allen\'s test operable on the right, borderline on the left.
**ECG**: tachycardic sinus rhythm with a heart rate of 109/min, left
axis deviation, regular R-wave progression in chest leads, mild
S-persistence in V6, no significant ST-T wave changes.
**Transthoracic and transesophageal echocardiography on 11/27/2020**:
[Kinetics]{.underline}: Hypokinesia of the lateral and anterior walls,
otherwise normokinetic and synergistic. Systolic function (right
ventricle): TAPSE 18 mm (\> 16 mm), RV-S\' 17.6 cm/s (\> 10 cm/s).
[Valves]{.underline}: Mitral valve - Delicate leaflets, good opening
motion, no significant insufficiency. Lambl\'s excrescences on the
atrial side. Small fluttering structure at the subvalvular apparatus,
compatible with chordae tendineae. Aortic valve - Tricuspid, delicate
valve. Functionally intact (AV Vmax 1.0 m/s). Tricuspid valve -
Morphologically normal. Mild insufficiency. TR Vmax 1.9 m/s, sPAP 15
mmHg + CVP. Pulmonic valve - Morphologically and functionally normal.
[Other Findings:]{.underline} No pericardial effusion. Small Persistent
Foramen Ovale. Left atrial appendage free of intracavitary thrombi at
60°/90°/150°. Thoracic aorta with smooth-walled plaques, no dissections
or thrombi.
[Assessment]{.underline}: No structures suggestive of endocarditis. No
relevant valvular abnormalities. Incidentally, there is a moderately
reduced LVEF with wall motion abnormalities in the RIVA (right
ventricular anterior) region. We request a cardiology consultation and
further diagnostics.
**Phoniatric Consultation on 10/24/2021:**
[Medical History:]{.underline} Patient with a history of COVID
pneumonia, twice tested negative. Currently, the patient has Aspergillus
and pneumonia. Previously, the patient was in the ICU and intubated for
two weeks due to COVID. Following speech therapy for dysphagia, a
flexible endoscopic evaluation of swallowing (FEES) is requested.
[Findings]{.underline}: FEES reveals a normal configuration of the
larynx with good mobility of the tongue and lips. Normal gross mobility
of the vocal cords during phonation and respiration transitions. Full
glottic closure appears complete. Flexible transnasal swallow evaluation
(FEES) with blue dye: Sufficient oral bolus control for liquids, purees,
and solids. No drooling or leakage. Swallow reflex present. Voluntary
initiation of the swallow act is possible. Side-by-side swallowing of
test substances over the valleculae without evidence of
pre-/intra-/post-deglutitive penetration or aspiration for all test
consistencies. Rosenbeck\'s Penetration-Aspiration Scale score: 1
(Minimal retention in the valleculae with puree, which can be completely
cleared by swallowing). Normal sensitivity, strong cough reflex. No
nasal regurgitation.
[Assessment]{.underline}: Normal swallowing function.
[Current Recommendations:]{.underline} Able to consume regular diet and
thin liquids, as well as medications with water.
**Therapy and Progression:** The patient was admitted for further
treatment. Upon admission, the patient was in a reduced general
condition with significant mobility limitations.
Staphylococcus aureus was detected in a blood culture, leading to a
transesophageal echocardiogram (TEE) on 11/26/2020. No vegetations were
found, but a moderate hypokinesia of the left ventricle in the RIVA area
was observed. Cardiac enzymes were within normal limits. This was
interpreted as post-COVID myocarditis, differential diagnosis myocardial
injury in severe ARDS, coronary artery disease, or mixed picture.
In consultation with the cardiology colleagues, a cautious heart failure
medication regimen with beta-blockers and ACE inhibitors was initiated.
We recommend an elective coronary angiography in the future. Currently,
the patient was symptom-free with low cardiac markers and a normal ECG,
so acute diagnostic procedures were not indicated.
The antibiotic therapy with Cefazolin was continued until 11/05/2021
(last sterile blood cultures from 10/24/2021). Staphylococcus
epidermidis detected in the blood culture on 10/20/21 and at the tip of
the central venous catheter on 10/22/21 were considered
catheter-associated. The catheter was immediately removed. The patient
did not develop a fever during the hospital stay. Inflammatory markers
improved over time.
An abdominal ultrasound was performed due to an unclear liver lesion,
which was found to be a congenital cyst. Echinococcus serology was
negative.
In consultation with the psychiatric colleagues, Quetiapine medication
was cautiously resumed for known depression, but it had to be
discontinued later due to significant QTc prolongation. Long-term oxygen
therapy of 2 liters was indicated.
Our ophthalmology colleagues recommended the resumption of MTX therapy
given the patient\'s stable vision. We request this therapy be initiated
and an outpatient follow-up appointment in ophthalmology arranged after
the patient completes rehabilitation.
With physiotherapy, the patient achieved mobilization up to walking.
Swallowing and articulation difficulties also significantly improved.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency** **Route**
---------------------------------- --------------- ------------------------------ ------------
Metoprolol Succinate (Toprol XL) 23.8 mg 1-0-1-0 Oral
Dicloxacillin Sodium (Dynapen) 2176 mg 1-1-1-0 Oral
Voriconazole (Vfend) 200 mg 2-0-2-0 Oral
Acetaminophen (Tylenol) 500 mg As needed Oral
Ipratropium Bromide (Atrovent) 0.26 mg/2 ml 6-0-0-0 Inhalation
Albuterol Sulfate (ProAir) 1.5 mg/2.5 ml 6-0-0-0 Inhalation
Amitriptyline (Elavil) 28.3 mg 0-0-1-0 Oral
Citalopram (Celexa) 50 mg 1-0-0-0 Oral
Melatonin 2 mg 0-0-2-0 Oral
Montelukast (Singulair) 10 mg 1-0-0-0 Oral
Pantoprazole (Protonix) 45 mg 0-0-1-0 Oral
Eplerenone (Inspra) 25 mg 1-0-0-0 Oral
Ramipril (Altace) 2.5 mg 0-0-1-0 Oral
Folic Acid 5 mg 0-0-1-0 48h after MTX intake Oral
Methotrexate (Trexall) 15 mg 1-0-0-0 Once a Week Oral
### Patient Report 4
**Dear colleague, **
We thank you for referring your patient, Mr. Brian Carter, born on
04/24/1956 to our outpatient care on 02/03/2022.
**Diagnoses**: Suspected Post-Intensive-Care Syndrome with:
- Dysphagia
- ICU-acquired weakness
- Depressive mood, anxiety
**Other Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8 and NO therapy
- Intubation on 09/30, initial extubation on 10/13
- Reporting to the health department by the referring physician
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
<!-- -->
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** Mr. Carter was treated in the intensive care unit
for a total of 24 days in September and October 2021 due to COVID-19.
Following intensive care treatment, he underwent neurological
rehabilitation from 11/08/2021 to 01/18/2022, with the following
rehabilitation results: \"Mr. I. benefited well from the therapies.
Particularly, physiotherapy helped regain confidence in walking. During
treatment, breathing difficulties improved, and oxygen supplementation
was no longer necessary.\" An antidepressant therapy with Mirtazapine
was initiated for sleep disorders and mood swings, resulting in a
reduction in sleep disturbances.
**Assessment**: Since the illness, Mr. Carter reports general fatigue,
quick fatigue, and weakness, especially in the lower extremities. He is
currently not undergoing physiotherapy or any other treatments.
Regarding psychopharmacological therapy, the patient has been seeing a
psychiatrist once a month based on anamnesis. After a short exertion, he
experiences dyspnea and regularly needs to take breaks. Room air
saturation was at 94%. Physical examination revealed significant
expiratory wheezing and prolonged expiration bilaterally. Furthermore,
the patient reports cognitive impairments with marked forgetfulness and
difficulty concentrating. This is evident in the reduced results of the
MiniCog (2/3 words, normal clock, 4 points) and animal naming tests
(correct single naming of 10 animals, 3 points). Additionally, the
patient reports an exacerbation of symptoms of depression known since
2011, including sadness, fatigue, sleep disturbances, and anxiety. These
worsened during the ICU stay. The current medication includes Citalopram
40 mg and Mirtazapine 7.5 mg, which have somewhat improved previously
worsened sleep disturbances. Psychotherapy is not currently taking place
but is strongly recommended.
Dysphagia diagnosed during the intensive care unit stay has slightly
improved, allowing Mr. Carter to consume regular food again. However, he
still experiences dysphagia and coughing during each meal. An
appointment at the swallowing clinic has been scheduled by us (see
below).
**Current Recommendations:**
As swallowing difficulties persist, an appointment has been scheduled at
our local swallowing clinic. We also recommend a pulmonary evaluation.
Contact has already been made, and the colleagues from Pulmonology will
get in touch with Mr. Carter. Furthermore, due to a previously existing
depressive mood with currently exacerbated symptoms, we recommend
connecting the patient with an outpatient psychotherapist. Some
therapists have already been suggested by the patient\'s general
practitioner, and we strongly recommend further contact. A prescription
for physiotherapy has been issued for pronounced muscle weakness and
suspected ICU-acquired weakness. Further physiotherapeutic engagement
with the general practitioner\'s assistance is urgently required.
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
----------------------- ------------- ---------------------
Neutrophils 49.0 % 42.0-77.0 %
Lymphocytes 31.9 % 20.0-44.0 %
Monocytes 7.2 % 2.0-9.5 %
Basophils 0.7 % 0.0-1.8 %
Eosinophils 10.8 % 0.5-5.5 %
Immature Granulocytes 0.4 % 0.0-1.0 %
Sodium 139 mEq/L 136-145 mEq/L
Potassium 3.5 mEq/L 3.5-4.5 mEq/L
Creatinine 0.99 mg/dL 0.70-1.20 mg/dL
BUN 25 mg/dL 17-48 mg/dL
Total Bilirubin 0.45 mg/dL \< 1.20 mg/dL
CRP 3.9 mg/dL \< 5.0 mg/dL
ALT 21 U/L \< 41 U/L
AST 20 U/L \< 50 U/L
Alkaline Phosphatase 65 U/L 40-130 U/L
Gamma-GT 38 U/L 8-61 U/L
LDH 160 U/L 135-250 U/L
Lipase 20 U/L 13-60 U/L
Procalcitonin 0.03 µg/L \< 0.50 µg/L
Hemoglobin 13.2 g/dL 13.5-17.0 g/dL
Hematocrit 44.2 % 39.5-50.5 %
Red Blood Cells 5.2 M/µL 4.3-5.8 M/µL
White Blood Cells 7.62 K/µL 3.90-10.50 K/µL
Platelets 281 K/µL 150-370 K/µL | Toprol XL |
According to the reviewer of "Boys Don't Cry," Brandon Teena feels more connected to their true identity by engaging in all of the following acts EXCEPT:
A. Confiding in their family
B. Getting dirty
C. Flirting with women
D. Drinking in a bar
| Boys Do Bleed Fight Club is silly stuff, sensationalism that mistakes itself for satire, but it's also a brash and transporting piece of moviemaking, like Raging Bull on acid. The film opens with--literally--a surge of adrenalin, which travels through the bloodstream and into the brain of its protagonist, Jack (Edward Norton), who's viewed, as the camera pulls out of his insides, with a gun stuck in his mouth. How'd he get into this pickle? He's going to tell you, breezily, and the director, David Fincher, is going to illustrate his narrative--violently. Fincher ( Seven , 1995; The Game , 1997) is out to bombard you with so much feverish imagery that you have no choice but to succumb to the movie's reeling, punch-drunk worldview. By the end, you might feel as if you, too, have a mouthful of blood. Not to mention a hole in your head. Fight Club careers from one resonant satirical idea to the next without quite deciding whether its characters are full of crap or are Gen X prophets. It always gives you a rush, though. At first, it goofs on the absurd feminization of an absurdly macho culture. An increasingly desperate insomniac, Jack finds relief (and release) only at meetings for the terminally ill. At a testicular cancer group, he's enfolded in the ample arms of Bob (the singer Meat Loaf Aday), a former bodybuilder who ruined his health with steroids and now has "bitch tits." Jack and Bob subscribe to a new form of male bonding: They cling to each other and sob. But Jack's idyll is rudely disrupted by--wouldn't you know it?--a woman. A dark-eyed, sepulchral head case named Marla Singer (Helena Bonham Carter) begins showing up at all the same disparate meetings for essentially the same voyeuristic ends, and the presence of this "tourist" makes it impossible for Jack to emote. Jack finds another outlet, though. On a plane, he meets Tyler Durden (Brad Pitt), a cryptic hipster with a penchant for subversive acts both large (he makes high-priced soaps from liposuctioned human fat) and small (he splices frames from porn flicks into kiddie movies). When Jack's apartment mysteriously explodes--along with his carefully chosen IKEA furniture--he moves into Tyler's squalid warehouse and helps to found a new religion: Fight Club, in which young males gather after hours in the basement of a nightclub to pound one another (and be pounded) to a bloody pulp. That last parenthesis isn't so parenthetical. In some ways, it's the longing to be beaten into oblivion that's the strongest. "Self-improvement," explains Tyler, "is masturbation"; self-destruction is the new way. Tyler's manifesto calls for an end to consumerism ("Things you own end up owning you"), and since society is going down ("Martha Stewart is polishing brass on the Titanic "), the only creative outlet left is annihilation. "It's only after we've lost everything that we're free to do anything," he says. Fincher and his screenwriter, Jim Uhls, seem to think they've broken new ground in Fight Club , that their metaphor for our discontents hits harder than anyone else's. Certainly it produces more bloody splatter. But 20 years ago, the same impulse was called punk and, as Greil Marcus documents in Lipstick Traces , it was other things before that. Yes, the mixture of Johnny Rotten, Jake La Motta, and Jesus is unique; and the Faludi-esque emasculation themes are more explicit. But there's something deeply movie-ish about the whole conceit, as if the novelist and director were weaned on Martin Scorsese pictures and never stopped dreaming of recapturing that first masochistic rush. The novel, the first by Chuck Palahniuk (the surname sounds like Eskimo for "palooka"--which somehow fits), walks a line between the straight and ironic--it isn't always clear if its glib sociological pronouncements are meant to be taken straight or as the ravings of a delusional mama's boy. But onscreen, when Pitt announces to the assembled fighters that they are the "middle children of history" with "no purpose and no place"--emasculated on one hand by the lack of a unifying crisis (a world war or depression) and on the other by lack of material wealth as promised by television--he seems meant to be intoning gospel. "We are a generation of men raised by women," Tyler announces, and adds, "If our fathers bail, what does that tell you about God?" (I give up: What?) F ight Club could use a few different perspectives: a woman's, obviously, but also an African-American's--someone who'd have a different take on the "healing" properties of violence. It's also unclear just what has emasculated Jack: Is it that he's a materialist or that the materials themselves (i.e., IKEA's lacquered particle boards) don't measure up to his fantasies of opulence? Is he motivated by spiritual hunger or envy? Tyler's subsequent idea of confining his group's mayhem to franchise coffee bars and corporate-subsidized art is a witty one--it's like a parody of neo-Nazism as re-enacted by yuppies. It might have been a howl if performed by, say, the troupe of artsy German nihilists in Joel and Ethan Coen's The Big Lebowski (1998). Somehow Brad Pitt doesn't have the same piquancy. Actually, Pitt isn't as terrible as usual: He's playing not a character but a conceit, and he can bask in his movie-idol arrogance, which seems to be the most authentic emotion he has. But the film belongs to Norton. As a ferocious skinhead in last year's American History X , Norton was taut and ropy, his long torso curled into a sneer; here, he's skinny and wilting, a quivering pansy. Even when he fights he doesn't transform--he's a raging wimp. The performance is marvelous, and it makes poetic sense in light of the movie's climactic twist. But that twist will annoy more people than it will delight, if only because it shifts the drama from the realm of the sociological to that of the psychoanalytic. The finale, scored with the Pixies' great "Where Is My Mind?" comes off facetiously--as if Fincher is throwing the movie away. Until then, however, he has done a fabulous job of keeping it spinning. The most thrilling thing about Fight Club isn't what it says but how Uhls and Fincher pull you into its narrator's head and simulate his adrenalin rushes. A veteran of rock videos, Fincher is one of those filmmakers who helps make the case that MTV--along with digital editing--has transformed cinema for better as well as worse. The syntax has become more intricate. Voice-over narration, once considered uncinematic, is back in style, along with novelistic asides, digressions, fantasies, and flashbacks. To make a point, you can jazzily interject anything--even, as in Three Kings , a shot of a bullet slicing through internal organs. Films like Fight Club might not gel, but they have a breathless, free-associational quality that points to new possibilities in storytelling. Or maybe old possibilities: The language of movies hasn't seemed this unfettered since the pre-sound days of Sergei Eisenstein and Abel Gance. An actress named Hilary Swank gives one of the most rapturous performances I've ever seen as the cross-dressing Brandon Teena (a k a Teena Brandon) in Kimberly Peirce's stark and astonishingly beautiful debut feature, Boys Don't Cry . The movie opens with Teena being shorn of her hated female tresses and becoming "Brandon," who swaggers around in tight jeans and leather jackets. The joy is in watching the actor transform, and I don't just mean Swank: I mean Teena Brandon playing Brandon Teena--the role she has been longing for her whole life. In a redneck Nebraska bar, Brandon throws back a shot of whiskey and the gesture--a macho cliché--becomes an act of self-discovery. Every gesture does. "You're gonna have a shiner in the morning," someone tells Brandon after a barroom brawl, and he takes the news with a glee that's almost mystical: "I am????? Oh, shit!!!" he cries, grinning. That might be my favorite moment in the picture, because Swank's ecstatic expression carries us through the next hour, as Brandon acts out his urban-cowboy fantasies--"surfing" from the bumper of a pickup truck, rolling in the mud, and straddling a barstool with one hand on a brewski and the other on the shoulder of a gorgeous babe. That the people with whom Brandon feels most at home would kill him if they knew his true gender is the movie's most tragic irony--and the one that lifts it out of the realm of gay-martyr hagiography and into something more complex and irreducible: a meditation on the irrelevance of gender. Peirce's triumph is to make these scenes at once exuberant (occasionally hilarious) and foreboding, so that all the seeds of Brandon's killing are right there on the screen. John (Peter Sarsgaard), one of his future rapists and murderers, calls him "little buddy" and seems almost attracted to him; Sarsgaard's performance is a finely chiseled study of how unresolved emotion can suddenly resolve itself into violence. Though harrowing, the second half of Boys Don't Cry isn't as great as the first. The early scenes evoke elation and dread simultaneously, the later ones just dread; and the last half-hour is unrelieved torture. What keeps the movie tantalizing is Chloë Sevigny's Lana, who might or might not know that Brandon is a girl but who's entranced by him anyway. With her lank hair, hooded eyes, and air of sleepy sensuality, Sevigny--maybe even more than Swank--embodies the mystery of sex that's at the core of Boys Don't Cry . Everything she does is deliberate, ironic, slightly unreadable--and unyielding. She's could be saying, "I'm in this world but not of it. ... You'd never dream what's underneath." I n brief: If a friend tells you you'll love Happy Texas , rethink the friendship. This clunky mistaken-identity comedy about escaped cons who impersonate gay pageant directors doesn't even make sense on its own low farcical terms; it's mostly one lame homo joke after another. The only bright spot is Steve Zahn, who could be the offspring of Michael J. Fox and Crispin Glover if they'd mated on the set of Back to the Future (1985). It's hard to make a serious case for Lawrence Kasdan's Mumford , which has apparently flopped but which you can still catch at second- and third-tier theaters. It looks peculiar--a Norman Rockwell painting with noir shadows. And its tale of a small town healed by a depressive (Loren Dean) posing as a psychologist is full of doddering misconceptions about psychotherapy. I almost don't know why I loved it, but the relaxed pacing and the witty turns by Martin Short, Ted Danson, David Paymer, and Mary McDonnell surely helped. I can't decide if the weirdly affectless Dean is inspired or inept, but my indecision suggests why he works in the role. There's no doubt, however, about his even more depressive love object, Hope Davis, who posseses the cinema's most expressive honking-nasal voice and who slumps through the movie like the world's most lyrical anti-ballerina. Even her puffy cheeks are eloquent: They made me think of Mumford as the home of the psychological mumps. | A. Confiding in their family |
Which statement best describes the purpose of this text?
A. To propose potential pathways that AI could take to eliminate social and environmental problems in the near future
B. To explain how industries are approaching collaboration and making decisions in AI with regard to social responses
C. To demonstrate how humans are taking advantages of AI-related opportunities while dodging the risks
D. To make an argument in support of more checks and balances within the institution of AI development
| AI: what's the worst that could happen? The Centre for the Future of Intelligence is seeking to investigate the implications of artificial intelligence for humanity, and make sure humans take advantage of the opportunities while dodging the risks. It launched at the University of Cambridge last October, and is a collaboration between four universities and colleges – Cambridge, Oxford, Imperial and Berkeley – backed with a 10-year, £10m grant from the Leverhulme Trust. Because no single discipline is ideally suited to this task, the centre emphasises the importance of interdisciplinary knowledge-sharing and collaboration. It is bringing together a diverse community of some of the world's best researchers, philosophers, psychologists, lawyers and computer scientists. Executive director of the centre is Stephen Cave, a writer, philosopher and former diplomat. Harry Armstrong, head of futures at Nesta, which publishes The Long + Short, spoke with Cave about the impact of AI. Their conversation has been edited. Harry Armstrong: Do you see the interdisciplinary nature of the centre as one of its key values and one of the key impacts you hope it will have on the field? Stephen Cave: Thinking about the impact of AI is not something that any one discipline owns or does in any very systematic way. So if academia is going to rise to the challenge and provide thought leadership on this hugely important issue, then we’re going to need to do it by breaking down current disciplinary boundaries and bringing people with very different expertise together. That means bringing together the technologists and the experts at developing these algorithms together with social scientists, philosophers, legal scholars and so forth. I think there are many areas of science where more interdisciplinary engagement would be valuable. Biotech’s another example. In that sense AI isn’t unique, but I think because thinking about AI is still in very early stages, we have an opportunity to shape the way in which we think about it, and build that community. We want to create a space where many different disciplines can come together and develop a shared language, learn from each other’s approaches, and hopefully very quickly move to be able to actually develop new ideas, new conclusions, together. But the first step is learning how to talk to each other. At a recent talk, Naomi Klein said that addressing the challenge of climate change could not have come at a worse time. The current dominant political and economic ideologies, along with growing isolationist sentiment, runs contrary to the bipartisan, collaborative approaches needed to solve global issues like climate change. Do you see the same issues hampering a global effort to respond to the challenges AI raises? Climate change suffers from the problem that the costs are not incurred in any direct way by the industrialists who own the technology and are profiting from it. With AI, that has been the case so far; although not on the same scale. There has been disruption but so far, compared to industrialisation, the impact has been fairly small. That will probably change. AI companies, and in particular the big tech companies, are very concerned that this won't go like climate change, but rather it will go like GMOs: that people will have a gut reaction to this technology as soon as the first great swathe of job losses take hold. People speculate that 50m jobs could be lost in the US if trucking is automated, which is conceivable within 10 years. You could imagine a populist US government therefore simply banning driverless cars. So I think there is anxiety in the tech industry that there could be a serious reaction against this technology at any point. And so my impression is that there is a feeling within these companies that these ethical and social implications need to be taken very seriously, now. And that a broad buy-in by society into some kind of vision of the future in which this technology plays a role is required, if a dangerous – or to them dangerous – counteraction is to be avoided. My personal experience working with these tech companies is that they are concerned for their businesses and genuinely want to do the right thing. Of course there are intellectual challenges and there is money to be made, but equally they are people who don't think when they get up in the morning that they're going to put people out of jobs or bring about the downfall of humanity. As the industry matures it's developing a sense of responsibility. So I think we've got a real opportunity, despite the general climate, and in some ways because of it. There's a great opportunity to bring industry on board to make sure the technology is developed in the right way. One of the dominant narratives around not only AI but technology and automation more generally is that we, as humans, are at the mercy of technological progress. If you try and push against this idea you can be labelled as being anti-progress and stuck in the past. But we do have a lot more control than we give ourselves credit for. For example, routineness and susceptibility to automation are not inevitable features of occupations, job design is hugely important. How do we design jobs? How do we create jobs that allow people to do the kind of work they want to do? There can be a bit of a conflict between being impacted by what's happening and having some sort of control over what we want to happen. Certainly, we encounter technological determinism a lot. And it's understandable. For us as individuals, of course it does feel like it always is happening and we just have to cope. No one individual can do much about it, other than adapt. But that's different when we consider ourselves at a level of a society, as a polis [city state], or as an international community. I think we can shape the way in which technology develops. We have various tools. In any given country, we have regulations. There's a possibility of international regulation. Technology is emerging from a certain legal, political, normative, cultural, and social framework. It's coming from a certain place. And it is shaped by all of those things. And I think the more we understand a technology's relationship with those things, and the more we then consciously try to shape those things, the more we are going to influence the technology. So, for example, developing a culture of responsible innovation. For example, a kind of Hippocratic oath for AI developers. These things are within the realms of what is feasible, and I think will help to shape the future. One of the problems with intervention, generally, is that we cannot control the course of events. We can attempt to, but we don't know how things are going to evolve. The reality is, societies are much too complex for us to be able to shape them in any very specific way, as plenty of ideologies and political movements have found to their cost. There are often unforeseen consequences that can derail a project. I think, nonetheless, there are things we can do. We can try to imagine how things might go very badly wrong, and then work hard to develop systems that will stop that from happening. We can also try collectively to imagine how things could go very right. The kind of society that we actually want to live in that uses this technology. And I'm sure that will be skewed in all sorts of ways, and we might imagine things that seem wonderful and actually have terrible by-products. This conversation cannot be in the hands of any one group. It oughtn't be in the hands of Silicon Valley billionaires alone. They've got their role to play, but this is a conversation we need to be having as widely as possible. The centre is developing some really interesting projects but perhaps one of the most interesting is the discussion of what intelligence might be. Could you go into a bit more detail about the kinds of questions you are trying to explore in this area? You mean kinds of intelligence? Yeah. I think this is very important because historically, we've had an overwhelming tendency to anthropomorphise. We define what intelligence is, historically, as being human-like. And then within that, being like certain humans. And it's taken a very long time for the academic community to accept that there could be such a thing as non-human intelligence at all. We know that crows, for example, who have had a completely different evolutionary history, or octopuses, who have an even more different evolutionary history, might have a kind of intelligence that's very different to ours. That in some ways rivals our own, and so forth. But luckily, we have got to that point in recent years of accepting that we are not the only form of intelligence. But now, AI is challenging that from a different direction. Just as we are accepting that the natural world offers this enormous range of different intelligences, we are at the same time inventing new intelligences that are radically different to humans. And I think, still, this anthropomorphic picture of the kind of humanoid android, the robot, dominates our idea of what AI is too much. And too many people, and the industry as well, talk about human-level artificial intelligence as a goal, or general AI, which basically means like a human. But actually what we're building is nothing like a human. When the first pocket calculator was made, it didn't do maths like a human. It was vastly better. It didn't make the occasional mistake. When we set about creating these artificial agents to solve these problems, because they have a completely different evolutionary history to humans, they solve problems in very different ways. And until now, people have been fairly shy about describing them as intelligent. Or rather, in the history of AIs, we think solving a particular problem would require intelligence. Then we solve it. And then that's no longer intelligence, because we've solved it. Chess is a good example. But the reality is, we are creating a whole new world of different artificial agents. And we need to understand that world. We need to understand all the different ways of being clever, if you like. How you can be extremely sophisticated at some particular rational process, and yet extremely bad at another one in a way that bears no relation to the way humans are on these axes. And this is important, partly because we need to expand our sense of what is intelligent, like we have done with the natural world. Because lots of things follow from saying something is intelligent. Historically, we have a long tradition in Western philosophy of saying those who are intelligent should rule. So if intelligence equates to power, then obviously we need to think about what we mean by intelligence. Who has it and who doesn't. Or how it equates to rights and responsibilities. It certainly is a very ambitious project to create the atlas of intelligence. There was a point I read in something you wrote on our ideas of intelligence that I thought was very interesting. We actually tend to think of intelligence at the societal level when we think about human ability, rather than at the individual level but in the end conflate the two. I think that's a very good point, when we think about our capabilities, we think about what we can achieve as a whole, not individually. But when we talk about AI, we tend to think about that individual piece of technology, or that individual system. So for example if we think about the internet of things and AI, we should discuss intelligence as something encompassed by the whole. Yeah, absolutely. Yes, right now, perhaps it is a product of our anthropomorphising bias. But there is a tendency to see a narrative of AI versus humanity, as if it's one or the other. And yet, obviously, there are risks in this technology long before it acquires any kind of manipulative agency. Robotic technology is dangerous. Or potentially dangerous. But at the same time, most of what we're using technology for is to enhance ourselves, to increase our capacities. And a lot of what AI is going to be doing is augmenting us – we're going to be working as teams, AI-human teams. Where do you think this AI-human conflict, or concept of a conflict, comes from? Do you think that's just a reflection of historical conversations we've had about automation, or do you think it is a deeper fear? I do think it comes both from some biases that might well be innate, such as anthropomorphism, or our human tendency to ascribe agency to other objects, particularly moving ones, is well-established and probably has sound evolutionary roots. If it moves, it's probably wise to start asking yourself questions like, "What is it? What might it want? Where might it be going? Might it be hungry? Do I look like food to it?" I think it makes sense, it's natural for us to think in terms of agency. And when we do, it's natural for us to project our own ways of being and acting. And we, as primates, are profoundly co-operative. But at the same time, we're competitive and murderous. We have a strong sense of in-group versus out-group, which is responsible for both a great deal of cooperation, within the in-group, but also terrible crimes. Murder, rape, pillage, genocide; and they're pointed at the out-group. And so I think it's very natural for us to see AIs in terms of agents. We anthropomorphise them as these kind of android robots. And then we think about, well, you know, are they part of our in-group, or are they some other group? If they're some other group, it's us against them. Who's going to win? Well, let's see. So I think that's very natural, I think that's very human. There is this long tradition, in Western culture in particular, with associating intelligence and dominance and power. It's interesting to speculate about how, and I wish I knew more about it, and I'd like to see more research on this, about how different cultures perceive AI. It's well known that Japan is very accepting of technology and robots, for example. You can think, well, we in the West have long been justifying power relations of a certain kind on the basis that we're 'cleverer'. That's why men get to vote and women don't, or whatever. In a culture where power is not based on intelligence but, say, on a caste system, which is purely hereditary, we’d build an AI, and it would just tune in, drop out, attain enlightenment, just sit in the corner. Or we beg it to come back and help us find enlightenment. It might be that we find a completely different narrative to the one that's dominant in the West. One of the projects the centre is running is looking into what kind of AI breakthroughs may come, when and what the social consequences could be. What do you think the future holds? What are your fears – what do you think could go right and wrong in the short, medium and long term? That's a big question. Certainly I don't lie awake at night worried that robots are going to knock the door down and come in with a machine gun. If the robots take over the world, it won't be by knocking the door down. At the moment, I think it's certainly as big a risk that we have a GMO moment, and there's a powerful reaction against the technology which prevents us from reaping the benefits, which are enormous. I think that's as big a risk as the risks from the technologies themselves. I think one worry that we haven't talked about is that we've become extremely dependent upon this technology. And that we essentially become deskilled. There's an extent to which the history of civilisation is the history of the domestication of the human species sort of by ourselves, and also by our technology, to some extent. And AI certainly allows for that to reach a whole new level. Just think about GPs with diagnostic tools. Even now, my GP consults the computer fairly regularly. But as diagnostic tools get better, what are they going to be doing other than just typing something into the computer and reading out what comes back? At which point, you might as well do away with the GP. But then, who does know about medicine? And so we do need to worry about deskilling and about becoming dependent. And it is entirely possible that you can imagine a society in which we're all sort of prosperous, in a sense. Our basic bodily needs are provided for, perhaps, in a way, to an extent that we've never before even dreamed of. Unprecedented in human history. And yet, we're stripped of any kind of meaningful work. We have no purpose. We're escaping to virtual reality. And then you could imagine all sorts of worrying countercultures or Luddite movements or what have you. I guess that's the kind of scenario that – I haven't sketched it terribly well – but that's the kind of thing that worries me more than missile-toting giant robots. As to utopian, yes, that's interesting. I certainly mentioned a couple of things. One thing that I hope is that this new technological revolution enables us to undo some of the damage of the last one. That's a very utopian thought and not terribly realistic, but we use fossil fuels so incredibly efficiently. The idea that driverless cars that are shared, basically a kind of shared service located off a Brownfield site does away with 95 per cent of all cars, freeing up a huge amount of space in the city to be greener, many fewer cars need to be produced, they would be on the road much less, there'd be fewer traffic jams. It's just one example, but the idea that we can live much more resource-efficiently, because we are living more intelligently through using these tools. And therefore can undo some of the damage of the last Industrial Revolution. That's my main utopian hope, I guess. Vintage toy robot image by josefkubes/Shutterstock This article was originally published on TheLong+Short. Read the original article. | B. To explain how industries are approaching collaboration and making decisions in AI with regard to social responses |
Which evaluation metrics do they use for language modelling? | ### Introduction
Recent success in language modelling and representation learning have largely focused on learning the semantic structures of language BIBREF0. Syntactic information, such as part-of-speech (POS) sequences, is an essential part of language and can be important for tasks such as authorship identification, writing-style analysis, translation, etc. Methods that learn syntactic representations have received relatively less attention, with focus mostly on evaluating the semantic information contained in representations produced by language models. Multilingual embeddings have been shown to achieve top performance in many downstream tasks BIBREF1, BIBREF2. By training over large corpora, these models have shown to generalize to similar but unseen contexts. However, words contain multiple types of information: semantic, syntactic, and morphologic. Therefore, it is possible that syntactically different passages have similar embeddings due to their semantic properties. On tasks like the ones mentioned above, discriminating using patterns that include semantic information may result in poor generalization, specially when datasets are not sufficiently representative. In this work, we study methods that learn sentence-level embeddings that explicitly capture syntactic information. We focus on variations of sequence-to-sequence models BIBREF3, trained using a multilingual corpus with universal part-of-speech (UPOS) tags for the target languages only. By using target-language UPOS tags in the training process, we are able to learn sentence-level embeddings for source languages that lack UPOS tagging data. This property can be leveraged to learn syntactic embeddings for low-resource languages. Our main contributions are: to study whether sentence-level syntactic embeddings can be learned efficiently, to evaluate the structure of the learned embedding space, and to explore the potential of learning syntactic embeddings for low-resource languages. We evaluate the syntactic structure of sentence-level embeddings by performing nearest-neighbour (NN) search in the embedding space. We show that these embeddings exhibit properties that correlate with similarities between UPOS sequences of the original sentences. We also evaluate the embeddings produced by language models such as BERT BIBREF0 and show that they contain some syntactic information. We further explore our method in the few-shot setting for low-resource source languages without large, high quality treebank datasets. We show its transfer-learning capabilities on artificial and real low-resource languages. Lastly, we show that training on multilingual parallel corpora significantly improves the learned syntactic embeddings. This is similar to existing results for models trained (or pre-trained) on multiple languages BIBREF4, BIBREF2 for downstream tasks BIBREF5. ### Related Work
Training semantic embeddings based on multilingual data was studied by MUSE BIBREF1 and LASER BIBREF2 at the word and sentence levels respectively. Multi-task training for disentangling semantic and syntactic information was studied in BIBREF6. This work also used a nearest neighbour method to evaluate the syntactic properties of models, though their focus was on disentanglement rather than embedding quality. The syntactic content of language models was studied by examining syntax trees BIBREF7, subject-object agreement BIBREF8, and evaluation on syntactically altered datasets BIBREF9, BIBREF10. These works did not examine multilingual models. Distant supervision BIBREF11, BIBREF12 has been used to learn POS taggers for low-resource languages using cross-lingual corpora. The goal of these works is to learn word-level POS tags, rather than sentence-level syntactic embeddings. Furthermore, our method does not require explicit POS sequences for the low-resource language, which results in a simpler training process than distant supervision. ### Method ::: Architecture
We iterated upon the model architecture proposed in LASER BIBREF2. The model consists of a two-layer Bi-directional LSTM (BiLSTM) encoder and a single-layer LSTM decoder. The encoder is language agnostic as no language context is provided as input. In contrast to LASER, we use the concatenation of last hidden and cell states of the encoder to initialize the decoder through a linear projection. At each time-step, the decoder takes an embedding of the previous POS target concatenated with an embedding representing the language context, as well as a max-pooling over encoder outputs. Figure FIGREF2 shows the architecture of the proposed model. The input embeddings for the encoder were created using a jointly learned Byte-Pair-Encoding (BPE) vocabulary BIBREF13 for all languages by using sentencepiece. ### Method ::: Training
Training was performed using an aligned parallel corpus. Given a source-target aligned sentence pair (as in machine translation), we: Convert the sentence in the source language into BPE Look up embeddings for BPE as the input to the encoder Convert the sentence in a target language into UPOS tags, in the tagset of the target language. Use the UPOS tags in step 3 as the targets for a cross-entropy loss. Hence, the task is to predict the UPOS sequence computed from the translated input sentence. The UPOS targets were obtained using StandfordNLP BIBREF14 . Dropout with a drop probability of 0.2 was applied to the encoder. The Adam optimizer BIBREF15 was used with a constant learning rate of $0.0001$. Table TABREF4 shows a full list of the hyperparameters used in the training procedure. ### Method ::: Dataset
To create our training dataset, we followed an approach similar to LASER. The dataset contains 6 languages: English, Spanish, German, Dutch, Korean and Chinese Mandarin. These languages use 3 different scripts, 2 different language orderings, and belong to 4 language families. English, Spanish, German, and Dutch use a Latin-based script. However, Spanish is a Romantic language while the others are Germanic languages. Chinese Mandarin and Korean are included because they use non-latin based scripts and originate from language families distinct from the other languages. Although the grammatical rules vary between the selected languages, they share a number of key characteristics such as the Subject-Verb-Object ordering, except Korean (which mainly follows the Subject-Object-Verb order). We hope to extend our work to other languages with different scripts and sentence structures, such as Arabic, Japanese, Hindi, etc. in the future. The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16. They were chosen for their high availability in multiple languages. Statistics of the final training dataset are shown in Table TABREF14. Rows and columns correspond to source and target languages respectively. ### Method ::: Dataset ::: Tatoeba
Tatoeba is a freely available crowd-annotated dataset for language learning. We selected all sentences in English, Spanish, German, Dutch, and Korean. We pruned the dataset to contain only sentences with at least one translation to any of the other languages. The final training set contains 1.36M translation sentence pairs from this source. ### Method ::: Dataset ::: OpenSubtitles
We augmented our training data by using the 2018 OpenSubtitles dataset. OpenSubtitles is a publicly available dataset based on movie subtitles BIBREF16. We created our training dataset from selected aligned subtitles by taking the unique translations among the first million sentences, for each aligned parallel corpus. We further processed the data by pruning to remove samples with less than 3 words, multiple sentences, or incomplete sentences. The resulting dataset contains 1.9M translation sentence pairs from this source. ### Experiments
We aim to address the following questions: Can syntactic structures be embedded? For multiple languages? Can parallel corpora be used to learn syntactic structure for low-resource languages? Does multilingual pre-training improve syntactic embeddings? We address question 1 in Secs. SECREF20 and SECREF28 by evaluating the quality of syntactic and semantic embeddings in several ways. Questions 2 and 3 are addressed in Sec. SECREF30 by studying the transfer-learning performance of syntactic embeddings. ### Experiments ::: Quality of Syntactic Embeddings
We studied the quality of the learned syntactic embeddings by using a nearest-neighbour (NN) method. First, we calculated the UPOS sequence of all sentences in the Tatoeba dataset by using a tagger. Sentences were then assigned to distinct groups according to their UPOS sequence, i.e., all sentences belonging to the same group had the same UPOS sequence. For all languages except Korean, a held-out test set was created by randomly sampling groups that contained at least 6 sentences. For Korean, all groups containing at least 6 sentences were kept as the test set since the dataset is small. During evaluation, we applied max-pooling to the outputs of the encoder to obtain the syntactic embeddings of the held-out sentences. For each syntactic embedding, we find its top nearest neighbour (1-NN) and top-5 nearest neighbours (5-NN) in the embedding space for the held-out sentences, based on their UPOS group. Given $n$ sentences $S = \lbrace s_0, \dots , s_{n-1}\rbrace $ and their embeddings $E = \lbrace e_0, \dots , e_{n-1}\rbrace $, for each $s_i$ there is a set of $k$ gold nearest neighbours $G(i, k) = \lbrace g_0, \dots , g_{k-1}\rbrace $, $G(i, k) \subseteq S$ such that $d(s_i, g) \le d(s_i, s) \textrm { for all } g \in G(i, k) \textrm { and } s \in S \setminus G(i, k)$, where $d(\cdot , \cdot )$ is the cosine distance. Given embedding $e_i$, we calculate cosine distances $\lbrace d(e_i, e_j) \textrm { for } e_j \in E, e_j \ne e_i\rbrace $ and sort them into non-decreasing order $d_{j_0} \le d_{j_1} \le \dots \le d_{j_{n-2}}$. We consider the ordering to be unique as the probability of embedding cosine distances being equal is very small. The set of embedded $k$-nearest neighbours of $s_i$ is defined as Finally, the $k$-nearest neighbours accuracy for $s_i$ is given by A good embedding model should cluster the embeddings for similar inputs in the embedding space. Hence, the 5-NN test can be seen as an indicator of how cohesive the embedding space is. The results are shown in Table TABREF22. The differences in the number of groups in each language are due to different availabilities of sentences and sentence-types in the Tatoeba dataset. The high nearest neighbours accuracy indicates that syntax information was successfully captured by the embeddings. Table TABREF22 also shows that the syntactic information of multiple languages was captured by a single embedding model. ### Experiments ::: Quality of Syntactic Embeddings ::: Language Model
A number of recent works BIBREF7, BIBREF8 have probed language models to determine if they contain syntactic information. We applied the same nearest neighbours experiment (with the same test sets) on a number of existing language models: Universal Sentence Encoder (USE) BIBREF17, LASER, and BERT. For USE we used models available from TensorHub. For LASER we used models and created embeddings from the official repository . For BERT, we report the results using max (BERT$_{max}$) and average-pooling (BERT$_{avg}$), obtained from the BERT embedding toolkit with the multilingual cased model (104 languages, 12-layers, 768-hidden units, 12-heads), and `pooled-output' (BERT$_{output}$) from the TensorHub version of the model with the same parameters. We computed the nearest neighbours experiment for all languages in the training data for the above models. The results are shown in Table TABREF27. The results show that general purpose language models do capture syntax information, which varies greatly across languages and models. The nearest neighbours accuracy of our syntactic embeddings in Table TABREF22 significantly outperforms the general purpose language models. Arguably these language models were trained using different training data. However, this is a reasonable comparison because many real-world applications rely on released pre-trained language models for syntactically related information. Hence, we want to show that we can use much smaller models trained with direct supervision, to obtain syntactic embeddings with similar or better quality. Nonetheless, the training method used in this work can certainly be extended to architectures similar to BERT or USE. ### Experiments ::: Functional Dissimilarity
The experiments in the previous section showed that the proposed syntactic embeddings formed cohesive clusters in the embedding space, based on UPOS sequence similarities. We further studied the spatial relationships within the embeddings. Word2Vec BIBREF18 examined spatial relationships between embeddings and compared them to the semantic relationships between words. Operations on vectors in the embedding space such as $King - Man + Woman = Queen$ created vectors that also correlated with similar operations in semantics. Such semantic comparisons do not directly translate to syntactic embeddings. However, syntax information shifts with edits on POS sequences. Hence, we examined the spatial relationships between syntactic embeddings by comparing their cosine similarities with the edit distances between UPOS sequence pairs. Given $n$ UPOS sequences $U = \lbrace u_0,...,u_{n-1}\rbrace $, we compute the matrix $L \in \mathbb {R}^{n \times n}$, where $l_{ij} = l(u_i, u_j)$, the complement of the normalized Levenshtein distance between $u_i$ and $u_j$. Given the set of embedding vectors $\lbrace e_0,...,e_{n-1}\rbrace $ where $e_i$ is the embedding for sentence $s_i$, we also compute $D \in \mathbb {R}^{n \times n}$, where $d_{ij} = d(e_i, e_j)$. We further normalize $d_{ij}$ to be within $[0, 1]$ by min-max normalization to obtain $\hat{D} = \operatorname{minMax}(D)$. Following BIBREF19, we define the functional dissimilarity score by Intuitively, UPOS sequences that are similar (smaller edit distance) should be embedded close to each other in the embedding space, and embeddings that are further away should have dissimilar UPOS sequences. Hence, the functional dissimilarity score is low if the relative changes in UPOS sequences are reflected in the embedding space. The score is high if such changes are not reflected. The functional dissimilarity score was computed using sentences from the test set in CoNLL 2017 Universal Dependencies task BIBREF20 for the relevant languages with the provided UPOS sequences. Furthermore, none of the evaluated models, including the proposed method, were trained with CoNLL2017 data. We compared the functional dissimilarity scores of our syntactic representations against embeddings obtained from BERT and LASER, to further demonstrate that simple network structures with explicit supervision may be sufficient to capture syntactic structure. All the results are shown in Table TABREF29. We only show the best (lowest) results from BERT. ### Experiments ::: Transfer Performance of Syntactic Embeddings
Many NLP tasks utilize POS as features, but human annotated POS sequences are difficult and expensive to obtain. Thus, it is important to know if we can learn sentences-level syntactic embeddings for low-sources languages without treebanks. We performed zero-shot transfer of the syntactic embeddings for French, Portuguese and Indonesian. French and Portuguese are simulated low-resource languages, while Indonesian is a true low-resource language. We reported the 1-NN and 5-NN accuracies for all languages using the same evaluation setting as described in the previous section. The results are shown in Table TABREF31 (top). We also fine-tuned the learned syntactic embeddings on the low-resource language for a varying number of training data and languages. The results are shown in Table TABREF31 (bottom). In this table, the low-resource language is denoted as the `source', while the high-resource language(s) is denoted as the `target'. With this training method, no UPOS tag information was provided to the model for the `source' languages, where supervising information comes solely from parallel sentences and UPOS tags in high-resource languages. The results show that for a new language (French and Portuguese) that is similar to the family of pre-training languages, there are two ways to achieve higher 1-NN accuracy. If the number of unique sentences in the new language is small, accuracy can be improved by increasing the size of the parallel corpora used to fine-tune. If only one parallel corpus is available, accuracy can be improved by increasing the number of unique sentence-pairs used to fine-tune. For a new language that is dissimilar to the family of pre-training languages, e.g. Indonesian in Table TABREF31, the above methods only improved nearest neighbours accuracy slightly. This may be caused by differing data distribution or by tagger inaccuracies. The results for Indonesian do indicate that some syntactic structure can be learned by using our method, even for a dissimilar language. A future direction is to conduct a rigorous analysis of transfer learning between languages from the same versus different language families. ### Conclusion
We examined the possibility of creating syntactic embeddings by using a multilingual method based on sequence-to-sequence models. In contrast to prior work, our method only requires parallel corpora and UPOS tags in the target language. We studied the quality of learned embeddings by examining nearest neighbours in the embedding space and investigating their functional dissimilarity. These results were compared against recent state-of-the-art language models. We also showed that pre-training with a parallel corpus allowed the syntactic embeddings to be transferred to low-resource languages via few-shot fine-tuning. Our evaluations indicated that syntactic structure can be learnt by using simple network architectures and explicit supervision. Future directions include improving the transfer performance for low-resource languages, disentangling semantic and syntactic embeddings, and analyzing the effect of transfer learning between languages belong to the same versus different language families. Table 1: Hyperparameters Figure 1: Proposed architecture. Table 2: Training Dataset Statistics Table 3: Syntactic Nearest-Neighbour Accuracy (%) Table 4: Syntactic Nearest-Neighbour for Language Models (%) Table 5: Functional Dissimilarity Scores (Lower is Better) Table 6: Syntactic Nearest-Neighbour on New languages (%) | functional dissimilarity score, nearest neighbours experiment |
What dataset is used for this study? | ### Introduction
Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study. MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation. Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11. In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter. ### Introduction ::: MH17 Related (Dis-)Information Flow on Twitter
We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role. To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed. Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian. ### Introduction ::: Contributions
We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available. ### Competing Narratives about the MH17 Crash
We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists. Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat. The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin’s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash. ### Dataset
For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016. BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates. ### Classification Models
For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN). ### Classification Models ::: Hashtag-Based Baseline
Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi. ### Classification Models ::: Logistic Regression Classifier
As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia. ### Classification Models ::: Convolutional Neural Network Classifier
As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4. ### Experimental Setup
We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits. ### Experimental Setup ::: Tweet Preprocessing
Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding. ### Experimental Setup ::: Evaluation Metrics
We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task. ### Results
The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes. ### Results ::: Comparison Between Models
We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class. ### Results ::: Per-Class Performance
With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset. Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class. In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task. ### Data Augmentation Experiments using Cross-Lingual Transfer
The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time. We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #КиевСкажиПравду (Kiew tell the truth) and russian #Киевсбилбоинг (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation. ### Error Analysis
In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error: the correct class can be directly inferred from the text content easily, even without background knowledge the correct class can be inferred from the text content, given that event-specific knowledge is provided the correct class can be inferred from the text content if the text is interpreted correctly For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following. ### Error Analysis ::: Category I Errors
Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them. ### Error Analysis ::: Category II Errors
Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash. ### Error Analysis ::: Category III Errors
Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o). Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home. From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37. ### Integrating Automatic Predictions into the Retweet Network
Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38). Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge. ### Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges
In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class). ### Conclusion
In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible. ### Acknowledgements
We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the ‘Digital Disinformation’ project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012). Table 1: Label distribution and dataset sizes. Tweets are considered original if their preprocessed text is unique. All tweets comprise original tweets, retweets and duplicates. Table 2: Example tweets for each of the three classes. Table 3: Classification results on the English MH17 dataset measured as F1 and area under the precision-recall curve (AUC). Figure 1: Confusion matrices for the CNN (left) and the logistic regression model (right). The y-axis shows the true label while the x-axis shows the model prediction. Table 4: Examples for the different error categories. Error category I are cases where the correct class can easily be inferred from the text. For error category II, the correct class can be inferred from the text with event-specific knowledge. For error category III, it is necessary to resolve humour/satire in order to infer the intended meaning that the speaker wants to communicate. Figure 2: The left plot shows the original k10 retweet network as computed by Golovchenko et al. (2018) together with the new edges that were added after manually re-annotating the classifier predictions. The right plot only visualizes the new edges that we could add by filtering the classifier predictions. Pro-Russian edges are colored in red, pro-Ukrainian edges are colored in dark blue and neutral edges are colored in grey. Both plots were made using The Force Atlas 2 layout in gephi (Bastian et al., 2009). Table 5: Number of labeled edges in the k10 network before and after augmentation with predicted labels. Candidates are previously unlabeled edges for which the model makes a confident prediction. The total number of edges in the network is 24,602. | MH17 Twitter dataset |
What likely happens to the narrator after the story ends?
A. He eventually makes his meeting but is too shaken up to successfully close the sale
B. He and Julia get together after Julia's divorce
C. The narrator stays with Julia's sister on his trip and misses his meeting
D. He probably returns to his unsatisfying life negotiating printing orders
| Nuts to wild talents! Mine was no satisfaction, never earned me a penny—and now it had me fighting for my life in ... THE LITTLE RED BAG By JERRY SOHL [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, January 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] About an hour out of San Francisco on the flight to Los Angeles, I made the discovery. I had finished reading the Chronicle , folded and put it beside me, turned and looked out the window, expecting to see the San Joaquin Valley but finding only a sea of clouds instead. So I returned my attention to the inside of the plane, to the overstuffed gray-haired woman asleep beside me, to the backs of heads in seats before me, across the aisle to other heads, and down to the blonde. I had seen her in the concourse and at the gate, a shapely thing. Now she had crossed her legs and I was privileged to view a trim ankle and calf, and her profile as she stared moodily across the aisle and out a window where there was nothing to see. I slid my eyes past her to others. A crossword-puzzle worker, a togetherness-type-magazine reader. Inventory completed, I went back to looking at the clouds, knowing I should be thinking about the printing order I was going to Los Angeles for, and not wanting to. So I started going through the purse of the woman next to me. Perhaps that sounds bad. It wasn't. I'd been doing it for years and nobody ever complained. It started when I was a kid, this business of being able to explore the insides of things like purses and sealed boxes and locked drawers and—well, human beings. But human beings aren't worth the trouble. It's like swimming through spaghetti. And I've got to stay away from electric wires. They hurt. Now don't ask me how they hurt. Maybe you think it's fun. For the most part, it really isn't. I always knew what was in Christmas presents before I unwrapped them, and therefore Christmas was always spoiled for me as a kid. I can't feel the color of anything, just its consistency. An apple senses about the same as a potato, except for the core and the stem. I can't even tell if there's writing on a piece of paper. So you see it isn't much. Just the feel of shapes, the hardnesses and softnesses. But I've learned to become pretty good at guessing. Like this woman next to me. She had a short, cylindrical metal object in her purse with waxlike stuff inside it—a lipstick. A round, hard object with dust inside—a compact. Handkerchief, chewing gum, a small book, probably an address book, money in a change purse—a few bills and coins. Not much else. I was a little disappointed. I've run across a gun or two in my time. But I never say anything. I learned the wisdom of keeping my mouth shut in the fourth grade when Miss Winters, a stern, white-haired disciplinarian, ordered me to eat my sack lunch in the classroom with her instead of outside with some of the other kids. This was the punishment for some minor infraction. Lunchtime was nearly over and we'd both finished eating; she said she'd be gone for a few moments and that I was to erase the blackboard during her absence, which I dutifully did. Class had hardly resumed when she started looking around the desk for her favorite mechanical pencil, asking if any of us had seen it, and looking straight at me. I didn't want her to think I had taken it while she was out of the room, so I probed the contents of her purse, which she always kept in the upper right drawer of her desk. "It's in your purse," I blurted out. I was sent home with a stinging note. Since then I've kept quiet. At one time I assumed everybody was able to sense. I've known better for years. Still, I wonder how many other people are as close-mouthed about their special gift as I am about mine. I used to think that some day I'd make a lot of money out of it, but how? I can't read thoughts. I can't even be sure what some of the things I sense in probing really are. But I've learned to move things. Ever so little. A piece of paper. A feather. Once I stopped one of those little glass-enclosed light or heat-powered devices with vanes you see now and then in a jeweler's window. And I can stop clocks. Take this morning, for example. I had set my alarm for five-thirty because I had to catch the seven o'clock plane at San Francisco International Airport. This being earlier than I usually get up, it seems all I did during the night was feel my way past the escapement and balance wheel to see where the notch for the alarm was. The last time I did it there was just the merest fraction of an inch between the pawl and the notch. So I sighed and moved to the balance wheel and its delicate ribbon of spiraling steel. I hung onto the wheel, exerting influence to decrease the restoring torque. The wheel slowed down until there was no more ticking. It took quite a bit of effort, as it always does, but I did it, as I usually do. I can't stand the alarm. When I first learned to do this, I thought I had it made. I even went to Las Vegas to try my hand, so to speak, with the ratchets and pawls and cams and springs on the slot machines. But there's nothing delicate about a slot machine, and the spring tensions are too strong. I dropped quite a lot of nickels before I finally gave up. So I'm stuck with a talent I've found little real use for. Except that it amuses me. Sometimes. Not like this time on the plane. The woman beside me stirred, sat up suddenly and looked across me out the window. "Where are we?" she asked in a surprised voice. I told her we were probably a little north of Bakersfield. She said, "Oh," glanced at her wristwatch and sank back again. Soon the stewardesses would bring coffee and doughnuts around, so I contented myself with looking at the clouds and trying to think about Amos Magaffey, who was purchasing agent for a Los Angeles amusement chain, and how I was going to convince him our printing prices were maybe a little higher but the quality and service were better. My mind wandered below where I was sitting, idly moving from one piece of luggage to another, looking for my beat-up suitcase. I went through slips and slippers, lingerie and laundry, a jig saw puzzle and a ukulele. I never did find my suitcase because I found the bomb first. The bomb was in a small bag—a woman's bag judging by the soft, flimsy things you'd never find in a man's—and I didn't know it was a bomb right away. I thought it was just a clock, one of those small, quiet alarms. I was going to pass it by and go on, but what held me was that something was taped to it. By the feel, I knew it must be electrician's tape. Interested and curious, I explored the clock more closely, found two wires. One went to a battery and the other to hard round cylinders taped together. The hairs stood up at the base of my neck when I suddenly realized what it was. The clock's balance wheel was rocking merrily. Quickly I went up past the train of gears to the alarm wheel. If this was anything like my own alarm clock, this one had something like ten minutes to go. It was forty minutes to Burbank and Lockheed Air Terminal. My mind was churning when I turned from the window to look around at the unconcerned passengers, the woman at my side asleep again. I thought: Which one of these.... No, none of them would know it was there. I glanced out the window again; clouds were still in the way. We'd be leaving the valley for the mountain range north of Los Angeles soon, if we hadn't left it already. No place to land the plane there. But of course that had been the plan! My heart was beating in jackhammer rhythm; my mouth was dry and my mind was numb. Tell somebody about the bomb before it's too late! No, they'd think I put it there. Besides, what good would it do? There would be panic and they'd never get the plane down in time—if they believed me. "Sir." My head jerked around. The stewardess stood in the aisle, smiling, extending a tray to me, a brown plastic tray bearing a small paper cup of tomato juice, a cup of coffee, a cellophane-wrapped doughnut, paper spoon, sugar and dehydrated cream envelopes, and a napkin. I goggled at her, managed to croak, "No, thanks." She gave me an odd look and moved along. My seatmate had accepted hers and was tearing at the cellophane. I couldn't bear to watch her. I closed my eyes, forced my mind back to the luggage compartment, spent a frantic moment before I found the bag again. I had to stop that balance wheel, just as I stopped my alarm clock every morning. I tried to close everything off—the throb of engines, the rush of air, the woman sipping coffee noisily beside me—and I went into the clock and surrounded the seesawing wheel. When it went forward, I pulled it back; when it went back, I pulled it forward. I struggled with it, and it was like trying to work with greasy hands, and I was afraid I wasn't going to be able to stop it. Then, little by little, it started to slow its beat. But I could not afford to relax. I pushed and pulled and didn't dare release my hold until it came to a dead stop. "Anything the matter?" My eyelids flew open and I looked into the eyes of the woman next to me. There was sugar from the doughnut around her mouth and she was still chewing. "No," I said, letting out my breath. "I'm all right." "You were moaning, it sounded like. And you kept moving your head back and forth." "Must have been dreaming," I said as I rang for the stewardess. When she came I told her I'd take some of that coffee now. No, nothing else, just coffee. I didn't tell her how much I needed it. I sat there clammy with sweat until she returned. Coffee never tasted so good. All right, so I had stopped the bomb's timer. My mind raced ahead to the landing. When they unloaded the luggage, the balance wheel would start again. I wouldn't be able to stay with it, keeping it still. I considered telling the authorities as soon as we landed, or maybe calling in ahead, but wouldn't that just bring suspicion, questions. Maybe I could convince them I could stop a clock—but not before the bomb exploded. And then what? My secret would be out and my life would be changed. I'd be a man not to be trusted, a prying man, a man literally with gimlet eyes. Mountain crags jutted through the clouds. We were in the range north of the city. Here and there were clear spots and I could see roads below, but there were also clouds far above us. It was very beautiful, but it was also very bumpy, and we started to slip and slide. To my horror I found that the balance wheel was rocking again. Closing my eyes and gritting my teeth, I forced my senses to the wheel, tugging and pulling and shoving and pushing until it finally stopped. A jab in the shoulder. I jumped, startled. "Your cup," my seat partner said, pointing. I looked down at the coffee cup I had crushed in my hands. Then I looked up into the eyes of the stewardess. I handed it to her. She took it without a word and went away. "Were you really asleep that time?" "Not really," I said. I was tempted to tell the woman I was subject to fits, but I didn't. It was only a few minutes to landing, but they became the longest minutes of my life as time after time I stopped the rocking wheel when the plane dipped and bumped to a landing. Leaving the apron with the other passengers, I tried to walk as unconcernedly as they through the exit gate. I would have liked walking through the terminal and out the entrance and away, but I could not. I had my suitcase to get, for one thing. The damned bomb was the other. So I strolled out into the concourse again to look at the plane and watch the baggagemen at work, transferring the luggage to two airfield carts. They weren't as careful as I would have been. It was impossible to tell from this distance just which bag contained the bomb; I could hardly identify my own scarred suitcase. The assortment of bags—a strange conglomeration of sizes and colors—was packed in some places six deep, and it rolled toward the gate where I was standing. I didn't know whether to stay or run, imagining the balance wheel now happily rocking again. The load went past me down a ramp to the front of the air terminal where the luggage was unloaded and placed in a long rack. I went with it. There was a flurry of ticket matching, hands grabbing for suitcases, and a general exodus on the part of my fellow passengers, too fast to determine who had got the one with the bomb. Now all that was left was the attendant and I had two bags—my own battered veteran of years, and a fine new red overnight case, small enough to be the one. I lit a cigarette, reached out. Inside were a woman's things and—a clock. The escapement was clicking vigorously. I didn't moan this time. I just closed my eyes, stretched toward and grabbed the balance wheel I was getting to know like my own. I entered into a union with it so strong that after I had reduced it to immobility, it was like waking when I opened my eyes. The baggage claim attendant was staring at me. For only a moment I stared back. Then I quickly reached for my baggage check and presented it to him. His hand hovered over the handle of the little red bag and I was ready to yell at him. But then, matching numbers on the tags with his eyes, his hand grasped the handle of my own suitcase and pushed it toward me. "Thanks," I said, taking it. I glanced ever so casually toward the remaining bag. "One left over, eh?" "Yeah." He was so bored I was tempted to tell him what was in it. But he was eying me with a "well-why-don't-you-get-along?" look. I said, "What happens if nobody claims it?" "Take it inside. Why?" He was getting too curious. "Oh, I just wondered, that's all." I stepped on my cigarette and walked toward the air terminal entrance and put my suitcase on the stone steps there. A redcap came hurrying over. "Cab?" I shook my head. "Just waiting." Just waiting for somebody to pick up a bomb. I lit another cigarette and glanced now and then toward the baggage claim area. The red bag was still there. All sorts of theories ran through my head as to why it should still be there, and none satisfied me. I should not have been there, that much I knew; I should be with a man named Amos Magaffey on Sixth Street at ten o'clock, discussing something very mundane, the matter of a printing order. But what could I do? If I left the airport, the attendant would eventually take the bag inside and there would be an explosion, and I wouldn't be able to live with myself. No. I had to stay to keep the balance wheel stationary until—until what? A man in tan gabardine, wearing a police cap and badge, walked out of the entrance to stand on the stone steps beside me while he put on a pair of dark glasses. A member of the airport police detail. I could tell him. I could take him down to the little red bag and explain the whole thing. Then it would be his baby and I would be off on my own business. But he moved on down the steps, nodded at the redcap, and started across the street to the parking area. I could have called to him, "Hey, officer, let me tell you about a bomb in a little red bag." But I didn't. I didn't because I caught a movement at the baggage claim counter out of the side of my eye. The attendant had picked up the bag and was walking with it up the ramp to the rear of the air terminal. Picking up my own suitcase, I went inside in time to see him enter through a side door and deposit the bag on the scales at the airline desk and say something to the clerk. The clerk nodded and moved the bag to the rear room. I could visualize the balance wheel once again rocking like crazy. How many minutes—or seconds—were left? I was sweating when I moved to the counter, and it wasn't because of the sunshine I'd been soaking in. I had to get as close to the bag as I could if I was going to stop the clock again. "Can I help you?" the clerk asked. "No. I'm waiting for someone." I turned my back to him, put down my suitcase, leaned against the counter and reached out for the wheel. I found I could reach the device, but it was far away. When I tried to dampen it, the wheel escaped my grasp. "Do you have my suitcase?" I blinked my eyes open and looked around. The blonde in the plane stood there looking very fresh and bright and unconcerned. In her right hand she had a green baggage claim check. The clerk took it, nodded, and in a moment brought out the overnight case and set it on the scales. The girl thanked him, picked it up, glanced at me indifferently, and then started for the entrance with it. "Just a moment," I found myself saying, grabbing my bag and hurrying after her. At her side and a little ahead of her, I said, "Listen to me." She looked annoyed and increased her stride toward the door. "It's a matter of life or death," I said. I wanted to wrest the bag from her and hurl it out through the doorway into the street, but I restrained myself. She stopped and stared. I noticed a short, fat man in a rumpled suitcoat and unpressed pants staring, too. Ignoring him, I said, "Please put the bag down. Over there." I indicated a spot beside a telephone booth where it would be out of the way. She didn't move. She just said, "Why?" "For God's sake!" I took the case. She offered no resistance. I put her bag and mine next to the booth. When I turned around she was standing there looking at me as if I had gone out of my mind. Her eyes were blue and brown-flecked, very pretty eyes, and my thought at the moment was, I'm glad the bomb didn't go off; these eyes wouldn't be looking at me or anything else right now if it had. "I've got to talk to you. It's very important." The girl said, "Why?" I was beginning to think it was the only word she knew. At the same time I was wondering why anyone would want to kill someone so lovely. "I'll explain in a moment. Please stand right here while I make a telephone call." I moved toward the phone booth, paused and said, "And don't ask me why." She gave me a speculative look. I must not have seemed a complete idiot because she said, "All right, but—" I didn't listen for the rest. I went into the booth, closed the door, pretended to drop a coin and dial a number. But all the time I was in there, I was reaching out through the glass for the clock. At this range it wasn't difficult to stop the balance wheel. Just the same, when I came out I was wringing wet. "Now will you please tell me what this is all about?" she said stiffly. "Gladly. Let me buy you a cup of coffee and I'll explain." She glanced at the bags. I told her they'd be all right. We followed the short, fat man into the coffee shop. Over coffee I explained it all to her, how I had this extrasensory ability, how she was the first person I had ever revealed it to, and how I had discovered what was in her overnight bag. During the telling, her untouched coffee grew a skin, her face grew pale, her eyes grew less curious and more troubled. There were tears there when I finished. I asked her who put the bomb in her bag. "Joe did," she said in a toneless voice, not looking at me any more but staring vacantly across the room. "Joe put it there." Behind her eyes she was reliving some recent scene. "Who is Joe?" "My husband." I thought she was going to really bawl, but she got control again. "This trip was his idea, my coming down here to visit my sister." Her smile was bleak. "I see now why he wanted to put in those books. I'd finished packing and was in the bathroom. He said he'd put in some books we'd both finished reading—for my sister. That's when he must have put the—put it in there." I said gently, "Why would he want to do a thing like that?" "I don't know." She shook her head. "I just don't know." And she was close to bawling again. Then she recovered and said, "I'm not sure I want to know." I admired her for saying it. Joe must have been crazy. "It's all right now?" she asked. I nodded. "As long as we don't move it." I told her I didn't know how much more time there was, that I'd been thinking it over and that the only way out seemed to be to tell the airport policeman. After I explained it to her, the girl—she said her name was Julia Claremont—agreed to tell him she thought there was a bomb in her bag, that she had noticed a ticking and had become worried because she knew she hadn't packed a clock. It wasn't good, but it would have to do. "We've got to get it deactivated," I said, watching the fat man pay for his coffee and leave. "The sooner the better." I finished my coffee in one gulp and went to pay the bill with her. I asked her why she didn't claim the bag at the same time the other people had. She said she had called her sister and the phone was busy for a long while. "She was supposed to meet me, and when she wasn't here, I got worried. She said she isn't feeling well and asked me to take a cab." She smiled a little. It was a bright, cheery thing. I had the feeling it was all for me. "That's where I was going when you caught up with me." It had become a very nice day. But the bottom dropped out of it again when we reached the lobby. The two bags weren't there. I ran to the entrance and nearly collided with the redcap. "See anybody go out of here with a little red bag and an old battered suitcase?" "Bag? Suitcase?" he mumbled. Then he became excited. "Why, a man just stepped out of here—" He turned to look down the street. "That's him." The dumpy man I'd seen was walking off; Julia's bag in his right hand, mine in his left. He seemed in no hurry. "Hey!" I shouted, starting toward him. The man turned, took one look at me, and started to run. He came abreast an old gray, mud-spattered coupe, ran around, opened the door and threw both bags into the rear seat as he got in. The car was a hundred feet away and gathering speed by the time I reached where it had been parked. I watched it for a moment, then walked back to the entranceway where Julia was standing with the redcap, who said, "That man steal them suitcases?" "That he did," I said. Just then the airport policeman started across the street from the parking lot. Redcap said, "Better tell him about it." The policeman was sympathetic and concerned. He said, "We'd better get over to the office." But we never left the spot because an explosion some blocks distant shattered the air. Julia's hand grasped my arm. Hard. "Jets," the redcap said, eying the sky. "I don't know," the policeman said. "Didn't sound much like a jet to me." We stood there. I could visualize the wreckage of an old gray coupe in the middle of a street, but I couldn't visualize the driver. That was all right. I didn't want to see him. I didn't know what Julia was thinking. She said, "About those bags," and looked at me. The officer said, "Yes, miss?" "I—I don't care about mine. I didn't have much of anything in it." "I feel the same way," I said. "Would it be all right if we didn't bother to report it?" "Well," the policeman said, "I can't make you report it." "I'd rather not then," Julia said. She turned to me. "I'd like some air. Can't we walk a little?" "Sure," I said. We started down the street, her arm in mine, as the air began to fill with the distant sounds of sirens. | B. He and Julia get together after Julia's divorce |
What categories does the dataset come from? | ### Introduction
In our online world, social media users tweet, post, and message an incredible number of times each day, and the interconnected, information-heavy nature of our lives makes stress more prominent and easily observable than ever before. With many platforms such as Twitter, Reddit, and Facebook, the scientific community has access to a massive amount of data to study the daily worries and stresses of people across the world. Stress is a nearly universal phenomenon, and we have some evidence of its prevalence and recent increase. For example, the American Psychological Association (APA) has performed annual studies assessing stress in the United States since 2007 which demonstrate widespread experiences of chronic stress. Stress is a subjective experience whose effects and even definition can vary from person to person; as a baseline, the APA defines stress as a reaction to extant and future demands and pressures, which can be positive in moderation. Health and psychology researchers have extensively studied the connection between too much stress and physical and mental health BIBREF0, BIBREF1. In this work, we present a corpus of social media text for detecting the presence of stress. We hope this corpus will facilitate the development of models for this problem, which has diverse applications in areas such as diagnosing physical and mental illness, gauging public mood and worries in politics and economics, and tracking the effects of disasters. Our contributions are as follows: Dreaddit, a dataset of lengthy social media posts in five categories, each including stressful and non-stressful text and different ways of expressing stress, with a subset of the data annotated by human annotators; Supervised models, both discrete and neural, for predicting stress, providing benchmarks to stimulate further work in the area; and Analysis of the content of our dataset and the performance of our models, which provides insight into the problem of stress detection. In the remainder of this paper, we will review relevant work, describe our dataset and its annotation, provide some analysis of the data and stress detection problem, present and discuss results of some supervised models on our dataset, and finally conclude with our summary and future work. ### Related Work
Because of the subjective nature of stress, relevant research tends to focus on physical signals, such as cortisol levels in saliva BIBREF2, electroencephalogram (EEG) readings BIBREF3, or speech data BIBREF4. This work captures important aspects of the human reaction to stress, but has the disadvantage that hardware or physical presence is required. However, because of the aforementioned proliferation of stress on social media, we believe that stress can be observed and studied purely from text. Other threads of research have also made this observation and generally use microblog data (e.g., Twitter). The most similar work to ours includes BIBREF5, who use Long Short-Term Memory Networks (LSTMs) to detect stress in speech and Twitter data; BIBREF6, who examine the Facebook and Twitter posts of users who score highly on a diagnostic stress questionnaire; and BIBREF7, who detect stress on microblogging websites using a Convolutional Neural Network (CNN) and factor graph model with a suite of discrete features. Our work is unique in that it uses data from Reddit, which is both typically longer and not typically as conducive to distant labeling as microblogs (which are labeled in the above work with hashtags or pattern matching, such as “I feel stressed”). The length of our posts will ultimately enable research into the causes of stress and will allow us to identify more implicit indicators. We also limit ourselves to text data and metadata (e.g., posting time, number of replies), whereas BIBREF5 also train on speech data and BIBREF7 include information from photos, neither of which is always available. Finally, we label individual parts of longer posts for acute stress using human annotators, while BIBREF6 label users themselves for chronic stress with the users' voluntary answers to a psychological questionnaire. Researchers have used Reddit data to examine a variety of mental health conditions such as depression BIBREF8 and other clinical diagnoses such as general anxiety BIBREF9, but to our knowledge, our corpus is the first to focus on stress as a general experience, not only a clinical concept. ### Dataset ::: Reddit Data
Reddit is a social media website where users post in topic-specific communities called subreddits, and other users comment and vote on these posts. The lengthy nature of these posts makes Reddit an ideal source of data for studying the nuances of phenomena like stress. To collect expressions of stress, we select categories of subreddits where members are likely to discuss stressful topics: Interpersonal conflict: abuse and social domains. Posters in the abuse subreddits are largely survivors of an abusive relationship or situation sharing stories and support, while posters in the social subreddit post about any difficulty in a relationship (often but not exclusively romantic) and seek advice for how to handle the situation. Mental illness: anxiety and Post-Traumatic Stress Disorder (PTSD) domains. Posters in these subreddits seek advice about coping with mental illness and its symptoms, share support and successes, seek diagnoses, and so on. Financial need: financial domain. Posters in the financial subreddits generally seek financial or material help from other posters. We include ten subreddits in the five domains of abuse, social, anxiety, PTSD, and financial, as detailed in tab:data-spread, and our analysis focuses on the domain level. Using the PRAW API, we scrape all available posts on these subreddits between January 1, 2017 and November 19, 2018; in total, 187,444 posts. As we will describe in sec:annotation, we assign binary stress labels to 3,553 segments of these posts to form a supervised and semi-supervised training set. An example segment is shown in fig:stress-example. Highlighted phrases are indicators that the writer is stressed: the writer mentions common physical symptoms (nausea), explicitly names fear and dread, and uses language indicating helplessness and help-seeking behavior. The average length of a post in our dataset is 420 tokens, much longer than most microblog data (e.g., Twitter's character limit as of this writing is 280 characters). While we label segments that are about 100 tokens long, we still have much additional data from the author on which to draw. We feel this is important because, while our goal in this paper is to predict stress, having longer posts will ultimately allow more detailed study of the causes and effects of stress. In tab:data-examples, we provide examples of labeled segments from the various domains in our dataset. The samples are fairly typical; the dataset contains mostly first-person narrative accounts of personal experiences and requests for assistance or advice. Our data displays a range of topics, language, and agreement levels among annotators, and we provide only a few examples. Lengthier examples are available in the appendix. ### Dataset ::: Data Annotation
We annotate a subset of the data using Amazon Mechanical Turk in order to begin exploring the characteristics of stress. We partition the posts into contiguous five-sentence chunks for labeling; we wish to annotate segments of the posts because we are ultimately interested in what parts of the post depict stress, but we find through manual inspection that some amount of context is important. Our posts, however, are quite long, and it would be difficult for annotators to read and annotate entire posts. This type of data will allow us in the future not only to classify the presence of stress, but also to locate its expressions in the text, even if they are diffused throughout the post. We set up an annotation task in which English-speaking Mechanical Turk Workers are asked to label five randomly selected text segments (of five sentences each) after taking a qualification test; Workers are allowed to select “Stress”, “Not Stress”, or “Can't Tell” for each segment. In our instructions, we define stress as follows: “The Oxford English Dictionary defines stress as `a state of mental or emotional strain or tension resulting from adverse or demanding circumstances'. This means that stress results from someone being uncertain that they can handle some threatening situation. We are interested in cases where that someone also feels negatively about it (sometimes we can find an event stressful, but also find it exciting and positive, like a first date or an interview).”. We specifically ask Workers to decide whether the author is expressing both stress and a negative attitude about it, not whether the situation itself seems stressful. Our full instructions are available in the appendix. We submit 4,000 segments, sampled equally from each domain and uniformly within domains, to Mechanical Turk to be annotated by at least five Workers each and include in each batch one of 50 “check questions” which have been previously verified by two in-house annotators. After removing annotations which failed the check questions, and data points for which at least half of the annotators selected “Can't Tell”, we are left with 3,553 labeled data points from 2,929 different posts. We take the annotators' majority vote as the label for each segment and record the percentage of annotators who agreed. The resulting dataset is nearly balanced, with 52.3% of the data (1,857 instances) labeled stressful. Our agreement on all labeled data is $\kappa =0.47$, using Fleiss's Kappa BIBREF10, considered “moderate agreement” by BIBREF11. We observe that annotators achieved perfect agreement on 39% of the data, and for another 32% the majority was 3/5 or less. This suggests that our data displays significant variation in how stress is expressed, which we explore in the next section. ### Data Analysis
While all our data has the same genre and personal narrative style, we find distinctions among domains with which classification systems must contend in order to perform well, and distinctions between stressful and non-stressful data which may be useful when developing such systems. Posters in each subreddit express stress, but we expect that their different functions and stressors lead to differences in how they do so in each subreddit, domain, and broad category. By domain. We examine the vocabulary patterns of each domain on our training data only, not including unlabeled data so that we may extend our analysis to the label level. First, we use the word categories from the Linguistic Inquiry and Word Count (LIWC) BIBREF12, a lexicon-based tool that gives scores for psychologically relevant categories such as sadness or cognitive processes, as a proxy for topic prevalence and expression variety. We calculate both the percentage of tokens per domain which are included in a specific LIWC word list, and the percentage of words in a specific LIWC word list that appear in each domain (“coverage” of the domain). Results of the analysis are highlighted in tab:domain-liwc. We first note that variety of expression depends on domain and topic; for example, the variety in the expression of negative emotions is particularly low in the financial domain (with 1.54% of words being negative emotion (“negemo”) words and only 31% of “negemo” words used). We also see clear topic shifts among domains: the interpersonal domains contain roughly 1.5 times as many social words, proportionally, as the others; and domains are stratified by their coverage of the anxiety word list (with the most in the mental illness domains and the least in the financial domain). We also examine the overall lexical diversity of each domain by calculating Yule's I measure BIBREF13. fig:domain-yule shows the lexical diversity of our data, both for all words in the vocabulary and for only words in LIWC's “negemo” word list. Yule's I measure reflects the repetitiveness of the data (as opposed to the broader coverage measured by our LIWC analysis). We notice exceptionally low lexical diversity for the mental illness domains, which we believe is due to the structured, clinical language surrounding mental illnesses. For example, posters in these domains discuss topics such as symptoms, medical care, and diagnoses (fig:stress-example, tab:data-examples). When we restrict our analysis to negative emotion words, this pattern persists only for anxiety; the PTSD domain has comparatively little lexical variety, but what it does have contributes to its variety of expression for negative emotions. By label. We perform similar analyses on data labeled stressful or non-stressful by a majority of annotators. We confirm some common results in the mental health literature, including that stressful data uses more first-person pronouns (perhaps reflecting increased self-focus) and that non-stressful data uses more social words (perhaps reflecting a better social support network). Additionally, we calculate measures of syntactic complexity, including the percentage of words that are conjunctions, average number of tokens per labeled segment, average number of clauses per sentence, Flesch-Kincaid Grade Level BIBREF14, and Automated Readability Index BIBREF15. These scores are comparable for all splits of our data; however, as shown in tab:label-complexity, we do see non-significant but persistent differences between stressful and non-stressful data, with stressful data being generally longer and more complex but also rated simpler by readability indices. These findings are intriguing and can be explored in future work. By agreement. Finally, we examine the differences among annotator agreement levels. We find an inverse relationship between the lexical variety and the proportion of annotators who agree, as shown in fig:agreement-diversity. While the amount of data and lexical variety seem to be related, Yule's I measure controls for length, so we believe that this trend reflects a difference in the type of data that encourages high or low agreement. ### Methods
In order to train supervised models, we group the labeled segments by post and randomly select 10% of the posts ($\approx $ 10% of the labeled segments) to form a test set. This ensures that while there is a reasonable distribution of labels and domains in the train and test set, the two do not explicitly share any of the same content. This results in a total of 2,838 train data points (51.6% labeled stressful) and 715 test data points (52.4% labeled stressful). Because our data is relatively small, we train our traditional supervised models with 10-fold cross-validation; for our neural models, we break off a further random 10% of the training data for validation and average the predictions of 10 randomly-initialized trained models. In addition to the words of the posts (both as bag-of-n-grams and distributed word embeddings), we include features in three categories: Lexical features. Average, maximum, and minimum scores for pleasantness, activation, and imagery from the Dictionary of Affect in Language (DAL) BIBREF16; the full suite of 93 LIWC features; and sentiment calculated using the Pattern sentiment library BIBREF17. Syntactic features. Part-of-speech unigrams and bigrams, the Flesch-Kincaid Grade Level, and the Automated Readability Index. Social media features. The UTC timestamp of the post; the ratio of upvotes to downvotes on the post, where an upvote roughly corresponds to a reaction of “like” and a downvote to “dislike” (upvote ratio); the net score of the post (karma) (calculated by Reddit, $n_\text{upvotes} - n_\text{downvotes}$); and the total number of comments in the entire thread under the post. ### Methods ::: Supervised Models
We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees. We tune the parameters for these models using grid search and 10-fold cross-validation, and obtain results for different combinations of input and features. For input representation, we experiment with bag-of-n-grams (for $n \in \lbrace 1..3\rbrace $), Google News pre-trained Word2Vec embeddings (300-dimensional) BIBREF18, Word2Vec embeddings trained on our large unlabeled corpus (300-dimensional, to match), and BERT embeddings trained on our unlabeled corpus (768-dimensional, the top-level [CLS] embedding) BIBREF19. We experiment with subsets of the above features, including separating the features by category (lexical, syntactic, social) and by magnitude of the Pearson correlation coefficient ($r$) with the training labels. Finally, we stratify the training data by annotator agreement, including separate experiments on only data for which all annotators agreed, data for which at least 4/5 annotators agreed, and so on. We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23). Because neural models require large amounts of data, we do not cull the data by annotator agreement for these experiments and use all the labeled data we have. We experiment with training embeddings with random initialization as well as initializing with our domain-specific Word2Vec embeddings, and we also concatenate the best feature set from our non-neural experiments onto the representations after the recurrent and convolutional/pooling layers respectively. Finally, we apply BERT directly to our task, fine-tuning the pretrained BERT-base on our classification task for three epochs (as performed in BIBREF19 when applying BERT to any task). Our parameter settings for our various models are available in the appendix. ### Results and Discussion
We present our results in tab:supervised-results. Our best model is a logistic regression classifier with Word2Vec embeddings trained on our unlabeled corpus, high-correlation features ($\ge $ 0.4 absolute Pearson's $r$), and high-agreement data (at least 4/5 annotators agreed); this model achieves an F-score of 79.8 on our test set, a significant improvement over the majority baseline, the n-gram baseline, and the pre-trained embedding model, (all by the approximate randomization test, $p < 0.01$). The high-correlation features used by this model are LIWC's clout, tone, and “I” pronoun features, and we investigate the use of these features in the other model types. Particularly, we apply different architectures (GRNN and CNN) and different input representations (pretrained Word2Vec, domain-specific BERT). We find that our logistic regression classifier described above achieves comparable performance to BERT-base (approximate randomization test, $p > 0.5$) with the added benefits of increased interpretability and less intensive training. Additionally, domain-specific word embeddings trained on our unlabeled corpus (Word2Vec, BERT) significantly outperform n-grams or pretrained embeddings, as expected, signaling the importance of domain knowledge in this problem. We note that our basic deep learning models do not perform as well as our traditional supervised models or BERT, although they consistently, significantly outperform the majority baseline. We believe this is due to a serious lack of data; our labeled dataset is orders of magnitude smaller than neural models typically require to perform well. We expect that neural models can make good use of our large unlabeled dataset, which we plan to explore in future work. We believe that the superior performance of the pretrained BERT-base model (which uses no additional features) on our dataset supports this hypothesis as well. In tab:data-and-feat-comparison, we examine the impact of different feature sets and levels of annotator agreement on our logistic regressor with domain-specific Word2Vec embeddings and find consistent patterns supporting this model. First, we see a tradeoff between data size and data quality, where lower-agreement data (which can be seen as lower-quality) results in worse performance, but the larger 80% agreement data consistently outperforms the smaller perfect agreement data. Additionally, LIWC features consistently perform well while syntactic features consistently do not, and we see a trend towards the quality of features over their quantity; those with the highest Pearson correlation with the train set (which all happen to be LIWC features) outperform sets with lower correlations, which in turn outperform the set of all features. This suggests that stress detection is a highly lexical problem, and in particular, resources developed with psychological applications in mind, like LIWC, are very helpful. Finally, we perform an error analysis of the two best-performing models. Although the dataset is nearly balanced, both BERT-base and our best logistic regression model greatly overclassify stress, as shown in tab:confusion-matrices, and they broadly overlap but do differ in their predictions (disagreeing with one another on approximately 100 instances). We note that the examples misclassified by both models are often, though not always, ones with low annotator agreement (with the average percent agreement for misclassified examples being 0.55 for BERT and 0.61 for logistic regression). Both models seem to have trouble with less explicit expressions of stress, framing negative experiences in a positive or retrospective way, and stories where another person aside from the poster is the focus; these types of errors are difficult to capture with the features we used (primarily lexical), and further work should be aware of them. We include some examples of these errors in tab:error-analysis-paper, and further illustrative examples are available in the appendix. ### Conclusion and Future Work
In this paper, we present a new dataset, Dreaddit, for stress classification in social media, and find the current baseline at 80% F-score on the binary stress classification problem. We believe this dataset has the potential to spur development of sophisticated, interpretable models of psychological stress. Analysis of our data and our models shows that stress detection is a highly lexical problem benefitting from domain knowledge, but we note there is still room for improvement, especially in incorporating the framing and intentions of the writer. We intend for our future work to use this dataset to contextualize stress and offer explanations using the content features of the text. Additional interesting problems applicable to this dataset include the development of effective distant labeling schemes, which is a significant first step to developing a quantitative model of stress. ### Acknowledgements
We would like to thank Fei-Tzin Lee, Christopher Hidey, Diana Abagyan, and our anonymous reviewers for their insightful comments during the writing of this paper. This research was funded in part by a Presidential Fellowship from the Fu Foundation School of Engineering and Applied Science at Columbia University. ### Data Samples
We include several full posts (with identifying information removed and whitespace collapsed) in fig:data-appendix-1,fig:data-appendix-2,fig:data-appendix-3,fig:data-appendix-4. Posts are otherwise reproduced exactly as obtained (with spelling errors, etc.). The selected examples are deliberately of a reasonable but fairly typical length for readability and space concerns; recall that our average post length is 420 tokens, longer for interpersonal subreddits and shorter for other subreddits. ### Full Annotation Guidelines
We provide our annotation instructions in full in fig:annotation. Mechanical Turk Workers were given these instructions and examples followed by five text segments (one of which was one of our 50 check questions) and allowed to select “Stress”, “Not Stress', or “Can't Tell” for each. Workers were given one hour to complete the HIT and paid $0.12 for each HIT where they correctly answered the check question, with a limit of 30 total submissions per Worker. ### Parameter Settings
We tune our traditional supervised models' parameters using grid search, all as implemented in Python's scikit-learn library BIBREF25. Our best model uses unbalanced class weights, L2 penalty, and a constant term C=10, with other parameters at their default values. All cross-validation runs were initialized with the same random seed for comparability and reproducibility. We train each of our neural models with the Adam optimizer BIBREF24 for up to ten epochs with early stopping measured on the validation set. We apply a dropout rate of 0.5 during training in the recurrent layers and after the convolutional layers. We set our hidden sizes (i.e., the output of the recurrent and pooling layers) as well as our batch size to 128, and tune our learning rate to $5\cdot 10^{-4}$; we set these parameters relatively small to try to work with our small data. We also experiment with scheduling the learning rate on plateau of the validation loss, and with pre-training the models on a much larger sentiment dataset, the Stanford Sentiment Treebank BIBREF26, to help combat the problem of small data, but this does not improve the performance of our neural networks. ### Error Analysis Examples
As a supplement to our error analysis discussion in sec:results, we provide additional examples of test data points which one or both of our best models (BERT-base or our best logistic regressor with embeddings trained on our unlabeled corpus and high-correlation discrete features) failed to classify correctly in tab:error-analysis-appendix. Figure 1: An example of stress being expressed in social media from our dataset, from a post in r/anxiety (reproduced exactly as found). Some possible expressions of stress are highlighted. Table 1: Data Statistics. We include ten total subreddits from five domains in our dataset. Because some subreddits are more or less popular, the amount of data in each domain varies. We endeavor to label a comparable amount of data from each domain for training and testing. Table 2: Data Examples. Examples from our dataset with their domains, assigned labels, and number of annotators who agreed on the majority label (reproduced exactly as found, except that a link to the GoFundMe has been removed in the last example). Annotators labeled these five-sentence segments of larger posts. Table 3: LIWC Analysis by Domain. Results from our analysis using LIWC word lists. Each term in quotations refers to a specific word list curated by LIWC; percentage refers to the percent of words in the domain that are included in that word list, and coverage refers to the percent of words in that word list which appear in the domain. Figure 2: Lexical Diversity by Domain. Yule’s I measure (on the y-axes) is plotted against domain size (on the x-axes) and each domain is plotted as a point on two graphics. a) measures the lexical diversity of all words in the vocabulary, while b) deletes all words that were not included in LIWC’s negative emotion word list. Table 4: LIWC Analysis by Label. Results from our analysis using LIWC word lists, with the same definitions as in Table 3. First-person pronouns (“1st-Person”) use the LIWC “I” word list. Table 5: Complexity by Label. Measures of syntactic complexity for stressful and non-stressful data. Figure 3: Lexical Diversity by Agreement. Yule’s I measure (on the y-axis) is plotted against domain size (on the x-axis) for each level of annotator agreement. Perfect means all annotators agreed; High, 4/5 or more; Medium, 3/5 or more; and Low, everything else. Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p < 0.01). Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold. Table 8: Confusion Matrices. Confusion matrices of our best models and the gold labels. 0 represents data labeled not stressed while 1 represents data labeled stressed. Table 9: Error Analysis Examples. Examples of test samples our models failed to classify correctly.“BERT” refers to the state-of-the-art BERT-base model, while “LogReg” is our best logistic regressor described in section 6. Figure 8: Our full annotation instructions. Table 10: Additional Error Analysis Examples. Supplementary examples for our error analysis.“BERT” refers to the state-of-the-art BERT-base model, while “LogReg” is our best logistic regressor described in section 6. | abuse, social, anxiety, PTSD, and financial |
Why did Val become so tired during her trek across the desert?
A. She did not have the technology that enabled Ron to persist
B. She became consumed with resentment for having traveled to Mars
C. She had trouble adjusting to the Martian climate and terrain
D. Uranium was seeping through her space suit
| THE HUNTED HEROES By ROBERT SILVERBERG The planet itself was tough enough—barren, desolate, forbidding; enough to stop the most adventurous and dedicated. But they had to run head-on against a mad genius who had a motto: Death to all Terrans! "Let's keep moving," I told Val. "The surest way to die out here on Mars is to give up." I reached over and turned up the pressure on her oxymask to make things a little easier for her. Through the glassite of the mask, I could see her face contorted in an agony of fatigue. And she probably thought the failure of the sandcat was all my fault, too. Val's usually about the best wife a guy could ask for, but when she wants to be she can be a real flying bother. It was beyond her to see that some grease monkey back at the Dome was at fault—whoever it was who had failed to fasten down the engine hood. Nothing but what had stopped us could stop a sandcat: sand in the delicate mechanism of the atomic engine. But no; she blamed it all on me somehow: So we were out walking on the spongy sand of the Martian desert. We'd been walking a good eight hours. "Can't we turn back now, Ron?" Val pleaded. "Maybe there isn't any uranium in this sector at all. I think we're crazy to keep on searching out here!" I started to tell her that the UranCo chief had assured me we'd hit something out this way, but changed my mind. When Val's tired and overwrought there's no sense in arguing with her. I stared ahead at the bleak, desolate wastes of the Martian landscape. Behind us somewhere was the comfort of the Dome, ahead nothing but the mazes and gullies of this dead world. He was a cripple in a wheelchair—helpless as a rattlesnake. "Try to keep going, Val." My gloved hand reached out and clumsily enfolded hers. "Come on, kid. Remember—we're doing this for Earth. We're heroes." She glared at me. "Heroes, hell!" she muttered. "That's the way it looked back home, but, out there it doesn't seem so glorious. And UranCo's pay is stinking." "We didn't come out here for the pay, Val." "I know, I know, but just the same—" It must have been hell for her. We had wandered fruitlessly over the red sands all day, both of us listening for the clicks of the counter. And the geigers had been obstinately hushed all day, except for their constant undercurrent of meaningless noises. Even though the Martian gravity was only a fraction of Earth's, I was starting to tire, and I knew it must have been really rough on Val with her lovely but unrugged legs. "Heroes," she said bitterly. "We're not heroes—we're suckers! Why did I ever let you volunteer for the Geig Corps and drag me along?" Which wasn't anywhere close to the truth. Now I knew she was at the breaking point, because Val didn't lie unless she was so exhausted she didn't know what she was doing. She had been just as much inflamed by the idea of coming to Mars to help in the search for uranium as I was. We knew the pay was poor, but we had felt it a sort of obligation, something we could do as individuals to keep the industries of radioactives-starved Earth going. And we'd always had a roving foot, both of us. No, we had decided together to come to Mars—the way we decided together on everything. Now she was turning against me. I tried to jolly her. "Buck up, kid," I said. I didn't dare turn up her oxy pressure any higher, but it was obvious she couldn't keep going. She was almost sleep-walking now. We pressed on over the barren terrain. The geiger kept up a fairly steady click-pattern, but never broke into that sudden explosive tumult that meant we had found pay-dirt. I started to feel tired myself, terribly tired. I longed to lie down on the soft, spongy Martian sand and bury myself. I looked at Val. She was dragging along with her eyes half-shut. I felt almost guilty for having dragged her out to Mars, until I recalled that I hadn't. In fact, she had come up with the idea before I did. I wished there was some way of turning the weary, bedraggled girl at my side back into the Val who had so enthusiastically suggested we join the Geigs. Twelve steps later, I decided this was about as far as we could go. I stopped, slipped out of the geiger harness, and lowered myself ponderously to the ground. "What'samatter, Ron?" Val asked sleepily. "Something wrong?" "No, baby," I said, putting out a hand and taking hers. "I think we ought to rest a little before we go any further. It's been a long, hard day." It didn't take much to persuade her. She slid down beside me, curled up, and in a moment she was fast asleep, sprawled out on the sands. Poor kid , I thought. Maybe we shouldn't have come to Mars after all. But, I reminded myself, someone had to do the job. A second thought appeared, but I squelched it: Why the hell me? I looked down at Valerie's sleeping form, and thought of our warm, comfortable little home on Earth. It wasn't much, but people in love don't need very fancy surroundings. I watched her, sleeping peacefully, a wayward lock of her soft blonde hair trailing down over one eyebrow, and it seemed hard to believe that we'd exchanged Earth and all it held for us for the raw, untamed struggle that was Mars. But I knew I'd do it again, if I had the chance. It's because we wanted to keep what we had. Heroes? Hell, no. We just liked our comforts, and wanted to keep them. Which took a little work. Time to get moving. But then Val stirred and rolled over in her sleep, and I didn't have the heart to wake her. I sat there, holding her, staring out over the desert, watching the wind whip the sand up into weird shapes. The Geig Corps preferred married couples, working in teams. That's what had finally decided it for us—we were a good team. We had no ties on Earth that couldn't be broken without much difficulty. So we volunteered. And here we are. Heroes. The wind blasted a mass of sand into my face, and I felt it tinkle against the oxymask. I glanced at the suit-chronometer. Getting late. I decided once again to wake Val. But she was tired. And I was tired too, tired from our wearying journey across the empty desert. I started to shake Val. But I never finished. It would be so nice just to lean back and nuzzle up to her, down in the sand. So nice. I yawned, and stretched back. I awoke with a sudden startled shiver, and realized angrily I had let myself doze off. "Come on, Val," I said savagely, and started to rise to my feet. I couldn't. I looked down. I was neatly bound in thin, tough, plastic tangle-cord, swathed from chin to boot-bottoms, my arms imprisoned, my feet caught. And tangle-cord is about as easy to get out of as a spider's web is for a trapped fly. It wasn't Martians that had done it. There weren't any Martians, hadn't been for a million years. It was some Earthman who had bound us. I rolled my eyes toward Val, and saw that she was similarly trussed in the sticky stuff. The tangle-cord was still fresh, giving off a faint, repugnant odor like that of drying fish. It had been spun on us only a short time ago, I realized. "Ron—" "Don't try to move, baby. This stuff can break your neck if you twist it wrong." She continued for a moment to struggle futilely, and I had to snap, "Lie still, Val!" "A very wise statement," said a brittle, harsh voice from above me. I looked up and saw a helmeted figure above us. He wasn't wearing the customary skin-tight pliable oxysuits we had. He wore an outmoded, bulky spacesuit and a fishbowl helmet, all but the face area opaque. The oxygen cannisters weren't attached to his back as expected, though. They were strapped to the back of the wheelchair in which he sat. Through the fishbowl I could see hard little eyes, a yellowed, parchment-like face, a grim-set jaw. I didn't recognize him, and this struck me odd. I thought I knew everyone on sparsely-settled Mars. Somehow I'd missed him. What shocked me most was that he had no legs. The spacesuit ended neatly at the thighs. He was holding in his left hand the tanglegun with which he had entrapped us, and a very efficient-looking blaster was in his right. "I didn't want to disturb your sleep," he said coldly. "So I've been waiting here for you to wake up." I could just see it. He might have been sitting there for hours, complacently waiting to see how we'd wake up. That was when I realized he must be totally insane. I could feel my stomach-muscles tighten, my throat constrict painfully. Then anger ripped through me, washing away the terror. "What's going on?" I demanded, staring at the half of a man who confronted us from the wheelchair. "Who are you?" "You'll find out soon enough," he said. "Suppose now you come with me." He reached for the tanglegun, flipped the little switch on its side to MELT, and shot a stream of watery fluid over our legs, keeping the blaster trained on us all the while. Our legs were free. "You may get up now," he said. "Slowly, without trying to make trouble." Val and I helped each other to our feet as best we could, considering our arms were still tightly bound against the sides of our oxysuits. "Walk," the stranger said, waving the tanglegun to indicate the direction. "I'll be right behind you." He holstered the tanglegun. I glimpsed the bulk of an outboard atomic rigging behind him, strapped to the back of the wheelchair. He fingered a knob on the arm of the chair and the two exhaust ducts behind the wheel-housings flamed for a moment, and the chair began to roll. Obediently, we started walking. You don't argue with a blaster, even if the man pointing it is in a wheelchair. "What's going on, Ron?" Val asked in a low voice as we walked. Behind us the wheelchair hissed steadily. "I don't quite know, Val. I've never seen this guy before, and I thought I knew everyone at the Dome." "Quiet up there!" our captor called, and we stopped talking. We trudged along together, with him following behind; I could hear the crunch-crunch of the wheelchair as its wheels chewed into the sand. I wondered where we were going, and why. I wondered why we had ever left Earth. The answer to that came to me quick enough: we had to. Earth needed radioactives, and the only way to get them was to get out and look. The great atomic wars of the late 20th Century had used up much of the supply, but the amount used to blow up half the great cities of the world hardly compared with the amount we needed to put them back together again. In three centuries the shattered world had been completely rebuilt. The wreckage of New York and Shanghai and London and all the other ruined cities had been hidden by a shining new world of gleaming towers and flying roadways. We had profited by our grandparents' mistakes. They had used their atomics to make bombs. We used ours for fuel. It was an atomic world. Everything: power drills, printing presses, typewriters, can openers, ocean liners, powered by the inexhaustible energy of the dividing atom. But though the energy is inexhaustible, the supply of nuclei isn't. After three centuries of heavy consumption, the supply failed. The mighty machine that was Earth's industry had started to slow down. And that started the chain of events that led Val and me to end up as a madman's prisoners, on Mars. With every source of uranium mined dry on Earth, we had tried other possibilities. All sorts of schemes came forth. Project Sea-Dredge was trying to get uranium from the oceans. In forty or fifty years, they'd get some results, we hoped. But there wasn't forty or fifty years' worth of raw stuff to tide us over until then. In a decade or so, our power would be just about gone. I could picture the sort of dog-eat-dog world we'd revert back to. Millions of starving, freezing humans tooth-and-clawing in it in the useless shell of a great atomic civilization. So, Mars. There's not much uranium on Mars, and it's not easy to find or any cinch to mine. But what little is there, helps. It's a stopgap effort, just to keep things moving until Project Sea-Dredge starts functioning. Enter the Geig Corps: volunteers out on the face of Mars, combing for its uranium deposits. And here we are, I thought. After we walked on a while, a Dome became visible up ahead. It slid up over the crest of a hill, set back between two hummocks on the desert. Just out of the way enough to escape observation. For a puzzled moment I thought it was our Dome, the settlement where all of UranCo's Geig Corps were located, but another look told me that this was actually quite near us and fairly small. A one-man Dome, of all things! "Welcome to my home," he said. "The name is Gregory Ledman." He herded us off to one side of the airlock, uttered a few words keyed to his voice, and motioned us inside when the door slid up. When we were inside he reached up, clumsily holding the blaster, and unscrewed the ancient spacesuit fishbowl. His face was a bitter, dried-up mask. He was a man who hated. The place was spartanly furnished. No chairs, no tape-player, no decoration of any sort. Hard bulkhead walls, rivet-studded, glared back at us. He had an automatic chef, a bed, and a writing-desk, and no other furniture. Suddenly he drew the tanglegun and sprayed our legs again. We toppled heavily to the floor. I looked up angrily. "I imagine you want to know the whole story," he said. "The others did, too." Valerie looked at me anxiously. Her pretty face was a dead white behind her oxymask. "What others?" "I never bothered to find out their names," Ledman said casually. "They were other Geigs I caught unawares, like you, out on the desert. That's the only sport I have left—Geig-hunting. Look out there." He gestured through the translucent skin of the Dome, and I felt sick. There was a little heap of bones lying there, looking oddly bright against the redness of the sands. They were the dried, parched skeletons of Earthmen. Bits of cloth and plastic, once oxymasks and suits, still clung to them. Suddenly I remembered. There had been a pattern there all the time. We didn't much talk about it; we chalked it off as occupational hazards. There had been a pattern of disappearances on the desert. I could think of six, eight names now. None of them had been particularly close friends. You don't get time to make close friends out here. But we'd vowed it wouldn't happen to us. It had. "You've been hunting Geigs?" I asked. " Why? What've they ever done to you?" He smiled, as calmly as if I'd just praised his house-keeping. "Because I hate you," he said blandly. "I intend to wipe every last one of you out, one by one." I stared at him. I'd never seen a man like this before; I thought all his kind had died at the time of the atomic wars. I heard Val sob, "He's a madman!" "No," Ledman said evenly. "I'm quite sane, believe me. But I'm determined to drive the Geigs—and UranCo—off Mars. Eventually I'll scare you all away." "Just pick us off in the desert?" "Exactly," replied Ledman. "And I have no fears of an armed attack. This place is well fortified. I've devoted years to building it. And I'm back against those hills. They couldn't pry me out." He let his pale hand run up into his gnarled hair. "I've devoted years to this. Ever since—ever since I landed here on Mars." "What are you going to do with us?" Val finally asked, after a long silence. He didn't smile this time. "Kill you," he told her. "Not your husband. I want him as an envoy, to go back and tell the others to clear off." He rocked back and forth in his wheelchair, toying with the gleaming, deadly blaster in his hand. We stared in horror. It was a nightmare—sitting there, placidly rocking back and forth, a nightmare. I found myself fervently wishing I was back out there on the infinitely safer desert. "Do I shock you?" he asked. "I shouldn't—not when you see my motives." "We don't see them," I snapped. "Well, let me show you. You're on Mars hunting uranium, right? To mine and ship the radioactives back to Earth to keep the atomic engines going. Right?" I nodded over at our geiger counters. "We volunteered to come to Mars," Val said irrelevantly. "Ah—two young heroes," Ledman said acidly. "How sad. I could almost feel sorry for you. Almost." "Just what is it you're after?" I said, stalling, stalling. "Atomics cost me my legs," he said. "You remember the Sadlerville Blast?" he asked. "Of course." And I did, too. I'd never forget it. No one would. How could I forget that great accident—killing hundreds, injuring thousands more, sterilizing forty miles of Mississippi land—when the Sadlerville pile went up? "I was there on business at the time," Ledman said. "I represented Ledman Atomics. I was there to sign a new contract for my company. You know who I am, now?" I nodded. "I was fairly well shielded when it happened. I never got the contract, but I got a good dose of radiation instead. Not enough to kill me," he said. "Just enough to necessitate the removal of—" he indicated the empty space at his thighs. "So I got off lightly." He gestured at the wheelchair blanket. I still didn't understand. "But why kill us Geigs? We had nothing to do with it." "You're just in this by accident," he said. "You see, after the explosion and the amputation, my fellow-members on the board of Ledman Atomics decided that a semi-basket case like myself was a poor risk as Head of the Board, and they took my company away. All quite legal, I assure you. They left me almost a pauper!" Then he snapped the punchline at me. "They renamed Ledman Atomics. Who did you say you worked for?" I began, "Uran—" "Don't bother. A more inventive title than Ledman Atomics, but not quite as much heart, wouldn't you say?" He grinned. "I saved for years; then I came to Mars, lost myself, built this Dome, and swore to get even. There's not a great deal of uranium on this planet, but enough to keep me in a style to which, unfortunately, I'm no longer accustomed." He consulted his wrist watch. "Time for my injection." He pulled out the tanglegun and sprayed us again, just to make doubly certain. "That's another little souvenir of Sadlerville. I'm short on red blood corpuscles." He rolled over to a wall table and fumbled in a container among a pile of hypodermics. "There are other injections, too. Adrenalin, insulin. Others. The Blast turned me into a walking pin-cushion. But I'll pay it all back," he said. He plunged the needle into his arm. My eyes widened. It was too nightmarish to be real. I wasn't seriously worried about his threat to wipe out the entire Geig Corps, since it was unlikely that one man in a wheelchair could pick us all off. No, it wasn't the threat that disturbed me, so much as the whole concept, so strange to me, that the human mind could be as warped and twisted as Ledman's. I saw the horror on Val's face, and I knew she felt the same way I did. "Do you really think you can succeed?" I taunted him. "Really think you can kill every Earthman on Mars? Of all the insane, cockeyed—" Val's quick, worried head-shake cut me off. But Ledman had felt my words, all right. "Yes! I'll get even with every one of you for taking away my legs! If we hadn't meddled with the atom in the first place, I'd be as tall and powerful as you, today—instead of a useless cripple in a wheelchair." "You're sick, Gregory Ledman," Val said quietly. "You've conceived an impossible scheme of revenge and now you're taking it out on innocent people who've done nothing, nothing at all to you. That's not sane!" His eyes blazed. "Who are you to talk of sanity?" Uneasily I caught Val's glance from a corner of my eye. Sweat was rolling down her smooth forehead faster than the auto-wiper could swab it away. "Why don't you do something? What are you waiting for, Ron?" "Easy, baby," I said. I knew what our ace in the hole was. But I had to get Ledman within reach of me first. "Enough," he said. "I'm going to turn you loose outside, right after—" " Get sick! " I hissed to Val, low. She began immediately to cough violently, emitting harsh, choking sobs. "Can't breathe!" She began to yell, writhing in her bonds. That did it. Ledman hadn't much humanity left in him, but there was a little. He lowered the blaster a bit and wheeled one-hand over to see what was wrong with Val. She continued to retch and moan most horribly. It almost convinced me. I saw Val's pale, frightened face turn to me. He approached and peered down at her. He opened his mouth to say something, and at that moment I snapped my leg up hard, tearing the tangle-cord with a snicking rasp, and kicked his wheelchair over. The blaster went off, burning a hole through the Dome roof. The automatic sealers glued-in instantly. Ledman went sprawling helplessly out into the middle of the floor, the wheelchair upended next to him, its wheels slowly revolving in the air. The blaster flew from his hands at the impact of landing and spun out near me. In one quick motion I rolled over and covered it with my body. Ledman clawed his way to me with tremendous effort and tried wildly to pry the blaster out from under me, but without success. I twisted a bit, reached out with my free leg, and booted him across the floor. He fetched up against the wall of the Dome and lay there. Val rolled over to me. "Now if I could get free of this stuff," I said, "I could get him covered before he comes to. But how?" "Teamwork," Val said. She swivelled around on the floor until her head was near my boot. "Push my oxymask off with your foot, if you can." I searched for the clamp and tried to flip it. No luck, with my heavy, clumsy boot. I tried again, and this time it snapped open. I got the tip of my boot in and pried upward. The oxymask came off, slowly, scraping a jagged red scratch up the side of Val's neck as it came. "There," she breathed. "That's that." I looked uneasily at Ledman. He was groaning and beginning to stir. Val rolled on the floor and her face lay near my right arm. I saw what she had in mind. She began to nibble the vile-tasting tangle-cord, running her teeth up and down it until it started to give. She continued unfailingly. Finally one strand snapped. Then another. At last I had enough use of my hand to reach out and grasp the blaster. Then I pulled myself across the floor to Ledman, removed the tanglegun, and melted the remaining tangle-cord off. My muscles were stiff and bunched, and rising made me wince. I turned and freed Val. Then I turned and faced Ledman. "I suppose you'll kill me now," he said. "No. That's the difference between sane people and insane," I told him. "I'm not going to kill you at all. I'm going to see to it that you're sent back to Earth." " No! " he shouted. "No! Anything but back there. I don't want to face them again—not after what they did to me—" "Not so loud," I broke in. "They'll help you on Earth. They'll take all the hatred and sickness out of you, and turn you into a useful member of society again." "I hate Earthmen," he spat out. "I hate all of them." "I know," I said sarcastically. "You're just all full of hate. You hated us so much that you couldn't bear to hang around on Earth for as much as a year after the Sadlerville Blast. You had to take right off for Mars without a moment's delay, didn't you? You hated Earth so much you had to leave." "Why are you telling all this to me?" "Because if you'd stayed long enough, you'd have used some of your pension money to buy yourself a pair of prosthetic legs, and then you wouldn't need this wheelchair." Ledman scowled, and then his face went belligerent again. "They told me I was paralyzed below the waist. That I'd never walk again, even with prosthetic legs, because I had no muscles to fit them to." "You left Earth too quickly," Val said. "It was the only way," he protested. "I had to get off—" "She's right," I told him. "The atom can take away, but it can give as well. Soon after you left they developed atomic-powered prosthetics—amazing things, virtually robot legs. All the survivors of the Sadlerville Blast were given the necessary replacement limbs free of charge. All except you. You were so sick you had to get away from the world you despised and come here." "You're lying," he said. "It's not true!" "Oh, but it is," Val smiled. I saw him wilt visibly, and for a moment I almost felt sorry for him, a pathetic legless figure propped up against the wall of the Dome at blaster-point. But then I remembered he'd killed twelve Geigs—or more—and would have added Val to the number had he had the chance. "You're a very sick man, Ledman," I said. "All this time you could have been happy, useful on Earth, instead of being holed up here nursing your hatred. You might have been useful, on Earth. But you decided to channel everything out as revenge." "I still don't believe it—those legs. I might have walked again. No—no, it's all a lie. They told me I'd never walk," he said, weakly but stubbornly still. I could see his whole structure of hate starting to topple, and I decided to give it the final push. "Haven't you wondered how I managed to break the tangle-cord when I kicked you over?" "Yes—human legs aren't strong enough to break tangle-cord that way." "Of course not," I said. I gave Val the blaster and slipped out of my oxysuit. "Look," I said. I pointed to my smooth, gleaming metal legs. The almost soundless purr of their motors was the only noise in the room. "I was in the Sadlerville Blast, too," I said. "But I didn't go crazy with hate when I lost my legs." Ledman was sobbing. "Okay, Ledman," I said. Val got him into his suit, and brought him the fishbowl helmet. "Get your helmet on and let's go. Between the psychs and the prosthetics men, you'll be a new man inside of a year." "But I'm a murderer!" "That's right. And you'll be sentenced to psych adjustment. When they're finished, Gregory Ledman the killer will be as dead as if they'd electrocuted you, but there'll be a new—and sane—Gregory Ledman." I turned to Val. "Got the geigers, honey?" For the first time since Ledman had caught us, I remembered how tired Val had been out on the desert. I realized now that I had been driving her mercilessly—me, with my chromium legs and atomic-powered muscles. No wonder she was ready to fold! And I'd been too dense to see how unfair I had been. She lifted the geiger harnesses, and I put Ledman back in his wheelchair. Val slipped her oxymask back on and fastened it shut. "Let's get back to the Dome in a hurry," I said. "We'll turn Ledman over to the authorities. Then we can catch the next ship for Earth." "Go back? Go back? If you think I'm backing down now and quitting you can find yourself another wife! After we dump this guy I'm sacking in for twenty hours, and then we're going back out there to finish that search-pattern. Earth needs uranium, honey, and I know you'd never be happy quitting in the middle like that." She smiled. "I can't wait to get out there and start listening for those tell-tale clicks." I gave a joyful whoop and swung her around. When I put her down, she squeezed my hand, hard. "Let's get moving, fellow hero," she said. I pressed the stud for the airlock, smiling. THE END Transcriber's Note: This etext was produced from Amazing Stories September 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. She did not have the technology that enabled Ron to persist |
Regarding Mr. Rudolph, what was the size of the left renal pelvis mass according to the histopathological report?
Choose the correct answer from the following options:
A. 2.5 cm
B. 3.4 cm
C. 4.3 cm
D. 5.1 cm
E. 6.0 cm
| ### Patient Report 0
**Dear colleague, **
We would like to inform you about our patient, Mr. Peter Rudolph, born
on 05/26/1954, who was under our care from 01/16/2019 to 01/17/2019.
**Diagnosis**: Suspected malignant mass at pyeloureteral junction/left
renal pelvis and suspicious paraaortic lymph nodes.
**Other Diagnoses:**
- Atrial fibrillation: Post-ablation in 2013
- pTCA stenting in 2010 for acute myocardial infarction
- Suspected soft tissue rheumatism, currently no complaints
- Laparoscopic cholecystectomy in 2012
- Tonsillectomy
- Obesity
**Procedure:** Diagnostic ureterorenoscopy on the left with biopsy and
left DJ stent placement on 01/16/2019.
**Current Presentation:** Elective presentation for further endoscopic
evaluation of the unclear mass in the pyeloureteral junction area
involving the proximal ureter and renal pelvis. Additionally, abnormal
lymph nodes were observed in external imaging. The patient reports
occasional mild discomfort in the left upper abdomen.
**Physical Examination:** Soft abdomen, no pressure pain.
**CT Thorax (Plain) from 01/16/2019:**
Presence of axillary and mediastinal lymph nodes with borderline
enlarged lymph nodes ventral to the tracheal bifurcation (approximately
10 mm).
Calcification of aortic valves. Aortic and coronary sclerosis.
No suspicious lesions detected within the lungs. No pleural effusions.
No infiltrates.
History of cholecystectomy.
Known soft tissue density formation in the left renal hilum from the
previous examination.
The assessment of other upper abdominal organs that were visible and
could be evaluated natively was unremarkable.
No evidence of suspicious retrocrural lymph nodes. Vascular sclerosis.
**Skeletal Assessment:** Degenerative changes in the spine. No evidence
of suspicious lesions.
**Assessment:** No definitive evidence of metastatic lesions in the
lungs. Increased presence of mediastinal lymph nodes, some borderline
enlarged, ventral to the tracheal bifurcation. Differential diagnosis
includes nonspecific findings or lymph node metastases, which cannot be
excluded based solely on CT morphology.
**Main Diagnosis and Main Procedure from the Surgical Report:**
- Surgical Diagnosis: Unclear proximal ureter tumor on the left
- Unclear tumor in the left renal pelvis
- Surgical Procedure: Diagnostic ureterorenoscopy on the left
- Biopsy of the left ureter
- Retrograde urography on the left
- DJ catheter placement on the left
- Diagnostic urethroscopy
**Procedure:**
The patient underwent a diagnostic ureterorenoscopy, which proceeded
without complications. During the procedure, a total of eight biopsies
were successfully obtained from the ureter for histological evaluation.
Cytological samples were also collected from both the ureter and renal
pelvis. Although there was a stenosing tumor present, endoscopic passage
into the renal pelvis was successfully accomplished.
Following the diagnostic procedure, a left-sided double-J catheter was
placed under radiographic control. Additionally, a urinary catheter was
inserted. It was observed that the initial urine output appeared
hemorrhagic, but it subsequently cleared to a normal coloration.
For post-procedural management, plans are in place for the DJ catheter
to be removed, the timing of which will be guided by improvements in the
color of the urine as well as the patient\'s overall clinical status. A
sonogram will be performed prior to discharge as part of routine
follow-up. Moreover, the patient has been scheduled for counseling to
address the significantly elevated PSA values noted in recent lab tests.
**Diagnosis:** Unclear proximal ureter tumor on the left. Unclear tumor
in the left renal pelvis
**Type of Surgery:**
- Diagnostic ureterorenoscopy on the left
- Biopsy of the left ureter
- Retrograde urography on the left
- DJ catheter placement on the left
- Diagnostic urethroscopy
**Anesthesia Type:** Laryngeal mask
**Report:** Indication: Unclear mass in the left renal pelvis. Elective
diagnostic ureterorenoscopy for further assessment. Written consent is
obtained. The urine is sterile. The procedure is conducted under
antibacterial prophylaxis with Ampicillin/Sulbactam 3g.
1. Standard preparation, lithotomy position on the X-ray unit, sterile
scrubbing/disinfection, and sterile draping by nursing staff.
Verification and approval.
2. Anesthesiology and urology discussion. Surgery clearance. Antibiotic
administration.
3. Initial urethroscopy was unremarkable, with no signs of tumors.
4. Semi-rigid ureterorenoscopy with a 6.5/8.5 continuous-flow
ureterorenoscopy. Unremarkable ureterorenoscopy of the entire ureter
until just before the pyeloureteral junction, where a papillary
stenotic constriction was encountered, impeding further passage with
the endoscope. Cytology collection (20 mL) was performed. Retrograde
urography was conducted to visualize the proximal collecting system,
and biopsies were obtained from the accessible portions, with 8
biopsies taken using an access sheath. Even with flexible
Viperscope, further passage was not feasible.
5. A DJ catheter was inserted under radiographic guidance over a
guidewire. Collection of irrigation cytology (5 ml) from the renal
pelvis.
6. Insertion of a DJ catheter (7/28 Vortek) over the indwelling wire
and endoscope under radiographic control. Documentation of images.
7. Placement of a permanent catheter. Urine initially appeared bloody
but cleared rapidly.
**Conclusion:** Uncomplicated diagnostic ureterorenoscopy with biopsy of
the ureter (8 biopsies taken), cytology collection from the ureter and
renal pelvis, and endoscopic passage into the renal pelvis in the
presence of a stenosing tumor. DJ catheter placement on the left.
Endoscopic assessment of the urinary bladder and distal ureter revealed
no abnormalities. Follow-up steps:
- Removal of the urinary catheter based on urine appearance and
patient vigilance.
- Sonography before discharge.
- Further steps determined by histology.
- Recommend evaluation and clarification of the significantly elevated
PSA value.
**Internal Cytological Report Clinical Details: Sample Date: 01/16/2019
**
1. Left ureter (100 mL colorless, clear)
2. Left renal pelvis (50 mL brown) (Papanicolaou staining)
Both materials contain increased urinary sediment, along with
granulocytes, erythrocytes, and urothelial cells from various layers
with multi-nuclear surface cells. Material 1 also shows papillary
arrangements of urothelial cells, some of which have peripheral
hyperchromatic cell nuclei and altered nuclear-plasma ratios. Material 2
shows individual papillary urothelial cell arrangements with similar
nuclear quality, hyperchromasia, and eccentric placement within the
cytoplasm, as well as nuclear rounding. Numerous individual urothelial
cells are also present with significantly rounded and enlarged cell
nuclei, frequently in a peripheral location with hyperchromasia.
**Critical Findings Report:**
1. Detection of a papillary-structured urothelial population with
nuclear changes, which may be related to instrumentation. Malignant
urothelial proliferation cannot be definitively ruled out.
2. Abundant cell material with papillary and single atypical urothelia,
highly suspicious for urothelial carcinoma cells.
**Diagnostic Classification:** Suspicious
**Internal Histopathological Report**
**Clinical Details/Question:** Endoscopic suspicion of urothelial
carcinoma.
**Macroscopy:**
1. Left proximal ureter: Unfixed nephrectomy specimen measuring 9.2 x
6.5 x 5.2 cm with a maximum 4 cm wide perirenal fat tissue and
maximum 1 cm wide perihilar fat tissue. Also, a 5 cm long ureter,
max 1 cm hilar vessels, and a 2.1 x 1.3 x 0.8 cm adrenal gland at
the upper pole of the kidney. On the sections at the renal hilum,
there is a maximum 4.3 cm grayish induration. No clear infiltration
of vessels by the induration is visible macroscopically. No
connection between the induration and the adrenal gland. The minimal
distance from the induration to the specimen edge at the renal hilum
is focally \< 0.1 cm. Furthermore, the renal pelvis system is
dilated, and there is a maximum 0.4 cm grayish indurated nodule in
the perirenal fat tissue.
**Therapy and Progression:** After thorough preparation and patient
counseling, we successfully performed the above procedure on 01/16/2019
without complications. Intraoperatively, a stenotic process reaching the
proximal ureter was observed, preventing passage into the renal pelvis.
Cytology and biopsy were obtained, and a left DJ stent was placed. The
postoperative course was uneventful. We were able to remove the
transurethral catheter upon clearing of urine and discharged the patient
to your outpatient care.
**Current Recommendations:**
- We request regular follow-up urological evaluations.
- Given the histological findings and highly suspicious radiological
findings for a malignant mass, we recommend performing an isotope
renogram to assess separate kidney function. An appointment has been
scheduled for 03/05/2019. We ask the patient to visit our
preoperative outpatient clinic on the same day to prepare for left
nephroureterectomy.
- The surgical procedure is scheduled for 03/20/2019.
- In case of acute urological symptoms, immediate reevaluation is
welcome at any time.
### Patient Report 1
**Dear colleague, **
We would like to report to you regarding our mutual patient Mr. Peter
Rudolph, born on 05/26/1954, who was under our care from 03/17/2019 to
04/01/2019.
**Diagnosis:** Urothelial carcinoma of the renal pelvis, high grade,
maximum size 4.3 cm. TNM Classification (8th edition, 2017): pT3, pN0
(0/11), M1 (ADR), Pn1, L1, V1.
**Other Diagnoses:**
- Atrial fibrillation: History of ablation in 2013
- History of pTCA stenting in 2010 due to acute myocardial infarction
- Suspected soft tissue rheumatism
- History of laparoscopic cholecystectomy in 2012
- History of tonsillectomy
- Obesity
**Procedures:** Open left nephroureterectomy with lymphadenectomy on
03/18/2019.
**Histology: Critical Findings Report:**
[Renal pelvis carcinoma (left kidney):]{.underline} Extensive
infiltration of a high-grade urothelial carcinoma in the renal pelvis
with infiltration of the renal parenchyma and perihilar adipose tissue,
maximum size 4.3 cm (1.). In the included adrenal tissue, central
evidence of small carcinoma infiltrates, to be interpreted as distant
metastasis (M1) with no macroscopic evidence of direct infiltration and
central localization.
[Resection Status]{.underline}: Carcinoma-free resection margins of the
proximal left ureter and ureter with mild florid urocystitis at the
ureteral orifice. Margin-forming carcinoma infiltrates at the main
preparation hilar near the renal vein, with the cranial hilar resection
margins I and II being carcinoma-free.
[Nodal Status:]{.underline} Eleven metastasis-free lymph nodes in the
submissions as follows: 0/1 (2.), 0/3 (4.), 0/6 (5.), 0/1 (6.).
Final TNM Classification (8th edition, 2017): pT3, pN0(0/11), M1 (ADR),
Pn1, L1, V1.
**Current Presentation:** The patient was electively scheduled for the
above-mentioned procedure. The patient does not report any complaints in
the urological field.
**Physical Examination:** Abdomen is soft, no tenderness. Both renal
beds are free.
**Fast Track Report on 03/18/2019: **
**Microscopy:** Histologically, there are extensive infiltrations of a
carcinoma growing in large solid formations with focal necrosis and
highly pleomorphic cell nuclei. In block 1A, there is a section of a
urothelium-lined duct structure with a transition from normal epithelium
to highly atypical epithelium and invasive carcinoma infiltrates. Broad
infiltration into adjacent fat tissue and renal parenchyma is observed.
Focal perineural sheath infiltration.
**Critical Findings**: Left renal pelvis carcinoma: Extensive
infiltrates of high-grade urothelial carcinoma in the renal pelvis,
infiltrating the renal parenchyma and perihilar fat tissue, max 4.3 cm
(1.). No direct infiltration of the accompanying adrenal gland is found.
Isolated abnormal cells in the adrenal gland parenchyma, which will be
further characterized to exclude the smallest carcinoma extensions. An
update will be provided after the completion of investigations.
**Resection Status:** Carcinoma-free resection margins of the proximal
left ureter with mild florid urocystitis near the ureteral orifice.
Carcinoma-forming infiltrates on the main specimen hilus near the renal
vein, but postresected cranial hili I and II were free of carcinoma.
**Nodal status**: Eleven metastasis-free lymph nodes in the submissions
as follows: 0/1 (2nd.), 0/3 (4.), 0/6 (5.), 0/1 (6.).
TNM classification (8th edition 2017): pT3, pN0 (0/11), Pn1, L1, V1.
**Urinanalysis from 03/20/2019**
**Material: Urine, Midstream Collected on 10/13/2020 at 00:00**
- Antimicrobial Agents: Negative. No evidence of growth-inhibiting
substances in the sample material.
- Bacterial Count per ml: 1,000 - 10,000
- Assessment: Bacterial counts of 1000 CFU/mL or higher can be
clinically relevant, especially with corresponding clinical
symptoms, especially in pure cultures of uropathogenic
microorganisms from midstream urine or single-catheter urine, along
with concomitant leukocyturia.
- Epithelial Cells (microscopic): \<20 epithelial cells/μL
- Leukocytes (microscopic): \<20 leukocytes/μL
- Microorganisms (microscopic): \<20 microorganisms/μL
**Pathogen:** Citrobacter koseri
**Antibiogram:**
- Cefalexin: Susceptible (S) with a minimum inhibitory concentration
(MIC) of 8
- Ampicillin/Amoxicillin: Resistant (R) with MIC \>=32
- Amoxicillin+Clavulanic Acid: Susceptible (S) with MIC of 8
- Piperacillin+Tazobactam: Susceptible (S) with MIC \<=4
- Cefotaxime: Susceptible (S) with MIC \<=1
- Ceftazidime: Susceptible (S) with MIC of 0.25
- Cefepime: Susceptible (S) with MIC \<=0.12
- Meropenem: Susceptible (S) with MIC \<=0.25
- Ertapenem: Susceptible (S) with MIC \<=0.5
- Cotrimoxazole: Susceptible (S) with MIC \<=20
- Gentamicin: Susceptible (S) with MIC \<=1
- Ciprofloxacin: Susceptible (S) with MIC \<=0.25
- Levofloxacin: Susceptible (S) with MIC \<=0.12
- Fosfomycin: Susceptible (S) with MIC \<=16
**Therapy and Progression:** After thorough preparation and patient
education, we performed the above-mentioned procedure on 03/18/2019,
without complications. The postoperative course was uneventful except
for prolonged milky secretion from the indwelling wound drainage. Prior
to catheter removal, a single instillation of Mitomycin was
administered. Regular examinations were unremarkable. We discharged Mr.
Rudolph on 04/01/2019, in good general condition after removal of the
drainage, following an unremarkable final examination, for your esteemed
outpatient follow-up.
**Current Recommendations:**
- We request regular follow-up urological appointments. The first one
should take place within one week after discharge.
- Based on the histopathological findings with evidence of a
metastasis in the adrenal tissue, we recommend the administration of
adjuvant chemotherapy with Gemcitabine/Cisplatin. The patient wishes
for a local connection, which was initiated during the inpatient
stay.
- Anticoagulation: Following the recommendations of the current
guideline for prophylaxis of venous thromboembolism, we advise
continuing anticoagulation with low molecular weight heparins for a
total of 4 - 5 weeks post-operation after urological procedures in
the abdominal and pelvic area.
- With the current single kidney situation, we recommend regular
nephrological follow-up examinations.
- In case of acute urological complaints, immediate re-presentation
is, of course, welcome.
### Patient Report 2
**Dear colleague, **
We are writing to inform you about our patient Mr. Peter Rudolph, born
on 05/26/1954, who was under treatment at our outpatient clinic on
04/20/2020.
**Diagnosis:** Newly hepatic and previously known adrenal metastasized,
locally advanced urothelial carcinoma of the left renal pelvis
(diagnosed in 03/19).
**Previous Diagnoses and Treatment:**
- 03/19: Left nephroureterectomy with the detection of urothelial
carcinoma of the renal pelvis, pT3, pN0 (0/11), M1 (ADR), pn1, L1,
V1, high-grade. 04 - 07/19: Four cycles of adjuvant chemotherapy
with Gemcitabine/Cisplatin.
- Newly emerged, progressively enlarging liver metastasis in Segment 6
and Segment 7, in relation to the previously known adrenal
metastasized and locally advanced urothelial carcinoma of the renal
pelvis, following left nephroureterectomy and four cycles of
adjuvant chemotherapy with Gemcitabine/Cisplatin. Suspected
activation of a rheumatic disease.
**Other Diagnoses:**
- 2013: Atrial fibrillation with ablation
- 2010: pTCA stenting for acute myocardial infarction/CHD
- Suspected activated soft tissue rheumatism (currently under
Prednisolone pulse therapy)
- Unclear thyroid nodule
- 2012: Laparoscopic cholecystectomy
- Tonsillectomy (date unknown)
- 01/2019: Left psoas abscess with detection of multisensitive
Staphylococcus aureus
**Current Presentation:** Mr. Rudolph presents electively with current
imaging in our uro-oncological outpatient clinic for treatment and
discussion of the further therapy plan.
**Medical History:** In March 2019, Mr. Rudolph underwent a
nephroureterectomy with the detection of urothelial carcinoma of the
renal pelvis. Subsequently, four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin were performed due to the detection of locally
advanced urothelial carcinoma with primary metastasis to the left
adrenal gland. The chemotherapy was well-tolerated. In the summer, the
patient presented with abdominal pain, and subsequently, an extensive
psoas abscess was detected during our inpatient treatment. Planned
follow-up examinations have taken place since then, but the current
imaging now suggests a newly emerged hepatic metastasis.
**Therapy and Progression**: Mr. Rudolph is in a satisfactory general
condition. Bowel movements are unremarkable with 1-2 well-formed stools
per day. Urinary frequency is up to 5-6 times a day with one episode of
nocturia. There is no urinary hesitancy. Currently, the patient
complains of an activation of his previously unclarified rheumatic
disease. He describes increasing pain with swelling in the left distal
ankle more than the right. Additionally, the patient complains of
painful right knee, and a total endoprosthesis on this side was
apparently planned but postponed due to the current COVID-19 pandemic.
Furthermore, the patient reports pain in the distal and proximal
interphalangeal joints of both hands. Externally, the general
practitioner initiated a short-term cortisone pulse therapy with 3-day
intervals (initial dose 100mg) due to suspicion of soft tissue
rheumatism a week ago. Under this treatment, the pain has progressively
improved, and the patient is currently almost symptom-free. Otherwise,
there is a good social network, and no nursing care is required.
The urological findings indicate a newly emerged hepatic metastasis in
relation to the previously known adrenal metastasized, locally advanced
urothelial carcinoma of the left renal pelvis, following
nephroureterectomy and four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin. Due to the newly emerged metastasis within one
year after successful Cisplatin therapy, platinum resistance is
presumed. Therefore, the indication for initiating a second-line therapy
with the immune checkpoint inhibitor Pembrolizumab, Atezolizumab, or
Nivolumab now exists. A comprehensive explanation was provided, with a
particular focus on risks and side effects. Special attention was given
to the exacerbation of pre-existing rheumatic complaints, and it was
strongly advised that the patient consult a rheumatologist before
initiating systemic therapy with an immune checkpoint inhibitor. As the
patient is already well-connected to the outpatient oncologist and has a
long commute, the initiation of local therapy was discussed with the
patient. Telephonically, the patient has already been connected to the
mentioned practice. Therapy initiation is planned for this week and will
be communicated to the patient separately.
**Current Recommendations:**
- We request the initiation of systemic therapy with an immune
checkpoint inhibitor (Pembrolizumab, Atezolizumab, or Nivolumab).
The first follow-up staging examination should take place after 4
cycles of therapy using CT of the chest/abdomen/pelvis.
- Prior to initiating systemic therapy, we recommend consultation with
a local rheumatologist for further evaluation of rheumatic symptoms.
- In particular, if systemic therapy with an immune checkpoint
inhibitor is initiated despite existing rheumatic symptoms, regular
follow-up and clinical monitoring should be closely observed.
- Regarding the externally initiated high-dose Prednisolone course, we
recommend a rapid tapering, so that after reaching a threshold dose
of 10mg/day, immune checkpoint inhibitor therapy can be initiated.
- In the event of acute complications or side effects, immediate
medical evaluation may be necessary. In particular, the need for
timely high-dose cortisone therapy with Prednisolone was emphasized
if it is an immune-associated side effect.
- If immune checkpoint inhibitor therapy is not feasible, the
discussion of re-induction with Gemcitabine/Cisplatin or alternative
therapy with Vinflunine as a second-line treatment should be
considered.
**Current Medication: **
**Medication ** **Dosage** **Frequency**
------------------------- ------------ ---------------------
Aspirin 100 mg 1-0-0
Bisoprolol (Zebeta) 50 mg 1/2-0-0
Pantoprazole (Protonix) 40 mg 1-0-0
Prednisolone (Prelone) 80 mg According to scheme
### Patient Report 3
**Dear colleague, **
We are reporting on our patient, Mr. Peter Rudolph, born on 05/26/1954,
who was under our inpatient care from 11/04/2020 to 11/05/2020.
**Diagnosis**: Hepatic, lymphatic, and adrenal metastasized, locally
advanced urothelial carcinoma of the left renal pelvis (diagnosed in
03/19).
**Other Diagnoses:**
- 03/19: Left nephroureterectomy with the detection of urothelial
carcinoma of the renal pelvis and adrenal metastasis, pT3, pN0,
(0/11), M1 (ADR), pn1, L1, V1, high-grade.
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin.
- 04/20: Newly emerged liver metastasis in Segment 6 and Segment 7.
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg
q14d.
- 2013: Atrial fibrillation with ablation
<!-- -->
- 2010: pTCA stenting for acute myocardial infarction/CHD.
- Suspected activated soft tissue rheumatism (currently under
Prednisolone pulse therapy).
- Unclear thyroid nodule.
- 2012: Laparoscopic cholecystectomy.
- Tonsillectomy (date unknown).
- 01/2019: Left psoas abscess with detection of multisensitive
Staphylococcus aureus.
**Intervention**: CT-guided liver biopsy on 11/04/2020.
**Current Presentation:** Mr. Rudolph presents electively in our
urological clinic for the aforementioned procedure. Under immunotherapy
with Nivolumab 240 mg q14d, there has been significant disease
progression. A CT-guided liver biopsy was initially discussed with Mr.
Rudolph for further therapy evaluation. At the time of admission, the
patient is in good general condition.
**Therapy and Progression:** Following appropriate patient information
and preparation, we performed the above procedure without complications.
Postoperatively, there were no complications. We were able to discharge
Mr. Rudolph in good general condition after unremarkable laboratory
checks on 11/05/2020.
**Current Recommendations:**
- We request a follow-up visit with the outpatient urologist within 1
week of discharge for clinical monitoring.
- We recommend switching the systemic therapy to Vinflunine. The
patient can have this done locally through his outpatient urologist.
- Further sequencing will be conducted through our interdisciplinary
molecular tumor board, and the patient will be informed of this in
due course.
- In case of symptoms or complications (especially fever over 38.5°C,
chills, or flank pain), an immediate return to our clinic is welcome
at any time.
### Patient Report 4
**Dear colleague, **
We are providing you with an update on our patient, Mr. Peter Rudolph,
born on 05/26/1954, who presented himself at our outpatient clinic on
06/29/2021.
**Diagnosis**: Hepatic, lymphatic, and adrenal metastasized, locally
advanced urothelial carcinoma of the left renal pelvis (diagnosed in
03/19).
**Other Diagnoses:**
- 03/19: Left nephroureterectomy with the detection of urothelial
carcinoma of the renal pelvis and adrenal metastasis (pT3, pN0,
(0/11), M1 (ADR), pn1, L1, V1, high-grade)
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin
- 04/20: Initial diagnosis of liver metastases in Segment 6 and
Segment 7
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg
q14d.
- 10/20 - 06/21: Third-line chemotherapy with Vinflunin (external),
resulting in hepatic progression
- 01/21: Molecular tumor board: no evidence of a molecular target
- 2013: Atrial fibrillation with ablation
<!-- -->
- 2010: pTCA stenting for acute myocardial infarction/CHD
- Soft tissue rheumatism
- Unclear thyroid nodule
- 2012: Laparoscopic cholecystectomy
- Tonsillectomy (date unknown)
- 01/2019: Left psoas abscess with detection of multisensitive
Staphylococcus aureus
**Current Presentation:** Mr. Rudolph presented to out outpatient clinic
on 06/29/2021, accompanied by his wife, in subjectively satisfactory
condition. Given the negative PDL1 status and FGFR mutation status
observed in our institution\'s molecular tumor board, Mr. Rudolph was
now presented to us for reevaluation and discussion of further
procedures.
**External CT Thorax dated 06/07/2021: **
**Findings:** The last relevant preliminary examination was conducted on
03/03/2021. Currently, well-ventilated lungs bilaterally without
tumor-typical findings or infiltrates. The bronchial system is clear. No
pathologically enlarged lymph nodes in the mediastinum, hilar region, or
axillae. Relatively pronounced atherosclerotic vascular calcifications,
otherwise unremarkable imaging of the major pulmonary and mediastinal
vessels. Normal dimensions of the cardiac chambers. No pericardial
effusion or pleural effusion. Thyroid and esophagus appear normal. No
osteolysis or spinal canal stenosis.
**Assessment**: Continued absence of thoracic metastases.
**External CT Abdomen dated 06/07/2021: **
**Findings:** Comparison with CT Abdomen dated 03/03/2021. Significant
progression of numerous, some large liver metastases in both liver
lobes. For example, a formerly 4.2 x 2.5 cm measuring metastasis
subcapsular in liver segment 7 has now increased to 5.8 x 3.6 cm. A
formerly 1.2 x 1.1 cm measuring metastasis in liver segment 4a has
increased to 3.3 x 2.4 cm. Portal vein and hepatic veins are properly
contrasted. Post-cholecystectomy status. Unremarkable adrenal glands.
Post-left nephrectomy. The right kidney is unremarkable. The spleen is
unremarkable. The pancreas appears normal. Diverticula of the sigmoid
and colon. No suspicious inguinal, iliac, retroperitoneal, or mesenteric
lymph nodes.
Assessment: Significant progression of numerous, some large liver
metastases. Otherwise, no organ metastases. No lymph node metastases.
Post-left nephrectomy.
**Assessment**: The urological examination findings indicate progressive
hepatic metastasized urothelial carcinoma originating from the left
renal pelvis, despite third-line chemotherapy with Vinflunin. The
findings were thoroughly discussed in the urological-interdisciplinary
conference on 06/29/2021.
[Recommendations for further procedures include:]{.underline}
1. Chemotherapy with Gemcitabine and Paclitaxel.
2. A best-supportive-care strategy with symptom-oriented approach and
possible palliative medical support.
3. After approval, a targeted therapy with Enfortumab Vedotin could be
considered if further tumor-specific treatment is desired.
These recommendations were discussed with Mr. Rudolph and his wife
during an outpatient uro-oncology consultation. Mr. Rudolph demonstrated
adequate orientation regarding his medical condition, given the overall
limited therapeutic options. A final decision on one of the proposed
alternatives was not reached collectively, although Mr. Rudolph tended
towards a watchful waiting approach due to perceived significant side
effects from the previous third-line chemotherapy with Vinflunin.
Therefore, we left the final recommendation open with a tendency towards
the best-supportive-care strategy. A local palliative medicine
outpatient connection was also recommended. According to the patient,
there is a living will and a power of attorney for healthcare decisions
in place.
We have already provided feedback to the attending oncologist by phone.
**Current Recommendations:**
- In the presence of apparent treatment fatigue in the patient, a
best-supportive-care strategy with a symptom-oriented approach and
potential initiation of chemotherapy with Gemcitabine and Paclitaxel
could be considered at the current time in an individualized
setting.
- We request the continuation of uro-oncological care by the attending
oncologist.
- After the medication Enfortumab-Vedotin is approved, a discussion of
this therapy can take place, depending on the patient\'s overall
condition and the desire for further tumor-specific treatment.
**Medication upon Discharge: **
**Medication ** **Dosage** **Frequency**
------------------------- ------------ ---------------------
Aspirin 100 mg 1-0-0
Bisoprolol (Zebeta) 50 mg 1/2-0-0
Pantoprazole (Protonix) 40 mg 1-0-0
Prednisolone (Prelone) 80 mg According to scheme
### Patient Report 5
**Dear colleague, **
We are reporting on the examination conducted on Mr. Rudolph, born on
05/26/1954 on 08/26/2021.
**Diagnosis**: Stenosis of the left subclavian artery
**Other Diagnoses:**
- 03/19: Left nephroureterectomy with the detection of urothelial
carcinoma of the renal pelvis and adrenal metastasis, pT3, pN0,
(0/11), M1 (ADR), pn1, L1, V1, high-grade
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin
- 04/20: Newly emerged liver metastasis in Segment 6 and Segment 7
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg q14d
<!-- -->
- 2013: Atrial fibrillation with ablation
- 2010: pTCA stenting for acute myocardial infarction/CHD
- Suspected activated soft tissue rheumatism (currently under
Prednisolone pulse therapy)
- Unclear thyroid nodule
- 2012: Laparoscopic cholecystectomy
- Tonsillectomy
- 01/2019: Left psoas abscess with detection of multisensitive
Staphylococcus aureus
**Clinical Findings:**
[Fist Closure Test:]{.underline} Color Doppler sonography of the
shoulder-arm arteries: Bilateral triphasic flow in the subclavian
arteries. Bilateral triphasic flow in the brachial arteries, even with
arm elevation. Left vertebral artery shows orthograde flow, no flow
reversal during overhead work.
[Conclusion]{.underline}: Clinically and duplex sonographically, no
subclavian stenosis can be demonstrated.
Both hands are warm and rosy and show intact sensory-motor function. No
hand claudication or dizziness provoked during overhead work.
Pulse status: Bilateral palpable radial and ulnar arteries. Blood
pressure on the right 160 mmHg systolic, on the left 190 mmHg systolic.
Duplex: Bilateral subclavian arteries show triphasic flow. Bilateral
brachial arteries show triphasic flow, even with arm elevation. Left
vertebral artery demonstrates orthograde flow, with no flow reversal
during overhead work.
**Current Recommenations: **
The evaluation is performed to assess a potential left subclavian
stenosis with blood pressure side differences. Dizziness or arm
claudication, especially during overhead work, is denied.
### Patient Report 6
**Dear colleague, **
We report to you about Mr. Peter Rudolph, born on 05/26/1954, who was in
our inpatient treatment from 02/22/2022 to 02/29/2022.
**Diagnosis**: Symptomatic incisional hernia in the area of the old
laparotomy scar (status post left nephroureterectomy in 03/19.
**Other Diagnoses:**
- 03/19: Left nephroureterectomy with the detection of urothelial
carcinoma of the renal pelvis and adrenal metastasis, pT3, pN0,
(0/11), M1 (ADR), pn1, L1, V1, high-grade
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin
- 04/20: Newly emerged liver metastasis in Segment 6 and Segment 7
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg q14d
<!-- -->
- 2013: Atrial fibrillation with ablation
- 2010: pTCA stenting for acute myocardial infarction/CHD
- Suspected activated soft tissue rheumatism (currently under
Prednisolone pulse therapy)
- Unclear thyroid nodule
- 2012: Laparoscopic cholecystectomy
- Tonsillectomy
- 01/2019: Left psoas abscess with detection of multisensitive
Staphylococcus aureus
**Operation:** Alloplastic Incisional Hernia Repair in intubation
anesthesia on 02/23/2022.
**Current Presentation:** The patient was admitted for elective surgery
after indications were assessed and preoperative preparation was
conducted in our clinic for the above-mentioned diagnosis.
**Therapy and Progression:** Following routine preoperative
preparations, comprehensive informed consent, and premedication, we
performed the aforementioned procedure on 02/23/2022 in uncomplicated
ITN. There were no intraoperative complications.
The postoperative inpatient course progressed normally with dry and
non-irritated wound conditions. The drainage was timely removed as the
drainage volume decreased. Full mobilization and intensive respiratory
therapy exercises were initiated on the first postoperative day. Regular
clinical and laboratory check-ups indicated a normal healing process.
The diet was well tolerated, and the wounds healed primarily. We
discharged Mr. Rudolph for further outpatient care on 02/29/2022.
**Histology**: Skin/subcutaneous resection with scar fibrosis.
Fibrolipomatous hernial sac with obstructed vessels. No evidence of
malignancy.
**Medication upon Admission:**
**Medication ** **Dosage** **Frequency**
--------------------- ------------ ---------------
Aspirin 100 mg 1-0-0
Bisoprolol (Zebeta) 50 mg 1/2-0-0
**Procedure:** From a surgical perspective, we request wound
inspections. To prevent recurrence, avoid lifting heavy objects (\>5 kg)
for the next 8-12 weeks. Please consistently wear the abdominal binder
during the wound healing period (14 days). Additionally, avoid excessive
abdominal pressure, especially during bowel movements.
**Surgical Report: **
**Diagnoses:**
- Extensive incisional hernia in the area of the transverse upper
abdominal laparotomy scar, with a history of: Left
nephroureterectomy with the detection of urothelial carcinoma of the
renal pelvis and adrenal metastasis, pT3, pN0, (0/11), M1 (ADR),
pn1, L1, V1, high-grade.
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin.
- 04/20: Newly emerged liver metastasis in Segment 6 and Segment 7.
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg
q14d.
**Type of Surgery:** Incisional Hernia Repair with Optilene Mesh (30 x
30 cm), Adhesiolysis of the intestine
**Anesthesia Type:** Intubation anesthesia
**Report**: **Indication**: Mr. Rudolph presents with an extensive
incisional hernia following a history of nephrectomy and pancreatic
resection for clear cell renal cell carcinoma. The indication for hernia
repair with mesh was made.
**Operation**: The procedure was performed with the patient in a supine
position and in ITN. Sterile preparation, draping, and perioperative
antibiotic prophylaxis with Ampicillin/Sulbactam 3g were administered.
Initially, a skin incision was made to the left of the existing
transverse upper abdominal laparotomy scar, and a sparing spindly
excision of the scar was performed. Dissection into the depth revealed
the first hernia sac. This sac was dissected free and opened. Further
lateral to the left, a very large additional hernia sac was found. This
one was also completely dissected free and opened. The two hernia
defects were connected only by a narrow isthmus of thinned abdominal
wall fascia, which was cut, and the two hernia defects were united.
Furthermore, another hernia sac was found laterally to the right in the
area of the scar. Thus, the scar was opened across its entire width by
extending the skin incision to the right. The right lateral hernia sac
was also dissected free and opened. Now, the hernia sacs were removed
one after the other (histology specimens). The epifascial adipose tissue
was then mobilized so that the abdominal wall fascia was exposed and
could serve as the base for the mesh to be placed. The three hernia
defects were then closed with a total of three continuous sutures using
Vicryl. This was done after the abdominal wall fascia was also dissected
free intra-abdominally, where the intestines or large mesh adhered to
the abdominal wall. After the hernia defects were now closed, the 30x30
cm Optilene mesh was introduced after thorough irrigation and careful
electrocoagulation for hemostasis. It was fixed tightly but without
tension at the edges with Ethibond sutures of size 0. Subsequently, a
Palisade suture was placed around the closed hernia defects using
Prolene size 0 in a continuous technique. Final irrigation and
hemostasis were performed. Four 12 Redon drains were placed in the
wound, led out, and sutured. Subcutaneous sutures were made with Vicryl
2-0. Skin sutures were placed with Nylon 3-0, followed by a sterile
wound compression dressing.
**Internal Histopathological Report**
**Macroscopy:**
- Skin spindle: Fixed. Skin spindle measuring 9 x 0.5 x 1.5 cm with a
centrally located, slightly raised, and indurated scar.
- Hernia sac I: Fixed. Cap-shaped serosal lamella measuring 8 x 7.5 x
2 cm with a bulging cord-like fibrosis. The serosa is smooth and
shiny.
- Hernia sac II: Fixed. A 15 x 3 x 0.5 cm large, reddish-livid serosal
specimen with focal indurations, petechial hemorrhages, and adhesion
strands. Multiple cross-sections embedded.
- Hernia sac III: Fixed. A 3.5 x 1 x 0.3 cm serosal lamella with
scarred fibrosis. Processing: Blocks: 4, H&E. Microscopy:
- Skin/subcutaneous resection with scar fibrosis of the adjacent
stroma. 2-4. Fibrolipomatous tissue, superficially peritonealized.
Markedly congested blood vessels.
**Critical Findings Report:** Skin/subcutaneous resection with scar
fibrosis. 2-4. Fibrolipomatous hernia sac tissue with congested blood
vessels. No evidence of malignancy.
### Patient Report 7
**Dear colleague, **
We are writing to provide an update regarding Mr. Peter Rudolph, born on
05/26/1954, who presented to our surgical outpatient clinic on
03/04/2022.
**Diagnoses**: Status post umbilical hernia repair 10 days ago.
- Extensive incisional hernia in the area of the transverse upper
abdominal laparotomy scar, with a history of: Left
nephroureterectomy with the detection of urothelial carcinoma of the
renal pelvis and adrenal metastasis, pT3, pN0, (0/11), M1 (ADR),
pn1, L1, V1, high-grade.
- 04/19 - 07/19: Four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin.
- 04/20: Newly emerged liver metastasis in Segment 6 and Segment 7.
- 05/20 - 09/20: 10 cycles of immunotherapy with Nivolumab 240 mg
q14d.
**Medical History:** In March 2019, Mr. Rudolph underwent a
nephroureterectomy with the detection of urothelial carcinoma of the
renal pelvis. Subsequently, four cycles of adjuvant chemotherapy with
Gemcitabine/Cisplatin were performed due to the detection of locally
advanced urothelial carcinoma with primary metastasis to the left
adrenal gland. The chemotherapy was well-tolerated overall. On
02/22/2022, Mr. Rudolph presented with an extensive incisional hernia
following a history of left nephroureterectomy. The indication for
hernia repair with mesh was made.
**Physical Examination**: Unremarkable scar, sutures in place.
**Current Recommendation**: Follow-up appointment scheduled for Thursday
(12th postoperative day) for suture removal and progress assessment. | 4.3 cm |
The purpose for the narrator losing his memory is. . .
A. Earth is not something a Dondromogon leader should remember.
B. So he can be birthed on a clean slate as the new Dondromogon leader.
C. So that the Dondromogons will be suspicious of him
D. To better assimilate to Dondromogon culture.
| Warrior of Two Worlds By MANLY WADE WELLMAN He was the man of two planets, drawn through the blackness of space to save a nation from ruthless invaders. He was Yandro, the Stranger of the Prophecy—and he found that he was destined to fight both sides. [Transcriber's Note: This etext was produced from Planet Stories Summer 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] My senses came to me slowly and somehow shyly, as if not sure of their way or welcome. I felt first—pressure on my brow and chest, as if I lay face downward; then the tug and buffet of a strong, probing wind, insistent but not cold, upon my naked skin. Closing my hands, I felt them dig into coarse dirt. I turned my face downwind and opened my eyes. There was little to see, so thick was the dust cloud around me. Words formed themselves on my thick tongue, words that must have been spoken by so many reviving unfortunates through the ages: "Where am I?" And at once there was an answer: " You lie upon the world Dondromogon. " I knew the language of that answer, but where it came from—above, beneath, or indeed within me—I could not say. I lifted a hand, and knuckled dust from my eyes. "How did I get here?" I demanded of the speaker. "It was ordered—by the Masters of the Worlds—that you should be brought from your own home planet, called Earth in the System of the star called Sun. Do you remember Earth?" And I did not know whether I remembered or not. Vague matters stirred deep in me, but I could not for certain say they were memories. I asked yet again: "Who am I?" The voice had a note of triumph. "You do not know that. It is as well, for this will be a birth and beginning of your destined leadership on Dondromogon." "Destined—leadership—" I began to repeat, and fell silent. I had need to think. The voice was telling me that I had been snatched from worlds away, for a specified purpose here on whatever windswept planet Dondromogon might be. "Birth and beginning—destined leadership—" Fantastic! And yet, for all I could say to the contrary, unvarnishedly true. "Dondromogon?" I mumbled. "The name is strange to me." "It is a world the size of your native one," came words of information. "Around a star it spins, light-years away from the world of your birth. One face of Dondromogon ever looks to the light and heat, wherefore its metals run in glowing seas. The other face is ever away in cold darkness, with its air freezing into solid chunks. But because Dondromogon wavers on its axis, there are two lunes of its surface which from time to time shift from night to day. These are habitable." My eyes were tight shut against the dust, but they saw in imagination such a planet—one-half incandescent, one-half pitchy black. From pole to pole on opposite sides ran the two twilight zones, widest at the equators like the outer rind of two slices of melon. Of course, such areas, between the hot and cold hemispheres, would be buffeted by mighty gales ... the voice was to be heard again: "War is fought between the two strips of habitable ground. War, unceasing, bitter, with no quarter asked, given or expected. Dondromogon was found and settled long ago, by adventurers from afar. Now come invaders, to reap the benefits of discovery and toil." A pause. "You find that thought unpleasant? You wish to right that wrong?" "Anyone would wish that," I replied. "But how—" "You are going to ask how you were brought here. That is the mystery of the Masters ." The voice became grand. "Suffice it that you were needed, and that the time was ripe. There is a proper time, like a proper place, for each thing and each happening. Now, go to your destiny." I rose on my knees, shielding my face from the buffeting wind by lifting a forearm. Somewhere through the murky clouds showed a dim blocky silhouette, a building of sorts. The voice spoke no more. I had not the time to wonder about it. I got to my feet, bent double to keep from being blown over, and staggered toward the promised haven. I reached it, groped along until I found a door. There was no latch, handle or entry button, and I pounded heavily on the massive panels. The door opened from within, and I was blown inside, to fall sprawling. I struck my forehead upon a floor of stone or concrete, and so was half-stunned, but still I could distinguish something like the sound of agitated voices. Then I felt myself grasped, by both shoulders, and drawn roughly erect. The touch restored my senses, and I wrenched myself violently free. What had seized me? That was my first wonder. On this strange world called Dondromogon, what manner of intelligent life bade defiance to heat and cold and storm, and built these stout structures, and now laid hands—were they hands indeed?—upon me? I swung around, setting my back to a solid wall. My first glance showed me that my companions were creatures like myself—two-legged, fair-skinned men, shorter and slighter than I, but clad in metal-faced garments and wearing weapons in their girdles. I saw that each bore a swordlike device with a curved guard, set in a narrow sheath as long as my arm. Each also had a shorter weapon, with a curved stock to fit the palm of the hand, borne snugly in a holster. With such arms I had a faint sense of familiarity. "Who are you, and where are you from?" said one of the two, a broad-faced middle-aged fellow. "Don't lie any more than you can help." I felt a stirring of the hair on my neck, but kept my voice mild and level: "Why should I lie? Especially as I don't know who I am, or where I'm from, or anything that has happened longer ago than just a moment. I woke up out there in the dust storm, and I managed to come here for shelter." "He's a Newcomer spy," quoth the other. "Let's put him under arrest." "And leave this gate unguarded?" demanded the other. "Sound the signal," and he jerked his head toward a system of levers and gauges on the wall beside the door-jamb. "There's a bigger reward for capture than for warning," objected his friend in turn, "and whoever comes to take this man will claim 'capture.' I'll guard here, and you take him in, then we'll divide—" "No. Yours is the idea. I'll guard and you take him in." The second man studied me apprehensively. "He's big, and looks strong, even without weapons." "Don't be afraid," I urged. "I'll make no resistance, if you'll only conduct me to your commander. I can show him that I'm no spy or enemy." Both stared narrowly. "No spy? No enemy?" asked the broad-faced one who had first spoken. Then, to his comrade: "No reward, then." "I think there'll be a reward," was the rejoinder, and the second man's hand stole to the sword-weapon. With a whispering rasp it cleared from its scabbard. "If he's dead, we get pay for both warning and capture—" His thumb touched a button at the pommel of the hilt. The dull blade suddenly glowed like heated iron, and from it crackled and pulsed little rainbow rays. There was no time to think or plan or ponder. I moved in, with a knowing speed that surprised me as much as the two guards. Catching the fellow's weapon wrist, I clamped it firmly and bent it back and around. He whimpered and swore, and his glowing sword dropped. Its radiant blade almost fell on my naked foot. Before the clang of its fall was through echoing, I had caught it up, and set the point within inches of its owner's unprotected face. "Quiet, or I'll roast you," I told him. The other had drawn a weapon of his own, a pistol-form arrangement. I turned on him, but too late. He pressed the trigger, and from the muzzle came—not a projectile but a flying, spouting filament of cord that seemed to spring on me like a long thin snake and to fasten coil after coil around my body. The stuff that gushed from the gun-muzzle seemed plastic in form, but hardened so quickly upon contact with the air, it bound me like wire. Half a dozen adroit motions of the fellow's gun hand, and my arms were caught to my body. I dropped my sword to prevent it burning me, and tried to break away, but my bonds were too much for me. "Let me out of this," I growled, and kicked at the man with my still unbound foot. He snapped a half-hitch on my ankle, and threw me heavily. Triumphant laughter came from both adversaries. Then: "What's this?" The challenge was clear, rich, authoritative. Someone else had come, from a rearward door into the stone-walled vestibule where the encounter was taking place. A woman this time, not of great height, and robust but not heavy. She was dressed for vigorous action in dark slacks with buskins to make them snug around ankles and calves, a jerkin of stout material that was faced with metal armor plates and left bare her round, strong arms. A gold-worked fillet bound her tawny hair back from a rosy, bold-featured face—a nose that was positively regal, a mouth short and firm but not hard, and blue eyes that just now burned and questioned. She wore a holstered pistol, and a cross-belt supported several instruments of a kind I could not remember seeing before. A crimson cloak gave color and dignity to her costume, and plainly she was someone of position, for both the men stiffened to attention. "A spy," one ventured. "He pushed in, claimed he was no enemy, then tried to attack—" "They lie," I broke in, very conscious of my naked helplessness before her regard. "They wanted to kill me and be rewarded for a false story of vigilance. I only defended myself." "Get him on his feet," the young woman said, and the two guards obeyed. Then her eyes studied me again. "Gods! What a mountain of a man!" she exclaimed. "Can you walk, stranger?" "Barely, with these bonds." "Then manage to do so." She flung off her cloak and draped it over my nakedness. "Walk along beside me. No tricks, and I promise you fair hearing." We went through the door by which she had entered, into a corridor beyond. It was lighted by small, brilliant bulbs at regular intervals. Beyond, it gave into several passages. She chose one of them and conducted me along. "You are surely not of us," she commented. "Men I have seen who are heavier than you, but none taller. Whence came you?" I remembered the strange voice that had instructed me. "I am from a far world," I replied. "It is called—yes, Earth. Beyond that, I know nothing. Memory left me." "The story is a strange one," she commented. "And your name?" "I do not know that, either. Who are you?" "Doriza—a gentlewoman of the guard. My inspection tour brought me by chance to where you fought my outposts. But it is not for you to ask questions. Enter here." We passed through another door, and I found myself in an office. A man in richly-embossed armor platings sat there. He had a fringe of pale beard, and his eyes were bluer than the gentlewoman Doriza's. She made a gesture of salute, hand at shoulder height, and reported the matter. He nodded for her to fall back to a corner. "Stranger," he said to me, "can you think of no better tale to tell than you now offer?" "I tell the truth," was my reply, not very gracious. "You will have to prove that," he admonished me. "What proof have I?" I demanded. "On this world of yours—Dondromogon, isn't it called?—I'm no more than an hour old. Accident or shock has taken my memory. Let me have a medical examination. A scientist probably can tell what happened to put me in such a condition." "I am a scientist," offered Doriza, and came forward. Her eyes met mine, suddenly flickered and lowered. "His gaze," she muttered. The officer at the table was touching a button. An attendant appeared, received an order, and vanished again. In a few moments two other men came—one a heavily armed officer of rank, the other an elderly, bearded fellow in a voluminous robe that enfolded him in most dignified manner. This latter man opened wide his clear old eyes at sight of me. "The stranger of the prophecy!" he cried, in a voice that made us all jump. The officer rose from behind the table. "Are you totally mad, Sporr? You mystic doctors are too apt to become fuddled—" "But it is, it is!" The graybeard flourished a thin hand at me. "Look at him, you of little faith! Your mind dwells so much on material strength that you lose touch with the spiritual—" He broke off, and wheeled on the attendant who had led him in. "To my study," he commanded. "On the shelf behind my desk, bring the great gold-bound book that is third from the right." Then he turned back, and bowed toward me. "Surely you are Yandro, the Conquering Stranger," he said, intoning as if in formal prayer. "Pardon these short-sighted ones—deign to save us from our enemies—" The girl Doriza spoke to the officer: "If Sporr speaks truth, and he generally does, you have committed a blasphemy." The other made a little grimace. "This may be Yandro, though I'm a plain soldier and follow the classics very little. The First Comers are souls to worship, not to study. If indeed he is Yandro," and he was most respectful, "he will appreciate, like a good military mind, my caution against possible impostors." "Who might Yandro be?" I demanded, very uncomfortable in my bonds and loose draperies. Old Sporr almost crowed. "You see? If he was a true imposter, he would come equipped with all plausible knowledge. As it is—" "As it is, he may remember that the Conquering Stranger is foretold to come with no memory of anything," supplied the officer. "Score one against you, Sporr. You should have been able to instruct me, not I you." The attendant reentered, with a big book in his hands. It looked old and well-thumbed, with dim gold traceries on its binding. Sporr snatched it, and turned to a brightly colored picture. He looked once, his beard gaped, and he dropped to his knees. "Happy, happy the day," he jabbered, "that I was spared to see our great champion come among us in the flesh, as was foretold of ancient time by the First Comers!" Doriza and the officer crossed to his side, snatching the book. Their bright heads bent above it. Doriza was first to speak. "It is very like," she half-stammered. The officer faced me, with a sort of baffled respect. "I still say you will understand my caution," he addressed me, with real respect and shyness this time. "If you are Yandro himself, you can prove it. The prophecy even sketches a thumb-print—" And he held the book toward me. It contained a full-page likeness, in color, of myself wrapped in a scarlet robe. Under this was considerable printed description, and to one side a thumb-print, or a drawing of one, in black. "Behold," Doriza was saying, "matters which even expert identification men take into thought. The ears in the picture are like the ears of the real man—" "That could be plastic surgery," rejoined the officer. "Such things are artfully done by the Newcomers, and the red mantle he wears more easily assumed." Doriza shook her head. "That happens to be my cloak. I gave it to him because he was naked, and not for any treasonable masquerade. But the thumb-print—" "Oh, yes, the thumb-print," I repeated wearily. "By all means, study my thumbs, if you'll first take these bonds off of me." "Bonds," mumbled old Sporr. He got creakily up from his knees and bustled to me. From under his robe he produced a pouch, and took out a pencil-sized rod. Gingerly opening the red mantle, he touched my tether in several places with the glowing end of the rod. The coils dropped away from my grateful body and limbs. I thrust out my hands. "Thumb-prints?" I offered. Sporr had produced something else, a little vial of dark pigment. He carefully anointed one of my thumbs, and pressed it to the page. All three gazed. "The same," said Doriza. And they were all on their knees before me. "Forgive me, great Yandro," said the officer thickly. "I did not know." "Get up," I bade them. "I want to hear why I was first bound, and now worshipped." II They rose, but stood off respectfully. The officer spoke first. "I am Rohbar, field commander of this defense position," he said with crisp respect. "Sporr is a mystic doctor, full of godly wisdom. Doriza, a junior officer and chief of the guard. And you—how could you know?—are sent by the First Comers to save us from our enemies." "Enemies?" I repeated. "The Newcomers," supplemented Doriza. "They have taken the "Other Side" of Dondromogon, and would take our side as well. We defend ourselves at the poles. Now," and her voice rang joyously, "you will lead us to defeat and crush them utterly!" "Not naked like this," I said, and laughed. I must have sounded foolish, but it had its effect. "Follow me, deign to follow me," Sporr said. "Your clothing, your quarters, your destiny, all await you." We went out by the door at the rear, and Sporr respectfully gestured me upon a metal-plated platform. Standing beside me, he tinkered with a lever. We dropped smoothly away into a dark corridor, past level after level of light and sound. "Our cities are below ground," he quavered. "Whipped by winds above, we must scrabble in the depths for life's necessities—chemicals to transmute into food, to weave into clothing, to weld into tools and weapons—" The mention of food brought to me the thought that I was hungry. I said as much, even as our elevator platform came to the lowest level and stopped. "I have arranged for that," Sporr began, then fell silent, fingers combing his beard in embarrassment. "Arranged food for me?" I prompted sharply. "As if you know I had come? What—" "Pardon, great Yandro," babbled Sporr. "I was saying that I arranged food, as always, for whatever guest should come. Please follow." We entered a new small chamber, where a table was set with dishes of porcelain-like plastic. Sporr held a chair for me, and waited on me with the utmost gingerly respect. The food was a pungent and filling jelly, a little bundle of transparent leaves or scraps like cellophane and tasting of spice, and a tumbler of pink juice. I felt refreshed and satisfied, and thanked Sporr, who led me on to the next room. "Behold!" he said, with a dramatic gesture. "Your garments, even as they have been preserved against your coming!" It was a sleeping chamber, with a cot made fast to the wall, a metal locker or cupboard, with a glass door through which showed the garments of which Sporr spoke. The door closed softly behind me—I was left alone. Knowing that it was expected of me, I went to the locker and opened the door. The garments inside were old, I could see, but well kept and serviceable. I studied their type, and my hands, if not my mind, seemed familiar with them. There was a kiltlike item, belted at the waist and falling to mid-thigh. A resilient band at the top, with a series of belt-holes, made it adaptable to my own body or to any other. Then came an upper garment, a long strip of soft, close-woven fabric that spiralled around the torso from hip to armpit, the end looping over the left shoulder and giving full play to the arms. A gold-worked fillet bound the brows and swept back my longish hair, knotting at the nape of the neck. The only fitted articles were a pair of shoes, metal-soled and soft-uppered, that went on well enough and ran cross-garters up to below the knee, like buskins. The case also held a platinum chain for the neck, a belt-bag, and a handsome sword, with clips to fasten them in place. These things, too, I donned, and closed the glass door. The light struck it at such an angle as to make it serve for a full-length mirror. With some curiosity I gazed at my image. The close-fitting costume was rich and dark, with bright colors only for edgings and minor accessories. I myself—and it was as if I saw my body for the first time—towered rather bluffly, with great breadth of chest and shoulder, and legs robust enough to carry such bulk. The face was square but haggard, as if from some toil or pain which was now wiped from my recollection. That nose had been even bigger than it was now, but a fracture had shortened it somewhat. The eyes were deep set and dark and moody—small wonder!—the chin heavy, the mouth made grim by a scar at one corner. Black, shaggy hair hung down like brackets. All told, I looked like a proper person for physical labor, or even fierce fighting—but surely no inspirational leader or savior of a distressed people. I took the military cloak which Doriza had lent me and slung it over my shoulders. Turning, I clanked out on my metal-soled shoes. Sporr was waiting in the room where I had eaten. His eyes widened at sight of me, something like a grin of triumph flashed through his beard. Then he bowed, supple and humble, his palms together. "It is indeed Yandro, our great chief," he mumbled. Then he turned and crossed the room. A sort of mouthpiece sprouted from the wall. "I announce," he intoned into it. "I announce, I, Sporr, the reader and fore-teller of wisdom. Yandro is with us, he awaits his partners and friends. Let them meet him in the audience hall." Facing me again, he motioned most respectfully toward the door to the hall. I moved to open it, and he followed, muttering. Outside stood Doriza. Her blue eyes met mine, and her lips moved to frame a word. Then, suddenly, she was on her knee, catching my hand and kissing it. "I serve Yandro," she vowed tremulously. "Now and forever—and happy that I was fated to live when he returned for the rescue of all Dondromogon." "Please get up," I bade her, trying not to sound as embarrassed as I felt. "Come with me. There is still much that I do not understand." "I am Yandro's orderly and helper," she said. Rising, she ranged herself at my left hand. "Will Yandro come this way? He will be awaited in the audience hall." It seemed to me then that the corridors were vast and mixed as a labyrinth, but Doriza guided me without the slightest hesitation past one tangled crossway after another. My questions she answered with a mixture of awe and brightness. "It is necessary that we live like this," she explained. "The hot air of Dondromogon's sunlit face is ever rising, and the cold air from the dark side comes rushing under to fill the vacuum. Naturally, our strip of twilight country is never free of winds too high and fierce to fight. No crops can grow outside, no domestic animals flourish. We must pen ourselves away from the sky and soil, with stout walls and heavy sunken parapets. Our deep mines afford every element for necessities of life." I looked at my garments, and hers. There were various kinds of fabric, which I now saw plainly to be synthetic. "The other side, where those you call the Newcomers dwell and fight," I reminded. "Is it also windswept? Why can two people not join forces and face toil and nature together? They should fight, not each other, but the elements." Doriza had no answer that time, but Sporr spoke up behind us: "Great Yandro is wise as well as powerful. But the Newcomers do not want to help, not even to conquer. They want to obliterate us. There is nothing to do—not for lifetimes—but to fight them back at the two poles." We came to a main corridor. It had a line of armed guards, but no pedestrians or vehicles, though I thought I caught a murmur of far-off traffic. Doriza paused before a great portal, closed by a curtainlike sheet of dull metal. She spoke into a mouthpiece: "Doriza, gentlewoman of the guard, conducts Yandro, the Conquering Stranger, to greet his lieutenants!" I have said that the portal was closed by a curtainlike metal sheet; and like a curtain it lifted, letting us through into the auditorium. That spacious chamber had rows of benches, with galleries above, that might have seated a thousand. However, only a dozen or so were present, on metal chairs ranged across the stage upon which we entered. They were all men but two, and wore robes of black, plum-purple or red. At sight of me, they rose together, most respectfully. They looked at me, and I looked at them. My first thought was, that if these were people of authority and trust in the nation I seemed destined to save, my work was cut out for me. Not that they really seemed stupid—none had the look, or the subsequent action, of stupidity. But they were not pleasant. Their dozen pairs of eyes fixed me with some steadiness, but with no frankness anywhere. One man had a round, greedy-seeming face. Another was too narrow and cunning to look it. Of the women, one was nearly as tall as I and nobly proportioned, with hair of a red that would be inspiring were it not so blatantly dyed. The other was a little wisp of a brunette, with teeth too big for her scarlet mouth and bright eyes like some sort of a rodent. They all wore jewelry. Too much jewelry. My mind flew back to the two scrubby, venial guardsmen who had first welcomed me; to stuffy Rohbar, the commander; to Sporr, spry and clever enough, but somehow unwholesome; Doriza—no, she was not like these others, who may have lived too long in their earth-buried shelters. And Doriza now spoke to the gathering: "Yandro, folk of the Council! He deigns to give you audience." " Yandro! " They all spoke the name in chorus, and bowed toward me. Silence then, a silence which evidently I must break. I broke it: "Friends, I am among you with no more memory or knowledge than an infant. I hear wonderful things, of which I seem to be the center. Are they true?" "The tenth part of the wonders which concern mighty Yandro have not been told," intoned Sporr, ducking his bearded head in a bow, but fixing me with his wise old eyes. One of the group, called Council by Doriza, now moved a pace forward. He was the greedy-faced man, short but plump, and very conscious of the dignified folds of his purple robe. One carefully-tended hand brushed back his ginger-brown hair, then toyed with a little moustache. "I am Gederr, senior of this Council," he purred. "If Yandro permits, I will speak simply. Our hopes have been raised by Yandro's return—the return presaged of old by those who could see the future, and more recently by the death in battle of the Newcomer champion, called Barak." "Barak!" I repeated. "I—I—" And I paused. When I had to learn my own name, how could it be that I sensed memory of another's name? "Barak was a brute—mighty, but a brute." Thus Gederr continued. "Weapons in his hands were the instruments of fate. His hands alone caused fear and ruin. But it pleased our fortune-bringing stars to encompass his destruction." He grinned, and licked his full lips. "Now, even as they are without their battle-leader, so we have ours." "You honor me," I told him. "Yet I still know little. It seems that I am expected to aid and lead and save the people of this world called Dondromogon. But I must know them before I can help." Gederr turned his eyes upon the woman with the red hair, and gestured to her "Tell him, Elonie." Then he faced me. "Have we Yandro's permission to sit?" "By all means," I granted, a little impatiently, and sat down myself. The others followed suit—the Council on their range of chairs, Doriza on a bench near me, Sporr somewhere behind. The woman called Elonie remained upon her sandalled feet, great eyes the color of deep green water fixed upon me. | B. So he can be birthed on a clean slate as the new Dondromogon leader. |
Why was Templin leery of the children on Tunpesh?
A. They seemed to be much older than children and only disguised as such.
B. Their appearance gave him an eerie feeling about their potential danger.
C. He knew even children were capable of doing damage with a weapon.
D. They were too eager to come near strangers and that made him uneasy.
| THE FIRE and THE SWORD By FRANK M. ROBINSON Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction August 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Nothing could have seemed pleasanter than that peaceful planet. Then why was a non-suicidal man driven to suicide there? Yet it made sense. Why do people commit suicide? Templin tightened his safety belt and lay back on the acceleration bunk. The lights in the cabin dimmed to a dull, red glow that meant the time for takeoff was nearing. He could hear noises from deep within the ship and the tiny whir of the ventilator fan, filling the air with the sweetish smell of sleeping gas. To sleep the trip away was better than to face the dull monotony of the stars for days on end. Oh, they kill themselves for lots of reasons. Maybe ill health or financial messes or family difficulties. An unhappy love affair. Or more complex ones, if you went into it deeper. The failure to achieve an ambition, failure to live up to one's own ideals. Weltschmerz, perhaps. He could smell the bitter fragrance of tobacco smoke mingling with the gas. Eckert had lit a cigarette and was calmly blowing the smoke at the neon "No Smoking" sign, which winked on and off in mechanical disapproval. He turned his head slightly so he could just see Eckert in the bank facing him. Eckert, one of the good gray men in the Service. The old reliables, the ones who could take almost anything in their stride because, at one time or another, they had had to. It was Eckert who had come into his office several days ago and told him that Don Pendleton had killed himself. Only Pendleton wasn't the type. He was the kind who have everything to live for, the kind you instinctively know will amount to something someday. And that was a lousy way to remember him. The clichés always come first. Your memory plays traitor and boils friendship down to the status of a breakfast food testimonial. The soft red lights seemed to be dancing in the darkness of the cabin. Eckert was just a dull, formless blur opposite him. His cigarette was out. Eckert had come into his office without saying a word and had watched his scenery-window. It had been snowing in the window, the white flakes making a simple pattern drifting past the glass. Eckert had fiddled with the controls and changed it to sunshine, then to a weird mixture of hail amid the brassy, golden sunlight. And then Eckert had told him that Pendleton had taken the short way out. He shouldn't get sentimental. But how the hell else should he remember Pendleton? Try to forget it and drink a toast to him at the next class reunion? And never, never be so crude as to speculate why Pendleton should have done it? If, of course, he had.... The cabin was hazy in the reddish glow, the sleeping gas a heavy perfume. Eckert and he had talked it out and gone over the records. Pendleton had come of good stock. There had been no mental instability in his family for as far back as the genetic records went. He had been raised in a middle-class neighborhood and attended a local grammar school where he had achieved average grades and had given his instructors the normal amount of trouble. Later, when he had made up his mind to enter the Diplomatic Service, his grades had improved. He had worked hard at it, though he wasn't what you would call a grind. In high school and later in college, he was the well-balanced type, athletic, popular, hard-working. How long would it be before memories faded and all there was left of Pendleton was a page of statistics? He had been on this team, he had been elected president of that, he had graduated with such and such honors. But try getting a picture of him by reading the records, resurrect him from a page of black print. Would he be human? Would he be flesh and blood? Hell, no! In the statistics Pendleton was the All-Around Boy, the cold marble statue with the finely chiseled muscles and the smooth, blank sockets where the eyes should be. Maybe someday fate would play a trick on a hero-worshiping public and there would actually be kids like that. But they wouldn't be human; they wouldn't be born. Parents would get them by sending in so many box tops. He was drowsy; the room was filled with the gas now. It would be only a matter of minutes before he would be asleep. Pendleton had been in his second year as attache on Tunpesh, a small planet with a G-type sun. The Service had stumbled across it recently and decided the system was worth diplomatic recognition of some kind, so Pendleton had been sent there. He had been the first attache to be sent and naturally he had gone alone. There was no need to send more. Tunpesh had been inspected and certified and approved. The natives were primitive and friendly. Or maybe the Service had slipped up, as it sometimes did, and Tunpesh had received something less than a thorough survey. And then an unscheduled freighter had put in for repairs, one of the very few ships that ever came by Tunpesh. The captain had tried to pay his respects to Pendleton. Only Pendleton wasn't there. The natives said he had killed himself and showed the captain the little flower-covered plot where they had buried him. Tunpesh had been Pendleton's second assignment. The natives were oh-so-friendly. So friendly that he had made sure that a certain box was on board, filled with shiny atomic rifles, needle pistols, and the fat little gas guns. They might be needed. People like Pendleton didn't kill themselves, did they? No, they didn't. But sometimes they were murdered. It was almost black inside the cabin now; only a thin red line around the ceiling told how close they were to takeoff. His head was thick with drowsiness, his eyelids a heavy weight that he knew he couldn't keep open much longer. Eckert and he had been chosen to go to Tunpesh and investigate. The two of them, working together, should be able to find out why Pendleton had killed himself. But that wasn't the real reason. Maybe Eckert thought so, but he knew better. The real reason they were going there was to find out why Pendleton had been killed and who had killed him. That was it. Who had killed Cock Robin? The thin red line was practically microscopic now and Templin could feel his lashes lying gently on his cheeks. But he wasn't asleep—not quite. There was something buzzing about in the dim recesses of his mind. Their information on Tunpesh was limited. They knew that it had no trading concessions or armed forces and that nobody from neighboring systems seemed to know much about it or even visited it. But a staff anthropologist must have been routinely assigned to Tunpesh to furnish data and reports. "Ted?" he murmured sleepily. A faint stirring in the black bulk opposite him. "Yes?" "How come our anthropologist on Tunpesh didn't come across with more information?" A drowsy mumble from the other cot: "He wasn't there long enough. He committed suicide not long after landing." The room was a whirling pool of blackness into which his mind was slowly slipping. Takeoff was only seconds away. Why do people commit suicide? "It's a nice day, isn't it, Ted?" Eckert took a deep and pleasurable breath. "It's the type of day that makes you feel good just to be alive." Warm breezes rustled through Eckert's graying hair and tugged gently at his tunic. The air smelled as if it had been washed and faintly perfumed with the balsamy scent of something very much like pine. A few hundred yards away, a forest towered straight and slim and coolly inviting, and brilliantly colored birds whirled and fluttered in the foliage. The rocketport, where they were standing surrounded by their luggage, was a grassy valley where the all too infrequent ships could land and discharge cargo or make repairs. There was a blackened patch on it now, with little blast-ignited flames dying out around the edges. It won't be long before it will be green again , he thought. The grass looked as though it grew fast—it would certainly have plenty of time to grow before the next ship landed. He looked at the slim, dwindling shape that was the rocket, and was suddenly, acutely aware that he and Templin would be stranded for six months on a foreign and very possibly dangerous planet. And there would be no way of calling for help or of leaving before the six months were up. He stood there for a moment, drinking in the fresh air and feeling the warmth of the sun against his face. It might be a pleasant six months at that, away from the din and the hustle and confusion, spending the time in a place where the sun was warm and inviting. I must be getting old , he thought, thinking about the warmth and comfort. Like old dogs and octogenarians. Templin was looking at the scenery with a disappointed expression on his face. Eckert stole a side glance at him and for a fleeting moment felt vaguely concerned. "Don't be disappointed if it doesn't look like cloak-and-dagger right off, Ray. What seems innocent enough on the surface can prove to be quite dangerous underneath." "It's rather hard to think of danger in a setting like this." Eckert nodded agreement. "It wouldn't fit, would it? It would be like a famous singer suddenly doing a jazz number in an opera, or having the princess in a fairy tale turn out to be ugly." He gestured toward the village. "You could hardly class that as dangerous from its outward appearance, could you?" The rocketport was in a small valley, surrounded by low, wooded hills. The village started where the port left off and crawled and wound over the wooded ridges. Small houses of sun-baked, white-washed mud crouched in the shadow of huge trees and hugged the banks of a small stream. It looked fairly primitive, Eckert thought, and yet it didn't have the earmarks, the characteristics of most primitive villages. It didn't seem cluttered or dirty and you didn't feel like beating a hasty retreat when the wind was blowing toward you. A few adults were watching them curiously and the usual bunch of kids that always congregated around rocketports quickly gathered. Eckert stared at them for a moment, wondering what it was that seemed odd about them, and they stared back with all the alert dignity of childhood. They finally came out on the field and clustered around him and Templin. Templin studied them warily. "Better watch them, Ted. Even kids can be dangerous." It's because you never suspect kids , Eckert thought, you never think they'll do any harm. But they can be taught. They could do as much damage with a knife as a man could, for instance. And they might have other weapons. But the idea still didn't go with the warm sun and the blue sky and the piny scent of the trees. One of the adults of the village started to walk toward them. "The reception committee," Templin said tightly. His hand went inside his tunic. He couldn't be blamed for being jumpy, Eckert realized. This was his first time out, his first mission like this. And, of course, Pendleton had been a pretty good friend of his. "I'd be very careful what I did," Eckert said softly. "I would hate to start something merely because I misunderstood their intentions." The committee of one was a middle-aged man dressed in a simple strip of white cloth twisted about his waist and allowed to hang freely to his knees. When he got closer, Eckert became less sure of his age. He had the firm, tanned musculature of a much younger man, though a slightly seamed face and white hair aged him somewhat. Eckert still had the feeling that if you wanted to know his exact age, you'd have to look at his teeth or know something about his epiphyseal closures. "You are menshars from Earth?" The voice was husky and pleasant and the pronunciation was very clear. Eckert regarded him thoughtfully and made a few mental notes. He wasn't bowing and scraping like most natives who weren't too familiar with visitors from the sky, and yet he was hardly either friendly or hostile. "You learned our language from Pendleton and Reynolds?" Reynolds had been the anthropologist. "We have had visitors from Earth before." He hesitated a moment and then offered his hand, somewhat shyly, Eckert thought, in the Terrestrial sign of greeting. "You may call me Jathong if you wish." He paused a moment to say something in his native tongue to the kids who were around. They promptly scattered and picked up the luggage. "While you are here, you will need a place to stay. There is one ready, if you will follow me." He was polite, Eckert thought. He didn't ask what they were there for or how long they were going to stay. But then again, perhaps the natives were a better judge of that than he and Templin. The town was larger than he had thought at first, stretching over a wide expanse of the countryside. There wasn't, so far as he could see, much manufacturing above the level of handicrafts and simple weaving. Colored patches on far hillsides indicated the presence of farms, and practically every house in the village had its small garden. What manufacturing there was seemed to be carried on in the central square of the town, where a few adults and children squatted in the warm afternoon sun and worked industriously at potter's wheels and weaver's looms. The other part of the square was given over to the native bazaar where pots and bolts of cloth were for sale, and where numerous stalls were loaded with dried fruits and vegetables and the cleaned and plucked carcasses of the local variety of fowl. It was late afternoon when they followed Jathong into a small, white-washed house midway up a hill. "You are free to use this while you are here," he said. Eckert and Templin took a quick tour of the few rooms. They were well furnished, in a rustic sort of way, and what modern conveniences they didn't have they could easily do without. The youngsters who had carried their luggage left it outside and quietly faded away. It was getting dark; Eckert opened one of the boxes they had brought along, took out an electric lantern and lighted it. He turned to Jathong. "You've been very kind to us and we would like to repay you. You may take what you wish of anything within this box." He opened another of the boxes and displayed the usual trade goods—brightly colored cloth and finely worked jewelry and a few mechanical contrivances that Eckert knew usually appealed to the primitive imagination. Jathong ran his hand over the cloth and held some of the jewelry up to the light. Eckert knew by the way he looked at it that he wasn't at all impressed. "I am grateful," he said finally, "but there is nothing I want." He turned and walked away into the gathering darkness. "The incorruptible native." Templin laughed sarcastically. Eckert shrugged. "That's one of the things you do out of habit, try and buy some of the natives so you'll have friends in case you need them." He stopped for a moment, thinking. "Did you notice the context? He didn't say he didn't want what we showed him. He said there was nothing that he wanted. Implying that everything he wanted, he already had." "That's not very typical of a primitive society, is it?" "No, I'm afraid it's not." Eckert started unpacking some of the boxes. "You know, Ray, I got a kick out of the kids. They're a healthy-looking lot, aren't they?" "Too healthy," Templin said. "There didn't seem to be any sick ones or ones with runny noses or cuts or black eyes or bruises. It doesn't seem natural." "They're probably just well brought-up kids," Eckert said sharply. "Maybe they've been taught not to get in fights or play around in the mud on the way home from school." He felt faintly irritated, annoyed at the way Templin had put it, as if any deviation from an Earth norm was potentially dangerous. "Ted." Templin's voice was strained. "This could be a trap, you know." "In what way?" The words came out slowly. "The people are too casual, as though they're playing a rehearsed part. Here we are, from an entirely different solar system, landed in what must be to them an unusual manner. They couldn't have seen rockets more than three or four times before. It should still be a novelty to them. And yet how much curiosity did they show? Hardly any. Was there any fear? No. And the cute, harmless little kids." He looked at Eckert. "Maybe that's what we're supposed to think—just an idyllic, harmless society. Maybe that's what Pendleton thought, right to the very end." He was keyed up, jumpy, Eckert realized. He would probably be seeing things in every shadow and imagining danger to be lurking around every corner. "It hasn't been established yet that Pendleton was killed, Ray. Let's keep an open mind until we know for certain." He flicked out the light and lay back on the cool bed, letting his body relax completely. The cool night wind blew lazily through the wood slat blinds, carrying the fragrance of the trees and the grass, and he inhaled deeply and let his thoughts wander for a moment. It was going to be pleasant to live on Tunpesh for six months—even if the six months were all they had to live. The climate was superb and the people seemed a cut above the usual primitive culture. If he ever retired some day, he thought suddenly, he would have to remember Tunpesh. It would be pleasant to spend his old age here. And the fishing was probably excellent.... He turned his head a little to watch Templin get ready for bed. There were advantages in taking him along that Templin probably didn't even realize. He wondered what Templin would do if he ever found out that the actual reason he had been chosen to go was that his own psychological chart was very close to Pendleton's. Pendleton's own feelings and emotions would almost exactly be duplicated in Templin's. A few stray wisps of starlight pierced through the blinds and sparkled for an instant on a small metal box strapped to Templin's waist. A power pack, Eckert saw grimly, probably leading to the buttons on his tunic. A very convenient, portable, and hard to detect weapon. There were disadvantages in taking Templin, too. "Just how primitive do you think the society is, Ted?" Eckert put down the chain he had been whittling and reached for his pipe and tobacco. "I don't think it's primitive at all. There are too many disparities. Their knowledge of a lot of things is a little more than empirical knowledge; they associate the growth of crops with fertilizer and nitrogen in the soil as well as sunlight, rather than the blessings of some native god. And they differ a lot in other respects. Their art and their music are advanced. Free art exists along with purely decorative art, and their techniques are finely developed." "I'm glad you agree, then. Take a look at this." Templin threw a shiny bit of metal on the rough-hewn table. Eckert picked it up and inspected it. It was heavy and one side of it was extremely sharp. "What's it for?" "They've got a hospital set up here. Not a hospital like any we know, of course, but a hospital nonetheless. It's not used very much; apparently the natives don't get sick here. But occasionally there are hunting accidents and injuries that require surgery. The strip of metal there is a scalpel." He laughed shortly. "Primitive little gadget, but it works well—as well as any of ours." Eckert hefted it in his palm. "The most important thing is that they have the knowledge to use it. Surgery isn't a simple science." "Well, what do you think about it?" "The obvious. They evidently have as much technology as they want, at least in fields where they have to have it." "How come they haven't gone any further?" "Why should they? You can live without skycars and rocket ships, you know." "Did you ever wonder what kind of weapons they might have?" "The important thing," Eckert mused, "is not if they have them, but if they'd use them. And I rather doubt that they would. We've been here for two weeks now and they've been very kind to us, seeing that we've had food and water and what fuel we need." "It's known in the livestock trade as being fattened up for the slaughter," Templeton said. Eckert sighed and watched a fat bug waddle across a small patch of sunlight on the wooden floor. It was bad enough drawing an assignment in a totally foreign culture, even if the natives were humanoid. It complicated things beyond all measure when your partner in the project seemed likely to turn into a vendettist. It meant that Eckert would have to split his energies. He'd have to do what investigating he could among the Tunpeshans, and he'd have to watch Templin to see that he didn't go off half-cocked and spoil everything. "You're convinced that Pendleton was murdered, aren't you?" Templin nodded. "Sure." "Why?" "The Tunpeshans know why we're here. We've dropped enough hints along those lines. But nobody has mentioned Pendleton; nobody has volunteered any information about him. And he was an attache here for three years. Didn't anybody know him during that time? We've let slip a few discreet statements that we would like to talk to Pendleton's friends, yet nobody's come around. Apparently, in all the three years he was here, Pendleton didn't make any friends. And that's a little hard to believe. It's more likely that his friends have been silenced and any information about him is being withheld for a reason." "What reason?" Templin shrugged. "Murder. What other reason could there be?" Eckert rolled up the thin, slatted blinds and stared out at the scenery. A hundred feet down the road, a native woman was going to market, leading a species of food animal by the halter. "They grow their women nice, don't they?" "Physically perfect, like the men," Templin grumbled. "You could get an inferiority complex just from watching the people here. Everybody's so damn perfect. Nobody's sick, nobody's unhealthy, nobody is too fat or too thin, nobody's unhappy. The only variation is that they don't all look alike. Perfection. It gets boring after a while." "Does it? I hadn't noticed." Eckert turned away from the blinds. His voice was crisp. "I knew Don Pendleton quite well, too," he said. "But it isn't blinding me to what I'm here for. We came to find out what happened to him, not to substantiate any preconceived notions. What we find out may be vitally important to anybody serving here in the future. I would hate to see our efforts spoiled because you've already made up your mind." "You knew Pendleton," Templin repeated grimly. "Do you think it was suicide?" "I don't think there's such a thing as a suicide type, when you come down to it. I'm not ruling out the possibility of murder, either. I'm trying to keep an open mind." "What have we accomplished so far? What have we found out?" "We've got six months," Eckert said quietly. "Six months in which we'll try to live here inconspicuously and study the people and try to cultivate informants. We would get nowhere if we came barging in asking all sorts of questions. And don't forget, Ray, we're all alone on Tunpesh. If it is a case of murder, what happens when the natives find out that we know it is?" Templin's eyes dueled for a moment. Then he turned his back and walked to the window. "I suppose you're right," he said at last. "It's nice living here, Ted. Maybe I've been fighting it. But I can't help thinking that Don must have liked it here, too." One of the hardest things to learn in a foreign culture, Eckert thought, is when to enjoy yourself, when to work and when to worry. " Pelache, menshar? " " Sharra! " He took the small bowl of pelache nuts, helped himself to a few, and passed the bowl on. This was definitely the time to enjoy himself, not to work or worry. He had heard about the halera a few days ago, and, by judicious hinting to the proper authorities, he and Templin had been invited. It was a good chance to observe native customs. A little anthropology—with refreshments. The main courses started making the rounds and he took generous helpings of the roasted ulami and the broiled halunch and numerous dabs from the side dishes of steaming vegetables. Between every course, they passed around a small flagon of the hot, spiced native wine, but he noticed that nobody drank to excess. The old Greek ideal , he thought: moderation in everything. He looked at Templin, sitting across from him in the huge circle, and shrugged mentally. Templin looked as if he was about to break down and enjoy himself, but there was still a slight bulge under his tunic, where he had strapped his power pack. Any fool should have known that nothing would happen at a banquet like this. The only actual danger lay in Templin's getting excited and doing something he was bound to regret later on. And even that danger was not quite as likely now. There will be hell to pay , Eckert thought, if Templin ever finds out that I sabotaged his power pack. "You look thoughtful, menshar Eckert." Eckert took another sip of the wine and turned to the Tunpeshan on his left. He was a tall, muscular man with sharp eyes, a firm chin and a certain aura of authority. "I was wondering if my countryman Pendleton had offended your people in any way, Nayova." Now was as good a time as any to pump him for what he knew about Pendleton's death. "So far as I know, menshar Pendleton offended no one. I do not know what duties he had to perform here, but he was a generous and courteous man." Eckert gnawed the dainty meat off a slender ulami bone and tried to appear casual in his questioning. "I am sure he was, Nayova. I am sure, too, that you were as kind to him as you have been to Templin and myself. My Government is grateful to you for that." Nayova seemed pleased. "We tried to do as well for menshar Pendleton as we could. While he was here, he had the house that you have now and we saw that he was supplied with food and all other necessities." Eckert had a sudden clammy feeling which quickly passed away. What Nayova had said was something he'd make sure Templin never heard about. He wiped his mouth on a broad, flat leaf that had been provided and took another sip of the wine. "We were shocked to find out that menshar Pendleton had killed himself. We knew him quite well and we could not bring ourselves to believe he had done such a thing." Nayova's gaze slid away from him. "Perhaps it was the will of the Great One," he said vaguely. He didn't seem anxious to talk about it. Eckert stared bleakly at his wine glass and tried to put the pieces of information together. They probably had a taboo about self-destruction which would make it difficult to talk about. That would make it even harder for him to find out by direct questioning. A native fife trilled shrilly and a group of young men and women walked into the room. The circle broke to let them through and they came and knelt before Nayova. When he clapped his hands sharply, they retreated to the center of the circle and began the slow motions of a native dance. The sound of the fife softened and died and the slow monotonous beat of drums took its place. The beat slowly increased and so did the rhythm of the dancers. The small fires at the corners of the hut were allowed to dwindle and the center of the circle became filled with the motions of shadows intermixed with the swift, sure movements of glistening limbs. Eckert felt his eyebrows crawl upward. Apparently the dance was the Tunpeshan version of the rites de passage . He glanced across the circle at Templin. Templin's face—what he could see of it by the flickering light—was brick red. A voice spoke in his ear. "It is hard for us to imagine anybody doing what menshar Pendleton did. It is ..." and he used a native word that Eckert translated as being roughly equivalent to " obscene ." The dancers at the center of the circle finally bowed out with small garlands of flowers on their heads that signified their reaching adulthood. Acrobats then took the stage and went through a dizzying routine, and they in turn were succeeded by a native singer. They were all excellent, Eckert thought. If anything, they were too good. The bowl of pelache nuts made its way around again and Nayova leaned over to speak to him. "If there is any possibility that I can help you while you are here, menshar Eckert, you have but to ask." It would probably be a mistake to ask for a list of Pendleton's friends, but there was a way around that. "I would like to meet any of your people who had dealings with Pendleton, either in business or socially. I will do everything not to inconvenience them in any way." "I think they would be glad to help you. I shall ask them to go to you this coming week." | C. He knew even children were capable of doing damage with a weapon. |
As of 11/20/2020, which COPD severity level was Mr. Wells classified under?
Choose the correct answer from the following options:
A. Gold I
B. Gold II
C. Gold III
D. Gold IV
E. Not classified
| ### Patient Report 0
**Dear colleague, **
We report to you on Mr. Paul Wells, born on 04/02/1953, who was in our
inpatient treatment from 07/26/2019 to 07/28/2019.
**Diagnoses:** Suspected multifocal HCC segment IV, VII/VIII, first
diagnosed: 07/19.
- COPD, current severity level Gold III.
- Pulmonary emphysema, respiratory partial insufficiency with home
oxygen.
- Postnasal drip syndrome
**Current Presentation:** The elective presentation of Mr. Wells was
made in accordance with the decision of the interdisciplinary liver
board of 07/20/2019 for further diagnostics in the case of multiple
malignoma-specific hepatic space demands.
**Medical History: **In brief, Mr. Wells presented to the Medical Center
St. Luke's with persistent right-sided pain in the upper abdomen.
Computer tomography showed multiple intrahepatic masses of the right
liver lobe (SIV, SVII/VIII). For diagnostic clarification of the
malignoma-specific findings, the patient was presented to our liver
outpatient clinic. The tumor marker diagnostics have not been
conclusive. Analogous to the recommendation of the liver board, a liver
puncture, staging, and endoscopic exclusion of a primary in the
gastrointestinal tract should be initiated.
**Physical Examination:** Physical examination reveals an alert patient.
- Oral mucosa: Moist and rosy, no plaques typical of thrush, no
plaques typical of herpes.
- Hear: Heart sounds pure, rhythmic, normofrequency.
- Lungs: Laterally attenuated breath sound with wheezing.
- Abdomen: Abdomen soft, regular bowel sounds over all 4 quadrants, no
defensive tension, no resistances, diffuse pressure pain over the
upper abdomen. No renal tap pain, no spinal tap pain. Spleen
palpable under the costal arch.
- Extremities: No edema, freely movable
- Neurology: GCS 15, pupils directly and indirectly reactive to light,
no flapping tremor. No meningism.
**Therapy and Progression:** Mr. Wells presented an age-appropriate
general status and cardiopulmonary stability. Anamnestically, there was
no evidence of an acute infection. Skin or scleral icterus and pruritus
were denied. No B symptoms. No stool changes, no dysuria. There would be
regular alcohol consumption of about 3-4 beers a day, as well as
nicotine abuse (120 PY). The general performance in COPD Gold grade III
was strongly limited, with a walking distance reduced to 100m due to
dyspnea. He had a home oxygen demand with 4L/min O2 during the day, up
to 6L/min under load. At night, 2L/min O2. The last colonoscopy was
performed 4 years ago, with no anamnestic abnormalities. No known
allergies. Family history is positive for colorectal cancer (mother).
Clinical examination revealed the typical auscultation findings of
advanced COPD with attenuated breath sounds bilateral, with
hyperinflation and clear wheezing. Otherwise, there were no significant
findings. Laboratory chemistry did not reveal any higher-grade
abnormalities. On the day of admission, after detailed clarification,
the patient was able to undergo the complication-free sonographically
guided puncture of the liver cavity in SIV. Thereby, two punch cylinders
were preserved for histopathological processing. Histologically, the
findings presented as infiltrates of a macrotrabecular and
pseudoglandular growing, well-differentiated hepatocellular carcinoma
(G1). The postinterventional course was unremarkable. In particular, no
clinical or laboratory signs were found for bleeding.
CT staging revealed a size constant known in the short term.
Hypervascularized hepatic space demands in both lobes of the liver
without further malignancy suspect thoracoabdominal tumor detection and
without metastasis aspects. MR also revealed the large, partly exophytic
growing, partly centrally hemorrhaged HCC lesions in S3/4 and S7/8 to
the illustration. In addition, complete enforcement of the left lobe of
the liver was evident with smaller satellites and macroinvasion of the
left portal vein branch. There was a low cholestasis of the left biliary
system. Gastroscopy and colonoscopy were also performed. Here, a reflux
esophagitis, sigmoid diverticulosis, multiple colonic diverticula, and a
4mm polyp were removed from the sigmoid colon to prevent bleeding; a
hemoclip was applied. Histologically, no adenoma was found. An
appointment to discuss the findings in our HCC outpatient clinic has
been arranged. We recommend further therapy preparation and the
performance of an echocardiography.
We were able to discharge Mr. Wells on 7/28/19.
**Addition:**
**Ultrasound on 07/26/2019 10:15 AM:**
- Indication: Targeted liver puncture for suspected metastatic liver
malignancy
- Organ puncture: Quick: 114%, PTT: 28 s, and platelets: 475 G/L. A
valid declaration of consent is available. According to the patient,
he does not receive antiplatelet drugs.
- In segment IV, an approximately 8.3 x 6 cm echo-depleted mass with
central cystic fusion is accessible in the dorsal position of a
sonographically guided puncture at 6.5 cm puncture depth. After
extensive skin disinfection, local anesthesia with 10 mL Mecaine 1%
and puncture incision with a scalpel. Repeated puncture with 18 G
Magnum needles is performed. Two approximately 1 cm fragile whitish
cylinders obtained for histologic examination. Band-aid dressing.
- **Assessment:** Hepatic space demand
**MRI of the liver plain + contrast agent from 07/26/2019 1:15 PM:**
**Technique**: Coronary and axial T2 weighted sequences, axial
diffusion-weighted EPI sequence with ADC map (b: 0, 50, 300 and 600
s/mmÇ), axial dynamic T1 weighted sequences with Dixon fat suppression
and (liver-specific) contrast agent (Dotagraf/Primovist); slice
thickness: 4 mm. Premedication with 2 mL Buscopan.
**Liver**: Centrally hemorrhagic masses observed in liver segments 4, 7,
and 8 demonstrate T2 hyperintensity, marked diffusion restriction,
arterial phase enhancement, and venous phase washout. These
characteristics are congruent with histopathological diagnosis of
hepatocellular carcinoma. The largest lesion in segment 4 exhibits
pronounced exophytic growth but no evidence of organ invasion. Notably,
branches of the mammary arteries penetrate directly into the tumor.
Diffusion-weighted imaging further reveals disseminated foci throughout
the entire left hepatic lobe. Disruption of the peripheral left portal
vein branch indicative of macrovascular invasion, accompanied by
peripheral cholestasis in the left biliary system.
**Biliary Tract:** Bile ducts are emphasized on both left and right
sides, with no evidence of mechanical obstruction in drainage. The
common hepatic duct remains non-dilated.
**Pancreas and Spleen:** Both organs exhibit no abnormalities.
**Kidneys:** Normal signal characteristics observed.
**Bone Marrow:** Signal behavior is within normal limits.
Assessment: Radiological features highly suggestive of hepatocellular
carcinoma in liver segments 4, 7, and 8, with evidence of macrovascular
invasion and peripheral cholestasis in the left biliary system. No signs
of organ invasion or biliary obstruction. Pancreas, spleen, kidneys, and
bone marrow appear unremarkable.
**Assessment:**
Large liver lesions, some exophytic and some centrally hemorrhagic, are
observed in segments 3/4 and 7/8.
In addition, the left lobe of the liver is completely involved with
smaller satellite lesions and macroinvasion of the left portal branch.
Mild cholestasis of the left biliary system is noted.
Dilated bile ducts are also found on the right side with no apparent
mechanical obstruction to outflow.
**CT Chest/Abdomen/Pelvis with contrast agent from 07/27/2019 2:00 PM:**
**Clinical Indication:** Evaluation of an unclear liver lesion
(approximately 9 cm) in a patient with severe COPD. No prior
liver-related medical history.
**Question:** Are there any suspicious lesions in the liver?
**Pre-recordings:** Previous external CT abdomen dated 09/13/2021.
**Findings:**
**Technique:** CT imaging involved a multi-line spiral CT through the
chest, abdomen, and pelvis in the venous contrast phase. Oral contrast
agent with Gastrolux 1:33 in water was administered. Thin-layer
reconstructions and coronary and sagittal secondary reconstructions were
performed.
**Chest:** No axillary or mediastinal lymphadenopathy is observed. There
is marked coronary sclerosis, as well as calcification of the aortic and
mitral valves. Nonspecific nodules smaller than 2 mm are noted in the
posterolateral lower lobe on the right side and lateral middle lobe. No
pneumonic infiltrates are observed. There is reduced aeration with
presumed additional scarring changes at the base of the lung
bilaterally, along with centrilobular emphysema.
**Abdomen:** Known exophytic liver lesions are confirmed, with
involvement in segment III extending to the subhepatic region (0.1 cm
extension) and a 6 cm lesion in segment VIII. Further spotty
hypervascularized lesions are observed throughout the left lobe of the
liver. No pathological dilatation of intra- or extrahepatic bile ducts
is seen, and there is no evidence of portal vein thrombosis. There are
no pathologically enlarged lymph nodes at the hepatic portal,
retroperitoneal, or inguinal regions. No ascites or pneumoperitoneum is
noted. There is no pancreatic duct congestion, and the spleen is not
enlarged. Additionally, there is a Bosniak 1 left renal cyst measuring
3.6 cm. Pronounced sigmoid diverticulosis is observed, with no evidence
of other masses in the gastrointestinal tract. Skeletal imaging reveals
no malignancy-specific osteodestructions but shows ventral pontifying
spondylophytes of the thoracic spine with no fractures.
**Assessment:**
Short-term size-constant known hypervascularized hepatic space lesions
are present in both lobes of the liver.
No other malignancy-susceptible thoracoabdominal tumor evidence is
found, and there are no metastasis-specific lymph nodes.
**Gastroscopy from 07/28/2019**
**Findings:**
**Esophagus:** Unobstructed intubation of the esophageal orifice under
visualization. Mucosa appears inconspicuous, with the Z-line at 37 cm
and measuring less than 5 mm. Small mucosal lesions are observed but do
not straddle mucosal folds.
**Stomach:** The gastric lumen is completely distended under air
insufflation. There are streaky changes in the antrum, while the fundus
and cardia appear regular on inversion. The pylorus is inconspicuous and
passable.
**Duodenum:** Good development of the bulbus duodeni is noted, with good
insight into the pars descendens duodeni. The mucosa appears overall
inconspicuous.
**Assessment:** Findings suggest reflux esophagitis (Los Angeles
Classification Grade A) and antrum gastritis.
**Colonoscopy from 07/28/2019**
**Findings:**
**Colon:** Some residual fluid contamination is noted in the sigmoid
(Boston Bowel Preparation Scale \[BBPS\] 8). There is pronounced sigmoid
diverticulosis, along with multiple colonic diverticula. A 4mm polyp in
the lower sigma (Paris IIa, NICE 1) is observed and ablated with a cold
snare, with hemoclip application for bleeding prophylaxis. Other mucosal
findings appear inconspicuous, with normal vascular markings. There is
no indication of inflammatory or malignant processes.
**Maximum Insight:** Terminal ileum.
**Anus:** Inspection of the anal region reveals no pathological
findings. Palpation is inconspicuous, and the mucosa is smooth and
displaceable, with no resistance and no blood on the glove.
**Assessment:** Polypectomy was performed for sigmoid diverticulosis and
a colonic diverticulum, with histology revealing minimally hyperplastic
colorectal mucosa and no evidence of malignancy.
**Pathology from 08/27/2019**
**Clinical Information/Question:**
**Macroscopy:** Unclear liver tumor: numerous tissue samples up to a
maximum of 0.7 cm in size. Complete embedding.
Processing: One tissue block processed and stained with Hematoxylin and
Eosin (H&E), Gomori\'s trichrome, Iron stain, Diastase Periodic
Acid-Schiff (D-PAS), and Van Gieson stain.
**Microscopic Findings:**
- Liver architecture is presented in fragmented liver core biopsies
with observable lobular structures and two included portal fields.
- Hepatic trabeculae are notably wider than the typical 2-3 cell
width, featuring the formation of druse-like luminal structures.
- Sinusoidal dilatation is markedly observed.
- Hepatocytes show mildly enlarged nuclei with minimal cytologic
atypia and isolated mitotic figures.
- Gomori staining reveals a notable, partial loss of the fine
reticulin fiber network.
- Adjacent areas show fibrosed liver parenchyma containing hemosiderin
pigmentation.
- No significant evidence of parenchymal fatty degeneration is
observed.
**Assessment**: Histologic features indicative of marked sinusoidal
dilatation, trabecular widening, and partial loss of reticulin network,
alongside minimally atypical hepatocytes and fibrosed parenchyma with
hemosiderin pigment. No significant hepatic fat degeneration noted.
### Patient Report 1
**Dear colleague, **
We would like to report on Paul Wells, born on 04/02/1953, who was under
our outpatient treatment on 08/24/2019.
**Diagnoses:**
- Multifocal HCC (Hepatocellular Carcinoma) involving segments IV,
VII/VIII, with portal vein invasion, classified as BCLC C, diagnosed
in July 2019.
- Extensive HCC lesions, some exophytic and others centrally
hemorrhagic, in segments S3/4 and S7/8, complete involvement of the
left liver lobe with smaller satellite lesions, and macrovascular
invasion of the left portal vein.
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
**Other Diagnoses:**
- COPD with a current severity level of Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency requiring home oxygen therapy.
- Postnasal Drip Syndrome.
- History of nicotine use (120 pack-years).
- Hypertension (high blood pressure).
**Medical History:** Mr. Wells presented with persistent right upper
abdominal pain and was initially treated at St. Luke\'s Medical Center.
CT scans revealed multiple intrahepatic lesions in the right liver lobe
(SIV, SVII/VIII). Short-term follow-up CT staging revealed a known,
size-stable, hypervascularized hepatic lesion in both lobes of the
liver, with no evidence of other thoracoabdominal malignancies or
suspicious lymph nodes. MRI also confirmed the presence of large HCC
lesions, some exophytic and others centrally hemorrhagic, in segments
S3/4 and S7/8, along with complete infiltration of the left liver lobe
with smaller satellite lesions and macroinvasion of the left portal
vein. There was mild cholestasis in the left biliary system.
**Current Recommendations: **
- Liver function remains good based on laboratory tests.
- Mr. Wells has been extensively informed about systemic therapy
options with Atezolizumab/Bevacizumab and the possibility of
alternative therapy with a tyrosine kinase inhibitor.
- The decision has been made to initiate standard first-line therapy
with Atezolizumab/Bevacizumab. Detailed information regarding
potential side effects has been provided, with particular emphasis
on the need for immediate medical evaluation in case of signs of
gastrointestinal bleeding (blood in stool, black tarry stool, or
vomiting blood) or worsening pulmonary symptoms.
- The patient has been strongly advised to abstain from alcohol
completely.
- A follow-up evaluation through liver MRI and CT has been scheduled
for January 4, 2020, at our HCC (Hepatocellular Carcinoma) clinic.
The exact appointment time will be communicated to the patient
separately.
- We are available for any questions or concerns.
- In case of persistent or worsening symptoms, we recommend an
immediate follow-up appointment.
### Patient Report 2
**Dear colleague, **
We would like to provide an update regarding Mr. Paul Wells, born on
04/02/1953, who was under our inpatient care from 08/13/2020 to
08/14/2020.
**Medical History:**
We assume familiarity with Mr. Wells\'s comprehensive medical history as
described in the previous referral letter. At the time of admission, he
reported significantly reduced physical performance due to his known
severe COPD. Following the consensus of the Liver Board, we admitted Mr.
Wells for a SIRT simulation.
**Current Presentation:** Mr. Wells is a 66-year-old patient with normal
consciousness and reduced general condition. He is largely compensated
on 3 liters of oxygen per minute. His abdomen is soft with regular
peristalsis. A palpable tumor mass in the right upper abdomen is noted.
**DSA Coeliac-Mesenteric on 08/13/2020:**
- Uncomplicated SIRT simulation.
- Catheter position 1: Right hepatic artery.
- Catheter position 2: Left hepatic artery.
- Catheter position 3: Liver segment arteries 4a/4b.
- Uncomplicated and technically successful embolization of parasitic
tumor supply from the inferior and superior epigastric arteries.
**Perfusion Scintigraphy of the Liver and Lungs, including SPECT/CT on
08/13/2020:**
- The liver/lung shunt volume is 9.4%.
- There is intense radioactivity accumulation in multiple lesions in
both the right and left liver lobes.
**Therapy and Progression:** On 08/13/2020, we performed a DSA
coeliac-mesenteric angiography on Mr. Wells, administering a total of
approximately 159 MBq Tc99m-MAA into the liver\'s arterial circulation
(simulation). This procedure revealed that a significant portion of
radioactivity would reach the lung parenchyma during therapy, posing a
risk of worsening his already compromised lung function. In view of
these comorbidities, SIRT was not considered a viable treatment option.
Therefore, an interdisciplinary decision was made during the conference
to recommend systemic therapy. With an uneventful course, we discharged
Mr. Wells in stable general condition on 08/14/2020.
### Patient Report 3
**Dear colleague, **
We are reporting on Paul Wells, born on 04/02/1953, who presented to our
interdisciplinary clinic for Hepato- and Cholangiocellular Tumors on
10/24/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019.
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
**Other Diagnoses:**
- Suspected Polyneuropathy or Restless Legs Syndrome
- COPD, current severity Gold III.
- Pulmonary emphysema
- Respiratory partial insufficiency with home oxygen
- Postnasal-Drip Syndrome
- History of nicotine abuse (120 py)
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- History of severe pneumonia (Medical Center St. Luke's) in 10/2019
- Pneumogenic sepsis with detection of Streptococcus pneumoniae
- Arterial hypertension
- Atrial fibrillation
- Treatment with Apixaban
- Reflux esophagitis Grade A (Esophagogastroduodenoscopy in 08/2019).
**Current Presentation**: Mr. Wells presented to discuss follow-up after
systemic therapy with Atezolizumab/Bevacizumab due to his impaired
general condition.
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
The therapy had to be paused after a single administration due to a
substantial increase in transaminases (GPT 164 U/L, GOT 151 U/L),
suspected to be associated with immunotherapy-induced hepatitis. With
only minimal improvement in transaminases, Prednisolone therapy was
initiated on and tapered successfully after significant transaminase
regression. However, before the next planned administration, the patient
experienced severe pneumonic sepsis, requiring hospitalization on
10/2019. Following discharge, there was a recurrent infection requiring
inpatient antibiotic therapy.
Staging examinations in 01/2020 showed a very good tumor response.
Subsequently, Atezolizumab/Bevacizumab was re-administered on 01/23/2020
and 02/14/2020. However, in the following days, the patient experienced
significant side effects, including oral burning, appetite and weight
loss, low blood pressure, and worsening pulmonary status. Steroid
treatment improved the pulmonary situation, but due to poor tolerance,
therapy was paused after 02/14/2020.
Currently, Mr. Wells reports a satisfactory general condition, although
his pulmonary function remains limited but stable.
**Summary:** Laboratory results from external testing on 01/02/2020
indicate excellent liver function, with transaminases within normal
range. The latest CT examination shows continued tumor regression.
However, MRI quality is limited due to the patient\'s inability to hold
their breath adequately. Given the excellent tumor response and previous
significant side effects, it was decided to continue the treatment pause
until the next tumor staging.
**Current Recommendations:** A follow-up imaging appointment has been
scheduled for four months from now. We kindly request you send the
latest CT images (Chest/Abdomen/Pelvis, including dynamic liver CT) and
current blood values to our HCC clinic. Due to limited assessability,
another MRI is not advisable.
We remain at your disposal for any further inquiries. In case of
persistent or worsened symptoms, we recommend prompt reevaluation.
**Medication upon discharge:**
**Medication** **Dosage** **Frequency**
------------------------------------- ------------ -------------------------
Ipratropium/Fenoterol (Combivent) As needed As needed
Beclomethasone/Formoterol (Fostair) 6+200 mcg 2-0-2
Tiotropium (Spiriva) 2.5 mcg 2-0-0
Prednisolone (Prelone) 5 mg 2-0-0 (or as necessary)
Pantoprazole (Protonix) 40 mg 1-0-0
Fenoterol 0.1 mg As needed
Apixaban (Eliquis) 5 mg On hold
Olmesartan (Benicar) 20 mg 1-0-0
Lab results upon Discharge:
**Parameter** **Results** **Reference Range**
----------------------------- ------------- ---------------------
Sodium (Na) 144 mEq/L 134-145 mEq/L
Potassium (K) 3.7 mEq/L 3.4-5.2 mEq/L
Calcium (Ca) 2.37 mEq/L 2.15-2.65 mEq/L
Chloride (Cl) 106 mEq/L 95-112 mEq/L
Inorganic Phosphate (PO4) 0.93 mEq/L 0.8-1.5 mEq/L
Transferrin Saturation 20 % 16-45 %
Magnesium 0.78 mEq/L 0.75-1.06 mEq/L
Creatinine 1.88 mg/dL \<1.2 mg/dL
GFR 36 mL/min \<90 mL/min
BUN 60 mg/dL 14-46 mg/dL
Uric Acid 4.6 mg/dL 3.0-6.9 mg/dL
Total Bilirubin 0.5 mg/dL \<1 mg/dL
Albumin 4.0 g/dL 3.6-5.0 g/dL
Total Protein 6.8 g/dL 6.5-8.7 g/dL
CRP 0.19 mg/dL \<0.5 mg/dL
Transferrin 269 mg/dL 200-360 mg/dL
Ferritin 110 mcg/L 30-300 mcg/L
ALT 339 U/L \<45 U/L
AST 424 U/L \<50 U/L
GGT 904 U/L \<55 U/L
Lipase 61 U/L \<70 U/L
Thyroid-Stimulating Hormone 0.54 mIU/L 0.27-4.20 mIU/L
Hemoglobin 14.5 g/dL 14.0-17.5 g/dL
Hematocrit 43 % 40-52 %
Red Blood Cells 4.60 M/µL 4.6-6.2 M/µL
White Blood Cells 8.78 K/µL 4.5-11.0 K/µL
Platelets 205 K/µL 150-400 K/µL
MCV 94 fL 81-100 fL
MCH 31.5 pg 27-34 pg
MCHC 33.5 g/dL 32.4-35.0 g/dL
MPV 11 fL 7-12 fL
RDW 14.8 % 11.9-14.5 %
Neutrophils 3.72 K/µL 1.8-7.7 K/µL
Lymphocytes 2.37 K/µL 1.4-3.7 K/µL
Monocytes 0.93 K/µL 0.2-1.0 K/µL
Eosinophils 1.67 K/µL \<0.7 K/µL
Basophils 0.09 K/µL 0.01-0.10 K/µL
Erythroblasts Negative \<0.01 K/µL
Antithrombin Activity 85 % 80-120 %
### Patient Report 4
**Dear colleague, **
We are reporting an update of the medical condition of Mr. Paul Wells
born on 04/02/1953, who presented for a follow up in our outpatient
clinic on 11/20/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation.
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased alcohol consumption (3-4 beers/day).
**Other diagnoses:**
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with detection of Streptococcus pneumonia
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** Mr. Wells initially presented with right upper
abdominal pain, which led to the discovery of multiple intrahepatic
masses in liver segments IV, VII/VIII. Subsequent investigations
confirmed the diagnosis of HCC. He also suffers from chronic obstructive
pulmonary disease (COPD), emphysema, and respiratory insufficiency
requiring home oxygen therapy. Previous investigations and treatments
were documented in detail in our previous medical records.
**Physical Examination:**
- General Appearance: Alert, cooperative, and oriented.
- Vital Signs: Stable blood pressure, heart rate, respiratory rate,
and temperature. Oxygen Saturation (SpO2): Within the normal range.
- Respiratory System: Normal chest symmetry, no accessory muscle use.
Clear breath sounds, no wheezing or crackles. Regular respiratory
rate.
- Cardiovascular System: Regular heart rate and rhythm, no murmurs.
Strong radial and pedal pulses bilaterally. No lower extremity
edema.
- Gastrointestinal System: Soft, nontender abdomen. Bowel sounds
present in all quadrants. Spleen palpable under the costal arch.
- Neurological Examination: Alert and oriented. Cranial nerves, motor,
sensory, reflexes, coordination and gait normal. No focal
neurological deficits.
- Skin and Mucous Membranes: Intact skin, no rashes or lesions. Moist
oral mucosa without lesions.
- Extremities: No edema. Full range of motion in all joints. Normal
capillary refill.
- Lymphatic System:
- No palpable lymphadenopathy.
**MRI Liver (plain + contrast agent) on 11/20/2020 09:01 AM.**
- Imaging revealed stable findings in the liver. The previously
identified HCC lesions in segments IV, VII/VIII, including their
size and characteristics, remained largely unchanged. There was no
evidence of new lesions or metastases. Detailed MRI imaging provided
valuable insight into the nature of the lesions, their vascularity,
and possible effects on adjacent structures.
**CT Chest/Abdomen/Pelvis with contrast agent on 11/20/2020 12:45 PM.**
- Thoracoabdominal CT scan showed the same results as the previous
examination. Known space-occupying lesions in the liver remained
stable, and there was no evidence of malignancy or metastasis
elsewhere in the body. The examination also included a thorough
evaluation of the thoracic and pelvic regions to rule out possible
metastasis.
**Gastroscopy on 11/20/2020 13:45 PM.**
- Gastroscopy follow-up confirmed the previous diagnosis of reflux
esophagitis (Los Angeles classification grade A) and antral
gastritis. These findings were consistent with previous
investigations. It is important to note that while these findings
are unrelated to HCC, they contribute to Mr. Wells\' overall medical
profile and require ongoing treatment.
**Colonoscopy on 11/20/2020 15:15 PM.**
- Colonoscopy showed that the sigmoid colon polyp, which had been
removed during the previous examination, had not recurred. No new
abnormalities or malignancies were detected in the gastrointestinal
tract. This examination provides assurance that there is no
concurrent colorectal malignancy complicating Mr. Wells\' medical
condition.
**Pulmonary Function Testing:**
Mr. Wells\' COPD, emphysema, and respiratory insufficiency were
evaluated in detail. Pulmonary function tests confirmed his current
severity score of Gold III, indicating advanced COPD. Despite the
chronic nature of his disease, there has been no significant
deterioration since the last assessment.
**Oxygen Therapy:**
As previously documented, Mr. Wells requires home oxygen therapy. His
oxygen requirements have been constant, with no significant increase in
oxygen requirements during daily activities or at rest. This stability
in his oxygen demand is encouraging and indicates effective management
of his respiratory disease.
**Overall Assessment:** Based on the results of recent follow-up, Mr.
Paul Wells\' hepatocellular carcinoma (HCC) has not progressed
significantly. The previously noted HCC lesions have remained stable in
terms of size and characteristics. In addition, there is no evidence of
malignancy elsewhere in his thoracoabdominal region.
Mr. Wells\' COPD, emphysema, and respiratory insufficiency, which is
being treated with home oxygen therapy, have also not changed
significantly during this follow-up period. His cardiopulmonary
condition remains well controlled, with no acute deterioration.
Psychosocially, Mr. Wells continues to demonstrate resilience and
actively participates in his care. His strong support system continues
to contribute to his overall well-being.
Additional monitoring and follow-up appointments have been scheduled to
ensure continued management of Mr. Wells\' health. In addition,
discussions continue regarding potential treatment options and
interventions to provide him with the best possible care.
**Current Recommendations:** In light of the stability observed in Mr.
Wells\' HCC and overall medical condition, we recommend the following
steps for his continued care:
1. Regular Follow-up: Maintain a schedule of regular follow-up
appointments to monitor the status of the HCC, cardiopulmonary
function, and other associated conditions.
2. Lifestyle-Modification
### Patient Report 5
**Dear colleague, **
We report to you about Mr. Paul Wells born on 04/02/1953 who received
inpatient treatment from 02/04/2021 to 02/12/2021.
**Diagnosis**: Community-Acquired Pneumonia (CAP)
**Previous Diagnoses and Treatment:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation attempt on 08/13/2019: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation (up to 4x ULN).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
- Suspected PNP DD RLS (Restless Legs Syndrome).
<!-- -->
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with Streptococcus pneumoniae detection.
- History of unclear infection vs. pneumonia in 10/2019-01/2020.
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
Currently, Mr. Wells complains about progressively worsening respiratory
symptoms, which included shortness of breath, productive cough with
yellow-green sputum, pleuritic chest pain, fever, and chills, spanning a
period of five days.
**Physical Examination:**
Temperature: 38.6°C, Blood Pressure: 140/80 mm Hg, Heart Rate: 110 beats
per minute Respiratory Rate: 30 breaths per minute, Oxygen Saturation
(SpO2): 88% on room air
Breath Sounds: Auscultation revealed diminished breath sounds and coarse
crackles, notably in the right lower lobe.
The patient further reported pleuritic chest pain localized to the right
lower chest.
**Therapy and Progression:**
During his hospitalization, Mr. Wells was in stable cardiopulmonary
condition. We initiated an empiric antibiotic therapy with intravenous
Ceftriaxone and Azithromycin to treat community-acquired pneumonia
(CAP). Oxygen supplementation was provided to maintain adequate oxygen
saturation levels, and pain management strategies were implemented to
alleviate pleuritic chest pain. Additionally, pulmonary hygiene measures
and chest physiotherapy were applied to facilitate sputum clearance.
Frequent respiratory treatments with bronchodilators were administered
to mitigate airway obstruction, and continuous monitoring of vital
signs, oxygen saturation, and respiratory status was carried out.
Throughout his hospital stay, Mr. Wells exhibited gradual clinical
improvement, marked by several positive developments. These included the
resolution of fever, improved oxygen saturation levels, and a follow-up
chest X-ray demonstrating the resolution of the right lower lobe
consolidation. Furthermore, antibiotic therapy was adjusted based on
sputum culture results, which identified Streptococcus pneumoniae as the
causative pathogen. Mr. Wells continued to receive supportive care and
respiratory interventions.
We were thus able to discharge Mr. Wells in a good general condition. | Gold III |
What was the intention of Higgins' lawyer by saying that Higgins had put "The Scorpion" on his gun barrel himself?
A. To avoid a trial by admitting fault immediately and getting the job done quickly
B. In hopes of the judge and jury seeing the other vigilante acts of The Scorpion and cutting Higgins some slack.
C. In hopes of receiving mercy for the crimes.
D. To try to use an insanity defense for Higgins.
| CALL HIM NEMESIS By DONALD E. WESTLAKE Criminals, beware; the Scorpion is on your trail! Hoodlums fear his fury—and, for that matter, so do the cops! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, September 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The man with the handkerchief mask said, "All right, everybody, keep tight. This is a holdup." There were twelve people in the bank. There was Mr. Featherhall at his desk, refusing to okay a personal check from a perfect stranger. There was the perfect stranger, an itinerant garage mechanic named Rodney (Rod) Strom, like the check said. There were Miss English and Miss Philicoff, the girls in the gilded teller cages. There was Mister Anderson, the guard, dozing by the door in his brown uniform. There was Mrs. Elizabeth Clayhorn, depositing her husband's pay check in their joint checking account, and with her was her ten-year-old son Edward (Eddie) Clayhorn, Junior. There was Charlie Casale, getting ten dollars dimes, six dollars nickels and four dollars pennies for his father in the grocery store down the street. There was Mrs. Dolly Daniels, withdrawing money from her savings account again. And there were three bank robbers. The three bank robbers looked like triplets. From the ground up, they all wore scuffy black shoes, baggy-kneed and unpressed khaki trousers, brown cracked-leather jackets over flannel shirts, white handkerchiefs over the lower half of their faces and gray-and-white check caps pulled low over their eyes. The eyes themselves looked dangerous. The man who had spoken withdrew a small but mean-looking thirty-two calibre pistol from his jacket pocket. He waved it menacingly. One of the others took the pistol away from Mister Anderson, the guard, and said to him in a low voice, "Think about retirement, my friend." The third one, who carried a black satchel like a doctor's bag, walked quickly around behind the teller's counter and started filling it with money. It was just like the movies. The man who had first spoken herded the tellers, Mr. Featherhall and the customers all over against the back wall, while the second man stayed next to Mr. Anderson and the door. The third man stuffed money into the black satchel. The man by the door said, "Hurry up." The man with the satchel said, "One more drawer." The man with the gun turned to say to the man at the door, "Keep your shirt on." That was all Miss English needed. She kicked off her shoes and ran pelting in her stocking feet for the door. The man by the door spread his arms out and shouted, "Hey!" The man with the gun swung violently back, cursing, and fired the gun. But he'd been moving too fast, and so had Miss English, and all he hit was the brass plate on Mr. Featherhall's desk. The man by the door caught Miss English in a bear hug. She promptly did her best to scratch his eyes out. Meanwhile, Mr. Anderson went scooting out the front door and running down the street toward the police station in the next block, shouting, "Help! Help! Robbery!" The man with the gun cursed some more. The man with the satchel came running around from behind the counter, and the man by the door tried to keep Miss English from scratching his eyes out. Then the man with the gun hit Miss English on the head. She fell unconscious to the floor, and all three of them ran out of the bank to the car out front, in which sat a very nervous-looking fourth man, gunning the engine. Everyone except Miss English ran out after the bandits, to watch. Things got very fast and very confused then. Two police cars came driving down the block and a half from the precinct house to the bank, and the car with the four robbers in it lurched away from the curb and drove straight down the street toward the police station. The police cars and the getaway car passed one another, with everybody shooting like the ships in pirate movies. There was so much confusion that it looked as though the bank robbers were going to get away after all. The police cars were aiming the wrong way and, as they'd come down with sirens wailing, there was a clear path behind them. Then, after the getaway car had gone more than two blocks, it suddenly started jouncing around. It smacked into a parked car and stopped. And all the police went running down there to clap handcuffs on the robbers when they crawled dazedly out of their car. "Hey," said Eddie Clayhorn, ten years old. "Hey, that was something, huh, Mom?" "Come along home," said his mother, grabbing his hand. "We don't want to be involved." "It was the nuttiest thing," said Detective-Sergeant Stevenson. "An operation planned that well, you'd think they'd pay attention to their getaway car, you know what I mean?" Detective-Sergeant Pauling shrugged. "They always slip up," he said. "Sooner or later, on some minor detail, they always slip up." "Yes, but their tires ." "Well," said Pauling, "it was a stolen car. I suppose they just grabbed whatever was handiest." "What I can't figure out," said Stevenson, "is exactly what made those tires do that. I mean, it was a hot day and all, but it wasn't that hot. And they weren't going that fast. I don't think you could go fast enough to melt your tires down." Pauling shrugged again. "We got them. That's the important thing." "Still and all, it's nutty. They're free and clear, barrelling out Rockaway toward the Belt, and all at once their tires melt, the tubes blow out and there they are." Stevenson shook his head. "I can't figure it." "Don't look a gift horse in the mouth," suggested Pauling. "They picked the wrong car to steal." "And that doesn't make sense, either," said Stevenson. "Why steal a car that could be identified as easily as that one?" "Why? What was it, a foreign make?" "No, it was a Chevvy, two-tone, three years old, looked just like half the cars on the streets. Except that in the trunk lid the owner had burned in 'The Scorpion' in big black letters you could see half a block away." "Maybe they didn't notice it when they stole the car," said Pauling. "For a well-planned operation like this one," said Stevenson, "they made a couple of really idiotic boners. It doesn't make any sense." "What do they have to say about it?" Pauling demanded. "Nothing, what do you expect? They'll make no statement at all." The squad-room door opened, and a uniformed patrolman stuck his head in. "The owner of that Chevvy's here," he said. "Right," said Stevenson. He followed the patrolman down the hall to the front desk. The owner of the Chevvy was an angry-looking man of middle age, tall and paunchy. "John Hastings," he said. "They say you have my car here." "I believe so, yes," said Stevenson. "I'm afraid it's in pretty bad shape." "So I was told over the phone," said Hastings grimly. "I've contacted my insurance company." "Good. The car's in the police garage, around the corner. If you'd come with me?" On the way around, Stevenson said, "I believe you reported the car stolen almost immediately after it happened." "That's right," said Hastings. "I stepped into a bar on my route. I'm a wine and liquor salesman. When I came out five minutes later, my car was gone." "You left the keys in it?" "Well, why not?" demanded Hastings belligerently. "If I'm making just a quick stop—I never spend more than five minutes with any one customer—I always leave the keys in the car. Why not?" "The car was stolen," Stevenson reminded him. Hastings grumbled and glared. "It's always been perfectly safe up till now." "Yes, sir. In here." Hastings took one look at his car and hit the ceiling. "It's ruined!" he cried. "What did you do to the tires?" "Not a thing, sir. That happened to them in the holdup." Hastings leaned down over one of the front tires. "Look at that! There's melted rubber all over the rims. Those rims are ruined! What did you use, incendiary bullets?" Stevenson shook his head. "No, sir. When that happened they were two blocks away from the nearest policeman." "Hmph." Hastings moved on around the car, stopping short to exclaim, "What in the name of God is that? You didn't tell me a bunch of kids had stolen the car." "It wasn't a bunch of kids," Stevenson told him. "It was four professional criminals, I thought you knew that. They were using it in a bank holdup." "Then why did they do that ?" Stevenson followed Hastings' pointing finger, and saw again the crudely-lettered words, "The Scorpion" burned black into the paint of the trunk lid. "I really don't know," he said. "It wasn't there before the car was stolen?" "Of course not!" Stevenson frowned. "Now, why in the world did they do that?" "I suggest," said Hastings with heavy sarcasm, "you ask them that." Stevenson shook his head. "It wouldn't do any good. They aren't talking about anything. I don't suppose they'll ever tell us." He looked at the trunk lid again. "It's the nuttiest thing," he said thoughtfully.... That was on Wednesday. The Friday afternoon mail delivery to the Daily News brought a crank letter. It was in the crank letter's most obvious form; that is, the address had been clipped, a letter or a word at a time, from a newspaper and glued to the envelope. There was no return address. The letter itself was in the same format. It was brief and to the point: Dear Mr. Editor: The Scorpion has struck. The bank robbers were captured. The Scorpion fights crime. Crooks and robbers are not safe from the avenging Scorpion. WARN YOUR READERS! Sincerely yours, THE SCORPION The warning was duly noted, and the letter filed in the wastebasket. It didn't rate a line in the paper. II The bank robbery occurred in late June. Early in August, a Brooklyn man went berserk. It happened in Canarsie, a section in southeast Brooklyn near Jamaica Bay. This particular area of Canarsie was a residential neighborhood, composed of one and two family houses. The man who went berserk was a Motor Vehicle Bureau clerk named Jerome Higgins. Two days before, he had flunked a Civil Service examination for the third time. He reported himself sick and spent the two days at home, brooding, a bottle of blended whiskey at all times in his hand. As the police reconstructed it later, Mrs. Higgins had attempted to awaken him on the third morning at seven-thirty, suggesting that he really ought to stop being so foolish, and go back to work. He then allegedly poked her in the eye, and locked her out of the bedroom. Mrs. Higgins then apparently called her sister-in-law, a Mrs. Thelma Stodbetter, who was Mr. Higgins' sister. Mrs. Stodbetter arrived at the house at nine o'clock, and spent some time tapping at the still-locked bedroom door, apparently requesting Mr. Higgins to unlock the door and "stop acting like a child." Neighbors reported to the police that they heard Mr. Higgins shout a number of times, "Go away! Can't you let a man sleep?" At about ten-fifteen, neighbors heard shots from the Higgins residence, a two-story one-family pink stucco affair in the middle of a block of similar homes. Mr. Higgins, it was learned later, had suddenly erupted from his bedroom, brandishing a .30-.30 hunting rifle and, being annoyed at the shrieks of his wife and sister, had fired seven shells at them, killing his wife on the spot and wounding his sister in the hand and shoulder. Mrs. Stodbetter, wounded and scared out of her wits, raced screaming out the front door of the house, crying for the police and shouting, "Murder! Murder!" At this point, neighbors called the police. One neighbor additionally phoned three newspapers and two television stations, thereby earning forty dollars in "news-tips" rewards. By chance, a mobile television unit was at that moment on the Belt Parkway, returning from having seen off a prime minister at Idlewild Airport. This unit was at once diverted to Canarsie, where it took up a position across the street from the scene of carnage and went to work with a Zoomar lens. In the meantime, Mister Higgins had barricaded himself in his house, firing at anything that moved. The two cameramen in the mobile unit worked their hearts out. One concentrated on the movements of the police and firemen and neighbors and ambulance attendants, while the other used the Zoomar lens to search for Mr. Higgins. He found him occasionally, offering the at-home audience brief glimpses of a stocky balding man in brown trousers and undershirt, stalking from window to window on the second floor of the house. The show lasted for nearly an hour. There were policemen everywhere, and firemen everywhere, and neighbors milling around down at the corner, where the police had roped the block off, and occasionally Mr. Higgins would stick his rifle out a window and shoot at somebody. The police used loudspeakers to tell Higgins he might as well give up, they had the place surrounded and could eventually starve him out anyway. Higgins used his own good lungs to shout obscenities back and challenge anyone present to hand-to-hand combat. The police fired tear gas shells at the house, but it was a windy day and all the windows in the Higgins house were either open or broken. Higgins was able to throw all the shells back out of the house again. The show lasted for nearly an hour. Then it ended, suddenly and dramatically. Higgins had showed himself to the Zoomar lens again, for the purpose of shooting either the camera or its operator. All at once he yelped and threw the rifle away. The rifle bounced onto the porch roof, slithered down to the edge, hung for a second against the drain, and finally fell barrel first onto the lawn. Meanwhile, Higgins was running through the house, shouting like a wounded bull. He thundered down the stairs and out, hollering, to fall into the arms of the waiting police. They had trouble holding him. At first they thought he was actually trying to get away, but then one of them heard what it was he was shouting: "My hands! My hands!" They looked at his hands. The palms and the palm-side of the fingers were red and blistering, from what looked like severe burns. There was another burn on his right cheek and another one on his right shoulder. Higgins, thoroughly chastened and bewildered, was led away for burn ointment and jail. The television crew went on back to Manhattan. The neighbors went home and telephoned their friends. On-duty policemen had been called in from practically all of the precincts in Brooklyn. Among them was Detective-Sergeant William Stevenson. Stevenson frowned thoughtfully at Higgins as that unhappy individual was led away, and then strolled over to look at the rifle. He touched the stock, and it was somewhat warm but that was all. He picked it up and turned it around. There, on the other side of the stock, burned into the wood, were the crudely-shaped letters, "The Scorpion." You don't get to be Precinct Captain on nothing but political connections. Those help, of course, but you need more than that. As Captain Hanks was fond of pointing out, you needed as well to be both more imaginative than most—"You gotta be able to second-guess the smart boys"—and to be a complete realist—"You gotta have both feet on the ground." If these were somewhat contradictory qualities, it was best not to mention the fact to Captain Hanks. The realist side of the captain's nature was currently at the fore. "Just what are you trying to say, Stevenson?" he demanded. "I'm not sure," admitted Stevenson. "But we've got these two things. First, there's the getaway car from that bank job. The wheels melt for no reason at all, and somebody burns 'The Scorpion' onto the trunk. Then, yesterday, this guy Higgins out in Canarsie. He says the rifle all of a sudden got too hot to hold, and he's got the burn marks to prove it. And there on the rifle stock it is again. 'The Scorpion'." "He says he put that on there himself," said the captain. Stevenson shook his head. "His lawyer says he put it on there. Higgins says he doesn't remember doing it. That's half the lawyer's case. He's trying to build up an insanity defense." "He put it on there himself, Stevenson," said the captain with weary patience. "What are you trying to prove?" "I don't know. All I know is it's the nuttiest thing I ever saw. And what about the getaway car? What about those tires melting?" "They were defective," said Hanks promptly. "All four of them at once? And what about the thing written on the trunk?" "How do I know?" demanded the captain. "Kids put it on before the car was stolen, maybe. Or maybe the hoods did it themselves, who knows? What do they say?" "They say they didn't do it," said Stevenson. "And they say they never saw it before the robbery and they would have noticed it if it'd been there." The captain shook his head. "I don't get it," he admitted. "What are you trying to prove?" "I guess," said Stevenson slowly, thinking it out as he went along, "I guess I'm trying to prove that somebody melted those tires, and made that rifle too hot, and left his signature behind." "What? You mean like in the comic books? Come on, Stevenson! What are you trying to hand me?" "All I know," insisted Stevenson, "is what I see." "And all I know," the captain told him, "is Higgins put that name on his rifle himself. He says so." "And what made it so hot?" "Hell, man, he'd been firing that thing at people for an hour! What do you think made it hot?" "All of a sudden?" "He noticed it all of a sudden, when it started to burn him." "How come the same name showed up each time, then?" Stevenson asked desperately. "How should I know? And why not, anyway? You know as well as I do these things happen. A bunch of teen-agers burgle a liquor store and they write 'The Golden Avengers' on the plate glass in lipstick. It happens all the time. Why not 'The Scorpion'? It couldn't occur to two people?" "But there's no explanation—" started Stevenson. "What do you mean, there's no explanation? I just gave you the explanation. Look, Stevenson, I'm a busy man. You got a nutty idea—like Wilcox a few years ago, remember him? Got the idea there was a fiend around loose, stuffing all those kids into abandoned refrigerators to starve. He went around trying to prove it, and getting all upset, and pretty soon they had to put him away in the nut hatch. Remember?" "I remember," said Stevenson. "Forget this silly stuff, Stevenson," the captain advised him. "Yes, sir," said Stevenson.... The day after Jerome Higgins went berserk, the afternoon mail brought a crank letter to the Daily News : Dear Mr. Editor, You did not warn your readers. The man who shot all those people could not escape the Scorpion. The Scorpion fights crime. No criminal is safe from the Scorpion. WARN YOUR READERS. Sincerely yours, THE SCORPION Unfortunately, this letter was not read by the same individual who had seen the first one, two months before. At any rate, it was filed in the same place, and forgotten. III Hallowe'en is a good time for a rumble. There's too many kids around for the cops to keep track of all of them, and if you're picked up carrying a knife or a length of tire chain or something, why, you're on your way to a Hallowe'en party and you're in costume. You're going as a JD. The problem was this schoolyard. It was a block wide, with entrances on two streets. The street on the north was Challenger territory, and the street on the south was Scarlet Raider territory, and both sides claimed the schoolyard. There had been a few skirmishes, a few guys from both gangs had been jumped and knocked around a little, but that had been all. Finally, the War Lords from the two gangs had met, and determined that the matter could only be settled in a war. The time was chosen: Hallowe'en. The place was chosen: the schoolyard. The weapons were chosen: pocket knives and tire chains okay, but no pistols or zip-guns. The time was fixed: eleven P.M. And the winner would have undisputed territorial rights to the schoolyard, both entrances. The night of the rumble, the gangs assembled in their separate clubrooms for last-minute instructions. Debs were sent out to play chicken at the intersections nearest the schoolyard, both to warn of the approach of cops and to keep out any non-combatant kids who might come wandering through. Judy Canzanetti was a Deb with the Scarlet Raiders. She was fifteen years old, short and black-haired and pretty in a movie-magazine, gum-chewing sort of way. She was proud of being in the Auxiliary of the Scarlet Raiders, and proud also of the job that had been assigned to her. She was to stand chicken on the southwest corner of the street. Judy took up her position at five minutes to eleven. The streets were dark and quiet. Few people cared to walk this neighborhood after dark, particularly on Hallowe'en. Judy leaned her back against the telephone pole on the corner, stuck her hands in the pockets of her Scarlet Raider jacket and waited. At eleven o'clock, she heard indistinct noises begin behind her. The rumble had started. At five after eleven, a bunch of little kids came wandering down the street. They were all about ten or eleven years old, and most of them carried trick-or-treat shopping bags. Some of them had Hallowe'en masks on. They started to make the turn toward the schoolyard. Judy said, "Hey, you kids. Take off." One of them, wearing a red mask, turned to look at her. "Who, us?" "Yes, you! Stay out of that street. Go on down that way." "The subway's this way," objected the kid in the red mask. "Who cares? You go around the other way." "Listen, lady," said the kid in the red mask, aggrieved, "we got a long way to go to get home." "Yeah," said another kid, in a black mask, "and we're late as it is." "I couldn't care less," Judy told them callously. "You can't go down that street." "Why not?" demanded yet another kid. This one was in the most complete and elaborate costume of them all, black leotards and a yellow shirt and a flowing: black cape. He wore a black and gold mask and had a black knit cap jammed down tight onto his head. "Why can't we go down there?" this apparition demanded. "Because I said so," Judy told him. "Now, you kids get away from here. Take off." "Hey!" cried the kid in the black-and-yellow costume. "Hey, they're fighting down there!" "It's a rumble," said Judy proudly. "You twerps don't want to be involved." "Hey!" cried the kid in the black-and-yellow costume again. And he went running around Judy and dashing off down the street. "Hey, Eddie!" shouted one of the other kids. "Eddie, come back!" Judy wasn't sure what to do next. If she abandoned her post to chase the one kid who'd gotten through, then maybe all the rest of them would come running along after her. She didn't know what to do. A sudden siren and a distant flashing red light solved her problems. "Cheez," said one of the kids. "The cops!" "Fuzz!" screamed Judy. She turned and raced down the block toward the schoolyard, shouting, "Fuzz! Fuzz! Clear out, it's the fuzz!" But then she stopped, wide-eyed, when she saw what was going on in the schoolyard. The guys from both gangs were dancing. They were jumping around, waving their arms, throwing their weapons away. Then they all started pulling off their gang jackets and throwing them away, whooping and hollering. They were making such a racket themselves that they never heard Judy's warning. They didn't even hear the police sirens. And all at once both schoolyard entrances were full of cops, a cop had tight hold of Judy and the rumble was over. Judy was so baffled and terrified that everything was just one great big blur. But in the middle of it all, she did see the little kid in the yellow-and-black costume go scooting away down the street. And she had the craziest idea that it was all his fault. Captain Hanks was still in his realistic cycle this morning, and he was impatient as well. "All right, Stevenson," he said. "Make it fast, I've got a lot to do this morning. And I hope it isn't this comic-book thing of yours again." "I'm afraid it is, Captain," said Stevenson. "Did you see the morning paper?" "So what?" "Did you see that thing about the gang fight up in Manhattan?" Captain Hanks sighed. "Stevenson," he said wearily, "are you going to try to connect every single time the word 'scorpion' comes up? What's the problem with this one? These kid gangs have names, so what?" "Neither one of them was called 'The Scorpions,'" Stevenson told him. "One of them was the Scarlet Raiders and the other gang was the Challengers." "So they changed their name," said Hanks. "Both gangs? Simultaneously? To the same name?" "Why not? Maybe that's what they were fighting over." "It was a territorial war," Stevenson reminded him. "They've admitted that much. It says so in the paper. And it also says they all deny ever seeing that word on their jackets until after the fight." "A bunch of juvenile delinquents," said Hanks in disgust. "You take their word?" "Captain, did you read the article in the paper?" "I glanced through it." "All right. Here's what they say happened: They say they started fighting at eleven o'clock. And they just got going when all at once all the metal they were carrying—knives and tire chains and coins and belt buckles and everything else—got freezing cold, too cold to touch. And then their leather jackets got freezing cold, so cold they had to pull them off and throw them away. And when the jackets were later collected, across the name of the gang on the back of each one had been branded 'The Scorpion.'" "Now, let me tell you something," said Hanks severely. "They heard the police sirens, and they threw all their weapons away. Then they threw their jackets away, to try to make believe they hadn't been part of the gang that had been fighting. But they were caught before they could get out of the schoolyard. If the squad cars had showed up a minute later, the schoolyard wouldn't have had anything in it but weapons and jackets, and the kids would have been all over the neighborhood, nice as you please, minding their own business and not bothering anybody. That's what happened. And all this talk about freezing cold and branding names into jackets is just some smart-alec punk's idea of a way to razz the police. Now, you just go back to worrying about what's happening in this precinct and forget about kid gangs up in Manhattan and comic book things like the Scorpion, or you're going to wind up like Wilcox, with that refrigerator business. Now, I don't want to hear any more about this nonsense, Stevenson." "Yes, sir," said Stevenson. | D. To try to use an insanity defense for Higgins. |
Why does Chip seem to enjoy talking to Retief?
A. He thinks that Retief will be able to overthrow the captain.
B. He’s the cook, and generally nice to those he serves.
C. As he says, he likes to see a “feller” eat and enjoys cooking for him.
D. He doesn’t like the captain and likes that Retief doesn’t like him either.
| THE FROZEN PLANET By Keith Laumer [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, September 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] "It is rather unusual," Magnan said, "to assign an officer of your rank to courier duty, but this is an unusual mission." Retief sat relaxed and said nothing. Just before the silence grew awkward, Magnan went on. "There are four planets in the group," he said. "Two double planets, all rather close to an unimportant star listed as DRI-G 33987. They're called Jorgensen's Worlds, and in themselves are of no importance whatever. However, they lie deep in the sector into which the Soetti have been penetrating. "Now—" Magnan leaned forward and lowered his voice—"we have learned that the Soetti plan a bold step forward. Since they've met no opposition so far in their infiltration of Terrestrial space, they intend to seize Jorgensen's Worlds by force." Magnan leaned back, waiting for Retief's reaction. Retief drew carefully on his cigar and looked at Magnan. Magnan frowned. "This is open aggression, Retief," he said, "in case I haven't made myself clear. Aggression on Terrestrial-occupied territory by an alien species. Obviously, we can't allow it." Magnan drew a large folder from his desk. "A show of resistance at this point is necessary. Unfortunately, Jorgensen's Worlds are technologically undeveloped areas. They're farmers or traders. Their industry is limited to a minor role in their economy—enough to support the merchant fleet, no more. The war potential, by conventional standards, is nil." Magnan tapped the folder before him. "I have here," he said solemnly, "information which will change that picture completely." He leaned back and blinked at Retief. "All right, Mr. Councillor," Retief said. "I'll play along; what's in the folder?" Magnan spread his fingers, folded one down. "First," he said. "The Soetti War Plan—in detail. We were fortunate enough to make contact with a defector from a party of renegade Terrestrials who've been advising the Soetti." He folded another finger. "Next, a battle plan for the Jorgensen's people, worked out by the Theory group." He wrestled a third finger down. "Lastly; an Utter Top Secret schematic for conversion of a standard anti-acceleration field into a potent weapon—a development our systems people have been holding in reserve for just such a situation." "Is that all?" Retief said. "You've still got two fingers sticking up." Magnan looked at the fingers and put them away. "This is no occasion for flippancy, Retief. In the wrong hands, this information could be catastrophic. You'll memorize it before you leave this building." "I'll carry it, sealed," Retief said. "That way nobody can sweat it out of me." Magnan started to shake his head. "Well," he said. "If it's trapped for destruction, I suppose—" "I've heard of these Jorgensen's Worlds," Retief said. "I remember an agent, a big blond fellow, very quick on the uptake. A wizard with cards and dice. Never played for money, though." "Umm," Magnan said. "Don't make the error of personalizing this situation, Retief. Overall policy calls for a defense of these backwater worlds. Otherwise the Corps would allow history to follow its natural course, as always." "When does this attack happen?" "Less than four weeks." "That doesn't leave me much time." "I have your itinerary here. Your accommodations are clear as far as Aldo Cerise. You'll have to rely on your ingenuity to get you the rest of the way." "That's a pretty rough trip, Mr. Councillor. Suppose I don't make it?" Magnan looked sour. "Someone at a policy-making level has chosen to put all our eggs in one basket, Retief. I hope their confidence in you is not misplaced." "This antiac conversion; how long does it take?" "A skilled electronics crew can do the job in a matter of minutes. The Jorgensens can handle it very nicely; every other man is a mechanic of some sort." Retief opened the envelope Magnan handed him and looked at the tickets inside. "Less than four hours to departure time," he said. "I'd better not start any long books." "You'd better waste no time getting over to Indoctrination," Magnan said. Retief stood up. "If I hurry, maybe I can catch the cartoon." "The allusion escapes me," Magnan said coldly. "And one last word. The Soetti are patrolling the trade lanes into Jorgensen's Worlds; don't get yourself interned." "I'll tell you what," Retief said soberly. "In a pinch, I'll mention your name." "You'll be traveling with Class X credentials," Magnan snapped. "There must be nothing to connect you with the Corps." "They'll never guess," Retief said. "I'll pose as a gentleman." "You'd better be getting started," Magnan said, shuffling papers. "You're right," Retief said. "If I work at it, I might manage a snootful by takeoff." He went to the door. "No objection to my checking out a needler, is there?" Magnan looked up. "I suppose not. What do you want with it?" "Just a feeling I've got." "Please yourself." "Some day," Retief said, "I may take you up on that." II Retief put down the heavy travel-battered suitcase and leaned on the counter, studying the schedules chalked on the board under the legend "ALDO CERISE—INTERPLANETARY." A thin clerk in a faded sequined blouse and a plastic snakeskin cummerbund groomed his fingernails, watching Retief from the corner of his eye. Retief glanced at him. The clerk nipped off a ragged corner with rabbitlike front teeth and spat it on the floor. "Was there something?" he said. "Two twenty-eight, due out today for the Jorgensen group," Retief said. "Is it on schedule?" The clerk sampled the inside of his right cheek, eyed Retief. "Filled up. Try again in a couple of weeks." "What time does it leave?" "I don't think—" "Let's stick to facts," Retief said. "Don't try to think. What time is it due out?" The clerk smiled pityingly. "It's my lunch hour," he said. "I'll be open in an hour." He held up a thumb nail, frowned at it. "If I have to come around this counter," Retief said, "I'll feed that thumb to you the hard way." The clerk looked up and opened his mouth. Then he caught Retief's eye, closed his mouth and swallowed. "Like it says there," he said, jerking a thumb at the board. "Lifts in an hour. But you won't be on it," he added. Retief looked at him. "Some ... ah ... VIP's required accommodation," he said. He hooked a finger inside the sequined collar. "All tourist reservations were canceled. You'll have to try to get space on the Four-Planet Line ship next—" "Which gate?" Retief said. "For ... ah...?" "For the two twenty-eight for Jorgensen's Worlds," Retief said. "Well," the clerk said. "Gate 19," he added quickly. "But—" Retief picked up his suitcase and walked away toward the glare sign reading To Gates 16-30 . "Another smart alec," the clerk said behind him. Retief followed the signs, threaded his way through crowds, found a covered ramp with the number 228 posted over it. A heavy-shouldered man with a scarred jawline and small eyes was slouching there in a rumpled gray uniform. He put out a hand as Retief started past him. "Lessee your boarding pass," he muttered. Retief pulled a paper from an inside pocket, handed it over. The guard blinked at it. "Whassat?" "A gram confirming my space," Retief said. "Your boy on the counter says he's out to lunch." The guard crumpled the gram, dropped it on the floor and lounged back against the handrail. "On your way, bub," he said. Retief put his suitcase carefully on the floor, took a step and drove a right into the guard's midriff. He stepped aside as the man doubled and went to his knees. "You were wide open, ugly. I couldn't resist. Tell your boss I sneaked past while you were resting your eyes." He picked up his bag, stepped over the man and went up the gangway into the ship. A cabin boy in stained whites came along the corridor. "Which way to cabin fifty-seven, son?" Retief asked. "Up there." The boy jerked his head and hurried on. Retief made his way along the narrow hall, found signs, followed them to cabin fifty-seven. The door was open. Inside, baggage was piled in the center of the floor. It was expensive looking baggage. Retief put his bag down. He turned at a sound behind him. A tall, florid man with an expensive coat belted over a massive paunch stood in the open door, looking at Retief. Retief looked back. The florid man clamped his jaws together, turned to speak over his shoulder. "Somebody in the cabin. Get 'em out." He rolled a cold eye at Retief as he backed out of the room. A short, thick-necked man appeared. "What are you doing in Mr. Tony's room?" he barked. "Never mind! Clear out of here, fellow! You're keeping Mr. Tony waiting." "Too bad," Retief said. "Finders keepers." "You nuts?" The thick-necked man stared at Retief. "I said it's Mr. Tony's room." "I don't know Mr. Tony. He'll have to bull his way into other quarters." "We'll see about you, mister." The man turned and went out. Retief sat on the bunk and lit a cigar. There was a sound of voices in the corridor. Two burly baggage-smashers appeared, straining at an oversized trunk. They maneuvered it through the door, lowered it, glanced at Retief and went out. The thick-necked man returned. "All right, you. Out," he growled. "Or have I got to have you thrown out?" Retief rose and clamped the cigar between his teeth. He gripped a handle of the brass-bound trunk in each hand, bent his knees and heaved the trunk up to chest level, then raised it overhead. He turned to the door. "Catch," he said between clenched teeth. The trunk slammed against the far wall of the corridor and burst. Retief turned to the baggage on the floor, tossed it into the hall. The face of the thick-necked man appeared cautiously around the door jamb. "Mister, you must be—" "If you'll excuse me," Retief said, "I want to catch a nap." He flipped the door shut, pulled off his shoes and stretched out on the bed. Five minutes passed before the door rattled and burst open. Retief looked up. A gaunt leathery-skinned man wearing white ducks, a blue turtleneck sweater and a peaked cap tilted raffishly over one eye stared at Retief. "Is this the joker?" he grated. The thick-necked man edged past him, looked at Retief and snorted, "That's him, sure." "I'm captain of this vessel," the first man said. "You've got two minutes to haul your freight out of here, buster." "When you can spare the time from your other duties," Retief said, "take a look at Section Three, Paragraph One, of the Uniform Code. That spells out the law on confirmed space on vessels engaged in interplanetary commerce." "A space lawyer." The captain turned. "Throw him out, boys." Two big men edged into the cabin, looking at Retief. "Go on, pitch him out," the captain snapped. Retief put his cigar in an ashtray, and swung his feet off the bunk. "Don't try it," he said softly. One of the two wiped his nose on a sleeve, spat on his right palm, and stepped forward, then hesitated. "Hey," he said. "This the guy tossed the trunk off the wall?" "That's him," the thick-necked man called. "Spilled Mr. Tony's possessions right on the deck." "Deal me out," the bouncer said. "He can stay put as long as he wants to. I signed on to move cargo. Let's go, Moe." "You'd better be getting back to the bridge, Captain," Retief said. "We're due to lift in twenty minutes." The thick-necked man and the Captain both shouted at once. The Captain's voice prevailed. "—twenty minutes ... uniform Code ... gonna do?" "Close the door as you leave," Retief said. The thick-necked man paused at the door. "We'll see you when you come out." III Four waiters passed Retief's table without stopping. A fifth leaned against the wall nearby, a menu under his arm. At a table across the room, the Captain, now wearing a dress uniform and with his thin red hair neatly parted, sat with a table of male passengers. He talked loudly and laughed frequently, casting occasional glances Retief's way. A panel opened in the wall behind Retief's chair. Bright blue eyes peered out from under a white chef's cap. "Givin' you the cold shoulder, heh, Mister?" "Looks like it, old-timer," Retief said. "Maybe I'd better go join the skipper. His party seems to be having all the fun." "Feller has to be mighty careless who he eats with to set over there." "I see your point." "You set right where you're at, Mister. I'll rustle you up a plate." Five minutes later, Retief cut into a thirty-two ounce Delmonico backed up with mushrooms and garlic butter. "I'm Chip," the chef said. "I don't like the Cap'n. You can tell him I said so. Don't like his friends, either. Don't like them dern Sweaties, look at a man like he was a worm." "You've got the right idea on frying a steak, Chip. And you've got the right idea on the Soetti, too," Retief said. He poured red wine into a glass. "Here's to you." "Dern right," Chip said. "Dunno who ever thought up broiling 'em. Steaks, that is. I got a Baked Alaska coming up in here for dessert. You like brandy in yer coffee?" "Chip, you're a genius." "Like to see a feller eat," Chip said. "I gotta go now. If you need anything, holler." Retief ate slowly. Time always dragged on shipboard. Four days to Jorgensen's Worlds. Then, if Magnan's information was correct, there would be four days to prepare for the Soetti attack. It was a temptation to scan the tapes built into the handle of his suitcase. It would be good to know what Jorgensen's Worlds would be up against. Retief finished the steak, and the chef passed out the baked Alaska and coffee. Most of the other passengers had left the dining room. Mr. Tony and his retainers still sat at the Captain's table. As Retief watched, four men arose from the table and sauntered across the room. The first in line, a stony-faced thug with a broken ear, took a cigar from his mouth as he reached the table. He dipped the lighted end in Retief's coffee, looked at it, and dropped it on the tablecloth. The others came up, Mr. Tony trailing. "You must want to get to Jorgensen's pretty bad," the thug said in a grating voice. "What's your game, hick?" Retief looked at the coffee cup, picked it up. "I don't think I want my coffee," he said. He looked at the thug. "You drink it." The thug squinted at Retief. "A wise hick," he began. With a flick of the wrist, Retief tossed the coffee into the thug's face, then stood and slammed a straight right to the chin. The thug went down. Retief looked at Mr. Tony, still standing open-mouthed. "You can take your playmates away now, Tony," he said. "And don't bother to come around yourself. You're not funny enough." Mr. Tony found his voice. "Take him, Marbles!" he growled. The thick-necked man slipped a hand inside his tunic and brought out a long-bladed knife. He licked his lips and moved in. Retief heard the panel open beside him. "Here you go, Mister," Chip said. Retief darted a glance; a well-honed french knife lay on the sill. "Thanks, Chip," Retief said. "I won't need it for these punks." Thick-neck lunged and Retief hit him square in the face, knocking him under the table. The other man stepped back, fumbling a power pistol from his shoulder holster. "Aim that at me, and I'll kill you," Retief said. "Go on, burn him!" Mr. Tony shouted. Behind him, the captain appeared, white-faced. "Put that away, you!" he yelled. "What kind of—" "Shut up," Mr. Tony said. "Put it away, Hoany. We'll fix this bum later." "Not on this vessel, you won't," the captain said shakily. "I got my charter to consider." "Ram your charter," Hoany said harshly. "You won't be needing it long." "Button your floppy mouth, damn you!" Mr. Tony snapped. He looked at the man on the floor. "Get Marbles out of here. I ought to dump the slob." He turned and walked away. The captain signaled and two waiters came up. Retief watched as they carted the casualty from the dining room. The panel opened. "I usta be about your size, when I was your age," Chip said. "You handled them pansies right. I wouldn't give 'em the time o' day." "How about a fresh cup of coffee, Chip?" Retief said. "Sure, Mister. Anything else?" "I'll think of something," Retief said. "This is shaping up into one of those long days." "They don't like me bringing yer meals to you in yer cabin," Chip said. "But the cap'n knows I'm the best cook in the Merchant Service. They won't mess with me." "What has Mr. Tony got on the captain, Chip?" Retief asked. "They're in some kind o' crooked business together. You want some more smoked turkey?" "Sure. What have they got against my going to Jorgensen's Worlds?" "Dunno. Hasn't been no tourists got in there fer six or eight months. I sure like a feller that can put it away. I was a big eater when I was yer age." "I'll bet you can still handle it, Old Timer. What are Jorgensen's Worlds like?" "One of 'em's cold as hell and three of 'em's colder. Most o' the Jorgies live on Svea; that's the least froze up. Man don't enjoy eatin' his own cookin' like he does somebody else's." "That's where I'm lucky, Chip. What kind of cargo's the captain got aboard for Jorgensen's?" "Derned if I know. In and out o' there like a grasshopper, ever few weeks. Don't never pick up no cargo. No tourists any more, like I says. Don't know what we even run in there for." "Where are the passengers we have aboard headed?" "To Alabaster. That's nine days' run in-sector from Jorgensen's. You ain't got another one of them cigars, have you?" "Have one, Chip. I guess I was lucky to get space on this ship." "Plenty o' space, Mister. We got a dozen empty cabins." Chip puffed the cigar alight, then cleared away the dishes, poured out coffee and brandy. "Them Sweaties is what I don't like," he said. Retief looked at him questioningly. "You never seen a Sweaty? Ugly lookin' devils. Skinny legs, like a lobster; big chest, shaped like the top of a turnip; rubbery lookin' head. You can see the pulse beatin' when they get riled." "I've never had the pleasure," Retief said. "You prob'ly have it perty soon. Them devils board us nigh ever trip out. Act like they was the Customs Patrol or somethin'." There was a distant clang, and a faint tremor ran through the floor. "I ain't superstitious ner nothin'," Chip said. "But I'll be triple-damned if that ain't them boarding us now." Ten minutes passed before bootsteps sounded outside the door, accompanied by a clicking patter. The doorknob rattled, then a heavy knock shook the door. "They got to look you over," Chip whispered. "Nosy damn Sweaties." "Unlock it, Chip." The chef opened the door. "Come in, damn you," he said. A tall and grotesque creature minced into the room, tiny hoof-like feet tapping on the floor. A flaring metal helmet shaded the deep-set compound eyes, and a loose mantle flapped around the knobbed knees. Behind the alien, the captain hovered nervously. "Yo' papiss," the alien rasped. "Who's your friend, Captain?" Retief said. "Never mind; just do like he tells you." "Yo' papiss," the alien said again. "Okay," Retief said. "I've seen it. You can take it away now." "Don't horse around," the captain said. "This fellow can get mean." The alien brought two tiny arms out from the concealment of the mantle, clicked toothed pincers under Retief's nose. "Quick, soft one." "Captain, tell your friend to keep its distance. It looks brittle, and I'm tempted to test it." "Don't start anything with Skaw; he can clip through steel with those snappers." "Last chance," Retief said. Skaw stood poised, open pincers an inch from Retief's eyes. "Show him your papers, you damned fool," the captain said hoarsely. "I got no control over Skaw." The alien clicked both pincers with a sharp report, and in the same instant Retief half-turned to the left, leaned away from the alien and drove his right foot against the slender leg above the bulbous knee-joint. Skaw screeched and floundered, greenish fluid spattering from the burst joint. "I told you he was brittle," Retief said. "Next time you invite pirates aboard, don't bother to call." "Jesus, what did you do! They'll kill us!" the captain gasped, staring at the figure flopping on the floor. "Cart poor old Skaw back to his boat," Retief said. "Tell him to pass the word. No more illegal entry and search of Terrestrial vessels in Terrestrial space." "Hey," Chip said. "He's quit kicking." The captain bent over Skaw, gingerly rolled him over. He leaned close and sniffed. "He's dead." The captain stared at Retief. "We're all dead men," he said. "These Soetti got no mercy." "They won't need it. Tell 'em to sheer off; their fun is over." "They got no more emotions than a blue crab—" "You bluff easily, Captain. Show a few guns as you hand the body back. We know their secret now." "What secret? I—" "Don't be no dumber than you got to, Cap'n," Chip said. "Sweaties die easy; that's the secret." "Maybe you got a point," the captain said, looking at Retief. "All they got's a three-man scout. It could work." He went out, came back with two crewmen. They hauled the dead alien gingerly into the hall. "Maybe I can run a bluff on the Soetti," the captain said, looking back from the door. "But I'll be back to see you later." "You don't scare us, Cap'n," Chip said. "Him and Mr. Tony and all his goons. You hit 'em where they live, that time. They're pals o' these Sweaties. Runnin' some kind o' crooked racket." "You'd better take the captain's advice, Chip. There's no point in your getting involved in my problems." "They'd of killed you before now, Mister, if they had any guts. That's where we got it over these monkeys. They got no guts." "They act scared, Chip. Scared men are killers." "They don't scare me none." Chip picked up the tray. "I'll scout around a little and see what's goin' on. If the Sweaties figure to do anything about that Skaw feller they'll have to move fast; they won't try nothin' close to port." "Don't worry, Chip. I have reason to be pretty sure they won't do anything to attract a lot of attention in this sector just now." Chip looked at Retief. "You ain't no tourist, Mister. I know that much. You didn't come out here for fun, did you?" "That," Retief said, "would be a hard one to answer." IV Retief awoke at a tap on his door. "It's me, Mister. Chip." "Come on in." The chef entered the room, locking the door. "You shoulda had that door locked." He stood by the door, listening, then turned to Retief. "You want to get to Jorgensen's perty bad, don't you, Mister?" "That's right, Chip." "Mr. Tony give the captain a real hard time about old Skaw. The Sweaties didn't say nothin'. Didn't even act surprised, just took the remains and pushed off. But Mr. Tony and that other crook they call Marbles, they was fit to be tied. Took the cap'n in his cabin and talked loud at him fer half a hour. Then the cap'n come out and give some orders to the Mate." Retief sat up and reached for a cigar. "Mr. Tony and Skaw were pals, eh?" "He hated Skaw's guts. But with him it was business. Mister, you got a gun?" "A 2mm needler. Why?" "The orders cap'n give was to change course fer Alabaster. We're by-passin' Jorgensen's Worlds. We'll feel the course change any minute." Retief lit the cigar, reached under the mattress and took out a short-barreled pistol. He dropped it in his pocket, looked at Chip. "Maybe it was a good thought, at that. Which way to the Captain's cabin?" "This is it," Chip said softly. "You want me to keep an eye on who comes down the passage?" Retief nodded, opened the door and stepped into the cabin. The captain looked up from his desk, then jumped up. "What do you think you're doing, busting in here?" "I hear you're planning a course change, Captain." "You've got damn big ears." "I think we'd better call in at Jorgensen's." "You do, huh?" the captain sat down. "I'm in command of this vessel," he said. "I'm changing course for Alabaster." "I wouldn't find it convenient to go to Alabaster," Retief said. "So just hold your course for Jorgensen's." "Not bloody likely." "Your use of the word 'bloody' is interesting, Captain. Don't try to change course." The captain reached for the mike on his desk, pressed the key. "Power Section, this is the captain," he said. Retief reached across the desk, gripped the captain's wrist. "Tell the mate to hold his present course," he said softly. "Let go my hand, buster," the captain snarled. Eyes on Retief's, he eased a drawer open with his left hand, reached in. Retief kneed the drawer. The captain yelped and dropped the mike. "You busted it, you—" "And one to go," Retief said. "Tell him." "I'm an officer of the Merchant Service!" "You're a cheapjack who's sold his bridge to a pack of back-alley hoods." "You can't put it over, hick." "Tell him." The captain groaned and picked up the mike. "Captain to Power Section," he said. "Hold your present course until you hear from me." He dropped the mike and looked up at Retief. "It's eighteen hours yet before we pick up Jorgensen Control. You going to sit here and bend my arm the whole time?" Retief released the captain's wrist and turned to the door. "Chip, I'm locking the door. You circulate around, let me know what's going on. Bring me a pot of coffee every so often. I'm sitting up with a sick friend." "Right, Mister. Keep an eye on that jasper; he's slippery." "What are you going to do?" the captain demanded. Retief settled himself in a chair. "Instead of strangling you, as you deserve," he said, "I'm going to stay here and help you hold your course for Jorgensen's Worlds." The captain looked at Retief. He laughed, a short bark. "Then I'll just stretch out and have a little nap, farmer. If you feel like dozing off sometime during the next eighteen hours, don't mind me." Retief took out the needler and put it on the desk before him. "If anything happens that I don't like," he said, "I'll wake you up. With this." | D. He doesn’t like the captain and likes that Retief doesn’t like him either. |
How is gang membership verified? | ### Introduction and Motivation
The crime and violence street gangs introduce into neighborhoods is a growing epidemic in cities around the world. Today, over 1.23 million people in the United States are members of a street gang BIBREF0 , BIBREF1 , which is a coalition of peers, united by mutual interests, with identifiable leadership and internal organization, who act collectively to conduct illegal activity and to control a territory, facility, or enterprise BIBREF2 . They promote criminal activities such as drug trafficking, assault, robbery, and threatening or intimidating a neighborhood BIBREF1 . Moreover, data from the Centers for Disease Control in the United States suggests that the victims of at least 1.3% of all gang-related homicides are merely innocent bystanders who live in gang occupied neighborhoods BIBREF3 . Street gang members have established online presences coinciding with their physical occupation of neighborhoods. The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 . Gang members typically use social networking sites and social media to develop online respect for their street gang BIBREF5 and to post intimidating, threatening images or videos BIBREF6 . This “Cyber-” or “Internet banging” BIBREF7 behavior is precipitated by the fact that an increasing number of young members of the society are joining gangs BIBREF8 , and these young members have become enamored with technology and with the notion of sharing information quickly and publicly through social media. Stronger police surveillance in the physical spaces where gangs congregate further encourages gang members to seek out virtual spaces such as social media to express their affiliation, to sell drugs, and to celebrate their illegal activities BIBREF9 . Gang members are able to post publicly on Twitter without fear of consequences because there are few tools law enforcement can use to surveil this medium BIBREF10 . Police departments across the United States instead rely on manual processes to search social media for gang member profiles and to study their posts. For example, the New York City police department employs over 300 detectives to combat teen violence triggered by insults, dares, and threats exchanged on social media, and the Toronto police department teaches officers about the use of social media in investigations BIBREF11 . Officer training is broadly limited to understanding policies on using Twitter in investigations and best practices for data storage BIBREF12 . The safety and security of city neighborhoods can thus be improved if law enforcement were equipped with intelligent tools to study social media for gang activity. The need for better tools for law enforcement cannot be underscored enough. Recent news reports have shown that many incidents involving gangs start on Twitter, escalate over time, and lead to an offline event that could have been prevented by an early warning. For example, the media reported on a possible connection between the death of a teenage rapper from Illinois and the final set of tweets he posted. One of his last tweets linked to a video of him shouting vulgar words at a rival gang member who, in return, replied “I'ma kill you” on social media. In a following tweet, the teenage rapper posted “im on 069”, revealing his location, and was shot dead soon after that post. Subsequent investigation revealed that the rivalry leading to his death began and was carried out entirely on social media. Other reporting has revealed how innocent bystanders have also become targets in online fights, leaving everyone in a neighborhood at risk. This paper investigates whether gang member profiles can be identified automatically on Twitter, which can enable better surveillance of gang members on social media. Classifying Twitter profiles into particular types of users has been done in other contexts BIBREF13 , BIBREF14 , BIBREF15 , but gang member profiles pose unique challenges. For example, many Twitter profile classifiers search for contextual clues in tweets and profile descriptions BIBREF16 , but gang member profiles use a rapidly changing lexicon of keywords and phrases that often have only a local, geographic context. This is illustrated in Figure FIGREF6 , which shows the Twitter profile descriptions of two verified deceased gang members. The profile of @OsoArrogantJoJo provides evidence that he belongs to a rival gang of the Black Disciples by #BDK, a hashtag that is only known to those involved with gang culture in Chicago. @PappyNotPapi's profile mentions #PBG and our investigations revealed that this hashtag is newly founded and stands for the Pooh Bear Gang, a gang that was formerly known as the Insane Cutthroat Gangsters. Given the very local, rapidly changing lexicon of gang members on social media, building a database of keywords, phrases, and other identifiers to find gang members nationally is not feasible. Instead, this study proposes heterogeneous sets of features derived not only from profile and tweet text but also from the emoji usage, profile images, and links to YouTube videos reflecting their music culture. A large set of gang member profiles, obtained through a careful data collection process, is compared against non-gang member profiles to find contrasting features. Experimental results show that using these sets of features, we can build a classifier that has a low false positive rate and a promising INLINEFORM0 -score of 0.7755. This paper is organized as follows. Section SECREF2 discusses the related literature and positions how this work differs from other related works. Section SECREF3 discusses the data collection, manual feature selection and our approach to identify gang member profiles. Section SECREF4 gives a detailed explanation for evaluation of the proposed method and the results in detail. Section SECREF5 concludes the work reported while discussing the future work planned. ### Related Work
Gang violence is a well studied social science topic dating back to 1927 BIBREF17 . However, the notions of “Cyber-” or “Internet banging”, which is defined as “the phenomenon of gang affiliates using social media sites to trade insults or make violent threats that lead to homicide or victimization” BIBREF7 , was only recently introduced BIBREF18 , BIBREF10 . Patton et al. introduced the concept of “Internet banging” and studied how social media is now being used as a tool for gang self-promotion and as a way for gang members to gain and maintain street credibility BIBREF7 . They also discussed the relationship between gang-related crime and hip-hop culture, giving examples on how hip-hop music shared on social media websites targeted at harassing rival gang members often ended up in real-world collisions among those gangs. Decker et al. and Patton et al. have also reported that street gangs perform Internet banging with social media posts of videos depicting their illegal behaviors, threats to rival gangs, and firearms BIBREF19 , BIBREF20 . The ability to take action on these discoveries is limited by the tools available to discover gang members on social media and to analyze the content they post BIBREF18 . Recent attempts to improve our abilities include a proposed architecture for a surveillance system that can learn the structure, function, and operation of gangs through what they post on social media BIBREF10 . However, the architecture requires a set of gang member profiles for input, thus assuming that they have already been discovered. Patton et al. BIBREF20 devised a method to automatically collect tweets from a group of gang members operating in Detroit, MI. However, their approach required the profile names of the gang members to be known beforehand, and data collection was localized to a single city in the country. This work builds upon existing methods to automatically discover gang member profiles on Twitter. This type of user profile classification problem has been explored in a diverse set of applications such as political affiliation BIBREF13 , ethnicity BIBREF13 , gender BIBREF15 , predicting brand loyalty BIBREF13 , and user occupations BIBREF16 . However, these approaches may utilize an abundance of positive examples in their training data, and only rely on a single feature type (typically, tweet text). Whereas most profile classifiers focus on a single type of feature (e.g. profile text), we consider the use of a variety of feature types, including emoji, YouTube links, and photo features. ### Discovering Gang Member Profiles
This section discusses the methodology we followed to study and classify the Twitter profiles of gang members automatically. It includes a semi-automatic data collection process to discover a large set of verifiable gang member profiles, an evaluation of the tweets of gang and non-gang member posts to identify promising features, and the deployment of multiple supervised learning algorithms to perform the classification. ### Data collection
Discovering gang member profiles on Twitter to build training and testing datasets is a challenging task. Past strategies to find these profiles were to search for keywords, phrases, and events that are known to be related to gang activity in a particular city a priori BIBREF10 , BIBREF20 . However, such approaches are unlikely to yield adequate data to train an automatic classifier since gang members from different geographic locations and cultures use local languages, location-specific hashtags, and share information related to activities in a local region BIBREF10 . Such region-specific tweets and profiles may be used to train a classifier to find gang members within a small region but not across the Twitterverse. To overcome these limitations, we adopted a semi-automatic workflow, illustrated in Figure FIGREF7 , to build a dataset of gang member profiles suitable for training a classifier. The steps of the workflow are: 1. Seed Term Discovery: Following the success of identifying gang member profiles from Chicago BIBREF10 , we began our data collection with discovering universal terms used by gang members. We first searched for profiles with hashtags for Chicago gangs noted in BIBREF10 , namely #BDK (Black Disciple Killers) and #GDK (Gangster Disciples Killers). Those profiles were analyzed and manually verified as explained in Step 3. Analysis of these profiles identified a small set of hashtags they all use in their profile descriptions. Searching Twitter profiles using those hashtags, we observed that gang members across the U.S. use them, thus we consider those terms to be location neutral. For example, gang members post #FreeDaGuys in their profile to support their fellow members who are in jail, #RIPDaGuys to convey the grieving for fallen gang members, and #FuckDaOpps to show their hatred towards police officers. We used these terms as keywords to discover Twitter profiles irrespective of geographical location. We used the Followerwonk Web service API and Twitter REST API to search Twitter profile descriptions by keywords #FreeDaGuys, #FreeMyNigga, #RIPDaGuys, and #FuckDaOpps. Since there are different informal ways people spell a word in social media, we also considered variations on the spelling of each keyword; for example, for #FreeDaGuys, we searched both #FreeDaGuys, and #FreeTheGuys. 2. Gang Affiliated Rappers' Twitter Profile Discovery: Finding profiles by a small set of keywords is unlikely to yield sufficient data. Thus, we sought additional gang member profiles with an observation from Patton et al. BIBREF7 that the influence of hip-hop music and culture on offline gang member activities can also be seen in their social media posts. We thus also consider the influence of hip-hop culture on Twitter by exploring the Twitter network of known gangster rappers who were murdered in 2015 due to gang-related incidents. We searched for these rapper profiles on Twitter and manually checked that the rapper was affiliated to a gang. 3. Manual verification of Twitter profiles: We verified each profile discovered manually by examining the profile picture, profile background image, recent tweets, and recent pictures posted by a user. During these checks, we searched for terms, activities, and symbols that we believed could be associated with a gang. For example, profiles whose image or background included guns in a threatening way, stacks of money, showing gang hand signs and gestures, and humans holding or posing with a gun, appeared likely to be from a gang member. Such images were often identified in profiles of users who submitted tweets that contain messages of support or sadness for prisoners or recently fallen gang members, or used a high volume of threatening and intimidating slang language. Only profiles where the images, words, and tweets all suggested gang affiliation were labeled as gang affiliates and added to our dataset. Although this manual verification does have a degree of subjectivity, in practice, the images and words used by gang members on social media are so pronounced that we believe any reasonable analyst would agree that they are gang members. We found that not all the profiles collected belonged to gang members; we observed relatives and followers of gang members posting the same hashtags as in Step 1 to convey similar feelings in their profile descriptions. 4. Using Retweets to discover more profiles: From the set of verified profiles, we explored their retweet and follower networks as a way to expand the dataset. We first considered authors of tweets which were retweeted by a gang member in our seed set. In Twitter, “retweeting” is a mechanism by which a user can share someone else's tweet to their follower audience. Assuming that a user only retweets things that they believe or their audience would be interested in, it may be reasonable to assume that gang members would only be interested in sharing what other gang members have to say, and hence, the authors of gang members' retweets could also be gang members. 5. Using Followers and Followees to discover more profiles: We analyzed followers and followees of our seed gang member profiles to find more gang member profiles. A Twitter user can follow other Twitter users so that the individual will be subscribed to their tweets as a follower and they will be able to start a private conversation by sending direct messages to the individual. Motivated by the sociological concept of homophily, which claims that individuals have a tendency to associate and bond with similar others, we hypothesized that the followers and followees of Twitter profiles from the seed set may also be gang members. Manual verification of Twitter profiles collected from retweets, followers, and followees of gang members showed that a majority of those profiles are non-gang members who are either family members, hip-hop artists, women or profiles with pornographic content. To ensure that our dataset is not biased towards a specific gang or geographic location, only a limited number of profiles were collected via retweets, followers and followees. Table TABREF8 summarizes the number of profiles manually verified as gang members from Twitter profiles collected in step 1, 2, 4 and 5. Altogether we collected 400 gang member's Twitter profiles. This is a large number compared to previous studies of gang member activities on social media that curated a maximum of 91 profiles BIBREF10 . Moreover, we believe the profiles collected represent a diverse set of gang members that are not biased toward a particular geographic area or lingo as our data collection process used location-independent terms proven to be used by gang members when they express themselves. ### Data analysis
We next explore differences between gang and non-gang member Twitter usage to find promising features for classifying profiles. For this purpose, profiles of non-gang members were collected from the Twitter Streaming API. We collected a random sample of tweets and the profiles of the users who authored the tweets in the random sample. We manually verified that all Twitter profiles collected in this approach belong to non-gang members. The profiles selected were then filtered by location to remove non-U.S. profiles by reverse geo-coding the location stated in their profile description by the Google Maps API. Profiles with location descriptions that were unspecified or did not relate to a location in the U.S. were discarded. We collected 2,000 non-gang member profiles in this manner. In addition, we added 865 manually verified non-gang member profiles collected using the location neutral keywords discussed in Section SECREF3 . Introducing these profiles, which have some characteristics of gang members (such as cursing frequently or cursing at law enforcement) but are not, captures local languages used by family/friends of gang members and ordinary people in a neighborhood where gangs operate. With the Twitter REST API, we collected the maximum number of most recent tweets that can be retrieved (3,200) along with profile descriptions and images (profile and cover photos) of every gang and non-gang member profile. The resulting dataset consists of 400 gang member Twitter profiles and 2,865 non-gang member Twitter profiles. The dataset has a total of 821,412 tweets from gang member profiles and 7,238,758 tweets from non-gang member profiles. Prior to analyzing any text content, we removed all of the seed words used to find gang member profiles, all stop words, and performed stemming across all tweets and profile descriptions. Figure FIGREF14 summarizes the words seen most often in the gang and non-gang members' tweets as clouds. They show a clear difference in language. For example, we note that gang members more frequently use curse words in comparison to ordinary users. Although cursing is frequent in tweets, they represent just 1.15% of all words used BIBREF21 . In contrast, we found 5.72% of all words posted by gang member accounts to be classified as a curse word, which is nearly five times more than the average curse word usage on Twitter. The clouds also reflect the fact that gang members often talk about drugs and money with terms such as smoke, high, hit, and money, while ordinary users hardly speak about finances and drugs. We also noticed that gang members talk about material things with terms such as got, money, make, real, need whereas ordinary users tend to vocalize their feelings with terms such as new, like, love, know, want, look, make, us. These differences make it clear that the individual words used by gang and non-gang members will be relevant features for gang profile classification. On Twitter, a user can give a self-description as a part of the user's profile. A comparison of the top 10 words in gang members' and non-gang members' Twitter profile descriptions is shown in Figure FIGREF21 . The first 10 words are the most frequently used words in non-gang members' profiles and the latter 10 words are the most frequently used words in gang members' profiles. Word comparison shows that gang members prefer to use curse words (nigga, fuck, shit) in their profile descriptions while non-gang members use words related to their feelings or interests (love, life, live, music, book). The terms rip and free which appear in approximately INLINEFORM0 of all gang member Twitter profiles, suggest that gang members use their profile descriptions as a space to grieve for their fallen or incarcerated gang members. The term gang in gang members' profile descriptions suggest that gang members like to self-identify themselves on Twitter. Such lexical features may therefore be of great importance for automatically identifying gang member profiles. We take counts of unigrams from gang and non-gang members' Twitter profile descriptions as classification features. It has been recognized that music is a key cultural component in an urban lifestyle and that gang members often want to emulate the scenarios and activities the music conveys BIBREF7 . Our analysis confirms that the influence of gangster rap is expressed in gang members' Twitter posts. We found that 51.25% of the gang members collected have a tweet that links to a YouTube video. Following these links, a simple keyword search for the terms gangsta and hip-hop in the YouTube video description found that 76.58% of the shared links are related to hip-hop music, gangster rap, and the culture that surrounds this music genre. Moreover, this high proportion is not driven by a small number of profiles that prolifically share YouTube links; eight YouTube links are shared on average by a gang member. Recognizing the frequency with which gang members post YouTube links on gangster rap and hip-hop, we consider the YouTube videos posted in a user's tweets as features for the classifier. In particular, for each YouTube video tweeted, we used the YouTube API to retrieve the video's description and its comments. Further analysis of YouTube data showed a difference between terms in gang members' YouTube data and non-gang members' YouTube data. For example, the top 5 terms (after stemming and stop word removal) used in YouTube videos shared by gang members are shit, like, nigga, fuck, lil while like, love, peopl, song, get are the top 5 terms in non-gang member video data. To represent a user profile based on their music interests, we generated a bag of words from the video descriptions and comments from all shared videos. Motivated by recent work involving the use of emojis by gang members BIBREF22 , we also studied if and how gang and non-gang members use emoji symbols in their tweets. Our analysis found that gang members have a penchant for using just a small set of emoji symbols that convey their anger and violent behavior through their tweets. Figure FIGREF24 illustrates the emoji distribution for the top 20 most frequent emojis used by gang member profiles in our dataset. The fuel pump emoji was the most frequently used emoji by the gang members, which is often used in the context of selling or consuming marijuana. The pistol emoji is the second most frequent in our dataset, which is often used with the guardsman emoji or the police cop emoji in an `emoji chain'. Figure FIGREF28 presents some prototypical `chaining' of emojis used by gang members. The chains may reflect their anger at law enforcement officers, as a cop emoji is often followed up with the emoji of a weapon, bomb, or explosion. We found that 32.25% of gang members in our dataset have chained together the police and the pistol emoji, compared to just 1.14% of non-gang members. Moreover, only 1.71% of non-gang members have used the hundred points emoji and pistol emoji together in tweets while 53% of gang members have used them. A variety of the angry face emoji such as devil face emoji and imp emoji were also common in gang member tweets. The frequency of each emoji symbol used across the set of user's tweets are thus considered as features for our classifier. In our profile verification process, we observed that most gang member profiles portray a context representative of gang culture. Some examples of these profile pictures are shown in Figure FIGREF32 , where the user holds or points weapons, is seen in a group fashion which displays a gangster culture, or is showing off graffiti, hand signs, tattoos and bulk cash. Descriptions of these images may thus empower our classifier. Thus, we translated profile images into features with the Clarifai web service. Clarifai offers a free API to query a deep learning system that tags images with a set of scored keywords that reflect what is seen in the image. We tagged the profile image and cover image for each profile using 20 tags identified by Clarifai. Figure FIGREF36 offers the 20 most often used tags applied to gang and non-gang member profiles. Since we take all the tags returned for an image, we see common words such as people and adult coming up in the top 20 tag set. However, gang member profile images were assigned unique tags such as trigger, bullet, worship while non-gang images were uniquely tagged with beach, seashore, dawn, wildlife, sand, pet. The set of tags returned by Clarifai were thus considered as features for the classifier. ### Learning algorithms
The unigrams of tweets, profile text, and linked YouTube video descriptions and comments, along with the distribution of emoji symbols and the profile image tags were used to train four different classification models: a Naive Bayes net, a Logistic Regression, a Random Forest, and a Support Vector Machine (SVM). These four models were chosen because they are known to perform well over text features, which is the dominant type of feature considered. The performance of the models are empirically compared to determine the most suitable classification technique for this problem. Data for the models are represented as a vector of term frequencies where the terms were collected from one or more feature sets described above. ### Evaluation
We next evaluate the performance of classifiers that use the above features to discover gang member profiles on Twitter. For this purpose, we use the training set discussed in Section SECREF3 with 400 gang member profiles (the `positive'/`gang' class) and 2,865 non-gang member profiles (the `negative'/`non-gang' class). We trained and evaluated the performance of the classifiers mentioned in Section SECREF31 under a 10-fold cross validation scheme. For each of the four learning algorithms, we consider variations involving only tweet text, emoji, profile, image, or music interest (YouTube comments and video description) features, and a final variant that considers all types of features together. The classifiers that use a single feature type were intended to help us study the quality of their predictive power by itself. When building these single-feature classifiers, we filtered the training dataset based on the availability of the single feature type in the training data. For example, we only used the twitter profiles that had at least a single emoji in their tweets to train classifiers that consider emoji features. We found 3,085 such profiles out of the 3,265 profiles in the training set. When all feature types were considered, we developed two different models: Because a Twitter profile may not have every feature type, Model(1) represents a practical scenario where not every Twitter profile contains every type of feature. In this model, the non-occurrence of a feature is represented by `zeroing out' the feature value during model training. Model(2) represents the ideal scenario where all profiles contain every feature type. For this model, we used 1,358 training instances (42% of all training instances), out of which 172 were gang members (43% of all gang members) and 1,186 were non-gang members (41% of all non-gang members). We used version 0.17.1 of scikit-learn machine learning library to implement the classifiers. For each 10-fold cross validation experiment, we report three evaluation metrics for the `gang' and `non-gang' classes, namely, the Precision = INLINEFORM0 , Recall = INLINEFORM1 , and INLINEFORM2 -score = INLINEFORM3 , where INLINEFORM4 is the number of true positives, INLINEFORM5 is the number of false positives, INLINEFORM6 is the number of true negatives, and INLINEFORM7 is the number of false negatives. We report these metrics for the positive `gang' and negative `non-gang' classes separately because of class imbalance in our dataset. ### Experimental results
Table TABREF37 presents the average precision, recall, and INLINEFORM0 -score over the 10 folds for the single-feature and combined feature classifiers. The table includes, in braces (`{ }'), the number of gang and non-gang profiles that contain a particular feature type, and hence the number of profiles used for the 10-fold cross validation. It is reasonable to expect that any Twitter profile is not that of a gang member, predicting a Twitter user as a non-gang member is much easier than predicting a Twitter user as a gang member. Moreover false positive classifications of the `gang' class may be detrimental to law enforcement investigations, which may go awry as they surveil an innocent person based on the classifier's suggestion. We thus believe that a small false positive rate of the `gang' class to be an especially important evaluation metric. We say that a classifier is `ideal' if it demonstrates high precision, recall, and INLINEFORM1 -score for the `gang' class while performing well on the `non-gang' class as well. The best performing classifier that considers single features is a Random Forest model over tweet features (T), with a reasonable INLINEFORM0 -score of 0.7229 for the `gang' class. It also features the highest INLINEFORM1 -score for the `non-gang' class (0.9671). Its strong performance is intuitive given the striking differences in language as shown in Figure FIGREF14 and discussed in Section UID22 . We also noted that music features offer promising results, with an INLINEFORM2 -score of 0.6505 with a Naive Bayes classifier, as well as emoji features with an INLINEFORM3 -score of 0.6067 also achieved by a Naive Bayes classifier. However, the use of profile data and image tags by themselves yield relatively poor INLINEFORM4 -scores no matter which classifier considered. There may be two reasons for this despite the differences we observed in Section SECREF17 . First, these two feature types did not generate a large number of specific features for learning. For example, descriptions are limited to just 160 characters per profile, leading to a limited number of unigrams (in our dataset, 10 on average) that can be used to train the classifiers. Second, the profile images were tagged by a third party Web service which is not specifically designed to identify gang hand signs, drugs and guns, which are often shared by gang members. This led to a small set of image tags in their profiles that were fairly generic, i.e., the image tags in Figure FIGREF36 such as `people', `man', and `adult'. Combining these diverse sets of features into a single classifier yields even better results. Our results for Model(1) show that the Random Forest achieves the highest INLINEFORM0 -scores for both `gang' (0.7364) and `non-gang' (0.9690) classes and yields the best precision of 0.8792, which corresponds to a low false positive rate when labeling a profile as a gang member. Despite the fact that it has lower positive recall compared to the second best performing classifier (a Random Forest trained over only tweet text features (T)), for this problem setting, we should be willing to increase the chance that a gang member will go unclassified if it means reducing the chance of applying a `gang' label to a non-gang member. When we tested Model(2), a Random Forrest classifier achieved an INLINEFORM1 -score of 0.7755 (improvement of 7.28% with respect to the best performing single feature type classifier (T)) for `gang' class with a precision of 0.8961 (improvement of 6.26% with respect to (T)) and a recall of 0.6994 (improvement of 9.26% with respect to (T)). Model(2) thus outperforms Model(1), and we expect its performance to improve with the availability of more training data with all feature types. px ### Evaluation Over Unseen Profiles
We also tested the trained classifiers using a set of Twitter profiles from a separate data collection process that may emulate the classifier's operation in a real-time setting. For this experiment, we captured real-time tweets from Los Angeles, CA and from ten South Side, Chicago neighborhoods that are known for gang-related activities BIBREF10 using the Twitter streaming API. We consider these areas with known gang presence on social media to ensure that some positive profiles would appear in our test set. We ultimately collected 24,162 Twitter profiles: 15,662 from Los Angeles, and 8,500 from Chicago. We populated data for each profile by using the 3,200 most recent tweets (the maximum that can be collected from Twitter's API) for each profile. Since the 24,162 profiles are far too many to label manually, we qualitatively study those profiles the classifier placed into the `gang' class. We used the training dataset to train our best performing random forest classifier (which use all feature types) and tested it on the test dataset. We then analyzed the Twitter profiles that our classifier labeled as belonging to the `gang' class. Each of those profiles had several features which overlap with gang members such as displaying hand signs and weapons in their profile images or in videos posted by them, gang names or gang-related hashtags in their profile descriptions, frequent use of curse words, and the use of terms such as “my homie" to refer to self-identified gang members. Representative tweets extracted from those profiles are depicted in Figure FIGREF41 . The most frequent words found in tweets from those profiles were shit, nigga, got, bitch, go, fuck etc. and their user profiles had terms such as free, artist, shit, fuck, freedagang, and ripthefallen. They had frequently used emojis such as face with tears of joy, hundred points symbol, fire, skull, money bag, and pistol. For some profiles, it was less obvious that the classifier correctly identified a gang member. Such profiles used the same emojis and curse words commonly found in gang members profiles, but their profile picture and tweet content was not indicative of a gang affiliation. In conclusion, we find that in a real-time-like setting, the classifier to be able to extract profiles with features that strongly suggest gang affiliation. Of course, these profiles demand further investigation and extensive evidence from other sources in order to draw a concrete conclusion, especially in the context of a law enforcement investigation. We refrain from reporting any profile names or specific details about the profiles labeled as a `gang' member to comply with the applicable IRB governing this human subject research. px ### Conclusion and Future Work
This paper presented an approach to address the problem of automatically identifying gang member profiles on Twitter. Despite the challenges in developing such automated systems, mainly due to difficulties in finding online gang member profiles for developing training datasets, we proposed an approach that uses features extracted from textual descriptions, emojis, images and videos shared on Twitter (textual features extracted from images, and videos). Exploratory analysis of these types of features revealed interesting, and sometimes striking differences in the ways gang and non-gang members use Twitter. Classifiers trained over features that highlight these differences, were evaluated under 10-fold cross validation. Our best classifier achieved a promising INLINEFORM0 -score of 0.7755 over the `gang' profiles when all types of features were considered. Future work will strengthen our training dataset by including more gang member Twitter profiles by searching for more location-independent keywords. We also plan to develop our own image classification system specifically designed to classify images found on gang member profiles. We would also like to experiment with building dictionaries that contain gang names to understand whether “having a gang name in the profile description” as a feature can improve our results. Finally, we would also like to study how can we further improve our classifier models using word embeddings BIBREF23 and social networks of known gang members. px ### Acknowledgement
We are thankful to Uday Kiran Yeda for helping us with data collection. We acknowledge partial support from the National Science Foundation (NSF) award: CNS-1513721: “Context-Aware Harassment Detection on Social Media”, National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression” and Grant No. 2014-PS-PSN-00006 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component of the U.S. Department of Justice's Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice, NSF or NIH. px Fig. 1: Twitter profile descriptions of known gang members. Pursuant to an IRB governing human subject research, we are prohibited from revealing personally identifiable information in this paper. We only report Twitter handles that have already been revealed in widely reported publications and were not collected by the research team for this work. Fig. 2: Gang member dataset creation. TABLE I: Number of gang member profiles captured. Fig. 3: Comparison of words used in tweets. Fig. 4: Word usage in profile descriptions: gang vs non-gang. Fig. 6: Examples for gang members’ tweets with emojis. Fig. 5: Emoji usage distribution: gang vs non-gang. Fig. 7: Sample gang member profile images. Fig. 8: Image tags distribution: gang vs non-gang. TABLE II: Classification results based on 10-fold cross validation. | Manual verification |
Why is the first part of the story so important?
A. It lets the reader know that it's Purnie's birthday (which becomes important later)
B. It lets the reader see how Purnie interacts with his family
C. It shows the reader a skill that Purnie's been practicing
D. It give great detail of the setting (which Purnie has to use later in the story to his advantage)
| BEACH SCENE By MARSHALL KING Illustrated by WOOD [Transcriber's Note: This etext was produced from Galaxy Magazine October 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] It was a fine day at the beach for Purnie's game—but his new friends played very rough! Purnie ran laughing and shouting through the forest until he could run no more. He fell headlong into a patch of blue moss and whooped with delight in having this day free for exploring. He was free to see the ocean at last. When he had caught his breath, he looked back through the forest. No sign of the village; he had left it far behind. Safe from the scrutiny of brothers and parents, there was nothing now to stop him from going to the ocean. This was the moment to stop time. "On your mark!" he shouted to the rippling stream and its orange whirlpools. He glanced furtively from side to side, pretending that some object might try to get a head start. "Get set!" he challenged the thin-winged bees that hovered over the abundant foliage. "Stop!" He shrieked this command upward toward the dense, low-hanging purple clouds that perennially raced across the treetops, making one wonder how tall the trees really were. His eyes took quick inventory. It was exactly as he knew it would be: the milky-orange stream had become motionless and its minute whirlpools had stopped whirling; a nearby bee hung suspended over a paka plant, its transparent wings frozen in position for a downward stroke; and the heavy purple fluid overhead held fast in its manufacture of whorls and nimbi. With everything around him in a state of perfect tableau, Purnie hurried toward the ocean. If only the days weren't so short! he thought. There was so much to see and so little time. It seemed that everyone except him had seen the wonders of the beach country. The stories he had heard from his brothers and their friends had taunted him for as long as he could remember. So many times had he heard these thrilling tales that now, as he ran along, he could clearly picture the wonderland as though he were already there. There would be a rockslide of petrified logs to play on, the ocean itself with waves higher than a house, the comical three-legged tripons who never stopped munching on seaweed, and many kinds of other wonderful creatures found only at the ocean. He bounced through the forest as though the world was reserved this day just for him. And who could say it wasn't? he thought. Wasn't this his fifth birthday? He ran along feeling sorry for four-year-olds, and even for those who were only four and a half, for they were babies and wouldn't dare try slipping away to the ocean alone. But five! "I'll set you free, Mr. Bee—just wait and see!" As he passed one of the many motionless pollen-gathering insects he met on the way, he took care not to brush against it or disturb its interrupted task. When Purnie had stopped time, the bees—like all the other creatures he met—had been arrested in their native activities, and he knew that as soon as he resumed time, everything would pick up where it had left off. When he smelled an acid sweetness that told him the ocean was not far off, his pulse quickened in anticipation. Rather than spoil what was clearly going to be a perfect day, he chose to ignore the fact that he had been forbidden to use time-stopping as a convenience for journeying far from home. He chose to ignore the oft-repeated statement that an hour of time-stopping consumed more energy than a week of foot-racing. He chose to ignore the negative maxim that "small children who stop time without an adult being present, may not live to regret it." He chose, instead, to picture the beaming praise of family and friends when they learned of his brave journey. The journey was long, the clock stood still. He stopped long enough to gather some fruit that grew along the path. It would serve as his lunch during this day of promise. With it under his arm he bounded along a dozen more steps, then stopped abruptly in his tracks. He found himself atop a rocky knoll, overlooking the mighty sea! He was so overpowered by the vista before him that his "Hurrah!" came out as a weak squeak. The ocean lay at the ready, its stilled waves awaiting his command to resume their tidal sweep. The breakers along the shoreline hung in varying stages of disarray, some having already exploded into towering white spray while others were poised in smooth orange curls waiting to start that action. And there were new friends everywhere! Overhead, a flock of spora were frozen in a steep glide, preparatory to a beach landing. Purnie had heard of these playful creatures many times. Today, with his brothers in school, he would have the pets all to himself. Further down the beach was a pair of two-legged animals poised in mid-step, facing the spot where Purnie now stood. Some distance behind them were eight more, each of whom were motionless in a curious pose of interrupted animation. And down in the water, where the ocean ran itself into thin nothingness upon the sand, he saw standing here and there the comical tripons, those three-legged marine buffoons who made handsome careers of munching seaweed. "Hi there!" Purnie called. When he got no reaction, he remembered that he himself was "dead" to the living world: he was still in a zone of time-stopping, on the inside looking out. For him, the world would continue to be a tableau of mannikins until he resumed time. "Hi there!" he called again; but now his mental attitude was that he expected time to resume. It did! Immediately he was surrounded by activity. He heard the roar of the crashing orange breakers, he tasted the dew of acid that floated from the spray, and he saw his new friends continue the actions which he had stopped while back in the forest. He knew, too, that at this moment, in the forest, the little brook picked up its flow where it had left off, the purple clouds resumed their leeward journey up the valley, and the bees continued their pollen-gathering without having missed a single stroke of their delicate wings. The brook, the clouds, and the insects had not been interrupted in the least; their respective tasks had been performed with continuing sureness. It was time itself that Purnie had stopped, not the world around him. He scampered around the rockpile and down the sandy cliff to meet the tripons who, to him, had just come to life. "I can stand on my head!" He set down his lunch and balanced himself bottoms-up while his legs pawed the air in an effort to hold him in position. He knew it was probably the worst head-stand he had ever done, for he felt weak and dizzy. Already time-stopping had left its mark on his strength. But his spirits ran on unchecked. The tripon thought Purnie's feat was superb. It stopped munching long enough to give him a salutory wag of its rump before returning to its repast. Purnie ran from pillar to post, trying to see and do everything at once. He looked around to greet the flock of spora, but they had glided to a spot further along the shore. Then, bouncing up to the first of the two-legged animals, he started to burst forth with his habitual "Hi there!" when he heard them making sounds of their own. "... will be no limit to my operations now, Benson. This planet makes seventeen. Seventeen planets I can claim as my own!" "My, my. Seventeen planets. And tell me, Forbes, just what the hell are you going to do with them—mount them on the wall of your den back in San Diego?" "Hi there, wanna play?" Purnie's invitation got nothing more than startled glance from the animals who quickly returned to their chatter. He scampered up the beach, picked up his lunch, and ran back to them, tagging along at their heels. "I've got my lunch, want some?" "Benson, you'd better tell your men back there to stop gawking at the scenery and get to work. Time is money. I didn't pay for this expedition just to give your flunkies a vacation." The animals stopped so suddenly that Purnie nearly tangled himself in their heels. "All right, Forbes, just hold it a minute. Listen to me. Sure, it's your money that put us here; it's your expedition all the way. But you hired me to get you here with the best crew on earth, and that's just what I've done. My job isn't over yet. I'm responsible for the safety of the men while we're here, and for the safe trip home." "Precisely. And since you're responsible, get 'em working. Tell 'em to bring along the flag. Look at the damn fools back there, playing in the ocean with a three-legged ostrich!" "Good God, man, aren't you human? We've only been on this planet twenty minutes! Naturally they want to look around. They half expected to find wild animals or worse, and here we are surrounded by quaint little creatures that run up to us like we're long-lost brothers. Let the men look around a minute or two before we stake out your claim." "Bah! Bunch of damn children." As Purnie followed along, a leg shot out at him and missed. "Benson, will you get this bug-eyed kangaroo away from me!" Purnie shrieked with joy at this new frolic and promptly stood on his head. In this position he got an upside down view of them walking away. He gave up trying to stay with them. Why did they move so fast, anyway? What was the hurry? As he sat down and began eating his lunch, three more of the creatures came along making excited noises, apparently trying to catch up to the first two. As they passed him, he held out his lunch. "Want some?" No response. Playing held more promise than eating. He left his lunch half eaten and went down to where they had stopped further along the beach. "Captain Benson, sir! Miles has detected strong radiation in the vicinity. He's trying to locate it now." "There you are, Forbes. Your new piece of real estate is going to make you so rich that you can buy your next planet. That'll make eighteen, I believe." "Radiation, bah! We've found low-grade ore on every planet I've discovered so far, and this one'll be no different. Now how about that flag? Let's get it up, Benson. And the cornerstone, and the plaque." "All right, lads. The sooner we get Mr. Forbes's pennant raised and his claim staked out, the sooner we can take time to look around. Lively now!" When the three animals went back to join the rest of their group, the first two resumed walking. Purnie followed along. "Well, Benson, you won't have to look far for materials to use for the base of the flag pole. Look at that rockpile up there. "Can't use them. They're petrified logs. The ones on top are too high to carry down, and if we move those on the bottom, the whole works will slide down on top of us." "Well—that's your problem. Just remember, I want this flag pole to be solid. It's got to stand at least—" "Don't worry, Forbes, we'll get your monument erected. What's this with the flag? There must be more to staking a claim than just putting up a flag." "There is, there is. Much more. I've taken care of all requirements set down by law to make my claim. But the flag? Well, you might say it represents an empire, Benson. The Forbes Empire. On each of my flags is the word FORBES, a symbol of development and progress. Call it sentiment if you will." "Don't worry, I won't. I've seen real-estate flags before." "Damn it all, will you stop referring to this as a real-estate deal? What I'm doing is big, man. Big! This is pioneering." "Of course. And if I'm not mistaken, you've set up a neat little escrow system so that you not only own the planets, but you will virtually own the people who are foolish enough to buy land on them." "I could have your hide for talking to me like this. Damn you, man! It's people like me who pay your way. It's people like me who give your space ships some place to go. It's people like me who pour good money into a chancey job like this, so that people like you can get away from thirteen-story tenement houses. Did you ever think of that?" "I imagine you'll triple your money in six months." When they stopped, Purnie stopped. At first he had been interested in the strange sounds they were making, but as he grew used to them, and as they in turn ignored his presence, he hopped alongside chattering to himself, content to be in their company. He heard more of these sounds coming from behind, and he turned to see the remainder of the group running toward them. "Captain Benson! Here's the flag, sir. And here's Miles with the scintillometer. He says the radiation's getting stronger over this way!" "How about that, Miles?" "This thing's going wild, Captain. It's almost off scale." Purnie saw one of the animals hovering around him with a little box. Thankful for the attention, he stood on his head. "Can you do this?" He was overjoyed at the reaction. They all started making wonderful noises, and he felt most satisfied. "Stand back, Captain! Here's the source right here! This little chuck-walla's hotter than a plutonium pile!" "Let me see that, Miles. Well, I'll be damned! Now what do you suppose—" By now they had formed a widening circle around him, and he was hard put to think of an encore. He gambled on trying a brand new trick: he stood on one leg. "Benson, I must have that animal! Put him in a box." "Now wait a minute, Forbes. Universal Law forbids—" "This is my planet and I am the law. Put him in a box!" "With my crew as witness, I officially protest—" "Good God, what a specimen to take back. Radio-active animals! Why, they can reproduce themselves, of course! There must be thousands of these creatures around here someplace. And to think of those damn fools on Earth with their plutonium piles! Hah! Now I'll have investors flocking to me. How about it, Benson—does pioneering pay off or doesn't it?" "Not so fast. Since this little fellow is radioactive, there may be great danger to the crew—" "Now look here! You had planned to put mineral specimens in a lead box, so what's the difference? Put him in a box." "He'll die." "I have you under contract, Benson! You are responsible to me, and what's more, you are on my property. Put him in a box." Purnie was tired. First the time-stopping, then this. While this day had brought more fun and excitement than he could have hoped for, the strain was beginning to tell. He lay in the center of the circle happily exhausted, hoping that his friends would show him some of their own tricks. He didn't have to wait long. The animals forming the circle stepped back and made way for two others who came through carrying a box. Purnie sat up to watch the show. "Hell, Captain, why don't I just pick him up? Looks like he has no intention of running away." "Better not, Cabot. Even though you're shielded, no telling what powers the little fella has. Play it safe and use the rope." "I swear he knows what we're saying. Look at those eyes." "All right, careful now with that line." "Come on, baby. Here you go. That's a boy!" Purnie took in these sounds with perplexed concern. He sensed the imploring quality of the creature with the rope, but he didn't know what he was supposed to do. He cocked his head to one side as he wiggled in anticipation. He saw the noose spinning down toward his head, and, before he knew it, he had scooted out of the circle and up the sandy beach. He was surprised at himself for running away. Why had he done it? He wondered. Never before had he felt this fleeting twinge that made him want to protect himself. He watched the animals huddle around the box on the beach, their attention apparently diverted to something else. He wished now that he had not run away; he felt he had lost his chance to join in their fun. "Wait!" He ran over to his half-eaten lunch, picked it up, and ran back into the little crowd. "I've got my lunch, want some?" The party came to life once more. His friends ran this way and that, and at last Purnie knew that the idea was to get him into the box. He picked up the spirit of the tease, and deliberately ran within a few feet of the lead box, then, just as the nearest pursuer was about to push him in, he sidestepped onto safer ground. Then he heard a deafening roar and felt a warm, wet sting in one of his legs. "Forbes, you fool! Put away that gun!" "There you are, boys. It's all in knowing how. Just winged him, that's all. Now pick him up." The pang in his leg was nothing: Purnie's misery lay in his confusion. What had he done wrong? When he saw the noose spinning toward him again, he involuntarily stopped time. He knew better than to use this power carelessly, but his action now was reflex. In that split second following the sharp sting in his leg, his mind had grasped in all directions to find an acceptable course of action. Finding none, it had ordered the stoppage of time. The scene around him became a tableau once more. The noose hung motionless over his head while the rest of the rope snaked its way in transverse waves back to one of the two-legged animals. Purnie dragged himself through the congregation, whimpering from his inability to understand. As he worked his way past one creature after another, he tried at first to not look them in the eye, for he felt sure he had done something wrong. Then he thought that by sneaking a glance at them as he passed, he might see a sign pointing to their purpose. He limped by one who had in his hand a small shiny object that had been emitting smoke from one end; the smoke now billowed in lifeless curls about the animal's head. He hobbled by another who held a small box that had previously made a hissing sound whenever Purnie was near. These things told him nothing. Before starting his climb up the knoll, he passed a tripon which, true to its reputation, was comical even in fright. Startled by the loud explosion, it had jumped four feet into the air before Purnie had stopped time. Now it hung there, its beak stuffed with seaweed and its three legs drawn up into a squatting position. Leaving the assorted statues behind, he limped his way up the knoll, torn between leaving and staying. What an odd place, this ocean country! He wondered why he had not heard more detail about the beach animals. Reaching the top of the bluff, he looked down upon his silent friends with a feeling of deep sorrow. How he wished he were down there playing with them. But he knew at last that theirs was a game he didn't fit into. Now there was nothing left but to resume time and start the long walk home. Even though the short day was nearly over, he knew he didn't dare use time-stopping to get himself home in nothing flat. His fatigued body and clouded mind were strong signals that he had already abused this faculty. When Purnie started time again, the animal with the noose stood in open-mouthed disbelief as the rope fell harmlessly to the sand—on the spot where Purnie had been standing. "My God, he's—he's gone." Then another of the animals, the one with the smoking thing in his hand, ran a few steps toward the noose, stopped and gaped at the rope. "All right, you people, what's going on here? Get him in that box. What did you do with him?" The resumption of time meant nothing at all to those on the beach, for to them time had never stopped. The only thing they could be sure of was that at one moment there had been a fuzzy creature hopping around in front of them, and the next moment he was gone. "Is he invisible, Captain? Where is he?" "Up there, Captain! On those rocks. Isn't that him?" "Well, I'll be damned!" "Benson, I'm holding you personally responsible for this! Now that you've botched it up, I'll bring him down my own way." "Just a minute, Forbes, let me think. There's something about that fuzzy little devil that we should.... Forbes! I warned you about that gun!" Purnie moved across the top of the rockpile for a last look at his friends. His weight on the end of the first log started the slide. Slowly at first, the giant pencils began cascading down the short distance to the sand. Purnie fell back onto solid ground, horrified at the spectacle before him. The agonizing screams of the animals below filled him with hysteria. The boulders caught most of them as they stood ankle-deep in the surf. Others were pinned down on the sand. "I didn't mean it!" Purnie screamed. "I'm sorry! Can't you hear?" He hopped back and forth near the edge of the rise, torn with panic and shame. "Get up! Please get up!" He was horrified by the moans reaching his ears from the beach. "You're getting all wet! Did you hear me? Please get up." He was choked with rage and sorrow. How could he have done this? He wanted his friends to get up and shake themselves off, tell him it was all right. But it was beyond his power to bring it about. The lapping tide threatened to cover those in the orange surf. Purnie worked his way down the hill, imploring them to save themselves. The sounds they made carried a new tone, a desperate foreboding of death. "Rhodes! Cabot! Can you hear me?" "I—I can't move, Captain. My leg, it's.... My God, we're going to drown!" "Look around you, Cabot. Can you see anyone moving?" "The men on the beach are nearly buried, Captain. And the rest of us here in the water—" "Forbes. Can you see Forbes? Maybe he's—" His sounds were cut off by a wavelet gently rolling over his head. Purnie could wait no longer. The tides were all but covering one of the animals, and soon the others would be in the same plight. Disregarding the consequences, he ordered time to stop. Wading down into the surf, he worked a log off one victim, then he tugged the animal up to the sand. Through blinding tears, Purnie worked slowly and carefully. He knew there was no hurry—at least, not as far as his friends' safety was concerned. No matter what their condition of life or death was at this moment, it would stay the same way until he started time again. He made his way deeper into the orange liquid, where a raised hand signalled the location of a submerged body. The hand was clutching a large white banner that was tangled among the logs. Purnie worked the animal free and pulled it ashore. It was the one who had been carrying the shiny object that spit smoke. Scarcely noticing his own injured leg, he ferried one victim after another until there were no more in the surf. Up on the beach, he started unraveling the logs that pinned down the animals caught there. He removed a log from the lap of one, who then remained in a sitting position, his face contorted into a frozen mask of agony and shock. Another, with the weight removed, rolled over like an iron statue into a new position. Purnie whimpered in black misery as he surveyed the chaotic scene before him. At last he could do no more; he felt consciousness slipping away from him. He instinctively knew that if he lost his senses during a period of time-stopping, events would pick up where they had left off ... without him. For Purnie, this would be death. If he had to lose consciousness, he knew he must first resume time. Step by step he plodded up the little hill, pausing every now and then to consider if this were the moment to start time before it was too late. With his energy fast draining away, he reached the top of the knoll, and he turned to look down once more on the group below. Then he knew how much his mind and body had suffered: when he ordered time to resume, nothing happened. His heart sank. He wasn't afraid of death, and he knew that if he died the oceans would roll again and his friends would move about. But he wanted to see them safe. He tried to clear his mind for supreme effort. There was no urging time to start. He knew he couldn't persuade it by bits and pieces, first slowly then full ahead. Time either progressed or it didn't. He had to take one viewpoint or the other. Then, without knowing exactly when it happened, his mind took command.... His friends came to life. The first one he saw stir lay on his stomach and pounded his fists on the beach. A flood of relief settled over Purnie as sounds came from the animal. "What's the matter with me? Somebody tell me! Am I nuts? Miles! Schick! What's happening?" "I'm coming, Rhodes! Heaven help us, man—I saw it, too. We're either crazy or those damn logs are alive!" "It's not the logs. How about us? How'd we get out of the water? Miles, we're both cracking." "I'm telling you, man, it's the logs, or rocks or whatever they are. I was looking right at them. First they're on top of me, then they're piled up over there!" "Damnit, the logs didn't pick us up out of the ocean, did they? Captain Benson!" "Are you men all right?" "Yes sir, but—" "Who saw exactly what happened?" "I'm afraid we're not seeing right, Captain. Those logs—" "I know, I know. Now get hold of yourselves. We've got to round up the others and get out of here while time is on our side." "But what happened, Captain?" "Hell, Rhodes, don't you think I'd like to know? Those logs are so old they're petrified. The whole bunch of us couldn't lift one. It would take super-human energy to move one of those things." "I haven't seen anything super-human. Those ostriches down there are so busy eating seaweed—" "All right, let's bear a hand here with the others. Some of them can't walk. Where's Forbes?" "He's sitting down there in the water, Captain, crying like a baby. Or laughing. I can't tell which." "We'll have to get him. Miles, Schick, come along. Forbes! You all right?" "Ho-ho-ho! Seventeen! Seventeen! Seventeen planets, Benson, and they'll do anything I say! This one's got a mind of its own. Did you see that little trick with the rocks? Ho-ho!" "See if you can find his gun, Schick; he'll either kill himself or one of us. Tie his hands and take him back to the ship. We'll be along shortly." "Hah-hah-hah! Seventeen! Benson, I'm holding you personally responsible for this. Hee-hee!" Purnie opened his eyes as consciousness returned. Had his friends gone? He pulled himself along on his stomach to a position between two rocks, where he could see without being seen. By the light of the twin moons he saw that they were leaving, marching away in groups of two and three, the weak helping the weaker. As they disappeared around the curving shoreline, the voices of the last two, bringing up the rear far behind the others, fell faintly on his ears over the sound of the surf. "Is it possible that we're all crazy, Captain?" "It's possible, but we're not." "I wish I could be sure." "See Forbes up ahead there? What do you think of him?" "I still can't believe it." "He'll never be the same." "Tell me something. What was the most unusual thing you noticed back there?" "You must be kidding, sir. Why, the way those logs were off of us suddenly—" "Yes, of course. But I mean beside that." "Well, I guess I was kind of busy. You know, scared and mixed up." "But didn't you notice our little pop-eyed friend?" "Oh, him. I'm afraid not, Captain. I—I guess I was thinking mostly of myself." "Hmmm. If I could only be sure I saw him. If only someone else saw him too." "I'm afraid I don't follow you, sir." "Well, damn it all, you know that Forbes took a pot shot at him. Got him in the leg. That being the case, why would the fuzzy little devil come back to his tormentors—back to us—when we were trapped under those logs?" "Well, I guess as long as we were trapped, he figured we couldn't do him any more harm.... I'm sorry, that was a stupid answer. I guess I'm still a little shaky." "Forget it. Look, you go ahead to the ship and make ready for take-off. I'll join you in a few minutes. I think I'll go back and look around. You know. Make sure we haven't left anyone." "No need to do that. They're all ahead of us. I've checked." "That's my responsibility, Cabot, not yours. Now go on." As Purnie lay gathering strength for the long trek home, he saw through glazed eyes one of the animals coming back along the beach. When it was nearly directly below him, he could hear it making sounds that by now had become familiar. "Where are you?" Purnie paid little attention to the antics of his friend; he was beyond understanding. He wondered what they would say at home when he returned. "We've made a terrible mistake. We—" The sounds faded in and out on Purnie's ears as the creature turned slowly and called in different directions. He watched the animal walk over to the pile of scattered logs and peer around and under them. "If you're hurt I'd like to help!" The twin moons were high in the sky now, and where their light broke through the swirling clouds a double shadow was cast around the animal. With foggy awareness, Purnie watched the creature shake its head slowly, then walk away in the direction of the others. Purnie's eyes stared, without seeing, at the panorama before him. The beach was deserted now, and his gaze was transfixed on a shimmering white square floating on the ocean. Across it, the last thing Purnie ever saw, was emblazoned the word FORBES. | C. It shows the reader a skill that Purnie's been practicing |
Which would Alis be least likely to say?
A. "I'd love to leave Superior."
B. "Most people in Superior are a little different."
C. "I know how to get us back down."
D. "Don, I'd love to get to know you better."
| And Then the Town Took Off by RICHARD WILSON ACE BOOKS, INC. 23 West 47th Street, New York 36, N.Y. AND THEN THE TOWN TOOK OFF Copyright ©, 1960, by Ace Books, Inc. All Rights Reserved For Felicitas K. Wilson THE SIOUX SPACEMAN Copyright ©, 1960, by Ace Books, Inc. Printed in U.S.A. THE CITY THAT RAN OFF THE MAP The town of Superior, Ohio, certainly was living up to its name! In what was undoubtedly the most spectacular feat of the century, it simply picked itself up one night and rose two full miles above Earth! Radio messages stated simply that Superior had seceded from Earth. But Don Cort, stranded on that rising town, was beginning to suspect that nothing was simple about Superior except its citizens. Calmly they accepted their rise in the world as being due to one of their local townspeople, a crackpot professor. But after a couple of weeks of floating around, it began to be obvious that the professor had no idea how to get them down. So then it was up to Cort: either find a way to anchor Superior, or spend the rest of his days on the smallest—and the nuttiest—planet in the galaxy! I The town of Superior, Ohio, disappeared on the night of October 31. A truck driver named Pierce Knaubloch was the first to report it. He had been highballing west along Route 202, making up for the time he'd spent over a second cup of coffee in a diner, when he screeched to a stop. If he'd gone another twenty-five feet he'd have gone into the pit where Superior had been. Knaubloch couldn't see the extent of the pit because it was too dark, but it looked big. Bigger than if a nitro truck had blown up, which was his first thought. He backed up two hundred feet, set out flares, then sped off to a telephone. The state police converged on the former site of Superior from several directions. Communicating by radiophone across the vast pit, they confirmed that the town undoubtedly was missing. They put in a call to the National Guard. The guard surrounded the area with troops—more than a thousand were needed—to keep people from falling into the pit. A pilot who flew over it reported that it looked as if a great ice-cream scoop had bitten into the Ohio countryside. The Pennsylvania Railroad complained that one of its passenger trains was missing. The train's schedule called for it to pass through but not stop at Superior at 11:58. That seemed to fix the time of the disappearance at midnight. The truck driver had made his discovery shortly after midnight. Someone pointed out that October 31 was Halloween and that midnight was the witching hour. Somebody else said nonsense, they'd better check for radiation. A civil defense official brought up a Geiger counter, but no matter how he shook it and rapped on it, it refused to click. A National Guard officer volunteered to take a jeep down into the pit, having found a spot that seemed navigable. He was gone a long time but when he came out the other side he reported that the pit was concave, relatively smooth, and did not smell of high explosives. He'd found no people, no houses—no sign of anything except the pit itself. The Governor of Ohio asked Washington whether any unidentified planes had been over the state. Washington said no. The Pentagon and the Atomic Energy Commission denied that they had been conducting secret experiments. Nor had there been any defense plants in Superior that might have blown up. The town's biggest factory made kitchen sinks and the next biggest made bubble gum. A United Airlines pilot found Superior early on the morning of November 1. The pilot, Captain Eric Studley, who had never seen a flying saucer and hoped never to see one, was afraid now that he had. The object loomed out of a cloudbank at twelve thousand feet and Studley changed course to avoid it. He noted with only minimum satisfaction that his co-pilot also saw the thing and wondered why it wasn't moving at the terrific speed flying saucers were allegedly capable of. Then he saw the church steeple on it. A few minutes later he had relayed a message from Superior, formerly of Ohio, addressed to whom it might concern: It said that Superior had seceded from Earth. One other radio message came from Superior, now airborne, on that first day. A ham radio operator reported an unidentified voice as saying plaintively: " Cold up here!" Don Cort had been dozing in what passed for the club car on the Buckeye Cannonball when the train braked to a stop. He looked out the window, hoping this was Columbus, where he planned to catch a plane east. But it wasn't Columbus. All he could see were some lanterns jogging as trainmen hurried along the tracks. The conductor looked into the car. The redhead across the aisle in whom Don had taken a passing interest earlier in the evening asked, "Why did we stop?" "Somebody flagged us down," the conductor said. "We don't make a station stop at Superior on this run." The girl's hair was a subtle red, but false. When Don had entered the club car he'd seen her hatless head from above and noticed that the hair along the part was dark. Her eyes had been on a book and Don had the opportunity for a brief study of her face. The cheeks were full and untouched by make-up. There were lines at the corners of her mouth which indicated a tendency to arrange her expression into one of disapproval. The lips were full, like the cheeks, but it was obvious that the scarlet lipstick had contrived a mouth a trifle bigger than the one nature had given her. Her glance upward at that moment interrupted his examination, which had been about to go on to her figure. Later, though, he was able to observe that it was more than adequate. If the girl had given Don Cort more than that one glance, or if it had been a trained, all-encompassing glance, she would have seen a man in his mid-twenties—about her age—lean, tall and straight-shouldered, with once-blond hair now verging on dark brown, a face neither handsome nor ugly, and a habit of drawing the inside of his left cheek between his teeth and nibbling at it thoughtfully. But it was likely that all she noticed then was the brief case he carried, attached by a chain to a handcuff on his left wrist. "Will we be here long?" Don asked the conductor. He didn't want to miss his plane at Columbus. The sooner he got to Washington, the sooner he'd get rid of the brief case. The handcuff it was attached to was one reason why his interest in the redhead had been only passing. "Can't say," the conductor told him. He let the door close again and went down to the tracks. Don hesitated, shrugged at the redhead, said, "Excuse me," and followed the conductor. About a dozen people were milling around the train as it sat in the dark, hissing steam. Don made his way up to the locomotive and found a bigger knot of people gathered in front of the cowcatcher. Some sort of barricade had been put up across the tracks and it was covered with every imaginable kind of warning device. There were red lanterns, both battery and electric; flashlights; road flares; and even an old red shirt. Don saw two men who must have been the engineer and the fireman talking to an old bearded gentleman wearing a civil defense helmet, a topcoat and riding boots. "You'd go over the edge, I tell you," the old gentleman was saying. "If you don't get this junk off the line," the engineer said, "I'll plow right through it. Off the edge! you crazy or something?" "Look for yourself," the old man in the white helmet said. "Go ahead. Look." The engineer was exasperated. He turned to the fireman. "You look. Humor the old man. Then let's go." The bearded man—he called himself Professor Garet—went off with the fireman. Don followed them. They had tramped a quarter of a mile along the gravel when the fireman stopped. "Okay," he said "where's the edge? I don't see nothing." The tracks seemed to stretch forever into the darkness. "It's another half mile or so," the professor said. "Well, let's hurry up. We haven't got all night." The old man chuckled. "I'm afraid you have." They came to it at last, stopping well back from it. Professor Garet swelled with pride, it seemed, as he made a theatrical gesture. "Behold," he said. "Something even Columbus couldn't find. The edge of the world." True, everything seemed to stop, and they could see stars shining low on the horizon where stars could not properly be expected to be seen. Don Cort and the fireman walked cautiously toward the edge while the professor ambled ahead with the familiarity of one who had been there before. But there was a wind and they did not venture too close. Nevertheless, Don could see that it apparently was a neat, sharp edge, not one of your old ragged, random edges such as might have been caused by an explosion. This one had the feeling of design behind it. Standing on tiptoe and repressing a touch of giddiness, Don looked over the edge. He didn't have to stand on tiptoe any more than he had to sit on the edge of his seat during the exciting part of a movie, but the situation seemed to call for it. Over the edge could be seen a big section of Ohio. At least he supposed it was Ohio. Don looked at the fireman, who had an unbelieving expression on his face, then at the bearded old man, who was smiling and nodding. "You see what I mean," he said. "You would have gone right over. I believe you would have had a two-mile fall." "Of course you could have stayed aboard the train," the man driving the old Pontiac said, "but I really think you'll be more comfortable at Cavalier." Don Cort, sitting in the back seat of the car with the redhead from the club car, asked, "Cavalier?" "The college. The institute, really; it's not accredited. What did you say your name was, miss?" "Jen Jervis," she said. "Geneva Jervis, formally." "Miss Jervis. I'm Civek. You know Mr. Cort, I suppose." The girl smiled sideways. "We have a nodding acquaintance." Don nodded and grinned. "There's plenty of room in the dormitories," Civek said. "People don't exactly pound on the gates and scream to be admitted to Cavalier." "Are you connected with the college?" Don asked. "Me? No. I'm the mayor of Superior. The old town's really come up in the world, hasn't it?" "Overnight," Geneva Jervis said. "If what Mr. Cort and the fireman say is true. I haven't seen the edge myself." "You'll have a better chance to look at it in the morning," the mayor said, "if we don't settle back in the meantime." "Was there any sort of explosion?" Don asked. "No. There wasn't any sensation at all, as far as I noticed. I was watching the late show—or trying to. My house is down in a hollow and reception isn't very good, especially with old English movies. Well, all of a sudden the picture sharpened up and I could see just as plain. Then the phone rang and it was Professor Garet." "The old fellow with the whiskers and the riding boots?" Jen Jervis asked. "Yes. Osbert Garet, Professor of Magnology at the Cavalier Institute of Applied Sciences." "Professor of what?" "Magnology. As I say, the school isn't accredited. Well, Professor Garet telephoned and said, 'Hector'—that's my name, Hector Civek—'everything's up in the air.' He was having his little joke, of course. I said, 'What?' and then he told me." "Told you what?" Jen Jervis asked. "I mean, does he have any theory about it?" "He has a theory about everything. I think what he was trying to convey was that this—this levitation confirmed his magnology principle." "What's that?" Don asked. "I haven't the faintest idea. I'm a politician, not a scientist. Professor Garet went on about it for a while, on the telephone, about magnetism and gravity, but I think he was only calling as a courtesy, so the mayor wouldn't look foolish the next morning, not knowing his town had flown the coop." "What's the population of Superior?" "Three thousand, including the students at the institute. Three thousand and forty, counting you people from the train. I guess you'll be with us for a while." "What do you mean by that?" Jen Jervis asked. "Well, I don't see how you can get down. Do you?" "Does Superior have an airport?" Don asked. "I've got to get back to—to Earth." It sounded odd to put it that way. "Nope," Civek said. "No airport. No place for a plane to land, either." "Maybe not a plane," Don said, "but a helicopter could land just about anywhere." "No helicopters here, either." "Maybe not. But I'll bet they're swarming all over you by morning." "Hm," said Hector Civek. Don couldn't quite catch his expression in the rearview mirror. "I suppose they could, at that. Well, here's Cavalier. You go right in that door, where the others are going. There's Professor Garet. I've got to see him—excuse me." The mayor was off across the campus. Don looked at Geneva Jervis, who was frowning. "Are you thinking," he asked, "that Mayor Civek was perhaps just a little less than completely honest with us?" "I'm thinking," she said, "that I should have stayed with Aunt Hattie another night, then taken a plane to Washington." "Washington?" Don said. "That's where I'm going. I mean where I was going before Superior became airborne. What do you do in Washington, Miss Jervis?" "I work for the Government. Doesn't everybody?" "Not everybody. Me, for instance." "No?" she said. "Judging by that satchel you're handcuffed to, I'd have thought you were a courier for the Pentagon. Or maybe State." He laughed quickly and loudly because she was getting uncomfortably close. "Oh, no. Nothing so glamorous. I'm a messenger for the Riggs National Bank, that's all. Where do you work?" "I'm with Senator Bobby Thebold, S.O.B." Don laughed again. "He sure is." " Mister Cort!" she said, annoyed. "You know as well as I do that S.O.B. stands for Senate Office Building. I'm his secretary." "I'm sorry. We'd better get out and find a place to sleep. It's getting late." " Places to sleep," she corrected. She looked angry. "Of course," Don said, puzzled by her emphasis. "Come on. Where they put you, you'll probably be surrounded by co-eds, even if I could get out of this cuff." He took her bag in his free hand and they were met by a gray-haired woman who introduced herself as Mrs. Garet. "We'll try to make you comfortable," she said. "What a night, eh? The professor is simply beside himself. We haven't had so much excitement since the cosmolineator blew up." They had a glimpse of the professor, still in his CD helmet, going around a corner, gesticulating wildly to someone wearing a white laboratory smock. II Don Cort had slept, but not well. He had tried to fold the brief case to pull it through his sleeve so he could take his coat off, but whatever was inside the brief case was too big. Cavalier had given him a room to himself at one end of a dormitory and he'd taken his pants off but had had to sleep with his coat and shirt on. He got up, feeling gritty, and did what little dressing was necessary. It was eight o'clock, according to the watch on the unhandcuffed wrist, and things were going on. He had a view of the campus from his window. A bright sun shone on young people moving generally toward a squat building, and other people going in random directions. The first were students going to breakfast, he supposed, and the others were faculty members. The air was very clear and the long morning shadows distinct. Only then did he remember completely that he and the whole town of Superior were up in the air. He went through the dormitory. A few students were still sleeping. The others had gone from their unmade beds. He shivered as he stepped outdoors. It was crisp, if not freezing, and his breath came out visibly. First he'd eat, he decided, so he'd be strong enough to go take a good look over the edge, in broad daylight, to the Earth below. The mess hall, or whatever they called it, was cafeteria style and he got in line with a tray for juice, eggs and coffee. He saw no one he knew, but as he was looking for a table a willowy blonde girl smiled and gestured to the empty place opposite her. "You're Mr. Cort," she said. "Won't you join me?" "Thanks," he said, unloading his tray. "How did you know?" "The mystery man with the handcuff. You'd be hard to miss. I'm Alis—that's A-l-i-s, not A-l-i-c-e—Garet. Are you with the FBI? Or did you escape from jail?" "How do you do. No, just a bank messenger. What an unusual name. Professor Garet's daughter?" "The same," she said. "Also the only. A pity, because if there'd been two of us I'd have had a fifty-fifty chance of going to OSU. As it is, I'm duty-bound to represent the second generation at the nut factory." "Nut factory? You mean Cavalier?" Don struggled to manipulate knife and fork without knocking things off the table with his clinging brief case. "Here, let me cut your eggs for you," Alis said. "You'd better order them scrambled tomorrow. Yes, Cavalier. Home of the crackpot theory and the latter-day alchemist." "I'm sure it's not that bad. Thanks. As for tomorrow, I hope to be out of here by then." "How do you get down from an elephant? Old riddle. You don't; you get down from ducks. How do you plan to get down from Superior?" "I'll find a way. I'm more interested at the moment in how I got up here." "You were levitated, like everybody else." "You make it sound deliberate, Miss Garet, as if somebody hoisted a whole patch of real estate for some fell purpose." "Scarcely fell , Mr. Cort. As for it being deliberate, that seems to be a matter of opinion. Apparently you haven't seen the papers." "I didn't know there were any." "Actually there's only one, the Superior Sentry , a weekly. This is an extra. Ed Clark must have been up all night getting it out." She opened her purse and unfolded a four-page tabloid. Don blinked at the headline: Town Gets High "Ed Clark's something of an eccentric, like everybody else in Superior," Alis said. Don read the story, which seemed to him a capricious treatment of an apparently grave situation. Residents having business beyond the outskirts of town today are advised not to. It's a long way down. Where Superior was surrounded by Ohio, as usual, today Superior ends literally at the town line. A Citizens' Emergency Fence-Building Committee is being formed, but in the meantime all are warned to stay well away from the edge. The law of gravity seems to have been repealed for the town but it is doubtful if the same exemption would apply to a dubious individual bent on investigating.... Don skimmed the rest. "I don't see anything about it being deliberate." Alis had been creaming and sugaring Don's coffee. She pushed it across to him and said, "It's not on page one. Ed Clark and Mayor Civek don't get along, so you'll find the mayor's statement in a box on page three, bottom." Don creased the paper the other way, took a sip of coffee, nodded his thanks, and read: Mayor Claims Secession From Earth Mayor Hector Civek, in a proclamation issued locally by hand and dropped to the rest of the world in a plastic shatter-proof bottle, said today that Superior has seceded from Earth. His reasons were as vague as his explanation. The "reasons" include these: (1) Superior has been discriminated against by county, state and federal agencies; (2) Cavalier Institute has been held up to global derision by orthodox (presumably meaning accredited) colleges and universities; and (3) chicle exporters have conspired against the Superior Bubble Gum Company by unreasonably raising prices. The "explanation" consists of a 63-page treatise on applied magnology by Professor Osbert Garet of Cavalier which the editor (a) does not understand; (b) lacks space to publish; and which (it being atrociously handwritten) he (c) has not the temerity to ask his linotype operator to set. Don said, "I'm beginning to like this Ed Clark." "He's a doll," Alis said. "He's about the only one in town who stands up to Father." "Does your father claim that he levitated Superior off the face of the Earth?" "Not to me he doesn't. I'm one of those banes of his existence, a skeptic. He gave up trying to magnolize me when I was sixteen. I had a science teacher in high school—not in Superior, incidentally—who gave me all kinds of embarrassing questions to ask Father. I asked them, being a natural-born needler, and Father has disowned me intellectually ever since." "How old are you, Miss Garet, if I may ask?" She sat up straight and tucked her sweater tightly into her skirt, emphasizing her good figure. To a male friend Don would have described the figure as outstanding. She had mocking eyes, a pert nose and a mouth of such moist red softness that it seemed perpetually waiting to be kissed. All in all she could have been the queen of a campus much more densely populated with co-eds than Cavalier was. "You may call me Alis," she said. "And I'm nineteen." Don grinned. "Going on?" "Three months past. How old are you , Mr. Cort?" "Don's the name I've had for twenty-six years. Please use it." "Gladly. And now, Don, unless you want another cup of coffee, I'll go with you to the end of the world." "On such short notice?" Don was intrigued. Last night the redhead from the club car had repelled an advance that hadn't been made, and this morning a blonde was apparently making an advance that hadn't been solicited. He wondered where Geneva Jervis was, but only vaguely. "I'll admit to the double entendre ," Alis said. "What I meant—for now—was that we can stroll out to where Superior used to be attached to the rest of Ohio and see how the Earth is getting along without us." "Delighted. But don't you have any classes?" "Sure I do. Non-Einsteinian Relativity 1, at nine o'clock. But I'm a demon class-cutter, which is why I'm still a Senior at my advanced age. On to the brink!" They walked south from the campus and came to the railroad track. The train was standing there with nowhere to go. It had been abandoned except for the conductor, who had dutifully spent the night aboard. "What's happening?" he asked when he saw them. "Any word from down there?" "Not that I know of," Don said. He introduced him to Alis Garet. "What are you going to do?" "What can I do?" the conductor asked. "You can go over to Cavalier and have breakfast," Alis said. "Nobody's going to steal your old train." The conductor reckoned as how he might just do that, and did. "You know," Don said, "I was half-asleep last night but before the train stopped I thought it was running alongside a creek for a while." "South Creek," Alis said. "That's right. It's just over there." "Is it still? I mean hasn't it all poured off the edge by now? Was that Superior's water supply?" Alis shrugged. "All I know is you turn on the faucet and there's water. Let's go look at the creek." They found it coursing along between the banks. "Looks just about the same," she said. "That's funny. Come on; let's follow it to the edge." The brink, as Alis called it, looked even more awesome by daylight. Everything stopped short. There were the remnants of a cornfield, with the withered stalks cut down, then there was nothing. There was South Creek surging along, then nothing. In the distance a clump of trees, with a few autumn leaves still clinging to their branches, simply ended. "Where is the water going?" Don asked. "I can't make it out." "Down, I'd say. Rain for the Earth-people." "I should think it'd be all dried up by now. I'm going to have a look." "Don't! You'll fall off!" "I'll be careful." He walked cautiously toward the edge. Alis followed him, a few feet behind. He stopped a yard from the brink and waited for a spell of dizziness to pass. The Earth was spread out like a topographer's map, far below. Don took another wary step, then sat down. "Chicken," said Alis. She laughed uncertainly, then she sat down, too. "I still can't see where the water goes," Don said. He stretched out on his stomach and began to inch forward. "You stay there." Finally he had inched to a point where, by stretching out a hand, he could almost reach the edge. He gave another wriggle and the fingers of his right hand closed over the brink. For a moment he lay there, panting, head pressed to the ground. "How do you feel?" Alis asked. "Scared. When I get my courage back I'll pick up my head and look." Alis put a hand out tentatively, then purposefully took hold of his ankle and held it tight. "Just in case a high wind comes along," she said. "Thanks. It helps. Okay, here we go." He lifted his head. "Damn." "What?" "It still isn't clear. Do you have a pocket mirror?" "I have a compact." She took it out of her bag with her free hand and tossed it to him. It rolled and Don had to grab to keep it from going over the edge. Alis gave a little shriek. Don was momentarily unnerved and had to put his head back on the ground. "Sorry," she said. Don opened the compact and carefully transferred it to his right hand. He held it out beyond the edge and peered into it, focusing it on the end of the creek. "Now I've got it. The water isn't going off the edge!" "It isn't? Then where is it going?" "Down, of course, but it's as if it's going into a well, or a vertical tunnel, just short of the edge." "Why? How?" "I can't see too well, but that's my impression. Hold on now. I'm coming back." He inched away from the edge, then got up and brushed himself off. He returned her compact. "I guess you know where we go next." "The other end of the creek?" "Exactly." South Creek did not bisect Superior, as Don thought it might, but flowed in an arc through a southern segment of it. They had about two miles to go, past South Creek Bridge—which used to lead to Ladenburg, Alis said—past Raleigh Country Club (a long drive would really put the ball out of play, Don thought) and on to the edge again. But as they approached what they were forced to consider the source of the creek, they found a wire fence at the spot. "This is new," Alis said. The fence, which had a sign on it, warning—electrified , was semicircular, with each end at the edge and tarpaulins strung behind it so they could see the mouth of the creek. The water flowed from under the tarp and fence. "Look how it comes in spurts," Alis said. "As if it's being pumped." Smaller print on the sign said: Protecting mouth of South Creek, one of two sources of water for Superior. Electrical charge in fence is sufficient to kill. It was signed: Vincent Grande, Chief of Police, Hector Civek, Mayor . "What's the other source, besides the faucet in your bathroom?" Don asked. "North Lake, maybe," Alis said. "People fish there but nobody's allowed to swim." "Is the lake entirely within the town limits?" "I don't know." "If it were on the edge, and if I took a rowboat out on it, I wonder what would happen?" "I know one thing—I wouldn't be there holding your ankle while you found out." She took his arm as they gazed past the electrified fence at the Earth below and to the west. "It's impressive, isn't it?" she said. "I wonder if that's Indiana way over there?" He patted her hand absent-mindedly. "I wonder if it's west at all. I mean, how do we know Superior is maintaining the same position up here as it used to down there?" "We could tell by the sun, silly." "Of course," he said, grinning at his stupidity. "And I guess we're not high enough to see very far. If we were we'd be able to see the Great Lakes—or Lake Erie, anyway." They were musing about the geography when a plane came out of a cloudbank and, a second later, veered sharply. They could make out UAL on the underside of a wing. As it turned they imagined they could see faces peering out of the windows. They waved and thought they saw one or two people wave back. Then the plane climbed toward the east and was gone. "Well," Don said as they turned to go back to Cavalier, "now we know that they know. Maybe we'll begin to get some answers. Or, if not answers, then transportation." "Transportation?" Alis squeezed the arm she was holding. "Why? Don't you like it here?" "If you mean don't I like you, the answer is yes, of course I do. But if I don't get out of this handcuff soon so I can take a bath and get into clean clothes, you're not going to like me." "You're still quite acceptable, if a bit whiskery." She stopped, still holding his arm, and he turned so they were face to face. "So kiss me," she said, "before you deteriorate." They were in the midst of an extremely pleasant kiss when the brief case at the end of Don's handcuff began to talk to him. | C. "I know how to get us back down." |
What does "the Life" best represent?
A. Freedom to live authentically
B. Escapism and abandonment of responsibility
C. Temptation and deviation from shared goals
D. Immortality and a return to wholeness
| By H. B. Fyfe THE TALKATIVE TREE Dang vines! Beats all how some plants have no manners—but what do you expect, when they used to be men! All things considered—the obscure star, the undetermined damage to the stellar drive and the way the small planet's murky atmosphere defied precision scanners—the pilot made a reasonably good landing. Despite sour feelings for the space service of Haurtoz, steward Peter Kolin had to admit that casualties might have been far worse. Chief Steward Slichow led his little command, less two third-class ration keepers thought to have been trapped in the lower hold, to a point two hundred meters from the steaming hull of the Peace State . He lined them up as if on parade. Kolin made himself inconspicuous. "Since the crew will be on emergency watches repairing the damage," announced the Chief in clipped, aggressive tones, "I have volunteered my section for preliminary scouting, as is suitable. It may be useful to discover temporary sources in this area of natural foods." Volunteered HIS section! thought Kolin rebelliously. Like the Supreme Director of Haurtoz! Being conscripted into this idiotic space fleet that never fights is bad enough without a tin god on jets like Slichow! Prudently, he did not express this resentment overtly. His well-schooled features revealed no trace of the idea—or of any other idea. The Planetary State of Haurtoz had been organized some fifteen light-years from old Earth, but many of the home world's less kindly techniques had been employed. Lack of complete loyalty to the state was likely to result in a siege of treatment that left the subject suitably "re-personalized." Kolin had heard of instances wherein mere unenthusiastic posture had betrayed intentions to harbor treasonable thoughts. "You will scout in five details of three persons each," Chief Slichow said. "Every hour, each detail will send one person in to report, and he will be replaced by one of the five I shall keep here to issue rations." Kolin permitted himself to wonder when anyone might get some rest, but assumed a mildly willing look. (Too eager an attitude could arouse suspicion of disguising an improper viewpoint.) The maintenance of a proper viewpoint was a necessity if the Planetary State were to survive the hostile plots of Earth and the latter's decadent colonies. That, at least, was the official line. Kolin found himself in a group with Jak Ammet, a third cook, and Eva Yrtok, powdered foods storekeeper. Since the crew would be eating packaged rations during repairs, Yrtok could be spared to command a scout detail. Each scout was issued a rocket pistol and a plastic water tube. Chief Slichow emphasized that the keepers of rations could hardly, in an emergency, give even the appearance of favoring themselves in regard to food. They would go without. Kolin maintained a standard expression as the Chief's sharp stare measured them. Yrtok, a dark, lean-faced girl, led the way with a quiet monosyllable. She carried the small radio they would be permitted to use for messages of utmost urgency. Ammet followed, and Kolin brought up the rear. To reach their assigned sector, they had to climb a forbidding ridge of rock within half a kilometer. Only a sparse creeper grew along their way, its elongated leaves shimmering with bronze-green reflections against a stony surface; but when they topped the ridge a thick forest was in sight. Yrtok and Ammet paused momentarily before descending. Kolin shared their sense of isolation. They would be out of sight of authority and responsible for their own actions. It was a strange sensation. They marched down into the valley at a brisk pace, becoming more aware of the clouds and atmospheric haze. Distant objects seemed blurred by the mist, taking on a somber, brooding grayness. For all Kolin could tell, he and the others were isolated in a world bounded by the rocky ridge behind them and a semi-circle of damp trees and bushes several hundred meters away. He suspected that the hills rising mistily ahead were part of a continuous slope, but could not be sure. Yrtok led the way along the most nearly level ground. Low creepers became more plentiful, interspersed with scrubby thickets of tangled, spike-armored bushes. Occasionally, small flying things flickered among the foliage. Once, a shrub puffed out an enormous cloud of tiny spores. "Be a job to find anything edible here," grunted Ammet, and Kolin agreed. Finally, after a longer hike than he had anticipated, they approached the edge of the deceptively distant forest. Yrtok paused to examine some purple berries glistening dangerously on a low shrub. Kolin regarded the trees with misgiving. "Looks as tough to get through as a tropical jungle," he remarked. "I think the stuff puts out shoots that grow back into the ground to root as they spread," said the woman. "Maybe we can find a way through." In two or three minutes, they reached the abrupt border of the odd-looking trees. Except for one thick trunked giant, all of them were about the same height. They craned their necks to estimate the altitude of the monster, but the top was hidden by the wide spread of branches. The depths behind it looked dark and impenetrable. "We'd better explore along the edge," decided Yrtok. "Ammet, now is the time to go back and tell the Chief which way we're— Ammet! " Kolin looked over his shoulder. Fifty meters away, Ammet sat beside the bush with the purple berries, utterly relaxed. "He must have tasted some!" exclaimed Kolin. "I'll see how he is." He ran back to the cook and shook him by the shoulder. Ammet's head lolled loosely to one side. His rather heavy features were vacant, lending him a doped appearance. Kolin straightened up and beckoned to Yrtok. For some reason, he had trouble attracting her attention. Then he noticed that she was kneeling. "Hope she didn't eat some stupid thing too!" he grumbled, trotting back. As he reached her, whatever Yrtok was examining came to life and scooted into the underbrush with a flash of greenish fur. All Kolin saw was that it had several legs too many. He pulled Yrtok to her feet. She pawed at him weakly, eyes as vacant as Ammet's. When he let go in sudden horror, she folded gently to the ground. She lay comfortably on her side, twitching one hand as if to brush something away. When she began to smile dreamily, Kolin backed away. The corners of his mouth felt oddly stiff; they had involuntarily drawn back to expose his clenched teeth. He glanced warily about, but nothing appeared to threaten him. "It's time to end this scout," he told himself. "It's dangerous. One good look and I'm jetting off! What I need is an easy tree to climb." He considered the massive giant. Soaring thirty or forty meters into the thin fog and dwarfing other growth, it seemed the most promising choice. At first, Kolin saw no way, but then the network of vines clinging to the rugged trunk suggested a route. He tried his weight gingerly, then began to climb. "I should have brought Yrtok's radio," he muttered. "Oh, well, I can take it when I come down, if she hasn't snapped out of her spell by then. Funny … I wonder if that green thing bit her." Footholds were plentiful among the interlaced lianas. Kolin progressed rapidly. When he reached the first thick limbs, twice head height, he felt safer. Later, at what he hoped was the halfway mark, he hooked one knee over a branch and paused to wipe sweat from his eyes. Peering down, he discovered the ground to be obscured by foliage. "I should have checked from down there to see how open the top is," he mused. "I wonder how the view will be from up there?" "Depends on what you're looking for, Sonny!" something remarked in a soughing wheeze. Kolin, slipping, grabbed desperately for the branch. His fingers clutched a handful of twigs and leaves, which just barely supported him until he regained a grip with the other hand. The branch quivered resentfully under him. "Careful, there!" whooshed the eerie voice. "It took me all summer to grow those!" Kolin could feel the skin crawling along his backbone. "Who are you?" he gasped. The answering sigh of laughter gave him a distinct chill despite its suggestion of amiability. "Name's Johnny Ashlew. Kinda thought you'd start with what I am. Didn't figure you'd ever seen a man grown into a tree before." Kolin looked about, seeing little but leaves and fog. "I have to climb down," he told himself in a reasonable tone. "It's bad enough that the other two passed out without me going space happy too." "What's your hurry?" demanded the voice. "I can talk to you just as easy all the way down, you know. Airholes in my bark—I'm not like an Earth tree." Kolin examined the bark of the crotch in which he sat. It did seem to have assorted holes and hollows in its rough surface. "I never saw an Earth tree," he admitted. "We came from Haurtoz." "Where's that? Oh, never mind—some little planet. I don't bother with them all, since I came here and found out I could be anything I wanted." "What do you mean, anything you wanted?" asked Kolin, testing the firmness of a vertical vine. "Just what I said," continued the voice, sounding closer in his ear as his cheek brushed the ridged bark of the tree trunk. "And, if I do have to remind you, it would be nicer if you said 'Mr. Ashlew,' considering my age." "Your age? How old—?" "Can't really count it in Earth years any more. Lost track. I always figured bein' a tree was a nice, peaceful life; and when I remembered how long some of them live, that settled it. Sonny, this world ain't all it looks like." "It isn't, Mr. Ashlew?" asked Kolin, twisting about in an effort to see what the higher branches might hide. "Nope. Most everything here is run by the Life—that is, by the thing that first grew big enough to do some thinking, and set its roots down all over until it had control. That's the outskirts of it down below." "The other trees? That jungle?" "It's more'n a jungle, Sonny. When I landed here, along with the others from the Arcturan Spark , the planet looked pretty empty to me, just like it must have to—Watch it, there, Boy! If I didn't twist that branch over in time, you'd be bouncing off my roots right now!" "Th-thanks!" grunted Kolin, hanging on grimly. "Doggone vine!" commented the windy whisper. " He ain't one of my crowd. Landed years later in a ship from some star towards the center of the galaxy. You should have seen his looks before the Life got in touch with his mind and set up a mental field to help him change form. He looks twice as good as a vine!" "He's very handy," agreed Kolin politely. He groped for a foothold. "Well … matter of fact, I can't get through to him much, even with the Life's mental field helping. Guess he started living with a different way of thinking. It burns me. I thought of being a tree, and then he came along to take advantage of it!" Kolin braced himself securely to stretch tiring muscles. "Maybe I'd better stay a while," he muttered. "I don't know where I am." "You're about fifty feet up," the sighing voice informed him. "You ought to let me tell you how the Life helps you change form. You don't have to be a tree." "No?" " Uh -uh! Some of the boys that landed with me wanted to get around and see things. Lots changed to animals or birds. One even stayed a man—on the outside anyway. Most of them have to change as the bodies wear out, which I don't, and some made bad mistakes tryin' to be things they saw on other planets." "I wouldn't want to do that, Mr. Ashlew." "There's just one thing. The Life don't like taking chances on word about this place gettin' around. It sorta believes in peace and quiet. You might not get back to your ship in any form that could tell tales." "Listen!" Kolin blurted out. "I wasn't so much enjoying being what I was that getting back matters to me!" "Don't like your home planet, whatever the name was?" "Haurtoz. It's a rotten place. A Planetary State! You have to think and even look the way that's standard thirty hours a day, asleep or awake. You get scared to sleep for fear you might dream treason and they'd find out somehow." "Whooeee! Heard about them places. Must be tough just to live." Suddenly, Kolin found himself telling the tree about life on Haurtoz, and of the officially announced threats to the Planetary State's planned expansion. He dwelt upon the desperation of having no place to hide in case of trouble with the authorities. A multiple system of such worlds was agonizing to imagine. Somehow, the oddity of talking to a tree wore off. Kolin heard opinions spouting out which he had prudently kept bottled up for years. The more he talked and stormed and complained, the more relaxed he felt. "If there was ever a fellow ready for this planet," decided the tree named Ashlew, "you're it, Sonny! Hang on there while I signal the Life by root!" Kolin sensed a lack of direct attention. The rustle about him was natural, caused by an ordinary breeze. He noticed his hands shaking. "Don't know what got into me, talking that way to a tree," he muttered. "If Yrtok snapped out of it and heard, I'm as good as re-personalized right now." As he brooded upon the sorry choice of arousing a search by hiding where he was or going back to bluff things out, the tree spoke. "Maybe you're all set, Sonny. The Life has been thinkin' of learning about other worlds. If you can think of a safe form to jet off in, you might make yourself a deal. How'd you like to stay here?" "I don't know," said Kolin. "The penalty for desertion—" "Whoosh! Who'd find you? You could be a bird, a tree, even a cloud." Silenced but doubting, Kolin permitted himself to try the dream on for size. He considered what form might most easily escape the notice of search parties and still be tough enough to live a long time without renewal. Another factor slipped into his musings: mere hope of escape was unsatisfying after the outburst that had defined his fuming hatred for Haurtoz. I'd better watch myself! he thought. Don't drop diamonds to grab at stars! "What I wish I could do is not just get away but get even for the way they make us live … the whole damn set-up. They could just as easy make peace with the Earth colonies. You know why they don't?" "Why?" wheezed Ashlew. "They're scared that without talk of war, and scouting for Earth fleets that never come, people would have time to think about the way they have to live and who's running things in the Planetary State. Then the gravy train would get blown up—and I mean blown up!" The tree was silent for a moment. Kolin felt the branches stir meditatively. Then Ashlew offered a suggestion. "I could tell the Life your side of it," he hissed. "Once in with us, you can always make thinking connections, no matter how far away. Maybe you could make a deal to kill two birds with one stone, as they used to say on Earth…." Chief Steward Slichow paced up and down beside the ration crate turned up to serve him as a field desk. He scowled in turn, impartially, at his watch and at the weary stewards of his headquarters detail. The latter stumbled about, stacking and distributing small packets of emergency rations. The line of crewmen released temporarily from repair work was transient as to individuals but immutable as to length. Slichow muttered something profane about disregard of orders as he glared at the rocky ridges surrounding the landing place. He was so intent upon planning greetings with which to favor the tardy scouting parties that he failed to notice the loose cloud drifting over the ridge. It was tenuous, almost a haze. Close examination would have revealed it to be made up of myriads of tiny spores. They resembled those cast forth by one of the bushes Kolin's party had passed. Along the edges, the haze faded raggedly into thin air, but the units evidently formed a cohesive body. They drifted together, approaching the men as if taking intelligent advantage of the breeze. One of Chief Slichow's staggering flunkies, stealing a few seconds of relaxation on the pretext of dumping an armful of light plastic packing, wandered into the haze. He froze. After a few heartbeats, he dropped the trash and stared at ship and men as if he had never seen either. A hail from his master moved him. "Coming, Chief!" he called but, returning at a moderate pace, he murmured, "My name is Frazer. I'm a second assistant steward. I'll think as Unit One." Throughout the cloud of spores, the mind formerly known as Peter Kolin congratulated itself upon its choice of form. Nearer to the original shape of the Life than Ashlew got , he thought. He paused to consider the state of the tree named Ashlew, half immortal but rooted to one spot, unable to float on a breeze or through space itself on the pressure of light. Especially, it was unable to insinuate any part of itself into the control center of another form of life, as a second spore was taking charge of the body of Chief Slichow at that very instant. There are not enough men , thought Kolin. Some of me must drift through the airlock. In space, I can spread through the air system to the command group. Repairs to the Peace State and the return to Haurtoz passed like weeks to some of the crew but like brief moments in infinity to other units. At last, the ship parted the air above Headquarters City and landed. The unit known as Captain Theodor Kessel hesitated before descending the ramp. He surveyed the field, the city and the waiting team of inspecting officers. "Could hardly be better, could it?" he chuckled to the companion unit called Security Officer Tarth. "Hardly, sir. All ready for the liberation of Haurtoz." "Reformation of the Planetary State," mused the captain, smiling dreamily as he grasped the handrail. "And then—formation of the Planetary Mind!" END Transcriber's Note: This e-text was produced from Worlds of If January 1962 . Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. | A. Freedom to live authentically |
What is the open-access delivered by repositories called?
A. Green OA
B. Libre OA
C. Gold OA
D. Gratis OA
| What Is Open Access? Shifting from ink on paper to digital text suddenly allows us to make perfect copies of our work. Shifting from isolated computers to a globe-spanning network of connected computers suddenly allows us to share perfect copies of our work with a worldwide audience at essentially no cost. About thirty years ago this kind of free global sharing became something new under the sun. Before that, it would have sounded like a quixotic dream. Digital technologies have created more than one revolution. Let’s call this one the access revolution. Why don’t more authors take advantage of the access revolution to reach more readers? The answer is pretty clear. Authors who share their works in this way aren’t selling them, and even authors with purposes higher than money depend on sales to make a living. Or at least they appreciate sales. Let’s sharpen the question, then, by putting to one side authors who want to sell their work. We can even acknowledge that we’re putting aside the vast majority of authors. Imagine a tribe of authors who write serious and useful work, and who follow a centuries-old custom of giving it away without charge. I don’t mean a group of rich authors who don’t need money. I mean a group of authors defined by their topics, genres, purposes, incentives, and institutional circumstances, not by their wealth. In fact, very few are wealthy. For now, it doesn’t matter who these authors are, how rare they are, what they write, or why they follow this peculiar custom. It’s enough to know that their employers pay them salaries, freeing them to give away their work, that they write for impact rather than money, and that they score career points when they make the kind of impact they hoped to make. Suppose that selling their work would actually harm their interests by shrinking their audience, reducing their impact, and distorting their professional goals by steering them toward popular topics and away from the specialized questions on which they are experts. If authors like that exist, at least they should take advantage of the access revolution. The dream of global free access can be a reality for them, even if most other authors hope to earn royalties and feel obliged to sit out this particular revolution. These lucky authors are scholars, and the works they customarily write and publish without payment are peer-reviewed articles in scholarly journals. Open access is the name of the revolutionary kind of access these authors, unencumbered by a motive of financial gain, are free to provide to their readers. Open access (OA) literature is digital, online, free of charge, and free of most copyright and licensing restrictions. We could call it “barrier-free” access, but that would emphasize the negative rather than the positive. In any case, we can be more specific about which access barriers OA removes. A price tag is a significant access barrier. Most works with price tags are individually affordable. But when a scholar needs to read or consult hundreds of works for one research project, or when a library must provide access for thousands of faculty and students working on tens of thousands of topics, and when the volume of new work grows explosively every year, price barriers become insurmountable. The resulting access gaps harm authors by limiting their audience and impact, harm readers by limiting what they can retrieve and read, and thereby harm research from both directions. OA removes price barriers. Copyright can also be a significant access barrier. If you have access to a work for reading but want to translate it into another language, distribute copies to colleagues, copy the text for mining with sophisticated software, or reformat it for reading with new technology, then you generally need the permission of the copyright holder. That makes sense when the author wants to sell the work and when the use you have in mind could undermine sales. But for research articles we’re generally talking about authors from the special tribe who want to share their work as widely as possible. Even these authors, however, tend to transfer their copyrights to intermediaries—publishers—who want to sell their work. As a result, users may be hampered in their research by barriers erected to serve intermediaries rather than authors. In addition, replacing user freedom with permission-seeking harms research authors by limiting the usefulness of their work, harms research readers by limiting the uses they may make of works even when they have access, and thereby harms research from both directions. OA removes these permission barriers. Removing price barriers means that readers are not limited by their own ability to pay, or by the budgets of the institutions where they may have library privileges. Removing permission barriers means that scholars are free to use or reuse literature for scholarly purposes. These purposes include reading and searching, but also redistributing, translating, text mining, migrating to new media, long-term archiving, and innumerable new forms of research, analysis, and processing we haven’t yet imagined. OA makes work more useful in both ways, by making it available to more people who can put it to use, and by freeing those people to use and reuse it. Terminology When we need to, we can be more specific about access vehicles and access barriers. In the jargon, OA delivered by journals is called gold OA , and OA delivered by repositories is called green OA . Work that is not open access, or that is available only for a price, is called toll access (TA). Over the years I’ve asked publishers for a neutral, nonpejorative and nonhonorific term for toll-access publishers, and conventional publishers is the suggestion I hear most often. While every kind of OA removes price barriers, there are many different permission barriers we could remove if we wanted to. If we remove price barriers alone, we provide gratis OA , and if we remove at least some permission barriers as well, we provide libre OA . (Also see section 3.1 on green/gold and section 3.3 on gratis/libre.) OA was defined in three influential public statements: the Budapest Open Access Initiative (February 2002), the Bethesda Statement on Open Access Publishing (June 2003), and the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (October 2003). I sometimes refer to their overlap or common ground as the BBB definition of OA. My definition here is the BBB definition reduced to its essential elements and refined with some post-BBB terminology (green, gold, gratis, libre) for speaking precisely about subspecies of OA. Here’s how the Budapest statement defined OA: There are many degrees and kinds of wider and easier access to [research] literature. By “open access” to this literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited. Here’s how the Bethesda and Berlin statements put it: For a work to be OA, the copyright holder must consent in advance to let users “copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship.” Note that all three legs of the BBB definition go beyond removing price barriers to removing permission barriers, or beyond gratis OA to libre OA. But at the same time, all three allow at least one limit on user freedom: an obligation to attribute the work to the author. The purpose of OA is to remove barriers to all legitimate scholarly uses for scholarly literature, but there’s no legitimate scholarly purpose in suppressing attribution to the texts we use. (That’s why my shorthand definition says that OA literature is free of “most” rather than “all” copyright and licensing restrictions.) The basic idea of OA is simple: Make research literature available online without price barriers and without most permission barriers. Even the implementation is simple enough that the volume of peer-reviewed OA literature and the number of institutions providing it have grown at an increasing rate for more than a decade. If there are complexities, they lie in the transition from where we are now to a world in which OA is the default for new research. This is complicated because the major obstacles are not technical, legal, or economic, but cultural. (More in chapter 9 on the future.) In principle, any kind of digital content can be OA, since any digital content can be put online without price or permission barriers. Moreover, any kind of content can be digital: texts, data, images, audio, video, multimedia, and executable code. We can have OA music and movies, news and novels, sitcoms and software—and to different degrees we already do. But the term “open access” was coined by researchers trying to remove access barriers to research. The next section explains why. 1.1 What Makes OA Possible? OA is made possible by the internet and copyright-holder consent. But why would a copyright holder consent to OA? Two background facts suggest the answer. First, authors are the copyright holders for their work until or unless they transfer rights to someone else, such as a publisher. Second, scholarly journals generally don’t pay authors for their research articles, which frees this special tribe of authors to consent to OA without losing revenue. This fact distinguishes scholars decisively from musicians and moviemakers, and even from most other kinds of authors. This is why controversies about OA to music and movies don’t carry over to OA for research articles. Both facts are critical, but the second is nearly unknown outside the academic world. It’s not a new fact of academic life, arising from a recent economic downturn in the publishing industry. Nor is it a case of corporate exploitation of unworldly academics. Scholarly journals haven’t paid authors for their articles since the first scholarly journals, the Philosophical Transactions of the Royal Society of London and the Journal des sçavans , launched in London and Paris in 1665. The academic custom to write research articles for impact rather than money may be a lucky accident that could have been otherwise. Or it may be a wise adaptation that would eventually evolve in any culture with a serious research subculture. (The optimist in me wants to believe the latter, but the evolution of copyright law taunts that optimism.) This peculiar custom does more than insulate cutting-edge research from the market and free scholars to consent to OA without losing revenue. It also supports academic freedom and the kinds of serious inquiry that advance knowledge. It frees researchers to challenge conventional wisdom and defend unpopular ideas, which are essential to academic freedom. At the same time it frees them to microspecialize and defend ideas of immediate interest to just a handful people in the world, which are essential to pushing the frontiers of knowledge. This custom doesn’t guarantee that truth-seeking won’t be derailed by profit-seeking, and it doesn’t guarantee that we’ll eventually fill the smallest gaps in our collaborative understanding of the world. It doesn’t even guarantee that scholars won’t sometimes play for the crowd and detour into fad thinking. But it removes a major distraction by allowing them, if they wish, to focus on what is likely to be true rather than what is likely to sell. It’s a payment structure we need for good research itself, not just for good access to research, and it’s the key to the legal and economic lock that would otherwise shackle steps toward OA. Creative people who live by royalties, such as novelists, musicians, and moviemakers, may consider this scholarly tradition a burden and sacrifice for scholars. We might even agree, provided we don’t overlook a few facts. First, it’s a sacrifice that scholars have been making for nearly 350 years. OA to research articles doesn’t depend on asking royalty-earning authors to give up their royalties. Second, academics have salaries from universities, freeing them to dive deeply into their research topics and publish specialized articles without market appeal. Many musicians and moviemakers might envy that freedom to disregard sales and popular taste. Third, academics receive other, less tangible rewards from their institutions—like promotion and tenure—when their research is recognized by others, accepted, cited, applied, and built upon. It’s no accident that faculty who advance knowledge in their fields also advance their careers. Academics are passionate about certain topics, ideas, questions, inquiries, or disciplines. They feel lucky to have jobs in which they may pursue these passions and even luckier to be rewarded for pursuing them. Some focus single-mindedly on carrying an honest pebble to the pile of knowledge (as John Lange put it), having an impact on their field, or scooping others working on the same questions. Others focus strategically on building the case for promotion and tenure. But the two paths converge, which is not a fortuitous fact of nature but an engineered fact of life in the academy. As incentives for productivity, these intangible career benefits may be stronger for the average researcher than royalties are for the average novelist or musician. (In both domains, bountiful royalties for superstars tell us nothing about effective payment models for the long tail of less stellar professionals.) There’s no sense in which research would be more free, efficient, or effective if academics took a more “businesslike” position, behaved more like musicians and moviemakers, abandoned their insulation from the market, and tied their income to the popularity of their ideas. Nonacademics who urge academics to come to their senses and demand royalties even for journal articles may be more naive about nonprofit research than academics are about for-profit business. We can take this a step further. Scholars can afford to ignore sales because they have salaries and research grants to take the place of royalties. But why do universities pay salaries and why do funding agencies award grants? They do it to advance research and the range of public interests served by research. They don’t do it to earn profits from the results. They are all nonprofit. They certainly don’t do it to make scholarly writings into gifts to enrich publishers, especially when conventional publishers erect access barriers at the expense of research. Universities and funding agencies pay researchers to make their research into gifts to the public in the widest sense. Public and private funding agencies are essentially public and private charities, funding research they regard as useful or beneficial. Universities have a public purpose as well, even when they are private institutions. We support the public institutions with public funds, and we support the private ones with tax exemptions for their property and tax deductions for their donors. We’d have less knowledge, less academic freedom, and less OA if researchers worked for royalties and made their research articles into commodities rather than gifts. It should be no surprise, then, that more and more funding agencies and universities are adopting strong OA policies. Their mission to advance research leads them directly to logic of OA: With a few exceptions, such as classified research, research that is worth funding or facilitating is worth sharing with everyone who can make use of it. (See chapter 4 on OA policies.) Newcomers to OA often assume that OA helps readers and hurts authors, and that the reader side of the scholarly soul must beg the author side to make the necessary sacrifice. But OA benefits authors as well as readers. Authors want access to readers at least as much as readers want access to authors. All authors want to cultivate a larger audience and greater impact. Authors who work for royalties have reason to compromise and settle for the smaller audience of paying customers. But authors who aren’t paid for their writing have no reason to compromise. It takes nothing away from a disinterested desire to advance knowledge to recognize that scholarly publication is accompanied by a strong interest in impact and career building. The result is a mix of interested and disinterested motives. The reasons to make work OA are essentially the same as the reasons to publish. Authors who make their work OA are always serving others but not always acting from altruism. In fact, the idea that OA depends on author altruism slows down OA progress by hiding the role of author self-interest. Another aspect of author self-interest emerges from the well-documented phenomenon that OA articles are cited more often than non-OA articles, even when they are published in the same issue of the same journal. There’s growing evidence that OA articles are downloaded more often as well, and that journals converting to OA see a rise in their submissions and citation impact. There are many hypotheses to explain the correlation between OA and increased citations, but it’s likely that ongoing studies will show that much of the correlation is simply due to the larger audience and heightened visibility provided by OA itself. When you enlarge the audience for an article, you also enlarge the subset of the audience that will later cite it, including professionals in the same field at institutions unable to afford subscription access. OA enlarges the potential audience, including the potential professional audience, far beyond that for even the most prestigious and popular subscription journals. In any case, these studies bring a welcome note of author self-interest to the case for OA. OA is not a sacrifice for authors who write for impact rather than money. It increases a work’s visibility, retrievability, audience, usage, and citations, which all convert to career building. For publishing scholars, it would be a bargain even if it were costly, difficult, and time-consuming. But as we’ll see, it’s not costly, not difficult, and not time-consuming. My colleague Stevan Harnad frequently compares research articles to advertisements. They advertise the author’s research. Try telling advertisers that they’re making a needless sacrifice by allowing people to read their ads without having to pay for the privilege. Advertisers give away their ads and even pay to place them where they might be seen. They do this to benefit themselves, and scholars have the same interest in sharing their message as widely as possible. Because any content can be digital, and any digital content can be OA, OA needn’t be limited to royalty-free literature like research articles. Research articles are just ripe examples of low-hanging fruit. OA could extend to royalty-producing work like monographs, textbooks, novels, news, music, and movies. But as soon as we cross the line into OA for royalty-producing work, authors will either lose revenue or fear that they will lose revenue. Either way, they’ll be harder to persuade. But instead of concluding that royalty-producing work is off limits to OA, we should merely conclude that it’s higher-hanging fruit. In many cases we can still persuade royalty-earning authors to consent to OA. (See section 5.3 on OA for books.) Authors of scholarly research articles aren’t the only players who work without pay in the production of research literature. In general, scholarly journals don’t pay editors or referees either. In general, editors and referees are paid salaries by universities to free them, like authors, to donate their time and labor to ensure the quality of new work appearing in scholarly journals. An important consequence follows. All the key players in peer review can consent to OA without losing revenue. OA needn’t dispense with peer review or favor unrefereed manuscripts over refereed articles. We can aim for the prize of OA to peer-reviewed scholarship. (See section 5.1 on peer review.) Of course, conventional publishers are not as free as authors, editors, and referees to forgo revenue. This is a central fact in the transition to OA, and it explains why the interests of scholars and conventional publishers diverge more in the digital age than they diverged earlier. But not all publishers are conventional, and not all conventional publishers will carry print-era business models into the digital age. Academic publishers are not monolithic. Some new ones were born OA and some older ones have completely converted to OA. Many provide OA to some of their work but not all of it. Some are experimenting with OA, and some are watching the experiments of others. Most allow green OA (through repositories) and a growing number offer at least some kind of gold OA (through journals). Some are supportive, some undecided, some opposed. Among the opposed, some have merely decided not to provide OA themselves, while others lobby actively against policies to encourage or require OA. Some oppose gold but not green OA, while others oppose green but not gold OA. OA gains nothing and loses potential allies by blurring these distinctions. This variety reminds us (to paraphrase Tim O’Reilly) that OA doesn’t threaten publishing; it only threatens existing publishers who do not adapt. A growing number of journal publishers have chosen business models allowing them to dispense with subscription revenue and offer OA. They have expenses but they also have revenue to cover their expenses. In fact, some OA publishers are for-profit and profitable. (See chapter 7 on economics.) Moreover, peer review is done by dedicated volunteers who don’t care how a journal pays its bills, or even whether the journal is in the red or the black. If all peer-reviewed journals converted to OA overnight, the authors, editors, and referees would have the same incentives to participate in peer review that they had the day before. They needn’t stop offering their services, needn’t lower their standards, and needn’t make sacrifices they weren’t already making. They volunteer their time not because of a journal’s choice of business model but because of its contribution to research. They could carry on with solvent or insolvent subscription publishers, with solvent or insolvent OA publishers, or even without publishers. The Budapest Open Access Initiative said in February 2002: “An old tradition and a new technology have converged to make possible an unprecedented public good. The old tradition is the willingness of scientists and scholars to publish the fruits of their research in scholarly journals without payment. . . . The new technology is the internet.” To see what this willingness looks like without the medium to give it effect, look at scholarship in the age of print. Author gifts turned into publisher commodities, and access gaps for readers were harmfully large and widespread. (Access gaps are still harmfully large and widespread, but only because OA is not yet the default for new research.) To see what the medium looks like without the willingness, look at music and movies in the age of the internet. The need for royalties keeps creators from reaching everyone who would enjoy their work. A beautiful opportunity exists where the willingness and the medium overlap. A scholarly custom that evolved in the seventeenth century frees scholars to take advantage of the access revolution in the twentieth and twenty-first. Because scholars are nearly unique in following this custom, they are nearly unique in their freedom to take advantage of this revolution without financial risk. In this sense, the planets have aligned for scholars. Most other authors are constrained to fear rather than seize the opportunities created by the internet. 1.2 What OA Is Not We can dispel a cloud of objections and misunderstandings simply by pointing out a few things that OA is not. (Many of these points will be elaborated in later chapters.) OA isn’t an attempt to bypass peer review. OA is compatible with every kind of peer review, from the most conservative to the most innovative, and all the major public statements on OA insist on its importance. Because scholarly journals generally don’t pay peer-reviewing editors and referees, just as they don’t pay authors, all the participants in peer review can consent to OA without losing revenue. While OA to unrefereed preprints is useful and widespread, the OA movement isn’t limited to unrefereed preprints and, if anything, focuses on OA to peer-reviewed articles. (More in section 5.1 on peer review.) OA isn’t an attempt to reform, violate, or abolish copyright. It’s compatible with copyright law as it is. OA would benefit from the right kinds of copyright reforms, and many dedicated people are working on them. But it needn’t wait for reforms and hasn’t waited. OA literature avoids copyright problems in exactly the same way that conventional toll-access literature does. For older works, it takes advantage of the public domain, and for newer works, it rests on copyright-holder consent. (More in chapter 4 on policies and chapter 6 on copyright.) OA isn’t an attempt to deprive royalty-earning authors of income. The OA movement focuses on research articles precisely because they don’t pay royalties. In any case, inside and outside that focus, OA for copyrighted work depends on copyright-holder consent. Hence, royalty-earning authors have nothing to fear but persuasion that the benefits of OA might outweigh the risks to royalties. (More in section 5.3 on OA for books.) OA isn’t an attempt to deny the reality of costs. No serious OA advocate has ever argued that OA literature is costless to produce, although many argue that it is less expensive to produce than conventionally published literature, even less expensive than born-digital toll-access literature. The question is not whether research literature can be made costless, but whether there are better ways to pay the bills than charging readers and creating access barriers. (More in chapter 7 on economics.) Terminology We could talk about vigilante OA, infringing OA, piratical OA, or OA without consent. That sort of OA could violate copyrights and deprive royalty-earning authors of royalties against their will. But we could also talk about vigilante publishing, infringing publishing, piratical publishing, or publishing without consent. Both happen. However, we generally reserve the term “publishing” for lawful publishing, and tack on special adjectives to describe unlawful variations on the theme. Likewise, I’ll reserve the term “open access” for lawful OA that carries the consent of the relevant rightsholder. OA isn’t an attempt to reduce authors’ rights over their work. On the contrary, OA depends on author decisions and requires authors to exercise more rights or control over their work than they are allowed to exercise under traditional publishing contracts. One OA strategy is for authors to retain some of the rights they formerly gave publishers, including the right to authorize OA. Another OA strategy is for publishers to permit more uses than they formerly permitted, including permission for authors to make OA copies of their work. By contrast, traditional journal-publishing contracts demand that authors transfer all rights to publishers, and author rights or control cannot sink lower than that. (See chapters 4 on policies and 6 on copyright.) OA isn’t an attempt to reduce academic freedom. Academic authors remain free to submit their work to the journals or publishers of their choice. Policies requiring OA do so conditionally, for example, for researchers who choose to apply for a certain kind of grant. In addition, these policies generally build in exceptions, waiver options, or both. Since 2008 most university OA policies have been adopted by faculty deeply concerned to preserve and even enhance their prerogatives. (See chapter 4 on OA policies.) OA isn’t an attempt to relax rules against plagiarism. All the public definitions of OA support author attribution, even construed as a “restriction” on users. All the major open licenses require author attribution. Moreover, plagiarism is typically punished by the plagiarist’s institution rather than by courts, that is, by social norms rather than by law. Hence, even when attribution is not legally required, plagiarism is still a punishable offense and no OA policy anywhere interferes with those punishments. In any case, if making literature digital and online makes plagiarism easier to commit, then OA makes plagiarism easier to detect. Not all plagiarists are smart, but the smart ones will not steal from OA sources indexed in every search engine. In this sense, OA deters plagiarism. OA isn’t an attempt to punish or undermine conventional publishers. OA is an attempt to advance the interests of research, researchers, and research institutions. The goal is constructive, not destructive. If OA does eventually harm toll-access publishers, it will be in the way that personal computers harmed typewriter manufacturers. The harm was not the goal, but a side effect of developing something better. Moreover, OA doesn’t challenge publishers or publishing per se, just one business model for publishing, and it’s far easier for conventional publishers to adapt to OA than for typewriter manufacturers to adapt to computers. In fact, most toll-access publishers are already adapting, by allowing author-initiated OA, providing some OA themselves, or experimenting with OA. (See section 3.1 on green OA and chapter 8 on casualties.) OA doesn’t require boycotting any kind of literature or publisher. It doesn’t require boycotting toll-access research any more than free online journalism requires boycotting priced online journalism. OA doesn’t require us to strike toll-access literature from our personal reading lists, course syllabi, or libraries. Some scholars who support OA decide to submit new work only to OA journals, or to donate their time as editors or referees only to OA journals, in effect boycotting toll-access journals as authors, editors, and referees. But this choice is not forced by the definition of OA, by a commitment to OA, or by any OA policy, and most scholars who support OA continue to work with toll-access journals. In any case, even those scholars who do boycott toll-access journals as authors, editors, or referees don’t boycott them as readers. (Here we needn’t get into the complexity that some toll-access journals effectively create involuntary reader boycotts by pricing their journals out of reach of readers who want access.) OA isn’t primarily about bringing access to lay readers. If anything, the OA movement focuses on bringing access to professional researchers whose careers depend on access. But there’s no need to decide which users are primary and which are secondary. The publishing lobby sometimes argues that the primary beneficiaries of OA are lay readers, perhaps to avoid acknowledging how many professional researchers lack access, or perhaps to set up the patronizing counter-argument that lay people don’t care to read research literature and wouldn’t understand it if they tried. OA is about bringing access to everyone with an internet connection who wants access, regardless of their professions or purposes. There’s no doubt that if we put “professional researchers” and “everyone else” into separate categories, a higher percentage of researchers will want access to research literature, even after taking into account that many already have paid access through their institutions. But it’s far from clear why that would matter, especially when providing OA to all internet users is cheaper and simpler than providing OA to just a subset of worthy internet users. If party-goers in New York and New Jersey can both enjoy the Fourth of July fireworks in New York Harbor, then the sponsors needn’t decide that one group is primary, even if a simple study could show which group is more numerous. If this analogy breaks down, it’s because New Jersey residents who can’t see the fireworks gain nothing from New Yorkers who can. But research does offer this double or indirect benefit. When OA research directly benefits many lay readers, so much the better. But when it doesn’t, it still benefits everyone indirectly by benefiting researchers directly. (Also see section 5.5.1 on access for lay readers.) Finally, OA isn’t universal access. Even when we succeed at removing price and permission barriers, four other kinds of access barrier might remain in place: Filtering and censorship barriers Many schools, employers, ISPs, and governments want to limit what users can see. Language barriers Most online literature is in English, or another single language, and machine translation is still very weak. Handicap access barriers Most web sites are not yet as accessible to handicapped users as they should be. Connectivity barriers The digital divide keeps billions of people offline, including millions of scholars, and impedes millions of others with slow, flaky, or low-bandwidth internet connections. Most us want to remove all four of these barriers. But there’s no reason to save the term open access until we succeed. In the long climb to universal access, removing price and permission barriers is a significant plateau worth recognizing with a special name. | A. Green OA |
What was the previous state-of-the-art? | ### Introduction
Authorship attribution (AA) is the task of identifying the author of a text, given a set of author-labeled training texts. This task typically makes use of stylometric cues at the surface lexical and syntactic level BIBREF0 , although BIBREF1 and BIBREF2 go beyond the sentence level, showing that discourse information can help. However, they achieve limited performance gains and lack an in-depth analysis of discourse featurization techniques. More recently, convolutional neural networks (CNNs) have demonstrated considerable success on AA relying only on character-level INLINEFORM0 -grams BIBREF3 , BIBREF4 . The strength of these models is evidenced by findings that traditional stylometric features such as word INLINEFORM1 -grams and POS-tags do not improve, and can sometimes even hurt performance BIBREF3 , BIBREF5 . However, none of these CNN models make use of discourse. Our work builds upon these prior studies by exploring an effective method to (i) featurize the discourse information, and (ii) integrate discourse features into the best text classifier (i.e., CNN-based models), in the expectation of achieving state-of-the-art results in AA. BIBREF1 (henceforth F&H14) made the first comprehensive attempt at using discourse information for AA. They employ an entity-grid model, an approach introduced by BIBREF6 for the task of ordering sentences. This model tracks how the grammatical relations of salient entities (e.g., subj, obj, etc.) change between pairs of sentences in a document, thus capturing a form of discourse coherence. The grid is summarized into a vector of transition probabilities. However, because the model only records the transition between two consecutive sentences at a time, the coherence is local. BIBREF2 (henceforth F15) further extends the entity-grid model by replacing grammatical relations with discourse relations from Rhetorical Structure Theory BIBREF7 . Their study uses a linear-kernel SVM to perform pairwise author classifications, where a non-discourse model captures lexical and syntactic features. They find that adding the entity-grid with grammatical relations enhances the non-discourse model by almost 1% in accuracy, and using RST relations provides an improvement of 3%. The study, however, works with only one small dataset and their models produce overall unremarkable performance ( INLINEFORM0 85%). BIBREF8 propose an advanced Recursive Neural Network (RecNN) architecture to work with RST in the more general area of text categorization and present impressive results. However, we suspect that the massive number of parameters of RecNNs would likely cause overfitting when working with smaller datasets, as is often the case in AA tasks. In our paper, we opt for a state-of-the-art character bigram CNN classifier BIBREF4 , and investigate various ways in which the discourse information can be featurized and integrated into the CNN. Specifically, We explore these questions using two approaches to represent salient entities: grammatical relations, and RST discourse relations. We apply these models to datasets of varying sizes and genres, and find that adding any discourse information improves AA consistently on longer documents, but has mixed results on shorter documents. Further, embedding the discourse features in a parallel CNN at the input end yields better performance than concatenating them to the output layer as a feature vector (Section SECREF3 ). The global featurization is more effective than the local one. We also show that SVMs, which can only use discourse probability vectors, neither produce a competitive performance (even with fine-tuning), nor generalize in using the discourse information effectively. ### Background
Entity-grid model. Typical lexical features for AA are relatively superficial and restricted to within the same sentence. F&H14 hypothesize that discourse features beyond the sentence level also help authorship attribution. In particular, they propose an author has a particular style for representing entities across a discourse. Their work is based on the entity-grid model of BIBREF6 (henceforth B&L). The entity-grid model tracks the grammatical relation (subj, obj, etc.) that salient entities take on throughout a document as a way to capture local coherence . A salient entity is defined as a noun phrase that co-occurs at least twice in a document. Extensive literature has shown that subject and object relations are a strong signal for salience and it follows from the Centering Theory that you want to avoid rough shifts in the center BIBREF9 , BIBREF10 . B&L thus focus on whether a salient entity is a subject (s), object (o), other (x), or is not present (-) in a given sentence, as illustrated in Table TABREF1 . Every sentence in a document is encoded with the grammatical relation of all the salient entities, resulting in a grid similar to Table TABREF6 . The local coherence of a document is then defined on the basis of local entity transitions. A local entity transition is the sequence of grammatical relations that an entity can assume across INLINEFORM0 consecutive sentences, resulting in {s,o,x,-} INLINEFORM1 possible transitions. Following B&L, F&H14 consider sequences of length INLINEFORM2 =2, that is, transitions between two consecutive sentences, resulting in INLINEFORM3 =16 possible transitions. The probability for each transition is then calculated as the frequency of the transition divided by the total number of transitions. This step results in a single probability vector for every document, as illustrated in Table TABREF2 . B&L apply this model to a sentence ordering task, where the more coherent option, as evidenced by its transition probabilities, was chosen. In authorship attribution, texts are however assumed to already be coherent. F&H14 instead hypothesize that an author unconsciously employs the same methods for describing entities as the discourse unfolds, resulting in discernible transition probability patterns across multiple of their texts. Indeed, F&H14 find that adding the B&L vectors increases the accuracy of AA by almost 1% over a baseline lexico-syntactic model. RST discourse relations. F15 extends the notion of tracking salient entities to RST. Instead of using grammatical relations in the grid, RST discourse relations are specified. An RST discourse relation defines the relationship between two or more elementary discourse units (EDUs), which are spans of text that typically correspond to syntactic clauses. In a relation, an EDU can function as a nucleus (e.g., result.N) or as a satellite (e.g., summary.S). All the relations in a document then form a tree as in Figure FIGREF8 . F15 finds that RST relations are more effective for AA than grammatical relations. In our paper, we populate the entity-grid in the same way as F15's “Shallow RST-style” encoding, but use fine-grained instead of coarse-grained RST relations, and do not distinguish between intra-sentential and multi-sentential RST relations, or salient and non-salient entities. We explore various featurization techniques using the coding scheme. CNN model. shrestha2017 propose a convolutional neural network formulation for AA tasks (detailed in Section SECREF3 ). They report state-of-the-art performance on a corpus of Twitter data BIBREF11 , and compare their models with alternative architectures proposed in the literature: (i) SCH: an SVM that also uses character n-grams, among other stylometric features BIBREF11 ; (ii) LSTM-2: an LSTM trained on bigrams BIBREF12 ; (iii) CHAR: a Logistic Regression model that takes character n-grams BIBREF13 ; (iv) CNN-W: a CNN trained on word embeddings BIBREF14 . The authors show that the model CNN2 produces the best performance overall. Ruder:16 apply character INLINEFORM0 -gram CNNs to a wide range of datasets, providing strong empirical evidence that the architecture generalizes well. Further, they find that including word INLINEFORM1 -grams in addition to character INLINEFORM2 -grams reduces performance, which is in agreement with BIBREF5 's findings. ### Models
Building on shrestha2017's work, we employ their character-bigram CNN (CNN2), and propose two extensions which utilize discourse information: (i) CNN2 enhanced with relation probability vectors (CNN2-PV), and (ii) CNN2 enhanced with discourse embeddings (CNN2-DE). The CNN2-PV allows us to conduct a comparison with F&H14 and F15, which also use relation probability vectors. CNN2. CNN2 is the baseline model with no discourse features. Illustrated in Figure FIGREF10 (center), it consists of (i) an embedding layer, (ii) a convolution layer, (iii) a max-pooling layer, and (iv) a softmax layer. We briefly sketch the processing procedure and refer the reader to BIBREF4 for mathematical details. The network takes a sequence of character bigrams INLINEFORM0 as input, and outputs a multinomial INLINEFORM1 over class labels as the prediction. The model first looks up the embedding matrix to produce a sequence of embeddings for INLINEFORM2 (i.e., the matrix INLINEFORM3 ), then pushes the embedding sequence through convolutional filters of three bigram-window sizes INLINEFORM4 , each yielding INLINEFORM5 feature maps. We then apply the max-over-time pooling BIBREF15 to the feature maps from each filter, and concatenate the resulting vectors to obtain a single vector INLINEFORM6 , which then goes through the softmax layer to produce predictions. CNN2-PV. This model (Figure FIGREF10 , left+center) featurizes discourse information into a vector of relation probabilities. In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). The GR features are represented as grammatical relation transitions derived from the entity grid, e.g., INLINEFORM0 . The RST features are represented as RST discourse relations with their nuclearity, e.g., INLINEFORM1 . The probability vectors are then distributions over relation types. For GR, the vector is a distribution over all the entity role transitions, i.e., INLINEFORM2 (see Table TABREF2 ). For RST, the vector is a distribution over all the RST discourse relations, i.e., INLINEFORM3 Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer. CNN2-DE. In this model (Figure FIGREF10 , center+right), we embed discourse features in high-dimensional space (similar to char-bigram embeddings). Let INLINEFORM0 be a sequence of discourse features, we treat it in a similar fashion to the char-bigram sequence INLINEFORM1 , i.e. feeding it through a “parallel” convolutional net (Figure FIGREF10 right). The operation results in a pooling vector INLINEFORM2 . We concatenate INLINEFORM3 to the pooling vector INLINEFORM4 (which is constructed from INLINEFORM5 ) then feed INLINEFORM6 to the softmax layer for the final prediction. ### Experiments and Results
We begin by introducing the datasets (Section SECREF15 ), followed by detailing the featurization methods (Section SECREF17 ), the experiments (Section SECREF22 ), and finally reporting results (Section SECREF26 ). ### Datasets
The statistics for the three datasets used in the experiments are summarized in Table TABREF16 . novel-9. This dataset was compiled by F&H14: a collection of 19 novels by 9 nineteenth century British and American authors in the Project Gutenberg. To compare to F&H14, we apply the same resampling method (F&H14, Section 4.2) to correct the imbalance in authors by oversampling the texts of less-represented authors. novel-50. This dataset extends novel-9, compiling the works of 50 randomly selected authors of the same period. For each author, we randomly select 5 novels for a total 250 novels. IMDB62. IMDB62 consists of 62K movie reviews from 62 users (1,000 each) from the Internet Movie dataset, compiled by Seroussi:11. Unlike the novel datasets, the reviews are considerably shorter, with a mean of 349 words per text. ### Featurization
As described in Section SECREF2 , in both the GR and RST variants, from each input entry we start by obtaining an entity grid. CNN2-PV. We collect the probabilities of entity role transitions (in GR) or discourse relations (in RST) for the entries. Each entry corresponds to a probability distribution vector. CNN2-DE. We employ two schema for creating discourse feature sequences from an entity grid. While we always read the grid by column (by a salient entity), we vary whether we track the entity across a number of sentences (n rows at a time) or across the entire document (one entire column at a time), denoted as local and global reading respectively. For the GR discourse features, in the case of local reading, we process the entity roles one sentence pair at a time (Figure FIGREF18 , left). For example, in processing the pair INLINEFORM0 , we find the first non-empty role INLINEFORM1 for entity INLINEFORM2 in INLINEFORM3 . If INLINEFORM4 also has a non-empty role INLINEFORM5 in the INLINEFORM6 , we collect the entity role transition INLINEFORM7 . We then proceed to the following entity INLINEFORM8 , until we process all the entities in the grid and move to the next sentence pair. For the global reading, we instead read the entity roles by traversing one column of the entire document at a time (Figure FIGREF18 , right). The entity roles in all the sentences are read for one entity: we collect transitions for all the non-empty roles (e.g., INLINEFORM9 , but not INLINEFORM10 ). For the RST discourse features, we process non-empty discourse relations also through either local or global reading. In the local reading, we read all the discourse relations in a sentence (a row) then move on to the next sentence. In the global reading, we read in discourse relations for one entity at a time. This results in sequences of discourse relations for the input entries. ### Experiments
Baseline-dataset experiments. All the baseline-dataset experiments are evaluated on novel-9. As a comparison to previous work (F15), we evaluate our models using a pairwise classification task with GR discourse features. In her model, novels are partitioned into 1000-word chunks, and the model is evaluated with accuracy. Surpassing F15's SVM model by a large margin, we then further evaluate the more difficult multi-class task, i.e., all-class prediction simultaneously, with both GR and RST discourse features and the more robust F1 evaluation. In this multi-class task, we implement two SVMs to extend F15's SVM models: (i) SVM2: a linear-kernel SVM which takes char-bigrams as input, as our CNNs, and (ii) SVM2-PV: an updated SVM2 which takes also probability vector features. Further, we are interested in finding a performance threshold on the minimally-required input text length for discourse information to “kick in”. To this end, we chunk the novels into different sizes: 200-2000 words, at 200-word intervals, and evaluate our CNNs in the multi-class condition. Generalization-dataset experiments. To confirm that our models generalize, we pick the best models from the baseline-dataset experiments and evaluate on the novel-50 and IMDB62 datasets. For novel-50, the chunking size applied is 2000-word as per the baseline-dataset experiment results, and for IMDB62, texts are not chunked (i.e., we feed the models with the original reviews directly). For model comparison, we also run the SVMs (i.e., SVM2 and SVM2-PV) used in the baseline-dataset experiment. All the experiments conducted here are multi-class classification with macro-averaged F1 evaluation. Model configurations. Following F15, we perform 5-fold cross-validation. The embedding sizes are tuned on novel-9 (multi-class condition): 50 for char-bigrams; 20 for discourse features. The learning rate is 0.001 using the Adam Optimizer BIBREF18 . For all models, we apply dropout regularization of 0.75 BIBREF19 , and run 50 epochs (batch size 32). The SVMs in the baseline-dataset experiments use default settings, following F15. For the SVMs in the generalization-dataset experiments, we tuned the hyperparameters on novel-9 with a grid search, and found the optimal setting as: stopping condition tol is 1e-5, at a max-iteration of 1,500. ### Results
Baseline-dataset experiments. The results of the baseline-dataset experiments are reported in Table TABREF24 , TABREF25 and Figure FIGREF27 . In Table TABREF24 , Baseline denotes the dumb baseline model which always predicts the more-represented author of the pair. Both SVMs are from F15, and we report her results. SVM (LexSyn) takes character and word bi/trigrams and POS tags. SVM (LexSyn-PV) additionally includes probability vectors, similar to our CNN2-PV. In this part of the experiment, while the CNNs clear a large margin over SVMs, adding discourse in CNN2-PV brings only a small performance gain. Table TABREF25 reports the results from the multi-class classification task, the more difficult task. Here, probability vector features (i.e., PV) again fail to contribute much. The discourse embedding features, on the other hand, manage to increase the F1 score by a noticeable amount, with the maximal improvement seen in the CNN2-DE (global) model with RST features (by 2.6 points). In contrast, the discourse-enhanced SVM2-PVs increase F1 by about 1 point, with overall much lower scores in comparison to the CNNs. In general, RST features work better than GR features. The results of the varying-sizes experiments are plotted in Figure FIGREF27 . Again, we observe the overall pattern that discourse features improve the F1 score, and RST features procure superior performance. Crucially, however, we note there is no performance boost below the chunk size of 1000 for GR features, and below 600 for RST features. Where discourse features do help, the GR-based models achieve, on average, 1 extra point on F1, and the RST-based models around 2. Generalization-dataset experiments. Table TABREF28 summarizes the results of the generalization-dataset experiments. On novel-50, most discourse-enhanced models improve the performance of the baseline non-discourse CNN2 to varying degrees. The clear pattern again emerges that RST features work better, with the best F1 score evidenced in the CNN2-DE (global) model (3.5 improvement in F1). On IMDB62, as expected with short text inputs (mean=349 words/review), the discourse features in general do not add further contribution. Even the best model CNN2-DE brings only marginal improvement, confirming our findings from varying the chunk size on novel-9, where discourse features did not help at this input size. Equipped with discourse features, SVM2-PV performs slightly better than SVM2 on novel-50 (by 0.4 with GR, 0.9 with RST features). On IMDB62, the same pattern persists for the SVMs: discourse features do not make noticeable improvements (by 0.0 and 0.5 with GR and RST respectively). ### Analysis
General analysis. Overall, we have shown that discourse information can improve authorship attribution, but only when properly encoded. This result is critical in demonstrating the particular value of discourse information, because typical stylometric features such as word INLINEFORM0 -grams and POS tags do not add additional performance improvements BIBREF3 , BIBREF5 . In addition, the type of discourse information and the way in which it is featurized are tantamount to this performance improvement: RST features provide overall stronger improvement, and the global reading scheme for discourse embedding works better than the local one. The discourse embedding proves to be a superior featurization technique, as evidenced by the generally higher performance of CNN2-DE models over CNN2-PV models. With an SVM, where the option is not available, we are only able to use relation probability vectors to obtain a very modest performance improvement. Further, we found an input-length threshold for the discourse features to help (Section SECREF26 ). Not surprisingly, discourse does not contribute on shorter texts. Many of the feature grids are empty for these shorter texts– either there are no coreference chains or they are not correctly resolved. Currently we only have empirical results on short novel chunks and movie reviews, but believe the finding would generalize to Twitter or blog posts. Discourse embeddings. It does not come as a surprise that discourse embedding-based models perform better than their relation probability-based peers. The former (i) leverages the weight learning of the entire computational graph of the CNN rather than only the softmax layer, as the PV models do, and (ii) provides a more fine-grained featurization of the discourse information. Rather than merely taking a probability over grammatical relation transitions (in GR) or discourse relation types (in RST), in DE-based models we learn the dependency between grammatical relation transitions/discourse relations through the INLINEFORM0 -sized filter sweeps. To further study the information encoded in the discourse embeddings, we perform t-SNE clustering BIBREF20 on them, using the best performing model CNN2-DE (global). We examine the closest neighbors of each embedding, and observe that similar discourse relations tend to go together (e.g., explanation and interpretation; consequence and result). Some examples are given in Table TABREF29 . However, it is unclear how this pattern helps improve classification performance. We intend to investigate this question in future work. Global vs. Local featurization. As described in Section SECREF17 , the global reading processes all the discourse features for one entity at a time, while the local approach reads one sentence (or one sentence pair) at a time. In all the relevant experiments, global featurization showed a clear performance advantage (on average 1 point gain in F1). Recall that the creation of the grids (both GR and RST) depend on coreference chains of entities (Section SECREF2 ), and only the global reading scheme takes advantage of the coreference pattern whereas the local reading breaks the chains. To find out whether coreference pattern is the key to the performance difference, we further ran a probe experiment where we read RST discourse relations in the order in which EDUs are arranged in the RST tree (i.e., left-to-right), and evaluated this model on novel-50 and IMDB62 with the same hyperparameter setting. The F1 scores turned out to be very close to the CNN2-DE (local) model, at 97.5 and 90.9. Based on this finding, we tentatively confirm the importance of the coreference pattern, and intend to further investigate how exactly it matters for the classification performance. GR vs. RST. RST features in general effect higher performance gains than GR features (Table TABREF28 ). The RST parser produces a tree of discourse relations for the input text, thus introducing a “global view.” The GR features, on the other hand, are more restricted to a “local view” on entities between consecutive sentences. While a deeper empirical investigation is needed, one can intuitively imagine that identifying authorship by focusing on the local transitions between grammatical relations (as in GR) is more difficult than observing how the entire text is organized (as in RST). ### Conclusion
We have conducted an in-depth investigation of techniques that (i) featurize discourse information, and (ii) effectively integrate discourse features into the state-of-the-art character-bigram CNN classifier for AA. Beyond confirming the overall superiority of RST features over GR features in larger and more difficult datasets, we present a discourse embedding technique that is unavailable for previously proposed discourse-enhanced models. The new technique enabled us to push the envelope of the current performance ceiling by a large margin. Admittedly, in using the RST features with entity-grids, we lose the valuable RST tree structure. In future work, we intend to adopt more sophisticated methods such as RecNN, as per Ji:17, to retain more information from the RST trees while reducing the parameter size. Further, we aim to understand how discourse embeddings contribute to AA tasks, and find alternatives to coreference chains for shorter texts. Table 2: The probability vector for the excerpt in Table 1 capturing transition probabilities of length 2. Figure 1: RST tree for the first sentence of the excerpt in Table 1. Table 3: The entity grid for the excerpt in Table 1, where columns are salient entities and rows are sentences. Each cell contains the grammatical relation of the given entity for the given sentence (subject s, object o, another grammatical relation x, or not present -). If an entity occurs multiple times in a sentence, only the highest-ranking relation is recorded. Figure 2: The bigram character CNN models Figure 3: Two variants for creating sequences of grammatical relation transitions in an entity grid. Table 4: Statistics for datasets. Table 5: Accuracy for pairwise author classification on the novel-9 dataset, using either a dumb baseline, an SVM with and without discourse to replicate F15, or a bigram-character CNN (CNN2) with and without discourse. Table 6: Macro-averaged F1 score for multi-class author classification on the novel-9 dataset, using either no discourse (None), grammatical relations (GR), or RST relations (RST). These experiments additionally include the Discourse Embedding (DE) models for GR and RST. Figure 4: Macro-averaged F1 score for multi-class author classification on the novel-9 dataset in varied chunk sizes. Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST). Table 8: Nearest neighbors of example embeddings with t-SNE clustering (top 5) | character bigram CNN classifier |
Which medication caused a possible dose-dependent intolerance in Mr. Fisher?
Choose the correct answer from the following options:
A. Fluvastatin
B. Cyclosporin A
C. Mycophenolic Acid
D. Prednisone
E. Metoprolol
| ### Patient Report 0
**Dear colleague, **
We are reporting to you regarding our patient, Mr. Alan Fisher, born on
12/09/1953. He was under our inpatient care from 04/19/2009 to
04/28/2009.
**Diagnoses:**
- Progressive deterioration of renal transplant function (creeping
creatinine) without evidence of biopsy-proven rejection
- Isovolumetric tubular epithelial vacuolization
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** Mr. Fisher was admitted for a renal transplant
biopsy due to progressive deterioration of transplant function (creeping
creatinine). His recent creatinine values had increased to around 1.4 --
1.6 mg/dL, while they had previously been around 1.1 mg/dL.
**Therapy and Progression:** Following appropriate preparation and
informed consent, a complication-free transplant puncture was performed.
The biopsy showed isometric tubular epithelial vacuolization without
significant findings. This was followed by adjustment of Cyclosporin-A
levels and the addition of a lymphocyte proliferation inhibitor to the
existing immunosuppressive dual therapy. There was a significant
increase in Cyclosporin-A levels at one point due to accidental double
dosing by the patient, but levels returned to the target range. This
might explain the current rise in creatinine. Another explanation could
be recurrent hypotensive blood pressure dysregulations, leading to the
discontinuation of Minoxidile medication. For chronic atrial
fibrillation, anticoagulation therapy with Marcumar was restarted during
hospitalization and should be continued as an outpatient according to
the target INR. Low molecular weight heparin administration could be
discontinued.
**Physical Examination:** Patient in good general condition. Oriented in
all aspects. No dyspnea. No cyanosis. No edema. Warm and dry skin.
Normal nasal and pharyngeal findings. Pupils round, equal, and react
promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa
unremarkable. No jugular vein distension. No carotid bruits heard.
Palpation of lymph nodes unremarkable. Palpation of the thyroid gland
unremarkable, freely movable.
Lungs: Normal chest shape, moderately mobile, resonant percussion sound,
vesicular breath sounds bilaterally, no wheezing or crackles heard.
Heart: Irregular heart action, normal rate; heart sounds clear, no
pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all
quadrants; soft abdomen, markedly obese, no tenderness, no palpable
masses, liver and spleen not palpable due to limited access, non-tender
kidneys. Large reducible incisional hernia on the right side following
nephrectomy.
Extremities: Occluded fistula on the right forearm. Normal peripheral
pulses; joints freely movable. Strength, motor function, and sensation
are unremarkable.
**Kidney Biopsy on 04/19/2009:** Complication-free biopsy of the
transplant kidney.
Findings: Erythematous macules.
Recommendation: Follow-up in 3 months.
**Ultrasound of Transplant Kidney on 04/20/2009:** Transplant kidney
well visualized, located in the left iliac fossa, measurable,
oval-shaped. Parenchymal echogenicity normal, normal corticomedullary
differentiation. No evidence of arteriovenous fistula or hematoma after
kidney biopsy.
**Pathological-anatomical assessment on 04/19/2009:**
**Macroscopic Findings:** Singular Nodule Identified: Dimensions
measuring 8 mm.
**Microscopic Examination:**
Sampled Tissue: Renal cortex
Identified Components:
- Glomeruli: Nine observed
- Interlobular Artery: One segment present
- Absence of medullary tissue
**Diagnostic Observations:** There were no signs of inflammation or
scarring in the renal cortex. The glomeruli appeared normocellular, and
no signs of inflammation or pathological changes were observed in them.
The peritubular capillaries were free of inflammation, and the specific
test for C4d staining yielded negative results. The arterioles within
the tissue had thin walls, and there was no evidence of inflammation in
this vascular component.
The interlobular artery was also thin-walled and showed no evidence of
inflammation.
A notable finding was extensive damage to the tubular epithelium. The
damage was characterized by isometric microvesicular cytoplasmic
transformation, which exceeded 80%. Importantly, there was no evidence
of cell necrosis and only minimal flattening of cells was observed. In
addition, no pathological imprints, microcalcifications, or nuclear
inclusion bodies were observed in the tubular epithelium.
**Summary:** The predominant pathological finding in this case is
substantial tubular damage. Consequently, it is highly advisable to
closely monitor immunosuppression levels in the patient\'s management.
Further comprehensive evaluation is strongly recommended to determine
the underlying cause of the observed tubular damage and to address the
clinical question concerning the presence of Chronic Allograft
Nephropathy or the potential involvement of an infection in the clinical
presentation.
**Chest X-ray (2 views) on 04/22/2009:**
[Findings]{.underline}: No pneumothorax, no effusion. No evidence of
pneumonia. No focal findings. Left-biased heart without decompensation.
Mediastinum centrally positioned, not widened. Unremarkable depiction of
central hilar structures. Thoracic hyperkyphosis.
**Current Recommenations:** We request regular outpatient monitoring of
retention parameters (initially every 2-3 weeks) and are available for
further questions at the provided telephone number.
**Lab results upon Discharge**
**Parameter** **Results** **Reference Range**
---------------------------------------------- ------------- ---------------------
Sodium 144 mEq/L 134-145 mEq/L
Potassium 3.7 mEq/L 3.4-5.2 mEq/L
Calcium 9.48 mg/dL 8.6-10.6 mg/dL
Chloride 106 mEq/L 95-112 mEq/L
Phosphorus 2.88 mg/dL 2.5-4.5 mg/dL
Transferrin Saturation 20 % 16-45 %
Magnesium 1.9 mg/dL 1.8-2.6 mg/dL
Creatinine 1.88 mg/dL \<1.2 mg/dL
Glomerular Filtration Rate 36 mL/min \>90 mL/min
BUN (Blood Urea Nitrogen) 60 mg/dL 14-46 mg/dL
Uric Acid 4.6 mg/dL 3.0-6.9 mg/dL
Total Bilirubin 0.5 mg/dL \<1 mg/dL
Albumin 4.0 g/dL 3.6-5.0 g/dL
Total Protein 6.8 g/dL 6.5-8.7 g/dL
C-Reactive Protein 0.19 mg/dL \<0.5 mg/dL
Transferrin 269 mg/dL 200-360 mg/dL
Ferritin 110 ng/mL 30-300 ng/mL
ALT (Alanine Aminotransferase) 17 U/L \<45 U/L
AST (Aspartate Aminotransferase) 20 U/L \<50 U/L
Alkaline Phosphatase 119 U/L 40-129 U/L
GGT (Gamma-Glutamyltransferase) 94 U/L \<55 U/L
Lipase 61 U/L \<70 U/L
TSH (Thyroid-Stimulating Hormone) 0.54 mIU/L 0.27-4.20 mIU/L
Hemoglobin 14.5 g/dL 14.0-17.5 g/dL
Hematocrit 43% 40-52%
Red Blood Cells 4.60 M/uL 4.6-6.2 M/uL
White Blood Cells 8.78 K/uL 4.5-11.0 K/uL
Platelets 205 K/uL 150-400 K/uL
MCV 94 fL 81-100 fL
MCH 31.5 pg 27-34 pg
MCHC 33.5 g/dL 32.4-35.0 g/dL
MPV 11 fL 7-12 fL
RDW 14.8 % 11.9-14.5 %
Neutrophils 3.72 K/uL 1.8-7.7 K/uL
Lymphocytes 2.37 K/uL 1.4-3.7 K/uL
Monocytes 0.93 K/uL 0.2-1.0 K/uL
Eosinophils 1.67 K/uL \<0.7 K/uL
Basophils 0.09 K/uL 0.01-0.10 K/uL
Nucleated Red Blood Cells Negative \<0.01 K/uL
APTT (Activated Partial Thromboplastin Time) 45.1 sec 26-40 sec
Antithrombin Activity 85 % 80-120 %
**Medication upon discharge**
**Medication ** **Dosage** **Frequency**
-------------------------------- ------------ ---------------
Cyclosporine (Neoral) 1 mg 1-0-1
Mycophenolic Acid (Myfortic) 180 mg 1-0-1
Prednisone (Deltasone) 5 mg 1-0-0
Aspirin 81 mg 1-0-0
Candesartan (Atacand) 16 mg 0-0-1
Metoprolol (Lopressor) 50 mg 1-1-1-1
Isosorbide Dinitrate (Isordil) 60 mg 1-0-0
Torsemide (Demadex) 10 mg As directed
Ranitidine (Zantac) 300 mg 0-0-1
Fluvastatin (Lescol) 20 mg 0-0-1
Allopurinol (Zyloprim) 100 mg 0-1-0
Tamsulosin (Flomax) 0.4 mg 1-0-0
### Patient Report 1
**Dear colleague, **
We are writing to provide an update on our patient, Mr. Alan Fisher,
born on 12/09/1953.
He was under our inpatient care from 10/02/2018 to 10/03/2018.
**Diagnoses:**
- Urosepsis
- Acute postrenal kidney failure
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Previous Surgeries:** Previous prostate vesiculectomy with regional
lymphadenectomy
**Planned procedure:** Urethro-cystoscopy with catheter placement for
urethral stricture
**Medical History:** The patient was admitted through our emergency
department upon referral by the outpatient urologist due to suspicion of
a urethral stricture. Mr. Fisher reports a worsening urinary retention
for approximately 6 months. Despite multiple unsuccessful attempts at
catheter placement, ureterocystoscopy with catheter insertion was
performed. Intraoperatively, purulent cystitis and a bladder outlet
obstruction were observed.
Mr. Fisher regularly attends follow-up examinations for his history of
kidney transplantation in 1995 and previous prostate vesiculectomy with
regional lymphadenectomy in 01/2018.
**Physical Examination:** Neurology: RASS 0, alert, CAM-ICU negative, no
new focal neurology
Lungs: Bilateral air entry, no rales or wheezing, sufficient gas
exchange on 2L/O2 Cardiovascular: Normal sinus rhythm, normotensive on
0.01 µg/kg/min NA
Abdomen: Soft, no guarding, sparse peristalsis, advanced oral diet,
regular bowel movements
Diuresis: Normal urine output, retention values within normal range,
goal: balanced fluid status
Skin/Wounds: Non-irritated, no peripheral edema
**Therapy and Progression**: We received Mr. Fisher, who was awake and
spontaneously breathing under a 2L O2 mask via nasal cannula, to our
intensive care unit due to urosepsis. To maintain an adequate
circulation, low-dose catecholamine therapy was required but could be
discontinued on the first postoperative day. Pulmonary function remained
stable with intensive non-invasive ventilation and breathing training.
Given his immunosuppression, we escalated the intraoperatively initiated
anti-infective therapy from Ceftriaxone to Piperacillin/Tazobactam.
Pneumococcal and Legionella rapid tests were negative. Following
appropriate volume resuscitation and diuretic therapy with Furosemide,
diuresis became sufficient. Oral diet progression occurred without
complications. Anticoagulation was initially in prophylactic dosing with
Heparin and later switched to therapeutic dosing with Enoxaparin.
**Current Recommendations:**
- Switch unfractionated Heparin to Fragmin
- baseline Crea 2mg/dL, target CyA level: 50-60ng/mL, Myfortic
continued.
- Urological care of the stricture in progress, leave catheter until
then.
- Mobilization
**Medication upon Discharge:**
**Medication (Brand)** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Torsemide (Demadex) 10 mg 1-1-0-0
Prednisone (Deltasone) 5 mg 1-1-0-0
Pantoprazole (Protonix) 20 mg 1-1-0-0
Mycophenolate Mofetil (CellCept) 360 mg 1-0-1-0
Metoprolol Succinate (Toprol-XL) 100 mg 1-0-1-0
Magnesium Oxide 400 mg 1-0-0-0
Ciclosporin (Neoral) 100 mg 60-0-70-0
Candesartan (Atacand) 16 mg 0-0.5-0-0
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Allopurinol (Zyloprim) 100 mg 1-0-0-0
Aspirin 81 mg 1-0-0-0
Paracetamol (Tylenol) 500 mg As needed
### Patient Report 2
**Dear colleague, **
We are reporting to you about our patient, Mr. Alan Fisher, born on
12/09/1953.
He was under our inpatient care from 11/04/2018 to 11/12/2018.
**Current Symptoms:** Decreased diuresis, rising creatinine, frustrating
catheterization.
**Diagnoses:**
- Acute on chronic graft failure
<!-- -->
- Creatinine increased from 1.56 mg/dL to a maximum of 2.35 mg/dL.
- Likely postrenal origin due to urethral stricture; sonographically,
Grade II urinary stasis with urinary retention and residual urine
formation.
- Frustrating catheterization due to urethral stricture
- Urethro-cystoscopy with bougie and catheter placement
- Parainfectious component in purulent cystitis with urosepsis
- Discharged with indwelling catheter
- Inpatient readmission to the colleagues in Urology for internal
urethrotomy
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** The patient was admitted through our emergency
department upon referral by an outpatient urologist due to suspected
urethral stricture. Mr. Fisher reports increasing difficulty urinating
for approximately 6 months. He has to \"squeeze out\" his bladder
completely. Frustrating catheterization was performed due to urinary
retention. Intraoperatively, purulent cystitis and bladder outlet
stenosis were observed. Mr. Fisher regularly undergoes follow-up
examinations for a history of kidney transplantation in 1995 and a
prostate vesiculectomy with regional lymphadenectomy in 01/2018.
**Vegetative Findings:** The patient had a bowel movement 4 days ago,
indwelling catheter irritation (3L of diuresis the previous day), no
nausea/vomiting, no fever or night sweats, weight loss of 30kg from
February to April 2020.
**Physical Capacity:** Limited, can still climb 2 stairs but needs to
take a break due to shortness of breath.
**Physical Examination:** Temperature 37.4°C, Blood pressure 128/72
mmHg; Pulse 72/min; Respiratory rate 15/min, O2 saturation under 2L O2:
96%
Awake, alert, cooperative, oriented to time, place, person, and
situation.
[Head/Neck:]{.underline} Non-tender nerve exit points; Clear paranasal
sinuses; moist and pink mucous membranes; unremarkable dentition; moist
and glossy tongue; non-palpable thyroid enlargement.
[Chest]{.underline}: Normal configuration; Non-tender spine; free renal
beds bilaterally.
[Heart]{.underline}: Rhythmic, clear heart sounds, normal rate, no
splitting; non-distended jugular veins. [Lungs]{.underline}: Vesicular
breath sounds; Resonant percussion note; no adventitious sounds; no
stridor; normal chest expansion.
[Abdomen]{.underline}: Protuberant, known incisional hernia, normal
peristalsis in all quadrants; soft; no pathological resistance; no
tenderness; liver palpable below the costal margin; spleen not palpable.
[Lymph nodes:]{.underline} No pathologically enlarged cervical nodes
palpable; axillary and inguinal nodes not palpable.
Skin: No pathological skin findings.
[Extremities:]{.underline} Warm; mild bilateral ankle edema.
[Pulse status (right/left):]{.underline} A. carotis +/+, A. radialis
+/+, A. femoralis +/+, A. tibialis post. +/+, A. dorsalis ped. +/+
[Neurological]{.underline}: Oriented and unremarkable.
**Therapy and Progression:** The patient was admitted through our
emergency department upon referral by an outpatient urologist due to
suspected urethral stricture, which had been causing increasingly
difficult urination for approximately 6 months. Sonography showed Grade
II urinary stasis with urinary retention and residual urine. Frustrating
catheterization was performed, followed by ureterocystoscopy with bougie
and catheter placement. Intraoperatively, purulent cystitis and bladder
outlet stenosis were observed. Laboratory tests revealed acute kidney
transplant failure, with creatinine increasing from 1.56 mg/dL to 2.35
mg/dL, along with significantly elevated infection parameters: CRP up to
186 mg/dL, PCT 12.82 µg/L, and leukocytosis of 21.6/nL. After obtaining
blood cultures, empirical antibiotic therapy with Ceftriaxone was
initiated. Upon detecting Pseudomonas aeruginosa, therapy was switched
to Piperacillin/Tazobactam on 12/06/20 and continued until 12/13/20.
Under this treatment, infection parameters significantly improved, and
Mr. Fisher remained afebrile. Kidney retention parameters also decreased
to a discharge creatinine of 2.05 mg/dL. Regarding the urethral
stricture, he was initially discharged with an indwelling catheter. A
follow-up appointment for internal urethrotomy and potentially Allium
stent placement was scheduled for 4 weeks later. During the hospital
stay, ciclosporin levels remained within the target range. Following
prostate vesiculectomy earlier in the year, anticoagulation was switched
from Enoxaparin to Apixaban 2.5 mg twice daily, and Aspirin therapy was
discontinued.
**Recommendations**: We recommend regular monitoring of kidney retention
parameters and infection parameters. Regarding the urethral stricture,
the patient will be discharged with an indwelling catheter. We scheduled
a follow-up with colleagues in Urology for internal urethrotomy and
potentially Allium stent placement. Pause oral anticoagulation with
Apixaban one day before inpatient admission.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Ciclosporin (Neoral) 100 mg 60-0-70-0
Mycophenolic Acid (Myfortic) 360 mg 1-0-1-0
Prednisone (Deltasone) 5 mg 1-0-0-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Candesartan (Atacand) 8 mg 0-1-0-0
Torsemide (Demadex) 10 mg 1-1-0-0
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Pantoprazole (Protonix) 20 mg 1-0-0-0
Vitamin D3 (Cholecalciferol) 20,000 IU Pause
Magnesium Oxide 400 mg 1-0-0-0
### Patient Report 3
**Dear colleague, **
We are reporting to you regarding our patient, Mr. Alan Fisher, born on
12/09/1953, who was under outpatient care on 07/01/2019.
**Current Symptoms:** Pain on the left side at rib level**,** Dyspnea
**Diagnoses:**
- Infection of unclear origin
- CT Thorax and Abdomen showed no focus
- Urine dipstick and cultures were bland
- Antibiotics: Meropenem from 06/11/2019 to 06/19/2019
- Acute Transplant Dysfunction
- Serum Creatinine: 2.4 -\> 4.5 -\> 2.6 mg/dl
- Renal ultrasound: 123 x 54 x 24 mm, not dilated, some areas of
increased echogenicity, no twinkling, no acoustic shadowing, no
signs of urolithiasis.
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** Initial presentation was at the local emergency
department on referral from the primary care physician for suspected
acute coronary syndrome. Mr. Fisher described left-sided rib pain, which
was related to breathing and pressure, as well as dyspnea for a few
days. Laboratory tests showed acute-on-chronic kidney failure and
elevated infection parameters. A urine dipstick test was negative for
nitrites and leukocytes. Chest CT ruled out pulmonary pathology, and
acute coronary syndrome was also excluded. Mr. Fisher reported a urinary
tract infection about 4 weeks ago, which was treated with antibiotics as
an outpatient.
**Physical Examination:** Alert, oriented, cooperative, and responsive
to time, place, person, and situation
[Head/Neck:]{.underline} Non-tender nerve exit points; clear nasal
sinuses; moist pink mucous membranes; unremarkable dental status; moist
tongue
[Chest]{.underline}: Normal configuration; no tenderness in the spine;
both renal beds free
[Heart]{.underline}: Arrhythmic heart sounds, pure, tachycardic, not
split
[Lungs]{.underline}: Vesicular breath sounds; somewhat decreased breath
sounds bilaterally; no adventitious sounds; no stridor
[Abdomen]{.underline}: Regular peristalsis in all quadrants; soft; right
lower abdomen notably distended with increased vascular markings, liver
and spleen not palpable, transplant kidney non-tender
[Lymph Nodes:]{.underline} No pathologically enlarged cervical lymph
nodes palpable
[Skin]{.underline}: No pathological skin findings
[Extremities:]{.underline} Warm; no edema; cyanosis of toes bilaterally
after prolonged leg dependency
- Pulse status (right/left): Carotid artery +/+, Radial artery +/+,
Posterior tibial artery +/+
- Neurology: Normal cranial nerves; round, moderately dilated pupils;
prompt bilateral pupillary light reflex; no sensory or motor
deficits; ubiquitous muscle strength 5/5
**Therapy and Progression:** We admitted the patient for further
diagnosis and treatment. Initially suspected acute coronary syndrome was
ruled out. Laboratory results showed elevated retention and infection
parameters. With volume substitution, we achieved baseline creatinine
levels again. The transplant kidney appeared non-dilated and
well-perfused. For the infection, the patient received the mentioned
imaging studies, which did not reveal any definitive findings. Our urine
analyses and cultures also showed bland results. It should be noted that
prior outpatient treatment for suspected urinary tract infection was
likely with cotrimoxazole. Ultimately, considering the recent
antibiograms, we decided on a calculated antibiotic therapy with
Meropenem. This led to a significant improvement in infection
parameters. The last measured Ciclosporin level was slightly
subtherapeutic, so we adjusted the dosage accordingly. We recommend
follow-up with the primary care physician.
**Chest CT on 06/10/2019:**
[Clinical Information, Question, Justification]{.underline}: Patient
with a history of kidney transplantation. Bursting pain on both sides at
the ribcage. Cough. Elevated inflammatory markers. Question about
infiltrates, pleural effusion, congestion.
[Technique]{.underline}: Digital overview radiographs. Plain 80-line CT
of the chest. MPR (Multiplanar reconstruction). DLP (Dose-Length
Product) 120.6 mGy\*cm.
[Findings]{.underline}: No previous images available for comparison.
Symmetric thyroid. Minimal pericardial effusion, accentuated at the
base, measuring up to 8 mm in width (Series 5, Image 293). Coronary
atherosclerosis. No pathologically enlarged lymph nodes in the
mediastinum, axilla, or hilum on plain images. Multisegmental calcified
(micro)nodules. No suspicious pulmonary nodules indicative of
malignancy. No pneumonic infiltrates. No pleural effusions. No
pneumothorax. No pulmonary venous congestion. Delicate scar tissue at
the bases bilaterally. Small axial hiatal hernia. Rounded soft tissue
structure in the right adrenal space (Series 5, measuring 411 x 10 mm).
Incidentally captured at the image margins is a shrunken left kidney.
Spondylosis deformans of the thoracic spine. Interpretation: No
pneumonic infiltrates. No pleural effusions. No pulmonary venous
congestion. Minimal pericardial effusion. Multisegmental calcified
(micro)nodules, likely post-inflammatory.
**Abdomen/Pelvis CT on 06/14/2019:**
[Clinical Information, Question, Justification:]{.underline} Acute
kidney failure. Question regarding kidney or ureteral stones.
[Technique]{.underline}: Plain 80-line CT of the abdomen. MPR. DLP 947
mGy\*cm. Findings and [Interpretation:]{.underline}
The left transplant kidney shows pelvic dilation with an expanded renal
pelvis and ureter (hydronephrosis grade II) but no evidence of stones.
Status post-right nephrectomy. Shrunken left kidney. Known large,
broad-based right-sided abdominal wall weakness with prolapsed
intestinal loops and mesenteric fat tissue without evidence of
incarceration. No ileus. Diverticulosis of the sigmoid colon. Small
axial hiatal hernia. No free or encapsulated fluid or free air in the
abdomen with the right diaphragmatic dome not fully visualized.
Cholecystolithiasis. No cholestasis. Vascular sclerosis. No
lymphadenopathy. Bilaterally aerated lung bases captured without change.
Unchanged irregularly thickened and coarsely structured right iliac
bone, consistent with Paget\'s disease.
**Recommendations:**
Ciclosporin level monitoring
**Medication upon discharge:**
**Medication ** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Candesartan Cilexetil (Atacand) 8 mg 0-1-0-0
Prednisone (Deltasone) 5 mg 1-0-0-0
Vitamin D3 (Cholecalciferol) 20,000 IU 1 x/week
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Magnesium Oxide 400 mg 1-0-0-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Mycophenolic Acid (Myfortic) 360 mg 1-0-1-0
Pantoprazole (Protonix) 20 mg 1-0-0-0
Ciclosporin (Neoral) 100 mg 70-0-70-0
### Patient Report 4
**Dear colleague, **
We would like to inform you about our patient, Mr. Alan Fisher, born on
12/09/1953, who was under our inpatient care from 02/19/2020 to
03/01/2020.
**Current Symptoms:** Decreased general condition, weakness,
decompensation
**Diagnosis**: Acute episode of recurrent urinary tract infection with
detection of E. faecalis, E. faecium, and Enterobacter cloacae in urine
(blood cultures sterile).
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** The patient was admitted through our internal
medicine emergency department. He presented with worsening general
condition and increasing weakness, following the recommendation of our
local nephrological telemedicine. He particularly noticed the increasing
weakness when getting up, describing his legs as feeling like rubber. He
also experienced shortness of breath. His walking distance was greater
than 100 meters. There was no fever, chills, nausea, vomiting, dysuria,
or changes in bowel movements. Before the outpatient visit, the patient
had collected urine for 24 hours, totaling 1700 ml, with a fluid intake
of approximately 2 liters. His blood pressure at home was approximately
120/60 mmHg. In the emergency department, he had negative urinary
dipstick results and a non-specific chest X-ray. Blood and urine
cultures were obtained, and he was subsequently transferred to our
general ward. No angina pectoris symptoms. The patient had normal bowel
movements, specifically no melena, and no blood-tinged stools. Urine was
described as clear and light.
**Physical Examination:** Alert, oriented, cooperative, oriented to
time, place, person, and situation. Height 179 cm; Weight 114 kg
[Head/neck:]{.underline} No tender nerve exit points; Clear nasal
sinuses; No tenderness over the skull; Mucous membranes pink and moist;
Dental status is rehabilitated; Tongue moist and glossy
[Thorax]{.underline}: Normally shaped; Spine without tenderness; Renal
regions free of tenderness
[Heart]{.underline}: Heart sounds are faint, arrhythmic, clear, regular
rate, no splitting of heart sounds; Jugular veins are not distended
[Lungs]{.underline}: Faint vesicular breath sounds; Resonant percussion
note; Dullness on the left, no added sounds; No stridor; Normal breath
excursion
[Abdomen]{.underline}: Large right abdominal wall hernia, normal
peristalsis in all quadrants; Soft; No pathological resistances; No
tenderness (especially not over the left lower abdomen)
[Skin status:]{.underline} No pathological skin findings
Extremities: Warm; Mild edema.
[Neurology:]{.underline} Alert. No focal deficits
**Treatment and Progression:** The patient was admitted through our
emergency department due to a decrease in general condition and
weakness, accompanied by significantly elevated laboratory infection
parameters and slightly worsened retention parameters (Creatinine max
3.4 mg/dL compared to the current baseline of 3 mg/dL). Upon admission,
apart from a known and persistent leukocyturia since 2020, there were no
indications of any other infectious focus. We were able to detect
Enterobacter cloacae and Enterococcus faecalis in the urine, and we
initially treated the patient with intravenous Tazobactam. Blood
cultures remained sterile. The patient\'s general condition improved
within a few days, along with a regression of infection parameters.
For further investigation of recurrent urinary tract infections (UTIs)
and in the context of a history of urethral stricture treatment in
February 2021 with bougienage of the urethra one year ago, a urological
consultation was arranged. During this consultation, there was suspicion
of a recurrence of the urethral stricture due to a significant residual
urine volume of 175 ml. A scheduled readmission for repeat surgical
management was set for May 16, 2022. Due to the lack of normalization of
elevated infection parameters and significant residual urine, a urinary
catheter was inserted. Subsequently, Enterococcus faecium was detected,
and we continued treatment with oral Linezolid after the completion of
intravenous antibiotic therapy.
The antibiotic treatment was planned to continue on an outpatient basis
for a total of 10 days. We kindly request an outpatient follow-up to
monitor infection parameters next week. The urinary catheter will be
maintained until the urological follow-up appointment, and the patient
has been provided with a prescription for medication.
Furthermore, the patient exhibited atrial tachyarrhythmia. We reduced
the heart rate using Digoxin, as the patient was already on maximum
beta-blocker therapy. The atrial tachyarrhythmia significantly improved
under this treatment.
Additionally, there was a non-puncture-worthy pleural effusion and a
chronic pericardial effusion, which was not hemodynamically relevant.
There were no clinical indications of pericarditis.
**Current Recommendations:**
1. Inpatient admission to Urology Department.
2. Outpatient laboratory monitoring and referral issuance by the
primary care physician.
### Patient Report 5
**Dear colleague, **
We are reporting on mutual patient, Mr. Alan Fisher, born on 12/09/1953,
who was under our inpatient care from 03/14/2020 to 03/15/2020.
**Diagnoses**: Anastomotic stricture following history of prostatectomy
and history of urethrotomy interna.
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Procedure**:
- Urethrotomy interna according to Sachse
- Calculated intravenous antibiotic therapy with Meropenem starting on
03/14/2020
- Extension of therapy to include antifungal treatment with
Fluconazole on 03/15/2020
**Medical History:** The patient presents with a recurrence of
symptomatic urethral stricture at the anastomosis site following
prostatectomy. The main symptoms are frequent urination, dysuria, and
residual urine formation up to 175 ml. In January 2019, urethrotomy
interna was already performed. Since the last hospitalization due to a
urinary tract infection, the patient has had a continuous
catheterization.
**Physical Examination:** Patient in a reduced general condition and
obese nutritional status. The abdomen is soft, without signs of
resistance or pain. Kidney beds on both sides are indolent.
**Urine Diagnostics**: Urine dipstick: Leukocytes 500, Nitrite negative,
Erythrocytes 50
**Microbiology**: Candida in urine, collected by the general
practitioner on 03/11/2020.
**Chest X-ray in two planes on 02/19/2020:**
[Clinical Information, Question, Justification for the
Examination]{.underline}: Deterioration of general condition. History of
recurrent sepsis. History of lung transplantation. Infiltrates?
**Findings**: The heart is shifted to the left and has a mitral
configuration. No signs of acute congestion. The mediastinum shows no
signs of emphysema, is centrally located, and of normal width. No active
pneumonia in the ventilated lung regions. Progressive costophrenic angle
effusion on the left. No pleural effusion on the right, as far as can be
assessed. No pneumothorax. Degenerative changes in the spine.
Hyperkyphosis of the thoracic spine.
**Therapy and Progression:** The above-mentioned procedure was performed
without complication. Scar tissue at the level of the bladder sphincter
was incised. The postoperative course was uneventful. The transurethral
indwelling catheter was removed on the 19th postoperative day. At the
time of discharge, the patient could urinate without residual urine with
a good urinary stream. We discharged the patient on 03/19/2020 for
further outpatient care.
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
---------------------------------------- ------------------- ------------------
Magnesium Oxide 400 mg 1-0-0-0
Atorvastatin (Lipitor) 43.3 mg 0-0-1-0
Candesartan Cilexetil (Atacand) 16 mg 1-1-0-1
Prednisone (Deltasone) 5 mg 1-0-0-0
Vitamin D3 (Cholecalciferol, oily) 20,000 IU 1x every 2 weeks
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Mycophenolic Acid (Myfortic) 385 mg 1-0-1-0
Pantoprazole (Protonix) 22.6 mg 1-0-0-0
Piperacillin/Tazobactam (Zosyn) 4.17 g and 0.54 g 1-1-1-0
Cyclosporine, microemulsified (Neoral) 10 mg 1-0-1-0
Cyclosporine, microemulsified (Neoral) 50 mg 1-0-1-0
Torsemide (Demadex) 10 mg 2-1-0-0
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
------------------------------------------- ------------------ ---------------------
Sodium 141 mEq/L 136-145 mEq/L
Potassium 3.9 mEq/L 3.5-4.5 mEq/L
Creatinine 3.02 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR) 19 mL/min/1.73m² \-
Total Bilirubin 0.73 mg/dL \< 1.20 mg/dL
Direct Bilirubin 0.41 mg/dL \< 0.30 mg/dL
C-reactive Protein 78.3 mg/dL \< 5.0 mg/dL
Alanine Aminotransferase 35 U/L \< 41 U/L
Aspartate Aminotransferase 33 U/L \< 50 U/L
Alkaline Phosphatase 273 U/L 40-130 U/L
Gamma-Glutamyl Transferase 184 U/L 8-61 U/L
Lipase 102 U/L 13-60 U/L
Hemoglobin 12.3 g/dL 12.5-17.2 g/dL
Hematocrit 39.0% 37.0-49.0%
Red Blood Cells 4.2 M/uL 4.0-5.6 M/uL
White Blood Cells 10.41 K/uL 3.90-10.50 K/uL
Platelets 488 K/uL 150-370 K/uL
Mean Corpuscular Volume 92.4 fL 80.0-101.0 fL
Mean Corpuscular Hemoglobin 29.1 pg 27.0-34.0 pg
Mean Corpuscular Hemoglobin Concentration 31.5 g/dL 31.5-36.0 g/dL
Mean Platelet Volume 10.3 fL 7.0-12.0 fL
Red Cell Distribution Width 13.5% 11.5-15.0% | Fluvastatin |
What language pairs did they experiment with? | ### Introduction
Neural machine translation (NMT, § SECREF2 ; kalchbrenner13emnlp, sutskever14nips) is a variant of statistical machine translation (SMT; brown93cl), using neural networks. NMT has recently gained popularity due to its ability to model the translation process end-to-end using a single probabilistic model, and for its state-of-the-art performance on several language pairs BIBREF0 , BIBREF1 . One feature of NMT systems is that they treat each word in the vocabulary as a vector of continuous-valued numbers. This is in contrast to more traditional SMT methods such as phrase-based machine translation (PBMT; koehn03phrasebased), which represent translations as discrete pairs of word strings in the source and target languages. The use of continuous representations is a major advantage, allowing NMT to share statistical power between similar words (e.g. “dog” and “cat”) or contexts (e.g. “this is” and “that is”). However, this property also has a drawback in that NMT systems often mistranslate into words that seem natural in the context, but do not reflect the content of the source sentence. For example, Figure FIGREF2 is a sentence from our data where the NMT system mistakenly translated “Tunisia” into the word for “Norway.” This variety of error is particularly serious because the content words that are often mistranslated by NMT are also the words that play a key role in determining the whole meaning of the sentence. In contrast, PBMT and other traditional SMT methods tend to rarely make this kind of mistake. This is because they base their translations on discrete phrase mappings, which ensure that source words will be translated into a target word that has been observed as a translation at least once in the training data. In addition, because the discrete mappings are memorized explicitly, they can be learned efficiently from as little as a single instance (barring errors in word alignments). Thus we hypothesize that if we can incorporate a similar variety of information into NMT, this has the potential to alleviate problems with the previously mentioned fatal errors on low-frequency words. In this paper, we propose a simple, yet effective method to incorporate discrete, probabilistic lexicons as an additional information source in NMT (§ SECREF3 ). First we demonstrate how to transform lexical translation probabilities (§ SECREF7 ) into a predictive probability for the next word by utilizing attention vectors from attentional NMT models BIBREF2 . We then describe methods to incorporate this probability into NMT, either through linear interpolation with the NMT probabilities (§ UID10 ) or as the bias to the NMT predictive distribution (§ UID9 ). We construct these lexicon probabilities by using traditional word alignment methods on the training data (§ SECREF11 ), other external parallel data resources such as a handmade dictionary (§ SECREF13 ), or using a hybrid between the two (§ SECREF14 ). We perform experiments (§ SECREF5 ) on two English-Japanese translation corpora to evaluate the method's utility in improving translation accuracy and reducing the time required for training. ### Neural Machine Translation
The goal of machine translation is to translate a sequence of source words INLINEFORM0 into a sequence of target words INLINEFORM1 . These words belong to the source vocabulary INLINEFORM2 , and the target vocabulary INLINEFORM3 respectively. NMT performs this translation by calculating the conditional probability INLINEFORM4 of the INLINEFORM5 th target word INLINEFORM6 based on the source INLINEFORM7 and the preceding target words INLINEFORM8 . This is done by encoding the context INLINEFORM9 a fixed-width vector INLINEFORM10 , and calculating the probability as follows: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are respectively weight matrix and bias vector parameters. The exact variety of the NMT model depends on how we calculate INLINEFORM0 used as input. While there are many methods to perform this modeling, we opt to use attentional models BIBREF2 , which focus on particular words in the source sentence when calculating the probability of INLINEFORM1 . These models represent the current state of the art in NMT, and are also convenient for use in our proposed method. Specifically, we use the method of luong15emnlp, which we describe briefly here and refer readers to the original paper for details. First, an encoder converts the source sentence INLINEFORM0 into a matrix INLINEFORM1 where each column represents a single word in the input sentence as a continuous vector. This representation is generated using a bidirectional encoder INLINEFORM2 Here the INLINEFORM0 function maps the words into a representation BIBREF3 , and INLINEFORM1 is a stacking long short term memory (LSTM) neural network BIBREF4 , BIBREF5 , BIBREF6 . Finally we concatenate the two vectors INLINEFORM2 and INLINEFORM3 into a bidirectional representation INLINEFORM4 . These vectors are further concatenated into the matrix INLINEFORM5 where the INLINEFORM6 th column corresponds to INLINEFORM7 . Next, we generate the output one word at a time while referencing this encoded input sentence and tracking progress with a decoder LSTM. The decoder's hidden state INLINEFORM0 is a fixed-length continuous vector representing the previous target words INLINEFORM1 , initialized as INLINEFORM2 . Based on this INLINEFORM3 , we calculate a similarity vector INLINEFORM4 , with each element equal to DISPLAYFORM0 INLINEFORM0 can be an arbitrary similarity function, which we set to the dot product, following luong15emnlp. We then normalize this into an attention vector, which weights the amount of focus that we put on each word in the source sentence DISPLAYFORM0 This attention vector is then used to weight the encoded representation INLINEFORM0 to create a context vector INLINEFORM1 for the current time step INLINEFORM2 Finally, we create INLINEFORM0 by concatenating the previous hidden state INLINEFORM1 with the context vector, and performing an affine transform INLINEFORM2 Once we have this representation of the current state, we can calculate INLINEFORM0 according to Equation ( EQREF3 ). The next word INLINEFORM1 is chosen according to this probability, and we update the hidden state by inputting the chosen word into the decoder LSTM DISPLAYFORM0 If we define all the parameters in this model as INLINEFORM0 , we can then train the model by minimizing the negative log-likelihood of the training data INLINEFORM1 ### Integrating Lexicons into NMT
In § SECREF2 we described how traditional NMT models calculate the probability of the next target word INLINEFORM0 . Our goal in this paper is to improve the accuracy of this probability estimate by incorporating information from discrete probabilistic lexicons. We assume that we have a lexicon that, given a source word INLINEFORM1 , assigns a probability INLINEFORM2 to target word INLINEFORM3 . For a source word INLINEFORM4 , this probability will generally be non-zero for a small number of translation candidates, and zero for the majority of words in INLINEFORM5 . In this section, we first describe how we incorporate these probabilities into NMT, and explain how we actually obtain the INLINEFORM6 probabilities in § SECREF4 . ### Converting Lexicon Probabilities into Conditioned Predictive Proabilities
First, we need to convert lexical probabilities INLINEFORM0 for the individual words in the source sentence INLINEFORM1 to a form that can be used together with INLINEFORM2 . Given input sentence INLINEFORM3 , we can construct a matrix in which each column corresponds to a word in the input sentence, each row corresponds to a word in the INLINEFORM4 , and the entry corresponds to the appropriate lexical probability: INLINEFORM5 This matrix can be precomputed during the encoding stage because it only requires information about the source sentence INLINEFORM0 . Next we convert this matrix into a predictive probability over the next word: INLINEFORM0 . To do so we use the alignment probability INLINEFORM1 from Equation ( EQREF5 ) to weight each column of the INLINEFORM2 matrix: INLINEFORM3 This calculation is similar to the way how attentional models calculate the context vector INLINEFORM0 , but over a vector representing the probabilities of the target vocabulary, instead of the distributed representations of the source words. The process of involving INLINEFORM1 is important because at every time step INLINEFORM2 , the lexical probability INLINEFORM3 will be influenced by different source words. ### Combining Predictive Probabilities
After calculating the lexicon predictive probability INLINEFORM0 , next we need to integrate this probability with the NMT model probability INLINEFORM1 . To do so, we examine two methods: (1) adding it as a bias, and (2) linear interpolation. In our first bias method, we use INLINEFORM0 to bias the probability distribution calculated by the vanilla NMT model. Specifically, we add a small constant INLINEFORM1 to INLINEFORM2 , take the logarithm, and add this adjusted log probability to the input of the softmax as follows: INLINEFORM3 We take the logarithm of INLINEFORM0 so that the values will still be in the probability domain after the softmax is calculated, and add the hyper-parameter INLINEFORM1 to prevent zero probabilities from becoming INLINEFORM2 after taking the log. When INLINEFORM3 is small, the model will be more heavily biased towards using the lexicon, and when INLINEFORM4 is larger the lexicon probabilities will be given less weight. We use INLINEFORM5 for this paper. We also attempt to incorporate the two probabilities through linear interpolation between the standard NMT probability model probability INLINEFORM0 and the lexicon probability INLINEFORM1 . We will call this the linear method, and define it as follows: INLINEFORM2 where INLINEFORM0 is an interpolation coefficient that is the result of the sigmoid function INLINEFORM1 . INLINEFORM2 is a learnable parameter, and the sigmoid function ensures that the final interpolation level falls between 0 and 1. We choose INLINEFORM3 ( INLINEFORM4 ) at the beginning of training. This notation is partly inspired by allamanis16icml and gu16acl who use linear interpolation to merge a standard attentional model with a “copy” operator that copies a source word as-is into the target sentence. The main difference is that they use this to copy words into the output while our method uses it to influence the probabilities of all target words. ### Constructing Lexicon Probabilities
In the previous section, we have defined some ways to use predictive probabilities INLINEFORM0 based on word-to-word lexical probabilities INLINEFORM1 . Next, we define three ways to construct these lexical probabilities using automatically learned lexicons, handmade lexicons, or a combination of both. ### Automatically Learned Lexicons
In traditional SMT systems, lexical translation probabilities are generally learned directly from parallel data in an unsupervised fashion using a model such as the IBM models BIBREF7 , BIBREF8 . These models can be used to estimate the alignments and lexical translation probabilities INLINEFORM0 between the tokens of the two languages using the expectation maximization (EM) algorithm. First in the expectation step, the algorithm estimates the expected count INLINEFORM0 . In the maximization step, lexical probabilities are calculated by dividing the expected count by all possible counts: INLINEFORM1 The IBM models vary in level of refinement, with Model 1 relying solely on these lexical probabilities, and latter IBM models (Models 2, 3, 4, 5) introducing more sophisticated models of fertility and relative alignment. Even though IBM models also occasionally have problems when dealing with the rare words (e.g. “garbage collecting” effects BIBREF9 ), traditional SMT systems generally achieve better translation accuracies of low-frequency words than NMT systems BIBREF6 , indicating that these problems are less prominent than they are in NMT. Note that in many cases, NMT limits the target vocabulary BIBREF10 for training speed or memory constraints, resulting in rare words not being covered by the NMT vocabulary INLINEFORM0 . Accordingly, we allocate the remaining probability assigned by the lexicon to the unknown word symbol INLINEFORM1 : DISPLAYFORM0 ### Manual Lexicons
In addition, for many language pairs, broad-coverage handmade dictionaries exist, and it is desirable that we be able to use the information included in them as well. Unlike automatically learned lexicons, however, handmade dictionaries generally do not contain translation probabilities. To construct the probability INLINEFORM0 , we define the set of translations INLINEFORM1 existing in the dictionary for particular source word INLINEFORM2 , and assume a uniform distribution over these words: INLINEFORM3 Following Equation ( EQREF12 ), unknown source words will assign their probability mass to the INLINEFORM0 tag. ### Hybrid Lexicons
Handmade lexicons have broad coverage of words but their probabilities might not be as accurate as the learned ones, particularly if the automatic lexicon is constructed on in-domain data. Thus, we also test a hybrid method where we use the handmade lexicons to complement the automatically learned lexicon. Specifically, inspired by phrase table fill-up used in PBMT systems BIBREF11 , we use the probability of the automatically learned lexicons INLINEFORM1 by default, and fall back to the handmade lexicons INLINEFORM2 only for uncovered words: DISPLAYFORM0 ### Experiment & Result
In this section, we describe experiments we use to evaluate our proposed methods. ### Settings
Dataset: We perform experiments on two widely-used tasks for the English-to-Japanese language pair: KFTT BIBREF12 and BTEC BIBREF13 . KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus. BTEC is an easier translation task than KFTT, because KFTT covers a broader domain, has a larger vocabulary of rare words, and has relatively long sentences. The details of each corpus are depicted in Table TABREF19 . We tokenize English according to the Penn Treebank standard BIBREF14 and lowercase, and tokenize Japanese using KyTea BIBREF15 . We limit training sentence length up to 50 in both experiments and keep the test data at the original length. We replace words of frequency less than a threshold INLINEFORM0 in both languages with the INLINEFORM1 symbol and exclude them from our vocabulary. We choose INLINEFORM2 for BTEC and INLINEFORM3 for KFTT, resulting in INLINEFORM4 k, INLINEFORM5 k for BTEC and INLINEFORM6 k, INLINEFORM7 k for KFTT. NMT Systems: We build the described models using the Chainer toolkit. The depth of the stacking LSTM is INLINEFORM0 and hidden node size INLINEFORM1 . We concatenate the forward and backward encodings (resulting in a 1600 dimension vector) and then perform a linear transformation to 800 dimensions. We train the system using the Adam BIBREF16 optimization method with the default settings: INLINEFORM0 . Additionally, we add dropout BIBREF17 with drop rate INLINEFORM1 at the last layer of each stacking LSTM unit to prevent overfitting. We use a batch size of INLINEFORM2 and we run a total of INLINEFORM3 iterations for all data sets. All of the experiments are conducted on a single GeForce GTX TITAN X GPU with a 12 GB memory cache. At test time, we use beam search with beam size INLINEFORM0 . We follow luong15acl in replacing every unknown token at position INLINEFORM1 with the target token that maximizes the probability INLINEFORM2 . We choose source word INLINEFORM3 according to the highest alignment score in Equation ( EQREF5 ). This unknown word replacement is applied to both baseline and proposed systems. Finally, because NMT models tend to give higher probabilities to shorter sentences BIBREF18 , we discount the probability of INLINEFORM4 token by INLINEFORM5 to correct for this bias. Traditional SMT Systems: We also prepare two traditional SMT systems for comparison: a PBMT system BIBREF19 using Moses BIBREF20 , and a hierarchical phrase-based MT system BIBREF21 using Travatar BIBREF22 , Systems are built using the default settings, with models trained on the training data, and weights tuned on the development data. Lexicons: We use a total of 3 lexicons for the proposed method, and apply bias and linear method for all of them, totaling 6 experiments. The first lexicon (auto) is built on the training data using the automatically learned lexicon method of § SECREF11 separately for both the BTEC and KFTT experiments. Automatic alignment is performed using GIZA++ BIBREF8 . The second lexicon (man) is built using the popular English-Japanese dictionary Eijiro with the manual lexicon method of § SECREF13 . Eijiro contains 104K distinct word-to-word translation entries. The third lexicon (hyb) is built by combining the first and second lexicon with the hybrid method of § SECREF14 . Evaluation: We use standard single reference BLEU-4 BIBREF23 to evaluate the translation performance. Additionally, we also use NIST BIBREF24 , which is a measure that puts a particular focus on low-frequency word strings, and thus is sensitive to the low-frequency words we are focusing on in this paper. We measure the statistical significant differences between systems using paired bootstrap resampling BIBREF25 with 10,000 iterations and measure statistical significance at the INLINEFORM0 and INLINEFORM1 levels. Additionally, we also calculate the recall of rare words from the references. We define “rare words” as words that appear less than eight times in the target training corpus or references, and measure the percentage of time they are recovered by each translation system. ### Effect of Integrating Lexicons
In this section, we first a detailed examination of the utility of the proposed bias method when used with the auto or hyb lexicons, which empirically gave the best results, and perform a comparison among the other lexicon integration methods in the following section. Table TABREF20 shows the results of these methods, along with the corresponding baselines. First, compared to the baseline attn, our bias method achieved consistently higher scores on both test sets. In particular, the gains on the more difficult KFTT set are large, up to 2.3 BLEU, 0.44 NIST, and 30% Recall, demonstrating the utility of the proposed method in the face of more diverse content and fewer high-frequency words. Compared to the traditional pbmt systems hiero, particularly on KFTT we can see that the proposed method allows the NMT system to exceed the traditional SMT methods in BLEU. This is despite the fact that we are not performing ensembling, which has proven to be essential to exceed traditional systems in several previous works BIBREF6 , BIBREF0 , BIBREF1 . Interestingly, despite gains in BLEU, the NMT methods still fall behind in NIST score on the KFTT data set, demonstrating that traditional SMT systems still tend to have a small advantage in translating lower-frequency words, despite the gains made by the proposed method. In Table TABREF27 , we show some illustrative examples where the proposed method (auto-bias) was able to obtain a correct translation while the normal attentional model was not. The first example is a mistake in translating “extramarital affairs” into the Japanese equivalent of “soccer,” entirely changing the main topic of the sentence. This is typical of the errors that we have observed NMT systems make (the mistake from Figure FIGREF2 is also from attn, and was fixed by our proposed method). The second example demonstrates how these mistakes can then affect the process of choosing the remaining words, propagating the error through the whole sentence. Next, we examine the effect of the proposed method on the training time for each neural MT method, drawing training curves for the KFTT data in Figure FIGREF26 . Here we can see that the proposed bias training methods achieve reasonable BLEU scores in the upper 10s even after the first iteration. In contrast, the baseline attn method has a BLEU score of around 5 after the first iteration, and takes significantly longer to approach values close to its maximal accuracy. This shows that by incorporating lexical probabilities, we can effectively bootstrap the learning of the NMT system, allowing it to approach an appropriate answer in a more timely fashion. It is also interesting to examine the alignment vectors produced by the baseline and proposed methods, a visualization of which we show in Figure FIGREF29 . For this sentence, the outputs of both methods were both identical and correct, but we can see that the proposed method (right) placed sharper attention on the actual source word corresponding to content words in the target sentence. This trend of peakier attention distributions in the proposed method held throughout the corpus, with the per-word entropy of the attention vectors being 3.23 bits for auto-bias, compared with 3.81 bits for attn, indicating that the auto-bias method places more certainty in its attention decisions. ### Comparison of Integration Methods
Finally, we perform a full comparison between the various methods for integrating lexicons into the translation process, with results shown in Table TABREF31 . In general the bias method improves accuracy for the auto and hyb lexicon, but is less effective for the man lexicon. This is likely due to the fact that the manual lexicon, despite having broad coverage, did not sufficiently cover target-domain words (coverage of unique words in the source vocabulary was 35.3% and 9.7% for BTEC and KFTT respectively). Interestingly, the trend is reversed for the linear method, with it improving man systems, but causing decreases when using the auto and hyb lexicons. This indicates that the linear method is more suited for cases where the lexicon does not closely match the target domain, and plays a more complementary role. Compared to the log-linear modeling of bias, which strictly enforces constraints imposed by the lexicon distribution BIBREF27 , linear interpolation is intuitively more appropriate for integrating this type of complimentary information. On the other hand, the performance of linear interpolation was generally lower than that of the bias method. One potential reason for this is the fact that we use a constant interpolation coefficient that was set fixed in every context. gu16acl have recently developed methods to use the context information from the decoder to calculate the different interpolation coefficients for every decoding step, and it is possible that introducing these methods would improve our results. ### Additional Experiments
To test whether the proposed method is useful on larger data sets, we also performed follow-up experiments on the larger Japanese-English ASPEC dataset BIBREF28 that consist of 2 million training examples, 63 million tokens, and 81,000 vocabulary size. We gained an improvement in BLEU score from 20.82 using the attn baseline to 22.66 using the auto-bias proposed method. This experiment shows that our method scales to larger datasets. ### Related Work
From the beginning of work on NMT, unknown words that do not exist in the system vocabulary have been focused on as a weakness of these systems. Early methods to handle these unknown words replaced them with appropriate words in the target vocabulary BIBREF10 , BIBREF29 according to a lexicon similar to the one used in this work. In contrast to our work, these only handle unknown words and do not incorporate information from the lexicon in the learning procedure. There have also been other approaches that incorporate models that learn when to copy words as-is into the target language BIBREF30 , BIBREF31 , BIBREF32 . These models are similar to the linear approach of § UID10 , but are only applicable to words that can be copied as-is into the target language. In fact, these models can be thought of as a subclass of the proposed approach that use a lexicon that assigns a all its probability to target words that are the same as the source. On the other hand, while we are simply using a static interpolation coefficient INLINEFORM0 , these works generally have a more sophisticated method for choosing the interpolation between the standard and “copy” models. Incorporating these into our linear method is a promising avenue for future work. In addition mi16acl have also recently proposed a similar approach by limiting the number of vocabulary being predicted by each batch or sentence. This vocabulary is made by considering the original HMM alignments gathered from the training corpus. Basically, this method is a specific version of our bias method that gives some of the vocabulary a bias of negative infinity and all other vocabulary a uniform distribution. Our method improves over this by considering actual translation probabilities, and also considering the attention vector when deciding how to combine these probabilities. Finally, there have been a number of recent works that improve accuracy of low-frequency words using character-based translation models BIBREF33 , BIBREF34 , BIBREF35 . However, luong16acl have found that even when using character-based models, incorporating information about words allows for gains in translation accuracy, and it is likely that our lexicon-based method could result in improvements in these hybrid systems as well. ### Conclusion & Future Work
In this paper, we have proposed a method to incorporate discrete probabilistic lexicons into NMT systems to solve the difficulties that NMT systems have demonstrated with low-frequency words. As a result, we achieved substantial increases in BLEU (2.0-2.3) and NIST (0.13-0.44) scores, and observed qualitative improvements in the translations of content words. For future work, we are interested in conducting the experiments on larger-scale translation tasks. We also plan to do subjective evaluation, as we expect that improvements in content word translation are critical to subjective impressions of translation results. Finally, we are also interested in improvements to the linear method where INLINEFORM0 is calculated based on the context, instead of using a fixed value. ### Acknowledgment
We thank Makoto Morishita and Yusuke Oda for their help in this project. We also thank the faculty members of AHC lab for their supports and suggestions. This work was supported by grants from the Ministry of Education, Culture, Sport, Science, and Technology of Japan and in part by JSPS KAKENHI Grant Number 16H05873. Figure 1: An example of a mistake made by NMT on low-frequency content words. Table 1: Corpus details. Table 2: Accuracies for the baseline attentional NMT (attn) and the proposed bias-based method using the automatic (auto-bias) or hybrid (hyb-bias) dictionaries. Bold indicates a gain over the attn baseline, † indicates a significant increase at p < 0.05, and ∗ indicates p < 0.10. Traditional phrase-based (pbmt) and hierarchical phrase based (hiero) systems are shown for reference. Figure 2: Training curves for the baseline attn and the proposed bias method. Table 3: Examples where the proposed auto-bias improved over the baseline system attn. Underlines indicate words were mistaken in the baseline output but correct in the proposed model’s output. Figure 3: Attention matrices for baseline attn and proposed bias methods. Lighter colors indicate stronger attention between the words, and boxes surrounding words indicate the correct alignments. Table 4: A comparison of the bias and linear lexicon integration methods on the automatic, manual, and hybrid lexicons. The first line without lexicon is the traditional attentional NMT. | English-Japanese |
How many Viceroys are neither Vegan nor Sundan?
A. 15
B. 20
C. 25
D. 10
| UPSTARTS By L. J. STECHER, JR. Illustrated by DILLON The sight of an Earthman on Vega III, where it was impossible for an outlander to be, brought angry crowds to surround John Crownwall as he strode toward the palace of Viceroy Tronn Ffallk, ruler of Sector XII of the Universal Holy Empire of Sunda. He ignored the snarling, the spitting, the waving of boneless prehensile fingers, as he ignored the heavy gravity and heavier air of the unfamiliar planet. John Crownwall, florid, red-headed and bulky, considered himself to be a bold man. But here, surrounded by this writhing, slithering mass of eight-foot creatures, he felt distinctly unhappy. Crownwall had heard about creatures that slavered, but he had never before seen it done. These humanoids had large mouths and sharp teeth, and they unquestionably slavered. He wished he knew more about them. If they carried out the threats of their present attitude, Earth would have to send Marshall to replace him. And if Crownwall couldn't do the job, thought Crownwall, then it was a sure bet that Marshall wouldn't have a chance. He climbed the great ramp, with its deeply carved Greek key design, toward the mighty entrance gate of the palace. His manner demonstrated an elaborate air of unconcern that he felt sure was entirely wasted on these monsters. The clashing teeth of the noisiest of them were only inches from the quivering flesh of his back as he reached the upper level. Instantly, and unexpectedly to Crownwall, the threatening crowd dropped back fearfully, so that he walked the last fifty meters alone. Crownwall all but sagged with relief. A pair of guards, their purple hides smoothly polished and gleaming with oil, crossed their ceremonial pikes in front of him as he approached the entrance. "And just what business do you have here, stranger?" asked the senior of the guards, his speaking orifice framing with difficulty the sibilances of Universal Galactic. "What business would I have at the Viceroy's Palace?" asked Crownwall. "I want to see Ffallk." "Mind your tongue," growled the guard. "If you mean His Effulgence, Right Hand of the Glorious Emperor, Hereditary Ruler of the Seventy Suns, Viceroy of the Twelfth Sector of the Universal Holy Empire"—Universal Galactic had a full measure of ceremonial words—"he sees only those whom he summons. If you know what's good for you, you'll get out of here while you can still walk. And if you run fast enough, maybe you can even get away from that crowd out there, but I doubt it." "Just tell him that a man has arrived from Earth to talk to him. He'll summon me fast enough. Meanwhile, my highly polished friends, I'll just wait here, so why don't you put those heavy pikes down?" Crownwall sat on the steps, puffed alight a cigarette, and blew expert smoke rings toward the guards. An elegant courtier, with elaborately jeweled harness, bustled from inside the palace, obviously trying to present an air of strolling nonchalance. He gestured fluidly with a graceful tentacle. "You!" he said to Crownwall. "Follow me. His Effulgence commands you to appear before him at once." The two guards withdrew their pikes and froze into immobility at the sides of the entrance. Crownwall stamped out his smoke and ambled after the hurrying courtier along tremendous corridors, through elaborate waiting rooms, under guarded doorways, until he was finally bowed through a small curtained arch. At the far side of the comfortable, unimpressive room, a plump thing, hide faded to a dull violet, reclined on a couch. Behind him stood a heavy and pompous appearing Vegan in lordly trappings. They examined Crownwall with great interest for a few moments. "It's customary to genuflect when you enter the Viceroy's presence," said the standing one at last. "But then I'm told you're an Earthling. I suppose we can expect you to be ignorant of those niceties customary among civilized peoples." "It's all right, Ggaran," said the Viceroy languidly. He twitched a tentacle in a beckoning gesture. "Come closer, Earthling. I bid you welcome to my capital. I have been looking forward to your arrival for some time." Crownwall put his hands in his pockets. "That's hardly possible," he said. "It was only decided yesterday, back on Earth, that I would be the one to make the trip here. Even if you could spy through buildings on Earth from space, which I doubt, your communications system can't get the word through that fast." "Oh, I didn't mean you in particular," the Vegan said with a negligent wave. "Who can tell one Earthling from another? What I meant was that I expected someone from Earth to break through our blockade and come here. Most of my advisors—even Ggaran here—thought it couldn't be done, but I never doubted that you'd manage it. Still, if you were on your home planet only yesterday, that's astonishing even to me. Tell me, how did you manage to get here so fast, and without even alerting my detection web?" "You're doing the talking," said Crownwall. "If you wanted someone from Earth to come here to see you, why did you put the cordon around Earth? And why did you drop a planet-buster in the Pacific Ocean, and tell us that it was triggered to go off if we tried to use the distorter drive? That's hardly the action of somebody who expects visitors." Ffallk glanced up at Ggaran. "I told you that Earthlings were unbelievably bold." He turned back to Crownwall. "If you couldn't come to me in spite of the trifling inconveniences I put in your way, your presence here would be useless to both of us. But you did come, so I can tell you that although I am the leader of one of the mightiest peoples in the Galaxy, whereas there are scarcely six billions of you squatting on one minor planet, we still need each other. Together, there is nothing we can't do." "I'm listening," said Crownwall. "We offer you partnership with us to take over the rule of the Galaxy from the Sunda—the so-called Master Race." "It would hardly be an equal partnership, would it, considering that there are so many more of you than there are of us?" His Effulgence twitched his ear stalks in amusement. "I'm Viceroy of one of the hundred Sectors of the Empire. I rule over a total of a hundred Satrapies; these average about a hundred Provinces each. Provinces consist, in general, of about a hundred Clusters apiece, and every Cluster has an average of a hundred inhabited solar systems. There are more inhabited planets in the Galaxy than there are people on your single world. I, personally, rule three hundred trillion people, half of them of my own race. And yet I tell you that it would be an equal partnership." "I don't get it. Why?" "Because you came to me." Crownwall shrugged. "So?" The Vegan reached up and engulfed the end of a drinking tube with his eating orifice. "You upstart Earthlings are a strange and a frightening race," he said. "Frightening to the Sunda, especially. When you showed up in the spaceways, it was decreed that you had to be stopped at once. There was even serious discussion of destroying Earth out of hand, while it is still possible. "Your silly little planet was carefully examined at long range in a routine investigation just about fifty thousand years ago. There were at that time three different but similar racial strains of pulpy bipeds, numbering a total of perhaps a hundred thousand individuals. They showed many signs of an ability to reason, but a complete lack of civilization. While these creatures could by no means be classed among the intelligent races, there was a general expectation, which we reported to the Sunda, that they would some day come to be numbered among the Servants of the Emperor. So we let you alone, in order that you could develop in your own way, until you reached a high enough civilization to be useful—if you were going to. "Intelligence is very rare in the Galaxy. In all, it has been found only fifteen times. The other races we have watched develop, and some we have actively assisted to develop. It took the quickest of them just under a million years. One such race we left uncontrolled too long—but no matter. "You Earthlings, in defiance of all expectation and all reason, have exploded into space. You have developed in an incredibly short space of time. But even that isn't the most disconcerting item of your development. As an Earthling, you have heard of the details of the first expedition of your people into space, of course?" " Heard about it?" exclaimed Crownwall. "I was on it." He settled down comfortably on a couch, without requesting permission, and thought back to that first tremendous adventure; an adventure that had taken place little more than ten years before. The Star Seeker had been built in space, about forty thousand kilometers above the Earth. It had been manned by a dozen adventurous people, captained by Crownwall, and had headed out on its ion drive until it was safely clear of the warping influence of planetary masses. Then, after several impatient days of careful study and calculation, the distorter drive had been activated, for the first time in Earth's history, and, for the twelve, the stars had winked out. The men of Earth had decided that it should work in theory. They had built the drive—a small machine, as drives go—but they had never dared to try it, close to a planet. To do so, said their theory, would usually—seven point three four times out of 10—destroy the ship, and everything in space for thousands of miles around, in a ravening burst of raw energy. So the drive had been used for the first time without ever having been tested. And it had worked. In less than a week's time, if time has any meaning under such circumstances, they had flickered back into normal space, in the vicinity of Alpha Centauri. They had quickly located a dozen planets, and one that looked enough like Earth to be its twin sister. They had headed for that planet confidently and unsuspectingly, using the ion drive. Two weeks later, while they were still several planetary diameters from their destination, they had been shocked to find more than two score alien ships of space closing in on them—ships that were swifter and more maneuverable than their own. These ships had rapidly and competently englobed the Star Seeker , and had then tried to herd it away from the planet it had been heading toward. Although caught by surprise, the Earthmen had acted swiftly. Crownwall recalled the discussion—the council of war, they had called it—and their unanimous decision. Although far within the dangerous influence of a planetary mass, they had again activated the distorter drive, and they had beaten the odds. On the distorter drive, they had returned to Earth as swiftly as they had departed. Earth had immediately prepared for war against her unknown enemy. "Your reaction was savage," said Ggaran, his tentacles stiffening with shock at the memory. "You bloody-minded Earthlings must have been aware of the terrible danger." Ffallk rippled in agreement. "The action you took was too swift and too foolhardy to be believed. You knew that you could have destroyed not only yourself, but also all who live on that planet. You could also have wrecked the planet itself and the ships and those of my own race who manned them. We had tried to contact you, but since you had not developed subspace radio, we were of course not successful. Our englobement was just a routine quarantine. With your total lack of information about us, what you did was more than the height of folly. It was madness." "Could we have done anything else that would have kept you from landing on Earth and taking us over?" asked Crownwall. "Would that have been so bad?" said Ggaran. "We can't tolerate wild and warlike races running free and uncontrolled in the Galaxy. Once was enough for that." "But what about my question? Was there any other way for us to stay free?" "Well, no. But you didn't have enough information to realize that when you acted so precipitously. As a matter of fact, we didn't expect to have much trouble, even after your surprising action. Of course, it took us a little time to react. We located your planet quickly enough, and confirmed that you were a new race. But by the time we could try to set up communications and send ambassadors, you had already organized a not inconsiderable defense. Your drones blew up our unmanned ships as fast as we could send them down to your planet. And by the time we had organized properly for war against you, it was obvious that we could not conquer you. We could only destroy you." "That old fool on Sunda, the Emperor, decided that we should blow you up, but by that time I had decided," said His Effulgence, "that you might be useful to me—that is, that we might be useful to each other. I traveled halfway across the Galaxy to meet him, to convince him that it would be sufficient just to quarantine you. When we had used your radio system to teach a few of you the Universal Galactic tongue, and had managed to get what you call the 'planet-buster' down into the largest of your oceans, he figured we had done our job. "With his usual lack of imagination, he felt sure that we were safe from you—after all, there was no way for you to get off the planet. Even if you could get down to the bottom of the ocean and tamper with the bomb, you would only succeed in setting it off, and that's what the Sunda had been in favor of in the first place. "But I had different ideas. From what you had already done, I suspected it wouldn't be long before one of you amazing Earthlings would dream up some device or other, head out into space, and show up on our planet. So I've been waiting for you, and here you are." "It was the thinking of a genius," murmured Ggaran. "All right, then, genius, here I am," said Crownwall. "So what's the pitch?" "Ggaran, you explain it to the Earthling," said His Effulgence. Ggaran bowed. "The crustaceans on Sunda—the lobsterlike creatures that rule the Galaxy—are usurpers. They have no rights to their position of power. Our race is much older than theirs. We were alone when we found the Sundans—a primitive tribe, grubbing in the mud at the edge of their shallow seas, unable even to reason. In those days we were desperately lonely. We needed companionship among the stars, and we helped them develop to the point where, in their inferior way, they were able to reason, almost as well as we, The People, can. And then they cheated us of our rightful place. "The Emperor at Sunda is one of them. They provide sixty-eight of the hundred Viceroys; we provide only seventeen. It is a preposterous and intolerable situation. "For more than two million years we have waited for the opportunity for revenge. And now that you have entered space, that opportunity is at hand." "If you haven't been able to help yourselves for two million years," asked Crownwall, "how does the sight of me give you so much gumption all of a sudden?" Ggaran's tentacles writhed, and he slavered in fury, but the clashing of his teeth subsided instantly at a soothing wave from His Effulgence. "War in space is almost an impossibility," said the aged ruler. "We can destroy planets, of course, but with few exceptions, we cannot conquer them. I rule a total of seven races in my Sector. I rule them, but I don't let them intermingle. Each race settles on the planets that best suit it. Each of those planets is quite capable of defending itself from raids, or even large-scale assaults that would result in its capture and subjugation—just as your little Earth can defend itself. "Naturally, each is vulnerable to economic blockade—trade provides a small but vital portion of the goods each planet uses. All that a world requires for a healthy and comfortable life cannot be provided from the resources of that single world alone, and that gives us a very considerable measure of control. "And it is true that we can always exterminate any planet that refuses to obey the just and legal orders of its Viceroy. So we achieve a working balance in our Empire. We control it adequately, and we live in peace. "The Sundans, for example, though they took the rule of the Empire that was rightfully ours away from us, through trickery, were unable to take over the Sectors we control. We are still powerful. And soon we will be all-powerful. In company with you Earthlings, that is." Crownwall nodded. "In other words, you think that we Earthmen can break up this two-million-year-old stalemate. You've got the idea that, with our help, you can conquer planets without the necessity of destroying them, and thereby take over number one spot from these Sunda friends of yours." "Don't call those damn lobsters friends," growled Ggaran. He subsided at the Viceroy's gesture. "Exactly," said His Effulgence to Crownwall. "You broke our blockade without any trouble. Our instruments didn't even wiggle when you landed here on my capital world. You can do the same on the worlds of the Sunda. Now, just tell us how you did it, and we're partners." Crownwall lifted one eyebrow quizzically, but remained silent. He didn't expect his facial gesture to be interpreted correctly, but he assumed that his silence would be. He was correct. "Of course," His Effulgence said, "we will give you any assurances that your people may desire in order to feel safe, and we will guarantee them an equal share in the government of the Galaxy." "Bunk," said Crownwall. His Effulgence lifted a tentacle swiftly, before Ggaran, lunging angrily forward, could speak. "Then what do you want of us?" "It seems to me that we need no wordy assurances from each other," said Crownwall, and he puffed a cigarette aglow. "We can arrange something a little more trustworthy, I believe. On your side, you have the power to destroy our only planet at any time. That is certainly adequate security for our own good behavior and sincerity. "It is impossible for us of Earth to destroy all of your planets. As you have said, there are more planets that belong to you than there are human beings on Earth. But there is a way for us to be reasonably sure that you will behave yourselves. You will transfer to us, at once, a hundred of your planet-destroying bombs. That will be a sufficient supply to let us test some of them, to see that they are in good working order. Then, if you try any kind of double-cross, we will be able to use our own methods—which you cannot prevent—to send one of those bombs here to destroy this planet. "And if you try to move anywhere else, by your clumsy distorter drive, we can follow you, and destroy any planet you choose to land on. You would not get away from us. We can track you without any difficulty. "We wouldn't use the bombs lightly, to be sure, because of what would happen to Earth. And don't think that blowing up our planet would save you, because we naturally wouldn't keep the bombs on Earth. How does that sound to you?" "Ridiculous," snorted Ggaran. "Impossible." After several minutes of silent consideration, "It is an excellent plan," said His Effulgence. "It is worthy of the thinking of The People ourselves. You Earthlings will make very satisfactory allies. What you request will be provided without delay. Meanwhile, I see no reason why we cannot proceed with our discussions." "Nor do I," consented Crownwall. "But your stooge here doesn't seem very happy about it all." His Effulgence wiggled his tentacles. "I'm afraid that Ggaran had expected to take what you Earthlings have to offer without giving anything in return. I never had any such ideas. I have not underestimated you, you see." "That's nice," said Crownwall graciously. "And now," Ggaran put in, "I think it's time for you to tell us something about how you get across light-years of space in a few hours, without leaving any traces for us to detect." He raised a tentacle to still Crownwall's immediate exclamation of protest. "Oh, nothing that would give us a chance to duplicate it—just enough to indicate how we can make use of it, along with you—enough to allow us to begin to make intelligent plans to beat the claws off the Master Race." After due consideration, Crownwall nodded. "I don't see why not. Well, then, let me tell you that we don't travel in space at all. That's why I didn't show up on any of your long-range detection instruments. Instead, we travel in time. Surely any race that has progressed as far as your own must know, at least theoretically, that time travel is entirely possible. After all, we knew it, and we haven't been around nearly as long as you have." "We know about it," said Ffallk, "but we've always considered it useless—and very dangerous—knowledge." "So have we, up until the time you planted that bomb on us. Anyone who tried to work any changes in his own past would be almost certain to end up finding himself never having been born. So we don't do any meddling. What we have discovered is a way not only of moving back into the past, but also of making our own choice of spatial references while we do it, and of changing our spatial anchor at will. "For example, to reach this planet, I went back far enough, using Earth as the spatial referent, to move with Earth a little more than a third of the way around this spiral nebula that is our Galaxy. Then I shifted my frame of reference to that of the group of galaxies of which ours is such a distinguished member. "Then of course, as I continued to move in time, the whole Galaxy moved spatially with reference to my own position. At the proper instant I shifted again, to the reference frame of this Galaxy itself. Then I was stationary in the Galaxy, and as I continued time traveling, your own mighty sun moved toward me as the Galaxy revolved. I chose a point where there was a time intersection of your planet's position and my own. When you got there, I just changed to the reference plane of this planet I'm on now, and then came on back with it to the present. So here I am. It was a long way around to cover a net distance of 26 light-years, but it was really very simple. "And there's no danger of meeting myself, or getting into any anachronistic situation. As you probably know, theory shows that these are excluded times for me, as is the future—I can't stop in them." "Are you sure that you haven't given us a little too much information for your own safety?" asked Ffallk softly. "Not at all. We were enormously lucky to have learned how to control spatial reference frames ourselves. I doubt if you could do it in another two million years." Crownwall rose to his feet. "And now, Your Effulgence, I think it's about time I went back to my ship and drove it home to Earth to make my report, so we can pick up those bombs and start making arrangements." "Excellent," said Ffallk. "I'd better escort you; my people don't like strangers much." "I'd noticed that," Crownwall commented drily. "Since this is a very important occasion, I think it best that we make this a Procession of Full Ceremony. It's a bother, but the proprieties have to be observed." Ggaran stepped out into the broad corridor and whistled a shrill two-tone note, using both his speaking and his eating orifices. A cohort of troops, pikes at the ready and bows strapped to their backs, leaped forward and formed a double line leading from His Effulgence's sanctum to the main door. Down this lane, carried by twenty men, came a large sedan chair. "Protocol takes a lot of time," said His Effulgence somewhat sadly, "but it must be observed. At least, as Ambassador, you can ride with me in the sedan, instead of walking behind it, like Ggaran." "I'm glad of that," said Crownwall. "Too bad Ggaran can't join us." He climbed into the chair beside Ffallk. The bearers trotted along at seven or eight kilometers an hour, carrying their contraption with absolute smoothness. Blasts from horns preceded them as they went. When they passed through the huge entrance doors of the palace and started down the ramp toward the street, Crownwall was astonished to see nobody on the previously crowded streets, and mentioned it to Ffallk. "When the Viceroy of the Seventy Suns," said the Viceroy of the Seventy Suns, "travels in state, no one but my own entourage is permitted to watch. And my guests, of course," he added, bowing slightly to Crownwall. "Of course," agreed Crownwall, bowing back. "Kind of you, I'm sure. But what happens if somebody doesn't get the word, or doesn't hear your trumpeters, or something like that?" Ggaran stepped forward, already panting slightly. "A man with knots in all of his ear stalks is in a very uncomfortable position," he explained. "Wait. Let me show you. Let us just suppose that that runner over there"—he gestured toward a soldier with a tentacle—"is a civilian who has been so unlucky as to remain on the street after His Effulgence's entourage arrived." He turned to one of the bowmen who ran beside the sedan chair, now strung and at the ready. "Show him!" he ordered peremptorily. In one swift movement the bowman notched an arrow, drew and fired. The arrow hissed briefly, and then sliced smoothly through the soldier's throat. "You see," said Ggaran complacently, "we have very little trouble with civilians who violate this particular tradition." His Effulgence beckoned to the bowman to approach. "Your results were satisfactory," he said, "but your release was somewhat shaky. The next time you show such sloppy form, you will be given thirty lashes." He leaned back on the cushion and spoke again to Crownwall. "That's the trouble with these requirements of civilization. The men of my immediate guard must practice with such things as pikes and bows and arrows, which they seldom get an opportunity to use. It would never do for them to use modern weapons on occasions of ceremony, of course." "Of course," said Crownwall, then added, "It's too bad that you can't provide them with live targets a little more often." He stifled a shudder of distaste. "Tell me, Your Effulgence, does the Emperor's race—the Master Race—also enjoy the type of civilization you have just had demonstrated for me?" "Oh, no. They are far too brutal, too morally degraded, to know anything of these finer points of etiquette and propriety. They are really an uncouth bunch. Why, do you know, I am certain that they would have had the bad taste to use an energy weapon to dispose of the victim in a case such as you just witnessed! They are really quite unfit to rule. They can scarcely be called civilized at all. But we will soon put a stop to all of that—your race and mine, of course." "I sincerely hope so," said Crownwall. Refreshments were served to His Effulgence and to Crownwall during the trip, without interrupting the smooth progress of the sedan. The soldiers of the cohort, the bearers and Ggaran continued to run—without food, drink or, except for Ggaran, evidence of fatigue. After several hours of travel, following Crownwall's directions, the procession arrived at the copse in which he had concealed his small transportation machine. The machine, for spatial mobility, was equipped with the heavy and grossly inefficient anti-gravity field generator developed by Kowalsky. It occupied ten times the space of the temporal translation and coordination selection systems combined, but it had the great advantage of being almost undetectable in use. It emitted no mass or radiation. After elaborate and lengthy farewells, Crownwall climbed into his machine and fell gently up until he was out of the atmosphere, before starting his enormous journey through time back to Earth. More quickly than it had taken him to reach his ship from the palace of His Effulgence, he was in the Council Chamber of the Confederation Government of Earth, making a full report on his trip to Vega. When he had finished, the President sighed deeply. "Well," he said, "we gave you full plenipotentiary powers, so I suppose we'll have to stand behind your agreements—especially in view of the fact that we'll undoubtedly be blown into atoms if we don't. But from what you say, I'd rather be in bed with a rattler than have a treaty with a Vegan. They sound ungodly murderous to me. There are too many holes in that protection plan of yours. It's only a question of time before they'll find some way around it, and then—poof—we'll all be dust." "Things may not be as bad as they seem," answered Crownwall complacently. "After I got back a few million years, I'm afraid I got a little careless and let my ship dip down into Vega III's atmosphere for a while. I was back so far that the Vegans hadn't appeared yet. Now, I didn't land—or deliberately kill anything—but I'd be mighty surprised if we didn't find a change or two. Before I came in here, I asked Marshall to take the ship out and check on things. He should be back with his report before long. Why don't we wait and see what he has to say?" Marshall was excited when he was escorted into the Council Chamber. He bowed briefly to the President and began to speak rapidly. "They're gone without trace— all of them !" he cried. "I went clear to Sunda and there's no sign of intelligent life anywhere! We're all alone now!" "There, you see?" exclaimed Crownwall. "Our enemies are all gone!" He looked around, glowing with victory, at the others at the table, then slowly quieted and sat down. He turned his head away from their accusing eyes. "Alone," he said, and unconsciously repeated Marshall's words: "We're all alone now." In silence, the others gathered their papers together and left the room, leaving Crownwall sitting at the table by himself. He shivered involuntarily, and then leaped to his feet to follow after them. Loneliness, he found, was something that he couldn't face alone. —L. J. STECHER, JR. Transcriber's Note: This etext was produced from Galaxy Magazine June 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. 15 |
What were the evaluation metrics? | ### Introduction
Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, several works tried to model the task-oriented dialogue as the Seq2Seq generation of response from the dialogue history BIBREF5, BIBREF6, BIBREF7. This kind of modeling scheme frees the task-oriented dialogue system from the manually designed pipeline modules and heavy annotation labor for these modules. Different from typical text generation, the successful conversations for task-oriented dialogue system heavily depend on accurate knowledge base (KB) queries. Taking the dialogue in Figure FIGREF1 as an example, to answer the driver's query on the gas station, the dialogue system is required to retrieve the entities like “200 Alester Ave” and “Valero”. For the task-oriented system based on Seq2Seq generation, there is a trend in recent study towards modeling the KB query as an attention network over the entire KB entity representations, hoping to learn a model to pay more attention to the relevant entities BIBREF6, BIBREF7, BIBREF8, BIBREF9. Though achieving good end-to-end dialogue generation with over-the-entire-KB attention mechanism, these methods do not guarantee the generation consistency regarding KB entities and sometimes yield responses with conflict entities, like “Valero is located at 899 Ames Ct” for the gas station query (as shown in Figure FIGREF1). In fact, the correct address for Valero is 200 Alester Ave. A consistent response is relatively easy to achieve for the conventional pipeline systems because they query the KB by issuing API calls BIBREF10, BIBREF11, BIBREF12, and the returned entities, which typically come from a single KB row, are consistently related to the object (like the “gas station”) that serves the user's request. This indicates that a response can usually be supported by a single KB row. It's promising to incorporate such observation into the Seq2Seq dialogue generation model, since it encourages KB relevant generation and avoids the model from producing responses with conflict entities. To achieve entity-consistent generation in the Seq2Seq task-oriented dialogue system, we propose a novel framework which query the KB in two steps. In the first step, we introduce a retrieval module — KB-retriever to explicitly query the KB. Inspired by the observation that a single KB row usually supports a response, given the dialogue history and a set of KB rows, the KB-retriever uses a memory network BIBREF13 to select the most relevant row. The retrieval result is then fed into a Seq2Seq dialogue generation model to filter the irrelevant KB entities and improve the consistency within the generated entities. In the second step, we further perform attention mechanism to address the most correlated KB column. Finally, we adopt the copy mechanism to incorporate the retrieved KB entity. Since dialogue dataset is not typically annotated with the retrieval results, training the KB-retriever is non-trivial. To make the training feasible, we propose two methods: 1) we use a set of heuristics to derive the training data and train the retriever in a distant supervised fashion; 2) we use Gumbel-Softmax BIBREF14 as an approximation of the non-differentiable selecting process and train the retriever along with the Seq2Seq dialogue generation model. Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever. Both the retrievers trained with distant-supervision and Gumbel-Softmax technique outperform the compared systems in the automatic and human evaluations. Analysis empirically verifies our assumption that more than 80% responses in the dataset can be supported by a single KB row and better retrieval results lead to better task-oriented dialogue generation performance. ### Definition
In this section, we will describe the input and output of the end-to-end task-oriented dialogue system, and the definition of Seq2Seq task-oriented dialogue generation. ### Definition ::: Dialogue History
Given a dialogue between a user ($u$) and a system ($s$), we follow eric:2017:SIGDial and represent the $k$-turned dialogue utterances as $\lbrace (u_{1}, s_{1} ), (u_{2} , s_{2} ), ... , (u_{k}, s_{k})\rbrace $. At the $i^{\text{th}}$ turn of the dialogue, we aggregate dialogue context which consists of the tokens of $(u_{1}, s_{1}, ..., s_{i-1}, u_{i})$ and use $\mathbf {x} = (x_{1}, x_{2}, ..., x_{m})$ to denote the whole dialogue history word by word, where $m$ is the number of tokens in the dialogue history. ### Definition ::: Knowledge Base
In this paper, we assume to have the access to a relational-database-like KB $B$, which consists of $|\mathcal {R}|$ rows and $|\mathcal {C}|$ columns. The value of entity in the $j^{\text{th}}$ row and the $i^{\text{th}}$ column is noted as $v_{j, i}$. ### Definition ::: Seq2Seq Dialogue Generation
We define the Seq2Seq task-oriented dialogue generation as finding the most likely response $\mathbf {y}$ according to the input dialogue history $\mathbf {x}$ and KB $B$. Formally, the probability of a response is defined as where $y_t$ represents an output token. ### Our Framework
In this section, we describe our framework for end-to-end task-oriented dialogues. The architecture of our framework is demonstrated in Figure FIGREF3, which consists of two major components including an memory network-based retriever and the seq2seq dialogue generation with KB Retriever. Our framework first uses the KB-retriever to select the most relevant KB row and further filter the irrelevant entities in a Seq2Seq response generation model to improve the consistency among the output entities. While in decoding, we further perform the attention mechanism to choose the most probable KB column. We will present the details of our framework in the following sections. ### Our Framework ::: Encoder
In our encoder, we adopt the bidirectional LSTM BIBREF15 to encode the dialogue history $\mathbf {x}$, which captures temporal relationships within the sequence. The encoder first map the tokens in $\mathbf {x}$ to vectors with embedding function $\phi ^{\text{emb}}$, and then the BiLSTM read the vector forwardly and backwardly to produce context-sensitive hidden states $(\mathbf {h}_{1}, \mathbf {h}_2, ..., \mathbf {h}_{m})$ by repeatedly applying the recurrence $\mathbf {h}_{i}=\text{BiLSTM}\left( \phi ^{\text{emb}}\left( x_{i}\right) , \mathbf {h}_{i-1}\right)$. ### Our Framework ::: Vanilla Attention-based Decoder
Here, we follow eric:2017:SIGDial to adopt the attention-based decoder to generation the response word by word. LSTM is also used to represent the partially generated output sequence $(y_{1}, y_2, ...,y_{t-1})$ as $(\tilde{\mathbf {h}}_{1}, \tilde{\mathbf {h}}_2, ...,\tilde{\mathbf {h}}_t)$. For the generation of next token $y_t$, their model first calculates an attentive representation $\tilde{\mathbf {h}}^{^{\prime }}_t$ of the dialogue history as Then, the concatenation of the hidden representation of the partially outputted sequence $\tilde{\mathbf {h}}_t$ and the attentive dialogue history representation $\tilde{\mathbf {h}}^{^{\prime }}_t$ are projected to the vocabulary space $\mathcal {V}$ by $U$ as to calculate the score (logit) for the next token generation. The probability of next token $y_t$ is finally calculated as ### Our Framework ::: Entity-Consistency Augmented Decoder
As shown in section SECREF7, we can see that the generation of tokens are just based on the dialogue history attention, which makes the model ignorant to the KB entities. In this section, we present how to query the KB explicitly in two steps for improving the entity consistence, which first adopt the KB-retriever to select the most relevant KB row and the generation of KB entities from the entities-augmented decoder is constrained to the entities within the most probable row, thus improve the entity generation consistency. Next, we perform the column attention to select the most probable KB column. Finally, we show how to use the copy mechanism to incorporate the retrieved entity while decoding. ### Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection
In our framework, our KB-retriever takes the dialogue history and KB rows as inputs and selects the most relevant row. This selection process resembles the task of selecting one word from the inputs to answer questions BIBREF13, and we use a memory network to model this process. In the following sections, we will first describe how to represent the inputs, then we will talk about our memory network-based retriever ### Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: Dialogue History Representation:
We encode the dialogue history by adopting the neural bag-of-words (BoW) followed the original paper BIBREF13. Each token in the dialogue history is mapped into a vector by another embedding function $\phi ^{\text{emb}^{\prime }}(x)$ and the dialogue history representation $\mathbf {q}$ is computed as the sum of these vectors: $\mathbf {q} = \sum ^{m}_{i=1} \phi ^{\text{emb}^{\prime }} (x_{i}) $. ### Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: KB Row Representation:
In this section, we describe how to encode the KB row. Each KB cell is represented as the cell value $v$ embedding as $\mathbf {c}_{j, k} = \phi ^{\text{value}}(v_{j, k})$, and the neural BoW is also used to represent a KB row $\mathbf {r}_{j}$ as $\mathbf {r}_{j} = \sum _{k=1}^{|\mathcal {C}|} \mathbf {c}_{j,k}$. ### Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: Memory Network-Based Retriever:
We model the KB retrieval process as selecting the row that most-likely supports the response generation. Memory network BIBREF13 has shown to be effective to model this kind of selection. For a $n$-hop memory network, the model keeps a set of input matrices $\lbrace R^{1}, R^{2}, ..., R^{n+1}\rbrace $, where each $R^{i}$ is a stack of $|\mathcal {R}|$ inputs $(\mathbf {r}^{i}_1, \mathbf {r}^{i}_2, ..., \mathbf {r}^{i}_{|\mathcal {R}|})$. The model also keeps query $\mathbf {q}^{1}$ as the input. A single hop memory network computes the probability $\mathbf {a}_j$ of selecting the $j^{\text{th}}$ input as For the multi-hop cases, layers of single hop memory network are stacked and the query of the $(i+1)^{\text{th}}$ layer network is computed as and the output of the last layer is used as the output of the whole network. For more details about memory network, please refer to the original paper BIBREF13. After getting $\mathbf {a}$, we represent the retrieval results as a 0-1 matrix $T \in \lbrace 0, 1\rbrace ^{|\mathcal {R}|\times \mathcal {|C|}}$, where each element in $T$ is calculated as In the retrieval result, $T_{j, k}$ indicates whether the entity in the $j^{\text{th}}$ row and the $k^{\text{th}}$ column is relevant to the final generation of the response. In this paper, we further flatten T to a 0-1 vector $\mathbf {t} \in \lbrace 0, 1\rbrace ^{|\mathcal {E}|}$ (where $|\mathcal {E}|$ equals $|\mathcal {R}|\times \mathcal {|C|}$) as our retrieval row results. ### Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Column Selection
After getting the retrieved row result that indicates which KB row is the most relevant to the generation, we further perform column attention in decoding time to select the probable KB column. For our KB column selection, following the eric:2017:SIGDial we use the decoder hidden state $(\tilde{\mathbf {h}}_{1}, \tilde{\mathbf {h}}_2, ...,\tilde{\mathbf {h}}_t)$ to compute an attention score with the embedding of column attribute name. The attention score $\mathbf {c}\in R^{|\mathcal {E}|}$ then become the logits of the column be selected, which can be calculated as where $\mathbf {c}_j$ is the attention score of the $j^{\text{th}}$ KB column, $\mathbf {k}_j$ is represented with the embedding of word embedding of KB column name. $W^{^{\prime }}_{1}$, $W^{^{\prime }}_{2}$ and $\mathbf {t}^{T}$ are trainable parameters of the model. ### Our Framework ::: Entity-Consistency Augmented Decoder ::: Decoder with Retrieved Entity
After the row selection and column selection, we can define the final retrieved KB entity score as the element-wise dot between the row retriever result and the column selection score, which can be calculated as where the $v^{t}$ indicates the final KB retrieved entity score. Finally, we follow eric:2017:SIGDial to use copy mechanism to incorporate the retrieved entity, which can be defined as where $\mathbf {o}_t$’s dimensionality is $ |\mathcal {V}|$ +$|\mathcal {E}|$. In $\mathbf {v}^t$ , lower $ |\mathcal {V}|$ is zero and the rest$|\mathcal {E}|$ is retrieved entity scores. ### Training the KB-Retriever
As mentioned in section SECREF9, we adopt the memory network to train our KB-retriever. However, in the Seq2Seq dialogue generation, the training data does not include the annotated KB row retrieval results, which makes supervised training the KB-retriever impossible. To tackle this problem, we propose two training methods for our KB-row-retriever. 1) In the first method, inspired by the recent success of distant supervision in information extraction BIBREF16, BIBREF17, BIBREF18, BIBREF19, we take advantage of the similarity between the surface string of KB entries and the reference response, and design a set of heuristics to extract training data for the KB-retriever. 2) In the second method, instead of training the KB-retriever as an independent component, we train it along with the training of the Seq2Seq dialogue generation. To make the retrieval process in Equation DISPLAY_FORM13 differentiable, we use Gumbel-Softmax BIBREF14 as an approximation of the $\operatornamewithlimits{argmax}$ during training. ### Training the KB-Retriever ::: Training with Distant Supervision
Although it's difficult to obtain the annotated retrieval data for the KB-retriever, we can “guess” the most relevant KB row from the reference response, and then obtain the weakly labeled data for the retriever. Intuitively, for the current utterance in the same dialogue which usually belongs to one topic and the KB row that contains the largest number of entities mentioned in the whole dialogue should support the utterance. In our training with distant supervision, we further simplify our assumption and assume that one dialogue which is usually belongs to one topic and can be supported by the most relevant KB row, which means for a $k$-turned dialogue, we construct $k$ pairs of training instances for the retriever and all the inputs $(u_{1}, s_{1}, ..., s_{i-1}, u_{i} \mid i \le k)$ are associated with the same weakly labeled KB retrieval result $T^*$. In this paper, we compute each row's similarity to the whole dialogue and choose the most similar row as $T^*$. We define the similarity of each row as the number of matched spans with the surface form of the entities in the row. Taking the dialogue in Figure FIGREF1 for an example, the similarity of the 4$^\text{th}$ row equals to 4 with “200 Alester Ave”, “gas station”, “Valero”, and “road block nearby” matching the dialogue context; and the similarity of the 7$^\text{th}$ row equals to 1 with only “road block nearby” matching. In our model with the distantly supervised retriever, the retrieval results serve as the input for the Seq2Seq generation. During training the Seq2Seq generation, we use the weakly labeled retrieval result $T^{*}$ as the input. ### Training the KB-Retriever ::: Training with Gumbel-Softmax
In addition to treating the row retrieval result as an input to the generation model, and training the kb-row-retriever independently, we can train it along with the training of the Seq2Seq dialogue generation in an end-to-end fashion. The major difficulty of such a training scheme is that the discrete retrieval result is not differentiable and the training signal from the generation model cannot be passed to the parameters of the retriever. Gumbel-softmax technique BIBREF14 has been shown an effective approximation to the discrete variable and proved to work in sentence representation. In this paper, we adopt the Gumbel-Softmax technique to train the KB retriever. We use as the approximation of $T$, where $\mathbf {g}_{j}$ are i.i.d samples drawn from $\text{Gumbel}(0,1)$ and $\tau $ is a constant that controls the smoothness of the distribution. $T^{\text{approx}}_{j}$ replaces $T^{\text{}}_{j}$ in equation DISPLAY_FORM13 and goes through the same flattening and expanding process as $\mathbf {V}$ to get $\mathbf {v}^{\mathbf {t}^{\text{approx}^{\prime }}}$ and the training signal from Seq2Seq generation is passed via the logit To make training with Gumbel-Softmax more stable, we first initialize the parameters by pre-training the KB-retriever with distant supervision and further fine-tuning our framework. ### Training the KB-Retriever ::: Experimental Settings
We choose the InCar Assistant dataset BIBREF6 including three distinct domains: navigation, weather and calendar domain. For weather domain, we follow wen2018sequence to separate the highest temperature, lowest temperature and weather attribute into three different columns. For calendar domain, there are some dialogues without a KB or incomplete KB. In this case, we padding a special token “-” in these incomplete KBs. Our framework is trained separately in these three domains, using the same train/validation/test split sets as eric:2017:SIGDial. To justify the generalization of the proposed model, we also use another public CamRest dataset BIBREF11 and partition the datasets into training, validation and testing set in the ratio 3:1:1. Especially, we hired some human experts to format the CamRest dataset by equipping the corresponding KB to every dialogues. All hyper-parameters are selected according to validation set. We use a three-hop memory network to model our KB-retriever. The dimensionalities of the embedding is selected from $\lbrace 100, 200\rbrace $ and LSTM hidden units is selected from $\lbrace 50, 100, 150, 200, 350\rbrace $. The dropout we use in our framework is selected from $\lbrace 0.25, 0.5, 0.75\rbrace $ and the batch size we adopt is selected from $\lbrace 1,2\rbrace $. L2 regularization is used on our model with a tension of $5\times 10^{-6}$ for reducing overfitting. For training the retriever with distant supervision, we adopt the weight typing trick BIBREF20. We use Adam BIBREF21 to optimize the parameters in our model and adopt the suggested hyper-parameters for optimization. We adopt both the automatic and human evaluations in our experiments. ### Training the KB-Retriever ::: Baseline Models
We compare our model with several baselines including: Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding. Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context. KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities. Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output. DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding. In InCar dataset, for the Attn seq2seq, Ptr-UNK and Mem2seq, we adopt the reported results from madotto2018mem2seq. In CamRest dataset, for the Mem2Seq, we adopt their open-sourced code to get the results while for the DSR, we run their code on the same dataset to obtain the results. ### Results
Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. The experimental results are illustrated in Table TABREF30. In the first block of Table TABREF30, we show the Human, rule-based and KV Net (with*) result which are reported from eric:2017:SIGDial. We argue that their results are not directly comparable because their work uses the entities in thier canonicalized forms, which are not calculated based on real entity value. It's noticing that our framework with two methods still outperform KV Net in InCar dataset on whole BLEU and Entity F metrics, which demonstrates the effectiveness of our framework. In the second block of Table TABREF30, we can see that our framework trained with both the distant supervision and the Gumbel-Softmax beats all existing models on two datasets. Our model outperforms each baseline on both BLEU and F1 metrics. In InCar dataset, Our model with Gumbel-Softmax has the highest BLEU compared with baselines, which which shows that our framework can generate more fluent response. Especially, our framework has achieved 2.5% improvement on navigate domain, 1.8% improvement on weather domain and 3.5% improvement on calendar domain on F1 metric. It indicates that the effectiveness of our KB-retriever module and our framework can retrieve more correct entity from KB. In CamRest dataset, the same trend of improvement has been witnessed, which further show the effectiveness of our framework. Besides, we observe that the model trained with Gumbel-Softmax outperforms with distant supervision method. We attribute this to the fact that the KB-retriever and the Seq2Seq module are fine-tuned in an end-to-end fashion, which can refine the KB-retriever and further promote the dialogue generation. ### Results ::: The proportion of responses that can be supported by a single KB row
In this section, we verify our assumption by examining the proportion of responses that can be supported by a single row. We define a response being supported by the most relevant KB row as all the responded entities are included by that row. We study the proportion of these responses over the test set. The number is 95% for the navigation domain, 90% for the CamRest dataset and 80% for the weather domain. This confirms our assumption that most responses can be supported by the relevant KB row. Correctly retrieving the supporting row should be beneficial. We further study the weather domain to see the rest 20% exceptions. Instead of being supported by multiple rows, most of these exceptions cannot be supported by any KB row. For example, there is one case whose reference response is “It 's not rainy today”, and the related KB entity is sunny. These cases provide challenges beyond the scope of this paper. If we consider this kind of cases as being supported by a single row, such proportion in the weather domain is 99%. ### Results ::: Generation Consistency
In this paper, we expect the consistent generation from our model. To verify this, we compute the consistency recall of the utterances that have multiple entities. An utterance is considered as consistent if it has multiple entities and these entities belong to the same row which we annotated with distant supervision. The consistency result is shown in Table TABREF37. From this table, we can see that incorporating retriever in the dialogue generation improves the consistency. ### Results ::: Correlation between the number of KB rows and generation consistency
To further explore the correlation between the number of KB rows and generation consistency, we conduct experiments with distant manner to study the correlation between the number of KB rows and generation consistency. We choose KBs with different number of rows on a scale from 1 to 5 for the generation. From Figure FIGREF32, as the number of KB rows increase, we can see a decrease in generation consistency. This indicates that irrelevant information would harm the dialogue generation consistency. ### Results ::: Visualization
To gain more insights into how the our retriever module influences the whole KB score distribution, we visualized the KB entity probability at the decoding position where we generate the entity 200_Alester_Ave. From the example (Fig FIGREF38), we can see the $4^\text{th}$ row and the $1^\text{th}$ column has the highest probabilities for generating 200_Alester_Ave, which verify the effectiveness of firstly selecting the most relevant KB row and further selecting the most relevant KB column. ### Results ::: Human Evaluation
We provide human evaluation on our framework and the compared models. These responses are based on distinct dialogue history. We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5. In each judgment, the expert is presented with the dialogue history, an output of a system with the name anonymized, and the gold response. The evaluation results are illustrated in Table TABREF37. Our framework outperforms other baseline models on all metrics according to Table TABREF37. The most significant improvement is from correctness, indicating that our model can retrieve accurate entity from KB and generate more informative information that the users want to know. ### Related Work
Sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 has gained more popular and they are applied for the open-domain dialogs BIBREF24, BIBREF25 in the end-to-end training method. Recently, the Seq2Seq can be used for learning task oriented dialogs and how to query the structured KB is the remaining challenges. Properly querying the KB has long been a challenge in the task-oriented dialogue system. In the pipeline system, the KB query is strongly correlated with the design of language understanding, state tracking, and policy management. Typically, after obtaining the dialogue state, the policy management module issues an API call accordingly to query the KB. With the development of neural network in natural language processing, efforts have been made to replacing the discrete and pre-defined dialogue state with the distributed representation BIBREF10, BIBREF11, BIBREF12, BIBREF26. In our framework, our retrieval result can be treated as a numeric representation of the API call return. Instead of interacting with the KB via API calls, more and more recent works tried to incorporate KB query as a part of the model. The most popular way of modeling KB query is treating it as an attention network over the entire KB entities BIBREF6, BIBREF27, BIBREF8, BIBREF28, BIBREF29 and the return can be a fuzzy summation of the entity representations. madotto2018mem2seq's practice of modeling the KB query with memory network can also be considered as learning an attentive preference over these entities. wen2018sequence propose the implicit dialogue state representation to query the KB and achieve the promising performance. Different from their modes, we propose the KB-retriever to explicitly query the KB, and the query result is used to filter the irrelevant entities in the dialogue generation to improve the consistency among the output entities. ### Conclusion
In this paper, we propose a novel framework to improve entities consistency by querying KB in two steps. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce the KB retriever to return the most relevant KB row, which is used to filter the irrelevant KB entities and encourage consistent generation. In the second step, we further perform attention mechanism to select the most relevant KB column. Experimental results show the effectiveness of our method. Extensive analysis further confirms the observation and reveal the correlation between the success of KB query and the success of task-oriented dialogue generation. ### Acknowledgments
We thank the anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61976072, 61632011 and 61772153. Figure 1: An example of a task-oriented dialogue that incorporates a knowledge base (KB). The fourth row in KB supports the second turn of the dialogue. A dialogue system will produce a response with conflict entities if it includes the POI in the fourth row and the address in the fifth row, like “Valero is located at 899 Ames Ct”. Figure 2: The workflow of our Seq2Seq task-oriented dialogue generation model with KB-retriever. For simplification, we draw the single-hop memory network instead of the multiple-hop one we use in our model. Table 1: Comparison of our model with baselines Figure 3: Correlation between the number of KB rows and generation consistency on navigation domain. Table 2: The generation consistency and Human Evaluation on navigation domain. Cons. represents Consistency. Cor. represents Correctness. Flu. represents Fluency and Hum. represents Humanlikeness. Figure 4: KB score distribution. The distribution is the timestep when generate entity 200 Alester Ave for response “ Valero is located at 200 Alester Ave” | BLEU, Micro Entity F1, quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5 |
What caused Ben to physically assault Cobb?
A. Cobb physically assaulted Ben first.
B. Cobb's vocal disgust for spacemen.
C. Ben was trying to prove a point about his masculinity.
D. He thought he was someone else.
| A Coffin for Jacob By EDWARD W. LUDWIG Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] With never a moment to rest, the pursuit through space felt like a game of hounds and hares ... or was it follow the leader? Ben Curtis eased his pale, gaunt body through the open doorway of the Blast Inn, the dead man following silently behind him. His fear-borne gaze traveled into the dimly illumined Venusian gin mill. The place was like an evil caldron steaming with a brew whose ingredients had been culled from the back corners of three planets. Most of the big room lay obscured behind a shimmering veil of tobacco smoke and the sweet, heavy fumes of Martian Devil's Egg. Here and there, Ben saw moving figures. He could not tell if they were Earthmen, Martians or Venusians. Someone tugged at his greasy coat. He jumped, thinking absurdly that it was the dead man's hand. " Coma esta, senor? " a small voice piped. " Speken die Deutsch? Desirez-vous d'amour? Da? Nyet? " Ben looked down. The speaker was an eager-eyed Martian boy of about ten. He was like a red-skinned marionette with pipestem arms and legs, clad in a torn skivvy shirt and faded blue dungarees. "I'm American," Ben muttered. "Ah, buena ! I speak English tres fine, senor . I have Martian friend, she tres pretty and tres fat. She weigh almost eighty pounds, monsieur . I take you to her, si ?" Ben shook his head. He thought, I don't want your Martian wench. I don't want your opium or your Devil's Egg or your Venusian kali. But if you had a drug that'd bring a dead man to life, I'd buy and pay with my soul. "It is deal, monsieur ? Five dollars or twenty keelis for visit Martian friend. Maybe you like House of Dreams. For House of Dreams—" "I'm not buying." The dirty-faced kid shrugged. "Then I show you to good table,— tres bien . I do not charge you, senor ." The boy grabbed his hand. Because Ben could think of no reason for resisting, he followed. They plunged into shifting layers of smoke and through the drone of alcohol-cracked voices. They passed the bar with its line of lean-featured, slit-eyed Earthmen—merchant spacemen. They wormed down a narrow aisle flanked by booths carved from Venusian marble that jutted up into the semi-darkness like fog-blanketed tombstones. Several times, Ben glimpsed the bulky figures of CO 2 -breathing Venusians, the first he'd ever seen. They were smoky gray, scaly, naked giants, toads in human shape. They stood solitary and motionless, aloof, their green-lidded eyes unblinking. They certainly didn't look like telepaths, as Ben had heard they were, but the thought sent a fresh rivulet of fear down his spine. Once he spied a white-uniformed officer of Hoover City's Security Police. The man was striding down an aisle, idly tapping his neuro-club against the stone booths. Keep walking , Ben told himself. You look the same as anyone else here. Keep walking. Look straight ahead. The officer passed. Ben breathed easier. "Here we are, monsieur ," piped the Martian boy. "A tres fine table. Close in the shadows." Ben winced. How did this kid know he wanted to sit in the shadows? Frowning, he sat down—he and the dead man. He listened to the lonely rhythms of the four-piece Martian orchestra. The Martians were fragile, doll-like creatures with heads too large for their spindly bodies. Their long fingers played upon the strings of their cirillas or crawled over the holes of their flutes like spider legs. Their tune was sad. Even when they played an Earth tune, it still seemed a song of old Mars, charged with echoes of lost voices and forgotten grandeur. For an instant, Ben's mind rose above the haunting vision of the dead man. He thought, What are they doing here, these Martians? Here, in a smoke-filled room under a metalite dome on a dust-covered world? Couldn't they have played their music on Mars? Or had they, like me, felt the challenge of new worlds? He sobered. It didn't matter. He ordered a whiskey from a Chinese waiter. He wet his lips but did not drink. His gaze wandered over the faces of the Inn's other occupants. You've got to find him , he thought. You've got to find the man with the red beard. It's the only way you can escape the dead man. The dead man was real. His name was Cobb. He was stout and flabby and about forty and he hated spacemen. His body was buried now—probably in the silent gray wastes outside Luna City. But he'd become a kind of invisible Siamese twin, as much a part of Ben as sight in his eyes. Sometimes the image would be shuffling drunkenly beside him, its lips spitting whiskey-slurred curses. Again, its face would be a pop-eyed mask of surprise as Ben's fist thudded into its jaw. More often, the face would be frozen in the whiteness of death. The large eyes would stare. Blood would trickle from a corner of the gaping mouth. You can forget a living man. You can defeat him or submit to him or ignore him, and the matter is over and done. You can't escape from a memory that has burned into your mind. It had begun a week ago in Luna City. The flight from White Sands had been successful. Ben, quietly and moderately, wanted to celebrate. He stopped alone in a rocketfront bar for a beer. The man named Cobb plopped his portly and unsteady posterior on the stool next to him. "Spacemen," he muttered, "are getting like flies. Everywhere, all you see's spacemen." He was a neatly dressed civilian. Ben smiled. "If it weren't for spacemen, you wouldn't be here." "The name's Cobb." The man hiccoughed. "Spacemen in their white monkey suits. They think they're little tin gods. Betcha you think you're a little tin god." He downed a shot of whiskey. Ben stiffened. He was twenty-four and dressed in the white, crimson-braided uniform of the Odyssey's junior astrogation officer. He was three months out of the Academy at White Sands and the shining uniform was like a key to all the mysteries of the Universe. He'd sought long for that key. At the age of five—perhaps in order to dull the memory of his parents' death in a recent strato-jet crash—he'd spent hours watching the night sky for streaking flame-tails of Moon rockets. At ten, he'd ground his first telescope. At fourteen, he'd converted an abandoned shed on the government boarding-school grounds to a retreat which housed his collection of astronomy and rocketry books. At sixteen, he'd spent every weekend holiday hitchhiking from Boys Town No. 5 in the Catskills to Long Island Spaceport. There, among the grizzled veterans of the old Moon Patrol, he'd found friends who understood his dream and who later recommended his appointment to the U. S. Academy for the Conquest of Space. And a month ago, he'd signed aboard the Odyssey —the first ship, it was rumored, equipped to venture as far as the asteroids and perhaps beyond. Cobb was persistent: "Damn fools shoulda known enough to stay on Earth. What the hell good is it, jumpin' from planet to planet?" The guy's drunk , Ben thought. He took his drink and moved three stools down the bar. Cobb followed. "You don't like the truth, eh, kid? You don't like people to call you a sucker." Ben rose and started to leave the bar, but Cobb grabbed his arm and held him there. "Thas what you are—a sucker. You're young now. Wait ten years. You'll be dyin' of radiation rot or a meteor'll get you. Wait and see, sucker!" Until this instant, Ben had suppressed his anger. Now, suddenly and without warning, it welled up into savage fury. His fist struck the man on the chin. Cobb's eyes gaped in shocked horror. He spun backward. His head cracked sickeningly on the edge of the bar. The sound was like a punctuation mark signaling the end of life. He sank to the floor, eyes glassy, blood tricking down his jaw. Ben knew that he was dead. Then, for a single absurd second, Ben was seized with terror—just as, a moment before, he'd been overwhelmed with anger. He ran. For some twenty minutes, he raced through a dizzying, nightmare world of dark rocketfront alleys and shouting voices and pursuing feet. At last, abruptly, he realized that he was alone and in silence. He saw that he was still on the rocketfront, but in the Tycho-ward side of the city. He huddled in a dark corner of a loading platform and lit a cigarette. A thousand stars—a thousand motionless balls of silver fire—shone above him through Luna City's transparent dome. He was sorry he'd hit Cobb, of course. He was not sorry he'd run. Escaping at least gave him a power of choice, of decision. You can do two things , he thought. You can give yourself up, and that's what a good officer would do. That would eliminate the escape charge. You'd get off with voluntary manslaughter. Under interplanetary law, that would mean ten years in prison and a dishonorable discharge. And then you'd be free. But you'd be through with rockets and space. They don't want new men over thirty-four for officers on rockets or even for third-class jet-men on beat-up freighters—they don't want convicted killers. You'd get the rest of the thrill of conquering space through video and by peeking through electric fences of spaceports. Or— There were old wives' tales of a group of renegade spacemen who operated from the Solar System's frontiers. The spacemen weren't outlaws. They were misfits, rejectees from the clearing houses on Earth. And whereas no legally recognized ship had ventured past Mars, the souped-up renegade rigs had supposedly hit the asteroids. Their headquarters was Venus. Their leader—a subject of popular and fantastic conjecture in the men's audiozines—was rumored to be a red-bearded giant. So , Ben reflected, you can take a beer-and-pretzels tale seriously. You can hide for a couple of days, get rid of your uniform, change your name. You can wait for a chance to get to Venus. To hell with your duty. You can try to stay in space, even if you exile yourself from Earth. After all, was it right for a single second, a single insignificant second, to destroy a man's life and his dream? He was lucky. He found a tramp freighter whose skipper was on his last flight before retirement. Discipline was lax, investigation of new personnel even more so. Ben Curtis made it to Venus. There was just one flaw in his decision. He hadn't realized that the memory of the dead man's face would haunt him, torment him, follow him as constantly as breath flowed into his lungs. But might not the rumble of atomic engines drown the murmuring dead voice? Might not the vision of alien worlds and infinite spaceways obscure the dead face? So now he sat searching for a perhaps nonexistent red-bearded giant, and hoping and doubting and fearing, all at once. "You look for someone, senor ?" He jumped. "Oh. You still here?" " Oui. " The Martian kid grinned, his mouth full of purple teeth. "I keep you company on your first night in Hoover City, n'est-ce-pas ?" "This isn't my first night here," Ben lied. "I've been around a while." "You are spacemen?" Ben threw a fifty-cent credit piece on the table. "Here. Take off, will you?" Spiderlike fingers swept down upon the coin. " Ich danke, senor. You know why city is called Hoover City?" Ben didn't answer. "They say it is because after women come, they want first thing a thousand vacuum cleaners for dust. What is vacuum cleaner, monsieur ?" Ben raised his hand as if to strike the boy. " Ai-yee , I go. You keep listen to good Martian music." The toothpick of a body melted into the semi-darkness. Minutes passed. There were two more whiskeys. A ceaseless parade of faces broke through the smoky veil that enclosed him—reddish balloon faces, scaly reptilian faces, white-skinned, slit-eyed faces, and occasionally a white, rouged, powdered face. But nowhere was there a face with a red beard. A sense of hopelessness gripped Ben Curtis. Hoover City was but one of a dozen cities of Venus. Each had twenty dives such as this. He needed help. But his picture must have been 'scoped to Venusian visiscreens. A reward must have been offered for his capture. Whom could he trust? The Martian kid, perhaps? Far down the darkened aisle nearest him, his eyes caught a flash of white. He tensed. Like the uniform of a Security Policeman, he thought. His gaze shifted to another aisle and another hint of whiteness. And then he saw another and another and another. Each whiteness became brighter and closer, like shrinking spokes of a wheel with Ben as their focal point. You idiot! The damned Martian kid! You should have known! Light showered the room in a dazzling explosion. Ben, half blinded, realized that a broad circle of unshaded globes in the ceiling had been turned on. The light washed away the room's strangeness and its air of brooding wickedness, revealing drab concrete walls and a debris-strewn floor. Eyes blinked and squinted. There were swift, frightened movements and a chorus of angry murmurs. The patrons of the Blast Inn were like tatter-clad occupants of a house whose walls have been ripped away. Ben Curtis twisted his lean body erect. His chair tumbled backward, falling. The white-clad men charged, neuro-clubs upraised. A woman screamed. The music ceased. The Martian orchestra slunk with feline stealth to a rear exit. Only the giant Venusians remained undisturbed. They stood unmoving, their staring eyes shifting lazily in Ben's direction. "Curtis!" one of the policemen yelled. "You're covered! Hold it!" Ben whirled away from the advancing police, made for the exit into which the musicians had disappeared. A hissing sound traveled past his left ear, a sound like compressed air escaping from a container. A dime-sized section of the concrete wall ahead of him crumbled. He stumbled forward. They were using deadly neuro-pistols now, not the mildly stunning neuro-clubs. Another hiss passed his cheek. He was about twelve feet from the exit. Another second , his brain screamed. Just another second— Or would the exits be guarded? He heard the hiss. It hit directly in the small of his back. There was no pain, just a slight pricking sensation, like the shallow jab of a needle. He froze as if yanked to a stop by a noose. His body seemed to be growing, swelling into balloon proportions. He knew that the tiny needle had imbedded itself deep in his flesh, knew that the paralyzing mortocain was spreading like icy fire into every fiber and muscle of his body. He staggered like a man of stone moving in slow motion. He'd have fifteen—maybe twenty—seconds before complete lethargy of mind and body overpowered him. In the dark world beyond his fading consciousness, he heard a voice yell, "Turn on the damn lights!" Then a pressure and a coldness were on his left hand. He realized that someone had seized it. A soft feminine voice spoke to him. "You're wounded? They hit you?" "Yes." His thick lips wouldn't let go of the word. "You want to escape—even now?" "Yes." "You may die if you don't give yourself up." "No, no." He tried to stumble toward the exit. "All right then. Not that way. Here, this way." Heavy footsteps thudded toward them. A few yards away, a flashlight flicked on. Hands were guiding him. He was aware of being pushed and pulled. A door closed behind him. The glare of the flashlight faded from his vision—if he still had vision. "You're sure?" the voice persisted. "I'm sure," Ben managed to say. "I have no antidote. You may die." His mind fought to comprehend. With the anti-paralysis injection, massage and rest, a man could recover from the effects of mortocain within half a day. Without treatment, the paralysis could spread to heart and lungs. It could become a paralysis of death. An effective weapon: the slightest wound compelled the average criminal to surrender at once. "Anti ... anti ..." The words were as heavy as blobs of mercury forced from his throat. "No ... I'm sure ... sure." He didn't hear the answer or anything else. Ben Curtis had no precise sensation of awakening. Return to consciousness was an intangible evolution from a world of black nothingness to a dream-like state of awareness. He felt the pressure of hands on his naked arms and shoulders, hands that massaged, manipulated, fought to restore circulation and sensitivity. He knew they were strong hands. Their strength seemed to transfer itself to his own body. For a long time, he tried to open his eyes. His lids felt welded shut. But after a while, they opened. His world of darkness gave way to a translucent cloak of mist. A round, featureless shape hovered constantly above him—a face, he supposed. He tried to talk. Although his lips moved slightly, the only sound was a deep, staccato grunting. But he heard someone say, "Don't try to talk." It was the same gentle voice he'd heard in the Blast Inn. "Don't talk. Just lie still and rest. Everything'll be all right." Everything all right , he thought dimly. There were long periods of lethargy when he was aware of nothing. There were periods of light and of darkness. Gradually he grew aware of things. He realized that the soft rubber mouth of a spaceman's oxygen mask was clamped over his nose. He felt the heat of electric blankets swathed about his body. Occasionally a tube would be in his mouth and he would taste liquid food and feel a pleasant warmth in his stomach. Always, it seemed, the face was above him, floating in the obscuring mist. Always, it seemed, the soft voice was echoing in his ears: "Swallow this now. That's it. You must have food." Or, "Close your eyes. Don't strain. It won't be long. You're getting better." Better , he'd think. Getting better.... At last, after one of the periods of lethargy, his eyes opened. The mist brightened, then dissolved. He beheld the cracked, unpainted ceiling of a small room, its colorless walls broken with a single, round window. He saw the footboard of his aluminite bed and the outlines of his feet beneath a faded blanket. Finally he saw the face and figure that stood at his side. "You are better?" the kind voice asked. The face was that of a girl probably somewhere between twenty-five and thirty. Her features, devoid of makeup, had an unhealthy-looking pallor, as if she hadn't used a sunlamp for many weeks. Yet, at the same time, her firm slim body suggested a solidity and a strength. Her straight brown hair was combed backward, tight upon her scalp, and drawn together in a knot at the nape of her neck. "I—I am better," he murmured. His words were still slow and thick. "I am going to live?" "You will live." He thought for a moment. "How long have I been here?" "Nine days." "You took care of me?" He noted the deep, dark circles beneath her sleep-robbed eyes. She nodded. "You're the one who carried me when I was shot?" "Yes." "Why?" Suddenly he began to cough. Breath came hard. She held the oxygen mask in readiness. He shook his head, not wanting it. "Why?" he asked again. "It would be a long story. Perhaps I'll tell you tomorrow." A new thought, cloaked in sudden fear, entered his murky consciousness. "Tell me, will—will I be well again? Will I be able to walk?" He lay back then, panting, exhausted. "You have nothing to worry about," the girl said softly. Her cool hand touched his hot forehead. "Rest. We'll talk later." His eyes closed and breath came easier. He slept. When he next awoke, his gaze turned first to the window. There was light outside, but he had no way of knowing if this was morning, noon or afternoon—or on what planet. He saw no white-domed buildings of Hoover City, no formal lines of green-treed parks, no streams of buzzing gyro-cars. There was only a translucent and infinite whiteness. It was as if the window were set on the edge of the Universe overlooking a solemn, silent and matterless void. The girl entered the room. "Hi," she said, smiling. The dark half-moons under her eyes were less prominent. Her face was relaxed. She increased the pressure in his rubberex pillows and helped him rise to a sitting position. "Where are we?" he asked. "Venus." "We're not in Hoover City?" "No." He looked at her, wondering. "You won't tell me?" "Not yet. Later, perhaps." "Then how did you get me here? How did we escape from the Inn?" She shrugged. "We have friends who can be bribed. A hiding place in the city, the use of a small desert-taxi, a pass to leave the city—these can be had for a price." "You'll tell me your name?" "Maggie." "Why did you save me?" Her eyes twinkled mischievously. "Because you're a good astrogator." His own eyes widened. "How did you know that?" She sat on a plain chair beside his bed. "I know everything about you, Lieutenant Curtis." "How did you learn my name? I destroyed all my papers—" "I know that you're twenty-four. Born July 10, 1971. Orphaned at four, you attended Boys Town in the Catskills till you were 19. You graduated from the Academy at White Sands last June with a major in Astrogation. Your rating for the five-year period was 3.8—the second highest in a class of fifty-seven. Your only low mark in the five years was a 3.2 in History of Martian Civilization. Want me to go on?" Fascinated, Ben nodded. "You were accepted as junior astrogation officer aboard the Odyssey . You did well on your flight from Roswell to Luna City. In a barroom fight in Luna City, you struck and killed a man named Arthur Cobb, a pre-fab salesman. You've been charged with second degree murder and escape. A reward of 5,000 credits has been offered for your capture. You came to Hoover City in the hope of finding a renegade group of spacemen who operate beyond Mars. You were looking for them in the Blast Inn." He gaped incredulously, struggling to rise from his pillows. "I—don't get it." "There are ways of finding out what we want to know. As I told you, we have many friends." He fell back into his pillows, breathing hard. She rose quickly. "I'm sorry," she said. "I shouldn't have told you yet. I felt so happy because you're alive. Rest now. We'll talk again soon." "Maggie, you—you said I'd live. You didn't say I'd be able to walk again." She lowered her gaze. "I hope you'll be able to." "But you don't think I will, do you?" "I don't know. We'll try walking tomorrow. Don't think about it now. Rest." He tried to relax, but his mind was a vortex of conjecture. "Just one more question," he almost whispered. "Yes?" "The man I killed—did he have a wife?" She hesitated. He thought, Damn it, of all the questions, why did I ask that? Finally she said, "He had a wife." "Children?" "Two. I don't know their ages." She left the room. He sank into the softness of his bed. As he turned over on his side, his gaze fell upon an object on a bureau in a far corner of the room. He sat straight up, his chest heaving. The object was a tri-dimensional photo of a rock-faced man in a merchant spaceman's uniform. He was a giant of a man with a neatly trimmed red beard ! Ben stared at the photo for a long time. At length, he slipped into restless sleep. Images of faces and echoes of words spun through his brain. The dead man returned to him. Bloodied lips cursed at him. Glassy eyes accused him. Somewhere were two lost children crying in the night. And towering above him was a red-bearded man whose great hands reached down and beckoned to him. Ben crawled through the night on hands and knees, his legs numb and useless. The crying of the children was a chilling wail in his ears. His head rose and turned to the red-bearded man. His pleading voice screamed out to him in a thick, harsh cackle. Yet even as he screamed, the giant disappeared, to be replaced by white-booted feet stomping relentlessly toward him. He awoke still screaming.... A night without darkness passed. Ben lay waiting for Maggie's return, a question already formed in his mind. She came and at once he asked, "Who is the man with the red beard?" She smiled. "I was right then when I gave you that thumbnail biog. You were looking for him, weren't you?" "Who is he?" She sat on the chair beside him. "My husband," she said softly. He began to understand. "And your husband needs an astrogator? That's why you saved me?" "We need all the good men we can get." "Where is he?" She cocked her head in mock suspicion. "Somewhere between Mercury and Pluto. He's building a new base for us—and a home for me. When his ship returns, I'll be going to him." "Why aren't you with him now?" "He said unexplored space is no place for a woman. So I've been studying criminal reports and photos from the Interplanetary Bureau of Investigation and trying to find recruits like yourself. You know how we operate?" He told her the tales he'd heard. She nodded. "There are quite a few of us now—about a thousand—and a dozen ships. Our base used to be here on Venus, down toward the Pole. The dome we're in now was designed and built by us a few years ago after we got pushed off Mars. We lost a few men in the construction, but with almost every advance in space, someone dies." "Venus is getting too civilized. We're moving out and this dome is only a temporary base when we have cases like yours. The new base—I might as well tell you it's going to be an asteroid. I won't say which one." "Don't get the idea that we're outlaws. Sure, about half our group is wanted by the Bureau, but we make honest livings. We're just people like yourself and Jacob." "Jacob? Your husband?" She laughed. "Makes you think of a Biblical character, doesn't it? Jacob's anything but that. And just plain 'Jake' reminds one of a grizzled old uranium prospector and he isn't like that, either." She lit a cigarette. "Anyway, the wanted ones stay out beyond the frontiers. Jacob and those like him can never return to Earth—not even to Hoover City—except dead. The others are physical or psycho rejects who couldn't get clearance if they went back to Earth. They know nothing but rocketing and won't give up. They bring in our ships to frontier ports like Hoover City to unload cargo and take on supplies." "Don't the authorities object?" "Not very strongly. The I. B. I. has too many problems right here to search the whole System for a few two-bit crooks. Besides, we carry cargoes of almost pure uranium and tungsten and all the stuff that's scarce on Earth and Mars and Venus. Nobody really cares whether it comes from the asteroids or Hades. If we want to risk our lives mining it, that's our business." She pursed her lips. "But if they guessed how strong we are or that we have friends planted in the I. B. I.—well, things might be different. There probably would be a crackdown." Ben scowled. "What happens if there is a crackdown? And what will you do when Space Corps ships officially reach the asteroids? They can't ignore you then." "Then we move on. We dream up new gimmicks for our crates and take them to Jupiter, Saturn, Uranus, Neptune, Pluto. In time, maybe, we'll be pushed out of the System itself. Maybe it won't be the white-suited boys who'll make that first hop to the stars. It could be us, you know—if we live long enough. But that Asteroid Belt is murder. You can't follow the text-book rules of astrogation out there. You make up your own." Ben stiffened. "And that's why you want me for an astrogator." Maggie rose, her eyes wistful. "If you want to come—and if you get well." She looked at him strangely. "Suppose—" He fought to find the right words. "Suppose I got well and decided not to join Jacob. What would happen to me? Would you let me go?" Her thin face was criss-crossed by emotion—alarm, then bewilderment, then fear. "I don't know. That would be up to Jacob." He lay biting his lip, staring at the photo of Jacob. She touched his hand and it seemed that sadness now dominated the flurry of emotion that had coursed through her. "The only thing that matters, really," she murmured, "is your walking again. We'll try this afternoon. Okay?" "Okay," he said. When she left, his eyes were still turned toward Jacob's photo. He was like two people, he thought. Half of him was an officer of the Space Corps. Perhaps one single starry-eyed boy out of ten thousand was lucky enough to reach that goal. He remembered a little picture book his mother had given him when she was alive. Under the bright pictures of spacemen were the captions: "A Space Officer Is Honest" "A Space Officer Is Loyal." "A Space Officer Is Dutiful." Honesty, loyalty, duty. Trite words, but without those concepts, mankind would never have broken away from the planet that held it prisoner for half a million years. Without them, Everson, after three failures and a hundred men dead, would never have landed on the Moon twenty-seven years ago. | B. Cobb's vocal disgust for spacemen. |
Where are the characters from?
A. The land of Meizque.
B. The city-state of Koltah.
C. They all live aboard a ship.
D. Different city-states within the whole system.
| VOYAGE TO FAR N'JURD By KRIS NEVILLE Illustrated by MACK [Transcriber's Note: This etext was produced from Galaxy Magazine April 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] They would never live to see the trip's end. So they made a few changes in their way of life—and many in their way of death! I "I don't see why we have to be here," a crewman said. "He ain't liable to say anything." "He shore better," the man in front of him said loudly. "Be still," his wife said. "People's lookin' at ya." "I don't care a smidgen," he said, "if en they ayre." "Please," she said. "Joanne Marie," he said, "you know that when I aims ta do somethin', I'm jest natcher'lly bound to do hit. An' iffen I aims ta talk...." "Here comes the priest. Now, be still." The man looked up. "So he do; an' I'll tell ya, hit shore is time he's a-gittin' hyere. I ain't got no all night fer ta sit." The crewman to his left bent over and whispered, "I'll bet he's gonna tell us it's gonna be another postponement." "Iffen he does, I'm jest a-gonna stand up an' yell right out that I ain't gonna stand fer hit no longer." "Now, dear," said Joanne Marie, "the captain can hear ya, if you're gonna talk so loud." "I hope he does; I jest hope he does. He's th' one that's a-keepin' us all from our Reward, an' I jest hope he does heyar me, so he'll know I'm a-gittin' mighty tyird uv waitin'." "You tell 'im!" someone said from two rows behind him. The captain, in the officer's section, sat very straight and tall. He was studiously ignoring the crew. This confined his field of vision to the left half of the recreation area. While the priest stood before the speaker's rostrum waiting for silence, the captain reached back with great dignity and scratched his right shoulder blade. Nestir, the priest, was dressed out in the full ceremonial costume of office. His high, strapless boots glistened with polish. His fez perched jauntily on his shiny, shaven head. The baldness was symbolic of diligent mental application to abstruse points of doctrine. Cotian exentiati pablum re overum est : "Grass grows not in the middle of a busy thoroughfare." The baldness was the result of the diligent application of an effective depilatory. His blood-red cloak had been freshly cleaned for the occasion, and it rustled around him in silky sibilants. "Men," he said. And then, more loudly, "Men!" The hiss and sputter of conversation guttered away. "Men," he said. "The other evening," he said, "—Gelday it was, to be exact—one of the crew came to me with a complaint." "Well, I'll be damned," Joanne Marie's husband said loudly. Nestir cleared his throat. "It was about the Casting Off. That's why I called you all together today." He stared away, at a point over the head and to the rear of the audience. "It puts me in mind of the parable of the six Vergios." Joanne Marie's husband sighed deeply. "Three, you will recall, were wise. When Prophet was at Meizque, they came to him and said, 'Prophet, we are afflicted. We have great sores upon our bodies.' The Prophet looked at them and did see that it was true. Then he blessed them and took out His knife and lay open their sores. For which the three wise Vergios were passing grateful. And within the last week, they were dead of infection. But three were foolish and hid their sores; and these three did live." The captain rubbed his nose. " Calex i pundendem hoy , my children. 'Secrecy makes for a long life,' as it says in the Jarcon ." Nestir tugged behind him at his cloak. "I want you all to remember that little story. I want you all to take it away from here with you and think about it, tonight, in the privacy of your cabins. "And like the three wise Vergios who went to the Prophet, one of the crewmen came to me. He came to me, and he said: 'Father, I am weary of sailing.' "Yes, he said, 'I am weary of sailing.' "Now, don't you think I don't know that. Every one of you—every blessed one of you—is weary of sailing. I know that as well as I know my own name, yes. "But because he came to me and said, 'Father, I am weary of sailing,' I went to the captain, and I said, 'Captain, the men are weary of sailing.' "And then the captain said: 'All right, Father,' he said, 'I will set the day for the Festival of the Casting Off!'" The little fellow was pleased by the rustle of approval from the audience. "God damn, hit's about time!" Joanne Marie's husband said. Nestir cleared his throat again. "Hummm. Uh. And the day is not very far distant," said Nestir. "I knowed there was a catch to hit," Joanne Marie's husband said. "I know you will have many questions; yes, I know you will have—ah, ah—well, many questions. You are thinking: 'What kind of a Festival can we have here on this ship?' You are thinking: 'What a fine thing—ah, what a good thing, that is—ah, how nice it would be to have the Casting Off at home, among friends.'" Nestir waved his hands. "Well, I just want to tell you: I come from Koltah. And you know that Koltah never let any city state outdo her in a Festival, uh-huh. "The arena in Koltah is the greatest arena in the whole system. We have as many as sixty thousand accepted applicants. All of them together in the arena is a—uh, uh, well—a sight to behold. People come from all over to behold it. I never will forget the Festival at which my father was accepted. He.... "Well, the point I want to make is this: I just wanted to tell you that I know what a Festival should be, and the captain and I will do everything in our power to make our Casting Off as wonderful as any anywhere. "And I want to tell you that if you'll come to me with your suggestions, I'll do all I can to see that we do this thing just the way you want it done. I want you to be proud of this Casting Off Festival, so you can look back on it and say, uh, uh—this day was the real high point of your whole life!" Everyone but Joanne Marie's husband cheered. He sat glumly muttering to himself. Nestir bobbed his shiny head at them and beamed his cherubic smile. And noticed that there was a little blonde, one of the crewmen's wives, in the front row that had very cute ankles. While they were still cheering and stomping and otherwise expressing their enthusiasm and approval, Nestir walked off the speaker's platform and into the officer's corridor. He wiped his forehead indecorously on the hem of his cloak and felt quite relieved that the announcement was over with and the public speaking done. II Dinner that evening was a gala occasion aboard the ship. The steward ordered the holiday feast prepared in celebration of Nestir's announcement. And, for the officers, he broke out of the special cellar the last case allotment for Crew One of the delicate Colta Barauche ('94). He ordered the messman to put a bottle of it to the right of each plate. The captain came down from his stateroom after the meal had begun. He nodded curtly to the officers when he entered the mess hall, walked directly to his place at the head of the table, sat down and morosely began to work the cork out of his wine bottle with his teeth. "You'll spoil the flavor, shaking it that way," the third mate cautioned. He was particularly fond of that year. The captain twisted the bottle savagely, and the cork came free with a little pop. He removed the cork from between his teeth, placed it very carefully beside his fork, and poured himself a full glass of the wine. "Very probably," he said sadly. "I don't think hit'll do hit," the first mate said. "He hain't shook hard enough to matter." The captain picked up the glass, brought it toward his lips—then, suddenly having thought of something, he put it back down and turned to Nestir. "I say. Have you decided on this Carstar thing yet, Father?" The little priest looked up. He laid his knife across the rim of his plate. "It has ramifications," he said. When the third mate saw that his opinion on the wine was not immediately to be justified, he settled back in his chair with a little sigh of disapproval. "Well, what do you think your decision will be, Father?" the steward asked. Nestir picked up his knife and fork and cut off a piece of meat. "Hummmm," he said. "It's hard to say. The whole issue involves, as a core point, the principle of casta cum mae stotiti ." The first mate nodded sagely. "The intent, of course, could actually be—ah— sub mailloux ; and in that event, naturally, the decision would be even more difficult. I wish I could talk to higher authority about it; but of course I haven't the time. I'll have to decide something." "He had a very pretty wife," the third mate said. "Yes, very." Nestir agreed. "But as I was saying, if it could be proven that the culstem fell due to no negligence on his part, either consciously or subconsciously, then the obvious conclusion would be that no stigma would be attached." He speared his meat and chewed it thoughtfully. "But it wasn't at all bloody," the wife of the second mate said. "I scarcely think he felt it at all. It happened too fast." Nestir swallowed the mouthful of food and washed it down with a gulp of wine. "The problem, my dear Helen," he said, "is one of intent. To raise the issue of concomitant agonies is to confuse the whole matter. For instance. Take Wilson, in my home state of Koltah. Certainly he died as miserable a death as anyone could desire." "Yes," said the second mate's wife. "I remember that. I read about it in the newspapers." "But it was a case of obvious intent ," continued Nestir, "and therefore constituted a clear out attempt to avoid his duty by hastening to his Reward." Upon hearing the word duty, the captain brightened. "That," he said to Nestir, "my dear Father, is the cardinal point of the whole game, y'know." He scratched the back of his left hand. "Duty. And I must say, I think you're being quite short-sighted about the Casting Off date. After all, it's not only a question of how we go, but also a question of leaving only after having done our duty. And that's equally important." "The Synod of Cathau—" Nestir began. "Plague take it, Father! Really, now, I must say. The Synod of Cathau! Certainly you've misinterpreted that. Anticipation can be a joy, y'know: almost equal to the very Reward. Anticipation should spur man in duty. It's all noble and self sacrificing." He scratched the back of his right hand. The second mate had been trying to get a word in edgewise for several minutes; he finally succeeded by utilizing the temporary silence following the captain's outburst. "You don't need to worry about your Casting Off, Captain. You can leave that to me. I assure you, I have in mind a most ingenious method." The captain was not visibly cheered; he was still brooding about the sad absence of a sense of duty on the part of Nestir. "I will welcome it," he said, "at the proper time, sir. And I certainly hope—" His eyes swept the table. "I certainly hope to be Cast Off by an officer. It would be very humiliating, y'know, to have a crew member do it." "Oh, very," said the steward. "I don't know," the second mate's wife said, "whether you better count on my husband or not. I have my own plans for him." "This problem of Carstar interests me," the third mate said. "Did I ever tell you about my wife? She strangled our second baby." "He was a very annoying child," his wife said. "He probably wouldn't have lived, anyway," the third mate said. "Puny baby." "That," said Nestir, "is not at all like the Carstar case. Not at all. Yours is a question of saliex y cuminzund ." The first mate nodded. "It seems to me that the whole thing would depend on the intent of the strangler." "Captain," the steward said, "you really must let me give you some of that salve." "That's very kind of you, but I...." "No bother at all," the steward said. "As I see it," Nestir said, "if the intent was the natural maternal instinct of the mother to release her child from its duty, then...." "Oh, not at all," the third mate's wife said. "I did it to make him stop crying." "Well, in that case, I see no reason why he shouldn't get his Reward." "I certainly hope so," the third mate said. "Jane worries about it all the time." "I do not," Jane contradicted. "Now, honey, you know you do so." At that moment, he lost interest in his wife and leaned across the table toward the captain, "Well?" he asked. The captain rolled the wine over his tongue. "You were right, of course." The third mate turned triumphantly to the first mate. "There, I told you so." The first mate shrugged. "I never do say nothin' right," he said. "I hain't got no luck. I've spent more years un all ya, carpenterin' up a duty log that's better un even th' captain's. An' hit's Martha an' me that gotta wait an' help th' next crew. Lord above knows how long time hit'll be afore we uns'll got ta have a Festival." "Oh, really, now. Now. Duty, duty," the captain reprimanded him mildly. "Duty! Duty! Duty! You all ur in a conspiracy. You all want me ta die uv old age." "Nonsense," said the steward. "We don't want anything of the sort. After all, someone has to orient the new crew." "Quite right," said the captain. "You ought to be proud." The first mate slammed his napkin in the middle of his food and stalked out of the mess hall. "Quite touchy today," Nestir observed. "By the way," the third mate said. "Wanda gave me a petition to give to you, Father." "Wanda?" "Yes. She's sixteen, now." "Wanda who?" the steward asked. "Wanda Miller, the bosun's daughter." "I know her," Helen said. "She's the oldest child on the ship, and she wants you to sign her adult petition so she can be in the Festival, Father." "She's so young...." "Sixteen, Father." "After all, one must have done some duty," the captain said. "He wants you to sign it so he can take her in the Changing of the Wives," Jane said. Nestir fidgeted uncomfortably. "Well, I'll look at her record," he said. "It's an idea," the second mate said. "Otherwise, we'll be short one woman." "There wouldn't be one short if he had brought a wife," the first mate's wife said, looking squarely at the captain. "Now, Martha. I place duty above pleasure. You're just angry, y'know, because you have to stay with your husband." "All right, so I am. But it's true. And if Carstar hadn't been killed, there would have been two short." She shot a wicked glance at Nestir. "Why don't you and him share a woman—" "Martha!" "Although the Prophet knows what woman in her right mind would consent to...." "Well," said Nestir hesitantly. "Listen," the third mate said, "the second's right. If you don't sign it, someone will have to do without a woman." Nestir blushed. "I'll look it over very carefully, but you must realize that the priestcraft...." "Actually, in a way, it would be her duty to, you see. Think of it like that: as her way to do her duty." "She's too young for you, dear," Jane said to her husband. "Oh, I don't know," the steward said. "Sometimes they're the best, I hear." III The third mate, whose name was Harry, stood before the mirror combing his hair. He had been combing his hair for the last fifteen minutes. "I suppose the crew is celebrating?" his wife said. "I suppose." She stood up and walked over to the dresser. Absently she began to finger the articles on it. "You really shouldn't have told them about little Glenn tonight." "Pish-tush." "No, Harry. I mean it. Helen looked at me strangely all through dinner. She has three children, you know." "You're imagining things." "But she does have three children." "I mean about her looking at you." "Oh." Harry fiddled with his tie without speaking. "I mean, as much as to say: 'Well, I raised all of mine.'" "But honey, about little Glenn. That was an accident, almost. You didn't really mean to choke him that hard." "But still ... it ... I mean, there was Helen, looking at me like I wasn't doing my duty. You know." "No," he said. "That's nonsense, Jane. Sheer nonsense. You know what the priest said." He polished one of his brass buttons with the sleeve of his coat. "Harry?" "Yes?" "I don't think all that is necessary just to go on duty." "Probably not." She walked to the bed and sat down. "Harry?" "Yes, dear?" "Don't you really think she's awful young?" "Huh-uh." "I mean, why don't you pick someone else? Like Mary? She's awful sweet. I'll bet she'd be better." "Probably." "She's a lot of fun." He brushed at his hair again. "Who do you want, Jane?" "Oh, I don't know." She looked down at her legs, raised them up from the floor and held them out in front of her. "I think I'd kind of like Nestir. With his funny bald head. I hope he asks me." "I'll mention it to him." "Would you really, Harry? That would be sweet." "Sure, honey." He looked down at his watch. "Harry? Are you going to meet Wanda in the control room?" "Uh-huh." "I thought so. Well, remember this, dear: It isn't the day of the Changing of the Wives yet. Don't forget." "Honey! You don't think for a minute that...." "No, dear. I know you wouldn't. But just don't , I mean." He walked over and kissed her forehead and patted her cheek. "Course not," he said, comfortingly. He left her sitting on the bed and strolled down the officers' corridor, whistling. He made a mental note to have the bosun send some of the crew in tomorrow to wash down these bulkheads. They needed it. In one corner a spider spun its silver web. He jogged up the companionway, turned left and felt the air as fresh as spring when he stepped under the great ventilator. And beneath it lay one of the crew. He kicked the man several times in the ribs until he came to consciousness. "Can't sleep here, my man," Harry explained. "Awww. Go way an' le' me 'lone, huh?" "Here. Here." He pulled the fellow erect and slapped him in the face briskly. "This is the officers' corridor." "Oh? Ish it? Schorry. Shore schorry, shir. So schorry." Harry assisted him to the crew's corridor where he sank to the floor and relapsed once more into a profound slumber. Harry continued on to the control room. When he entered it, the second mate was yawning. "Hi, John. Sleepy?" "Uh-huh. You're early." "Don't mind, do you?" "No ... Quiet tonight. Had to cut the motors an hour ago. Control technician passed out." "Oh?" The second mate took out a cigarette and lit it. "Can't blow the ship up, you know. Look like hell on the record. Hope the captain don't find out about it, though. He'll figure the man was neglecting his duty." He blew a smoke ring. "Might even bar him from the Festival." "Yeah," said Harry, "the captain's funny that way." The second mate blew another smoke ring. "Well," Harry said. "Uh. Harry? Are you really going to take that Wanda girl?" "If Nestir lets me." "Say. Harry. Do you suppose your wife would...?" Harry crossed to the second mate and put a hand on his shoulder. "Sorry, old fellow. She's got it in her head to take Nestir." He shrugged. "I don't exactly approve, of course, but ... I'm sure if he doesn't want her, she'd be glad to hear your offer." "Aw, that's all right," John said. "Don't really matter. Say. By the way. Have I told you what I intend to do to the captain? I've got it all thought out. You know that saber I picked up on Queglat? Well...." "Look. How about telling me another time?" "Uh, Sure. If you say so. Uh?" "I'm kind of expecting Wanda." "Oh. Sure. I should have known you weren't here early for nothing. In that case, I better be shoving off. Luck." "Thanks. See you at breakfast." "Right-o." After the second mate left, Harry walked over to the control panel. The jet lights were dead. He picked up the intercom and switched over the engine call bell. "'Lo," he said into the microphone. "This is the bridge.... Oh, hi, Barney. Harry.... Have you got a sober control technician down there yet...? Fine. We'll start the jets again. If the captain comes in now—well, you know how he is.... Okay, thanks. Night." He replaced the microphone. He reached over and threw the forward firing lever. The jet lights came on and the ship began to brake acceleration again. Having done that, he switched on the space viewer. The steady buzz of the equipment warming sounded in his ears. Wanda would be sure to want to look at the stars. She was simple minded. "Hello." He swiveled around. "Oh, hello, Wanda, honey." "Hello, Haireee. Are you glad little ol' me could come, huh?" "Sure am." "Me, too. Can I look at the—oh. It's already on." "Uh-huh. Look. Wanda." "Hum?" "I talked to Nestir today." "Goody. What did he say, huh? I can be an adult and get to play in the Festival, can I?" "I don't know, yet. He's thinking about it. That's why I want to see you. He's going to check your record. And Wanda?" "Them stars shore are purty." "Wanda, listen to me." "I'm a-listenin', Haireee." "You're simply going to have to stop carrying that doll around with you if you want to be an adult." In Nestir's cabin the next morning, the captain and the priest held a conference. "No, Captain. I'm afraid I can't agree to that," Nestir said. The captain said, "Oh, don't be unreasonable, Father. After all, this is a ship, y'know. And I am, after all, the captain." Nestir shook his head. "The crew and the officers will participate together in the Festival. I will not put the officers' corridor off limits, and—Oh! Yes? Come in!" The door opened. "Father?" "Yes, my son? Come in." "Thank you, Father. Good morning, Captain, sir." "Sit down, my son. Now, Captain, as I was saying: no segregation. It's contrary to the spirit, if not the wording, of the Jarcon ." "But Father! A crewman! In the officers' corridor! Think!" "Before the Prophet, we are all equal. I'm sorry, Captain. Now on Koltah, we practiced it with very good results, and...." "I say, really—" "Father?" said the crewman who had just entered. "Yes, my son. In one moment. Now, Captain. As I have been explaining: The arena method has advantages. In Koltah we always used it. But here—due to the—ah—exigencies of deep space—I feel convinced that a departure from normal procedure is warranted. It is not without precedent. Such things were fairly common, in astoli tavoro , up until centralization, three hundred years before Allth. Indeed, in my home city—Koltah—in the year of the seventh plague, a most unusual expedient was adopted. It seems...." "You're perfectly correct, of course," the captain said. "That's just what I wanted to see you about, Father," the crewman said. "Now, in my city state of Ni, for the Festivals, we...." "Shut up," said the captain softly. "Yes, sir." "Now, as I was saying, Captain, when the methods used in...." "If you'll excuse me, Father, I really should return to duty," said the crewman. "Quite all right, my son. Close the door after you." "I must say, fellow, your sense of duty is commendable." "Well, uh, thank you, sir. And thank you, Father, for your time." "Quite all right, my son. That's what I'm here for. Come in as often as you like." The crewman closed the door after him. He had been gone only a moment, scarcely time for Nestir to get properly launched on his account, when Harry, the third mate, knocked on the door and was admitted. "Oh? Good morning, Captain. I didn't know you were here." Then, to the priest: "I'll come back later, Father." "Nonsense," said the captain. "Come in." "Well, I had hoped to see the Father for a minute on ... private business." "I have to be toddling along," said the captain. "But Captain! I haven't finished telling you about...." "I'll just go down and get a cup of coffee," the captain said. "I'll call you when I'm through," said Harry. The captain left the room. "It's about Wanda, Father," said the third mate. The priest studied the table top. He rearranged some papers. "Ah, yes. The young girl." "Well, I mean, it's not only about Wanda," said Harry. "You see, my wife, Jane, that is...." "Yes?" said the priest. He took his pen out of the holder. "I think, with the proper ... ah ... you know. What I mean is, I think she might look with favor on you in the Changing of the Wives, if I said a few well chosen words in your behalf." "That is very flattering, my son." He returned the pen to the holder. "Such bounty, as it says in the Jarcon , is cull tensio ." "And with your permission, Father...." "Ah...." "She's a very pretty woman." "Ah.... Quite so." "Well, about Wanda. I really shouldn't mention this. But Father, if we are short one woman...." "Hummmm." "I mean, the girls might think a man gets rusty." "I see what you mean." Nestir blinked his eyes. "It wouldn't be fair, all things considered." He stood up. "I may tell you, my son, that, in thinking this matter over last night, I decided that Wanda—ah—Miller, yes, has had sufficient duty to merit participation in the Festival." "Justice is a priestly virtue," Harry said. "And you really think your wife would...?" "Oh, yes, Father." "Well, ahem. But...." "Yes, Father?" " Ad dulce verboten. " "Uh?" "That is to say, in order for a woman to join in the ritual of the Changing of the Wives, she must, ahem, be married." "I never thought of that," said the third mate disconsolately. "I think that can be arranged, however," said Nestir. "If you go by the mess hall on your way out, please tell the captain we can continue our discussion at his pleasure." IV "Sit down, Captain," said Nestir, when the captain entered. "No. Over there, in the comfortable chair. There. Are you comfortable, Captain?" "Of course I am." "Good. I have a question to ask you, Captain." "I say?" Nestir rubbed his bald head. "Sir," he said by way of preamble, "I know you have the greatest sensibility in questions of duty." "That's quite so, y'know. I pride myself upon it, if I do say so." "Exactly. Argot y calpex. No sacrifice is too great." "True; true." "Well, then, say the first day of Wenslaus, that would be—ah, a Zentahday—I may depend upon you to wed Wanda Miller, the bosun's daughter, yes?" "No," said the captain. "Come now, sir. I realize she is the daughter of a crewman, but—" "Father," said the captain, "did I ever tell you about the time I led an expeditionary force against Zelthalta?" "I don't believe you have." "Then I will tell you. Came about this way. I was given command of fifty-three thousand Barains. Savage devils. Uncivilized, but fine fighters. I was to march them ninety-seven miles across the desert that...." "Captain! I fear I must be very severe with you. I will be forced to announce in the mess hall this evening that you have refused to do your duty when it was plainly and properly called to your attention." "Very well, Father," the captain said after several minutes. "I will do it." He was trembling slightly. That morning was to be the time of the captain's wedding. He had insisted that it be done in privacy. For the ceremony, he refused to make the slightest change in his everyday uniform; nor would he consent to Nestir's suggestion that he carry a nosegay of hydroponic flowers. He had intended, after the ceremony, to go about his duty as if nothing out of the ordinary had happened; but after it was done with, the vast indignity of it came home to him even more poignantly than he had imagined it would. Without a word, he left the priest's stateroom and walked slowly, ponderously, with great dignity, to his own. It was a very fine stateroom. The finest, but for Nestir's, in the whole ship. The velvet and gold drapes (his single esthetic joy) were scented with exotic perfume. The carpet was an inch and a half thick. He walked through his office without breaking his stride. The bed was large and fluffy. An unbroken expanse of white coverlette jutting out from the far bulkhead. It looked as soft as feather down. Without even a sigh, he threw himself upon the bed and lay very, very quiet. His left leg was suspended in the air, intersecting, at the thigh, the plane of the coverlet at forty-five degrees; the number of degrees remained stiffly, unrelaxingly forty-five. Only after a long, long time did he roll over on his back and then it was merely to stare fixedly at the ceiling. It is entirely possible that he would have lain there until Doomsday had not his introspection been, around noon, interrupted by an apologetic tap on the door. "Come in," he whispered, hoping she would not hear him and go away. But she heard him. "Husband," Wanda said simply. She closed the door behind her and stood staring at him. "Madam," he said, "I hope you will have the kindness not to refer to me by that indecent appelation a second time." "Gee. You say the cutest things. I'm awful glad you had to marry me, huh." The captain stood up, adjusted his coat and his shoulders, and walked across the room to the dressing table. He opened the left-hand drawer, removed a bottle, poured himself half a water-glass full and drank it off. "Ah," he said. He returned to the bed and sat down. "Can'tcha even say hello ta little ol' me, huh?" she asked. "Hello," he said. "Madam, sit down. I intend to give you an instructive lecture in the natural order of...." "Huh?" "Ah," he said. "Quite true, of course." She walked over to the chair and sat down. "I don't like them," she said. "Them cloth things over there." "Those, Madam," he said, "are priceless drapes I had imported from the province of San Xalthan. They have a long, strange history. "About three thousand years ago, a family by the name of Soong was forced to flee from the city of Xan because the eldest son of the family had become involved in a conspiracy against the illustrious King Fod. As the Soong family was traveling...." "I don't like 'em anyway," said Wanda. "Madam," said the captain, "kindly bring me that." "This?" "Yes. Thank you." He took the doll from her. He got up again, walked to the chest of drawers, searched around for a penknife. Finally he located it under a stack of socks. | D. Different city-states within the whole system. |
Why did Jaro sneak out of his hostelry?
A. he wanted his money from Mr. Peet
B. he wanted to meet Joan
C. he was in need of more Latonka
D. he wanted to figure out the mystery
| Red Witch of Mercury By EMMETT McDOWELL Death was Jaro Moynahan's stock in trade, and every planet had known his touch. But now, on Mercury, he was selling his guns into the weirdest of all his exploits—gambling his life against the soft touch of a woman's lips. [Transcriber's Note: This etext was produced from Planet Stories Summer 1945. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] On the stage of Mercury Sam's Garden , a tight-frocked, limber-hipped, red-head was singing " The Lady from Mars ." The song was a rollicking, ribald ditty, a favorite of the planters and miners, the space pilots and army officers who frequented the garden. The girl rendered it with such gusto that the audience burst into a roar of applause. She bent her head in acknowledgment so that her bronze red hair fell down about her face. There was perspiration on her upper lip and temples. Her crimson mouth wore a fixed smile. Her eyes were frightened. The man, who had accompanied the singer on the piano, sat at the foot of the stage, his back to the crowded tables. He did not look up at the singer but kept his pale, immature face bent over the keys, while his fingers lightly, automatically picked out the tune. Sweat trickled down the back of his neck, plastered his white coat to his back. Without looking up, he said: "Have you spotted him?" His voice was pitched to reach the singer alone. The girl, with an almost imperceptible gesture, shook her head. The night was very hot; but then it is always hot on Mercury, the newest, the wildest, the hottest of Earth's frontiers. Fans spaced about the garden's walls sluggishly stirred the night air, while the men and women sitting at the tables drank heavily of Latonka, the pale green wine of Mercury. Only the native waiters, the enigmatic, yellow-eyed Mercurians, seemed unaffected by the heat. They didn't sweat at all. Up on the stage the singer was about to begin another number when she stiffened. "Here he is," she said to the pianist without moving her lips. The pianist swung around on his stool, lifted his black eyes to the gate leading to the street. Just within the entrance, a tall, thin man was standing. He looked like a gaunt gray wolf loitering in the doorway. His white duraloes suit hung faultlessly. His black hair was close-cropped, his nose thin and aquiline. For a moment he studied the crowded garden before making his way to a vacant table. "Go on," said the pianist in a flat voice. The red-head shivered. Stepping from the stage she picked her way through the tables until she came to the one occupied by the newcomer. "May I join you?" she asked in a low voice. The man arose. "Of course. I was expecting you. Here, sit down." He pulled out a chair, motioned for the waiter. The Mercurian, his yellow incurious eyes like two round topazes, sidled up. "Bring us a bottle of Latonka from the Veederman region, well iced." The waiter slipped away. "So," said the red-head; "you have come. I did not think you would be in time." Her hands were clenched in her lap. The knuckles were white. The man said nothing. "I did not want to call you in, Jaro Moynahan." It was the first time she had used his name. "You have the reputation of being unpredictable. I don't trust you, but since...." She stopped as the waiter placed glasses on the table and deftly poured the pale green wine. The man, Jaro Moynahan, raised his glass. "Here's to the revolution," he said. His low voice carried an odd, compelling note. His eyes, light blue and amused, were pale against his brown face. The girl drew in her breath. "No! Mercury is not ready for freedom. Only a handful of fanatics are engineering the revolution. The real Mercurian patriots are against it, but they are afraid to protest. You've got to believe me. The revolution is scheduled to break during the Festival of the Rains. If it does, the Terrestrials here will be massacred. The Mercurians hate them. We haven't but a handful of troops." Jaro Moynahan wiped the sweat from his forehead with a fine duraweb handkerchief. "I had forgotten how abominably hot it can be here." The girl ignored the interruption. "There is one man; he is the leader, the very soul of the revolution. The Mercurians worship him. They will do whatever he says. Without him they would be lost. He is the rebel, Karfial Hodes. I am to offer you ten thousand Earth notes to kill Karfial Hodes." Jaro Moynahan refilled their empty glasses. He was a big man, handsome in a gaunt fashion. Only his eyes were different. They were flat and a trifle oblique with straight brows. The pupils were a pale and penetrating blue that could probe like a surgeon's knife. Now he caught the girl's eyes and held them with his own as a man spears a fish. "Why call me all the way from Mars for that? Why not have that gunman at the piano rub Hodes out?" The girl started, glanced at the pianist, said with a shiver: "We can't locate Karfial Hodes. Don't look at me that way, Jaro. You frighten me. I'm telling the truth. We can't find him. That's why we called you. You've got to find him, Jaro. He's stirring up all Mercury." "Who's putting up the money?" "I can't tell you." "Ah," said Jaro Moynahan; "so that's the way it is." "That's the way it is." "There isn't much time," he said after a moment. "The Rains are due any day now." "No," the girl replied. "But we think he's here in the city." "Why? What makes you think that?" "He was seen," she began, then stopped with a gasp. The lights had gone out. It was as unexpected as a shot in the back. One moment the garden was glowing in light, the next the hot black night swooped down on the revelers, pressing against their eyes like dark wool. The fans about the walls slowed audibly and stopped. It grew hotter, closer. Jaro Moynahan slipped sideways from the table. He felt something brush his sleeve. Somewhere a girl giggled. "What's coming off here?" growled a petulant male voice. Other voices took up the plaint. Across the table from Jaro there was the feel of movement; he could sense it. An exclamation was suddenly choked off as if a hand had been clamped over the girl's mouth. "Red!" said Jaro in a low voice. There was no answer. "Red!" he repeated, louder. Unexpectedly, the deep, ringing voice of Mercury Sam boomed out from the stage. "It's all right. The master fuse blew out. The lights will be on in a moment." On the heels of his speech the lights flashed on, driving the night upward. The fans recommenced their monotonous whirring. Jaro Moynahan glanced at the table. The red-headed singer was gone. So was the pianist. Jaro Moynahan sat quietly back down and poured himself another glass of Latonka. The pale green wine had a delicate yet exhilarating taste. It made him think of cool green grapes beaded with dew. On the hot, teeming planet of Mercury it was as refreshing as a cold plunge. He wondered who was putting up the ten thousand Earth notes? Who stood to lose most in case of a revolution? The answer seemed obvious enough. Who, but Albert Peet. Peet controlled the Latonka trade for which there was a tremendous demand throughout the Universe. And what had happened to the girl. Had the rebels abducted her. If so, he suspected that they had caught a tartar. The Red Witch had the reputation of being able to take care of herself. He beckoned a waiter, paid his bill. As the Mercurian started to leave, a thought struck Jaro. These yellow-eyed Mercurians could see as well in the dark as any alley-prowling cat. For centuries they had lived most their lives beneath ground to escape the terrible rays of the sun. Only at night did they emerge to work their fields and ply their trades. He peeled off a bill, put it in the waiter's hands. "What became of the red-headed singer?" The Mercurian glanced at the bill, then back at the Earthman. There was no expression in his yellow eyes. "She and the man, the queer white one who plays the piano, slipped out the gate to the street." Jaro shrugged, dismissed the waiter. He had not expected to get much information from the waiter, but he was not a man to overlook any possibility. If the girl had been abducted, only Mercurians could have engineered it in the dark; and the Mercurians were a clannish lot. Back on the narrow alley-like street Jaro Moynahan headed for his hostelry. By stretching out his arms he could touch the buildings on either side: buildings with walls four feet thick to keep out the heat of the sun. Beneath his feet, he knew, stretched a labyrinth of rooms and passages. Somewhere in those rat-runs was Karfial Hodes, the revolutionist, and the girl. At infrequent intervals green globes cut a hole in the night, casting a faint illumination. He had just passed one of these futile street lamps when he thought he detected a footfall behind him. It was only the whisper of a sound, but as he passed beyond the circle of radiation, he flattened himself in a doorway. Nothing stirred. There was no further sound. Again he started forward, but now he was conscious of shadows following him. They were never visible, but to his trained ears there came stealthy, revealing noises: the brush of cloth against the baked earth walls, the sly shuffle of a step. He ducked down a bisecting alley, faded into a doorway. Immediately all sounds of pursuit stopped. But as soon as he emerged he was conscious again of the followers. In the dense, humid night, he was like a blind man trying to elude the cat-eyed Mercurians. Jaro Moynahan In the East a sullen red glow stained the heavens like the reflection of a fire. The Mercurian dawn was about to break. With an oath, he set out again for his hostelry. He made no further effort to elude the followers. Once back in his room, Jaro Moynahan stripped off his clothes, unbuckled a shoulder holster containing a compressed air slug gun, stepped under the shower. His body was lean and brown as his face and marked with innumerable scars. There were small round puckered scars and long thin ones, and his left shoulder bore the unmistakable brownish patch of a ray burn. Stepping out of the shower, he dried, rebuckled on the shoulder holster, slipped into pajamas. The pajamas were blue with wide gaudy stripes. Next he lit a cigarette and stretching out on the bed began to contemplate his toes with singular interest. He had, he supposed, killed rather a lot of men. He had fought in the deadly little wars of the Moons of Jupiter for years, then the Universal Debacle of 3368, after that the Martian Revolution as well as dozens of skirmishes between the Federated Venusian States. No, there was little doubt but that he had killed quite a number of men. But this business of hunting a man through the rat-runs beneath the city was out of his line. Furthermore, there was something phony about the entire set up. The Mercurians, he knew, had been agitating for freedom for years. Why, at this time when the Earth Congress was about to grant them self-government, should they stage a revolution? A loud, authoritative rapping at the door interrupted further speculation. He swung his bare feet over the edge of the bed, stood up and ground out his cigarette. Before he could reach the door the rapping came again. Throwing off the latch, he stepped back, balancing on the balls of his feet. "Come in," he called. The door swung open. A heavy set man entered, shut and locked the door, then glanced around casually. His eyes fastened on Jaro. He licked his lips. "Mr. Moynahan, the—ah—professional soldier, I believe." His voice was high, almost feminine. "I'm Albert Peet." He held out a fat pink hand. Jaro said nothing. He ignored the hand, waited, poised like a cat. Mr. Peet licked his lips again. "I have come, Mr. Moynahan, on a matter of business, urgent business. I had not intended to appear in this matter. I preferred to remain behind the scenes, but the disappearance of Miss Mikail has—ah—forced my hand." He paused. Jaro still said nothing. Miss Mikail must be the red-headed singer, whom at different times he had known under a dozen different aliases. He doubted that even she remembered her right name. "Miss Mikail made you a proposition?" Albert Peet's voice was tight. "Yes," said Jaro. "You accepted?" "Why, no. As it happened she was abducted before I had the chance." Mr. Peet licked his lips. "But you will, surely you will. Unless Karfial Hodes is stopped immediately there will be a bloody uprising all over the planet during the Festival of the Rains. Earth doesn't realize the seriousness of the situation." "Then I was right; it is you who are putting up the ten thousand Earth notes." "Not entirely," said Peet uncomfortably. "There are many of us here, Mercurians as well as Earthmen, who recognize the danger. We have—ah—pooled our resources." "But you stand to lose most in case of a successful revolution?" "Perhaps. I have a large interest in the Latonka trade. It is—ah—lucrative." Jaro Moynahan lit a cigarette, sat down on the edge of the bed. "Why beat about the bush," he asked with a sudden grin. "Mr. Peet, you've gained control of the Latonka trade. Other Earthmen are in control of the mines and the northern plantations. Together you form perhaps the strongest combine the Universe has ever seen. You actually run Mercury, and you've squeezed out every possible penny. Every time self-government has come before the Earth Congress you've succeeded in blocking it. You are, perhaps, the most cordially-hated group anywhere. I don't wonder that you are afraid of a revolution." Mr. Peet took out a handkerchief and mopped his forehead. "Fifteen thousand Earth notes I can offer you. But no more. That is as high as I can go." Jaro laughed. "How did you know Red had been kidnapped?" "We have a very efficient information system. I had the report of Miss Mikail's abduction fifteen minutes after the fact." Jaro raised his eyebrows. "Perhaps then you know where she is?" Mr. Peet shook his head. "No. Karfial Hodes' men abducted her." A second rapping at the door caused them to exchange glances. Jaro went to the door, opened it. The pianist at the gardens was framed in the entrance. His black eyes burned holes in his pale boyish face. His white suit was blotched with sweat and dirt. "They told me Mr. Peet was here," he said. "It's for you," said Jaro over his shoulder. Mr. Peet came to the door. "Hello, Stanley. I thought Hodes had you? Where's Miss Mikail?" "I got away. Look, Mr. Peet, I got to see you alone." Albert Peet said, "Would you excuse me, Mr. Moynahan?" He licked his lips. "I'll just step out into the hall a moment." He went out, drawing the door shut after him. Jaro lit a cigarette. He padded nervously back and forth across the room, his bare feet making no noise. He sat down on the edge of the bed. He got up and ground out the cigarette. He went to the door, but did not open it. Instead, he took another turn about the room. Again he came to a halt before the door, pressed his ear against the panel. For a long time he listened but could distinguish no murmur of voices. With an oath he threw open the door. The hall was empty. II Jaro returned to his room, stripped off his pajamas, climbed back into his suit. He tested the slug gun. It was a flat, ugly weapon which hurled a slug the size of a quarter. He preferred it because, though he seldom shot to kill, it stopped a man like a well placed mule's hoof. He adjusted the gun lightly in its holster in order that it wouldn't stick if he were called upon to use it in a hurry. Then he went out into the hall. At the desk he inquired if any messages had come for him. There were none, but the clerk had seen Mr. Peet with a young fellow take the incline to the underground. Above the clerk's head a newsograph was reeling off the current events almost as soon as they happened. Jaro read: " Earth Congress suspends negotiations on Mercurian freedom pending investigation of rumored rebellion. Terrestrials advised to return to Earth. Karfial Hodes, Mercurian patriot, being sought. " Jaro descended the incline to the network of burrows which served as streets during the flaming days. Here in the basements and sub-basements were located the shops and dram houses where the Mercurians sat around little tables drinking silently of the pale green Latonka. The burrows were but poorly lit, the natives preferring the cool gloom, and Jaro had to feel his way, rubbing shoulders with the strange, silent populace. But when he reached the Terrestrial quarter of the city, bright radoxide lights took the place of the green globes, and there was a sprinkling of Colonial guards among the throng. Jaro halted before a door bearing a placard which read: "LATONKA TRUST" He pushed through the door into a rich carpeted reception room. At the far end was a second door beside which sat a desk, door and desk being railed off from the rest of the office. The door into Albert Peet's inner sanctum was ajar. Jaro could distinguish voices; then quite clearly he heard Albert Peet say in a high girlish tone: "Stanley, I thought I left you in the native quarter. Why did you follow me? How many times have I told you never to come here?" The reply was unintelligible. Then the pale-faced young man came through the door shutting it after himself. At the sight of Jaro Moynahan he froze. "What're you sneaking around here for?" Jaro settled himself warily, his light blue eyes flicking over the youth. "Let's get this straight," he said mildly. "I've known your kind before. Frankly, ever since I saw you I've had to repress a desire to step on you as I might a spider." The youth's black eyes were hot as coals, his fingers twitching. His hands began to creep upward. "You dirty ..." he began, but he got no further. Jaro Moynahan shot him in the shoulder. The compressed air slug gun had seemed to leap into Jaro's hand. The big slug, smacked the gunman's shoulder with a resounding thwack, hurled him against the wall. Jaro vaulted the rail, deftly relieved him of two poisoned needle guns. "I'll get you for this," said Stanley, his mouth twisted in pain. "You've broken my shoulder. I'll kill you." The door to the inner sanctum swung open. "What's happened?" cried Albert Peet in distress. "What's wrong with you, Stanley?" "This dirty slob shot me in the shoulder." "But how badly?" Peet was wringing his hands. "Nothing serious," said Jaro. "He'll have his arm in a sling for a while. That's all." "Stanley," said Mr. Peet. "You're bleeding all over my carpet. Why can't you go in the washroom. There's a tile floor in there. If you hadn't disobeyed this wouldn't have happened. You and your fights. Has anyone called a doctor? Where's Miss Webb? Miss Webb! Oh, Miss Webb! That girl. Miss Webb!" Stanley climbed to his feet, swayed a moment drunkenly, then wobbled out a door on the left just as a tall brunette hurried in from the right. She had straight black hair which hung not quite to her shoulders, and dark brown eyes, and enough of everything else to absorb Jaro's attention. "Oh!" exclaimed Miss Webb as she caught sight of the blood staining the carpet. Joan Webb "There's been an—ah—accident," said Mr. Peet, and he licked his lips. "Call a doctor, Miss Webb." Miss Webb raised an eyebrow, went to the visoscreen. In a moment she had tuned in the prim starched figure of a nurse seated at a desk. "Could Dr. Baer rush right over here? There's been an accident." "Rush over where?" said the girl in the visoscreen. "These gadgets aren't telepathic, honey." "Oh," said Miss Webb, "the offices of the Latonka Trust." The girl in the visoscreen thawed like ice cream in the sun. "I'm sure Dr. Baer can come. He'll be there in a moment." "Thank you," said Miss Webb. She flicked the machine off, then added: "You trollop." Mr. Peet regarded Jaro Moynahan with distress. "Really, Mr. Moynahan, was it necessary to shoot Stanley? Isn't that—ah—a little extreme? I'm afraid it might incapacitate him, and I had a job for him." "Oh," cried Miss Webb, her brown eyes crackling. "Did you shoot that poor boy? Aren't you the big brave man?" "Poor boy?" said Jaro mildly. "Venomous little rattlesnake. I took these toys away from him." He held out the poisoned dart guns. "You take them, Mr. Peet. Frankly, they give me the creeps. They might go off. A scratch from one of those needles would be enough." Mr. Peet accepted the guns gingerly. He held them as if they might explode any minute. He started to put them in his pocket, thought better of it, glanced around helplessly. "Here, Miss Webb," he said, "do something with these. Put them in my desk." Miss Webb's eyes grew round as marbles. "I wouldn't touch one of those nasty little contraptions for all the Latonka on Mercury." "Here, I'll take them," said Stanley coming back into the room. He had staunched the flow of blood. His face was even whiter, if possible. Jaro eyed him coldly as with his good hand the youth dropped the dart guns back into their holsters. "Act like you want to use those and I'll put a slug in your head next time." "Now, Mr. Moynahan." Mr. Peet licked his lips nervously. "Stanley, go into my office. The doctor will be here in a moment. Miss Webb, you may go home. I'll have no more work for you today." Albert Peet led Stanley through the door. Jaro and Miss Webb were alone. With his eye on the door, Jaro said: "When you go out, turn left toward the native quarter. Wait for me in the first grog shop you come to." Miss Webb raised her eyebrows. "What's this? A new technique?" "Look," began Jaro annoyed. "My eyes are practically popping out of my head now," she interrupted. "Another morning like this and I take the first space liner back to Earth." She jammed her hat on backward, snatched her bag from the desk drawer. "I'm not trying to pick you up. This is...." "How disappointing." Jaro began again patiently. "Wait for me in the first grog shop. There's something I must know. It's important." He cleared his throat. "Don't you find the heat rather uncomfortable, Miss Webb. But perhaps you've become accustomed to it." Mr. Peet came back into the room. "Why, no, I mean yes," replied Miss Webb, a blank expression in her eyes. "Goodbye, Miss Webb," said Mr. Peet firmly. Jaro grinned and winked at her. Miss Webb tottered out of the room. As the door closed behind the girl, Albert Peet licked his lips, said: "Mr. Moynahan, I suppose my disappearance back at your room requires some explanation. But the fact is that Stanley brought an important bit of news." He paused. Jaro said nothing. "You might be interested to know that Miss Mikail is quite safe. Karfial Hodes has her, but Stanley assures me she will be quite safe." Again he paused. As Jaro remained silent, his neck mottled up pinkly. "The fact is, Mr. Moynahan, that we won't need you after all. I realize that we've put you to considerable trouble and we're prepared to pay you whatever you believe your time is worth. Say five hundred Earth notes?" "That's fair enough," replied Jaro. Albert Peet sighed. "I have the check made out." "Only," continued Jaro coldly, "I'm not ready to be bought off. I think I'll deal myself a hand in this game." Mr. Peet's face fell. "You won't reconsider?" "Sorry," said Jaro; "but I've got a date. I'm late now." He started to leave. "Stanley!" called Albert Peet. The pale-faced young man appeared in the doorway, the dart gun in his good hand. Jaro Moynahan dropped on his face, jerking out his slug gun as he fell. There was a tiny plop like a cap exploding. He heard the whisper of the poisoned dart as it passed overhead. Then he fired from the floor. The pale-faced young man crumpled like an empty sack. Jaro got up, keeping an eye on Albert Peet, brushed off his knees. "You've killed him," said Peet. "If I were you, Mr. Moynahan, I would be on the next liner back to Earth." Without answering, Jaro backed watchfully from the room. Once Jaro Moynahan had regained the street, he mopped his forehead with his handkerchief. Whatever was going on, these boys played for keeps. Warily he started down the passage toward the native quarter. At the first basement grog shop he turned in. His eyes swept the chamber, then he grinned. At a corner table, a tall glass of Latonka before her, sat Miss Webb. Her hat was still on backwards, and she was perched on the edge of her chair as if ready to spring up and away like a startled faun. " Bang! " said Jaro coming up behind her and poking a long brown finger in the small of her back. Miss Webb uttered a shriek, jerked so violently that her hat tilted over one eye. She regarded him balefully from beneath the brim. "Never a dull moment," she gritted. Still grinning, Jaro sat down. "I'm Jaro Moynahan, Miss Webb. I think Albert Peet forgot to introduce us. There's some skullduggery going on here that I'm particularly anxious to get to the bottom of. I thought you might be able to help me." "Yes," replied Miss Webb sweetly. A native waiter, attracted no doubt by her scream, came over and took Jaro's order. "All right," Jaro smiled, but his pale blue eyes probed the girl thoughtfully. "I'll have to confide certain facts which might be dangerous for you to know. Are you game, Miss Webb?" "Since we're going to be so chummy," she replied; "you might begin by calling me Joan. You make me feel downright ancient." "Well then," he said. "In the first place, I just killed that baby-faced gunman your boss had in his office." " Awk! " said Joan, choking on the Latonka. "It was self-defense," he hastened to assure her. "He took a pot shot at me with that poisoned dart gun." "But the police!" she cried, as she caught her breath. "There'll never be an investigation. Albert Peet will see to that. I was called here on what I supposed was a legitimate revolution. Instead I was offered ten thousand Earth notes to assassinate the leader of the revolution." "What revolution? I'm going around in circles." "The Mercurians, of course." "I don't believe it," said the girl. "The Mercurians are the most peaceable people in the Universe. They've been agitating for freedom, yes. But they believe in passive resistance. I don't believe you could induce a Mercurian to kill, even in self-protection. That's why Albert Peet and the rest of the combine had such an easy time gaining control of the Latonka trade." "Score one," breathed Jaro, "I begin to see light. Miss Webb—ah, Joan—I've a notion that we're going to be a great team. How do you happen to be Albert Peet's private secretary?" "A gal's gotta eat. But the truth is, I was quitting. The Latonka Trust is almost on the rocks. Their stock has been dropping like a meteor." Jaro Moynahan raised his oblique brows but did not interrupt. "Albert Peet," she continued, "has been trying to sell out but nobody will touch the stock, not since it looks as if the Earth Congress is going to grant the Mercurians their freedom. Everybody knows that the first thing the Mercurians will do, will be to boot out the Latonka Trust." "What about this Karfial Hodes?" said Jaro. "I've heard that he's inciting the Mercurians to rebellion. The newscaster had a line about the revolution too. The government has advised all Terrestrials to return to Earth." "It's not true," Joan flared. "It's all a pack of lies invented by the Latonka Trust. I know." "But I should think rumors like that would run down the Latonka stock." | D. he wanted to figure out the mystery |
What are the characteristics of the dataset? | ### Introduction
Ultrasound tongue imaging (UTI) uses standard medical ultrasound to visualize the tongue surface during speech production. It provides a non-invasive, clinically safe, and increasingly inexpensive method to visualize the vocal tract. Articulatory visual biofeedback of the speech production process, using UTI, can be valuable for speech therapy BIBREF0 , BIBREF1 , BIBREF2 or language learning BIBREF3 , BIBREF4 . Ultrasound visual biofeedback combines auditory information with visual information of the tongue position, allowing users, for example, to correct inaccurate articulations in real-time during therapy or learning. In the context of speech therapy, automatic processing of ultrasound images was used for tongue contour extraction BIBREF5 and the animation of a tongue model BIBREF6 . More broadly, speech recognition and synthesis from articulatory signals BIBREF7 captured using UTI can be used with silent speech interfaces in order to help restore spoken communication for users with speech or motor impairments, or to allow silent spoken communication in situations where audible speech is undesirable BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . Similarly, ultrasound images of the tongue have been used for direct estimation of acoustic parameters for speech synthesis BIBREF13 , BIBREF14 , BIBREF15 . Speech and language therapists (SLTs) have found UTI to be very useful in speech therapy. In this work we explore the automatic processing of ultrasound tongue images in order to assist SLTs, who currently largely rely on manual processing when using articulatory imaging in speech therapy. One task that could assist SLTs is the automatic classification of tongue shapes from raw ultrasound. This can facilitate the diagnosis and treatment of speech sound disorders, by allowing SLTs to automatically identify incorrect articulations, or by quantifying patient progress in therapy. In addition to being directly useful for speech therapy, the classification of tongue shapes enables further understanding of phonetic variability in ultrasound tongue images. Much of the previous work in this area has focused on speaker-dependent models. In this work we investigate how automatic processing of ultrasound tongue imaging is affected by speaker variation, and how severe degradations in performance can be avoided when applying systems to data from previously unseen speakers through the use of speaker adaptation and speaker normalization approaches. Below, we present the main challenges associated with the automatic processing of ultrasound data, together with a review of speaker-independent models applied to UTI. Following this, we present the experiments that we have performed (Section SECREF2 ), and discuss the results obtained (Section SECREF3 ). Finally we propose some future work and conclude the paper (Sections SECREF4 and SECREF5 ). ### Ultrasound Tongue Imaging
There are several challenges associated with the automatic processing of ultrasound tongue images. Image quality and limitations. UTI output tends to be noisy, with unrelated high-contrast edges, speckle noise, or interruptions of the tongue surface BIBREF16 , BIBREF17 . Additionally, the oral cavity is not entirely visible from the image, missing the lips, the palate, or the pharyngeal wall. Inter-speaker variation. Age and physiology may affect the output, with children imaging better than adults due to more moisture in the mouth and less tissue fat BIBREF16 . However, dry mouths lead to poor imaging, which might occur in speech therapy if a child is nervous during a session. Similarly, the vocal tracts of children across different ages may be more variable than those of adults. Probe placement. Articulators that are orthogonal to the ultrasound beam direction image well, while those at an angle tend to image poorly. Incorrect or variable probe placement during recordings may lead to high variability between otherwise similar tongue shapes. This may be controlled using helmets BIBREF18 , although it is unreasonable to expect the speaker to remain still throughout the recording session, especially if working with children. Therefore, probe displacement should be expected to be a factor in image quality and consistency. Limited data. Although ultrasound imaging is becoming less expensive to acquire, there is still a lack of large publicly available databases to evaluate automatic processing methods. The UltraSuite Repository BIBREF19 , which we use in this work, helps alleviate this issue, but it still does not compare to standard speech recognition or image classification databases, which contain hundreds of hours of speech or millions of images. ### Related Work
Earlier work concerned with speech recognition from ultrasound data has mostly been focused on speaker-dependent systems BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 . An exception is the work of Xu et al. BIBREF24 , which investigates the classification of tongue gestures from ultrasound data using convolutional neural networks. Some results are presented for a speaker-independent system, although the investigation is limited to two speakers generalizing to a third. Fabre et al BIBREF5 present a method for automatic tongue contour extraction from ultrasound data. The system is evaluated in a speaker-independent way by training on data from eight speakers and evaluating on a single held out speaker. In both of these studies, a large drop in accuracy was observed when using speaker-independent systems in comparison to speaker-dependent systems. Our investigation differs from previous work in that we focus on child speech while using a larger number of speakers (58 children). Additionally, we use cross-validation to evaluate the performance of speaker-independent systems across all speakers, rather than using a small held out subset. ### Ultrasound Data
We use the Ultrax Typically Developing dataset (UXTD) from the publicly available UltraSuite repository BIBREF19 . This dataset contains synchronized acoustic and ultrasound data from 58 typically developing children, aged 5-12 years old (31 female, 27 male). The data was aligned at the phone-level, according to the methods described in BIBREF19 , BIBREF25 . For this work, we discarded the acoustic data and focused only on the B-Mode ultrasound images capturing a midsaggital view of the tongue. The data was recorded using an Ultrasonix SonixRP machine using Articulate Assistant Advanced (AAA) software at INLINEFORM0 121fps with a 135 field of view. A single ultrasound frame consists of 412 echo returns from each of the 63 scan lines (63x412 raw frames). For this work, we only use UXTD type A (semantically unrelated words, such as pack, tap, peak, tea, oak, toe) and type B (non-words designed to elicit the articulation of target phones, such as apa, eepee, opo) utterances. ### Data Selection
For this investigation, we define a simplified phonetic segment classification task. We determine four classes corresponding to distinct places of articulation. The first consists of bilabial and labiodental phones (e.g. /p, b, v, f, .../). The second class includes dental, alveolar, and postalveolar phones (e.g. /th, d, t, z, s, sh, .../). The third class consists of velar phones (e.g. /k, g, .../). Finally, the fourth class consists of alveolar approximant /r/. Figure FIGREF1 shows examples of the four classes for two speakers. For each speaker, we divide all available utterances into disjoint train, development, and test sets. Using the force-aligned phone boundaries, we extract the mid-phone frame for each example across the four classes, which leads to a data imbalance. Therefore, for all utterances in the training set, we randomly sample additional examples within a window of 5 frames around the center phone, to at least 50 training examples per class per speaker. It is not always possible to reach the target of 50 examples, however, if no more data is available to sample from. This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples. Because the amount of data varies per speaker, we compute a sampling score, which denotes the proportion of sampled examples to the speaker's total training examples. We expect speakers with high sampling scores (less unique data overall) to underperform when compared with speakers with more varied training examples. ### Preprocessing and Model Architectures
For each system, we normalize the training data to zero mean and unit variance. Due to the high dimensionality of the data (63x412 samples per frame), we have opted to investigate two preprocessing techniques: principal components analysis (PCA, often called eigentongues in this context) and a 2-dimensional discrete cosine transform (DCT). In this paper, Raw input denotes the mean-variance normalized raw ultrasound frame. PCA applies principal components analysis to the normalized training data and preserves the top 1000 components. DCT applies the 2D DCT to the normalized raw ultrasound frame and the upper left 40x40 submatrix (1600 coefficients) is flattened and used as input. The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. The networks are optimized for 40 epochs with a mini-batch of 32 samples using stochastic gradient descent. Based on preliminary experiments on the validation set, hyperparameters such learning rate, decay rate, and L2 weight vary depending on the input format (Raw, PCA, or DCT). Generally, Raw inputs work better with smaller learning rates and heavier regularization to prevent overfitting to the high-dimensional data. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes. The convolutional layers use 16 filters, 8x8 and 4x4 kernels respectively, and rectified units. The fully-connected layers use dropout with a drop probability of 0.2. Because CNN systems take longer to converge, they are optimized over 200 epochs. For all systems, at the end of every epoch, the model is evaluated on the development set, and the best model across all epochs is kept. ### Training Scenarios and Speaker Means
We train speaker-dependent systems separately for each speaker, using all of their training data (an average of 185 examples per speaker). These systems use less data overall than the remaining systems, although we still expect them to perform well, as the data matches in terms of speaker characteristics. Realistically, such systems would not be viable, as it would be unreasonable to collect large amounts of data for every child who is undergoing speech therapy. We further evaluate all trained systems in a multi-speaker scenario. In this configuration, the speaker sets for training, development, and testing are equal. That is, we evaluate on speakers that we have seen at training time, although on different utterances. A more realistic configuration is a speaker-independent scenario, which assumes that the speaker set available for training and development is disjoint from the speaker set used at test time. This scenario is implemented by leave-one-out cross-validation. Finally, we investigate a speaker adaptation scenario, where training data for the target speaker becomes available. This scenario is realistic, for example, if after a session, the therapist were to annotate a small number of training examples. In this work, we use the held-out training data to finetune a pretrained speaker-independent system for an additional 6 epochs in the DNN systems and 20 epochs for the CNN systems. We use all available training data across all training scenarios, and we investigate the effect of the number of samples on one of the top performing systems. This work is primarily concerned with generalizing to unseen speakers. Therefore, we investigate a method to provide models with speaker-specific inputs. A simple approach is to use the speaker mean, which is the pixel-wise mean of all raw frames associated with a given speaker, illustrated in Figure FIGREF8 . The mean frame might capture an overall area of tongue activity, average out noise, and compensate for probe placement differences across speakers. Speaker means are computed after mean variance normalization. For PCA-based systems, matrix decomposition is applied on the matrix of speaker means for the training data with 50 components being kept, while the 2D DCT is applied normally to each mean frame. In the DNN systems, the speaker mean is appended to the input vector. In the CNN system, the raw speaker mean is given to the network as a second channel. All model configurations are similar to those described earlier, except for the DNN using Raw input. Earlier experiments have shown that a larger number of parameters are needed for good generalization with a large number of inputs, so we use layers of 1024 nodes rather than 512. ### Results and Discussion
Results for all systems are presented in Table TABREF10 . When comparing preprocessing methods, we observe that PCA underperforms when compared with the 2 dimensional DCT or with the raw input. DCT-based systems achieve good results when compared with similar model architectures, especially when using smaller amounts of data as in the speaker-dependent scenario. When compared with raw input DNNs, the DCT-based systems likely benefit from the reduced dimensionality. In this case, lower dimensional inputs allow the model to generalize better and the truncation of the DCT matrix helps remove noise from the images. Compared with PCA-based systems, it is hypothesized the observed improvements are likely due to the DCT's ability to encode the 2-D structure of the image, which is ignored by PCA. However, the DNN-DCT system does not outperform a CNN with raw input, ranking last across adapted systems. When comparing training scenarios, as expected, speaker-independent systems underperform, which illustrates the difficulty involved in the generalization to unseen speakers. Multi-speaker systems outperform the corresponding speaker-dependent systems, which shows the usefulness of learning from a larger database, even if variable across speakers. Adapted systems improve over the dependent systems, except when using DCT. It is unclear why DCT-based systems underperform when adapting pre-trained models. Figure FIGREF11 shows the effect of the size of the adaptation data when finetuning a pre-trained speaker-independent system. As expected, the more data is available, the better that system performs. It is observed that, for the CNN system, with roughly 50 samples, the model outperforms a similar speaker-dependent system with roughly three times more examples. Speaker means improve results across all scenarios. It is particularly useful for speaker-independent systems. The ability to generalize to unseen speakers is clear in the CNN system. Using the mean as a second channel in the convolutional network has the advantage of relating each pixel to its corresponding speaker mean value, allowing the model to better generalize to unseen speakers. Figure FIGREF12 shows pair-wise scatterplots for the CNN system. Training scenarios are compared in terms of the effect on individual speakers. It is observed, for example, that the performance of a speaker-adapted system is similar to a multi-speaker system, with most speakers clustered around the identity line (bottom left subplot). Figure FIGREF12 also illustrates the variability across speakers for each of the training scenarios. The classification task is easier for some speakers than others. In an attempt to understand this variability, we can look at correlation between accuracy scores and various speaker details. For the CNN systems, we have found some correlation (Pearson's product-moment correlation) between accuracy and age for the dependent ( INLINEFORM0 ), multi-speaker ( INLINEFORM1 ), and adapted ( INLINEFORM2 ) systems. A very small correlation ( INLINEFORM3 ) was found for the independent system. Similarly, some correlation was found between accuracy and sampling score ( INLINEFORM4 ) for the dependent system, but not for the remaining scenarios. No correlation was found between accuracy and gender (point biserial correlation). ### Future Work
There are various possible extensions for this work. For example, using all frames assigned to a phone, rather than using only the middle frame. Recurrent architectures are natural candidates for such systems. Additionally, if using these techniques for speech therapy, the audio signal will be available. An extension of these analyses should not be limited to the ultrasound signal, but instead evaluate whether audio and ultrasound can be complementary. Further work should aim to extend the four classes to more a fine-grained place of articulation, possibly based on phonological processes. Similarly, investigating which classes lead to classification errors might help explain some of the observed results. Although we have looked at variables such as age, gender, or amount of data to explain speaker variation, there may be additional factors involved, such as the general quality of the ultrasound image. Image quality could be affected by probe placement, dry mouths, or other factors. Automatically identifying or measuring such cases could be beneficial for speech therapy, for example, by signalling the therapist that the data being collected is sub-optimal. ### Conclusion
In this paper, we have investigated speaker-independent models for the classification of phonetic segments from raw ultrasound data. We have shown that the performance of the models heavily degrades when evaluated on data from unseen speakers. This is a result of the variability in ultrasound images, mostly due to differences across speakers, but also due to shifts in probe placement. Using the mean of all ultrasound frames for a new speaker improves the generalization of the models to unseen data, especially when using convolutional neural networks. We have also shown that adapting a pre-trained speaker-independent system using as few as 50 ultrasound frames can outperform a corresponding speaker-dependent system. Fig. 1. Ultrasound samples for the four output classes based on place of articulation. The top row contains samples from speaker 12 (male, aged six), and the bottom row from speaker 13 (female, aged eleven). All samples show a midsaggital view of the oral cavity with the tip of the tongue facing right. Each sample is the mid-point frame of a phone uttered in an aCa context (e.g. apa, ata, ara, aka). See the UltraSuite repository2 for details on interpreting ultrasound tongue images. Fig. 2. Ultrasound mean image for speaker 12 (top row) and speaker 13 (bottom row). Means on the left column are taken over the training data, while means on the right are taken over the test data. Table 1. Phonetic segment accuracy for the four training scenarios. Fig. 3. Accuracy scores for adapted CNN Raw, varying amount of adaptation examples. We separately restrict training and development data to either n or all examples, whichever is smallest. Fig. 4. Pair-wise scatterplots for the CNN system without speaker mean. Each sample is a speaker with axes representing accuracy under a training scenario. Percentages in top left and bottom right corners indicate amount of speakers above or below the dashed identity line, respectively. Speaker accuracies are compared after being rounded to two decimal places. | synchronized acoustic and ultrasound data from 58 typically developing children, aged 5-12 years old (31 female, 27 male), data was aligned at the phone-level, 121fps with a 135 field of view, single ultrasound frame consists of 412 echo returns from each of the 63 scan lines (63x412 raw frames) |
What architectural factors were investigated? | ### Introduction
Any finite training set is consistent with multiple generalizations. Therefore, the way that a learner generalizes to unseen examples depends not only on the training data but also on properties of the learner. Suppose a learner is told that a blue triangle is an example of a blick. A learner preferring shape-based generalizations would conclude that blick means “triangle,” while a learner preferring color-based generalizations would conclude that blick means “blue object” BIBREF0. Factors that guide a learner to choose one generalization over another are called inductive biases. What properties of a learner cause it to have a particular inductive bias? We investigate this question with respect to sequence-to-sequence neural networks BIBREF1, BIBREF2. As a test case for studying differences in how models generalize, we use the syntactic task of English question formation, such as transforming SECREF1 into SECREF1: . Ṫhe zebra ForestGreendoes chuckle. ForestGreenDoes the zebra chuckle? Following BIBREF3's (BIBREF3) empirical claims about children's linguistic input, we constrain our training set to be consistent with two possible rules illustrated in Figure FIGREF1: move-main (a rule based on hierarchical syntactic structure) and move-first (a rule based on linear order). We then evaluate each trained model on examples where the rules make different predictions, such as SECREF1: given SECREF1, move-main would generate SECREF1 while move-first would generate SECREF1: . Ẏour zebras that bluedon't dance ForestGreendo chuckle. ForestGreenDo your zebras that bluedon't dance chuckle? blueDon't your zebras that dance ForestGreendo chuckle? Since no such examples appear in the training set, a model's behavior on them reveals which rule the model is biased toward. This task allows us to study a particular bias, namely a bias for hierarchical generalization, which is important for models of language because it has been argued to underlie human language acquisition BIBREF4. To test which models have a hierarchical bias, we use the question formation task and a second task: tense reinflection. For both tasks, our training set is ambiguous between a hierarchical generalization and a linear generalization. If a model chooses the hierarchical generalization for only one task, this preference is likely due to task-specific factors rather than a general hierarchical bias. On the other hand, a consistent preference for hierarchical generalizations across tasks would provide converging evidence that a model has a hierarchical bias. We find that all the factors we tested can qualitatively affect how a model generalizes on the question formation task. These factors are the type of recurrent unit, the type of attention, and the choice of sequential vs. tree-based model structure. Even though all these factors affected the model's decision between move-main and move-first, only the use of a tree-based model can be said to impart a hierarchical bias, since this was the only model type that chose a hierarchical generalization across both of our tasks. Specific findings that support these general conclusions include: Generalization behavior is profoundly affected by the type of recurrent unit and the type of attention, and also by the interactions between these factors. LSTMs and GRUs have qualitatively different inductive biases. The difference appears at least partly due to the fact that the values in GRU hidden states are bounded within a particular interval BIBREF5. Only a model built around the correct tree structure displayed a robust hierarchical bias across tasks. Sequentially-structured models failed to generalize hierarchically even when the input contained explicit marking of each sentence's hierarchical structure. Overall, we conclude that many factors can qualitatively affect a model's inductive biases, but human-like syntactic generalization may require specific types of high-level structure, at least when learning from text alone. ### The question formation task ::: Background
The classic discussion of the acquisition of English question formation begins with two empirical claims: (i) disambiguating examples such as SECREF1 rarely occur in a child's linguistic input, but (ii) all learners of English nevertheless acquire move-main rather than move-first. chomsky1965,chomsky1980 uses these points to argue that humans must have an innate bias toward learning syntactic rules that are based on hierarchy rather than linear order (this argument is known as the argument from the poverty of the stimulus). There has been a long debate about this line of argument. Though some have discussed the validity of Chomsky's empirical claims BIBREF6, BIBREF7, BIBREF8, BIBREF9, most of the debate has been about which mechanisms could explain the preference for move-main. These mechanisms include an assumption of substitutability BIBREF10, a bias for simplicity BIBREF11, exploitation of statistical patterns BIBREF12, BIBREF13, and semantic knowledge BIBREF14; see clark2010linguistic for in-depth discussion. These past works focus on the content of the bias that favors move-main (i.e., which types of generalizations the bias supports), but we instead focus on the source of this bias (i.e., which factors of the learner give rise to the bias). In the book Rethinking Innateness, elman1998rethinking argue that innate biases in humans must arise from architectural constraints on the neural connections in the brain rather than from constraints stated at the symbolic level, under the assumption that symbolic constraints are unlikely to be specified in the genome. Here we use artificial neural networks to investigate whether syntactic inductive biases can emerge from architectural constraints. ### The question formation task ::: Framing of the task
Following frank2007 and mccoy2018revisiting, we train models to take a declarative sentence as input and to either output the same sentence unchanged, or transform that sentence into a question. The sentences were generated from a context-free grammar containing only the sentence types shown in Figure FIGREF5 and using a 75-word vocabulary; the full grammar is at the project website.fn:website The different types of sentences vary in the linear position of the main auxiliary, such that a model cannot identify the main auxiliary with a simple positional heuristic. The task to be performed is indicated by the final input token, as in SECREF7 and SECREF7: . Input:your zebra does read . declXX Output: your zebra does read . declXX . Input:your zebra does read . questX Output:does your zebra read ? questX During training, all question formation examples are consistent with both move-first and move-main, such that there is no direct evidence favoring one rule over the other (see Figure FIGREF5). To assess how models generalize, we evaluate them on a generalization set consisting of examples where move-main and move-first make different predictions due to the presence of a relative clause on the subject (see sentence SECREF1). ### The question formation task ::: Evaluation metrics
We focus on two metrics. The first is full-sentence accuracy on the test set. That is, for examples drawn from the same distribution as the training set, does the model get the output exactly right? For testing generalization to the withheld example type, a natural metric would be full-sentence accuracy on the generalization set. However, in preliminary experiments we found that most models rarely produced the exact output predicted by either move-main or move-first, as they tend to truncate the output, confuse similar words, and make other extraneous errors. To abstract away from such errors, we use first-word accuracy on the generalization set. With both move-first and move-main, the first word of the question is the auxiliary that has been moved from within the sentence. If the auxiliaries in the relative and main clauses are distinct, this word alone is sufficient to differentiate the two rules. For example, in the bottom right cell of Figure FIGREF5, move-main predicts having do at the start, while move-first predicts don't. Models almost always produced either the main auxiliary or the first auxiliary as the first word of the output (over 98% of the time for most models), so a low first-word accuracy can be interpreted as high consistency with move-first. ### The question formation task ::: Architecture
We used the sequence-to-sequence architecture in Figure FIGREF12 BIBREF2. This model consists of two neural networks: the encoder and the decoder. The encoder is fed the input sentence one word at a time; after each word, the encoder updates its hidden state, a vector representation of the information encountered so far. After the encoder has been fed the entire input, its final hidden state ($E_6$ in Figure FIGREF12) is fed to the decoder, which generates an output sequence one word at a time based on its own hidden state, which is updated after each output word. The weights that the encoder and decoder use to update their hidden states and generate outputs are learned via gradient descent; for more details, see Appendix SECREF10. ### The question formation task ::: Overview of experiments
Holding the task constant, we first varied two aspects of the architecture that have no clear connection to question formation, namely the recurrent unit and the type of attention; both of these aspects have been central to major advances in natural language processing BIBREF15, BIBREF16, so we investigate them here to see whether their contributions might be partially explained by linguistically-relevant inductive biases that they impart. We also tested a more clearly task-relevant modification of the architecture, namely the use of tree-based models rather than the sequential structure in Figure FIGREF12. ### Recurrent unit and attention ::: Recurrent unit
The recurrent unit is the component that updates the hidden state after each word for the encoder and decoder. We used three types of recurrent units: simple recurrent networks (SRNs; BIBREF17), gated recurrent units (GRUs; BIBREF18), and long short-term memory (LSTM) units BIBREF19. In SRNs and GRUs, the hidden state is represented by a single vector, while LSTMs use two vectors (the hidden state and the cell state). In addition, GRUs and LSTMs both use gates, which control what information is retained across time steps, while SRNs do not; GRUs and LSTMs differ from each other in the number and types of gates they use. ### Recurrent unit and attention ::: Attention
In the basic model in Figure FIGREF12, the final hidden state of the encoder is the decoder's only source of information about the input. To avoid having such a bottleneck, many contemporary sequence-to-sequence models use attention BIBREF16, a feature that enables the decoder to consider all encoder hidden states ($E_0$ through $E_6$ in Figure FIGREF12) when generating hidden state $D_i$. A model without attention has the only inputs to $D_i$ being $D_{i-1}$ and $y_{i-1}$ (the previous output); attention adds a third input, $c_i = \sum _j \alpha _i[j] E_j$, which is a weighted sum of the encoder's hidden states ($E_0$ through $E_n$) using a weight vector $\alpha _i$ whose $j^{th}$ element is denoted by $\alpha _i[j]$. Implementations of attention vary in how the weights $\alpha _i[j]$ are derived BIBREF20, BIBREF21, BIBREF22. Attention can be solely location-based, where each $\alpha _i$ is determined solely from $D_{i-1}$ (and potentially also $y_{i-1}$), so that the model chooses where to attend without first checking what it is attending to. Alternately, attention could be content-based, in which case each $\alpha _i[j]$ is determined from both $D_{i-1}$ and $E_j$, such that the model does consider what it might attend to before attending to it. We test both location-based and content-based attention, and we also test models without attention. ### Recurrent unit and attention ::: Results
We trained models with all nine possible combinations of recurrent unit and attention type, using the hyperparameters and training procedure described in Appendix SECREF10. The results are in Figure FIGREF13. The SRN without attention failed on the test set, mainly because it often confused words that had the same part of speech, a known weakness of SRNs BIBREF23. Therefore, its generalization set behavior is uninformative. The other architectures performed strongly on the test set ($>$ 50% full-sentence accuracy), so we now consider their generalization set performance. The GRU with location-based attention and the SRN with content-based attention both preferred move-main, while the remaining architectures preferred move-first. These results suggest that both the recurrent unit and the type of attention can qualitatively affect a model's inductive biases. Moreover, the interactions of these factors can have drastic effects: with SRNs, content-based attention led to behavior consistent with move-main while location-based attention led to behavior consistent with move-first; these types of attention had opposite effects with GRUs. ### Recurrent unit and attention ::: Differences between LSTMs and GRUs
One striking result in Figure FIGREF13 is that LSTMs and GRUs display qualitative differences, even though the two architectures are often viewed as interchangeable and achieve similar performance in applied tasks BIBREF24. One difference between LSTMs and GRUs is that a squashing function is applied to the hidden state of a GRU to keep its values within the range $(-1,1)$, while the cell state of an LSTM is not bounded. weiss2018 demonstrate that such squashing leads to a qualitative difference in how well these models generalize counting behavior. Such squashing may also explain the qualitative differences that we observe: counting the input elements is equivalent to keeping track of their linear positions, so we might expect that a tendency to count would make the linear generalization more accessible. To test whether squashing increases a model's preference for move-main, we created a modified LSTM that included squashing in the calculation of its cell state, and a modified GRU that did not have the squashing usually present in GRUs. See Appendix SECREF11 for more details. Using the same training setup as before, we trained models with these modified recurrent units and with location-based attention. LSTMs and GRUs with squashing chose move-main more often than the corresponding models without squashing (Figure FIGREF20), suggesting that such squashing is one factor that causes GRUs to behave differently than LSTMs. ### Recurrent unit and attention ::: Hyperparameters and random seed
In addition to variation across architectures, we also observed considerable variation across multiple instances of the same architecture that differed only in random seed; the random seeds determined both the initial weights of each model and the order in which training examples were sampled. For example, the generalization set first-word accuracy for SRNs with content-based attention ranged from 0.17 to 0.90. Based on our exploration of hyperparameters, it also appears that the learning rate and hidden size can qualitatively affect generalization. The effects of these details are difficult to interpret systematically, and we leave the characterization of their effects for future work. Results for all individual re-runs are at the project website.fn:website ### Tree models
So far we have tested whether properties that are not interpretably related to hierarchical structure nevertheless affect how a model generalizes on a syntactic task. We now turn to a related but opposite question: when a model's design is meant to give it a hierarchical inductive bias, does this design succeed at giving the model this bias? ### Tree models ::: Tree model that learns implicit structure
The first hierarchical model that we test is the Ordered Neurons LSTM (ON-LSTM; BIBREF25). This model is not given the tree structure of each sentence as part of its input. Instead, its processing is structured in a way that leads to the implicit construction of a soft parse tree. This implicit tree structure is created by imposing a stack-like constraint on the updates to the values in the cell state of an LSTM: the degree to which the $i^{\textrm {th}}$ value is updated must always be less than or equal to the degree to which the $j^{\textrm {th}}$ value is updated for all $j \le i$. This hierarchy of cell-state values adds an implicit tree structure to the model, where each level in the tree is defined by a soft depth in the cell state to which that level extends. We re-implemented the ON-LSTM and trained 100 instances of it using the hyperparameters specified in Appendix SECREF10. This model achieved a test set full-sentence accuracy of 0.93 but a generalization set first-word accuracy of 0.05, showing a strong preference for move-first over move-main, contrary to what one would expect from a model with a hierarchical inductive bias. This lack of hierarchical behavior might be explained by BIBREF26's (BIBREF26) finding that ON-LSTMs do not perform much better than standard LSTMs at implicitly recovering hierarchical structure, even though ON-LSTMs (but not standard LSTMs) were designed in a way intended to impart a hierarchical bias. According to BIBREF26, the ON-LSTM's apparent success reported in shen2018ordered was largely due to the method used to analyze the model rather than the model itself. ### Tree models ::: Tree models given explicit structure
The ON-LSTM results show that hierarchically structured processing alone is not sufficient to induce a bias for move-main, suggesting that constraints on which trees are used may also be necessary. We therefore tested a second type of hierarchical model, namely Tree-RNNs, that were explicitly fed the correct parse tree. Parse trees can be used to guide the encoder, the decoder, or both; Figure FIGREF28 shows a model where both the encoder and decoder are tree-based. For the tree-based encoder, we use the Tree-GRU from chen2017improved. This model composes the vector representations for a pair of sister nodes to generate a vector representing their parent. It performs this composition bottom-up, starting with the word embeddings at the leaves and ending with a single vector representing the root ($E_4$ in Figure FIGREF28); this vector acts as the encoding of the input. For the tree-based decoder, we use a model based on the Tree-LSTM decoder from BIBREF27, but using a GRU instead of an LSTM, for consistency with the tree encoder. This tree decoder is the mirror image of the tree encoder: starting with the vector representation of the root node ($D_0$ in Figure FIGREF28), it takes the vector representation of a parent node and outputs two vectors, one for the left child and one for the right child, until it reaches a leaf node, where it outputs a word. We test models with a tree-based encoder and sequential decoder, a sequential encoder and tree-based decoder, or a tree-based encoder and tree-based decoder, all without attention; we investigate these variations to determine whether hierarchical generalization is determined by the encoder, the decoder, or both. The results for these models are in Figure FIGREF31, along with the previous results of the fully sequential GRU (sequential encoder + sequential decoder) without attention for comparison. The model with a tree-based encoder and sequential decoder preferred move-first, like the fully sequential model. Only the models with a tree-based decoder preferred move-main, consistent with the finding of mccoy2018rnns that it is the decoder that determines an encoder-decoder model's representations. However, the model with a sequential encoder and a tree decoder failed on the test set, so the only model that both succeeded on the test set and showed a bias toward a move-main generalization was the fully tree-based model (Tree/Tree). The behavior of this Tree/Tree model was striking in another way as well: Its generalization set full-sentence accuracy was 69%, while all other models—even those that achieved high first-word accuracy on the generalization set—had close to 0% generalization set full-sentence accuracy. The ON-LSTM and Tree-GRU results show that an architecture designed to have a certain inductive bias might, but will not necessarily, display the intended bias. ### Tense reinflection
We have shown that several models reliably preferred move-main over move-first. However, this behavior alone does not necessarily mean that these models have a hierarchical bias, because a preference for move-main might arise not from a hierarchical bias but rather from some task-specific factors such as the prevalence of certain n-grams BIBREF28, BIBREF29. A true hierarchical bias would lead a model to adopt hierarchical generalizations across training tasks; by contrast, we hypothesize that other factors (such as a bias for focusing on n-gram statistics) will be more sensitive to details of the task and will thus be unlikely to consistently produce hierarchical preferences. To test the robustness of the hierarchical preferences of our models, then, we introduce a second task, tense reinflection. ### Tense reinflection ::: Reinflection task
The reinflection task uses English subject-verb agreement to illuminate a model's syntactic generalizations BIBREF30. The model is fed a past-tense English sentence as input. It must then output that sentence either unchanged or transformed to the present tense, with the final word of the input indicating the task to be performed: . my yak swam . past $\rightarrow $ my yak swam . . my yak swam . present $\rightarrow $ my yak swims . Because the past tense in English does not inflect for number (e.g., the past tense of swim is swam whether the subject is singular or plural), the model must determine from context whether each verb being turned to present tense should be singular or plural. Example SECREF32 is consistent with two salient rules for determining which aspects of the context are relevant: . agree-subject: Each verb should agree with its hierarchically-determined subject. . agree-recent: Each verb should agree with the linearly most recent noun. Though these rules make the same prediction for SECREF32, they make different predictions for other examples, such as SECREF32, for which agree-subject predicts SECREF32 while agree-recent predicts SECREF32: . ṁy zebra by the yaks swam . present my zebra by the yaks swims . my zebra by the yaks swim . Similar to the setup for the question formation experiments, we trained models on examples for which agree-subject and agree-recent made the same predictions and evaluated the trained models on examples where the rules make different predictions. We ran this experiment with all 9 sequential models ([SRN, GRU, LSTM] x [no attention, location-based attention, content-based attention]), the ON-LSTM, and the model with a tree-based encoder and tree-based decoder that were provided the correct parse trees, using the hyperparameters in Appendix SECREF10. The example sentences were generated using the same context-free grammar used for the question formation task, except with inflected verbs instead of auxiliary/verb bigrams (e.g., reads instead of does read). We evaluated these models on the full-sentence accuracy on the test set and also main-verb accuracy for the generalization set—that is, the proportion of generalization set examples for which the main verb was correctly predicted, such as when swims rather than swim was chosen in the output for SECREF32. Models usually chose the correct lemma for the main verb (at least 87% of the time for all tense reinflection models), with most main verb errors involving the correct verb but with incorrect inflection (i.e., being singular instead of plural, or vice versa). Thus, a low main-verb accuracy can be interpreted as consistency with agree-recent. All sequential models, even the ones that generalized hierarchically with question formation, overwhelmingly chose agree-recent for this reinflection task (Figure FIGREF33), consistent with the results of a similar experiment done by ravfogel2019studying. The ON-LSTM also preferred agree-recent. By contrast, the fully tree-based model preferred the hierarchical generalization agree-subject. Thus, although the question formation experiments showed qualitative differences in sequential models' inductive biases, this experiment shows that those differences cannot be explained by positing that there is a general hierarchical bias in some of our sequential models. What the relevant bias for these models is remains unclear; we only claim to show that it is not a hierarchical bias. Overall, the model with both a tree-based encoder and a tree-based decoder is the only model we tested that plausibly has a generic hierarchical bias, as it is the only one that behaved consistently with such a bias across both tasks. ### Are tree models constrained to generalize hierarchically?
It may seem that the tree-based models are constrained by their structure to make only hierarchical generalizations, rendering their hierarchical generalization trivial. In this section, we test whether they are in fact constrained in this way, and similarly whether sequential models are constrained to make only linear generalizations. Earlier, the training sets for our two tasks were ambiguous between two generalizations, but we now used training sets that unambiguously supported either a linear transformation or a hierarchical transformation. For example, we used a move-main training set that included some examples like SECREF6, while the move-first training set included some examples like SECREF6: . ṁy yaks that do read don't giggle . quest $\rightarrow $ don't my yaks that do read giggle ? my yaks that do read don't giggle . quest $\rightarrow $ do my yaks that read don't giggle ? Similarly, for the tense reinflection task, we created an agree-subject training set and an agree-recent training set. For each of these four training sets, we trained 100 sequential GRUs and 100 Tree/Tree GRUs, all without attention. Each model learned to perform linear and hierarchical transformations with similar accuracy: On the move-main and move-first datasets, both the sequential and tree-based models achieved 100% first-word accuracy. On both the agree-subject and agree-recent datasets, the sequential model achieved 91% main-verb accuracy and the tree-based model achieved 99% main-verb accuracy. Thus, the fact that the tree-based model preferred hierarchical generalizations when the training set was ambiguous arose not from any constraint imposed by the tree structure but rather from the model's inductive biases—biases that can be overridden given appropriate training data. ### Tree structure vs. tree information
Our sequential and tree-based models differ not only in structure but also in the information they have been provided: the tree-based models have been given correct parse trees for their input and output sentences, while the sequential models have not been given parse information. Therefore, it is unclear whether the hierarchical generalization displayed by the tree-based models arose from the tree-based model structure, from the parse information provided to the models, or both. To disentangle these factors, we ran two further experiments. First, we retrained the Tree/Tree GRU but using uniformly right-branching trees (as in (11b)) instead of correct parses (as in (11a)). Thus, these models make use of tree structure but not the kind of parse structure that captures linguistic information. Second, we retrained the sequential GRU without attention but modified the input and output by adding brackets that indicate each sentence's parse; for example, SECREF7 would be changed to SECREF7. Thus, these models are provided with parse information in the input but such structure does not guide the neural network computation as it does with tree RNNs. . a. [ [ [ my yak ] [ does giggle ] ] $.$ ] b. [ my [ yak [ does [ giggle $.$ ] ] ] ] . ṁy yak does giggle . quest $\rightarrow $ does my yak giggle ? [ [ [ my yak ] [ does giggle ] . ] quest ] $\rightarrow $ [ [ does [ [ my yak ] giggle ] ] ? ] We ran 100 instances of each experiment using different random seeds. For the experiment with bracketed input, the brackets significantly increased the lengths of the sentences, making the learning task harder; we therefore found it necessary to use a patience of 6 instead of the patience of 3 we used elsewhere, but all other hyperparameters remained as described in Appendix SECREF10. For both tasks, neither the sequential GRU that was given brackets in its input nor the Tree/Tree model that was given right-branching trees displayed a hierarchical bias (Figure FIGREF36). The lack of hierarchical bias in the sequential GRU with bracketed input indicates that simply providing parse information in the input and target output is insufficient to induce a model to favor hierarchical generalization; it appears that such parse information must be integrated into the model's structure to be effective. On the other hand, the lack of a hierarchical bias in the Tree/Tree model using right-branching trees shows that simply having tree structure is also insufficient; it is necessary to have the correct tree structure. ### Will models generalize across transformations?
Each experiment discussed so far involved a single linguistic transformation. By contrast, humans acquiring language are not exposed to phenomena in isolation but rather to a complete language encompassing many phenomena. This fact has been pointed to as a possible way to explain hierarchical generalization in humans without needing to postulate any innate preference for hierarchical structure. While one phenomenon, such as question formation, might be ambiguous in the input, there might be enough direct evidence among other phenomena to conclude that the language as a whole is hierarchical, a fact which learners can then extend to the ambiguous phenomenon BIBREF8, BIBREF11, under the non-trivial assumption that the learner will choose to treat the disparate phenomena in a unified fashion. While our training sets are ambiguous with respect to whether the phenomenon underlying the mapping is structurally driven, they do contain other cues that the language is more generally governed by hierarchical regularities. First, certain structural units are reused across positions in a sentence; for example, prepositional phrases can appear next to subjects or objects. Such reuse of structure can be represented more efficiently with a hierarchical grammar than a linear one. Second, in the question formation task, subject-verb agreement can also act as a cue to hierarchical structure: e.g., in the sentence my walrus by the yaks does read, the inflection of does depends on the verb's hierarchically-determined subject (walrus) rather than the linearly closest noun (yaks). For the sequential RNNs we have investigated, it appears that these indirect cues to hierarchical structure were not sufficient to guide the models towards hierarchical generalizations. However, perhaps the inclusion of some more direct evidence for hierarchy would be more successful. To take a first step toward investigating this possibility, we use a multi-task learning setup, where we train a single model to perform both question formation and tense reinflection. We set up the training set such that one task was unambiguously hierarchical while the other was ambiguous between the hierarchical generalization and the linear generalization. This gave two settings: One where question formation was ambiguous, and one where tense reinflection was ambiguous. We trained 100 instances of a GRU without attention on each setting and assessed how each model generalized for the task that was ambiguous. For both cases, generalization behavior in the multi-task setting differed only minimally from the single-task setting (Figure FIGREF41). One potential explanation for the lack of transfer across tasks is that the two tasks operated over different sentence structures: the question formation sentences always contained overt auxiliaries on their verbs (e.g., my walrus does giggle), while the tense reinflection sentences did not (e.g., my walrus giggles). To test this possibility, we reran the multi-task experiments but with overt auxiliaries added to the tense reinflection sentences (Figure FIGREF41, “Multi-task + auxiliaries” row). In this setting, the model still generalized linearly when it was question formation that was ambiguous. However, when it was tense reinflection that was ambiguous, the model generalized hierarchically. We hypothesize that the directionality of this transfer is due to the fact that the question formation training set includes unambiguous long-distance subject-verb agreement as in SECREF8, which might help the model on generalization-set examples for tense reinflection such as SECREF8: . my zebras by the yak do read . decl $\rightarrow $ my zebras by the yak do read . . my zebras by the yak did read . present $\rightarrow $ my zebras by the yak do read . By contrast, the tense reinflection training set does not contain any outputs of the type withheld from the question formation training set. If this explanation is correct, it would mean that the improvement on the tense reinflection task derived not from the question formation transformation but rather from the subject-verb agreement incidentally present in the question formation dataset. Therefore, even the single potential case of generalization across transformations is likely spurious. Recent NLP work has also found that neural networks do not readily transfer knowledge across tasks; e.g., pretrained models often perform worse than non-pretrained models BIBREF31. This lack of generalization across tasks might be due to the tendency of multi-task neural networks to create largely independent representations for different tasks even when a shared representation could be used BIBREF32. Therefore, to make cross-phenomenon generalizations, neural networks may need to be given an explicit bias for sharing processing across phenomena. ### Discussion
We have found that all factors we tested can qualitatively affect a model's inductive biases but that a hierarchical bias—which has been argued to underlie children's acquisition of syntax—only arose in a model whose inputs and computations were governed by syntactic structure. ### Discussion ::: Relation to Rethinking Innateness
Our experiments were motivated in part by the book Rethinking Innateness BIBREF33 which argued that humans' inductive biases must arise from constraints on the wiring patterns of the brain. Our results support two conclusions from this book. First, those authors argued that “Dramatic effects can be produced by small changes” (p. 359). This claim is supported by our observation that low-level factors, such as the size of the hidden state, qualitatively affect how models generalize (Section SECREF26). Second, they argued that “[w]hat appear to be single events or behaviors may have a multiplicity of underlying causes” (p. 359); in our case, we found that a model's generalization behavior results from some combination of factors that interact in hard-to-interpret ways; e.g., changing the type of attention had different effects in SRNs than in GRUs. The dramatic effects of these low-level factors offer some support for the claim that humans' inductive biases can arise from fine-grained architectural constraints in the brain. However, this support is only partial. Our only model that robustly displayed the kind of preference for hierarchical generalization that is necessary for language learning did not derive such a preference from low-level architectural properties but rather from the explicit encoding of linguistic structure. ### Discussion ::: Relation to human language acquisition
Our experiments showed that some tree-based models displayed a hierarchical bias, while non-tree-based models never displayed such a bias, even when provided with strong cues to hierarchical structure in their input (through bracketing or multi-task learning). These findings suggest that the hierarchical preference displayed by humans when acquiring English requires making explicit reference to hierachical structure, and cannot be argued to emerge from more general biases applied to input containing cues to hierarchical structure. Moreover, since the only successful hierarchical model was one that took the correct parse trees as input, our results suggest that a child's set of biases includes biases governing which specific trees will be learned. Such biases could involve innate knowledge of likely tree structures, but they do not need to; they might instead involve innate tendencies to bootstrap parse trees from other sources, such as prosody BIBREF34 or semantics BIBREF35. With such information, children might learn their language's basic syntax before beginning to acquire question formation, and this knowledge might then guide their acquisition of question formation. There are three important caveats for extending our conclusions to humans. First, humans may have a stronger bias to share processing across phenomena than neural networks do, in which case multi-task learning would be a viable explanation for the biases displayed by humans even though it had little effect on our models. Indeed, this sort of cross-phenomenon consistency is similar in spirit to the principle of systematicity, and it has long been argued that humans have a strong bias for systematicity while neural networks do not BIBREF36, BIBREF37. Second, some have argued that children's input actually does contain utterances unambiguously supporting a hierarchical transformation BIBREF8, whereas we have assumed a complete lack of such examples. Finally, our training data omit many cues to hierarchical structure that are available to children, including prosody and real-world grounding. It is possible that, with data closer to a child's input, more general inductive biases might succeed. However, there is still significant value in studying what can be learned from strings alone, because we are unlikely to understand how the multiple components of a child's input interact without a better understanding of each component. Furthermore, during the acquisition of abstract aspects of language, real-world grounding is not always useful in the absence of linguistic biases BIBREF38. More generally, it is easily possible for learning to be harder when there is more information available than when there is less information available BIBREF39. Thus, our restricted experimental setup may actually make learning easier than in the more informationally-rich scenario faced by children. ### Discussion ::: Practical takeaways
Our results leave room for three possible approaches to imparting a model with a hierarchical bias. First, one could search the space of hyperparameters and random seeds to find a setting that leads to the desired generalization. However, this may be ineffective: At least in our limited exploration of these factors, we did not find a hyperparameter setting that led to hierarchical generalization across tasks for any non-tree-based model. A second option is to add a pre-training task or use multi-task learning BIBREF40, BIBREF41, BIBREF42, where the additional task is designed to highlight hierarchical structure. Most of our multi-task experiments only achieved modest improvements over the single-task setting, suggesting that this approach is also not very viable. However, it is possible that further secondary tasks would bring further gains, making this approach more effective. A final option is to use more interpretable architectures with explicit hierachical structure. Our results suggest that this approach is the most viable, as it yielded models that reliably generalized hierarchically. However, this approach only worked when the architectural bias was augmented with rich assumptions about the input to the learner, namely that it provided correct hierarchical parses for all sentences. We leave for future work an investigation of how to effectively use tree-based models without providing correct parses. ### Acknowledgments
For helpful comments we thank Joe Pater, Paul Smolensky, the JHU Computation and Psycholinguistics lab, the JHU Neurosymbolic Computation lab, the Computational Linguistics at Yale (CLAY) lab, the anonymous reviewers, and audiences at the University of Pavia Center for Neurocognition, Epistemology, and Theoretical Syntax, the Penn State Dept. of Computer Science and Engineering, and the MIT Dept. of Brain and Cognitive Sciences. Any errors are our own. This material is based upon work supported by the NSF Graduate Research Fellowship Program under Grant No. 1746891, and by NSF Grant Nos. BCS-1920924 and BCS-1919321. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Our experiments were conducted with resources from the Maryland Advanced Research Computing Center (MARCC). ### Architecture and training details
We used a word embedding size of 256 (with word embeddings learned from scratch), a hidden size of 256, a learning rate of 0.001, and a batch size of 5. Models were evaluated on a validation set after every 1000 training batches, and we halted training if the model had been trained for at least 30,000 batches and had shown no improvement over 3 consecutive evaluations on the validation set (the number 3 in this context is called the patience). The training set contained 100,000 examples, while the validation, test, and generalization sets contained 10,000 examples each. The datasets were held constant across experiments, but models sampled from the training set in different orders across experiments. During training, we used teacher forcing on 50% of examples. ### Equations for squashing experiments
The equations governing a standard LSTM are: To create a new LSTM whose cell state exhibits squashing, like the hidden state of the GRU, we modified the LSTM cell state update in (DISPLAY_FORM45) to (DISPLAY_FORM47), where the new coefficients now add to 1: The equations governing a standard GRU are: The GRU's hidden state is squashed because its update gate $z$ merges the functions of the input and forget gates ($i$ and $f$) of the LSTM (cf. equations DISPLAY_FORM45 and DISPLAY_FORM48). As a result, the input and forget weights are tied in the GRU but not the LSTM. To create a non-squashed GRU, we added an input gate $i$ and changed the hidden state update (Equation DISPLAY_FORM48) to Equation DISPLAY_FORM49 to make $z$ act solely as a forget gate: | type of recurrent unit, type of attention, choice of sequential vs. tree-based model structure |
What positive critique does the film reviewer offer for "Elizabeth"? juicy melodrama
A. It relies on juxtaposition-based cinematography that makes for a compelling theatrical performance
B. It takes necessary liberties with history's version of Elizabeth's reign to make her story more interesting to movie-goers
C. It takes the best aspects of both Jacobean and Shakespearean interpretations of Elizabeth I and combines them into one melodramatic depiction
D. It is the best interpretation of Elizabeth I's ascent to the throne and subsequent reign
| Warrior Queens Elizabeth is a lurid paraphrase of the old Groucho Marx line about Doris Day: "I knew the Virgin Queen before she was a virgin." As the movie tells it, she was a sylvan, redheaded princess (Cate Blanchett) given to gamboling with her fella (Joseph Fiennes) between periods of internment in the Tower of London on charges of conspiring to overthrow her half-sister, the heatedly Catholic Queen Mary (Kathy Burke). The daughter of the second wife of Henry VIII, Anne Boleyn, and therefore dubbed a bastard by the papists, the Protestant Elizabeth ascends the throne to find the air still thick with smoke from roasted heretics, a team of skulking Catholics plotting her assassination, and a council of advisers (lords, bishops, sundry old boys) who snigger openly at the prospect of taking orders from a woman. Only a strategic marriage to a Spaniard or a Frenchman will mollify all factions, her advisers insist, but the pickings prove dismal. (Her French suitor enjoys wearing dresses.) After skulls are smashed, throats slit, and bosoms skewered in the name of Christ, Elizabeth decides to: a) "unsex" herself and become a symbol--the Virgin Queen, married only to England; and b) entertain dissenting opinions exclusively from those whose heads are affixed to spikes. You can't be both a queenly queen and a womanly woman, says the script (by Michael Hirst)--at least not in 1554. (The director, Shekhar Kapur, made the same point in his grim 1994 Indian epic The Bandit Queen , against a backdrop of scrubby plains along the Ganges.) Is this feminist take historically accurate? Probably, although the evidence suggests that Elizabeth had developed a head for stratagems earlier in life (her position had been precarious since the beheading of her mother) and came to the throne with few girlish illusions about How Things Work in a barbarous state. That said, the movie's approach makes for juicy melodrama. The tone of Elizabeth comes nearer to the nihilistic relish of Jacobeans such as John Ford and John Webster than to the more sorrowful horror of the Elizabethan dramatists Ben Jonson and William Shakespeare. It's even closer to a Jacobean drama of our own age: The Godfather (1972), which it emulates by cutting back-and-forth between queen and courtiers in prayer and the roundup and slaughter of Catholics on their privies, in bed with their mistresses, and so on. Their severed heads look on, wide-eyed, as Elizabeth directs her hair to be shorn--images of her girlhood flashing by as her locks rain down--and then walks weightily to her throne, now a chalk-faced gorgon. With all due respect to Blanchett, Bette Davis, and Glenda Jackson, my favorite Elizabeth I remains Miranda Richardson's capricious, baby-talking psychopath on the BBC comedy Blackadder II . (Casting about for a new lord high executioner, she mews to Rowan Atkinson, "There are thousands of Catholics simply dying to have their heads sneaked off --and there's no one to organize it.") But Blanchett comes in a close second, pulling off the transition from hapless young woman to coolly ruthless monarch with uncommon subtlety. Gradually expunging all empathy from her moist, pink eyes and permitting her visage to ossify, she gives this carnival of carnage an awe-inspiring center. A more subversive sort of queen is on display in Velvet Goldmine , Todd Haynes' musical fantasia on the early '70s era of "glam" or "glitter" rock. Here the monarch is a David Bowie-esque singer called Brian Slade (Jonathan Rhys-Meyers) and his spidery, space-age alter ego, Maxwell Demon. The movie opens with a spaceship depositing an infant Oscar Wilde on the stoop of a Dublin townhouse. Then it skips ahead to track a jade pin (it signifies hedonistic liberation) from the custody of a young Wilde to a swishy fringe creature called Jack Fairy to the regal Slade, a bisexual superstar who carries the news to all the young dudes. After that, we're in an Orwellian 1984 that's presided over by a vaguely fascist president and by arena rockers who serve as propagandists for a repressively conformist state. Whatever happened to Brian Slade, the glitter kids, the visionary exhibitionists and gleeful poseurs? Borrowing its framework from Citizen Kane , the movie follows a reporter (Christian Bale) assigned to reconstruct Slade's life and solve the mystery of his whereabouts. Whatever you make of Velvet Goldmine (opinions have ranged from rapturous to casually dismissive), it's like no other musical ever made. It's determinedly swirling, discursive, elliptical. Now the story is told by an omniscient narrator, now a TV reporter, now a participant. Now it's flashing back, now forward. Every other line of dialogue is a cue for one of its dazzling numbers, largely covers of songs by Brian Eno, Bryan Ferry, and T. Rex. The narrative is a challenge to keep up with, but then, great artists often invent their own syntax. In the '80s, Haynes employed Barbie dolls to depict the rise and wasting away from anorexia of the singer Karen Carpenter. Lucky audiences who caught Superstar: The Karen Carpenter Story (it was shelved when Richard Carpenter served the producers with an order to cease and desist exhibition) began by laughing at this elaborately posed, soft-rock femme, only to discover by the climax that the cultural forces that were eating at her (and that kept her from eating) had grown heartbreakingly palpable. Poison (1991), Haynes' Genêt-inspired exploration of transgression, didn't overcome its own artiness. But Safe (1995), the story of a Reagan-era housewife (Julianne Moore) convinced that her environment is poisoning her, is an entrancing meditation on the power of culture to crush the individual. Despite its ironic detachment, the film draws you into its heroine's sickly state: Breathing oxygen from a canister inside a high-tech igloo, she dwindles to nearly nothing, the modern incarnation of the Incredible Shrinking Man. (It was partly my passion for Haynes' films that led me to accept a job offer from his indefatigable producer Christine Vachon last year to collaborate on a nuts-and-bolts book about producing, Shooting To Kill . So my review of Velvet Goldmine --like my review of Vachon's other recent release, Happiness --should be read as the work of a partisan. But not a blind partisan.) In Velvet Goldmine , Haynes sets out to demonstrate the power of popular music to change people's lives--to tell them it's OK to fashion themselves into anything they please. The core of the movie turns out not to be the Bowie figure but the journalist, Arthur Stuart, who was a witness to the events he's now reconstructing. Bale is such an expressive performer that Stuart's remembrance of things past attains a Proustian intensity. To him, Slade was a sexual messiah. I've never seen a more vivid distillation of rock's allure than the scene in which he reverently opens the new Brian Slade album--its centerfold image is a lithe, naked, green-tinged Maxwell Demon--slips the vinyl out of its paper jacket and, after gingerly setting the LP on the turntable, props a chair under the doorknob to keep the uncomprehending world at bay. But if Haynes wants Velvet Goldmine to be an anthem to the principles Bowie once embodied--the embrace of artifice and the smashing of conventional sexual roles--he also wants to portray the rocker as a hollow opportunist who abandoned glam and bisexuality for the life of a corporate superstar, throwing in his lot with the forces of repression. That's a lot to cover. An actor of stature might have bridged these two impulses, but the beautiful, brazenly slim-hipped Rhys-Meyers doesn't make his lines sound as if he's thinking them up on the spot, and Slade's self-destructive passion for Curt Wild (Ewan McGregor), the film's fuzzy, sweet Iggy Pop figure, seems less an emotional imperative than a thematic one. A case can be made that Velvet Goldmine isn't fully filled in, and that Haynes, who has never shaken off his background as a semiotics major, has made a movie that's all signifiers. I sometimes found myself wishing he would let the picture catch its breath, that the performers would stop coming at me in stroboscopic flashes. But then I'd be swept up in the sinuous motion of his filmmaking, in the elation of watching point of view passed like a baton from hand to hand, in the liberating force of his language and soundtrack. Velvet Goldmine might seem like a collection of baubles, but those baubles are strung. Is Brad Pitt the worst actor on earth? The case could be made, and Meet Joe Black could serve as Exhibit A. Pitt plays two roles in this seven course schlockfest. He's (briefly) a slick but wholesome yuppie and then (interminably) Death, who takes over the young man's body when he's thumped by a couple of cars in the movie's most promising moment. Bleached so blond that he looks like an irradiated android, Pitt expels all expression from his face and all tone from his voice. He speaks very, very slowly. The stunt half-works, at least until he's supposed to undergo an inner transformation and acquire human emotions--whereupon his face remains just as blank. Pitt's conception of the role is an idée fixe by someone who doesn't appear to have an idée in his head. Martin Brest, the director, is known for shooting a ton of footage and then "finding" his films in the editing room. What do you suppose he "found" when he scrutinized these miles of celluloid with Pitt doing nothing and taking his sweet time doing it? The first adaptation of this story (originally a play) was the 1934 Death Takes a Holiday , which came in at a perky 78 minutes. A conceit this fragile needs to whiz along to keep our disbelief in suspension, but Meet Joe Black grinds on for three hours (longer than either Beloved or Saving Private Ryan ), and Pitt acts as if he has leased the screen by the year. Anthony Hopkins plays the zillionaire communications baron whom Death enlists in the hope of understanding the human condition--an odd choice for a tour guide, since most people's condition doesn't involve personal helicopters, sprawling mansions on Long Island Sound, or Manhattan apartments that sport Olympic-size swimming pools. Four screenwriters, among them the great Bo Goldman ( Melvin and Howard , 1980; Shoot the Moon , 1982), labored on this moldy script, which features characters who ask questions that begin "Am I to understand that ...?" and a corporate villain who directs another character to "wake up and smell the thorns." It apparently never occurred to even one of these overpaid scribes to eliminate Hopkins' rueful realization that he'd "never write the great American novel"--no kidding, given his flagrantly Welsh accent. Actually, Hopkins gives this humanistic magnate considerable weight, so that whether or not Death takes him before he can stop to smell the roses and make amends to his neglected children becomes a matter of some suspense. The rest of the cast works with equal fortitude, especially Jeffrey Tambor (Hank "Hey now!" Kingsley on The Larry Sanders Show ) as Hopkins' milksop son-in-law and Marcia Gay Harden as his party planning, perpetually wilting elder daughter. As the younger daughter, the dark eyed, spaghetti thin Claire Forlani has to carry the picture's bathos on her exquisite shoulders. Her tremulous thoroughbred act wears thin, but it's hardly her fault: She has to emote like mad opposite a black pit of death--or is that the Black Death of Pitt? | A. It relies on juxtaposition-based cinematography that makes for a compelling theatrical performance |
People around him describe Dink as a…
A. brute
B. ladies’ man
C. hard-working entrepreneur
D. nerd
| CINDERELLA STORY By ALLEN KIM LANG What a bank! The First Vice-President was a cool cat—the elevator and the money operators all wore earmuffs—was just as phony as a three-dollar bill! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, May 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I The First Vice-President of the William Howard Taft National Bank and Trust Company, the gentleman to whom Miss Orison McCall was applying for a job, was not at all the public picture of a banker. His suit of hound's-tooth checks, the scarlet vest peeping above the vee of his jacket, were enough to assure Orison that the Taft Bank was a curious bank indeed. "I gotta say, chick, these references of yours really swing," said the Vice-President, Mr. Wanji. "Your last boss says you come on real cool in the secretary-bit." "He was a very kind employer," Orison said. She tried to keep from staring at the most remarkable item of Mr. Wanji's costume, a pair of furry green earmuffs. It was not cold. Mr. Wanji returned to Orison her letters of reference. "What color bread you got eyes for taking down, baby?" he asked. "Beg pardon?" "What kinda salary you bucking for?" he translated, bouncing up and down on the toes of his rough-leather desert boots. "I was making one-twenty a week in my last position," Miss McCall said. "You're worth more'n that, just to jazz up the decor," Mr. Wanji said. "What you say we pass you a cee-and-a-half a week. Okay?" He caught Orison's look of bewilderment. "One each, a Franklin and a Grant," he explained further. She still looked blank. "Sister, you gonna work in a bank, you gotta know who's picture's on the paper. That's a hunnerd-fifty a week, doll." "That will be most satisfactory, Mr. Wanji," Orison said. It was indeed. "Crazy!" Mr. Wanji grabbed Orison's right hand and shook it with athletic vigor. "You just now joined up with our herd. I wanna tell you, chick, it's none too soon we got some decent scenery around this tomb, girlwise." He took her arm and led her toward the bank of elevators. The uniformed operator nodded to Mr. Wanji, bowed slightly to Orison. He, too, she observed, wore earmuffs. His were more formal than Mr. Wanji's, being midnight blue in color. "Lift us to five, Mac," Mr. Wanji said. As the elevator door shut he explained to Orison, "You can make the Taft Bank scene anywhere between the street floor and floor five. Basement and everything higher'n fifth floor is Iron Curtain Country far's you're concerned. Dig, baby?" "Yes, sir," Orison said. She was wondering if she'd be issued earmuffs, now that she'd become an employee of this most peculiar bank. The elevator opened on five to a tiny office, just large enough to hold a single desk and two chairs. On the desk were a telephone and a microphone. Beside them was a double-decked "In" and "Out" basket. "Here's where you'll do your nine-to-five, honey," Mr. Wanji said. "What will I be doing, Mr. Wanji?" Orison asked. The Vice-President pointed to the newspaper folded in the "In" basket. "Flip on the microphone and read the paper to it," he said. "When you get done reading the paper, someone will run you up something new to read. Okay?" "It seems a rather peculiar job," Orison said. "After all, I'm a secretary. Is reading the newspaper aloud supposed to familiarize me with the Bank's operation?" "Don't bug me, kid," Mr. Wanji said. "All you gotta do is read that there paper into this here microphone. Can do?" "Yes, sir," Orison said. "While you're here, Mr. Wanji, I'd like to ask you about my withholding tax, social security, credit union, coffee-breaks, union membership, lunch hour and the like. Shall we take care of these details now? Or would you—" "You just take care of that chicken-flickin' kinda stuff any way seems best to you, kid," Mr. Wanji said. "Yes, sir," Orison said. This laissez-faire policy of Taft Bank's might explain why she'd been selected from the Treasury Department's secretarial pool to apply for work here, she thought. Orison McCall, girl Government spy. She picked up the newspaper from the "In" basket, unfolded it to discover the day's Wall Street Journal , and began at the top of column one to read it aloud. Wanji stood before the desk, nodding his head as he listened. "You blowing real good, kid," he said. "The boss is gonna dig you the most." Orison nodded. Holding her newspaper and her microphone, she read the one into the other. Mr. Wanji flicked his fingers in a good-by, then took off upstairs in the elevator. By lunchtime Orison had finished the Wall Street Journal and had begun reading a book an earmuffed page had brought her. The book was a fantastic novel of some sort, named The Hobbit . Reading this peculiar fare into the microphone before her, Miss McCall was more certain than ever that the Taft Bank was, as her boss in Washington had told her, the front for some highly irregular goings-on. An odd business for a Federal Mata Hari, Orison thought, reading a nonsense story into a microphone for an invisible audience. Orison switched off her microphone at noon, marked her place in the book and took the elevator down to the ground floor. The operator was a new man, ears concealed behind scarlet earmuffs. In the car, coming down from the interdicted upper floors, were several gentlemen with briefcases. As though they were members of a ballet-troupe, these gentlemen whipped off their hats with a single motion as Orison stepped aboard the elevator. Each of the chivalrous men, hat pressed to his heart, wore a pair of earmuffs. Orison nodded bemused acknowledgment of their gesture, and got off in the lobby vowing never to put a penny into this curiousest of banks. Lunch at the stand-up counter down the street was a normal interlude. Girls from the ground-floor offices of Taft Bank chattered together, eyed Orison with the coolness due so attractive a competitor, and favored her with no gambit to enter their conversations. Orison sighed, finished her tuna salad on whole-wheat, then went back upstairs to her lonely desk and her microphone. By five, Orison had finished the book, reading rapidly and becoming despite herself engrossed in the saga of Bilbo Baggins, Hobbit. She switched off the microphone, put on her light coat, and rode downstairs in an elevator filled with earmuffed, silent, hat-clasping gentlemen. What I need, Orison thought, walking rapidly to the busline, is a double Scotch, followed by a double Scotch. And what the William Howard Taft National Bank and Trust Company needs is a joint raid by forces of the U.S. Treasury Department and the American Psychiatric Association. Earmuffs, indeed. Fairy-tales read into a microphone. A Vice-President with the vocabulary of a racetrack tout. And what goes on in those upper floors? Orison stopped in at the restaurant nearest her apartment house—the Windsor Arms—and ordered a meal and a single Martini. Her boss in Washington had told her that this job of hers, spying on Taft Bank from within, might prove dangerous. Indeed it was, she thought. She was in danger of becoming a solitary drinker. Home in her apartment, Orison set the notes of her first day's observations in order. Presumably Washington would call tonight for her initial report. Item: some of the men at the Bank wore earmuffs, several didn't. Item: the Vice-President's name was Mr. Wanji: Oriental? Item: the top eight floors of the Taft Bank Building seemed to be off-limits to all personnel not wearing earmuffs. Item: she was being employed at a very respectable salary to read newsprint and nonsense into a microphone. Let Washington make sense of that, she thought. In a gloomy mood, Orison McCall showered and dressed for bed. Eleven o'clock. Washington should be calling soon, inquiring after the results of her first day's spying. No call. Orison slipped between the sheets at eleven-thirty. The clock was set; the lights were out. Wasn't Washington going to call her? Perhaps, she thought, the Department had discovered that the Earmuffs had her phone tapped. "Testing," a baritone voice muttered. Orison sat up, clutching the sheet around her throat. "Beg pardon?" she said. "Testing," the male voice repeated. "One, two, three; three, two, one. Do you read me? Over." Orison reached under the bed for a shoe. Gripping it like a Scout-ax, she reached for the light cord with her free hand and tugged at it. The room was empty. "Testing," the voice repeated. "What you're testing," Orison said in a firm voice, "is my patience. Who are you?" "Department of Treasury Monitor J-12," the male voice said. "Do you have anything to report, Miss McCall?" "Where are you, Monitor?" she demanded. "That's classified information," the voice said. "Please speak directly to your pillow, Miss McCall." Orison lay down cautiously. "All right," she whispered to her pillow. "Over here," the voice instructed her, coming from the unruffled pillow beside her. Orison transferred her head to the pillow to her left. "A radio?" she asked. "Of a sort," Monitor J-12 agreed. "We have to maintain communications security. Have you anything to report?" "I got the job," Orison said. "Are you ... in that pillow ... all the time?" "No, Miss McCall," the voice said. "Only at report times. Shall we establish our rendezvous here at eleven-fifteen, Central Standard Time, every day?" "You make it sound so improper," Orison said. "I'm far enough away to do you no harm, Miss McCall," the monitor said. "Now, tell me what happened at the bank today." Orison briefed her pillow on the Earmuffs, on her task of reading to a microphone, and on the generally mimsy tone of the William Howard Taft National Bank and Trust Company. "That's about it, so far," she said. "Good report," J-12 said from the pillow. "Sounds like you've dropped into a real snakepit, beautiful." "How do you know ... why do you think I'm beautiful?" Orison asked. "Native optimism," the voice said. "Good night." J-12 signed off with a peculiar electronic pop that puzzled Orison for a moment. Then she placed the sound: J-12 had kissed his microphone. Orison flung the shoe and the pillow under her bed, and resolved to write Washington for permission to make her future reports by registered mail. II At ten o'clock the next morning, reading page four of the current Wall Street Journal , Orison was interrupted by the click of a pair of leather heels. The gentleman whose heels had just slammed together was bowing. And she saw with some gratification that he was not wearing earmuffs. "My name," the stranger said, "is Dink Gerding. I am President of this bank, and wish at this time to welcome you to our little family." "I'm Orison McCall," she said. A handsome man, she mused. Twenty-eight? So tall. Could he ever be interested in a girl just five-foot-three? Maybe higher heels? "We're pleased with your work, Miss McCall," Dink Gerding said. He took the chair to the right of her desk. "It's nothing," Orison said, switching off the microphone. "On the contrary, Miss McCall. Your duties are most important," he said. "Reading papers and fairy-tales into this microphone is nothing any reasonably astute sixth-grader couldn't do as well," Orison said. "You'll be reading silently before long," Mr. Gerding said. He smiled, as though this explained everything. "By the way, your official designation is Confidential Secretary. It's me whose confidences you're to keep secret. If I ever need a letter written, may I stop down here and dictate it?" "Please do," Orison said. This bank president, for all his grace and presence, was obviously as kookie as his bank. "Have you ever worked in a bank before, Miss McCall?" Mr. Gerding asked, as though following her train of thought. "No, sir," she said. "Though I've been associated with a rather large financial organization." "You may find some of our methods a little strange, but you'll get used to them," he said. "Meanwhile, I'd be most grateful if you'd dispense with calling me 'sir.' My name is Dink. It is ridiculous, but I'd enjoy your using it." "Dink?" she asked. "And I suppose you're to call me Orison?" "That's the drill," he said. "One more question, Orison. Dinner this evening?" Direct, she thought. Perhaps that's why he's president of a bank, and still so young. "We've hardly met," she said. "But we're on a first-name basis already," he pointed out. "Dance?" "I'd love to," Orison said, half expecting an orchestra to march, playing, from the elevator. "Then I'll pick you up at seven. Windsor Arms, if I remember your personnel form correctly." He stood, lean, all bone and muscle, and bowed slightly. West Point? Hardly. His manners were European. Sandhurst, perhaps, or Saint Cyr. Was she supposed to reply with a curtsy? Orison wondered. "Thank you," she said. He was a soldier, or had been: the way, when he turned, his shoulders stayed square. The crisp clicking of his steps, a military metronome, to the elevator. When the door slicked open Orison, staring after Dink, saw that each of the half-dozen men aboard snapped off their hats (but not their earmuffs) and bowed, the earmuffed operator bowing with them. Small bows, true; just head-and-neck. But not to her. To Dink Gerding. Orison finished the Wall Street Journal by early afternoon. A page came up a moment later with fresh reading-matter: a copy of yesterday's Congressional Record . She launched into the Record , thinking as she read of meeting again this evening that handsome madman, that splendid lunatic, that unlikely bank-president. "You read so well , darling," someone said across the desk. Orison looked up. "Oh, hello," she said. "I didn't hear you come up." "I walk ever so lightly," the woman said, standing hip-shot in front of the desk, "and pounce ever so hard." She smiled. Opulent, Orison thought. Built like a burlesque queen. No, she thought, I don't like her. Can't. Wouldn't if I could. Never cared for cats. "I'm Orison McCall," she said, and tried to smile back without showing teeth. "Delighted," the visitor said, handing over an undelighted palm. "I'm Auga Vingt. Auga, to my friends." "Won't you sit down, Miss Vingt?" "So kind of you, darling," Auga Vingt said, "but I shan't have time to visit. I just wanted to stop and welcome you as a Taft Bank co-worker. One for all, all for one. Yea, Team. You know." "Thanks," Orison said. "Common courtesy," Miss Vingt explained. "Also, darling, I'd like to draw your attention to one little point. Dink Gerding—you know, the shoulders and muscles and crewcut? Well, he's posted property. Should you throw your starveling charms at my Dink, you'd only get your little eyes scratched out. Word to the wise, n'est-ce pas ?" "Sorry you have to leave so suddenly," Orison said, rolling her Wall Street Journal into a club and standing. "Darling." "So remember, Tiny, Dink Gerding is mine. You're all alone up here. You could get broken nails, fall down the elevator shaft, all sorts of annoyance. Understand me, darling?" "You make it very clear," Orison said. "Now you'd best hurry back to your stanchion, Bossy, before the hay's all gone." "Isn't it lovely, the way you and I reached an understanding right off?" Auga asked. "Well, ta-ta." She turned and walked to the elevator, displaying, Orison thought, a disgraceful amount of ungirdled rhumba motion. The elevator stopped to pick up the odious Auga. A passenger, male, stepped off. "Good morning, Mr. Gerding," Miss Vingt said, bowing. "Carry on, Colonel," the stranger replied. As the elevator door closed, he stepped up to Orison's desk. "Good morning. Miss McCall," he said. "What is this?" Orison demanded. "Visiting-day at the zoo?" She paused and shook her head. "Excuse me, sir," she said. "It's just that ... Vingt thing...." "Auga is rather intense," the new Mr. Gerding said. "Yeah, intense," Orison said. "Like a kidney-stone." "I stopped by to welcome you to the William Howard Taft National Bank and Trust Company family, Miss McCall," he said. "I'm Kraft Gerding, Dink's elder brother. I understand you've met Dink already." "Yes, sir," Orison said. The hair of this new Mr. Gerding was cropped even closer than Dink's. His mustache was gray-tipped, like a patch of frosted furze; and his eyes, like Dink's, were cobalt blue. The head, Orison mused, would look quite at home in one of Kaiser Bill's spike-topped Pickelhauben ; but the ears were in evidence, and seemed normal. Mr. Kraft Gerding bowed—what continental manners these bankers had!—and Orison half expected him to free her hand from the rolled-up paper she still clutched and plant a kiss on it. Instead, Kraft Gerding smiled a smile as frosty as his mustache and said, "I understand that my younger brother has been talking with you, Miss McCall. Quite proper, I know. But I must warn you against mixing business with pleasure." Orison jumped up, tossing the paper into her wastebasket. "I quit!" she shouted. "You can take this crazy bank ... into bankruptcy, for all I care. I'm not going to perch up here, target for every uncaged idiot in finance, and listen to another word." "Dearest lady, my humblest pardon," Kraft Gerding said, bowing again, a bit lower. "Your work is splendid; your presence is Taft Bank's most charming asset; my only wish is to serve and protect you. To this end, dear lady, I feel it my duty to warn you against my brother. A word to the wise...." " N'est-ce pas? " Orison said. "Well, Buster, here's a word to the foolish. Get lost." Kraft Gerding bowed and flashed his gelid smile. "Until we meet again?" "I'll hold my breath," Orison promised. "The elevator is just behind you. Push a button, will you? And bon voyage ." Kraft Gerding called the elevator, marched aboard, favored Orison with a cold, quick bow, then disappeared into the mysterious heights above fifth floor. First the unspeakable Auga Vingt, then the obnoxious Kraft Gerding. Surely, Orison thought, recovering the Wall Street Journal from her wastebasket and smoothing it, no one would convert a major Midwestern bank into a lunatic asylum. How else, though, could the behavior of the Earmuffs be explained? Could madmen run a bank? Why not, she thought. History is rich in examples of madmen running nations, banks and all. She began again to read the paper into the microphone. If she finished early enough, she might get a chance to prowl those Off-Limits upper floors. Half an hour further into the paper, Orison jumped, startled by the sudden buzz of her telephone. She picked it up. " Wanji e-Kal, Datto. Dink ger-Dink d'summa. " Orison scribbled down this intelligence in bemused Gregg before replying, "I'm a local girl. Try me in English." "Oh. Hi, Miss McCall," the voice said. "Guess I goofed. I'm in kinda clutch. This is Wanji. I got a kite for Mr. Dink Gerding. If you see him, tell him the escudo green is pale. Got that, doll?" "Yes, Mr. Wanji. I'll tell Mr. Gerding." Orison clicked the phone down. What now, Mata Hari? she asked herself. What was the curious language Mr. Wanji had used? She'd have to report the message to Washington by tonight's pillow, and let the polyglots of Treasury Intelligence puzzle it out. Meanwhile, she thought, scooting her chair back from her desk, she had a vague excuse to prowl the upper floors. The Earmuffs could only fire her. Orison folded the paper and put it in the "Out" basket. Someone would be here in a moment with something new to read. She'd best get going. The elevator? No. The operators had surely been instructed to keep her off the upstairs floors. But the building had a stairway. III The door on the sixth floor was locked. Orison went on up the stairs to seven. The glass of the door there was painted black on the inside, and the landing was cellar-dark. Orison closed her eyes for a moment. There was a curious sound. The buzzing of a million bees, barely within the fringes of her hearing. Somehow, a very pleasant sound. She opened her eyes and tried the knob. The door opened. Orison was blinded by the lights, brilliant as noonday sun. The room extended through the entire seventh floor, its windows boarded shut, its ceiling a mass of fluorescent lamps. Set about the floor were galvanized steel tanks, rectangular and a little bigger than bathtubs. Orison counted the rows of tanks. Twelve rows, nine tiers. One hundred and eight tanks. She walked closer. The tubs were laced together by strands of angel-hair, delicate white lattices scintillating with pink. She walked to the nearest of the tubs and looked in. It was half full of a greenish fluid, seething with tiny pink bubbles. For a moment Orison thought she saw Benjamin Franklin winking up at her from the liquid. Then she screamed. The pink bubbles, the tiny flesh-colored flecks glinting light from the spun-sugar bridges between the tanks, were spiders. Millions upon millions of spiders, each the size of a mustard-seed; crawling, leaping, swinging, spinning webs, seething in the hundred tanks. Orison put her hands over her ears and screamed again, backing toward the stairway door. Into a pair of arms. "I had hoped you'd be happy here, Miss McCall," Kraft Gerding said. Orison struggled to release herself. She broke free only to have her wrists seized by two Earmuffs that had appeared with the elder Gerding. "It seems that our Pandora doesn't care for spiders," he said. "Really, Miss McCall, our little pets are quite harmless. Were we to toss you into one of these tanks...." Orison struggled against her two sumo -sized captors, whose combined weights exceeded hers by some quarter-ton, without doing more than lifting her feet from the floor. "... your flesh would be unharmed, though they spun and darted all around you. Our Microfabridae are petrovorous, Miss McCall. Of course, once they discovered your teeth, and through them a skeleton of calcium, a delicacy they find most toothsome, you'd be filleted within minutes." "Elder Compassion wouldn't like your harming the girl, Sire," one of the earmuffed sumo -wrestlers protested. "Elder Compassion has no rank," Kraft Gerding said. "Miss McCall, you must tell me what you were doing here, or I'll toss you to the spiders." "Dink ... Dink!" Orison shouted. "My beloved younger brother is otherwise engaged than in the rescue of damsels in distress," Kraft said. "Someone, after all, has to mind the bank." "I came to bring a message to Dink," Orison said. "Let me go, you acromegalic apes!" "The message?" Kraft Gerding demanded. "Something about escudo green. Put me down!" Suddenly she was dropped. Her mountainous keepers were on the floor as though struck by lightning, their arms thrown out before them, their faces abject against the floor. Kraft Gerding was slowly lowering himself to one knee. Dink had entered the spider-room. Without questions, he strode between the shiko-ing Earmuffs and put his arms around Orison. "They can't harm you," he said. She turned to press her face against his chest. "You're all right, child. Breathe deep, swallow, and turn your brain back on. All right, now?" "All right," she said, still trembling. "They were going to throw me to the spiders." "Kraft told you that?" Dink Gerding released her and turned to the kneeling man. "Stand up, Elder Brother." "I...." Dink brought his right fist up from hip-level, crashing it into Kraft's jaw. Kraft Gerding joined the Earmuffs on the floor. "If you'd care to stand again, Elder Brother, you may attempt to recover your dignity without regard for the difference in our rank." Kraft struggled to one knee and remained kneeling, gazing up at Dink through half-closed eyes. "No? Then get out of here, all of you. Samma! " Kraft Gerding arose, stared for a moment at Dink and Orison, then, with the merest hint of a bow, led his two giant Earmuffs to the elevator. "I wish you hadn't come up here, Orison," Dink said. "Why did you do it?" "Have you read the story of Bluebeard?" Orison asked. She stood close to Dink, keeping her eyes on the nearest spidertank. "I had to see what it was you kept up here so secretly, what it was that I was forbidden to see. My excuse was to have been that I was looking for you, to deliver a message from Mr. Wanji. He said I was to tell you that the escudo green is pale." "You're too curious, and Wanji is too careless," Dink said. "Now, what is this thing you have about spiders?" "I've always been terrified of them," Orison said. "When I was a little girl, I had to stay upstairs all day one Sunday because there was a spider hanging from his thread in the stairway. I waited until Dad came home and took it down with a broom. Even then, I didn't have appetite for supper." "Strange," Dink said. He walked over to the nearest tank and plucked one of the tiny pink creatures from a web-bridge. "This is no spider, Orison," he said. She backed away from Dink Gerding and the minuscule creature he cupped in the palm of his hand. "These are Microfabridae, more nearly related to shellfish than to spiders," he said. "They're stone-and-metal eaters. They literally couldn't harm a fly. Look at it, Orison." He extended his palm. Orison forced herself to look. The little creature, flesh-colored against his flesh, was nearly invisible, scuttling around the bowl of his hand. "Pretty little fellow, isn't he?" Dink asked. "Here. You hold him." "I'd rather not," she protested. "I'd be happier if you did," Dink said. Orison extended her hand as into a furnace. Dink brushed the Microfabridus from his palm to hers. It felt crisp and hard, like a legged grain of sand. Dink took a magnifier from his pocket and unfolded it, to hold it over Orison's palm. "He's like a baby crawdad," Orison said. "A sort of crustacean," Dink agreed. "We use them in a commercial process we're developing. That's why we keep this floor closed off and secret. We don't have a patent on the use of Microfabridae, you see." "What do they do?" Orison asked. "That's still a secret," Dink said, smiling. "I can't tell even you that, not yet, even though you're my most confidential secretary." "What's he doing now?" Orison asked, watching the Microfabridus, perched up on the rear four of his six microscopic legs, scratching against her high-school class-ring with his tiny chelae. "They like gold," Dink explained, peering across her shoulder, comfortably close. "They're attracted to it by a chemical tropism, as children are attracted to candy. Toss him back into his tank, Orison. We'd better get you down where you belong." Orison brushed the midget crustacean off her finger into the nearest tank, where he joined the busy boil of his fellows. She felt her ring. It was pitted where the Microfabridus had been nibbling. "Strange, using crawdads in a bank," she said. She stood silent for a moment. "I thought I heard music," she said. "I heard it when I came in. Something like the sighing of wind in winter trees." "That's the hymn of the Microfabridae," Dink said. "They all sing together while they work, a chorus of some twenty million voices." He took her arm. "If you listen very carefully, you'll find the song these little workers sing the most beautiful music in the world." Orison closed her eyes, leaning back into Dink's arms, listening to the music that seemed on the outermost edge of her hearing. Wildness, storm and danger were its theme, counterpointed by promises of peace and harbor. She heard the wash of giant waves in the song, the crash of breakers against granite, cold and insatiable. And behind this, the quiet of sheltered tide-pools, the soft lub of sea-arms landlocked. "It's an ancient song," Dink said. "The Microfabridae have been singing it for a million years." He released her, and opened a wood-covered wooden box. He scooped up a cupful of the sand inside. "Hold out your hands," he told Orison. He filled them with the sand. "Throw our singers some supper for their song," he said. Orison went with her cupped hands to the nearest tank and sprinkled the mineral fishfood around inside it. The Microfabridae leaped from the liquid like miniature porpoises, seizing the grains of sand in mid-air. "They're so very strange," Orison said. At the bottom of the tank she thought she saw Ben Franklin again, winking at her through the bubbling life. Nonsense, she thought, brushing her hands. | B. ladies’ man |
Which inter-annotator metric do they use? | ### Introduction
Researchers have recognized that performance improvements in natural language processing (NLP) tasks such as summarization BIBREF0, question answering BIBREF1, and machine translation BIBREF2 can come from recognizing discourse-level properties of text. These include properties such as the how new entities are introduced into the text, how entities are subsequently referenced (e.g., coreference chains), and how clauses and sentences relate to one another. Corpora in which such properties have been manually annotated by experts can be used as training data for such tasks, or seed data for creating additional "silver annotated” data. Penn Discourse Treebank (PDTB), a lexically grounded method for annotation, is a shallow approach to discourse structure which can be adapted to different genres. Annotating discourse relations both within and across sentences, it aims to have wide application in the field of natural language processing. PDTB can effectively help extract discourse semantic features, thus serving as a useful substrate for the development and evaluation of neural models in many downstream NLP applications. Few Chinese corpora are both annotated for discourse properties and publicly available. The available annotated texts are primarily newspaper articles. The work described here annotates another type of text – the planned monologues found in TED talks, following the annotation style used in the Penn Discourse TreeBank, but adapted to take account of properties of Chinese described in Section 3. TED talks (TED is short for technology, entertainment, design), as examples of planned monologues delivered to a live audience BIBREF3, are scrupulously translated to various languages. Although TED talks have been annotated for discourse relations in several languages BIBREF4, this is the first attempt to annotate TED talks in Chinese (either translated into Chinese, or presented in Chinese), providing data on features of Chinese spoken discourse. Our annotation by and large follows the annotation scheme in the PDTB-3, adapted to features of Chinese spoken discourse described below. The rest of the paper is organized as follows: in Section 2, we review the related existing discourse annotation work. In Section 3, we briefly introduce PDTB-3 BIBREF5 and our adapted annotation scheme by examples. In Section 4, we elaborate our annotation process and the results of our inteannotator-agreement study. Finally, in Section 5, we display the results of our annotation and preliminarily analyze corpus statistics, which we compare to the relation distribution of the CUHK Discourse TreeBank for Chinese. (CUHK-DTBC)BIBREF6. ### Related work
Following the release of the Penn Discourse Treebank (PDTB-2) in 2008 BIBREF7, several remarkable Chinese discourse corpora have since adapted the PDTB framework BIBREF8, including the Chinese Discourse Treebank BIBREF9, HIT Chinese Discourse Treebank (HIT-CDTB) zhou2014cuhk, and the Discourse Treebank for Chinese (DTBC) BIBREF6. Specifically, Xue proposed the Chinese Discourse Treebank (CDTB) Project BIBREF10. From their annotation work, they discussed the matters such as features of Chinese discourse connectives, definition and scope of arguments, and senses disambiguation, and they argued that determining the argument scope is the most challenging part of the annotation. To further promote their research, zhou2012pdtb presented a PDTB-style discourse corpus for Chinese. They also discussed the key characteristics of Chinese text which differs from English, e.g., the parallel connectives, comma-delimited intra-sentential implicit relations etc. Their data set contains 98 documents from the Chinese Treebank BIBREF10. In 2015, Zhou and Xue expanded their corpus to 164 documents, with more than 5000 relations being annotated. huang-chen-2011-chinese constructed a Chinese discourse corpus with 81 articles. They adopted the top-level senses from PDTB sense hierarchy and focused on the annotation of inter-sentential discourse relations. zhang2014chinese analyzed the differences between Chinese and English, and then presented a new Chinese discourse relation hierarchy based on the PDTB system, in which the discourse relations are divided into 6 types: temporal, causal, condition, comparison, expansion and conjunction. And they constructed a Chinese Discourse Relation corpus called HIT-CDTB based on this hierarchy. Then, zhou2014cuhk presented the first open discourse treebank for Chinese, the CUHK Discourse Treebank for Chinese. They adapted the annotation scheme of Penn Discourse Treebank 2 (PDTB-2) to Chinese language and made adjustments to 3 aspects according to the previous study of Chinese linguistics. However, they just reannotated the documents of the Chinese Treebank and did not annotate inter-sentence level discourse relations. It is worth noting that, all these corpora display a similar unbalanced distribution that is likely to be associated with them all being limited to text from the same NEWS genre. In particular, these two senses (Expansion and Conjunction) represent 80 % of the relations annotated in the CDTB. In addition, although annotating spoken TED talks has been done on other several languages before BIBREF4, to our knowledge, there is no recent annotation work for Chinese spoken discourses, or particularly for Chinese Ted talks. However, there is some evidence that noticeable differences in the use of discourse connectives and discourse relations can be found between written and spoken discourses BIBREF11. Here, by using the new PDTB-3 sense hierarchy and annotator, which has not been used for Chinese annotation before, we annotated Chinese Ted talks to help others be aware of the differences between the Chinese discourse structure of written and spoken texts and will make our corpus publicly available to benefit the discourse-level NLP researches for spoken discourses. ### PDTB and our Annotation Scheme
The annotation scheme we adopted in this work is based on the framework of PDTB, incorporating the most recent PDTB (PDTB-3) relational taxonomy and sense hierarchy BIBREF5, shown in Table 1. PDTB follows a lexically grounded approach to the representation of discourse relations BIBREF12. Discourse relations are taken to hold between two abstract object arguments, named Arg1 and Arg2 using syntactic conventions, and are triggered either by explicit connectives or, otherwise, by adjacency between clauses and sentences. As we can see from Table 1, the PDTB-3 sense hierarchy has 4 top-level senses (Expansion, Temporal, Contingency, Contrast) and second- and third-level senses for some cases. With obvious differences ranging from the conventions used in annotation, to differences in senses hierarchy, PDTB-3 gives rigorous attention to achieving as much consistency as possible while annotating discourse relations. Previously, all Chinese annotation work using PDTB style followed the settings of PDTB-2. Some researchers tried to adapt it in lines of the Chinese characteristics. For example, zhou2012pdtb annotated the parallel connectives continuously rather than discontinuously due to the greater use of parallel connectives in Chinese and a reduced use of explicit connectives in general. zhou2014cuhk added some additional senses into the hierarchy. However, PDTB-3, as a new and enriched version, not only has paid greater attention to intra-sentential senses, but also has incorporated some of those additional senses. Therefore, we just made several modifications including removing, adding, or disambiguating for the practical use of PDTB-3 into our Chinese annotation. In practice, using the PDTB annotator tool, we annotated an explicit connective, identified its two arguments in which the connective occurs, and then labeled the sense. For implicit relations, when we inferred the type of relation between two arguments, we tried to insert a connective for this relation, and also the inserted connective is not so strictly restricted, extending to expressions that can convey the sense of the arguments. If a connective conveys more than one sense or more than one relation can be inferred, multiple senses would be assigned to the token. Our adaptations towards PDTB-3 will be introduced from the perspectives of arguments, relations and senses as follows. ### PDTB and our Annotation Scheme ::: Arguments
The argument-labelling conventions used in the PDTB-2 had to be modified to deal with the wider variety of discourse relations that needed to be annotated consistently within sentences in the PDTB-3. In particular, in labelling intra-sentential discourse relations, a distinction was made between relations whose arguments were in coordinating syntactic structures and ones whose arguments were in subordinating syntactic structures. For coordinating structures, arguments were labelled by position (Arg1 first, then Arg2), while for subordinating structures, the argument in subordinate position was labelled Arg2, and the other, Arg1, independent of position. For discourse in Chinese, this can introduce an unwanted ambiguity. Example 1 is a typical example for illustrate this phenomenon. In the examples throughout the paper, explicit connectives are underlined, while implicit Discourse Connectives and the lexicalizing expression for Alternative Lexicalizations are shown in parentheses and square brackets respectively. The position of the arguments is indicated by the attached composite labels to the right square brackets, and the relation lables and sense lables can be seen in the parentheses at the end of arguments. When the arguments, relations or senses are ambiguous, there may be no corresponding labels shown in the examples. UTF8gbsn 因为 你 让 我 生气， 所以，我 要让 Because you make me angry, so I want 你 更难过。(Explicit, Cause.Result) you to be sadder. “You made me angry, so I return it double back.” While“because”and“so”are rarely found together as connectives in a sentence in English, it is not uncommon to find them used concurrently as a paired connective in Chinese. Therefore, due to this difference, the annotators tend to have no idea about which clause is subordinate. Therefore, if we regard the first clause as subordinating structure and “因 为”(because)as connective, then the sense would be Contingency.Cause.Reason. By contrast, the sense would be Contingency.Cause.Result, when the second clause is regarded as Arg2. To get rid of this kind of ambiguity, we just take the first as Arg1 and the second Arg2 regardless of the fact that the parallel connectives are surbodinating or coordinating. ### PDTB and our Annotation Scheme ::: Relations
There are two new types of relation in PDTB-3: AltlexC and Hypophora. Hypophora is an explicitly marked question-response pairs, first used in annotating the TED- MDB BIBREF4. In Hypophora relations, Arg1 expresses a question and Arg2 offers an answer, with no explicit or implicit connective being annotated (Example 2). Because of the nature of TED talks, many relations in both the TED-MDB and in our Chinese TED talks are examples of “Hypophora”. However, not all discourse relations whose first argument is a question are Hypophora. Example 3, instead of seeking information and giving answer, is just a rhetorical question expressing negation by imposing a dramatic effect. [我到底 要 讲 什么Arg1]? I on earth am going to talk about what ? [最后 我决定 要 讲 教育Arg2]。(Hypophora) Finally, I decided to talk about education . “what am I gonna say? Finally, I decided to talk about education.” 他说 ： “ 我 是 三 天 一 小 哭 、 五 天 He said, " I am three days a little cry, five days 一 大 哭 。 " 这样 你 有 比较 健康 吗 ？ a lot cry." In this way, you are more healthier? 都 是 悲伤 ， 并 不 是 每 一 个 人 ，每 一 次 All are sadness, not everyone, every time 感受 到 悲伤 的 时候 ，都 一定 会 流泪 、 甚至 大哭 。 feel sad 's time, would shed tears、even cry. “He said, "Three times I cry a little, and five times I cry a lot." Is that healthier? Everyone gets sad, but that's not to say that whenever someone feels sad, they necessarily will cry.” In addition, we found a new issue when identifying Hypophora, which is shown in Example 4. In this example, we have a series of questions, rather than a series of assertions or a question-response pair. We attempted to capture the rhetorical links by taking advantage of our current inventory of discourse relations. Here, two implicit relations were annotated for this example, and the senses are Arg2-as-detail and Result+SpeechAct respectively. Therefore, when there are subsequent texts related to a question or a sequence of questions, we would not just annotated them as Hypophora but had to do such analysis as what we did for the examples shown. [情绪 ， 它 到底 是 什么Rel1-Arg1 ]? (具体来说) Emotion, it on earth is what? (Specially) [它 是 好还是 不 好Rel1-Arg2,Rel2-Arg1]？(Implicit，Arg2-as-detail) It is good or bad? (所以)[你 会 想要 拥有 它 吗Rel2-Arg2]？ (Implicit，Result+SpeechAct) (So) You want to have it ? “What is it exactly? Is it good or bad? Do you want to have them?” Besides, it is widely accepted that the ellipsis of subject or object are frequently seen in Chinese. Then for EntRel, if facing this situation where one of the entities in Arg1 or Arg2 is omitted, we still need to annotate this as EntRel (Example 5). In this following example, we can see in Arg2, the pronoun which means “that”is omitted, but in fact which refers to the phenomenon mentioned in Arg1, so here there is still an EntRel relation between this pair of arguments. [我们会以讽刺的口吻来谈论， 并且会 We in ironic terms talk about, and 加上引号 : “进步”Arg1 ] add quotes: “Progress”. [我想是有原因的， 我们也知道 是 什么 I think there are reasons, we also know are what 原因Arg2]。(EntRel) reasons. “We talk about it in ironic terms with little quotes around it:“Progress.”Okay, there are reasons for that, and I think we know what those reasons are.” ### PDTB and our Annotation Scheme ::: Senses
The great improvement in the sense hierarchy in PDTB-3 enables us to capture more senses with additional types and assign the senses more clearly. For example, the senses under the category of Expansion such as level of detail, manner, disjunction and similarity are indispensable to our annotation. Therefore, we nearly adopted the sense hierarchy in PDTB-3, just with few adaptations. On the one hand, we removed the third level sense “Negative condition+SpeechAct”, since it was not used to label anything in the corpus. On the other hand, we added the Level-2 sense “Expansion.Progression”. This type of sense applies when Arg1 and Arg2 are coordinating structure with different emphasis. The first argument is annotated as Arg1 and the second as Arg2. This sense is usually conveyed by such typical connectives as “不 但 (not only)... 而 且 (but also)...”, “甚 至 (even)... 何 况 (let alone)...”,“... 更 (even more)...”(Example 6). [我 去 了 聋人 俱乐部 ，观看 了 聋人 的 I went to deaf clubs, saw the deaf person’s 表演 Arg1]。[我甚至 去 了 田纳西州 纳什维尔的 performances. I even went to the Nashville ’s “ 美国 聋人 小姐 ” 选秀赛Arg2]。(Explicit, Progression.Arg2-as-progr) “the Miss Deaf” America contest. “ I went to deaf clubs. I saw performances of deaf theater and of deaf poetry. I even went to the Miss Deaf America contest in Nashville.” Another issue about sense is the inconsistency when we annotated the implicit relations. zhou2012pdtb did not insert connective for implicit relations, but we did this for further researches with regard to implicit relations. However, we found that in some cases where different connectives can be inserted into the same arguments to express the same relation, the annotators found themselves in a dilemma. We can see that Example 7 and Example 8 respectively insert “so” and “because” into the arguments between which there is a causal relation, but the senses in these two examples would be Cause.Result and Cause.Reason. The scheme we adopted for this is that we only take the connectives that we would insert into account, and the position and sense relations of arguments would depend on the inserted connectives. [“克服 逆境 ” 这一说法 对我 “Overcome the adversity” this phrase for me 根本 不 成立 Arg1]，（所以）[别人 让 我 completely not justified, (so) others asked me 就 这一话题 说 几 句 的时候， 我很不自在Arg2]。(Implicit, Cause.Result) to this topic talk about some, I felt uneasy. （因为）[“克服 逆境 ” 这一说法 (Because) “overcome the adversity” this phrase 对 我来说根本 不 成立Arg2], [ 别人 for me completely not justified, (so) others 让 我就 这一话题 说几句的时候，我很 asked me to this topic, talk about some, I felt 不自在Arg1]。(Implicit, Cause.Reason) uneasy. ““overcome the adversity” this phrase never sat right with me, and I always felt uneasy trying to answer people's questions about it.” ### Annotation Procedure
In this section, we describe our annotation process in creating the Chinese TED discourse treebank. To ensure annotation quality, the whole annotation process has three stages: annotators training, annotation, post-annotation. The training process intends to improve the annotators’ annotation ability, while after the formal annotation, the annotated work was carefully checked by the supervisor, and the possible errors and inconsistencies were dealt with through discussions and further study. ### Annotation Procedure ::: Annotator training
The annotator team consists of a professor as the supervisor, an experienced annotator and a researcher of PDTB as counselors, two master degree candidates as annotators. Both of the annotators have a certain theoretical foundation of linguistics. To guarantee annotation quality, the annotators were trained through the following steps: firstly, the annotators read the PDTB-3 annotation manual, the PDTB-2 annotation manual and also other related papers carefully; next, the annotators tried to independently annotate same texts, finding out their own uncertainties or problems respectively and discussing these issues together; then, the annotators were asked to create sample annotations on TED talks transcripts for each sense from the top level to the third. They discussed the annotations with the researchers of the team and tried to settle disputes. When sample annotations are created, this part of process is completed; based on the manuals, previous annotation work and also the annotators’ own pre-annotation work, they made a Chinese tutorial on PDTB guidelines, in which major difficulties and perplexities, such as the position and the span of the arguments, the insert of connectives, and the distinction of different categories of relations and senses, are explained clearly in detail by typical samples. This Chinese tutorial is beneficial for those who want to carry out similar Chinese annotation, so we made this useful tutorial available to those who want to carry out similar annotation; finally, to guarantee annotation consistency, the annotators were required to repeat their annotation-discussion process until their annotation results show the Kappa value > 0.8 for each of the indicators for agreement. ### Annotation Procedure ::: Corpus building
At present, our corpus has been released publiclyFOOTREF19. Our corpus consists of two parts with equal number of texts: (1) 8 English TED talks translated into Chinese, just like the talks in the TED-MDB, all of which were originally presented in English and translated into other languages (including German, Lithuanian, Portuguese,Polish, Russian and Turkish) BIBREF4. (2) 8 Chinese TED talks originally presented in Taipei and translated into English. We got the texts by means of extracting Chinese and English subtitles from TED talks videos . Firstly, we just annotated the talks given in English and translated in Chinese. But after considering the possible divergencies between translated texts and the original texts, we did our annotation for the Taipei TED talks, which were delivered in Chinese. The parallel English texts are also being annotated for discourse relations, but they are not ready for carrying out a systematic comparison between them. At the current stage, we annotated 3212 relations for the TED talks transcripts with 55307 words，and the length of each talk (in words) and the number of annotated relations in each talks can be found from Table 2. These TED talks we annotated were prudently selected from dozens of candidate texts. The quality of texts which is principally embodied in content, logic, punctuation and the translation are the major concerns for us. Moreover, when selecting the texts from the Taipei talks, we ruled out those texts which are heavy in dialogues. Some speakers try to interact with the audience, asking the questions, and then commenting on how they have replied. However, what we were annotating was not dialogues. In spite of critically picking over the texts, we still spent considerable time on dealing with them before annotation such as inserting punctuation and correcting the translation. Moreover, before annotation, we did word segmentation by using Stanford Segmenter and corrected improper segmentation. While annotating, we assigned the vast majority of the relations a single sense and a small proportion of relations multiple senses. Unlike previous similar Chinese corpora which primarily or just annotated the relations between sentences, we annotated not only discourse relations between sentences but intra-sentential discourse relations as well. To ensure building a high-quality corpus, the annotators regularly discussed their difficulties and confusions with the researcher and the experienced annotator in the whole process of annotation. After discussion, the annotators reached agreement or retained the differences for few ambiguities. ### Annotation Procedure ::: Agreement study
We measured intra-annotator agreement between two annotators in three aspects: relations, senses, arguments. To be specific, the annotators’ consistency in annotating the type of a specific relation or sense and the position and scope of arguments are measured. To assess the consistency of annotations and also eliminate coincidental annotations, we used agreement rates, which is calculated by dividing the number of senses under each category where the annotators annotate consistently by the total number of each kind of sense. And considering the potential impact of unbalanced distribution of senses, we also used the Kappa value. And the final agreement study was carried out for the first 300 relations in our corpus. We obtained high agreement results and Kappa value for the discourse relation type and top-level senses ($\ge {0.9} $ ). However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses. Table 3 also shows that agreement on argument order is almost 1.0 (kappa = 0.99). This means that the guidelines were sufficiently clear that the annotators rarely had difficulty in deciding the location of Arg1 and Arg2 when the senses are determined. Concerning the scope of arguments, which is seen as the most challenging part in the annotation work BIBREF10, our agreement and Kappa value on this are 0.88 and 0.86 respectively, while the agreement of the scope of arguments depends on whether the scopes of two arguments the anotators annotated are completely the same. Under such strict requirement, our consistency in this respect is still significantly higher than that of other annotation work done before, for we strictly obeyed the rules of “minimality principle” mentioned in the PDTB-3 annotation manual and got a clearer perspective of supplementary information. Therefore, the annotators are better at excluding the information that do not fall within the scope of the discourse relation. It is useful to determine where the annotators disagreed most with each other. The three senses where most disagreement occurred are shown in Table 4. The disagreements were primarily in labelling implicit relations. The highest level of disagreement occurred with Expansion.Conjunction and Expansion.Detail, accounting for 12.5 % among all the inconsistent senses. It is because, more often than not, the annotators failed to judge whether the two arguments make the same contribution with respect to that situation or both arguments describing the same has different level of details. The second highest level of disagreement is reflected in Conjunction and Asynchronous, accounting for 9.3 %. Besides, Contrast and Concession are two similar senses, which are usually signaled by the same connectives like “但是”, “而”, “不过”, and all these words can be translated into“but”in English. Hence, the annotators sometimes tend to be inconsistent when distinguishing them. ### Results
In regard to discourse relations, there are 3212 relations, of which 1237 are explicit relations (39%) and 1174 are implicit relation (37%) (Figure 1). The remaining 801 relations include Hypophora, AltLex, EntRel, and NoRel. Among these 4 kinds of relations, what is worth mentioning is AltLex(Alternative Lexicalizations ),which only constitutes 3% but is of tremendous significance, for we are able to discover inter- or intra-sentential relations when there is no explicit expressions but AltLex expressions conveying the relations. but AltLex expressions(eg, 这导致了(this cause), 一个例子是(one example is... ), 原 因 是 (the reason is), etc.). Originally in English, AltLex is supposed to contain both an anaphoric or deictic reference to an actual argument and an indication of the type of sense BIBREF13. While for Chinese, the instances of Altlex do not differ significantly from those annotated in English. To prove this, two examples are given as below (Example 9 and Example 10). From our annotation, we realized that Altlex deserves more attention, for which can effectively help to recogonize types of discourse relations automatically. [“国内 许多被截肢者, 无法使用 in this country, many of the amputees, cannot use 他们的假肢Arg1],[这其中的原因是] [他们由于 their prostheses, the reason was their 假肢接受腔 无法 与残肢 适配 而 prosthetic sockets cannot their leg fit well so that 感到疼痛Arg2].(AltLex, Cause.Reason) felt painful.] “Many of the amputees in the country would not use their prostheses. The reason, I would come to find out, was that their prosthetic sockets were painful because they did not fit well.” 三年级的时候考进秀朗小学的游泳班, in third grade, got in the swimming class at Xiu Lang [ 这个班每天的 游泳 elementary school, this class everyday’s swimming 训练量高达 3000 米 Arg1], 我发现 [这样的训练量 volumm reach 3000 meters, I realized the training load 使][我 无法同时兼顾两种乐器 Arg2]。(AltLex, Cause.Result) make me cannot learn the two instruments at the same time “I got in the swimming class at Xiu Lang elementary school when I was in third grade. We had to swim up to 3000 meters every day. I realized the training load was too much for me to learn the two instruments at the same time.” Obviously, there is approximately the same number of explicit and implicit relations in the corpus. This may indicate that explicit connectives and relations are more likely to present in Chinese spoken texts rather than Chinese written texts. The figures shown by Table 4 illustrate the distributions of class level senses. We make a comparison for the class level senses between our corpus and the CUHK Discourse Treebank for Chinese (CUHK-DTBC). CUHK Discourse Treebank for Chinese is a corpus annotating news reports. Therefore, our comparison with it may shed light on the differences of discourse structures in different genres. According to the statistics of CUHK-DTBC for 400 documents and our corpus, while more than half of the senses is Expansion in CUHK-DTBC, it just represents 37.5% in our corpus. In addition, it is highlighted that the ranks of the class level senses are the same in both corpora, although all of the other three senses in our corpus are more than those in CUHK-DTBC. The most frequent second-level senses in our corpus can be seen from Table 5. We can find that 20% of the senses is Cause (including Reason and Result), followed by Conjunction and Concession, each with 13%. The top 10 most frequent senses take up 86% of all senses annotated, which reveals that other senses also can validate their existence in our corpus. Therefore, these findings show that, compared with other corpora about Chinese shallow relations where the majority of the documents are news report, our corpus evidently show a more balanced and varied distribution from perspectives of both relations and senses, which in large measure proves the differences in discourse relations between Chinese written texts and Chinese spoken texts. ### Conclusions and Future Work
In this paper, we describe our scheme and process in annotating shallow discourse relations using PDTB-style. In view of the differences between English and Chinese, we made adaptations for the PDTB-3 scheme such as removing AltLexC and adding Progression into our sense hierarchy. To ensure the annotation quality, we formulated detailed annotation criteria and quality assurance strategies. After serious training, we annotated 3212 discourse relations, and we achieved a satisfactory consistency of labelling with a Kappa value of greater than 0.85 for most of the indicators. Finally, we display our annotation results in which the distribution of discourse relations and senses differ from that in other corpora which annotate news report or newspaper texts. Our corpus contains more Contingency, Temporal and Comparison relations instead of being governed by Expansion. In future work, we are planning to 1) expand our corpus by annotating more TED talks or other spoken texts; 2) build a richer and diverse connective set and AltLex expressions set; 3) use the corpus in developing a shallow discourse parser for Chinese spoken discourses; 4) also explore automatic approaches for implicit discourse relations recognition. ### Acknowledgement
The present research was supported by the National Natural Science Foundation of China (Grant No. 61861130364) and the Royal Society (London) (NAF$\backslash $R1$\backslash $180122). We would like to thank the anonymous reviewers for their insightful comments. Table 1: PDTB-3 Sense Hierarchy (Webber et al., 2019) Table 2: The length and the number of relations of each text Table 3: Agreement study Figure 1: Relation distribution Table 4: Disagreements between annotators: Percentage of cases Table 5: Distribution of class level senses in our corpus and 400 documents of CUHK-DTBC Table 6: The most frequent Level-2 senses in our corpus | agreement rates, Kappa value |
What factor necessitates the change in frequency of performed C-sections?
A. Uterine environment
B. Practitioner training
C. Cranial growth
D. Advanced technology
| Obstetrics for beginners It's my first go at delivering a baby by caesarean section – and the foetal head is impacted, jammed in its mother's pelvis. To be honest I'm struggling. Incisions have been made in the lower part of the mother's abdomen and womb. I've pushed my gloved hand inside and managed to slide my fingers between the baby's head and the surrounding uterine tissue. But it's difficult. The baby is tightly wedged in. I've had to push hard to get my hand to the far side of its head, and even though I'm now cupping and grasping it in the approved manner, I can't seem to pull it out. Dare I grip its head more firmly? Dare I pull harder? The baby's mother – she's called Debra – remains impassive throughout these agonised fumblings. Her face reveals nothing of what she may be feeling. But then Debra has no feelings. Indeed she has no face… So you can stop worrying. Debra – Desperate Debra to use her full trade name – is a simulator designed to help doctors practise their skill at dealing with impacted foetuses: babies that get stuck trying to exit the womb by the normal route. She comprises the lower two thirds (ie from the mid-chest region downwards) of a life-sized but limbless female torso made of flesh-coloured silicone rubber. She comes with a vulva, a pre-cut incision in her abdomen and, most importantly, a uterus containing a foetal head that should, in the normal way of things, be free to emerge between her legs. But this fetus is going nowhere until an obstetrician – or in this case me – can successfully grasp and pull it out. The clever and sophisticated simulator I'm playing with started life as a lash-up in an obstetrician's home workshop: a Heath Robinson-style contraption barely recognisable as a model of the human body. But it wasn't at that stage intended as a simulator for training medical staff. Its sole purpose was to test the effectiveness of a novel device called a Tydeman tube. Paradoxically, although the testing equipment, Debra, is now commercially available, the device it was intended to test has yet to reach the market. The inventor of the tube and of Desperate Debra is Dr Graham Tydeman, a consultant in obstetrics and gynaecology at Victoria Hospital in Kirkcaldy, Fife. Only after he'd built Debra did he realise that she might serve a purpose beyond his original intention. His is a decade-long tale of inspired insights, thwarted aims and shifting purposes; but with a good outcome. Although the Tydeman tube is still in gestation, Desperate Debra herself is now thriving. To understand the desperation of Debra and how the Tydeman tube might help to relieve it requires a brief foray into basic obstetric knowhow. Evolution has endowed us with heads proportionally so large that even when labour runs according to plan, the delivery process involves a bit of a squeeze. For the baby's head to get stuck on the way out may not be usual, but it's by no means a rarity. The standard response is to perform a caesarean section. Every year some 160,000 babies are born in the UK this way, with almost two thirds of them classified as emergencies. One audit has suggested that roughly 8,000 babies get stuck and have to be delivered by caesarean at a stage when their mothers are fully dilated. "Some of the babies will be so close to coming out by the normal route," says Tydeman, "that it's then difficult to get them back up and remove them through the hole in the woman's tummy." Which women are most at risk of this setback seems to be largely unpredictable. "We just observe that it happens… It's been discussed in the medical literature since the 1940s, but until 10 years ago, and throughout my training and most of my life as a consultant, it wasn't really talked about." Considering the universality of childbirth, impaction and the best way of dealing with it are topics that seem to have gone remarkably unstudied. "There are strong opinions about why it happens and what to do, but very little research evidence," says Tydeman, adding that many of these opinions are contradictory. In a protracted birth that's destined to end with a caesarean, the longer the labour is allowed to go on before the obstetrician decides to intervene, the greater the likelihood that the baby's head will become impacted. However, concern over the rising number of babies born by caesarean has made doctors more wary of doing them – one consequence of which is that medical staff may allow a difficult birth to continue for longer before they resort to surgery. This could be boosting the frequency of impaction. But, again, no one is certain. When obstetricians doing planned caesareans slice open a mother's womb, what they usually see is the baby's head. By slipping a hand round and below it they can easily guide the baby out. "When you do a caesarean for an impacted baby," says Tydeman, "you make the incision in the same place, but what you might come across is a shoulder because the baby's so much further down [the birth canal]." As I'd discovered for myself, sliding a hand around the baby's head is then far more difficult. "It makes your fingers hurt," says Tydeman. "It makes your pulse rate go up to about 200, and you break out in a sweat because know you've only got about five or 10 minutes before there are serious consequences. The clock is ticking." If a baby's head is jammed down in the mother's pelvic region, common sense suggests that it might help if a second person gives a gentle backward push on the area of its head visible through the mother's dilated cervix. "In our unit," says Tydeman, "when the woman is fully dilated and you'd expect the baby to come out normally [but it doesn't]… a registrar will be asking for a push-up about one in five times." Although registrars are doctors still in training, they're nonetheless experienced; which suggests requests for push-ups during unplanned caesareans are far from uncommon. The Tydeman tube is a gadget intended to make this manoeuvre safer and more effective. Creativity and innovation have many unlikely sources. What seems to have inspired Tydeman to develop his device was the characteristic sound of a Wellington boot being pulled free of wet, muddy ground: a slurpy, sucking, gurgling noise. When an impacted foetal head is pulled free of the uterus it's often accompanied by a similar sucking noise, the result of air rushing in between the obstetrician's fingers to fill the space vacated. "What occurred to me years ago was that if the air can't get in, why not put a tube up into the vagina so that it can get in from below the baby's head." From time to time, if he felt he felt the baby might stick, Tydeman would slip a length of sterile silicone tubing through the woman's vagina and up into the womb next to the baby's head. Allowing air in by this route would release any suction forces tending to hold it where it was. Tydeman didn't do much with the idea until 10 years ago when one trainee, who was experiencing real difficulty getting heads out, prompted him to think again about the problem. Around the same time, he met professor of obstetrics Andrew Shennan and consultant midwife Annette Briley, both of the Women's Health Academic Centre at St Thomas's hospital. Between them they came up with a device – the Tydeman tube – to make pushing on the foetus more controlled while simultaneously releasing any vacuum that might be holding it in place. The instrument is made up of a rigid plastic tube opening into a softer silicone cup. Pressure to the foetal head is applied using four pads projecting forward from the cup's interior. Holding the device by the tube, the user places the cup against the part of the head exposed through the dilated cervix, and presses. This pushes the baby back up into the uterus while releasing any suction pressure that may have been holding it, so allowing the obstetrician to extract it more easily. Because pressure is distributed equally between the four pads with a greater combined surface area than that of a user's fingertips, the risk of inadvertent damage is minimised. The team found some money to employ a product designer who used computer-aided design technology and 3D printing to make a prototype. "We were at the point of getting one made in silicone," says Tydeman, "when we realised that before we started experimenting on women we really ought to test it on a simulator." No such simulator existed – so he decided to make one himself. That Tydeman was able to do this comes as no great surprise once you've glanced at his website. His career may be rooted in medicine but his interests encompass sculpture, furniture making and much else. He works in wood, glass, metals and plastic. "I've got a big workshop with a lathe and a forge," he says. "I make stuff. I always have, ever since I was a child. My dad was a woodwork teacher, my mum was very creative with fabric." Although tests carried out with the Debra prototype showed that the tube would work as intended, Tydeman and his colleagues then faced what he calls a kind of medical catch-22. "We had the tube finished about three years ago… but we were more interested in trying to save lives than selling a product. We thought that the right thing to do before commercialising it was to be sure we'd got the best design." They tried it on a dozen or so women in labour, and concluded that it did what it supposed to. But they held off trying to market it because they wanted to do more extensive, more rigorous clinical studies. This presented a problem. "If you've applied for research money," says Tydeman, "but you've already got what seems to be a commercially viable design, potential funders are going to say that the company aiming to sell it should pay for the work." On the other hand, commercial interest is easier to drum up if you've already got evidence that a device is safe and effective. That said, the team didn't want to leave the tube sitting on the shelf. So they eventually decided to go ahead and find a commercial partner willing to manufacture and market it. They have now identified one, and are fairly confident it will soon be in production. With sufficient users it should then be possible to compile factual – as opposed to anecdotal – evidence of benefit. Not ideal, Tydeman concedes, but the best they can do at present. In the meantime, back to Desperate Debra: so named, Tydeman says, not after any particular person but because the appellation is memorably alliterative. He put together the original Debra in a weekend. The skin was made out of a neoprene wetsuit fixed to a scaffolding formed from plastic tubing he'd found 20 years ago in skip outside a Glasgow pub; the head was cast in silicone from a model he'd made in plasticine, and the rest comprised old springs and other bits of stuff lying around his workshop. "It wasn't actually that difficult," Tydeman says. When originally conceived, remember, Debra was simply a means of testing the effectiveness of the tube. What she looked like was neither here nor there. It was only once Debra was reborn as a teaching aid that she needed sprucing up. Tydeman can remember the exact moment when the idea of her having a greater role dawned on him. "I was on the sleeper train down from Scotland to London," he says. "Debra was with me because the first Tydeman tube had become available at St Thomas's… It was about midnight, I'd had my free whisky and I suddenly thought, 'Blow me! Even if the tube doesn't work, Debra could be useful as a teaching aid'." The following morning, at St Thomas's, Tydeman asked a visiting professor of obstetrics to have a look at Debra and tell him what she thought. She put her hand into Debra's womb, grasped the foetal head and said it felt just like the real thing. "Terribly flattering," Tydeman laughs. With a grant from the Guy's and St Thomas's Charity fund they made Debra more presentable. Tydeman showed the prototype to Adam Rouilly, an established company specialising in medical models and simulators. They were impressed. A year later, the first of Debra's smartened-up sisters was on the market. In Debra as she is now, the precise extent and nature of her desperation can be fine-tuned according to need. The foetal head inside her uterus can be moved to mimic the various positions that an unborn baby may adopt. By tightening a spring inside Debra's body, it's also possible vary the degree of impaction from mild to so severe that the head is virtually impossible to extract. In this way she simulates the full range of difficulty that obstetricians are likely to encounter. So how valuable in training medical staff is a simulator like this? Very, according to Annette Briley. Imagine it's the middle of the night and an unplanned emergency caesarean is required: "Some poor junior doctor might find himself trying to manage it on his own." To have practised the knack of extracting a firmly impacted baby from a simulator is lot better than first honing your skill on a real woman. At St Thomas's, midwives in training also get an opportunity to practise on Debra. The chances that midwives will find themselves having to do the actual extraction of an infant are slim; but they're quite likely to be asked to help the obstetrician by pushing a stuck baby from below. Debra's anatomy allows them to practise this skill; and to learn where and how hard to push on the infant skull. "Any practice you've done in the cold light of day will help you stay calm and composed in an emergency, and that's what we're aiming for," says Briley. It's still too soon to make a final judgement about Debra’s impact. "When we first brought Debra out," Briley recalls, "some of the really experienced professors said things like, 'We always managed without one. Why would you need this?' But ask them to have a go at using it and then they admit it's really good." Medicine as a whole has an oddly ambivalent relationship to innovation. Some new findings, techniques or equipment take years to penetrate the profession; others are seized upon immediately. A proper study of the clinical effectiveness of the Tydeman tube will necessarily involve women giving birth. Assessing the value of Debra as a simulator didn't require human subjects; and the team has already conducted such a study. Thirty obstetricians, from three NHS maternity units and with varying levels of experience, took part. They all received a brief explanation of how Debra works, and were then asked to try a timed removal of the foetal head at three different levels of difficulty. Overall, 87 per cent reported that the simulator offered a realistic experience of dealing with an impacted head, and 93 per cent thought it would be valuable as a training device. The use of simulators to teach technical skills is now common in medical schools. You can learn to sew up a knife wound, catheterise a bladder or intubate an airway. You can practise cardiopulmonary resuscitation or ear syringing or even go through the motions of a keyhole surgical procedure. The technology required to do these things may cost a few pounds, or tens of thousands. Either way, given that most of these devices were invented during the past three or four decades, it comes as something of a surprise to learn that simulation for medical purposes can be traced back as far as the Chinese Song dynasty of 960-1279 AD. One of the treatments of choice in that era was, naturally, acupuncture. But how to teach tyro-acupuncturists where to place the needles? Simple. A life-size bronze statue dotted with small holes indicated the points of insertion. And how then to test the students' grasp of their subject? If the statute was hollow, filled with liquid and given an outer coating of wax to mask the holes, a correct needle insertion would be followed by a leak. Given the universality of childbirth it's no surprise that, then as now, the womb turns out to be the most simulated of our organs. For the benefit of 18th-century midwives and doctors-in-training, the Bologna surgeon Giovanni Antonio Galli devised a birthing simulator comprising a glass uterus supported by an artificial pelvis and containing a flexible foetus. Trainees had to deliver the baby while wearing a blindfold. Only the tutor could witness the fumbling of their hands. As the material for a convincing simulation, glass clearly has its drawbacks. But another 18th-century contraption used a pink cloth-covered mannequin comprising a female torso complete with genitalia, a set of implantable foetuses of various ages, and even – a real coup de théâtre , this – a facility for exuding suitably coloured liquids at the appropriate moment. Oddly, as medicine became more scientific, most of these devices fell by the wayside. As an academic review of these and other devices has pointed out, much of the 20th century was something of Dark Ages for simulation. Its value in professional training has had to be rediscovered: an endeavour in which inventive people like Graham Tydeman, sometimes with workshops rich in discarded junk, are still fruitfully engaged. This article was originally published on TheLong+Short. Read the original article. | C. Cranial growth |
How is correctness of automatic derivation proved? | ### Introduction
Accurate and efficient computation of derivatives is vital for a wide variety of computing applications, including numerical optimization, solution of nonlinear equations, sensitivity analysis, and nonlinear inverse problems. Virtually every process could be described with a mathematical function, which can be thought of as an association between elements from different sets. Derivatives track how a varying quantity depends on another quantity, for example how the position of a planet varies as time varies. Derivatives and gradients (vectors of partial derivatives of multivariable functions) allow us to explore the properties of a function and thus the described process as a whole. Gradients are an essential component in gradient-based optimization methods, which have become more and more important in recent years, in particular with its application training of (deep) neural networks BIBREF0. Several different techniques are commonly used to compute the derivatives of a given function, either exactly or approximately BIBREF1, BIBREF0, BIBREF2. The most prevalent techniques are: Numerical differentiation, based on the finite difference method, provides a way to evaluate derivatives approximately. While simple, numerical differentiation can be slow (the run-time complexity grows linearly with the number of input variables) and may have problems with accuracy due to round-off and truncation errors. Symbolic differentiation, based on transformations of symbolic expressions of functions, provides exact closed-form expressions for the derivatives. It faces difficulties when the function to be differentiated is not available in a closed form, which is often the case for computer programs which may contain control flow. Symbolic differentiation can produce derivative expressions that are computationally expensive to evaluate due to difficulties in exploiting common subexpressions. Automatic differentiation (AD) computes derivatives accurately to the precision of the original function, supports control flow and uses at most a small constant factor more time and space than it takes to evaluate the original function, at the expense of increased implementation complexity and introducing more software dependencies. Numerical and symbolic differentiation methods are slow at computing gradients of functions with many input variables, as is often needed for gradient-based optimization algorithms. Both methods have problems calculating higher-order derivatives, where the complexity and errors due to numerical precision increase. Automatic differentiation largely avoids the problems of numerical and symbolic differentiation. In this paper, we describe the implementation of automatic differentiation techniques in ROOT, which is the data analysis framework broadly used High-Energy Physics BIBREF3. This implementation is based on Clad BIBREF4, BIBREF5, which is an automatic differentiation plugin for computation expressed in C/C++. ### Background
Here, we briefly discuss main algorithmic and implementation principles behind AD. An in-depth overview and more formal description can be found in BIBREF1 and BIBREF2, respectively. ### Background ::: AD and its Modes
AD is based on the decomposition of the procedure (e.g. a source code that computes the original function) into a sequence of simple mathematical operations (e.g. $+, -, *, /, \sin , \cos , \exp $) that can be expressed using a series of intermediate results. Subsequently, derivatives of every intermediate result are evaluated and combined via the chain rule of calculus to obtain the derivatives of the whole sequence. The control flow (e.g. branches, loops) can be incorporated by differentiating the control flow of the original function during the derivative evaluation. Two main modes of AD, which differ in the order of application of the chain rule, are used: Forward mode operates in a top-down approach and computes the derivative of every intermediate result with respect to a single selected input variable of the function. As soon as a final result of the function is reached, the partial derivative with respect to the selected input is available. A single evaluation of the forward mode can only compute partial derivatives with respect to a single input variable. Thus, when the whole gradient is required, forward mode must be invoked once per every input variable, leading to $m \cdot c_{F} \cdot n$ runtime complexity, where $m$ is the number of input variables, $n$ is the algorithmic complexity of the original function and $c_{F} < 3 $ is a small constant factor overhead of a single invocation of the forward mode BIBREF2. Reverse mode operates in a bottom-up approach and computes the derivative of a function's output with respect to every intermediate result. Once every input variable of the function is reached, the whole gradient of an output is available. Note that, independently on the number of input variables $N$, a single evaluation of the reverse mode is sufficient to get the whole gradient of a function's output, leading to $c_{R} \cdot n$ runtime complexity, where $n$ is the complexity of the original function and $c_{R} \le 4$ is a small constant factor overhead BIBREF2. This is a huge advantage in settings with a single scalar output and many inputs, which is often the case in machine-learning problems where $N >> 10^6$ that makes the forward mode infeasible. As a disadvantage, reverse mode implementations are more complicated, and dynamic memory allocations may be required when dynamic control flow is involved. Depending on the original function, this may cause a single evaluation of the reverse mode to be somewhat slower compared to a single evaluation of the forward mode. ### Background ::: AD Implementations
AD techniques have been implemented in a variety of programming languages and paradigms, ranging from classical tools for Fortran BIBREF6 and C BIBREF7, to recent active work on tools specific to machine-learning applications BIBREF8, BIBREF9, and modern general-purpose programming languages BIBREF10, BIBREF11. We refer the reader to www.autodiff.org for a comprehensive list of available AD implementations for various languages. In particular, several implementations exist for C++, e.g. BIBREF12, BIBREF13, BIBREF14. Majority of implementations of AD fall into one of the two categories of implementation techniques: Tools based on operator overloading utilize features of programming languages like C++ and Python to define custom types and overload mathematical operators (e.g. +, -, *, /) and functions (e.g. $\exp , \sin , \cos $) on them. Such implementations are often based on custom AD-enabled types that wrap values of both the original and derivative functions and redefine operators to simultaneously act on original and derivative values. In C++, such tools are often implemented as a library that introduces templated differentiable types and corresponding mathematical operations. Then, functions called on the custom type return both original and derivative values. This is a powerful technique but has two primary limitations: legacy code and performance. Functions must be either polymorphic (templated) or explicitly defined on AD-enabled type to be differentiated. Differentiation of pre-existing source code using builtin types such as double and float is not possible. Users are required to use additional level of abstraction in the form of library-specific types instead of first-class language features. Moreover, the performance of the derivative generation can be suboptimal due to the C++ metaprogramming system which usually constructs deep template instantiation chains. Performance can be even more problematic when creating a higher order derivatives. Tools based on source transformation analyze the source code of the original function and build another source code for the derivative function. Such techniques typically accept and generate any code using built-in features of the original language and do not require custom libraries. On the other hand, they require an additional pass over the source file to analyze and generate derivative code. Source transformation can fully utilize source-level optimizations and has reasonably good performance. Implementation is more complicated and it is problematic to achieve full coverage of C++ language features. While full integration with a compiler can make AD a first-class language feature that is transparent for the user, most current implementations for C++ are based on custom parsers that do not have full coverage of the vast variety of C++ language constructs and require a separate step before compilation. ### Architecture and Implementation
Automatic differentiation in ROOT is based on Clad BIBREF4, BIBREF5. Clad is a source transformation AD tool for C++. It is based on LLVM compiler infrastructure BIBREF15 and is implemented as a plugin for C++ compiler Clang, which allows Clad to be transparently integrated into the compilation phase and to utilize large parts of the compiler. Clad relies on Clang's parsing and code generation functionality and can differentiate complicated C++ constructs. Clad supports both forward and reverse mode. It is available as a standalone Clang plugin that, when attached to the compiler, produces derivatives in the compilation phase. On top of that, Clad is integrated directly into ROOT to provide AD functionality as an integral part of the framework. ROOT has a C++ interpreter Cling BIBREF16 which is built on the top of LLVM and Clang. This allows Clad to be attached to Cling as a plugin in a similar way as it can be attached to Clang. In this section, we discuss 1) architecture of Clad and its interaction with Cling; and 2) details of its integration into ROOT. Clad operates on Clang AST (abstract syntax tree) by analyzing the AST of the original function and generating the AST of the derivative. Clad provides two API functions: clad::differentiate for forward mode and clad::gradient for reverse mode, which can be used directly in the source code to mark a function for differentiation (see BIBREF5 for more details on usage and code examples). The information flow of interactions with Cling during differentiation (Figure FIGREF13) is: A function is marked for differentiation with the C++ construct clad::differentiate or clad::gradient (step 1). Cling in ROOT performs incremental compilation and receives an abstract syntax tree (AST) representation of the code (step 2). Cling detects the differentiation marker and sends the AST of the original function to Clad, which transforms the AST to produce the AST of the derivative (step 3). Clad returns the derivative AST to Cling for code generation and execution by the low level LLVM primitives (steps 4, 5, 6, 7). Alternatively, if Clad was configured for non-interactive use, the generated AST can be converted to a C++ source code and written to a text file. The generated code then can be compiled with any C++ compiler (steps 8, 9). Inside of ROOT, interface functions clad::differentiate and clad::gradient are accessible via include <Math/CladDerivator.h>. Clad is also directly integrated into the TFormula class that encapsulates the concept of multidimensional mathematical functions in ROOT. TFormula is a primitive in ROOT's math package which is connected to the Cling interpreter. In the context of TFormula, Clad can differentiate functions available in the interpreter. The TFormula::GenerateGradientPar method uses Clad to differentiate the underlying code of the formula with respect to its parameters and generate the code for the gradient. TFormula::GradientPar method then evaluates the gradient at a specified point. ### Results
In this section, we empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) in ROOT. We show that AD can drastically improve accuracy and performance of derivative evaluation, compared to ND. ### Results ::: Accuracy
As stated in Section SECREF1, numerical differentiation may give imprecise results while AD computes the derivatives exactly. We show an example of a function where this difference is apparent: AD provides exact result while ND suffers from the loss of accuracy. 2 The function is the PDF of Breit-Wigner distribution (Eq. DISPLAY_FORM19), whose derivative with respect to $\Gamma $ (Eq. DISPLAY_FORM20) has critical points at $\Gamma =\pm {2x}$. In ROOT, the function is implemented as in (Listing SECREF18). linenos=false inline double breitwignerpdf(double x, double gamma, double x0 = 0) double gammahalf = gamma/2.0; return gammahalf/(MPI * ((x-x0)*(x-x0) + gammahalf*gammahalf)); listingBreit-Wigner PDF implementation in ROOT When evaluating the derivative of breitwignerpdf with respect to gamma at x=1, gamma=2, ND in ROOT the yields a result close to 0 with an absolute error of $10^{-13}$ despite the fact that the function is smooth and well-conditioned at this point. The approximation error becomes larger when the derivative is evaluated further from the critical point. In contrast, the automatic differentiation (in both modes) yields the exact result of 0. ### Results ::: Performance
Section SECREF2 showed that reverse mode AD computes gradients in a single pass with a runtime complexity of at most $4 \cdot n$, which depends only on the complexity $n$ and not the dimensionality $dim$ of the original function. On the other hand, numerical differentiation requires a separate evaluation of the original function for every dimension to compute the entire gradient, making the overall the run-time complexity of gradient evaluation via central finite difference method $2 \cdot dim \cdot n$. Hence, in theory, reverse mode achieves an asymptotic speedup of $O(dim)$ over the numerical differentiation and can be up to $dim / 2$ times faster. We experimentally verify this by comparing the performance of gradient evaluation produced by reverse mode AD against our an implementation of numerical differentiation via the central finite difference method. We use the two functions in Listing SECREF21: sum, which computes the sum of all values in a vector; and mvn, which implements the PDF of a multivariate normal distribution. Both functions have a parameter dim which defines the dimension, and gradients are taken with respect to dim-dimensional vector p. While closed-form expressions of these gradients are well-known, these functions make a good basis of a benchmark as they perform typical operations that are commonly found inside more complicated functions (e.g. +, *, pow, exp inside loop). linenos=false double sum(double* p, int dim) double r = 0.0; for (int i = 0; i < dim; i++) r += p[i]; return r; linenos=false double mvn(double* x, double* p /*means*/, double sigma, int dim) double t = 0; for (int i = 0; i < dim; i++) t += (x[i] - p[i])*(x[i] - p[i]); t = -t / (2*sigma*sigma); return std::pow(2*MPI, -n/2.0) * std::pow(sigma, -0.5) * std::exp(t); listingImplementations of sum and mvn functions Gradients of sum produced by numerical differentiation and Clad are shown in Listing SECREF21. linenos=false double* sumnumgrad(double* p, int dim, double eps = 1e-8) double result = new double[dim]; for (int i = 0; i < dim; i++) double pi = p[i]; p[i] = pi + eps; double v1 = sum(p, dim); p[i] = pi - eps; double v2 = sum(p, dim); result[i] = (v1 - v2)/(2 * eps); p[i] = pi; return result; linenos=false void sumadgrad(double *p, int dim, double *result) double dr = 0; unsigned long t0; int di = 0; clad::tape<int> t1 = ; double r = 0.; t0 = 0; for (int i = 0; i < dim; i++) t0++; r += p[clad::push(t1, i)]; double sumreturn = r; dr += 1; for (; t0; t0–) double rd0 = dr; dr += rd0; result[clad::pop(t1)] += rd0; dr -= rd0; listingGradient of sum: (left) using finite differences, (right) generated by Clad We perform the evaluation for values of dim between 5 and 20480. Figure FIGREF22 shows the comparison for (a) sum; (b) mvn and confirms the expected theoretical speedup of $O(dim)$, with AD-generated gradient being $~dim/4$ times faster for sum and $~dim/25$ times faster for mvn (slowdown is due to more expensive operations like pow, exp). ### Results ::: Performance in TFormula
Figure FIGREF26 shows the performance comparisons of reverse-mode AD and ND for the task of evaluating gradients of TFormula's builtin primitive probability density functions. The functions are gaus ($dim=3$), expo ($dim=2$), crystalball ($dim=5$), breitwigner ($dim=5$) and cheb2 ($dim=4$). Despite the low dimensionality ($dim \le 5$), AD gives significant (approx. 10x) speedups. The speedups are even larger than expected factor of $dim/2$ that follows from theoretical results, apparently due to additional overhead of the implementation of numerical differentiation in ROOT, which tries to find the optimal step size for its finite difference method to improve accuracy. In Figure FIGREF26, we perform fitting of a Gaussian distribution to a histogram of random samples via gradient-based optimization. In ROOT, this functionality is implemented in TFormula-based TF1 class. We can therefore use AD due to the integration of Clad into TFormula. Figure FIGREF26 compares the performance of the AD-based TF1 fitting with the numerical fitting in the Hist package. As in previous experiments, we show that AD scales better with problem dimensionality (number of histogram bins) on this task. The integration of Clad into TFormula makes it straightforward to use AD for fitting in ROOT. ### Conclusion
We discussed our implementation of automatic differentiation in ROOT based on Clad. We demonstrated that Clad is integrated into ROOT and can be easily used in various contexts inside ROOT (e.g. histogram fitting). Furthermore, we showed that automatic differentiation in ROOT achieves significant improvements in accuracy and performance over numerical differentiation. The performance and accuracy are promising and encourage further work in the development of Clad and its integration in ROOT. Possible further improvements for Clad include optimizations to code transformation and design of a consistent interface for derivatives and gradients computation. This functionality can be further extended, including the computation of Jacobians and higher-order derivatives. In order to achieve optimal performance, the evaluation of individual derivatives could be executed in parallel. Besides, the Clad API should enable a flexible execution method based on the needs of its user. ### Acknowledgments
This work has been supported by U.S. NSF grants PHY-1450377 and 1450323. Figure 1: Information flow of Clad in ROOT Figure 2: Comparison of reverse mode AD and ND with increasing dimension Figure 3: Performance benchmarks in ROOT | empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) |
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