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Why is Billy so drawn to Grannie Annie?
A. She knows about the Green Flame and Billy wants to know more about them.
B. Her writing wows him.
C. She's a famous author. He's naturally drawn to that fame.
D. She's an eccentric adventurer at heart, and compelling.
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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."
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D. She's an eccentric adventurer at heart, and compelling.
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What are the baselines?
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### Introduction
Semantic parsers map sentences onto logical forms that can be used to query databases BIBREF0 , BIBREF1 , instruct robots BIBREF2 , extract information BIBREF3 , or describe visual scenes BIBREF4 . In this paper we consider the problem of semantically parsing questions into Freebase logical forms for the goal of question answering. Current systems accomplish this by learning task-specific grammars BIBREF5 , strongly-typed CCG grammars BIBREF6 , BIBREF7 , or neural networks without requiring any grammar BIBREF8 . These methods are sensitive to the words used in a question and their word order, making them vulnerable to unseen words and phrases. Furthermore, mismatch between natural language and Freebase makes the problem even harder. For example, Freebase expresses the fact that “Czech is the official language of Czech Republic” (encoded as a graph), whereas to answer a question like “What do people in Czech Republic speak?” one should infer people in Czech Republic refers to Czech Republic and What refers to the language and speak refers to the predicate official language. We address the above problems by using paraphrases of the original question. Paraphrasing has shown to be promising for semantic parsing BIBREF9 , BIBREF10 , BIBREF11 . We propose a novel framework for paraphrasing using latent-variable PCFGs (L-PCFGs). Earlier approaches to paraphrasing used phrase-based machine translation for text-based QA BIBREF12 , BIBREF13 , or hand annotated grammars for KB-based QA BIBREF10 . We find that phrase-based statistical machine translation (MT) approaches mainly produce lexical paraphrases without much syntactic diversity, whereas our grammar-based approach is capable of producing both lexically and syntactically diverse paraphrases. Unlike MT based approaches, our system does not require aligned parallel paraphrase corpora. In addition we do not require hand annotated grammars for paraphrase generation but instead learn the grammar directly from a large scale question corpus. The main contributions of this paper are two fold. First, we present an algorithm (§ "Paraphrase Generation Using Grammars" ) to generate paraphrases using latent-variable PCFGs. We use the spectral method of narayan-15 to estimate L-PCFGs on a large scale question treebank. Our grammar model leads to a robust and an efficient system for paraphrase generation in open-domain question answering. While CFGs have been explored for paraphrasing using bilingual parallel corpus BIBREF14 , ours is the first implementation of CFG that uses only monolingual data. Second, we show that generated paraphrases can be used to improve semantic parsing of questions into Freebase logical forms (§ "Semantic Parsing using Paraphrasing" ). We build on a strong baseline of reddylargescale2014 and show that our grammar model competes with MT baseline even without using any parallel paraphrase resources. ### Paraphrase Generation Using Grammars
Our paraphrase generation algorithm is based on a model in the form of an L-PCFG. L-PCFGs are PCFGs where the nonterminals are refined with latent states that provide some contextual information about each node in a given derivation. L-PCFGs have been used in various ways, most commonly for syntactic parsing BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 . In our estimation of L-PCFGs, we use the spectral method of narayan-15, instead of using EM, as has been used in the past by matsuzaki-2005 and petrov-2006. The spectral method we use enables the choice of a set of feature functions that indicate the latent states, which proves to be useful in our case. It also leads to sparse grammar estimates and compact models. The spectral method works by identifying feature functions for “inside” and “outside” trees, and then clusters them into latent states. Then it follows with a maximum likelihood estimation step, that assumes the latent states are represented by clusters obtained through the feature function clustering. For more details about these constructions, we refer the reader to cohen-13 and narayan-15. The rest of this section describes our paraphrase generation algorithm. ### Paraphrases Generation Algorithm
We define our paraphrase generation task as a sampling problem from an L-PCFG $G_{\mathrm {syn}}$ , which is estimated from a large corpus of parsed questions. Once this grammar is estimated, our algorithm follows a pipeline with two major steps. We first build a word lattice $W_q$ for the input question $q$ . We use the lattice to constrain our paraphrases to a specific choice of words and phrases that can be used. Once this lattice is created, a grammar $G_{\mathrm {syn}}^{\prime }$ is then extracted from $G_{\mathrm {syn}}$ . This grammar is constrained to the lattice. We experiment with three ways of constructing word lattices: naïve word lattices representing the words from the input question only, word lattices constructed with the Paraphrase Database BIBREF14 and word lattices constructed with a bi-layered L-PCFG, described in § "Bi-Layered L-PCFGs" . For example, Figure 1 shows an example word lattice for the question What language do people in Czech Republic speak? using the lexical and phrasal rules from the PPDB. Once $G_{\mathrm {syn}}^{\prime }$ is generated, we sample paraphrases of the input question $q$ . These paraphrases are further filtered with a classifier to improve the precision of the generated paraphrases. We train the L-PCFG $G_{\mathrm {syn}}$ on the Paralex corpus BIBREF9 . Paralex is a large monolingual parallel corpus, containing 18 million pairs of question paraphrases with 2.4M distinct questions in the corpus. It is suitable for our task of generating paraphrases since its large scale makes our model robust for open-domain questions. We construct a treebank by parsing 2.4M distinct questions from Paralex using the BLLIP parser BIBREF25 . Given the treebank, we use the spectral algorithm of narayan-15 to learn an L-PCFG for constituency parsing to learn $G_{\mathrm {syn}}$ . We follow narayan-15 and use the same feature functions for the inside and outside trees as they use, capturing contextual syntactic information about nonterminals. We refer the reader to narayan-15 for more detailed description of these features. In our experiments, we set the number of latent states to 24. Once we estimate $G_{\mathrm {syn}}$ from the Paralex corpus, we restrict it for each question to a grammar $G_{\mathrm {syn}}^{\prime }$ by keeping only the rules that could lead to a derivation over the lattice. This step is similar to lexical pruning in standard grammar-based generation process to avoid an intermediate derivation which can never lead to a successful derivation BIBREF26 , BIBREF27 . Sampling a question from the grammar $G_{\mathrm {syn}}^{\prime }$ is done by recursively sampling nodes in the derivation tree, together with their latent states, in a top-down breadth-first fashion. Sampling from the pruned grammar $G_{\mathrm {syn}}^{\prime }$ raises an issue of oversampling words that are more frequent in the training data. To lessen this problem, we follow a controlled sampling approach where sampling is guided by the word lattice $W_q$ . Once a word $w$ from a path $e$ in $W_q$ is sampled, all other parallel or conflicting paths to $e$ are removed from $W_q$ . For example, generating for the word lattice in Figure 1 , when we sample the word citizens, we drop out the paths “human beings”, “people's”, “the population”, “people” and “members of the public” from $W_q$ and accordingly update the grammar. The controlled sampling ensures that each sampled question uses words from a single start-to-end path in $W_q$ . For example, we could sample a question what is Czech Republic 's language? by sampling words from the path (what, language, do, people 's, in, Czech, Republic, is speaking, ?) in Figure 1 . We repeat this sampling process to generate multiple potential paraphrases. The resulting generation algorithm has multiple advantages over existing grammar generation methods. First, the sampling from an L-PCFG grammar lessens the lexical ambiguity problem evident in lexicalized grammars such as tree adjoining grammars BIBREF27 and combinatory categorial grammars BIBREF28 . Our grammar is not lexicalized, only unary context-free rules are lexicalized. Second, the top-down sampling restricts the combinatorics inherent to bottom-up search BIBREF29 . Third, we do not restrict the generation by the order information in the input. The lack of order information in the input often raises the high combinatorics in lexicalist approaches BIBREF30 . In our case, however, we use sampling to reduce this problem, and it allows us to produce syntactically diverse questions. And fourth, we impose no constraints on the grammar thereby making it easier to maintain bi-directional (recursive) grammars that can be used both for parsing and for generation BIBREF31 . ### Bi-Layered L-PCFGs
As mentioned earlier, one of our lattice types is based on bi-layered PCFGs introduced here. In their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions. To create the bi-layered L-PCFG, we again use the spectral algorithm of narayan-15 to estimate a grammar $G_{\mathrm {par}}$ from the Paralex corpus. We use the word alignment of paraphrase question pairs in Paralex to map inside and outside trees of each nonterminals in the treebank to bag of word features. The number of latent states we use is 1,000. Once the two feature functions (syntactic in $G_{\mathrm {syn}}$ and semantic in $G_{\mathrm {par}}$ ) are created, each nonterminal in the training treebank is assigned two latent states (cluster identifiers). Figure 2 shows an example annotation of trees for three paraphrase questions from the Paralex corpus. We compute the parameters of the bi-layered L-PCFG $G_{\mathrm {layered}}$ with a simple frequency count maximum likelihood estimate over this annotated treebank. As such, $G_{\mathrm {layered}}$ is a combination of $G_{\mathrm {syn}}$ and $G_{\mathrm {par}}$ , resulting in 24,000 latent states (24 syntactic x 1000 semantic). Consider an example where we want to generate paraphrases for the question what day is nochebuena. Parsing it with $G_{\mathrm {layered}}$ will lead to the leftmost hybrid structure as shown in Figure 2 . The assignment of the first latent states for each nonterminals ensures that we retrieve the correct syntactic representation of the sentence. Here, however, we are more interested in the second latent states assigned to each nonterminals which capture the paraphrase information of the sentence at various levels. For example, we have a unary lexical rule (NN-*-142 day) indicating that we observe day with NN of the paraphrase type 142. We could use this information to extract unary rules of the form (NN-*-142 $w$ ) in the treebank that will generate words $w$ which are paraphrases to day. Similarly, any node WHNP-*-291 in the treebank will generate paraphrases for what day, SBARQ-*-403, for what day is nochebuena. This way we will be able to generate paraphrases when is nochebuena and when is nochebuena celebrated as they both have SBARQ-*-403 as their roots. To generate a word lattice $W_q$ for a given question $q$ , we parse $q$ with the bi-layered grammar $G_{\mathrm {layered}}$ . For each rule of the form $X$ - $m_1$ - $m_2 \rightarrow w$ in the bi-layered tree with $X \in {\cal P}$ , $m_1 \in \lbrace 1, \ldots , 24 \rbrace $ , $m_2 \in \lbrace 1, \ldots , 1000 \rbrace $ and $q$0 a word in $q$1 , we extract rules of the form $q$2 - $q$3 - $q$4 from $q$5 such that $q$6 . For each such $q$7 , we add a path $q$8 parallel to $q$9 in the word lattice. ### Paraphrase Classification
Our sampling algorithm overgenerates paraphrases which are incorrect. To improve its precision, we build a binary classifier to filter the generated paraphrases. We randomly select 100 distinct questions from the Paralex corpus and generate paraphrases using our generation algorithm with various lattice settings. We randomly select 1,000 pairs of input-sampled sentences and manually annotate them as “correct” or “incorrect” paraphrases. We train our classifier on this manually created training data. We follow madnani2012, who used MT metrics for paraphrase identification, and experiment with 8 MT metrics as features for our binary classifier. In addition, we experiment with a binary feature which checks if the sampled paraphrase preserves named entities from the input sentence. We use WEKA BIBREF32 to replicate the classifier of madnani2012 with our new feature. We tune the feature set for our classifier on the development data. ### Semantic Parsing using Paraphrasing
In this section we describe how the paraphrase algorithm is used for converting natural language to Freebase queries. Following reddylargescale2014, we formalize the semantic parsing problem as a graph matching problem, i.e., finding the Freebase subgraph (grounded graph) that is isomorphic to the input question semantic structure (ungrounded graph). This formulation has a major limitation that can be alleviated by using our paraphrase generation algorithm. Consider the question What language do people in Czech Republic speak?. The ungrounded graph corresponding to this question is shown in Figure 3 . The Freebase grounded graph which results in correct answer is shown in Figure 3 . Note that these two graphs are non-isomorphic making it impossible to derive the correct grounding from the ungrounded graph. In fact, at least 15% of the examples in our development set fail to satisfy isomorphic assumption. In order to address this problem, we use paraphrases of the input question to generate additional ungrounded graphs, with the aim that one of those paraphrases will have a structure isomorphic to the correct grounding. Figure 3 and Figure 3 are two such paraphrases which can be converted to Figure 3 as described in sec:groundedGraphs. For a given input question, first we build ungrounded graphs from its paraphrases. We convert these graphs to Freebase graphs. To learn this mapping, we rely on manually assembled question-answer pairs. For each training question, we first find the set of oracle grounded graphs—Freebase subgraphs which when executed yield the correct answer—derivable from the question's ungrounded graphs. These oracle graphs are then used to train a structured perceptron model. These steps are discussed in detail below. ### Ungrounded Graphs from Paraphrases
We use GraphParser BIBREF7 to convert paraphrases to ungrounded graphs. This conversion involves three steps: 1) parsing the paraphrase using a CCG parser to extract syntactic derivations BIBREF33 , 2) extracting logical forms from the CCG derivations BIBREF34 , and 3) converting the logical forms to an ungrounded graph. The ungrounded graph for the example question and its paraphrases are shown in Figure 3 , Figure 3 and Figure 3 , respectively. ### Grounded Graphs from Ungrounded Graphs
The ungrounded graphs are grounded to Freebase subgraphs by mapping entity nodes, entity-entity edges and entity type nodes in the ungrounded graph to Freebase entities, relations and types, respectively. For example, the graph in Figure 3 can be converted to a Freebase graph in Figure 3 by replacing the entity node Czech Republic with the Freebase entity CzechRepublic, the edge (speak.arg $_2$ , speak.in) between $x$ and Czech Republic with the Freebase relation (location.country.official_language.2, location.country.official_language.1), the type node language with the Freebase type language.human_language, and the target node remains intact. The rest of the nodes, edges and types are grounded to null. In a similar fashion, Figure 3 can be grounded to Figure 3 , but not Figure 3 to Figure 3 . If no paraphrase is isomorphic to the target grounded grounded graph, our grounding fails. ### Learning
We use a linear model to map ungrounded graphs to grounded ones. The parameters of the model are learned from question-answer pairs. For example, the question What language do people in Czech Republic speak? paired with its answer $\lbrace \textsc {CzechLanguage}\rbrace $ . In line with most work on question answering against Freebase, we do not rely on annotated logical forms associated with the question for training and treat the mapping of a question to its grounded graph as latent. Let $q$ be a question, let $p$ be a paraphrase, let $u$ be an ungrounded graph for $p$ , and let $g$ be a grounded graph formed by grounding the nodes and edges of $u$ to the knowledge base $\mathcal {K}$ (throughout we use Freebase as the knowledge base). Following reddylargescale2014, we use beam search to find the highest scoring tuple of paraphrase, ungrounded and grounded graphs $(\hat{p}, \hat{u}, \hat{g})$ under the model $\theta \in \mathbb {R}^n$ : $
({\hat{p},\hat{u},\hat{g}}) = \operatornamewithlimits{arg\,max}_{(p,u,g)} \theta \cdot \Phi (p,u,g,q,\mathcal {K})\,,
$ where $\Phi (p, u, g, q, \mathcal {K}) \in \mathbb {R}^n$ denotes the features for the tuple of paraphrase, ungrounded and grounded graphs. The feature function has access to the paraphrase, ungrounded and grounded graphs, the original question, as well as to the content of the knowledge base and the denotation $|g|_\mathcal {K}$ (the denotation of a grounded graph is defined as the set of entities or attributes reachable at its target node). See sec:details for the features employed. The model parameters are estimated with the averaged structured perceptron BIBREF35 . Given a training question-answer pair $(q,\mathcal {A})$ , the update is: $
\theta ^{t+1} \leftarrow \theta ^{t} + \Phi (p^+, u^+, g^+, q,
\mathcal {K}) - \Phi (\hat{p}, \hat{u}, \hat{g}, q, \mathcal {K})\,,
$ where $({p^+,u^+,g^+})$ denotes the tuple of gold paraphrase, gold ungrounded and grounded graphs for $q$ . Since we do not have direct access to the gold paraphrase and graphs, we instead rely on the set of oracle tuples, $\mathcal {O}_{\mathcal {K}, \mathcal {A}}(q)$ , as a proxy: $
(p^{+},u^{+},{g^{+}}) = \operatornamewithlimits{arg\,max}_{(p,u,g) \in \mathcal {O}_{\mathcal {K},\mathcal {A}}(q)} \theta \cdot \Phi ({p,u,g,q,\mathcal {K}})\,,
$ where $\mathcal {O}_{\mathcal {K}, \mathcal {A}}(q)$ is defined as the set of tuples ( $p$ , $u$ , $g$ ) derivable from the question $q$ , whose denotation $|g|_\mathcal {K}$ has minimal $F_1$ -loss against the gold answer $\mathcal {A}$ . We find the oracle graphs for each question a priori by performing beam-search with a very large beam. ### Experimental Setup
Below, we give details on the evaluation dataset and baselines used for comparison. We also describe the model features and provide implementation details. ### Evaluation Data and Metric
We evaluate our approach on the WebQuestions dataset BIBREF5 . WebQuestions consists of 5,810 question-answer pairs where questions represents real Google search queries. We use the standard train/test splits, with 3,778 train and 2,032 test questions. For our development experiments we tune the models on held-out data consisting of 30% training questions, while for final testing we use the complete training data. We use average precision (avg P.), average recall (avg R.) and average F $_1$ (avg F $_1$ ) proposed by berantsemantic2013 as evaluation metrics. ### Baselines
We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases. We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions. ### Implementation Details
For WebQuestions, we use 8 handcrafted part-of-speech patterns (e.g., the pattern (DT)?(JJ.? $\mid $ NN.?){0,2}NN.? matches the noun phrase the big lebowski) to identify candidate named entity mention spans. We use the Stanford CoreNLP caseless tagger for part-of-speech tagging BIBREF38 . For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. We generate ungrounded graphs for these paraphrases and treat the final entity disambiguation and paraphrase selection as part of the semantic parsing problem. We use the features from reddylargescale2014. These include edge alignments and stem overlaps between ungrounded and grounded graphs, and contextual features such as word and grounded relation pairs. In addition to these features, we add two new real-valued features – the paraphrase classifier's score and the entity disambiguation lattice score. We use beam search to infer the highest scoring graph pair for a question. The search operates over entity-entity edges and entity type nodes of each ungrounded graph. For an entity-entity edge, there are two operations: ground the edge to a Freebase relation, or skip the edge. Similarly, for an entity type node, there are two operations: ground the node to a Freebase type, or skip the node. We use a beam size of 100 in all our experiments. ### Results and Discussion
In this section, we present results from five different systems for our question-answering experiments: original, mt, naive, ppdb and bilayered. First two are baseline systems. Other three systems use paraphrases generated from an L-PCFG grammar. naive uses a word lattice with a single start-to-end path representing the input question itself, ppdb uses a word lattice constructed using the PPDB rules, and bilayered uses bi-layered L-PCFG to build word lattices. Note that naive does not require any parallel resource to train, ppdb requires an external paraphrase database, and bilayered, like mt, needs a parallel corpus with paraphrase pairs. We tune our classifier features and GraphParser features on the development data. We use the best setting from tuning for evaluation on the test data. ### Conclusion
We described a grammar method to generate paraphrases for questions, and applied it to a question answering system based on semantic parsing. We showed that using paraphrases for a question answering system is a useful way to improve its performance. Our method is rather generic and can be applied to any question answering system. ### Acknowledgements
The authors would like to thank Nitin Madnani for his help with the implementation of the paraphrase classifier. We would like to thank our anonymous reviewers for their insightful comments. This research was supported by an EPSRC grant (EP/L02411X/1), the H2020 project SUMMA (under grant agreement 688139), and a Google PhD Fellowship for the second author. Figure 1: An example word lattice for the question What language do people in Czech Republic speak? using the lexical and phrasal rules from the PPDB. Figure 2: Trees used for bi-layered L-PCFG training. The questions what day is nochebuena, when is nochebuena and when is nochebuena celebrated are paraphrases from the Paralex corpus. Each nonterminal is decorated with a syntactic label and two identifiers, e.g., for WP-7-254, WP is the syntactic label assigned by the BLLIP parser, 7 is the syntactic latent state, and 254 is the semantic latent state. Figure 3: Ungrounded graphs for an input question and its paraphrases along with its correct grounded graph. The green squares indicate NL or Freebase entities, the yellow rectangles indicate unary NL predicates or Freebase types, the circles indicate NL or Freebase events, the edge labels indicate binary NL predicates or Freebase relations, and the red diamonds attach to the entity of interest (the answer to the question). Table 1: Oracle statistics and results on the WebQuestions development set. Table 2: Results on WebQuestions test dataset.
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GraphParser without paraphrases, monolingual machine translation based model for paraphrase generation
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What is likely to happen to the crew when they return to the planet?
A. They'll fall victim like those before them, and have their supplies stolen.
B. They'll return, still believing it's the Garden of Eden.
C. They'll learn the truth about the Old Serpent and Adam, and leave.
D. They'll carry through with their settlement plans and cash in.
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IT WAS A DULL, ROUTINE LITTLE WORLD. IT DIDN'T EVEN HAVE A CITY. EVERYTHING IT HAD WAS IN THE GARDEN BY R. A. LAFFERTY [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, March 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The protozoic recorder chirped like a bird. Not only would there be life traces on that little moon, but it would be a lively place. So they skipped several steps in the procedure. The chordata discerner read Positive over most of the surface. There was spinal fluid on that orb, rivers of it. So again they omitted several tests and went to the cognition scanner. Would it show Thought on the body? Naturally they did not get results at once, nor did they expect to; it required a fine adjustment. But they were disappointed that they found nothing for several hours as they hovered high over the rotation. Then it came—clearly and definitely, but from quite a small location only. "Limited," said Steiner, "as though within a pale. As though there were but one city, if that is its form. Shall we follow the rest of the surface to find another, or concentrate on this? It'll be twelve hours before it's back in our ken if we let it go now." "Let's lock on this one and finish the scan. Then we can do the rest of the world to make sure we've missed nothing," said Stark. There was one more test to run, one very tricky and difficult of analysis, that with the Extraordinary Perception Locator. This was designed simply to locate a source of superior thought. But this might be so varied or so unfamiliar that often both the machine and the designer of it were puzzled as to how to read the results. The E. P. Locator had been designed by Glaser. But when the Locator had refused to read Positive when turned on the inventor himself, bad blood developed between machine and man. Glaser knew that he had extraordinary perception. He was a much honored man in his field. He told the machine so heatedly. The machine replied, with such warmth that its relays chattered, that Glaser did not have extraordinary perception; he had only ordinary perception to an extraordinary degree. There is a difference , the machine insisted. It was for this reason that Glaser used that model no more, but built others more amenable. And it was for this reason also that the owners of Little Probe had acquired the original machine so cheaply. And there was no denying that the Extraordinary Perception Locator (or Eppel) was a contrary machine. On Earth it had read Positive on a number of crack-pots, including Waxey Sax, a jazz tootler who could not even read music. But it had also read Positive on ninety per cent of the acknowledged superior minds of the Earth. In space it had been a sound guide to the unusual intelligences encountered. Yet on Suzuki-Mi it had read Positive on a two-inch-long worm, only one of them out of billions. For the countless identical worms no trace of anything at all was shown by the test. So it was with mixed expectations that Steiner locked onto the area and got a flick. He then narrowed to a smaller area (apparently one individual, though this could not be certain) and got very definite action. Eppel was busy. The machine had a touch of the ham in it, and assumed an air of importance when it ran these tests. Finally it signaled the result, the most exasperating result it ever produces: the single orange light. It was the equivalent of the shrug of the shoulders in a man. They called it the "You tell me light." So among the intelligences there was at least one that might be extraordinary, though possibly in a crackpot way. It is good to be forewarned. "Scan the remainder of the world, Steiner," said Stark, "and the rest of us will get some sleep. If you find no other spot then we will go down on that one the next time it is in position under us, in about twelve hours." "You don't want to visit any of the other areas first? Somewhere away from the thoughtful creature?" "No. The rest of the world may be dangerous. There must be a reason that thought is in one spot only. If we find no others then we will go down boldly and visit this." So they all, except Steiner, went off to their bunks then: Stark, the Captain; Gregory Gilbert, the executive officer; Wolfgang Langweilig, the engineer; Casper Craig, super-cargo, tycoon and 51% owner of the Little Probe, and F. R. Briton, S.J., a Jesuit priest who was linguist and checker champion of the craft. Dawn did not come to the moon-town. The Little Probe hovered stationary in the light and the moon-town came up under the dawn. Then the Probe went down to visit whatever was there. "There's no town," said Steiner. "Not a building. Yet we're on the track of the minds. There's nothing but a meadow and some boscage, a sort of fountain or pool, and four streams coming out of it." "Keep on towards the minds," said Stark. "They're our target." "Not a building, not two sticks or stones placed together. That looks like an Earth-type sheep there. And that looks like an Earth-lion, I'm almost afraid to say. And those two ... why, they could well be Earth-people. But with a difference. Where is that bright light coming from?" "I don't know, but they're right in the middle of it. Land here. We'll go to meet them at once. Timidity has never been an efficacious tool with us." Well, they were people. And one could only wish that all people were like them. There was a man and a woman, and they were clothed either in very bright garments or in no garments at all, but only in a very bright light. "Talk to them, Father Briton," said Stark. "You are the linguist." "Howdy," said the priest. He may or may not have been understood, but the two of them smiled at him, so he went on. "Father Briton from Philadelphia," he said, "on detached service. And you, my good man, what is your handle, your monicker, your tag?" "Ha-Adamah," said the man. "And your daughter, or niece?" It may be that the shining man frowned momentarily at this; but the woman smiled, proving that she was human. "The woman is named Hawwah," said the man. "The sheep is named sheep, the lion is named lion, the horse is named horse and the hoolock is named hoolock." "I understand. It is possible that this could go on and on. How is it that you use the English tongue?" "I have only one tongue; but it is given to us to be understood by all; by the eagle, by the squirrel, by the ass, by the English." "We happen to be bloody Yankees, but we use a borrowed tongue. You wouldn't have a drink on you for a tubful of thirsty travellers, would you?" "The fountain." "Ah—I see." But the crew all drank of the fountain to be sociable. It was water, but water that excelled, cool and with all its original bubbles like the first water ever made. "What do you make of them?" asked Stark. "Human," said Steiner. "It may even be that they are a little more than human. I don't understand that light that surrounds them. And they seem to be clothed, as it were, in dignity." "And very little else," said Father Briton, "though that light trick does serve a purpose. But I'm not sure they'd pass in Philadelphia." "Talk to them again," said Stark. "You're the linguist." "That isn't necessary here, Captain. Talk to them yourself." "Are there any other people here?" Stark asked the man. "The two of us. Man and woman." "But are there any others?" "How would there be any others? What other kind of people could there be than man and woman?" "But is there more than one man or woman?" "How could there be more than one of anything?" The captain was a little puzzled by this, but he went on doggedly: "Ha-Adamah, what do you think that we are? Are we not people?" "You are not anything till I name you. But I will name you and then you can be. You are named Captain. He is named Priest. He is named Engineer. He is named Flunky." "Thanks a lot," said Steiner. "But are we not people?" persisted Captain Stark. "No. We are the people. There are no people but two. How could there be other people?" "And the damnest thing about it," muttered Langweilig, "is, how are you going to prove him wrong? But it does give you a small feeling." "Can we have something to eat?" asked the Captain. "Pick from the trees," said Ha-Adamah, "and then it may be that you will want to sleep on the grass. Being not of human nature (which does not need sleep or rest), it may be that you require respite. But you are free to enjoy the garden and its fruits." "We will," said Captain Stark. They wandered about the place, but they were uneasy. There were the animals. The lion and lioness were enough to make one cautious, though they offered no harm. The two bears had a puzzling look, as though they wanted either to frolic with you or to mangle you. "If there are only two people here," said Casper Craig, "then it may be that the rest of the world is not dangerous at all. It looked fertile wherever we scanned it, though not so fertile as this central bit. And those rocks would bear examining." "Flecked with gold, and possibly with something else," said Stark. "A very promising site." "And everything grows here," added Steiner. "Those are Earth-fruits and I never saw finer. I've tasted the grapes and plums and pears. The figs and dates are superb, the quince is as flavorsome as a quince can be, the cherries are excellent. And I never did taste such oranges. But I haven't yet tried the—" and he stopped. "If you're thinking what I'm afraid to think," said Gilbert, "then it will be the test at least: whether we're having a pleasant dream or whether this is reality. Go ahead and eat one." "I won't be the first to eat one. You eat." "Ask him first. You ask him." "Ha-Adamah, is it allowed to eat the apples?" "Certainly. Eat. It is the finest fruit in the garden." "Well, the analogy breaks down there," said Stark. "I was almost beginning to believe in the thing. But if it isn't that, then what. Father Briton, you are the linguist, but in Hebrew does not Ha-Adamah and Hawwah mean—?" "Of course they do. You know that as well as I." "I was never a believer. But would it be possible for the exact same proposition to maintain here as on Earth?" "All things are possible." And it was then that Ha-Adamah, the shining man, gave a wild cry: "No, no. Do not approach it. It is not allowed to eat of that one!" It was the pomegranate tree, and he was warning Langweilig away from it. "Once more, Father," said Stark, "you should be the authority; but does not the idea that it was the apple that was forbidden go back only to a medieval painting?" "It does. The name of the fruit is not mentioned in Genesis. In Hebrew exegesis, however, the pomegranate is usually indicated." "I thought so. Question the man further, Father. This is too incredible." "It is a little odd. Adam, old man, how long have you been here?" "Forever less six days is the answer that has been given to me. I never did understand the answer, however." "And have you gotten no older in all that time?" "I do not understand what 'older' is. I am as I have been from the beginning." "And do you think that you will ever die?" "To die I do not understand. I am taught that it is a property of fallen nature to die, and that does not pertain to me or mine." "And are you completely happy here?" "Perfectly happy according to my preternatural state. But I am taught that it might be possible to lose that happiness, and then to seek it vainly through all the ages. I am taught that sickness and ageing and even death could come if this happiness were ever lost. I am taught that on at least one other unfortunate world it has actually been lost." "Do you consider yourself a knowledgeable man?" "Yes, since I am the only man, and knowledge is natural to man. But I am further blessed. I have a preternatural intellect." Then Stark cut in once more: "There must be some one question you could ask him, Father. Some way to settle it. I am becoming nearly convinced." "Yes, there is a question that will settle it. Adam, old man, how about a game of checkers?" "This is hardly the time for clowning," said Stark. "I'm not clowning, Captain. How about it, Adam? I'll give you choice of colors and first move." "No. It would be no contest. I have a preternatural intellect." "Well, I beat a barber who was champion of Germantown. And I beat the champion of Morgan County, Tennessee, which is the hottest checker center on Earth. I've played against, and beaten, machines. But I never played a preternatural mind. Let's just set up the board, Adam, and have a go at it." "No. It would be no contest. I would not like to humble you." They were there for three days. They were delighted with the place. It was a world with everything, and it seemed to have only two inhabitants. They went everywhere except into the big cave. "What is there, Adam?" asked Captain Stark. "The great serpent lives there. I would not disturb him. He has long been cranky because plans he had for us did not materialize. But we are taught that should ever evil come to us, which it cannot if we persevere, it will come by him." They learned no more of the real nature of the sphere in their time there. Yet all but one of them were convinced of the reality when they left. And they talked of it as they took off. "A crowd would laugh if told of it," said Stark, "but not many would laugh if they had actually seen the place, or them. I am not a gullible man, but I am convinced of this: that this is a pristine and pure world and that ours and all the others we have visited are fallen worlds. Here are the prototypes of our first parents before their fall. They are garbed in light and innocence, and they have the happiness that we have been seeking for centuries. It would be a crime if anyone disturbed that happiness." "I too am convinced," said Steiner. "It is Paradise itself, where the lion lies down with the lamb, and where the serpent has not prevailed. It would be the darkest of crimes if we or others should play the part of the serpent, and intrude and spoil." "I am probably the most skeptical man in the world," said Casper Craig the tycoon, "but I do believe my eyes. I have been there and seen it. It is indeed an unspoiled Paradise; and it would be a crime calling to the wide heavens for vengeance for anyone to smirch in any way that perfection. "So much for that. Now to business. Gilbert, take a gram: Ninety Million Square Miles of Pristine Paradise for Sale or Lease. Farming, Ranching, exceptional opportunities for Horticulture. Gold, Silver, Iron, Earth-Type Fauna. Terms. Special Rates for Large Settlement Parties. Write, Gram, or call in person at any of our planetary offices as listed below. Ask for Brochure—Eden Acres Unlimited." Down in the great cave that Old Serpent, a two-legged one among whose names were "Snake-Oil Sam," spoke to his underlings: "It'll take them fourteen days to get back with the settlers. We'll have time to overhaul the blasters. We haven't had any well-equipped settlers for six weeks. It used to be we'd hardly have time to strip and slaughter and stow before there was another batch to take care of." "I think you'd better write me some new lines," said Adam. "I feel like a goof saying those same ones to each bunch." "You are a goof, and therefore perfect for the part. I was in show business long enough to know never to change a line too soon. I did change Adam and Eve to Ha-Adamah and Hawwah, and the apple to the pomegranate. People aren't becoming any smarter—but they are becoming better researched, and they insist on authenticity. "This is still a perfect come-on here. There is something in human nature that cannot resist the idea of a Perfect Paradise. Folks will whoop and holler to their neighbors to come in droves to spoil and mar it. It isn't greed or the desire for new land so much—though that is strong too. Mainly it is the feverish passion to befoul and poison what is unspoiled. Fortunately I am sagacious enough to take advantage of this trait. And when you start to farm a new world on a shoestring you have to acquire your equipment as you can." He looked proudly around at the great cave with its mountains and tiers of materials, heavy machinery of all sorts, titanic crates of foodstuff space-sealed; wheeled, tracked, propped, vaned and jetted vehicles; and power packs to run a world. He looked at the three dozen space ships stripped and stacked, and at the rather large pile of bone-meal in one corner. "We will have to have another lion," said Eve. "Bowser is getting old, and Marie-Yvette abuses him and gnaws his toes. And we do have to have a big-maned lion to lie down with the lamb." "I know it, Eve. The lion is a very important prop. Maybe one of the crackpot settlers will bring a new lion." "And can't you mix another kind of shining paint? This itches. It's hell." "I'm working on it." Casper Craig was still dictating the gram: "Amazing quality of longevity seemingly inherent in the locale. Climate ideal. Daylight or half-light. All twenty-one hours from Planet Delphina and from Sol. Pure water for all industrial purposes. Scenic and storied. Zoning and pre-settlement restrictions to insure congenial neighbors. A completely planned globular settlement in a near arm of our own galaxy. Low taxes and liberal credit. Financing our specialty—" "And you had better have an armed escort when you return," said Father Briton. "Why in cosmos would we want an armed escort?" "It's as phony as a seven-credit note!" "You, a man of the cloth doubt it? And us ready skeptics convinced by our senses? Why do you doubt?" "It is only the unbelieving who believe so easily in obvious frauds. Theologically unsound, dramaturgically weak, philologically impossible, zoologically rigged, salted conspicuously with gold and shot through with anachronisms. And moreover he was afraid to play me at checkers." "What?" "If I have a preternatural intellect I wouldn't be afraid of a game of checkers with anyone. Yet there was an unusual mind there somewhere; it was just that he chose not to make our acquaintance personally." "They looked at the priest thoughtfully. "But it was Paradise in one way," said Steiner at last. "How?" "All the time we were there the woman did not speak."
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A. They'll fall victim like those before them, and have their supplies stolen.
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Of the following options, what traits best describe Purnie?
A. Kind and Naive
B. Resourceful and prepared
C. Careful and brave
D. Cautious and dilligent
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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.
|
A. Kind and Naive
|
In relation to Mr. Doe's radiotherapy in 2015, how many Gy was each session?
Choose the correct answer from the following options:
A. 1 Gy
B. 2 Gy
C. 3 Gy
D. 4 Gy
E. 5 Gy
|
### Patient Report 0
**Dear colleague, ****Dear colleague, **
We are writing to provide an update regarding Mr. Paul Doe, born on
08/08/1965, who was treated in our clinic from 05/28/14 to 06/20/14.
**Diagnoses: **
- pT1, pN0 (0/21, ECE negative), cM0, Pn0, G2, RX, L0, V0, left
midline tongue carcinoma
- Arterial hypertension
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Idiopathic thrombocytopenia
- Non-insulin-dependent diabetes mellitus type II
- Hypothyroidism
- Nicotine abuse
<!-- -->
- Panendoscopy with sampling on 04/14/2014 and 04/26/2014
**Current Presentation**: With histologically confirmed carcinoma in the
region of the base of the tongue on the left side, Mr. Doe presents for
surgical treatment of the findings. In accordance with the tumor board
decision, resection is performed via a lateral pharyngotomy and neck
dissection on both sides.
**Physical Examination:** Patient in stable general condition (85 kg,
188 cm). MUST score: 0, pain NRS 8/10 intermittent (adjusted with
Acetaminophen) \| fatigue I°, dysphagia I° \| aspiration 0°, ulcer 0°,
trismus 0°, taste disturbance I°, xerostomia I°, osteonecrosis 0°,
hypothyroidism I° (L-thyroxine increased to 150 μg 1-0-0), hoarseness
0°, hearing loss 0° (subjectively reduced), dyspnea: 0°, pneumonitis 0°,
nausea/vomiting 0°.
No suspicious lymph nodes palpable \| movement restrictions 0°,
subcutaneous fibrosis: I°, hyperpigmentation: I° cervical, mucositis 0°,
lymphedema I° (lymphatic drainage prescribed), telangiectasia 0°.
A tumorous mass can be inspected at the base of the left tongue. Tongue
mobility is unremarkable.
**CT chest, abdomen, pelvis on 05/28/14:**
Emphasized mediastinal as well as abdominal lymph
nodes.** **Vasosclerosis. Otherwise, there is no evidence for the
presence of distant metastases with a suspected base of tongue
carcinoma. Liver cirrhosis.
**CT neck on 06/11/14:**
Suspected left tongue base carcinoma crossing midline with extension
into the left vallecula and compression of the left piriform sinus with
suspected lymph node metastases in levels I-III ipsilateral.
Contralateral prominent but not certainly suspicious lymph nodes. The
prominent structure on the left supraclavicular side can also be
interpreted as circumscribed cystiform ectasia of the thoracic duct.
**Ultrasound abdomen on 06/15/14:**
Image of liver cirrhosis status post cholecystectomy.
Hepatosplenomegaly. Moderate aortic sclerosis.
**X-ray pap swallow on 06/17/14:**
Clear tracheal aspiration in the absence of epiglottis envelope. The
cough reflex is preserved. Otherwise, essentially unremarkable
swallowing act.
**Histology**: Invasive, moderately differentiated, squamous cell
carcinoma with keratinization of the medial left base of the tongue,
maximum extent 1.0 cm. Carcinoma- and dysplasia-free biopsies of the
left tonsil, the oropharyngeal tumor/tongue base on the left, deep
resection of the tumor, lower tonsillar pole transition on the left
tongue base, tongue base on the left and medial left tongue base, as
well as median tongue base.
Metastasis-free lymph nodes in Neck-dissection Level IIa to IV on the
left (0/16), Neck-dissection Level IIb on the left (0/1), and
Neck-dissection Level II to IV on the right (0/3), occasionally with
lymphofollicular hyperplasia. Carcinoma-free bone of the left lateral
thigh of the hyoid.
**Final UICC classification:** pT1. pN0 (0/21). L0. V0. Pn0. G2. RX.
**Therapy and Progression**: After the usual clinical and laboratory
preparations, we performed the above-mentioned therapy on 05/29/14 in
intubation anesthesia without complications. For perioperative infection
prophylaxis, the patient received intravenous antibiotic therapy with
Ampicillin and Sulbactam 3g three times daily for the duration of his
hospital stay.
During this procedure, the left lingual artery was interrupted
prophylactically. No postoperative bleeding and no wound healing
disturbances occurred.
A porridge swallow examination showed no evidence of a fistula. On the
following day, the patient was decannulated in consultation with the
colleagues of the speech therapy. After this, a food build-up was
carried out in cooperation with speech therapists. At the time of
discharge, the patient was receiving regular oral nutrition. The stoma
continued to shrink. The patient was monitored, and if necessary, the
tracheostoma was closed with local anesthesia. Histological findings
were pT1. Due to an RX status, adjuvant radiotherapy will be performed
as decided by the tumor board. A prophylactic presentation at the
colleagues of the MKG as a preparatory measure for the upcoming
radiotherapy. We asked for a control re-presentation in our outpatient
clinic on 06/26/14 at 3:00 PM. Further controls take place at the half
and at the end of the radiotherapy and further in 4-6 weeks rhythm. In
case of acute complaints, an immediate re-presentation is possible at
any time.
**Type of surgery**: Lateral pharyngotomy with resection of the base of
the tongue on the left as well as selective neck dissection on both
sides level II-IV with ligature of the lingual artery on the left side,
creation of a stable tracheostoma and tonsillectomy on the left side.
**Surgery report: **First, tracheotomy in a typical manner. A horizontal
incision was made on the skin, positioned approximately two transverse
finger widths above the jugulum. Subsequently, the subcutaneous tissue
and the platysma colli were incised. To facilitate access to the
trachea, the laryngeal muscles were carefully displaced to the side. The
thyroid isthmus was undermined and clamped bilaterally. A precise
transection of the thyroid isthmus followed, with both halves of the
thyroid gland being meticulously sutured using 0- Vicryl. The thyroid
halves were repositioned to expose the trachea. A visceral tracheotomy
was performed, and re-intubation was achieved utilizing a U-tube. The
surgical procedure then transitioned to a neck dissection on the left
side. This phase began with an incision along the anterior edge of the
sternocleidomastoid muscle. The subcutaneous tissue and platysma colli
were carefully cut, with due respect to the auricularis magnus nerve.
Dissection continued dorsally along the sternocleidomastoid muscle to
reach the anterior border of the trapezius muscle. Further exposure
involved the accessorius nerve in a cranialward direction, with
preservation of this neural structure. Dissection proceeded along the
cervical vascular sheath, revealing the common carotid artery, internal
jugular vein, and vagus nerve up to the digastric muscle. Below this
level, exposure of the hypoglossal nerve was achieved.
Successive dissection involved the lymph node fat package, progressing
from level II to level IV in a cranial to caudal and ventral to dorsal
direction. Throughout this process, careful attention was paid to
sparing the aforementioned neural and vascular structures. Subsequently,
access to the lateral pharyngectomy area was gained, allowing
visualization of the external carotid artery along with its branches,
including the superior thyroid artery, superior laryngeal artery, and
lingual artery. Notably, the lingual artery was interrupted during this
stage.
Further exploration revealed the superior laryngeal nerve and
hypoglossal nerve intersecting in a loop above the internal carotid
artery and externally below the external jugular vein. Additional
dissection in a ventral direction followed. The hypoglossal nerve was
prepared meticulously. Exposure of the hyoid bone was achieved, with a
posterior resection of half of the hyoid bone. Importantly, the
hypoglossal nerve was spared during this procedure. Subsequent to these
steps, the lateral pharynx wall was opened, exposing the base of the
hyoid. The next phase of the procedure involved enoral tumor
tonsillectomy on the left side. Starting from the left side, the
surgical team identified the tonsil capsule at the anterior palatal arch
using a Henke spatula. The upper tonsillar pole was then dislodged and
dissected with the Rosenblatt instrument, proceeding from cranial to
caudal. Hemostasis was meticulously achieved through swab pressure and
electrocautery. The excised tonsil tissue was sent for frozen section
examination for further analysis.
**Frozen Section Report: **No evidence of malignancy was found. The
resection was carried out at the junction of the caudal tonsillar pole
and the base of the tongue. At this location, tissue from the base of
the tongue was resected and sent for a frozen section examination, which
revealed no indication of malignancy. Subsequently, a medial resection
of the base of the tongue was performed, confirming the presence of
squamous cell carcinoma in the frozen section analysis. Mucosal suturing
with inverting sutures was then conducted. On the left side, a neck
dissection procedure was performed. The dissection extended along the
sternocleidomastoid muscle, reaching dorsally to the anterior border of
the trapezius muscle. This approach allowed for cranial exposure of the
accessorius nerve while sparing the same. Dissection continued along the
cervical vascular sheath, exposing the common carotid artery, internal
jugular vein, and the vagus nerve up to the digastric muscle. Below
this, the hypoglossal nerve was exposed. Subsequently, the lymph node
fat package was dissected systematically from level II to level IV,
progressing from cranial to caudal and ventral to dorsal, while
carefully preserving the mentioned structures. The surgical procedure
concluded with the placement of a drain, subcutaneous suturing, and skin
suturing.
**Frozen section report:** Invasive squamous cell carcinoma.
**Microscopy:**
Even after paraffin embedding, mucosal cross-sections show a covering of
stratified, non-keratinizing squamous epithelium with occasional
significant stratification disturbances extending into superficial cell
layers. This transitions into invasive growth with solid clusters of
polygonal tumor cells, some of which exhibit identifiable intercellular
bridges. The cell nuclei are enlarged, round to oval, with occasional
small nucleoli and mild to moderate nuclear pleomorphism. Dyskeratosis
is observed in some areas.
**Lab results upon Discharge: **
**Parameter** **Result** **Reference Range**
-------------------- ----------------------- -----------------------
Sodium 141 mEq/L 135 - 145 mEq/L
Potassium 4.7 mEq/L 3.5 - 5.0 mEq/L
Creatinine 1.0 mg/dL 0.7 - 1.3 mg/dL
Calcium 9.04 mg/dL 8.8 - 10.6 mg/dL
GFR (MDRD) \> 60 mL/min/1.73m\^2 \> 60 mL/min/1.73m\^2
GFR (CKD-EPI,CREA) 80 mL/min/1.73m\^2 \> 90 mL/min/1.73m\^2
C-reactive protein 1.0 mg/dL \< 0.5 mg/dL
### Patient Report 1
**Dear colleague, **
We are writing to provide an update regarding Mr. Paul Doe, born on
08/08/1965, who presented to our outpatient clinic on 09/14/2014.
**Diagnosis: **Tongue base Carcinoma ICD-10: C01, stage: pT1 pN0 (0/21)
L0 V0 Pn0 G2 RX
**Other Diagnoses:**
- Arterial hypertension
- Diabetes mellitus type II
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
- Since 03/2014: Odynophagia
<!-- -->
- 05/14/2014: Panendoscopy, biopsy, and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
**Histology:**
Invasive, moderately differentiated, squamous cell carcinoma with
keratinization of the medial left base of the tongue, maximum extent 1.0
cm. Carcinoma- and dysplasia-free biopsies of the left tonsil, the
oropharyngeal tumor/tongue base on the left, deep resection of the
tumor, lower tonsillar pole transition on the left tongue base, tongue
base on the left and medial left tongue base, as well as median tongue
base.
Metastasis-free lymph nodes in Neck-dissection Level IIa to IV on the
left (0/16), Neck-dissection Level IIb on the left (0/1), and
Neck-dissection Level II to IV on the right (0/3), occasionally with
lymphofollicular hyperplasia. Carcinoma-free bone of the left lateral
thigh of the hyoid.
**Final UICC classification:** min pT1, pN0 (0/21). L0. V0. Pn0. G2. RX.
**Current Radiotherapy:**
**Indication**: According to the decision made by the interdisciplinary
tumor board for head and neck tumors, it was determined by our medical
team that, in the postoperative condition following the resection of a
tongue base carcinoma with an unclear resection status, there is an
indication for radiation therapy of the former tumor site.
**Technique:** Percutaneous radiotherapy of the former primary tumor
region with 6-MeVPhotons, in Rapid-Arc technique, with a single dose of
2 Gy up to a total dose of 60 Gy.
**Radiotherapy 07/27/2014 - 09/06/2014:**
During the course of radiotherapy, the patient experienced enoral
mucositis (grade II according to CTCAE) leading to subsequent
odynophagia and dysphagia. We managed these symptoms with oral rinses
and initiated pain management using Acetaminophen, resulting in an
acceptable reduction of pain over time. At the end of the therapy, the
patient\'s general condition remained stable (ECOG performance status:
70%). Second-degree mucositis enoral persisted, causing ongoing
dysphagia and odynophagia. Additionally, the patient exhibited localized
radiodermatitis (grade II according to CTCAE) within the radiation
field. The patient did not report xerostomia or dysgeusia.
**Current Recommendations:**
The patient received comprehensive instructions on continued skincare
and side-effect management. An initial follow-up appointment with the
radio-oncology team has been scheduled in our outpatient clinic. We
kindly request the patient to provide a renewed referral for
radiotherapy on the day of the appointment.
The ongoing oncological treatment plan will be determined by the
patient\'s Ear, Nose, and Throat specialists. Regular follow-up
examinations are strongly recommended. Additionally, for patients who
have completed radiation therapy in the ENT region, we advise lifelong
adherence to fluoride prophylaxis and antibiotic therapy during any
dental procedures.
### Patient Report 2
**Dear colleague, **
We report about our patient, Mr. Doe, born on 08/08/1965, who presented
to our outpatient clinic for phoniatrics and pedaudiology on 10/10/2014.
**Diagnoses: **
- Tongue base carcinoma ICD-10: C01, stage: pT1 pN0 (0/21) L0 V0 Pn0
G2 RX
- Since 03/2014: Odynophagia
- 05/14/2014: Panendoscopy, biopsy, and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
**Other Diagnoses:**
- Arterial hypertension
- Diabetes mellitus type II
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Medical History: **We may kindly assume the detailed history as known.
**Phoniatrics: Fiberoptic endoscopic swallow examination:**
Tongue motor function is well preserved, sensitivity of lip and tongue
laterally equal,
Mucous membranes non-irritant on all sides. Tracheal mucosa
non-irritant, no evidence of saliva intratracheal. Neopharynx
inconspicuous, air bubbles visible above Provox outlet on pressing
attempt. Tongue retraction slightly limited. Velopharyngeal closure
good.
**Therapy and Course:** After completion of the adjuvant radiotherapy
approximately 6 weeks ago, phonation via the Provox voice prosthesis was
no longer possible after this had initially worked after the operation.
Additionally, there were issues with regurgitation of ingested
substances, regardless of their consistency. In some cases, nasal
penetration with fluids occurred. There were no indications of
aspiration. A self-assessment, involving the use of blue-colored liquid,
revealed no signs of leakage from the Provox device.
**Current Recommendations:** Oncological follow-up in 12 months.
### Patient Report 3
**Dear colleague, **
We report about our patient, Mr. Doe, born on 08/08/1965 who presented
at our outpatient clinic for radio-oncological follow-up on 10/09/2020.
**Diagnoses: **
- Tongue base Carcinoma ICD-10: C01, stage: pT1 pN0 (0/21) L0 V0 Pn0
G2 RX
- Since 03/2014: Odynophagia
- 05/14/2014: Panendoscopy, biopsy, and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
**Other Diagnoses:**
- Arterial hypertension
- Diabetes mellitus type II
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Current Presentation: **The patient presented to our general
outpatient clinic for a radio-oncological follow-up on 10/09/2020 in the
presence of his wife.
**Physical Examination**: Patient in stable general condition (85 kg,188
cm). MUST score: 0, pain NRS 8/10 intermittent (adjusted with
Acetaminophen) \| fatigue I°, dysphagia I° \| aspiration 0°, ulcer 0°,
trismus 0°, taste disturbance I°, xerostomia I°, osteonecrosis 0°,
hypothyroidism I°, hoarseness 0°, hearing loss 0° (subjectively
reduced), dyspnea: 0°, pneumonitis 0°, nausea/vomiting 0°.
No suspicious lymph nodes palpable \| movement restrictions 0°,
subcutaneous fibrosis: I°, hyperpigmentation: I° cervical, mucositis 0°,
lymphedema I° (lymphatic drainage prescribed), telangiectasia 0°.
**MRI scan of the neck from 10/09/2020:**
Clear post-therapeutic changes in the resection and radiation area after
adjuvant RTx following tumor resection with laryngectomy for extensive
recurrence of oropharyngeal cancer.
Size constant, but still clearly accentuated lymph nodes in level Ib/IIa
on the left. Regredience of seroma formation under the left
sternocleidomastoid muscle.
**Current Recommendations: **Primary oncological care and follow-up,
including imaging, will be provided by the ENT clinic according to the
guidelines. A re-appointment for a further radio-oncological follow-up
at the follow-up appointment at the Radiation Therapy Tumor Therapy
Center has been scheduled. After head and neck radiation therapy,
regular fluoridation of the teeth and guideline-based antibiotic
prophylaxis is required prior to major dental procedures. We also
recommend temporomandibular joint opening exercises to prevent
temporomandibular joint fibrosis and consecutive temporomandibular joint
opening obstruction. We also refer to regular control of thyroid
function parameters and, if necessary, initiation of substitution
therapy after radiotherapy to the neck.
### Patient Report 4
**Dear colleague, **
We hereby report on our patient Mr. Paul Doe, born 08/08/1965 for
radio-oncological follow-up on 09/24/2021.
**Diagnoses: **
- Tongue base carcinoma ICD-10: C01, stage: pT1 pN0 (0/21) L0 V0 Pn0
G2 RX
- Since 03/2014: Odynophagia
- 05/14/2014: Panendoscopy, biopsy, and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
**Other Diagnoses:**
- Arterial hypertension
- Diabetes mellitus type II
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Current Presentation: **The patient presented to our general
outpatient clinic for radio-oncological follow-up on 09/24/2021.
**Physical Examination: **Patient in reduced general condition (KPS 60%,
86 kg,188 cm). Weight loss 0°, MUST score: 0, pain VAS 1-2/10, fatigue
I°, dysphagia I° with solid food (liquids occasionally flow out of the
nose again when swallowing), aspiration 0°, ulcer 0°, trismus 0°, taste
disorder I° (present in approx. 80%), xerostomia I°, osteonecrosis 0°,
hypothyroidism II°, hoarseness II°, hearing loss, I°, dyspnea: 0°,
pneumonitis 0°, nausea/vomiting 0°.
No suspicious lymph nodes palpable, movement restrictions I° head
reclination restricted with tension and pain, subcutaneous fibrosis: I°,
hyperpigmentation: I°, mucositis 0°, lymphedema I° (lymphatic drainage),
telangiectasia 0°.
**MR neck plain + contrast agent on 09/24/2021**:
[Technique]{.underline}: STIR triplanar, T1 ax -/+ contrast agent, T1
mDixon cor after
contrast agent.
[Findings]{.underline}: Known status post tumor resection with
laryngectomy for extensive recurrence of oropharyngeal Carcinoma;
Follow-up after RTx. Somewhat increasing swelling of nasopharynx to
oropharynx. From the uvula the swelling is stable. As far as can be
assessed, no clear recurrence-specific tissue proliferation or contrast
uptake. Unchanged accentuated lymph nodes in level Ib/IIa on the left
(one exemplary measured lymph node borderline large, idem to preliminary
examination). Mastoid cells minimally displaced on the left. Moderate
degenerative changes of the cervical spine. Assessment. Increasing
swelling of the naso- to oropharynx. Neopharynx unchanged swollen. No
evidence of malignancy-suspicious lymph nodes.
**CT scan of the thorax on 09/24/2021**:
Size-constant visualization of interlobar oval compaction in the left
upper lobe corresponding to an interlobar lymph node. New to the
previous examination, two small nodular condensations appear, basal in
the right and in the left lower lobe, differentially inflammatory;
follow-up is recommended. Unchanged the prominent mediastinal lymph
nodes, constant in size and number.
**Current Recommendations:**
Primary oncologic care and follow-up including imaging will take place
via the ENT clinic on 01/14/22 at 11:00 AM. A re-appointment for the
next radio-oncological follow-up has been arranged for 01/14/2022 at
1:00 PM in our radiotherapy outpatient clinic in the Tumor Therapy
Center.
**Lab results upon Discharge:**
**Hematology**
**Parameter ** **Result** **Reference**
---------------- ------------- -----------------------
WBC 6,900 /μL 4,500 - 11,000 /μL
RBC 2.7M /μL 4.5M - 5.9M /μL
Hemoglobin 8.2 g/dL 14 - 18 g/dL
Hematocrit 25.1 % 40 - 48 %
MCH 31.27 pg 27 - 33 pg
MCV 94 fL 82 - 92 fL
MCHC 32.7 g/dL 32 - 36 g/dL
Platelets 638,000 /μL 150,000 - 450,000 /μL
**Serum chemistry**
**Parameter ** **Result** **Reference**
---------------- ------------ -----------------
Sodium 144 mEq/L 135 - 145 mEq/L
Potassium 4.8 mEq/L 3.5 - 5.0 mEq/L
Creatinine 1.3 mg/dL 0.7 - 1.3 mg/dL
ALT 21 U/L 10 - 50 U/L
eGFR 55 mL/min \> 90 mL/min
CRP 2.9 mg/dL \< 0.5 mg/dL
**Coagulation**
**Parameter ** **Result** **Reference**
---------------- ------------ ---------------
PT 93 % 70 - 120 %
INR 1.1 0.8 - 1.2
aPTT 31 sec 26 - 37 sec
**Thyroid hormones**
**Parameter ** **Result** **Reference**
---------------- ------------- ------------------
TSH 1.16 μIU/mL 0.4 - 4.2 μIU/mL
fT3 2.38 pg/mL 2.3 - 4.2 pg/mL
fT4 1.70 ng/dL 0.9 - 1.7 ng/dL
### Patient Report 5
**Dear colleague, **
We are reporting on Mr. Paul Doe, born on 08/08/1965, who was admitted
to our hospital from 01/10/2022 to 01/27/2022.
**Diagnosis**: Metachronous pulmonary metastatic squamous cell carcinoma
**Diagnoses: **
- Tongue base Carcinoma ICD-10: C01, stage: pT1 pN0 (0/21) L0 V0 Pn0
G2 RX
- Since 03/2014 Odynophagia
- 05/14/2014 Panendoscopy, biopsy, and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014 Tumor resection and selective neck dissection LI-III
**Other Diagnoses:**
- Arterial hypertension
- Diabetes mellitus type II
- Status post liver surgery 2013
- Status post endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Planned Surgical Procedure:**
- Perioperative bronchoscopy
- Left-sided video-assisted thoracoscopic surgery
- Pleurolysis
- Anatomical upper lobe resection
- Systematic mediastinal, hilar, and interlobar lymph node dissection
- Placement of double chest drains
**Medical History**: Mr. Paul Doe initially presented with a diagnosis
of pT1, pN0 (0/21, ECE negative), cM0, Pn0, G2, RX, L0, V0, left midline
tongue carcinoma. Panendoscopy with specimen collection on 04/14/2014
and 04/26/2014 confirmed carcinoma.
Surgical resection was performed via lateral pharyngotomy and neck
dissection. Histology confirmed squamous cell carcinoma. He subsequently
received radiotherapy.
During a follow-up CT examination, a suspicious lesion was identified in
the lung, which raised concerns regarding the possibility of metastasis
originating from the previously diagnosed left midline tongue carcinoma
(pT1, pN0, G2, RX).
**Current Presentation:** Mr. Doe was admitted for further examination
and treatment to assess the behavior and extent of the lesion.
Clinically, Mr. Doe was in stable general condition and had no symptoms
suggestive of B symptoms.
**Therapy and Progression**: The above-mentioned procedure was performed
without complications on 01/10/2022. Histologically, the final resection
specimen confirmed the presence of a 2.9 cm squamous cell carcinoma,
consistent with a metastatic recurrence of the previously known
hypopharyngeal carcinoma. The dissected lymph nodes were free of tumor.
The postoperative course was uneventful. In the absence of any
complications, surgical sutures were removed on day 10 after surgery. A
current chest X-ray showed a regular postoperative outcome with
sufficient expansion of the left lobe. Further monitoring is recommended
by the treating colleagues in this regard. Mr. Doe is also connected to
the radiation therapy team for ongoing follow-up.
If there are any complications or questions, please contact the relevant
ward or reach out to our Central Patient Management. Outside regular
working hours, you can contact the on-call colleague in the Abdominal
Surgery department for assistance.
For further information on the patient\'s discharge management, treating
providers are available for inquiries from Monday to Friday, 9 AM to 7
PM, as well as on weekends and holidays from 10 AM to 2 PM.
Mr. Doe was discharged from the hospital on 01/27/2022.
**Addition:**
**Histology Report:** Resected left upper lobe specimen with a 2.9 cm
solid carcinoma. The histological picture is consistent with a
metastasis from the previously diagnosed non-keratinizing squamous cell
carcinoma. There were focal vascular invasions. No pleural invasion was
observed. The resection was complete, with all dissected lymph nodes
showing no tumor involvement.
**Chest X-ray, anterior-posterior view from 01/21/2022**:
[Clinical Information:]{.underline} History of VATS with wedge
resection, yesterday\'s drain removal
[Question:]{.underline} Follow-up, pneumothorax after drain removal?
Infiltrates? Atelectasis?
[Findings:]{.underline} Left chest drainage tube has been removed. Left
apical pneumothorax line, measuring approximately 2.3 cm. Continued
extensive shadowing of the left upper field, most likely postoperative,
infiltrate cannot be definitively ruled out. Slightly hypotransparent
left lung in comparison, most likely due to residual postoperative
reduced ventilation. No effusion. Widened cardiac silhouette. Regression
of dystelectasis in the right lower field. No acute signs of pulmonary
venous congestion. Trachea is mid-positioned and not stenosed.
Left-sided port catheter still in place.
[Summary]{.underline}: Left apical corax with a width of 2.3 cm.
Continued extensive shadowing of the left upper field, most likely
postoperative, with infiltrate not definitively excluded. Residual
postoperative reduced ventilation on the left side. Regression of
dystelectasis in the right lower field. No effusion. No acute signs of
pulmonary venous congestion. Follow-up recommended.
**Examinations Chest X-ray, anterior-posterior view from 01/23/2022:**
[Question]{.underline}**:** Follow-up.
[Findings]{.underline}: Left apical pneumothorax, measuring
approximately 1.4 cm. Extensive shadowing in projection onto the left
upper lobe, differentials include postoperative changes, incipient
infiltrate not excluded. Dystelectasis of the right lower field. No
evidence of pleural effusion or acute pulmonary venous congestion.
Cardiomegaly. Indwelling chest drainage with the catheter tip projecting
onto the left upper lobe. Well-positioned port catheter tip projecting
onto the right atrial entrance plane. No evidence of pleural effusion.
Assessment Left apical pneumothorax, measuring approximately 1.4 cm,
with indwelling left chest drainage. Extensive shadowing in projection
onto the left upper lobe, differentials include postoperative changes,
incipient infiltrate not excluded. Dystelectasis of the right lower
field. No significant pleural effusion. No acute pulmonary venous
congestion. Cardiomegaly.
**Chest X-ray, anterior-posterior view from 01/25/2022**
[Previous Examinations]{.underline}: Appearance of a diffuse
postoperative shadow in the left upper field. No evidence of pleural
effusion, inflammatory infiltrate, or pulmonary venous congestion.
[Findings]{.underline}: Left-sided chest port with the tip projecting
onto the superior vena cava. The upper mediastinum is narrow, the
trachea is mid-positioned and patent.
[Assessment]{.underline}: The pneumothorax appears to be largely
resolved.
**Urinanalysis**:
Material: Urine, midstream sample collected on 01/11/2022
- Antimicrobial inhibitors negative
- No evidence of growth-inhibiting substances in the sample material.
- Colony Count (CFU) / mL \<1,000, Assessment: A low colony count
typically does not support a urinary tract infection.
- Epithelial cells (microscopic) \<20 epithelial cells/μL
- Leukocytes (microscopic) \<20 leukocytes/μL
- Microorganisms (microscopic) 20-100 microorganisms/μL Pathogen
Enterococci
**Lab values upon Discharge: **
**Parameter** **Result** **Reference Range**
-------------------- --------------------- ---------------------
Sodium 141 mEq/L 135 - 145 mEq/L
Potassium 4.7 mEq/L 3.5 - 5.0 mEq/L
Creatinine 1.1 mg/dL 0.7 - 1.3 mg/dL
Calcium 10.4 mg/dL 8.8 - 10.6 mg/dL
eGFR (MDRD) \> 60 mL/min/1.73m² \> 60 mL/min/1.73m²
eGFR (CKD-EPI) 85 mL/min/1.73m² \> 90 mL/min/1.73m²
C-Reactive Protein 5.0 mg/dL \< 0.5 mg/dL
### Patient Report 6
**Dear colleague, **
We are reporting on Mr. Paul Doe, born on 08/08/1965, who presented to
our surgical outpatient clinic on 01/24/2022.
**Diagnoses**: Metachronous pulmonary metastatic squamous cell carcinoma
at the base of the tongue.
**Surgical Procedure from 01/11/2022:**
- Perioperative bronchoscopy
- Left-sided video-assisted thoracoscopic surgery
- Pleurolysis
- Anatomical upper lobe resection
- Systematic mediastinal, hilar, and interlobar lymph node dissection
- Placement of double chest drains
**Previous Diagnoses and Therapies:**
- Recurrent oropharyngeal carcinoma ICD-10: C01
- Stage: rpT2 rpN2(2/24, ECE -) L1 V0 Pn0 G2 R0
- Tumor localization: base of tongue, crossing midline
- Since 04/2014: Odynophagia
- 04/14/2014: Panendoscopy, biopsy and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
- 08-10/2015: radiotherapy of former PTR with 60 Gy à 2 Gy.
- OSAS with CPAP incompliance
<!-- -->
- Liver cirrhosis with alcohol abuse
- Non-insulin-dependent diabetes mellitus type II
- Arterial hypertension
- History of endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Current Presentation:** Mr. Doe presented for postoperative follow-up
after his left-sided videothoracoscopic upper lobe resection due to
previously diagnosed pulmonary metastatic hypopharyngeal carcinoma.
**Medical History:** The patient\'s general condition is good. He is
currently on intermittent as-needed analgesia with Acetaminophen. The
final resection specimen histologically confirmed the presence of a 2.9
cm squamous cell carcinoma, consistent with a metastatic recurrence of
the previously known hypopharyngeal carcinoma. The dissected lymph nodes
were free of tumor.
**Therapy and Progression**: During the follow-up appointment, Mr. Doe
underwent a clinical examination and a chest X-ray to assess his
postoperative condition. The examination revealed no new concerning
findings, and Mr. Doe continued to remain in stable general condition.
His surgical incision site was inspected, showing signs of satisfactory
healing without any signs of infection or complications.
Furthermore, Mr. Doe\'s lung function was evaluated through spirometry,
which indicated adequate pulmonary function post-surgery. He was also
provided with personalized recommendations for respiratory exercises to
optimize his lung function during the recovery period.
**Current Recommendations:**
- Mr. Doe is connected to the radiation therapy team for ongoing
follow-up.
### Patient Report 7
**Dear colleague, **
We are reporting on Mr. Paul Doe, born on 08/08/1965, who presented to
our surgical outpatient clinic on 01/24/2022.
**Diagnoses**: Metachronous pulmonary metastatic squamous cell carcinoma
at the base of the tongue.
**Surgery on 01/11/2022:**
- Left-sided video-assisted thoracoscopic surgery
- Anatomical upper lobe resection
- Systematic mediastinal, hilar, and interlobar lymph node dissection
**Previous Diagnoses and Therapies:**
- Recurrent oropharyngeal carcinoma ICD-10: C01
- Stage: rpT2 rpN2(2/24, ECE -) L1 V0 Pn0 G2 R0
- Tumor localization: base of tongue, crossing midline
- Since 04/2014: Odynophagia
- 04/14/2014: Panendoscopy, biopsy and initial diagnosis of left
tongue base carcinoma.
- 05/29/2014: Tumor resection and selective neck dissection LI-III
- 08-10/2015 Radiotherapy of former PTR with 60 Gy à 2 Gy.
**Other Diagnoses:**
- OSAS with CPAP incompliance
- Liver cirrhosis with alcohol abuse
- Non-insulin-dependent diabetes mellitus type II
- Arterial hypertension
- History of endoprosthetic hip treatment
- Hypothyroidism
- Oral thrush
- Hypacusis
**Current Presentation:** Mr. Doe presented for routine follow-up.
Clinically, he remains in stable general condition, with no signs of
B-symptoms.
**Medical History**: The surgical procedure performed on 01/11/2022
involved perioperative bronchoscopy, left VATS, pleurolysis, anatomical
upper lobe resection, systematic mediastinal, hilar, and interlobar
lymph node dissection, as well as the placement of double chest drains.
Histologically, the final resection specimen confirmed a 2.9 cm solid
carcinoma, consistent with metastasis from the previously diagnosed
squamous cell carcinoma. Lymph nodes dissected during the procedure were
tumor-free. Mr. Doe\'s postoperative course was uneventful, and surgical
sutures were removed on day 10 after surgery.
**Physical Examination:** Patient in good general condition. Weight loss
0°, MUST score: 0, pain VAS 1-2/10, fatigue I°, dysphagia I° with solid
food (liquids occasionally flow out of the nose again when swallowing),
aspiration 0°, ulcer 0°, trismus 0°, taste disorder I° (present in
approx. 80%), xerostomia I°, osteonecrosis 0°, hypothyroidism II°,
hoarseness II°, hearing loss, I°, dyspnea: 0°, pneumonitis 0°,
nausea/vomiting 0°.
No suspicious lymph nodes palpable, movement restrictions I° head
reclination restricted with tension and pain, subcutaneous fibrosis: I°,
hyperpigmentation: I°, mucositis 0°, lymphedema I° (lymphatic drainage),
telangiectasia 0°.
**Current Recommendations:** Mr. Doe is advised to continue his
follow-up appointments.
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
-------------------------------- -------------- ---------------------
Neutrophils 72.2 % 42.0-77.0 %
Lymphocytes 8.6 % 20.0-44.0 %
Monocytes 11.6 % 2.0-9.5 %
Basophils 1.4 % 0.0-1.8 %
Eosinophils 6.0 % 0.5-5.5 %
Immature Granulocytes 0.2 % 0.0-1.0 %
Sodium 137 mEq/L 136-145 mEq/L
Potassium 4.2 mEq/L 3.5-4.5 mEq/L
Calcium 9.24 mg/dL 8.8-10.2 mg/dL
Chloride 100 mEq/L 98-107 mEq/L
Creatinine 1.27 mg/dL 0.70-1.20 mg/dL
BUN 48 mg/dL 17-48 mg/dL
Uric Acid 5.2 mg/dL 3.6-8.2 mg/dL
CRP 0.8 mg/L \< 5.0 mg/L
PSA 2.31 ng/mL \< 4.40 ng/mL
ALT 12 U/L \< 41 U/L
AST 38 U/L \< 50 U/L
Alkaline Phosphatase 115 U/L 40-130 U/L
GGT 20 U/L 8-61 U/L
LDH 335 U/L 135-250 U/L
Testosterone \<0.03 ng/mL 1.32-8.92 ng/mL
TSH 1.42 mIU/L 0.27-4.20 mIU/L
Hemoglobin 10.1 g/dL 12.5-17.2 g/dL
Hematocrit 28.5 % 37.0-49.0 %
RBC 3.3 M/µL 4.0-5.6 M/µL
WBC 4.98 K/µL 3.90-10.50 K/µL
Platelets 281 K/µL 150-370 K/µL
MCV 85.6 fL 80.0-101.0 fL
MCH 30.3 pg 27.0-34.0 pg
MCHC 35.4 g/dL 31.5-36.0 g/dL
MPV 9.2 fL 7.0-12.0 fL
RDW 13.4 % 11.5-15.0 %
Absolute Neutrophils 3.59 K/µL 1.50-7.70 K/µL
Absolute Immature Granulocytes 0.010 K/µL \< 0.050 K/µL
Absolute Lymphocytes 0.43 K/µL 1.10-4.50 K/µL
Absolute Monocytes 0.58 K/µL 0.10-0.90 K/µL
Absolute Eosinophils 0.30 K/µL 0.02-0.50 K/µL
Absolute Basophils 0.07 K/µL 0.00-0.20 K/µL
Reticulocytes 31.3 K/µL 25.0-105.0 K/µL
Reticulocyte % 0.94 % 0.50-2.00 %
Ret-Hb 33.9 pg 28.5-34.5 pg
PT 112 % \> 78 %
INR 0.95 \< 1.25
aPTT 30.2 sec. 25.0-38.0 sec.
|
2 Gy
|
What wouldn't the author agree with?
A. new swear words will exist in the future
B. it's important for a culture to have profane words
C. swear words have changed over the centuries
D. all people should become more comfortable with swearing
|
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. all people should become more comfortable with swearing
|
On which dataset(s) do they compute their word embeddings?
|
### Introduction
Word embeddings have been used to improve the performance of many NLP tasks including language modelling BIBREF1 , machine translation BIBREF2 , and sentiment analysis BIBREF3 . The broad applicability of word embeddings to NLP implies that improvements to their quality will likely have widespread benefits for the field. The word embedding problem is to learn a mapping INLINEFORM0 ( INLINEFORM1 100-300 in most applications) that encodes meaningful semantic and/or syntactic information. For instance, in many word embeddings, INLINEFORM2 car INLINEFORM3 truck INLINEFORM4 , since the words are semantically similar. More complex relationships than similarity can also be encoded in word embeddings. For example, we can answer analogy queries of the form INLINEFORM0 ? using simple arithmetic in many state-of-the-art embeddings BIBREF4 . The answer to bed INLINEFORM1 sleep INLINEFORM2 chair INLINEFORM3 INLINEFORM4 is given by the word whose vector representation is closest to INLINEFORM5 sleep INLINEFORM6 bed INLINEFORM7 chair INLINEFORM8 ( INLINEFORM9 sit INLINEFORM10 ). Other embeddings may encode such information in a nonlinear way BIBREF5 . BIBREF4 demonstrates the additive compositionality of their word2vec vectors: one can sum vectors produced by their embedding to compute vectors for certain phrases rather than just vectors for words. Later in this paper, we will show that our embeddings naturally give rise to a form of multiplicative compositionality that has not yet been explored in the literature. Almost all recent word embeddings rely on the distributional hypothesis BIBREF6 , which states that a word's meaning can be inferred from the words that tend to surround it. To utilize the distributional hypothesis, many embeddings are given by a low-rank factor of a matrix derived from co-occurrences in a large unsupervised corpus, see BIBREF7 , BIBREF8 , BIBREF9 and BIBREF10 . Approaches that rely on matrix factorization only utilize pairwise co-occurrence information in the corpus. We aim to extend this approach by creating word embeddings given by factors of tensors containing higher order co-occurrence data. ### Related work
Some common word embeddings related to co-occurrence based matrix factorization include GloVe BIBREF7 , word2vec BIBREF9 , LexVec BIBREF10 , and NNSE BIBREF8 . In contrast, our work studies word embeddings given by factorization of tensors. An overview of tensor factorization methods is given in BIBREF11 . Our work uses factorization of symmetric nonnegative tensors, which has been studied in the past BIBREF12 , BIBREF13 . In general, factorization of tensors has been applied to NLP in BIBREF14 and factorization of nonnegative tensors BIBREF15 . Recently, factorization of symmetric tensors has been used to create a generic word embedding BIBREF16 but the idea was not explored extensively. Our work studies this idea in much greater detail, fully demonstrating the viability of tensor factorization as a technique for training word embeddings. Composition of word vectors to create novel representations has been studied in depth, including additive, multiplicative, and tensor-based methods BIBREF17 , BIBREF18 . Typically, composition is used to create vectors that represent phrases or sentences. Our work, instead, shows that pairs of word vectors can be composed multiplicatively to create different vector representations for the various meanings of a single polysemous word. ### Notation
Throughout this paper we will write scalars in lowercase italics INLINEFORM0 , vectors in lowercase bold letters INLINEFORM1 , matrices with uppercase bold letters INLINEFORM2 , and tensors (of order INLINEFORM3 ) with Euler script notation INLINEFORM4 , as is standard in the literature. ### Pointwise Mutual Information
Pointwise mutual information (PMI) is a useful property in NLP that quantifies the likelihood that two words co-occur BIBREF9 . It is defined as: INLINEFORM0 where INLINEFORM0 is the probability that INLINEFORM1 and INLINEFORM2 occur together in a given fixed-length context window in the corpus, irrespective of order. It is often useful to consider the positive PMI (PPMI), defined as: INLINEFORM0 since negative PMI values have little grounded interpretation BIBREF19 , BIBREF9 , BIBREF15 . Given an indexed vocabulary INLINEFORM0 , one can construct a INLINEFORM1 PPMI matrix INLINEFORM2 where INLINEFORM3 . Many existing word embedding techniques involve factorizing this PPMI matrix BIBREF9 , BIBREF8 , BIBREF10 . PMI can be generalized to INLINEFORM0 variables. While there are many ways to do so BIBREF20 , in this paper we use the form defined by: INLINEFORM1 where INLINEFORM0 is the probability that all of INLINEFORM1 occur together in a given fixed-length context window in the corpus, irrespective of their order. In this paper we study 3-way PPMI tensors INLINEFORM0 , where INLINEFORM1 , as this is the natural higher-order generalization of the PPMI matrix. We leave the study of creating word embeddings with INLINEFORM2 -dimensional PPMI tensors ( INLINEFORM3 ) to future work. ### Tensor factorization
Just as the rank- INLINEFORM0 matrix decomposition is defined to be the product of two factor matrices ( INLINEFORM1 ), the canonical rank- INLINEFORM2 tensor decomposition for a third order tensor is defined to be the product of three factor matrices BIBREF11 : DISPLAYFORM0 where INLINEFORM0 is the outer product: INLINEFORM1 . This is also commonly referred to as the rank-R CP Decomposition. Elementwise, this is written as: INLINEFORM2 where INLINEFORM0 is elementwise vector multiplication and INLINEFORM1 is the INLINEFORM2 row of INLINEFORM3 . In our later section on multiplicative compositionality, we will see this formulation gives rise to a meaningful interpretation of the elementwise product between vectors in our word embeddings. Symmetric CP Decomposition. In this paper, we will consider symmetric CP decomposition of nonnegative tensors BIBREF21 , BIBREF11 . Since our INLINEFORM0 -way PPMI is nonnegative and invariant under permutation, the PPMI tensor INLINEFORM1 is nonnegative and supersymmetric, i.e. INLINEFORM2 for any permutation INLINEFORM3 . In the symmetric CP decomposition, instead of factorizing INLINEFORM0 , we factorize INLINEFORM1 as the triple product of a single factor matrix INLINEFORM2 such that INLINEFORM3 In this formulation, we use INLINEFORM0 to be the word embedding so the vector for INLINEFORM1 is the INLINEFORM2 row of INLINEFORM3 similar to the formulations in BIBREF9 , BIBREF8 , BIBREF7 . It is known that the optimal rank- INLINEFORM0 CP decomposition exists for symmetric nonnegative tensors such as the PPMI tensor BIBREF21 . However, finding such a decomposition is NP hard in general BIBREF22 so we must consider approximate methods. In this work, we only consider the symmetric CP decomposition, leaving the study of other tensor decompositions (such as the Tensor Train or HOSVD BIBREF23 , BIBREF11 ) to future work. ### Computing the Symmetric CP Decomposition
The INLINEFORM0 size of the third order PPMI tensor presents a number of computational challenges. In practice, INLINEFORM1 can vary from INLINEFORM2 to INLINEFORM3 , resulting in a tensor whose naive representation requires at least INLINEFORM4 bytes = 4 TB of floats. Even the sparse representation of the tensor takes up such a large fraction of memory that standard algorithms such as successive rank-1 approximation BIBREF12 , BIBREF24 and alternating least-squares BIBREF11 are infeasible for our uses. Thus, in this paper we will consider a stochastic online formulation similar to that of BIBREF25 . We optimize the CP decomposition in an online fashion, using small random subsets INLINEFORM0 of the nonzero tensor entries to update the decomposition at time INLINEFORM1 . In this minibatch setting, we optimize the decomposition based on the current minibatch and the previous decomposition at time INLINEFORM2 . To update INLINEFORM3 (and thus the symmetric decomposition), we first define a decomposition loss INLINEFORM4 and minimize this loss with respect to INLINEFORM5 using Adam BIBREF26 . At each time INLINEFORM0 , we take INLINEFORM1 to be all co-occurrence triples (weighted by PPMI) in a fixed number of sentences (around 1,000) from the corpus. We continue training until we have depleted the entire corpus. For INLINEFORM0 to accurately model INLINEFORM1 , we also include a certain proportion of elements with zero PPMI (or “negative samples”) in INLINEFORM2 , similar to that of BIBREF10 . We use an empirically found proportion of negative samples for training, and leave discovery of the optimal negative sample proportion to future work. ### Word Embedding Proposals
CP-S. The first embedding we propose is based on symmetic CP decomposition of the PPMI tensor INLINEFORM0 as discussed in the mathematical preliminaries section. The optimal setting for the word embedding INLINEFORM1 is: INLINEFORM2 Since we cannot feasibly compute this exactly, we minimize the loss function defined as the squared error between the values in INLINEFORM0 and their predicted values: INLINEFORM1 using the techniques discussed in the previous section. JCP-S. A potential problem with CP-S is that it is only trained on third order information. To rectify this issue, we propose a novel joint tensor factorization problem we call Joint Symmetric Rank- INLINEFORM0 CP Decomposition. In this problem, the input is the fixed rank INLINEFORM1 and a list of supersymmetric tensors INLINEFORM2 of different orders but whose axis lengths all equal INLINEFORM3 . Each tensor INLINEFORM4 is to be factorized via rank- INLINEFORM5 symmetric CP decomposition using a single INLINEFORM6 factor matrix INLINEFORM7 . To produce a solution, we first define the loss at time INLINEFORM0 to be the sum of the reconstruction losses of each different tensor: INLINEFORM1 where INLINEFORM0 is an INLINEFORM1 -dimensional supersymmetric PPMI tensor. We then minimize the loss with respect to INLINEFORM2 . Since we are using at most third order tensors in this work, we assign our word embedding INLINEFORM3 to be: INLINEFORM4 This problem is a specific instance of Coupled Tensor Decomposition, which has been studied in the past BIBREF27 , BIBREF28 . In this problem, the goal is to factorize multiple tensors using at least one factor matrix in common. A similar formulation to our problem can be found in BIBREF29 , which studies blind source separation using the algebraic geometric aspects of jointly factorizing numerous supersymmetric tensors (to unknown rank). In contrast to our work, they outline some generic rank properties of such a decomposition rather than attacking the problem numerically. Also, in our formulation the rank is fixed and an approximate solution must be found. Exploring the connection between the theoretical aspects of joint decomposition and quality of word embeddings would be an interesting avenue for future work. To the best of our knowledge this is the first study of Joint Symmetric Rank- INLINEFORM0 CP Decomposition. ### Shifted PMI
In the same way BIBREF9 considers factorization of positive shifted PMI matrices, we consider factorization of positive shifted PMI tensors INLINEFORM0 , where INLINEFORM1 for some constant shift INLINEFORM2 . We empirically found that different levels of shifting resulted in different qualities of word embeddings – the best shift we found for CP-S was a shift of INLINEFORM3 , whereas any nonzero shift for JCP-S resulted in a worse embedding across the board. When we discuss evaluation we report the results given by factorization of the PPMI tensors shifted by the best value we found for each specific embedding. ### Computational notes
When considering going from two dimensions to three, it is perhaps necessary to discuss the computational issues in such a problem size increase. However, it should be noted that the creation of pre-trained embeddings can be seen as a pre-processing step for many future NLP tasks, so if the training can be completed once, it can be used forever thereafter without having to take training time into account. Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively. ### Evaluation
In this section we present a quantitative evaluation comparing our embeddings to an informationless embedding and two strong baselines. Our baselines are: For a fair comparison, we trained each model on the same corpus of 10 million sentences gathered from Wikipedia. We removed stopwords and words appearing fewer than 2,000 times (130 million tokens total) to reduce noise and uninformative words. Our word2vec and NNSE baselines were trained using the recommended hyperparameters from their original publications, and all optimizers were using using the default settings. Hyperparameters are always consistent across evaluations. Because of the dataset size, the results shown should be considered a proof of concept rather than an objective comparison to state-of-the-art pre-trained embeddings. Due to the natural computational challenges arising from working with tensors, we leave creation of a full-scale production ready embedding based on tensor factorization to future work. As is common in the literature BIBREF4 , BIBREF8 , we use 300-dimensional vectors for our embeddings and all word vectors are normalized to unit length prior to evaluation. ### Quantitative tasks
Outlier Detection. The Outlier Detection task BIBREF0 is to determine which word in a list INLINEFORM0 of INLINEFORM1 words is unrelated to the other INLINEFORM2 which were chosen to be related. For each INLINEFORM3 , one can compute its compactness score INLINEFORM4 , which is the compactness of INLINEFORM5 . INLINEFORM6 is explicitly computed as the mean similarity of all word pairs INLINEFORM7 . The predicted outlier is INLINEFORM8 , as the INLINEFORM9 related words should form a compact cluster with high mean similarity. We use the WikiSem500 dataset BIBREF30 which includes sets of INLINEFORM0 words per group gathered based on semantic similarity. Thus, performance on this task is correlated with the amount of semantic information encoded in a word embedding. Performance on this dataset was shown to be well-correlated with performance at the common NLP task of sentiment analysis BIBREF30 . The two metrics associated with this task are accuracy and Outlier Position Percentage (OPP). Accuracy is the fraction of cases in which the true outlier correctly had the highest compactness score. OPP measures how close the true outlier was to having the highest compactness score, rewarding embeddings more for predicting the outlier to be in 2nd place rather than INLINEFORM0 when sorting the words by their compactness score INLINEFORM1 . 3-way Outlier Detection. As our tensor-based embeddings encode higher order relationships between words, we introduce a new way to compute INLINEFORM0 based on groups of 3 words rather than pairs of words. We define the compactness score for a word INLINEFORM1 to be: INLINEFORM2 where INLINEFORM0 denotes similarity between a group of 3 vectors. INLINEFORM1 is defined as: INLINEFORM2 We call this evaluation method OD3. The purpose of OD3 is to evaluate the extent to which an embedding captures 3rd order relationships between words. As we will see in the results of our quantitative experiments, our tensor methods outperform the baselines on OD3, which validates our approach. This approach can easily be generalized to OD INLINEFORM0 INLINEFORM1 , but again we leave the study of higher order relationships to future work. Simple supervised tasks. BIBREF5 points out that the primary application of word embeddings is transfer learning to NLP tasks. They argue that to evaluate an embedding's ability to transfer information to a relevant task, one must measure the embedding's accessibility of information for actual downstream tasks. To do so, one must cite the performance of simple supervised tasks as training set size increases, which is commonly done in transfer learning evaluation BIBREF5 . If an algorithm using a word embedding performs well with just a small amount of training data, then the information encoded in the embedding is easily accessible. The simple supervised downstream tasks we use to evaluate the embeddings are as follows: Supervised Analogy Recovery. We consider the task of solving queries of the form a : b :: c : ? using a simple neural network as suggested in BIBREF5 . The analogy dataset we use is from the Google analogy testbed BIBREF4 . Sentiment analysis. We also consider sentiment analysis as described by BIBREF31 . We use the suggested Large Movie Review dataset BIBREF32 , containing 50,000 movie reviews. All code is implemented using scikit-learn or TensorFlow and uses the suggested train/test split. Word similarity. To standardize our evaluation methodology, we evaluate the embeddings using word similarity on the common MEN and MTurk datasets BIBREF33 , BIBREF34 . For an overview of word similarity evaluation, see BIBREF31 . ### Quantitative results
Outlier Detection results. The results are shown in Table TABREF20 . The first thing to note is that CP-S outperforms the other methods across each Outlier Detection metric. Since the WikiSem500 dataset is semantically focused, performance at this task demonstrates the quality of semantic information encoded in our embeddings. On OD2, the baselines perform more competitively with our CP Decomposition based models, but when OD3 is considered our methods clearly excel. Since the tensor-based methods are trained directly on third order information and perform much better at OD3, we see that OD3 scores reflect the amount of third order information in a word embedding. This is a validation of OD3, as our 3rd order embeddings would naturally out perform 2nd order embeddings at a task that requires third order information. Still, the superiority of our tensor-based embeddings at OD2 demonstrates the quality of the semantic information they encode. Supervised analogy results. The results are shown in Figure FIGREF18 . At the supervised semantic analogy task, CP-S vastly outperforms the baselines at all levels of training data, further signifying the amount of semantic information encoded by this embedding technique. Also, when only 10% of the training data is presented, our tensor methods are the only ones that attain nonzero performance – even in such a limited data setting, use of CP-S's vectors results in nearly 40% accuracy. This phenomenon is also observed in the syntactic analogy tasks: our embeddings consistently outperform the others until 100% of the training data is presented. These two observations demonstrate the accessibility of the information encoded in our word embeddings. We can thus conclude that this relational information encoded in the tensor-based embeddings is more easily accessible than that of CBOW and NNSE. Thus, our methods would likely be better suited for transfer learning to actual NLP tasks, particularly those in data-sparse settings. Sentiment analysis results. The results are shown in Figure FIGREF19 . In this task, JCP-S is the dominant method across all levels of training data, but the difference is more obvious when training data is limited. This again indicates that for this specific task the information encoded by our tensor-based methods is more readily available as that of the baselines. It is thus evident that exploiting both second and third order co-occurrence data leads to higher quality semantic information being encoded in the embedding. At this point it is not clear why JCP-S so vastly outperforms CP-S at this task, but its superiority to the other strong baselines demonstrates the quality of information encoded by JCP-S. This discrepancy is also illustrative of the fact that there is no single “best word embedding” BIBREF5 – different embeddings encode different types of information, and thus should be used where they shine rather than for every NLP task. Word Similarity results. We show the results in Table TABREF21 . As we can see, our embeddings very clearly outperform the random embedding at this task. They even outperform CBOW on both of these datasets. It is worth including these results as the word similarity task is a very common way of evaluating embedding quality in the literature. However, due to the many intrinsic problems with evaluating word embeddings using word similarity BIBREF35 , we do not discuss this further. ### Multiplicative Compositionality
We find that even though they are not explicitly trained to do so, our tensor-based embeddings capture polysemy information naturally through multiplicative compositionality. We demonstrate this property qualitatively and provide proper motivation for it, leaving automated utilization to future work. In our tensor-based embeddings, we found that one can create a vector that represents a word INLINEFORM0 in the context of another word INLINEFORM1 by taking the elementwise product INLINEFORM2 . We call INLINEFORM3 a “meaning vector” for the polysemous word INLINEFORM4 . For example, consider the word star, which can denote a lead performer or a celestial body. We can create a vector for star in the “lead performer” sense by taking the elementwise product INLINEFORM0 . This produces a vector that lies near vectors for words related to lead performers and far from those related to star's other senses. To motivate why this works, recall that the values in a third order PPMI tensor INLINEFORM0 are given by: INLINEFORM1 where INLINEFORM0 is the word vector for INLINEFORM1 . If words INLINEFORM2 have a high PPMI, then INLINEFORM3 will also be high, meaning INLINEFORM4 will be close to INLINEFORM5 in the vector space by cosine similarity. For example, even though galaxy is likely to appear in the context of the word star in in the “celestial body” sense, INLINEFORM0 PPMI(star, actor, galaxy) is low whereas INLINEFORM1 PPMI(star, actor, drama) is high. Thus , INLINEFORM2 represents the meaning of star in the “lead performer” sense. In Table TABREF22 we present the nearest neighbors of multiplicative and additive composed vectors for a variety of polysemous words. As we can see, the words corresponding to the nearest neighbors of the composed vectors for our tensor methods are semantically related to the intended sense both for multiplicative and additive composition. In contrast, for CBOW, only additive composition yields vectors whose nearest neighbors are semantically related to the intended sense. Thus, our embeddings can produce complementary sets of polysemous word representations that are qualitatively valid whereas CBOW (seemingly) only guarantees meaningful additive compositionality. We leave automated usage of this property to future work. ### Conclusion
Our key contributions are as follows: Tensor factorization appears to be a highly applicable and effective tool for learning word embeddings, with many areas of potential future work. Leveraging higher order data in training word embeddings is useful for encoding new types of information and semantic relationships compared to models that are trained using only pairwise data. This indicates that such techniques will prove useful for training word embeddings to be used in downstream NLP tasks. Table 1: Outlier detection scores across all embeddings Figure 1: Supervised analogy task performance vs. % training data Figure 2: Semantic analogy accuracy vs. % training data Figure 3: Sentiment analysis task performance vs. % dataset size Figure 4: Outlier detection quality vs. embedding dimension for JCP-S Table 2: Outlier detection scores for various PPMI shifts of CP-S. Second highest values are italicized. Table 3: Phrase vectors and their nearest neighbors
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10 million sentences gathered from Wikipedia
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Why do people keep asking Hank what he saw?
A. Hank was dead for months. People want to know about the afterlife.
B. Hank was on the moon for months. People want to know what life was like there.
C. Hank was dead for months. People want to know which religion got it right.
D. Hank was out in space for months. People want to know what he saw on Mars.
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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
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A. Hank was dead for months. People want to know about the afterlife.
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Why is Melrose so opposed to Lessing publishing his book?
A. The field of psionics is new. If Lessing turns out to be wrong, the whole field of study could be discredited.
B. Lessing is Melrose's closest friend. He doesn't want to see Lessing embarrassed if his theory is proved wrong.
C. Melrose runs a task force against the publishing of junk science.
D. Melrose is also studying psionics and wants to delay Lessing by any means so that he can publish first.
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BRAMBLE BUSH BY ALAN E. NOURSE [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, August 1957. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There was a man in our town, and he was wondrous wise; He jumped into a bramble bush and scratched out both his eyes. And when he saw what he had done, with all his might and main He jumped into another bush and scratched them in again. MOTHER GOOSE Dr. David Lessing found Jack Dorffman and the boy waiting in his office when he arrived at the Hoffman Center that morning. Dorffman looked as though he'd been running all night. There were dark pouches under his eyes; his heavy unshaven face seemed to sag at every crease. Lessing glanced sharply at his Field Director and sank down behind his desk with a sigh. "All right, Jack—what's wrong?" "This kid is driving me nuts," said Dorffman through clenched teeth. "He's gone completely hay-wire. Nobody's been able to get near him for three weeks, and now at six o'clock this morning he decides he's leaving the Farm. I talk to him, I sweat him down, I do everything but tie him to the bed, and I waste my time. He's leaving the Farm. Period." "So you bring him down here," said Lessing sourly. "The worst place he could be, if something's really wrong." He looked across at the boy. "Tommy? Come over and sit down." There was nothing singular about the boy's appearance. He was thin, with a pale freckled face and the guileless expression of any normal eight-year-old as he blinked across the desk at Lessing. The awkward grey monitor-helmet concealed a shock of sandy hair. He sat with a mute appeal in his large grey eyes as Lessing flipped the reader-switch and blinked in alarm at the wildly thrashing pattern on the tape. The boy was terrorized. He was literally pulsating with fear. Lessing sat back slowly. "Tell me about it, Tommy," he said gently. "I don't want to go back to the Farm," said the boy. "Why?" "I just don't. I hate it there." "Are you frightened?" The boy bit his lip and nodded slowly. "Of me? Of Dr. Dorffman?" "No. Oh, no!" "Then what?" Again the mute appeal in the boy's eyes. He groped for words, and none came. Finally he said, "If I could only take this off—" He fingered the grey plastic helmet. "You think that would make you feel better?" "It would, I know it would." Lessing shook his head. "I don't think so, Tommy. You know what the monitor is for, don't you?" "It stops things from going out." "That's right. And it stops things from going in. It's an insulator. You need it badly. It would hurt you a great deal if you took it off, away from the Farm." The boy fought back tears. "But I don't want to go back there—" The fear-pattern was alive again on the tape. "I don't feel good there. I never want to go back." "Well, we'll see. You can stay here for a while." Lessing nodded at Dorffman and stepped into an adjoining room with him. "You say this has been going on for three weeks ?" "I'm afraid so. We thought it was just a temporary pattern—we see so much of that up there." "I know, I know." Lessing chewed his lip. "I don't like it. We'd better set up a battery on him and try to spot the trouble. And I'm afraid you'll have to set it up. I've got that young Melrose from Chicago to deal with this morning—the one who's threatening to upset the whole Conference next month with some crazy theories he's been playing with. I'll probably have to take him out to the Farm to shut him up." Lessing ran a hand through sparse grey hair. "See what you can do for the boy downstairs." "Full psi precautions?" asked Dorffman. "Certainly! And Jack—in this case, be sure of it. If Tommy's in the trouble I think he's in, we don't dare risk a chance of Adult Contact now. We could end up with a dead boy on our hands." Two letters were waiting on Lessing's desk that morning. The first was from Roberts Bros., announcing another shift of deadline on the book, and demanding the galley proofs two weeks earlier than scheduled. Lessing groaned. As director of psionic research at the Hoffman Medical Center, he had long since learned how administrative detail could suck up daytime hours. He knew that his real work was at the Farm—yet he hadn't even been to the Farm in over six weeks. And now, as the book approached publication date, Lessing wondered if he would ever really get back to work again. The other letter cheered him a bit more. It bore the letterhead of the International Psionics Conference: Dear Dr. Lessing: In recognition of your position as an authority on human Psionic behavior patterns, we would be gratified to schedule you as principle speaker at the Conference in Chicago on October 12th. A few remarks in discussion of your forthcoming book would be entirely in order— They were waiting for it, then! He ran the galley proofs into the scanner excitedly. They knew he had something up his sleeve. His earlier papers had only hinted at the direction he was going—but the book would clear away the fog. He scanned the title page proudly. "A Theory of Psionic Influence on Infant and Child Development." A good title—concise, commanding, yet modest. They would read it, all right. And they would find it a light shining brightly in the darkness, a guide to the men who were floundering in the jungle of a strange and baffling new science. For they were floundering. When they were finally forced to recognize that this great and powerful force did indeed exist in human minds, with unimaginable potential if it could only be unlocked, they had plunged eagerly into the search, and found themselves in a maddening bramble bush of contradictions and chaos. Nothing worked, and everything worked too well. They were trying to study phenomena which made no sense, observing things that defied logic. Natural laws came crashing down about their ears as they stood sadly by and watched things happen which natural law said could never happen. They had never been in this jungle before, nor in any jungle remotely like it. The old rules didn't work here, the old methods of study failed. And the more they struggled, the thicker and more impenetrable the bramble bush became— But now David Lessing had discovered a pathway through that jungle, a theory to work by— At his elbow the intercom buzzed. "A gentleman to see you," the girl said. "A Dr. Melrose. He's very impatient, sir." He shut off the scanner and said, "Send him in, please." Dr. Peter Melrose was tall and thin, with jet black hair and dark mocking eyes. He wore a threadbare sport coat and a slouch. He offered Lessing a bony hand, then flung himself into a chair as he stared about the office in awe. "I'm really overwhelmed," he said after a moment. "Within the stronghold of psionic research at last. And face to face with the Master in the trembling flesh!" Lessing frowned. "Dr. Melrose, I don't quite understand—" "Oh, it's just that I'm impressed," the young man said airily. "Of course, I've seen old dried-up Authorities before—but never before a brand spanking new one, just fresh out of the pupa, so to speak!" He touched his forehead in a gesture of reverence. "I bow before the Oracle. Speak, oh Motah, live forever! Cast a pearl at my feet!" "If you've come here to be insulting," Lessing said coldly, "you're just wasting time." He reached for the intercom switch. "I think you'd better wait before you do that," Melrose said sharply, "because I'm planning to take you apart at the Conference next month unless I like everything I see and hear down here today. And if you don't think I can do it, you're in for quite a dumping." Lessing sat back slowly. "Tell me—just what, exactly, do you want?" "I want to hear this fairy tale you're about to publish in the name of 'Theory'," Melrose said. "I want to see this famous Farm of yours up in Connecticut and see for myself how much pressure these experimental controls you keep talking about will actually bear. But mostly, I want to see just what in psionic hell you're so busy making yourself an Authority about." There was no laughter in the man's sharp brown eyes. "You couldn't touch me with a ten foot pole at this conference," snapped Lessing. The other man grinned. "Try me! We shook you up a little bit last year, but you didn't seem to get the idea." "Last year was different." Lessing scowled. "As for our 'fairy tale', we happen to have a staggering body of evidence that says that it's true." "If the papers you've already published are a preview, we think it's false as Satan." "And our controls are above suspicion." "So far, we haven't found any way to set up logical controls," said Melrose. "We've done a lot of work on it, too." "Oh, yes—I've heard about your work. Not bad, really. A little misdirected, is all." "According to your Theory, that is." "Wildly unorthodox approach to psionics—but at least you're energetic enough." "We haven't been energetic enough to find an orthodox approach that got us anywhere. We doubt if you have, either. But maybe we're all wrong." Melrose grinned unpleasantly. "We're not unreasonable, your Majesty. We just ask to be shown. If you dare, that is." Lessing slammed his fist down on the desk angrily. "Have you got the day to take a trip?" "I've got 'til New Year." Lessing shouted for his girl. "Get Dorffman up here. We're going to the Farm this afternoon." The girl nodded, then hesitated. "But what about your lunch?" "Bother lunch." He gave Melrose a sidelong glare. "We've got a guest here who's got a lot of words he's going to eat for us...." Ten minutes later they rode the elevator down to the transit levels and boarded the little shuttle car in the terminal below the Hoffman Center. They sat in silence as the car dipped down into the rapid-transit channels beneath the great city, swinging northward in the express circuit through Philadelphia and Camden sectors, surfacing briefly in Trenton sector, then dropping underground once again for the long pull beneath Newark, Manhattan and Westchester sectors. In less than twenty minutes the car surfaced on a Parkway channel and buzzed north and east through the verdant Connecticut countryside. "What about Tommy?" Lessing asked Dorffman as the car sped along through the afternoon sun. "I just finished the prelims. He's not cooperating." Lessing ground his teeth. "I should be running him now instead of beating the bushes with this—" He broke off to glare at young Melrose. Melrose grinned. "I've heard you have quite a place up here." "It's—unconventional, at any rate," Lessing snapped. "Well, that depends on your standards. Sounds like a country day school, from what I've heard. According to your papers, you've even used conventional statistical analysis on your data from up here." "Until we had to throw it out. We discovered that what we were trying to measure didn't make sense in a statistical analysis." "Of course, you're sure you were measuring something ." "Oh, yes. We certainly were." "Yet you said that you didn't know what." "That's right," said Lessing. "We don't." "And you don't know why your instruments measure whatever they're measuring." The Chicago man's face was thoughtful. "In fact, you can't really be certain that your instruments are measuring the children at all. It's not inconceivable that the children might be measuring the instruments , eh?" Lessing blinked. "It's conceivable." "Mmmm," said Melrose. "Sounds like a real firm foundation to build a theory on." "Why not?" Lessing growled. "It wouldn't be the first time the tail wagged the dog. The psychiatrists never would have gotten out of their rut if somebody hadn't gotten smart and realized that one of their new drugs worked better in combatting schizophrenia when the doctor took the medicine instead of the patient. That was quite a wall to climb." "Yes, wasn't it," mused Melrose, scratching his bony jaw. "Only took them seventy years to climb it, thanks to a certain man's theories. I wonder how long it'll take psionics to crawl out of the pit you're digging for it?" "We're not digging any pit," Lessing exploded angrily. "We're exploring—nothing more. A phenomenon exists. We've known that, one way or another, for centuries. The fact that it doesn't seem to be bound by the same sort of natural law we've observed elsewhere doesn't mean that it isn't governed by natural law. But how can we define the law? How can we define the limits of the phenomenon, for that matter? We can't work in the dark forever—we've got to have a working hypothesis to guide us." "So you dreamed up this 'tadpole' idea," said Melrose sourly. "For a working hypothesis—yes. We've known for a long time that every human being has extrasensory potential to one degree or another. Not just a few here and there—every single one. It's a differentiating quality of the human mind. Just as the ability to think logically in a crisis instead of giving way to panic is a differentiating quality." "Fine," said Melrose. "Great. We can't prove that, of course, but I'll play along." Lessing glared at him. "When we began studying this psi-potential, we found out some curious things. For one thing, it seemed to be immensely more powerful and active in infants and children than in adults. Somewhere along the line as a child grows up, something happens. We don't know what. We do know that the child's psi-potential gradually withdraws deeper and deeper into his mind, burying itself farther and farther out of reach, just the way a tadpole's tail is absorbed deeper and deeper into the growing frog until there just isn't any tail any more." Lessing paused, packing tobacco into his pipe. "That's why we have the Farm—to try to discover why. What forces that potential underground? What buries it so deeply that adult human beings can't get at it any more?" "And you think you have an answer," said Melrose. "We think we might be near an answer. We have a theory that explains the available data." The shuttle car bounced sharply as it left the highway automatics. Dorffman took the controls. In a few moments they were skimming through the high white gates of the Farm, slowing down at the entrance to a long, low building. "All right, young man—come along," said Lessing. "I think we can show you our answer." In the main office building they donned the close-fitting psionic monitors required of all personnel at the Farm. They were of a hard grey plastic material, with a network of wiring buried in the substance, connected to a simple pocket-sized power source. "The major problem," Lessing said, "has been to shield the children from any external psionic stimuli, except those we wished to expose them to. Our goal is a perfectly controlled psi environment. The monitors are quite effective—a simple Renwick scrambler screen." "It blocks off all types of psi activity?" asked Melrose. "As far as we can measure, yes." "Which may not be very far." Jack Dorffman burst in: "What Dr. Lessing is saying is that they seem effective for our purposes." "But you don't know why," added Melrose. "All right, we don't know why. Nobody knows why a Renwick screen works—why blame us?" They were walking down the main corridor and out through an open areaway. Behind the buildings was a broad playground. A baseball game was in progress in one corner; across the field a group of swings, slides, ring bars and other playground paraphernalia was in heavy use. The place was teeming with youngsters, all shouting in a fury of busy activity. Occasionally a helmeted supervisor hurried by; one waved to them as she rescued a four-year-old from the parallel bars. They crossed into the next building, where classes were in progress. "Some of our children are here only briefly," Lessing explained as they walked along, "and some have been here for years. We maintain a top-ranking curriculum—your idea of a 'country day school' wasn't so far afield at that—with scholarships supported by Hoffman Center funds. Other children come to us—foundlings, desertees, children from broken homes, children of all ages from infancy on. Sometimes they stay until they have reached college age, or go on to jobs. As far as psionics research is concerned, we are not trying to be teachers. We are strictly observers. We try to place the youngsters in positions where they can develope what potential they have— without the presence of external psionic influences they would normally be subject to. The results have been remarkable." He led them into a long, narrow room with chairs and ash trays, facing a wide grey glass wall. The room fell into darkness, and through the grey glass they could see three children, about four years old, playing in a large room. "They're perfectly insulated from us," said Lessing. "A variety of recording instruments are working. And before you ask, Dr. Melrose, they are all empirical instruments, and they would all defy any engineer's attempts to determine what makes them go. We don't know what makes them go, and we don't care—they go. That's all we need. Like that one, for instance—" In the corner a flat screen was flickering, emitting a pale green fluorescent light. It hung from the wall by two plastic rods which penetrated into the children's room. There was no sign of a switch, nor a power source. As the children moved about, the screen flickered. Below it, a recording-tape clicked along in little spurts and starts of activity. "What are they doing?" Melrose asked after watching the children a few moments. "Those three seem to work as a team, somehow. Each one, individually, had a fairly constant recordable psi potential of about seventeen on the arbitrary scale we find useful here. Any two of them scale in at thirty-four to thirty-six. Put the three together and they operate somewhere in the neighborhood of six hundred on the same scale." Lessing smiled. "This is an isolated phenomenon—it doesn't hold for any other three children on the Farm. Nor did we make any effort to place them together—they drew each other like magnets. One of our workers spent two weeks trying to find out why the instruments weren't right. It wasn't the instruments, of course." Lessing nodded to an attendant, and peered around at Melrose. "Now, I want you to watch this very closely." He opened a door and walked into the room with the children. The fluorescent screen continued to flicker as the children ran to Lessing. He inspected the block tower they were building, and stooped down to talk to them, his lips moving soundlessly behind the observation wall. The children laughed and jabbered, apparently intrigued by the game he was proposing. He walked to the table and tapped the bottom block in the tower with his thumb. The tower quivered, and the screen blazed out with green light, but the tower stood. Carefully Lessing jogged all the foundation blocks out of place until the tower hung in midair, clearly unsupported. The children watched it closely, and the foundation blocks inched still further out of place.... Then, quite casually, Lessing lifted off his monitor. The children continued staring at the tower as the screen gave three or four violent bursts of green fire and went dark. The block tower fell with a crash. Moments later Lessing was back in the observation room, leaving the children busily putting the tower back together. There was a little smile on his lips as he saw Melrose's face. "Perhaps you're beginning to see what I'm driving at," he said slowly. "Yes," said Melrose. "I think I'm beginning to see." He scratched his jaw. "You think that it's adult psi-contact that drives the child's potential underground—that somehow adult contact acts like a damper, a sort of colossal candle-snuffer." "That's what I think," said Lessing. "How do you know those children didn't make you take off your monitor?" Lessing blinked. "Why should they?" "Maybe they enjoy the crash when the blocks fall down." "But that wouldn't make any difference, would it? The blocks still fall down." Melrose paced down the narrow room. "This is very good," he said suddenly, his voice earnest. "You have fine facilities here, good workers. And in spite of my flippancy, Dr. Lessing, I have never imagined for a moment that you were not an acute observer and a careful, highly imaginative worker. But suppose I told you, in perfect faith, that we have data that flatly contradicts everything you've told me today. Reproducible data, utterly incompatable with yours. What would you say to that?" "I'd say you were wrong," said Lessing. "You couldn't have such data. According to the things I am certain are true, what you're saying is sheer nonsense." "And you'd express that opinion in a professional meeting?" "I would." "And as an Authority on psionic behavior patterns," said Melrose slowly, "you would kill us then and there. You would strangle us professionally, discredit anything we did, cut us off cold." The tall man turned on him fiercely. "Are you blind, man? Can't you see what danger you're in? If you publish your book now, you will become an Authority in a field where the most devastating thing that could possibly happen would be— the appearance of an Authority ." Lessing and Dorffman rode back to the Hoffman Center in grim silence. At first Lessing pretended to work; finally he snapped off the tape recorder in disgust and stared out the shuttle-car window. Melrose had gone on to Idlewild to catch a jet back to Chicago. It was a relief to see him go, Lessing thought, and tried to force the thin, angry man firmly out of his mind. But somehow Melrose wouldn't force. "Stop worrying about it," Dorffman urged. "He's a crackpot. He's crawled way out on a limb, and now he's afraid your theory is going to cut it off under him. Well, that's his worry, not yours." Dorffman's face was intense. "Scientifically, you're on unshakeable ground. Every great researcher has people like Melrose sniping at him. You just have to throw them off and keep going." Lessing shook his head. "Maybe. But this field of work is different from any other, Jack. It doesn't follow the rules. Maybe scientific grounds aren't right at all, in this case." Dorffman snorted. "Surely there's nothing wrong with theorizing—" "He wasn't objecting to the theory. He's afraid of what happens after the theory." "So it seems. But why?" "Have you ever considered what makes a man an Authority?" "He knows more about his field than anybody else does." "He seems to, you mean. And therefore, anything he says about it carries more weight than what anybody else says. Other workers follow his lead. He developes ideas, formulates theories—and then defends them for all he's worth ." "But why shouldn't he?" "Because a man can't fight for his life and reputation and still keep his objectivity," said Lessing. "And what if he just happens to be wrong? Once he's an Authority the question of what's right and what's wrong gets lost in the shuffle. It's what he says that counts." "But we know you're right," Dorffman protested. "Do we?" "Of course we do! Look at our work! Look at what we've seen on the Farm." "Yes, I know." Lessing's voice was weary. "But first I think we'd better look at Tommy Gilman, and the quicker we look, the better—" A nurse greeted them as they stepped off the elevator. "We called you at the Farm, but you'd already left. The boy—" She broke off helplessly. "He's sick, Doctor. He's sicker than we ever imagined." "What happened?" "Nothing exactly—happened. I don't quite know how to describe it." She hurried them down the corridor and opened a door into a large children's playroom. "See what you think." The boy sat stolidly in the corner of the room. He looked up as they came in, but there was no flicker of recognition or pleasure on his pale face. The monitor helmet was still on his head. He just sat there, gripping a toy fire engine tightly in his hands. Lessing crossed the room swiftly. "Tommy," he said. The boy didn't even look at him. He stared stupidly at the fire engine. "Tommy!" Lessing reached out for the toy. The boy drew back in terror, clutching it to his chest. "Go away," he choked. "Go away, go away—" When Lessing persisted the boy bent over swiftly and bit him hard on the hand. Lessing sat down on the table. "Tommy, listen to me." His voice was gentle. "I won't try to take it again. I promise." "Go away." "Do you know who I am?" Tommy's eyes shifted haltingly to Lessing's face. He nodded. "Go away." "Why are you afraid, Tommy?" "I hurt. My head hurts. I hurt all over. Go away." "Why do you hurt?" "I—can't get it—off," the boy said. The monitor , Lessing thought suddenly. Something had suddenly gone horribly wrong—could the boy really be sensing the source of the trouble? Lessing felt a cold knot gather in the pit of his stomach. He knew what happened when adult psi-contact struck a psi-high youngster's mind. He had seen it a hundred times at the Farm. But even more—he had felt it in his own mind, bursting from the child. Like a violent physical blow, the hate and fear and suspicion and cruelty buried and repressed in the adult mind, crushing suddenly into the raw receptors of the child's mind like a smothering fog—it was a fearful thing. A healthy youngster could survive it, even though the scar remained. But this youngster was sick— And yet an animal instinctively seeks its own protection . With trembling fingers Lessing reached out and opened the baffle-snap on the monitor. "Take it off, Tommy," he whispered. The boy blinked in amazement, and pulled the grey helmet from his head. Lessing felt the familiar prickly feeling run down his scalp as the boy stared at him. He could feel deep in his own mind the cold chill of terror radiating from the boy. Then, suddenly, it began to fade. A sense of warmth—peace and security and comfort—swept in as the fear faded from the boy's face. The fire engine clattered to the floor. They analyzed the tapes later, punching the data cards with greatest care, filing them through the machines for the basic processing and classification that all their data underwent. It was late that night when they had the report back in their hands. Dorffman stared at it angrily. "It's obviously wrong," he grated. "It doesn't fit. Dave, it doesn't agree with anything we've observed before. There must be an error." "Of course," said Lessing. "According to the theory. The theory says that adult psi-contact is deadly to the growing child. It smothers their potential through repeated contact until it dries up completely. We've proved that, haven't we? Time after time. Everything goes according to the theory—except Tommy. But Tommy's psi-potential was drying up there on the Farm, until the distortion was threatening the balance of his mind. Then he made an adult contact, and we saw how he bloomed." Lessing sank down to his desk wearily. "What are we going to do, Jack? Formulate a separate theory for Tommy?" "Of course not," said Dorffman. "The instruments were wrong. Somehow we misread the data—" "Didn't you see his face ?" Lessing burst out. "Didn't you see how he acted ? What do you want with an instrument reading?" He shook his head. "It's no good, Jack. Something different happened here, something we'd never counted on. It's something the theory just doesn't allow for." They sat silently for a while. Then Dorffman said: "What are you going to do?" "I don't know," said Lessing. "Maybe when we fell into this bramble bush we blinded ourselves with the urge to classify—to line everything up in neat rows like pins in a paper. Maybe we were so blind we missed the path altogether." "But the book is due! The Conference speech—" "I think we'll make some changes in the book," Lessing said slowly. "It'll be costly—but it might even be fun. It's a pretty dry, logical presentation of ideas, as it stands. Very austere and authoritarian. But a few revisions could change all that—" He rubbed his hands together thoughtfully. "How about it, Jack? Do we have nerve enough to be laughed at? Do you think we could stand a little discredit, making silly asses of ourselves? Because when I finish this book, we'll be laughed out of existence. There won't be any Authority in psionics for a while—and maybe that way one of the lads who's really sniffing out the trail will get somebody to listen to him! "Get a pad, get a pencil! We've got work to do. And when we finish, I think we'll send a carbon copy out Chicago way. Might even persuade that puppy out there to come here and work for me—"
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A. The field of psionics is new. If Lessing turns out to be wrong, the whole field of study could be discredited.
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How were Jig and Bucky attacked by the Vapor snakes?
A. They had been released by someone on purpose
B. Bucky had released them while inebriated
C. They had gone into the wrong enclosure.
D. They had escaped their tanks in search of food.
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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.
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A. They had been released by someone on purpose
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What made the narrator's mission so difficult?
A. No one cooperated with the narrator to help him find the right Joe.
B. He became incapacitated by the hot weather on Venus.
C. He became physically lost on Venus.
D. The inhabitants of Venus were all very much the same.
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A PLANET NAMED JOE By S. A. LOMBINO There were more Joes on Venus than you could shake a ray-gun at. Perhaps there was method in Colonel Walsh's madness—murder-madness—when he ordered Major Polk to scan the planet for a guy named Joe. [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.] Colonel Walsh had a great sense of humor. I hated his guts ever since we went through the Academy together, but he had a great sense of humor. For example, he could have chosen a Second Looie for the job on Venus. He might even have picked a Captain. But he liked me about as much as I liked him, and so he decided the job was just right for a Major. At least, that's what he told me. I stood at attention before his desk in the Patrol Station. We were somewhere in Area Two on Earth, takeoff point for any operations in Space II. The duty was fine, and I liked it a lot. Come to think of it, the most I ever did was inspect a few defective tubes every now and then. The rest was gravy, and Colonel Walsh wasn't going to let me get by with gravy. "It will be a simple assignment, Major," he said to me, peering over his fingers. He held them up in front of him like a cathedral. "Yes, sir," I said. "It will involve finding one man, a Venusian native." I wanted to say, "Then why the hell don't you send a green kid on the job? Why me?" Instead, I nodded and watched him playing with his fingers. "The man is a trader of sorts. Rather intelligent." He paused, then added, "For a native, that is." I had never liked Walsh's attitude toward natives. I hadn't liked the way he'd treated the natives on Mars ever since he'd taken over there. Which brought to mind an important point. "I always figured Venus was under the jurisdiction of Space III, sir. I thought our activities were confined to Mars." He folded his fingers like a deck of cards and dropped them on his desk as if he were waiting for me to cut. "Mmmm," he said, "yes, that's true. But this is a special job. It so happens this Venusian is the one man who can help us understand just what's happening on Mars." I tried to picture a Venusian understanding Mars and I didn't get very far. "He's had many dealings with the natives there," Walsh explained. "If anyone can tell us the reasons for the revolt, he can." If Walsh really wanted to know the reasons for the revolt, I could give them to him in one word: Walsh. I had to laugh at the way he called it "revolt." It had been going on for six months now and we'd lost at least a thousand men from Space II. Revolt. "And this man is on Venus now?" I asked for confirmation. I'd never been to Venus, being in Space II ever since I'd left the Moon run. It was just like Walsh to ship me off to a strange place. "Yes, Major," he said. "This man is on Venus." At the Academy he had called me Fred. That was before I'd reported him for sleeping on Boiler Watch. He'd goofed off on a pile of uranium that could've, and almost did, blow the barracks sky-high that night. He still thought it was my fault, as if I'd done the wrong thing by reporting him. And now, through the fouled-up machinery that exists in any military organization, he outranked me. "And the man's name, sir?" "Joe." A tight smile played on his face. "Joe what?" I asked. "Just Joe." "Just Joe?" "Yes," Walsh said. "A native, you know. They rarely go in for more than first names. But then, it should be simple to find a man with a name like Joe. Among the natives, I mean." "I don't know, sir." "A relatively simple assignment," Walsh said. "Can you tell me anything else about this man? Physical appearance? Personal habits? Anything?" Walsh seemed to consider this for a moment. "Well, physically he's like any of the other Venusians, so I can't give you much help there. He does have a peculiar habit, though." "What's that?" "He has an affinity for Terran cigarettes." I sighed. "Well, it's not very much to go on." "You'll find him," Walsh said, grinning. "I'm sure of it." The trip to Venus came off without a hitch. I did a lot of thinking on that trip. I thought about Mars and the revolt there. And I thought about Colonel Leonard Walsh and how he was supposed to be quelling that revolt. Ever since Walsh had taken command, ever since he'd started pushing the natives around, there'd been trouble. It was almost as if the whole damned planet had blown up in our faces the moment he took over. Swell guy, Walsh. Venus was hotter than I'd expected it to be. Much too hot for the tunic I was wearing. It smelled, too. A funny smell I couldn't place. Like a mixture of old shoe and after-shave. There were plants everywhere I looked. Big plants and small ones, some blooming with flowers I'd never seen before, and some as bare as cactus. I recognized a blue figure as one of the natives the pilot had told me about. He was tall, looking almost human except that everything about him was elongated. His features, his muscles, everything seemed to have been stretched like a rubber band. I kept expecting him to pop back to normal. Instead, he flashed a double row of brilliant teeth at me. I wondered if he spoke English. "Hey, boy," I called. He ambled over with long-legged strides that closed the distance between us in seconds. "Call me Joe," he said. I dropped my bags and stared at him. Maybe this was going to be a simple assignment after all. "I sure am glad to see you, Joe," I said. "Same here, Toots," he answered. "The guys back in Space II are searching high and low for you," I told him. "You've got the wrong number," he said, and I was a little surprised at his use of Terran idiom. "You are Joe, aren't you? Joe the trader?" "I'm Joe, all right," he said. "Only thing I ever traded, though, was a pocketknife. Got a set of keys for it." "Oh," I said, my voice conveying my disappointment. I sighed and began wondering just how I should go about contacting the Joe I was looking for. My orders said I was to report to Captain Bransten immediately upon arrival. I figured the hell with Captain Bransten. I outranked him anyway, and there wasn't much he could do if I decided to stop for a drink first. "Where's the Officer's Club?" I asked the Venusian. "Are you buying information or are you just curious?" "Can you take me there?" I asked. "Sure thing, Toots." He picked up my bags and started walking up a heavily overgrown path. We'd probably walked for about ten minutes when he dropped my bags and said, "There it is." The Officer's Club was a plasteel hut with window shields that protected it from the heat of the sun. It didn't look too comfortable but I really wanted that drink. I reached into my tunic and slipped the native thirty solars. He stared at the credits curiously and then shrugged his shoulders. "Oh well, you're new here. We'll let it go." He took off then, while I stared after him, wondering just what he'd meant. Had I tipped him too little? I shrugged and looked over at the Officer's Club. From the outside it looked as hot as hell. On the inside it was about two degrees short of that mark. I began to curse Walsh for taking me away from my nice soft job in Space II. There wasn't much inside the club. A few tables and chairs, a dart game and a bar. Behind the bar a tall Venusian lounged. I walked over and asked, "What are you serving, pal?" "Call me Joe," he answered. He caught me off balance. "What?" "Joe," he said again. A faint glimmer of understanding began to penetrate my thick skull. "You wouldn't happen to be Joe the trader? The guy who knows all about Mars, would you?" "I never left home," he said simply. "What are you drinking?" That rat! That dirty, filthy, stinking, unprincipled.... But then, it should be simple to find a man with a name like Joe. Among the natives, I mean. Sure. Oh sure. Real simple. Walsh was about the lowest, most contemptible.... "What are you drinking, pal?" the Venusian asked again. "Skip it," I said. "How do I get to the captain's shack?" "Follow your nose, pal. Can't miss it." I started to pick up my bag as another Venusian entered. He waved at the bartender. "Hello, Joe," he said. "How's it going?" "Not so hot, Joe," the bartender replied. I listened in fascination. Joe, Joe, Joe. So this was Walsh's idea of a great gag. Very funny. Very.... "You Major Polk, sweetheart?" the Venusian who'd just come in asked. "Yes," I said, still thinking of Colonel Walsh. "You better get your butt over to the captain's shack," he said. "He's about ready to post you as overdue." "Sure," I said wearily. "Will you take my bags, please?" "Roger," he answered. He picked up the bags and nodded at the bar. "So long, Joe," he said to the bartender. "See you, Joe," the bartender called back. Captain Bransten was a mousey, unimpressive sort of man. He was wearing a tropical tunic, but he still resembled a wilted lily more than he did an officer. "Have a seat, Major," he offered. He reached for a cigarette box on the desk and extended it to me. He coughed in embarrassment when he saw it was empty. Quickly, he pressed a button on his desk and the door popped open. A tall, blue Venusian stepped lithely into the room. "Sir?" the Venusian asked. "We're out of cigarettes, Joe," the Captain said. "Will you get us some, please?" "Sure thing," the Venusian answered. He smiled broadly and closed the door behind him. Another Joe , I thought. Another damned Joe. "They steal them," Captain Bransten said abruptly. "Steal what?" I asked. "Cigarettes. I sometimes think the cigarette is one of the few things they like about Terran culture." So Walsh had taken care of that angle too. He does have a peculiar habit, though. He has an affinity for Terran cigarettes. Cigarettes was the tip I should have given; not solars. "All right," I said, "suppose we start at the beginning." Captain Bransten opened his eyes wide. "Sir?" he asked. "What's with all this Joe business? It may be a very original name but I think its popularity here is a little outstanding." Captain Bransten began to chuckle softly. I personally didn't think it was so funny. I tossed him my withering Superior Officer's gaze and waited for his explanation. "I hadn't realized this was your first time on Venus," he said. "Is there a local hero named Joe?" I asked. "No, no, nothing like that," he assured me. "It's a simple culture, you know. Not nearly as developed as Mars." "I can see that," I said bitingly. "And the natives are only now becoming acquainted with Terran culture. Lots of enlisted men, you know." I began to get the idea. And I began to appreciate Walsh's doubtful ancestry more keenly. "It's impossible to tell exactly where it all started, of course," Bransten was saying. I was beginning to get angry. Very angry. I was thinking of Walsh sitting back in a nice cozy foam chair back on Earth. "Get to the point, Captain!" I barked. "Easy, sir," Bransten said, turning pale. I could see that the Captain wasn't used to entertaining Majors. "The enlisted men. You know how they are. They'll ask a native to do something and they'll call him Joe. 'Hey, Joe, give me a hand with this.' Or 'Listen, Joe, how'd you like to earn some cigarettes?' Do you follow?" "I follow, all right," I said bitterly. "Well," Bransten went on, "that sort of thing mushrooms. The natives are a simple, almost childish people. It appealed to them—the Joe business, I mean. Now they're all Joe. They like it. That and the cigarettes." He cleared his throat and looked at me apologetically as if he were personally responsible for Venusian culture. In fact, he looked as if he were responsible for having put Venus in the heavens in the first place. "Do you understand, Major? Just a case of extended idiom, that's all." Just a case of extended idiot , I thought. An idiot on a wild goose chase a hell of a long way from home. "I understand perfectly," I snapped. "Where are my quarters?" Bransten asked a Venusian named Joe to show me my quarters, reminding me that chow was at thirteen hundred. As I was leaving, the first Venusian came back with the cigarettes Bransten had ordered. I could tell by the look on his face that he probably had half a carton stuffed into his pockets. I shrugged and went to change into a tropical tunic. I called Earth right after chow. The Captain assured me that this sort of thing was definitely against regulations, but he submitted when I twinkled my little gold leaf under his nose. Walsh's face appeared on the screen. He was smiling, looking like a fat pussy cat. "What is it, Major?" he asked. "This man Joe," I said. "Can you give me any more on him?" Walsh's grin grew wider. "Why, Major," he said, "you're not having any difficulties, are you?" "None at all," I snapped back. "I just thought I'd be able to find him a lot sooner if...." "Take your time, Major," Walsh beamed. "There's no rush at all." "I thought...." "I'm sure you can do the job," Walsh cut in. "I wouldn't have sent you otherwise." Hell, I was through kidding around. "Look...." "He's somewhere in the jungle, you know," Walsh said. I wanted to ram my fist into the screen, right smack up against those big white teeth. Instead, I cut off the transmission and watched the surprised look on his face as his screen went blank millions of miles away. He blinked at the screen, trying to realize I'd deliberately hung up on him. "Polk!" he shouted, "can you hear me?" I smiled, saw the twisted hatred on his features, and then the screen on my end went blank, too. He's somewhere in the jungle, you know. I thanked Captain Bransten for his hospitality and went back to my quarters. As I saw it, there were two courses for me to follow. One: I could say the hell with Walsh and Venus. That would mean hopping the next ship back to Earth. It would also mean disobeying the direct order of a superior officer. It might mean demotion, and it might mean getting bounced out of the Service altogether. Two: I could assume there really was a guy name Joe somewhere in that jungle, a Joe separate and apart from the other Joes on this planet, a trader Joe who knew the Martians well. I could always admit failure, of course, and return empty handed. Mission not accomplished. Or, I might really find a guy who was trader Joe. I made my decision quickly. I wanted to stay in the Service, and besides Walsh may have been on the level for the first time in his life. Maybe there was a Joe here who could help us on Mars. If there was I'd try to find him. It was still a hell of a trick though. I cursed Walsh again and pushed the buzzer near my bed. A tall Venusian stepped into the room. "Joe?" I asked, just to be sure. "Who else, boss?" he answered. "I'm trying to locate someone," I said. "I'll need a guide to take me into the jungle. Can you get me one?" "It'll cost you, boss," the Venusian said. "How much?" "Two cartons of cigarettes at least." "Who's the guide?" I asked. "How's the price sound?" "Fine, fine," I said impatiently. And the Captain had said they were almost a childish people! "His name is Joe," the Venusian told me. "Best damn guide on the planet. Take you anywhere you want to go, do anything you want to do. Courageous. Doesn't know the meaning of fear. I've known him to...." "Skip it," I said, cutting the promotion short. "Tell him to show up around fifteen hundred with a complete list of what we'll need." The Venusian started to leave. "And Joe," I said, stopping him at the door, "I hope you're not overlooking your commission on the deal." His face broke into a wide grin. "No danger of that, boss," he said. When he was gone I began figuring out a plan of action. Obviously, I'd just have to traipse through the jungle looking for a guy named Joe on a planet where everyone was named Joe. Everybody, at least, but the Captain, the small garrison attached to the Station, and me. I began wondering why Walsh had gone to so much trouble to get rid of me. The job, as I saw it, would take a hell of a long time. It seemed like a silly thing to do, just to get even with a guy for something that had happened years ago. He surely must have realized that I'd be back again, sooner or later. Maybe he had another little junket all set for me. Or maybe he didn't expect me to come back. The thought hadn't occurred to me before this, and I began to consider it seriously. Walsh was no good, rotten clear through. He was failing at the job of keeping Mars in hand, and he probably realized that a few more mistakes on his part would mean the end of his career with Space II. I chuckled as I thought of him isolated in some God-forsaken place like Space V or Space VII. This probably bothered him a lot, too. But what probably bothered him more was the fact that I was next in command. If he were transferred, I'd be in charge of Space II, and I could understand how much that would appeal to Walsh. I tried to figure the thing out sensibly, tried to weigh his good points against his bad. But it all came back to the same thing. A guy who would deliberately go to sleep on Boiler Watch with a ton of uranium ready to blast a barracks to smithereens if it wasn't watched, would deliberately do just about anything. Sending me off on a wild goose chase after a character named Joe may have been a gag. But it may have been something a little grimmer than a gag, and I made up my mind to be extremely careful from here on in. The guide arrived at fifteen hundred on the dot. He was tall, elongated, looked almost like all the other Venusians I'd seen so far. "I understand you need a Grade A guide, sir," he said. "Are you familiar with the jungle?" I asked him. "Born and raised there, sir. Know it like the back of my hand." "Has Joe told you what the payment will be?" "Yes, sir. A carton and a half of cigarettes." I thought about Joe deducting his commission and smiled. "When can we leave?" "Right away, sir. We won't need much really. I've made a list of supplies and I can get them in less than an hour. I suggest you wear light clothing, boots, and a hat." "Will I need a weapon?" He looked at me, his eyes faintly amused. "Why, what for, sir?" "Never mind," I said. "What's your name, by the way?" He lifted his eyebrows, and his eyes widened in his narrow face. He was definitely surprised. "Joe," he said. "Didn't you know?" When we'd been out for a while I discovered why Joe had suggested the boots and the hat. The undergrowth was often sharp and jagged and it would have sliced my legs to ribbons were they not protected by the high boots. The hat kept the strong sun off my head. Joe was an excellent guide and a pleasant companion. He seemed to be enjoying a great romp, seemed to love the jungle and take a secret pleasure in the work he was doing. There were times when I couldn't see three feet ahead of me. He'd stand stock still for a few minutes, his head barely moving, his eyes darting from one plant to another. Then he'd say, "This way," and take off into what looked like more impenetrable jungle invariably to find a little path leading directly to another village. Each village was the same. The natives would come running out of their huts, tall and blue, shouting, "Cigarettes, Joe? Cigarettes?" It took me a while to realize they were addressing me and not my guide. Everybody was Joe. It was one beautiful, happy, joyous round of stinking, hot jungle. And I wasn't getting any nearer my man. Nor had I any idea how I was supposed to find him. I began to feel pretty low about the whole affair. Joe, on the other hand, enjoyed every moment of the trip. In each village he greeted the natives cheerfully, told them stories, swapped gossip and jokes. And when it was time to leave, he would say goodbye to all his friends and we would plunge into the twisted foliage again. His spirits were always high and he never failed to say the right thing that would give a momentary lift to my own depressed state of mind. He would talk for hours on end as we hacked our way through the jungle. "I like Venus," he said once. "I would never leave it." "Have you ever been to Earth?" I asked. "No," Joe replied. "I like Terrans too, you understand. They are good for Venus. And they are fun." "Fun?" I asked, thinking of a particular species of Terran: species Leonard Walsh. "Yes, yes," he said wholeheartedly. "They joke and they laugh and ... well, you know." "I suppose so," I admitted. Joe smiled secretly, and we pushed on. I began to find, more and more, that I had started to talk freely to Joe. In the beginning he had been just my guide. There had been the strained relationship of employer and employee. But as the days lengthened into weeks, the formal atmosphere began to crumble. I found myself telling him all about Earth, about the people there, about my decision to attend the Academy, the rigid tests, the grind, even the Moon run. Joe was a good listener, nodding sympathetically, finding experiences in his own life to parallel my own. And as our relationship progressed from a casual one to a definitely friendly one, Joe seemed more enthusiastic than ever to keep up our grinding pace to find what we were looking for. Once we stopped in a clearing to rest. Joe lounged on the matted greenery, his long body stretched out in front of him, the knife gleaming in his belt. I'd seen him slash his way through thick, tangled vines with that knife, his long, muscular arms powerfully slicing through them like strips of silk. "How far are we from the Station?" I asked. "Three or four Earth weeks," he replied. I sighed wearily. "Where do we go from here?" "There are more villages," he said. "We'll never find him." "Possibly," Joe mused, the smile creeping over his face again. "A wild goose chase. A fool's errand." "We'd better get started," Joe said simply. I got to my feet and we started the march again. Joe was still fresh, a brilliant contrast to me, weary and dejected. Somehow, I had the same feeling I'd had a long time ago on my sixteenth birthday. One of my friends had taken me all over the city, finally dropping me off at my own house where the whole gang was gathered for a surprise party. Joe reminded me of that friend. "There's a village ahead," he said, and the grin on his face was large now, his eyes shining. Something was missing here. Natives. There were no natives rushing out to greet us. No cries of "Cigarettes? Cigarettes?" I caught up with Joe. "What's the story?" I whispered. He shrugged knowingly and continued walking. And then I saw the ship, nose pointing into space, catching the rays of the sun like a great silver bullet. "What...?" I started. "It's all right," Joe said, smiling. The ship looked vaguely familiar. I noticed the crest of Space II near the nose, and a lot of things became clear then. I also saw Walsh standing near one of the huts, a stun gun in his hand. "Hello, Major," he called, almost cheerfully. The gun didn't look cheerful, though. It was pointed at my head. "Fancy meeting you here, Colonel," I said, trying to match his joviality. Somehow it didn't quite come off. Joe was walking beside me, waving at the colonel, beaming all over with happiness. "I see you found your man," Walsh said. I turned rapidly. Joe nodded and kept grinning, a grin that told me he was getting a big kick out of all this. Like a kid playing a game. I faced Walsh again. "Okay, what's it all about, pal?" "Colonel," Walsh corrected me. "You mustn't forget to say Colonel, Major ." He emphasized my rank, and he said it with a sort of ruthless finality. I waited. I could see he was just busting to tell me how clever he'd been. Besides, there wasn't much I could do but wait. Not with Walsh pointing the stun gun at my middle. "We've come a long way since the Academy, haven't we, Major?" "If you mean in miles," I said, looking around at the plants, "we sure have." Walsh grinned a little. "Always the wit," he said drily. And then the smile faded from his lips and his eyes took on a hard lustre. "I'm going to kill you, you know." He said it as if he were saying, "I think it'll rain tomorrow." Joe almost clapped his hands together with glee. He was really enjoying this. Another of those funny Terran games. "You gave me a powerful handicap to overcome," Walsh said. "I suppose I should thank you, really." "You're welcome," I said. "It wasn't easy living down the disgrace you caused me." "It was your own damn fault," I said. "You knew what you were doing when you decided to cork off." Beside me, Joe chuckled a little, enjoying the game immensely. "You didn't have to report me," Walsh said. "No? Maybe I should have forgotten all about it? Maybe I should have nudged you and served you orange juice? So you could do it again sometime and maybe blow up the whole damn Academy!" Walsh was silent for a long time. When he spoke his voice was barely audible. The heat was oppressive, as if it were concentrated on this little spot in the jungle, focusing all its penetration on a small, unimportant drama. I could hear Joe breathing beside me. "I'm on my way out," Walsh rasped. "Finished, do you understand?" "Good," I said. And I meant it. "This Mars thing. A terrible fix. Terrible." Beside me, a slight frown crossed Joe's face. Apparently he couldn't understand the seriousness of our voices. What had happened to the game, the fun? "You brought the Mars business on yourself," I told Walsh. "There was never any trouble before you took command." "The natives," he practically shouted. "They ... they...." Joe caught his breath sharply, and I wondered what Walsh was going to say about the natives. Apparently he'd realized that Joe was a native. Or maybe Joe's knife had something to do with it. "What about the natives?" I asked. "Nothing," Walsh said. "Nothing." He was silent for a while. "A man of my calibre," he said then, his face grim. "Dealing with savages." He caught himself again and threw a hasty glance at Joe. The perplexed frown had grown heavier on Joe's face. He looked at the colonel in puzzlement.
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D. The inhabitants of Venus were all very much the same.
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How often does the Lunar colony get supplies delivered from earth?
A. Every twelve days
B. Every three months
C. Once a month
D. Every two weeks
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SCRIMSHAW The old man just wanted to get back his memory—and the methods he used were gently hellish, from the viewpoint of the others.... BY MURRAY LEINSTER Illustrated by Freas Pop Young was the one known man who could stand life on the surface of the Moon's far side, and, therefore, he occupied the shack on the Big Crack's edge, above the mining colony there. Some people said that no normal man could do it, and mentioned the scar of a ghastly head-wound to explain his ability. One man partly guessed the secret, but only partly. His name was Sattell and he had reason not to talk. Pop Young alone knew the whole truth, and he kept his mouth shut, too. It wasn't anybody else's business. The shack and the job he filled were located in the medieval notion of the physical appearance of hell. By day the environment was heat and torment. By night—lunar night, of course, and lunar day—it was frigidity and horror. Once in two weeks Earth-time a rocketship came around the horizon from Lunar City with stores for the colony deep underground. Pop received the stores and took care of them. He handed over the product of the mine, to be forwarded to Earth. The rocket went away again. Come nightfall Pop lowered the supplies down the long cable into the Big Crack to the colony far down inside, and freshened up the landing field marks with magnesium marking-powder if a rocket-blast had blurred them. That was fundamentally all he had to do. But without him the mine down in the Crack would have had to shut down. The Crack, of course, was that gaping rocky fault which stretches nine hundred miles, jaggedly, over the side of the Moon that Earth never sees. There is one stretch where it is a yawning gulf a full half-mile wide and unguessably deep. Where Pop Young's shack stood it was only a hundred yards, but the colony was a full mile down, in one wall. There is nothing like it on Earth, of course. When it was first found, scientists descended into it to examine the exposed rock-strata and learn the history of the Moon before its craters were made. But they found more than history. They found the reason for the colony and the rocket landing field and the shack. The reason for Pop was something else. The shack stood a hundred feet from the Big Crack's edge. It looked like a dust-heap thirty feet high, and it was. The outside was surface moondust, piled over a tiny dome to be insulation against the cold of night and shadow and the furnace heat of day. Pop lived in it all alone, and in his spare time he worked industriously at recovering some missing portions of his life that Sattell had managed to take away from him. He thought often of Sattell, down in the colony underground. There were galleries and tunnels and living-quarters down there. There were air-tight bulkheads for safety, and a hydroponic garden to keep the air fresh, and all sorts of things to make life possible for men under if not on the Moon. But it wasn't fun, even underground. In the Moon's slight gravity, a man is really adjusted to existence when he has a well-developed case of agoraphobia. With such an aid, a man can get into a tiny, coffinlike cubbyhole, and feel solidity above and below and around him, and happily tell himself that it feels delicious. Sometimes it does. But Sattell couldn't comfort himself so easily. He knew about Pop, up on the surface. He'd shipped out, whimpering, to the Moon to get far away from Pop, and Pop was just about a mile overhead and there was no way to get around him. It was difficult to get away from the mine, anyhow. It doesn't take too long for the low gravity to tear a man's nerves to shreds. He has to develop kinks in his head to survive. And those kinks— The first men to leave the colony had to be knocked cold and shipped out unconscious. They'd been underground—and in low gravity—long enough to be utterly unable to face the idea of open spaces. Even now there were some who had to be carried, but there were some tougher ones who were able to walk to the rocketship if Pop put a tarpaulin over their heads so they didn't have to see the sky. In any case Pop was essential, either for carrying or guidance. Sattell got the shakes when he thought of Pop, and Pop rather probably knew it. Of course, by the time he took the job tending the shack, he was pretty certain about Sattell. The facts spoke for themselves. Pop had come back to consciousness in a hospital with a great wound in his head and no memory of anything that had happened before that moment. It was not that his identity was in question. When he was stronger, the doctors told him who he was, and as gently as possible what had happened to his wife and children. They'd been murdered after he was seemingly killed defending them. But he didn't remember a thing. Not then. It was something of a blessing. But when he was physically recovered he set about trying to pick up the threads of the life he could no longer remember. He met Sattell quite by accident. Sattell looked familiar. Pop eagerly tried to ask him questions. And Sattell turned gray and frantically denied that he'd ever seen Pop before. All of which happened back on Earth and a long time ago. It seemed to Pop that the sight of Sattell had brought back some vague and cloudy memories. They were not sharp, though, and he hunted up Sattell again to find out if he was right. And Sattell went into panic when he returned. Nowadays, by the Big Crack, Pop wasn't so insistent on seeing Sattell, but he was deeply concerned with the recovery of the memories that Sattell helped bring back. Pop was a highly conscientious man. He took good care of his job. There was a warning-bell in the shack, and when a rocketship from Lunar City got above the horizon and could send a tight beam, the gong clanged loudly, and Pop got into a vacuum-suit and went out the air lock. He usually reached the moondozer about the time the ship began to brake for landing, and he watched it come in. He saw the silver needle in the sky fighting momentum above a line of jagged crater-walls. It slowed, and slowed, and curved down as it drew nearer. The pilot killed all forward motion just above the field and came steadily and smoothly down to land between the silvery triangles that marked the landing place. Instantly the rockets cut off, drums of fuel and air and food came out of the cargo-hatch and Pop swept forward with the dozer. It was a miniature tractor with a gigantic scoop in front. He pushed a great mound of talc-fine dust before him to cover up the cargo. It was necessary. With freight costing what it did, fuel and air and food came frozen solid, in containers barely thicker than foil. While they stayed at space-shadow temperature, the foil would hold anything. And a cover of insulating moondust with vacuum between the grains kept even air frozen solid, though in sunlight. At such times Pop hardly thought of Sattell. He knew he had plenty of time for that. He'd started to follow Sattell knowing what had happened to his wife and children, but it was hearsay only. He had no memory of them at all. But Sattell stirred the lost memories. At first Pop followed absorbedly from city to city, to recover the years that had been wiped out by an axe-blow. He did recover a good deal. When Sattell fled to another continent, Pop followed because he had some distinct memories of his wife—and the way he'd felt about her—and some fugitive mental images of his children. When Sattell frenziedly tried to deny knowledge of the murder in Tangier, Pop had come to remember both his children and some of the happiness of his married life. Even when Sattell—whimpering—signed up for Lunar City, Pop tracked him. By that time he was quite sure that Sattell was the man who'd killed his family. If so, Sattell had profited by less than two days' pay for wiping out everything that Pop possessed. But Pop wanted it back. He couldn't prove Sattell's guilt. There was no evidence. In any case, he didn't really want Sattell to die. If he did, there'd be no way to recover more lost memories. Sometimes, in the shack on the far side of the Moon, Pop Young had odd fancies about Sattell. There was the mine, for example. In each two Earth-weeks of working, the mine-colony nearly filled up a three-gallon cannister with greasy-seeming white crystals shaped like two pyramids base to base. The filled cannister would weigh a hundred pounds on Earth. Here it weighed eighteen. But on Earth its contents would be computed in carats, and a hundred pounds was worth millions. Yet here on the Moon Pop kept a waiting cannister on a shelf in his tiny dome, behind the air-apparatus. It rattled if he shook it, and it was worth no more than so many pebbles. But sometimes Pop wondered if Sattell ever thought of the value of the mine's production. If he would kill a woman and two children and think he'd killed a man for no more than a hundred dollars, what enormity would he commit for a three-gallon quantity of uncut diamonds? But he did not dwell on such speculation. The sun rose very, very slowly in what by convention was called the east. It took nearly two hours to urge its disk above the horizon, and it burned terribly in emptiness for fourteen times twenty-four hours before sunset. Then there was night, and for three hundred and thirty-six consecutive hours there were only stars overhead and the sky was a hole so terrible that a man who looked up into it—what with the nagging sensation of one-sixth gravity—tended to lose all confidence in the stability of things. Most men immediately found it hysterically necessary to seize hold of something solid to keep from falling upward. But nothing felt solid. Everything fell, too. Wherefore most men tended to scream. But not Pop. He'd come to the Moon in the first place because Sattell was here. Near Sattell, he found memories of times when he was a young man with a young wife who loved him extravagantly. Then pictures of his children came out of emptiness and grew sharp and clear. He found that he loved them very dearly. And when he was near Sattell he literally recovered them—in the sense that he came to know new things about them and had new memories of them every day. He hadn't yet remembered the crime which lost them to him. Until he did—and the fact possessed a certain grisly humor—Pop didn't even hate Sattell. He simply wanted to be near him because it enabled him to recover new and vivid parts of his youth that had been lost. Otherwise, he was wholly matter-of-fact—certainly so for the far side of the Moon. He was a rather fussy housekeeper. The shack above the Big Crack's rim was as tidy as any lighthouse or fur-trapper's cabin. He tended his air-apparatus with a fine precision. It was perfectly simple. In the shadow of the shack he had an unfailing source of extreme low temperature. Air from the shack flowed into a shadow-chilled pipe. Moisture condensed out of it here, and CO 2 froze solidly out of it there, and on beyond it collected as restless, transparent liquid air. At the same time, liquid air from another tank evaporated to maintain the proper air pressure in the shack. Every so often Pop tapped the pipe where the moisture froze, and lumps of water ice clattered out to be returned to the humidifier. Less often he took out the CO 2 snow, and measured it, and dumped an equivalent quantity of pale-blue liquid oxygen into the liquid air that had been purified by cold. The oxygen dissolved. Then the apparatus reversed itself and supplied fresh air from the now-enriched fluid, while the depleted other tank began to fill up with cold-purified liquid air. Outside the shack, jagged stony pinnacles reared in the starlight, and craters complained of the bombardment from space that had made them. But, outside, nothing ever happened. Inside, it was quite different. Working on his memories, one day Pop made a little sketch. It helped a great deal. He grew deeply interested. Writing-material was scarce, but he spent most of the time between two particular rocket-landings getting down on paper exactly how a child had looked while sleeping, some fifteen years before. He remembered with astonishment that the child had really looked exactly like that! Later he began a sketch of his partly-remembered wife. In time—he had plenty—it became a really truthful likeness. The sun rose, and baked the abomination of desolation which was the moonscape. Pop Young meticulously touched up the glittering triangles which were landing guides for the Lunar City ships. They glittered from the thinnest conceivable layer of magnesium marking-powder. He checked over the moondozer. He tended the air apparatus. He did everything that his job and survival required. Ungrudgingly. Then he made more sketches. The images to be drawn came back more clearly when he thought of Sattell, so by keeping Sattell in mind he recovered the memory of a chair that had been in his forgotten home. Then he drew his wife sitting in it, reading. It felt very good to see her again. And he speculated about whether Sattell ever thought of millions of dollars' worth of new-mined diamonds knocking about unguarded in the shack, and he suddenly recollected clearly the way one of his children had looked while playing with her doll. He made a quick sketch to keep from forgetting that. There was no purpose in the sketching, save that he'd lost all his young manhood through a senseless crime. He wanted his youth back. He was recovering it bit by bit. The occupation made it absurdly easy to live on the surface of the far side of the Moon, whether anybody else could do it or not. Sattell had no such device for adjusting to the lunar state of things. Living on the Moon was bad enough anyhow, then, but living one mile underground from Pop Young was much worse. Sattell clearly remembered the crime Pop Young hadn't yet recalled. He considered that Pop had made no overt attempt to revenge himself because he planned some retaliation so horrible and lingering that it was worth waiting for. He came to hate Pop with an insane ferocity. And fear. In his mind the need to escape became an obsession on top of the other psychotic states normal to a Moon-colonist. But he was helpless. He couldn't leave. There was Pop. He couldn't kill Pop. He had no chance—and he was afraid. The one absurd, irrelevant thing he could do was write letters back to Earth. He did that. He wrote with the desperate, impassioned, frantic blend of persuasion and information and genius-like invention of a prisoner in a high-security prison, trying to induce someone to help him escape. He had friends, of a sort, but for a long time his letters produced nothing. The Moon swung in vast circles about the Earth, and the Earth swung sedately about the Sun. The other planets danced their saraband. The rest of humanity went about its own affairs with fascinated attention. But then an event occurred which bore directly upon Pop Young and Sattell and Pop Young's missing years. Somebody back on Earth promoted a luxury passenger-line of spaceships to ply between Earth and Moon. It looked like a perfect set-up. Three spacecraft capable of the journey came into being with attendant reams of publicity. They promised a thrill and a new distinction for the rich. Guided tours to Lunar! The most expensive and most thrilling trip in history! One hundred thousand dollars for a twelve-day cruise through space, with views of the Moon's far side and trips through Lunar City and a landing in Aristarchus, plus sound-tapes of the journey and fame hitherto reserved for honest explorers! It didn't seem to have anything to do with Pop or with Sattell. But it did. There were just two passenger tours. The first was fully booked. But the passengers who paid so highly, expected to be pleasantly thrilled and shielded from all reasons for alarm. And they couldn't be. Something happens when a self-centered and complacent individual unsuspectingly looks out of a spaceship port and sees the cosmos unshielded by mists or clouds or other aids to blindness against reality. It is shattering. A millionaire cut his throat when he saw Earth dwindled to a mere blue-green ball in vastness. He could not endure his own smallness in the face of immensity. Not one passenger disembarked even for Lunar City. Most of them cowered in their chairs, hiding their eyes. They were the simple cases of hysteria. But the richest girl on Earth, who'd had five husbands and believed that nothing could move her—she went into catatonic withdrawal and neither saw nor heard nor moved. Two other passengers sobbed in improvised strait jackets. The first shipload started home. Fast. The second luxury liner took off with only four passengers and turned back before reaching the Moon. Space-pilots could take the strain of space-flight because they had work to do. Workers for the lunar mines could make the trip under heavy sedation. But it was too early in the development of space-travel for pleasure-passengers. They weren't prepared for the more humbling facts of life. Pop heard of the quaint commercial enterprise through the micro-tapes put off at the shack for the men down in the mine. Sattell probably learned of it the same way. Pop didn't even think of it again. It seemed to have nothing to do with him. But Sattell undoubtedly dealt with it fully in his desperate writings back to Earth. Pop matter-of-factly tended the shack and the landing field and the stores for the Big Crack mine. Between-times he made more drawings in pursuit of his own private objective. Quite accidentally, he developed a certain talent professional artists might have approved. But he was not trying to communicate, but to discover. Drawing—especially with his mind on Sattell—he found fresh incidents popping up in his recollection. Times when he was happy. One day he remembered the puppy his children had owned and loved. He drew it painstakingly—and it was his again. Thereafter he could remember it any time he chose. He did actually recover a completely vanished past. He envisioned a way to increase that recovery. But there was a marked shortage of artists' materials on the Moon. All freight had to be hauled from Earth, on a voyage equal to rather more than a thousand times around the equator of the Earth. Artists' supplies were not often included. Pop didn't even ask. He began to explore the area outside the shack for possible material no one would think of sending from Earth. He collected stones of various sorts, but when warmed up in the shack they were useless. He found no strictly lunar material which would serve for modeling or carving portraits in the ground. He found minerals which could be pulverized and used as pigments, but nothing suitable for this new adventure in the recovery of lost youth. He even considered blasting, to aid his search. He could. Down in the mine, blasting was done by soaking carbon black—from CO 2 —in liquid oxygen, and then firing it with a spark. It exploded splendidly. And its fumes were merely more CO 2 which an air-apparatus handled easily. He didn't do any blasting. He didn't find any signs of the sort of mineral he required. Marble would have been perfect, but there is no marble on the Moon. Naturally! Yet Pop continued to search absorbedly for material with which to capture memory. Sattell still seemed necessary, but— Early one lunar morning he was a good two miles from his shack when he saw rocket-fumes in the sky. It was most unlikely. He wasn't looking for anything of the sort, but out of the corner of his eye he observed that something moved. Which was impossible. He turned his head, and there were rocket-fumes coming over the horizon, not in the direction of Lunar City. Which was more impossible still. He stared. A tiny silver rocket to the westward poured out monstrous masses of vapor. It decelerated swiftly. It curved downward. The rockets checked for an instant, and flamed again more violently, and checked once more. This was not an expert approach. It was a faulty one. Curving surface-ward in a sharply changing parabola, the pilot over-corrected and had to wait to gather down-speed, and then over-corrected again. It was an altogether clumsy landing. The ship was not even perfectly vertical when it settled not quite in the landing-area marked by silvery triangles. One of its tail-fins crumpled slightly. It tilted a little when fully landed. Then nothing happened. Pop made his way toward it in the skittering, skating gait one uses in one-sixth gravity. When he was within half a mile, an air-lock door opened in the ship's side. But nothing came out of the lock. No space-suited figure. No cargo came drifting down with the singular deliberation of falling objects on the Moon. It was just barely past lunar sunrise on the far side of the Moon. Incredibly long and utterly black shadows stretched across the plain, and half the rocketship was dazzling white and half was blacker than blackness itself. The sun still hung low indeed in the black, star-speckled sky. Pop waded through moondust, raising a trail of slowly settling powder. He knew only that the ship didn't come from Lunar City, but from Earth. He couldn't imagine why. He did not even wildly connect it with what—say—Sattell might have written with desperate plausibility about greasy-seeming white crystals out of the mine, knocking about Pop Young's shack in cannisters containing a hundred Earth-pounds weight of richness. Pop reached the rocketship. He approached the big tail-fins. On one of them there were welded ladder-rungs going up to the opened air-lock door. He climbed. The air-lock was perfectly normal when he reached it. There was a glass port in the inner door, and he saw eyes looking through it at him. He pulled the outer door shut and felt the whining vibration of admitted air. His vacuum suit went slack about him. The inner door began to open, and Pop reached up and gave his helmet the practiced twisting jerk which removed it. Then he blinked. There was a red-headed man in the opened door. He grinned savagely at Pop. He held a very nasty hand-weapon trained on Pop's middle. "Don't come in!" he said mockingly. "And I don't give a damn about how you are. This isn't social. It's business!" Pop simply gaped. He couldn't quite take it in. "This," snapped the red-headed man abruptly, "is a stickup!" Pop's eyes went through the inner lock-door. He saw that the interior of the ship was stripped and bare. But a spiral stairway descended from some upper compartment. It had a handrail of pure, transparent, water-clear plastic. The walls were bare insulation, but that trace of luxury remained. Pop gazed at the plastic, fascinated. The red-headed man leaned forward, snarling. He slashed Pop across the face with the barrel of his weapon. It drew blood. It was wanton, savage brutality. "Pay attention!" snarled the red-headed man. "A stickup, I said! Get it? You go get that can of stuff from the mine! The diamonds! Bring them here! Understand?" Pop said numbly: "What the hell?" The red-headed man hit him again. He was nerve-racked, and, therefore, he wanted to hurt. "Move!" he rasped. "I want the diamonds you've got for the ship from Lunar City! Bring 'em!" Pop licked blood from his lips and the man with the weapon raged at him. "Then phone down to the mine! Tell Sattell I'm here and he can come on up! Tell him to bring any more diamonds they've dug up since the stuff you've got!" He leaned forward. His face was only inches from Pop Young's. It was seamed and hard-bitten and nerve-racked. But any man would be quivering if he wasn't used to space or the feel of one-sixth gravity on the Moon. He panted: "And get it straight! You try any tricks and we take off! We swing over your shack! The rocket-blast smashes it! We burn you down! Then we swing over the cable down to the mine and the rocket-flame melts it! You die and everybody in the mine besides! No tricks! We didn't come here for nothing!" He twitched all over. Then he struck cruelly again at Pop Young's face. He seemed filled with fury, at least partly hysterical. It was the tension that space-travel—then, at its beginning—produced. It was meaningless savagery due to terror. But, of course, Pop was helpless to resent it. There were no weapons on the Moon and the mention of Sattell's name showed the uselessness of bluff. He'd pictured the complete set-up by the edge of the Big Crack. Pop could do nothing. The red-headed man checked himself, panting. He drew back and slammed the inner lock-door. There was the sound of pumping. Pop put his helmet back on and sealed it. The outer door opened. Outrushing air tugged at Pop. After a second or two he went out and climbed down the welded-on ladder-bars to the ground. He headed back toward his shack. Somehow, the mention of Sattell had made his mind work better. It always did. He began painstakingly to put things together. The red-headed man knew the routine here in every detail. He knew Sattell. That part was simple. Sattell had planned this multi-million-dollar coup, as a man in prison might plan his break. The stripped interior of the ship identified it. It was one of the unsuccessful luxury-liners sold for scrap. Or perhaps it was stolen for the journey here. Sattell's associates had had to steal or somehow get the fuel, and somehow find a pilot. But there were diamonds worth at least five million dollars waiting for them, and the whole job might not have called for more than two men—with Sattell as a third. According to the economics of crime, it was feasible. Anyhow it was being done. Pop reached the dust-heap which was his shack and went in the air lock. Inside, he went to the vision-phone and called the mine-colony down in the Crack. He gave the message he'd been told to pass on. Sattell to come up, with what diamonds had been dug since the regular cannister was sent up for the Lunar City ship that would be due presently. Otherwise the ship on the landing strip would destroy shack and Pop and the colony together. "I'd guess," said Pop painstakingly, "that Sattell figured it out. He's probably got some sort of gun to keep you from holding him down there. But he won't know his friends are here—not right this minute he won't." A shaking voice asked questions from the vision-phone. "No," said Pop, "they'll do it anyhow. If we were able to tell about 'em, they'd be chased. But if I'm dead and the shacks smashed and the cable burnt through, they'll be back on Earth long before a new cable's been got and let down to you. So they'll do all they can no matter what I do." He added, "I wouldn't tell Sattell a thing about it, if I were you. It'll save trouble. Just let him keep on waiting for this to happen. It'll save you trouble." Another shaky question. "Me?" asked Pop. "Oh, I'm going to raise what hell I can. There's some stuff in that ship I want." He switched off the phone. He went over to his air apparatus. He took down the cannister of diamonds which were worth five millions or more back on Earth. He found a bucket. He dumped the diamonds casually into it. They floated downward with great deliberation and surged from side to side like a liquid when they stopped. One-sixth gravity. Pop regarded his drawings meditatively. A sketch of his wife as he now remembered her. It was very good to remember. A drawing of his two children, playing together. He looked forward to remembering much more about them. He grinned. "That stair-rail," he said in deep satisfaction. "That'll do it!" He tore bed linen from his bunk and worked on the emptied cannister. It was a double container with a thermware interior lining. Even on Earth newly-mined diamonds sometimes fly to pieces from internal stress. On the Moon, it was not desirable that diamonds be exposed to repeated violent changes of temperature. So a thermware-lined cannister kept them at mine-temperature once they were warmed to touchability. Pop packed the cotton cloth in the container. He hurried a little, because the men in the rocket were shaky and might not practice patience. He took a small emergency-lamp from his spare spacesuit. He carefully cracked its bulb, exposing the filament within. He put the lamp on top of the cotton and sprinkled magnesium marking-powder over everything. Then he went to the air-apparatus and took out a flask of the liquid oxygen used to keep his breathing-air in balance. He poured the frigid, pale-blue stuff into the cotton. He saturated it. All the inside of the shack was foggy when he finished. Then he pushed the cannister-top down. He breathed a sigh of relief when it was in place. He'd arranged for it to break a frozen-brittle switch as it descended. When it came off, the switch would light the lamp with its bare filament. There was powdered magnesium in contact with it and liquid oxygen all about. He went out of the shack by the air lock. On the way, thinking about Sattell, he suddenly recovered a completely new memory. On their first wedding anniversary, so long ago, he and his wife had gone out to dinner to celebrate. He remembered how she looked: the almost-smug joy they shared that they would be together for always, with one complete year for proof. Pop reflected hungrily that it was something else to be made permanent and inspected from time to time. But he wanted more than a drawing of this! He wanted to make the memory permanent and to extend it— If it had not been for his vacuum suit and the cannister he carried, Pop would have rubbed his hands. Tall, jagged crater-walls rose from the lunar plain. Monstrous, extended inky shadows stretched enormous distances, utterly black. The sun, like a glowing octopod, floated low at the edge of things and seemed to hate all creation. Pop reached the rocket. He climbed the welded ladder-rungs to the air lock. He closed the door. Air whined. His suit sagged against his body. He took off his helmet. When the red-headed man opened the inner door, the hand-weapon shook and trembled. Pop said calmly: "Now I've got to go handle the hoist, if Sattell's coming up from the mine. If I don't do it, he don't come up." The red-headed man snarled. But his eyes were on the cannister whose contents should weigh a hundred pounds on Earth. "Any tricks," he rasped, "and you know what happens!" "Yeah," said Pop. He stolidly put his helmet back on. But his eyes went past the red-headed man to the stair that wound down, inside the ship, from some compartment above. The stair-rail was pure, clear, water-white plastic, not less than three inches thick. There was a lot of it! The inner door closed. Pop opened the outer. Air rushed out. He climbed painstakingly down to the ground. He started back toward the shack. There was the most luridly bright of all possible flashes. There was no sound, of course. But something flamed very brightly, and the ground thumped under Pop Young's vacuum boots. He turned. The rocketship was still in the act of flying apart. It had been a splendid explosion. Of course cotton sheeting in liquid oxygen is not quite as good an explosive as carbon-black, which they used down in the mine. Even with magnesium powder to start the flame when a bare light-filament ignited it, the cannister-bomb hadn't equaled—say—T.N.T. But the ship had fuel on board for the trip back to Earth. And it blew, too. It would be minutes before all the fragments of the ship returned to the Moon's surface. On the Moon, things fall slowly. Pop didn't wait. He searched hopefully. Once a mass of steel plating fell only yards from him, but it did not interrupt his search. When he went into the shack, he grinned to himself. The call-light of the vision-phone flickered wildly. When he took off his helmet the bell clanged incessantly. He answered. A shaking voice from the mining-colony panted: "We felt a shock! What happened? What do we do?" "Don't do a thing," advised Pop. "It's all right. I blew up the ship and everything's all right. I wouldn't even mention it to Sattell if I were you." He grinned happily down at a section of plastic stair-rail he'd found not too far from where the ship exploded. When the man down in the mine cut off, Pop got out of his vacuum suit in a hurry. He placed the plastic zestfully on the table where he'd been restricted to drawing pictures of his wife and children in order to recover memories of them. He began to plan, gloatingly, the thing he would carve out of a four-inch section of the plastic. When it was carved, he'd paint it. While he worked, he'd think of Sattell, because that was the way to get back the missing portions of his life—the parts Sattell had managed to get away from him. He'd get back more than ever, now! He didn't wonder what he'd do if he ever remembered the crime Sattell had committed. He felt, somehow, that he wouldn't get that back until he'd recovered all the rest. Gloating, it was amusing to remember what people used to call such art-works as he planned, when carved by other lonely men in other faraway places. They called those sculptures scrimshaw. But they were a lot more than that! THE END Transcriber's Note: This etext was produced from Astounding Science Fiction 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.
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D. Every two weeks
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Why would a psychologist be a better programmer than a scientist in response to the WBM having picked a psychologist over a scientist for a programming job?
A. A psychologist would know how to program a chess game to avoid cheating.
B. A psychologist can easily learn programming and has the background to be more effective at it than a scientist.
C. A psychologist knows the rules of chess more than a scientist does.
D. A psychologist could better predict a person's thinking during a chess game than a scientist could.
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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?"
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D. A psychologist could better predict a person's thinking during a chess game than a scientist could.
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Why does Price believe it is important for everyone in the world to be Cured?
A. He believes untreated, repressed fears may arise at any time and manifest as violence towards others.
B. As a former psychiatrist, he believes it is essential for everyone to address their deep-seated issues, and pairing Cures with appropriate psychiatric therapy is the only way to do that.
C. He is a demagogical psychopath who wants to take advantage of people's fears and use them to gain control over society.
D. He is an idealist who believes that humanity can be perfected by the use of scientific and mechanical Cures.
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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.
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A. He believes untreated, repressed fears may arise at any time and manifest as violence towards others.
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What is FY2018 days payable outstanding (DPO) for Walmart? DPO is defined as: 365 * (average accounts payable between FY2017 and FY2018) / (FY2018 COGS + change in inventory between FY2017 and FY2018). Round your answer to two decimal places. Please base your judgments on the information provided primarily in the statement of financial position and the P&L statement.
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Evidence 0:
Walmart Inc.
Consolidated Statements of Income
Fiscal Years Ended January 31,
(Amounts in millions, except per share data)
2018
2017
2016
Revenues:
Net sales
$
495,761
$
481,317 $
478,614
Membership and other income
4,582
4,556
3,516
Total revenues
500,343
485,873
482,130
Costs and expenses:
Cost of sales
373,396
361,256
360,984
Operating, selling, general and administrative expenses
106,510
101,853
97,041
Operating income
20,437
22,764
24,105
Interest:
Debt
1,978
2,044
2,027
Capital lease and financing obligations
352
323
521
Interest income
(152)
(100)
(81)
Interest, net
2,178
2,267
2,467
Loss on extinguishment of debt
3,136
Income before income taxes
15,123
20,497
21,638
Provision for income taxes
4,600
6,204
6,558
Consolidated net income
10,523
14,293
15,080
Consolidated net income attributable to noncontrolling interest
(661)
(650)
(386)
Consolidated net income attributable to Walmart
$
9,862
$
13,643 $
14,694
Net income per common share:
Basic net income per common share attributable to Walmart
$
3.29
$
4.40 $
4.58
Diluted net income per common share attributable to Walmart
3.28
4.38
4.57
Weighted-average common shares outstanding:
Basic
2,995
3,101
3,207
Diluted
3,010
3,112
3,217
Dividends declared per common share
$
2.04
$
2.00 $
1.96
See accompanying notes.
55
Evidence 1:
Walmart Inc.
Consolidated Balance Sheets
As of January 31,
(Amounts in millions)
2018
2017
ASSETS
Current assets:
Cash and cash equivalents
$
6,756
$
6,867
Receivables, net
5,614
5,835
Inventories
43,783
43,046
Prepaid expenses and other
3,511
1,941
Total current assets
59,664
57,689
Property and equipment:
Property and equipment
185,154
179,492
Less accumulated depreciation
(77,479)
(71,782)
Property and equipment, net
107,675
107,710
Property under capital lease and financing obligations:
Property under capital lease and financing obligations
12,703
11,637
Less accumulated amortization
(5,560)
(5,169)
Property under capital lease and financing obligations, net
7,143
6,468
Goodwill
18,242
17,037
Other assets and deferred charges
11,798
9,921
Total assets
$
204,522
$
198,825
LIABILITIES AND EQUITY
Current liabilities:
Short-term borrowings
$
5,257
$
1,099
Accounts payable
46,092
41,433
Accrued liabilities
22,122
20,654
Accrued income taxes
645
921
Long-term debt due within one year
3,738
2,256
Capital lease and financing obligations due within one year
667
565
Total current liabilities
78,521
66,928
Long-term debt
30,045
36,015
Long-term capital lease and financing obligations
6,780
6,003
Deferred income taxes and other
8,354
9,344
Commitments and contingencies
Equity:
Common stock
295
305
Capital in excess of par value
2,648
2,371
Retained earnings
85,107
89,354
Accumulated other comprehensive loss
(10,181)
(14,232)
Total Walmart shareholders' equity
77,869
77,798
Noncontrolling interest
2,953
2,737
Total equity
80,822
80,535
Total liabilities and equity
$
204,522
$
198,825
See accompanying notes.
57
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42.69
|
what classic language models are mentioned in the paper?
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### Introduction
Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 . Specifically focused on language modeling, this paper examines an issue that to our knowledge has not been explored: advances in neural language models have come at a significant cost in terms of increased computational complexity. Computing the probability of a token sequence using non-neural techniques requires a number of phrase lookups and perhaps a few arithmetic operations, whereas model inference with NLMs require large matrix multiplications consuming perhaps millions of floating point operations (FLOPs). These performance tradeoffs are worth discussing. In truth, language models exist in a quality–performance tradeoff space. As model quality increases (e.g., lower perplexity), performance as measured in terms of energy consumption, query latency, etc. tends to decrease. For applications primarily running in the cloud—say, machine translation—practitioners often solely optimize for the lowest perplexity. This is because such applications are embarrassingly parallel and hence trivial to scale in a data center environment. There are, however, applications of NLMs that require less one-sided optimizations. On mobile devices such as smartphones and tablets, for example, NLMs may be integrated into software keyboards for next-word prediction, allowing much faster text entry. Popular Android apps that enthusiastically tout this technology include SwiftKey and Swype. The greater computational costs of NLMs lead to higher energy usage in model inference, translating into shorter battery life. In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\times $ longer and requires 32 $\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point. ### Background and Related Work
BIBREF3 evaluate recent neural language models; however, their focus is not on the computational footprint of each model, but rather the perplexity. To further reduce perplexity, many neural language model extensions exist, such as continuous cache pointer BIBREF5 and mixture of softmaxes BIBREF6 . Since our focus is on comparing “core” neural and non-neural approaches, we disregard these extra optimizations techniques in all of our models. Other work focus on designing lightweight models for resource-efficient inference on mobile devices. BIBREF7 explore LSTMs BIBREF8 with binary weights for language modeling; BIBREF9 examine shallow feedforward neural networks for natural language processing. AWD-LSTM. BIBREF4 show that a simple three-layer LSTM, with proper regularization and optimization techniques, can achieve state of the art on various language modeling datasets, surpassing more complex models. Specifically, BIBREF4 apply randomized backpropagation through time, variational dropout, activation regularization, embedding dropout, and temporal activation regularization. A novel scheduler for optimization, non-monotonically triggered ASGD (NT-ASGD) is also introduced. BIBREF4 name their three-layer LSTM model trained with such tricks, “AWD-LSTM.” Quasi-Recurrent Neural Networks. Quasi-recurrent neural networks (QRNNs; BIBREF10 ) achieve current state of the art in word-level language modeling BIBREF11 . A quasi-recurrent layer comprises two separate parts: a convolution layer with three weights, and a recurrent pooling layer. Given an input $\mathbf {X} \in \mathbb {R}^{k \times n}$ , the convolution layer is $
\mathbf {Z} = \tanh (\mathbf {W}_z \cdot \mathbf {X})\\
\mathbf {F} = \sigma (\mathbf {W}_f \cdot \mathbf {X})\\
\mathbf {O} = \sigma (\mathbf {W}_o \cdot \mathbf {X})
$ where $\sigma $ denotes the sigmoid function, $\cdot $ represents masked convolution across time, and $\mathbf {W}_{\lbrace z, f, o\rbrace } \in \mathbb {R}^{m \times k \times r}$ are convolution weights with $k$ input channels, $m$ output channels, and a window size of $r$ . In the recurrent pooling layer, the convolution outputs are combined sequentially: $
\mathbf {c}_t &= \mathbf {f}_t \odot \mathbf {c}_{t-1} + (1 -
\mathbf {f}_t) \odot \mathbf {z}_t\\
\mathbf {h}_t &= \mathbf {o}_t \odot \mathbf {c}_t
$ Multiple QRNN layers can be stacked for deeper hierarchical representation, with the output $\mathbf {h}_{1:t}$ being fed as the input into the subsequent layer: In language modeling, a four-layer QRNN is a standard architecture BIBREF11 . Perplexity–Recall Scale. Word-level perplexity does not have a strictly monotonic relationship with recall-at- $k$ , the fraction of top $k$ predictions that contain the correct word. A given R@ $k$ imposes a weak minimum perplexity constraint—there are many free parameters that allow for large variability in the perplexity given a certain R@ $k$ . Consider the corpus, “choo choo train,” with an associated unigram model $P(\text{``choo''}) = 0.1$ , $P(\text{``train''}) = 0.9$ , resulting in an R@1 of $1/3$ and perplexity of $4.8$ . Clearly, R@1 $ =1/3$ for all $P(\text{``choo''}) \le 0.5$ ; thus, perplexity can drop as low as 2 without affecting recall. ### Experimental Setup
We conducted our experiments on Penn Treebank (PTB; BIBREF12 ) and WikiText-103 (WT103; BIBREF13 ). Preprocessed by BIBREF14 , PTB contains 887K tokens for training, 70K for validation, and 78K for test, with a vocabulary size of 10,000. On the other hand, WT103 comprises 103 million tokens for training, 217K for validation, and 245K for test, spanning a vocabulary of 267K unique tokens. For the neural language model, we used a four-layer QRNN BIBREF10 , which achieves state-of-the-art results on a variety of datasets, such as WT103 BIBREF11 and PTB. To compare against more common LSTM architectures, we also evaluated AWD-LSTM BIBREF4 on PTB. For the non-neural approach, we used a standard five-gram model with modified Kneser-Ney smoothing BIBREF15 , as explored in BIBREF16 on PTB. We denote the QRNN models for PTB and WT103 as ptb-qrnn and wt103-qrnn, respectively. For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set. ### Hyperparameters and Training
The QRNN models followed the exact training procedure and architecture delineated in the official codebase from BIBREF11 . For ptb-qrnn, we trained the model for 550 epochs using NT-ASGD BIBREF4 , then finetuned for 300 epochs using ASGD BIBREF17 , all with a learning rate of 30 throughout. For wt103-qrnn, we followed BIBREF11 and trained the QRNN for 14 epochs, using the Adam optimizer with a learning rate of $10^{-3}$ . We also applied regularization techniques from BIBREF4 ; all the specific hyperparameters are the same as those in the repository. Our model architecture consists of 400-dimensional tied embedding weights BIBREF18 and four QRNN layers, with 1550 hidden units per layer on PTB and 2500 per layer on WT103. Both QRNN models have window sizes of $r=2$ for the first layer and $r=1$ for the rest. For the KN-5 model, we trained an off-the-shelf five-gram model using the popular SRILM toolkit BIBREF19 . We did not specify any special hyperparameters. ### Infrastructure
We trained the QRNNs with PyTorch (0.4.0; commit 1807bac) on a Titan V GPU. To evaluate the models under a resource-constrained environment, we deployed them on a Raspberry Pi 3 (Model B) running Raspbian Stretch (4.9.41-v7+). The Raspberry Pi (RPi) is not only a standard platform, but also a close surrogate to mobile phones, using the same Cortex-A7 in many phones. We then transferred the trained models to the RPi, using the same frameworks for evaluation. We plugged the RPi into a Watts Up Pro meter, a power meter that can be read programatically over USB at a frequency of 1 Hz. For the QRNNs, we used the first 350 words of the test set, and averaged the ms/query and mJ/query. For KN-5, we used the entire test set for evaluation, since the latency was much lower. To adjust for the base power load, we subtracted idle power draw from energy usage. For a different perspective, we further evaluated all the models under a desktop environment, using an i7-4790k CPU and Titan V GPU. Because the base power load for powering a desktop is much higher than running neural language models, we collected only latency statistics. We used the entire test set, since the QRNN runs quickly. In addition to energy and latency, another consideration for the NLP developer selecting an operating point is the cost of underlying hardware. For our setup, the RPi costs $35 USD, the CPU costs $350 USD, and the GPU costs $3000 USD. ### Results and Discussion
To demonstrate the effectiveness of the QRNN models, we present the results of past and current state-of-the-art neural language models in Table 1 ; we report the Skip- and AWD-LSTM results as seen in the original papers, while we report our QRNN results. Skip LSTM denotes the four-layer Skip LSTM in BIBREF3 . BIBREF20 focus on Hebbian softmax, a model extension technique—Rae-LSTM refers to their base LSTM model without any extensions. In our results, KN-5 refers to the traditional five-gram model with modified Kneser-Ney smoothing, and AWD is shorthand for AWD-LSTM. Perplexity–recall scale. In Figure 1 , using KN-5 as the model, we plot the log perplexity (cross entropy) and R@3 error ( $1 - \text{R@3}$ ) for every sentence in PTB and WT103. The horizontal clusters arise from multiple perplexity points representing the same R@3 value, as explained in Section "Infrastructure" . We also observe that the perplexity–recall scale is non-linear—instead, log perplexity appears to have a moderate linear relationship with R@3 error on PTB ( $r=0.85$ ), and an even stronger relationship on WT103 ( $r=0.94$ ). This is partially explained by WT103 having much longer sentences, and thus less noisy statistics. From Figure 1 , we find that QRNN models yield strongly linear log perplexity–recall plots as well, where $r=0.88$ and $r=0.93$ for PTB and WT103, respectively. Note that, due to the improved model quality over KN-5, the point clouds are shifted downward compared to Figure 1 . We conclude that log perplexity, or cross entropy, provides a more human-understandable indicator of R@3 than perplexity does. Overall, these findings agree with those from BIBREF21 , which explores the log perplexity–word error rate scale in language modeling for speech recognition. Quality–performance tradeoff. In Table 2 , from left to right, we report perplexity results on the validation and test sets, R@3 on test, and finally per-query latency and energy usage. On the RPi, KN-5 is both fast and power-efficient to run, using only about 7 ms/query and 6 mJ/query for PTB (Table 2 , row 1), and 264 ms/q and 229 mJ/q on WT103 (row 5). Taking 220 ms/query and consuming 300 mJ/query, AWD-LSTM and ptb-qrnn are still viable for mobile phones: The modern smartphone holds upwards of 10,000 joules BIBREF22 , and the latency is within usability standards BIBREF23 . Nevertheless, the models are still 49 $\times $ slower and 32 $\times $ more power-hungry than KN-5. The wt103-qrnn model is completely unusable on phones, taking over 1.2 seconds per next-word prediction. Neural models achieve perplexity drops of 60–80% and R@3 increases of 22–34%, but these improvements come at a much higher cost in latency and energy usage. In Table 2 (last two columns), the desktop yields very different results: the neural models on PTB (rows 2–3) are 9 $\times $ slower than KN-5, but the absolute latency is only 8 ms/q, which is still much faster than what humans perceive as instantaneous BIBREF23 . If a high-end commodity GPU is available, then the models are only twice as slow as KN-5 is. From row 5, even better results are noted with wt103-qrnn: On the CPU, the QRNN is only 60% slower than KN-5 is, while the model is faster by 11 $\times $ on a GPU. These results suggest that, if only latency is considered under a commodity desktop environment, the QRNN model is humanly indistinguishable from the KN-5 model, even without using GPU acceleration. ### Conclusion
In the present work, we describe and examine the tradeoff space between quality and performance for the task of language modeling. Specifically, we explore the quality–performance tradeoffs between KN-5, a non-neural approach, and AWD-LSTM and QRNN, two neural language models. We find that with decreased perplexity comes vastly increased computational requirements: In one of the NLMs, a perplexity reduction by 2.5 $\times $ results in a 49 $\times $ rise in latency and 32 $\times $ increase in energy usage, when compared to KN-5. Table 1: Comparison of neural language models on Penn Treebank and WikiText-103. Figure 1: Log perplexity–recall error with KN-5. Figure 2: Log perplexity–recall error with QRNN. Table 2: Language modeling results on performance and model quality.
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Kneser–Ney smoothing
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What multimodality is available in the dataset?
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### Introduction
A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling the intrinsic dynamics of the procedures BIBREF0, BIBREF1, BIBREF2. That is, one must be aware of the entities present in the text, infer relations among them and even anticipate changes in the states of the entities after each action. For example, consider the cheeseburger recipe presented in Fig. FIGREF2. The instruction “salt and pepper each patty and cook for 2 to 3 minutes on the first side” in Step 5 entails mixing three basic ingredients, the ground beef, salt and pepper, together and then applying heat to the mix, which in turn causes chemical changes that alter both the appearance and the taste. From a natural language understanding perspective, the main difficulty arises when a model sees the word patty again at a later stage of the recipe. It still corresponds to the same entity, but its form is totally different. Over the past few years, many new datasets and approaches have been proposed that address this inherently hard problem BIBREF0, BIBREF1, BIBREF3, BIBREF4. To mitigate the aforementioned challenges, the existing works rely mostly on heavy supervision and focus on predicting the individual state changes of entities at each step. Although these models can accurately learn to make local predictions, they may lack global consistency BIBREF3, BIBREF4, not to mention that building such annotated corpora is very labor-intensive. In this work, we take a different direction and explore the problem from a multimodal standpoint. Our basic motivation, as illustrated in Fig. FIGREF2, is that accompanying images provide complementary cues about causal effects and state changes. For instance, it is quite easy to distinguish raw meat from cooked one in visual domain. In particular, we take advantage of recently proposed RecipeQA dataset BIBREF2, a dataset for multimodal comprehension of cooking recipes, and ask whether it is possible to have a model which employs dynamic representations of entities in answering questions that require multimodal understanding of procedures. To this end, inspired from BIBREF5, we propose Procedural Reasoning Networks (PRN) that incorporates entities into the comprehension process and allows to keep track of entities, understand their interactions and accordingly update their states across time. We report that our proposed approach significantly improves upon previously published results on visual reasoning tasks in RecipeQA, which test understanding causal and temporal relations from images and text. We further show that the dynamic entity representations can capture semantics of the state information in the corresponding steps. ### Visual Reasoning in RecipeQA
In our study, we particularly focus on the visual reasoning tasks of RecipeQA, namely visual cloze, visual coherence, and visual ordering tasks, each of which examines a different reasoning skill. We briefly describe these tasks below. Visual Cloze. In the visual cloze task, the question is formed by a sequence of four images from consecutive steps of a recipe where one of them is replaced by a placeholder. A model should select the correct one from a multiple-choice list of four answer candidates to fill in the missing piece. In that regard, the task inherently requires aligning visual and textual information and understanding temporal relationships between the cooking actions and the entities. Visual Coherence. The visual coherence task tests the ability to identify the image within a sequence of four images that is inconsistent with the text instructions of a cooking recipe. To succeed in this task, a model should have a clear understanding of the procedure described in the recipe and at the same time connect language and vision. Visual Ordering. The visual ordering task is about grasping the temporal flow of visual events with the help of the given recipe text. The questions show a set of four images from the recipe and the task is to sort jumbled images into the correct order. Here, a model needs to infer the temporal relations between the images and align them with the recipe steps. ### Procedural Reasoning Networks
In the following, we explain our Procedural Reasoning Networks model. Its architecture is based on a bi-directional attention flow (BiDAF) model BIBREF6, but also equipped with an explicit reasoning module that acts on entity-specific relational memory units. Fig. FIGREF4 shows an overview of the network architecture. It consists of five main modules: An input module, an attention module, a reasoning module, a modeling module, and an output module. Note that the question answering tasks we consider here are multimodal in that while the context is a procedural text, the question and the multiple choice answers are composed of images. Input Module extracts vector representations of inputs at different levels of granularity by using several different encoders. Reasoning Module scans the procedural text and tracks the states of the entities and their relations through a recurrent relational memory core unit BIBREF5. Attention Module computes context-aware query vectors and query-aware context vectors as well as query-aware memory vectors. Modeling Module employs two multi-layered RNNs to encode previous layers outputs. Output Module scores a candidate answer from the given multiple-choice list. At a high level, as the model is reading the cooking recipe, it continually updates the internal memory representations of the entities (ingredients) based on the content of each step – it keeps track of changes in the states of the entities, providing an entity-centric summary of the recipe. The response to a question and a possible answer depends on the representation of the recipe text as well as the last states of the entities. All this happens in a series of implicit relational reasoning steps and there is no need for explicitly encoding the state in terms of a predefined vocabulary. ### Procedural Reasoning Networks ::: Input Module
Let the triple $(\mathbf {R},\mathbf {Q},\mathbf {A})$ be a sample input. Here, $\mathbf {R}$ denotes the input recipe which contains textual instructions composed of $N$ words in total. $\mathbf {Q}$ represents the question that consists of a sequence of $M$ images. $\mathbf {A}$ denotes an answer that is either a single image or a series of $L$ images depending on the reasoning task. In particular, for the visual cloze and the visual coherence type questions, the answer contains a single image ($L=1$) and for the visual ordering task, it includes a sequence. We encode the input recipe $\mathbf {R}$ at character, word, and step levels. Character-level embedding layer uses a convolutional neural network, namely CharCNN model by BIBREF7, which outputs character level embeddings for each word and alleviates the issue of out-of-vocabulary (OOV) words. In word embedding layer, we use a pretrained GloVe model BIBREF8 and extract word-level embeddings. The concatenation of the character and the word embeddings are then fed to a two-layer highway network BIBREF10 to obtain a contextual embedding for each word in the recipe. This results in the matrix $\mathbf {R}^{\prime } \in \mathbb {R}^{2d \times N}$. On top of these layers, we have another layer that encodes the steps of the recipe in an individual manner. Specifically, we obtain a step-level contextual embedding of the input recipe containing $T$ steps as $\mathcal {S}=(\mathbf {s}_1,\mathbf {s}_2,\dots ,\mathbf {s}_T)$ where $\mathbf {s}_i$ represents the final state of a BiLSTM encoding the $i$-th step of the recipe obtained from the character and word-level embeddings of the tokens exist in the corresponding step. We represent both the question $\mathbf {Q}$ and the answer $\mathbf {A}$ in terms of visual embeddings. Here, we employ a pretrained ResNet-50 model BIBREF11 trained on ImageNet dataset BIBREF12 and represent each image as a real-valued 2048-d vector using features from the penultimate average-pool layer. Then these embeddings are passed first to a multilayer perceptron (MLP) and then its outputs are fed to a BiLSTM. We then form a matrix $\mathbf {Q}^{\prime } \in \mathbb {R}^{2d \times M}$ for the question by concatenating the cell states of the BiLSTM. For the visual ordering task, to represent the sequence of images in the answer with a single vector, we additionally use a BiLSTM and define the answering embedding by the summation of the cell states of the BiLSTM. Finally, for all tasks, these computations produce answer embeddings denoted by $\mathbf {a} \in \mathbb {R}^{2d \times 1}$. ### Procedural Reasoning Networks ::: Reasoning Module
As mentioned before, comprehending a cooking recipe is mostly about entities (basic ingredients) and actions (cooking activities) described in the recipe instructions. Each action leads to changes in the states of the entities, which usually affects their visual characteristics. A change rarely occurs in isolation; in most cases, the action affects multiple entities at once. Hence, in our reasoning module, we have an explicit memory component implemented with relational memory units BIBREF5. This helps us to keep track of the entities, their state changes and their relations in relation to each other over the course of the recipe (see Fig. FIGREF14). As we will examine in more detail in Section SECREF4, it also greatly improves the interpretability of model outputs. Specifically, we set up the memory with a memory matrix $\mathbf {E} \in \mathbb {R}^{d_E \times K}$ by extracting $K$ entities (ingredients) from the first step of the recipe. We initialize each memory cell $\mathbf {e}_i$ representing a specific entity by its CharCNN and pre-trained GloVe embeddings. From now on, we will use the terms memory cells and entities interchangeably throughout the paper. Since the input recipe is given in the form of a procedural text decomposed into a number of steps, we update the memory cells after each step, reflecting the state changes happened on the entities. This update procedure is modelled via a relational recurrent neural network (R-RNN), recently proposed by BIBREF5. It is built on a 2-dimensional LSTM model whose matrix of cell states represent our memory matrix $\mathbf {E}$. Here, each row $i$ of the matrix $\mathbf {E}$ refers to a specific entity $\mathbf {e}_i$ and is updated after each recipe step $t$ as follows: where $\mathbf {s}_{t}$ denotes the embedding of recipe step $t$ and $\mathbf {\phi }_{i,t}=(\mathbf {h}_{i,t},\mathbf {e}_{i,t})$ is the cell state of the R-RNN at step $t$ with $\mathbf {h}_{i,t}$ and $\mathbf {e}_{i,t}$ being the $i$-th row of the hidden state of the R-RNN and the dynamic representation of entity $\mathbf {e}_{i}$ at the step $t$, respectively. The R-RNN model exploits a multi-headed self-attention mechanism BIBREF13 that allows memory cells to interact with each other and attend multiple locations simultaneously during the update phase. In Fig. FIGREF14, we illustrate how this interaction takes place in our relational memory module by considering a sample cooking recipe and by presenting how the attention matrix changes throughout the recipe. In particular, the attention matrix at a specific time shows the attention flow from one entity (memory cell) to another along with the attention weights to the corresponding recipe step (offset column). The color intensity shows the magnitude of the attention weights. As can be seen from the figure, the internal representations of the entities are actively updated at each step. Moreover, as argued in BIBREF5, this can be interpreted as a form of relational reasoning as each update on a specific memory cell is operated in relation to others. Here, we should note that it is often difficult to make sense of these attention weights. However, we observe that the attention matrix changes very gradually near the completion of the recipe. ### Procedural Reasoning Networks ::: Attention Module
Attention module is in charge of linking the question with the recipe text and the entities present in the recipe. It takes the matrices $\mathbf {Q^{\prime }}$ and $\mathbf {R}^{\prime }$ from the input module, and $\mathbf {E}$ from the reasoning module and constructs the question-aware recipe representation $\mathbf {G}$ and the question-aware entity representation $\mathbf {Y}$. Following the attention flow mechanism described in BIBREF14, we specifically calculate attentions in four different directions: (1) from question to recipe, (2) from recipe to question, (3) from question to entities, and (4) from entities to question. The first two of these attentions require computing a shared affinity matrix $\mathbf {S}^R \in \mathbb {R}^{N \times M}$ with $\mathbf {S}^R_{i,j}$ indicating the similarity between $i$-th recipe word and $j$-th image in the question estimated by where $\mathbf {w}^{\top }_{R}$ is a trainable weight vector, $\circ $ and $[;]$ denote elementwise multiplication and concatenation operations, respectively. Recipe-to-question attention determines the images within the question that is most relevant to each word of the recipe. Let $\mathbf {\tilde{Q}} \in \mathbb {R}^{2d \times N}$ represent the recipe-to-question attention matrix with its $i$-th column being given by $ \mathbf {\tilde{Q}}_i=\sum _j \mathbf {a}_{ij}\mathbf {Q}^{\prime }_j$ where the attention weight is computed by $\mathbf {a}_i=\operatorname{softmax}(\mathbf {S}^R_{i}) \in \mathbb {R}^M$. Question-to-recipe attention signifies the words within the recipe that have the closest similarity to each image in the question, and construct an attended recipe vector given by $ \tilde{\mathbf {r}}=\sum _{i}\mathbf {b}_i\mathbf {R}^{\prime }_i$ with the attention weight is calculated by $\mathbf {b}=\operatorname{softmax}(\operatorname{max}_{\mathit {col}}(\mathbf {S}^R)) \in \mathbb {R}^{N}$ where $\operatorname{max}_{\mathit {col}}$ denotes the maximum function across the column. The question-to-recipe matrix is then obtained by replicating $\tilde{\mathbf {r}}$ $N$ times across the column, giving $\tilde{\mathbf {R}} \in \mathbb {R}^{2d \times N}$. Then, we construct the question aware representation of the input recipe, $\mathbf {G}$, with its $i$-th column $\mathbf {G}_i \in \mathbb {R}^{8d \times N}$ denoting the final embedding of $i$-th word given by Attentions from question to entities, and from entities to question are computed in a way similar to the ones described above. The only difference is that it uses a different shared affinity matrix to be computed between the memory encoding entities $\mathbf {E}$ and the question $\mathbf {Q}^{\prime }$. These attentions are then used to construct the question aware representation of entities, denoted by $\mathbf {Y}$, that links and integrates the images in the question and the entities in the input recipe. ### Procedural Reasoning Networks ::: Modeling Module
Modeling module takes the question-aware representations of the recipe $\mathbf {G}$ and the entities $\mathbf {Y}$, and forms their combined vector representation. For this purpose, we first use a two-layer BiLSTM to read the question-aware recipe $\mathbf {G}$ and to encode the interactions among the words conditioned on the question. For each direction of BiLSTM , we use its hidden state after reading the last token as its output. In the end, we obtain a vector embedding $\mathbf {c} \in \mathbb {R}^{2d \times 1}$. Similarly, we employ a second BiLSTM, this time, over the entities $\mathbf {Y}$, which results in another vector embedding $\mathbf {f} \in \mathbb {R}^{2d_E \times 1}$. Finally, these vector representations are concatenated and then projected to a fixed size representation using $\mathbf {o}=\varphi _o(\left[\mathbf {c}; \mathbf {f}\right]) \in \mathbb {R}^{2d \times 1}$ where $\varphi _o$ is a multilayer perceptron with $\operatorname{tanh}$ activation function. ### Procedural Reasoning Networks ::: Output Module
The output module takes the output of the modeling module, encoding vector embeddings of the question-aware recipe and the entities $\mathbf {Y}$, and the embedding of the answer $\mathbf {A}$, and returns a similarity score which is used while determining the correct answer. Among all the candidate answer, the one having the highest similarity score is chosen as the correct answer. To train our proposed procedural reasoning network, we employ a hinge ranking loss BIBREF15, similar to the one used in BIBREF2, given below. where $\gamma $ is the margin parameter, $\mathbf {a}_+$ and $\mathbf {a}_{-}$ are the correct and the incorrect answers, respectively. ### Experiments
In this section, we describe our experimental setup and then analyze the results of the proposed Procedural Reasoning Networks (PRN) model. ### Experiments ::: Entity Extraction
Given a recipe, we automatically extract the entities from the initial step of a recipe by using a dictionary of ingredients. While determining the ingredients, we exploit Recipe1M BIBREF16 and Kaggle What’s Cooking Recipes BIBREF17 datasets, and form our dictionary using the most commonly used ingredients in the training set of RecipeQA. For the cases when no entity can be extracted from the recipe automatically (20 recipes in total), we manually annotate those recipes with the related entities. ### Experiments ::: Training Details
In our experiments, we separately trained models on each task, as well as we investigated multi-task learning where a single model is trained to solve all these tasks at once. In total, the PRN architecture consists of $\sim $12M trainable parameters. We implemented our models in PyTorch BIBREF18 using AllenNLP library BIBREF6. We used Adam optimizer with a learning rate of 1e-4 with an early stopping criteria with the patience set to 10 indicating that the training procedure ends after 10 iterations if the performance would not improve. We considered a batch size of 32 due to our hardware constraints. In the multi-task setting, batches are sampled round-robin from all tasks, where each batch is solely composed of examples from one task. We performed our experiments on a system containing four NVIDIA GTX-1080Ti GPUs, and training a single model took around 2 hours. We employed the same hyperparameters for all the baseline systems. We plan to share our code and model implementation after the review process. ### Experiments ::: Baselines
We compare our model with several baseline models as described below. We note that the results of the first two are previously reported in BIBREF2. Hasty Student BIBREF2 is a heuristics-based simple model which ignores the recipe and gives an answer by examining only the question and the answer set using distances in the visual feature space. Impatient Reader BIBREF19 is a simple neural model that takes its name from the fact that it repeatedly computes attention over the recipe after observing each image in the query. BiDAF BIBREF14 is a strong reading comprehension model that employs a bi-directional attention flow mechanism to obtain a question-aware representation and bases its predictions on this representation. Originally, it is a span-selection model from the input context. Here, we adapt it to work in a multimodal setting and answer multiple choice questions instead. BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities. However, it does not make any updates on the memory cells. That is, it uses the static entity embeeddings initialized with GloVe word vectors. We propose this baseline to test the significance of the use of relational memory updates. ### Experiments ::: Results
Table TABREF29 presents the quantitative results for the visual reasoning tasks in RecipeQA. In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy BIBREF20. In Fig. FIGREF28, we illustrate the entity embeddings space by projecting the learned embeddings from the step-by-step memory snapshots through time with t-SNE to 3-d space from 200-d vector space. Color codes denote the categories of the cooking recipes. As can be seen, these step-aware embeddings show clear clustering of these categories. Moreover, within each cluster, the entities are grouped together in terms of their state characteristics. For instance, in the zoomed parts of the figure, chopped and sliced, or stirred and whisked entities are placed close to each other. Fig. FIGREF30 demonstrates the entity arithmetics using the learned embeddings from each entity step. Here, we show that the learned embedding from the memory snapshots can effectively capture the contextual information about the entities at each time point in the corresponding step while taking into account of the recipe data. This basic arithmetic operation suggests that the proposed model can successfully capture the semantics of each entity's state in the corresponding step. ### Related Work
In recent years, tracking entities and their state changes have been explored in the literature from a variety of perspectives. In an early work, BIBREF21 proposed a dynamic memory based network which updates entity states using a gating mechanism while reading the text. BIBREF22 presented a more structured memory augmented model which employs memory slots for representing both entities and their relations. BIBREF23 suggested a conceptually similar model in which the pairwise relations between attended memories are utilized to encode the world state. The main difference between our approach and these works is that by utilizing relational memory core units we also allow memories to interact with each other during each update. BIBREF24 showed that similar ideas can be used to compile supporting memories in tracking dialogue state. BIBREF25 has shown the importance of coreference signals for reading comprehension task. More recently, BIBREF26 introduced a specialized recurrent layer which uses coreference annotations for improving reading comprehension tasks. On language modeling task, BIBREF27 proposed a language model which can explicitly incorporate entities while dynamically updating their representations for a variety of tasks such as language modeling, coreference resolution, and entity prediction. Our work builds upon and contributes to the growing literature on tracking states changes in procedural text. BIBREF0 presented a neural model that can learn to explicitly predict state changes of ingredients at different points in a cooking recipe. BIBREF1 proposed another entity-aware model to track entity states in scientific processes. BIBREF3 demonstrated that the prediction quality can be boosted by including hard and soft constraints to eliminate unlikely or favor probable state changes. In a follow-up work, BIBREF4 exploited the notion of label consistency in training to enforce similar predictions in similar procedural contexts. BIBREF28 proposed a model that dynamically constructs a knowledge graph while reading the procedural text to track the ever-changing entities states. As discussed in the introduction, however, these previous methods use a strong inductive bias and assume that state labels are present during training. In our study, we deliberately focus on unlabeled procedural data and ask the question: Can multimodality help to identify and provide insights to understanding state changes. ### Conclusion
We have presented a new neural architecture called Procedural Reasoning Networks (PRN) for multimodal understanding of step-by-step instructions. Our proposed model is based on the successful BiDAF framework but also equipped with an explicit memory unit that provides an implicit mechanism to keep track of the changes in the states of the entities over the course of the procedure. Our experimental analysis on visual reasoning tasks in the RecipeQA dataset shows that the model significantly improves the results of the previous models, indicating that it better understands the procedural text and the accompanying images. Additionally, we carefully analyze our results and find that our approach learns meaningful dynamic representations of entities without any entity-level supervision. Although we achieve state-of-the-art results on RecipeQA, clearly there is still room for improvement compared to human performance. We also believe that the PRN architecture will be of value to other visual and textual sequential reasoning tasks. ### Acknowledgements
We thank the anonymous reviewers and area chairs for their invaluable feedback. This work was supported by TUBA GEBIP fellowship awarded to E. Erdem; and by the MMVC project via an Institutional Links grant (Project No. 217E054) under the Newton-Katip Çelebi Fund partnership funded by the Scientific and Technological Research Council of Turkey (TUBITAK) and the British Council. We also thank NVIDIA Corporation for the donation of GPUs used in this research. Figure 1: A recipe for preparing a cheeseburger (adapted from the cooking instructions available at https: //www.instructables.com/id/In-N-Out-Double-Double-Cheeseburger-Copycat). Each basic ingredient (entity) is highlighted by a different color in the text and with bounding boxes on the accompanying images. Over the course of the recipe instructions, ingredients interact with each other, change their states by each cooking action (underlined in the text), which in turn alter the visual and physical properties of entities. For instance, the tomato changes it form by being sliced up and then stacked on a hamburger bun. Figure 2: An illustration of our Procedural Reasoning Networks (PRN). For a sample question from visual coherence task in RecipeQA, while reading the cooking recipe, the model constantly performs updates on the representations of the entities (ingredients) after each step and makes use of their representations along with the whole recipe when it scores a candidate answer. Please refer to the main text for more details. Figure 3: Sample visualizations of the self-attention weights demonstrating both the interactions among the ingredients and between the ingredients and the textual instructions throughout the steps of a sample cooking recipe from RecipeQA (darker colors imply higher attention weights). The attention maps do not change much after the third step as the steps after that mostly provide some redundant information about the completed recipe. Figure 4: t-SNE visualizations of learned embeddings from each memory snapshot mapping to each entity and their corresponding states from each step for visual cloze task. Table 1: Quantitative comparison of the proposed PRN model against the baselines. Figure 5: Step-aware entity representations can be used to discover the changes occurred in the states of the ingredients between two different recipe steps. The difference vector between two entities can then be added to other entities to find their next states. For instance, in the first example, the difference vector encodes the chopping action done on onions. In the second example, it encodes the pouring action done on the water. When these vectors are added to the representations of raw tomatoes and milk, the three most likely next states capture the semantics of state changes in an accurate manner.
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images and text
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Which term best describes Sara's relationship with her parents?
A. inflammatory
B. tenuous
C. strained
D. obligatory
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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.
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C. strained
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Historical figures have proposed all of the following theories regarding physiognomy EXCEPT for the idea that:
A. humans share similar characteristics to animals based on their facial features and mannerisms
B. humans can use physiognomy to select which employees, slaves, and mates may be most compatible with them
C. humans are constantly influenced by physiognomy on a daily basis
D. humans will never be able to eliminate the effects of physiognomy from their decision-making
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Face value When the BBC broadcast the recent documentary by Louis Theroux that looked back at the time he spent in the company of Jimmy Savile, there was disbelief across social media that no one had stepped in to stop Savile from committing his crimes. Some blamed the BBC, some blamed those in Savile's immediate circle, but others blamed a simple error of human judgment. "He literally couldn't look more like a paedophile," read one post – one of many to state a supposedly incontrovertible truth: that Savile's criminal tendencies could have been detected from the shape of his features, his eyes, his hair. Moreover, this has nothing to do with the benefit of hindsight and should have been picked up at the time. His looks, they suggested, were a moral indicator, with a wealth of compelling visual evidence to support the claim. We know that paedophiles, murderers and other violent criminals come in many shapes and sizes. If we knew nothing about their criminal history, some of their photos might even appear attractive. But the idea that someone's features betray their character is something rooted deep within us; it's the reason why certain photos perform well on dating apps, or why trustworthy-looking politicians might rack up votes. But how wrong are our hunches of perceived criminality? A recent paper, published by Xiaolin Wu and Xi Zhang of Shanghai's Jiao Tong University, claims to be the first to use machine learning and neural networks to attempt a fully automated inference of criminality from facial images, removing prejudice from the equation and testing the validity of our gut feelings. "What facial features influence the average Joe's impulsive and yet consensual judgments on social attributes?" they ask. Through a study of 1,856 images ("controlled for race, gender, age and facial expression") they claim to have established the validity of "automated, face-induced inference on criminality, despite the historical controversy surrounding this line of enquiry." In other words, they believe that they've found a relationship between looking like a criminal and actually being one. It's a claim that's been made many times over the years. Physiognomy, the 'science' of judging people by their appearance, was first theorised by the ancient Greeks in around the 5th century BC. Aristotle's pronouncement that "it is possible to infer character from features" led to a number of works relating to 'Physiognomica', a word derived from physis (nature), nomos (law) and (or) gnomon (judge or interpreter). All of Greek society, it was claimed, could benefit from this skill: it could assist with choosing an employee, a slave or a spouse, while its inherent vagueness made it intriguing to philosophers and useful for scientists who bent the theories to support their own beliefs. It became a recognised science in the Islamic world, and was used and taught in Europe throughout late antiquity and the early Middle Ages, despite nagging doubts among thinkers and physicians of the day. In the early 16th century, Leonardo da Vinci claimed not to "concern myself with false physiognomy, because these chimeras have no scientific foundation." Theories of physiognomy, however, would persist beyond the Renaissance. In 1586, Italian scholar Giambattista della Porta published a book, De humana physiognomonia libri IIII, which established him as the 'father of Physiognomy'. Della Porta's thinking was based on the 'doctrine of signatures'; the idea that the appearance of plants and animals offers clues to their nature. For example, as one writer of the time suggested, walnuts are good for curing headaches because they're shaped a bit like a human head. The theories in della Porta's book were supported by dozens of detailed illustrations which, by comparing human faces to those of animals, suggested that they must surely share similar character traits. In the 17th century, Swiss poet Johann Caspar Lavater took della Porta's methodology and ran with it, commissioning artists to illustrate his popular Essays On Physiognomy – which, to the chagrin of his contemporary, the writer Hannah More, sold for "fifteen guineas a set… while in vain we boast that philosophy [has] broken down all the strongholds of prejudice, ignorance, and superstition." Lavater's work was criticised for being ridden with bias (black faces rarely emerged well from his analyses) but he was right in one respect: "Whether they are or are not sensible of it," he wrote, "all men are daily influenced by physiognomy." Many studies have been done into our psychological response to faces, and it's clear that a so-called halo effect will inevitably work its magic. "Attractive people are regarded as better at everything," says Professor Peter Hancock, lecturer in Psychology at Stirling University. "And we can't shake that off because there's some truth to it. Good genes produce intelligent people, attractive faces, fit bodies, and we imagine that they're going to be good at everything else, too. We don't have good insight into our own behaviour. We tend to think we understand what we're doing, but we don't." Hancock describes attending a conference where one speaker showed a series of black faces and white faces to students (who were mostly white) and asked them what they thought the experiment was about. "They knew that he was trying to assess whether they would rate the black ones as more criminal," says Hancock. "But then they did!" We attribute social characteristics based on opinions we already hold about certain kinds of faces: whether they look unusual in some way, whether they resemble a partner, a family member or even ourselves, or perhaps have some other cultural association. Physiognomy ultimately stems from what Alexander Todorov, professor of psychology at Princeton University, calls an 'overgeneralisation hypothesis'. "People," he wrote, "use easily accessible facial information (eg an expression such as a smile, cues to gender and ethnic group) to make social attributions congruent with this information (eg a nice person)." In a social media age, the pictures we choose to represent ourselves online are a form of self-presentation driven by those social attributions and the knowledge that our pictures are being judged. Experiments at Princeton found that we take less than one tenth of a second to form an opinion of strangers from their pictures, and those opinions tend to stand firm even if we're exposed to those pictures for a longer period of time. That tendency to judge instantly gives rise to a number of selfie tropes that are deemed to elicit positive responses, particularly when it comes to photos on dating profiles: certain angles, particular expressions, minute adjustments of eyebrows and lips that might appear to be about narcissism and vanity, but are more about a fear of being incorrectly assessed. After all, false suppositions based on people's faces are hugely influential within society, and in extreme cases they can have a huge impact on people's lives. When retired teacher Christopher Jefferies was held by police in connection with the murder of Joanna Yeates in Bristol back in 2010, more than half a dozen newspapers gave his unusual appearance particular scrutiny and made assumptions accordingly, which in turn influenced public opinion. This culminated in substantial damages for defamation, two convictions for contempt of court and a painful ordeal for Jefferies, who was entirely innocent. This kind of deep-seated bias looms large throughout physiognomic works of the 19th and 20th centuries, from absurdities such as Vaught's Practical Character Reader of 1902 (handy if you want to find out what a "deceitful chin" looks like) to more inherently troubling volumes such as Cesare Lombroso's Criminal Man. After performing a number of autopsies on criminals, the Italian physician claimed to have discovered a number of common characteristics, and it's worth listing them if only to establish the supposed criminality of pretty much everyone you know: Unusually short or tall height; small head, but large face; fleshy lips, but thin upper lip; protuberances on head and around ear; wrinkles on forehead and face; large sinus cavities or bumpy face; tattoos; receding hairline; large incisors; bushy eyebrows, tending to meet across nose; large eye sockets but deep-set eyes; beaked or flat nose; strong jaw line; small and sloping forehead; small or weak chin; thin neck; sloping shoulders but large chest; large, protruding ears; long arms; high cheek bones; pointy or snubbed fingers or toes. In a woeful misreading of Darwinian theory, Lombroso unwittingly founded the field of anthropological criminology, and more specifically the idea of the born criminal: a hereditary quality that posed a danger to society and must be rooted out. His theories became discredited during the 20th century, but the kind of bias displayed by Lombroso can still be found in legal systems across the world; studies show that people with stereotypically 'untrustworthy' faces tend to receive harsher treatment than those who don't. There's evidently some consensus over people's attitudes toward certain faces, but it doesn't follow that the consensus is correct. The only attributes that we're reasonably good at detecting, according to research done at the University of Michigan in the 1960s and later tested at the University of Stirling in 2007, are extroversion and conscientiousness. For other traits there's insufficient evidence that our hunches are correct, with anomalies explained by our evolved aversion to 'ugliness', established links between broader faces and powerful physiques, or cultural associations with certain demographics which are reinforced with nagging regularity by newspapers, books, television and film. Data-driven studies, based upon huge quantities of facial data, would seem to offer the final word on this. Since 2005, computational models have used various techniques to test for links between social attributes and facial features, resulting in suggestions that our faces can betray, for example, political leanings, sexual orientation and criminality. One BBC Future article from 2015 even describes the 'discipline' of physiognomy as 'gaining credibility'. But Todorov details many problems with these studies, pointing out the challenging nature of doing such experiments with sufficient rigour – not least because different images of the same people can prompt wildly differing results. The aforementioned study at Shanghai's Jiao Tong University, with its enthusiastic, data-driven analyses of such questions as "What features of a human face betray its owner's propensity for crimes?" prompted a wave of press coverage. The vision outlined in these articles is of an unethical dystopia where neural networks can assess our faces and establish a likely score for criminality – but Todorov is scathing about this paper, too. "The main problem is the sampling of the images," he says. "There is not enough information about the [nature of] the images of the people who were convicted. Second, clearly, there are huge differences between the two samples [of convicts and non-convicts] [in terms of] education and socio-economic status." In other words, your appearance is affected by the kind of life you've led, so the classifiers within the computer program are simply distinguishing between different demographics rather than detecting a propensity for criminal behaviour. Todorov is also wary of these classifiers misidentifying more 'innocent' people than identifying actual criminals, and accuracy is a concern shared by Peter Hancock. "Networks don't assess faces in the same way that we do," he says. "One of our systems, which is a deep network, has a recognition engine which generates an ordered list of how similar various faces are. And sometimes you get good matches – but other times you look at them and say, well, it's the wrong race! To humans they look completely different. And that underlines the fact that the networks are working in a different sort of way, and actually you don't really know how they're working. They're the ultimate black box." This isn't to say that the use of big data, and particularly the use of composite imagery (digitally blending together certain types of faces) doesn't give us useful information and fascinating correlations. "You can, for example, take a given face and use computer software to make it look more or less trustworthy," says Hancock. "I remember a colleague playing with this and he made a less trustworthy version of George W Bush – and how shifty did he look! I'm surprised that they're not using these techniques in political advertising, because you couldn't tell that anything had been done [to the picture], but when you look at it you think 'I wouldn't trust him'." The revitalisation of the theory of physiognomy by the Shanghai students is, according to Todorov, deeply problematic on a theoretical level. "Are we back to Lombroso's theory," he asks, "that criminals were anomalous creatures, evolutionary degenerates? How does one become criminal, and what role do various life forces play into this? There are people making claims that you just need to look at the face to predict personality and behaviour, but many of these people have not given much thought to their underlying assumptions." While it's true that we judge books by their covers, covers are more than just faces; we piece together all kinds of cues from people to form our impressions of them. Jimmy Savile's appearance was unusual by any standards, but we absorbed a great deal of information about him over the years that will have influenced our opinions – not least from the original Louis Theroux programme from 2000 that was reexamined in that recent BBC documentary. Savile's vague resemblance to the Child Catcher from the film Chitty Chitty Bang Bang is convenient but ultimately misleading, and the way it reinforces the idea of what a paedophile might 'look like' is unfortunate; not least because it helps to sustain a low-level belief in the 'science' of physiognomy, despite its tendency to crumble under the slightest cross examination. This article was originally published on TheLong+Short. Read the original article.
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D. humans will never be able to eliminate the effects of physiognomy from their decision-making
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From the text, what can be inferred about the thoughts in Pendleton's demise?
A. The information did not match up with his cause of death being suicide.
B. Everyone was in agreement that Pendleton abandoned his position and returned home by choice.
C. Everyone was in agreement that Pendleton was still alive and in hiding.
D. The information matched up with his cause of death being suicide.
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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."
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A. The information did not match up with his cause of death being suicide.
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Who is with Red when Wayne enters the Four Aces Club?
A. A hefty psycho who drinks too much
B. A hefty psycho who has killed five people
C. A hefty psycho with a cat’s face
D. A hefty psycho who has abducted Red
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THE RECRUIT BY BRYCE WALTON It was dirty work, but it would make him a man. And kids had a right to grow up—some of them! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, July 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Wayne, unseen, sneered down from the head of the stairs. The old man with his thick neck, thick cigar, evening highball, potgut and bald head without a brain in it. His slim mother with nervously polite smiles and voice fluttering, assuring the old man by her frailty that he was big in the world. They were squareheads one and all, marking moron time in a gray dream. Man, was he glad to break out. The old man said, "He'll be okay. Let him alone." "But he won't eat. Just lies there all the time." "Hell," the old man said. "Sixteen's a bad time. School over, waiting for the draft and all. He's in between. It's rough." Mother clasped her forearms and shook her head once slowly. "We got to let him go, Eva. It's a dangerous time. You got to remember about all these dangerous repressed impulses piling up with nowhere to go, like they say. You read the books." "But he's unhappy." "Are we specialists? That's the Youth Board's headache, ain't it? What do we know about adolescent trauma and like that? Now get dressed or we'll be late." Wayne watched the ritual, grinning. He listened to their purposeless noises, their blabbing and yakking as if they had something to say. Blab-blab about the same old bones, and end up chewing them in the same old ways. Then they begin all over again. A freak sideshow all the way to nowhere. Squareheads going around either unconscious or with eyes looking dead from the millennium in the office waiting to retire into limbo. How come he'd been stuck with parental images like that? One thing—when he was jockeying a rocket to Mars or maybe firing the pants off Asiatic reds in some steamy gone jungle paradise, he'd forget his punkie origins in teeveeland. But the old man was right on for once about the dangerous repressed impulses. Wayne had heard about it often enough. Anyway there was no doubt about it when every move he made was a restrained explosion. So he'd waited in his room, and it wasn't easy sweating it out alone waiting for the breakout call from HQ. "Well, dear, if you say so," Mother said, with the old resigned sigh that must make the old man feel like Superman with a beerbelly. They heard Wayne slouching loosely down the stairs and looked up. "Relax," Wayne said. "You're not going anywhere tonight." "What, son?" his old man said uneasily. "Sure we are. We're going to the movies." He could feel them watching him, waiting; and yet still he didn't answer. Somewhere out in suburban grayness a dog barked, then was silent. "Okay, go," Wayne said. "If you wanta walk. I'm taking the family boltbucket." "But we promised the Clemons, dear," his mother said. "Hell," Wayne said, grinning straight into the old man. "I just got my draft call." He saw the old man's Adam's apple move. "Oh, my dear boy," Mother cried out. "So gimme the keys," Wayne said. The old man handed the keys over. His understanding smile was strained, and fear flicked in his sagging eyes. "Do be careful, dear," his mother said. She ran toward him as he laughed and shut the door on her. He was still laughing as he whoomed the Olds between the pale dead glow of houses and roared up the ramp onto the Freeway. Ahead was the promising glitter of adventure-calling neon, and he looked up at the high skies of night and his eyes sailed the glaring wonders of escape. He burned off some rubber finding a slot in the park-lot. He strode under a sign reading Public Youth Center No. 947 and walked casually to the reception desk, where a thin man with sergeant's stripes and a pansy haircut looked out of a pile of paperwork. "Where you think you're going, my pretty lad?" Wayne grinned down. "Higher I hope than a typewriter jockey." "Well," the sergeant said. "How tough we are this evening. You have a pass, killer?" "Wayne Seton. Draft call." "Oh." The sergeant checked his name off a roster and nodded. He wrote on a slip of paper, handed the pass to Wayne. "Go to the Armory and check out whatever your lusting little heart desires. Then report to Captain Jack, room 307." "Thanks, sarge dear," Wayne said and took the elevator up to the Armory. A tired fat corporal with a naked head blinked up at tall Wayne. Finally he said, "So make up your mind, bud. Think you're the only kid breaking out tonight?" "Hold your teeth, pop," Wayne said, coolly and slowly lighting a cigarette. "I've decided." The corporal's little eyes studied Wayne with malicious amusement. "Take it from a vet, bud. Sooner you go the better. It's a big city and you're starting late. You can get a cat, not a mouse, and some babes are clever hellcats in a dark alley." "You must be a genius," Wayne said. "A corporal with no hair and still a counterboy. I'm impressed. I'm all ears, Dad." The corporal sighed wearily. "You can get that balloon head ventilated, bud, and good." Wayne's mouth twitched. He leaned across the counter toward the shelves and racks of weapons. "I'll remember that crack when I get my commission." He blew smoke in the corporal's face. "Bring me a Smith and Wesson .38, shoulder holster with spring-clip. And throw in a Skelly switchblade for kicks—the six-inch disguised job with the double springs." The corporal waddled back with the revolver and the switchblade disguised in a leather comb case. He checked them on a receipt ledger, while Wayne examined the weapons, broke open the revolver, twirled the cylinder and pushed cartridges into the waiting chamber. He slipped the knife from the comb case, flicked open the blade and stared at its gleam in the buttery light as his mouth went dry and the refracted incandescence of it trickled on his brain like melted ice, exciting and scary. He removed his leather jacket. He slung the holster under his left armpit and tested the spring clip release several times, feeling the way the serrated butt dropped into his wet palm. He put his jacket back on and the switchblade case in his pocket. He walked toward the elevator and didn't look back as the corporal said, "Good luck, tiger." Captain Jack moved massively. The big stone-walled office, alive with stuffed lion and tiger and gunracks, seemed to grow smaller. Captain Jack crossed black-booted legs and whacked a cane at the floor. It had a head shaped like a grinning bear. Wayne felt the assured smile die on his face. Something seemed to shrink him. If he didn't watch himself he'd begin feeling like a pea among bowling balls. Contemptuously amused little eyes glittered at Wayne from a shaggy head. Shoulders hunched like stuffed sea-bags. "Wayne Seton," said Captain Jack as if he were discussing something in a bug collection. "Well, well, you're really fired up aren't you? Really going out to eat 'em. Right, punk?" "Yes, sir," Wayne said. He ran wet hands down the sides of his chinos. His legs seemed sheathed in lead as he bit inwardly at shrinking fear the way a dog snaps at a wound. You big overblown son, he thought, I'll show you but good who is a punk. They made a guy wait and sweat until he screamed. They kept a guy on the fire until desire leaped in him, ran and billowed and roared until his brain was filled with it. But that wasn't enough. If this muscle-bound creep was such a big boy, what was he doing holding down a desk? "Well, this is it, punk. You go the distance or start a butterfly collection." The cane darted up. A blade snicked from the end and stopped an inch from Wayne's nose. He jerked up a shaky hand involuntarily and clamped a knuckle-ridged gag to his gasping mouth. Captain Jack chuckled. "All right, superboy." He handed Wayne his passcard. "Curfew's off, punk, for 6 hours. You got 6 hours to make out." "Yes, sir." "Your beast is primed and waiting at the Four Aces Club on the West Side. Know where that is, punk?" "No, sir, but I'll find it fast." "Sure you will, punk," smiled Captain Jack. "She'll be wearing yellow slacks and a red shirt. Black hair, a cute trick. She's with a hefty psycho who eats punks for breakfast. He's butchered five people. They're both on top of the Undesirable list, Seton. They got to go and they're your key to the stars." "Yes, sir," Wayne said. "So run along and make out, punk," grinned Captain Jack. A copcar stopped Wayne as he started over the bridge, out of bright respectable neon into the murky westside slum over the river. Wayne waved the pass card, signed by Captain Jack, under the cop's quivering nose. The cop shivered and stepped back and waved him on. The Olds roared over the bridge as the night's rain blew away. The air through the open window was chill and damp coming from Slumville, but Wayne felt a cold that wasn't of the night or the wind. He turned off into a rat's warren of the inferiors. Lights turned pale, secretive and sparse, the uncared-for streets became rough with pitted potholes, narrow and winding and humid with wet unpleasant smells. Wayne's fearful exhilaration increased as he cruised with bated breath through the dark mazes of streets and rickety tenements crawling with the shadows of mysterious promise. He found the alley, dark, a gloom-dripping tunnel. He drove cautiously into it and rolled along, watching. His belly ached with expectancy as he spotted the sick-looking dab of neon wanly sparkling. FOUR ACES CLUB He parked across the alley. He got out and stood in shadows, digging the sultry beat of a combo, the wild pulse of drums and spinning brass filtering through windows painted black. He breathed deep, started over, ducked back. A stewbum weaved out of a bank of garbage cans, humming to himself, pulling at a rainsoaked shirt clinging to a pale stick body. He reminded Wayne of a slim grub balanced on one end. The stewbum stumbled. His bearded face in dim breaking moonlight had a dirty, greenish tinge as he sensed Wayne there. He turned in a grotesque uncoordinated jiggling and his eyes were wide with terror and doom. "I gotta hide, kid. They're on me." Wayne's chest rose and his hands curled. The bum's fingers drew at the air like white talons. "Help me, kid." He turned with a scratchy cry and retreated before the sudden blast of headlights from a Cad bulleting into the alley. The Cad rushed past Wayne and he felt the engine-hot fumes against his legs. Tires squealed. The Cad stopped and a teener in black jacket jumped out and crouched as he began stalking the old rummy. "This is him! This is him all right," the teener yelled, and one hand came up swinging a baseball bat. A head bobbed out of the Cad window and giggled. The fumble-footed rummy tried to run and plopped on wet pavement. The teener moved in, while a faint odor of burnt rubber hovered in the air as the Cad cruised in a slow follow-up. Wayne's breath quickened as he watched, feeling somehow blank wonder at finding himself there, free and breaking out at last with no curfew and no law but his own. He felt as though he couldn't stop anything. Living seemed directionless, but he still would go with it regardless, until something dropped off or blew to hell like a hot light-bulb. He held his breath, waiting. His body was tensed and rigid as he moved in spirit with the hunting teener, an omniscient shadow with a hunting license and a ghetto jungle twenty miles deep. The crawling stewbum screamed as the baseball bat whacked. The teener laughed. Wayne wanted to shout. He opened his mouth, but the yell clogged up somewhere, so that he remained soundless yet with his mouth still open as he heard the payoff thuds where the useless wino curled up with stick arms over his rheumy face. The teener laughed, tossed the bat away and began jumping up and down with his hobnailed, mail-order air force boots. Then he ran into the Cad. A hootch bottle soared out, made a brittle tink-tink of falling glass. "Go, man!" The Cad wooshed by. It made a sort of hollow sucking noise as it bounced over the old man twice. Then the finlights diminished like bright wind-blown sparks. Wayne walked over and sneered down at the human garbage lying in scummed rain pools. The smell of raw violence, the scent of blood, made his heart thump like a trapped rubber ball in a cage. He hurried into the Four Aces, drawn by an exhilarating vision ... and pursued by the hollow haunting fears of his own desires. He walked through the wavering haze of smoke and liquored dizziness and stood until his eyes learned the dark. He spotted her red shirt and yellow legs over in the corner above a murky lighted table. He walked toward her, watching her little subhuman pixie face lift. The eyes widened with exciting terror, turned even paler behind a red slash of sensuous mouth. Briefed and waiting, primed and eager for running, she recognized her pursuer at once. He sat at a table near her, watching and grinning and seeing her squirm. She sat in that slightly baffled, fearful and uncomprehending attitude of being motionless, as though they were all actors performing in a weirdo drama being staged in that smoky thick-aired dive. Wayne smiled with wry superiority at the redheaded psycho in a dirty T-shirt, a big bruiser with a gorilla face. He was tussling his mouse heavy. "What's yours, teener?" the slug-faced waiter asked. "Bring me a Crusher, buddyroo," Wayne said, and flashed his pass card. "Sure, teener." Red nuzzled the mouse's neck and made drooly noises. Wayne watched and fed on the promising terror and helplessness of her hunted face. She sat rigid, eyes fixed on Wayne like balls of frozen glass. Red looked up and stared straight at Wayne with eyes like black buttons imbedded in the waxlike skin of his face. Then he grinned all on one side. One huge hand scratched across the wet table top like a furious cat's. Wayne returned the challenging move but felt a nervous twitch jerk at his lips. A numbness covered his brain like a film as he concentrated on staring down Red the psycho. But Red kept looking, his eyes bright but dead. Then he began struggling it up again with the scared little mouse. The waiter sat the Crusher down. Wayne signed a chit; tonight he was in the pay of the state. "What else, teener?" "One thing. Fade." "Sure, teener," the waiter said, his breathy words dripping like syrup. Wayne drank. Liquored heat dripped into his stomach. Fire tickled his veins, became hot wire twisting in his head. He drank again and forced out a shaky breath. The jazz beat thumped fast and muted brass moaned. Drumpulse, stabbing trumpet raped the air. Tension mounted as Wayne watched her pale throat convulsing, the white eyelids fluttering. Red fingered at her legs and salivated at her throat, glancing now and then at Wayne, baiting him good. "Okay, you creep," Wayne said. He stood up and started through the haze. The psycho leaped and a table crashed. Wayne's .38 dropped from its spring-clip holster and the blast filled the room. The psycho screamed and stumbled toward the door holding something in. The mouse darted by, eluded Wayne's grasp and was out the door. Wayne went out after her in a laughing frenzy of release. He felt the cold strange breath of moist air on his sweating skin as he sprinted down the alley into a wind full of blowing wet. He ran laughing under the crazy starlight and glimpsed her now and then, fading in and out of shadows, jumping, crawling, running with the life-or-death animation of a wild deer. Up and down alleys, a rat's maze. A rabbit run. Across vacant lots. Through shattered tenement ruins. Over a fence. There she was, falling, sliding down a brick shute. He gained. He moved up. His labored breath pumped more fire. And her scream was a rejuvenation hypo in his blood. She quivered above him on the stoop, panting, her eyes afire with terror. "You, baby," Wayne gasped. "I gotcha." She backed into darkness, up there against the sagging tenement wall, her arms out and poised like crippled wings. Wayne crept up. She gave a squeaking sob, turned, ran. Wayne leaped into gloom. Wood cracked. He clambered over rotten lumber. The doorway sagged and he hesitated in the musty dark. A few feet away was the sound of loose trickling plaster, a whimpering whine. "No use running," Wayne said. "Go loose. Give, baby. Give now." She scurried up sagging stairs. Wayne laughed and dug up after her, feeling his way through debris. Dim moonlight filtered through a sagging stairway from a shattered skylight three floors up. The mouse's shadow floated ahead. He started up. The entire stair structure canted sickeningly. A railing ripped and he nearly went with it back down to the first floor. He heard a scream as rotten boards crumbled and dust exploded from cracks. A rat ran past Wayne and fell into space. He burst into the third-floor hallway and saw her half-falling through a door under the jagged skylight. Wayne took his time. He knew how she felt waiting in there, listening to his creeping, implacable footfalls. Then he yelled and slammed open the door. Dust and stench, filth so awful it made nothing of the dust. In the corner he saw something hardly to be called a bed. More like a nest. A dirty, lumpy pile of torn mattress, felt, excelsior, shredded newspapers and rags. It seemed to crawl a little under the moon-streaming skylight. She crouched in the corner panting. He took his time moving in. He snickered as he flashed the switchblade and circled it like a serpent's tongue. He watched what was left of her nerves go to pieces like rotten cloth. "Do it quick, hunter," she whispered. "Please do it quick." "What's that, baby?" "I'm tired running. Kill me first. Beat me after. They won't know the difference." "I'm gonna bruise and beat you," he said. "Kill me first," she begged. "I don't want—" She began to cry. She cried right up in his face, her wide eyes unblinking, and her mouth open. "You got bad blood, baby," he snarled. He laughed but it didn't sound like him and something was wrong with his belly. It was knotting up. "Bad, I know! So get it over with, please. Hurry, hurry." She was small and white and quivering. She moaned but kept staring up at him. He ripped off his rivet-studded belt and swung once, then groaned and shuffled away from her. He kept backing toward the door. She crawled after him, begging and clutching with both arms as she wriggled forward on her knees. "Don't run. Please. Kill me! It'll be someone else if you don't. Oh, God, I'm so tired waiting and running!" "I can't," he said, and sickness soured in his throat. "Please." "I can't, I can't!" He turned and ran blindly, half-fell down the cracking stairs. Doctor Burns, head of the readjustment staff at the Youth Center, studied Wayne with abstract interest. "You enjoyed the hunt, Seton? You got your kicks?" "Yes, sir." "But you couldn't execute them?" "No, sir." "They're undesirables. Incurables. You know that, Seton?" "Yes, sir." "The psycho you only wounded. He's a five-times murderer. And that girl killed her father when she was twelve. You realize there's nothing can be done for them? That they have to be executed?" "I know." "Too bad," the doctor said. "We all have aggressive impulses, primitive needs that must be expressed early, purged. There's murder in all of us, Seton. The impulse shouldn't be denied or suppressed, but educated . The state used to kill them. Isn't it better all around, Seton, for us to do it, as part of growing up? What was the matter, Seton?" "I—felt sorry for her." "Is that all you can say about it?" "Yes, sir." The doctor pressed a buzzer. Two men in white coats entered. "You should have got it out of your system, Seton, but now it's still in there. I can't turn you out and have it erupt later—and maybe shed clean innocent blood, can I?" "No, sir," Wayne mumbled. He didn't look up. "I'm sorry I punked out." "Give him the treatment," the doctor said wearily. "And send him back to his mother." Wayne nodded and they led him away. His mind screamed still to split open some prison of bone and lay bare and breathing wide. But there was no way out for the trapped. Now he knew about the old man and his poker-playing pals. They had all punked out. Like him.
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B. A hefty psycho who has killed five people
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What made Captain Hannah want to give the narrator a black eye?
A. Because the narrator offered no help in transporting the marocca plants.
B. Because the welts on Captain Hannah were angering the captain.
C. Because the narrator made an unfair deal to transport the plants to Gloryanna III.
D. Because Captain Hannah's transportation of the marocca plants was frustrating and gruesome.
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CAKEWALK TO GLORYANNA BY L. J. STECHER, JR. [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.] The job was easy. The profit was enormous. The only trouble was—the cargo had a will of its own! Captain Hannah climbed painfully down from the Delta Crucis , hobbled across the spaceport to where Beulah and I were waiting to greet him and hit me in the eye. Beulah—that's his elephant, but I have to take care of her for him because Beulah's baby belongs to me and Beulah has to take care of it—kept us apart until we both cooled down a little. Then, although still somewhat dubious about it, she let us go together across the field to the spaceport bar. I didn't ask Captain Hannah why he had socked me. Although he has never been a handsome man, he usually has the weathered and austere dignity that comes from plying the remote reaches among the stars. Call it the Look of Eagles. Captain Hannah had lost the Look of Eagles. His eyes were swollen almost shut; every inch of him that showed was a red mass of welts piled on more welts, as though he had tangled with a hive of misanthropic bees. The gold-braided hat of his trade was not clamped in its usual belligerent position slightly over one eye. It was riding high on his head, apparently held up by more of the ubiquitous swellings. I figured that he figured that I had something to do with the way he looked. "Shipping marocca to Gloryanna III didn't turn out to be a cakewalk after all?" I suggested. He glared at me in silence. "Perhaps you would like a drink first, and then you would be willing to tell me about it?" I decided that his wince was intended for a nod, and ordered rhial. I only drink rhial when I've been exposed to Captain Hannah. It was almost a pleasure to think that I was responsible, for a change, for having him take the therapy. "A Delta Class freighter can carry almost anything," he said at last, in a travesty of his usual forceful voice. "But some things it should never try." He lapsed back into silence after this uncharacteristic admission. I almost felt sorry for him, but just then Beulah came racking across the field with her two-ton infant in tow, to show her off to Hannah. I walled off my pity. He had foisted those two maudlin mastodons off onto me in one of our earlier deals, and if I had somehow been responsible for his present troubles, it was no more than he deserved. I rated winning for once. "You did succeed in getting the marocca to Gloryanna III?" I asked anxiously, after the elephants had been admired and sent back home. The success of that venture—even if the job had turned out to be more difficult than we had expected—meant an enormous profit to both of us. The fruit of the marocca is delicious and fabulously expensive. The plant grew only on the single planet Mypore II. Transshipped seeds invariably failed to germinate, which explained its rarity. The Myporians were usually, and understandably, bitterly, opposed to letting any of the living plants get shipped off their planet. But when I offered them a sizable piece of cash plus a perpetual share of the profits for letting us take a load of marocca plants to Gloryanna III, they relented and, for the first time in history, gave their assent. In fact, they had seemed delighted. "I got them there safely," said Captain Hannah. "And they are growing all right?" I persisted. "When I left, marocca was growing like mad," said Captain Hannah. I relaxed and leaned back in my chair. I no longer felt the need of rhial for myself. "Tell me about it," I suggested. "It was you who said that we should carry those damn plants to Gloryanna III," he said balefully. "I ought to black your other eye." "Simmer down and have some more rhial," I told him. "Sure I get the credit for that. Gloryanna III is almost a twin to Mypore II. You know that marocca takes a very special kind of environment. Bright sun most of the time—that means an almost cloudless environment. A very equable climate. Days and nights the same length and no seasons—that means no ecliptical and no axial tilt. But our tests showed that the plants had enough tolerance to cause no trouble in the trip in Delta Crucis ." A light dawned. "Our tests were no good?" "Your tests were no good," agreed the captain with feeling. "I'll tell you about it first, and then I'll black your other eye," he decided. "You'll remember that I warned you that we should take some marocca out into space and solve any problems we might find before committing ourselves to hauling a full load of it?" asked Captain Hannah. "We couldn't," I protested. "The Myporians gave us a deadline. If we had gone through all of that rigamarole, we would have lost the franchise. Besides, they gave you full written instructions about what to do under all possible circumstances." "Sure. Written in Myporian. A very difficult language to translate. Especially when you're barricaded in the head." I almost asked him why he had been barricaded in the bathroom of the Delta Crucis , but I figured it was safer to let him tell me in his own way, in his own time. "Well," he said, "I got into parking orbit around Mypore without any trouble. The plastic film kept the water in the hydroponic tanks without any trouble, even in a no-gravity condition. And by the time I had lined up for Gloryanna and Jumped, I figured, like you said, that the trip would be a cakewalk. "Do you remember how the plants always keep their leaves facing the sun? They twist on their stems all day, and then they go on twisting them all night, still pointing at the underground sun, so that they're aimed right at sunrise. So the stem looks like a corkscrew?" I nodded. "Sure. That's why they can't stand an axial tilt. They 'remember' the rate and direction of movement, and keep it up during the night time. So what? We had that problem all figured out." "You think so? That solution was one of yours, too, wasn't it?" He gazed moodily at his beaker of rhial. "I must admit it sounded good to me, too. In Limbo, moving at multiple light-speeds, the whole Universe, of course, turns into a bright glowing spot in our direction of motion, with everything else dark. So I lined up the Delta Crucis perpendicular to her direction of motion, put a once-every-twenty-one hour spin on her to match the rotation rates of Mypore II and Gloryanna III, and uncovered the view ports to let in the light. It gradually brightened until 'noon time', with the ports pointing straight at the light source, and then dimmed until we had ten and one-half hours of darkness. "Of course, it didn't work." "For Heaven's sake, why not?" "For Heaven's sake why should it? With no gravity for reference, how were the plants supposed to know that the 'sun' was supposed to be moving?" "So what did you do?" I asked, when that had sunk in. "If the stem doesn't keep winding, the plants die; and they can only take a few extra hours of night time before they run down." "Oh," said Captain Hannah in quiet tones of controlled desperation, "it was very simple. I just put enough spin on the ship to make artificial gravity, and then I strung a light and moved it every fifteen minutes for ten and one-half hours, until I had gone halfway around the room. Then I could turn the light off and rest for ten and one-half hours. The plants liked it fine. "Of course, first I had to move all the hydroponic tanks from their original positions perpendicular to the axial thrust line of the ship to a radial position. And because somehow we had picked up half of the plants in the northern hemisphere of Mypore and the other half in the southern hemisphere, it turned out that half of the plants had a sinistral corkscrew and the other half had a dextral. So I had to set the plants up in two different rooms, and run an artificial sun for each, going clockwise with one, widdershins with the other. "I won't even talk about what I went through while I was shifting the hydroponic tanks, when all the plastic membranes that were supposed to keep the water in place started to break." "I'd like to know," I said sincerely. He stared at me in silence for a moment. "Well, it filled the cabin with great solid bubbles of water. Water bubbles will oscillate and wobble like soap bubbles," he went on dreamily, "but of course, they're not empty, like soap bubbles. The surface acts a little like a membrane, so that sometimes two of the things will touch and gently bounce apart without joining. But just try touching one of them. You could drown—I almost did. Several times. "I got a fire pump—an empty one. You know the kind; a wide cylinder with a piston with a handle, and a hose that you squirt the water out of, or can suck water in with. The way you use it is, you float up on a big ball of water, with the pump piston down—closed. You carefully poke the end of the hose into the ball of water, letting only the metal tip touch. Never the hose. If you let the hose touch, the water runs up it and tries to drown you. Then you pull up on the piston, and draw all the water into the cylinder. Of course, you have to hold the pump with your feet while you pull the handle with your free hand." "Did it work?" I asked eagerly. "Eventually. Then I stopped to think of what to do with the water. It was full of minerals and manure and such, and I didn't want to introduce it into the ship's tanks." "But you solved the problem?" "In a sense," said the captain. "I just emptied the pump back into the air, ignored the bubbles, repositioned the tanks, put spin on the ship and then ladled the liquid back into the tanks with a bucket." "Didn't you bump into a lot of the bubbles and get yourself dunked a good deal while you were working with the tanks?" He shrugged. "I couldn't say. By that time I was ignoring them. It was that or suicide. I had begun to get the feeling that they were stalking me. So I drew a blank." "Then after that you were all right, except for the tedium of moving the lights around?" I asked him. I answered myself at once. "No. There must be more. You haven't told me why you hid out in the bathroom, yet." "Not yet," said Captain Hannah. "Like you, I figured I had the situation fairly well under control, but like you, I hadn't thought things through. The plastic membranes hadn't torn when we brought the tanks in board the Delta Crucis . It never occurred to me to hunt around for the reasons for the change. But I wouldn't have had long to hunt anyway, because in a few hours the reasons came looking for me. "They were a tiny skeeter-like thing. A sort of midge or junior grade mosquito. They had apparently been swimming in the water during their larval stage. Instead of making cocoons for themselves, they snipped tiny little pieces of plastic to use as protective covers in the pupal stage. I guess they were more like butterflies than mosquitoes in their habits. And now they were mature. "There were thousands and thousands of them, and each one of them made a tiny, maddening whine as it flew." "And they bit? That explains your bumps?" I asked sympathetically. "Oh, no. These things didn't bite, they itched. And they got down inside of everything they could get down inside, and clung. That included my ears and my eyes and my nose. "I broke out a hand sprayer full of a DDT solution, and sprayed it around me to try to clear the nearby air a little, so that I could have room to think. The midges loved it. But the plants that were in reach died so fast that you could watch their leaves curl up and drop off. "I couldn't figure whether to turn up the fans and dissipate the cloud—by spreading it all through the ship—or whether to try to block off the other plant room, and save it at least. So I ended up by not doing anything, which was the right thing to do. No more plants died from the DDT. "So then I did a few experiments, and found that the regular poison spray in the ship's fumigation system worked just fine. It killed the bugs without doing the plants any harm at all. Of course, the fumigation system is designed to work with the fumigator off the ship, because it's poisonous to humans too. "I finally blocked the vents and the door edges in the head, after running some remote controls into there, and then started the fumigation system going. While I was sitting there with nothing much to do, I tried to translate what I could of the Myporian instructions. It was on page eleven that it mentioned casually that the midges—the correct word is carolla—are a necessary part of the life cycle of the marocca. The larvae provide an enzyme without which the plants die. "Of course. I immediately stopped slapping at the relatively few midges that had made their way into the head with me, and started to change the air in the ship to get rid of the poison. I knew it was too late before I started, and for once I was right. "The only live midges left in the ship were the ones that had been with me during the fumigation process. I immediately tried to start a breeding ground for midges, but the midges didn't seem to want to cooperate. Whatever I tried to do, they came back to me. I was the only thing they seemed to love. I didn't dare bathe, or scratch, or even wriggle, for fear of killing more of them. And they kept on itching. It was just about unbearable, but I bore it for three interminable days while the midges died one by one. It was heartbreaking—at least, it was to me. "And it was unnecessary, too. Because apparently the carolla had already laid their eggs, or whatever it is that they do, before I had fumigated them. After my useless days of agony, a new batch came swarming out. And this time there were a few of a much larger thing with them—something like an enormous moth. The new thing just blundered around aimlessly. "I lit out for the head again, to keep away from that intolerable whining. This time I took a luxurious shower and got rid of most of the midges that came through the door with me. I felt almost comfortable, in fact, until I resumed my efforts to catch up on my reading. "The mothlike things—they are called dingleburys—also turn out to provide a necessary enzyme. They are supposed to have the same timing of their life cycle as the carolla. Apparently the shaking up I had given their larvae in moving the tanks and dipping the water up in buckets and all that had inhibited them in completing their cycle the first time around. "And the reason they had the same life cycle as the carolla was that the adult dinglebury will eat only the adult carolla, and it has to fill itself full to bursting before it will reproduce. If I had the translation done correctly, they were supposed to dart gracefully around, catching carolla on the wing and stuffing themselves happily. "I had to find out what was wrong with my awkward dingleburys. And that, of course, meant going out into the ship again. But I had to do that anyway, because it was almost 'daylight', and time for me to start shifting the lights again. "The reason for the dingleburys' problem is fairly obvious. When you set up artificial gravity by spinning a ship, the gravity is fine down near the skin where the plants are. But the gravity potential is very high, and it gets very light up where things fly around, going to zero on the middle line of the ship. And the unfamiliar gravity gradient, together with the Coriolis effect and all, makes the poor dingleburys dizzy, so they can't catch carolla. "And if you think I figured all that out about dingleburys getting dizzy at the time, in that madhouse of a ship, then you're crazy. What happened was that I saw that there was one of the creatures that didn't seem to be having any trouble, but was acting like the book said it should. I caught it and examined it. The poor thing was blind, and was capturing her prey by sound alone. "So I spent the whole day—along with my usual chore of shifting the lights—blindfolding dingleburys. Which is a hell of a sport for a man who is captain of his own ship." I must say that I agreed with him, but it seemed to be a good time for me to keep my mouth shut. "Well, after the dingleburys had eaten and propagated, they became inquisitive. They explored the whole ship, going into places I wouldn't have believed it to be possible for them to reach, including the inside of the main computer, which promptly shorted out. I finally figured that one of the things had managed to crawl up the cooling air exhaust duct, against the flow of air, to see what was going on inside. "I didn't dare to get rid of the things without checking my book, of course, so it was back to the head for me. 'Night' had come again—and it was the only place I could get any privacy. There were plenty of the carolla left to join me outside. "I showered and swatted and started to read. I got as far as where it said that the dingleburys continued to be of importance, and then I'm afraid I fell asleep. "I got up with the sun the next morning. Hell, I had to, considering that it was I who turned the sun on! I found that the dingleburys immediately got busy opening small buds on the stems of the marocca plants. Apparently they were pollinating them. I felt sure that these buds weren't the marocca blossoms from which the fruit formed—I'd seen a lot of those while we were on Mypore II and they were much bigger and showier than these little acorn-sized buds. "Of course, I should have translated some more of my instruction book, but I was busy. "Anyway, the action of the dingleburys triggered the violent growth phase of the marocca plants. Did you know that they plant marocca seedlings, back on Mypore II, at least a hundred feet apart? If you'll recall, a mature field, which was the only kind we ever saw, is one solid mass of green growth. "The book says that it takes just six hours for a marocca field to shift from the seedling stage to the mature stage. It didn't seem that long. You could watch the stuff grow—groping and crawling along; one plant twining with another as they climbed toward the light. "It was then that I began to get worried. If they twined around the light, they would keep me from moving it, and they would shadow it so it wouldn't do its job right. In effect, their growth would put out the sun. "I thought of putting up an electrically charged fence around the light, but the bugs had put most of my loose equipment out of action, so I got a machete. When I took a swing at one of the vines, something bit me on the back of the neck so hard it almost knocked me down. It was one of the dingleburys, and it was as mad as blazes. It seems that one of the things they do is to defend the marocca against marauders. That was the first of my welts, and it put me back in the head in about two seconds. "And what's more, I found that I couldn't kill the damn things. Not if I wanted to save the plants. The growth only stops at the end of six hours, after the blossoms appear and are visited by the dingleburys. No dingleburys, no growth stoppage. "So for the next several hours I had to keep moving those lights, and keep them clear of the vines, and keep the vines from shadowing each other to the point where they curled up and died, and I had to do it gently , surrounded by a bunch of worried dingleburys. "Every time they got a little too worried, or I slipped and bumped into a plant too hard, or looked crosseyed at them, they bit me. If you think I look bad now, you should have seen me just about the time the blossoms started to burst. "I was worried about those blossoms. I felt sure that they would smell terrible, or make me sick, or hypnotize me, or something. But they just turned out to be big, white, odorless flowers. They did nothing for me or to me. They drove the dingleburys wild, though, I'm happy to say. Made them forget all about me. "While they were having their orgy, I caught up on my reading. It was necessary for me to cut back the marocca vines. For one thing, I couldn't get up to the area of the bridge. For another, the main computer was completely clogged. I could use the auxiliary, on the bridge, if I could get to it, but it's a poor substitute. For another thing, I would have to cut the stuff way back if I was ever going to get the plants out of the ship. And I was a little anxious to get my Delta Crucis back to normal as soon as possible. But before cutting, I had to translate the gouge. "It turns out that it's all right to cut marocca as soon as it stops growing. To keep the plants from dying, though, you have to mulch the cuttings and then feed them back to the plants, where the roots store whatever they need against the time of the next explosive period of growth. Of course, if you prefer you can wait for the vines to die back naturally, which takes several months. "There was one little catch, of course. The cuttings from the vines will poison the plants if they are fed back to them without having been mixed with a certain amount of processed mulch. Enzymes again. And there was only one special processor on board. "I was the special processor. That's what the instructions said—I translated very carefully—it required an 'organic processor'. "So I had to eat pounds of that horrible tasting stuff every day, and process it the hard way. "I didn't even have time to scratch my bites. I must have lost weight everywhere but in the swollen places, and they looked worse than they do now. The doctor says it may take a year before the bumps all go away—if they ever do—but I have improved a lot already. "For a while I must have been out of my head. I got so caught up in the rhythm of the thing that I didn't even notice when we slipped out of Limbo into real space near Gloryanna III. It was three days, the Control Tower on Gloryanna III told me, that they tried continuously to raise me on the communications gear before I heard the alarm bell and answered them, so I had to do a good deal of backtracking before I could get into parking orbit around the planet, and then set Delta Crucis down safely. Even as shaky as I was, Delta Crucis behaved like a lady. "I hadn't chopped off all of the new growth, although I had the plants down to manageable size. Some of the blossoms left on the plants had formed fruit, and the fruit had ripened and dried, and the seeds had developed fully. They were popping and spreading fine dust-like spores all over the ship, those last few hours before I landed. "By that time, though, an occasional sneezing fit and watering eyes didn't bother me any. I was far beyond the point where hay fever could add to my troubles. "When I opened the airlock door, though, the spores drifting outside set the customs inspectors to sneezing and swearing more than seemed reasonable at the time." Captain Hannah inhaled a sip of rhial, and seemed to be enjoying the powerful stuff. He acted as if he thought he had finished. "Well, go on," I urged him. "The marocca plants were still in good shape, weren't they?" Hannah nodded. "They were growing luxuriously." He nodded his head a couple of more times, in spite of the discomfort it must have given him. He said, "They made me burn the entire crop right away, of course. They didn't get all of the carolla or dingleburys, though. Or spores." "Gloryanna III is the original home planet of marocca. They hated the stuff, of course, but they liked the profit. Then, when a plague almost wiped out the dingleburys, they introduced khorram furs as a cash crop. It wasn't as lucrative, but it was so much more pleasant that they outlawed marocca. Took them almost fifty years to stamp it out completely. Meanwhile, some clever native shipped a load of the stuff to Mypore II. He took his time, did it without any trouble and made his fortune. And got out again quickly. "The Gloryannans were going to hold my Delta Crucis as security to pay for the cost of stamping out marocca all over again—those spores sprout fast—and for a time I was worried. "Of course, when I showed them our contract—that you alone were responsible for everything once I landed the plants safely on Gloryanna III, they let me go. "They'll send you the bill. They don't figure it will take them more than a few months to complete the job." Captain Hannah stopped talking and stood up, painfully and a little unsteadily. I'm afraid I didn't even notice when he blacked my other eye. I was too busy reaching for the rhial. END
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D. Because Captain Hannah's transportation of the marocca plants was frustrating and gruesome.
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Which two terms best describe Jerry's tone toward Greta?
A. misogynistic and dismissive
B. lustful and manipulative
C. rueful and vexed
D. condescending and harsh
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The saucer was interesting, but where was the delegate? The DELEGATE FROM VENUS By HENRY SLESAR ILLUSTRATOR NOVICK Everybody was waiting to see what the delegate from Venus looked like. And all they got for their patience was the biggest surprise since David clobbered Goliath. " Let me put it this way," Conners said paternally. "We expect a certain amount of decorum from our Washington news correspondents, and that's all I'm asking for." Jerry Bridges, sitting in the chair opposite his employer's desk, chewed on his knuckles and said nothing. One part of his mind wanted him to play it cagey, to behave the way the newspaper wanted him to behave, to protect the cozy Washington assignment he had waited four years to get. But another part of him, a rebel part, wanted him to stay on the trail of the story he felt sure was about to break. "I didn't mean to make trouble, Mr. Conners," he said casually. "It just seemed strange, all these exchanges of couriers in the past two days. I couldn't help thinking something was up." "Even if that's true, we'll hear about it through the usual channels," Conners frowned. "But getting a senator's secretary drunk to obtain information—well, that's not only indiscreet, Bridges. It's downright dirty." Jerry grinned. "I didn't take that kind of advantage, Mr. Conners. Not that she wasn't a toothsome little dish ..." "Just thank your lucky stars that it didn't go any further. And from now on—" He waggled a finger at him. "Watch your step." Jerry got up and ambled to the door. But he turned before leaving and said: "By the way. What do you think is going on?" "I haven't the faintest idea." "Don't kid me, Mr. Conners. Think it's war?" "That'll be all, Bridges." The reporter closed the door behind him, and then strolled out of the building into the sunlight. He met Ruskin, the fat little AP correspondent, in front of the Pan-American Building on Constitution Avenue. Ruskin was holding the newspaper that contained the gossip-column item which had started the whole affair, and he seemed more interested in the romantic rather than political implications. As he walked beside him, he said: "So what really happened, pal? That Greta babe really let down her hair?" "Where's your decorum?" Jerry growled. Ruskin giggled. "Boy, she's quite a dame, all right. I think they ought to get the Secret Service to guard her. She really fills out a size 10, don't she?" "Ruskin," Jerry said, "you have a low mind. For a week, this town has been acting like the 39 Steps , and all you can think about is dames. What's the matter with you? Where will you be when the big mushroom cloud comes?" "With Greta, I hope," Ruskin sighed. "What a way to get radioactive." They split off a few blocks later, and Jerry walked until he came to the Red Tape Bar & Grill, a favorite hangout of the local journalists. There were three other newsmen at the bar, and they gave him snickering greetings. He took a small table in the rear and ate his meal in sullen silence. It wasn't the newsmen's jibes that bothered him; it was the certainty that something of major importance was happening in the capitol. There had been hourly conferences at the White House, flying visits by State Department officials, mysterious conferences involving members of the Science Commission. So far, the byword had been secrecy. They knew that Senator Spocker, chairman of the Congressional Science Committee, had been involved in every meeting, but Senator Spocker was unavailable. His secretary, however, was a little more obliging ... Jerry looked up from his coffee and blinked when he saw who was coming through the door of the Bar & Grill. So did every other patron, but for different reasons. Greta Johnson had that effect upon men. Even the confining effect of a mannishly-tailored suit didn't hide her outrageously feminine qualities. She walked straight to his table, and he stood up. "They told me you might be here," she said, breathing hard. "I just wanted to thank you for last night." "Look, Greta—" Wham! Her hand, small and delicate, felt like a slab of lead when it slammed into his cheek. She left a bruise five fingers wide, and then turned and stalked out. He ran after her, the restaurant proprietor shouting about the unpaid bill. It took a rapid dog-trot to reach her side. "Greta, listen!" he panted. "You don't understand about last night. It wasn't the way that lousy columnist said—" She stopped in her tracks. "I wouldn't have minded so much if you'd gotten me drunk. But to use me, just to get a story—" "But I'm a reporter , damn it. It's my job. I'd do it again if I thought you knew anything." She was pouting now. "Well, how do you suppose I feel, knowing you're only interested in me because of the Senator? Anyway, I'll probably lose my job, and then you won't have any use for me." "Good-bye, Greta," Jerry said sadly. "What?" "Good-bye. I suppose you won't want to see me any more." "Did I say that?" "It just won't be any use. We'll always have this thing between us." She looked at him for a moment, and then touched his bruised cheek with a tender, motherly gesture. "Your poor face," she murmured, and then sighed. "Oh, well. I guess there's no use fighting it. Maybe if I did tell you what I know, we could act human again." "Greta!" "But if you print one word of it, Jerry Bridges, I'll never speak to you again!" "Honey," Jerry said, taking her arm, "you can trust me like a brother." "That's not the idea," Greta said stiffly. In a secluded booth at the rear of a restaurant unfrequented by newsmen, Greta leaned forward and said: "At first, they thought it was another sputnik." " Who did?" "The State Department, silly. They got reports from the observatories about another sputnik being launched by the Russians. Only the Russians denied it. Then there were joint meetings, and nobody could figure out what the damn thing was." "Wait a minute," Jerry said dizzily. "You mean to tell me there's another of those metal moons up there?" "But it's not a moon. That's the big point. It's a spaceship." "A what ?" "A spaceship," Greta said coolly, sipping lemonade. "They have been in contact with it now for about three days, and they're thinking of calling a plenary session of the UN just to figure out what to do about it. The only hitch is, Russia doesn't want to wait that long, and is asking for a hurry-up summit meeting to make a decision." "A decision about what?" "About the Venusians, of course." "Greta," Jerry said mildly, "I think you're still a little woozy from last night." "Don't be silly. The spaceship's from Venus; they've already established that. And the people on it—I guess they're people—want to know if they can land their delegate." "Their what?" "Their delegate. They came here for some kind of conference, I guess. They know about the UN and everything, and they want to take part. They say that with all the satellites being launched, that our affairs are their affairs, too. It's kind of confusing, but that's what they say." "You mean these Venusians speak English?" "And Russian. And French. And German. And everything I guess. They've been having radio talks with practically every country for the past three days. Like I say, they want to establish diplomatic relations or something. The Senator thinks that if we don't agree, they might do something drastic, like blow us all up. It's kind of scary." She shivered delicately. "You're taking it mighty calm," he said ironically. "Well, how else can I take it? I'm not even supposed to know about it, except that the Senator is so careless about—" She put her fingers to her lips. "Oh, dear, now you'll really think I'm terrible." "Terrible? I think you're wonderful!" "And you promise not to print it?" "Didn't I say I wouldn't?" "Y-e-s. But you know, you're a liar sometimes, Jerry. I've noticed that about you." The press secretary's secretary, a massive woman with gray hair and impervious to charm, guarded the portals of his office with all the indomitable will of the U. S. Marines. But Jerry Bridges tried. "You don't understand, Lana," he said. "I don't want to see Mr. Howells. I just want you to give him something." "My name's not Lana, and I can't deliver any messages." "But this is something he wants to see." He handed her an envelope, stamped URGENT. "Do it for me, Hedy. And I'll buy you the flashiest pair of diamond earrings in Washington." "Well," the woman said, thawing slightly. "I could deliver it with his next batch of mail." "When will that be?" "In an hour. He's in a terribly important meeting right now." "You've got some mail right there. Earrings and a bracelet to match." She looked at him with exasperation, and then gathered up a stack of memorandums and letters, his own envelope atop it. She came out of the press secretary's office two minutes later with Howells himself, and Howells said: "You there, Bridges. Come in here." "Yes, sir !" Jerry said, breezing by the waiting reporters with a grin of triumph. There were six men in the room, three in military uniform. Howells poked the envelope towards Jerry, and snapped: "This note of yours. Just what do you think it means?" "You know better than I do, Mr. Howells. I'm just doing my job; I think the public has a right to know about this spaceship that's flying around—" His words brought an exclamation from the others. Howells sighed, and said: "Mr. Bridges, you don't make it easy for us. It's our opinion that secrecy is essential, that leakage of the story might cause panic. Since you're the only unauthorized person who knows of it, we have two choices. One of them is to lock you up." Jerry swallowed hard. "The other is perhaps more practical," Howells said. "You'll be taken into our confidence, and allowed to accompany those officials who will be admitted to the landing site. But you will not be allowed to relay the story to the press until such a time as all correspondents are informed. That won't give you a 'scoop' if that's what you call it, but you'll be an eyewitness. That should be worth something." "It's worth a lot," Jerry said eagerly. "Thanks, Mr. Howells." "Don't thank me, I'm not doing you any personal favor. Now about the landing tonight—" "You mean the spaceship's coming down?" "Yes. A special foreign ministers conference was held this morning, and a decision was reached to accept the delegate. Landing instructions are being given at Los Alamos, and the ship will presumably land around midnight tonight. There will be a jet leaving Washington Airport at nine, and you'll be on it. Meanwhile, consider yourself in custody." The USAF jet transport wasn't the only secrecy-shrouded aircraft that took off that evening from Washington Airport. But Jerry Bridges, sitting in the rear seat flanked by two Sphinx-like Secret Service men, knew that he was the only passenger with non-official status aboard. It was only a few minutes past ten when they arrived at the air base at Los Alamos. The desert sky was cloudy and starless, and powerful searchlights probed the thick cumulus. There were sleek, purring black autos waiting to rush the air passengers to some unnamed destination. They drove for twenty minutes across a flat ribbon of desert road, until Jerry sighted what appeared to be a circle of newly-erected lights in the middle of nowhere. On the perimeter, official vehicles were parked in orderly rows, and four USAF trailer trucks were in evidence, their radarscopes turning slowly. There was activity everywhere, but it was well-ordered and unhurried. They had done a good job of keeping the excitement contained. He was allowed to leave the car and stroll unescorted. He tried to talk to some of the scurrying officials, but to no avail. Finally, he contented himself by sitting on the sand, his back against the grill of a staff car, smoking one cigarette after another. As the minutes ticked off, the activity became more frenetic around him. Then the pace slowed, and he knew the appointed moment was approaching. Stillness returned to the desert, and tension was a tangible substance in the night air. The radarscopes spun slowly. The searchlights converged in an intricate pattern. Then the clouds seemed to part! "Here she comes!" a voice shouted. And in a moment, the calm was shattered. At first, he saw nothing. A faint roar was started in the heavens, and it became a growl that increased in volume until even the shouting voices could no longer be heard. Then the crisscrossing lights struck metal, glancing off the gleaming body of a descending object. Larger and larger the object grew, until it assumed the definable shape of a squat silver funnel, falling in a perfect straight line towards the center of the light-ringed area. When it hit, a dust cloud obscured it from sight. A loudspeaker blared out an unintelligible order, but its message was clear. No one moved from their position. Finally, a three-man team, asbestos-clad, lead-shielded, stepped out from the ring of spectators. They carried geiger counters on long poles before them. Jerry held his breath as they approached the object; only when they were yards away did he appreciate its size. It wasn't large; not more than fifteen feet in total circumference. One of the three men waved a gloved hand. "It's okay," a voice breathed behind him. "No radiation ..." Slowly, the ring of spectators closed tighter. They were twenty yards from the ship when the voice spoke to them. "Greetings from Venus," it said, and then repeated the phrase in six languages. "The ship you see is a Venusian Class 7 interplanetary rocket, built for one-passenger. It is clear of all radiation, and is perfectly safe to approach. There is a hatch which may be opened by an automatic lever in the side. Please open this hatch and remove the passenger." An Air Force General whom Jerry couldn't identify stepped forward. He circled the ship warily, and then said something to the others. They came closer, and he touched a small lever on the silvery surface of the funnel. A door slid open. "It's a box!" someone said. "A crate—" "Colligan! Moore! Schaffer! Lend a hand here—" A trio came forward and hoisted the crate out of the ship. Then the voice spoke again; Jerry deduced that it must have been activated by the decreased load of the ship. "Please open the crate. You will find our delegate within. We trust you will treat him with the courtesy of an official emissary." They set to work on the crate, its gray plastic material giving in readily to the application of their tools. But when it was opened, they stood aside in amazement and consternation. There were a variety of metal pieces packed within, protected by a filmy packing material. "Wait a minute," the general said. "Here's a book—" He picked up a gray-bound volume, and opened its cover. "'Instructions for assembling Delegate,'" he read aloud. "'First, remove all parts and arrange them in the following order. A-1, central nervous system housing. A-2 ...'" He looked up. "It's an instruction book," he whispered. "We're supposed to build the damn thing." The Delegate, a handsomely constructed robot almost eight feet tall, was pieced together some three hours later, by a team of scientists and engineers who seemed to find the Venusian instructions as elementary as a blueprint in an Erector set. But simple as the job was, they were obviously impressed by the mechanism they had assembled. It stood impassive until they obeyed the final instruction. "Press Button K ..." They found button K, and pressed it. The robot bowed. "Thank you, gentlemen," it said, in sweet, unmetallic accents. "Now if you will please escort me to the meeting place ..." It wasn't until three days after the landing that Jerry Bridges saw the Delegate again. Along with a dozen assorted government officials, Army officers, and scientists, he was quartered in a quonset hut in Fort Dix, New Jersey. Then, after seventy-two frustrating hours, he was escorted by Marine guard into New York City. No one told him his destination, and it wasn't until he saw the bright strips of light across the face of the United Nations building that he knew where the meeting was to be held. But his greatest surprise was yet to come. The vast auditorium which housed the general assembly was filled to its capacity, but there were new faces behind the plaques which designated the member nations. He couldn't believe his eyes at first, but as the meeting got under way, he knew that it was true. The highest echelons of the world's governments were represented, even—Jerry gulped at the realization—Nikita Khrushchev himself. It was a summit meeting such as he had never dreamed possible, a summit meeting without benefit of long foreign minister's debate. And the cause of it all, a placid, highly-polished metal robot, was seated blithely at a desk which bore the designation: VENUS. The robot delegate stood up. "Gentlemen," it said into the microphone, and the great men at the council tables strained to hear the translator's version through their headphones, "Gentlemen, I thank you for your prompt attention. I come as a Delegate from a great neighbor planet, in the interests of peace and progress for all the solar system. I come in the belief that peace is the responsibility of individuals, of nations, and now of worlds, and that each is dependent upon the other. I speak to you now through the electronic instrumentation which has been created for me, and I come to offer your planet not merely a threat, a promise, or an easy solution—but a challenge." The council room stirred. "Your earth satellites have been viewed with interest by the astronomers of our world, and we foresee the day when contact between our planets will be commonplace. As for ourselves, we have hitherto had little desire to explore beyond our realm, being far too occupied with internal matters. But our isolation cannot last in the face of your progress, so we believe that we must take part in your affairs. "Here, then, is our challenge. Continue your struggle of ideas, compete with each other for the minds of men, fight your bloodless battles, if you know no other means to attain progress. But do all this without unleashing the terrible forces of power now at your command. Once unleashed, these forces may or may not destroy all that you have gained. But we, the scientists of Venus, promise you this—that on the very day your conflict deteriorates into heedless violence, we will not stand by and let the ugly contagion spread. On that day, we of Venus will act swiftly, mercilessly, and relentlessly—to destroy your world completely." Again, the meeting room exploded in a babble of languages. "The vessel which brought me here came as a messenger of peace. But envision it, men of Earth, as a messenger of war. Unstoppable, inexorable, it may return, bearing a different Delegate from Venus—a Delegate of Death, who speaks not in words, but in the explosion of atoms. Think of thousands of such Delegates, fired from a vantage point far beyond the reach of your retaliation. This is the promise and the challenge that will hang in your night sky from this moment forward. Look at the planet Venus, men of Earth, and see a Goddess of Vengeance, poised to wreak its wrath upon those who betray the peace." The Delegate sat down. Four days later, a mysterious explosion rocked the quiet sands of Los Alamos, and the Venus spacecraft was no more. Two hours after that, the robot delegate, its message delivered, its mission fulfilled, requested to be locked inside a bombproof chamber. When the door was opened, the Delegate was an exploded ruin. The news flashed with lightning speed over the world, and Jerry Bridges' eyewitness accounts of the incredible event was syndicated throughout the nation. But his sudden celebrity left him vaguely unsatisfied. He tried to explain his feeling to Greta on his first night back in Washington. They were in his apartment, and it was the first time Greta had consented to pay him the visit. "Well, what's bothering you?" Greta pouted. "You've had the biggest story of the year under your byline. I should think you'd be tickled pink." "It's not that," Jerry said moodily. "But ever since I heard the Delegate speak, something's been nagging me." "But don't you think he's done good? Don't you think they'll be impressed by what he said?" "I'm not worried about that. I think that damn robot did more for peace than anything that's ever come along in this cockeyed world. But still ..." Greta snuggled up to him on the sofa. "You worry too much. Don't you ever think of anything else? You should learn to relax. It can be fun." She started to prove it to him, and Jerry responded the way a normal, healthy male usually does. But in the middle of an embrace, he cried out: "Wait a minute!" "What's the matter?" "I just thought of something! Now where the hell did I put my old notebooks?" He got up from the sofa and went scurrying to a closet. From a debris of cardboard boxes, he found a worn old leather brief case, and cackled with delight when he found the yellowed notebooks inside. "What are they?" Greta said. "My old school notebooks. Greta, you'll have to excuse me. But there's something I've got to do, right away!" "That's all right with me," Greta said haughtily. "I know when I'm not wanted." She took her hat and coat from the hall closet, gave him one last chance to change his mind, and then left. Five minutes later, Jerry Bridges was calling the airlines. It had been eleven years since Jerry had walked across the campus of Clifton University, heading for the ivy-choked main building. It was remarkable how little had changed, but the students seemed incredibly young. He was winded by the time he asked the pretty girl at the desk where Professor Martin Coltz could be located. "Professor Coltz?" She stuck a pencil to her mouth. "Well, I guess he'd be in the Holland Laboratory about now." "Holland Laboratory? What's that?" "Oh, I guess that was after your time, wasn't it?" Jerry felt decrepit, but managed to say: "It must be something new since I was here. Where is this place?" He followed her directions, and located a fresh-painted building three hundred yards from the men's dorm. He met a student at the door, who told him that Professor Coltz would be found in the physics department. The room was empty when Jerry entered, except for the single stooped figure vigorously erasing a blackboard. He turned when the door opened. If the students looked younger, Professor Coltz was far older than Jerry remembered. He was a tall man, with an unruly confusion of straight gray hair. He blinked when Jerry said: "Hello, Professor. Do you remember me? Jerry Bridges?" "Of course! I thought of you only yesterday, when I saw your name in the papers—" They sat at facing student desks, and chatted about old times. But Jerry was impatient to get to the point of his visit, and he blurted out: "Professor Coltz, something's been bothering me. It bothered me from the moment I heard the Delegate speak. I didn't know what it was until last night, when I dug out my old college notebooks. Thank God I kept them." Coltz's eyes were suddenly hooded. "What do you mean, Jerry?" "There was something about the Robot's speech that sounded familiar—I could have sworn I'd heard some of the words before. I couldn't prove anything until I checked my old notes, and here's what I found." He dug into his coat pocket and produced a sheet of paper. He unfolded it and read aloud. "'It's my belief that peace is the responsibility of individuals, of nations, and someday, even of worlds ...' Sound familiar, Professor?" Coltz shifted uncomfortably. "I don't recall every silly thing I said, Jerry." "But it's an interesting coincidence, isn't it, Professor? These very words were spoken by the Delegate from Venus." "A coincidence—" "Is it? But I also remember your interest in robotics. I'll never forget that mechanical homing pigeon you constructed. And you've probably learned much more these past eleven years." "What are you driving at, Jerry?" "Just this, Professor. I had a little daydream, recently, and I want you to hear it. I dreamed about a group of teachers, scientists, and engineers, a group who were suddenly struck by an exciting, incredible idea. A group that worked in the quiet and secrecy of a University on a fantastic scheme to force the idea of peace into the minds of the world's big shots. Does my dream interest you, Professor?" "Go on." "Well, I dreamt that this group would secretly launch an earth satellite of their own, and arrange for the nose cone to come down safely at a certain time and place. They would install a marvelous electronic robot within the cone, ready to be assembled. They would beam a radio message to earth from the cone, seemingly as if it originated from their 'spaceship.' Then, when the Robot was assembled, they would speak through it to demand peace for all mankind ..." "Jerry, if you do this—" "You don't have to say it, Professor, I know what you're thinking. I'm a reporter, and my business is to tell the world everything I know. But if I did it, there might not be a world for me to write about, would there? No, thanks, Professor. As far as I'm concerned, what I told you was nothing more than a daydream." Jerry braked the convertible to a halt, and put his arm around Greta's shoulder. She looked up at the star-filled night, and sighed romantically. Jerry pointed. "That one." Greta shivered closer to him. "And to think what that terrible planet can do to us!" "Oh, I dunno. Venus is also the Goddess of Love." He swung his other arm around her, and Venus winked approvingly. THE END Transcriber's Note: This etext was produced from Amazing Science Fiction Stories October 1958. 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.
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B. lustful and manipulative
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How is the training data collected?
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### Introduction
End-to-end models such as Listen, Attend & Spell (LAS) BIBREF0 or the Recurrent Neural Network Transducer (RNN-T) BIBREF1 are sequence models that directly define $P(W | X)$, the posterior probability of the word or subword sequence $W$ given an audio frame sequence $X$, with no chaining of sub-module probabilities. State-of-the-art, or near state-of-the-art results have been reported for these models on challenging tasks BIBREF2, BIBREF3. End-to-end ASR models in essence do not include independently trained symbols-only or acoustics-only sub-components. As such, they do not provide a clear role for language models $P(W)$ trained only on text/transcript data. There are, however, many situations where we would like to use a separate LM to complement or modify a given ASR system. In particular, no matter how plentiful the paired {audio, transcript} training data, there are typically orders of magnitude more text-only data available. There are also many practical applications of ASR where we wish to adapt the language model, e.g., biasing the recognition grammar towards a list of specific words or phrases for a specific context. The research community has been keenly aware of the importance of this issue, and has responded with a number of approaches, under the rubric of “Fusion”. The most popular of these is “Shallow Fusion” BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, which is simple log-linear interpolation between the scores from the end-to-end model and the separately-trained LM. More structured approaches, “Deep Fusion” BIBREF9, “Cold Fusion” BIBREF10 and “Component Fusion” BIBREF11 jointly train an end-to-end model with a pre-trained LM, with the goal of learning the optimal combination of the two, aided by gating mechanisms applied to the set of joint scores. These methods have not replaced the simple Shallow Fusion method as the go-to method in most of the ASR community. Part of the appeal of Shallow Fusion is that it does not require model retraining – it can be applied purely at decoding time. The Density Ratio approach proposed here can be seen as an extension of Shallow Fusion, sharing some of its simplicity and practicality, but offering a theoretical grounding in Bayes' rule. After describing the historical context, theory and practical implementation of the proposed Density Ratio method, this article describes experiments comparing the method to Shallow Fusion in a cross-domain scenario. An RNN-T model was trained on large-scale speech data with semi-supervised transcripts from YouTube videos, and then evaluated on data from a live Voice Search service, using an RNN-LM trained on Voice Search transcripts to try to boost performance. Then, exploring the transition between cross-domain and in-domain, limited amounts of Voice Search speech data were used to fine-tune the YouTube-trained RNN-T model, followed by LM fusion via both the Density Ratio method and Shallow Fusion. The ratio method was found to produce consistent gains over Shallow Fusion in all scenarios examined. ### A Brief History of Language Model incorporation in ASR
Generative models and Bayes' rule. The Noisy Channel Model underlying the origins of statistical ASR BIBREF12 used Bayes' rule to combine generative models of both the acoustics $p(X|W)$ and the symbol sequence $P(W)$: for an acoustic feature vector sequence $X = {\mbox{\bf x}}_1, ..., {\mbox{\bf x}}_T$ and a word or sub-word sequence $W = s_1, ..., s_U$ with possible time alignments $S_W = \lbrace ..., {\bf s}, ...\rbrace $. ASR decoding then uses the posterior probability $P(W|X)$. A prior $p({\bf s}| W)$ on alignments can be implemented e.g. via a simple 1st-order state transition model. Though lacking in discriminative power, the paradigm provides a clear theoretical framework for decoupling the acoustic model (AM) $p(X|W)$ and LM $P(W)$. Hybrid model for DNNs/LSTMs within original ASR framework. The advent of highly discriminative Deep Neural Networks (DNNs) BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17 and Long Short Term Memory models (LSTMs) BIBREF18, BIBREF19 posed a challenge to the original Noisy Channel Model, as they produce phoneme- or state- level posteriors $P({\bf s}(t) | {\mbox{\bf x}}_t)$, not acoustic likelihoods $p({\mbox{\bf x}}_t | {\bf s}(t))$. The “hybrid” model BIBREF20 proposed the use of scaled likelihoods, i.e. posteriors divided by separately estimated state priors $P(w)$. For bidirectional LSTMs, the scaled-likelihood over a particular alignment ${\bf s}$ is taken to be using $k(X)$ to represent a $p(X)$-dependent term shared by all hypotheses $W$, that does not affect decoding. This “pseudo-generative” score can then be plugged into the original model of Eq. (DISPLAY_FORM2) and used for ASR decoding with an arbitrary LM $P(W)$. For much of the ASR community, this approach still constitutes the state-of-the-art BIBREF2, BIBREF21, BIBREF22. Shallow Fusion. The most popular approach to LM incorporation for end-to-end ASR is a linear interpolation, with no claim to direct interpretability according to probability theory, and often a reward for sequence length $|W|$, scaled by a factor $\beta $ BIBREF5, BIBREF7, BIBREF8, BIBREF23. ### Language Model incorporation into End-to-end ASR, using Bayes' rule ::: A Sequence-level Hybrid Pseudo-Generative Model
The model makes the following assumptions: The source domain $\psi $ has some true joint distribution $P_{\psi }(W, X)$ over text and audio; The target domain $\tau $ has some other true joint distribution $P_{\tau }(W, X)$; A source domain end-to-end model (e.g. RNN-T) captures $P_{\psi }(W | X)$ reasonably well; Separately trained LMs (e.g. RNN-LMs) capture $P_{\psi }(W)$ and $P_{\tau }(W)$ reasonably well; $p_{\psi }(X | W)$ is roughly equal to $p_{\tau }(X | W)$, i.e. the two domains are acoustically consistent; and The target domain posterior, $P_{\tau }(W | X)$, is unknown. The starting point for the proposed Density Ratio Method is then to express a “hybrid” scaled acoustic likelihood for the source domain, in a manner paralleling the original hybrid model BIBREF20: Similarly, for the target domain: Given the stated assumptions, one can then estimate the target domain posterior as: with $k(X) = p_{\psi }(X) / p_{\tau }(X)$ shared by all hypotheses $W$, and the ratio $P_{\tau }(W) / {P_{\psi }(W)}$ (really a probablity mass ratio) giving the proposed method its name. In essence, this model is just an application of Bayes' rule to end-to-end models and separate LMs. The approach can be viewed as the sequence-level version of the classic hybrid model BIBREF20. Similar use of Bayes' rule to combine ASR scores with RNN-LMs has been described elsewhere, e.g. in work connecting grapheme-level outputs with word-level LMs BIBREF6, BIBREF24, BIBREF25. However, to our knowledge this approach has not been applied to end-to-end models in cross-domain settings, where one wishes to leverage a language model from the target domain. For a perspective on a “pure” (non-hybrid) deep generative approach to ASR, see BIBREF26. ### Language Model incorporation into End-to-end ASR, using Bayes' rule ::: Top-down fundamentals of RNN-T
The RNN Transducer (RNN-T) BIBREF1 defines a sequence-level posterior $P(W|X)$ for a given acoustic feature vector sequence $X = {\mbox{\bf x}}_1, ..., {\mbox{\bf x}}_T$ and a given word or sub-word sequence $W = s_1, ..., s_U$ in terms of possible alignments $S_W = \lbrace ..., ({\bf s}, {\bf t}), ... \rbrace $ of $W$ to $X$. The tuple $({\bf s}, {\bf t})$ denotes a specific alignment sequence, a symbol sequence and corresponding sequence of time indices, consistent with the sequence $W$ and utterance $X$. The symbols in ${\bf s}$ are elements of an expanded symbol space that includes optional, repeatable blank symbols used to represent acoustics-only path extensions, where the time index is incremented, but no non-blank symbols are added. Conversely, non-blank symbols are only added to a partial path time-synchronously. (I.e., using $i$ to index elements of ${\bf s}$ and ${\bf t}$, $t_{i+1} = t_i + 1$ if $s_{i+1}$ is blank, and $t_{i + 1} = t_i$ if $s_{i+1}$ is non-blank). $P(W|X)$ is defined by summing over alignment posteriors: Finally, $P(s_{i+1} | X, t_i, s_{1:i})$ is defined using an LSTM-based acoustic encoder with input $X$, an LSTM-based label encoder with non-blank inputs $s$, and a feed-forward joint network combining outputs from the two encoders to produce predictions for all symbols $s$, including the blank symbol. The Forward-Backward algorithm can be used to calculate Eq. (DISPLAY_FORM16) efficiently during training, and Viterbi-based beam search (based on the argmax over possible alignments) can be used for decoding when $W$ is unknown BIBREF1, BIBREF27. ### Language Model incorporation into End-to-end ASR, using Bayes' rule ::: Application of Shallow Fusion to RNN-T
Shallow Fusion (Eq. (DISPLAY_FORM4)) can be implemented in RNN-T for each time-synchronous non-blank symbol path extension. The LM score corresponding to the same symbol extension can be “fused” into the log-domain score used for decoding: This is only done when the hypothesized path extension $s_{i+1}$ is a non-blank symbol; the decoding score for blank symbol path extensions is the unmodified $\log P(s_{i+1} | X, t_i, s_{1:i})$. ### Language Model incorporation into End-to-end ASR, using Bayes' rule ::: Application of the Density Ratio Method to RNN-T
Eq. (DISPLAY_FORM14) can be implemented via an estimated RNN-T “pseudo-posterior”, when $s_{i+1}$ is a non-blank symbol: This estimate is not normalized over symbol outputs, but it plugs into Eq. () and Eq. (DISPLAY_FORM16) to implement the RNN-T version of Eq. (DISPLAY_FORM14). In practice, scaling factors $\lambda _\psi $ and $\lambda _\tau $ on the LM scores, and a non-blank reward $\beta $, are used in the final decoding score: ### Language Model incorporation into End-to-end ASR, using Bayes' rule ::: Implementation
The ratio method is very simple to implement. The procedure is essentially to: Train an end-to-end model such as RNN-T on a given source domain training set $\psi $ (paired audio/transcript data); Train a neural LM such as RNN-LM on text transcripts from the same training set $\psi $; Train a second RNN-LM on the target domain $\tau $; When decoding on the target domain, modify the RNN-T output by the ratio of target/training RNN-LMs, as defined in Eq. (DISPLAY_FORM21), and illustrated in Fig. FIGREF1. The method is purely a decode-time method; no joint training is involved, but it does require tuning of the LM scaling factor(s) (as does Shallow Fusion). A held-out set can be used for that purpose. ### Training, development and evaluation data ::: Training data
The following data sources were used to train the RNN-T and associated RNN-LMs in this study. Source-domain baseline RNN-T: approximately 120M segmented utterances (190,000 hours of audio) from YouTube videos, with associated transcripts obtained from semi-supervised caption filtering BIBREF28. Source-domain normalizing RNN-LM: transcripts from the same 120M utterance YouTube training set. This corresponds to about 3B tokens of the sub-word units used (see below, Section SECREF30). Target-domain RNN-LM: 21M text-only utterance-level transcripts from anonymized, manually transcribed audio data, representative of data from a Voice Search service. This corresponds to about 275M sub-word tokens. Target-domain RNN-T fine-tuning data: 10K, 100K, 1M and 21M utterance-level {audio, transcript} pairs taken from anonymized, transcribed Voice Search data. These fine-tuning sets roughly correspond to 10 hours, 100 hours, 1000 hours and 21,000 hours of audio, respectively. ### Training, development and evaluation data ::: Dev and Eval Sets
The following data sources were used to choose scaling factors and/or evaluate the final model performance. Source-domain Eval Set (YouTube). The in-domain performance of the YouTube-trained RNN-T baseline was measured on speech data taken from Preferred Channels on YouTube BIBREF29. The test set is taken from 296 videos from 13 categories, with each video averaging 5 minutes in length, corresponding to 25 hours of audio and 250,000 word tokens in total. Target-domain Dev & Eval sets (Voice Search). The Voice Search dev and eval sets each consist of approximately 7,500 anonymized utterances (about 33,000 words and corresponding to about 8 hours of audio), distinct from the fine-tuning data described earlier, but representative of the same Voice Search service. ### Cross-domain evaluation: YouTube-trained RNN-T @!START@$\rightarrow $@!END@ Voice Search
The first set of experiments uses an RNN-T model trained on {audio, transcript} pairs taken from segmented YouTube videos, and evaluates the cross-domain generalization of this model to test utterances taken from a Voice Search dataset, with and without fusion to an external LM. ### Cross-domain evaluation: YouTube-trained RNN-T @!START@$\rightarrow $@!END@ Voice Search ::: RNN-T and RNN-LM model settings
The overall structure of the models used here is as follows: RNN-T: Acoustic features: 768-dimensional feature vectors obtained from 3 stacked 256-dimensional logmel feature vectors, extracted every 20 msec from 16 kHz waveforms, and sub-sampled with a stride of 3, for an effective final feature vector step size of 60 msec. Acoustic encoder: 6 LSTM layers x (2048 units with 1024-dimensional projection); bidirectional. Label encoder (aka “decoder” in end-to-end ASR jargon): 1 LSTM layer x (2048 units with 1024-dimensional projection). RNN-T joint network hidden dimension size: 1024. Output classes: 10,000 sub-word “morph” units BIBREF30 , input via a 512-dimensional embedding. Total number of parameters: approximately 340M RNN-LMs for both source and target domains were set to match the RNN-T decoder structure and size: 1 layer x (2048 units with 1024-dimensional projection). Output classes: 10,000 morphs (same as the RNN-T). Total number of parameters: approximately 30M. The RNN-T and the RNN-LMs were independently trained on 128-core tensor processing units (TPUs) using full unrolling and an effective batch size of 4096. All models were trained using the Adam optimization method BIBREF31 for 100K-125K steps, corresponding to about 4 passes over the 120M utterance YouTube training set, and 20 passes over the 21M utterance Voice Search training set. The trained RNN-LM perplexities (shown in Table TABREF28) show the benefit to Voice Search test perplexity of training on Voice Search transcripts. ### Cross-domain evaluation: YouTube-trained RNN-T @!START@$\rightarrow $@!END@ Voice Search ::: Experiments and results
In the first set of experiments, the constraint $\lambda _\psi = \lambda _\tau $ was used to simplify the search for the LM scaling factor in Eq. DISPLAY_FORM21. Fig. FIGREF40 and Fig. FIGREF41 illustrate the different relative sensitivities of WER to the LM scaling factor(s) for Shallow Fusion and the Density Ratio method, as well as the effect of the RNN-T sequence length scaling factor, measured on the dev set. The LM scaling factor affects the relative value of the symbols-only LM score vs. that of the acoustics-aware RNN-T score. This typically alters the balance of insertion vs. deletion errors. In turn, this effect can be offset (or amplified) by the sequence length scaling factor $\beta $ in Eq. (DISPLAY_FORM4), in the case of RNN-T, implemented as a non-blank symbol emission reward. (The blank symbol only consumes acoustic frames, not LM symbols BIBREF1). Given that both factors have related effects on overall WER, the LM scaling factor(s) and the sequence length scaling factor need to be tuned jointly. Fig. FIGREF40 and Fig. FIGREF41 illustrate the different relative sensitivities of WER to these factors for Shallow Fusion and the Density Ratio method, measured on the dev set. In the second set of experiments, $\beta $ was fixed at -0.1, but the constraint $\lambda _\psi = \lambda _\tau $ was lifted, and a range of combinations was evaluated on the dev set. The results are shown in Fig. FIGREF43. The shading in Figs. FIGREF40, FIGREF41 and FIGREF43 uses the same midpoint value of 15.0 to highlight the results. The best combinations of scaling factors from the dev set evaluations (see Fig. FIGREF40, Fig. FIGREF41 and Fig. FIGREF43) were used to generate the final eval set results, WERs and associated deletion, insertion and substitution rates, shown in Table TABREF44. These results are summarized in Table TABREF45, this time showing the exact values of LM scaling factor(s) used. ### Fine-tuning a YouTube-trained RNN-T using limited Voice Search audio data
The experiments in Section SECREF5 showed that an LM trained on text from the target Voice Search domain can boost the cross-domain performance of an RNN-T. The next experiments examined fine-tuning the original YouTube-trained RNN-T on varied, limited amounts of Voice Search {audio, transcript} data. After fine-tuning, LM fusion was applied, again comparing Shallow Fusion and the Density Ratio method. Fine-tuning simply uses the YouTube-trained RNN-T model to warm-start training on the limited Voice Search {audio, transcript} data. This is an effective way of leveraging the limited Voice Search audio data: within a few thousand steps, the fine-tuned model reaches a decent level of performance on the fine-tuning task – though beyond that, it over-trains. A held-out set can be used to gauge over-training and stop training for varying amounts of fine-tuning data. The experiments here fine-tuned the YouTube-trained RNN-T baseline using 10 hours, 100 hours and 1000 hours of Voice Search data, as described in Section SECREF27. (The source domain RNN-LM was not fine-tuned). For each fine-tuned model, Shallow Fusion and the Density Ratio method were used to evaluate incorporation of the Voice Search RNN-LM, described in Section SECREF5, trained on text transcripts from the much larger set of 21M Voice Search utterances. As in Section SECREF5, the dev set was used to tune the LM scaling factor(s) and the sequence length scaling factor $\beta $. To ease parameter tuning, the constraint $\lambda _\psi = \lambda _\tau $ was used for the Density Ratio method. The best combinations of scaling factors from the dev set were then used to generate the final eval results, which are shown in Table TABREF45 ### Discussion
The experiments described here examined the generalization of a YouTube-trained end-to-end RNN-T model to Voice Search speech data, using varying quantities (from zero to 100%) of Voice Search audio data, and 100% of the available Voice Search text data. The results show that in spite of the vast range of acoustic and linguistic patterns covered by the YouTube-trained model, it is still possible to improve performance on Voice Search utterances significantly via Voice Search specific fine-tuning and LM fusion. In particular, LM fusion significantly boosts performance when only a limited quantity of Voice Search fine-tuning data is used. The Density Ratio method consistently outperformed Shallow Fusion for the cross-domain scenarios examined, with and without fine-tuning to audio data from the target domain. Furthermore, the gains in WER over the baseline are significantly larger for the Density Ratio method than for Shallow Fusion, with up to 28% relative reduction in WER (17.5% $\rightarrow $ 12.5%) compared to up to 17% relative reduction (17.5% $\rightarrow $ 14.5%) for Shallow Fusion, in the no fine-tuning scenario. Notably, the “sweet spot” of effective combinations of LM scaling factor and sequence length scaling factor is significantly larger for the Density Ratio method than for Shallow Fusion (see Fig. FIGREF40 and Fig. FIGREF41). Compared to Shallow Fusion, larger absolute values of the scaling factor can be used. A full sweep of the LM scaling factors ($\lambda _\psi $ and $\lambda _\tau $) can improve over the constrained setting $\lambda _\psi = \lambda _\tau $, though not by much. Fig. FIGREF43 shows that the optimal setting of the two factors follows a roughly linear pattern along an off-diagonal band. Fine-tuning using transcribed Voice Search audio data leads to a large boost in performance over the YouTube-trained baseline. Nonetheless, both fusion methods give gains on top of fine-tuning, especially for the limited quantities of fine-tuning data. With 10 hours of fine-tuning, the Density Ratio method gives a 20% relative gain in WER, compared to 12% relative for Shallow Fusion. For 1000 hours of fine-tuning data, the Density Ratio method gives a 10.5% relative gave over the fine-tuned baseline, compared to 7% relative for Shallow Fusion. Even for 21,000 hours of fine-tuning data, i.e. the entire Voice Search training set, the Density Ratio method gives an added boost, from 7.8% to 7.4% WER, a 5% relative improvement. A clear weakness of the proposed method is the apparent need for scaling factors on the LM outputs. In addition to the assumptions made (outlined in Section SECREF5), it is possible that this is due to the implicit LM in the RNN-T being more limited than the RNN-LMs used. ### Summary
This article proposed and evaluated experimentally an alternative to Shallow Fusion for incorporation of an external LM into an end-to-end RNN-T model applied to a target domain different from the source domain it was trained on. The Density Ratio method is simple conceptually, easy to implement, and grounded in Bayes' rule, extending the classic hybrid ASR model to end-to-end models. In contrast, the most commonly reported approach to LM incorporation, Shallow Fusion, has no clear interpretation from probability theory. Evaluated on a YouTube $\rightarrow $ Voice Search cross-domain scenario, the method was found to be effective, with up to 28% relative gains in word error over the non-fused baseline, and consistently outperforming Shallow Fusion by a significant margin. The method continues to produce gains when fine-tuning to paired target domain data, though the gains diminish as more fine-tuning data is used. Evaluation using a variety of cross-domain evaluation scenarios is needed to establish the general effectiveness of the method. ### Summary ::: Acknowledgments
The authors thank Matt Shannon and Khe Chai Sim for valuable feedback regarding this work. Fig. 1. Estimating a target domain pseudo-posterior via combination of source domain RNN-T, source domain RNN-LM, and target domain RNN-LM. Fig. 2. Dev set WERs for Shallow Fusion LM scaling factor λ vs. sequence length scaling factor β. Table 1. Training set size and test set perplexity for the morph-level RNN-LMs (training domain → testing domain) used in this study. Fig. 3. Dev set WERs for Density Ratio LM scaling factor λ vs. sequence length scaling factor β. Here λ = λψ = λτ . Fig. 4. Dev set WERs for different combinations of λτ and λψ; sequence length scaling factor β = −0.1 Table 2. In-domain and target domain performance of a YouTube-trained RNN-T, evaluated with and without fusion to a Voice Search LM (and normalizing YouTube LM in the case of the Density Ratio method). Table 3. Fine tuning the YouTube-trained RNN-T baseline to the voice search target domain for different quantities of Voice Search fine-tuning data, evaluated with and without LM fusion on Voice Search test utterances. (Results for the “no fine-tuning” baseline carried over from Table 2).
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from YouTube videos, with associated transcripts obtained from semi-supervised caption filtering, from a Voice Search service
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Why is the machine operator willing to risk his job by sending Eric and Nada to Venus?
A. The operator is an alcoholic, and alcohol has been outlawed.
B. The operator thinks he sent them to Hong Kong,
C. The operator is a friend of Eric's, and he owes Eric a favor.
D. It does not occur to him that he is risking his job to send them.
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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
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A. The operator is an alcoholic, and alcohol has been outlawed.
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What existing corpus is used for comparison in these experiments?
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### Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2 . Due to renewed focus on finding ways to foster healthy political conversation, the profile of factcheckers has been raised. Factcheckers positively influence public debate by publishing good quality information and asking politicians and journalists to retract misleading or false statements. By calling out lies and the blurring of the truth, they make those in positions of power accountable. This is a result of labour intensive work that involves monitoring the news for spurious claims and carrying out rigorous research to judge credibility. So far, it has only been possible to scale their output upwards by hiring more personnel. This is problematic because newsrooms need significant resources to employ factcheckers. Publication budgets have been decreasing, resulting in a steady decline in the size of their workforce BIBREF0 . Factchecking is not a directly profitable activity, which negatively affects the allocation of resources towards it in for-profit organisations. It is often taken on by charities and philanthropists instead. To compensate for this shortfall, our strategy is to harness the latest developments in NLP to make factchecking more efficient and therefore less costly. To this end, the new field of automated factchecking has captured the imagination of both non-profits and start-ups BIBREF3 , BIBREF4 , BIBREF5 . It aims to speed up certain aspects of the factchecking process rather than create AI that can replace factchecking personnel. This includes monitoring claims that are made in the news, aiding decisions about which statements are the most important to check and automatically retrieving existing factchecks that are relevant to a new claim. The claim detection and claim clustering methods that we set out in this paper can be applied to each of these. We sought to devise a system that would automatically detect claims in articles and compare them to previously submitted claims. Storing the results to allow a factchecker's work on one of these claims to be easily transferred to others in the same cluster. ### Related Work
It is important to decide what sentences are claims before attempting to cluster them. The first such claim detection system to have been created is ClaimBuster BIBREF6 , which scores sentences with an SVM to determine how likely they are to be politically pertinent statements. Similarly, ClaimRank BIBREF7 uses real claims checked by factchecking institutions as training data in order to surface sentences that are worthy of factchecking. These methods deal with the question of what is a politically interesting claim. In order to classify the objective qualities of what set apart different types of claims, the ClaimBuster team created PolitiTax BIBREF8 , a taxonomy of claims, and factchecking organisation Full Fact BIBREF9 developed their preferred annotation schema for statements in consultation with their own factcheckers. This research provides a more solid framework within which to construct claim detection classifiers. The above considers whether or not a sentence is a claim, but often claims are subsections of sentences and multiple claims might be found in one sentence. In order to accommodate this, BIBREF10 proposes extracting phrases called Context Dependent Claims (CDC) that are relevant to a certain `Topic'. Along these lines, BIBREF11 proposes new definitions for frames to be incorporated into FrameNet BIBREF12 that are specific to facts, in particular those found in a political context. Traditional text clustering methods, using TFIDF and some clustering algorithm, are poorly suited to the problem of clustering and comparing short texts, as they can be semantically very similar but use different words. This is a manifestation of the the data sparsity problem with Bag-of-Words (BoW) models. BIBREF16 . Dimensionality reduction methods such as Latent Dirichlet Allocation (LDA) can help solve this problem by giving a dense approximation of this sparse representation BIBREF17 . More recently, efforts in this area have used text embedding-based systems in order to capture dense representation of the texts BIBREF18 . Much of this recent work has relied on the increase of focus in word and text embeddings. Text embeddings have been an increasingly popular tool in NLP since the introduction of Word2Vec BIBREF19 , and since then the number of different embeddings has exploded. While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time Some considerations to bear in mind when deciding on an embedding scheme to use are: the size of the final vector, the complexity of the model itself and, if using a pretrained implementation, the data the model has been trained on and whether it is trained in a supervised or unsupervised manner. The size of the embedding can have numerous results downstream. In our example we will be doing distance calculations on the resultant vectors and therefore any increase in length will increase the complexity of those distance calculations. We would therefore like as short a vector as possible, but we still wish to capture all salient information about the claim; longer vectors have more capacity to store information, both salient and non-salient. A similar effect is seen for the complexity of the model. A more complicated model, with more trainable parameters, may be able to capture finer details about the text, but it will require a larger corpus to achieve this, and will require more computational time to calculate the embeddings. We should therefore attempt to find the simplest embedding system that can accurately solve our problem. When attempting to use pretrained models to help in other areas, it is always important to ensure that the models you are using are trained on similar material, to increase the chance that their findings will generalise to the new problem. Many unsupervised text embeddings are trained on the CommonCrawl dataset of approx. 840 billion tokens. This gives a huge amount of data across many domains, but requires a similarly huge amount of computing power to train on the entire dataset. Supervised datasets are unlikely ever to approach such scale as they require human annotations which can be expensive to assemble. The SNLI entailment dataset is an example of a large open source dataset BIBREF20 . It features pairs of sentences along with labels specifying whether or not one entails the other. Google's Universal Sentence Encoder (USE) BIBREF14 is a sentence embedding created with a hybrid supervised/unsupervised method, leveraging both the vast amounts of unsupervised training data and the extra detail that can be derived from a supervised method. The SNLI dataset and the related MultiNLI dataset are often used for this because textual entailment is seen as a good basis for general Natural Language Understanding (NLU) BIBREF21 . ### Method
It is much easier to build a dataset and reliably evaluate a model if the starting definitions are clear and objective. Questions around what is an interesting or pertinent claim are inherently subjective. For example, it is obvious that a politician will judge their opponents' claims to be more important to factcheck than their own. Therefore, we built on the methodologies that dealt with the objective qualities of claims, which were the PolitiTax and Full Fact taxonomies. We annotated sentences from our own database of news articles based on a combination of these. We also used the Full Fact definition of a claim as a statement about the world that can be checked. Some examples of claims according to this definition are shown in Table TABREF3 . We decided the first statement was a claim since it declares the occurrence of an event, while the second was considered not to be a claim as it is an expression of feeling. Full Fact's approach centred around using sentence embeddings as a feature engineering step, followed by a simple classifier such as logistic regression, which is what we used. They used Facebook's sentence embeddings, InferSent BIBREF13 , which was a recent breakthrough at the time. Such is the speed of new development in the field that since then, several papers describing textual embeddings have been published. Due to the fact that we had already evaluated embeddings for clustering, and therefore knew our system would rely on Google USE Large BIBREF14 , we decided to use this instead. We compared this to TFIDF and Full Fact's results as baselines. The results are displayed in Table TABREF4 . However, ClaimBuster and Full Fact focused on live factchecking of TV debates. Logically is a news aggregator and we analyse the bodies of published news stories. We found that in our corpus, the majority of sentences are claims and therefore our model needed to be as selective as possible. In practice, we choose to filter out sentences that are predictions since generally the substance of the claim cannot be fully checked until after the event has occurred. Likewise, we try to remove claims based on personal experience or anecdotal evidence as they are difficult to verify. ### Choosing an embedding
In order to choose an embedding, we sought a dataset to represent our problem. Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions. The graphs in figure 1 show the distances between duplicate and non-duplicate questions using different embedding systems. The X axis shows the euclidean distance between vectors and the Y axis frequency. A perfect result would be a blue peak to the left and an entirely disconnected orange spike to the right, showing that all non-duplicate questions have a greater euclidean distance than the least similar duplicate pair of questions. As can be clearly seen in the figure above, Elmo BIBREF23 and Infersent BIBREF13 show almost no separation and therefore cannot be considered good models for this problem. A much greater disparity is shown by the Google USE models BIBREF14 , and even more for the Google USE Large model. In fact the Google USE Large achieved a F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates. In order to test whether these results generalised to our domain, we devised a test that would make use of what little data we had to evaluate. We had no original data on whether sentences were semantically similar, but we did have a corpus of articles clustered into stories. Working on the assumption that similar claims would be more likely to be in the same story, we developed an equation to judge how well our corpus of sentences was clustered, rewarding clustering which matches the article clustering and the total number of claims clustered. The precise formula is given below, where INLINEFORM0 is the proportion of claims in clusters from one story cluster, INLINEFORM1 is the proportion of claims in the correct claim cluster, where they are from the most common story cluster, and INLINEFORM2 is the number of claims placed in clusters. A,B and C are parameters to tune. INLINEFORM3 figureFormula to assess the correctness of claim clusters based on article clusters This method is limited in how well it can represent the problem, but it can give indications as to a good or bad clustering method or embedding, and can act as a check that the findings we obtained from the Quora dataset will generalise to our domain. We ran code which vectorized 2,000 sentences and then used the DBScan clustering method BIBREF24 to cluster using a grid search to find the best INLINEFORM0 value, maximizing this formula. We used DBScan as it mirrored the clustering method used to derive the original article clusters. The results for this experiment can be found in Table TABREF10 . We included TFIDF in the experiment as a baseline to judge other results. It is not suitable for our eventual purposes, but it the basis of the original keyword-based model used to build the clusters . That being said, TFIDF performs very well, with only Google USE Large and Infersent coming close in terms of `accuracy'. In the case of Infersent, this comes with the penalty of a much smaller number of claims included in the clusters. Google USE Large, however, clusters a greater number and for this reason we chose to use Google's USE Large. Since Google USE Large was the best-performing embedding in both the tests we devised, this was our chosen embedding to use for clustering. However as can be seen from the results shown above, this is not a perfect solution and the inaccuracy here will introduce inaccuracy further down the clustering pipeline. ### Clustering Method
We decided to follow a methodology upon the DBScan method of clustering BIBREF24 . DBScan considers all distances between pairs of points. If they are under INLINEFORM0 then those two are linked. Once the number of connected points exceeds a minimum size threshold, they are considered a cluster and all other points are considered to be unclustered. This method is advantageous for our purposes because unlike other methods, such as K-Means, it does not require the number of clusters to be specified. To create a system that can build clusters dynamically, adding one point at a time, we set the minimum cluster size to one, meaning that every point is a member of a cluster. A potential disadvantage of this method is that because points require only one connection to a cluster to join it, they may only be related to one point in the cluster, but be considered in the same cluster as all of them. In small examples this is not a problem as all points in the cluster should be very similar. However as the number of points being considered grows, this behaviour raises the prospect of one or several borderline clustering decisions leading to massive clusters made from tenuous connections between genuine clusters. To mitigate this problem we used a method described in the Newslens paper BIBREF25 to solve a similar problem when clustering entire articles. We stored all of our claims in a graph with the connections between them added when the distance between them was determined to be less than INLINEFORM0 . To determine the final clusters we run a Louvain Community Detection BIBREF26 over this graph to split it into defined communities. This improved the compactness of a cluster. When clustering claims one by one, this algorithm can be performed on the connected subgraph featuring the new claim, to reduce the computation required. As this method involves distance calculations between the claim being added and every existing claim, the time taken to add one claim will increase roughly linearly with respect to the number of previous claims. Through much optimization we have brought the computational time down to approximately 300ms per claim, which stays fairly static with respect to the number of previous claims. ### Next Steps
The clustering described above is heavily dependent on the embedding used. The rate of advances in this field has been rapid in recent years, but an embedding will always be an imperfect representation of an claim and therefore always an area of improvement. A domain specific-embedding will likely offer a more accurate representation but creates problems with clustering claims from different domains. They also require a huge amount of data to give a good model and that is not possible in all domains. Table 1: Examples of claims taken from real articles. Table 2: Claim Detection Results. Figure 1: Analysis of Different Embeddings on the Quora Question Answering Dataset Table 3: Comparing Sentence Embeddings for Clustering News Claims.
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Quora duplicate question dataset BIBREF22
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How was the dataset collected?
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### None
0pt0.03.03 * 0pt0.030.03 * 0pt0.030.03 We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide's map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task. ### Introduction
As artificial intelligence plays an ever more prominent role in everyday human lives, it becomes increasingly important to enable machines to communicate via natural language—not only with humans, but also with each other. Learning algorithms for natural language understanding, such as in machine translation and reading comprehension, have progressed at an unprecedented rate in recent years, but still rely on static, large-scale, text-only datasets that lack crucial aspects of how humans understand and produce natural language. Namely, humans develop language capabilities by being embodied in an environment which they can perceive, manipulate and move around in; and by interacting with other humans. Hence, we argue that we should incorporate all three fundamental aspects of human language acquisition—perception, action and interactive communication—and develop a task and dataset to that effect. We introduce the Talk the Walk dataset, where the aim is for two agents, a “guide” and a “tourist”, to interact with each other via natural language in order to achieve a common goal: having the tourist navigate towards the correct location. The guide has access to a map and knows the target location, but does not know where the tourist is; the tourist has a 360-degree view of the world, but knows neither the target location on the map nor the way to it. The agents need to work together through communication in order to successfully solve the task. An example of the task is given in Figure FIGREF3 . Grounded language learning has (re-)gained traction in the AI community, and much attention is currently devoted to virtual embodiment—the development of multi-agent communication tasks in virtual environments—which has been argued to be a viable strategy for acquiring natural language semantics BIBREF0 . Various related tasks have recently been introduced, but in each case with some limitations. Although visually grounded dialogue tasks BIBREF1 , BIBREF2 comprise perceptual grounding and multi-agent interaction, their agents are passive observers and do not act in the environment. By contrast, instruction-following tasks, such as VNL BIBREF3 , involve action and perception but lack natural language interaction with other agents. Furthermore, some of these works use simulated environments BIBREF4 and/or templated language BIBREF5 , which arguably oversimplifies real perception or natural language, respectively. See Table TABREF15 for a comparison. Talk The Walk is the first task to bring all three aspects together: perception for the tourist observing the world, action for the tourist to navigate through the environment, and interactive dialogue for the tourist and guide to work towards their common goal. To collect grounded dialogues, we constructed a virtual 2D grid environment by manually capturing 360-views of several neighborhoods in New York City (NYC). As the main focus of our task is on interactive dialogue, we limit the difficulty of the control problem by having the tourist navigating a 2D grid via discrete actions (turning left, turning right and moving forward). Our street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication. We argue that for artificial agents to solve this challenging problem, some fundamental architecture designs are missing, and our hope is that this task motivates their innovation. To that end, we focus on the task of localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism. To model the interaction between language and action, this architecture repeatedly conditions the spatial dimensions of a convolution on the communicated message sequence. This work makes the following contributions: 1) We present the first large scale dialogue dataset grounded in action and perception; 2) We introduce the MASC architecture for localization and show it yields improvements for both emergent and natural language; 4) Using localization models, we establish initial baselines on the full task; 5) We show that our best model exceeds human performance under the assumption of “perfect perception” and with a learned emergent communication protocol, and sets a non-trivial baseline with natural language. ### Talk The Walk
We create a perceptual environment by manually capturing several neighborhoods of New York City (NYC) with a 360 camera. Most parts of the city are grid-like and uniform, which makes it well-suited for obtaining a 2D grid. For Talk The Walk, we capture parts of Hell's Kitchen, East Village, the Financial District, Williamsburg and the Upper East Side—see Figure FIGREF66 in Appendix SECREF14 for their respective locations within NYC. For each neighborhood, we choose an approximately 5x5 grid and capture a 360 view on all four corners of each intersection, leading to a grid-size of roughly 10x10 per neighborhood. The tourist's location is given as a tuple INLINEFORM0 , where INLINEFORM1 are the coordinates and INLINEFORM2 signifies the orientation (north, east, south or west). The tourist can take three actions: turn left, turn right and go forward. For moving forward, we add INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 to the INLINEFORM7 coordinates for the respective orientations. Upon a turning action, the orientation is updated by INLINEFORM8 where INLINEFORM9 for left and INLINEFORM10 for right. If the tourist moves outside the grid, we issue a warning that they cannot go in that direction and do not update the location. Moreover, tourists are shown different types of transitions: a short transition for actions that bring the tourist to a different corner of the same intersection; and a longer transition for actions that bring them to a new intersection. The guide observes a map that corresponds to the tourist's environment. We exploit the fact that urban areas like NYC are full of local businesses, and overlay the map with these landmarks as localization points for our task. Specifically, we manually annotate each corner of the intersection with a set of landmarks INLINEFORM0 , each coming from one of the following categories: Bar Playfield Bank Hotel Shop Subway Coffee Shop Restaurant Theater The right-side of Figure FIGREF3 illustrates how the map is presented. Note that within-intersection transitions have a smaller grid distance than transitions to new intersections. To ensure that the localization task is not too easy, we do not include street names in the overhead map and keep the landmark categories coarse. That is, the dialogue is driven by uncertainty in the tourist's current location and the properties of the target location: if the exact location and orientation of the tourist were known, it would suffice to communicate a sequence of actions. ### Task
For the Talk The Walk task, we randomly choose one of the five neighborhoods, and subsample a 4x4 grid (one block with four complete intersections) from the entire grid. We specify the boundaries of the grid by the top-left and bottom-right corners INLINEFORM0 . Next, we construct the overhead map of the environment, i.e. INLINEFORM1 with INLINEFORM2 and INLINEFORM3 . We subsequently sample a start location and orientation INLINEFORM4 and a target location INLINEFORM5 at random. The shared goal of the two agents is to navigate the tourist to the target location INLINEFORM0 , which is only known to the guide. The tourist perceives a “street view” planar projection INLINEFORM1 of the 360 image at location INLINEFORM2 and can simultaneously chat with the guide and navigate through the environment. The guide's role consists of reading the tourist description of the environment, building a “mental map” of their current position and providing instructions for navigating towards the target location. Whenever the guide believes that the tourist has reached the target location, they instruct the system to evaluate the tourist's location. The task ends when the evaluation is successful—i.e., when INLINEFORM3 —or otherwise continues until a total of three failed attempts. The additional attempts are meant to ease the task for humans, as we found that they otherwise often fail at the task but still end up close to the target location, e.g., at the wrong corner of the correct intersection. ### Data Collection
We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). We use the MTurk interface of ParlAI BIBREF6 to render 360 images via WebGL and dynamically display neighborhood maps with an HTML5 canvas. Detailed task instructions, which were also given to our workers before they started their task, are shown in Appendix SECREF15 . We paired Turkers at random and let them alternate between the tourist and guide role across different HITs. ### Dataset Statistics
The Talk The Walk dataset consists of over 10k successful dialogues—see Table FIGREF66 in the appendix for the dataset statistics split by neighborhood. Turkers successfully completed INLINEFORM0 of all finished tasks (we use this statistic as the human success rate). More than six hundred participants successfully completed at least one Talk The Walk HIT. Although the Visual Dialog BIBREF2 and GuessWhat BIBREF1 datasets are larger, the collected Talk The Walk dialogs are significantly longer. On average, Turkers needed more than 62 acts (i.e utterances and actions) before they successfully completed the task, whereas Visual Dialog requires 20 acts. The majority of acts comprise the tourist's actions, with on average more than 44 actions per dialogue. The guide produces roughly 9 utterances per dialogue, slightly more than the tourist's 8 utterances. Turkers use diverse discourse, with a vocabulary size of more than 10K (calculated over all successful dialogues). An example from the dataset is shown in Appendix SECREF14 . The dataset is available at https://github.com/facebookresearch/talkthewalk. ### Experiments
We investigate the difficulty of the proposed task by establishing initial baselines. The final Talk The Walk task is challenging and encompasses several important sub-tasks, ranging from landmark recognition to tourist localization and natural language instruction-giving. Arguably the most important sub-task is localization: without such capabilities the guide can not tell whether the tourist reached the target location. In this work, we establish a minimal baseline for Talk The Walk by utilizing agents trained for localization. Specifically, we let trained tourist models undertake random walks, using the following protocol: at each step, the tourist communicates its observations and actions to the guide, who predicts the tourist's location. If the guide predicts that the tourist is at target, we evaluate its location. If successful, the task ends, otherwise we continue until there have been three wrong evaluations. The protocol is given as pseudo-code in Appendix SECREF12 . ### Tourist Localization
The designed navigation protocol relies on a trained localization model that predicts the tourist's location from a communicated message. Before we formalize this localization sub-task in Section UID21 , we further introduce two simplifying assumptions—perfect perception and orientation-agnosticism—so as to overcome some of the difficulties we encountered in preliminary experiments. paragraph4 0.1ex plus0.1ex minus.1ex-1em Perfect Perception Early experiments revealed that perceptual grounding of landmarks is difficult: we set up a landmark classification problem, on which models with extracted CNN BIBREF7 or text recognition features BIBREF8 barely outperform a random baseline—see Appendix SECREF13 for full details. This finding implies that localization models from image input are limited by their ability to recognize landmarks, and, as a result, would not generalize to unseen environments. To ensure that perception is not the limiting factor when investigating the landmark-grounding and action-grounding capabilities of localization models, we assume “perfect perception”: in lieu of the 360 image view, the tourist is given the landmarks at its current location. More formally, each state observation INLINEFORM0 now equals the set of landmarks at the INLINEFORM1 -location, i.e. INLINEFORM2 . If the INLINEFORM3 -location does not have any visible landmarks, we return a single “empty corner” symbol. We stress that our findings—including a novel architecture for grounding actions into an overhead map, see Section UID28 —should carry over to settings without the perfect perception assumption. paragraph4 0.1ex plus0.1ex minus.1ex-1em Orientation-agnosticism We opt to ignore the tourist's orientation, which simplifies the set of actions to [Left, Right, Up, Down], corresponding to adding [(-1, 0), (1, 0), (0, 1), (0, -1)] to the current INLINEFORM0 coordinates, respectively. Note that actions are now coupled to an orientation on the map—e.g. up is equal to going north—and this implicitly assumes that the tourist has access to a compass. This also affects perception, since the tourist now has access to views from all orientations: in conjunction with “perfect perception”, implying that only landmarks at the current corner are given, whereas landmarks from different corners (e.g. across the street) are not visible. Even with these simplifications, the localization-based baseline comes with its own set of challenges. As we show in Section SECREF34 , the task requires communication about a short (random) path—i.e., not only a sequence of observations but also actions—in order to achieve high localization accuracy. This means that the guide needs to decode observations from multiple time steps, as well as understand their 2D spatial arrangement as communicated via the sequence of actions. Thus, in order to get to a good understanding of the task, we thoroughly examine whether the agents can learn a communication protocol that simultaneously grounds observations and actions into the guide's map. In doing so, we thoroughly study the role of the communication channel in the localization task, by investigating increasingly constrained forms of communication: from differentiable continuous vectors to emergent discrete symbols to the full complexity of natural language. The full navigation baseline hinges on a localization model from random trajectories. While we can sample random actions in the emergent communication setup, this is not possible for the natural language setup because the messages are coupled to the trajectories of the human annotators. This leads to slightly different problem setups, as described below. paragraph4 0.1ex plus0.1ex minus.1ex-1em Emergent language A tourist, starting from a random location, takes INLINEFORM0 random actions INLINEFORM1 to reach target location INLINEFORM2 . Every location in the environment has a corresponding set of landmarks INLINEFORM3 for each of the INLINEFORM4 coordinates. As the tourist navigates, the agent perceives INLINEFORM5 state-observations INLINEFORM6 where each observation INLINEFORM7 consists of a set of INLINEFORM8 landmark symbols INLINEFORM9 . Given the observations INLINEFORM10 and actions INLINEFORM11 , the tourist generates a message INLINEFORM12 which is communicated to the other agent. The objective of the guide is to predict the location INLINEFORM13 from the tourist's message INLINEFORM14 . paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural language In contrast to our emergent communication experiments, we do not take random actions but instead extract actions, observations, and messages from the dataset. Specifically, we consider each tourist utterance (i.e. at any point in the dialogue), obtain the current tourist location as target location INLINEFORM0 , the utterance itself as message INLINEFORM1 , and the sequence of observations and actions that took place between the current and previous tourist utterance as INLINEFORM2 and INLINEFORM3 , respectively. Similar to the emergent language setting, the guide's objective is to predict the target location INLINEFORM4 models from the tourist message INLINEFORM5 . We conduct experiments with INLINEFORM6 taken from the dataset and with INLINEFORM7 generated from the extracted observations INLINEFORM8 and actions INLINEFORM9 . ### Model
This section outlines the tourist and guide architectures. We first describe how the tourist produces messages for the various communication channels across which the messages are sent. We subsequently describe how these messages are processed by the guide, and introduce the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding into the 2D overhead map in order to predict the tourist's location. ### The Tourist
For each of the communication channels, we outline the procedure for generating a message INLINEFORM0 . Given a set of state observations INLINEFORM1 , we represent each observation by summing the INLINEFORM2 -dimensional embeddings of the observed landmarks, i.e. for INLINEFORM3 , INLINEFORM4 , where INLINEFORM5 is the landmark embedding lookup table. In addition, we embed action INLINEFORM6 into a INLINEFORM7 -dimensional embedding INLINEFORM8 via a look-up table INLINEFORM9 . We experiment with three types of communication channel. paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vectors The tourist has access to observations of several time steps, whose order is important for accurate localization. Because summing embeddings is order-invariant, we introduce a sum over positionally-gated embeddings, which, conditioned on time step INLINEFORM0 , pushes embedding information into the appropriate dimensions. More specifically, we generate an observation message INLINEFORM1 , where INLINEFORM2 is a learned gating vector for time step INLINEFORM3 . In a similar fashion, we produce action message INLINEFORM4 and send the concatenated vectors INLINEFORM5 as message to the guide. We can interpret continuous vector communication as a single, monolithic model because its architecture is end-to-end differentiable, enabling gradient-based optimization for training. paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete symbols Like the continuous vector communication model, with discrete communication the tourist also uses separate channels for observations and actions, as well as a sum over positionally gated embeddings to generate observation embedding INLINEFORM0 . We pass this embedding through a sigmoid and generate a message INLINEFORM1 by sampling from the resulting Bernoulli distributions: INLINEFORM0 The action message INLINEFORM0 is produced in the same way, and we obtain the final tourist message INLINEFORM1 through concatenating the messages. The communication channel's sampling operation yields the model non-differentiable, so we use policy gradients BIBREF9 , BIBREF10 to train the parameters INLINEFORM0 of the tourist model. That is, we estimate the gradient by INLINEFORM1 where the reward function INLINEFORM0 is the negative guide's loss (see Section SECREF25 ) and INLINEFORM1 a state-value baseline to reduce variance. We use a linear transformation over the concatenated embeddings as baseline prediction, i.e. INLINEFORM2 , and train it with a mean squared error loss. paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language Because observations and actions are of variable-length, we use an LSTM encoder over the sequence of observations embeddings INLINEFORM0 , and extract its last hidden state INLINEFORM1 . We use a separate LSTM encoder for action embeddings INLINEFORM2 , and concatenate both INLINEFORM3 and INLINEFORM4 to the input of the LSTM decoder at each time step: DISPLAYFORM0 where INLINEFORM0 a look-up table, taking input tokens INLINEFORM1 . We train with teacher-forcing, i.e. we optimize the cross-entropy loss: INLINEFORM2 . At test time, we explore the following decoding strategies: greedy, sampling and a beam-search. We also fine-tune a trained tourist model (starting from a pre-trained model) with policy gradients in order to minimize the guide's prediction loss. ### The Guide
Given a tourist message INLINEFORM0 describing their observations and actions, the objective of the guide is to predict the tourist's location on the map. First, we outline the procedure for extracting observation embedding INLINEFORM1 and action embeddings INLINEFORM2 from the message INLINEFORM3 for each of the types of communication. Next, we discuss the MASC mechanism that takes the observations and actions in order to ground them on the guide's map in order to predict the tourist's location. paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous For the continuous communication model, we assign the observation message to the observation embedding, i.e. INLINEFORM0 . To extract the action embedding for time step INLINEFORM1 , we apply a linear layer to the action message, i.e. INLINEFORM2 . paragraph4 0.1ex plus0.1ex minus.1ex-1em Discrete For discrete communication, we obtain observation INLINEFORM0 by applying a linear layer to the observation message, i.e. INLINEFORM1 . Similar to the continuous communication model, we use a linear layer over action message INLINEFORM2 to obtain action embedding INLINEFORM3 for time step INLINEFORM4 . paragraph4 0.1ex plus0.1ex minus.1ex-1em Natural Language The message INLINEFORM0 contains information about observations and actions, so we use a recurrent neural network with attention mechanism to extract the relevant observation and action embeddings. Specifically, we encode the message INLINEFORM1 , consisting of INLINEFORM2 tokens INLINEFORM3 taken from vocabulary INLINEFORM4 , with a bidirectional LSTM: DISPLAYFORM0 where INLINEFORM0 is the word embedding look-up table. We obtain observation embedding INLINEFORM1 through an attention mechanism over the hidden states INLINEFORM2 : DISPLAYFORM0 where INLINEFORM0 is a learned control embedding who is updated through a linear transformation of the previous control and observation embedding: INLINEFORM1 . We use the same mechanism to extract the action embedding INLINEFORM2 from the hidden states. For the observation embedding, we obtain the final representation by summing positionally gated embeddings, i.e., INLINEFORM3 . We represent the guide's map as INLINEFORM0 , where in this case INLINEFORM1 , where each INLINEFORM2 -dimensional INLINEFORM3 location embedding INLINEFORM4 is computed as the sum of the guide's landmark embeddings for that location. paragraph4 0.1ex plus0.1ex minus.1ex-1em Motivation While the guide's map representation contains only local landmark information, the tourist communicates a trajectory of the map (i.e. actions and observations from multiple locations), implying that directly comparing the tourist's message with the individual landmark embeddings is probably suboptimal. Instead, we want to aggregate landmark information from surrounding locations by imputing trajectories over the map to predict locations. We propose a mechanism for translating landmark embeddings according to state transitions (left, right, up, down), which can be expressed as a 2D convolution over the map embeddings. For simplicity, let us assume that the map embedding INLINEFORM0 is 1-dimensional, then a left action can be realized through application of the following INLINEFORM1 kernel: INLINEFORM2 which effectively shifts all values of INLINEFORM3 one position to the left. We propose to learn such state-transitions from the tourist message through a differentiable attention-mask over the spatial dimensions of a 3x3 convolution. paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC We linearly project each predicted action embedding INLINEFORM0 to a 9-dimensional vector INLINEFORM1 , normalize it by a softmax and subsequently reshape the vector into a 3x3 mask INLINEFORM2 : DISPLAYFORM0 We learn a 3x3 convolutional kernel INLINEFORM0 , with INLINEFORM1 features, and apply the mask INLINEFORM2 to the spatial dimensions of the convolution by first broadcasting its values along the feature dimensions, i.e. INLINEFORM3 , and subsequently taking the Hadamard product: INLINEFORM4 . For each action step INLINEFORM5 , we then apply a 2D convolution with masked weight INLINEFORM6 to obtain a new map embedding INLINEFORM7 , where we zero-pad the input to maintain identical spatial dimensions. paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction model We repeat the MASC operation INLINEFORM0 times (i.e. once for each action), and then aggregate the map embeddings by a sum over positionally-gated embeddings: INLINEFORM1 . We score locations by taking the dot-product of the observation embedding INLINEFORM2 , which contains information about the sequence of observed landmarks by the tourist, and the map. We compute a distribution over the locations of the map INLINEFORM3 by taking a softmax over the computed scores: DISPLAYFORM0 paragraph4 0.1ex plus0.1ex minus.1ex-1em Predicting T While emergent communication models use a fixed length trasjectory INLINEFORM0 , natural language messages may differ in the number of communicated observations and actions. Hence, we predict INLINEFORM1 from the communicated message. Specifically, we use a softmax regression layer over the last hidden state INLINEFORM2 of the RNN, and subsequently sample INLINEFORM3 from the resulting multinomial distribution: DISPLAYFORM0 We jointly train the INLINEFORM0 -prediction model via REINFORCE, with the guide's loss as reward function and a mean-reward baseline. ### Comparisons
To better analyze the performance of the models incorporating MASC, we compare against a no-MASC baseline in our experiments, as well as a prediction upper bound. paragraph4 0.1ex plus0.1ex minus.1ex-1em No MASC We compare the proposed MASC model with a model that does not include this mechanism. Whereas MASC predicts a convolution mask from the tourist message, the “No MASC” model uses INLINEFORM0 , the ordinary convolutional kernel to convolve the map embedding INLINEFORM1 to obtain INLINEFORM2 . We also share the weights of this convolution at each time step. paragraph4 0.1ex plus0.1ex minus.1ex-1em Prediction upper-bound Because we have access to the class-conditional likelihood INLINEFORM0 , we are able to compute the Bayes error rate (or irreducible error). No model (no matter how expressive) with any amount of data can ever obtain better localization accuracy as there are multiple locations consistent with the observations and actions. ### Results and Discussion
In this section, we describe the findings of various experiments. First, we analyze how much information needs to be communicated for accurate localization in the Talk The Walk environment, and find that a short random path (including actions) is necessary. Next, for emergent language, we show that the MASC architecture can achieve very high localization accuracy, significantly outperforming the baseline that does not include this mechanism. We then turn our attention to the natural language experiments, and find that localization from human utterances is much harder, reaching an accuracy level that is below communicating a single landmark observation. We show that generated utterances from a conditional language model leads to significantly better localization performance, by successfully grounding the utterance on a single landmark observation (but not yet on multiple observations and actions). Finally, we show performance of the localization baseline on the full task, which can be used for future comparisons to this work. ### Analysis of Localization Task
paragraph4 0.1ex plus0.1ex minus.1ex-1em Task is not too easy The upper-bound on localization performance in Table TABREF32 suggest that communicating a single landmark observation is not sufficient for accurate localization of the tourist ( INLINEFORM0 35% accuracy). This is an important result for the full navigation task because the need for two-way communication disappears if localization is too easy; if the guide knows the exact location of the tourist it suffices to communicate a list of instructions, which is then executed by the tourist. The uncertainty in the tourist's location is what drives the dialogue between the two agents. paragraph4 0.1ex plus0.1ex minus.1ex-1em Importance of actions We observe that the upperbound for only communicating observations plateaus around 57% (even for INLINEFORM0 actions), whereas it exceeds 90% when we also take actions into account. This implies that, at least for random walks, it is essential to communicate a trajectory, including observations and actions, in order to achieve high localization accuracy. ### Emergent Language Localization
We first report the results for tourist localization with emergent language in Table TABREF32 . paragraph4 0.1ex plus0.1ex minus.1ex-1em MASC improves performance The MASC architecture significantly improves performance compared to models that do not include this mechanism. For instance, for INLINEFORM0 action, MASC already achieves 56.09 % on the test set and this further increases to 69.85% for INLINEFORM1 . On the other hand, no-MASC models hit a plateau at 43%. In Appendix SECREF11 , we analyze learned MASC values, and show that communicated actions are often mapped to corresponding state-transitions. paragraph4 0.1ex plus0.1ex minus.1ex-1em Continuous vs discrete We observe similar performance for continuous and discrete emergent communication models, implying that a discrete communication channel is not a limiting factor for localization performance. ### Natural Language Localization
We report the results of tourist localization with natural language in Table TABREF36 . We compare accuracy of the guide model (with MASC) trained on utterances from (i) humans, (ii) a supervised model with various decoding strategies, and (iii) a policy gradient model optimized with respect to the loss of a frozen, pre-trained guide model on human utterances. paragraph4 0.1ex plus0.1ex minus.1ex-1em Human utterances Compared to emergent language, localization from human utterances is much harder, achieving only INLINEFORM0 on the test set. Here, we report localization from a single utterance, but in Appendix SECREF45 we show that including up to five dialogue utterances only improves performance to INLINEFORM1 . We also show that MASC outperform no-MASC models for natural language communication. paragraph4 0.1ex plus0.1ex minus.1ex-1em Generated utterances We also investigate generated tourist utterances from conditional language models. Interestingly, we observe that the supervised model (with greedy and beam-search decoding) as well as the policy gradient model leads to an improvement of more than 10 accuracy points over the human utterances. However, their level of accuracy is slightly below the baseline of communicating a single observation, indicating that these models only learn to ground utterances in a single landmark observation. paragraph4 0.1ex plus0.1ex minus.1ex-1em Better grounding of generated utterances We analyze natural language samples in Table TABREF38 , and confirm that, unlike human utterances, the generated utterances are talking about the observed landmarks. This observation explains why the generated utterances obtain higher localization accuracy. The current language models are most successful when conditioned on a single landmark observation; We show in Appendix UID43 that performance quickly deteriorates when the model is conditioned on more observations, suggesting that it can not produce natural language utterances about multiple time steps. ### Localization-based Baseline
Table TABREF36 shows results for the best localization models on the full task, evaluated via the random walk protocol defined in Algorithm SECREF12 . paragraph4 0.1ex plus0.1ex minus.1ex-1em Comparison with human annotators Interestingly, our best localization model (continuous communication, with MASC, and INLINEFORM0 ) achieves 88.33% on the test set and thus exceed human performance of 76.74% on the full task. While emergent models appear to be stronger localizers, humans might cope with their localization uncertainty through other mechanisms (e.g. better guidance, bias towards taking particular paths, etc). The simplifying assumption of perfect perception also helps. paragraph4 0.1ex plus0.1ex minus.1ex-1em Number of actions Unsurprisingly, humans take fewer steps (roughly 15) than our best random walk model (roughly 34). Our human annotators likely used some form of guidance to navigate faster to the target. ### Conclusion
We introduced the Talk The Walk task and dataset, which consists of crowd-sourced dialogues in which two human annotators collaborate to navigate to target locations in the virtual streets of NYC. For the important localization sub-task, we proposed MASC—a novel grounding mechanism to learn state-transition from the tourist's message—and showed that it improves localization performance for emergent and natural language. We use the localization model to provide baseline numbers on the Talk The Walk task, in order to facilitate future research. ### Related Work
The Talk the Walk task and dataset facilitate future research on various important subfields of artificial intelligence, including grounded language learning, goal-oriented dialogue research and situated navigation. Here, we describe related previous work in these areas. paragraph4 0.1ex plus0.1ex minus.1ex-1em Related tasks There has been a long line of work involving related tasks. Early work on task-oriented dialogue dates back to the early 90s with the introduction of the Map Task BIBREF11 and Maze Game BIBREF25 corpora. Recent efforts have led to larger-scale goal-oriented dialogue datasets, for instance to aid research on visually-grounded dialogue BIBREF2 , BIBREF1 , knowledge-base-grounded discourse BIBREF29 or negotiation tasks BIBREF36 . At the same time, there has been a big push to develop environments for embodied AI, many of which involve agents following natural language instructions with respect to an environment BIBREF13 , BIBREF50 , BIBREF5 , BIBREF39 , BIBREF19 , BIBREF18 , following-up on early work in this area BIBREF38 , BIBREF20 . An early example of navigation using neural networks is BIBREF28 , who propose an online learning approach for robot navigation. Recently, there has been increased interest in using end-to-end trainable neural networks for learning to navigate indoor scenes BIBREF27 , BIBREF26 or large cities BIBREF17 , BIBREF40 , but, unlike our work, without multi-agent communication. Also the task of localization (without multi-agent communication) has recently been studied BIBREF18 , BIBREF48 . paragraph4 0.1ex plus0.1ex minus.1ex-1em Grounded language learning Grounded language learning is motivated by the observation that humans learn language embodied (grounded) in sensorimotor experience of the physical world BIBREF15 , BIBREF45 . On the one hand, work in multi-modal semantics has shown that grounding can lead to practical improvements on various natural language understanding tasks BIBREF14 , BIBREF31 . In robotics, researchers dissatisfied with purely symbolic accounts of meaning attempted to build robotic systems with the aim of grounding meaning in physical experience of the world BIBREF44 , BIBREF46 . Recently, grounding has also been applied to the learning of sentence representations BIBREF32 , image captioning BIBREF37 , BIBREF49 , visual question answering BIBREF12 , BIBREF22 , visual reasoning BIBREF30 , BIBREF42 , and grounded machine translation BIBREF43 , BIBREF23 . Grounding also plays a crucial role in the emergent research of multi-agent communication, where, agents communicate (in natural language or otherwise) in order to solve a task, with respect to their shared environment BIBREF35 , BIBREF21 , BIBREF41 , BIBREF24 , BIBREF36 , BIBREF47 , BIBREF34 . ### Implementation Details
For the emergent communication models, we use an embedding size INLINEFORM0 . The natural language experiments use 128-dimensional word embeddings and a bidirectional RNN with 256 units. In all experiments, we train the guide with a cross entropy loss using the ADAM optimizer with default hyper-parameters BIBREF33 . We perform early stopping on the validation accuracy, and report the corresponding train, valid and test accuracy. We optimize the localization models with continuous, discrete and natural language communication channels for 200, 200, and 25 epochs, respectively. To facilitate further research on Talk The Walk, we make our code base for reproducing experiments publicly available at https://github.com/facebookresearch/talkthewalk. ### Additional Natural Language Experiments
First, we investigate the sensitivity of tourist generation models to the trajectory length, finding that the model conditioned on a single observation (i.e. INLINEFORM0 ) achieves best performance. In the next subsection, we further analyze localization models from human utterances by investigating MASC and no-MASC models with increasing dialogue context. ### Tourist Generation Models
After training the supervised tourist model (conditioned on observations and action from human expert trajectories), there are two ways to train an accompanying guide model. We can optimize a location prediction model on either (i) extracted human trajectories (as in the localization setup from human utterances) or (ii) on all random paths of length INLINEFORM0 (as in the full task evaluation). Here, we investigate the impact of (1) using either human or random trajectories for training the guide model, and (2) the effect of varying the path length INLINEFORM1 during the full-task evaluation. For random trajectories, guide training uses the same path length INLINEFORM2 as is used during evaluation. We use a pre-trained tourist model with greedy decoding for generating the tourist utterances. Table TABREF40 summarizes the results. paragraph4 0.1ex plus0.1ex minus.1ex-1em Human vs random trajectories We only observe small improvements for training on random trajectories. Human trajectories are thus diverse enough to generalize to random trajectories. paragraph4 0.1ex plus0.1ex minus.1ex-1em Effect of path length There is a strong negative correlation between task success and the conditioned trajectory length. We observe that the full task performance quickly deteriorates for both human and random trajectories. This suggests that the tourist generation model can not produce natural language utterances that describe multiple observations and actions. Although it is possible that the guide model can not process such utterances, this is not very likely because the MASC architectures handles such messages successfully for emergent communication. We report localization performance of tourist utterances generated by beam search decoding of varying beam size in Table TABREF40 . We find that performance decreases from 29.05% to 20.87% accuracy on the test set when we increase the beam-size from one to eight. ### Localization from Human Utterances
We conduct an ablation study for MASC on natural language with varying dialogue context. Specifically, we compare localization accuracy of MASC and no-MASC models trained on the last [1, 3, 5] utterances of the dialogue (including guide utterances). We report these results in Table TABREF41 . In all cases, MASC outperforms the no-MASC models by several accuracy points. We also observe that mean predicted INLINEFORM0 (over the test set) increases from 1 to 2 when more dialogue context is included. ### Visualizing MASC predictions
Figure FIGREF46 shows the MASC values for a learned model with emergent discrete communications and INLINEFORM0 actions. Specifically, we look at the predicted MASC values for different action sequences taken by the tourist. We observe that the first action is always mapped to the correct state-transition, but that the second and third MASC values do not always correspond to right state-transitions. ### Evaluation on Full Setup
We provide pseudo-code for evaluation of localization models on the full task in Algorithm SECREF12 , as well as results for all emergent communication models in Table TABREF55 . INLINEFORM0 INLINEFORM1 INLINEFORM0 take new action INLINEFORM1 INLINEFORM2 Performance evaluation of location prediction model on full Talk The Walk setup ### Landmark Classification
While the guide has access to the landmark labels, the tourist needs to recognize these landmarks from raw perceptual information. In this section, we study landmark classification as a supervised learning problem to investigate the difficulty of perceptual grounding in Talk The Walk. The Talk The Walk dataset contains a total of 307 different landmarks divided among nine classes, see Figure FIGREF62 for how they are distributed. The class distribution is fairly imbalanced, with shops and restaurants as the most frequent landmarks and relatively few play fields and theaters. We treat landmark recognition as a multi-label classification problem as there can be multiple landmarks on a corner. For the task of landmark classification, we extract the relevant views of the 360 image from which a landmark is visible. Because landmarks are labeled to be on a specific corner of an intersection, we assume that they are visible from one of the orientations facing away from the intersection. For example, for a landmark on the northwest corner of an intersection, we extract views from both the north and west direction. The orientation-specific views are obtained by a planar projection of the full 360-image with a small field of view (60 degrees) to limit distortions. To cover the full field of view, we extract two images per orientation, with their horizontal focus point 30 degrees apart. Hence, we obtain eight images per 360 image with corresponding orientation INLINEFORM0 . We run the following pre-trained feature extractors over the extracted images: For the text recognition model, we use a learned look-up table INLINEFORM0 to embed the extracted text features INLINEFORM1 , and fuse all embeddings of four images through a bag of embeddings, i.e., INLINEFORM2 . We use a linear layer followed by a sigmoid to predict the probability for each class, i.e. INLINEFORM3 . We also experiment with replacing the look-up embeddings with pre-trained FastText embeddings BIBREF16 . For the ResNet model, we use a bag of embeddings over the four ResNet features, i.e. INLINEFORM4 , before we pass it through a linear layer to predict the class probabilities: INLINEFORM5 . We also conduct experiments where we first apply PCA to the extracted ResNet and FastText features before we feed them to the model. To account for class imbalance, we train all described models with a binary cross entropy loss weighted by the inverted class frequency. We create a 80-20 class-conditional split of the dataset into a training and validation set. We train for 100 epochs and perform early stopping on the validation loss. The F1 scores for the described methods in Table TABREF65 . We compare to an “all positive” baseline that always predicts that the landmark class is visible and observe that all presented models struggle to outperform this baseline. Although 256-dimensional ResNet features achieve slightly better precision on the validation set, it results in much worse recall and a lower F1 score. Our results indicate that perceptual grounding is a difficult task, which easily merits a paper of its own right, and so we leave further improvements (e.g. better text recognizers) for future work. ### Dataset Details
paragraph4 0.1ex plus0.1ex minus.1ex-1em Dataset split We split the full dataset by assigning entire 4x4 grids (independent of the target location) to the train, valid or test set. Specifically, we design the split such that the valid set contains at least one intersection (out of four) is not part of the train set. For the test set, all four intersections are novel. See our source code, available at URL ANONYMIZED, for more details on how this split is realized. paragraph4 0.1ex plus0.1ex minus.1ex-1em Example Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT Guide: Hello, what are you near? Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT Tourist: Hello, in front of me is a Brooks Brothers Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT Guide: Is that a shop or restaurant? Tourist: ACTION:TURNLEFT Tourist: It is a clothing shop. Tourist: ACTION:TURNLEFT Guide: You need to go to the intersection in the northwest corner of the map Tourist: ACTION:TURNLEFT Tourist: There appears to be a bank behind me. Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT Guide: Ok, turn left then go straight up that road Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT Guide: There should be shops on two of the corners but you need to go to the corner without a shop. Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT Guide: let me know when you get there. Tourist: on my left is Radio city Music hall Tourist: ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT Tourist: I can't go straight any further. Guide: ok. turn so that the theater is on your right. Guide: then go straight Tourist: That would be going back the way I came Guide: yeah. I was looking at the wrong bank Tourist: I'll notify when I am back at the brooks brothers, and the bank. Tourist: ACTION:TURNRIGHT Guide: make a right when the bank is on your left Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT Tourist: Making the right at the bank. Tourist: ACTION:FORWARD ACTION:FORWARD Tourist: I can't go that way. Tourist: ACTION:TURNLEFT Tourist: Bank is ahead of me on the right Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT Guide: turn around on that intersection Tourist: I can only go to the left or back the way I just came. Tourist: ACTION:TURNLEFT Guide: you're in the right place. do you see shops on the corners? Guide: If you're on the corner with the bank, cross the street Tourist: I'm back where I started by the shop and the bank. Tourist: ACTION:TURNRIGHT Guide: on the same side of the street? Tourist: crossing the street now Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT Tourist: there is an I love new york shop across the street on the left from me now Tourist: ACTION:TURNRIGHT ACTION:FORWARD Guide: ok. I'll see if it's right. Guide: EVALUATE_LOCATION Guide: It's not right. Tourist: What should I be on the look for? Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT ACTION:TURNRIGHT Guide: There should be shops on two corners but you need to be on one of the corners without the shop. Guide: Try the other corner. Tourist: this intersection has 2 shop corners and a bank corner Guide: yes. that's what I see on the map. Tourist: should I go to the bank corner? or one of the shop corners? or the blank corner (perhaps a hotel) Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNRIGHT ACTION:TURNRIGHT Guide: Go to the one near the hotel. The map says the hotel is a little further down but it might be a little off. Tourist: It's a big hotel it's possible. Tourist: ACTION:FORWARD ACTION:TURNLEFT ACTION:FORWARD ACTION:TURNRIGHT Tourist: I'm on the hotel corner Guide: EVALUATE_LOCATION Figure 1: Example of the Talk The Walk task: two agents, a “tourist” and a “guide”, interact with each other via natural language in order to have the tourist navigate towards the correct location. The guide has access to a map and knows the target location but not the tourist location, while the tourist does not know the way but can navigate in a 360-degree street view environment. Table 1: Talk The Walk grounds human generated dialogue in (real-life) perception and action. Table 2: Accuracy results for tourist localization with emergent language, showing continuous (Cont.) and discrete (Disc.) communication, along with the prediction upper bound. T denotes the length of the path and a 3 in the “MASC” column indicates that the model is conditioned on the communicated actions. Table 3: Localization accuracy of tourist communicating in natural language. Table 5: Localization given last {1, 3, 5} dialogue utterances (including the guide). We observe that 1) performance increases when more utterances are included; and 2) MASC outperforms no-MASC in all cases; and 3) mean T̂ increases when more dialogue context is included. Table 7: Full task performance of localization models trained on human and random trajectories. There are small benefits for training on random trajectories, but the most important hyperparameter is to condition the tourist utterance on a single observation (i.e. trajectories of size T = 0.) Table 6: Localization performance using pretrained tourist (via imitation learning) with beam search decoding of varying beam size. We find that larger beam-sizes lead to worse localization performance. Table 8: Samples from the tourist models communicating in natural language. Figure 2: We show MASC values of two action sequences for tourist localization via discrete communication with T = 3 actions. In general, we observe that the first action always corresponds to the correct state-transition, whereas the second and third are sometimes mixed. For instance, in the top example, the first two actions are correctly predicted but the third action is not (as the MASC corresponds to a “no action”). In the bottom example, the second action appears as the third MASC. Table 9: Accuracy of localization models on full task, using evaluation protocol defined in Algorithm 1. We report the average over 3 runs. Figure 3: Result of running the text recognizer of [20] on four examples of the Hell’s Kitchen neighborhood. Top row: two positive examples. Bottom row: example of false negative (left) and many false positives (right) Figure 4: Frequency of landmark classes Table 10: Results for landmark classification. Figure 5: Map of New York City with red rectangles indicating the captured neighborhoods of the Talk The Walk dataset. Figure 6: Set of instructions presented to turkers before starting their first task. Figure 7: (cont.) Set of instructions presented to turkers before starting their first task.
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crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk)
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Why are the robots incinerating all the living creatures?
A. A radio frequency from Waiamea scrambled the robots' programming.
B. Pete did not read the directions when assembling the robots.
C. Pete lost his mind on the journey to Waiamea and programmed the robots to kill everyone and everything.
D. Pete built the robots to hunt by following brain waves.
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SURVIVAL TACTICS By AL SEVCIK ILLUSTRATOR NOVICK The robots were built to serve Man; to do his work, see to his comforts, make smooth his way. Then the robots figured out an additional service—putting Man out of his misery. There was a sudden crash that hung sharply in the air, as if a tree had been hit by lightning some distance away. Then another. Alan stopped, puzzled. Two more blasts, quickly together, and the sound of a scream faintly. Frowning, worrying about the sounds, Alan momentarily forgot to watch his step until his foot suddenly plunged into an ant hill, throwing him to the jungle floor. "Damn!" He cursed again, for the tenth time, and stood uncertainly in the dimness. From tall, moss-shrouded trees, wrist-thick vines hung quietly, scraping the spongy ground like the tentacles of some monstrous tree-bound octopus. Fitful little plants grew straggly in the shadows of the mossy trunks, forming a dense underbrush that made walking difficult. At midday some few of the blue sun's rays filtered through to the jungle floor, but now, late afternoon on the planet, the shadows were long and gloomy. Alan peered around him at the vine-draped shadows, listening to the soft rustlings and faint twig-snappings of life in the jungle. Two short, popping sounds echoed across the stillness, drowned out almost immediately and silenced by an explosive crash. Alan started, "Blaster fighting! But it can't be!" Suddenly anxious, he slashed a hurried X in one of the trees to mark his position then turned to follow a line of similar marks back through the jungle. He tried to run, but vines blocked his way and woody shrubs caught at his legs, tripping him and holding him back. Then, through the trees he saw the clearing of the camp site, the temporary home for the scout ship and the eleven men who, with Alan, were the only humans on the jungle planet, Waiamea. Stepping through the low shrubbery at the edge of the site, he looked across the open area to the two temporary structures, the camp headquarters where the power supplies and the computer were; and the sleeping quarters. Beyond, nose high, stood the silver scout ship that had brought the advance exploratory party of scientists and technicians to Waiamea three days before. Except for a few of the killer robots rolling slowly around the camp site on their quiet treads, there was no one about. "So, they've finally got those things working." Alan smiled slightly. "Guess that means I owe Pete a bourbon-and-soda for sure. Anybody who can build a robot that hunts by homing in on animals' mind impulses ..." He stepped forward just as a roar of blue flame dissolved the branches of a tree, barely above his head. Without pausing to think, Alan leaped back, and fell sprawling over a bush just as one of the robots rolled silently up from the right, lowering its blaster barrel to aim directly at his head. Alan froze. "My God, Pete built those things wrong!" Suddenly a screeching whirlwind of claws and teeth hurled itself from the smoldering branches and crashed against the robot, clawing insanely at the antenna and blaster barrel. With an awkward jerk the robot swung around and fired its blaster, completely dissolving the lower half of the cat creature which had clung across the barrel. But the back pressure of the cat's body overloaded the discharge circuits. The robot started to shake, then clicked sharply as an overload relay snapped and shorted the blaster cells. The killer turned and rolled back towards the camp, leaving Alan alone. Shakily, Alan crawled a few feet back into the undergrowth where he could lie and watch the camp, but not himself be seen. Though visibility didn't make any difference to the robots, he felt safer, somehow, hidden. He knew now what the shooting sounds had been and why there hadn't been anyone around the camp site. A charred blob lying in the grass of the clearing confirmed his hypothesis. His stomach felt sick. "I suppose," he muttered to himself, "that Pete assembled these robots in a batch and then activated them all at once, probably never living to realize that they're tuned to pick up human brain waves, too. Damn! Damn!" His eyes blurred and he slammed his fist into the soft earth. When he raised his eyes again the jungle was perceptibly darker. Stealthy rustlings in the shadows grew louder with the setting sun. Branches snapped unaccountably in the trees overhead and every now and then leaves or a twig fell softly to the ground, close to where he lay. Reaching into his jacket, Alan fingered his pocket blaster. He pulled it out and held it in his right hand. "This pop gun wouldn't even singe a robot, but it just might stop one of those pumas." They said the blast with your name on it would find you anywhere. This looked like Alan's blast. Slowly Alan looked around, sizing up his situation. Behind him the dark jungle rustled forbiddingly. He shuddered. "Not a very healthy spot to spend the night. On the other hand, I certainly can't get to the camp with a pack of mind-activated mechanical killers running around. If I can just hold out until morning, when the big ship arrives ... The big ship! Good Lord, Peggy!" He turned white; oily sweat punctuated his forehead. Peggy, arriving tomorrow with the other colonists, the wives and kids! The metal killers, tuned to blast any living flesh, would murder them the instant they stepped from the ship! A pretty girl, Peggy, the girl he'd married just three weeks ago. He still couldn't believe it. It was crazy, he supposed, to marry a girl and then take off for an unknown planet, with her to follow, to try to create a home in a jungle clearing. Crazy maybe, but Peggy and her green eyes that changed color with the light, with her soft brown hair, and her happy smile, had ended thirty years of loneliness and had, at last, given him a reason for living. "Not to be killed!" Alan unclenched his fists and wiped his palms, bloody where his fingernails had dug into the flesh. There was a slight creak above him like the protesting of a branch too heavily laden. Blaster ready, Alan rolled over onto his back. In the movement, his elbow struck the top of a small earthy mound and he was instantly engulfed in a swarm of locust-like insects that beat disgustingly against his eyes and mouth. "Fagh!" Waving his arms before his face he jumped up and backwards, away from the bugs. As he did so, a dark shapeless thing plopped from the trees onto the spot where he had been lying stretched out. Then, like an ambient fungus, it slithered off into the jungle undergrowth. For a split second the jungle stood frozen in a brilliant blue flash, followed by the sharp report of a blaster. Then another. Alan whirled, startled. The planet's double moon had risen and he could see a robot rolling slowly across the clearing in his general direction, blasting indiscriminately at whatever mind impulses came within its pickup range, birds, insects, anything. Six or seven others also left the camp headquarters area and headed for the jungle, each to a slightly different spot. Apparently the robot hadn't sensed him yet, but Alan didn't know what the effective range of its pickup devices was. He began to slide back into the jungle. Minutes later, looking back he saw that the machine, though several hundred yards away, had altered its course and was now headed directly for him. His stomach tightened. Panic. The dank, musty smell of the jungle seemed for an instant to thicken and choke in his throat. Then he thought of the big ship landing in the morning, settling down slowly after a lonely two-week voyage. He thought of a brown-haired girl crowding with the others to the gangway, eager to embrace the new planet, and the next instant a charred nothing, unrecognizable, the victim of a design error or a misplaced wire in a machine. "I have to try," he said aloud. "I have to try." He moved into the blackness. Powerful as a small tank, the killer robot was equipped to crush, slash, and burn its way through undergrowth. Nevertheless, it was slowed by the larger trees and the thick, clinging vines, and Alan found that he could manage to keep ahead of it, barely out of blaster range. Only, the robot didn't get tired. Alan did. The twin moons cast pale, deceptive shadows that wavered and danced across the jungle floor, hiding debris that tripped him and often sent him sprawling into the dark. Sharp-edged growths tore at his face and clothes, and insects attracted by the blood matted against his pants and shirt. Behind, the robot crashed imperturbably after him, lighting the night with fitful blaster flashes as some winged or legged life came within its range. There was movement also, in the darkness beside him, scrapings and rustlings and an occasional low, throaty sound like an angry cat. Alan's fingers tensed on his pocket blaster. Swift shadowy forms moved quickly in the shrubs and the growling became suddenly louder. He fired twice, blindly, into the undergrowth. Sharp screams punctuated the electric blue discharge as a pack of small feline creatures leaped snarling and clawing back into the night. Mentally, Alan tried to figure the charge remaining in his blaster. There wouldn't be much. "Enough for a few more shots, maybe. Why the devil didn't I load in fresh cells this morning!" The robot crashed on, louder now, gaining on the tired human. Legs aching and bruised, stinging from insect bites, Alan tried to force himself to run holding his hands in front of him like a child in the dark. His foot tripped on a barely visible insect hill and a winged swarm exploded around him. Startled, Alan jerked sideways, crashing his head against a tree. He clutched at the bark for a second, dazed, then his knees buckled. His blaster fell into the shadows. The robot crashed loudly behind him now. Without stopping to think, Alan fumbled along the ground after his gun, straining his eyes in the darkness. He found it just a couple of feet to one side, against the base of a small bush. Just as his fingers closed upon the barrel his other hand slipped into something sticky that splashed over his forearm. He screamed in pain and leaped back, trying frantically to wipe the clinging, burning blackness off his arm. Patches of black scraped off onto branches and vines, but the rest spread slowly over his arm as agonizing as hot acid, or as flesh being ripped away layer by layer. Almost blinded by pain, whimpering, Alan stumbled forward. Sharp muscle spasms shot from his shoulder across his back and chest. Tears streamed across his cheeks. A blue arc slashed at the trees a mere hundred yards behind. He screamed at the blast. "Damn you, Pete! Damn your robots! Damn, damn ... Oh, Peggy!" He stepped into emptiness. Coolness. Wet. Slowly, washed by the water, the pain began to fall away. He wanted to lie there forever in the dark, cool, wetness. For ever, and ever, and ... The air thundered. In the dim light he could see the banks of the stream, higher than a man, muddy and loose. Growing right to the edge of the banks, the jungle reached out with hairy, disjointed arms as if to snag even the dirty little stream that passed so timidly through its domain. Alan, lying in the mud of the stream bed, felt the earth shake as the heavy little robot rolled slowly and inexorably towards him. "The Lord High Executioner," he thought, "in battle dress." He tried to stand but his legs were almost too weak and his arm felt numb. "I'll drown him," he said aloud. "I'll drown the Lord High Executioner." He laughed. Then his mind cleared. He remembered where he was. Alan trembled. For the first time in his life he understood what it was to live, because for the first time he realized that he would sometime die. In other times and circumstances he might put it off for a while, for months or years, but eventually, as now, he would have to watch, still and helpless, while death came creeping. Then, at thirty, Alan became a man. "Dammit, no law says I have to flame-out now !" He forced himself to rise, forced his legs to stand, struggling painfully in the shin-deep ooze. He worked his way to the bank and began to dig frenziedly, chest high, about two feet below the edge. His arm where the black thing had been was swollen and tender, but he forced his hands to dig, dig, dig, cursing and crying to hide the pain, and biting his lips, ignoring the salty taste of blood. The soft earth crumbled under his hands until he had a small cave about three feet deep in the bank. Beyond that the soil was held too tightly by the roots from above and he had to stop. The air crackled blue and a tree crashed heavily past Alan into the stream. Above him on the bank, silhouetting against the moons, the killer robot stopped and its blaster swivelled slowly down. Frantically, Alan hugged the bank as a shaft of pure electricity arced over him, sliced into the water, and exploded in a cloud of steam. The robot shook for a second, its blaster muzzle lifted erratically and for an instant it seemed almost out of control, then it quieted and the muzzle again pointed down. Pressing with all his might, Alan slid slowly along the bank inches at a time, away from the machine above. Its muzzle turned to follow him but the edge of the bank blocked its aim. Grinding forward a couple of feet, slightly overhanging the bank, the robot fired again. For a split second Alan seemed engulfed in flame; the heat of hell singed his head and back, and mud boiled in the bank by his arm. Again the robot trembled. It jerked forward a foot and its blaster swung slightly away. But only for a moment. Then the gun swung back again. Suddenly, as if sensing something wrong, its tracks slammed into reverse. It stood poised for a second, its treads spinning crazily as the earth collapsed underneath it, where Alan had dug, then it fell with a heavy splash into the mud, ten feet from where Alan stood. Without hesitation Alan threw himself across the blaster housing, frantically locking his arms around the barrel as the robot's treads churned furiously in the sticky mud, causing it to buck and plunge like a Brahma bull. The treads stopped and the blaster jerked upwards wrenching Alan's arms, then slammed down. Then the whole housing whirled around and around, tilting alternately up and down like a steel-skinned water monster trying to dislodge a tenacious crab, while Alan, arms and legs wrapped tightly around the blaster barrel and housing, pressed fiercely against the robot's metal skin. Slowly, trying to anticipate and shift his weight with the spinning plunges, Alan worked his hand down to his right hip. He fumbled for the sheath clipped to his belt, found it, and extracted a stubby hunting knife. Sweat and blood in his eyes, hardly able to move on the wildly swinging turret, he felt down the sides to the thin crack between the revolving housing and the stationary portion of the robot. With a quick prayer he jammed in the knife blade—and was whipped headlong into the mud as the turret literally snapped to a stop. The earth, jungle and moons spun in a pinwheeled blur, slowed, and settled to their proper places. Standing in the sticky, sweet-smelling ooze, Alan eyed the robot apprehensively. Half buried in mud, it stood quiet in the shadowy light except for an occasional, almost spasmodic jerk of its blaster barrel. For the first time that night Alan allowed himself a slight smile. "A blade in the old gear box, eh? How does that feel, boy?" He turned. "Well, I'd better get out of here before the knife slips or the monster cooks up some more tricks with whatever it's got for a brain." Digging little footholds in the soft bank, he climbed up and stood once again in the rustling jungle darkness. "I wonder," he thought, "how Pete could cram enough brain into one of those things to make it hunt and track so perfectly." He tried to visualize the computing circuits needed for the operation of its tracking mechanism alone. "There just isn't room for the electronics. You'd need a computer as big as the one at camp headquarters." In the distance the sky blazed as a blaster roared in the jungle. Then Alan heard the approaching robot, crunching and snapping its way through the undergrowth like an onrushing forest fire. He froze. "Good Lord! They communicate with each other! The one I jammed must be calling others to help." He began to move along the bank, away from the crashing sounds. Suddenly he stopped, his eyes widened. "Of course! Radio! I'll bet anything they're automatically controlled by the camp computer. That's where their brain is!" He paused. "Then, if that were put out of commission ..." He jerked away from the bank and half ran, half pulled himself through the undergrowth towards the camp. Trees exploded to his left as another robot fired in his direction, too far away to be effective but churning towards him through the blackness. Alan changed direction slightly to follow a line between the two robots coming up from either side, behind him. His eyes were well accustomed to the dark now, and he managed to dodge most of the shadowy vines and branches before they could snag or trip him. Even so, he stumbled in the wiry underbrush and his legs were a mass of stinging slashes from ankle to thigh. The crashing rumble of the killer robots shook the night behind him, nearer sometimes, then falling slightly back, but following constantly, more unshakable than bloodhounds because a man can sometimes cover a scent, but no man can stop his thoughts. Intermittently, like photographers' strobes, blue flashes would light the jungle about him. Then, for seconds afterwards his eyes would see dancing streaks of yellow and sharp multi-colored pinwheels that alternately shrunk and expanded as if in a surrealist's nightmare. Alan would have to pause and squeeze his eyelids tight shut before he could see again, and the robots would move a little closer. To his right the trees silhouetted briefly against brilliance as a third robot slowly moved up in the distance. Without thinking, Alan turned slightly to the left, then froze in momentary panic. "I should be at the camp now. Damn, what direction am I going?" He tried to think back, to visualize the twists and turns he'd taken in the jungle. "All I need is to get lost." He pictured the camp computer with no one to stop it, automatically sending its robots in wider and wider forays, slowly wiping every trace of life from the planet. Technologically advanced machines doing the job for which they were built, completely, thoroughly, without feeling, and without human masters to separate sense from futility. Finally parts would wear out, circuits would short, and one by one the killers would crunch to a halt. A few birds would still fly then, but a unique animal life, rare in the universe, would exist no more. And the bones of children, eager girls, and their men would also lie, beside a rusty hulk, beneath the alien sun. "Peggy!" As if in answer, a tree beside him breathed fire, then exploded. In the brief flash of the blaster shot, Alan saw the steel glint of a robot only a hundred yards away, much nearer than he had thought. "Thank heaven for trees!" He stepped back, felt his foot catch in something, clutched futilely at some leaves and fell heavily. Pain danced up his leg as he grabbed his ankle. Quickly he felt the throbbing flesh. "Damn the rotten luck, anyway!" He blinked the pain tears from his eyes and looked up—into a robot's blaster, jutting out of the foliage, thirty yards away. Instinctively, in one motion Alan grabbed his pocket blaster and fired. To his amazement the robot jerked back, its gun wobbled and started to tilt away. Then, getting itself under control, it swung back again to face Alan. He fired again, and again the robot reacted. It seemed familiar somehow. Then he remembered the robot on the river bank, jiggling and swaying for seconds after each shot. "Of course!" He cursed himself for missing the obvious. "The blaster static blanks out radio transmission from the computer for a few seconds. They even do it to themselves!" Firing intermittently, he pulled himself upright and hobbled ahead through the bush. The robot shook spasmodically with each shot, its gun tilted upward at an awkward angle. Then, unexpectedly, Alan saw stars, real stars brilliant in the night sky, and half dragging his swelling leg he stumbled out of the jungle into the camp clearing. Ahead, across fifty yards of grass stood the headquarters building, housing the robot-controlling computer. Still firing at short intervals he started across the clearing, gritting his teeth at every step. Straining every muscle in spite of the agonizing pain, Alan forced himself to a limping run across the uneven ground, carefully avoiding the insect hills that jutted up through the grass. From the corner of his eye he saw another of the robots standing shakily in the dark edge of the jungle waiting, it seemed, for his small blaster to run dry. "Be damned! You can't win now!" Alan yelled between blaster shots, almost irrational from the pain that ripped jaggedly through his leg. Then it happened. A few feet from the building's door his blaster quit. A click. A faint hiss when he frantically jerked the trigger again and again, and the spent cells released themselves from the device, falling in the grass at his feet. He dropped the useless gun. "No!" He threw himself on the ground as a new robot suddenly appeared around the edge of the building a few feet away, aimed, and fired. Air burned over Alan's back and ozone tingled in his nostrils. Blinding itself for a few seconds with its own blaster static, the robot paused momentarily, jiggling in place. In this instant, Alan jammed his hands into an insect hill and hurled the pile of dirt and insects directly at the robot's antenna. In a flash, hundreds of the winged things erupted angrily from the hole in a swarming cloud, each part of which was a speck of life transmitting mental energy to the robot's pickup devices. Confused by the sudden dispersion of mind impulses, the robot fired erratically as Alan crouched and raced painfully for the door. It fired again, closer, as he fumbled with the lock release. Jagged bits of plastic and stone ripped past him, torn loose by the blast. Frantically, Alan slammed open the door as the robot, sensing him strongly now, aimed point blank. He saw nothing, his mind thought of nothing but the red-clad safety switch mounted beside the computer. Time stopped. There was nothing else in the world. He half-jumped, half-fell towards it, slowly, in tenths of seconds that seemed measured out in years. The universe went black. Later. Brilliance pressed upon his eyes. Then pain returned, a multi-hurting thing that crawled through his body and dragged ragged tentacles across his brain. He moaned. A voice spoke hollowly in the distance. "He's waking. Call his wife." Alan opened his eyes in a white room; a white light hung over his head. Beside him, looking down with a rueful smile, stood a young man wearing space medical insignia. "Yes," he acknowledged the question in Alan's eyes, "you hit the switch. That was three days ago. When you're up again we'd all like to thank you." Suddenly a sobbing-laughing green-eyed girl was pressed tightly against him. Neither of them spoke. They couldn't. There was too much to say. THE END Transcriber's Note: This etext was produced from Amazing Science Fiction Stories October 1958. 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.
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D. Pete built the robots to hunt by following brain waves.
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Which models do they use as baselines on the Atomic dataset?
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### Introduction
Recently, event-centered commonsense knowledge has attracted much attention BIBREF0, BIBREF1, BIBREF2, BIBREF3, because of understanding events is an important component of NLP. Given a daily-life event, human can easily understand it and reason about its causes, effects, and so on. However, it still remains a challenging task for NLP systems. This is partly due to most of them are trained for task-specific datasets or objectives, which results in models that are adapt at finding task-specific underlying correlation patterns but have limited capability in simple and explainable commonsense reasoning BIBREF4. To facilitate this, BIBREF5 (BIBREF5) build the Event2Mind dataset and BIBREF4 (BIBREF4) present the Atomic dataset, mainly focus on nine If-Then reasoning types to describe causes, effects, intents and participant characteristic about events. Together with these datasets, a simple RNN-based encoder-decoder framework is proposed to conduct the If-Then reasoning. However, there still remains two challenging problems. First, as illustrated in Figure FIGREF1, given an event “PersonX finds a new job”, the plausible feeling of PersonX about that event could be multiple (such as “needy/stressed out” and “relieved/joyful”). Previous work showed that for the one-to-many problem, conventional RNN-based encoder-decoder models tend to generate generic responses, rather than meaningful and specific answers BIBREF6, BIBREF7. Second, as a commonsense reasoning problem, rich background knowledge is necessary for generating reasonable inferences. For example, as shown in Figure FIGREF1, the feeling of PersonX upon the event “PersonX finds a new job” could be multiple. However, after given a context “PersonX was fired”, the plausible inferences would be narrowed down to “needy” or “stressed out”. To better solve these problems, we propose a context-aware variational autoencoder (CWVAE) together with a two-stage training procedure. Variational Autoencoder (VAE) based models have shown great potential in modeling the one-to-many problem and generate diversified inferences BIBREF8, BIBREF9. In addition to the traditional VAE structure, we introduces an extra context-aware latent variable in CWVAE to learn the event background knowledge. In the pretrain stage, CWVAE is trained on an auxiliary dataset (consists of three narrative story corpora and contains rich event background knowledge), to learn the event background information by using the context-aware latent variable. Subsequently, in the finetune stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target (e.g., intents, reactions, etc.). Experiments on the Event2Mind and Atomic dataset show that our proposed approach outperforms baseline methods in both the accuracy and diversity of inferences. The code is released at https://github.com/sjcfr/CWVAE. ### Background
Before specifically describing two dataset —- Event2Mind and Atomic used in this paper as well as the If-Then reasoning task, for clarity, we define the following terminologies: Base event: the prerequisite event in If-Then reasoning, organized as a verb phrase with a predicate and its arguments, such as the event “PersonX finds a new job” shown in Figure FIGREF1. Inference dimension: a particular If-Then reasoning type, e.g., intents, effects of the base event. Details are shown in Table TABREF2 and Table TABREF3. Target: the inferential results. For example, as shown in Figure FIGREF1, given a base event “PersonX finds a new job” and one inference dimension “xReact”, the targets could be “relieved” or “needy”. Notice that each inference dimension can have multiple targets. Event2Mind Dataset contains 25K base events and 300K targets, annotated through crowdsourcing. Event2Mind is organized in a hierarchical form: each base event has three types of inference dimensions, and given a base event, under one of inference dimensions, several targets may simultaneously exist. Table TABREF2 shows the (base event-inference dimension-target) hierarchical structure through an example from Event2Mind. Atomic Dataset Inspired by Event2Mind, the Atomic dataset shares the same hierarchical structure as Event2Mind, while scales up the size of dataset and expands the scope to nine types of inference dimensions. Table TABREF3 shows the (base event-inference dimension-target) hierarchical structure through an example from Atomic. Though Atomic covers the inference dimensions of Event2Mind, the base event collection of Event2Mind is nonidentical to that of Atomic. Problem Definition The If-Then reasoning task could be formally defined as a conditional one-to-many generation problem: given a base event $x$ and one inference dimension $d$, the model is required to generate targets $y=f(x, d)$ as close to the ground truths as possible. Both $x$ and $y$ consist of sequence of words: $x=\lbrace x_1,\dots , x_{m}\rbrace $, and $y=\lbrace y_1,\dots , y_{n}\rbrace $, where $m$ and $n$ denotes the length of $x$ and $y$, respectively. Conditional Variational Autoencoder The variational autoencoder (VAE) defines a generative framework suited for one-to-many generation problem BIBREF10. While conditional variational autoencoder (CVAE) BIBREF11 is an extension of VAE on the conditional generation problem. As shown in Figure FIGREF5 (a), CVAE characterizes the conditional one-to-many generation problem using three random variables: event $x$, target $y$ and a latent variable $z$, which is used for modeling the latent distribution of semantic over targets given an event. Hence, under a certain inference dimension, with regard to the latent semantic variable $z$, the conditional generation problem could be expressed as $p(y|x)=\int p(y|x,z)p(z|x)dz$. CVAE models $p(y|x,z)$ and $p(z|x)$ using deep neural networks (parameterized by $\theta $) $p_{\theta }(y|x,z)$ and $p_{\theta }(z|x)$. Then as illustrated in Figure FIGREF5 (b), $y$ could be generated from $x$ and $z$. CVAE is trained to maximize the conditional likelihood $p(y|x)$, which involves an intractable marginalization over the latent variable $z$. Instead, following BIBREF10 (BIBREF10), a practical way is to introduce another deep network (parameterized by $\phi $) $q_{\phi }(z|x,y)$ to approximate the true posterior distribution $p(z|x,y)$ and maximize the evidence lower bound (ELBO) of the log-likelihood function: Therefore, CVAE is composed of three neural networks in general. We refer to $p_{\theta }(z|x)$ as a prior network, $q_{\phi }(z|x,y)$ as a recognition network, and $p_{\theta }(y|x,z)$ as a neural decoder. ### Context-aware Variational Autoencoder
Traditional CVAE can model the event-target relation. In other words, given an observed event, CVAE can generate its corresponding targets. While in this paper we model the If-Then reasoning as a [(background), event]-target process. It means that in addition to the observed event, we also want to involve the event background knowledge (which can be learned from event contexts) to generate the reasonable targets. To this end, we propose a context-aware variational autoencoder (CWVAE), with two additional latent variables: a context-acquiring latent variable $z_c$ to directly acquire context information, and a context-aware latent variable $z_{c^{\prime }}$ to learn background knowledge from $z_c$, as shown in Figure FIGREF6 (a). However, the event context information is absent in the Event2Mind and Atomic dataset. To learn from the external event context information, we design the following two-stage training procedure for CWVAE. Pretrain: Learning Event Background Knowledge from Auxiliary Dataset In the pretrain stage, CWVAE is trained on three narrative story corpora with rich event context information. As shown in Figure FIGREF6 (a), context-acquiring latent variable $z_c$ is directly conditioned on the context $c$. Hence, $z_c$ could be employed for acquiring background knowledge from event contexts. Then, we minimize the distance between $z_c$ and the context-aware latent variable $z_{c^{\prime }}$, by which the event background knowledge is transferred from $z_c$ to $z_{c^{\prime }}$. Finetune: Adapt Event Background Knowledge to Each Inference Dimension In the finetune stage, as shown in Figure FIGREF6 (b), CWVAE is trained on the Event2Mind and Atomic dataset without the event context information. Pretrained CWVAE is finetuned to learn the specific inferential knowledge of each inference dimension. After the training procedure, as shown in Figure FIGREF6 (c), samples of $z$ is generated based on $x$ and samples of $z_{c^{\prime }}$, where $z_{c^{\prime }}$ contains rich event background knowledge helpful for If-Then reasoning. ### Context-aware Variational Autoencoder ::: Architecture of CWVAE
As shown in Figure FIGREF8, CWVAE is mainly composed of four parts: a neural encoder that provides distributed representations of base events/targets, a recognition network for inferring $q_{\phi }(z|x,y)$, $q_{\phi }(z_c|x,c)$ and $q_{\phi }(z|z_{c^{\prime }}, x)$, a prior network for modeling $p_{\theta }(z_{c^{\prime }}|x)$ and $p_{\theta }(z|x, z_{c^{\prime }})$, and a neural decoder that integrates the information from $z$ and $z_{c^{\prime }}$ to generate targets. Neural Encoder We employ a bidirectional GRU as neural encoder, which encodes context $c$, event $x$ and target $y$ into distributed representations $h^c=\lbrace h_1^c,\dots ,h_{l_c}^c\rbrace $, $h^x=\lbrace h_1^x,\dots ,h_{l_x}^x\rbrace $ and $h^y=\lbrace h_1^y,\dots ,h_{l_y}^y\rbrace $, where $l_c$, $l_x$ and $l_y$ is the length of $c$, $x$ and $y$, respectively. Recognition Network The recognition network models $q_{\phi }(z|x,y)$, $q_{\phi }(z_c|x,c)$, $q_{\phi }(z|z_{c^{\prime }}, x)$ based on $h^x$, $h^y$ and $h^c$. Following traditional VAE, the above-mentioned three distributions are assumed to be multivariate Gaussian distribution with a diagonal covariance structure: where $\mu $ denotes the mean of the distribution, $\sigma $ denotes the standard deviation of the distribution, and $I$ denotes the identity matrix. Given $h^x$, $h^y$ and $h^c$, we propose a novel attention-based inferer (ABI) module to estimate the mean and standard deviation of $q_{\phi }(z_{c}|x,c)$, $q_{\phi }(z_{c^{\prime }}|x,y)$ and $q_{\phi }(z|x,y)$: Briefly, through the attention mechanism, ABI can capture the semantic interaction between input sequences, and estimate the parameters of distributions based on it. We will introduce the specific structure of ABI in below. Prior Network Prior Network models $p_{\theta }(z_{c^{\prime }}|x)$ and $p_{\theta }(z|x, z_{c^{\prime }})$ based on $h^x$. The distribution of $p_{\theta }(z_{c^{\prime }}|x)$ and $p_{\theta }(z|x, z_{c^{\prime }})$ are still assumed to be multivariate Gaussian, whereas the parameters are different: where $\mu ^{^{\prime }}$ denotes the mean of the distribution, $\sigma ^{^{\prime }}$ denotes the standard deviation of the distribution and $I$ denotes the identity matrix. Then the attention-based inferer module is still employed to estimate parameters of distributions: Neural Decoder Given the base event $x$, the semantic latent variable $z$, and the context-aware latent variable $z_{c^{\prime }}$, the neural decoder defines the generation probability of $y$ as following: where $p(y_j|y<j, z, z_{c^{\prime }}, x)=g(y_{j-1}, s_{j-1}, e_j)$, $g(\cdot )$ is an attention-based feed forward model, $e_j=\sum _i \alpha _{ji}h_i^{x}$ is the context vector and $s_{j-1}$ is the hidden state of the decoder. We obtain $g(\cdot )$ and $e_j$ the same way as BIBREF12 (BIBREF12). Whereas our decoder differs from BIBREF12 (BIBREF12) in that our model integrates the context-aware latent variable $z_{c^{\prime }}$ and semantic latent variable $z$ in the computation of $s_j=\mathrm {GRU}([E_{yj};s_{j-1},z,z_{j-1}])$, where $E_{yj}$ is the word embeddings of target words. Note that through concatenating $z$ and $z_{c^{\prime }}$ with $E_{yj}$ and $s_{j-1}$, $s_j$ could be affected by context-aware latent variable $z_{c^{\prime }}$ and semantic latent variable $z$. This allows model to directly access to the event background knowledge from $z_{c^{\prime }}$. In addition, the randomness of $z$ and $z_{c^{\prime }}$ would increase the diversity of model generation. Attention-based Inferer Attention mechanism has shown strong ability in capturing semantic interactions BIBREF13. Inspired by the co-attention mechanism BIBREF14, we propose an attention-based inferer (ABI) to estimate the mean and standard deviation of a distribution belongs to $p_{\theta }(\cdot )$ or $q_{\phi }(\cdot )$ by capturing semantic interactions of input sequences. Specifically, given two input sequences (e.g., representations of contexts and events) $a=\lbrace a_1,\dots ,a_{l_a}\rbrace $ and $b=\lbrace b_1,\dots ,b_{l_b}\rbrace $ with length $l_a$ and $l_b$, we first obtain the attention scores from each side through: where $W_a \in \mathbb {R}^{d\times d_a}$ and $W_b \in \mathbb {R}^{d\times d_b}$ are parameter weights. With these attention scores, the context vectors of both sequences are given by: Then we perform a mean pooling operation on context vectors of both sequences: To obtain the mean and standard deviation, the pooled context vectors $\bar{c^a}$ and $\bar{c^b}$ which carry semantic interaction between two sequences, are concatenated and projected into a latent semantic space through a nonlinear transformation: Finally the mean and standard deviation are generated through a nonlinear transformation over $h_z$: ### Context-aware Variational Autoencoder ::: Optimizing
With the incorporation of $z_{c^{\prime }}$, the original loglikelihood could be decomposed as: Then following traditional CVAE, the ELBO of CWVAE is defined as follows: which is the objective function at the finetune stage. While in the pretrain stage, as we aim to learn background knowledge through minimizing the distance between $z_c$ and $z_{c^{\prime }}$, in addition to $L^{ELBO}$, a context-aware regulation term is introduced: where the context aware regularization term is the KL distance between $z$ and $z_{c^{\prime }}$. Through minimizing the context aware regularization term, we aim to pass event context knowledge from $z_c$ to the context aware latent variable $z_{c^{\prime }}$. ### Context-aware Variational Autoencoder ::: Training Details
To test the performance of CWVAE, we split the Event2Mind and Atomic dataset into training, development and test sets (80%, 10%, 10%) in the same way as BIBREF5 (BIBREF5) and BIBREF4 (BIBREF4), respectively. We initialize the embedding layer from 300d GloVe word embeddings. The neural encoder is chosen to be biGRU with 300 hidden units. For the ABI module, size of $W_a$ and $W_b$ is set to be $100 \times d_a$ and $100 \times d_b$ respectively. The dimension of $z_c$, $z_{c^{\prime }}$ and $z$ is all set as 40. The neural decoder is set to be GRU with 300d hidden state. Regulation coefficient $\lambda $ of context-aware regulation term is set to be 0.1. Models are trained using an Adam optimizer BIBREF15 with a learning rate of 0.001. ### Experiments ::: Auxiliary Dataset
The auxiliary dataset is built upon three human-written story corpora: ROCStories BIBREF16, VIST BIBREF17 and WritingPrompts BIBREF18. ROCStories and VIST are composed of short stories with five sentences. We filter out stories of more than 1,000 words in WritingPrompts, and cut the remaining stories into five-sentence-paragraphs. For each five-sentence-paragraph, we define the first three sentences as contexts of the base event, the fourth sentence as the base event, and the fifth sentence as the inference target. For example, as shown in Table TABREF25, the first three sentences describe a context that Jason was unsatisfied about his job and applied for a new job. Hence, after happening the event “he got the job”, a plausible react about the event could be “jason was much happier at his new job”. In total, the auxiliary dataset contains 192,316 $(context, event, target)$ triples. ### Experiments ::: Baselines
We compared our proposed model with the following four baseline methods: RNN-based Seq2Seq proposed by BIBREF4 (BIBREF4) for the If-Then reasoning on Atomic. Variational Seq2Seq combines a latent variable with the encoder-decoder structure through converting the last hidden state of RNN encoder into a Gaussian distributed latent variable BIBREF8. VRNMT Propose by BIBREF19 (BIBREF19), VRNMT combines CVAE with attention-based encoder-decoder framework through introduces a latent variable to model the semantic distribution of targets. CWVAE-Unpretrained refers to the CWVAE model without the pretrain stage. Note that, for each baseline method, we train distinct models for each distinct inference dimension, respectively. ### Experiments ::: Evaluation Metrics ::: Automatic Evaluation
We first compare the perplexity of CWVAE with baseline methods. Perplexity measures the probability of model to regenerate the exact targets, which is particular suitable for evaluating the model performance on one-to-many problem BIBREF20. Further, we employ BLEU score to evaluate the accuracy of generations BIBREF21, and the number of distinct n-gram to evaluate the diversity of generations BIBREF6. The distinct is normalized to $[0, 1]$ by dividing the total number of generated tokens. ### Experiments ::: Evaluation Metrics ::: Human Evaluation
Since automatic evaluation of generations is still a challenging task BIBREF22, we also conduct human evaluations on the model performance. Five human experts are employed to evaluate the coherence, diversity and fluency of generated targets. Experts are asked to vote for if a generation is fluent or coherent for each generated target, and give a 1-5 score for the diversity of generations. For both Event2Mind and Atomic datasets, 100 events are randomly selected from the test set. For each method, top 10 generated targets of each base event are used for evaluation. Finally we report three overall averaged scores of coherence, diversity and fluency on both datasets, respectively. ### Experiments ::: Overall Results
We list the perplexity and BLEU score of CWVAE and baseline methods on Event2Mind and Atomic in Table TABREF31 and Table TABREF33, respectively, and show the distinct-1 and distinct-2 score on Event2Mind and Atomic in Table TABREF32 and Table TABREF34, respectively. We find that: (1) As shown in Table TABREF32 and Table TABREF34, comparison between RNN-based Seq2Seq and variational-based methods, including Variational Seq2Seq, VRNMT, CWVAE-unpretrained and CWVAE shows that, variational-based methods could increase the diversity of generations. This confirms one of our motivations that variational-based methods could capture the latent semantic distribution within targets and increase the diversity of If-Then reasoning. (2) Comparing CWVAE-unpretrained with other baseline methods shows that, in general CWVAE improves the accuracy and diversity on both dataset. These results indicate the efficiency of CWVAE in capturing the latent semantic distribution of targets, and generate more reasonable inferential results. (3) Comparison between CWVAE and CWVAE-unpretrained shows that the pretrain stage could enhance the performance of CWVAE in both the accuracy and diversity. This is mainly because event knowledge could offer the guidance for If-Then reasoning. In the pretrain stage, CWVAE could capture the event background knowledge through context-aware latent variable, and such knowledge could be be adapted to our task through the fintune stage. To further evaluate the effectiveness of our proposed approach, we also conduct human evaluations, the results of which are shown in Table TABREF39 and Table TABREF40. On both datasets, CWVAE-based methods achieve consistent better coherence, diversity and fluency performances. While comparing with CWVAE-Unpretrained, the pretrain procedure could improves the performance on coherence and fluency. The main reasons are twofold: first, the CWVAE has advantage in capturing the semantic distribution of targets; second, event background learned from the pretrain stage is helpful for the If-Then reasoning. ### Experiments ::: Case Study
Table TABREF41 provides an example of model generations given the base event “PersonX works tirelessly” and the inference dimension “xIntent”. The generations under CWVAE mainly contain four kinds of semantics: (1) be productive, (2) finish his work soon, (3) accomplish goal, (4) earn more money. While the semantics of generations using baseline RNN-based Seq2Seq model is relatively limited. Furthermore, the first three kinds of semantic overlap the three ground truth targets, and the fourth kind of semantic is in accordance with daily-life commonsense. Compared to RNN-based Seq2Seq model, our approach can increase the diversity and rationality of generations, meanwhile keep the accuracy. ### Related Work ::: Event-Centered Commonsense Reasoning
Understanding events and constructing event-centered commonsense knowledge are crucial to many NLP applications, such as intention recognition BIBREF23 and dialog generation BIBREF24. Recently a growing number of studies focus on event-centered commonsense reasoning, which mainly concentrates on two areas, script event prediction and story ending generation/choosing. Script event prediction concerns with the temporal relationships between script events BIBREF25, which requires models to choose a correct subsequent triple-organized event among the candidates BIBREF2. Prior work mainly focused on modeling event pairs BIBREF25, event chains BIBREF2 and event graph BIBREF3 to predict the subsequent event. Story ending generation focuses on generating plausible story endings BIBREF16, which requires models to understand the story context, and keep generated endings logically consistent with it BIBREF26, BIBREF27. The above tasks mainly investigate the logical orders of events, whereas the If-Then reasoning task focuses on inferring the mental state of event participants. ### Related Work ::: Variational AutoEncoder-Decoder Based Natural Language Generation
VAE BIBREF10 has been widely applied in various of text generation tasks, such as dialogue and machine translation. In dialogue generation, BIBREF9 (BIBREF9) adapts VAE with encoder-decoder framework to model the latent semantic distribution of answers, which can increase the diversity of generations. For the task of machine translation, BIBREF19 (BIBREF19) and BIBREF28 (BIBREF28) employ a latent variable to capture the semantic interaction between the source and target sentence, and regard the latent variable as a supplementation of attention mechanism. While BIBREF29 (BIBREF29) use the latent variable to model topic distributions in text generation. In this paper, we introduce an additional context-aware latent variable to effectively learn background knowledge and conduct If-Then reasoning on the guidance of it. ### Conclusion
In this paper, we propose a novel context-aware VAE (CWVAE) framework with two training stages for If-Then commonsense reasoning. By introducing an additional context-aware latent variable, CWVAE is able to learn external background knowledge, and conduct If-Then reasoning under its guidance. In the pretrain stage, CWVAE learns event background knowledge, then in the finetune stage CWVAE adapts such knowledge to each inference dimension. Experimental results demonstrate that CWVAE outperforms baseline methods in both the accuracy and diversity of generations. ### Acknowledgments
We thank the anonymous reviewers for their constructive comments, and gratefully acknowledge the support of the National Key Research and Development Program of China (SQ2018AAA010010), the National Key Research and Development Program of China (2018YFB1005103), the National Natural Science Foundation of China (NSFC) via Grant 61702137. Figure 1: A illustration of two challenging problems in IfThen reasoning. (a) Given an observed event, the feelings about this event could be multiple. (b) Background knowledge is need for generating reasonable inferences, which is absent in the dataset (marked by dashed lines). Table 1: Hierarchical structure of Event2Mind dataset. For specific inference dimensions, “x” and “o” refers to PersonX and others respectively. Table 2: Hierarchical structure of Atomic dataset. For specific inference dimensions, “x” and “o” refers to PersonX and others respectively. Figure 2: Illustration of inference and generation process of CVAE in a directed graph. Dashed lines represent the inference of z. Solid lines represent the generation process. Figure 3: Illustration of pretrain, finetune and generation process of CWVAE in a directed graph. Dashed lines represent the inference of z, zc and zc′ . Solid lines represent the generation process. Red circle denotes the context-aware latent variable. Figure 4: Architecture of CWVAE. We mark Neural encoder in green, prior network in blue, recognition network in brown and neural decoder in orange, respectively. Table 3: An example for the construction of auxiliary dataset. For a five-sentence-paragraph, the first three sentences are taken as event context, while the fourth and fifth sentence is taken as base event and target respectively. Table 4: Average perplexity and BLEU score (reported in percentages) for the top 10 generations under each inference dimension of Event2Mind. The the best result for each dimension is emboldened. Table 5: Distinct-1 and distinct-2 scores for the top 10 generations under each inference dimension of Event2Mind. The the best result for each dimension is emboldened. Table 6: Average perplexity and BLEU scores (reported in percentages) for the top 10 generations under each inference dimension of Atomic. The the best result for each dimension is emboldened. Table 7: Distinct-1 and distinct-2 scores for the top 10 generations under each inference dimension of Atomic. The the best result for each dimension is emboldened. Table 9: Human evaluation results on Atomic. Table 8: Human evaluation results on Event2Mind. Table 10: An example of inferences made by CWVAE and RNN-based Seq2Seq model under inference dimension “xIntent”.
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RNN-based Seq2Seq, Variational Seq2Seq, VRNMT , CWVAE-Unpretrained
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What is not true about Dr. Melrose?
A. He is curious to learn what others think about the issues in the field
B. He wants to try to teach the children to use their abilities
C. He is dedicated to the pursuit of true understanding of phenomena
D. He has no qualms about tearing down a fellow researcher
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BRAMBLE BUSH BY ALAN E. NOURSE [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, August 1957. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There was a man in our town, and he was wondrous wise; He jumped into a bramble bush and scratched out both his eyes. And when he saw what he had done, with all his might and main He jumped into another bush and scratched them in again. MOTHER GOOSE Dr. David Lessing found Jack Dorffman and the boy waiting in his office when he arrived at the Hoffman Center that morning. Dorffman looked as though he'd been running all night. There were dark pouches under his eyes; his heavy unshaven face seemed to sag at every crease. Lessing glanced sharply at his Field Director and sank down behind his desk with a sigh. "All right, Jack—what's wrong?" "This kid is driving me nuts," said Dorffman through clenched teeth. "He's gone completely hay-wire. Nobody's been able to get near him for three weeks, and now at six o'clock this morning he decides he's leaving the Farm. I talk to him, I sweat him down, I do everything but tie him to the bed, and I waste my time. He's leaving the Farm. Period." "So you bring him down here," said Lessing sourly. "The worst place he could be, if something's really wrong." He looked across at the boy. "Tommy? Come over and sit down." There was nothing singular about the boy's appearance. He was thin, with a pale freckled face and the guileless expression of any normal eight-year-old as he blinked across the desk at Lessing. The awkward grey monitor-helmet concealed a shock of sandy hair. He sat with a mute appeal in his large grey eyes as Lessing flipped the reader-switch and blinked in alarm at the wildly thrashing pattern on the tape. The boy was terrorized. He was literally pulsating with fear. Lessing sat back slowly. "Tell me about it, Tommy," he said gently. "I don't want to go back to the Farm," said the boy. "Why?" "I just don't. I hate it there." "Are you frightened?" The boy bit his lip and nodded slowly. "Of me? Of Dr. Dorffman?" "No. Oh, no!" "Then what?" Again the mute appeal in the boy's eyes. He groped for words, and none came. Finally he said, "If I could only take this off—" He fingered the grey plastic helmet. "You think that would make you feel better?" "It would, I know it would." Lessing shook his head. "I don't think so, Tommy. You know what the monitor is for, don't you?" "It stops things from going out." "That's right. And it stops things from going in. It's an insulator. You need it badly. It would hurt you a great deal if you took it off, away from the Farm." The boy fought back tears. "But I don't want to go back there—" The fear-pattern was alive again on the tape. "I don't feel good there. I never want to go back." "Well, we'll see. You can stay here for a while." Lessing nodded at Dorffman and stepped into an adjoining room with him. "You say this has been going on for three weeks ?" "I'm afraid so. We thought it was just a temporary pattern—we see so much of that up there." "I know, I know." Lessing chewed his lip. "I don't like it. We'd better set up a battery on him and try to spot the trouble. And I'm afraid you'll have to set it up. I've got that young Melrose from Chicago to deal with this morning—the one who's threatening to upset the whole Conference next month with some crazy theories he's been playing with. I'll probably have to take him out to the Farm to shut him up." Lessing ran a hand through sparse grey hair. "See what you can do for the boy downstairs." "Full psi precautions?" asked Dorffman. "Certainly! And Jack—in this case, be sure of it. If Tommy's in the trouble I think he's in, we don't dare risk a chance of Adult Contact now. We could end up with a dead boy on our hands." Two letters were waiting on Lessing's desk that morning. The first was from Roberts Bros., announcing another shift of deadline on the book, and demanding the galley proofs two weeks earlier than scheduled. Lessing groaned. As director of psionic research at the Hoffman Medical Center, he had long since learned how administrative detail could suck up daytime hours. He knew that his real work was at the Farm—yet he hadn't even been to the Farm in over six weeks. And now, as the book approached publication date, Lessing wondered if he would ever really get back to work again. The other letter cheered him a bit more. It bore the letterhead of the International Psionics Conference: Dear Dr. Lessing: In recognition of your position as an authority on human Psionic behavior patterns, we would be gratified to schedule you as principle speaker at the Conference in Chicago on October 12th. A few remarks in discussion of your forthcoming book would be entirely in order— They were waiting for it, then! He ran the galley proofs into the scanner excitedly. They knew he had something up his sleeve. His earlier papers had only hinted at the direction he was going—but the book would clear away the fog. He scanned the title page proudly. "A Theory of Psionic Influence on Infant and Child Development." A good title—concise, commanding, yet modest. They would read it, all right. And they would find it a light shining brightly in the darkness, a guide to the men who were floundering in the jungle of a strange and baffling new science. For they were floundering. When they were finally forced to recognize that this great and powerful force did indeed exist in human minds, with unimaginable potential if it could only be unlocked, they had plunged eagerly into the search, and found themselves in a maddening bramble bush of contradictions and chaos. Nothing worked, and everything worked too well. They were trying to study phenomena which made no sense, observing things that defied logic. Natural laws came crashing down about their ears as they stood sadly by and watched things happen which natural law said could never happen. They had never been in this jungle before, nor in any jungle remotely like it. The old rules didn't work here, the old methods of study failed. And the more they struggled, the thicker and more impenetrable the bramble bush became— But now David Lessing had discovered a pathway through that jungle, a theory to work by— At his elbow the intercom buzzed. "A gentleman to see you," the girl said. "A Dr. Melrose. He's very impatient, sir." He shut off the scanner and said, "Send him in, please." Dr. Peter Melrose was tall and thin, with jet black hair and dark mocking eyes. He wore a threadbare sport coat and a slouch. He offered Lessing a bony hand, then flung himself into a chair as he stared about the office in awe. "I'm really overwhelmed," he said after a moment. "Within the stronghold of psionic research at last. And face to face with the Master in the trembling flesh!" Lessing frowned. "Dr. Melrose, I don't quite understand—" "Oh, it's just that I'm impressed," the young man said airily. "Of course, I've seen old dried-up Authorities before—but never before a brand spanking new one, just fresh out of the pupa, so to speak!" He touched his forehead in a gesture of reverence. "I bow before the Oracle. Speak, oh Motah, live forever! Cast a pearl at my feet!" "If you've come here to be insulting," Lessing said coldly, "you're just wasting time." He reached for the intercom switch. "I think you'd better wait before you do that," Melrose said sharply, "because I'm planning to take you apart at the Conference next month unless I like everything I see and hear down here today. And if you don't think I can do it, you're in for quite a dumping." Lessing sat back slowly. "Tell me—just what, exactly, do you want?" "I want to hear this fairy tale you're about to publish in the name of 'Theory'," Melrose said. "I want to see this famous Farm of yours up in Connecticut and see for myself how much pressure these experimental controls you keep talking about will actually bear. But mostly, I want to see just what in psionic hell you're so busy making yourself an Authority about." There was no laughter in the man's sharp brown eyes. "You couldn't touch me with a ten foot pole at this conference," snapped Lessing. The other man grinned. "Try me! We shook you up a little bit last year, but you didn't seem to get the idea." "Last year was different." Lessing scowled. "As for our 'fairy tale', we happen to have a staggering body of evidence that says that it's true." "If the papers you've already published are a preview, we think it's false as Satan." "And our controls are above suspicion." "So far, we haven't found any way to set up logical controls," said Melrose. "We've done a lot of work on it, too." "Oh, yes—I've heard about your work. Not bad, really. A little misdirected, is all." "According to your Theory, that is." "Wildly unorthodox approach to psionics—but at least you're energetic enough." "We haven't been energetic enough to find an orthodox approach that got us anywhere. We doubt if you have, either. But maybe we're all wrong." Melrose grinned unpleasantly. "We're not unreasonable, your Majesty. We just ask to be shown. If you dare, that is." Lessing slammed his fist down on the desk angrily. "Have you got the day to take a trip?" "I've got 'til New Year." Lessing shouted for his girl. "Get Dorffman up here. We're going to the Farm this afternoon." The girl nodded, then hesitated. "But what about your lunch?" "Bother lunch." He gave Melrose a sidelong glare. "We've got a guest here who's got a lot of words he's going to eat for us...." Ten minutes later they rode the elevator down to the transit levels and boarded the little shuttle car in the terminal below the Hoffman Center. They sat in silence as the car dipped down into the rapid-transit channels beneath the great city, swinging northward in the express circuit through Philadelphia and Camden sectors, surfacing briefly in Trenton sector, then dropping underground once again for the long pull beneath Newark, Manhattan and Westchester sectors. In less than twenty minutes the car surfaced on a Parkway channel and buzzed north and east through the verdant Connecticut countryside. "What about Tommy?" Lessing asked Dorffman as the car sped along through the afternoon sun. "I just finished the prelims. He's not cooperating." Lessing ground his teeth. "I should be running him now instead of beating the bushes with this—" He broke off to glare at young Melrose. Melrose grinned. "I've heard you have quite a place up here." "It's—unconventional, at any rate," Lessing snapped. "Well, that depends on your standards. Sounds like a country day school, from what I've heard. According to your papers, you've even used conventional statistical analysis on your data from up here." "Until we had to throw it out. We discovered that what we were trying to measure didn't make sense in a statistical analysis." "Of course, you're sure you were measuring something ." "Oh, yes. We certainly were." "Yet you said that you didn't know what." "That's right," said Lessing. "We don't." "And you don't know why your instruments measure whatever they're measuring." The Chicago man's face was thoughtful. "In fact, you can't really be certain that your instruments are measuring the children at all. It's not inconceivable that the children might be measuring the instruments , eh?" Lessing blinked. "It's conceivable." "Mmmm," said Melrose. "Sounds like a real firm foundation to build a theory on." "Why not?" Lessing growled. "It wouldn't be the first time the tail wagged the dog. The psychiatrists never would have gotten out of their rut if somebody hadn't gotten smart and realized that one of their new drugs worked better in combatting schizophrenia when the doctor took the medicine instead of the patient. That was quite a wall to climb." "Yes, wasn't it," mused Melrose, scratching his bony jaw. "Only took them seventy years to climb it, thanks to a certain man's theories. I wonder how long it'll take psionics to crawl out of the pit you're digging for it?" "We're not digging any pit," Lessing exploded angrily. "We're exploring—nothing more. A phenomenon exists. We've known that, one way or another, for centuries. The fact that it doesn't seem to be bound by the same sort of natural law we've observed elsewhere doesn't mean that it isn't governed by natural law. But how can we define the law? How can we define the limits of the phenomenon, for that matter? We can't work in the dark forever—we've got to have a working hypothesis to guide us." "So you dreamed up this 'tadpole' idea," said Melrose sourly. "For a working hypothesis—yes. We've known for a long time that every human being has extrasensory potential to one degree or another. Not just a few here and there—every single one. It's a differentiating quality of the human mind. Just as the ability to think logically in a crisis instead of giving way to panic is a differentiating quality." "Fine," said Melrose. "Great. We can't prove that, of course, but I'll play along." Lessing glared at him. "When we began studying this psi-potential, we found out some curious things. For one thing, it seemed to be immensely more powerful and active in infants and children than in adults. Somewhere along the line as a child grows up, something happens. We don't know what. We do know that the child's psi-potential gradually withdraws deeper and deeper into his mind, burying itself farther and farther out of reach, just the way a tadpole's tail is absorbed deeper and deeper into the growing frog until there just isn't any tail any more." Lessing paused, packing tobacco into his pipe. "That's why we have the Farm—to try to discover why. What forces that potential underground? What buries it so deeply that adult human beings can't get at it any more?" "And you think you have an answer," said Melrose. "We think we might be near an answer. We have a theory that explains the available data." The shuttle car bounced sharply as it left the highway automatics. Dorffman took the controls. In a few moments they were skimming through the high white gates of the Farm, slowing down at the entrance to a long, low building. "All right, young man—come along," said Lessing. "I think we can show you our answer." In the main office building they donned the close-fitting psionic monitors required of all personnel at the Farm. They were of a hard grey plastic material, with a network of wiring buried in the substance, connected to a simple pocket-sized power source. "The major problem," Lessing said, "has been to shield the children from any external psionic stimuli, except those we wished to expose them to. Our goal is a perfectly controlled psi environment. The monitors are quite effective—a simple Renwick scrambler screen." "It blocks off all types of psi activity?" asked Melrose. "As far as we can measure, yes." "Which may not be very far." Jack Dorffman burst in: "What Dr. Lessing is saying is that they seem effective for our purposes." "But you don't know why," added Melrose. "All right, we don't know why. Nobody knows why a Renwick screen works—why blame us?" They were walking down the main corridor and out through an open areaway. Behind the buildings was a broad playground. A baseball game was in progress in one corner; across the field a group of swings, slides, ring bars and other playground paraphernalia was in heavy use. The place was teeming with youngsters, all shouting in a fury of busy activity. Occasionally a helmeted supervisor hurried by; one waved to them as she rescued a four-year-old from the parallel bars. They crossed into the next building, where classes were in progress. "Some of our children are here only briefly," Lessing explained as they walked along, "and some have been here for years. We maintain a top-ranking curriculum—your idea of a 'country day school' wasn't so far afield at that—with scholarships supported by Hoffman Center funds. Other children come to us—foundlings, desertees, children from broken homes, children of all ages from infancy on. Sometimes they stay until they have reached college age, or go on to jobs. As far as psionics research is concerned, we are not trying to be teachers. We are strictly observers. We try to place the youngsters in positions where they can develope what potential they have— without the presence of external psionic influences they would normally be subject to. The results have been remarkable." He led them into a long, narrow room with chairs and ash trays, facing a wide grey glass wall. The room fell into darkness, and through the grey glass they could see three children, about four years old, playing in a large room. "They're perfectly insulated from us," said Lessing. "A variety of recording instruments are working. And before you ask, Dr. Melrose, they are all empirical instruments, and they would all defy any engineer's attempts to determine what makes them go. We don't know what makes them go, and we don't care—they go. That's all we need. Like that one, for instance—" In the corner a flat screen was flickering, emitting a pale green fluorescent light. It hung from the wall by two plastic rods which penetrated into the children's room. There was no sign of a switch, nor a power source. As the children moved about, the screen flickered. Below it, a recording-tape clicked along in little spurts and starts of activity. "What are they doing?" Melrose asked after watching the children a few moments. "Those three seem to work as a team, somehow. Each one, individually, had a fairly constant recordable psi potential of about seventeen on the arbitrary scale we find useful here. Any two of them scale in at thirty-four to thirty-six. Put the three together and they operate somewhere in the neighborhood of six hundred on the same scale." Lessing smiled. "This is an isolated phenomenon—it doesn't hold for any other three children on the Farm. Nor did we make any effort to place them together—they drew each other like magnets. One of our workers spent two weeks trying to find out why the instruments weren't right. It wasn't the instruments, of course." Lessing nodded to an attendant, and peered around at Melrose. "Now, I want you to watch this very closely." He opened a door and walked into the room with the children. The fluorescent screen continued to flicker as the children ran to Lessing. He inspected the block tower they were building, and stooped down to talk to them, his lips moving soundlessly behind the observation wall. The children laughed and jabbered, apparently intrigued by the game he was proposing. He walked to the table and tapped the bottom block in the tower with his thumb. The tower quivered, and the screen blazed out with green light, but the tower stood. Carefully Lessing jogged all the foundation blocks out of place until the tower hung in midair, clearly unsupported. The children watched it closely, and the foundation blocks inched still further out of place.... Then, quite casually, Lessing lifted off his monitor. The children continued staring at the tower as the screen gave three or four violent bursts of green fire and went dark. The block tower fell with a crash. Moments later Lessing was back in the observation room, leaving the children busily putting the tower back together. There was a little smile on his lips as he saw Melrose's face. "Perhaps you're beginning to see what I'm driving at," he said slowly. "Yes," said Melrose. "I think I'm beginning to see." He scratched his jaw. "You think that it's adult psi-contact that drives the child's potential underground—that somehow adult contact acts like a damper, a sort of colossal candle-snuffer." "That's what I think," said Lessing. "How do you know those children didn't make you take off your monitor?" Lessing blinked. "Why should they?" "Maybe they enjoy the crash when the blocks fall down." "But that wouldn't make any difference, would it? The blocks still fall down." Melrose paced down the narrow room. "This is very good," he said suddenly, his voice earnest. "You have fine facilities here, good workers. And in spite of my flippancy, Dr. Lessing, I have never imagined for a moment that you were not an acute observer and a careful, highly imaginative worker. But suppose I told you, in perfect faith, that we have data that flatly contradicts everything you've told me today. Reproducible data, utterly incompatable with yours. What would you say to that?" "I'd say you were wrong," said Lessing. "You couldn't have such data. According to the things I am certain are true, what you're saying is sheer nonsense." "And you'd express that opinion in a professional meeting?" "I would." "And as an Authority on psionic behavior patterns," said Melrose slowly, "you would kill us then and there. You would strangle us professionally, discredit anything we did, cut us off cold." The tall man turned on him fiercely. "Are you blind, man? Can't you see what danger you're in? If you publish your book now, you will become an Authority in a field where the most devastating thing that could possibly happen would be— the appearance of an Authority ." Lessing and Dorffman rode back to the Hoffman Center in grim silence. At first Lessing pretended to work; finally he snapped off the tape recorder in disgust and stared out the shuttle-car window. Melrose had gone on to Idlewild to catch a jet back to Chicago. It was a relief to see him go, Lessing thought, and tried to force the thin, angry man firmly out of his mind. But somehow Melrose wouldn't force. "Stop worrying about it," Dorffman urged. "He's a crackpot. He's crawled way out on a limb, and now he's afraid your theory is going to cut it off under him. Well, that's his worry, not yours." Dorffman's face was intense. "Scientifically, you're on unshakeable ground. Every great researcher has people like Melrose sniping at him. You just have to throw them off and keep going." Lessing shook his head. "Maybe. But this field of work is different from any other, Jack. It doesn't follow the rules. Maybe scientific grounds aren't right at all, in this case." Dorffman snorted. "Surely there's nothing wrong with theorizing—" "He wasn't objecting to the theory. He's afraid of what happens after the theory." "So it seems. But why?" "Have you ever considered what makes a man an Authority?" "He knows more about his field than anybody else does." "He seems to, you mean. And therefore, anything he says about it carries more weight than what anybody else says. Other workers follow his lead. He developes ideas, formulates theories—and then defends them for all he's worth ." "But why shouldn't he?" "Because a man can't fight for his life and reputation and still keep his objectivity," said Lessing. "And what if he just happens to be wrong? Once he's an Authority the question of what's right and what's wrong gets lost in the shuffle. It's what he says that counts." "But we know you're right," Dorffman protested. "Do we?" "Of course we do! Look at our work! Look at what we've seen on the Farm." "Yes, I know." Lessing's voice was weary. "But first I think we'd better look at Tommy Gilman, and the quicker we look, the better—" A nurse greeted them as they stepped off the elevator. "We called you at the Farm, but you'd already left. The boy—" She broke off helplessly. "He's sick, Doctor. He's sicker than we ever imagined." "What happened?" "Nothing exactly—happened. I don't quite know how to describe it." She hurried them down the corridor and opened a door into a large children's playroom. "See what you think." The boy sat stolidly in the corner of the room. He looked up as they came in, but there was no flicker of recognition or pleasure on his pale face. The monitor helmet was still on his head. He just sat there, gripping a toy fire engine tightly in his hands. Lessing crossed the room swiftly. "Tommy," he said. The boy didn't even look at him. He stared stupidly at the fire engine. "Tommy!" Lessing reached out for the toy. The boy drew back in terror, clutching it to his chest. "Go away," he choked. "Go away, go away—" When Lessing persisted the boy bent over swiftly and bit him hard on the hand. Lessing sat down on the table. "Tommy, listen to me." His voice was gentle. "I won't try to take it again. I promise." "Go away." "Do you know who I am?" Tommy's eyes shifted haltingly to Lessing's face. He nodded. "Go away." "Why are you afraid, Tommy?" "I hurt. My head hurts. I hurt all over. Go away." "Why do you hurt?" "I—can't get it—off," the boy said. The monitor , Lessing thought suddenly. Something had suddenly gone horribly wrong—could the boy really be sensing the source of the trouble? Lessing felt a cold knot gather in the pit of his stomach. He knew what happened when adult psi-contact struck a psi-high youngster's mind. He had seen it a hundred times at the Farm. But even more—he had felt it in his own mind, bursting from the child. Like a violent physical blow, the hate and fear and suspicion and cruelty buried and repressed in the adult mind, crushing suddenly into the raw receptors of the child's mind like a smothering fog—it was a fearful thing. A healthy youngster could survive it, even though the scar remained. But this youngster was sick— And yet an animal instinctively seeks its own protection . With trembling fingers Lessing reached out and opened the baffle-snap on the monitor. "Take it off, Tommy," he whispered. The boy blinked in amazement, and pulled the grey helmet from his head. Lessing felt the familiar prickly feeling run down his scalp as the boy stared at him. He could feel deep in his own mind the cold chill of terror radiating from the boy. Then, suddenly, it began to fade. A sense of warmth—peace and security and comfort—swept in as the fear faded from the boy's face. The fire engine clattered to the floor. They analyzed the tapes later, punching the data cards with greatest care, filing them through the machines for the basic processing and classification that all their data underwent. It was late that night when they had the report back in their hands. Dorffman stared at it angrily. "It's obviously wrong," he grated. "It doesn't fit. Dave, it doesn't agree with anything we've observed before. There must be an error." "Of course," said Lessing. "According to the theory. The theory says that adult psi-contact is deadly to the growing child. It smothers their potential through repeated contact until it dries up completely. We've proved that, haven't we? Time after time. Everything goes according to the theory—except Tommy. But Tommy's psi-potential was drying up there on the Farm, until the distortion was threatening the balance of his mind. Then he made an adult contact, and we saw how he bloomed." Lessing sank down to his desk wearily. "What are we going to do, Jack? Formulate a separate theory for Tommy?" "Of course not," said Dorffman. "The instruments were wrong. Somehow we misread the data—" "Didn't you see his face ?" Lessing burst out. "Didn't you see how he acted ? What do you want with an instrument reading?" He shook his head. "It's no good, Jack. Something different happened here, something we'd never counted on. It's something the theory just doesn't allow for." They sat silently for a while. Then Dorffman said: "What are you going to do?" "I don't know," said Lessing. "Maybe when we fell into this bramble bush we blinded ourselves with the urge to classify—to line everything up in neat rows like pins in a paper. Maybe we were so blind we missed the path altogether." "But the book is due! The Conference speech—" "I think we'll make some changes in the book," Lessing said slowly. "It'll be costly—but it might even be fun. It's a pretty dry, logical presentation of ideas, as it stands. Very austere and authoritarian. But a few revisions could change all that—" He rubbed his hands together thoughtfully. "How about it, Jack? Do we have nerve enough to be laughed at? Do you think we could stand a little discredit, making silly asses of ourselves? Because when I finish this book, we'll be laughed out of existence. There won't be any Authority in psionics for a while—and maybe that way one of the lads who's really sniffing out the trail will get somebody to listen to him! "Get a pad, get a pencil! We've got work to do. And when we finish, I think we'll send a carbon copy out Chicago way. Might even persuade that puppy out there to come here and work for me—"
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B. He wants to try to teach the children to use their abilities
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By all accounts, Martin should be grateful for the descendants,
A. yet he resents them. He rebels against the new way of life they try to petition upon him.
B. and he is. He is appreciative of the fact that they want to make him an integral part of securing their future way of existence.
C. and he is. They have introduced Martin to a much better lifestyle than he was accustomed to.
D. but he is unappreciative of the lifestyle they bring to him because he would rather have things the way he was accustomed to. He sees that even though his lifestyle was rudimentary in comparison, it was genuine.
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THE MAN OUTSIDE By EVELYN E. SMITH Illustrated by DILLON [Transcriber's Note: This etext was produced from Galaxy Science Fiction August 1957. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] No one, least of all Martin, could dispute that a man's life should be guarded by his kin—but by those who hadn't been born yet? Nobody in the neighborhood was surprised when Martin's mother disappeared and Ninian came to take care of him. Mothers had a way of disappearing around those parts and the kids were often better off without them. Martin was no exception. He'd never had it this good while he was living with his old lady. As for his father, Martin had never had one. He'd been a war baby, born of one of the tides of soldiers—enemies and allies, both—that had engulfed the country in successive waves and bought or taken the women. So there was no trouble that way. Sometimes he wondered who Ninian really was. Obviously that story about her coming from the future was just a gag. Besides, if she really was his great-great-grand-daughter, as she said, why would she tell him to call her " Aunt Ninian "? Maybe he was only eleven, but he'd been around and he knew just what the score was. At first he'd thought maybe she was some new kind of social worker, but she acted a little too crazy for that. He loved to bait her, as he had loved to bait his mother. It was safer with Ninian, though, because when he pushed her too far, she would cry instead of mopping up the floor with him. "But I can't understand," he would say, keeping his face straight. "Why do you have to come from the future to protect me against your cousin Conrad?" "Because he's coming to kill you." "Why should he kill me? I ain't done him nothing." Ninian sighed. "He's dissatisfied with the current social order and killing you is part of an elaborate plan he's formulated to change it. You wouldn't understand." "You're damn right. I don't understand. What's it all about in straight gas?" "Oh, just don't ask any questions," Ninian said petulantly. "When you get older, someone will explain the whole thing to you." So Martin held his peace, because, on the whole, he liked things the way they were. Ninian really was the limit, though. All the people he knew lived in scabrous tenement apartments like his, but she seemed to think it was disgusting. "So if you don't like it, clean it up," he suggested. She looked at him as if he were out of his mind. "Hire a maid, then!" he jeered. And darned if that dope didn't go out and get a woman to come clean up the place! He was so embarrassed, he didn't even dare show his face in the streets—especially with the women buttonholing him and demanding to know what gave. They tried talking to Ninian, but she certainly knew how to give them the cold shoulder. One day the truant officer came to ask why Martin hadn't been coming to school. Very few of the neighborhood kids attended classes very regularly, so this was just routine. But Ninian didn't know that and she went into a real tizzy, babbling that Martin had been sick and would make up the work. Martin nearly did get sick from laughing so hard inside. But he laughed out of the other side of his mouth when she went out and hired a private tutor for him. A tutor—in that neighborhood! Martin had to beat up every kid on the block before he could walk a step without hearing "Fancy Pants!" yelled after him. Ninian worried all the time. It wasn't that she cared what these people thought of her, for she made no secret of regarding them as little better than animals, but she was shy of attracting attention. There were an awful lot of people in that neighborhood who felt exactly the same way, only she didn't know that, either. She was really pretty dumb, Martin thought, for all her fancy lingo. "It's so hard to think these things out without any prior practical application to go by," she told him. He nodded, knowing what she meant was that everything was coming out wrong. But he didn't try to help her; he just watched to see what she'd do next. Already he had begun to assume the detached role of a spectator. When it became clear that his mother was never going to show up again, Ninian bought one of those smallish, almost identical houses that mushroom on the fringes of a city after every war, particularly where intensive bombing has created a number of desirable building sites. "This is a much better neighborhood for a boy to grow up in," she declared. "Besides, it's easier to keep an eye on you here." And keep an eye on him she did—she or a rather foppish young man who came to stay with them occasionally. Martin was told to call him Uncle Raymond. From time to time, there were other visitors—Uncles Ives and Bartholomew and Olaf, Aunts Ottillie and Grania and Lalage, and many more—all cousins to one another, he was told, all descendants of his. Martin was never left alone for a minute. He wasn't allowed to play with the other kids in the new neighborhood. Not that their parents would have let them, anyway. The adults obviously figured that if a one-car family hired private tutors for their kid, there must be something pretty wrong with him. So Martin and Ninian were just as conspicuous as before. But he didn't tip her off. She was grown up; she was supposed to know better than he did. He lived well. He had food to eat that he'd never dreamed of before, warm clothes that no one had ever worn before him. He was surrounded by more luxury than he knew what to do with. The furniture was the latest New Grand Rapids African modern. There were tidy, colorful Picasso and Braque prints on the walls. And every inch of the floor was modestly covered by carpeting, though the walls were mostly unabashed glass. There were hot water and heat all the time and a freezer well stocked with food—somewhat erratically chosen, for Ninian didn't know much about meals. The non-glass part of the house was of neat, natural-toned wood, with a neat green lawn in front and a neat parti-colored garden in back. Martin missed the old neighborhood, though. He missed having other kids to play with. He even missed his mother. Sure, she hadn't given him enough to eat and she'd beaten him up so hard sometimes that she'd nearly killed him—but then there had also been times when she'd hugged and kissed him and soaked his collar with her tears. She'd done all she could for him, supporting him in the only way she knew how—and if respectable society didn't like it, the hell with respectable society. From Ninian and her cousins, there was only an impersonal kindness. They made no bones about the fact that they were there only to carry out a rather unpleasant duty. Though they were in the house with him, in their minds and in their talk they were living in another world—a world of warmth and peace and plenty where nobody worked, except in the government service or the essential professions. And they seemed to think even that kind of job was pretty low-class, though better than actually doing anything with the hands. In their world, Martin came to understand, nobody worked with hands; everything was done by machinery. All the people ever did was wear pretty clothes and have good times and eat all they wanted. There was no devastation, no war, no unhappiness, none of the concomitants of normal living. It was then that Martin began to realize that either the whole lot of them were insane, or what Ninian had told him at first was the truth. They came from the future. When Martin was sixteen, Raymond took him aside for the talk Ninian had promised five years before. "The whole thing's all my brother Conrad's fault. You see, he's an idealist," Raymond explained, pronouncing the last word with distaste. Martin nodded gravely. He was a quiet boy now, his brief past a dim and rather ridiculous memory. Who could ever imagine him robbing a grocery store or wielding a broken bottle now? He still was rather undersized and he'd read so much that he'd weakened his eyes and had to wear glasses. His face was pallid, because he spent little time in the sun, and his speech rather overbred, his mentors from the future having carefully eradicated all current vulgarities. "And Conrad really got upset over the way Earth has been exploiting the not so intelligent life-forms on the other planets," Raymond continued. "Which is distressing—though, of course, it's not as if they were people. Besides, the government has been talking about passing laws to do away with the—well, abuses and things like that, and I'm sure someday everything will come out all right. However, Conrad is so impatient." "I thought, in your world, machines did all the work," Martin suggested. "I've told you—our world is precisely the same as this one!" Raymond snapped. "We just come a couple of centuries or so later, that's all. But remember, our interests are identical. We're virtually the same people ... although it is amazing what a difference two hundred odd years of progress and polish can make in a species, isn't it?" He continued more mildly: "However, even you ought to be able to understand that we can't make machinery without metal. We need food. All that sort of thing comes from the out-system planets. And, on those worlds, it's far cheaper to use native labor than to ship out all that expensive machinery. After all, if we didn't give the natives jobs, how would they manage to live?" "How did they live before? Come to think of it, if you don't work, how do you live now?... I don't mean in the now for me, but the now for you," Martin explained laboriously. It was so difficult to live in the past and think in the future. "I'm trying to talk to you as if you were an adult," Raymond said, "but if you will persist in these childish interruptions—" "I'm sorry," Martin said. But he wasn't, for by now he had little respect left for any of his descendants. They were all exceedingly handsome and cultivated young people, with superior educations, smooth ways of speaking and considerable self-confidence, but they just weren't very bright. And he had discovered that Raymond was perhaps the most intelligent of the lot. Somewhere in that relatively short span of time, his line or—more frightening—his race had lost something vital. Unaware of the near-contempt in which his young ancestor held him, Raymond went on blandly: "Anyhow, Conrad took it upon himself to feel particularly guilty, because, he decided, if it hadn't been for the fact that our great-grandfather discovered the super-drive, we might never have reached the stars. Which is ridiculous—his feeling guilty, I mean. Perhaps a great-grandfather is responsible for his great-grandchildren, but a great-grandchild can hardly be held accountable for his great-grandfather." "How about a great-great-grandchild?" Martin couldn't help asking. Raymond flushed a delicate pink. "Do you want to hear the rest of this or don't you?" "Oh, I do!" Martin said. He had pieced the whole thing together for himself long since, but he wanted to hear how Raymond would put it. "Unfortunately, Professor Farkas has just perfected the time transmitter. Those government scientists are so infernally officious—always inventing such senseless things. It's supposed to be hush-hush, but you know how news will leak out when one is always desperate for a fresh topic of conversation." Anyhow, Raymond went on to explain, Conrad had bribed one of Farkas' assistants for a set of the plans. Conrad's idea had been to go back in time and "eliminate!" their common great-grandfather. In that way, there would be no space-drive, and, hence, the Terrestrials would never get to the other planets and oppress the local aborigines. "Sounds like a good way of dealing with the problem," Martin observed. Raymond looked annoyed. "It's the adolescent way," he said, "to do away with it, rather than find a solution. Would you destroy a whole society in order to root out a single injustice?" "Not if it were a good one otherwise." "Well, there's your answer. Conrad got the apparatus built, or perhaps he built it himself. One doesn't inquire too closely into such matters. But when it came to the point, Conrad couldn't bear the idea of eliminating our great-grandfather—because our great-grandfather was such a good man, you know." Raymond's expressive upper lip curled. "So Conrad decided to go further back still and get rid of his great-grandfather's father—who'd been, by all accounts, a pretty worthless character." "That would be me, I suppose," Martin said quietly. Raymond turned a deep rose. "Well, doesn't that just go to prove you mustn't believe everything you hear?" The next sentence tumbled out in a rush. "I wormed the whole thing out of him and all of us—the other cousins and me—held a council of war, as it were, and we decided it was our moral duty to go back in time ourselves and protect you." He beamed at Martin. The boy smiled slowly. "Of course. You had to. If Conrad succeeded in eliminating me, then none of you would exist, would you?" Raymond frowned. Then he shrugged cheerfully. "Well, you didn't really suppose we were going to all this trouble and expense out of sheer altruism, did you?" he asked, turning on the charm which all the cousins possessed to a consternating degree. Martin had, of course, no illusions on that score; he had learned long ago that nobody did anything for nothing. But saying so was unwise. "We bribed another set of plans out of another of the professor's assistants," Raymond continued, as if Martin had answered, "and—ah—induced a handicraft enthusiast to build the gadget for us." Induced , Martin knew, could have meant anything from blackmail to the use of the iron maiden. "Then we were all ready to forestall Conrad. If one of us guarded you night and day, he would never be able to carry out his plot. So we made our counter-plan, set the machine as far back as it would go—and here we are!" "I see," Martin said. Raymond didn't seem to think he really did. "After all," he pointed out defensively, "whatever our motives, it has turned into a good thing for you. Nice home, cultured companions, all the contemporary conveniences, plus some handy anachronisms—I don't see what more you could ask for. You're getting the best of all possible worlds. Of course Ninian was a ninny to locate in a mercantile suburb where any little thing out of the way will cause talk. How thankful I am that our era has completely disposed of the mercantiles—" "What did you do with them?" Martin asked. But Raymond rushed on: "Soon as Ninian goes and I'm in full charge, we'll get a more isolated place and run it on a far grander scale. Ostentation—that's the way to live here and now; the richer you are, the more eccentricity you can get away with. And," he added, "I might as well be as comfortable as possible while I suffer through this wretched historical stint." "So Ninian's going," said Martin, wondering why the news made him feel curiously desolate. Because, although he supposed he liked her in a remote kind of way, he had no fondness for her—or she, he knew, for him. "Well, five years is rather a long stretch for any girl to spend in exile," Raymond explained, "even though our life spans are a bit longer than yours. Besides, you're getting too old now to be under petticoat government." He looked inquisitively at Martin. "You're not going to go all weepy and make a scene when she leaves, are you?" "No...." Martin said hesitantly. "Oh, I suppose I will miss her. But we aren't very close, so it won't make a real difference." That was the sad part: he already knew it wouldn't make a difference. Raymond clapped him on the shoulder. "I knew you weren't a sloppy sentimentalist like Conrad. Though you do have rather a look of him, you know." Suddenly that seemed to make Conrad real. Martin felt a vague stirring of alarm. He kept his voice composed, however. "How do you plan to protect me when he comes?" "Well, each one of us is armed to the teeth, of course," Raymond said with modest pride, displaying something that looked like a child's combination spaceman's gun and death ray, but which, Martin had no doubt, was a perfectly genuine—and lethal—weapon. "And we've got a rather elaborate burglar alarm system." Martin inspected the system and made one or two changes in the wiring which, he felt, would increase its efficiency. But still he was dubious. "Maybe it'll work on someone coming from outside this house , but do you think it will work on someone coming from outside this time ?" "Never fear—it has a temporal radius," Raymond replied. "Factory guarantee and all that." "Just to be on the safe side," Martin said, "I think I'd better have one of those guns, too." "A splendid idea!" enthused Raymond. "I was just about to think of that myself!" When it came time for the parting, it was Ninian who cried—tears at her own inadequacy, Martin knew, not of sorrow. He was getting skillful at understanding his descendants, far better than they at understanding him. But then they never really tried. Ninian kissed him wetly on the cheek and said she was sure everything would work out all right and that she'd come see him again. She never did, though, except at the very last. Raymond and Martin moved into a luxurious mansion in a remote area. The site proved a well-chosen one; when the Second Atomic War came, half a dozen years later, they weren't touched. Martin was never sure whether this had been sheer luck or expert planning. Probably luck, because his descendants were exceedingly inept planners. Few people in the world then could afford to live as stylishly as Martin and his guardian. The place not only contained every possible convenience and gadget but was crammed with bibelots and antiques, carefully chosen by Raymond and disputed by Martin, for, to the man from the future, all available artifacts were antiques. Otherwise, Martin accepted his new surroundings. His sense of wonder had become dulled by now and the pink pseudo-Spanish castle—"architecturally dreadful, of course," Raymond had said, "but so hilariously typical"—impressed him far less than had the suburban split-level aquarium. "How about a moat?" Martin suggested when they first came. "It seems to go with a castle." "Do you think a moat could stop Conrad?" Raymond asked, amused. "No," Martin smiled, feeling rather silly, "but it would make the place seem safer somehow." The threat of Conrad was beginning to make him grow more and more nervous. He got Raymond's permission to take two suits of armor that stood in the front hall and present them to a local museum, because several times he fancied he saw them move. He also became an adept with the ray gun and changed the surrounding landscape quite a bit with it, until Raymond warned that this might lead Conrad to them. During those early years, Martin's tutors were exchanged for the higher-degreed ones that were now needful. The question inevitably arose of what the youth's vocation in that life was going to be. At least twenty of the cousins came back through time to hold one of their vigorous family councils. Martin was still young enough to enjoy such occasions, finding them vastly superior to all other forms of entertainment. "This sort of problem wouldn't arise in our day, Martin," Raymond commented as he took his place at the head of the table, "because, unless one specifically feels a call to some profession or other, one just—well, drifts along happily." "Ours is a wonderful world," Grania sighed at Martin. "I only wish we could take you there. I'm sure you would like it." "Don't be a fool, Grania!" Raymond snapped. "Well, Martin, have you made up your mind what you want to be?" Martin affected to think. "A physicist," he said, not without malice. "Or perhaps an engineer." There was a loud, excited chorus of dissent. He chuckled inwardly. "Can't do that," Ives said. "Might pick up some concepts from us. Don't know how; none of us knows a thing about science. But it could happen. Subconscious osmosis, if there is such a thing. That way, you might invent something ahead of time. And the fellow we got the plans from particularly cautioned us against that. Changing history. Dangerous." "Might mess up our time frightfully," Bartholomew contributed, "though, to be perfectly frank, I can't quite understand how." "I am not going to sit down and explain the whole thing to you all over again, Bart!" Raymond said impatiently. "Well, Martin?" "What would you suggest?" Martin asked. "How about becoming a painter? Art is eternal. And quite gentlemanly. Besides, artists are always expected to be either behind or ahead of their times." "Furthermore," Ottillie added, "one more artist couldn't make much difference in history. There were so many of them all through the ages." Martin couldn't hold back his question. "What was I, actually, in that other time?" There was a chilly silence. "Let's not talk about it, dear," Lalage finally said. "Let's just be thankful we've saved you from that !" So drawing teachers were engaged and Martin became a very competent second-rate artist. He knew he would never be able to achieve first rank because, even though he was still so young, his work was almost purely intellectual. The only emotion he seemed able to feel was fear—the ever-present fear that someday he would turn a corridor and walk into a man who looked like him—a man who wanted to kill him for the sake of an ideal. But the fear did not show in Martin's pictures. They were pretty pictures. Cousin Ives—now that Martin was older, he was told to call the descendants cousin —next assumed guardianship. Ives took his responsibilities more seriously than the others did. He even arranged to have Martin's work shown at an art gallery. The paintings received critical approval, but failed to evoke any enthusiasm. The modest sale they enjoyed was mostly to interior decorators. Museums were not interested. "Takes time," Ives tried to reassure him. "One day they'll be buying your pictures, Martin. Wait and see." Ives was the only one of the descendants who seemed to think of Martin as an individual. When his efforts to make contact with the other young man failed, he got worried and decided that what Martin needed was a change of air and scenery. "'Course you can't go on the Grand Tour. Your son hasn't invented space travel yet. But we can go see this world. What's left of it. Tourists always like ruins best, anyway." So he drew on the family's vast future resources and bought a yacht, which Martin christened The Interregnum . They traveled about from sea to ocean and from ocean to sea, touching at various ports and making trips inland. Martin saw the civilized world—mostly in fragments; the nearly intact semi-civilized world and the uncivilized world, much the same as it had been for centuries. It was like visiting an enormous museum; he couldn't seem to identify with his own time any more. The other cousins appeared to find the yacht a congenial head-quarters, largely because they could spend so much time far away from the contemporary inhabitants of the planet and relax and be themselves. So they never moved back to land. Martin spent the rest of his life on The Interregnum . He felt curiously safer from Conrad there, although there was no valid reason why an ocean should stop a traveler through time. More cousins were in residence at once than ever before, because they came for the ocean voyage. They spent most of their time aboard ship, giving each other parties and playing an avant-garde form of shuffleboard and gambling on future sporting events. That last usually ended in a brawl, because one cousin was sure to accuse another of having got advance information about the results. Martin didn't care much for their company and associated with them only when not to have done so would have been palpably rude. And, though they were gregarious young people for the most part, they didn't court his society. He suspected that he made them feel uncomfortable. He rather liked Ives, though. Sometimes the two of them would be alone together; then Ives would tell Martin of the future world he had come from. The picture drawn by Raymond and Ninian had not been entirely accurate, Ives admitted. True, there was no war or poverty on Earth proper, but that was because there were only a couple of million people left on the planet. It was an enclave for the highly privileged, highly interbred aristocracy, to which Martin's descendants belonged by virtue of their distinguished ancestry. "Rather feudal, isn't it?" Martin asked. Ives agreed, adding that the system had, however, been deliberately planned, rather than the result of haphazard natural development. Everything potentially unpleasant, like the mercantiles, had been deported. "Not only natives livin' on the other worlds," Ives said as the two of them stood at the ship's rail, surrounded by the limitless expanse of some ocean or other. "People, too. Mostly lower classes, except for officials and things. With wars and want and suffering," he added regretfully, "same as in your day.... Like now, I mean," he corrected himself. "Maybe it is worse, the way Conrad thinks. More planets for us to make trouble on. Three that were habitable aren't any more. Bombed. Very thorough job." "Oh," Martin murmured, trying to sound shocked, horrified—interested, even. "Sometimes I'm not altogether sure Conrad was wrong," Ives said, after a pause. "Tried to keep us from getting to the stars, hurting the people—I expect you could call them people—there. Still—" he smiled shamefacedly—"couldn't stand by and see my own way of life destroyed, could I?" "I suppose not," Martin said. "Would take moral courage. I don't have it. None of us does, except Conrad, and even he—" Ives looked out over the sea. "Must be a better way out than Conrad's," he said without conviction. "And everything will work out all right in the end. Bound to. No sense to—to anything, if it doesn't." He glanced wistfully at Martin. "I hope so," said Martin. But he couldn't hope; he couldn't feel; he couldn't even seem to care. During all this time, Conrad still did not put in an appearance. Martin had gotten to be such a crack shot with the ray pistol that he almost wished his descendant would show up, so there would be some excitement. But he didn't come. And Martin got to thinking.... He always felt that if any of the cousins could have come to realize the basic flaw in the elaborate plan they had concocted, it would have been Ives. However, when the yacht touched at Tierra del Fuego one bitter winter, Ives took a severe chill. They sent for a doctor from the future—one of the descendants who had been eccentric enough to take a medical degree—but he wasn't able to save Ives. The body was buried in the frozen ground at Ushuaia, on the southern tip of the continent, a hundred years or more before the date of his birth. A great many of the cousins turned up at the simple ceremony. All were dressed in overwhelming black and showed a great deal of grief. Raymond read the burial service, because they didn't dare summon a clerical cousin from the future; they were afraid he might prove rather stuffy about the entire undertaking. "He died for all of us," Raymond concluded his funeral eulogy over Ives, "so his death was not in vain." But Martin disagreed. The ceaseless voyaging began again. The Interregnum voyaged to every ocean and every sea. Some were blue and some green and some dun. After a while, Martin couldn't tell one from another. Cousin after cousin came to watch over him and eventually they were as hard for him to tell apart as the different oceans. All the cousins were young, for, though they came at different times in his life, they had all started out from the same time in theirs. Only the young ones had been included in the venture; they did not trust their elders. As the years went by, Martin began to lose even his detached interest in the land and its doings. Although the yacht frequently touched port for fuel or supplies—it was more economical to purchase them in that era than to have them shipped from the future—he seldom went ashore, and then only at the urging of a newly assigned cousin anxious to see the sights. Most of the time Martin spent in watching the sea—and sometimes he painted it. There seemed to be a depth to his seascapes that his other work lacked. When he was pressed by the current cousin to make a land visit somewhere, he decided to exhibit a few of his sea paintings. That way, he could fool himself into thinking that there was some purpose to this journey. He'd come to believe that perhaps what his life lacked was purpose, and for a while he kept looking for meaning everywhere, to the cousin's utter disgust. "Eat, drink and be merry, or whatever you Romans say when you do as you do," the cousin—who was rather woolly in history; the descendants were scraping bottom now—advised. Martin showed his work in Italy, so that the cousin could be disillusioned by the current crop of Romans. He found that neither purpose nor malice was enough; he was still immeasurably bored. However, a museum bought two of the paintings. Martin thought of Ives and felt an uncomfortable pang of a sensation he could no longer understand. "Where do you suppose Conrad has been all this time?" Martin idly asked the current cousin—who was passing as his nephew by now. The young man jumped, then glanced around him uncomfortably. "Conrad's a very shrewd fellow," he whispered. "He's biding his time—waiting until we're off guard. And then—pow!—he'll attack!" "Oh, I see," Martin said. He had often fancied that Conrad would prove to be the most stimulating member of the whole generation. But it seemed unlikely that he would ever have a chance for a conversation with the young man. More than one conversation, anyhow. "When he does show up, I'll protect you," the cousin vowed, touching his ray gun. "You haven't a thing to worry about." Martin smiled with all the charm he'd had nothing to do but acquire. "I have every confidence in you," he told his descendant. He himself had given up carrying a gun long ago. There was a war in the Northern Hemisphere and so The Interregnum voyaged to southern waters. There was a war in the south and they hid out in the Arctic. All the nations became too drained of power—fuel and man and will—to fight, so there was a sterile peace for a long time. The Interregnum roamed the seas restlessly, with her load of passengers from the future, plus one bored and aging contemporary. She bore big guns now, because of the ever-present danger of pirates.
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D. but he is unappreciative of the lifestyle they bring to him because he would rather have things the way he was accustomed to. He sees that even though his lifestyle was rudimentary in comparison, it was genuine.
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Who received the worste abuse of all who are mentioned?
A. Ross
B. Gill
C. Mehta
D. Shawn
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Goings On About Town One of the funniest moments in Brendan Gill's 1975 memoir, Here at "The New Yorker ," comes during a luncheon at the now vanished Ritz in Manhattan. At the table are Gill; William Shawn, then editor of The New Yorker ; and the reclusive English writer Henry Green. Green's new novel, Loving , has just received a very favorable review in The New Yorker . Shawn--"with his usual hushed delicacy of speech and manner"--inquires of the novelist whether he could possibly reveal what prompted the creation of such an exquisite work. Green obliges. "I once asked an old butler in Ireland what had been the happiest times of his life," he says. "The butler replied, 'Lying in bed on Sunday morning, eating tea and toast with cunty fingers.' " This was not the explanation Shawn was expecting, Gill tells us. "Discs of bright red begin to burn in his cheeks." Was Shawn blushing out of prudishness, as we are meant to infer? This was, after all, a man renowned for his retiring propriety, a man who sedulously barred anything smacking of the salacious--from lingerie ads to four-letter words--from the magazine he stewarded from 1952 until 1987, five years before his death. But after reading these two new memoirs about Shawn, I wonder. "He longed for the earthiest and wildest kinds of sexual adventures," Lillian Ross discloses in hers, adding that he lusted after Hannah Arendt, Evonne Goolagong, and Madonna. As for Ved Mehta, he reports that Shawn's favorite thing to watch on television was "people dancing uninhibitedly" ( Soul Train , one guesses). I suspect Shawn did not blush at the "cunty fingers" remark out of prudery. He blushed because it had hit too close to home. Both these memoirs must be read by everyone--everyone, that is, who takes seriously the important business of sorting out precisely how he or she feels about The New Yorker , then and now. Of the two, Mehta's is far and away the more entertaining. This may seem odd, for Mehta is reputed to be a very dull writer whereas Ross is a famously zippy one. Moreover, Mehta writes as Shawn's adoring acolyte, whereas Ross writes as his longtime adulterous lover. Just knowing that Mrs. Shawn is still alive adds a certain tension to reading much of what this Other Woman chooses to divulge. Evidently, "Bill" and Lillian loved each other with a fine, pure love, a love that was more than love, a love coveted by the winged seraphs of heaven. "We had indeed become one," she tells us, freely venting the inflations of her heart. Shawn was managing editor of The New Yorker when he hired Ross in 1945 as the magazine's second woman reporter (the first was Andy Logan). He was short and balding but had pale blue eyes to die for. As for Ross, "I was aware of the fact that I was not unappealing." During a late-night editorial session, she says, Shawn blurted out his love. A few weeks later at the office, their eyes met. Without a word--even, it seems, to the cab driver--they hied uptown to the Plaza, where matters were consummated. Thereafter, the couple set up housekeeping together in an apartment 20 blocks downtown from the Shawn residence on upper Fifth Avenue and stoically endured the sufferings of Shawn's wife, who did not want a divorce. Now, Ross seems like a nice lady, and I certainly have nothing against adultery, which I hear is being carried on in the best circles these days. But the public flaunting of adultery--especially when spouses and children are around--well, it brings out the bourgeois in me. It also made me feel funny about William Shawn, whom I have always regarded as a great man. I loved his New Yorker . The prose it contained--the gray stuff around the cartoons--was balm for the soul: unfailingly clear, precise, logical, and quietly stylish. So what if the articles were occasionally boring? It was a sweet sort of boredom, serene and restorative, not at all like the kind induced by magazines today, which is more akin to nervous exhaustion. Besides, the moral tone of the magazine was almost wholly admirable--it was ahead of the pack on Hiroshima, civil rights, Vietnam, Watergate, the environment--and this was very much Shawn's doing. I do not like to think of him in an illicit love nest, eating tea and toast with cunty fingers. Happily, Ross has sprinkled her memoir with clues that it is not to be taken as entirely factual. To say that Shawn was "a man who grieved over all living creatures" is forgivable hyperbole; but later to add that he "mourned" for Si Newhouse when Newhouse unceremoniously fired him in 1987 (a couple of years after buying the magazine)--well, that's a bit much. Even Jesus had his limits. Elsewhere, Ross refers to her lover's "very powerful masculinity," only to note on the very next page that "if he suffered a paper cut on a finger and saw blood, he would come into my office, looking pale." She declares that "Bill was incapable of engendering a cliché, in deed as well as in word." But then she puts the most toe-curling clichés into his mouth: "Why am I more ghost than man?" Or: "We must arrest our love in midflight. And we fix it forever as of today, a point of pure light that will reach into eternity." (File that under Romantic Effusions We Doubt Ever Got Uttered.) Nor is Ross incapable of a melodramatic cliché herself. "Why can't we just live, just live ?" she cries in anguish when she and Shawn, walking hand in hand out of Central Park, chance to see Shawn's wife slowly making her way down the block with a burden of packages. And what does she think of Mrs. Shawn? "I found her to be sensitive and likeable." Plus, she could "do a mean Charleston." There is nothing more poignant than the image of an openly cheated-upon and humiliated wife doing "a mean Charleston." William Shawn's indispensability as an editor is amply manifest in Ross' memoir. Word repetition? "Whatever reporting Bill asked me to do turned out to be both challenging and fun. ... For me, reporting and writing for the magazine was fun, pure fun. ... It was never 'work' for me. It was fun." Even in praising his skill as an editor, she betrays the presence of its absence. "All writers, of course, have needed the one called the 'editor,' who singularly, almost mystically, embodies the many-faceted, unique life force infusing the entire enchilada." Nice touch, that enchilada. When cocktail party malcontents mocked Shawn's New Yorker in the late '70s and early '80s, they would make fun of such things as E.J. Kahn's five-part series on "Grains of the World" or Elizabeth Drew's supposedly soporific reporting from Washington. But Ved Mehta was always the butt of the worst abuse. Shawn was allowing him to publish an autobiography in the pages of the magazine that was mounting up to millions of words over the years, and the very idea of it seemed to bore people silly. After the publication of two early installments, "Daddyji" and "Mamaji," each the length of a book, one critic cried: "Enoughji!" But it kept coming. And I, for one, was grateful. Here was a boy growing up in Punjab during the fall of the Raj and the Partition, a boy who had been blinded by meningitis at the age of 3, roller-skating through the back streets of Lahore as Sikhs slaughtered Hindus and Hindus slaughtered Muslims and civilization was collapsing and then, decades later, having made his way from India to an Arkansas school for the blind to Balliol College, Oxford, to The New Yorker , re-creating the whole thing in Proustian detail and better-than-Proustian prose ... ! Mehta's multivolume autobiography, titled Continents of Exile , has loss as its overarching theme: loss of sight, of childhood, of home and country, and now--with this volume--loss of Mr. Shawn's New Yorker . The memoir takes us from the time the author was hired as a staff writer in the early '60s up to 1994, when he was "terminated" by the loathed Tina Brown in her vandalization of his cherished magazine. Mehta evidently loved William Shawn at least as much as Lillian Ross did, although his love was not requited in the same way. He likens the revered editor to the character Prince Myshkin in The Idiot : innocent and vulnerable, someone who must be protected. And long-suffering, one might infer: "He was so careful of not hurting anyone's feelings that he often listened to utterly fatuous arguments for hours on end." Like Ross, Mehta struggles to express William Shawn's ineffable virtues. "It is as if, Mehta, he were beyond our human conception," Janet Flanner tells him once to calm him down. At times I wondered whether the author, in his ecstasies of devotion, had not inadvertently committed plagiarism. His words on Mr. Shawn sound suspiciously like those of Mr. Pooter on his boss Mr. Perkupp in The Diary of a Nobody . Compare. Mehta on Shawn: "His words were so generous that I could scarcely find my tongue, even to thank him." Pooter on Perkupp: "My heart was too full to thank him." Mehta: "I started saying to myself compulsively, 'I wish Mr. Shawn would ring,' at the oddest times of the day or night. ... How I longed for the parade of proofs, the excitement of rewriting and perfecting!" Pooter: "Mr. Perkupp, I will work night and day to serve you!" I am not sure I have made it sound this way so far, but Mehta's book is completely engrossing--the most enjoyable book, I think, I have ever reviewed. It oozes affection and conviction, crackles with anger, and is stuffed with thumping good stories. Many are about Mehta's daft colleagues at The New Yorker , such as the guy in the next office: His door was always shut, but I could hear him through the wall that separated his cubicle from mine typing without pause. ... Even the changing of the paper in the typewriter seemed somehow to be incorporated into the rhythmic rat-tat-tat ... year after year went by to the sound of his typing but without a word from his typewriter appearing in the magazine. Or the great and eccentric Irish writer Maeve Breenan, who fetched up as a bag lady. Or the legendary St. Clair McKelway, whose decisive breakdown came when he hailed a cab and prevailed upon the driver to take him to the New Yorker office at 24 West 43 rd St. "O.K., Mac, if that's what you want." He was in Boston at the time. (McKelway later told Mehta that if the cabby had not called him "Mac," his nickname, an alarm might have gone off in his head.) Mehta's writerly persona, a disarming mixture of the feline and the naive, is perfect for relating the little scandals that worried The New Yorker in the late '70s (plagiarism, frozen turbot), the drama of finding a worthy candidate to succeed the aging Shawn as editor, the purchase of the magazine by the evil Si Newhouse ("We all took fright") and the resultant plague of Gottliebs and Florios visited upon it, and what he sees as the final debacle: Tinaji. Lillian Ross, by contrast, takes a rather cheerful view of the Brown dispensation. Indeed, the new editor even coaxed Ross into re-joining the magazine, just as she was booting Mehta out. "I found that she possessed--under the usual disguises--her own share of Bill's kind of naivete, insight, and sensitivity," Ross says of Brown. "She, too, 'got it.' " A few months after Brown was appointed editor, Shawn died at the age of 85. He had long since stopped reading his beloved magazine, in sorrow and relief. That's if you believe Mehta. Ross assures us that Mr. Shawn was reading Tina Brown's New Yorker "with new interest" in the weeks prior to his death. Has Tina Brown betrayed the legacy of William Shawn, as Mehta fiercely believes, or has she continued and built upon it, as Ross is evidently convinced? Have the changes she has wrought enlivened a stodgy magazine or vulgarized a dignified one--or both? These are weighty questions, and one is of course loath to compromise one's life chances by hazarding unripe opinions in a public forum such as this.
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C. Mehta
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Which of these is true about the Red Mask?
A. He is entirely harmless and it just looks like he's trouble
B. He is a passenger looking for some entertainment
C. He throws the passengers' belongings overboard
D. He does not hesitate to use physical violence
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COUNTERWEIGHT By JERRY SOHL Every town has crime—but especially a town that is traveling from star to star! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, November 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Sure I'm a Nilly, and I've died seven times, always in the blackness of the outer reaches, and I'm not alone, although there aren't very many of us, never were. It made sense. Interstellar was new and they wanted him on the ship because he was a trained observer. They wanted facts, not gibberish. But to ask a man to give up two years of his life—well, that was asking a lot. Two years in a sardine can. Still, it had an appeal Keith Ellason knew he couldn't deny, a newsman's joy of the clean beat, a planetary system far afield, a closeup view of the universe, history in the making. Interstellar Chief Rexroad knocked the dottle from his pipe in a tray, saying, "Transworld Press is willing to let you have a leave of abscence, if you're interested." He knew Secretary Phipps from years of contacting, and now Phipps said, "Personally, I don't want to see anybody else on the job. You've got a fine record in this sort of thing." Keith Ellason smiled, but just barely. "You should have called me for the first trip." Phipps nodded. "I wish we had had you on the Weblor I ." "Crewmen," Rexroad said, "make poor reporters." The Weblor I had taken off on the first trip to Antheon five years before with a thousand families, reached the planet with less than five hundred surviving colonists. Upon the return to Earth a year later, the crew's report of suffering and chaos during the year's outgoing voyage was twisted, distorted and fragmentary. Ellason remembered it well. The decision of Interstellar was that the colonists started a revolution far out in space, that it was fanned by the ignorance of Captain Sessions in dealing with such matters. "Space affects men in a peculiar way," Phipps said. "We have conquered the problem of small groups in space—witness the discovery of Antheon, for example—but when there are large groups, control is more difficult." "Sessions," Rexroad said, "was a bully. The trouble started at about the halfway point. It ended with passengers engaging in open warfare with each other and the crew. Sessions was lucky to escape with his life." "As I recall," Ellason said, "there was something about stunners." Phipps rubbed his chin. "No weapons were allowed on the ship, but you must remember the colonists were selected for their intelligence and resourcefulness. They utilized these attributes to set up weapon shops to arm themselves." "The second trip is history," Rexroad said. "And a puzzle." Ellason nodded. "The ship disappeared." "Yes. We gave control to the colonists." "Assuming no accident in space," Phipps said, "it was a wrong decision. They probably took over the ship." "And now," Ellason said, "you're going to try again." Rexroad said very gravely, "We've got the finest captain in Interplanetary. Harvey Branson. No doubt you've heard of him. He's spent his life in our own system, and he's handpicking his own crew. We have also raised prerequisites for applicants. We don't think anything is going to happen, but if it does, we want to get an impersonal, unprejudiced view. That's where you come in. You do the observing, the reporting. We'll evaluate it on your return." "If I return," said Ellason. "I suppose that's problematical," Phipps said, "but I think you will. Captain Branson and his fifty crewmen want to return as badly as you do." He grinned. "You can write that novel you're always talking about on your return trip on the Weblor II ." Being a Nilly is important, probably as important as running the ship, and I think it is this thought that keeps us satisfied, willing to be what we are. The Weblor II had been built in space, as had its predecessor, the Weblor I , at a tremendous cost. Basically, it was an instrument which would open distant vistas to colonization, reducing the shoulder-to-shoulder pressure of a crowded solar system. A gigantic, hollow spike, the ship would never land anywhere, but would circle Antheon as it circled Earth, shuttling its cargo and passengers to the promised land, the new frontier. A space-borne metropolis, it would be the home for three thousand persons outward bound, only the crew on the return trip. It was equipped with every conceivable facility and comfort—dining rooms, assembly hall, individual and family compartments, recreation areas, swimming pool, library, theater. Nothing had been overlooked. The captain's briefing room was crowded, the air was heavy with the breathing of so many men, and the ventilators could not quite clear the air of tobacco smoke that drifted aimlessly here and there before it was caught and whisked away. In the tradition of newspaperman and observer, Keith Ellason tried to be as inconspicuous as possible, pressing against a bulkhead, but Captain Branson's eyes sought his several times as Branson listened to final reports from his engineers, record keepers, fuel men, computermen, and all the rest. He grunted his approval or disapproval, made a suggestion here, a restriction there. There was no doubt that Branson was in charge, yet there was a human quality about him that Ellason liked. The captain's was a lean face, well tanned, and his eyes were chunks of blue. "Gentlemen," Branson said at last, as Ellason knew he would, "I want to introduce Keith Ellason, whose presence Interstellar has impressed upon us. On loan from Transworld, he will have an observer status." He introduced him to the others. All of them seemed friendly; Ellason thought it was a good staff. Branson detained him after the others had gone. "One thing, Mr. Ellason. To make it easier for you, I suggest you think of this journey strictly from the observer viewpoint. There will be no story for Transworld at the end." Ellason was startled. While he had considered the possibility, he had not dwelt on it. Now it loomed large in his mind. "I don't understand, Captain Branson. It seems to me—" "Let me put it differently. Let me say that you will not understand why I say that until the journey ends." He smiled. "Perhaps I shouldn't have mentioned it." Ellason left the captain's quarters with an odd taste in his mouth. Now why had Branson said that? Why hadn't Rexroad or Phipps said something, if it was important? He made himself comfortable in his seven-foot-by-seven-foot cubicle, which is to say he dropped on his bed, found it more comfortable than he thought it would be, put his arms behind his head, stared at the ceiling. Metal walls, no windows, one floor vent, one ceiling vent, and a solitary ceiling molding tube-light. This would be his home for a year, just as there were homes like it for three thousand others, except that the family rooms would be larger. His quarters were near the front of the spike near the officers' quarters. He felt rather than heard the dull rumble. It was a sound he knew would be with him for two years—one year going and one year returning. He looked at his watch, picked up his notebook and made an entry. The ship right now would be slipping ever so slowly away from Earth. He got up. He'd have to go forward to the observation dome to see that. Last view of Earth for two years. The penetration of space by large groups is the coming out from under the traditions of thousands of years, and as these planet-orginated rules fall away, the floundering group seeks a new control, for they are humanity adrift, rudderless, for whom the stars are no longer bearings but nonexistent things, and values are altered if they are not shown the way. The theft of Carver Janssen's attache case occurred on the thirty-first day out. In Ellason's mind the incident, though insignificant from the standpoint of the ship as a whole, could very well be the cause of dissension later on. His notes covering it were therefore very thorough. Janssen's case contained vegetable and flower seeds—thousands of them, according to the Captain's Bulletin, the ship's daily newsletter which went to all hands and passengers. In the Bulletin the captain appealed to the thief to return the case to Mr. Janssen. He said it was significant that all en route had passed stability tests, and that it was to the ship's discredit that someone with criminal tendencies should have been permitted aboard. Ellason had to smile at that. What did Captain Branson think of those colonists who killed each other on the Weblor I ? They had passed stability tests too. This, then, was what happened when you took three thousand strangers and stuck them in a can for a year. When Ellason saw Branson about it, the captain said, "Of course I realize it takes only a little thing like this to set things off. I know people get tired of seeing each other, playing the same tapes, looking at the stars from the observation dome, walking down the same corridors, reading the same books, eating the same meals, though God knows we try to vary it as much as we can. Space creates rough edges. But the point is, we know all this, and knowing it, we shouldn't let it happen. We've got to find that thief." "What would he want seeds for? Have you thought of that?" "Of course. They'd have real value on Antheon." Ellason sought out Carver Janssen. He was a middle-aged man with a tired face and sad eyes. He said, "Now what am I going to Antheon for? I could only take along so much baggage and I threw out some comfort items to make room for the seeds. I'm a horticulturist, and Interstellar asked me to go along. But what use am I now? Where am I going to get seeds like those? Do you know how long it took me to collect them? They're not ordinary seeds, Mr. Ellason." There was an appeal from Janssen in the next day's newsletter describing the seeds, telling of their value, and requesting their return in the interests of the Antheon colony and of humanity. On the thirty-fourth day a witness turned up who said he had seen a man emerging from Janssen's compartment with the black case. "I didn't think anything of it at the time," Jamieson Dievers said. Branson asked him to describe the man. "Oh, he was about six feet tall, stocky build, and he wore a red rubber mask that covered his head completely." "Didn't you think that was important?" Branson asked in an outraged voice. "A man wearing a red mask?" Dievers shrugged. "This is a spaceship. How would I know whether a red mask—or a blue or green one—does or doesn't belong on a spaceship?" Although Dievers' account appeared in the newsletter, it was largely discounted. "If it is true," Branson told Ellason, "the theft must be the work of a psychotic. But I don't believe Jamieson Dievers. It may well be he's the psychotic." He snorted. "Red rubber mask! I think I'll have Dievers put through psychiatry." Attendant to taking notes on this incident, Ellason noted a strange thing. Janssen lived in that part of the ship known as the First Quadrant, and those who lived in that quadrant—more than seven hundred men, women and children—felt that the thief must surely live in Quadrant Two or Four. Elias Cromley, who had the compartment next to Janssen's, sounded the consensus when he said, "Surely a man wouldn't steal from his own quadrant, now would he, Mr. Ellason?" And so, Ellason observed in his notebook, are wars created. Seen in space, stars are unmoving, silent, sterile bright eyes ever watchful and accusing. To men unused to it, such a sight numbs, compresses, stultifies. He introduces a countermeasure, proof he exists, which is any overt act, sometimes violent. On the forty-fifth day June Failright, the young wife of one of the passenger meteorologists, ran screaming down one of the long corridors of the Third Quadrant. She told the captain she had been attacked in her compartment while her husband was in the ship's library. She was taken to one of the ship's doctors, who confirmed it. She said the culprit was a husky man wearing a red rubber mask, and though her description of what he had done did not appear in the story in the newsletter, it lost no time in penetrating every compartment of the ship. Ellason was present when a delegation from the Third Quadrant called on Captain Branson, demanding action. Branson remained seated behind his desk, unperturbed, saying, "I have no crewmen to spare for police duty." The delegation commenced speaking vehemently, to be quieted by Branson's raised hand. "I sympathize," Branson said, "but it is up to each quadrant to deal with its problems, whatever they may be. My job is to get us to Antheon." The group left in a surly mood. "You wonder at my reluctance, Mr. Ellason," Captain Branson said. "But suppose I assign the crew to patrol duties, the culprit isn't caught, and further incidents occur. What then? It soon becomes the crew's fault. And soon the colonists will begin thinking these things might be the crew's doing in the first place." "Yes," Ellason said, "but what if the intruder is a crewman?" "I know my men," Branson said flatly. "You could have a shake-down for the mask and the seed case." "Do you think it is a member of the crew?" Branson's eyes were bright. "No, I trust my men. I won't violate that trust." Ellason left, feeling uneasy. If he were Branson, he'd initiate an investigation, if nothing else than to prove the crew guiltless. Why couldn't Branson see the wisdom of setting an example for the colonists? As a Nilly, I knew that space breeds hate. There is a seed of malevolence in every man. It sometimes blossoms out among the stars. On the Weblor II it was ready for ripening. Raymond Palugger was killed in the ship's hospital on the sixty-first day. Palugger, a Fourth Quadrant passenger, had complained of feeling ill, had been hospitalized with a diagnosis of ileus. He had put his money belt in the drawer of the small stand beside his bed. A man in a red mask was seen hurrying from the hospital area, and a staff investigation revealed that Palugger had died trying to prevent the theft of the belt. Captain Branson did not wait for the newsletter. Through the ship's speaker system, he reported that Palugger had a fortune in credits in the belt and had died of a severe beating. He said that since the incident occurred in the staff section of the ship, his crew would be forced to submit to a thorough inspection in an effort to find the mask, the seed case, the money and the man. "I will not countenance such an act by a crewman," Branson said. "If and when he is found, he will be severely dealt with. But he might not be a member of the crew. I am ordering an assembly of all passengers at nine tomorrow morning in the auditorium. I will speak to you all then." Faces were angry, tongues were sharp at the meeting, eyes suspicious and tempers short. Above it all was the overpowering presence of Captain Branson speaking to them. "It is not my desire to interfere in passenger affairs," he said. "Insofar as the ship is concerned, it is my duty to make certain no crewman is guilty. This I am doing. But my crew is not and cannot be a police force for you. It is up to you people to police and protect yourselves." "How can we protect ourselves without stunners?" one colonist called out. "Has Red Mask a gun?" Branson retorted. "It seems to me you have a better weapon than any gun." "What's that?" "This ship is only so wide, so long and so deep. If every inch is searched, you'll find your man. He has to be somewhere aboard." The colonists quieted. Benjamin Simpson, one of the older men, was elected president of the newly formed Quadrant Council. One man from each of the quadrants was named to serve under him. Each of these men in turn selected five others from his own group. Those assembled waited in the hall while each team of six inspected the compartments of the others. These compartments were then locked, everyone returned to his compartment, and the larger search was conducted. It took twenty hours. No mask was found. No mask, no case, no money, no man. The captain reported that his search had been equally fruitless. At another assembly the following day it was decided to make the inspection teams permanent, to await further moves on the part of Red Mask. The Quadrant Council held periodic meetings to set up a method of trial for him when he was caught. It was all recorded in the newsletter and by Keith Ellason. We Nillys know about hate and about violence. We know too that where there is hate there is violence, and where there is violence there is death. During sleep time on the seventy-ninth day Barbara Stoneman, awakened by a strange sound, sat up in the bed of her compartment to find a man in a red mask in her room. Her cries brought neighbors into the corridor. The flight of the man was witnessed by many, and several men tried to stop him. But the intruder was light on his feet and fast. He escaped. The Quadrant Council confronted the captain, demanding weapons. "Are you out of your minds?" Branson exclaimed. Tom Tilbury, Fourth Quadrant leader, said, "We want to set up a police force, Captain. We want stunners." "There's no law against it," Branson said, "but it's a rule of mine that no weapons are to be issued en route." "If we had had a gun, we'd have got Red Mask," Tilbury said. "And I might have a murder on my conscience." Tilbury said, "We've also thought of that. Suppose you supply us with half-power stunners? That way we can stun but not kill." They got their guns. Now there were twenty-four policemen on duty in the corridors—eight on at a time. Ellason observed that for the first time the passengers seemed relaxed. Let Red Mask move against armed men, they said. Yeah, let him see what happens now. Red Mask did. On the 101st day he was seen in a corridor in Quadrant Four. Emil Pierce, policeman on duty, managed to squeeze off several shots at his retreating figure. Red Mask was seen again on the 120th day, on the 135th day, and the 157th day. He was seen, shot at, but not hit. He was also unable to commit any crime. We've got him on the run, the colonists said. He's afraid to do anything, now that we've got police protection, they said smugly. The Quadrant Council congratulated itself. The passengers were proud of themselves. A special congratulatory message from Captain Branson appeared one day in the Bulletin newsletter. The colonists settled down to living out the rest of the voyage until the landing on Antheon. But on the 170th day calamity struck. Red Mask appropriated one of the stunners, made his way down one whole corridor section in Quadrant Two, put occupants to sleep as he went, taking many articles of value and leaving disorder behind. Ellason interviewed as many victims as he could, noted it all in his book. The things taken were keepsakes, photographs and items of personal value. It seemed to be the work of a madman. If Red Mask wanted to make everyone furious, he certainly succeeded. "What does he want that stuff for?" Casey Stromberg, a passenger doctor, asked. "I can see him taking my narcotics, my doctor's kit—but my dead wife's picture? That I don't understand." It was the same with others. "The man's insane, Mr. Ellason. Positively insane." Many people said it. The council issued orders that all passengers from now on would be required to lock their compartments at all times. More guns were obtained from the captain. More policemen were appointed. Ellason was busy noting it all in his book. It became filled with jottings about innocent people being accidentally stunned when trigger-happy policemen thought their movements suspicious, about one man's suspicion of another and the ensuing search of compartments, people who saw Red Mask here, saw him there. Hardly a day went by without some new development. "Oh, yes, Mr. Ellason, we're going to get him," said Tilbury, now chief of police, cracking his knuckles, his eyes glowing at the thought. "We're bound to get him. We've got things worked out to the finest detail. He won't be able to get through our fingers now. Just let him make so much as a move." "And what will you do when you get him?" "Kill him," Tilbury said, licking his lips, his eyes glowing more fiercely than ever. "Without a trial?" "Oh, there'll be a trial, Mr. Ellason, but you don't think any jury'd let him live after all the things he's done, do you?" Red Mask was stunned in Quadrant Four in a corridor by a policeman named Terryl Placer on the 201st day. The criminal was carried to the assembly room surrounded by guards, for he surely would have been mauled, if not killed, by angry colonists who crowded around. In the assembly hall his mask was whipped off. The crowd gasped. Nobody knew him. Ellason's first thought was that he must be a stowaway, but then he remembered the face, and Captain Branson, who came to have a look at him, unhappily admitted the man was a member of the crew. His name was Harrel Critten and he was a record keeper third class. "Well, Critten," Branson roared at him, "what have you got to say for yourself?" "Go to hell," Critten said quietly. As if it were an afterthought, he spat at the captain. Branson looked as if he were going to kill the man himself right there and then. It was a long trial—from the 220th to the 241st day—and there didn't seem to be much doubt about the outcome, for Critten didn't help his own cause during any of it. Lemuel Tarper, who was appointed prosecutor, asked him, "What did you do with the loot, Critten?" Critten looked him square in the eye and said, "I threw it out one of the escape chutes. Does that answer your question?" "Threw it away?" Tarper and the crowd were incredulous. "Sure," Critten said. "You colonists got the easy life as passengers, just sitting around. I had to work my head off keeping records for you lazy bastards." The verdict was, of course, death. They executed Harrel Critten on the morning of the 270th day with blasts from six stunners supplied with full power. It was witnessed by a great crowd in the assembly hall. A detail from the ship's crew disposed of his body through a chute. It was all duly recorded in Keith Ellason's notebooks. Dying is easy for a Nilly. Especially if it's arranged for beforehand, which it always is. The Weblor II was only one day out of orbit when Captain Branson sent for Ellason and introduced him to the executed man. "Hello," Critten said, grinning from ear to ear. "I figured as much," Ellason said. "I've been doing a lot of thinking." "You're perhaps a little too good as an observer," Branson said. "Or maybe it was because you really weren't one of the colonists. But no matter, Critten did a good job. He was trained by an old friend of mine for this job, Gelthorpe Nill. Nill used to be in counter-espionage when there were wars." "You were excellent," Ellason said. "Can't say I enjoyed the role," said Critten, "but I think it saved lives." "Let me get this straight. Interstellar thought that it was idleness and boredom that caused the killings on the Weblor I , so they had you trained to be a scapegoat. Is that right?" Critten nodded. "When great numbers are being transported, they are apt to magnify each little event because so little happens. It was my job to see that they directed none of their venom against each other or the crew, only toward me." Branson smiled. "It made the time pass quickly and interestingly for the passengers." "To say nothing of me," Critten said. "And you, Mr. Ellason, were along to observe it all," Captain Branson put in. "Interstellar wanted an accurate picture of this. If it worked, they told me they'd use it on other trips to Antheon." Ellason nodded. "No time for brooding, for differences of opinion on small matters. Just time to hate Mr. Critten. Unanimously." "Probably," Critten said, "you are wondering about the execution." "Naturally." "We removed the charges before the guns were used." "And Carver Janssen's case?" "He'll get it back when he's shuttled to Antheon. And all the other items will be returned. They're all tagged with their owner's names. Captain Branson will say they were found somewhere on the ship. You see, I was a liar." "How about that assault on June Failright?" Critten grinned again. "She played right into our hands. She ran out into the hall claiming I'd attacked her, which I did not. She was certainly amazed when the ship's physicians agreed with her. Of course Captain Branson told them to do that." "And the murder?" "Raymond Palugger died in the hospital all right, but he died from his illness on the operating table. We turned it into an advantage by making it look suspicious." Ellason brightened. "And by that time everybody was seeing Red Mask everywhere and the colonists organized against him." "Gave them something to do," Branson said. "Every time things got dull, I livened them up. I got a stunner and robbed along the corridor. That really stirred them. Lucky nobody got hurt during any of it, including that Stoneman woman. I was trying to rob her when she woke up." Branson cleared his throat. "Ah, Ellason about that story. You understand you can't write it, don't you?" Ellason said regretfully that he did understand. "The colonists will never know the truth," Branson went on. "There will be other ships outward bound." Critten sighed. "And I'll have to be caught again." Yes, we're anonymous, nameless, we Nillys, for that's what we call each other, and are a theme, with variations, in the endless stretches of deep space, objects of hatred and contempt, professional heels, dying once a trip when the time is ripe, antidote to boredom, and we'll ply our trade, our little tragedies, on a thousand ships bringing humanity to new worlds.
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A. He is entirely harmless and it just looks like he's trouble
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What type and size of word embeddings were used?
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### Introduction
Microblogging environments, which allow users to post short messages, have gained increased popularity in the last decade. Twitter, which is one of the most popular microblogging platforms, has become an interesting platform for exchanging ideas, following recent developments and trends, or discussing any possible topic. Since Twitter has an enormously wide range of users with varying interests and sharing preferences, a significant amount of content is being created rapidly. Therefore, mining such platforms can extract valuable information. As a consequence, extracting information from Twitter has become a hot topic of research. For Twitter text mining, one popular research area is opinion mining or sentiment analysis, which is surely useful for companies or political parties to gather information about their services and products BIBREF0 . Another popular research area is content analysis, or more specifically topic modeling, which is useful for text classification and filtering applications on Twitter BIBREF1 . Moreover, event monitoring and trend analysis are also other examples of useful application areas on microblog texts BIBREF2 . In order to build successful social media analysis applications, it is necessary to employ successful processing tools for Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). NER is a critical stage for various NLP applications including machine translation, question answering and opinion mining. The aim of NER is to classify and locate atomic elements in a given text into predefined categories like the names of the persons, locations, and organizations (PLOs). NER on well-written texts is accepted as a solved problem for well-studied languages like English. However, it still needs further work for morphologically rich languages like Turkish due to their complex structure and relatively scarce language processing tools and data sets BIBREF3 . In addition, most of the NER systems are designed for formal texts. The performance of such systems drops significantly when applied on informal texts. To illustrate, the state-of-the-art Turkish NER system has CoNLL F-score of 91.94% on news data, but the performance drops to F-score of 19.28% when this system is adopted to Twitter data BIBREF4 . There are several challenges for NER on tweets, which are also summarized in Kucuk-2014-1, due to the very short text length and informal structure of the language used. Missing proper grammar rules and punctuation, lack of capitalization and apostrophes, usage of hashtags, abbreviations, and slang words are some of those challenges. In Twitter, using contracted forms and metonymic expressions instead of full organization or location names is very common as well. The usage of non-diacritic characters and the limited annotated data bring additional challenges for processing Turkish tweets. Due to the dynamic language used in Twitter, heavy feature engineering is not feasible for Twitter NER. Demir-2014 developed a semi-supervised approach for Turkish NER on formal (newswire) text using word embeddings obtained from unlabeled data. They obtained promising results without using any gazetteers and language dependent features. We adopted this approach for informal texts and evaluated it on Turkish tweets, where we achieved the state-of-the-art F-score performance. Our results show that using word embeddings for Twitter NER in Turkish can result in better F-score performance compared to using text normalization as a pre-processing step. In addition, utilizing in-domain word embeddings can be a promising approach for Twitter NER. ### Related Work
There are various important studies of NER on Twitter for English. Ritter-2011 presented a two-phase NER system for tweets, T-NER, using Conditional Random Fields (CRF) and including tweet-specific features. Liu-2011 proposed a hybrid NER approach based on K-Nearest Neighbors and linear CRF. Liu-2012 presented a factor graph-based method for NER on Twitter. Li-2012 described an unsupervised approach for tweets, called TwiNER. Bontcheva-2013 described an NLP pipeline for tweets, called TwitIE. Very recently, Cherry-2015 have shown the effectiveness of Brown clusters and word vectors on Twitter NER for English. For Turkish NER on formal texts, Tur-2003 presented the first study with a Hidden Markov Model based approach. Tatar-2011 presented an automatic rule learning system. Yeniterzi-2011 used CRF for Turkish NER, and Kucuk-2012 proposed a hybrid approach. A CRF-based model by Seker-2012 is the state-of-the-art Turkish NER system with CoNLL F-score of 91.94%, using gazetteers. Demir-2014 achieved a similar F-score of 91.85%, without gazetteers and language dependent features, using a semi-supervised model with word embeddings. For Turkish NER on Twitter, Celikkaya-2013 presented the first study by adopting the CRF-based NER of Seker-2012 with a text normalizer. Kucuk-2014-1 adopted a multilingual rule-based NER by extending the resources for Turkish. Kucuk-2014-2 adopted a rule-based approach for Turkish tweets, where diacritics-based expansion to lexical resources and relaxing the capitalization yielded an F-score of 48% with strict CoNLL-like metric. ### NER for Turkish Tweets using Semi-supervised Learning
To build a NER model with a semi-supervised learning approach on Turkish tweets, we used a neural network based architecture consisting of unsupervised and supervised stages. ### Unsupervised Stage
In the unsupervised stage, our aim is to learn distributed word representations, or word embeddings, in continuous vector space where semantically similar words are expected to be close to each other. Word vectors trained on large unlabeled Turkish corpus can provide additional knowledge base for NER systems trained with limited amount of labeled data in the supervised stage. A word representation is usually a vector associated with each word, where each dimension represents a feature. The value of each dimension is defined to be representing the amount of activity for that specific feature. A distributed representation represents each word as a dense vector of continuous values. By having lower dimensional dense vectors, and by having real values at each dimension, distributed word representations are helpful to solve the sparsity problem. Distributed word representations are trained with a huge unlabeled corpus using unsupervised learning. If this unlabeled corpus is large enough, then we expect that the distributed word representations will capture the syntactic and semantic properties of each word and this will provide a mechanism to obtain similar representations for semantically and syntactically close words. Vector space distributed representations of words are helpful for learning algorithms to reach better results in many NLP tasks, since they provide a method for grouping similar words together. The idea of using distributed word representations in vector space is applied to statistical language modeling for the first time by using a neural network based approach with a significant success by Bengio-2003. The approach is based on learning a distributed representation of each word, where each dimension of such a word embedding represents a hidden feature of this word and is used to capture the word's semantic and grammatical properties. Later on, Collobert-2011 proposed to use distributed word representations together with the supervised neural networks and achieved state-of-the art results in different NLP tasks, including NER for English. We used the public tool, word2vec, released by Mikolov-2013 to obtain the word embeddings. Their neural network approach is similar to the feed-forward neural networks BIBREF5 , BIBREF6 . To be more precise, the previous words to the current word are encoded in the input layer and then projected to the projection layer with a shared projection matrix. After that, the projection is given to the non-linear hidden layer and then the output is given to softmax in order to receive a probability distribution over all the words in the vocabulary. However, as suggested by Mikolov-2013, removing the non-linear hidden layer and making the projection layer shared by all words is much faster, which allowed us to use a larger unlabeled corpus and obtain better word embeddings. Among the methods presented in Mikolov-2013, we used the continuous Skip-gram model to obtain semantic representations of Turkish words. The Skip-gram model uses the current word as an input to the projection layer with a log-linear classifier and attempts to predict the representation of neighboring words within a certain range. In the Skip-gram model architecture we used, we have chosen 200 as the dimension of the obtained word vectors. The range of surrounding words is chosen to be 5, so that we will predict the distributed representations of the previous 2 words and the next 2 words using the current word. Our vector size and range decisions are aligned with the choices made in the previous study for Turkish NER by Demir-2014. The Skip-gram model architecture we used is shown in Figure FIGREF3 . ### Supervised Stage
In this stage, a comparably smaller amount of labeled data is used for training the final NER models. We used the publicly available neural network implementation by Turian-2010, which actually follows the study by Ratinov-2009, where a regularized averaged multiclass perceptron is used. Note that although non-local features are proven to be useful for the NER task on formal text types such as news articles, their usage and benefit is questionable for informal and short text types. Due to the fact that each tweet is treated as a single document with only 140 characters, it is difficult to make use of non-local features such as context aggregation and prediction history for the NER task on tweets. On the other hand, local features are mostly related to the previous and next tokens of the current token. With this motivation, we explored both local and non-local features but observed that we achieve better results without non-local features. As a result, to construct our NER model on tweets, we used the following local features: Context: All tokens in the current window of size two. Capitalization: Boolean feature indicating whether the first character of a token is upper-case or not. This feature is generated for all the tokens in the current window. Previous tags: Named entity tag predictions of the previous two tokens. Word type information: Type information of tokens in the current window, i.e. all-capitalized, is-capitalized, all-digits, contains-apostrophe, and is-alphanumeric. Token prefixes: First characters with length three and four, if exists, of current token. Token suffixes: Last characters with length one to four, if exists, of current token. Word embeddings: Vector representations of words in the current window. In addition to tailoring the features used by Ratinov-2009 for tweets, there are other Twitter-specific aspects of our NER system such as using word embeddings trained on an unlabeled tweet corpus, applying normalization on labeled tweets, and extracting Twitter-specific keywords like hashtags, mentions, smileys, and URLs from both labeled and unlabeled Turkish tweets. For text normalization as a pre-processing step of our system, we used the Turkish normalization interface developed for social media text with ill formed word detection and candidate word generation BIBREF8 . Along with the features used, the representation scheme for named entities is also important in terms of performance for a NER system. Two popular such encoding schemes are BIO and BILOU. The BIO scheme identifies the Beginning, the Inside and the Outside of the named entities, whereas the BILOU scheme identifies the Beginning, the Inside and the Last tokens of multi-token named entities, plus the Outside if it is not a named entity and the Unit length if the entity has single token. Since it is shown by Ratinov-2009 that BILOU representation scheme significantly outperforms the BIO encoding scheme, we make use of BILOU encoding for tagging named entities in our study. Furthermore, we applied normalization to numerical expressions as described in Turian-2010, which helps to achieve a degree of abstraction to numerical expressions. ### Unlabeled Data
In the unsupervised stage, we used two types of unlabeled data to obtain Turkish word embeddings. The first one is a Turkish news-web corpus containing 423M words and 491M tokens, namely the BOUN Web Corpus BIBREF9 , BIBREF10 . The second one is composed of 21M Turkish tweets with 241M words and 293M tokens, where we combined 1M tweets from TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı. We applied tokenization on both Turkish news-web corpus and Turkish tweets corpus using the publicly available Zemberek tool developed for Turkish. We have also applied lower-casing on both corpora in order to limit the number of unique words. Since our combined tweets corpus is composed of Twitter-specific texts, we applied what we call Twitter processing where we replaced mentions, hashtags, smileys and URLs with certain keywords. ### Labeled Data
In the supervised stage, we used two types of labeled data to train and test our NER models. The first one is Turkish news data annotated with ENAMEX-type named entities, or PLOs BIBREF11 . It includes 14481 person, 9409 location, and 9034 organization names in the training partition of 450K words. This data set is popularly used for performance evaluation of NER systems for Turkish, including the ones presented by Seker-2012, by Yeniterzi-2011 and by Demir-2014. The second type of labeled data is annotated Turkish tweets, where we used two different sets. The first set, TwitterDS-1, has around 5K tweets with 54K tokens and 1336 annotated PLOs BIBREF4 . The second set, TwitterDS-2, which is publicly available, has 2320 tweets with around 21K tokens and 980 PLOs in total BIBREF12 . The counts for each of the ENAMEX-type named entities for these Turkish Twitter data sets are provided in Table TABREF21 . ### Experiments and Results
We designed a number of experimental settings to investigate their effects on Turkish Twitter NER. These settings are as follows: the text type of annotated data used for training, the text type of unlabeled data used to learn the word embeddings, using the capitalization feature or not, and applying text normalization. We evaluated all models on ENAMEX types with the CoNLL metric and reported phrase-level overall F-score performance results. To be more precise, the F-score values presented in Table TABREF23 , Table TABREF26 and Table TABREF27 are micro-averaged over the classes using the strict metric. ### NER Models Trained on News
Most of our NER models are trained on annotated Turkish news data by Tur-2003 and tested on tweets, due to the limited amount of annotated Turkish tweets. In addition to using TwitterDS-1 and TwitterDS-2 as test sets, we detected 291 completely non-Turkish tweets out of 5040 in TwitterDS-1 and filtered them out using the isTurkish tool BIBREF13 to obtain TwitterDS-1_FT. We also used the normalized versions of these data sets. As shown in Table TABREF23 , turning off the capitalization feature is better when text normalization is not applied (bold entries), but the best results are achieved when normalization is applied and the capitalization feature is used (underlined bold entries). To observe the effects of the type of the source text used to learn the word embeddings, we have three models as Web, Twt, and Web+Twt where we used the Turkish web corpus, tweet corpus, and their combination respectively to learn the word embeddings. Including in-domain data from a relatively smaller tweet corpus together with a larger web corpus yields in better Twitter NER performance. We examined the effects of word embeddings on the performance of our NER models, and compared them to the improvements achieved by applying normalization on Turkish tweets. The baseline NER model is built by using the features explained in section 3.2, except the capitalization and word embeddings features. Using word embeddings obtained with unsupervised learning from a large corpus of web articles and tweets results in better NER performance than applying a Twitter-specific text normalizer, as shown in Table TABREF26 . This is crucial since Turkish text normalization for unstructured data is a challenging task and requires successful morphological analysis, whereas extracting word embeddings for any language or domain is much easier, yet more effective. ### NER Models Trained on Tweets
Although an ideal Turkish NER model for Twitter should be trained on similar informal texts, all previous Turkish Twitter NER systems are trained on news data due to the limited amount of annotated Turkish tweets. We also experimented training NER models on relatively smaller labeled Twitter data with 10-fold cross-validation. Our best phrase-level F-score of 46.61% achieved on TwitterDS-1_FT is increased to 48.96% when trained on the much smaller tweets data, TwitterDS-2, instead of news data. ### Comparison with the State-of-the-art
The best F-scores of the previously published Turkish Twitter NER systems BIBREF4 , BIBREF12 , BIBREF14 as well as our proposed NER system are shown in Table TABREF27 . We used the same training set with the first system BIBREF4 in our study, but the second NER system BIBREF12 uses a different multilingual news data and the third system BIBREF14 , which is rule based, does not have a training phase at all. All of these previous NER systems use gazetteer lists for named entities, which are manually constructed and highly language-dependent, whereas our system does not. Note that there is no publicly available gazetteer lists in Turkish. Kucuk-2014-2 achieved the state-of-the-art performance results for Turkish Twitter NER with their best model settings (shown in italic). These settings are namely using gazetteers list, with capitalization feature turned off, and with no normalization, together by expanding their gazetteer lists of named entities with diacritics variations. Our proposed system outperforms the state-of-the-art results on both Turkish Twitter data sets, even without using gazetteers (shown in bold). We achieved our best performance results with Turkish word embeddings obtained from our Web+Tweets corpus, when we apply normalization on tweets and keep the capitalization as a feature. ### Conclusion
We adopted a neural networks based semi-supervised approach using word embeddings for the NER task on Turkish tweets. At the first stage, we attained distributed representations of words by employing a fast unsupervised learning method on a large unlabeled corpus. At the second stage, we exploited these word embeddings together with language independent features in order to train our neural network on labeled data. We compared our results on two different Turkish Twitter data sets with the state-of-the-art NER systems proposed for Twitter data in Turkish and showed that our system outperforms the state-of-the-art results on both data sets. Our results also show that using word embeddings from an unlabeled corpus can lead to better performance than applying Twitter-specific text normalization. We discussed the promising benefits of using in-domain data to learn word embeddings at the unsupervised stage as well. Since the only language dependent part of our Turkish Twitter NER system is text normalization, and since even without text normalization it outperforms the previous state-of-the-art results, we believe that our approach can be adapted to other morphologically rich languages. Our Turkish Twitter NER system, namely TTNER, is publicly available. We believe that there is still room for improvement for NLP tasks on Turkish social media data. As a future work, we aim to construct a much larger in-domain resource, i.e., unlabeled Turkish tweets corpus, and investigate the full benefits of attaining word embeddings from in-domain data on Twitter NER. ### Acknowledgements
This research is partially supported by Boğaziçi University Research Fund Grant Number 11170. We would also like to thank The Scientific and Technological Research Council of Turkey (TÜBİTAK), The Science Fellowships and Grant Programmes Department (BİDEB) for providing financial support with 2210 National Scholarship Programme for MSc Students. Figure 1: Skip-grammodel architecture to learn continuous vector representation of words in order to predict surrounding words (Mikolov et al., 2013). Table 2: Phrase-level overall F-score performance results of the NER models trained on news. Table 1: Number of PLOs in Turkish Twitter data sets. Table 3: Phrase-level overall F-score performance results to compare word embeddings and normalization. Table 4: Phrase-level overall F-score performance results compared to the state-of-the-art.
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word2vec, 200 as the dimension of the obtained word vectors
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Which item doesn't get mentioned while they're in the year 2150?
A. flying vehicles
B. diamond makers
C. an elevator that you can't feel move
D. a modified English language
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... and it comes out here By LESTER DEL REY Illustrated by DON SIBLEY [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There is one fact no sane man can quarrel with ... everything has a beginning and an end. But some men aren't sane; thus it isn't always so! No, you're wrong. I'm not your father's ghost, even if I do look a bit like him. But it's a longish story, and you might as well let me in. You will, you know, so why quibble about it? At least, you always have ... or do ... or will. I don't know, verbs get all mixed up. We don't have the right attitude toward tenses for a situation like this. Anyhow, you'll let me in. I did, so you will. Thanks. You think you're crazy, of course, but you'll find out you aren't. It's just that things are a bit confused. And don't look at the machine out there too long—until you get used to it, you'll find it's hard on the eyes, trying to follow where the vanes go. You'll get used to it, of course, but it will take about thirty years. You're wondering whether to give me a drink, as I remember it. Why not? And naturally, since we have the same tastes, you can make the same for me as you're having. Of course we have the same tastes—we're the same person. I'm you thirty years from now, or you're me. I remember just how you feel; I felt the same way when he—that is, of course, I or we—came back to tell me about it, thirty years ago. Here, have one of these. You'll get to like them in a couple more years. And you can look at the revenue stamp date, if you still doubt my story. You'll believe it eventually, though, so it doesn't matter. Right now, you're shocked. It's a real wrench when a man meets himself for the first time. Some kind of telepathy seems to work between two of the same people. You sense things. So I'll simply go ahead talking for half an hour or so, until you get over it. After that you'll come along with me. You know, I could try to change things around by telling what happened to me; but he—I—told me what I was going to do, so I might as well do the same. I probably couldn't help telling you the same thing in the same words, even if I tried—and I don't intend to try. I've gotten past that stage in worrying about all this. So let's begin when you get up in half an hour and come out with me. You'll take a closer look at the machine, then. Yes, it'll be pretty obvious it must be a time machine. You'll sense that, too. You've seen it, just a small little cage with two seats, a luggage compartment, and a few buttons on a dash. You'll be puzzling over what I'll tell you, and you'll be getting used to the idea that you are the man who makes atomic power practical. Jerome Boell, just a plain engineer, the man who put atomic power in every home. You won't exactly believe it, but you'll want to go along. I'll be tired of talking by then, and in a hurry to get going. So I cut off your questions, and get you inside. I snap on a green button, and everything seems to cut off around us. You can see a sort of foggy nothing surrounding the cockpit; it is probably the field that prevents passage through time from affecting us. The luggage section isn't protected, though. You start to say something, but by then I'm pressing a black button, and everything outside will disappear. You look for your house, but it isn't there. There is exactly nothing there—in fact, there is no there . You are completely outside of time and space, as best you can guess how things are. You can't feel any motion, of course. You try to reach a hand out through the field into the nothing around you and your hand goes out, all right, but nothing happens. Where the screen ends, your hand just turns over and pokes back at you. Doesn't hurt, and when you pull your arm back, you're still sound and uninjured. But it looks frightening and you don't try it again. Then it comes to you slowly that you're actually traveling in time. You turn to me, getting used to the idea. "So this is the fourth dimension?" you ask. Then you feel silly, because you'll remember that I said you'd ask that. Well, I asked it after I was told, then I came back and told it to you, and I still can't help answering when you speak. "Not exactly," I try to explain. "Maybe it's no dimension—or it might be the fifth; if you're going to skip over the so-called fourth without traveling along it, you'd need a fifth. Don't ask me. I didn't invent the machine and I don't understand it." "But...." I let it go, and so do you. If you don't, it's a good way of going crazy. You'll see later why I couldn't have invented the machine. Of course, there may have been a start for all this once. There may have been a time when you did invent the machine—the atomic motor first, then the time-machine. And when you closed the loop by going back and saving yourself the trouble, it got all tangled up. I figured out once that such a universe would need some seven or eight time and space dimensions. It's simpler just to figure that this is the way time got bent back on itself. Maybe there is no machine, and it's just easier for us to imagine it. When you spend thirty years thinking about it, as I did—and you will—you get further and further from an answer. Anyhow, you sit there, watching nothing all around you, and no time, apparently, though there is a time effect back in the luggage space. You look at your watch and it's still running. That means you either carry a small time field with you, or you are catching a small increment of time from the main field. I don't know, and you won't think about that then, either. I'm smoking, and so are you, and the air in the machine is getting a bit stale. You suddenly realize that everything in the machine is wide open, yet you haven't seen any effects of air loss. "Where are we getting our air?" you ask. "Or why don't we lose it?" "No place for it to go," I explain. There isn't. Out there is neither time nor space, apparently. How could the air leak out? You still feel gravity, but I can't explain that, either. Maybe the machine has a gravity field built in, or maybe the time that makes your watch run is responsible for gravity. In spite of Einstein, you have always had the idea that time is an effect of gravity, and I sort of agree, still. Then the machine stops—at least, the field around us cuts off. You feel a dankish sort of air replace the stale air, and you breathe easier, though we're in complete darkness, except for the weak light in the machine, which always burns, and a few feet of rough dirty cement floor around. You take another cigaret from me and you get out of the machine, just as I do. I've got a bundle of clothes and I start changing. It's a sort of simple, short-limbed, one-piece affair I put on, but it feels comfortable. "I'm staying here," I tell you. "This is like the things they wear in this century, as near as I can remember it, and I should be able to pass fairly well. I've had all my fortune—the one you make on that atomic generator—invested in such a way I can get it on using some identification I've got with me, so I'll do all right. I know they still use some kind of money, you'll see evidence of that. And it's a pretty easygoing civilization, from what I could see. We'll go up and I'll leave you. I like the looks of things here, so I won't be coming back with you." You nod, remembering I've told you about it. "What century is this, anyway?" I'd told you that, too, but you've forgotten. "As near as I can guess, it's about 2150. He told me, just as I'm telling you, that it's an interstellar civilization." You take another cigaret from me, and follow me. I've got a small flashlight and we grope through a pile of rubbish, out into a corridor. This is a sub-sub-sub-basement. We have to walk up a flight of stairs, and there is an elevator waiting, fortunately with the door open. "What about the time machine?" you ask. "Since nobody ever stole it, it's safe." We get in the elevator, and I say "first" to it. It gives out a coughing noise and the basement openings begin to click by us. There's no feeling of acceleration—some kind of false gravity they use in the future. Then the door opens, and the elevator says "first" back at us. It's obviously a service elevator and we're in a dim corridor, with nobody around. I grab your hand and shake it. "You go that way. Don't worry about getting lost; you never did, so you can't. Find the museum, grab the motor, and get out. And good luck to you." You act as if you're dreaming, though you can't believe it's a dream. You nod at me and I move out into the main corridor. A second later, you see me going by, mixed into a crowd that is loafing along toward a restaurant, or something like it, that is just opening. I'm asking questions of a man, who points, and I turn and move off. You come out of the side corridor and go down a hall, away from the restaurant. There are quiet little signs along the hall. You look at them, realizing for the first time that things have changed. Steij:neri, Faunten, Z:rgat Dispenseri. The signs are very quiet and dignified. Some of them can be decoded to stationery shops, fountains, and the like. What a zergot is, you don't know. You stop at a sign that announces: Trav:l Biwrou—F:rst-Clas Twrz—Marz, Viin*s, and x: Trouj:n Planets. Spej:l reits tu aol s*nz wixin 60 lyt iirz! But there is only a single picture of a dull-looking metal sphere, with passengers moving up a ramp, and the office is closed. You begin to get the hang of the spelling they use, though. Now there are people around you, but nobody pays much attention to you. Why should they? You wouldn't care if you saw a man in a leopard-skin suit; you'd figure it was some part in a play and let it go. Well, people don't change much. You get up your courage and go up to a boy selling something that might be papers on tapes. "Where can I find the Museum of Science?" "Downayer rien turn lefa the sign. Stoo bloss," he tells you. Around you, you hear some pretty normal English, but there are others using stuff as garbled as his. The educated and uneducated? I don't know. You go right until you find a big sign built into the rubbery surface of the walk: Miuzi:m *v Syens . There's an arrow pointing and you turn left. Ahead of you, two blocks on, you can see a pink building, with faint aqua trimming, bigger than most of the others. They are building lower than they used to, apparently. Twenty floors up seems about the maximum. You head for it, and find the sidewalk is marked with the information that it is the museum. You go up the steps, but you see that it seems to be closed. You hesitate for a moment, then. You're beginning to think the whole affair is complete nonsense, and you should get back to the time machine and go home. But then a guard comes to the gate. Except for the short legs in his suit and the friendly grin on his face, he looks like any other guard. What's more, he speaks pretty clearly. Everyone says things in a sort of drawl, with softer vowels and slurred consonants, but it's rather pleasant. "Help you, sir? Oh, of course. You must be playing in 'Atoms and Axioms.' The museum's closed, but I'll be glad to let you study whatever you need for realism in your role. Nice show. I saw it twice." "Thanks," you mutter, wondering what kind of civilization can produce guards as polite as that. "I—I'm told I should investigate your display of atomic generators." He beams at that. "Of course." The gate is swung to behind you, but obviously he isn't locking it. In fact, there doesn't seem to be a lock. "Must be a new part. You go down that corridor, up one flight of stairs and left. Finest display in all the known worlds. We've got the original of the first thirteen models. Professor Jonas was using them to check his latest theory of how they work. Too bad he could not explain the principle, either. Someone will, some day, though. Lord, the genius of that twentieth century inventor! It's quite a hobby with me, sir. I've read everything I could get on the period. Oh—congratulations on your pronunciation. Sounds just like some of our oldest tapes." You get away from him, finally, after some polite thanks. The building seems deserted and you wander up the stairs. There's a room on your right filled with something that proclaims itself the first truly plastic diamond former, and you go up to it. As you come near, it goes through a crazy wiggle inside, stops turning out a continual row of what seem to be bearings, and slips something the size of a penny toward you. "Souvenir," it announces in a well-modulated voice. "This is a typical gem of the twentieth century, properly cut to 58 facets, known technically as a Jaegger diamond, and approximately twenty carats in size. You can have it made into a ring on the third floor during morning hours for one-tenth credit. If you have more than one child, press the red button for the number of stones you desire." You put it in your pocket, gulping a little, and get back to the corridor. You turn left and go past a big room in which models of spaceships—from the original thing that looks like a V-2, and is labeled first Lunar rocket, to a ten-foot globe, complete with miniature manikins—are sailing about in some kind of orbits. Then there is one labeled Wep:nz , filled with everything from a crossbow to a tiny rod four inches long and half the thickness of a pencil, marked Fynal Hand Arm . Beyond is the end of the corridor, and a big place that bears a sign, Mad:lz *v Atamic Pau:r Sorsez . By that time, you're almost convinced. And you've been doing a lot of thinking about what you can do. The story I'm telling has been sinking in, but you aren't completely willing to accept it. You notice that the models are all mounted on tables and that they're a lot smaller than you thought. They seem to be in chronological order, and the latest one, marked 2147—Rincs Dyn*pat: , is about the size of a desk telephone. The earlier ones are larger, of course, clumsier, but with variations, probably depending on the power output. A big sign on the ceiling gives a lot of dope on atomic generators, explaining that this is the first invention which leaped full blown into basically final form. You study it, but it mentions casually the inventor, without giving his name. Either they don't know it, or they take it for granted that everyone does, which seems more probable. They call attention to the fact that they have the original model of the first atomic generator built, complete with design drawings, original manuscript on operation, and full patent application. They state that it has all major refinements, operating on any fuel, producing electricity at any desired voltage up to five million, any chosen cyclic rate from direct current to one thousand megacycles, and any amperage up to one thousand, its maximum power output being fifty kilowatts, limited by the current-carrying capacity of the outputs. They also mention that the operating principle is still being investigated, and that only such refinements as better alloys and the addition of magnetric and nucleatric current outlets have been added since the original. So you go to the end and look over the thing. It's simply a square box with a huge plug on each side, and a set of vernier controls on top, plus a little hole marked, in old-style spelling, Drop BBs or wire here . Apparently that's the way it's fueled. It's about one foot on each side. "Nice," the guard says over your shoulder. "It finally wore out one of the cathogrids and we had to replace that, but otherwise it's exactly as the great inventor made it. And it still operates as well as ever. Like to have me tell you about it?" "Not particularly," you begin, and then realize bad manners might be conspicuous here. While you're searching for an answer, the guard pulls something out of his pocket and stares at it. "Fine, fine. The mayor of Altasecarba—Centaurian, you know—is arriving, but I'll be back in about ten minutes. He wants to examine some of the weapons for a monograph on Centaurian primitives compared to nineteenth century man. You'll pardon me?" You pardon him pretty eagerly and he wanders off happily. You go up to the head of the line, to that Rinks Dynapattuh, or whatever it transliterates to. That's small and you can carry it. But the darned thing is absolutely fixed. You can't see any bolts, but you can't budge it, either. You work down the line. It'd be foolish to take the early model if you can get one with built-in magnetic current terminals—Ehrenhaft or some other principle?—and nuclear binding-force energy terminals. But they're all held down by the same whatchamaycallem effect. And, finally, you're right back beside the original first model. It's probably bolted down, too, but you try it tentatively and you find it moves. There's a little sign under it, indicating you shouldn't touch it, since the gravostatic plate is being renewed. Well, you won't be able to change the time cycle by doing anything I haven't told you, but a working model such as that is a handy thing. You lift it; it only weighs about fifty pounds! Naturally, it can be carried. You expect a warning bell, but nothing happens. As a matter of fact, if you'd stop drinking so much of that scotch and staring at the time machine out there now, you'd hear what I'm saying and know what will happen to you. But of course, just as I did, you're going to miss a lot of what I say from now on, and have to find out for yourself. But maybe some of it helps. I've tried to remember how much I remembered, after he told me, but I can't be sure. So I'll keep on talking. I probably can't help it, anyhow. Pre-set, you might say. Well, you stagger down the corridor, looking out for the guard, but all seems clear. Then you hear his voice from the weapons room. You bend down and try to scurry past, but you know you're in full view. Nothing happens, though. You stumble down the stairs, feeling all the futuristic rays in the world on your back, and still nothing happens. Ahead of you, the gate is closed. You reach it and it opens obligingly by itself. You breathe a quick sigh of relief and start out onto the street. Then there's a yell behind you. You don't wait. You put one leg in front of the other and you begin racing down the walk, ducking past people, who stare at you with expressions you haven't time to see. There's another yell behind you. Something goes over your head and drops on the sidewalk just in front of your feet, with a sudden ringing sound. You don't wait to find out about that, either. Somebody reaches out a hand to catch you and you dart past. The street is pretty clear now and you jolt along, with your arms seeming to come out of the sockets, and that atomic generator getting heavier at every step. Out of nowhere, something in a blue uniform about six feet tall and on the beefy side appears—and the badge hasn't changed much. The cop catches your arm and you know you're not going to get away, so you stop. "You can't exert yourself that hard in this heat, fellow," the cop says. "There are laws against that, without a yellow sticker. Here, let me grab you a taxi." Reaction sets in a bit and your knees begin to buckle, but you shake your head and come up for air. "I—I left my money home," you begin. The cop nods. "Oh, that explains it. Fine, I won't have to give you an appearance schedule. But you should have come to me." He reaches out and taps a pedestrian lightly on the shoulder. "Sir, an emergency request. Would you help this gentleman?" The pedestrian grins, looks at his watch, and nods. "How far?" You did notice the name of the building from which you came and you mutter it. The stranger nods again, reaches out and picks up the other side of the generator, blowing a little whistle the cop hands him. Pedestrians begin to move aside, and you and the stranger jog down the street at a trot, with a nice clear path, while the cop stands beaming at you both. That way, it isn't so bad. And you begin to see why I decided I might like to stay in the future. But all the same, the organized cooperation here doesn't look too good. The guard can get the same and be there before you. And he is. He stands just inside the door of the building as you reach it. The stranger lifts an eyebrow and goes off at once when you nod at him, not waiting for thanks. And the guard comes up, holding some dinkus in his hand, about the size of a big folding camera and not too dissimilar in other ways. He snaps it open and you get set to duck. "You forgot the prints, monograph, and patent applications," he says. "They go with the generator—we don't like to have them separated. A good thing I knew the production office of 'Atoms and Axioms' was in this building. Just let us know when you're finished with the model and we'll pick it up." You swallow several sets of tonsils you had removed years before, and take the bundle of papers he hands you out of the little case. He pumps you for some more information, which you give him at random. It seems to satisfy your amiable guard friend. He finally smiles in satisfaction and heads back to the museum. You still don't believe it, but you pick up the atomic generator and the information sheets, and you head down toward the service elevator. There is no button on it. In fact, there's no door there. You start looking for other doors or corridors, but you know this is right. The signs along the halls are the same as they were. Then there's a sort of cough and something dilates in the wall. It forms a perfect door and the elevator stands there waiting. You get in, gulping out something about going all the way down, and then wonder how a machine geared for voice operation can make anything of that. What the deuce would that lowest basement be called? But the elevator has closed and is moving downward in a hurry. It coughs again and you're at the original level. You get out—and realize you don't have a light. You'll never know what you stumbled over, but, somehow, you move back in the direction of the time machine, bumping against boxes, staggering here and there, and trying to find the right place by sheer feel. Then a shred of dim light appears; it's the weak light in the time machine. You've located it. You put the atomic generator in the luggage space, throw the papers down beside it, and climb into the cockpit, sweating and mumbling. You reach forward toward the green button and hesitate. There's a red one beside it and you finally decide on that. Suddenly, there's a confused yell from the direction of the elevator and a beam of light strikes against your eyes, with a shout punctuating it. Your finger touches the red button. You'll never know what the shouting was about—whether they finally doped out the fact that they'd been robbed, or whether they were trying to help you. You don't care which it is. The field springs up around you and the next button you touch—the one on the board that hasn't been used so far—sends you off into nothingness. There is no beam of light, you can't hear a thing, and you're safe. It isn't much of a trip back. You sit there smoking and letting your nerves settle back to normal. You notice a third set of buttons, with some pencil marks over them—"Press these to return to yourself 30 years"—and you begin waiting for the air to get stale. It doesn't because there is only one of you this time. Instead, everything flashes off and you're sitting in the machine in your own back yard. You'll figure out the cycle in more details later. You get into the machine in front of your house, go to the future in the sub-basement, land in your back yard, and then hop back thirty years to pick up yourself, landing in front of your house. Just that. But right then, you don't care. You jump out and start pulling out that atomic generator and taking it inside. It isn't hard to disassemble, but you don't learn a thing; just some plates of metal, some spiral coils, and a few odds and ends—all things that can be made easily enough, all obviously of common metals. But when you put it together again, about an hour later, you notice something. Everything in it is brand-new and there's one set of copper wires missing! It won't work. You put some #12 house wire in, exactly like the set on the other side, drop in some iron filings, and try it again. And with the controls set at 120 volts, 60 cycles and 15 amperes, you get just that. You don't need the power company any more. And you feel a little happier when you realize that the luggage space wasn't insulated from time effects by a field, so the motor has moved backward in time, somehow, and is back to its original youth—minus the replaced wires the guard mentioned—which probably wore out because of the makeshift job you've just done. But you begin getting more of a jolt when you find that the papers are all in your own writing, that your name is down as the inventor, and that the date of the patent application is 1951. It will begin to soak in, then. You pick up an atomic generator in the future and bring it back to the past—your present—so that it can be put in the museum with you as the inventor so you can steal it to be the inventor. And you do it in a time machine which you bring back to yourself to take yourself into the future to return to take back to yourself.... Who invented what? And who built which? Before long, your riches from the generator are piling in. Little kids from school are coming around to stare at the man who changed history and made atomic power so common that no nation could hope to be anything but a democracy and a peaceful one—after some of the worst times in history for a few years. Your name eventually becomes as common as Ampere, or Faraday, or any other spelled without a capital letter. But you're thinking of the puzzle. You can't find any answer. One day you come across an old poem—something about some folks calling it evolution and others calling it God. You go out, make a few provisions for the future, and come back to climb into the time machine that's waiting in the building you had put around it. Then you'll be knocking on your own door, thirty years back—or right now, from your view—and telling your younger self all these things I'm telling you. But now.... Well, the drinks are finished. You're woozy enough to go along with me without protest, and I want to find out just why those people up there came looking for you and shouting, before the time machine left. Let's go.
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A. flying vehicles
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What did the beings use to ensure they killed every human?
A. Charles's brain-waves
B. The Bureau's Index
C. A machine they brought from their home planet
D. Spies throughout the world
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"Phone Me in Central Park" By JAMES McCONNELL There should be an epitaph for every man, big or little, but a really grand and special one for Loner Charlie. [Transcriber's Note: This etext was produced from Planet Stories Fall 1954. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Charles turned over on his side to look at her. She lay quietly in the other bed, the most beautiful woman he had ever seen. She was blonde to perfection, exquisitely shaped, and the rich promise of her body was exposed to his view. "Why?" he thought as he looked at her. "Why did it have to happen like this?" The whole thing was still like a dream to him, and as yet he couldn't decide whether it was a good or a bad dream. A year ago she had been unattainable, a face to conjure with in erotic dreams, far beyond his ken. A year ago she had been a public idol, the most popular actress of the day. And he had been a nobody, full of a nobody's idle hopes and schemes. And now he was lying in the bed next to hers in her swank Manhattan apartment in the most exclusive hotel in town. The unrealness of the situation overwhelmed him. His mind was a picture of confused thoughts. Meanings and answers to his questions slithered out of his reach. "God," he said. It was not an exclamation, nor yet an expletive. It was a mere statement of fact. A thought teased at him. Charles looked at the woman again and decided that she still looked beautiful in spite of the harshness of the room's lighting. He touched buttons by the edge of the bed and the illumination quieted to a soft glow, wrapping her in a radiant halo. Charles smiled wanly and got up. He stood by the bed looking at her. "I could have fallen in love with you once. A year ago, perhaps, or longer. But not now. Not now." He turned away and walked to the window. "Now the world is dead. The whole world is dead." New York lay quietly below him. It was the hour of indecision when day has not quite made up its mind to leave and night has not yet attacked in force. The streetlights were already on, making geometric patterns through the dusk of Central Park. Some of the billboards were shining, their relays activated by darkness-sensitized solenoids. A reddish-orange pallor hung from the sky. It had been very pleasant that afternoon. She had given of herself freely, warmly, and Charles had accepted. But then he had known that she would. It was not him, it was the circumstances. Under the circumstances, she would have given herself to any man— "Why did it have to be her—or me? Why should it have to happen to anybody! Why!" She would have given herself to any man— His thoughts beat a rapid crescendo, activating emotions, stimulating sensations of angry rage. He wanted to cry, to weep angry tears of protest. To any man, WHO HAPPENED TO BE THE LAST MAN ON EARTH! Charles picked up a heavy book end off the table and crashed it through the thick pane of window glass. A gust of wind from the outside breezed through the shattered opening, attacking his olfactory patch with the retching smell of decaying flesh. Charles ignored it. Even smells had lost their customary meanings. He felt the rage build up inside again, tearing at his viscera. His stomach clenched up like an angry fist. "But I don't want to be the last man alive!" he shouted. "I don't know what to do! I don't know where to go, how to act! I just don't know—" A paroxysm of sobbing shook his body. Trembling, he dropped to his knees, his head against the cold firmness of the sill, his hands clutched tightly around the jagged edges of the window pane. In spite of the sharp pain that raced through his system, in spite of the bright, warm, red stream that trickled down his face, he knelt by the window for several minutes. " Maybe I'm not the last! " The thought struck him with suddenness, promisingly, edged with swelling comfort to fill his emptiness. Charles got up slowly, noticing for the first time that his fingers were badly cut. He wrapped a handkerchief around them and forgot them. He had to know—he had to find out. As he turned to leave, he noticed again the woman lying in radiant state upon the bed. He walked to her side and leaned over, kissing her gently on the forehead. As he straightened up, his leg caught against her arm, pushing it slightly. The woman's arm slipped from its position and dangled from the edge of the bed like a crazy pendulum. Charles picked it up and folded it across her now cold breasts. He started to pull the sheet over her nude form, then stopped, smiling at his conventionality. After all, it didn't make any difference now. The phonograph was near the door. On sudden impulse he switched it on, turned the volume up full, and in grim jest left it playing Rachmaninoff's Isle of the Dead on full automatic. The music haunted him down the hall to the elevator that he had to run himself. The lobby was littered with debris, human and otherwise. Charles ignored it. The street that led towards the Bureau of Vital Statistics was a mess of desolate carnage. Charles overlooked it. Shop fronts smashed, stores looted, gyro-cars wrecked, proud buildings defaced. "That was it," he said to himself. "Pride. We called this the 'Proud Era.' Everything was better and bigger and nicer to have. Buildings were taller, men were healthier, most of the problems of humanity seemed licked, or nearly so. It was a time of free power, each small unit of population, each section of town operating on perpetual, ever-lasting, automatic atomic piles. "We were free. We seemed, almost, to have accomplished something. The world was running well. No wonder we called it the 'Proud Era.' Life was fun, just a bowl of cherries, until...." Two years ago the animals had started dying. Strangely enough the rats had gone first, to anybody's notice. Sales of poison dropped, scientific laboratories chained to a perpetual rodent-cycle began to complain bitterly. Then the lovers who hunted out and haunted the lonely lanes through the countryside began to remark that the locusts were late that year. The Southern states joyously reported that mosquito control was working to an unprecedented degree. The largest cotton crop ever was forecast and rumors from Mexico had it that no one had died from scorpion bite in several weeks. A month later the meat animals, the birds and the household pets began dropping as rapidly as the flies which had dropped earlier. Congress was called into special session, as were all of the national governments around the world. The U.N. met at emergency sessions to cope with the situation. The president of the world-wide Society for the Prevention of Cruelty to Animals committed suicide. Within a year it was obvious to everyone that man was the only animal left on earth. The panic which had begun with the death of the animals was quieted somewhat by the fact that humans seemed immune to the pandemic. But the lakes full of dead fish caused a great stink and residents along the coasts began to move inland. Sales of perfumes and deodorants soared. Then just one year ago, the first human became infected with the strange malady. Within six months, half of the world's population was gone. Less than a month ago no more than a few thousand people remained in New York. And now.... "I've got to find out," Charles told himself. He meant it, of course, but in a sense he was afraid—afraid that his trip to the Bureau might give him an answer he didn't dare listen to. "But I've got to try." He walked on down the bloody street. Before the plague the Bureau of Vital Statistics had been one of man's crowning achievements. Housed as it was in a huge metallic globe of a building, it contained computers which kept exact account of every human on earth. Compulsory registration and the classification of each individual by means of the discrete patterns of his brain waves had accomplished for man what no ordinary census could have. The machine knew who was alive, who was dead, and where everybody was. Once a year the Bureau issued The Index, an exact accounting of Earth's four billion inhabitants. Four billion names and addresses, compressed into microprint, a tremendous achievement even for the "Proud Era." In all of his life, Charles had never once glanced at The Index. The average person had little necessity to do so since the Bureau information service would answer questions free of charge at any time. Reaching the gigantic building, Charles pushed aside the body of a young man and walked into the main foyer. Passing behind once-guarded doors, he entered the giant computer room and paused in admiration. Only once, before the plague, had he seen the interior of this room. But he still remembered it and he still recalled the powerful emotional experience it had been those many years ago. All children had to have a brain-wave recording made by the Bureau during the first month of their life. And again at the age of 10 each child returned to the Bureau for a recheck. It was for this latter recording that Charles had come to the Bureau some twenty-two years before and a friendly guard had let him peep briefly into the computer room. The impression of intense activity, of organized confusion, of mechanical wonder had remained with him the rest of his life. "So different now," he thought, surveying the room. "Now it's empty, so empty." The machine seemed to reflect the stillness, the very deadness of the world. The silence became unbearable. Charles walked to the master control panel. With newly acquired dexterity he switched the computer screens on and watched them glow to life. All around the world sensitive receiving stations pulsed to activity, sending out searching fingers, hunting for elusive patterns of neutral energy, mapping and tabulating the results. The main computer screen dominated one wall of the room. Other smaller screens clustered around it. On these screens could be graphed the population of any and every part of the globe. An illuminated counter immediately above it would give the numerical strength of the area being sampled while the screen would show population density by individual pinpoints of light that merged to form brightness patterns. "I'll try New York first," he said to himself, knowing that he was a coward, afraid to check the whole world from the start. "I'll start with New York and work up." Charles activated the switches that would flash a schematic map of New York on the screen. "There's bound to be somebody else left here. After all, there were at least twenty of us just a couple of days ago." And one of them, a beautiful woman, had invited him up to her apartment, not because she liked him, but because.... The main screen focused itself, the patterns shifting into a recognizable perceptual image. "Why, it was just yesterday (or was it the day before?) that ten of us, at least, met here to check the figures. There were lots of us alive then." Including the blond young woman who had died just this afternoon.... Charles stopped talking and forced his eyes upwards. Peripheral vision caught first the vague outlines of the lower part of the map. His eyes continued to move, slowly, reluctantly. They caught the over-all relief of Greater New York City—and then concentrated on the single, shining dot at the very heart of the map—and he understood. His eyes stabbed quickly for the counter above the screen. One. He gasped. The counter read one . Charles was by himself, the last person alive in all of New York City. He began to tremble violently. The silence of the room began to press quickly in on him. His frantic fingers searched for the computer controls. New York State. One. The entire United States. One. The western hemisphere, including islands. (Was that a point of light in Brazil? No. Just a ghost image). One. The Pacific area, Asia, Australia, Asia Minor, Russia and the Near East, Africa and then Europe. England! There was a light in England! Someone else still lived! The counter clicked forward. Two! His trembling stopped. He breathed again. "Of course. London was at least as populous as New York City before the plague. It's only logical that—" He stopped. For even as he spoke, the light winked out! The counter clicked again. One. Alone. Alone! Charles screamed. The bottom dropped out from under him! Why? Such a simple question, but in those three letters lay the essence of human nature. Why. The drive of curiosity. Stronger, in a way, than the so-called "basic" drives: hunger, thirst, sex, shelter, warmth, companionship, elimination. Certainly more decisive in the history of the race. Man began to think, to differentiate himself from the other animals, when he first asked the question: "Why?" But thinking about "why" didn't answer the question itself, Charles thought. He looked around him. He was sitting on a bench in Central Park, alone except for a few stray corpses. But the park was fairly free of bodies. "You've got about ten minutes warning," he said to himself. "I guess that most people wanted to die inside of something—inside of anything. Not out in the unprotected open." The silence was like a weight hanging around his neck. Not an insect noise, not the chirp of a bird, not the sound of a car nor the scream of a plane. Not even a breeze to whisper among the leaves, he thought. Civilization equals life equals noise. Silence equals.... Why. His mind kept returning to the question. Of all the people on earth, me. The last. Why me? Average, that's what he was. Height: 5'11". Weight: 165. Age: 32. Status: Married, once upon a time. The Norm, with no significant departures, all down the line. Church member, but not a good one. Could that be it? Could the most normal be the most perfect? Had he led the best of all possible lives? Was that it? Had God, in His infinite wisdom and mercy, spared his life, saved him, singled him out because he was most nearly a saint, most nearly Christ-like, most nearly.... Lies—His mind snapped back to reality. He half smiled. Saint? Christ? The Second Coming? He was no saint. Charles sighed. What about—? Chance. That was it! The laws of probability, the bell-shaped curve, normal distribution, rectilinear regression. More people per square foot in New York than elsewhere. The first person who died was from New York, so the last person who gave way to the disease should come from here too. Spin the wheel; throw the dice; toss the coin. So simple to explain by the laws of chance. No need for any underlying assumptions about good and evil, no need for teleological arguments concerning cause and effect. Simply explain it by chance. Somebody had to be the last to go and that was— "No," Charles said, standing up in the quiet of the spring evening. "No, chance won't do it. No man can reckon with chance. The mind rejects such things. There must be something beyond mere accident. There must be!" He sighed slowly. "So now I'm a hermit, whether or not I like it," he said in derision to the gravel path as he walked along it. "A hermit in the midst of a city of millions of—No, I forgot. There aren't any more people, are there?" It was hard to realize, even now. "A hermit, alone—and I haven't even got a cave...." Charles stopped walking suddenly. No cave, he thought. No place to sleep out the long one, no place to rest while time came to change things around and make them for the better. No place to hide. And suddenly it was the most important thing in life to him to find his "cave." It took him almost an hour to find the proper tools, and better than two hours more of hard, nighttime work to get the hole dug to his satisfaction. It took almost three hours to find the right sort of casket, durable but not too heavy for one man to handle. He carted it out to a grassy plot close to the center of the park where the grave was. He let the coffin down slowly into the depression, then piled up loose dirt on the sloping sides of the hole so that the rain would wash it down over him. "I can't very well bury myself," he said. "I guess it will rain after I'm gone." He looked carefully down at the metallic container. Wait now. There was something wrong, something missing. It was—oh, yes, he caught it. It was the stone. There wasn't any stone to go at the head of the grave. "I'll have to fix that." A sheet of metal, bent double, served for the monument proper. A nearby tool shed yielded up a can of paint and a brush. By the glow of one of the streetlights Charles worked out the inscription. "It ought to be something impressive," he thought out loud. "Something fitting the occasion." What did one say on these situations? There was so little chance to practice up for things like this. But it ought to be good, it ought to be proper. "'In this now hallowed corner of the planet Earth—' No. That sounds too ... too...." Make it simple, he thought. And he finally wrote: HERE LIES THE BODY OF THE LAST MAN ON EARTH Yes. That was it. Simple. Let whoever came afterwards figure out the rest. Let them decide. He smiled and finished the painting. Charles was hungry. He got up and started for one of the restaurants near the park. Later on, when there was more time, he'd find a piece of granite and move it to the plot. He could spend his free time carving on it, copying the inscription. He would make it into a real shrine; maybe he would practice up a bit and try to carve a statue to go with the stone. Somehow, though, since things were ready and it didn't make too much difference, it seemed to Charles that he'd probably have a long time to wait. "Maybe it's just a disease, and I'm immune. I was immune to smallpox. The vaccination never took. That's probably it." He smiled. Strange, but now he wanted very much to go on living, alone or not. There were things he could do, ways to keep occupied. He wouldn't mind it so much. But he wanted more and more desperately with each passing second to retain his foothold on the tenuous path of physical existence. The tantalizing thought of "why" puzzled its way back into his mind. But it seemed less pressing now that he had almost come to the conclusion that he would live for a long time. Later, in a few days perhaps, he would think about it. In a little while he'd have plenty of opportunity for hunting down the answer. This seemed good to him, for now he thought he almost had the answer, if there were an answer. He thought he had seen the solution peering out at him from the recesses of his mind, and he didn't like the expression on its face. Better to forget. Charles reached the broad boulevard. There was a large cafe just across from him, its front window caved in by a large truck. He stumbled and almost fell as he stepped from the curb. "Look at me, nervous as a cat." He was trembling noticeably as he started across the street. "I—" He started to say something, to think something. But some hidden part of his mind clamped down, obscuring the thought, rejecting the concept. The tremor turned to a shake before he reached the far curb, and the first burst of wild pain came as he laid his shoulder against the door to the restaurant. This was the way the plague began, but—His mind quickly repressed the idea. It couldn't be the plague. He was immune! Another burst of pulsating, shattering pain crashed through his body, tearing down the defenses of his mind, putting an end of his thoughts of immunity. Colors flared before his eyes, a persistent, irresistible susurrus flooded his ears. He wanted to protest, but there was no one to listen to him. He appealed to every divinity he knew, all the time knowing it would be useless. His body, out of his voluntary control, tried to run off in all directions at once. Charles struggled to end his body's disorganized responses, to channelize all his energy into one direction. His mind came back into action. He set up his goal; everything else seemed irrelevant: he had to get back to the park, to his hermit's cave, to his long, narrow home. He couldn't die until then. Ten minutes. He was allotted ten minutes before the end. It could have been ten years or ten seconds, for now objective time meant nothing to him. It was not a matter of measuring seconds and minutes. It was a matter of forgetting time and measuring space. He concentrated on the grave; he forced his body to become an unwilling machine. While he could, he walked, forcing himself on. When his legs gave way, he crawled. When his knees buckled, he rolled. When his stomach protested, he vomited. It made no difference. Charles refused to think. Machines, especially half-broken machines, do not think; they only work. Sweating, straining, bleeding, retching, he pushed himself towards his goal, trying to add one final touch of grace and custom to the rude irrationalness of it all. His eyes gave out a few feet from the pit. He felt his way towards it. Convulsions shook his body like a cat shakes a captive mouse. He humped his body forward between the seizures, hands outstretched, searching for the grave. And then he was upon it. One arm reached out for grass, and clutched bare space instead. He was home. He gathered energy from his final reservoirs of strength for one final movement that would throw him headlong into the shallow grave. He tensed his muscles, pulled his limbs up under him and started to roll into the hole. Instantly the thought struck him with paralyzing devastation. The answer to it all poked its face out from the recesses of his mind and sapped the last bit of his energy, corroding his nerves and dying muscles. Now he knew, and the knowing was the end of it. He collapsed at the edge of the pit. Only one arm hung loosely down into it, swinging senseless in the air, pointing accusingly at the empty coffin. The world will end, not with a bang, nor with a whimper, but with the last man's anguished cry at the unreasonableness of it all. Charles screamed. The large, invisible, ovular being that hung suspended over the Empire State Building rested from its exertion. Soon it was approached by another of its kind. "It is finished?" asked the second. "Yes. Just now. I am resting." "I can feel the emptiness of it." "It was very good. Where were you?" "On the next planet out. No beauty to it at all; no system. How was yours?" "Beautiful," said the first. "It went according to the strictest semantic relationship following the purest mathematical principles. They made it easy for me." "Good." "Well, where to now?" "There's another system about four thoughts away. We're due there soon." "All right. Let's go." "What's that you have there?" "Oh, this?" replied the first. "It's a higher neural order compendium the Things here made up. It's what I used." "You can't take it with you, you know. They don't allow souvenirs." "I know." "Well?" "All right, all right. You're so good, see if you can compute the scatter probability." The first being moved imperceptably and the heavy plastoid binding of the book disappeared. The thousands of pages dropped softly, caught at the wind like hungry sails, separated, and pulled by the fingers of gravity, went their disparate ways. Here a page scuttled into a broken window of the Chrysler Building (read the names: Aabat, Aabbs, Aabbt). Here a page landed upright on the head of one of the library lions and sloughed softly to the ground (read the names: Looman, Loomana, Loomanabsky). Here another page crept in between the cracks of a pier on the riverfront, dropping gently to the caressing eddies of the water (read the names: Smith, Smitha, Smitj). And here two pages danced down into Central Park, pirouetted, promenaded, and finally came to rest against a propped-up piece of metal (read the names: Whit, Whita, Whitacomb). It was not until the dusty morning sun stirred up the breezes that they fluttered down into the shallow hole beneath, unnoticed. The writing on the metal, until then partially obscured by the papers, became legible: HERE LIES THE BODY OF THE LAST MAN ON EARTH— CHARLES J. ZZYZST GO TO HELL!
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B. The Bureau's Index
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What would've happened if Alan had not made it to the switch?
A. The robots would have gone on living unnoticed by people, doing as they wished with the planet.
B. The next group of people would have been caught by surprise and killed.
C. Pete would have been the last hope of the people on the planet's surface.
D. Peggy would have had to build new radio transmitters after the old ones were destroyed.
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SURVIVAL TACTICS By AL SEVCIK ILLUSTRATOR NOVICK The robots were built to serve Man; to do his work, see to his comforts, make smooth his way. Then the robots figured out an additional service—putting Man out of his misery. There was a sudden crash that hung sharply in the air, as if a tree had been hit by lightning some distance away. Then another. Alan stopped, puzzled. Two more blasts, quickly together, and the sound of a scream faintly. Frowning, worrying about the sounds, Alan momentarily forgot to watch his step until his foot suddenly plunged into an ant hill, throwing him to the jungle floor. "Damn!" He cursed again, for the tenth time, and stood uncertainly in the dimness. From tall, moss-shrouded trees, wrist-thick vines hung quietly, scraping the spongy ground like the tentacles of some monstrous tree-bound octopus. Fitful little plants grew straggly in the shadows of the mossy trunks, forming a dense underbrush that made walking difficult. At midday some few of the blue sun's rays filtered through to the jungle floor, but now, late afternoon on the planet, the shadows were long and gloomy. Alan peered around him at the vine-draped shadows, listening to the soft rustlings and faint twig-snappings of life in the jungle. Two short, popping sounds echoed across the stillness, drowned out almost immediately and silenced by an explosive crash. Alan started, "Blaster fighting! But it can't be!" Suddenly anxious, he slashed a hurried X in one of the trees to mark his position then turned to follow a line of similar marks back through the jungle. He tried to run, but vines blocked his way and woody shrubs caught at his legs, tripping him and holding him back. Then, through the trees he saw the clearing of the camp site, the temporary home for the scout ship and the eleven men who, with Alan, were the only humans on the jungle planet, Waiamea. Stepping through the low shrubbery at the edge of the site, he looked across the open area to the two temporary structures, the camp headquarters where the power supplies and the computer were; and the sleeping quarters. Beyond, nose high, stood the silver scout ship that had brought the advance exploratory party of scientists and technicians to Waiamea three days before. Except for a few of the killer robots rolling slowly around the camp site on their quiet treads, there was no one about. "So, they've finally got those things working." Alan smiled slightly. "Guess that means I owe Pete a bourbon-and-soda for sure. Anybody who can build a robot that hunts by homing in on animals' mind impulses ..." He stepped forward just as a roar of blue flame dissolved the branches of a tree, barely above his head. Without pausing to think, Alan leaped back, and fell sprawling over a bush just as one of the robots rolled silently up from the right, lowering its blaster barrel to aim directly at his head. Alan froze. "My God, Pete built those things wrong!" Suddenly a screeching whirlwind of claws and teeth hurled itself from the smoldering branches and crashed against the robot, clawing insanely at the antenna and blaster barrel. With an awkward jerk the robot swung around and fired its blaster, completely dissolving the lower half of the cat creature which had clung across the barrel. But the back pressure of the cat's body overloaded the discharge circuits. The robot started to shake, then clicked sharply as an overload relay snapped and shorted the blaster cells. The killer turned and rolled back towards the camp, leaving Alan alone. Shakily, Alan crawled a few feet back into the undergrowth where he could lie and watch the camp, but not himself be seen. Though visibility didn't make any difference to the robots, he felt safer, somehow, hidden. He knew now what the shooting sounds had been and why there hadn't been anyone around the camp site. A charred blob lying in the grass of the clearing confirmed his hypothesis. His stomach felt sick. "I suppose," he muttered to himself, "that Pete assembled these robots in a batch and then activated them all at once, probably never living to realize that they're tuned to pick up human brain waves, too. Damn! Damn!" His eyes blurred and he slammed his fist into the soft earth. When he raised his eyes again the jungle was perceptibly darker. Stealthy rustlings in the shadows grew louder with the setting sun. Branches snapped unaccountably in the trees overhead and every now and then leaves or a twig fell softly to the ground, close to where he lay. Reaching into his jacket, Alan fingered his pocket blaster. He pulled it out and held it in his right hand. "This pop gun wouldn't even singe a robot, but it just might stop one of those pumas." They said the blast with your name on it would find you anywhere. This looked like Alan's blast. Slowly Alan looked around, sizing up his situation. Behind him the dark jungle rustled forbiddingly. He shuddered. "Not a very healthy spot to spend the night. On the other hand, I certainly can't get to the camp with a pack of mind-activated mechanical killers running around. If I can just hold out until morning, when the big ship arrives ... The big ship! Good Lord, Peggy!" He turned white; oily sweat punctuated his forehead. Peggy, arriving tomorrow with the other colonists, the wives and kids! The metal killers, tuned to blast any living flesh, would murder them the instant they stepped from the ship! A pretty girl, Peggy, the girl he'd married just three weeks ago. He still couldn't believe it. It was crazy, he supposed, to marry a girl and then take off for an unknown planet, with her to follow, to try to create a home in a jungle clearing. Crazy maybe, but Peggy and her green eyes that changed color with the light, with her soft brown hair, and her happy smile, had ended thirty years of loneliness and had, at last, given him a reason for living. "Not to be killed!" Alan unclenched his fists and wiped his palms, bloody where his fingernails had dug into the flesh. There was a slight creak above him like the protesting of a branch too heavily laden. Blaster ready, Alan rolled over onto his back. In the movement, his elbow struck the top of a small earthy mound and he was instantly engulfed in a swarm of locust-like insects that beat disgustingly against his eyes and mouth. "Fagh!" Waving his arms before his face he jumped up and backwards, away from the bugs. As he did so, a dark shapeless thing plopped from the trees onto the spot where he had been lying stretched out. Then, like an ambient fungus, it slithered off into the jungle undergrowth. For a split second the jungle stood frozen in a brilliant blue flash, followed by the sharp report of a blaster. Then another. Alan whirled, startled. The planet's double moon had risen and he could see a robot rolling slowly across the clearing in his general direction, blasting indiscriminately at whatever mind impulses came within its pickup range, birds, insects, anything. Six or seven others also left the camp headquarters area and headed for the jungle, each to a slightly different spot. Apparently the robot hadn't sensed him yet, but Alan didn't know what the effective range of its pickup devices was. He began to slide back into the jungle. Minutes later, looking back he saw that the machine, though several hundred yards away, had altered its course and was now headed directly for him. His stomach tightened. Panic. The dank, musty smell of the jungle seemed for an instant to thicken and choke in his throat. Then he thought of the big ship landing in the morning, settling down slowly after a lonely two-week voyage. He thought of a brown-haired girl crowding with the others to the gangway, eager to embrace the new planet, and the next instant a charred nothing, unrecognizable, the victim of a design error or a misplaced wire in a machine. "I have to try," he said aloud. "I have to try." He moved into the blackness. Powerful as a small tank, the killer robot was equipped to crush, slash, and burn its way through undergrowth. Nevertheless, it was slowed by the larger trees and the thick, clinging vines, and Alan found that he could manage to keep ahead of it, barely out of blaster range. Only, the robot didn't get tired. Alan did. The twin moons cast pale, deceptive shadows that wavered and danced across the jungle floor, hiding debris that tripped him and often sent him sprawling into the dark. Sharp-edged growths tore at his face and clothes, and insects attracted by the blood matted against his pants and shirt. Behind, the robot crashed imperturbably after him, lighting the night with fitful blaster flashes as some winged or legged life came within its range. There was movement also, in the darkness beside him, scrapings and rustlings and an occasional low, throaty sound like an angry cat. Alan's fingers tensed on his pocket blaster. Swift shadowy forms moved quickly in the shrubs and the growling became suddenly louder. He fired twice, blindly, into the undergrowth. Sharp screams punctuated the electric blue discharge as a pack of small feline creatures leaped snarling and clawing back into the night. Mentally, Alan tried to figure the charge remaining in his blaster. There wouldn't be much. "Enough for a few more shots, maybe. Why the devil didn't I load in fresh cells this morning!" The robot crashed on, louder now, gaining on the tired human. Legs aching and bruised, stinging from insect bites, Alan tried to force himself to run holding his hands in front of him like a child in the dark. His foot tripped on a barely visible insect hill and a winged swarm exploded around him. Startled, Alan jerked sideways, crashing his head against a tree. He clutched at the bark for a second, dazed, then his knees buckled. His blaster fell into the shadows. The robot crashed loudly behind him now. Without stopping to think, Alan fumbled along the ground after his gun, straining his eyes in the darkness. He found it just a couple of feet to one side, against the base of a small bush. Just as his fingers closed upon the barrel his other hand slipped into something sticky that splashed over his forearm. He screamed in pain and leaped back, trying frantically to wipe the clinging, burning blackness off his arm. Patches of black scraped off onto branches and vines, but the rest spread slowly over his arm as agonizing as hot acid, or as flesh being ripped away layer by layer. Almost blinded by pain, whimpering, Alan stumbled forward. Sharp muscle spasms shot from his shoulder across his back and chest. Tears streamed across his cheeks. A blue arc slashed at the trees a mere hundred yards behind. He screamed at the blast. "Damn you, Pete! Damn your robots! Damn, damn ... Oh, Peggy!" He stepped into emptiness. Coolness. Wet. Slowly, washed by the water, the pain began to fall away. He wanted to lie there forever in the dark, cool, wetness. For ever, and ever, and ... The air thundered. In the dim light he could see the banks of the stream, higher than a man, muddy and loose. Growing right to the edge of the banks, the jungle reached out with hairy, disjointed arms as if to snag even the dirty little stream that passed so timidly through its domain. Alan, lying in the mud of the stream bed, felt the earth shake as the heavy little robot rolled slowly and inexorably towards him. "The Lord High Executioner," he thought, "in battle dress." He tried to stand but his legs were almost too weak and his arm felt numb. "I'll drown him," he said aloud. "I'll drown the Lord High Executioner." He laughed. Then his mind cleared. He remembered where he was. Alan trembled. For the first time in his life he understood what it was to live, because for the first time he realized that he would sometime die. In other times and circumstances he might put it off for a while, for months or years, but eventually, as now, he would have to watch, still and helpless, while death came creeping. Then, at thirty, Alan became a man. "Dammit, no law says I have to flame-out now !" He forced himself to rise, forced his legs to stand, struggling painfully in the shin-deep ooze. He worked his way to the bank and began to dig frenziedly, chest high, about two feet below the edge. His arm where the black thing had been was swollen and tender, but he forced his hands to dig, dig, dig, cursing and crying to hide the pain, and biting his lips, ignoring the salty taste of blood. The soft earth crumbled under his hands until he had a small cave about three feet deep in the bank. Beyond that the soil was held too tightly by the roots from above and he had to stop. The air crackled blue and a tree crashed heavily past Alan into the stream. Above him on the bank, silhouetting against the moons, the killer robot stopped and its blaster swivelled slowly down. Frantically, Alan hugged the bank as a shaft of pure electricity arced over him, sliced into the water, and exploded in a cloud of steam. The robot shook for a second, its blaster muzzle lifted erratically and for an instant it seemed almost out of control, then it quieted and the muzzle again pointed down. Pressing with all his might, Alan slid slowly along the bank inches at a time, away from the machine above. Its muzzle turned to follow him but the edge of the bank blocked its aim. Grinding forward a couple of feet, slightly overhanging the bank, the robot fired again. For a split second Alan seemed engulfed in flame; the heat of hell singed his head and back, and mud boiled in the bank by his arm. Again the robot trembled. It jerked forward a foot and its blaster swung slightly away. But only for a moment. Then the gun swung back again. Suddenly, as if sensing something wrong, its tracks slammed into reverse. It stood poised for a second, its treads spinning crazily as the earth collapsed underneath it, where Alan had dug, then it fell with a heavy splash into the mud, ten feet from where Alan stood. Without hesitation Alan threw himself across the blaster housing, frantically locking his arms around the barrel as the robot's treads churned furiously in the sticky mud, causing it to buck and plunge like a Brahma bull. The treads stopped and the blaster jerked upwards wrenching Alan's arms, then slammed down. Then the whole housing whirled around and around, tilting alternately up and down like a steel-skinned water monster trying to dislodge a tenacious crab, while Alan, arms and legs wrapped tightly around the blaster barrel and housing, pressed fiercely against the robot's metal skin. Slowly, trying to anticipate and shift his weight with the spinning plunges, Alan worked his hand down to his right hip. He fumbled for the sheath clipped to his belt, found it, and extracted a stubby hunting knife. Sweat and blood in his eyes, hardly able to move on the wildly swinging turret, he felt down the sides to the thin crack between the revolving housing and the stationary portion of the robot. With a quick prayer he jammed in the knife blade—and was whipped headlong into the mud as the turret literally snapped to a stop. The earth, jungle and moons spun in a pinwheeled blur, slowed, and settled to their proper places. Standing in the sticky, sweet-smelling ooze, Alan eyed the robot apprehensively. Half buried in mud, it stood quiet in the shadowy light except for an occasional, almost spasmodic jerk of its blaster barrel. For the first time that night Alan allowed himself a slight smile. "A blade in the old gear box, eh? How does that feel, boy?" He turned. "Well, I'd better get out of here before the knife slips or the monster cooks up some more tricks with whatever it's got for a brain." Digging little footholds in the soft bank, he climbed up and stood once again in the rustling jungle darkness. "I wonder," he thought, "how Pete could cram enough brain into one of those things to make it hunt and track so perfectly." He tried to visualize the computing circuits needed for the operation of its tracking mechanism alone. "There just isn't room for the electronics. You'd need a computer as big as the one at camp headquarters." In the distance the sky blazed as a blaster roared in the jungle. Then Alan heard the approaching robot, crunching and snapping its way through the undergrowth like an onrushing forest fire. He froze. "Good Lord! They communicate with each other! The one I jammed must be calling others to help." He began to move along the bank, away from the crashing sounds. Suddenly he stopped, his eyes widened. "Of course! Radio! I'll bet anything they're automatically controlled by the camp computer. That's where their brain is!" He paused. "Then, if that were put out of commission ..." He jerked away from the bank and half ran, half pulled himself through the undergrowth towards the camp. Trees exploded to his left as another robot fired in his direction, too far away to be effective but churning towards him through the blackness. Alan changed direction slightly to follow a line between the two robots coming up from either side, behind him. His eyes were well accustomed to the dark now, and he managed to dodge most of the shadowy vines and branches before they could snag or trip him. Even so, he stumbled in the wiry underbrush and his legs were a mass of stinging slashes from ankle to thigh. The crashing rumble of the killer robots shook the night behind him, nearer sometimes, then falling slightly back, but following constantly, more unshakable than bloodhounds because a man can sometimes cover a scent, but no man can stop his thoughts. Intermittently, like photographers' strobes, blue flashes would light the jungle about him. Then, for seconds afterwards his eyes would see dancing streaks of yellow and sharp multi-colored pinwheels that alternately shrunk and expanded as if in a surrealist's nightmare. Alan would have to pause and squeeze his eyelids tight shut before he could see again, and the robots would move a little closer. To his right the trees silhouetted briefly against brilliance as a third robot slowly moved up in the distance. Without thinking, Alan turned slightly to the left, then froze in momentary panic. "I should be at the camp now. Damn, what direction am I going?" He tried to think back, to visualize the twists and turns he'd taken in the jungle. "All I need is to get lost." He pictured the camp computer with no one to stop it, automatically sending its robots in wider and wider forays, slowly wiping every trace of life from the planet. Technologically advanced machines doing the job for which they were built, completely, thoroughly, without feeling, and without human masters to separate sense from futility. Finally parts would wear out, circuits would short, and one by one the killers would crunch to a halt. A few birds would still fly then, but a unique animal life, rare in the universe, would exist no more. And the bones of children, eager girls, and their men would also lie, beside a rusty hulk, beneath the alien sun. "Peggy!" As if in answer, a tree beside him breathed fire, then exploded. In the brief flash of the blaster shot, Alan saw the steel glint of a robot only a hundred yards away, much nearer than he had thought. "Thank heaven for trees!" He stepped back, felt his foot catch in something, clutched futilely at some leaves and fell heavily. Pain danced up his leg as he grabbed his ankle. Quickly he felt the throbbing flesh. "Damn the rotten luck, anyway!" He blinked the pain tears from his eyes and looked up—into a robot's blaster, jutting out of the foliage, thirty yards away. Instinctively, in one motion Alan grabbed his pocket blaster and fired. To his amazement the robot jerked back, its gun wobbled and started to tilt away. Then, getting itself under control, it swung back again to face Alan. He fired again, and again the robot reacted. It seemed familiar somehow. Then he remembered the robot on the river bank, jiggling and swaying for seconds after each shot. "Of course!" He cursed himself for missing the obvious. "The blaster static blanks out radio transmission from the computer for a few seconds. They even do it to themselves!" Firing intermittently, he pulled himself upright and hobbled ahead through the bush. The robot shook spasmodically with each shot, its gun tilted upward at an awkward angle. Then, unexpectedly, Alan saw stars, real stars brilliant in the night sky, and half dragging his swelling leg he stumbled out of the jungle into the camp clearing. Ahead, across fifty yards of grass stood the headquarters building, housing the robot-controlling computer. Still firing at short intervals he started across the clearing, gritting his teeth at every step. Straining every muscle in spite of the agonizing pain, Alan forced himself to a limping run across the uneven ground, carefully avoiding the insect hills that jutted up through the grass. From the corner of his eye he saw another of the robots standing shakily in the dark edge of the jungle waiting, it seemed, for his small blaster to run dry. "Be damned! You can't win now!" Alan yelled between blaster shots, almost irrational from the pain that ripped jaggedly through his leg. Then it happened. A few feet from the building's door his blaster quit. A click. A faint hiss when he frantically jerked the trigger again and again, and the spent cells released themselves from the device, falling in the grass at his feet. He dropped the useless gun. "No!" He threw himself on the ground as a new robot suddenly appeared around the edge of the building a few feet away, aimed, and fired. Air burned over Alan's back and ozone tingled in his nostrils. Blinding itself for a few seconds with its own blaster static, the robot paused momentarily, jiggling in place. In this instant, Alan jammed his hands into an insect hill and hurled the pile of dirt and insects directly at the robot's antenna. In a flash, hundreds of the winged things erupted angrily from the hole in a swarming cloud, each part of which was a speck of life transmitting mental energy to the robot's pickup devices. Confused by the sudden dispersion of mind impulses, the robot fired erratically as Alan crouched and raced painfully for the door. It fired again, closer, as he fumbled with the lock release. Jagged bits of plastic and stone ripped past him, torn loose by the blast. Frantically, Alan slammed open the door as the robot, sensing him strongly now, aimed point blank. He saw nothing, his mind thought of nothing but the red-clad safety switch mounted beside the computer. Time stopped. There was nothing else in the world. He half-jumped, half-fell towards it, slowly, in tenths of seconds that seemed measured out in years. The universe went black. Later. Brilliance pressed upon his eyes. Then pain returned, a multi-hurting thing that crawled through his body and dragged ragged tentacles across his brain. He moaned. A voice spoke hollowly in the distance. "He's waking. Call his wife." Alan opened his eyes in a white room; a white light hung over his head. Beside him, looking down with a rueful smile, stood a young man wearing space medical insignia. "Yes," he acknowledged the question in Alan's eyes, "you hit the switch. That was three days ago. When you're up again we'd all like to thank you." Suddenly a sobbing-laughing green-eyed girl was pressed tightly against him. Neither of them spoke. They couldn't. There was too much to say. THE END Transcriber's Note: This etext was produced from Amazing Science Fiction Stories October 1958. 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.
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B. The next group of people would have been caught by surprise and killed.
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Why doesn't Malloy go to the peace talks himself?
A. Malloy is too sick to travel to the peace conference. He also hates aliens.
B. Malloy needs to stay on Saarkkad IV to keep the drug supply lines flowing.
C. Malloy is too far from Saarkkad V to get to the peace conference on time. He also hates aliens.
D. Malloy has a psychological disorder that prevents him from leaving the house. He also hates aliens.
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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.
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D. Malloy has a psychological disorder that prevents him from leaving the house. He also hates aliens.
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Which isn't a feeling that the older man expects of the younger man?
A. anger
B. worry
C. confusion
D. surprise
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... and it comes out here By LESTER DEL REY Illustrated by DON SIBLEY [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There is one fact no sane man can quarrel with ... everything has a beginning and an end. But some men aren't sane; thus it isn't always so! No, you're wrong. I'm not your father's ghost, even if I do look a bit like him. But it's a longish story, and you might as well let me in. You will, you know, so why quibble about it? At least, you always have ... or do ... or will. I don't know, verbs get all mixed up. We don't have the right attitude toward tenses for a situation like this. Anyhow, you'll let me in. I did, so you will. Thanks. You think you're crazy, of course, but you'll find out you aren't. It's just that things are a bit confused. And don't look at the machine out there too long—until you get used to it, you'll find it's hard on the eyes, trying to follow where the vanes go. You'll get used to it, of course, but it will take about thirty years. You're wondering whether to give me a drink, as I remember it. Why not? And naturally, since we have the same tastes, you can make the same for me as you're having. Of course we have the same tastes—we're the same person. I'm you thirty years from now, or you're me. I remember just how you feel; I felt the same way when he—that is, of course, I or we—came back to tell me about it, thirty years ago. Here, have one of these. You'll get to like them in a couple more years. And you can look at the revenue stamp date, if you still doubt my story. You'll believe it eventually, though, so it doesn't matter. Right now, you're shocked. It's a real wrench when a man meets himself for the first time. Some kind of telepathy seems to work between two of the same people. You sense things. So I'll simply go ahead talking for half an hour or so, until you get over it. After that you'll come along with me. You know, I could try to change things around by telling what happened to me; but he—I—told me what I was going to do, so I might as well do the same. I probably couldn't help telling you the same thing in the same words, even if I tried—and I don't intend to try. I've gotten past that stage in worrying about all this. So let's begin when you get up in half an hour and come out with me. You'll take a closer look at the machine, then. Yes, it'll be pretty obvious it must be a time machine. You'll sense that, too. You've seen it, just a small little cage with two seats, a luggage compartment, and a few buttons on a dash. You'll be puzzling over what I'll tell you, and you'll be getting used to the idea that you are the man who makes atomic power practical. Jerome Boell, just a plain engineer, the man who put atomic power in every home. You won't exactly believe it, but you'll want to go along. I'll be tired of talking by then, and in a hurry to get going. So I cut off your questions, and get you inside. I snap on a green button, and everything seems to cut off around us. You can see a sort of foggy nothing surrounding the cockpit; it is probably the field that prevents passage through time from affecting us. The luggage section isn't protected, though. You start to say something, but by then I'm pressing a black button, and everything outside will disappear. You look for your house, but it isn't there. There is exactly nothing there—in fact, there is no there . You are completely outside of time and space, as best you can guess how things are. You can't feel any motion, of course. You try to reach a hand out through the field into the nothing around you and your hand goes out, all right, but nothing happens. Where the screen ends, your hand just turns over and pokes back at you. Doesn't hurt, and when you pull your arm back, you're still sound and uninjured. But it looks frightening and you don't try it again. Then it comes to you slowly that you're actually traveling in time. You turn to me, getting used to the idea. "So this is the fourth dimension?" you ask. Then you feel silly, because you'll remember that I said you'd ask that. Well, I asked it after I was told, then I came back and told it to you, and I still can't help answering when you speak. "Not exactly," I try to explain. "Maybe it's no dimension—or it might be the fifth; if you're going to skip over the so-called fourth without traveling along it, you'd need a fifth. Don't ask me. I didn't invent the machine and I don't understand it." "But...." I let it go, and so do you. If you don't, it's a good way of going crazy. You'll see later why I couldn't have invented the machine. Of course, there may have been a start for all this once. There may have been a time when you did invent the machine—the atomic motor first, then the time-machine. And when you closed the loop by going back and saving yourself the trouble, it got all tangled up. I figured out once that such a universe would need some seven or eight time and space dimensions. It's simpler just to figure that this is the way time got bent back on itself. Maybe there is no machine, and it's just easier for us to imagine it. When you spend thirty years thinking about it, as I did—and you will—you get further and further from an answer. Anyhow, you sit there, watching nothing all around you, and no time, apparently, though there is a time effect back in the luggage space. You look at your watch and it's still running. That means you either carry a small time field with you, or you are catching a small increment of time from the main field. I don't know, and you won't think about that then, either. I'm smoking, and so are you, and the air in the machine is getting a bit stale. You suddenly realize that everything in the machine is wide open, yet you haven't seen any effects of air loss. "Where are we getting our air?" you ask. "Or why don't we lose it?" "No place for it to go," I explain. There isn't. Out there is neither time nor space, apparently. How could the air leak out? You still feel gravity, but I can't explain that, either. Maybe the machine has a gravity field built in, or maybe the time that makes your watch run is responsible for gravity. In spite of Einstein, you have always had the idea that time is an effect of gravity, and I sort of agree, still. Then the machine stops—at least, the field around us cuts off. You feel a dankish sort of air replace the stale air, and you breathe easier, though we're in complete darkness, except for the weak light in the machine, which always burns, and a few feet of rough dirty cement floor around. You take another cigaret from me and you get out of the machine, just as I do. I've got a bundle of clothes and I start changing. It's a sort of simple, short-limbed, one-piece affair I put on, but it feels comfortable. "I'm staying here," I tell you. "This is like the things they wear in this century, as near as I can remember it, and I should be able to pass fairly well. I've had all my fortune—the one you make on that atomic generator—invested in such a way I can get it on using some identification I've got with me, so I'll do all right. I know they still use some kind of money, you'll see evidence of that. And it's a pretty easygoing civilization, from what I could see. We'll go up and I'll leave you. I like the looks of things here, so I won't be coming back with you." You nod, remembering I've told you about it. "What century is this, anyway?" I'd told you that, too, but you've forgotten. "As near as I can guess, it's about 2150. He told me, just as I'm telling you, that it's an interstellar civilization." You take another cigaret from me, and follow me. I've got a small flashlight and we grope through a pile of rubbish, out into a corridor. This is a sub-sub-sub-basement. We have to walk up a flight of stairs, and there is an elevator waiting, fortunately with the door open. "What about the time machine?" you ask. "Since nobody ever stole it, it's safe." We get in the elevator, and I say "first" to it. It gives out a coughing noise and the basement openings begin to click by us. There's no feeling of acceleration—some kind of false gravity they use in the future. Then the door opens, and the elevator says "first" back at us. It's obviously a service elevator and we're in a dim corridor, with nobody around. I grab your hand and shake it. "You go that way. Don't worry about getting lost; you never did, so you can't. Find the museum, grab the motor, and get out. And good luck to you." You act as if you're dreaming, though you can't believe it's a dream. You nod at me and I move out into the main corridor. A second later, you see me going by, mixed into a crowd that is loafing along toward a restaurant, or something like it, that is just opening. I'm asking questions of a man, who points, and I turn and move off. You come out of the side corridor and go down a hall, away from the restaurant. There are quiet little signs along the hall. You look at them, realizing for the first time that things have changed. Steij:neri, Faunten, Z:rgat Dispenseri. The signs are very quiet and dignified. Some of them can be decoded to stationery shops, fountains, and the like. What a zergot is, you don't know. You stop at a sign that announces: Trav:l Biwrou—F:rst-Clas Twrz—Marz, Viin*s, and x: Trouj:n Planets. Spej:l reits tu aol s*nz wixin 60 lyt iirz! But there is only a single picture of a dull-looking metal sphere, with passengers moving up a ramp, and the office is closed. You begin to get the hang of the spelling they use, though. Now there are people around you, but nobody pays much attention to you. Why should they? You wouldn't care if you saw a man in a leopard-skin suit; you'd figure it was some part in a play and let it go. Well, people don't change much. You get up your courage and go up to a boy selling something that might be papers on tapes. "Where can I find the Museum of Science?" "Downayer rien turn lefa the sign. Stoo bloss," he tells you. Around you, you hear some pretty normal English, but there are others using stuff as garbled as his. The educated and uneducated? I don't know. You go right until you find a big sign built into the rubbery surface of the walk: Miuzi:m *v Syens . There's an arrow pointing and you turn left. Ahead of you, two blocks on, you can see a pink building, with faint aqua trimming, bigger than most of the others. They are building lower than they used to, apparently. Twenty floors up seems about the maximum. You head for it, and find the sidewalk is marked with the information that it is the museum. You go up the steps, but you see that it seems to be closed. You hesitate for a moment, then. You're beginning to think the whole affair is complete nonsense, and you should get back to the time machine and go home. But then a guard comes to the gate. Except for the short legs in his suit and the friendly grin on his face, he looks like any other guard. What's more, he speaks pretty clearly. Everyone says things in a sort of drawl, with softer vowels and slurred consonants, but it's rather pleasant. "Help you, sir? Oh, of course. You must be playing in 'Atoms and Axioms.' The museum's closed, but I'll be glad to let you study whatever you need for realism in your role. Nice show. I saw it twice." "Thanks," you mutter, wondering what kind of civilization can produce guards as polite as that. "I—I'm told I should investigate your display of atomic generators." He beams at that. "Of course." The gate is swung to behind you, but obviously he isn't locking it. In fact, there doesn't seem to be a lock. "Must be a new part. You go down that corridor, up one flight of stairs and left. Finest display in all the known worlds. We've got the original of the first thirteen models. Professor Jonas was using them to check his latest theory of how they work. Too bad he could not explain the principle, either. Someone will, some day, though. Lord, the genius of that twentieth century inventor! It's quite a hobby with me, sir. I've read everything I could get on the period. Oh—congratulations on your pronunciation. Sounds just like some of our oldest tapes." You get away from him, finally, after some polite thanks. The building seems deserted and you wander up the stairs. There's a room on your right filled with something that proclaims itself the first truly plastic diamond former, and you go up to it. As you come near, it goes through a crazy wiggle inside, stops turning out a continual row of what seem to be bearings, and slips something the size of a penny toward you. "Souvenir," it announces in a well-modulated voice. "This is a typical gem of the twentieth century, properly cut to 58 facets, known technically as a Jaegger diamond, and approximately twenty carats in size. You can have it made into a ring on the third floor during morning hours for one-tenth credit. If you have more than one child, press the red button for the number of stones you desire." You put it in your pocket, gulping a little, and get back to the corridor. You turn left and go past a big room in which models of spaceships—from the original thing that looks like a V-2, and is labeled first Lunar rocket, to a ten-foot globe, complete with miniature manikins—are sailing about in some kind of orbits. Then there is one labeled Wep:nz , filled with everything from a crossbow to a tiny rod four inches long and half the thickness of a pencil, marked Fynal Hand Arm . Beyond is the end of the corridor, and a big place that bears a sign, Mad:lz *v Atamic Pau:r Sorsez . By that time, you're almost convinced. And you've been doing a lot of thinking about what you can do. The story I'm telling has been sinking in, but you aren't completely willing to accept it. You notice that the models are all mounted on tables and that they're a lot smaller than you thought. They seem to be in chronological order, and the latest one, marked 2147—Rincs Dyn*pat: , is about the size of a desk telephone. The earlier ones are larger, of course, clumsier, but with variations, probably depending on the power output. A big sign on the ceiling gives a lot of dope on atomic generators, explaining that this is the first invention which leaped full blown into basically final form. You study it, but it mentions casually the inventor, without giving his name. Either they don't know it, or they take it for granted that everyone does, which seems more probable. They call attention to the fact that they have the original model of the first atomic generator built, complete with design drawings, original manuscript on operation, and full patent application. They state that it has all major refinements, operating on any fuel, producing electricity at any desired voltage up to five million, any chosen cyclic rate from direct current to one thousand megacycles, and any amperage up to one thousand, its maximum power output being fifty kilowatts, limited by the current-carrying capacity of the outputs. They also mention that the operating principle is still being investigated, and that only such refinements as better alloys and the addition of magnetric and nucleatric current outlets have been added since the original. So you go to the end and look over the thing. It's simply a square box with a huge plug on each side, and a set of vernier controls on top, plus a little hole marked, in old-style spelling, Drop BBs or wire here . Apparently that's the way it's fueled. It's about one foot on each side. "Nice," the guard says over your shoulder. "It finally wore out one of the cathogrids and we had to replace that, but otherwise it's exactly as the great inventor made it. And it still operates as well as ever. Like to have me tell you about it?" "Not particularly," you begin, and then realize bad manners might be conspicuous here. While you're searching for an answer, the guard pulls something out of his pocket and stares at it. "Fine, fine. The mayor of Altasecarba—Centaurian, you know—is arriving, but I'll be back in about ten minutes. He wants to examine some of the weapons for a monograph on Centaurian primitives compared to nineteenth century man. You'll pardon me?" You pardon him pretty eagerly and he wanders off happily. You go up to the head of the line, to that Rinks Dynapattuh, or whatever it transliterates to. That's small and you can carry it. But the darned thing is absolutely fixed. You can't see any bolts, but you can't budge it, either. You work down the line. It'd be foolish to take the early model if you can get one with built-in magnetic current terminals—Ehrenhaft or some other principle?—and nuclear binding-force energy terminals. But they're all held down by the same whatchamaycallem effect. And, finally, you're right back beside the original first model. It's probably bolted down, too, but you try it tentatively and you find it moves. There's a little sign under it, indicating you shouldn't touch it, since the gravostatic plate is being renewed. Well, you won't be able to change the time cycle by doing anything I haven't told you, but a working model such as that is a handy thing. You lift it; it only weighs about fifty pounds! Naturally, it can be carried. You expect a warning bell, but nothing happens. As a matter of fact, if you'd stop drinking so much of that scotch and staring at the time machine out there now, you'd hear what I'm saying and know what will happen to you. But of course, just as I did, you're going to miss a lot of what I say from now on, and have to find out for yourself. But maybe some of it helps. I've tried to remember how much I remembered, after he told me, but I can't be sure. So I'll keep on talking. I probably can't help it, anyhow. Pre-set, you might say. Well, you stagger down the corridor, looking out for the guard, but all seems clear. Then you hear his voice from the weapons room. You bend down and try to scurry past, but you know you're in full view. Nothing happens, though. You stumble down the stairs, feeling all the futuristic rays in the world on your back, and still nothing happens. Ahead of you, the gate is closed. You reach it and it opens obligingly by itself. You breathe a quick sigh of relief and start out onto the street. Then there's a yell behind you. You don't wait. You put one leg in front of the other and you begin racing down the walk, ducking past people, who stare at you with expressions you haven't time to see. There's another yell behind you. Something goes over your head and drops on the sidewalk just in front of your feet, with a sudden ringing sound. You don't wait to find out about that, either. Somebody reaches out a hand to catch you and you dart past. The street is pretty clear now and you jolt along, with your arms seeming to come out of the sockets, and that atomic generator getting heavier at every step. Out of nowhere, something in a blue uniform about six feet tall and on the beefy side appears—and the badge hasn't changed much. The cop catches your arm and you know you're not going to get away, so you stop. "You can't exert yourself that hard in this heat, fellow," the cop says. "There are laws against that, without a yellow sticker. Here, let me grab you a taxi." Reaction sets in a bit and your knees begin to buckle, but you shake your head and come up for air. "I—I left my money home," you begin. The cop nods. "Oh, that explains it. Fine, I won't have to give you an appearance schedule. But you should have come to me." He reaches out and taps a pedestrian lightly on the shoulder. "Sir, an emergency request. Would you help this gentleman?" The pedestrian grins, looks at his watch, and nods. "How far?" You did notice the name of the building from which you came and you mutter it. The stranger nods again, reaches out and picks up the other side of the generator, blowing a little whistle the cop hands him. Pedestrians begin to move aside, and you and the stranger jog down the street at a trot, with a nice clear path, while the cop stands beaming at you both. That way, it isn't so bad. And you begin to see why I decided I might like to stay in the future. But all the same, the organized cooperation here doesn't look too good. The guard can get the same and be there before you. And he is. He stands just inside the door of the building as you reach it. The stranger lifts an eyebrow and goes off at once when you nod at him, not waiting for thanks. And the guard comes up, holding some dinkus in his hand, about the size of a big folding camera and not too dissimilar in other ways. He snaps it open and you get set to duck. "You forgot the prints, monograph, and patent applications," he says. "They go with the generator—we don't like to have them separated. A good thing I knew the production office of 'Atoms and Axioms' was in this building. Just let us know when you're finished with the model and we'll pick it up." You swallow several sets of tonsils you had removed years before, and take the bundle of papers he hands you out of the little case. He pumps you for some more information, which you give him at random. It seems to satisfy your amiable guard friend. He finally smiles in satisfaction and heads back to the museum. You still don't believe it, but you pick up the atomic generator and the information sheets, and you head down toward the service elevator. There is no button on it. In fact, there's no door there. You start looking for other doors or corridors, but you know this is right. The signs along the halls are the same as they were. Then there's a sort of cough and something dilates in the wall. It forms a perfect door and the elevator stands there waiting. You get in, gulping out something about going all the way down, and then wonder how a machine geared for voice operation can make anything of that. What the deuce would that lowest basement be called? But the elevator has closed and is moving downward in a hurry. It coughs again and you're at the original level. You get out—and realize you don't have a light. You'll never know what you stumbled over, but, somehow, you move back in the direction of the time machine, bumping against boxes, staggering here and there, and trying to find the right place by sheer feel. Then a shred of dim light appears; it's the weak light in the time machine. You've located it. You put the atomic generator in the luggage space, throw the papers down beside it, and climb into the cockpit, sweating and mumbling. You reach forward toward the green button and hesitate. There's a red one beside it and you finally decide on that. Suddenly, there's a confused yell from the direction of the elevator and a beam of light strikes against your eyes, with a shout punctuating it. Your finger touches the red button. You'll never know what the shouting was about—whether they finally doped out the fact that they'd been robbed, or whether they were trying to help you. You don't care which it is. The field springs up around you and the next button you touch—the one on the board that hasn't been used so far—sends you off into nothingness. There is no beam of light, you can't hear a thing, and you're safe. It isn't much of a trip back. You sit there smoking and letting your nerves settle back to normal. You notice a third set of buttons, with some pencil marks over them—"Press these to return to yourself 30 years"—and you begin waiting for the air to get stale. It doesn't because there is only one of you this time. Instead, everything flashes off and you're sitting in the machine in your own back yard. You'll figure out the cycle in more details later. You get into the machine in front of your house, go to the future in the sub-basement, land in your back yard, and then hop back thirty years to pick up yourself, landing in front of your house. Just that. But right then, you don't care. You jump out and start pulling out that atomic generator and taking it inside. It isn't hard to disassemble, but you don't learn a thing; just some plates of metal, some spiral coils, and a few odds and ends—all things that can be made easily enough, all obviously of common metals. But when you put it together again, about an hour later, you notice something. Everything in it is brand-new and there's one set of copper wires missing! It won't work. You put some #12 house wire in, exactly like the set on the other side, drop in some iron filings, and try it again. And with the controls set at 120 volts, 60 cycles and 15 amperes, you get just that. You don't need the power company any more. And you feel a little happier when you realize that the luggage space wasn't insulated from time effects by a field, so the motor has moved backward in time, somehow, and is back to its original youth—minus the replaced wires the guard mentioned—which probably wore out because of the makeshift job you've just done. But you begin getting more of a jolt when you find that the papers are all in your own writing, that your name is down as the inventor, and that the date of the patent application is 1951. It will begin to soak in, then. You pick up an atomic generator in the future and bring it back to the past—your present—so that it can be put in the museum with you as the inventor so you can steal it to be the inventor. And you do it in a time machine which you bring back to yourself to take yourself into the future to return to take back to yourself.... Who invented what? And who built which? Before long, your riches from the generator are piling in. Little kids from school are coming around to stare at the man who changed history and made atomic power so common that no nation could hope to be anything but a democracy and a peaceful one—after some of the worst times in history for a few years. Your name eventually becomes as common as Ampere, or Faraday, or any other spelled without a capital letter. But you're thinking of the puzzle. You can't find any answer. One day you come across an old poem—something about some folks calling it evolution and others calling it God. You go out, make a few provisions for the future, and come back to climb into the time machine that's waiting in the building you had put around it. Then you'll be knocking on your own door, thirty years back—or right now, from your view—and telling your younger self all these things I'm telling you. But now.... Well, the drinks are finished. You're woozy enough to go along with me without protest, and I want to find out just why those people up there came looking for you and shouting, before the time machine left. Let's go.
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A. anger
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What state of the art models were used in experiments?
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### Introduction
Teaching machines to converse with humans naturally and engagingly is a fundamentally interesting and challenging problem in AI research. Many contemporary state-of-the-art approaches BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 for dialogue generation follow the data-driven paradigm: trained on a plethora of query-response pairs, the model attempts to mimic human conversations. As a data-driven approach, the quality of generated responses in neural dialogue generation heavily depends on the training data. As such, in order to train a robust and well-behaved model, most works obtain large-scale query-response pairs by crawling human-generated conversations from publicly available sources such as OpenSubtitles BIBREF7. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly BIBREF8. Table TABREF1 shows samples drawn from OpenSubtitles BIBREF7, which contains millions of human-human conversations converted from movie transcripts. The response of the third sample “Yurakutei kikuhiko.” looks quite strange in terms of the given query, while the first sample is clearly easier to learn. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. Babies learn to speak by first imitating easy and exact utterances repeatedly taught by their patient parents. As children grow up, they learn grade by grade, from simple conversations to more complex ones. Inspired by such human behaviors of learning to converse, in this paper, we introduce curriculum learning to bring the neural dialogue model with easy-to-complex learning curriculum, where the model first learns from easy conversations and then gradually manages more complicated dialogues. Nevertheless, organizing a curriculum with increasing difficulty faces insurmountable obstacles: 1) automatic evaluation of dialogue complexity is a non-trivial task. BIBREF9 defined the difficulty for the training examples with respect to the sentence length and word rarity in neural machine translation. BIBREF10 expressed the difficulty regarding the value of the objective function. So far, there is no unified approach in measuring dialogue complexity. 2) Unlike the single metric of complexity in other tasks, dialogue complexity embodies multiple aspects of attributes BIBREF11—the specificity and repetitiveness of the response, the relevance between the query and the response, etc. As such, in this paper, we study the dialogue distributions along five aspects of attributes to gather multiple perspectives on dialogue complexity, resulting with five curricula accordingly. Conventional curriculum learning organizes the training samples into one curriculum, whereas we employ multiple curricula for dialogue learning. Enlightened by the phenomenon that children usually adjust the learning focus of multiple curricula dynamically in order to acquire a good mark, we further propose an adaptive multi-curricula learning framework, established upon the reinforcement learning paradigm, to automatically choose different curricula at different learning stages according to the learning status of the neural dialogue generation model. Detailed analysis and experiments demonstrate that the proposed framework effectively increases the learning efficiency and gains better performances on five state-of-the-art dialogue generation models regarding three publicly available conversational corpora. Code for this work is available on https://github.com/hengyicai/Adaptive_Multi-curricula_Learning_for_Dialog. ### Curriculum Plausibility
Intuitively, a well-organized curriculum should provide the model learning with easy dialogues first, and then gradually increase the curriculum difficulty. However, currently, there is no unified approach for dialogue complexity evaluation, where the complexity involves multiple aspects of attributes. In this paper, we prepare the syllabus for dialogue learning with respect to five dialogue attributes. To ensure the universality and general applicability of the curriculum, we perform an in-depth investigation on three publicly available conversation corpora, PersonaChat BIBREF12, DailyDialog BIBREF13 and OpenSubtitles BIBREF7, consisting of 140 248, 66 594 and 358 668 real-life conversation samples, respectively. ### Curriculum Plausibility ::: Conversational Attributes ::: Specificity
A notorious problem for neural dialogue generation model is that the model is prone to generate generic responses. The most unspecific responses are easy to learn, but are short and meaningless, while the most specific responses, consisting of too many rare words, are too difficult to learn, especially at the initial learning stage. Following BIBREF11, we measure the specificity of the response in terms of each word $w$ using Normalized Inverse Document Frequency (NIDF, ranging from 0 to 1): where $\text{IDF}(w)=\log {\frac{N_r}{N_w}}$. $N_r$ is the number of responses in the training set and $N_w$ is the number of those responses that contain $w$. $\text{idf}_{min}$ and $\text{idf}_{max}$ are the minimum and maximum IDFs, taken over all words in the vocabulary. The specificity of a response $r$ is measured as the mean NIDF of the words in $r$. ### Curriculum Plausibility ::: Conversational Attributes ::: Repetitiveness
Repetitive responses are easy to generate in current auto-regressive response decoding, where response generation loops frequently, whereas diverse and informative responses are much more complicated for neural dialogue generation. We measure the repetitiveness of a response $r$ as: where $I(\cdot )$ is an indicator function that takes the value 1 when $w_i \in \lbrace w_0, \cdots , w_{i-1}\rbrace $ is true and 0 otherwise. ### Curriculum Plausibility ::: Conversational Attributes ::: Query-relatedness
A conversation is considered to be coherent if the response correlates well with the given query. For example, given a query “I like to paint”, the response “What kind of things do you paint?” is more relevant and easier to learn than another loosely-coupled response “Do you have any pets?”. Following previous work BIBREF14, we measure the query-relatedness using the cosine similarities between the query and its corresponding response in the embedding space: $\textit {cos\_sim}(\textit {sent\_emb}(c), \textit {sent\_emb}(r))$, where $c$ is the query and $r$ is the response. The sentence embedding is computed by taking the average word embedding weighted by the smooth inverse frequency $\textit {sent\_emb}(e)=\frac{1}{|e|}\sum _{w\in {}e}\frac{0.001}{0.001 + p(w)}emb(w)$ of words BIBREF15, where $emb(w)$ and $p(w)$ are the embedding and the probability of word $w$ respectively. ### Curriculum Plausibility ::: Conversational Attributes ::: Continuity
A coherent response not only responds to the given query, but also triggers the next utterance. An interactive conversation is carried out for multiple rounds and a response in the current turn also acts as the query in the next turn. As such, we introduce the continuity metric, which is similar to the query-relatedness metric, to assess the continuity of a response $r$ with respect to the subsequent utterance $u$, by measuring the cosine similarities between them. ### Curriculum Plausibility ::: Conversational Attributes ::: Model Confidence
Despite the heuristic dialogue attributes, we further introduce the model confidence as an attribute, which distinguishes the easy-learnt samples from the under-learnt samples in terms of the model learning ability. A pretrained neural dialogue generation model assigns a relatively higher confidence probability for the easy-learnt samples than the under-learnt samples. Inspired by BIBREF16, BIBREF17, we employ the negative loss value of a dialogue sample under the pretrained model as the model confidence measure, indicating whether a sampled response is easy to be generated. Here we choose the attention-based sequence-to-sequence architecture with a cross-entropy objective as the underlying dialogue model. ### Curriculum Plausibility ::: Dialogue Analysis ::: Distributions among Attributes
The distributions of the data samples regarding the aforementioned five attributes are shown in Figure FIGREF11. Although the attribute score distributions on three corpora are similar, they also have disparities: 1) Outliers frequently appear among all the distributions, which exhibits the uneven dialogue complexity. 2) In terms of query-relatedness and continuity, to our surprise, the medians of the two distributions on PersonaChat are obviously smaller than the corresponding distributions on DailyDialog and OpenSubtitles. PersonaChat is manually created by crowd-sourcing, while DailyDialog and OpenSubtitles are collected from almost real-life conversations. 3) With respect to the model confidence (the negative loss value), the median of PersonaChat is relatively smaller, which illustrates that it is more difficult for the neural dialogue generation model to learn from PersonaChat. ### Curriculum Plausibility ::: Dialogue Analysis ::: Attributes Independence
So far, we have analyzed five dialogue attributes. A question might be raised that how well the proposed attributes correlate with each other. To validate the correlations of these conversation attributes, we summarize the statistics of the Kendall $\tau $ correlations for each dataset in Table TABREF12. We find that these attributes, in general, show little correlations with each other. This partially validates that dialogue complexity involves multiple perspectives. ### Curriculum Dialogue Learning
We propose an adaptive multi-curricula learning framework to accelerate dialogue learning and improve the performance of the neural dialogue generation model. ### Curriculum Dialogue Learning ::: Single Curriculum Dialogue Learning
We first illustrate how a dialogue generation model exploits the curriculum by taking single curriculum dialogue learning as an example, where the curriculum is arranged by sorting each sample in the dialogue training set $\mathcal {D}_{train}$ according to one attribute. Then, at training time step $t$, a batch of training examples is sampled from the top $f(t)$ portions of the total sorted training samples, where the progressing function $f(t)$ determines the learning rate of the curriculum. Following BIBREF9, we define the progressing function $f(t)$ as $f(t)\triangleq min(1, \sqrt{t\frac{1-c_0^2}{T} + c_0^2})$, where $c_0 > 0$ is set to 0.01 and $T$ is the duration of curriculum learning. At the early stage of the training process, the neural dialogue generation model learns from the samples drawing from the front part of the curriculum. As the advance of the curriculum, the difficulty gradually increases, as more complex training examples appear. After training $T$ batches, each batch of training instances is drawn from the whole training set, which is same as the conventional training procedure without a curriculum. ### Curriculum Dialogue Learning ::: Adaptive Multi-curricula Learning
Dialogue complexity consists of multi-perspectives of attributes. We extend the naive single curriculum learning into the multi-curricula setting, where we provide the neural dialogue generation model with five different learning curricula, and each curriculum is prepared by ordering the training set in terms of the corresponding attribute metric accordingly. Scheduling multiple curricula in the same learning pace is obviously inappropriate. Enlightened by the phenomenon that children usually adjust the learning progress of multiple curricula dynamically in order to acquire a good mark, we further introduce an adaptive multi-curricula learning framework, to automatically choose different curricula at different learning stages according to the learning status of the neural dialogue generation model. The adaptive multi-curricula learning framework is established upon the reinforcement learning (RL) paradigm. Figure FIGREF18 illustrates the overall learning process. The multi-curricula learning scheme is scheduled according to the model's performance on the validation set, where the scheduling mechanism acts as the policy $\pi $ interacting with the dialogue model to acquire the learning status $s$. The reward of the multi-curricula learning mechanism $m_t$ indicates how well the current dialogue model performs. A positive reward is expected if a multi-curricula scheduling action $a_t$ brings improvements on the model's performance, and the current mini-batch of training samples is drawn consulting with the scheduling action $a_t$. The neural dialogue generation model learns from those mini-batches, resulting with a new learning status $s_{t+1}$. The adaptive multi-curricula learning framework is optimized to maximize the reward. Such learning process loops continuously until the performance of the neural dialogue generation model converges. More specifically, the learning status of the dialogue model is represented as the state. Similar to other curriculum learning framework BIBREF18, BIBREF19, the learning status consists of several features, including the passed mini-batch number, the average historical training loss, the loss value on the training data, the margin value of predicted probabilities and the last validation metric values. To enable the proposed framework to be aware of the learning progress $\varrho _i$ regarding each attribute $i$, we also exploit $\varrho =\lbrace \varrho _0, \varrho _1, \cdots , \varrho _{k-1}\rbrace $ for state representations, where $k$ stands for the number of curricula, here $k=5$, and $\varrho _i$ can be simply measured as the learning steps on the attribute $i$. The multi-curricula learning framework samples a scheduling action $a_t$ per step by its policy $\Phi _\theta (a|s)$ with parameters $\theta $ to be learnt, and the scheduling action $a_t \in \lbrace 0, 1, \cdots , k-1\rbrace $ chooses one of the curricula. Then, a mini-batch of dialogue instances is sampled from the top $f(\varrho _i)$ portions of the chosen curriculum. The dialogue model is validated every $\Gamma $ training steps and the curriculum policy is updated at $\Gamma $-round intervals according to a reward $m_\Gamma $. To accelerate the neural dialogue learning, $m_\Gamma $ is defined as the ratio of two consecutive performance deviations on a held-out validation set: $m_\Gamma =\frac{\delta _{\Gamma }}{\delta _{\Gamma _{\text{prev}}}} - 1$. The performance deviation $\delta _{\Gamma }$ is calculated in terms of 13 automatic evaluation metrics $\lbrace \xi _1, \xi _2, \cdots , \xi _{13}\rbrace $ used in the experiments: where $\xi _i^{\Gamma }$ is the evaluation score of metric $i$ computed at the current validation turn and $\xi _i^{\Gamma _{\text{prev}}}$ is computed at the previous validation turn. Each score is normalized into $[0,1]$. The curriculum policy is trained by maximizing the expected reward: $J(\theta )=\mathbb {E}_{\Phi _\theta (a|s)}[M(s,a)]$, where $M(s,a)$ is the state-action value function. Since $M(s,a)$ is non-differentiable w.r.t. $\theta $, in this work, we use REINFORCE BIBREF20, a likelihood ratio policy gradient algorithm to optimize $J(\theta )$ based on the gradient: where $v_t$ is the sampled estimation of reward $M(s_t, a_t)$ from one episode execution of the policy $\Phi _\theta (a|s)$. In our implementation, $v_t$ is computed as the terminal reward $m_\Gamma $. ### Experiments ::: Experiment Settings
We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6. We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6. ### Experiments ::: Implementation and Reproducibility
Our experiments are performed using ParlAI BIBREF27. Regarding model implementations, we employ a 2-layer bidirectional LSTM as the encoder and a unidirectional one as the decoder for the SEQ2SEQ and CVAE. The hidden size is set to 512, and the latent size is set to 64 for CVAE. For the Transformer, the hidden size, attention heads and number of hidden layers are set to 512, 8 and 6, respectively. In terms of HRED and DialogWAE, the utterance encoder is a bidirectional GRU with 512 hidden units in each direction. The context encoder and decoder are both GRUs with 512 hidden units. Regarding the curriculum length $T$, we set its value in the following manner: we train the baseline model using the vanilla training procedure and compute the number of training steps it takes to reach approximately 110% of its final loss value. We then set $T$ to this value. Each model is trained using two protocols: the vanilla training procedure without using any curriculum and our proposed adaptive multi-curricula learning procedure, keeping other configurations the same. ### Experiments ::: Overall Performance and Human Evaluation
The automatic evaluation results of our proposed multi-curricula learning framework and the comparison models are listed in Table TABREF21. Compared with the vanilla training procedure, our curriculum learning framework 1) brings solid improvements for all the five dialogue models regarding almost all the evaluation metrics, 2) achieves competitive performance across three datasets, affirming the superiority and general applicability of our proposed framework. We also notice that the relative improvements of Distinct on OpenSubtitles are much larger (up to 122.46%) than the other two experiment datasets. We conjecture that the OpenSubtitles, with extremely uneven-complexity dialogue samples, benefits more from the multi-curricula learning paradigm. We conduct a human evaluation to validate the effectiveness of the proposed multi-curricula learning framework. We employ the DailyDialog as the evaluation corpus since it is closer to our daily conversations and easier for humans to make the judgment. We randomly sampled 100 cases from the test set and compared the generated responses of the models trained with the vanilla learning procedure and the multi-curricula learning framework. Three annotators, who have no knowledge about which system the response is from, are then required to evaluate among win (response$_1$ is better), loss (response$_2$ is better) and tie (they are equally good or bad) independently, considering four aspects: coherence, logical consistency, fluency and diversity. Cases with different rating results are counted as “tie”. Table TABREF25 reveals the results of the subjective evaluation. We observe that our multi-curricula learning framework outperforms the vanilla training method on all the five dialogue models and the kappa scores indicate that the annotators came to a fair agreement in the judgment. We checked the cases on which the vanilla training method loses to our multi-curricula learning method and found that the vanilla training method usually leads to irrelevant, generic and repetitive responses, while our method effectively alleviates such defects. ### Experiments ::: Model Analysis ::: Single vs Multi-curricula
To further glean the insights regarding the effects of the five conversational attributes on the proposed learning framework, we conduct the ablation test using the SEQ2SEQ model by only exploiting a single attribute during the curriculum learning. Table TABREF26 reports the ablation test results on the DailyDialog. We observe that the curriculum learning leads to consistent performance improvements, even with one single conversational attribute. When applying the multi-curricula learning method to the model, we observe the nearly best performance. ### Experiments ::: Model Analysis ::: Effects of Adaptive Multi-curricula Learning
Adaptive multi-curricula learning enables the model to choose different curricula at different learning stages according to the learning status of the underlying model. As shown in Table TABREF27, we notice the performance drops when replacing the RL-based curriculum policy with the random policy, indicating that choosing different curricula according to the learning status of the model benefits the model training. When training the model with anti-curriculum learning, i.e., feeding examples to the model in the complex-to-easy manner, we also observe consistent performance decreases, affirming the effectiveness of the easy-to-complex learning manner. ### Experiments ::: Model Analysis ::: Learning Efficiency
Figure FIGREF28 shows comparative results when training the SEQ2SEQ model on DailyDialog with different training protocols. As shown in Figure FIGREF28, our training method accelerates the learning effectively and consistently outperforms the baseline by a large margin in most cases. ### Experiments ::: Model Analysis ::: Multi-curricula Learning Route
To glean insights on how the proposed adaptive multi-curricula learning framework performs, we present the choosing curriculum distributions $\pi (a_t|s_t)$ during the model learning in Figure FIGREF29. We notice that the model focuses more on the curriculum of “query-relatedness” at the initial learning stage. As the learning proceeds, the model gradually turns its attention to other curricula. At the final stage, the model pays more attention to the “model confidence” curriculum. Such dynamic learning route is quite similar to the human learning behavior. ### Experiments ::: Model Analysis ::: Examples with Different Learning Frequencies
As shown in Table TABREF30, the most frequently learnt examples are comprehensively far better than those seldom learnt examples, which exhibits the effectiveness of the adaptive multi-curricula learning framework. ### Related Work
Neural dialogue generation. Neural generation models for dialogue, despite their ubiquity in current research, are still far from the real-world applications. Previous approaches enhancing neural dialogue generation models mainly focus on the learning systems by incorporating extra information to the dialogue models such as relevant dialogue history BIBREF5, topics BIBREF28, emotions BIBREF3, out-sourcing knowledge BIBREF4 or exemplars BIBREF29. Latent variables BIBREF0, BIBREF2 also benefit the model with more diverse response generations. In contrast with the previous researches, which pay most attention to the underlying dialogue models, in this work, we concentrate on the dialogue learning process and investigate how the performance of existing dialogue models can be improved on the conversation corpora with varying levels of complexity, by simply adapting the training protocols. BIBREF30 attributed the generic/uninteresting responses to the high-entropy utterances in the training set and proposed to improve dataset quality through data filtering. Though straightforward, the filtering threshold need be carefully chosen to prevent the data size decreasing too much. BIBREF8, BIBREF31 proposed to investigate instance weighting into dialogue systems. However, it is difficult to accurately define the “weight” of an example in conversation systems, since the dialogue data is of high diversity and complexity. Our proposed adaptive multi-curricula learning framework, concentrating on different curricula at evolving learning process according to the learning status of the underlying model, enables dialogue systems gradually proceed from easy to more complex samples in training and thus efficiently improves the response quality. Curriculum learning in NLP. BIBREF18 examined curriculum learning and demonstrated empirically that such curriculum approaches indeed help decrease training times and sometimes even improve generalization. BIBREF32 managed curriculum learning as an optimization problem. Curriculum learning has also been applied to many NLP tasks. To name a few, BIBREF10 applied self-paced learning for neural question answering. BIBREF33 proposed a curriculum learning based natural answer generation framework, dealing with low-quality QA-pairs first and then gradually learn more complete answers. BIBREF34 proposed curriculum pointer-generator networks for reading comprehension over long narratives. BIBREF9 applied curriculum learning for neural machine translation (NMT), aiming to reduce the need for specialized training heuristics and boost the performance of existing NMT systems. In our work, instead of organizing the curriculum only from a single aspect, we provide an adaptive multi-curricula dialogue learning framework, grounding our analysis on five conversation attributes regarding the dialogue complexity. ### Conclusion
In this paper, we propose an adaptive multi-curricula dialogue learning framework, to enable the dialogue models to gradually proceed from easy samples to more complex ones in training. We first define and analyze five conversational attributes regarding the complexity and easiness of dialogue samples, and then present an adaptive multi-curricula learning framework, which chooses different curricula at different training stages according to the learning status of the model. Extensive experiments conducted on three large-scale datasets and five state-of-the-art conversation models show that our proposed learning framework is able to boost the performance of existing dialogue systems. ### Acknowledgments
This work is supported by the National Natural Science Foundation of China-Joint Fund for Basic Research of General Technology under Grant U1836111 and U1736106. Hongshen Chen and Yonghao Song are the corresponding authors. Table 1: Examples of dialogues with different complexities in OpenSubtitles. Figure 1: Violin plot with whiskers regarding five conversation attributes in PersonaChat, DailyDialog and OpenSubtitles. For ease of comparison, the model confidence score is normalized by min-max normalization with the maximum and minimum confidence score on whole corpora. Table 2: Kendall τ correlations among the proposed conversational attributes on three datasets: (a) PersonaChat (b) DailyDialog and (c) OpenSubtitles. Figure 2: Overview of the proposed adaptive multi-curricula learning framework for neural dialogue generation. At training step t, the curriculum policy chooses one of the curricula to learn and the progressing function defines the learning progress on the selected curriculum. Table 3: Automatic evaluation results (%) on the test set of three datasets: (a) PersonaChat, (b) DailyDialog and (c) OpenSubtitles. “N” denotes that the model is trained using our proposed framework. The metrics Average, Extrema, Greedy and Coherence are abbreviated as Avg, Ext, Gre and Coh, respectively. The best results in each group are highlighted with bold. Table 4: The results of human evaluation on DailyDialog. Figure 4: Trajectory of the curriculum learning action distribution π(at|st) during the learning process. The bold line shows the mean and the shaded region shows the standard deviation. Figure 3: Comparison of the vanilla training and adaptive multi-curricula learning for six evaluation metrics with SEQ2SEQ on the validation set of DailyDialog. Dist-1, Intra1, Embedding Extrema and Ent-2 are denoted as “Distinct”, “Intra-dist”, “Embedding” and “Entropy”, respectively. Table 5: Ablation test (%) for the proposed five curriculum learning attributes on the validation set of the DailyDialog dataset with the SEQ2SEQ model. Table 6: Ablation test (%) of the curriculum learning framework on the DailyDialog dataset with SEQ2SEQ. Table 7: Examples with top and tail learning frequencies of the SEQ2SEQ model on DailyDialog, using the proposed learning framework.
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SEQ2SEQ, CVAE, Transformer, HRED, DialogWAE
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what types of features were used?
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### Introduction
Fake news are written and published with the intent to mislead in order to gain financially or politically, often targeting specific user groups. Another type of harmful content on the Internet are the so-called click-baits, which are distinguished by their sensational, exaggerated, or deliberately false headlines that grab attention and deceive the user into clicking an article with questionable content. While the motives behind these two types of fake news are different, they constitute a growing problem as they constitute a sizable fraction of the online news that users encounter on a daily basis. With the recent boom of Internet, mobile, and social networks, the spread of fake news increases exponentially. Using on-line methods for spreading harmful content makes the task of keeping the Internet clean significantly harder as it is very easy to publish an article and there is no easy way to verify its veracity. Currently, domains that consistently spread misinformation are being banned from various platforms, but this is a rather inefficient way to deal with fake news as websites that specialize in spreading misinformation are reappearing with different domain names. That is why our method is based purely on text analysis, without taking into account the domain name or website's reliability as a source of information. Our work is focused on exploring various stylistic and lexical features in order to detect misleading content, and on experiments with neural network architectures in order to evaluate how deep learning can be used for detecting fake news. Moreover, we created various language-specific resources that could be used in future work on fake news and clickbait detection for Bulgarian, including task-specific word embeddings and various lexicons and dictionaries extracted from the training data. ### Related Work
Trustworthiness and veracity analytics of on-line statements is an emerging research direction BIBREF0 . This includes predicting credibility of information shared in social media BIBREF1 , stance classification BIBREF2 and contradiction detection in rumours BIBREF3 . For example, Castillo:2011:ICT:1963405.1963500 studied the problem of finding false information about a newsworthy event. They compiled their own dataset, focusing on tweets using a variety of features including user reputation, author writing style, and various time-based features. Canini:2011 analysed the interaction of content and social network structure, and Morris:2012:TBU:2145204.2145274 studied how Twitter users judge truthfulness. They found that this is hard to do based on content alone, and instead users are influenced by heuristics such as user name. Rumour detection in social media represents yet another angle of information credibility. zubiaga2015analysing studied how people handle rumours in social media. They found that users with higher reputation are more trusted, and thus can spread rumours among other users without raising suspicions about the credibility of the news or of its source. lukasik-cohn-bontcheva:2015:ACL-IJCNLP and Ma:2015:DRU used temporal patterns to detect rumours and to predict their frequency, PlosONE:2016 focused on conversational threads, and RANLP2017:factchecking:external used deep learning to verify claims using the Web as a corpus. Veracity of information has been also studied in the context of online personal blogs BIBREF4 , community question answering forums BIBREF5 , and political debates BIBREF6 . Astroturfing and misinformation detection represent another relevant research direction. Their importance is motivated by the strong interest from political science, and research methods are driven by the presence of massive streams of micro-blogging data, e.g., on Twitter BIBREF7 . While astroturfing has been primarily studied in microblogs such as Twitter, here we focus on on-line news and click-baits instead. Identification of malicious accounts in social networks is another related research direction. This includes detecting spam accounts BIBREF8 , BIBREF9 , fake accounts BIBREF10 , BIBREF11 , compromised accounts and phishing accounts BIBREF12 . Fake profile detection has also been studied in the context of cyber-bullying BIBREF13 . A related problem is that of Web spam detection, which was addressed as a text classification problem BIBREF14 , e.g., using spam keyword spotting BIBREF15 , lexical affinity of arbitrary words to spam content BIBREF16 , frequency of punctuation and word co-occurrence BIBREF17 . Fake news detection is most closely related to the present work. While social media have been seen for years as the main vehicle for spreading information of questionable veracity, recently there has been a proliferation of fake news, often spread on social media, but also published in specialized websites. This has attracted research attention recently. For example, there has been work on studying credibility, trust, and expertise in news communities BIBREF18 . The credibility of the information published in on-line news portals has been questioned by a number of researchers BIBREF19 , BIBREF20 , BIBREF21 . As timing is crucial when it comes to publishing breaking news, it is simply not possible to double-check the facts and the sources, as is usually standard in respectable printed newspapers and magazines. This is one of the biggest concerns about on-line news media that journalists have BIBREF22 . Finally, conroy2015automatic review various methods for detecting fake news, e.g., using linguistic analysis, discourse, linked data, and social network features. All the above work was for English. The only work on fact checking for Bulgarian is that of BIBREF23 , but they focused on distinguishing serious news from humorous ones. In contrast, here we are interested in finding news that are not designed to sound funny, but to make the reader believe they are real. Unlike them, we use a deep learning approach. ### Fake News & Click-bait Dataset
We use a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned. The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks. The dataset was prepared for the Hack the Fake News hackathon. It was provided by the Bulgarian Association of PR Agencies and is available in Gitlab. The corpus was automatically collected, and then annotated by students of journalism. Each entry in the dataset consists of the following elements: URL of the original article, date of publication, article heading, article content, a label indicating whether the article is fake or not, and another label indicating whether it is a click-bait. The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; we further have 761 testing examples. However, there is 98% correlation between fake news and click-baits, i.e., a model trained on fake news would do well on click-baits and vice versa. Thus, below we focus on fake news detection only. One important aspect about the training dataset is that it contains many repetitions. This should not be surprising as it attempts to represent a natural distribution of factual vs. fake news on-line over a period of time. As publishers of fake news often have a group of websites that feature the same deceiving content, we should expect some repetition. In particular, the training dataset contains 434 unique articles with duplicates. These articles have three reposts each on average, with the most reposted article appearing 45 times. If we take into account the labels of the reposted articles, we can see that if an article is reposted, it is more likely to be fake news. The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322. We detect the duplicates based on their titles as far as they are distinctive enough and the content is sometimes slightly modified when reposted. This supports the hypothesis that fake news websites are likely to repost their content. This is also in line with previous research BIBREF24 , which has found it beneficial to find a pattern of how a rumour is reposted over time. ### Method
We propose a general framework for finding fake news focusing on the text only. We first create some resources, e.g., dictionaries of words strongly correlated with fake news, which are needed for feature extraction. Then, we design features that model a number of interesting aspects about an article, e.g., style, intent, etc. Moreover, we use a deep neural network to learn task-specific representations of the articles, which includes an attention mechanism that can focus on the most discriminative sentences and words. ### Language Resources
As our work is the first attempt at predicting click-baits in Bulgarian, it is organized around building new language-specific resources and analyzing the task. Word embeddings: We train 300-dimensional domain-specific word embeddings using word2vec BIBREF25 on 100,000 Bulgarian news articles from the same sources as the main dataset. The labelled dataset we use in our system is a subset of these articles. Finally, we end up with 207,270 unique words that occur in five or more documents. We use these embeddings for text representation, and as an input to our attention-based nevural network. Latent Dirichlet allocation (LDA): We use LDA BIBREF26 in order to build domain-specific topic models, which could be useful for inducing classes of words that signal fake/factual news. The LDA model is trained on the same 100,000 Bulgarian news articles as for training the word embeddings. In our experiments, these LDA classes proved helpful by themselves, but they did not have much to offer on top of the word embeddings. Thus, we ended up not using them in our final system, but we chose to still release them as other researchers might find them useful in the future. Fact-checking lexicon: Using lexicons of sentiment words has been shown to be very successful for the task of sentiment analysis BIBREF27 , and we applied the same idea to extract a fact-checking lexicon. In particular, we use point-wise mutual information (PMI) to find terms (words, word bi-grams, and named entities) that are highly correlated with the fake/factual news class. We calculated the PMI scores for uni-grams, bi-grams and on extracted named entities. Table TABREF9 shows some of the most significant words for the fake news class. We can see in the table some words that grab people attention, but are not very informative by themselves, such as mysterious or phenomenon. These words are largely context-independent and are likely to remain stable in their usage across different domains and even over an extended period of time. Thus, they should be useful beyond this task and this dataset. Other lexicons: Finally, we create four lexicons that can help to model the difference in language use between fake and factual news articles. In particular, we explored and merged/cleansed a number of on-line resources in order to put together the following lexicons: (i) common typos in Bulgarian written text, (ii) Bulgarian slang words, (iii) commonly used foreign words, and (iv) English words with Bulgarian equivalents. We separate the latter two, because of the frequent usage of English words in common language. We make these lexicons freely available for future research. ### Features
Fake news are written with the intent to deceive, and their authors often use a different style of writing compared to authors that create genuine content. This could be either deliberately, e.g., if the author wants to adapt the text to a specific target group or wants to provoke some particular emotional reaction in the reader, or unintentionally, e.g., because the authors of fake news have different writing style and personality compared to journalists in mainstream media. Disregarding the actual reason, we use features from author profiling and style detection BIBREF28 . Use of specific words that have strong correlation with one of the classes (48 features). We used the above-described PMI-based fact-checking lexicons to extract features based on the presence of lexicon words in the target article. We end up with the following features: 16 for uni-grams + 16 for bi-grams + 16 for named entities, where we have a feature for the sum and also for the average of the word scores for each of the target classes (click-bait, non-click-bait, fake, non-fake), and we had these features separately for the title and for the body of the article. Readability index (4 features): We calculate standard readability metrics including the type-token ratio, average word length, Flesch–Kincaid readability test BIBREF29 and Gunning-Fog index BIBREF30 . The last two metrics give scores to the text corresponding to the school grade the reader of the target article should have in order to be able to read and understand it easily. These metrics use statistics about the number of syllables, the number of words, and their length. Orthographic features (12 features): The orthographic features used in our system include: the number of words in the title and in the content; the number of characters in the title and in the content; the number of specific symbols in the title and in the content, counting the following as symbols $.!;#?:-+%(), ; the number of capital letters in the title and in the content; the fraction of capital letters to all letters in the title and in the content; the number of URLs in the content; the overlap between the words from the title and the words of the content, relying on the fact that click-baits tend to have content that does not quite match their title. These features can be very effective for modelling the author's style. Use of irregular vocabulary (4 features): During the initial analysis of our training dataset, we noticed the presence of a high number of foreign words. As it is not common in Bulgarian news articles to use words in another language, we thought that their presence could be a valuable feature to use. One of the reasons for their occurrence might be that they were translated from a foreign resource, or that they were borrowed. We further found that many articles that were labelled as fake news contained a high number of slang words, and we added this as a feature as well. Finally, we have a feature that counts the typos in the text. General lexical features are often used in natural language processing as they are somewhat task-independent and reasonably effective in terms of classification accuracy. In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). We should note that TF.IDF features should be used with caution as they may not remain relevant over time or in different contexts without retraining. The last type of hand-crafted features that we used are the grammatical features. First, we evaluate how often stop words are used in the content of the article. Extensive usage of stop words may indicate irregularities in the text, which would be missed by the above features. Additionally, we extract ten coarse-grained part-of-speech tags from the content of the article and we use part-of-speech occurrence ratios as features. This makes a total of twenty features, as we have separate features for the title and for the contents. All the above features are hand-crafted, evaluating a specific text metric or checking whether specific words highly correlate with one of the classes. However, we lack features that target the semantic representation of the text itself. Thus, we further use two types of word representations. Word embeddings (601 features). As we said above, we trained domain-specific word embeddings. In order to incorporate them as features, we calculate the average vector for the title and separately for the content of the news article. We end up with two 300-dimensional embedding representations of the semantics of the articles, which we use as 300+300=600 features. We also compute the cosine similarity between the average vector of the title and the average vector of the content, because we believe that this is a highly indicative measure for at least click-bait articles, whose content differs from what their title says. Task-specific embeddings. As a more advanced representation, we feed the text into an attention-based deep neural network, which we train to produce a task-specific embedding of the news articles. The network is designed to recognize words and sentences that contribute to the click-bait class attribution. The architecture is described in details in Section UID15 ### Some Features that we Ignored
As we mentioned above, our method is purely text-based. Thus, we ignored the publishing date of the article. In future work, it could be explored as a useful piece of information about the credibility of the article, as there is interesting research in this direction BIBREF24 . We also disregarded the article source (the URL) because websites that specialize in producing and distributing fake content are often banned and then later reappear under another name. We recognize that the credibility of a specific website could be a very informative feature, but, for the sake of creating a robust method for fake news detection, our system relies only on the text when predicting whether the target article is likely to be fake. We describe our features in more detail below. ### Model
Our framework for fake news detection is comprised of two components, which are used one after the other. First, we have an attention-based deep neural network model, which focuses on the segments of the text that are most indicative of the target class identification, and as a side effect learns task-specific representations of the news articles. We extract these representations from the last hidden layer in the network, and we feed it to the SVM classifier together with the hand-crafted features. The attention network BIBREF31 , BIBREF32 is a powerful mechanism, inspired by the human ability to spot important sections in images or text. We adopt the approach used in BIBREF33 and employ an attention neural networks to build attention over the text of a piece of news with respect to the title it has. As far as it is in the nature of click-baits to have titles that are different from the text of the news, the attentional layers of the neural network should spot when the two texts talk about the same thing and when they are not corresponding or accurate. We implemented the attention mechanism using Keras BIBREF34 with the Tensorflow back-end BIBREF35 . The architecture of the network with attention layers is shown in Figure FIGREF16 . Our neural model is based on Gated Recurrent Units (GRUs). GRUs are gating mechanism in RNNs which provide the ability to learn long-term dependencies and were first introduced in BIBREF36 . Given the document embedding, the GRUs build representations using input and forget gates, which help storing the valuable information through time. They build embeddings of the title and the text of the news, where at each step the unit has information only about the output from the previous step. This can be considered as a drawback, as far as we would considerably benefit if each step could construct its decision based not only on the previous step's output, but on all of the words that were processed so far. To improve this, the attention layer, for each step in the text sequence, uses the output of the steps in the title sequence. Thus, the layer learns weights, designating the strength of the relatedness between each word in the title and each word in the content. For the neural network, we take the first 50 symbols of the title and the content of the news, which we choose after experiments. We train the neural network for 20 epochs and the final classification is derived with sigmoid activation. The optimizer used for the training is Adam optimizer. We feed the neural network with the embedding of the words we built earlier with word2vec. As we will see below, the neural network is inferior in terms of performance to a feature-rich SVM (even though it performs well above the baseline). This is because it only has access to word embeddings, and does not use the manually-crafted features. Yet, its hidden layer represents a 128-dimensional task-specific embedding of the input article, and it turns out that using it as a list of 128 features in the SVM classifier yields even further great improvement, as we will see below. In this way, we combine a deep neural network with an attention mechanism with kernel-based SVM. We feed the above-described hand-crafted features together with the task-specific embeddings learned by the deep neural neural network (a total of 1,892 attributes combined) into a Support Vector Machines (SVM) classifier BIBREF37 . SVMs have proven to perform well in different classification settings, including in the case of small and noisy datasets. ### Experiments and Evaluation
We trained on the 2,815 training examples, and we tested on the 761 testing ones. The test dataset was provided apart from the training one, thus we didn't have to partition the original dataset to receive a testing one. The validation of the models was performed on a randomly chosen subset of sentences - one fifth of the original set. We scaled each feature individually by its maximum absolute value to end up with each feature having values in the [0;1] interval. We used an RBF kernel for the SVM, and we tuned the values of INLINEFORM0 and INLINEFORM1 using cross-validation. We trained the neural network using RMSProp BIBREF38 with a learning rate of 0.001 and mini-batches of size 32, chosen by performing experiments with cross-validation . We evaluated the model after each epoch and we kept the one that performed best on the development dataset. Table TABREF17 shows the performance of the features in groups as described in Section SECREF7 . We can see that, among the hand-crafted features, the lexical features yield the best results, i.e., words are the most indicative features. The good results of the stylometric features indicate that the intricacies of language use are highly discriminative. The next group is the one with the grammatical features, which shows good performance in terms of Precision. The last one are the embedding features, which although having low individual performance, contribute to the overall performance of the system as shown in next paragraph. Evaluating the final model, we set as a baseline the prediction of the majority class, i.e., the fake news class. This baseline has an F1 of 41.59% and accuracy of 71.22%. The performance of the built models can be seen in Table TABREF19 . Another stable baseline, apart from just taking the majority class, is the TF.IDF bag-of-words approach, which sets a high bar for the general model score. We then observe how much the attention mechanism embeddings improve the score (AttNN). Finally, we add the hand-crafted features (Feats), which further improve the performance. From the results, we can conclude that both the attention-based task-specific embeddings and the manual features are important for the task of finding fake news. ### Conclusion and Future Work
We have presented the first attempt to solve the fake news problem for Bulgarian. Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. We further produced and shared a number of relevant language resources for Bulgarian, which we created for solving the task. The evaluation results are encouraging and suggest the potential applicability of our approach in a real-world scenario. They further show the potential of combining attention-based task-specific embeddings with manually crafted features. An important advantage of the attention-based neural networks is that the produced representations can be easily visualized and potentially interpreted as shown in BIBREF31 . We consider the implementation of such visualization as an important future work on the task. ### Acknowledgements
We would like to thank Lachezar Bozhkov, who was part of our team in the Hack the Fake News hackathon, for his insight. This work is supported by the NSF of Bulgaria under Grant No. DN-02/11/2016 - ITDGate. Table 1: Words most strongly associated with the fake news class. Table 2: Performance of the individual groups of hand-crafted features. Figure 1: The architecture of our hierarchical attention deep neural network for click-bait news detection. Table 3: Performance of different models.
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stylometric, lexical, grammatical, and semantic
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How do Bal and Ethaniel feel about the humans?
A. Bal and Ethaniel think humans are very similar beings to themselves.
B. Bal and Ethaniel think humans are crude, rough, and desperate.
C. Bal and Ethaniel think humans are not very intelligent and superstitious.
D. Bal and Ethaniel are scared of the humans because humans seem to be trigger-happy.
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SECOND LANDING By FLOYD WALLACE A gentle fancy for the Christmas Season—an oft-told tale with a wistful twistful of Something that left the Earth with a wing and a prayer. Earth was so far away that it wasn't visible. Even the sun was only a twinkle. But this vast distance did not mean that isolation could endure forever. Instruments within the ship intercepted radio broadcasts and, within the hour, early TV signals. Machines compiled dictionaries and grammars and began translating the major languages. The history of the planet was tabulated as facts became available. The course of the ship changed slightly; it was not much out of the way to swing nearer Earth. For days the two within the ship listened and watched with little comment. They had to decide soon. "We've got to make or break," said the first alien. "You know what I'm in favor of," said the second. "I can guess," said Ethaniel, who had spoken first. "The place is a complete mess. They've never done anything except fight each other—and invent better weapons." "It's not what they've done," said Bal, the second alien. "It's what they're going to do, with that big bomb." "The more reason for stopping," said Ethaniel. "The big bomb can destroy them. Without our help they may do just that." "I may remind you that in two months twenty-nine days we're due in Willafours," said Bal. "Without looking at the charts I can tell you we still have more than a hundred light-years to go." "A week," said Ethaniel. "We can spare a week and still get there on time." "A week?" said Bal. "To settle their problems? They've had two world wars in one generation and that the third and final one is coming up you can't help feeling in everything they do." "It won't take much," said Ethaniel. "The wrong diplomatic move, or a trigger-happy soldier could set it off. And it wouldn't have to be deliberate. A meteor shower could pass over and their clumsy instruments could interpret it as an all-out enemy attack." "Too bad," said Bal. "We'll just have to forget there ever was such a planet as Earth." "Could you? Forget so many people?" "I'm doing it," said Bal. "Just give them a little time and they won't be here to remind me that I have a conscience." "My memory isn't convenient," said Ethaniel. "I ask you to look at them." Bal rustled, flicking the screen intently. "Very much like ourselves," he said at last. "A bit shorter perhaps, and most certainly incomplete. Except for the one thing they lack, and that's quite odd, they seem exactly like us. Is that what you wanted me to say?" "It is. The fact that they are an incomplete version of ourselves touches me. They actually seem defenseless, though I suppose they're not." "Tough," said Bal. "Nothing we can do about it." "There is. We can give them a week." "In a week we can't negate their entire history. We can't begin to undo the effect of the big bomb." "You can't tell," said Ethaniel. "We can look things over." "And then what? How much authority do we have?" "Very little," conceded Ethaniel. "Two minor officials on the way to Willafours—and we run directly into a problem no one knew existed." "And when we get to Willafours we'll be busy. It will be a long time before anyone comes this way again." "A very long time. There's nothing in this region of space our people want," said Ethaniel. "And how long can Earth last? Ten years? Even ten months? The tension is building by the hour." "What can I say?" said Bal. "I suppose we can stop and look them over. We're not committing ourselves by looking." They went much closer to Earth, not intending to commit themselves. For a day they circled the planet, avoiding radar detection, which for them was not difficult, testing, and sampling. Finally Ethaniel looked up from the monitor screen. "Any conclusions?" "What's there to think? It's worse than I imagined." "In what way?" "Well, we knew they had the big bomb. Atmospheric analysis showed that as far away as we were." "I know." "We also knew they could deliver the big bomb, presumably by some sort of aircraft." "That was almost a certainty. They'd have no use for the big bomb without aircraft." "What's worse is that I now find they also have missiles, range one thousand miles and upward. They either have or are near a primitive form of space travel." "Bad," said Ethaniel. "Sitting there, wondering when it's going to hit them. Nervousness could set it off." "It could, and the missiles make it worse," said Bal. "What did you find out at your end?" "Nothing worthwhile. I was looking at the people while you were investigating their weapons." "You must think something." "I wish I knew what to think. There's so little time," Ethaniel said. "Language isn't the difficulty. Our machines translate their languages easily and I've taken a cram course in two or three of them. But that's not enough, looking at a few plays, listening to advertisements, music, and news bulletins. I should go down and live among them, read books, talk to scholars, work with them, play." "You could do that and you'd really get to know them. But that takes time—and we don't have it." "I realize that." "A flat yes or no," said Bal. "No. We can't help them," said Ethaniel. "There is nothing we can do for them—but we have to try." "Sure, I knew it before we started," said Bal. "It's happened before. We take the trouble to find out what a people are like and when we can't help them we feel bad. It's going to be that way again." He rose and stretched. "Well, give me an hour to think of some way of going at it." It was longer than that before they met again. In the meantime the ship moved much closer to Earth. They no longer needed instruments to see it. The planet revolved outside the visionports. The southern plains were green, coursed with rivers; the oceans were blue; and much of the northern hemisphere was glistening white. Ragged clouds covered the pole, and a dirty pall spread over the mid-regions of the north. "I haven't thought of anything brilliant," said Ethaniel. "Nor I," said Bal. "We're going to have to go down there cold. And it will be cold." "Yes. It's their winter." "I did have an idea," said Bal. "What about going down as supernatural beings?" "Hardly," said Ethaniel. "A hundred years ago it might have worked. Today they have satellites. They are not primitives." "I suppose you're right," said Bal. "I did think we ought to take advantage of our physical differences." "If we could I'd be all for it. But these people are rough and desperate. They wouldn't be fooled by anything that crude." "Well, you're calling it," said Bal. "All right," said Ethaniel. "You take one side and I the other. We'll tell them bluntly what they'll have to do if they're going to survive, how they can keep their planet in one piece so they can live on it." "That'll go over big. Advice is always popular." "Can't help it. That's all we have time for." "Special instructions?" "None. We leave the ship here and go down in separate landing craft. You can talk with me any time you want to through our communications, but don't unless you have to." "They can't intercept the beams we use." "They can't, and even if they did they wouldn't know what to do with our language. I want them to think that we don't need to talk things over." "I get it. Makes us seem better than we are. They think we know exactly what we're doing even though we don't." "If we're lucky they'll think that." Bal looked out of the port at the planet below. "It's going to be cold where I'm going. You too. Sure we don't want to change our plans and land in the southern hemisphere? It's summer there." "I'm afraid not. The great powers are in the north. They are the ones we have to reach to do the job." "Yeah, but I was thinking of that holiday you mentioned. We'll be running straight into it. That won't help us any." "I know, they don't like their holidays interrupted. It can't be helped. We can't wait until it's over." "I'm aware of that," said Bal. "Fill me in on that holiday, anything I ought to know. Probably religious in origin. That so?" "It was religious a long time ago," said Ethaniel. "I didn't learn anything exact from radio and TV. Now it seems to be chiefly a time for eating, office parties, and selling merchandise." "I see. It has become a business holiday." "That's a good description. I didn't get as much of it as I ought to have. I was busy studying the people, and they're hard to pin down." "I see. I was thinking there might be some way we could tie ourselves in with this holiday. Make it work for us." "If there is I haven't thought of it." "You ought to know. You're running this one." Bal looked down at the planet. Clouds were beginning to form at the twilight edge. "I hate to go down and leave the ship up here with no one in it." "They can't touch it. No matter how they develop in the next hundred years they still won't be able to get in or damage it in any way." "It's myself I'm thinking about. Down there, alone." "I'll be with you. On the other side of the Earth." "That's not very close. I'd like it better if there were someone in the ship to bring it down in a hurry if things get rough. They don't think much of each other. I don't imagine they'll like aliens any better." "They may be unfriendly," Ethaniel acknowledged. Now he switched a monitor screen until he looked at the slope of a mountain. It was snowing and men were cutting small green trees in the snow. "I've thought of a trick." "If it saves my neck I'm for it." "I don't guarantee anything," said Ethaniel. "This is what I was thinking of: instead of hiding the ship against the sun where there's little chance it will be seen, we'll make sure that they do see it. Let's take it around to the night side of the planet and light it up." "Say, pretty good," said Bal. "They can't imagine that we'd light up an unmanned ship," said Ethaniel. "Even if the thought should occur to them they'll have no way of checking it. Also, they won't be eager to harm us with our ship shining down on them." "That's thinking," said Bal, moving to the controls. "I'll move the ship over where they can see it best and then I'll light it up. I'll really light it up." "Don't spare power." "Don't worry about that. They'll see it. Everybody on Earth will see it." Later, with the ship in position, glowing against the darkness of space, pulsating with light, Bal said: "You know, I feel better about this. We may pull it off. Lighting the ship may be just the help we need." "It's not we who need help, but the people of Earth," said Ethaniel. "See you in five days." With that he entered a small landing craft, which left a faintly luminescent trail as it plunged toward Earth. As soon as it was safe to do so, Bal left in another craft, heading for the other side of the planet. And the spaceship circled Earth, unmanned, blazing and pulsing with light. No star in the winter skies of the planet below could equal it in brilliancy. Once a man-made satellite came near but it was dim and was lost sight of by the people below. During the day the ship was visible as a bright spot of light. At evening it seemed to burn through the sunset colors. And the ship circled on, bright, shining, seeming to be a little piece clipped from the center of a star and brought near Earth to illuminate it. Never, or seldom, had Earth seen anything like it. In five days the two small landing craft that had left it arched up from Earth and joined the orbit of the large ship. The two small craft slid inside the large one and doors closed behind them. In a short time the aliens met again. "We did it," said Bal exultantly as he came in. "I don't know how we did it and I thought we were going to fail but at the last minute they came through." Ethaniel smiled. "I'm tired," he said, rustling. "Me too, but mostly I'm cold," said Bal, shivering. "Snow. Nothing but snow wherever I went. Miserable climate. And yet you had me go out walking after that first day." "From my own experience it seemed to be a good idea," said Ethaniel. "If I went out walking one day I noticed that the next day the officials were much more cooperative. If it worked for me I thought it might help you." "It did. I don't know why, but it did," said Bal. "Anyway, this agreement they made isn't the best but I think it will keep them from destroying themselves." "It's as much as we can expect," said Ethaniel. "They may have small wars after this, but never the big one. In fifty or a hundred years we can come back and see how much they've learned." "I'm not sure I want to," said Bal. "Say, what's an angel?" "Why?" "When I went out walking people stopped to look. Some knelt in the snow and called me an angel." "Something like that happened to me," said Ethaniel. "I didn't get it but I didn't let it upset me," said Bal. "I smiled at them and went about my business." He shivered again. "It was always cold. I walked out, but sometimes I flew back. I hope that was all right." In the cabin Bal spread his great wings. Renaissance painters had never seen his like but knew exactly how he looked. In their paintings they had pictured him innumerable times. "I don't think it hurt us that you flew," said Ethaniel. "I did so myself occasionally." "But you don't know what an angel is?" "No. I didn't have time to find out. Some creature of their folklore I suppose. You know, except for our wings they're very much like ourselves. Their legends are bound to resemble ours." "Sure," said Bal. "Anyway, peace on Earth." THE END Transcriber's Note: This etext was produced from Amazing Science Fiction Stories January 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.
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A. Bal and Ethaniel think humans are very similar beings to themselves.
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What datasets were used?
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### Introduction
Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIBREF2 , and opinion summarization BIBREF3 , among others. Despite the vast literature on opinion mining, the task on suggestion mining has given little attention. Suggestion mining BIBREF4 is the task of collecting and categorizing suggestions about a certain product. This is important because while opinions indirectly give hints on how to improve a product (e.g. analyzing reviews), suggestions are direct improvement requests (e.g. tips, advice, recommendations) from people who have used the product. To this end, BIBREF5 organized a shared task specifically on suggestion mining called SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. The shared task is composed of two subtasks, Subtask A and B. In Subtask A, systems are tasked to predict whether a sentence of a certain domain (i.e. electronics) entails a suggestion or not given a training data of the same domain. In Subtask B, systems are tasked to do suggestion prediction of a sentence from another domain (i.e. hotels). Organizers observed four main challenges: (a) sparse occurrences of suggestions; (b) figurative expressions; (c) different domains; and (d) complex sentences. While previous attempts BIBREF6 , BIBREF4 , BIBREF7 made use of human-engineered features to solve this problem, the goal of the shared task is to leverage the advancements seen on neural networks, by providing a larger dataset to be used on data-intensive models to achieve better performance. This paper describes our system JESSI (Joint Encoders for Stable Suggestion Inference). JESSI is built as a combination of two neural-based encoders using multiple pre-trained word embeddings, including BERT BIBREF8 , a pre-trained deep bidirectional transformer that is recently reported to perform exceptionally well across several tasks. The main intuition behind JESSI comes from our finding that although BERT gives exceptional performance gains when applied to in-domain samples, it becomes unstable when applied to out-of-domain samples, even when using a domain adversarial training BIBREF9 module. This problem is mitigated using two tricks: (1) jointly training BERT with a CNN-based encoder, and (2) using an RNN-based encoder on top of BERT before feeding to the classifier. JESSI is trained using only the datasets given on the shared task, without using any additional external data. Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%. ### Joint Encoders for Stable Suggestion Inference
We present our model JESSI, which stands for Joint Encoders for Stable Suggestion Inference, shown in Figure FIGREF4 . Given a sentence INLINEFORM0 , JESSI returns a binary suggestion label INLINEFORM1 . JESSI consists of four important components: (1) A BERT-based encoder that leverages general knowledge acquired from a large pre-trained language model, (2) A CNN-based encoder that learns task-specific sentence representations, (3) an MLP classifier that predicts the label given the joint encodings, and (4) a domain adversarial training module that prevents the model to distinguish between the two domains. ### Experiments
In this section, we show our results and experiments. We denote JESSI-A as our model for Subtask A (i.e., BERT INLINEFORM0 CNN+CNN INLINEFORM1 Att), and JESSI-B as our model for Subtask B (i.e., BERT INLINEFORM2 BiSRU+CNN INLINEFORM3 Att+DomAdv). The performance of the models is measured and compared using the F1-score. ### Conclusion
We presented JESSI (Joint Encoders for Stable Suggestion Inference), our system for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI builds upon jointly combined encoders, borrowing pre-trained knowledge from a language model BERT and a translation model CoVe. We found that BERT alone performs bad and unstably when tested on out-of-domain samples. We mitigate the problem by appending an RNN-based sentence encoder above BERT, and jointly combining a CNN-based encoder. Results from the shared task show that JESSI performs competitively among participating models, obtaining second place on Subtask A with an F-Score of 77.78%. It also performs well on Subtask B, with an F-Score of 79.59%, even without using any additional external data. ### Acknowledgement
This research was supported by the MSIT (Ministry of Science ICT), Korea, under (National Program for Excellence in SW) (2015-0-00910) and (Artificial Intelligence Contact Center Solution) (2018-0-00605) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) Figure 1: The overall architecture of JESSI for Subtask B. The thinner arrows correspond to the forward propagations, while the thicker arrows correspond to the backward propagations, where gradient calculations are indicated. For Subtask A, a CNN encoder is used instead of the BiSRU encoder, and the domain adversarial training module is not used. Table 1: Dataset Statistics Table 2: Ablation results for both subtasks using the provided trial sets. The + denotes a replacement of the BERT-based encoder, while the – denotes a removal of a specific component. Table 3: Summary statistics of the F-Scores of 10 runs of different models on the trial set of Subtask B when doing a 10-fold validation over the available training data. All models include the domain adversarial training module (+DOMADV), which is omitted for brevity. Table 4: F-Scores of JESSI and top three models for each subtask. Due to time constraints, we were not able to submit JESSI-B during the competition. For clarity, we also show our final official submission (CNN→ATT+DOMADV). Figure 2: Accuracy over various input sentence length on the test set.
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datasets given on the shared task, without using any additional external data
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What does the older man know the younger man will do?
A. exactly as he's been told
B. invent the next great invention
C. change the future
D. fight with him and try not to go
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... and it comes out here By LESTER DEL REY Illustrated by DON SIBLEY [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There is one fact no sane man can quarrel with ... everything has a beginning and an end. But some men aren't sane; thus it isn't always so! No, you're wrong. I'm not your father's ghost, even if I do look a bit like him. But it's a longish story, and you might as well let me in. You will, you know, so why quibble about it? At least, you always have ... or do ... or will. I don't know, verbs get all mixed up. We don't have the right attitude toward tenses for a situation like this. Anyhow, you'll let me in. I did, so you will. Thanks. You think you're crazy, of course, but you'll find out you aren't. It's just that things are a bit confused. And don't look at the machine out there too long—until you get used to it, you'll find it's hard on the eyes, trying to follow where the vanes go. You'll get used to it, of course, but it will take about thirty years. You're wondering whether to give me a drink, as I remember it. Why not? And naturally, since we have the same tastes, you can make the same for me as you're having. Of course we have the same tastes—we're the same person. I'm you thirty years from now, or you're me. I remember just how you feel; I felt the same way when he—that is, of course, I or we—came back to tell me about it, thirty years ago. Here, have one of these. You'll get to like them in a couple more years. And you can look at the revenue stamp date, if you still doubt my story. You'll believe it eventually, though, so it doesn't matter. Right now, you're shocked. It's a real wrench when a man meets himself for the first time. Some kind of telepathy seems to work between two of the same people. You sense things. So I'll simply go ahead talking for half an hour or so, until you get over it. After that you'll come along with me. You know, I could try to change things around by telling what happened to me; but he—I—told me what I was going to do, so I might as well do the same. I probably couldn't help telling you the same thing in the same words, even if I tried—and I don't intend to try. I've gotten past that stage in worrying about all this. So let's begin when you get up in half an hour and come out with me. You'll take a closer look at the machine, then. Yes, it'll be pretty obvious it must be a time machine. You'll sense that, too. You've seen it, just a small little cage with two seats, a luggage compartment, and a few buttons on a dash. You'll be puzzling over what I'll tell you, and you'll be getting used to the idea that you are the man who makes atomic power practical. Jerome Boell, just a plain engineer, the man who put atomic power in every home. You won't exactly believe it, but you'll want to go along. I'll be tired of talking by then, and in a hurry to get going. So I cut off your questions, and get you inside. I snap on a green button, and everything seems to cut off around us. You can see a sort of foggy nothing surrounding the cockpit; it is probably the field that prevents passage through time from affecting us. The luggage section isn't protected, though. You start to say something, but by then I'm pressing a black button, and everything outside will disappear. You look for your house, but it isn't there. There is exactly nothing there—in fact, there is no there . You are completely outside of time and space, as best you can guess how things are. You can't feel any motion, of course. You try to reach a hand out through the field into the nothing around you and your hand goes out, all right, but nothing happens. Where the screen ends, your hand just turns over and pokes back at you. Doesn't hurt, and when you pull your arm back, you're still sound and uninjured. But it looks frightening and you don't try it again. Then it comes to you slowly that you're actually traveling in time. You turn to me, getting used to the idea. "So this is the fourth dimension?" you ask. Then you feel silly, because you'll remember that I said you'd ask that. Well, I asked it after I was told, then I came back and told it to you, and I still can't help answering when you speak. "Not exactly," I try to explain. "Maybe it's no dimension—or it might be the fifth; if you're going to skip over the so-called fourth without traveling along it, you'd need a fifth. Don't ask me. I didn't invent the machine and I don't understand it." "But...." I let it go, and so do you. If you don't, it's a good way of going crazy. You'll see later why I couldn't have invented the machine. Of course, there may have been a start for all this once. There may have been a time when you did invent the machine—the atomic motor first, then the time-machine. And when you closed the loop by going back and saving yourself the trouble, it got all tangled up. I figured out once that such a universe would need some seven or eight time and space dimensions. It's simpler just to figure that this is the way time got bent back on itself. Maybe there is no machine, and it's just easier for us to imagine it. When you spend thirty years thinking about it, as I did—and you will—you get further and further from an answer. Anyhow, you sit there, watching nothing all around you, and no time, apparently, though there is a time effect back in the luggage space. You look at your watch and it's still running. That means you either carry a small time field with you, or you are catching a small increment of time from the main field. I don't know, and you won't think about that then, either. I'm smoking, and so are you, and the air in the machine is getting a bit stale. You suddenly realize that everything in the machine is wide open, yet you haven't seen any effects of air loss. "Where are we getting our air?" you ask. "Or why don't we lose it?" "No place for it to go," I explain. There isn't. Out there is neither time nor space, apparently. How could the air leak out? You still feel gravity, but I can't explain that, either. Maybe the machine has a gravity field built in, or maybe the time that makes your watch run is responsible for gravity. In spite of Einstein, you have always had the idea that time is an effect of gravity, and I sort of agree, still. Then the machine stops—at least, the field around us cuts off. You feel a dankish sort of air replace the stale air, and you breathe easier, though we're in complete darkness, except for the weak light in the machine, which always burns, and a few feet of rough dirty cement floor around. You take another cigaret from me and you get out of the machine, just as I do. I've got a bundle of clothes and I start changing. It's a sort of simple, short-limbed, one-piece affair I put on, but it feels comfortable. "I'm staying here," I tell you. "This is like the things they wear in this century, as near as I can remember it, and I should be able to pass fairly well. I've had all my fortune—the one you make on that atomic generator—invested in such a way I can get it on using some identification I've got with me, so I'll do all right. I know they still use some kind of money, you'll see evidence of that. And it's a pretty easygoing civilization, from what I could see. We'll go up and I'll leave you. I like the looks of things here, so I won't be coming back with you." You nod, remembering I've told you about it. "What century is this, anyway?" I'd told you that, too, but you've forgotten. "As near as I can guess, it's about 2150. He told me, just as I'm telling you, that it's an interstellar civilization." You take another cigaret from me, and follow me. I've got a small flashlight and we grope through a pile of rubbish, out into a corridor. This is a sub-sub-sub-basement. We have to walk up a flight of stairs, and there is an elevator waiting, fortunately with the door open. "What about the time machine?" you ask. "Since nobody ever stole it, it's safe." We get in the elevator, and I say "first" to it. It gives out a coughing noise and the basement openings begin to click by us. There's no feeling of acceleration—some kind of false gravity they use in the future. Then the door opens, and the elevator says "first" back at us. It's obviously a service elevator and we're in a dim corridor, with nobody around. I grab your hand and shake it. "You go that way. Don't worry about getting lost; you never did, so you can't. Find the museum, grab the motor, and get out. And good luck to you." You act as if you're dreaming, though you can't believe it's a dream. You nod at me and I move out into the main corridor. A second later, you see me going by, mixed into a crowd that is loafing along toward a restaurant, or something like it, that is just opening. I'm asking questions of a man, who points, and I turn and move off. You come out of the side corridor and go down a hall, away from the restaurant. There are quiet little signs along the hall. You look at them, realizing for the first time that things have changed. Steij:neri, Faunten, Z:rgat Dispenseri. The signs are very quiet and dignified. Some of them can be decoded to stationery shops, fountains, and the like. What a zergot is, you don't know. You stop at a sign that announces: Trav:l Biwrou—F:rst-Clas Twrz—Marz, Viin*s, and x: Trouj:n Planets. Spej:l reits tu aol s*nz wixin 60 lyt iirz! But there is only a single picture of a dull-looking metal sphere, with passengers moving up a ramp, and the office is closed. You begin to get the hang of the spelling they use, though. Now there are people around you, but nobody pays much attention to you. Why should they? You wouldn't care if you saw a man in a leopard-skin suit; you'd figure it was some part in a play and let it go. Well, people don't change much. You get up your courage and go up to a boy selling something that might be papers on tapes. "Where can I find the Museum of Science?" "Downayer rien turn lefa the sign. Stoo bloss," he tells you. Around you, you hear some pretty normal English, but there are others using stuff as garbled as his. The educated and uneducated? I don't know. You go right until you find a big sign built into the rubbery surface of the walk: Miuzi:m *v Syens . There's an arrow pointing and you turn left. Ahead of you, two blocks on, you can see a pink building, with faint aqua trimming, bigger than most of the others. They are building lower than they used to, apparently. Twenty floors up seems about the maximum. You head for it, and find the sidewalk is marked with the information that it is the museum. You go up the steps, but you see that it seems to be closed. You hesitate for a moment, then. You're beginning to think the whole affair is complete nonsense, and you should get back to the time machine and go home. But then a guard comes to the gate. Except for the short legs in his suit and the friendly grin on his face, he looks like any other guard. What's more, he speaks pretty clearly. Everyone says things in a sort of drawl, with softer vowels and slurred consonants, but it's rather pleasant. "Help you, sir? Oh, of course. You must be playing in 'Atoms and Axioms.' The museum's closed, but I'll be glad to let you study whatever you need for realism in your role. Nice show. I saw it twice." "Thanks," you mutter, wondering what kind of civilization can produce guards as polite as that. "I—I'm told I should investigate your display of atomic generators." He beams at that. "Of course." The gate is swung to behind you, but obviously he isn't locking it. In fact, there doesn't seem to be a lock. "Must be a new part. You go down that corridor, up one flight of stairs and left. Finest display in all the known worlds. We've got the original of the first thirteen models. Professor Jonas was using them to check his latest theory of how they work. Too bad he could not explain the principle, either. Someone will, some day, though. Lord, the genius of that twentieth century inventor! It's quite a hobby with me, sir. I've read everything I could get on the period. Oh—congratulations on your pronunciation. Sounds just like some of our oldest tapes." You get away from him, finally, after some polite thanks. The building seems deserted and you wander up the stairs. There's a room on your right filled with something that proclaims itself the first truly plastic diamond former, and you go up to it. As you come near, it goes through a crazy wiggle inside, stops turning out a continual row of what seem to be bearings, and slips something the size of a penny toward you. "Souvenir," it announces in a well-modulated voice. "This is a typical gem of the twentieth century, properly cut to 58 facets, known technically as a Jaegger diamond, and approximately twenty carats in size. You can have it made into a ring on the third floor during morning hours for one-tenth credit. If you have more than one child, press the red button for the number of stones you desire." You put it in your pocket, gulping a little, and get back to the corridor. You turn left and go past a big room in which models of spaceships—from the original thing that looks like a V-2, and is labeled first Lunar rocket, to a ten-foot globe, complete with miniature manikins—are sailing about in some kind of orbits. Then there is one labeled Wep:nz , filled with everything from a crossbow to a tiny rod four inches long and half the thickness of a pencil, marked Fynal Hand Arm . Beyond is the end of the corridor, and a big place that bears a sign, Mad:lz *v Atamic Pau:r Sorsez . By that time, you're almost convinced. And you've been doing a lot of thinking about what you can do. The story I'm telling has been sinking in, but you aren't completely willing to accept it. You notice that the models are all mounted on tables and that they're a lot smaller than you thought. They seem to be in chronological order, and the latest one, marked 2147—Rincs Dyn*pat: , is about the size of a desk telephone. The earlier ones are larger, of course, clumsier, but with variations, probably depending on the power output. A big sign on the ceiling gives a lot of dope on atomic generators, explaining that this is the first invention which leaped full blown into basically final form. You study it, but it mentions casually the inventor, without giving his name. Either they don't know it, or they take it for granted that everyone does, which seems more probable. They call attention to the fact that they have the original model of the first atomic generator built, complete with design drawings, original manuscript on operation, and full patent application. They state that it has all major refinements, operating on any fuel, producing electricity at any desired voltage up to five million, any chosen cyclic rate from direct current to one thousand megacycles, and any amperage up to one thousand, its maximum power output being fifty kilowatts, limited by the current-carrying capacity of the outputs. They also mention that the operating principle is still being investigated, and that only such refinements as better alloys and the addition of magnetric and nucleatric current outlets have been added since the original. So you go to the end and look over the thing. It's simply a square box with a huge plug on each side, and a set of vernier controls on top, plus a little hole marked, in old-style spelling, Drop BBs or wire here . Apparently that's the way it's fueled. It's about one foot on each side. "Nice," the guard says over your shoulder. "It finally wore out one of the cathogrids and we had to replace that, but otherwise it's exactly as the great inventor made it. And it still operates as well as ever. Like to have me tell you about it?" "Not particularly," you begin, and then realize bad manners might be conspicuous here. While you're searching for an answer, the guard pulls something out of his pocket and stares at it. "Fine, fine. The mayor of Altasecarba—Centaurian, you know—is arriving, but I'll be back in about ten minutes. He wants to examine some of the weapons for a monograph on Centaurian primitives compared to nineteenth century man. You'll pardon me?" You pardon him pretty eagerly and he wanders off happily. You go up to the head of the line, to that Rinks Dynapattuh, or whatever it transliterates to. That's small and you can carry it. But the darned thing is absolutely fixed. You can't see any bolts, but you can't budge it, either. You work down the line. It'd be foolish to take the early model if you can get one with built-in magnetic current terminals—Ehrenhaft or some other principle?—and nuclear binding-force energy terminals. But they're all held down by the same whatchamaycallem effect. And, finally, you're right back beside the original first model. It's probably bolted down, too, but you try it tentatively and you find it moves. There's a little sign under it, indicating you shouldn't touch it, since the gravostatic plate is being renewed. Well, you won't be able to change the time cycle by doing anything I haven't told you, but a working model such as that is a handy thing. You lift it; it only weighs about fifty pounds! Naturally, it can be carried. You expect a warning bell, but nothing happens. As a matter of fact, if you'd stop drinking so much of that scotch and staring at the time machine out there now, you'd hear what I'm saying and know what will happen to you. But of course, just as I did, you're going to miss a lot of what I say from now on, and have to find out for yourself. But maybe some of it helps. I've tried to remember how much I remembered, after he told me, but I can't be sure. So I'll keep on talking. I probably can't help it, anyhow. Pre-set, you might say. Well, you stagger down the corridor, looking out for the guard, but all seems clear. Then you hear his voice from the weapons room. You bend down and try to scurry past, but you know you're in full view. Nothing happens, though. You stumble down the stairs, feeling all the futuristic rays in the world on your back, and still nothing happens. Ahead of you, the gate is closed. You reach it and it opens obligingly by itself. You breathe a quick sigh of relief and start out onto the street. Then there's a yell behind you. You don't wait. You put one leg in front of the other and you begin racing down the walk, ducking past people, who stare at you with expressions you haven't time to see. There's another yell behind you. Something goes over your head and drops on the sidewalk just in front of your feet, with a sudden ringing sound. You don't wait to find out about that, either. Somebody reaches out a hand to catch you and you dart past. The street is pretty clear now and you jolt along, with your arms seeming to come out of the sockets, and that atomic generator getting heavier at every step. Out of nowhere, something in a blue uniform about six feet tall and on the beefy side appears—and the badge hasn't changed much. The cop catches your arm and you know you're not going to get away, so you stop. "You can't exert yourself that hard in this heat, fellow," the cop says. "There are laws against that, without a yellow sticker. Here, let me grab you a taxi." Reaction sets in a bit and your knees begin to buckle, but you shake your head and come up for air. "I—I left my money home," you begin. The cop nods. "Oh, that explains it. Fine, I won't have to give you an appearance schedule. But you should have come to me." He reaches out and taps a pedestrian lightly on the shoulder. "Sir, an emergency request. Would you help this gentleman?" The pedestrian grins, looks at his watch, and nods. "How far?" You did notice the name of the building from which you came and you mutter it. The stranger nods again, reaches out and picks up the other side of the generator, blowing a little whistle the cop hands him. Pedestrians begin to move aside, and you and the stranger jog down the street at a trot, with a nice clear path, while the cop stands beaming at you both. That way, it isn't so bad. And you begin to see why I decided I might like to stay in the future. But all the same, the organized cooperation here doesn't look too good. The guard can get the same and be there before you. And he is. He stands just inside the door of the building as you reach it. The stranger lifts an eyebrow and goes off at once when you nod at him, not waiting for thanks. And the guard comes up, holding some dinkus in his hand, about the size of a big folding camera and not too dissimilar in other ways. He snaps it open and you get set to duck. "You forgot the prints, monograph, and patent applications," he says. "They go with the generator—we don't like to have them separated. A good thing I knew the production office of 'Atoms and Axioms' was in this building. Just let us know when you're finished with the model and we'll pick it up." You swallow several sets of tonsils you had removed years before, and take the bundle of papers he hands you out of the little case. He pumps you for some more information, which you give him at random. It seems to satisfy your amiable guard friend. He finally smiles in satisfaction and heads back to the museum. You still don't believe it, but you pick up the atomic generator and the information sheets, and you head down toward the service elevator. There is no button on it. In fact, there's no door there. You start looking for other doors or corridors, but you know this is right. The signs along the halls are the same as they were. Then there's a sort of cough and something dilates in the wall. It forms a perfect door and the elevator stands there waiting. You get in, gulping out something about going all the way down, and then wonder how a machine geared for voice operation can make anything of that. What the deuce would that lowest basement be called? But the elevator has closed and is moving downward in a hurry. It coughs again and you're at the original level. You get out—and realize you don't have a light. You'll never know what you stumbled over, but, somehow, you move back in the direction of the time machine, bumping against boxes, staggering here and there, and trying to find the right place by sheer feel. Then a shred of dim light appears; it's the weak light in the time machine. You've located it. You put the atomic generator in the luggage space, throw the papers down beside it, and climb into the cockpit, sweating and mumbling. You reach forward toward the green button and hesitate. There's a red one beside it and you finally decide on that. Suddenly, there's a confused yell from the direction of the elevator and a beam of light strikes against your eyes, with a shout punctuating it. Your finger touches the red button. You'll never know what the shouting was about—whether they finally doped out the fact that they'd been robbed, or whether they were trying to help you. You don't care which it is. The field springs up around you and the next button you touch—the one on the board that hasn't been used so far—sends you off into nothingness. There is no beam of light, you can't hear a thing, and you're safe. It isn't much of a trip back. You sit there smoking and letting your nerves settle back to normal. You notice a third set of buttons, with some pencil marks over them—"Press these to return to yourself 30 years"—and you begin waiting for the air to get stale. It doesn't because there is only one of you this time. Instead, everything flashes off and you're sitting in the machine in your own back yard. You'll figure out the cycle in more details later. You get into the machine in front of your house, go to the future in the sub-basement, land in your back yard, and then hop back thirty years to pick up yourself, landing in front of your house. Just that. But right then, you don't care. You jump out and start pulling out that atomic generator and taking it inside. It isn't hard to disassemble, but you don't learn a thing; just some plates of metal, some spiral coils, and a few odds and ends—all things that can be made easily enough, all obviously of common metals. But when you put it together again, about an hour later, you notice something. Everything in it is brand-new and there's one set of copper wires missing! It won't work. You put some #12 house wire in, exactly like the set on the other side, drop in some iron filings, and try it again. And with the controls set at 120 volts, 60 cycles and 15 amperes, you get just that. You don't need the power company any more. And you feel a little happier when you realize that the luggage space wasn't insulated from time effects by a field, so the motor has moved backward in time, somehow, and is back to its original youth—minus the replaced wires the guard mentioned—which probably wore out because of the makeshift job you've just done. But you begin getting more of a jolt when you find that the papers are all in your own writing, that your name is down as the inventor, and that the date of the patent application is 1951. It will begin to soak in, then. You pick up an atomic generator in the future and bring it back to the past—your present—so that it can be put in the museum with you as the inventor so you can steal it to be the inventor. And you do it in a time machine which you bring back to yourself to take yourself into the future to return to take back to yourself.... Who invented what? And who built which? Before long, your riches from the generator are piling in. Little kids from school are coming around to stare at the man who changed history and made atomic power so common that no nation could hope to be anything but a democracy and a peaceful one—after some of the worst times in history for a few years. Your name eventually becomes as common as Ampere, or Faraday, or any other spelled without a capital letter. But you're thinking of the puzzle. You can't find any answer. One day you come across an old poem—something about some folks calling it evolution and others calling it God. You go out, make a few provisions for the future, and come back to climb into the time machine that's waiting in the building you had put around it. Then you'll be knocking on your own door, thirty years back—or right now, from your view—and telling your younger self all these things I'm telling you. But now.... Well, the drinks are finished. You're woozy enough to go along with me without protest, and I want to find out just why those people up there came looking for you and shouting, before the time machine left. Let's go.
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A. exactly as he's been told
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Why are the Soetti allowed to board the ship?
A. They need transport to Jorgenson’s Worlds as well.
B. They need to check the papers of each passenger, so the caption allows them to do so.
C. The Soetti aren’t - the captain fears them and they are illegally boarding.
D. The captain and Mr. Tony are in business with them.
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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."
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D. The captain and Mr. Tony are in business with them.
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What is Orison's main reason for going to floor seven?
A. To figure out what escudo green meant
B. To have a good reason to get fired
C. To find out what else is happening at the bank
D. To give Dink a message
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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.
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C. To find out what else is happening at the bank
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Which other unsupervised models are used for comparison?
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### Introduction
Improving unsupervised learning is of key importance for advancing machine learning methods, as to unlock access to almost unlimited amounts of data to be used as training resources. The majority of recent success stories of deep learning does not fall into this category but instead relied on supervised training (in particular in the vision domain). A very notable exception comes from the text and natural language processing domain, in the form of semantic word embeddings trained unsupervised BIBREF0 , BIBREF1 , BIBREF2 . Within only a few years from their invention, such word representations – which are based on a simple matrix factorization model as we formalize below – are now routinely trained on very large amounts of raw text data, and have become ubiquitous building blocks of a majority of current state-of-the-art NLP applications. While very useful semantic representations are available for words, it remains challenging to produce and learn such semantic embeddings for longer pieces of text, such as sentences, paragraphs or entire documents. Even more so, it remains a key goal to learn such general-purpose representations in an unsupervised way. Currently, two contrary research trends have emerged in text representation learning: On one hand, a strong trend in deep-learning for NLP leads towards increasingly powerful and complex models, such as recurrent neural networks (RNNs), LSTMs, attention models and even Neural Turing Machine architectures. While extremely strong in expressiveness, the increased model complexity makes such models much slower to train on larger datasets. On the other end of the spectrum, simpler “shallow” models such as matrix factorizations (or bilinear models) can benefit from training on much larger sets of data, which can be a key advantage, especially in the unsupervised setting. Surprisingly, for constructing sentence embeddings, naively using averaged word vectors was shown to outperform LSTMs (see BIBREF3 for plain averaging, and BIBREF4 for weighted averaging). This example shows potential in exploiting the trade-off between model complexity and ability to process huge amounts of text using scalable algorithms, towards the simpler side. In view of this trade-off, our work here further advances unsupervised learning of sentence embeddings. Our proposed model can be seen as an extension of the C-BOW BIBREF0 , BIBREF1 training objective to train sentence instead of word embeddings. We demonstrate that the empirical performance of our resulting general-purpose sentence embeddings very significantly exceeds the state of the art, while keeping the model simplicity as well as training and inference complexity exactly as low as in averaging methods BIBREF3 , BIBREF4 , thereby also putting the work by BIBREF4 in perspective. Contributions. The main contributions in this work can be summarized as follows: ### Model
Our model is inspired by simple matrix factor models (bilinear models) such as recently very successfully used in unsupervised learning of word embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF5 as well as supervised of sentence classification BIBREF6 . More precisely, these models can all be formalized as an optimization problem of the form DISPLAYFORM0 for two parameter matrices INLINEFORM0 and INLINEFORM1 , where INLINEFORM2 denotes the vocabulary. Here, the columns of the matrix INLINEFORM3 represent the learnt source word vectors whereas those of INLINEFORM4 represent the target word vectors. For a given sentence INLINEFORM5 , which can be of arbitrary length, the indicator vector INLINEFORM6 is a binary vector encoding INLINEFORM7 (bag of words encoding). Fixed-length context windows INLINEFORM0 running over the corpus are used in word embedding methods as in C-BOW BIBREF0 , BIBREF1 and GloVe BIBREF2 . Here we have INLINEFORM1 and each cost function INLINEFORM2 only depends on a single row of its input, describing the observed target word for the given fixed-length context INLINEFORM3 . In contrast, for sentence embeddings which are the focus of our paper here, INLINEFORM4 will be entire sentences or documents (therefore variable length). This property is shared with the supervised FastText classifier BIBREF6 , which however uses soft-max with INLINEFORM5 being the number of class labels. ### Proposed Unsupervised Model
We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings. Conceptually, the model can be interpreted as a natural extension of the word-contexts from C-BOW BIBREF0 , BIBREF1 to a larger sentence context, with the sentence words being specifically optimized towards additive combination over the sentence, by means of the unsupervised objective function. Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0 where INLINEFORM0 is the list of n-grams (including unigrams) present in sentence INLINEFORM1 . In order to predict a missing word from the context, our objective models the softmax output approximated by negative sampling following BIBREF0 . For the large number of output classes INLINEFORM2 to be predicted, negative sampling is known to significantly improve training efficiency, see also BIBREF7 . Given the binary logistic loss function INLINEFORM3 coupled with negative sampling, our unsupervised training objective is formulated as follows: INLINEFORM4 where INLINEFORM0 corresponds to the current sentence and INLINEFORM1 is the set of words sampled negatively for the word INLINEFORM2 . The negatives are sampled following a multinomial distribution where each word INLINEFORM5 is associated with a probability INLINEFORM6 , where INLINEFORM7 is the normalized frequency of INLINEFORM8 in the corpus. To select the possible target unigrams (positives), we use subsampling as in BIBREF6 , BIBREF5 , each word INLINEFORM0 being discarded with probability INLINEFORM1 where INLINEFORM2 . Where INLINEFORM3 is the subsampling hyper-parameter. Subsampling prevents very frequent words of having too much influence in the learning as they would introduce strong biases in the prediction task. With positives subsampling and respecting the negative sampling distribution, the precise training objective function becomes DISPLAYFORM0 ### Computational Efficiency
In contrast to more complex neural network based models, one of the core advantages of the proposed technique is the low computational cost for both inference and training. Given a sentence INLINEFORM0 and a trained model, computing the sentence representation INLINEFORM1 only requires INLINEFORM2 floating point operations (or INLINEFORM3 to be precise for the n-gram case, see ( EQREF8 )), where INLINEFORM4 is the embedding dimension. The same holds for the cost of training with SGD on the objective ( EQREF10 ), per sentence seen in the training corpus. Due to the simplicity of the model, parallel training is straight-forward using parallelized or distributed SGD. Also, in order to store higher-order n-grams efficiently, we use the standard hashing trick, see e.g. BIBREF8 , with the same hashing function as used in FastText BIBREF6 , BIBREF5 . ### Comparison to C-BOW
C-BOW BIBREF0 , BIBREF1 aims to predict a chosen target word given its fixed-size context window, the context being defined by the average of the vectors associated with the words at a distance less than the window size hyper-parameter INLINEFORM0 . If our system, when restricted to unigram features, can be seen as an extension of C-BOW where the context window includes the entire sentence, in practice there are few important differences as C-BOW uses important tricks to facilitate the learning of word embeddings. C-BOW first uses frequent word subsampling on the sentences, deciding to discard each token INLINEFORM1 with probability INLINEFORM2 or alike (small variations exist across implementations). Subsampling prevents the generation of n-grams features, and deprives the sentence of an important part of its syntactical features. It also shortens the distance between subsampled words, implicitly increasing the span of the context window. A second trick consists of using dynamic context windows: for each subsampled word INLINEFORM3 , the size of its associated context window is sampled uniformly between 1 and INLINEFORM4 . Using dynamic context windows is equivalent to weighing by the distance from the focus word INLINEFORM5 divided by the window size BIBREF9 . This makes the prediction task local, and go against our objective of creating sentence embeddings as we want to learn how to compose all n-gram features present in a sentence. In the results section, we report a significant improvement of our method over C-BOW. ### Model Training
Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets. The Wikipedia and Toronto books sentences have been tokenized using the Stanford NLP library BIBREF10 , while for tweets we used the NLTK tweets tokenizer BIBREF11 . For training, we select a sentence randomly from the dataset and then proceed to select all the possible target unigrams using subsampling. We update the weights using SGD with a linearly decaying learning rate. Also, to prevent overfitting, for each sentence we use dropout on its list of n-grams INLINEFORM0 , where INLINEFORM1 is the set of all unigrams contained in sentence INLINEFORM2 . After empirically trying multiple dropout schemes, we find that dropping INLINEFORM3 n-grams ( INLINEFORM4 ) for each sentence is giving superior results compared to dropping each token with some fixed probability. This dropout mechanism would negatively impact shorter sentences. The regularization can be pushed further by applying L1 regularization to the word vectors. Encouraging sparsity in the embedding vectors is particularly beneficial for high dimension INLINEFORM5 . The additional soft thresholding in every SGD step adds negligible computational cost. See also Appendix SECREF8 . We train two models on each dataset, one with unigrams only and one with unigrams and bigrams. All training parameters for the models are provided in Table TABREF25 in the supplementary material. Our C++ implementation builds upon the FastText library BIBREF6 , BIBREF5 . We will make our code and pre-trained models available open-source. ### Related Work
We discuss existing models which have been proposed to construct sentence embeddings. While there is a large body of works in this direction – several among these using e.g. labelled datasets of paraphrase pairs to obtain sentence embeddings in a supervised manner BIBREF12 , BIBREF3 , BIBREF13 to learn sentence embeddings – we here focus on unsupervised, task-independent models. While some methods require ordered raw text i.e., a coherent corpus where the next sentence is a logical continuation of the previous sentence, others rely only on raw text i.e., an unordered collection of sentences. Finally, we also discuss alternative models built from structured data sources. ### Unsupervised Models Independent of Sentence Ordering
The ParagraphVector DBOW model BIBREF14 is a log-linear model which is trained to learn sentence as well as word embeddings and then use a softmax distribution to predict words contained in the sentence given the sentence vector representation. They also propose a different model ParagraphVector DM where they use n-grams of consecutive words along with the sentence vector representation to predict the next word. BIBREF15 also presented an early approach to obtain compositional embeddings from word vectors. They use different compositional techniques including static averaging or Fisher vectors of a multivariate Gaussian to obtain sentence embeddings from word2vec models. BIBREF16 propose a Sequential (Denoising) Autoencoder, S(D)AE. This model first introduces noise in the input data: Firstly each word is deleted with probability INLINEFORM0 , then for each non-overlapping bigram, words are swapped with probability INLINEFORM1 . The model then uses an LSTM-based architecture to retrieve the original sentence from the corrupted version. The model can then be used to encode new sentences into vector representations. In the case of INLINEFORM2 , the model simply becomes a Sequential Autoencoder. BIBREF16 also propose a variant (S(D)AE + embs.) in which the words are represented by fixed pre-trained word vector embeddings. BIBREF4 propose a model in which sentences are represented as a weighted average of fixed (pre-trained) word vectors, followed by post-processing step of subtracting the principal component. Using the generative model of BIBREF17 , words are generated conditioned on a sentence “discourse” vector INLINEFORM0 : INLINEFORM1 where INLINEFORM0 and INLINEFORM1 and INLINEFORM2 , INLINEFORM3 are scalars. INLINEFORM4 is the common discourse vector, representing a shared component among all discourses, mainly related to syntax. It allows the model to better generate syntactical features. The INLINEFORM5 term is here to enable the model to generate some frequent words even if their matching with the discourse vector INLINEFORM6 is low. Therefore, this model tries to generate sentences as a mixture of three type of words: words matching the sentence discourse vector INLINEFORM0 , syntactical words matching INLINEFORM1 , and words with high INLINEFORM2 . BIBREF4 demonstrated that for this model, the MLE of INLINEFORM3 can be approximated by INLINEFORM4 , where INLINEFORM5 is a scalar. The sentence discourse vector can hence be obtained by subtracting INLINEFORM6 estimated by the first principal component of INLINEFORM7 's on a set of sentences. In other words, the sentence embeddings are obtained by a weighted average of the word vectors stripping away the syntax by subtracting the common discourse vector and down-weighting frequent tokens. They generate sentence embeddings from diverse pre-trained word embeddings among which are unsupervised word embeddings such as GloVe BIBREF2 as well as supervised word embeddings such as paragram-SL999 (PSL) BIBREF18 trained on the Paraphrase Database BIBREF19 . In a very different line of work, C-PHRASE BIBREF20 relies on additional information from the syntactic parse tree of each sentence, which is incorporated into the C-BOW training objective. BIBREF21 show that single layer CNNs can be modeled using a tensor decomposition approach. While building on an unsupervised objective, the employed dictionary learning step for obtaining phrase templates is task-specific (for each use-case), not resulting in general-purpose embeddings. ### Unsupervised Models Depending on Sentence Ordering
The SkipThought model BIBREF22 combines sentence level models with recurrent neural networks. Given a sentence INLINEFORM0 from an ordered corpus, the model is trained to predict INLINEFORM1 and INLINEFORM2 . FastSent BIBREF16 is a sentence-level log-linear bag-of-words model. Like SkipThought, it uses adjacent sentences as the prediction target and is trained in an unsupervised fashion. Using word sequences allows the model to improve over the earlier work of paragraph2vec BIBREF14 . BIBREF16 augment FastSent further by training it to predict the constituent words of the sentence as well. This model is named FastSent + AE in our comparisons. Compared to our approach, Siamese C-BOW BIBREF23 shares the idea of learning to average word embeddings over a sentence. However, it relies on a Siamese neural network architecture to predict surrounding sentences, contrasting our simpler unsupervised objective. Note that on the character sequence level instead of word sequences, FastText BIBREF5 uses the same conceptual model to obtain better word embeddings. This is most similar to our proposed model, with two key differences: Firstly, we predict from source word sequences to target words, as opposed to character sequences to target words, and secondly, our model is averaging the source embeddings instead of summing them. ### Models requiring structured data
DictRep BIBREF24 is trained to map dictionary definitions of the words to the pre-trained word embeddings of these words. They use two different architectures, namely BOW and RNN (LSTM) with the choice of learning the input word embeddings or using them pre-trained. A similar architecture is used by the CaptionRep variant, but here the task is the mapping of given image captions to a pre-trained vector representation of these images. ### Evaluation Tasks
We use a standard set of supervised as well as unsupervised benchmark tasks from the literature to evaluate our trained models, following BIBREF16 . The breadth of tasks allows to fairly measure generalization to a wide area of different domains, testing the general-purpose quality (universality) of all competing sentence embeddings. For downstream supervised evaluations, sentence embeddings are combined with logistic regression to predict target labels. In the unsupervised evaluation for sentence similarity, correlation of the cosine similarity between two embeddings is compared to human annotators. Downstream Supervised Evaluation. Sentence embeddings are evaluated for various supervised classification tasks as follows. We evaluate paraphrase identification (MSRP) BIBREF25 , classification of movie review sentiment (MR) BIBREF26 , product reviews (CR) BIBREF27 , subjectivity classification (SUBJ) BIBREF28 , opinion polarity (MPQA) BIBREF29 and question type classification (TREC) BIBREF30 . To classify, we use the code provided by BIBREF22 in the same manner as in BIBREF16 . For the MSRP dataset, containing pairs of sentences INLINEFORM0 with associated paraphrase label, we generate feature vectors by concatenating their Sent2Vec representations INLINEFORM1 with the component-wise product INLINEFORM2 . The predefined training split is used to tune the L2 penalty parameter using cross-validation and the accuracy and F1 scores are computed on the test set. For the remaining 5 datasets, Sent2Vec embeddings are inferred from input sentences and directly fed to a logistic regression classifier. Accuracy scores are obtained using 10-fold cross-validation for the MR, CR, SUBJ and MPQA datasets. For those datasets nested cross-validation is used to tune the L2 penalty. For the TREC dataset, as for the MRSP dataset, the L2 penalty is tuned on the predefined train split using 10-fold cross-validation, and the accuracy is computed on the test set. Unsupervised Similarity Evaluation. We perform unsupervised evaluation of the learnt sentence embeddings using the sentence cosine similarity, on the STS 2014 BIBREF31 and SICK 2014 BIBREF32 datasets. These similarity scores are compared to the gold-standard human judgements using Pearson's INLINEFORM0 BIBREF33 and Spearman's INLINEFORM1 BIBREF34 correlation scores. The SICK dataset consists of about 10,000 sentence pairs along with relatedness scores of the pairs. The STS 2014 dataset contains 3,770 pairs, divided into six different categories on the basis of the origin of sentences/phrases, namely Twitter, headlines, news, forum, WordNet and images. ### Results and Discussion
In Tables TABREF18 and TABREF19 , we compare our results with those obtained by BIBREF16 on different models. Table TABREF21 in the last column shows the dramatic improvement in training time of our models (and other C-BOW-inspired models) in contrast to neural network based models. All our Sent2Vec models are trained on a machine with 2x Intel Xeon E5 INLINEFORM0 2680v3, 12 cores @2.5GHz. Along with the models discussed in Section SECREF3 , this also includes the sentence embedding baselines obtained by simple averaging of word embeddings over the sentence, in both the C-BOW and skip-gram variants. TF-IDF BOW is a representation consisting of the counts of the 200,000 most common feature-words, weighed by their TF-IDF frequencies. To ensure coherence, we only include unsupervised models in the main paper. Performance of supervised and semi-supervised models on these evaluations can be observed in Tables TABREF29 and TABREF30 in the supplementary material. Downstream Supervised Evaluation Results. On running supervised evaluations and observing the results in Table TABREF18 , we find that on an average our models are second only to SkipThought vectors. Also, both our models achieve state of the art results on the CR task. We also observe that on half of the supervised tasks, our unigrams + bigram model is the best model after SkipThought. Our models are weaker on the MSRP task (which consists of the identification of labelled paraphrases) compared to state-of-the-art methods. However, we observe that the models which perform very strongly on this task end up faring very poorly on the other tasks, indicating a lack of generalizability. On rest of the tasks, our models perform extremely well. The SkipThought model is able to outperform our models on most of the tasks as it is trained to predict the previous and next sentences and a lot of tasks are able to make use of this contextual information missing in our Sent2Vec models. For example, the TREC task is a poor measure of how one predicts the content of the sentence (the question) but a good measure of how the next sentence in the sequence (the answer) is predicted. Unsupervised Similarity Evaluation Results. In Table TABREF19 , we see that our Sent2Vec models are state-of-the-art on the majority of tasks when comparing to all the unsupervised models trained on the Toronto corpus, and clearly achieve the best averaged performance. Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items. Also, C-PHRASE uses data three times the size of the Toronto book corpus. Interestingly, our model outperforms C-PHRASE when trained on Wikipedia, as shown in Table TABREF21 , despite the fact that we use no parse tree information. Official STS 2017 benchmark. In the official results of the most recent edition of the STS 2017 benchmark BIBREF35 , our model also significantly outperforms C-PHRASE, and in fact delivers the best unsupervised baseline method. For the Siamese C-BOW model trained on the Toronto corpus, supervised evaluation as well as similarity evaluation results on the SICK 2014 dataset are unavailable. Macro Average. To summarize our contributions on both supervised and unsupervised tasks, in Table TABREF21 we present the results in terms of the macro average over the averages of both supervised and unsupervised tasks along with the training times of the models. For unsupervised tasks, averages are taken over both Spearman and Pearson scores. The comparison includes the best performing unsupervised and semi-supervised methods described in Section SECREF3 . For models trained on the Toronto books dataset, we report a 3.8 INLINEFORM0 points improvement over the state of the art. Considering all supervised, semi-supervised methods and all datasets compared in BIBREF16 , we report a 2.2 INLINEFORM1 points improvement. We also see a noticeable improvement in accuracy as we use larger datasets like Twitter and Wikipedia. We furthermore see that the Sent2Vec models are faster to train when compared to methods like SkipThought and DictRep, owing to the SGD optimizer allowing a high degree of parallelizability. We can clearly see Sent2Vec outperforming other unsupervised and even semi-supervised methods. This can be attributed to the superior generalizability of our model across supervised and unsupervised tasks. Comparison with BIBREF4 . We also compare our work with BIBREF4 who also use additive compositionality to obtain sentence embeddings. However, in contrast to our model, they use fixed, pre-trained word embeddings to build a weighted average of these embeddings using unigram probabilities. While we couldn't find pre-trained state of the art word embeddings trained on the Toronto books corpus, we evaluated their method using GloVe embeddings obtained from the larger Common Crawl Corpus, which is 42 times larger than our twitter corpus, greatly favoring their method over ours. In Table TABREF22 , we report an experimental comparison to their model on unsupervised tasks. In the table, the suffix W indicates that their down-weighting scheme has been used, while the suffix R indicates the removal of the first principal component. They report values of INLINEFORM0 as giving the best results and used INLINEFORM1 for all their experiments. We observe that our results are competitive with the embeddings of BIBREF4 for purely unsupervised methods. It is important to note that the scores obtained from supervised task-specific PSL embeddings trained for the purpose of semantic similarity outperform our method on both SICK and average STS 2014, which is expected as our model is trained purely unsupervised. In order to facilitate a more detailed comparison, we also evaluated the unsupervised Glove + WR embeddings on downstream supervised tasks and compared them to our twitter models. To use BIBREF4 's method in a supervised setup, we precomputed and stored the common discourse vector INLINEFORM0 using 2 million random Wikipedia sentences. On an average, our models outperform their unsupervised models by a significant margin, this despite the fact that they used GloVe embeddings trained on larger corpora than ours (42 times larger). Our models also outperform their semi-supervised PSL + WR model. This indicates our model learns a more precise weighing scheme than the static one proposed by BIBREF4 . The effect of datasets and n-grams. Despite being trained on three very different datasets, all of our models generalize well to sometimes very specific domains. Models trained on Toronto Corpus are the state-of-the-art on the STS 2014 images dataset even beating the supervised CaptionRep model trained on images. We also see that addition of bigrams to our models doesn't help much when it comes to unsupervised evaluations but gives a significant boost-up in accuracy on supervised tasks. We attribute this phenomenon to the ability of bigrams models to capture some non-compositional features missed by unigrams models. Having a single representation for “not good" or “very bad" can boost the supervised model's ability to infer relevant features for the corresponding classifier. For semantic similarity tasks however, the relative uniqueness of bigrams results in pushing sentence representations further apart, which can explain the average drop of scores for bigrams models on those tasks. On learning the importance and the direction of the word vectors. Our model – by learning how to generate and compose word vectors – has to learn both the direction of the word embeddings as well as their norm. Considering the norms of the used word vectors as by our averaging over the sentence, we observe an interesting distribution of the “importance” of each word. In Figure FIGREF24 we show the profile of the INLINEFORM0 -norm as a function of INLINEFORM1 for each INLINEFORM2 , and compare it to the static down-weighting mechanism of BIBREF4 . We can observe that our model is learning to down-weight frequent tokens by itself. It is also down-weighting rare tokens and the INLINEFORM3 profile seems to roughly follow Luhn's hypothesis BIBREF36 , a well known information retrieval paradigm, stating that mid-rank terms are the most significant to discriminate content. ### Conclusion
In this paper, we introduce a novel, computationally efficient, unsupervised, C-BOW-inspired method to train and infer sentence embeddings. On supervised evaluations, our method, on an average, achieves better performance than all other unsupervised competitors with the exception of SkipThought. However, SkipThought vectors show a very poor performance on sentence similarity tasks while our model is state-of-the-art for these evaluations on average. Also, our model is generalizable, extremely fast to train, simple to understand and easily interpretable, showing the relevance of simple and well-grounded representation models in contrast to the models using deep architectures. Future work could focus on augmenting the model to exploit data with ordered sentences. Furthermore, we would like to investigate the model's ability to use pre-trained embeddings for downstream transfer learning tasks. ### L1 regularization of models
Optionally, our model can be additionally improved by adding an L1 regularizer term in the objective function, leading to slightly better generalization performance. Additionally, encouraging sparsity in the embedding vectors is beneficial for memory reasons, allowing higher embedding dimensions INLINEFORM0 . We propose to apply L1 regularization individually to each word (and n-gram) vector (both source and target vectors). Formally, the training objective function ( EQREF10 ) then becomes DISPLAYFORM0 where INLINEFORM0 is the regularization parameter. Now, in order to minimize a function of the form INLINEFORM0 where INLINEFORM1 is not differentiable over the domain, we can use the basic proximal-gradient scheme. In this iterative method, after doing a gradient descent step on INLINEFORM2 with learning rate INLINEFORM3 , we update INLINEFORM4 as DISPLAYFORM0 where INLINEFORM0 is called the proximal function BIBREF37 of INLINEFORM1 with INLINEFORM2 being the proximal parameter and INLINEFORM3 is the value of INLINEFORM4 after a gradient (or SGD) step on INLINEFORM5 . In our case, INLINEFORM0 and the corresponding proximal operator is given by DISPLAYFORM0 where INLINEFORM0 corresponds to element-wise product. Similar to the proximal-gradient scheme, in our case we can optionally use the thresholding operator on the updated word and n-gram vectors after an SGD step. The soft thresholding parameter used for this update is INLINEFORM0 and INLINEFORM1 for the source and target vectors respectively where INLINEFORM2 is the current learning rate, INLINEFORM3 is the INLINEFORM4 regularization parameter and INLINEFORM5 is the sentence on which SGD is being run. We observe that INLINEFORM0 regularization using the proximal step gives our models a small boost in performance. Also, applying the thresholding operator takes only INLINEFORM1 floating point operations for the updating the word vectors corresponding to the sentence and INLINEFORM2 for updating the target as well as the negative word vectors, where INLINEFORM3 is the number of negatives sampled and INLINEFORM4 is the embedding dimension. Thus, performing INLINEFORM5 regularization using soft-thresholding operator comes with a small computational overhead. We set INLINEFORM0 to be 0.0005 for both the Wikipedia and the Toronto Book Corpus unigrams + bigrams models. Table 1: Comparison of the performance of different models on different supervised evaluation tasks. An underline indicates the best performance for the dataset. Top 3 performances in each data category are shown in bold. The average is calculated as the average of accuracy for each category (For MSRP, we take the accuracy). ) Table 2: Unsupervised Evaluation Tasks: Comparison of the performance of different models on Spearman/Pearson correlation measures. An underline indicates the best performance for the dataset. Top 3 performances in each data category are shown in bold. The average is calculated as the average of entries for each correlation measure. Table 3: Best unsupervised and semi-supervised methods ranked by macro average along with their training times. ** indicates trained on GPU. * indicates trained on a single node using 30 threads. Training times for non-Sent2Vec models are due to Hill et al. (2016a). For CPU based competing methods, we were able to reproduce all published timings (+-10%) using our same hardware as for training Sent2Vec. Table 4: Comparison of the performance of the unsupervised and semi-supervised sentence embeddings by (Arora et al., 2017) with our models. Unsupervised comparisons are in terms of Pearson’s correlation, while comparisons on supervised tasks are stating the average described in Table 1. Figure 1: Left figure: the profile of the word vector L2norms as a function of log(fw) for each vocabulary word w, as learnt by our unigram model trained on Toronto books. Right figure: down-weighting scheme proposed by Arora et al. (2017): weight(w) = a a+fw . Table 5: Training parameters for the Sent2Vec models Table 6: Comparison of the performance of different Sent2Vec models with different semisupervised/supervised models on different downstream supervised evaluation tasks. An underline indicates the best performance for the dataset and Sent2Vec model performances are bold if they perform as well or better than all other non-Sent2Vec models, including those presented in Table 1. Table 7: Unsupervised Evaluation: Comparison of the performance of different Sent2Vec models with semi-supervised/supervised models on Spearman/Pearson correlation measures. An underline indicates the best performance for the dataset and Sent2Vec model performances are bold if they perform as well or better than all other non-Sent2Vec models, including those presented in Table 2. Table 8: Average sentence lengths for the datasets used in the comparison.
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Sequential (Denoising) Autoencoder, TF-IDF BOW, SkipThought, FastSent, Siamese C-BOW, C-BOW, C-PHRASE, ParagraphVector
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What was Kolin's primary motivation in transforming to his new form?
A. Desire for power over authority
B. Desire to out-smart Johnny Ashlew
C. Desire to liberate the people of Haurtoz
D. Desire to be free from conformity
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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.
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C. Desire to liberate the people of Haurtoz
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What is Niemand's tone toward the 'stress-and-strain of modern life' theory?
A. Inconsistent
B. Ambiguous
C. Dismissive
D. Vehement
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DISTURBING SUN By PHILIP LATHAM Illustrated by Freas [Transcriber's Note: This etext was produced from Astounding Science Fiction May 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] This, be it understood, is fiction—nothing but fiction—and not, under any circumstances, to be considered as having any truth whatever to it. It's obviously utterly impossible ... isn't it? An interview with Dr. I. M. Niemand, Director of the Psychophysical Institute of Solar and Terrestrial Relations, Camarillo, California. In the closing days of December, 1957, at the meeting of the American Association for the Advancement of Science in New York, Dr. Niemand delivered a paper entitled simply, "On the Nature of the Solar S-Regions." Owing to its unassuming title the startling implications contained in the paper were completely overlooked by the press. These implications are discussed here in an exclusive interview with Dr. Niemand by Philip Latham. LATHAM. Dr. Niemand, what would you say is your main job? NIEMAND. I suppose you might say my main job today is to find out all I can between activity on the Sun and various forms of activity on the Earth. LATHAM. What do you mean by activity on the Sun? NIEMAND. Well, a sunspot is a form of solar activity. LATHAM. Just what is a sunspot? NIEMAND. I'm afraid I can't say just what a sunspot is. I can only describe it. A sunspot is a region on the Sun that is cooler than its surroundings. That's why it looks dark. It isn't so hot. Therefore not so bright. LATHAM. Isn't it true that the number of spots on the Sun rises and falls in a cycle of eleven years? NIEMAND. The number of spots on the Sun rises and falls in a cycle of about eleven years. That word about makes quite a difference. LATHAM. In what way? NIEMAND. It means you can only approximately predict the future course of sunspot activity. Sunspots are mighty treacherous things. LATHAM. Haven't there been a great many correlations announced between sunspots and various effects on the Earth? NIEMAND. Scores of them. LATHAM. What is your opinion of these correlations? NIEMAND. Pure bosh in most cases. LATHAM. But some are valid? NIEMAND. A few. There is unquestionably a correlation between sunspots and disturbances of the Earth's magnetic field ... radio fade-outs ... auroras ... things like that. LATHAM. Now, Dr. Niemand, I understand that you have been investigating solar and terrestrial relationships along rather unorthodox lines. NIEMAND. Yes, I suppose some people would say so. LATHAM. You have broken new ground? NIEMAND. That's true. LATHAM. In what way have your investigations differed from those of others? NIEMAND. I think our biggest advance was the discovery that sunspots themselves are not the direct cause of the disturbances we have been studying on the Earth. It's something like the eruptions in rubeola. Attention is concentrated on the bright red papules because they're such a conspicuous symptom of the disease. Whereas the real cause is an invisible filterable virus. In the solar case it turned out to be these S-Regions. LATHAM. Why S-Regions? NIEMAND. We had to call them something. Named after the Sun, I suppose. LATHAM. You say an S-Region is invisible? NIEMAND. It is quite invisible to the eye but readily detected by suitable instrumental methods. It is extremely doubtful, however, if the radiation we detect is the actual cause of the disturbing effects observed. LATHAM. Just what are these effects? NIEMAND. Well, they're common enough, goodness knows. As old as the world, in fact. Yet strangely enough it's hard to describe them in exact terms. LATHAM. Can you give us a general idea? NIEMAND. I'll try. Let's see ... remember that speech from "Julius Caesar" where Cassius is bewailing the evil times that beset ancient Rome? I believe it went like this: "The fault, dear Brutus, is not in our stars but in ourselves that we are underlings." LATHAM. I'm afraid I don't see— NIEMAND. Well, Shakespeare would have been nearer the truth if he had put it the other way around. "The fault, dear Brutus, is not in ourselves but in our stars" or better "in the Sun." LATHAM. In the Sun? NIEMAND. That's right, in the Sun. I suppose the oldest problem in the world is the origin of human evil. Philosophers have wrestled with it ever since the days of Job. And like Job they have usually given up in despair, convinced that the origin of evil is too deep for the human mind to solve. Generally they have concluded that man is inherently wicked and sinful and that is the end of it. Now for the first time science has thrown new light on this subject. LATHAM. How is that? NIEMAND. Consider the record of history. There are occasional periods when conditions are fairly calm and peaceful. Art and industry flourished. Man at last seemed to be making progress toward some higher goal. Then suddenly— for no detectable reason —conditions are reversed. Wars rage. People go mad. The world is plunged into an orgy of bloodshed and misery. LATHAM. But weren't there reasons? NIEMAND. What reasons? LATHAM. Well, disputes over boundaries ... economic rivalry ... border incidents.... NIEMAND. Nonsense. Men always make some flimsy excuse for going to war. The truth of the matter is that men go to war because they want to go to war. They can't help themselves. They are impelled by forces over which they have no control. By forces outside of themselves. LATHAM. Those are broad, sweeping statements. Can't you be more specific? NIEMAND. Perhaps I'd better go back to the beginning. Let me see.... It all started back in March, 1955, when I started getting patients suffering from a complex of symptoms, such as profound mental depression, anxiety, insomnia, alternating with fits of violent rage and resentment against life and the world in general. These people were deeply disturbed. No doubt about that. Yet they were not psychotic and hardly more than mildly neurotic. Now every doctor gets a good many patients of this type. Such a syndrome is characteristic of menopausal women and some men during the climacteric, but these people failed to fit into this picture. They were married and single persons of both sexes and of all ages. They came from all walks of life. The onset of their attack was invariably sudden and with scarcely any warning. They would be going about their work feeling perfectly all right. Then in a minute the whole world was like some scene from a nightmare. A week or ten days later the attack would cease as mysteriously as it had come and they would be their old self again. LATHAM. Aren't such attacks characteristic of the stress and strain of modern life? NIEMAND. I'm afraid that old stress-and-strain theory has been badly overworked. Been hearing about it ever since I was a pre-med student at ucla . Even as a boy I can remember my grandfather deploring the stress and strain of modern life when he was a country doctor practicing in Indiana. In my opinion one of the most valuable contributions anthropologists have made in recent years is the discovery that primitive man is afflicted with essentially the same neurotic conditions as those of us who live a so-called civilized life. They have found savages displaying every symptom of a nervous breakdown among the mountain tribes of the Elgonyi and the Aruntas of Australia. No, Mr. Latham, it's time the stress-and-strain theory was relegated to the junk pile along with demoniac possession and blood letting. LATHAM. You must have done something for your patients— NIEMAND. A doctor must always do something for the patients who come to his office seeking help. First I gave them a thorough physical examination. I turned up some minor ailments—a slight heart murmur or a trace of albumin in the urine—but nothing of any significance. On the whole they were a remarkably healthy bunch of individuals, much more so than an average sample of the population. Then I made a searching inquiry into their personal life. Here again I drew a blank. They had no particular financial worries. Their sex life was generally satisfactory. There was no history of mental illness in the family. In fact, the only thing that seemed to be the matter with them was that there were times when they felt like hell. LATHAM. I suppose you tried tranquilizers? NIEMAND. Oh, yes. In a few cases in which I tried tranquilizing pills of the meprobamate type there was some slight improvement. I want to emphasize, however, that I do not believe in prescribing shotgun remedies for a patient. To my way of thinking it is a lazy slipshod way of carrying on the practice of medicine. The only thing for which I do give myself credit was that I asked my patients to keep a detailed record of their symptoms taking special care to note the time of exacerbation—increase in the severity of the symptoms—as accurately as possible. LATHAM. And this gave you a clue? NIEMAND. It was the beginning. In most instances patients reported the attack struck with almost the impact of a physical blow. The prodromal symptoms were usually slight ... a sudden feeling of uneasiness and guilt ... hot and cold flashes ... dizziness ... double vision. Then this ghastly sense of depression coupled with a blind insensate rage at life. One man said he felt as if the world were closing in on him. Another that he felt the people around him were plotting his destruction. One housewife made her husband lock her in her room for fear she would injure the children. I pored over these case histories for a long time getting absolutely nowhere. Then finally a pattern began to emerge. LATHAM. What sort of pattern? NIEMAND. The first thing that struck me was that the attacks all occurred during the daytime, between the hours of about seven in the morning and five in the evening. Then there were these coincidences— LATHAM. Coincidences? NIEMAND. Total strangers miles apart were stricken at almost the same moment. At first I thought nothing of it but as my records accumulated I became convinced it could not be attributed to chance. A mathematical analysis showed the number of coincidences followed a Poisson distribution very closely. I couldn't possibly see what daylight had to do with it. There is some evidence that mental patients are most disturbed around the time of full moon, but a search of medical literature failed to reveal any connection with the Sun. LATHAM. What did you do? NIEMAND. Naturally I said nothing of this to my patients. I did, however, take pains to impress upon them the necessity of keeping an exact record of the onset of an attack. The better records they kept the more conclusive was the evidence. Men and women were experiencing nearly simultaneous attacks of rage and depression all over southern California, which was as far as my practice extended. One day it occurred to me: if people a few miles apart could be stricken simultaneously, why not people hundreds or thousands of miles apart? It was this idea that prompted me to get in touch with an old colleague of mine I had known at UC medical school, Dr. Max Hillyard, who was in practice in Utica, New York. LATHAM. With what result? NIEMAND. I was afraid the result would be that my old roommate would think I had gone completely crazy. Imagine my surprise and gratification on receiving an answer by return mail to the effect that he also had been getting an increasing number of patients suffering with the same identical symptoms as my own. Furthermore, upon exchanging records we did find that in many cases patients three thousand miles apart had been stricken simultaneously— LATHAM. Just a minute. I would like to know how you define "simultaneous." NIEMAND. We say an attack is simultaneous when one occurred on the east coast, for example, not earlier or later than five minutes of an attack on the west coast. That is about as close as you can hope to time a subjective effect of this nature. And now another fact emerged which gave us another clue. LATHAM. Which was? NIEMAND. In every case of a simultaneous attack the Sun was shining at both New York and California. LATHAM. You mean if it was cloudy— NIEMAND. No, no. The weather had nothing to do with it. I mean the Sun had to be above the horizon at both places. A person might undergo an attack soon after sunrise in New York but there would be no corresponding record of an attack in California where it was still dark. Conversely, a person might be stricken late in the afternoon in California without a corresponding attack in New York where the Sun had set. Dr. Hillyard and I had been searching desperately for a clue. We had both noticed that the attacks occurred only during the daylight hours but this had not seemed especially significant. Here we had evidence pointing directly to the source of trouble. It must have some connection with the Sun. LATHAM. That must have had you badly puzzled at first. NIEMAND. It certainly did. It looked as if we were headed back to the Middle Ages when astrology and medicine went hand in hand. But since it was our only lead we had no other choice but to follow it regardless of the consequences. Here luck played somewhat of a part, for Hillyard happened to have a contact that proved invaluable to us. Several years before Hillyard had gotten to know a young astrophysicist, Henry Middletown, who had come to him suffering from a severe case of myositis in the arms and shoulders. Hillyard had been able to effect a complete cure for which the boy was very grateful, and they had kept up a desultory correspondence. Middletown was now specializing in radio astronomy at the government's new solar observatory on Turtle Back Mountain in Arizona. If it had not been for Middletown's help I'm afraid our investigation would never have gotten past the clinical stage. LATHAM. In what way was Middletown of assistance? NIEMAND. It was the old case of workers in one field of science being completely ignorant of what was going on in another field. Someday we will have to establish a clearing house in science instead of keeping it in tight little compartments as we do at present. Well, Hillyard and I packed up for Arizona with considerable misgivings. We were afraid Middletown wouldn't take our findings seriously but somewhat to our surprise he heard our story with the closest attention. I guess astronomers have gotten so used to hearing from flying saucer enthusiasts and science-fiction addicts that nothing surprises them any more. When we had finished he asked to see our records. Hillyard had them all set down for easy numerical tabulation. Middletown went to work with scarcely a word. Within an hour he had produced a chart that was simply astounding. LATHAM. Can you describe this chart for us? NIEMAND. It was really quite simple. But if it had not been for Middletown's experience in charting other solar phenomena it would never have occurred to us to do it. First, he laid out a series of about thirty squares horizontally across a sheet of graph paper. He dated these beginning March 1, 1955, when our records began. In each square he put a number from 1 to 10 that was a rough index of the number and intensity of the attacks reported on that day. Then he laid out another horizontal row below the first one dated twenty-seven days later. That is, the square under March 1st in the top row was dated March 28th in the row below it. He filled in the chart until he had an array of dozens of rows that included all our data down to May, 1958. When Middletown had finished it was easy to see that the squares of highest index number did not fall at random on the chart. Instead they fell in slightly slanting parallel series so that you could draw straight lines down through them. The connection with the Sun was obvious. LATHAM. In what way? NIEMAND. Why, because twenty-seven days is about the synodic period of solar rotation. That is, if you see a large spot at the center of the Sun's disk today, there is a good chance if it survives that you will see it at the same place twenty-seven days later. But that night Middletown produced another chart that showed the connection with the Sun in a way that was even more convincing. LATHAM. How was that? NIEMAND. I said that the lines drawn down through the days of greatest mental disturbance slanted slightly. On this second chart the squares were dated under one another not at intervals of twenty-seven days, but at intervals of twenty-seven point three days. LATHAM. Why is that so important? NIEMAND. Because the average period of solar rotation in the sunspot zone is not twenty-seven days but twenty-seven point three days. And on this chart the lines did not slant but went vertically downward. The correlation with the synodic rotation of the Sun was practically perfect. LATHAM. But how did you get onto the S-Regions? NIEMAND. Middletown was immediately struck by the resemblance between the chart of mental disturbance and one he had been plotting over the years from his radio observations. Now when he compared the two charts the resemblance between the two was unmistakable. The pattern shown by the chart of mental disturbance corresponded in a striking way with the solar chart but with this difference. The disturbances on the Earth started two days later on the average than the disturbances due to the S-Regions on the Sun. In other words, there was a lag of about forty-eight hours between the two. But otherwise they were almost identical. LATHAM. But if these S-Regions of Middletown's are invisible how could he detect them? NIEMAND. The S-Regions are invisible to the eye through an optical telescope, but are detected with ease by a radio telescope. Middletown had discovered them when he was a graduate student working on radio astronomy in Australia, and he had followed up his researches with the more powerful equipment at Turtle Back Mountain. The formation of an S-Region is heralded by a long series of bursts of a few seconds duration, when the radiation may increase up to several thousand times that of the background intensity. These noise storms have been recorded simultaneously on wavelengths of from one to fifteen meters, which so far is the upper limit of the observations. In a few instances, however, intense bursts have also been detected down to fifty cm. LATHAM. I believe you said the periods of mental disturbance last for about ten or twelve days. How does that tie-in with the S-Regions? NIEMAND. Very closely. You see it takes about twelve days for an S-Region to pass across the face of the Sun, since the synodic rotation is twenty-seven point three days. LATHAM. I should think it would be nearer thirteen or fourteen days. NIEMAND. Apparently an S-Region is not particularly effective when it is just coming on or just going off the disk of the Sun. LATHAM. Are the S-Regions associated with sunspots? NIEMAND. They are connected in this way: that sunspot activity and S-Region activity certainly go together. The more sunspots the more violent and intense is the S-Region activity. But there is not a one-to-one correspondence between sunspots and S-Regions. That is, you cannot connect a particular sunspot group with a particular S-Region. The same thing is true of sunspots and magnetic storms. LATHAM. How do you account for this? NIEMAND. We don't account for it. LATHAM. What other properties of the S-Regions have you discovered? NIEMAND. Middletown says that the radio waves emanating from them are strongly circularly polarized. Moreover, the sense of rotation remains constant while one is passing across the Sun. If the magnetic field associated with an S-Region extends into the high solar corona through which the rays pass, then the sense of rotation corresponds to the ordinary ray of the magneto-ionic theory. LATHAM. Does this mean that the mental disturbances arise from some form of electromagnetic radiation? NIEMAND. We doubt it. As I said before, the charts show a lag of about forty-eight hours between the development of an S-Region and the onset of mental disturbance. This indicates that the malignant energy emanating from an S-Region consists of some highly penetrating form of corpuscular radiation, as yet unidentified. [A] LATHAM. A question that puzzles me is why some people are affected by the S-Regions while others are not. NIEMAND. Our latest results indicate that probably no one is completely immune. All are affected in some degree. Just why some should be affected so much more than others is still a matter of speculation. LATHAM. How long does an S-Region last? NIEMAND. An S-Region may have a lifetime of from three to perhaps a dozen solar rotations. Then it dies out and for a time we are free from this malignant radiation. Then a new region develops in perhaps an entirely different region of the Sun. Sometimes there may be several different S-Regions all going at once. LATHAM. Why were not the S-Regions discovered long ago? NIEMAND. Because the radio exploration of the Sun only began since the end of World War II. LATHAM. How does it happen that you only got patients suffering from S-radiation since about 1955? NIEMAND. I think we did get such patients previously but not in large enough numbers to attract attention. Also the present sunspot cycle started its rise to maximum about 1954. LATHAM. Is there no way of escaping the S-radiation? NIEMAND. I'm afraid the only sure way is to keep on the unilluminated side of the Earth which is rather difficult to do. Apparently the corpuscular beam from an S-Region is several degrees wide and not very sharply defined, since its effects are felt simultaneously over the entire continent. Hillyard and Middletown are working on some form of shielding device but so far without success. LATHAM. What is the present state of S-Region activity? NIEMAND. At the present moment there happens to be no S-Region activity on the Sun. But a new one may develop at any time. Also, the outlook for a decrease in activity is not very favorable. Sunspot activity continues at a high level and is steadily mounting in violence. The last sunspot cycle had the highest maximum of any since 1780, but the present cycle bids fair to set an all time record. LATHAM. And so you believe that the S-Regions are the cause of most of the present trouble in the world. That it is not ourselves but something outside ourselves— NIEMAND. That is the logical outcome of our investigation. We are controlled and swayed by forces which in many cases we are powerless to resist. LATHAM. Could we not be warned of the presence of an S-Region? NIEMAND. The trouble is they seem to develop at random on the Sun. I'm afraid any warning system would be worse than useless. We would be crying WOLF! all the time. LATHAM. How may a person who is not particularly susceptible to this malignant radiation know that one of these regions is active? NIEMAND. If you have a feeling of restlessness and anxiety, if you are unable to concentrate, if you feel suddenly depressed and discouraged about yourself, or are filled with resentment toward the world, then you may be pretty sure that an S-Region is passing across the face of the Sun. Keep a tight rein on yourself. For it seems that evil will always be with us ... as long as the Sun shall continue to shine upon this little world. THE END [A] Middletown believes that the Intense radiation recently discovered from information derived from Explorer I and III has no connection with the corpuscular S-radiation.
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C. Dismissive
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What potential drawback does the author acknowledge regarding the popularity of Creative Commons licenses?
A. If everything becomes free, then no one can make any kind of profit
B. Too many people will not pay attention to when Creative Commons licenses expire
C. Corporations can potentially take advantage of people who use Creative Commons licenses
D. The Creative Commons license will eventually be replaced with something more equitable
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Just another free soul In his foreword to the book, Lessig writes that you understand your subjects “by learning to see them in a certain way.” What is that certain way? I think I’m trying to get a mental image of a person, certain expressions, or what I think that person is about. I’m trying to capture what I think they look like, which is many times a minority of their typical expressions, or their typical stance. So, if I’m taking pictures of Larry [Lessig], I want to have his signature hand gestures, and not just random ones. I think I’m trying to capture pictures of people that help others see what they’re about. Some photographers will make someone look the way the photographer wants them to look, and not the way they appear, so they’ll pick the one picture out of 100 where the guy looks more egotistical than he really is. Some photographers are almost medical, and are going after a perfect portrait. I’m somewhere in between. It’s amazing how many people will upload snapshots of people where the pictures don’t look like them at all. To me, uploading a picture that is not an easily recognizable picture of that person defeats the point, which I’m working toward, to try to express who they are. On the other hand, professional photographers usually have a subject whom they don’t know personally, so they end up having to try to capture an image that they’ve created based on who they think the person is or how they want that person to appear. You know how sculptors often say that they’re just freeing an image from a block? What I’m trying to do is free someone’s soul from his or her image. There are a lot of things that make this hard. A lot of people are uncomfortable in front of a camera, or might make expressions that aren’t very natural for them. And if the person is nervous, it’s very difficult to try to see what it is that you’re trying to capture. A lot of what I’m doing is, I just start shooting photos. After half an hour of having their picture taken, people start to ignore you. Or I’ll take pictures when I’m talking to people about what they’re doing, so after a while they get distracted by the conversation and forget about the camera. That’s something that I’m not perfect at, but I’m getting better. I think good photographers are also able to disarm people through conversation, but still, it’s difficult to have a disarming conversation with somebody you don’t know, or to make them laugh. Many times people make a face for me that they wouldn’t make for a professional photographer. For instance, a board meeting picture, like the one with Eric Saltzman: that was during a very tense discussion. I’ve found that people are at their most animated at these kinds of meetings, and look the most alive when they are under a lot of pressure, and super- focused. But usually if an outsider is in the room, they won’t get into that. I mean, it would be difficult for a cameraman to be in a room where a board is having a heated debate. But those are the things that I’m trying to capture, because most people don’t get to see that. At the Creative Commons board meeting, Larry asked me to put the camera away after awhile [laughs] because it was distracting. We were having a very heated discussion and I was taking all of these pictures. But he credited me later because afterward those pictures turned out the best. In your mind, what is a ‘Freesoul’ ? A freesoul is somewhat of a pun. On the one hand it means you are free, liberated. You, as a human spirit, are open. And then, it also has the meaning that you are unencumbered legally, that you are free, as in ‘free software.’ There’s a paradox: with many people’s Wikipedia articles to which I’ve contributed, when it comes to the picture, many of these people don’t have any free photos of themselves on the web, so while they are “notable” on Wikipedia, their images aren’t free of the copyright of the photographer, or the institution who hired the photographer to take the picture. Often, even the subject of the article can’t make an image available to the Wikimedia/Wikipedia community. This means that a lot of people who have a Net presence have a legally encumbered Net presence. People who are invited to conferences get asked all the time, “By the way, do you have a photo that we can use?” But they don’t. By making these pictures available under a Creative Commons license, now they do. This is solving the issue of legal freedom. The third part of the pun is that, since I’m asking for a model release from the subjects, I’m asking everyone to be much more open and giving about their image than most people typically are. I’m giving, you’re giving, we’re all giving to participate and to try to create this wonderful work, and allow others to create derivative works. Of course people can abuse that, just like they can abuse anything. But I want people to see the value in sharing over the fear in sharing. The fact is, it’s much more likely that somebody is going to use these pictures for something positive, rather than for something negative. The benefits greatly outweigh the risks. I think we spend way too much of our lives worrying about the risks, at the cost of a lot of the benefits. This is a celebration of all of the people who are willing to give. In a way, giving up your image and allowing anyone to use it: it’s the ultimate gift. In one way it’s kind of vain. [laughs] But in another way it’s wonderful. A Wikipedia article on some person but with no picture is sad. Besides Wikipedia, how do you imagine these photos being used? They can be used in textbooks and in mainstream media articles about the person. Now they can get a picture that represents the person, at least from my perspective. That said, I shouldn’t be the only person doing this. More people should do the same, and make the photographs available freely. For one, I feel that “free” CC licensed photos have a much higher chance of not disappearing. But I don’t know exactly how these photos are going to be used, so in a sense I’m curious. For example, recently I received the Harvard Berkman Center pamphlet. It was a report of what they’re doing, and they also had a bunch of my pictures in there. They all had attribution, and it made me feel really good. There were pictures of different Berkman Center members that I had taken in various places all over the world. I think that the subject is probably happy with this, and I’m happy, and the Berkman Center’s happy because they’re not all pictures of people sitting at desks in the Berkman Center. There’s one more important thing: Creative Commons is great for original creative works or derivative creative works, but when it involves human images, it gets very complicated. We all know the Virgin Mobile case, where Virgin used CC licensed images in an advertisement without getting permission from the models, and got in trouble. What we’re trying to do here is to expand beyond just copyright, to make it more thorough from a legal perspective. It’s also an important educational point, so people understand that, in addition to the Creative Commons licenses, we need people to provide other rights in cases where the law requires such rights to be cleared before reuse. What have you learned about the people in these networks, just in the past year? That’s a good question. I think that at least Creative Commons has become much more mainstream. Creative Commons has moved from a fringy academic discussion to a boardroom discussion. Yahoo announced that it will be using Creative Commons for all of their basic infrastructure, and integrating it all. Google has CC search in their advanced search. Microsoft is working with CC as well and have a plug-in. Nine Inch Nails released their album, Ghost, under a Creative Commons license. The list goes on. Many people are asking: can you make money and share? The answer is, yes. CC is becoming an important part of the business discussion. But one thing that happens when a movement like CC becomes a business thing, is that a lot of the pioneers fade into the background, and it becomes a part of industry. This happened to the Internet. And so while you still have the core people who still remember and hold the torch for the philosophical side, the Internet has become much more of a business. Now, when you go to many Internet conferences, it’s mostly salesmen in attendance. I believe that the success of the Internet has two parts. The first part is the market- driven business side, which has made the Internet affordable and ubiquitous. The second part is the strong movement of participants who fight to keep the Internet open and try to prevent the business side from corrupting the fundamental elements that make the Internet great. The Net Neutrality or Open Network discussion going on right now is a good example of the importance of continuing to balance these principles with business interests. Similarly, I think that business interests can help make Creative Commons ubiquitous and more easily accessible to everyone. However, I think it’s important to remember to keep pushing to make content more “free” and not allow businesses to use Creative Commons in exploitive or destructive ways. In addition to the business side, Creative Commons is being used by educators to create open courseware around the world and in the area of science and technology to promote sharing in research. And as of now, we have the license ported to at least 44 jurisdictions, and the number of countries with projects continues to grow. In many ways, the movement outside of the United States has become much bigger than the movement in the United States. Although the United States is still slightly farther ahead in terms of commercialization, the size of the whole free culture movement outside of the United States is huge now. The CC China Photo exhibit was just amazing. There were some great images, and a lot of the photographers were professionals. This is beyond what anybody has done in the US. A lot of the progress that we’re making is international. What are your personal realizations or experiences? Well, we’re all getting old, if you look at these pictures. But there’s another thing, though, about this book: the number of professional-quality amateurs has increased significantly due to the importance of digital in both professional and high-end amateur photography I hate to say it, a lot of people love the darkroom, but it really feels like the death of the darkroom with this year. With new 22 megapixel cameras coming in under $10,000, and Lightroom and some of this software at a couple hundred dollars, it doesn’t really make sense, except for particularly fussy artists, to do wet-work anymore. If you’re a commercial photographer or a high-end amateur, you can do anything you used to do in the darkroom. I think it has really lowered the bar. I don’t know how that affects the industry directly, but for me, it bridged a huge gap. I used to be darkroom geek. I loved my darkroom, and even when I didn’t have my darkroom anymore, I still was shooting 6x6 Hasselblad 120 film and processing it in a special lab, and then digitizing it. For me, that film was it. You could never get as good as medium-format film or large-format film At the time, the digital Hasselblad backs were too expensive, and were still not as good as 8x10 film. So there was this whole period where the darkroom was not all that exciting, but the digital wasn’t perfect. I went through a limbo period. I had invested so much in my Hasselblad system, and my Leica M6 set. I had bought the Leica R8, but I was kicking myself because it was terrible. But then the Leica M8 came out, and I bought one at the beginning of 2007. The M8 really got me to where I could use my old gear, and it had enough megapixels to be as good as some film. Another way of saying it was that there was a gear breakthrough at the beginning of last year. Okay, that’s pretty materialistic! So there was a technology breakthrough, let’s call it that, that allowed me to switch completely away from film, and I think this happened to a lot of photographers. It caused an explosion of content and an increase in the quality of content on sites like Flickr. It has allowed amateurs to create a business model with professionals. Interestingly, I think these new high-end amateurs are buying more photography books and photographs and are probably providing an increasing revenue stream for professional photographers. I think most amateurs, including myself, are paying homage to the professionals and not trying to “compete” with them. Despite the existence of social software, what is still important about meeting people face-to-face? For me, the right way to use a lot of the new social software is by making it easier to spend more physical time with the people you like best. Dopplr is a great example. When I visit a city, I will see all of the people who are in the city at the same time. When I went to London awhile ago, there were 47 people I knew in London, and a huge percentage of those people don’t live there. I would bet that more than half of the photos in this book are pictures of friends, and they’re not in their hometown. That’s the really interesting thing that is happening right now: it’s really increasing your ability to spend quality time with, actually, a smaller number of people. It allows you to actively filter. Your meetings don’t have to be random. If I look at the list of people in this book, although there are some obvious people missing whom I didn’t see last year, probably met more of my friends last year, my real friends, than I’ve met in any other year. I know my travels were crazy, but I think that the online world has allowed me to do that. What’s great about photography is that it captures the moment that I was sharing with that person. It’s not just a connection on a social network online, which is really pretty binary. I can look at all these photos and remember exactly what we were doing, what we were eating, what we were drinking, what we were talking about, and to me that’s a much more rich experience. It’s the combination of social software and photography. For me, reality is “the present” plus what you remember from the past. I think this project is really sharing memories with people. Blog posts contribute as well, but to me photography is a really good way of doing that. When I look at the expressions, I remember the moment and get a sense of presence. I think the main problem for me is the environmental impact of flying around. Just as I never believed that we would have a paperless office, being able to connect with people through social software mostly increases your travel, it doesn’t decrease it. It is great because you get to meet all these people. But it is bad for the environment, and bad for our jet lag. How would you characterize your contributions to free culture? I think it’s mostly incremental. I think there is very little we actually do all by ourselves, and I hate saying, “I did this” or “I did that.” I think that in most cases, focusing on individual contributions or achievements undervalues the importance of everyone else involved. Having said that, I think my main contribution is probably in supporting Creative Commons as a fan, board member, chairman of the board and now CEO. I think CC has a significant role, and helping to keep it on track and growing is probably the single most important role that I have in Free Culture. Specifically, I think that trying to keep an international focus and a balance between business and the non-business elements of the movement is essential. My job is to keep that focus and maintain that balance. Also, CC needs to run smoothly as an organization and there is a lot of operational work that we all need to do. My photography is a way for me to participate in a small measure on the creative side of the Free Culture movement, and helps me see things from that perspective as well. However, I believe in emergent democracy and the importance of trying to celebrate the community more than the heroes. Of course, I’m a huge fan of Larry’s and I have great respect for the leaders of our movement. But more than anything, I’m thankful for and respectful of all of the participants who aren’t so well known and who are essential to moving everything forward. Personally, I don’t think it’s ultimately meaningful to talk about one individual’s personal contribution to any movement. The real meaning is in the whole movement. I’m just one participant. Just another free soul.
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C. Corporations can potentially take advantage of people who use Creative Commons licenses
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Where does Chatterbox think world policing was effective?
A. Haiti
B. Kosovo
C. Bosnia
D. Rwanda
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Eleven-Twelfths of 1999 In Review When Chatterbox invited readers to nominate events, significant deaths, good and bad movies, etc., for 1999--a year likely to get little attention in the coming weeks, as news organizations choose instead to review the entire century or millennium--the response was overwhelming. Chatterbox had promised to publish his official "1999 In Review" item before Thanksgiving, but some distant memory of a scruple persuaded him to wait till November was over. Nothing ever happens in December. OK, that's not quite true. Hordes of protesters in Seattle are making the World Trade Organization's meeting there a much more exciting TV story than anyone expected it to be. Reader Dan Crist (who finds Chatterbox's habit of referring to himself in the third person "rather annoying and less than professional") points out that Japan bombed Pearl Harbor in Dec. 1941. Also, Chatterbox (moonlighting as "Today's Papers" columnist) observed not quite one year ago that the House of Representatives cast its second presidential-impeachment vote in U.S. history on Dec. 19, 1998. (That same news-filled day, the U.S. ended an air war against Iraq and Bob Livingston said he'd decided not to become House speaker after all.) Two months after the impeachment vote, the Senate failed to convict the president--a highly significant event of 1999 that, for some bizarre reason, slipped Chatterbox's mind until several indignant readers wrote in to remind him of it. By now, it should be clear that Chatterbox isn't much good at year-in-review journalism. Fortunately, Chatterbox's readers are very good at it. He will now turn this survey over to them. ( Disclaimer: Although Chatterbox previously stated that he wouldn't include opinions he disagreed with, that standard proved too confining. Where Chatterbox has solid information or opinions to the contrary, he occasionally interjects below. Obviously stupid or unnecessarily sour reader comments were discarded, but if you don't find your nominee below it doesn't necessarily mean that it was obviously stupid or unnecessarily sour. ) Here are 20 important things that happened in 1999: 1. Most Hated Celebrity--Ever? The New York Times reported on Nov. 10, 1999, that a new record had been set in the latest Times /CBS poll: [Its] highest negative rating ever scored by a person in the news. The honor went to Reform Party candidate Donald Trump, who managed to make an unfavorable impression upon some 70 percent of those polled. The paper noted that this achievement far eclipsed the last comparably negative rating--the 55 percent score attained by Linda Tripp. Presumably this came as no surprise to Mr. Trump, who, upon announcing the formation of a presidential exploratory committee on Oct. 7, 1999, had cited polls with "amazing results"--a remark that was widely misinterpreted at the time. -- Jodie Allen of U.S. News & World Report (and frequent Slate contributor) 2. Most Foolishly Ignored Parts of the World in 1999 The dog that did bark but no one noticed--the political turmoil in the three great South Asian nations of India, Pakistan, and Indonesia, which now are well on the way to passing the three northern Asian nations of China, Japan, and Russia in population (Indonesia is fourth, Pakistan just passed Japan to seventh, India will soon pass China to first). But Americans are still fixated on northern Asia--Clinton says he must deal with China, because "you can't ignore a billion people with nuclear weapons," but his own policy toward India shows that you sure can! --Jim Chapin 3. Worst/Best Films of 1999 Here's my nominee for worst movie of the year (complete category should be: "Worst Movie of the Year That Assumedly Adult Male Reviewers Slathered Over"): There's Something About Mary --a pathetically sophomoric, penis-obsessed mess that wouldn't even appeal to Larry Flynt! -- Felicia, Menlo Park, Cal. Chatterbox replies: You've got the wrong year. That was 1998 . [Chatterbox didn't have the heart to add that he thought There's Something About Mary was pretty funny, especially the joke about "the franks or the beans."] Felicia replies: Oops ... well then, the best of '99 was The Red Violin --lyrical, magical, musical, wonderful! [Chatterbox hasn't seen it.] 4. Most Shameless (and Unsuccessful) Attempt To Have It Both Ways in 1999 : Sen. Arlen Specter, citing Scottish law, finds Clinton "not proven" on the impeachment charges. --Andrew Solovay 5. Rest in Peace in 1999: Stanley Kubrick (multiple sources) John Kennedy Jr. (multiple sources) Susan Strasberg (anonymous tipster; Strasberg played Anne Frank in the original production of the Broadway adaptation, which some people think wasn't Jewish enough) Mel Torme (Steve Reiness) Mrs. Whozit [ Chatterbox interjects : her name was Anne Sheafe Miller], the first person ever to be saved by penicillin (Blair Bolles) 6. 1999: The Road Not Taken What an extraordinary year! A right-wing conspiracy topples the president, and the governor of Texas reveals himself in a series of debates to be a natural leader with an innate gift for connecting with his audience, a sure sign of his electoral success next year. A new Thomas Harris book brilliantly takes us deeper into the mind of a serial killer; a new Star Wars movie redefines the very nature of entertainment; a new Stanley Kubrick film changes the whole national dialogue about sex and marriage; a new TV series from the creator of SportsNight --oh, I can't even bring myself to bash that piece of do-gooder twaddle. If only McDonald's had come out with three more boldly adult-flavored hamburgers, it would have been a perfect year for dud megaevents--all leading up of course to Y2K, the limpest milestone in human history. --Mike Gebert 7 . Children Behaving Badly in 1999 Don't forget Woodstock 1999 --the concert of "peace and love" that ended in a literal blaze of glory when in an hours-long tribute to the original Woodstock, the mob started ripping down vendor booths and anything else that would burn and piling it onto the bonfires scattered about the scene. [ Chatterbox interjects: Didn't people get assaulted and raped, too?] I'm getting all sentimental just thinking about it. You also left out all the shooting rampages . Several were done in the name of God or love supposedly. They were all committed by "quiet, shy" people who "mostly kept to" themselves. I've started to hang around only loud, obnoxious people. --Susan Hoechstetter 8. A Lunatic Rhapsody for the New York Yankees The Yankees can actually be referred to as the glue that held the century together. Of course, as the 1999 World Series champions, they are a significant "story of the year." However, this one singular achievement must be considered in a broader context. 1999 represented the team's 25th championship of the century. This beats, by one, the most championships any one team won during the century. The Montreal Canadiens have won 23 Stanley Cups. However, the Yankees, an American team, playing in the "City of the Century" (so called by me to reflect the amazing growth and transformation of one city during this period), who play the "National Pastime," are truly an amazing story. The team's first championship occurred in 1921; therefore, they have won 25 of the last 78 years, nearly one in three. This level of sustained excellence is not matched in sports or in any other aspect of society. The 1999 win is possibly the most unique. With free-agency, expansion, and three levels of playoffs, it is much harder to win today than in past years. In fact, by winning three of the last four championships, they are the first team to accomplish this feat during the eras of free-agency and of divisional play. The Sultan of Swat, the Iron Man, the Yankee Clipper, the Mick, and Yogi--these strong, masculine names are synonymous with the team, the sport, and American history. They went hand in hand with two world wars, Superman, and America's superpower status. The 1999 squad does not feature "a name." This team, with its myriad of human-interest stories, its international roster, and no star, is representative of '90s man, male sensitivity, Pax American interests, and the new political paradigm. --Jim Landau from North Potomac, Md. (formerly of the Bronx) 9. A Big Shot Calls for Decriminalizing Drug Use in 1999 New Mexico Gov. Gary Johnson came out for ending drug prohibition. Though this by itself has no immediate effect, it makes it respectable, for the first time, for political leaders to discuss the subject, and thereby brings closer the day when the vast majority of crimes will no longer be committed, when billions of dollars will be freed to help the inner city instead of to ruin black people's lives, and when we will stop, as in Samuel Butler's Erewhon , imprisoning people for the crime of being sick. --Henry Cohen Chatterbox interjects: Didn't Baltimore Mayor Kurt Schmoke do the same thing 11 years ago? 10. Don't Worry in 1999 The Dalai Lama proclaimed that most important thing in the world is to be happy. --Margaret Taylor 11. The Athletic Bra Seen 'Round the World in 1999 Public interest and media attention to the women's World Cup in soccer. --Tom Horton 12. Another Overlooked Foreign-Policy Event in 1999 Presidential primary elections for the first time ever in Mexico. --Tom Horton 13. Policing the World Is Shown To Work in 1999 I nominate as the most under-reported story of the year (and the last few years) the continuing alarmist predictions by foreign-policy and military experts about peacekeeping efforts, which are then proved wrong and immediately forgotten. This year, the obvious one is Kosovo, but the year is also ending with East Timor, where the Aussies and their allies successfully stopped the slaughter with no casualties. These followed Haiti, Bosnia, and Rwanda as places where the West delayed sending in troops because of alarmist predictions. --Jerry Skurnik 14. Barbara Walters Did This One on Her Year-End Special, But It's Still Good Don't forget, Susan Lucci finally won an Emmy . --anonymous tipster 15. Annals of Justice in 1999 Matthew Shepard: the despicable defense . -- anonymous tipster 16. Get Me a New Century, Quick A sitting president was accused of rape. --Ananda Gupta Chatterbox interjects: Yes, but the evidence was shaky--something the Wall Street Journal 's editorial page, which broke the story, was not very forthcoming about. As Jack Shafer wrote in this column, Ronald Reagan, after he left office, was also accused of having once committed rape. The evidence there was shaky, too. 17. The Most Important Thing of All That Happened in 1999 In 1999, more than half of U.S. homes had a PC, for the first time (i.e., home-PC penetration passed 50 percent). Of course, most of these PCs crashed all the time, but it's still a significant development. By the way, Internet hookups in homes are still well below 50 percent. --Walt Mossberg, "Personal Technology" columnist for the Wall Street Journal (and occasional rock-music historian for this column) 18. All Dolled Up and Nowhere To Go in 1999 General Pinochet --Jodie Maurer 19. Senate Endorses Nuclear Proliferation in 1999 The Senate rejected the Comprehensive Test Ban Treaty , thereby decapitating nuclear-arms control and sending Iraq, Iran, and North Korea the message that the United States won't raise a big stink if they try to join India and Pakistan. The president woke up to this possibility at about the moment it was realized, and started lobbying for passage of the treaty a day after it became too late. --Josh Pollack 20. Unremarked Natural Disaster in 1999 The Indian Supercyclone is the biggest, this century at least. --Samir Raiyani Photographs of: Donald Trump by Peter Morgan/Reuters; Natalie Portman by Keith Hamshere/Lucasfilm Ltd./Reuters; New York Yankees players by Gary Hershorn/Reuters; KLA member by Hazir Reka/Reuters.
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B. Kosovo
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What Doc2Vec architectures other than PV-DBOW have been tried?
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### Abstract
Background PubMed is the biggest and most used bibliographic database worldwide, hosting more than 26M biomedical publications. One of its useful features is the “similar articles” section, allowing the end-user to find scientific articles linked to the consulted document in term of context. The aim of this study is to analyze whether it is possible to replace the statistic model PubMed Related Articles (pmra) with a document embedding method. Methods Doc2Vec algorithm was used to train models allowing to vectorize documents. Six of its parameters were optimised by following a grid-search strategy to train more than 1,900 models. Parameters combination leading to the best accuracy was used to train models on abstracts from the PubMed database. Four evaluations tasks were defined to determine what does or does not influence the proximity between documents for both Doc2Vec and pmra. Results The two different Doc2Vec architectures have different abilities to link documents about a common context. The terminological indexing, words and stems contents of linked documents are highly similar between pmra and Doc2Vec PV-DBOW architecture. These algorithms are also more likely to bring closer documents having a similar size. In contrary, the manual evaluation shows much better results for the pmra algorithm. Conclusions While the pmra algorithm links documents by explicitly using terminological indexing in its formula, Doc2Vec does not need a prior indexing. It can infer relations between documents sharing a similar indexing, without any knowledge about them, particularly regarding the PV-DBOW architecture. In contrary, the human evaluation, without any clear agreement between evaluators, implies future studies to better understand this difference between PV-DBOW and pmra algorithm. ### Background ::: PubMed
PubMed is the largest database of bio-medical articles worldwide with more than 29,000,000 freely available abstracts. Each article is identified by an unique PubMed IDentifier (PMID) and is indexed with the Medical Subject Headings (MeSH) terminology. In order to facilitate the Information Retrieval (IR) process for the end-user, PubMed launched in 2007 a service of related articles search, available both through its Graphical User Interface (GUI) and its Application Programming Interface (API). Regarding the GUI, while the user is reading a publication, a panel presents title of articles that may be linked to the current reading. For the API, the user must query eLink with a given PMID BIBREF0. The output will be a list of others PMIDs, each associated with the similarity score computed by the pmra (pubmed related article) model BIBREF1. ### Background ::: The pmra model
To do so, each document is tokenized into many topics $S_{i}$. Then, the probability $P(C|D)$ that the user will find relevant the document C when reading the document D will be calculated. For this purpose, the authors brought the concept of eliteness. Briefly, a topic $S_{i}$ is presented as elite topic for a given document if a word $W_{i}$ representing $S_{i}$ is used with a high frequency in this document. This work allows to bring closer documents sharing a maximum of elite topics. In the article presenting the pmra model, authors claim that “the deployed algorithm in PubMed also takes advantage of MeSH terms, which we do not discuss here”. We can thus assume that a similar score is computed thanks to the associated MeSH terms with both documents D and C. Such an indexing is highly time-consuming and has to be manually performed. ### Background ::: Documents embedding
Nowadays, embedding models allow to represent a text into a vector of fixed dimensions. The primary purpose of this mathematical representation of documents was to be able to use texts as input of deep neural networks. However, these models have been used by the IR community as well: once all fitted in the same multidimensional space, the cosine distance between two documents vectors can estimate the proximity between these two texts. In 2013, Mikolov et al. released a word embedding method called Word2Vec (W2V) BIBREF2. Briefly, this algorithm uses unsupervised learning to train a model which embeds a word as a vector while preserving its semantic meaning. Following this work, Mikolov and Le released in 2014 a method to vectorize complete texts BIBREF3. This algorithm, called Doc2Vec (D2V), is highly similar to W2V and comes with two architectures. The Distributed Memory Model of Paragraph Vectors (PV-DM) first trains a W2V model. This word embedding will be common for all texts from a given corpus C on which it was trained. Then, each document $D_{x}$ from C will be assigned to a randomly initialised vector of fixed length, which will be concatenated with vectors of words composing $D_{x}$ during the training time (words and documents vectors are sharing the same number of dimensions). This concatenation will be used by a final classifier to predict the next token of a randomly selected window of words. The accuracy of this task can be calculated and used to compute a loss function, used to back-propagate errors to the model, which leads to a modification of the document’s representation. The Distributed Bag of Words version of Paragraph Vector (PV-DBOW) is highly similar to the PV-DM, the main difference being the goal of the final classifier. Instead of concatenating vector from the document with word vectors, the goal here is to output words from this window just by using the mathematical representation of the document. ### Background ::: Related Work
Doc2Vec has been used for many cases of similar document retrieval. In 2016, Lee et al. used D2V to clusterize positive and negative sentiments with an accuracy of 76.4% BIBREF4. The same year, Lau and Baldwin showed that D2V provides a robust representation of documents, estimated with two tasks: document similarity to retrieve 12 different classes and sentences similarity scoring BIBREF5. Recently, studies started to use documents embedding on the PubMed corpus. In 2017, Gargiulo et al. used a combination of words vectors coming from the abstract to bring closer similar documents from Pubmed BIBREF6. Same year, Wang and Koopman used the PubMed database to compare D2V and their own document embedding method BIBREF7. Their designed accuracy measurement task was consisting in retrieving documents having a small cosine distance with the embedding of a query. Recently, Chen et al. released BioSentVec, a set of sentence vectors created from PubMed with the algorithm sent2vec BIBREF8, BIBREF9. However, their evaluation task was based on public sentences similarity datasets, when the goal here is to embed entire abstracts as vectors and to use them to search for similar articles versus the pmra model. In 2008, the related articles feature of PubMed has been compared (using a manual evaluation) with one that uses both a TF-IDF BIBREF10 representation of the documents and Lin’s distance BIBREF11 to compare their MeSH terms BIBREF12. Thus, no study was designed so far to compare documents embedding and the pmra algorithm. The objectives of this study were to measure the ability of these two models to infer the similarity between documents from PubMed and to search what impacts the most this proximity. To do so, different evaluation tasks were defined to cover a wide range of aspects of document analogy, from their context to their morphological similarities. ### Methods ::: Material
During this study, the optimisation of the model’s parameters and one of the evaluation tasks require associated MeSH terms with the abstracts from PubMed. Briefly, the MeSH is a medical terminology, used to index documents on PubMed to perform keywords-based queries. The MEDOC program was used to create a MySQL database filled with 26,345,267 articles from the PubMed bulk downloads on October 2018, 5th BIBREF13. Then, 16,048,372 articles having both an abstract and at least one associated MeSH term were selected for this study. For each, the PMID, title, abstract and MeSH terms were extracted. The titles and abstracts were lowered, tokenized and concatenated to compose the PubMed documents corpus. ### Methods ::: Optimisation
Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW). The hs option defines whether hierarchical softmax or negative sampling is used during the training. Finally, the vector_size parameter affects the number of dimensions composing the resulting vector. A list of possible values was defined for each of these six parameters. The full amount of possible combinations of these parameters were sent to slave nodes on a cluster, each node training a D2V model with a unique combination of parameters on 85% of 100,000 documents randomly selected from the corpus. Every article from the remaining 15% were then sent to each trained model and queried for the top-ten closest articles. For each model, a final accuracy score represented by the average of common MeSH terms percentage between each document $D_{i}$ from the 15,000 extracted texts and their returning top-ten closest documents was calculated. The combination of parameters with the highest score was kept for both PV-DBOW and PV-DM. ### Methods ::: Training
The final models were trained on a server powered by four XEON E7 (144 threads) and 1To of RAM. Among the total corpus (16,048,372 documents), 1% (160,482) was extracted as a test set (named TeS) and was discarded from the training. The final models were trained on 15,887,890 documents representing the training set called TrS. ### Methods ::: Evaluation
The goal here being to assess if D2V could effectively replace the related-document function on PubMed, five different document similarity evaluations were designed as seen on figure FIGREF9. These tasks were designed to cover every similarities, from the most general (the context) to the character-level similarity. Indeed, a reliable algorithm to find related documents should be able to bring closer texts sharing either a similar context, some important ideas (stems of words), an amount of non-stemmed vocabulary (e.g. verbs tenses are taken in account) and should not be based on raw character-similarity (two documents sharing the same proportion of letter “A” or having a similar length should not be brought together if they do not exhibit upper levels similarity). ### Methods ::: Evaluation ::: String length
To assess whether a similar length could lead to convergence of two documents, the size of the query document $D_{x}$ has been compared with the top-close document $C_{x}$ for 10,000 document randomly selected from the TeS after some pre-processing steps (stopwords and spaces were removed from both documents). ### Methods ::: Evaluation ::: Words co-occurrences
A matrix of words co-occurrence was constructed on the total corpus from PubMed. Briefly, each document was lowered and tokenized. A matrix was filled with the number of times that two words co-occur in a single document. Then, for 5,000 documents $D_{x}$ from the TeS, all models were queried for the top-close document $C_{x}$. All possible combinations between all words $WD_{x} \in D_{x}$ and all words $WC_{x} \in C_{x}$ (excluding stopwords) were extracted, 500 couples were randomly selected and the number of times each of them was co-occurring was extracted from the matrix. The average value of this list was calculated, reflecting the proximity between D and C regarding their words content. This score was also calculated between each $D_{x}$ and the top-close document $C_{x}$ returned by the pmra algorithm. ### Methods ::: Evaluation ::: Stems co-occurrences
The evaluation task explained above was also applied on 10,000 stemmed texts (using the Gensim’s PorterStemmer to only keep word’s roots). The influence of the conjugation form or other suffixes can be assessed. ### Methods ::: Evaluation ::: MeSH similarity
It is possible to compare the ability of both pmra and D2V to bring closer articles which were indexed with common labels. To do so, 5,000 documents $D_{x}$ randomly selected from the TeS were sent to both pmra and D2V architectures, and the top-five closer articles $C_{x}$ were extracted. The following rules were then applied to each MeSH found associated with $D_{x}$ for each document $C_{x_i}$ : add 1 to the score if this MeSH term is found in both $D_{x}$ and $C_{x_i}$, add 3 if this MeSH is defined as major topic and add 1 for each qualifier in common between $D_{x}$ and Cxi regarding this particular MeSH term. Then, the mean of these five scores was calculated for both pmra and D2V. ### Methods ::: Evaluation ::: Manual evaluation
Among all documents contained in the TeS, 10 articles $D_{x}$ have been randomly selected. All of them were sent to the pmra and to the most accurate of the two D2V architectures, regarding the automatic evaluations explained above. Each model was then queried for the ten closest articles for each $D_{x_i} \in D_{x}$ and the relevance between $D_{x_i}$ and every of the top-ten documents was blindly assessed by a three-modality scale used in other standard Information Retrieval test sets: bad (0), partial (1) or full relevance (2) BIBREF15. In addition, evaluators have been asked to rank publications according their relevant proximity with the query, the first being the closest from their perspective. Two medical doctors and two medical data librarians took part in this evaluation. ### Results ::: Optimisation
Regarding the optimisation, 1,920 different models were trained and evaluated. First, the dm parameter highly affects the accuracy. Indeed, the PV-DBOW architecture looks more precise with a highest accuracy of 25.78%, while the PV-DM reached only 18.08% of common MeSH terms in average between query and top-close documents. Then, embedding vectors having large number of dimensions ($> 256$) seem to lead to a better accuracy, for PV-DBOW at least. Finally, when set too low ($< 0.01$), the alpha parameter leads to poor accuracy. The best combination of parameters, obtained thanks to the PV-DBOW architecture, was selected. The best parameters regarding the PV-DM, but having the same vector_size value, were also kept (13.30% of accuracy). The concatenation of models is thus possible without dimensions reduction, this method being promoted by Mikolov and Lee BIBREF3. Selected values are listed on the table TABREF16. ### Results ::: Evaluation ::: String length
By looking at the length difference in term of characters between documents brought closer by D2V, a difference is visible between the two architectures (Figure FIGREF19C). In fact, while a very low correlation is visible under the PV-DM architecture (coefficient $-2.6e10^{-5}$) and under the pmra model ($-5.4e10^{-5}$), a stronger negative one is observed between the cosine distance computed by the PV-DBOW for two documents and their difference in terms of length (coefficient $-1.1e10^{-4}$). This correlation suggests that two documents having a similar size are more likely to be closer in the vectorial space created by the PV-DBOW (cosine distance closer to 1). ### Results ::: Evaluation ::: Words co-occurrences
Once scores from pmra have been normalized, the correlation between words co-occurrences and scores returned by both D2V and pmra were studied (Figure FIGREF19B). The very low slopes of the D2V trend lines ($-1.1e10^{-5}$ for the PV-DBOW and $-3e10^{-6}$ for PV-DM) indicate that the vocabulary content does not influence (positively or negatively) the proximity between two documents for this algorithm. By looking at the green dots or line, the pmra seems to give less importance to the co-occurrence of terms. A low slope is observed ($-5.8e10^{-5}$), indicating a slight negative correlation between word co-occurrence and computed score. ### Results ::: Evaluation ::: Stems co-occurrences
This test assigns a score reflecting the proximity between two documents regarding their vocabulary content, the impact of the conjugation, plural forms, etc was lowered by a stemming step. The D2V model returns a cosine score S for a pair of documents ($0 < S < 1$, the top-close document is not likely to have a negative cosine value), while the pmra returns a score between 18M and 75M in our case BIBREF0. These scores were normalized to fit between the same limits than the cosine distance. For PV-DBOW, PV-DM and pmra, the influence of the stems is almost insignificant with very flat slopes looking at the trend lines ($1e10^{-6}$, $-2e10^{-6}$ and $-2e10^{-6}$ respectively, see figure FIGREF19A). This indicates that the stem content of two documents will not affect (negatively or positively) their proximity for these models. ### Results ::: Evaluation ::: MeSH similarity
By studying the common MeSH labels between two close documents, it is possible to assess whether the context influence or not this proximity. By looking at the figure FIGREF23A, we can see that PV-DBOW and pmra are very close in term of MeSH score, indicating that they bring closer documents sharing a similar number of common MeSH labels in average. The pmra model seems to be more likely to output documents sharing a higher MeSH score (the distribution tail going further 4 with a mean equal to 1.58, standard deviation: 1.06), while the PV-DM brings closer documents that are less likely to share an important number of MeSH terms, with a majority of score between 0 and 1 (mean equal to 1.16, standard deviation: 0.73). The figure FIGREF23B shows the correlation between the MeSH score for documents returned by the pmra and those returned by both PV-DM and PV-DBOW models. The PV-DBOW algorithm looks way closer to the pmra in terms of common MeSH labels between two close documents with a slope of 1.0064. The PV-DM model is much less correlated, with a slope of 0.1633, indicating less MeSH in common for close articles. ### Results ::: Evaluation ::: Manual evaluation
Regarding the results obtained by both PV-DBOW and PV-DM sub-architectures, the PV-DBOW model has been used versus the pmra. Its close score in the MeSH evaluation task compared to the pmra's one indicates an ability to bring closer documents sharing same concepts. Thus, 10 randomly chosen documents were sent to the pmra and to the PV-DBOW models and they were asked to output the 10 closest documents for each. Their relevance was then assessed by four evaluators. The agreement between all evaluators regarding the three-modalities scale was assessed by computing the Cohen's kappa score $K$ thanks to the SKlearn Python's library (Figure FIGREF25) BIBREF16. First, we can notice that the highest $K$ was obtained by the two medical data librarian (EL and GK) with $K=0.61$, indicating a substantial agreement BIBREF17. In contrary, the lowest $K$ was computed using evaluations from the two Medical Doctors (SJD and JPL) with $K=0.49$, indicating barely a moderate agreement. The average agreement is represented by $K=0.55$, indicating a moderate global agreement. Regarding the ranking of all results (the first being the most accurate compared to the query, the last the worst one), the agreement can also be seen as moderate. The concordance rate has been defined between two evaluators for a given pair of results $A/B$ as the probability for A to be better ranked than B for both judges. For each couple of evaluators the mean agreement was computed by averaging ten pairs $result/query$ randomly selected. In order to evaluate the 95% bilateral confidence interval associated with the average concordance rate of each pair of judges the Student confidence interval estimation method has been used. Deviation from normal has been reduced by hyperbolic arc-tangent transformation. The global mean concordance by pooling all judges together was 0.751 (sd = 0.08). The minimal concordance was equal to 0.73 and the maximal one to 0.88. Regarding the evaluation itself, based on the three-modality scale (bad, partial or full relevance), models are clearly not equivalents (Figure FIGREF26). The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents. By looking at the results ranking, the mean position for D2V was 14.09 (ranging from 13.98 for JPL to 14.20 for EL). Regarding the pmra, this average position was equal to 6.89 (ranging from 6.47 for EL to 7.23 for SJD). ### Discussion
In this study, the ability of D2V to infer similarity between biomedical abstracts has been compared versus the pmra, the algorithm actually used in Pubmed. Regarding the strings length task, even if trending lines slopes are very close to zero, a slight negative correlation is observed between the difference in terms of character and scores calculated by PV-DBOW and pmra. This result can be relativized. Indeed, it was expected that two different abstracts regarding their number of characters are more likely to be different in term of context. The longest text can treat more subjects with different words (explaining D2V’s results) or to be associated with more MeSH labels (clarifying pmra ones’). Words or stems content analysis does not showed any particular correlation between common words/stems and scores computed by both D2V models or pmra. Inverse results could have been expected, regarding the way pmra is linking documents (using common terms between documents). The score brought to the pmra model by the MeSH terms should be quite important for the final scoring formula. However, among all possible couples of words between two documents, only 500 were randomly selected, due to computational limits. Random sampling effect could have led to these results. D2V takes in account many language features such as bi- or trigrams, synonyms, other related meanings and stopwords. No prior knowledge of analysis on the documents are needed. The pmra is based (in addition to words) on the manual MeSH indexing of the document, even if this aspect was not discussed in the Lin and Wilbur’s publication. This indexing step is highly time-consuming and employs more than 50 people to assign labels on documents from PubMed. The result displayed on the figure FIGREF23 could have been expected for the pmra algorithm, this model using the MeSH terms on the statistical formula used to link documents as well as elite or elitness terms. It was thus expected that two documents sharing a lot of indexing labels would have been seen close by the pmra. However, these MeSH descriptors were only used to select the appropriate parameters used to train the D2V models. The fact that D2V still manages, with the PV-DBOW architecture, to find documents that are close to each other regarding the MeSH indexing demonstrates its ability to capture an article’s subject solely with its abstract and title. Regarding the manual evaluation, D2V PV-DBOW model has been very largely underrated compared to the pmra model. Its results have been seen as not accurate more than three times compared to the Pubmed's model. Regarding the ranking of the results, the average position of the pmra is centred around 7, while D2V's one is around 14. However, the real signification of these results can be relativised. Indeed, the agreement between the four annotators is only moderate and no general consensus can be extracted. This study also has some limitations. First, the MeSH indexing of documents on PubMed can occur on full-text data, while both optimisation of the hyper-parameters and an evaluation task are based on abstracts' indexing. However, this bias should have a limited impact on the results. The indexing being based on the main topics from the documents, these subjects should also be cited in the abstract. About this manual indexing, a bias is brought by the indexers. It is well-known in the information retrieval community that intra- and inter-indexers bias exist. As the parameters optimisation step relied only on MeSH terms, it assumed that a model trained on articles’ abstracts can be optimised with MeSH terms which are selected according to the full text of the articles. In other words, this optimisation assumed an abstract is enough to semantically represent the whole text. But this is not completely true. If it was, MeSH terms would have not be selected on full texts in the first place. Also, the principle that a PubMed related article feature has to give articles which have a lot of MeSH terms in common has been followed throughout this work. To go further, as mentioned in the paper presenting D2V, the concatenation of vectors from both PV-DM and PV-DBOW for a single document could lead to a better accuracy. A third model could be designed by the merge of the two presented here. Another moot point on the text embedding community is about the part-of-speech tagging of the text before sending it to the model (during both training and utilisation). This supplementary information could lead to a better understanding of the text, particularly due to the disambiguation of homonyms. ### Conclusion
This study showed that Doc2Vec PV-DBOW, an unsupervised text embedding technique, can infer similarity between biomedical articles' abstract. It requires no prior knowledge on the documents such as text indexing and is not impacted by raw words content or document structure. This algorithm was able to link documents sharing MeSH labels in a similar way the pmra did. A manual evaluation returned very low scores for the D2V PV-DBOW model, but with a highly moderate agreement between evaluators. More investigation should be carried out to understand this difference between the evaluation based on the MeSH indexing (performed by humans) and the manual evaluation. Figure 1. Ranking of the five designed documents similarity evaluation tasks. Figure 2. Analysis of stems, words and length differences between texts broughts closer by D2V and pmra. Correlation plot between the stems co-occurrence score (A), words co-occurrence score (B), length difference (C) and scores returned by two D2V architectures (PV-DBOW, blue and PV-DM, orange) or the pmra model (green, normalized values). Outliers with z-score ¿ 3 were discarded from the plot. Figure 3. Study of both pmra and D2V models regarding their ability to bring closer documents sharing many MeSH labels. A (upper panel): frequency of the different MeSH scores for the pmra, PV-DM and PV-DBOW models. PV-DBOW and pmra are centred on the same value and have a similar distribution, indicating a common ability to link documents regarding their topic. However, the PV-DM algorithm looks less efficient. B (lower panel): correlation between MeSH scores calculated from the pmra and those from D2V. The slopes of the trend lines support the precedent result with a slope close to 1 for PV-DBOW while the PV-DM only reach 0.1, indicating a weaker correlation. Outliers with z-score ¿ 3 were discarded from the plot. Figure 4. Global agreement between four evaluators rating the accuracy of the D2V and pmra models. Colour scale indicates the strength of the agreement between two annotators. It ranges from 0.49 between the two medical doctors SJD and JPL to 0.61 between the two medical data librarian EL and GK. Figure 5. Pulled rating of both models D2V and pmra. The height indicates the number of times each model has been rated as bad, moderate or strong accuracy result by the evaluators. D2V has been mostly rated as badly relevant (80 times) while the pmra was mostly rated as good relevance.
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PV-DM
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What corpus was the source of the OpenIE extractions?
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### Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically constructed open vocabulary (subject; predicate; object) style tuples have broader coverage, but have only been used for simple questions where a single tuple suffices BIBREF2 , BIBREF3 . Our goal in this work is to develop a QA system that can perform reasoning with Open IE BIBREF4 tuples for complex multiple-choice questions that require tuples from multiple sentences. Such a system can answer complex questions in resource-poor domains where curated knowledge is unavailable. Elementary-level science exams is one such domain, requiring complex reasoning BIBREF5 . Due to the lack of a large-scale structured KB, state-of-the-art systems for this task either rely on shallow reasoning with large text corpora BIBREF6 , BIBREF7 or deeper, structured reasoning with a small amount of automatically acquired BIBREF8 or manually curated BIBREF9 knowledge. Consider the following question from an Alaska state 4th grade science test: Which object in our solar system reflects light and is a satellite that orbits around one planet? (A) Earth (B) Mercury (C) the Sun (D) the Moon This question is challenging for QA systems because of its complex structure and the need for multi-fact reasoning. A natural way to answer it is by combining facts such as (Moon; is; in the solar system), (Moon; reflects; light), (Moon; is; satellite), and (Moon; orbits; around one planet). A candidate system for such reasoning, and which we draw inspiration from, is the TableILP system of BIBREF9 . TableILP treats QA as a search for an optimal subgraph that connects terms in the question and answer via rows in a set of curated tables, and solves the optimization problem using Integer Linear Programming (ILP). We similarly want to search for an optimal subgraph. However, a large, automatically extracted tuple KB makes the reasoning context different on three fronts: (a) unlike reasoning with tables, chaining tuples is less important and reliable as join rules aren't available; (b) conjunctive evidence becomes paramount, as, unlike a long table row, a single tuple is less likely to cover the entire question; and (c) again, unlike table rows, tuples are noisy, making combining redundant evidence essential. Consequently, a table-knowledge centered inference model isn't the best fit for noisy tuples. To address this challenge, we present a new ILP-based model of inference with tuples, implemented in a reasoner called TupleInf. We demonstrate that TupleInf significantly outperforms TableILP by 11.8% on a broad set of over 1,300 science questions, without requiring manually curated tables, using a substantially simpler ILP formulation, and generalizing well to higher grade levels. The gains persist even when both solvers are provided identical knowledge. This demonstrates for the first time how Open IE based QA can be extended from simple lookup questions to an effective system for complex questions. ### Related Work
We discuss two classes of related work: retrieval-based web question-answering (simple reasoning with large scale KB) and science question-answering (complex reasoning with small KB). ### Tuple Inference Solver
We first describe the tuples used by our solver. We define a tuple as (subject; predicate; objects) with zero or more objects. We refer to the subject, predicate, and objects as the fields of the tuple. ### Tuple KB
We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. For each test set, we use the corresponding training questions $Q_\mathit {tr}$ to retrieve domain-relevant sentences from S. Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T). ### Tuple Selection
Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i\rbrace $ , we select the most relevant tuples from $T$ and $S$ as follows. Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ . We also filter out any tuples that overlap only with $tok(q)$ as they do not support any answer. We compute the normalized TF-IDF score treating the question, $q$ as a query and each tuple, $t$ as a document: $
&\textit {tf}(x, q)=1\; \textmd {if x} \in q ; \textit {idf}(x) = log(1 + N/n_x) \\
&\textit {tf-idf}(t, q)=\sum _{x \in t\cap q} idf(x)
$ where $N$ is the number of tuples in the KB and $n_x$ are the number of tuples containing $x$ . We normalize the tf-idf score by the number of tokens in $t$ and $q$ . We finally take the 50 top-scoring tuples $T_{qa}$ . On-the-fly tuples from text: To handle questions from new domains not covered by the training set, we extract additional tuples on the fly from S (similar to BIBREF17 knowlhunting). We perform the same ElasticSearch query described earlier for building T. We ignore sentences that cover none or all answer choices as they are not discriminative. We also ignore long sentences ( $>$ 300 characters) and sentences with negation as they tend to lead to noisy inference. We then run Open IE on these sentences and re-score the resulting tuples using the Jaccard score due to the lossy nature of Open IE, and finally take the 50 top-scoring tuples $T^{\prime }_{qa}$ . ### Support Graph Search
Similar to TableILP, we view the QA task as searching for a graph that best connects the terms in the question (qterms) with an answer choice via the knowledge; see Figure 1 for a simple illustrative example. Unlike standard alignment models used for tasks such as Recognizing Textual Entailment (RTE) BIBREF18 , however, we must score alignments between a set $T_{qa} \cup T^{\prime }_{qa}$ of structured tuples and a (potentially multi-sentence) multiple-choice question $qa$ . The qterms, answer choices, and tuples fields form the set of possible vertices, $\mathcal {V}$ , of the support graph. Edges connecting qterms to tuple fields and tuple fields to answer choices form the set of possible edges, $\mathcal {E}$ . The support graph, $G(V, E)$ , is a subgraph of $\mathcal {G}(\mathcal {V}, \mathcal {E})$ where $V$ and $E$ denote “active” nodes and edges, resp. We define the desired behavior of an optimal support graph via an ILP model as follows. Similar to TableILP, we score the support graph based on the weight of the active nodes and edges. Each edge $e(t, h)$ is weighted based on a word-overlap score. While TableILP used WordNet BIBREF19 paths to compute the weight, this measure results in unreliable scores when faced with longer phrases found in Open IE tuples. Compared to a curated KB, it is easy to find Open IE tuples that match irrelevant parts of the questions. To mitigate this issue, we improve the scoring of qterms in our ILP objective to focus on important terms. Since the later terms in a question tend to provide the most critical information, we scale qterm coefficients based on their position. Also, qterms that appear in almost all of the selected tuples tend not to be discriminative as any tuple would support such a qterm. Hence we scale the coefficients by the inverse frequency of the tokens in the selected tuples. Since Open IE tuples do not come with schema and join rules, we can define a substantially simpler model compared to TableILP. This reduces the reasoning capability but also eliminates the reliance on hand-authored join rules and regular expressions used in TableILP. We discovered (see empirical evaluation) that this simple model can achieve the same score as TableILP on the Regents test (target test set used by TableILP) and generalizes better to different grade levels. We define active vertices and edges using ILP constraints: an active edge must connect two active vertices and an active vertex must have at least one active edge. To avoid positive edge coefficients in the objective function resulting in spurious edges in the support graph, we limit the number of active edges from an active tuple, question choice, tuple fields, and qterms (first group of constraints in Table 1 ). Our model is also capable of using multiple tuples to support different parts of the question as illustrated in Figure 1 . To avoid spurious tuples that only connect with the question (or choice) or ignore the relation being expressed in the tuple, we add constraints that require each tuple to connect a qterm with an answer choice (second group of constraints in Table 1 ). We also define new constraints based on the Open IE tuple structure. Since an Open IE tuple expresses a fact about the tuple's subject, we require the subject to be active in the support graph. To avoid issues such as (Planet; orbit; Sun) matching the sample question in the introduction (“Which object $\ldots $ orbits around a planet”), we also add an ordering constraint (third group in Table 1 ). Its worth mentioning that TupleInf only combines parallel evidence i.e. each tuple must connect words in the question to the answer choice. For reliable multi-hop reasoning using OpenIE tuples, we can add inter-tuple connections to the support graph search, controlled by a small number of rules over the OpenIE predicates. Learning such rules for the Science domain is an open problem and potential avenue of future work. ### Experiments
Comparing our method with two state-of-the-art systems for 4th and 8th grade science exams, we demonstrate that (a) TupleInf with only automatically extracted tuples significantly outperforms TableILP with its original curated knowledge as well as with additional tuples, and (b) TupleInf's complementary approach to IR leads to an improved ensemble. Numbers in bold indicate statistical significance based on the Binomial exact test BIBREF20 at $p=0.05$ . We consider two question sets. (1) 4th Grade set (1220 train, 1304 test) is a 10x larger superset of the NY Regents questions BIBREF6 , and includes professionally written licensed questions. (2) 8th Grade set (293 train, 282 test) contains 8th grade questions from various states. We consider two knowledge sources. The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. This corpus is used by the IR solver and also used to create the tuple KB T and on-the-fly tuples $T^{\prime }_{qa}$ . Additionally, TableILP uses $\sim $ 70 Curated tables (C) designed for 4th grade NY Regents exams. We compare TupleInf with two state-of-the-art baselines. IR is a simple yet powerful information-retrieval baseline BIBREF6 that selects the answer option with the best matching sentence in a corpus. TableILP is the state-of-the-art structured inference baseline BIBREF9 developed for science questions. ### Results
Table 2 shows that TupleInf, with no curated knowledge, outperforms TableILP on both question sets by more than 11%. The lower half of the table shows that even when both solvers are given the same knowledge (C+T), the improved selection and simplified model of TupleInf results in a statistically significant improvement. Our simple model, TupleInf(C + T), also achieves scores comparable to TableILP on the latter's target Regents questions (61.4% vs TableILP's reported 61.5%) without any specialized rules. Table 3 shows that while TupleInf achieves similar scores as the IR solver, the approaches are complementary (structured lossy knowledge reasoning vs. lossless sentence retrieval). The two solvers, in fact, differ on 47.3% of the training questions. To exploit this complementarity, we train an ensemble system BIBREF6 which, as shown in the table, provides a substantial boost over the individual solvers. Further, IR + TupleInf is consistently better than IR + TableILP. Finally, in combination with IR and the statistical association based PMI solver (that scores 54.1% by itself) of BIBREF6 aristo2016:combining, TupleInf achieves a score of 58.2% as compared to TableILP's ensemble score of 56.7% on the 4th grade set, again attesting to TupleInf's strength. ### Error Analysis
We describe four classes of failures that we observed, and the future work they suggest. Missing Important Words: Which material will spread out to completely fill a larger container? (A)air (B)ice (C)sand (D)water In this question, we have tuples that support water will spread out and fill a larger container but miss the critical word “completely”. An approach capable of detecting salient question words could help avoid that. Lossy IE: Which action is the best method to separate a mixture of salt and water? ... The IR solver correctly answers this question by using the sentence: Separate the salt and water mixture by evaporating the water. However, TupleInf is not able to answer this question as Open IE is unable to extract tuples from this imperative sentence. While the additional structure from Open IE is useful for more robust matching, converting sentences to Open IE tuples may lose important bits of information. Bad Alignment: Which of the following gases is necessary for humans to breathe in order to live?(A) Oxygen(B) Carbon dioxide(C) Helium(D) Water vapor TupleInf returns “Carbon dioxide” as the answer because of the tuple (humans; breathe out; carbon dioxide). The chunk “to breathe” in the question has a high alignment score to the “breathe out” relation in the tuple even though they have completely different meanings. Improving the phrase alignment can mitigate this issue. Out of scope: Deer live in forest for shelter. If the forest was cut down, which situation would most likely happen?... Such questions that require modeling a state presented in the question and reasoning over the state are out of scope of our solver. ### Conclusion
We presented a new QA system, TupleInf, that can reason over a large, potentially noisy tuple KB to answer complex questions. Our results show that TupleInf is a new state-of-the-art structured solver for elementary-level science that does not rely on curated knowledge and generalizes to higher grades. Errors due to lossy IE and misalignments suggest future work in incorporating context and distributional measures. ### Appendix: ILP Model Details
To build the ILP model, we first need to get the questions terms (qterm) from the question by chunking the question using an in-house chunker based on the postagger from FACTORIE. ### Experiment Details
We use the SCIP ILP optimization engine BIBREF21 to optimize our ILP model. To get the score for each answer choice $a_i$ , we force the active variable for that choice $x_{a_i}$ to be one and use the objective function value of the ILP model as the score. For evaluations, we use a 2-core 2.5 GHz Amazon EC2 linux machine with 16 GB RAM. To evaluate TableILP and TupleInf on curated tables and tuples, we converted them into the expected format of each solver as follows. ### Using curated tables with TupleInf
For each question, we select the 7 best matching tables using the tf-idf score of the table w.r.t. the question tokens and top 20 rows from each table using the Jaccard similarity of the row with the question. (same as BIBREF9 tableilp2016). We then convert the table rows into the tuple structure using the relations defined by TableILP. For every pair of cells connected by a relation, we create a tuple with the two cells as the subject and primary object with the relation as the predicate. The other cells of the table are used as additional objects to provide context to the solver. We pick top-scoring 50 tuples using the Jaccard score. ### Using Open IE tuples with TableILP
We create an additional table in TableILP with all the tuples in $T$ . Since TableILP uses fixed-length $(subject; predicate; object)$ triples, we need to map tuples with multiple objects to this format. For each object, $O_i$ in the input Open IE tuple $(S; P; O_1; O_2 \ldots )$ , we add a triple $(S; P; O_i)$ to this table. Figure 1: An example support graph linking a question (top), two tuples from the KB (colored) and an answer option (nitrogen). Table 2: TUPLEINF is significantly better at structured reasoning than TABLEILP.9 Table 1: High-level ILP constraints; we report results for ~w = (2, 4, 4, 4, 2); the model can be improved with more careful parameter selection Table 3: TUPLEINF is complementarity to IR, resulting in a strong ensemble
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domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining
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Why does Thig change his mind about the invasion?
A. He remembers Ellen and the love he felt, and doesn't want to leave.
B. He has forgotten why he lives for the Hord.
C. He contracted a disease while on Earth that's making him make wild decisions.
D. He is fearful that Earth's influence will affect the Orthan as it did him.
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QUEST OF THIG By BASIL WELLS Thig of Ortha was the vanguard of the conquering "HORDE." He had blasted across trackless space to subdue a defenseless world—only to meet on Earth emotions that were more deadly than weapons. [Transcriber's Note: This etext was produced from Planet Stories Fall 1942. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Thig carefully smoothed the dark sand and seaweed of the lonely beach over the metal lid of the flexible ringed tunnel that linked the grubby ship from another planet with the upper air. He looked out across the heaving waters of the Sound toward Connecticut. He stared appraisingly around at the luxuriant green growth of foliage further inland; and started toward the little stretch of trees and brush, walking carefully because of the lesser gravitation. Thig was shorter than the average Earthman—although on Ortha he was well above the average in height—but his body was thick and powerfully muscled. His skull was well-shaped and large; his features were regular, perhaps a trifle oversize, and his hair and eyes were a curiously matching blend of reddish brown. Oddest of all, he wore no garments, other than the necessary belt and straps to support his rod-like weapon of white metal and his pouches for food and specimens. The Orthan entered the narrow strip of trees and crossed to the little-used highway on the other side. Here he patiently sat down to wait for an Earthman or an Earthwoman to pass. His task now was to bring a native, intact if possible, back to the carefully buried space cruiser where his two fellows and himself would drain the creature's mentality of all its knowledge. In this way they could learn whether a planet was suited for colonization by later swarms of Orthans. Already they had charted over a hundred celestial bodies but of them all only three had proven worthy of consideration. This latest planet, however, 72-P-3 on the chart, appeared to be an ideal world in every respect. Sunlight, plenty of water and a dense atmospheric envelope made of 72-P-3 a paradise among planets. The explorer from another world crouched into the concealment of a leafy shrub. A creature was approaching. Its squat body was covered with baggy strips of bluish cloth and it carried a jointed rod of metal and wood in its paw. It walked upright as did the men of Ortha. Thig's cold eyes opened a trifle wider as he stared into the thing's stupid face. It was as though he was looking into a bit of polished metal at the reflection of himself! The Earthman was opposite now and he must waste no more precious time. The mighty muscles of the Orthan sent him hurtling across the intervening space in two prodigious bounds, and his hands clamped across the mouth and neck of the stranger.... Lewis Terry was going fishing. For a week the typewriter mill that had ground out a thousand assorted yarns of the untamed West and the frigid desolation of the Northwoods had been silent. Lewis wondered if he was going stale. He had sat every day for eight hours in front of that shiny-buttoned bane of the typist, but there were no results. Feebly he had punched a key two days ago and a $ sign had appeared. He hadn't dared touch the machine since. For Mr. Terry, that hard-hitting writer of two-gun action, had never been further west of Long Island than Elizabeth, and he had promised his wife, Ellen, that he would take the three children and herself on a trailer tour of the West that very summer. Since that promise, he could not write a word. Visions of whooping red-skinned Apaches and be-chapped outlaws raiding his little trailer home kept rolling up out of his subconscious. Yet he had to write at least three novelets and a fistful of short stories in the next two weeks to finance the great adventure—or the trip was off. So Lewis left the weathered old cottage in the early dawn and headed for his tubby old boat at the landing in an attempt to work out a salable yarn.... "Hey!" he shouted as a naked man sprang out of the bushes beside the road. "What's the trouble?" Then he had no time for further speech, the massive arms of the stranger had wound around him and two hamlike hands shut off his speech and his wind. He fought futilely against trained muscles. The hand clamping his throat relaxed for a moment and hacked along the side of his head. Blackness flooded the brain of Lewis, and he knew no more. "There it is," announced Thig, dropping the limp body of the captured Earthman to the metal deck-plates. "It is a male of the species that must have built the cities we saw as we landed." "He resembles Thig," announced Kam. "But for the strange covering he wears he might be Thig." "Thig will be this creature!" announced Torp. "With a psychic relay we will transfer the Earthman's memories and meager store of knowledge to the brain of Thig! He can then go out and scout this world without arousing suspicion. While he is gone, I will take Kam and explore the two inner planets." "You are the commander," said Thig. "But I wish this beast did not wear these clumsy sheathing upon his body. On Ortha we do not hamper the use of our limbs so." "Do not question the word of your commander," growled Torp, swelling out his thick chest menacingly. "It is for the good of our people that you disguise yourself as an Earthman." "For the good of the Horde," Thig intoned almost piously as he lifted Terry's body and headed for the laboratory. Service for the Horde was all that the men of Ortha knew. Carefully cultured and brought to life in the laboratories of their Horde, they knew neither father nor mother. Affection and love were entirely lacking in their early training and later life. They were trained antlike from childhood that only the growth and power of the Horde were of any moment. Men and women alike toiled and died like unfeeling robots of flesh and bone for the Horde. The Horde was their religion, their love-life, their everything! So it was that the bodies of the Earthman and the Orthan were strapped on two parallel tables of chill metal and the twin helmets, linked to one another by the intricacies of the psychic relay, put upon their heads. For ten hours or more the droning hum of the relay sucked Terry's brain dry of knowledge. The shock upon the nervous system of the Earthman proved too violent and his heart faltered after a time and stopped completely. Twice, with subtle drugs they restored pseudo-life to his body and kept the electrical impulses throbbing from his tortured brain, but after the third suspension of life Thig removed his helmet. "There is nothing more to learn," he informed his impassive comrades. "Now, let us get on with the plastic surgery that is required. My new body must return to its barbaric household before undue attention is aroused. And when I return I will take along some of the gleaming baubles we found on the red planet—these people value them highly." An hour later, his scars and altered cartilage already healed and painless, Thig again scraped sand over the entrance to the space ship and set out along the moonlit beach toward the nearest path running inland to his home. Memory was laying the country bare about him, Terry's own childhood memories of this particular section of Long Island. Here was the place where Jake and Ted had helped him dig for the buried treasure that old 'Notch-ear' Beggs had told them so exactly about. Remembrance of that episode gave Thig an idea about the little lump of jewels in his pocket. He had found them in a chest along the beach! He was coming up on the porch now and at the sound of his foot on the sagging boards the screen door burst open and three little Earth-creatures were hugging at his legs. An odd sensation, that his acquired memories labeled as pleasure, sent a warm glow upward from around his heart. Then he saw the slender red-haired shape of a woman, the mate of the dead man he knew, and confusion struck his well-trained brain. Men had no mates on Ortha, sex had been overthrown with all the other primitive impulses of barbarism; so he was incapable of understanding the emotions that swept through his acquired memory. Unsteadily he took her in his arms and felt her warm lips pressed, trembling, against his own. That same hot wave of pulsing blood choked achingly up into his throat. "Lew, dear," Ellen was asking, "where have you been all day? I called up at the landing but you were not there. I wanted to let you know that Saddlebag Publications sent a check for $50 for "Reversed Revolvers" and three other editors asked for shorts soon." "Shoulda got a hundred bucks for that yarn," grunted Thig, and gasped. For the moment he had been Lewis Terry and not Thig! So thoroughly had he acquired the knowledge of Terry that he found himself unconsciously adopting the thinking and mannerism of the other. All the better this way, he realized—more natural. "Sorry I was late," he said, digging into his pocket for the glittering baubles, "but I was poking around on the beach where we used to hunt treasure and I found an old chest. Inside it I found nothing but a handful of these." He flashed the jewels in front of Ellen's startled eyes and she clung, unbelieving, to his arm. "Why, Lew," she gasped, "they're worth a fortune! We can buy that new trailer now and have a rebuilt motor in the car. We can go west right away.... Hollywood, the Grand Canyon, cowboys!" "Uh huh," agreed the pseudo Lewis, memories of the ferocious savages and gunmen of his stories rendering him acutely unhappy. Sincerely he hoped that the west had reformed. "I saved some kraut and weiners," Ellen said. "Get washed up while I'm warming them up. Kids ate all the bread so I had to borrow some from the Eskoes. Want coffee, too?" "Mmmmmm," came from the depths of the chipped white wash-basin. "Home again," whispered Ellen as she stood beside Thig twelve weeks later and gazed tearfully at the weathered little gray house. She knelt beside the front stoop and reached for the key hidden beneath it. "The west was wonderful; tremendous, vast and beautiful," she went on as they climbed the steps, "but nowhere was there any place as beautiful as our own little strip of sky and water." Thig sank into a dusty old swing that hung on creaking chains from the exposed rafters of the porch roof. He looked down at the dusty gray car and the bulbous silvery bulk of the trailer that had been their living quarters for almost three months. Strange thoughts were afloat in the chaos of his cool Orthan brain. Tonight or tomorrow night at the latest he must contact his two fellows and report that Earth was a planetary paradise. No other world, including Ortha, was so well-favored and rich. An expeditionary force to wipe the grotesque civilizations of Earth out of existence would, of course, be necessary before the first units of new Hordes could be landed. And there Thig balked. Why must they destroy these people, imperfect though their civilization might be, to make room for the Hordes? Thig tried to tell himself that it was the transmitted thoughts of the dead Earthman that made him feel so, but he was not too sure. For three months he had lived with people who loved, hated, wept and sacrificed for reasons that he had never known existed. He had learned the heady glory of thinking for himself and making his own decisions. He had experienced the primitive joy of matching his wits and tongue against the wits of other unpredictable human beings. There was no abrupt division of men and women into definite classes of endeavor. A laborer thought the same thoughts that a governor might think. Uncertainty added zest to every day's life. The Orthan had come to question the sole devotion of the individual to the Horde to the exclusion of all other interests. What, he wondered, would one new world—or a hundred—populated by the Hordes add to the progress of humanity? For a hundred thousand years the Orthan civilization had remained static, its energies directed into certain well-defined channels. They were mindless bees maintaining their vast mechanical hives. There was that moment on the brink of the Grand Canyon when Ellen had caught his arm breathlessly at all the beauty spread away there beneath them. There were mornings in the desert when the sun painted in lurid red the peaks above the harsh black-and-whites of the sagebrush and cactus slopes. There was the little boy, his body burning with fever, who nestled trustingly against his tense man's body and slept—the son of Ellen and the man he had destroyed. Thig groaned. He was a weakling to let sentimentality so get the better of his judgment. He would go now to the space ship and urge them to blast off for Ortha. He sprang off the porch and strode away down the road toward the beach. The children ran to him; wanted to go along. He sent them away harshly but they smiled and waved their brown little hands. Ellen came to the door and called after him. "Hurry home, dear," she said. "I'll have a bite ready in about an hour." He dared not say anything, for his voice would have broken and she would have known something was wrong. She was a very wise sort of person when something was troubling him. He waved his stubby paw of a hand to show that he had heard, and blindly hurried toward the Sound. Oddly enough, as he hurried away along the narrow path through the autumn woods, his mind busied itself with a new epic of the west that lived no longer. He mentally titled it: "Rustlers' Riot" and blocked in the outlines of his plot. One section of his brain was that of the careless author of gunslinging yarns, a section that seemed to be sapping the life from his own brain. He knew that the story would never be written, but he toyed with the idea. So far had Thig the emotionless, robot-being from Ortha drifted from the unquestioning worship of the Horde! "You have done well," announced Torp when Thig had completed his report on the resources and temperatures of various sections of Terra. "We now have located three worlds fit for colonization and so we will return to Ortha at once. "I will recommend the conquest of this planet, 72-P-3 at once and the complete destruction of all biped life upon it. The mental aberrations of the barbaric natives might lead to endless complications if they were permitted to exist outside our ordered way of life. I imagine that three circuits of the planet about its primary should prove sufficient for the purposes of complete liquidation." "But why," asked Thig slowly, "could we not disarm all the natives and exile them on one of the less desirable continents, Antarctica for example or Siberia? They are primitive humans even as our race was once a race of primitives. It is not our duty to help to attain our own degree of knowledge and comfort?" "Only the good of the Horde matters!" shouted Torp angrily. "Shall a race of feeble-witted beasts, such as these Earthmen, stand in the way of a superior race? We want their world, and so we will take it. The Law of the Horde states that all the universe is ours for the taking." "Let us get back to Ortha at once, then," gritted out Thig savagely. "Never again do I wish to set foot upon the soil of this mad planet. There are forces at work upon Earth that we of Ortha have long forgotten." "Check the blood of Thig for disease, Kam," ordered Torp shortly. "His words are highly irrational. Some form of fever perhaps native to this world. While you examine him I will blast off for Ortha." Thig followed Kam into the tiny laboratory and found a seat beside the squat scientist's desk. His eyes roamed over the familiar instruments and gauges, each in its own precise position in the cases along the walls. His gaze lingered longest on the stubby black ugliness of a decomposition blaster in its rack close to the deck. A blast of the invisible radiations from that weapon's hot throat and flesh or vegetable fiber rotted into flaky ashes. The ship trembled beneath their feet; it tore free from the feeble clutch of the sand about it, and they were rocketing skyward. Thig's broad fingers bit deep into the unyielding metal of his chair. Suddenly he knew that he must go back to Earth, back to Ellen and the children of the man he had helped destroy. He loved Ellen, and nothing must stand between them! The Hordes of Ortha must find some other world, an empty world—this planet was not for them. "Turn back!" he cried wildly. "I must go back to Earth. There is a woman there, helpless and alone, who needs me! The Horde does not need this planet." Kam eyed him coldly and lifted a shining hypodermic syringe from its case. He approached Thig warily, aware that disease often made a maniac of the finest members of the Horde. "No human being is more important than the Horde," he stated baldly. "This woman of whom you speak is merely one unit of the millions we must eliminate for the good of the Horde." Then it was that Thig went berserk. His fists slashed into the thick jaw of the scientist and his fingers ripped at the hard cords overlying the Orthan's vital throat tubes. His fingers and thumb gouged deep into Kam's startled throat and choked off any cry for assistance before it could be uttered. Kam's hand swept down to the holster swung from his intricate harness and dragged his blaster from it. Thig's other hand clamped over his and for long moments they swayed there, locked together in silent deadly struggle. The fate of a world hung in the balance as Kam's other hand fought against that lone arm of Thig. The scales swung in favor of Kam. Slowly the flaring snout of his weapon tilted upward until it reached the level of Thig's waist. Thig suddenly released his grip and dragged his enemy toward him. A sudden reversal of pressure on Kam's gun hand sent the weapon swivelling about full upon its owner's thick torso. Thig's fingers pressed down upon Kam's button finger, down upon the stud set into the grip of the decomposition blaster, and Kam's muscles turned to water. He shrieked. Before Thig's eyes half of his comrade's body sloughed away into foul corruption that swiftly gave way to hardened blobs of dessicated matter. Horror for what he had done—that he had slain one of his own Horde—made his limbs move woodenly. All of his thoughts were dulled for the moment. Painfully slow, he turned his body around toward the control blister, turned around on leaden feet, to look full into the narrowed icy eyes of his commander. He saw the heavy barrel of the blaster slashing down against his skull but he could not swing a fraction of an inch out of the way. His body seemed paralyzed. This was the end, he thought as he waited stupidly for the blow to fall, the end for Ellen and the kids and all the struggling races of Earth. He would never write another cowboy yarn—they would all be dead anyhow soon. Then a thunderclap exploded against his head and he dropped endlessly toward the deck. Blows rained against his skull. He wondered if Torp would ever cease to hammer at him and turn the deadly ray of the weapon upon him. Blood throbbed and pounded with every blow.... Bam, Bam, Bam, the blood pounded in his ears. Like repeated blows of a hammer they shook his booming head. No longer was Torp above him. He was in the corner of the laboratory, a crumpled blood-smeared heap of bruised flesh and bone. He was unfettered and the blood was caked upon his skull and in his matted hair. Torp must have thought he had killed him with those savage blows upon the head. Even Torp, thought Thig ruefully, gave way to the primitive rage of his ancestors at times; but to that very bit of unconscious atavism he now owed his life. A cool-headed robot of an Orthan would have efficiently used the blaster to destroy any possibility of remaining life in his unconscious body. Thig rolled slowly over so that his eye found the door into the control room. Torp would be coming back again to dispose of their bodies through the refuse lock. Already the body of Kam was gone. He wondered why he had been left until last. Perhaps Torp wished to take cultures of his blood and tissues to determine whether a disease was responsible for his sudden madness. The cases of fragile instruments were just above his head. Association of memories brought him the flash of the heavy blaster in its rack beneath them. His hand went up and felt the welcome hardness of the weapon. He tugged it free. In a moment he was on his knees crawling across the plates of the deck toward the door. Halfway across the floor he collapsed on his face, the metal of the gun making a harsh clang. He heard the feet of Torp scuffle out of silence and a choked cry in the man's throat squalled out into a senseless whinny. Thig raised himself up on a quivering elbow and slid the black length of the blaster in front of him. His eyes sought the doorway and stared full into the glaring vacant orbs of his commander. Torp leaned there watching him, his breath gurgling brokenly through his deep-bitten lips. The clawing marks of nails, fingernails, furrowed his face and chest. He was a madman! The deadly attack of Thig; his own violent avenging of Kam's death, and now the apparent return of the man he had killed come to life had all served to jolt his rigidly trained brain from its accustomed groove. The shock had been too much for the established thought-processes of the Orthan. So Thig shot him where he stood, mercifully, before that vacant mad stare set him, too, to gibbering and shrieking. Then he stepped over the skeleton-thing that had been Torp, using the new strength that victory had given him to drive him along. He had saved a world's civilization from extinction! The thought sobered him; yet, somehow, he was pleased that he had done so. After all, it had been the Earthwoman and the children he had been thinking of while he battled Kam, a selfish desire to protect them all. He went to the desk where Torp had been writing in the ship's log and read the last few nervously scrawled lines: Planet 72-P-3 unfit for colonization. Some pernicious disease that strikes at the brain centers and causes violent insanity is existent there. Thig, just returned from a survey of the planet, went mad and destroyed Kam. In turn I was forced to slay him. But it is not ended. Already I feel the insidious virus of.... And there his writing ended abruptly. Thig nodded. That would do it. He set the automatic pilot for the planet Ortha. Unless a rogue asteroid or a comet crossed the ship's path she would return safely to Ortha with that mute warning of danger on 72-P-3. The body of Torp would help to confirm his final message. Then Thig crossed the cabin to the auxiliary life boat there, one of a half-dozen space ships in miniature nested within the great ship's hull, and cut free from the mother vessel. He flipped the drive lever, felt the thrumming of the rockets driving him from the parent ship. The sensation of free flight against his new body was strangely exhilerating and heady. It was the newest of the emotions he had experienced on Earth since that day, so many months before, when he had felt the warmness of Ellen's lips tight against his. Thig flipped the drive lever, felt the thrumming of the rockets driving him from the parent ship. He swung about to the port, watched the flaming drive-rockets of the great exploratory ship hurl it toward far-away Ortha, and there was no regret in his mind that he was not returning to the planet of his first existence. He thought of the dull greys and blacks of his planet, of the monotonous routine of existence that had once been his—and his heart thrilled to the memories of the starry nights and perfect exciting days he had spent on his three month trip over Earth. He made a brief salute to the existence he had known, turned with a tiny sigh, and his fingers made brief adjustments in the controls. The rocket-thrum deepened, and the thin whistle of tenuous air clutching the ship echoed through the hull-plates. He thought of many things in those few moments. He watched the roundness of Earth flatten out, then take on the cup-like illusion that all planets had for an incoming ship. He reduced the drive of his rockets to a mere whisper, striving to control the impatience that crowded his mind. He shivered suddenly, remembering his utter callousness the first time he had sent a space ship whipping down toward the hills and valleys below. And there was a sickness within him when he fully realized that, despite his acquired memory and traits, he was an alien from outer space. He fingered the tiny scars that had completely obliterated the slight differences in his appearance from an Earthman's, and his fingers trembled a bit, as he bent and stared through the vision port. He said a brief prayer in his heart to a God whose presence he now felt very deeply. There were tears in the depths of his eyes, then, and memories were hot, bitter pains. Earth was not far below him. As he let gravity suck him earthward, he heaved a gasp of relief. He was no longer Thig, a creature of a Horde's creation, but Lewis Terry, writer of lurid gun-smoking tales of the West. He must remember that always. He had destroyed the real Terry and now, for the rest of his life, he must make up to the dead man's family. The knowledge that Ellen's love was not really meant for him would be a knife twisting in his heart but for her sake he must endure it. Her dreams and happiness must never be shattered. The bulge of Earth was flattening out now and he could see the outlines of Long Island in the growing twilight. A new plot was growing in the brain of Lewis Terry, a yarn about a cowboy suddenly transported to another world. He smiled ironically. He had seen those other worlds. Perhaps some day he would write about them.... He was Lewis Terry! He must remember that!
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A. He remembers Ellen and the love he felt, and doesn't want to leave.
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What is the general tone of this writing genre?
A. Sorrowful.
B. Academic.
C. Ironic.
D. Infuriated.
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It's Time To Keelhaul U-Haul! Like all superheroes worthy of the title, the Shopping Avenger has an Achilles' heel. In the case of the Shopping Avenger, his Achilles' heel is not animal, vegetable, or mineral but something less tangible. An explanation: Last week, the magazine you are currently reading forced the Shopping Avenger at gunpoint to read a series of treacle-filled self-help books, and then to . The Shopping Avenger, who can withstand radiation, extreme heat and cold, hail, bear attacks, and Eyes Wide Shut , almost succumbed to terminal jejuneness after reading these books. Except for one thing: One of the books, The Art of Happiness , which collects and simplifies the Dalai Lama's philosophy, got the Shopping Avenger to thinking. This, in a way, is the Shopping Avenger's Achilles' heel: thinking. Perhaps it is wrong, the Shopping Avenger thought, to complain about the petty insults and inconveniences of life in the materialistic '90s. The Shopping Avenger felt that perhaps he should counsel those who write seeking help to meditate, to accept bad service the way one accepts the change of seasons, and to extend a compassionate hand of forgiveness to those who provide poor customer care. But then the Shopping Avenger sat down, and the feeling passed. The Shopping Avenger does not make light of the Dalai Lama or of the notion that there is more to life than the impatient acquisition of material goods. If the Shopping Avenger were not, for a superhero, extremely nonjudgmental--as opposed to his alter ego, who is considered insufferably judgmental by his alter ego's wife--the Shopping Avenger would tell the occasional correspondent to let go of his petty grievance and get a life. But the Shopping Avenger also believes that the Dalai Lama has never tried to rent a truck from U-Haul. If he had tried to rent from U-Haul, he never would have escaped from Tibet. (For the complete back story, see "Shopping Avenger" column and one.) The complaints about U-Haul's nonreservation reservation policy continue to pour in through the electronic mail. One correspondent, B.R., wrote in with this cautionary tale: "Last weekend, I went to San Francisco to help my brother and his family move into their first house. My brother had reserved a moving truck with U-Haul for the big day. I warned my brother about U-Haul's 'not really a reservation per se' policy that I learned from the Shopping Avenger. He didn't believe such a thing would happen to him, so he didn't act on my warning." B.R. continues--as if you don't know what happened already--"I went to U-Haul with my brother to get our 'reserved' truck. The store had many customers standing around looking frustrated. When we got to the front of the line, the clerk informed us that our 'reserved' truck had not yet been returned. We asked if we could rent one of the many trucks sitting idle in the parking lot. The clerk laughed and said the keys to those trucks were lost." B.R. and his chastened brother--the Shopping Avenger is resisting the urge to gloat--went to Ryder. "Ryder had a truck available for us. The gentleman who helped us at Ryder said Ryder prides itself on being everything U-Haul is not." The Shopping Avenger has still not received a call from U-Haul spokeswoman Johna Burke explaining why U-Haul refuses to provide trucks to people who reserve trucks, but the Shopping Avenger is pleased to note that several correspondents have written in over the past month saying that, based on what they have read in this column, they will be taking their business to Ryder or Budget or elsewhere. The Shopping Avenger will undoubtedly return to the sorry state of affairs at U-Haul in the next episode, but now on to this month's airline debacle. Before we begin, though, the Shopping Avenger nearly forgot to announce the winner of last month's contest, in which readers were asked to answer the question, "What's the difference between pests and airlines?" The winner is one Tom Morgan, who wrote, "You can hire someone to kill pests." Tom is the winner of a year's supply of Turtle Wax, and he will receive his prize just as soon as the Shopping Avenger figures out how much Turtle Wax actually constitutes a year's supply. The new contest question: How much Turtle Wax comprises a year's supply of Turtle Wax? This month's airline in the spotlight is Southwest. Loyal readers will recall that last month the Shopping Avenger praised Southwest Airlines for its "sterling" customer service. This brought forth a small number of articulate dissensions. The most articulate, and the most troubling, came from M., who wrote, "Last year, flying from Baltimore to Chicago with my entire family (two really little kids included), we set down at Midway in a rainstorm. And waited for our bags. And waited for bags. And waited for bags." An hour later, M. says, the bags showed up, "soaked through. We took them to baggage services at SW and were faced with the most complicated, unclear, and confusing mechanism for filing a claim we experienced flyers have ever seen." When they arrived at their destination, M. and her family made a terrible discovery, "We discovered that our clothes were soaked through--the top clothes were so wet that the dye had bled through down to the lower levels, destroying lots of other clothes. Obviously, our bags had just been sitting out on the runway in the rain. To this day, I've never heard a thing from SW, despite calls and letters." This, of course, is where Shopping Avenger steps in. Shopping Avenger knows that Southwest is different from the average airline, in that it doesn't go out of its way to infuriate its paying customers (see: ), so I expected a quick and generous resolution to M.'s problem. What I got at first, though, was a load of corporate hoo-ha. "The airline's policy, which is consistent with all contracts of carriage at all airlines, requires that passengers file a report in person for lost or damaged luggage within four hours of arrival at their destination," a Southwest spokeswoman, Linda Rutherford, e-mailed me. "[M.] indicates she called for a few days, but did not file a report in person until April 12--three days later. Southwest, as a courtesy, took her report anyway and asked for follow up information and written inventory of the damage." Rutherford said that M. should have submitted detailed receipts and photographs of the damage in order to make a claim. Harrumph, the Shopping Avenger says. It is a bad hair day at Southwest when its officials defend themselves by comparing their airline to other airlines. I forwarded this message to M., who replied: "Wow. Well, of course I didn't file it at the airport on the 9 th because I didn't know the clothes were ruined at the airport. I didn't know until I opened the baggage at my hotel and saw the ruined stuff. (And it's worth noting that we had already waited for about an hour for our luggage with two little kids and impatient in-laws nipping at our heels.)" She goes on, "I did call that evening ... and was told that that sufficed. This is the first time I've been told that I had to file a complaint in person within four hours. ... When I filed on the 12 th , I was never told that I needed any receipts or photos or other type of documentation. The baggage folks seemed pretty uninterested in all of this. ... They know that the type of 'evidence' they want is impossible to obtain. They also know that on April 9 they screwed up the luggage retrieval and left bags out in the rain a long time." Southwest's response actually served to anger M. more than the original problem. "Before, they had a mildly annoyed but loyal customer (who would have been placated by an apology and thrilled with some modest token of their regret). Now they have a pissed-off customer." Things do look bad for Southwest, don't they? The Shopping Avenger sent M.'s response to Rutherford, who e-mailed back saying she thought the Shopping Avenger was asking for "policy information." The Shopping Avenger e-mailed back again, stressing to Rutherford that the Great Court of Consumer Justice would, if this case were brought to trial, undoubtedly find for the plaintiff (the Shopping Avenger serves as prosecutor, judge, and jury in the Great Court of Consumer Justice--defendants are represented by the president of U-Haul), and that Southwest was precipitously close to feeling the sword of retribution at its neck. But then she came through, provisionally, "Yep, you can be sure if [M.] will call me we will get everything squared away. I'm sorry it's taken this long for her to get someone who can help, but we will take care of it from here." Stay tuned, shoppers, to hear whether Southwest makes good it promise to compensate M. and apologize to her for her troubles. The story of M. reminds the Shopping Avenger of a central truth of consumer service: It's not the crime, it's the cover-up. Take the case of K., who found himself waiting in vain for Circuit City to repair his television. Televisions break, even 1-year-old televisions, as is the case with K's. But Circuit City, where he bought the television, gave him a terrible runaround. The Shopping Avenger dispatched his sidekick, Tad the Deputy Avenger, to get to the bottom of K.'s story. This is what he found: K. grew concerned, Tad the Deputy Avenger reports, after his television had been in the Circuit City shop for a week. When he called, he was told to "check back next week." When he asked if someone from the store could call him with more information, he was refused. Weeks went by. When K. told one Circuit City employee that he really would like to get his television back, the employee, K. says, asked him, "Don't you have another television in your house?" More than a month later--after hours and hours and hours of telephone calls and days missed at work--K. received his television back. Mistakes happen, but not, Tad the Deputy Avenger found out, at Circuit City. The case, K. was told by a Circuit City official, was "handled perfectly." Another official, Morgan Stewart in public relations, assured Deputy Avenger Tad that "We got to be a big and successful company by treating customers better than the other guy." The Shopping Avenger and his loyal sidekick would like to hear from other Circuit City customers: Does Circuit City, in fact, treat its customers better than the other guy? Stay tuned for answers. And next month, a Shopping Avenger clergy special: TWA screws with a Hasidic rabbi's travel plans, leaving the rabbi's wife crying at the airport. Find out if the Shopping Avenger can save TWA from certain heavenly punishment, in the next episode. Got a consumer score you want settled? Send e-mail to [email protected].
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C. Ironic.
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What are the benchmark models?
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### Introduction
Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many corpora have advanced the research of task-oriented dialogue systems, most of which are single domain conversations, including ATIS BIBREF6, DSTC 2 BIBREF7, Frames BIBREF8, KVRET BIBREF9, WOZ 2.0 BIBREF10 and M2M BIBREF11. Despite the significant contributions to the community, these datasets are still limited in size, language variation, or task complexity. Furthermore, there is a gap between existing dialogue corpora and real-life human dialogue data. In real-life conversations, it is natural for humans to transition between different domains or scenarios while still maintaining coherent contexts. Thus, real-life dialogues are much more complicated than those dialogues that are only simulated within a single domain. To address this issue, some multi-domain corpora have been proposed BIBREF12, BIBREF13. The most notable corpus is MultiWOZ BIBREF12, a large-scale multi-domain dataset which consists of crowdsourced human-to-human dialogues. It contains 10K dialogue sessions and 143K utterances for 7 domains, with annotation of system-side dialogue states and dialogue acts. However, the state annotations are noisy BIBREF14, and user-side dialogue acts are missing. The dependency across domains is simply embodied in imposing the same pre-specified constraints on different domains, such as requiring both a hotel and an attraction to locate in the center of the town. In comparison to the abundance of English dialogue data, surprisingly, there is still no widely recognized Chinese task-oriented dialogue corpus. In this paper, we propose CrossWOZ, a large-scale Chinese multi-domain (cross-domain) task-oriented dialogue dataset. An dialogue example is shown in Figure FIGREF1. We compare CrossWOZ to other corpora in Table TABREF5 and TABREF6. Our dataset has the following features comparing to other corpora (particularly MultiWOZ BIBREF12): The dependency between domains is more challenging because the choice in one domain will affect the choices in related domains in CrossWOZ. As shown in Figure FIGREF1 and Table TABREF6, the hotel must be near the attraction chosen by the user in previous turns, which requires more accurate context understanding. It is the first Chinese corpus that contains large-scale multi-domain task-oriented dialogues, consisting of 6K sessions and 102K utterances for 5 domains (attraction, restaurant, hotel, metro, and taxi). Annotation of dialogue states and dialogue acts is provided for both the system side and user side. The annotation of user states enables us to track the conversation from the user's perspective and can empower the development of more elaborate user simulators. In this paper, we present the process of dialogue collection and provide detailed data analysis of the corpus. Statistics show that our cross-domain dialogues are complicated. To facilitate model comparison, benchmark models are provided for different modules in pipelined task-oriented dialogue systems, including natural language understanding, dialogue state tracking, dialogue policy learning, and natural language generation. We also provide a user simulator, which will facilitate the development and evaluation of dialogue models on this corpus. The corpus and the benchmark models are publicly available at https://github.com/thu-coai/CrossWOZ. ### Related Work
According to whether the dialogue agent is human or machine, we can group the collection methods of existing task-oriented dialogue datasets into three categories. The first one is human-to-human dialogues. One of the earliest and well-known ATIS dataset BIBREF6 used this setting, followed by BIBREF8, BIBREF9, BIBREF10, BIBREF15, BIBREF16 and BIBREF12. Though this setting requires many human efforts, it can collect natural and diverse dialogues. The second one is human-to-machine dialogues, which need a ready dialogue system to converse with humans. The famous Dialogue State Tracking Challenges provided a set of human-to-machine dialogue data BIBREF17, BIBREF7. The performance of the dialogue system will largely influence the quality of dialogue data. The third one is machine-to-machine dialogues. It needs to build both user and system simulators to generate dialogue outlines, then use templates BIBREF3 to generate dialogues or further employ people to paraphrase the dialogues to make them more natural BIBREF11, BIBREF13. It needs much less human effort. However, the complexity and diversity of dialogue policy are limited by the simulators. To explore dialogue policy in multi-domain scenarios, and to collect natural and diverse dialogues, we resort to the human-to-human setting. Most of the existing datasets only involve single domain in one dialogue, except MultiWOZ BIBREF12 and Schema BIBREF13. MultiWOZ dataset has attracted much attention recently, due to its large size and multi-domain characteristics. It is at least one order of magnitude larger than previous datasets, amounting to 8,438 dialogues and 115K turns in the training set. It greatly promotes the research on multi-domain dialogue modeling, such as policy learning BIBREF18, state tracking BIBREF19, and context-to-text generation BIBREF20. Recently the Schema dataset is collected in a machine-to-machine fashion, resulting in 16,142 dialogues and 330K turns for 16 domains in the training set. However, the multi-domain dependency in these two datasets is only embodied in imposing the same pre-specified constraints on different domains, such as requiring a restaurant and an attraction to locate in the same area, or the city of a hotel and the destination of a flight to be the same (Table TABREF6). Table TABREF5 presents a comparison between our dataset with other task-oriented datasets. In comparison to MultiWOZ, our dataset has a comparable scale: 5,012 dialogues and 84K turns in the training set. The average number of domains and turns per dialogue are larger than those of MultiWOZ, which indicates that our task is more complex. The cross-domain dependency in our dataset is natural and challenging. For example, as shown in Table TABREF6, the system needs to recommend a hotel near the attraction chosen by the user in previous turns. Thus, both system recommendation and user selection will dynamically impact the dialogue. We also allow the same domain to appear multiple times in a user goal since a tourist may want to go to more than one attraction. To better track the conversation flow and model user dialogue policy, we provide annotation of user states in addition to system states and dialogue acts. While the system state tracks the dialogue history, the user state is maintained by the user and indicates whether the sub-goals have been completed, which can be used to predict user actions. This information will facilitate the construction of the user simulator. To the best of our knowledge, CrossWOZ is the first large-scale Chinese dataset for task-oriented dialogue systems, which will largely alleviate the shortage of Chinese task-oriented dialogue corpora that are publicly available. ### Data Collection
Our corpus is to simulate scenarios where a traveler seeks tourism information and plans her or his travel in Beijing. Domains include hotel, attraction, restaurant, metro, and taxi. The data collection process is summarized as below: Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database. For the taxi domain, there is no need to store the information. Instead, we can call the API directly if necessary. Goal Generation: a multi-domain goal generator was designed based on the database. The relation across domains is captured in two ways. One is to constrain two targets that locate near each other. The other is to use a taxi or metro to commute between two targets in HAR domains mentioned in the context. To make workers understand the task more easily, we crafted templates to generate natural language descriptions for each structured goal. Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states. Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. To evaluate the quality of the annotation of dialogue acts and states, three experts were employed to manually annotate dialogue acts and states for 50 dialogues. The results show that our annotations are of high quality. Finally, each dialogue contains a structured goal, a task description, user states, system states, dialogue acts, and utterances. ### Data Collection ::: Database Construction
We collected 465 attractions, 951 restaurants, and 1,133 hotels in Beijing from the Web. Some statistics are shown in Table TABREF11. There are three types of slots for each entity: common slots such as name and address; binary slots for hotel services such as wake-up call; nearby attractions/restaurants/hotels slots that contain nearby entities in the attraction, restaurant, and hotel domains. Since it is not usual to find another nearby hotel in the hotel domain, we did not collect such information. This nearby relation allows us to generate natural cross-domain goals, such as "find another attraction near the first one" and "find a restaurant near the attraction". Nearest metro stations of HAR entities form the metro database. In contrast, we provided the pseudo car type and plate number for the taxi domain. ### Data Collection ::: Goal Generation
To avoid generating overly complex goals, each goal has at most five sub-goals. To generate more natural goals, the sub-goals can be of the same domain, such as two attractions near each other. The goal is represented as a list of (sub-goal id, domain, slot, value) tuples, named as semantic tuples. The sub-goal id is used to distinguish sub-goals which may be in the same domain. There are two types of slots: informable slots which are the constraints that the user needs to inform the system, and requestable slots which are the information that the user needs to inquire from the system. As shown in Table TABREF13, besides common informable slots (italic values) whose values are determined before the conversation, we specially design cross-domain informable slots (bold values) whose values refer to other sub-goals. Cross-domain informable slots utilize sub-goal id to connect different sub-goals. Thus the actual constraints vary according to the different contexts instead of being pre-specified. The values of common informable slots are sampled randomly from the database. Based on the informable slots, users are required to gather the values of requestable slots (blank values in Table TABREF13) through conversation. There are four steps in goal generation. First, we generate independent sub-goals in HAR domains. For each domain in HAR domains, with the same probability $\mathcal {P}$ we generate a sub-goal, while with the probability of $1-\mathcal {P}$ we do not generate any sub-goal for this domain. Each sub-goal has common informable slots and requestable slots. As shown in Table TABREF15, all slots of HAR domains can be requestable slots, while the slots with an asterisk can be common informable slots. Second, we generate cross-domain sub-goals in HAR domains. For each generated sub-goal (e.g., the attraction sub-goal in Table TABREF13), if its requestable slots contain "nearby hotels", we generate an additional sub-goal in the hotel domain (e.g., the hotel sub-goal in Table TABREF13) with the probability of $\mathcal {P}_{attraction\rightarrow hotel}$. Of course, the selected hotel must satisfy the nearby relation to the attraction entity. Similarly, we do not generate any additional sub-goal in the hotel domain with the probability of $1-\mathcal {P}_{attraction\rightarrow hotel}$. This also works for the attraction and restaurant domains. $\mathcal {P}_{hotel\rightarrow hotel}=0$ since we do not allow the user to find the nearby hotels of one hotel. Third, we generate sub-goals in the metro and taxi domains. With the probability of $\mathcal {P}_{taxi}$, we generate a sub-goal in the taxi domain (e.g., the taxi sub-goal in Table TABREF13) to commute between two entities of HAR domains that are already generated. It is similar for the metro domain and we set $\mathcal {P}_{metro}=\mathcal {P}_{taxi}$. All slots in the metro or taxi domain appear in the sub-goals and must be filled. As shown in Table TABREF15, from and to slots are always cross-domain informable slots, while others are always requestable slots. Last, we rearrange the order of the sub-goals to generate more natural and logical user goals. We require that a sub-goal should be followed by its referred sub-goal as immediately as possible. To make the workers aware of this cross-domain feature, we additionally provide a task description for each user goal in natural language, which is generated from the structured goal by hand-crafted templates. Compared with the goals whose constraints are all pre-specified, our goals impose much more dependency between different domains, which will significantly influence the conversation. The exact values of cross-domain informable slots are finally determined according to the dialogue context. ### Data Collection ::: Dialogue Collection
We developed a specialized website that allows two workers to converse synchronously and make annotations online. On the website, workers are free to choose one of the two roles: tourist (user) or system (wizard). Then, two paired workers are sent to a chatroom. The user needs to accomplish the allocated goal through conversation while the wizard searches the database to provide the necessary information and gives responses. Before the formal data collection, we trained the workers to complete a small number of dialogues by giving them feedback. Finally, 90 well-trained workers are participating in the data collection. In contrast, MultiWOZ BIBREF12 hired more than a thousand workers to converse asynchronously. Each worker received a dialogue context to review and need to respond for only one turn at a time. The collected dialogues may be incoherent because workers may not understand the context correctly and multiple workers contributed to the same dialogue session, possibly leading to more variance in the data quality. For example, some workers expressed two mutually exclusive constraints in two consecutive user turns and failed to eliminate the system's confusion in the next several turns. Compared with MultiWOZ, our synchronous conversation setting may produce more coherent dialogues. ### Data Collection ::: Dialogue Collection ::: User Side
The user state is the same as the user goal before a conversation starts. At each turn, the user needs to 1) modify the user state according to the system response at the preceding turn, 2) select some semantic tuples in the user state, which indicates the dialogue acts, and 3) compose the utterance according to the selected semantic tuples. In addition to filling the required values and updating cross-domain informable slots with real values in the user state, the user is encouraged to modify the constraints when there is no result under such constraints. The change will also be recorded in the user state. Once the goal is completed (all the values in the user state are filled), the user can terminate the dialogue. ### Data Collection ::: Dialogue Collection ::: Wizard Side
We regard the database query as the system state, which records the constraints of each domain till the current turn. At each turn, the wizard needs to 1) fill the query according to the previous user response and search the database if necessary, 2) select the retrieved entities, and 3) respond in natural language based on the information of the selected entities. If none of the entities satisfy all the constraints, the wizard will try to relax some of them for a recommendation, resulting in multiple queries. The first query records original user constraints while the last one records the constraints relaxed by the system. ### Data Collection ::: Dialogue Annotation
After collecting the conversation data, we used some rules to annotate dialogue acts automatically. Each utterance can have several dialogue acts. Each dialogue act is a tuple that consists of intent, domain, slot, and value. We pre-define 6 types of intents and use the update of the user state and system state as well as keyword matching to obtain dialogue acts. For the user side, dialogue acts are mainly derived from the selection of semantic tuples that contain the information of domain, slot, and value. For example, if (1, Attraction, fee, free) in Table TABREF13 is selected by the user, then (Inform, Attraction, fee, free) is labelled. If (1, Attraction, name, ) is selected, then (Request, Attraction, name, none) is labelled. If (2, Hotel, name, near (id=1)) is selected, then (Select, Hotel, src_domain, Attraction) is labelled. This intent is specially designed for the "nearby" constraint. For the system side, we mainly applied keyword matching to label dialogue acts. Inform intent is derived by matching the system utterance with the information of selected entities. When the wizard selects multiple retrieved entities and recommend them, Recommend intent is labeled. When the wizard expresses that no result satisfies user constraints, NoOffer is labeled. For General intents such as "goodbye", "thanks" at both user and system sides, keyword matching is applied. We also obtained a binary label for each semantic tuple in the user state, which indicates whether this semantic tuple has been selected to be expressed by the user. This annotation directly illustrates the progress of the conversation. To evaluate the quality of the annotation of dialogue acts and states (both user and system states), three experts were employed to manually annotate dialogue acts and states for the same 50 dialogues (806 utterances), 10 for each goal type (see Section SECREF4). Since dialogue act annotation is not a classification problem, we didn't use Fleiss' kappa to measure the agreement among experts. We used dialogue act F1 and state accuracy to measure the agreement between each two experts' annotations. The average dialogue act F1 is 94.59% and the average state accuracy is 93.55%. We then compared our annotations with each expert's annotations which are regarded as gold standard. The average dialogue act F1 is 95.36% and the average state accuracy is 94.95%, which indicates the high quality of our annotations. ### Statistics
After removing uncompleted dialogues, we collected 6,012 dialogues in total. The dataset is split randomly for training/validation/test, where the statistics are shown in Table TABREF25. The average number of sub-goals in our dataset is 3.24, which is much larger than that in MultiWOZ (1.80) BIBREF12 and Schema (1.84) BIBREF13. The average number of turns (16.9) is also larger than that in MultiWOZ (13.7). These statistics indicate that our dialogue data are more complex. According to the type of user goal, we group the dialogues in the training set into five categories: 417 dialogues have only one sub-goal in HAR domains. 1573 dialogues have multiple sub-goals (2$\sim $3) in HAR domains. However, these sub-goals do not have cross-domain informable slots. 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3$\sim $5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots. 1,759 dialogues have multiple sub-goals (2$\sim $5) in HAR domains with cross-domain informable slots. 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3$\sim $5 sub-goals). The data statistics are shown in Table TABREF26. As mentioned in Section SECREF14, we generate independent multi-domain, cross multi-domain, and traffic domain sub-goals one by one. Thus in terms of the task complexity, we have S<M<CM and M<M+T<CM+T, which is supported by the average number of sub-goals, semantic tuples, and turns per dialogue in Table TABREF26. The average number of tokens also becomes larger when the goal becomes more complex. About 60% of dialogues (M+T, CM, and CM+T) have cross-domain informable slots. Because of the limit of maximal sub-goals number, the ratio of dialogue number of CM+T to CM is smaller than that of M+T to M. CM and CM+T are much more challenging than other tasks because additional cross-domain constraints in HAR domains are strict and will result in more "NoOffer" situations (i.e., the wizard finds no result that satisfies the current constraints). In this situation, the wizard will try to relax some constraints and issue multiple queries to find some results for a recommendation while the user will compromise and change the original goal. The negotiation process is captured by "NoOffer rate", "Multi-query rate", and "Goal change rate" in Table TABREF26. In addition, "Multi-query rate" suggests that each sub-goal in M and M+T is as easy to finish as the goal in S. The distribution of dialogue length is shown in Figure FIGREF27, which is an indicator of the task complexity. Most single-domain dialogues terminate within 10 turns. The curves of M and M+T are almost of the same shape, which implies that the traffic task requires two additional turns on average to complete the task. The curves of CM and CM+T are less similar. This is probably because CM goals that have 5 sub-goals (about 22%) can not further generate a sub-goal in traffic domains and become CM+T goals. ### Corpus Features
Our corpus is unique in the following aspects: Complex user goals are designed to favor inter-domain dependency and natural transition between multiple domains. In return, the collected dialogues are more complex and natural for cross-domain dialogue tasks. A well-controlled, synchronous setting is applied to collect human-to-human dialogues. This ensures the high quality of the collected dialogues. Explicit annotations are provided at not only the system side but also the user side. This feature allows us to model user behaviors or develop user simulators more easily. ### Benchmark and Analysis
CrossWOZ can be used in different tasks or settings of a task-oriented dialogue system. To facilitate further research, we provided benchmark models for different components of a pipelined task-oriented dialogue system (Figure FIGREF32), including natural language understanding (NLU), dialogue state tracking (DST), dialogue policy learning, and natural language generation (NLG). These models are implemented using ConvLab-2 BIBREF21, an open-source task-oriented dialog system toolkit. We also provided a rule-based user simulator, which can be used to train dialogue policy and generate simulated dialogue data. The benchmark models and simulator will greatly facilitate researchers to compare and evaluate their models on our corpus. ### Benchmark and Analysis ::: Natural Language Understanding
Task: The natural language understanding component in a task-oriented dialogue system takes an utterance as input and outputs the corresponding semantic representation, namely, a dialogue act. The task can be divided into two sub-tasks: intent classification that decides the intent type of an utterance, and slot tagging which identifies the value of a slot. Model: We adapted BERTNLU from ConvLab-2. BERT BIBREF22 has shown strong performance in many NLP tasks. We use Chinese pre-trained BERT BIBREF23 for initialization and then fine-tune the parameters on CrossWOZ. We obtain word embeddings and the sentence representation (embedding of [CLS]) from BERT. Since there may exist more than one intent in an utterance, we modify the traditional method accordingly. For dialogue acts of inform and recommend intents such as (intent=Inform, domain=Attraction, slot=fee, value=free) whose values appear in the sentence, we perform sequential labeling using an MLP which takes word embeddings ("free") as input and outputs tags in BIO schema ("B-Inform-Attraction-fee"). For each of the other dialogue acts (e.g., (intent=Request, domain=Attraction, slot=fee)) that do not have actual values, we use another MLP to perform binary classification on the sentence representation to predict whether the sentence should be labeled with this dialogue act. To incorporate context information, we use the same BERT to get the embedding of last three utterances. We separate the utterances with [SEP] tokens and insert a [CLS] token at the beginning. Then each original input of the two MLP is concatenated with the context embedding (embedding of [CLS]), serving as the new input. We also conducted an ablation test by removing context information. We trained models with both system-side and user-side utterances. Result Analysis: The results of the dialogue act prediction (F1 score) are shown in Table TABREF31. We further tested the performance on different intent types, as shown in Table TABREF35. In general, BERTNLU performs well with context information. The performance on cross multi-domain dialogues (CM and CM+T) drops slightly, which may be due to the decrease of "General" intent and the increase of "NoOffer" as well as "Select" intent in the dialogue data. We also noted that the F1 score of "Select" intent is remarkably lower than those of other types, but context information can improve the performance significantly. Since recognizing domain transition is a key factor for a cross-domain dialogue system, natural language understanding models need to utilize context information more effectively. ### Benchmark and Analysis ::: Dialogue State Tracking
Task: Dialogue state tracking is responsible for recognizing user goals from the dialogue context and then encoding the goals into the pre-defined system state. Traditional state tracking models take as input user dialogue acts parsed by natural language understanding modules, while recently there are joint models obtaining the system state directly from the context. Model: We implemented a rule-based model (RuleDST) and adapted TRADE (Transferable Dialogue State Generator) BIBREF19 in this experiment. RuleDST takes as input the previous system state and the last user dialogue acts. Then, the system state is updated according to hand-crafted rules. For example, If one of user dialogue acts is (intent=Inform, domain=Attraction, slot=fee, value=free), then the value of the "fee" slot in the attraction domain will be filled with "free". TRADE generates the system state directly from all the previous utterances using a copy mechanism. As mentioned in Section SECREF18, the first query of the system often records full user constraints, while the last one records relaxed constraints for recommendation. Thus the last one involves system policy, which is out of the scope of state tracking. We used the first query for these models and left state tracking with recommendation for future work. Result Analysis: We evaluated the joint state accuracy (percentage of exact matching) of these two models (Table TABREF31). TRADE, the state-of-the-art model on MultiWOZ, performs poorly on our dataset, indicating that more powerful state trackers are necessary. At the test stage, RuleDST can access the previous gold system state and user dialogue acts, which leads to higher joint state accuracy than TRADE. Both models perform worse on cross multi-domain dialogues (CM and CM+T). To evaluate the ability of modeling cross-domain transition, we further calculated joint state accuracy for those turns that receive "Select" intent from users (e.g., "Find a hotel near the attraction"). The performances are 11.6% and 12.0% for RuleDST and TRADE respectively, showing that they are not able to track domain transition well. ### Benchmark and Analysis ::: Dialogue Policy Learning
Task: Dialogue policy receives state $s$ and outputs system action $a$ at each turn. Compared with the state given by a dialogue state tracker, $s$ may have more information, such as the last user dialogue acts and the entities provided by the backend database. Model: We adapted a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy). The state $s$ consists of the last system dialogue acts, last user dialogue acts, system state of the current turn, the number of entities that satisfy the constraints in the current domain, and a terminal signal indicating whether the user goal is completed. The action $a$ is delexicalized dialogue acts of current turn which ignores the exact values of the slots, where the values will be filled back after prediction. Result Analysis: As illustrated in Table TABREF31, there is a large gap between F1 score of exact dialogue act and F1 score of delexicalized dialogue act, which means we need a powerful system state tracker to find correct entities. The result also shows that cross multi-domain dialogues (CM and CM+T) are harder for system dialogue act prediction. Additionally, when there is "Select" intent in preceding user dialogue acts, the F1 score of exact dialogue act and delexicalized dialogue act are 41.53% and 54.39% respectively. This shows that the policy performs poorly for cross-domain transition. ### Benchmark and Analysis ::: Natural Language Generation
Task: Natural language generation transforms a structured dialogue act into a natural language sentence. It usually takes delexicalized dialogue acts as input and generates a template-style sentence that contains placeholders for slots. Then, the placeholders will be replaced by the exact values, which is called lexicalization. Model: We provided a template-based model (named TemplateNLG) and SC-LSTM (Semantically Conditioned LSTM) BIBREF1 for natural language generation. For TemplateNLG, we extracted templates from the training set and manually added some templates for infrequent dialogue acts. For SC-LSTM we adapted the implementation on MultiWOZ and trained two SC-LSTM with system-side and user-side utterances respectively. Result Analysis: We calculated corpus-level BLEU as used by BIBREF1. We took all utterances with the same delexcalized dialogue acts as references (100 references on average), which results in high BLEU score. For user-side utterances, the BLEU score for TemplateNLG is 0.5780, while the BLEU score for SC-LSTM is 0.7858. For system-side, the two scores are 0.6828 and 0.8595. As exemplified in Table TABREF39, the gap between the two models can be attributed to that SC-LSTM generates common pattern while TemplateNLG retrieves original sentence which has more specific information. We do not provide BLEU scores for different goal types (namely, S, M, CM, etc.) because BLEU scores on different corpus are not comparable. ### Benchmark and Analysis ::: User Simulator
Task: A user simulator imitates the behavior of users, which is useful for dialogue policy learning and automatic evaluation. A user simulator at dialogue act level (e.g., the "Usr Policy" in Figure FIGREF32) receives the system dialogue acts and outputs user dialogue acts, while a user simulator at natural language level (e.g., the left part in Figure FIGREF32) directly takes system's utterance as input and outputs user's utterance. Model: We built a rule-based user simulator that works at dialogue act level. Different from agenda-based BIBREF24 user simulator that maintains a stack-like agenda, our simulator maintains the user state straightforwardly (Section SECREF17). The simulator will generate a user goal as described in Section SECREF14. At each user turn, the simulator receives system dialogue acts, modifies its state, and outputs user dialogue acts according to some hand-crafted rules. For example, if the system inform the simulator that the attraction is free, then the simulator will fill the "fee" slot in the user state with "free", and ask for the next empty slot such as "address". The simulator terminates when all requestable slots are filled, and all cross-domain informable slots are filled by real values. Result Analysis: During the evaluation, we initialized the user state of the simulator using the previous gold user state. The input to the simulator is the gold system dialogue acts. We used joint state accuracy (percentage of exact matching) to evaluate user state prediction and F1 score to evaluate the prediction of user dialogue acts. The results are presented in Table TABREF31. We can observe that the performance on complex dialogues (CM and CM+T) is remarkably lower than that on simple ones (S, M, and M+T). This simple rule-based simulator is provided to facilitate dialogue policy learning and automatic evaluation, and our corpus supports the development of more elaborated simulators as we provide the annotation of user-side dialogue states and dialogue acts. ### Benchmark and Analysis ::: Evaluation with User Simulation
In addition to corpus-based evaluation for each module, we also evaluated the performance of a whole dialogue system using the user simulator as described above. Three configurations were explored: Simulation at dialogue act level. As shown by the dashed connections in Figure FIGREF32, we used the aforementioned simulator at the user side and assembled the dialogue system with RuleDST and SL policy. Simulation at natural language level using TemplateNLG. As shown by the solid connections in Figure FIGREF32, the simulator and the dialogue system were equipped with BERTNLU and TemplateNLG additionally. Simulation at natural language level using SC-LSTM. TemplateNLG was replaced with SC-LSTM in the second configuration. When all the slots in a user goal are filled by real values, the simulator terminates. This is regarded as "task finish". It's worth noting that "task finish" does not mean the task is success, because the system may provide wrong information. We calculated "task finish rate" on 1000 times simulations for each goal type (See Table TABREF31). Findings are summarized below: Cross multi-domain tasks (CM and CM+T) are much harder to finish. Comparing M and M+T, although each module performs well in traffic domains, additional sub-goals in these domains are still difficult to accomplish. The system-level performance is largely limited by RuleDST and SL policy. Although the corpus-based performance of NLU and NLG modules is high, the two modules still harm the performance. Thus more powerful models are needed for all components of a pipelined dialogue system. TemplateNLG has a much lower BLEU score but performs better than SC-LSTM in natural language level simulation. This may be attributed to that BERTNLU prefers templates retrieved from the training set. ### Conclusion
In this paper, we present the first large-scale Chinese Cross-Domain task-oriented dialogue dataset, CrossWOZ. It contains 6K dialogues and 102K utterances for 5 domains, with the annotation of dialogue states and dialogue acts at both user and system sides. About 60% of the dialogues have cross-domain user goals, which encourage natural transition between related domains. Thanks to the rich annotation of dialogue states and dialogue acts at both user side and system side, this corpus provides a new testbed for a wide range of tasks to investigate cross-domain dialogue modeling, such as dialogue state tracking, policy learning, etc. Our experiments show that the cross-domain constraints are challenging for all these tasks. The transition between related domains is especially challenging to model. Besides corpus-based component-wise evaluation, we also performed system-level evaluation with a user simulator, which requires more powerful models for all components of a pipelined cross-domain dialogue system. ### Acknowledgments
This work was supported by the National Science Foundation of China (Grant No. 61936010/61876096) and the National Key R&D Program of China (Grant No. 2018YFC0830200). We would like to thank THUNUS NExT JointLab for the support. We would also like to thank Ryuichi Takanobu and Fei Mi for their constructive comments. We are grateful to our action editor, Bonnie Webber, and the anonymous reviewers for their valuable suggestions and feedback.
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BERTNLU from ConvLab-2, a rule-based model (RuleDST) , TRADE (Transferable Dialogue State Generator) , a vanilla policy trained in a supervised fashion from ConvLab-2 (SL policy)
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What is the purpose of a comanalysis?
A. It paralyzes patients in order to restore their nervous systems to equilibrium
B. It gives more direct access to the plagues of the human mind
C. It allows a manipulator to implant false memories
D. It permits a psychoanalyst to remove traumatic memories
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Transcriber’s note: This story was published in Galaxy magazine, June 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. [p 135 ] By CHARLES V. DE VET monkey on his back Under the cloud of cast-off identities lay the shape of another man— was it himself? Illustrated by DILLON HE was walking endlessly down a long, glass-walled corridor. Bright sunlight slanted in through one wall, on the blue knapsack across his shoulders. Who he was, and what he was doing here, was clouded. The truth lurked in some corner of his consciousness, but it was not reached by surface awareness. The corridor opened at last into a large high-domed room, much like a railway station or an air terminal. He walked straight ahead. At the sight of him a man leaning negligently against a stone pillar, to his right but within vision, straightened and barked an order to him, “Halt!” He lengthened his stride but gave no other sign. [p 136 ] Two men hurried through a doorway of a small anteroom to his left, calling to him. He turned away and began to run. Shouts and the sound of charging feet came from behind him. He cut to the right, running toward the escalator to the second floor. Another pair of men were hurrying down, two steps at a stride. With no break in pace he veered into an opening beside the escalator. At the first turn he saw that the aisle merely circled the stairway, coming out into the depot again on the other side. It was a trap. He glanced quickly around him. At the rear of the space was a row of lockers for traveler use. He slipped a coin into a pay slot, opened the zipper on his bag and pulled out a flat briefcase. It took him only a few seconds to push the case into the compartment, lock it and slide the key along the floor beneath the locker. There was nothing to do after that—except wait. The men pursuing him came hurtling around the turn in the aisle. He kicked his knapsack to one side, spreading his feet wide with an instinctive motion. Until that instant he had intended to fight. Now he swiftly reassessed the odds. There were five of them, he saw. He should be able to incapacitate two or three and break out. But the fact that they had been expecting him meant that others would very probably be waiting outside. His best course now was to sham ignorance. He relaxed. He offered no resistance as they reached him. They were not gentle men. A tall ruffian, copper-brown face damp with perspiration and body oil, grabbed him by the jacket and slammed him back against the lockers. As he shifted his weight to keep his footing someone drove a fist into his face. He started to raise his hands; and a hard flat object crashed against the side of his skull. The starch went out of his legs. “D O you make anything out of it?” the psychoanalyst Milton Bergstrom, asked. John Zarwell shook his head. “Did I talk while I was under?” “Oh, yes. You were supposed to. That way I follow pretty well what you’re reenacting.” “How does it tie in with what I told you before?” Bergstrom’s neat-boned, fair-skinned face betrayed no emotion other than an introspective stillness of his normally alert gaze. “I see no connection,” he decided, his words once again precise and meticulous. “We don’t have enough to go on. Do you feel able to try another comanalysis this afternoon yet?” “I don’t see why not.” Zarwell [p 137 ] opened the collar of his shirt. The day was hot, and the room had no air conditioning, still a rare luxury on St. Martin’s. The office window was open, but it let in no freshness, only the mildly rank odor that pervaded all the planet’s habitable area. “Good.” Bergstrom rose. “The serum is quite harmless, John.” He maintained a professional diversionary chatter as he administered the drug. “A scopolamine derivative that’s been well tested.” The floor beneath Zarwell’s feet assumed abruptly the near transfluent consistency of a damp sponge. It rose in a foot-high wave and rolled gently toward the far wall. Bergstrom continued talking, with practiced urbanity. “When psychiatry was a less exact science,” his voice went on, seeming to come from a great distance, “a doctor had to spend weeks, sometimes months or years interviewing a patient. If he was skilled enough, he could sort the relevancies from the vast amount of chaff. We are able now, with the help of the serum, to confine our discourses to matters cogent to the patient’s trouble.” The floor continued its transmutation, and Zarwell sank deep into viscous depths. “Lie back and relax. Don’t …” The words tumbled down from above. They faded, were gone. ZARWELL found himself standing on a vast plain. There was no sky above, and no horizon in the distance. He was in a place without space or dimension. There was nothing here except himself—and the gun that he held in his hand. A weapon beautiful in its efficient simplicity. He should know all about the instrument, its purpose and workings, but he could not bring his thoughts into rational focus. His forehead creased with his mental effort. Abruptly the unreality about him shifted perspective. He was approaching—not walking, but merely shortening the space between them—the man who held the gun. The man who was himself. The other “himself” drifted nearer also, as though drawn by a mutual attraction. The man with the gun raised his weapon and pressed the trigger. With the action the perspective shifted again. He was watching the face of the man he shot jerk and twitch, expand and contract. The face was unharmed, yet it was no longer the same. No longer his own features. The stranger face smiled approvingly at him. “O DD,” Bergstrom said. He brought his hands up and joined the tips of his fingers against his chest. “But it’s another piece in the [p 138 ] jig-saw. In time it will fit into place.” He paused. “It means no more to you than the first, I suppose?” “No,” Zarwell answered. He was not a talking man, Bergstrom reflected. It was more than reticence, however. The man had a hard granite core, only partially concealed by his present perplexity. He was a man who could handle himself well in an emergency. Bergstrom shrugged, dismissing his strayed thoughts. “I expected as much. A quite normal first phase of treatment.” He straightened a paper on his desk. “I think that will be enough for today. Twice in one sitting is about all we ever try. Otherwise some particular episode might cause undue mental stress, and set up a block.” He glanced down at his appointment pad. “Tomorrow at two, then?” Zarwell grunted acknowledgment and pushed himself to his feet, apparently unaware that his shirt clung damply to his body. THE sun was still high when Zarwell left the analyst’s office. The white marble of the city’s buildings shimmered in the afternoon heat, squat and austere as giant tree trunks, pock-marked and gray-mottled with windows. Zarwell was careful not to rest his hand on the flesh searing surface of the stone. The evening meal hour was approaching when he reached the Flats, on the way to his apartment. The streets of the old section were near-deserted. The only sounds he heard as he passed were the occasional cry of a baby, chronically uncomfortable in the day’s heat, and the lowing of imported cattle waiting in a nearby shed to be shipped to the country. All St. Martin’s has a distinctive smell, as of an arid dried-out swamp, with a faint taint of fish. But in the Flats the odor changes. Here is the smell of factories, warehouses, and trading marts; the smell of stale cooking drifting from the homes of the laborers and lower class techmen who live there. Zarwell passed a group of smaller children playing a desultory game of lic-lic for pieces of candy and cigarettes. Slowly he climbed the stairs of a stone flat. He prepared a supper for himself and ate it without either enjoyment or distaste. He lay down, fully clothed, on his bed. The visit to the analyst had done nothing to dispel his ennui. [p 139 ] The next morning when Zarwell awoke he lay for a moment, unmoving. The feeling was there again, like a scene waiting only to be gazed at directly to be perceived. It was as though a great wisdom lay at the edge of understanding. If he rested quietly it would all come to him. Yet always, when his mind lost its sleep-induced [p 140 ] lethargy, the moment of near understanding slipped away. This morning, however, the sense of disorientation did not pass with full wakefulness. He achieved no understanding, but the strangeness did not leave as he sat up. He gazed about him. The room did not seem to be his own. The furnishings, and the clothing he observed in a closet, might have belonged to a stranger. He pulled himself from his blankets, his body moving with mechanical reaction. The slippers into which he put his feet were larger than he had expected them to be. He walked about the small apartment. The place was familiar, but only as it would have been if he had studied it from blueprints, not as though he lived there. The feeling was still with him when he returned to the psychoanalyst. THE scene this time was more kaleidoscopic, less personal. A village was being ravaged. Men struggled and died in the streets. Zarwell moved among them, seldom taking part in the individual clashes, yet a moving force in the conflict . The background changed. He understood that he was on a different world. Here a city burned. Its resistance was nearing its end. Zarwell was riding a shaggy pony outside a high wall surrounding the stricken metropolis. He moved in and joined a party of short, bearded men, directing them as they battered at the wall with a huge log mounted on a many-wheeled truck. The log broke a breach in the concrete and the besiegers charged through, carrying back the defenders who sought vainly to plug the gap. Soon there would be rioting in the streets again, plundering and killing. Zarwell was not the leader of the invaders, only a lesser figure in the rebellion. But he had played a leading part in the planning of the strategy that led to the city’s fall. The job had been well done. Time passed, without visible break in the panorama. Now Zarwell was fleeing, pursued by the same bearded men who had been his comrades before. Still he moved with the same firm purpose, vigilant, resourceful, and well prepared for the eventuality that had befallen. He made his escape without difficulty. He alighted from a space ship on still another world—another shift in time—and the atmosphere of conflict engulfed him. Weary but resigned he accepted it, and did what he had to do … BERGSTROM was regarding him with speculative scrutiny. “You’ve had quite a past, apparently,” he observed. [p 141 ] Zarwell smiled with mild embarrassment. “At least in my dreams.” “Dreams?” Bergstrom’s eyes widened in surprise. “Oh, I beg your pardon. I must have forgotten to explain. This work is so routine to me that sometimes I forget it’s all new to a patient. Actually what you experienced under the drug were not dreams. They were recollections of real episodes from your past.” Zarwell’s expression became wary. He watched Bergstrom closely. After a minute, however, he seemed satisfied, and he let himself settle back against the cushion of his chair. “I remember nothing of what I saw,” he observed. “That’s why you’re here, you know,” Bergstrom answered. “To help you remember.” “But everything under the drug is so …” “Haphazard? That’s true. The recall episodes are always purely random, with no chronological sequence. Our problem will be to reassemble them in proper order later. Or some particular scene may trigger a complete memory return. “It is my considered opinion,” Bergstrom went on, “that your lost memory will turn out to be no ordinary amnesia. I believe we will find that your mind has been tampered with.” “Nothing I’ve seen under the drug fits into the past I do remember.” “That’s what makes me so certain,” Bergstrom said confidently. “You don’t remember what we have shown to be true. Conversely then, what you think you remember must be false. It must have been implanted there. But we can go into that later. For today I think we have done enough. This episode was quite prolonged.” “I won’t have any time off again until next week end,” Zarwell reminded him. “That’s right.” Bergstrom thought for a moment. “We shouldn’t let this hang too long. Could you come here after work tomorrow?” “I suppose I could.” “Fine,” Bergstrom said with satisfaction. “I’ll admit I’m considerably more than casually interested in your case by this time.” A WORK truck picked Zarwell up the next morning and he rode with a tech crew to the edge of the reclam area. Beside the belt bringing ocean muck from the converter plant at the seashore his bulldozer was waiting. He took his place behind the drive wheel and began working dirt down between windbreakers anchored in the rock. Along a makeshift road into the badlands trucks brought crushed lime and phosphorus to supplement the ocean sediment. The progress of life from the sea to the land was a mechanical [p 142 ] process of this growing world. Nearly two hundred years ago, when Earth established a colony on St. Martin’s, the land surface of the planet had been barren. Only its seas thrived with animal and vegetable life. The necessary machinery and technicians had been supplied by Earth, and the long struggle began to fit the world for human needs. When Zarwell arrived, six months before, the vitalized area already extended three hundred miles along the coast, and sixty miles inland. And every day the progress continued. A large percentage of the energy and resources of the world were devoted to that essential expansion. The reclam crews filled and sodded the sterile rock, planted binding grasses, grain and trees, and diverted rivers to keep it fertile. When there were no rivers to divert they blasted out springs and lakes in the foothills to make their own. Biologists developed the necessary germ and insect life from what they found in the sea. Where that failed, they imported microorganisms from Earth. Three rubber-tracked crawlers picked their way down from the mountains until they joined the road passing the belt. They were loaded with ore that would be smelted into metal for depleted Earth, or for other colonies short of minerals. It was St. Martin’s only export thus far. Zarwell pulled his sun helmet lower, to better guard his hot, dry features. The wind blew continuously on St. Martin’s, but it furnished small relief from the heat. After its three-thousand-mile journey across scorched sterile rock, it sucked the moisture from a man’s body, bringing a membrane-shrinking dryness to the nostrils as it was breathed in. With it came also the cloying taste of limestone in a worker’s mouth. Zarwell gazed idly about at the other laborers. Fully three-quarters of them were beri-rabza ridden. A cure for the skin fungus had not yet been found; the men’s faces and hands were scabbed and red. The colony had grown to near self-sufficiency, would soon have a moderate prosperity, yet they still lacked adequate medical and research facilities. Not all the world’s citizens were content. Bergstrom was waiting in his office when Zarwell arrived that evening. HE was lying motionless on a hard cot, with his eyes closed, yet with his every sense sharply quickened. Tentatively he tightened small muscles in his arms and legs. Across his wrists and thighs he felt straps binding him to the cot. “So that’s our big, bad man,” a coarse voice above him observed [p 143 ] caustically. “He doesn’t look so tough now, does he?” “It might have been better to kill him right away,” a second, less confident voice said. “It’s supposed to be impossible to hold him.” “Don’t be stupid. We just do what we’re told. We’ll hold him.” “What do you think they’ll do with him?” “Execute him, I suppose,” the harsh voice said matter-of-factly. “They’re probably just curious to see what he looks like first. They’ll be disappointed.” Zarwell opened his eyes a slit to observe his surroundings. It was a mistake. “He’s out of it,” the first speaker said, and Zarwell allowed his eyes to open fully. The voice, he saw, belonged to the big man who had bruised him against the locker at the spaceport. Irrelevantly he wondered how he knew now that it had been a spaceport. His captor’s broad face jeered down at Zarwell. “Have a good sleep?” he asked with mock solicitude. Zarwell did not deign to acknowledge that he heard. The big man turned. “You can tell the Chief he’s awake,” he said. Zarwell followed his gaze to where a younger man, with a blond lock of hair on his forehead, stood behind him. The youth nodded and went out, while the other pulled a chair up to the side of Zarwell’s cot. While their attention was away from him Zarwell had unobtrusively loosened his bonds as much as possible with arm leverage. As the big man drew his chair nearer, he made the hand farthest from him tight and compact and worked it free of the leather loop. He waited. The big man belched. “You’re supposed to be great stuff in a situation like this,” he said, his smoke-tan face splitting in a grin that revealed large square teeth. “How about giving me a sample?” “You’re a yellow-livered bastard,” Zarwell told him. The grin faded from the oily face as the man stood up. He leaned over the cot—and Zarwell’s left hand shot up and locked about his throat, joined almost immediately by the right. The man’s mouth opened and he tried to yell as he threw himself frantically backward. He clawed at the hands about his neck. When that failed to break the grip he suddenly reversed his weight and drove his fist at Zarwell’s head. Zarwell pulled the struggling body down against his chest and held it there until all agitated movement ceased. He sat up then, letting the body slide to the floor. The straps about his thighs came loose with little effort. THE analyst dabbed at his upper lip with a handkerchief. “The episodes are beginning to tie together,” he said, with an attempt at [p 144 ] nonchalance. “The next couple should do it.” Zarwell did not answer. His memory seemed on the point of complete return, and he sat quietly, hopefully. However, nothing more came and he returned his attention to his more immediate problem. Opening a button on his shirt, he pulled back a strip of plastic cloth just below his rib cage and took out a small flat pistol. He held it in the palm of his hand. He knew now why he always carried it. Bergstrom had his bad moment. “You’re not going to …” he began at the sight of the gun. He tried again. “You must be joking.” “I have very little sense of humor,” Zarwell corrected him. “You’d be foolish!” Bergstrom obviously realized how close he was to death. Yet surprisingly, after the first start, he showed little fear. Zarwell had thought the man a bit soft, too adjusted to a life of ease and some prestige to meet danger calmly. Curiosity restrained his trigger finger. “Why would I be foolish?” he asked. “Your Meninger oath of inviolable confidence?” Bergstrom shook his head. “I know it’s been broken before. But you need me. You’re not through, you know. If you killed me you’d still have to trust some other analyst.” “Is that the best you can do?” “No.” Bergstrom was angry now. “But use that logical mind you’re supposed to have! Scenes before this have shown what kind of man you are. Just because this last happened here on St. Martin’s makes little difference. If I was going to turn you in to the police, I’d have done it before this.” Zarwell debated with himself the truth of what the other had said. “Why didn’t you turn me in?” he asked. “Because you’re no mad-dog killer!” Now that the crisis seemed to be past, Bergstrom spoke more calmly, even allowed himself to relax. “You’re still pretty much in the fog about yourself. I read more in those comanalyses than you did. I even know who you are!” Zarwell’s eyebrows raised. “Who am I?” he asked, very interested now. Without attention he put his pistol away in a trouser pocket. Bergstrom brushed the question aside with one hand. “Your name makes little difference. You’ve used many. But you are an idealist. Your killings were necessary to bring justice to the places you visited. By now you’re almost a legend among the human worlds. I’d like to talk more with you on that later.” While Zarwell considered, Bergstrom pressed his advantage. “One more scene might do it,” he said. “Should we try again—if you trust me, that is?” [p 145 ] Zarwell made his decision quickly. “Go ahead,” he answered. ALL Zarwell’s attention seemed on the cigar he lit as he rode down the escalator, but he surveyed the terminal carefully over the rim of his hand. He spied no suspicious loungers. Behind the escalator he groped along the floor beneath the lockers until he found his key. The briefcase was under his arm a minute later. In the basement lave he put a coin in the pay slot of a private compartment and went in. As he zipped open the briefcase he surveyed his features in the mirror. A small muscle at the corner of one eye twitched spasmodically. One cheek wore a frozen quarter smile. Thirty-six hours under the paralysis was longer than advisable. The muscles should be rested at least every twenty hours. Fortunately his natural features would serve as an adequate disguise now. He adjusted the ring setting on the pistol-shaped instrument that he took from his case, and carefully rayed several small areas of his face, loosening muscles that had been tight too long. He sighed gratefully when he finished, massaging his cheeks and forehead with considerable pleasure. Another glance in the mirror satisfied him with the changes that had been made. He turned to his briefcase again and exchanged the gun for a small syringe, which he pushed into a trouser pocket, and a single-edged razor blade. Removing his fiber-cloth jacket he slashed it into strips with the razor blade and flushed it down the disposal bowl. With the sleeves of his blouse rolled up he had the appearance of a typical workman as he strolled from the compartment. Back at the locker he replaced the briefcase and, with a wad of gum, glued the key to the bottom of the locker frame. One step more. Taking the syringe from his pocket, he plunged the needle into his forearm and tossed the instrument down a waste chute. He took three more steps and paused uncertainly. When he looked about him it was with the expression of a man waking from a vivid dream. “Q UITE ingenious,” Graves murmured admiringly. “You had your mind already preconditioned for the shot. But why would you deliberately give yourself amnesia?” “What better disguise than to believe the part you’re playing?” “A good man must have done that job on your mind,” Bergstrom commented. “I’d have hesitated to try it myself. It must have taken a lot of trust on your part.” [p 146 ] “Trust and money,” Zarwell said drily. “Your memory’s back then?” Zarwell nodded. “I’m glad to hear that,” Bergstrom assured him. “Now that you’re well again I’d like to introduce you to a man named Vernon Johnson. This world …” Zarwell stopped him with an upraised hand. “Good God, man, can’t you see the reason for all this? I’m tired. I’m trying to quit.” “Quit?” Bergstrom did not quite follow him. “It started on my home colony,” Zarwell explained listlessly. “A gang of hoods had taken over the government. I helped organize a movement to get them out. There was some bloodshed, but it went quite well. Several months later an unofficial envoy from another world asked several of us to give them a hand on the same kind of job. The political conditions there were rotten. We went with him. Again we were successful. It seems I have a kind of genius for that sort of thing.” He stretched out his legs and regarded them thoughtfully. “I learned then the truth of Russell’s saying: ‘When the oppressed win their freedom they are as oppressive as their former masters.’ When they went bad, I opposed them. This time I failed. But I escaped again. I have quite a talent for that also. “I’m not a professional do-gooder.” Zarwell’s tone appealed to Bergstrom for understanding. “I have only a normal man’s indignation at injustice. And now I’ve done my share. Yet, wherever I go, the word eventually gets out, and I’m right back in a fight again. It’s like the proverbial monkey on my back. I can’t get rid of it.” He rose. “That disguise and memory planting were supposed to get me out of it. I should have known it wouldn’t work. But this time I’m not going to be drawn back in! You and your Vernon Johnson can do your own revolting. I’m through!” Bergstrom did not argue as he left. RESTLESSNESS drove Zarwell from his flat the next day—a legal holiday on St. Martin’s. At a railed-off lot he stopped and loitered in the shadow of an adjacent building watching workmen drilling an excavation for a new structure. When a man strolled to his side and stood watching the workmen, he was not surprised. He waited for the other to speak. “I’d like to talk to you, if you can spare a few minutes,” the stranger said. Zarwell turned and studied the man without answering. He was medium tall, with the body of an athlete, though perhaps ten years [p 147 ] beyond the age of sports. He had a manner of contained energy. “You’re Johnson?” he asked. The man nodded. Zarwell tried to feel the anger he wanted to feel, but somehow it would not come. “We have nothing to talk about,” was the best he could manage. “Then will you just listen? After, I’ll leave—if you tell me to.” Against his will he found himself liking the man, and wanting at least to be courteous. He inclined his head toward a curb wastebox with a flat top. “Should we sit?” Johnson smiled agreeably and they walked over to the box and sat down. “When this colony was first founded,” Johnson began without preamble, “the administrative body was a governor, and a council of twelve. Their successors were to be elected biennially. At first they were. Then things changed. We haven’t had an election now in the last twenty-three years. St. Martin’s is beginning to prosper. Yet the only ones receiving the benefits are the rulers. The citizens work twelve hours a day. They are poorly housed , poorly fed, poorly clothed. They …” Zarwell found himself not listening as Johnson’s voice went on. The story was always the same. But why did they always try to drag him into their troubles? Why hadn’t he chosen some other world on which to hide? The last question prompted a new thought. Just why had he chosen St. Martin’s? Was it only a coincidence? Or had he, subconsciously at least, picked this particular world? He had always considered himself the unwilling subject of glib persuaders … but mightn’t some inner compulsion of his own have put the monkey on his back? “… and we need your help.” Johnson had finished his speech. Zarwell gazed up at the bright sky. He pulled in a long breath, and let it out in a sigh. “What are your plans so far?” he asked wearily. — CHARLES V. DE VET
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B. It gives more direct access to the plagues of the human mind
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Who is the Watcher?
A. Someone who has been granted the honor of watching over the mountain region
B. A man who was exiled from society because of violent tendencies
C. An old man who has retracted from society
D. An alien in charge of protecting the planet
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WHEN THE MOUNTAIN SHOOK By Robert Abernathy Illustrated by Kelly Freas [Transcriber Note: This etext was produced from IF Worlds of Science Fiction March 1954. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Dark was the Ryzga mountain and forbidding; steep were its cliffs and sheer its crevasses. But its outward perils could not compare with the Ryzgas themselves, who slept within, ready to wake and conquer.... At sunset they were in sight of the Ryzga mountain. Strangely it towered among the cliffs and snow-slopes of the surrounding ranges: an immense and repellently geometric cone, black, its sides blood-tinted by the dying sun. Neena shivered, even though the surrounding cold could not reach her. The ice-wind blew from the glacier, but Var's love was round her as a warming cloak, a cloak that glowed softly golden in the deepening twilight, even as her love was about him. Var said, "The Watcher's cave should be three miles beyond this pass." He stood rigid, trying to catch an echo of the Watcher's thoughts, but there was nothing. Perhaps the old man was resting. From the other direction, the long way that they two had come, it was not difficult to sense the thought of Groz. That thought was powerful, and heavy with vengeance. "Hurry," said Neena. "They're closer than they were an hour ago." She was beautiful and defiant, facing the red sunset and the black mountain. Var sensed her fear, and the love that had conquered it. He felt a wave of tenderness and bitterness. For him she had come to this. For the flame that had sprung between them at the Truce of New Grass, she had challenged the feud of their peoples and had left her home, to follow him. Now, if her father and his kinsmen overtook them, it would be death for Var, and for Neena living shame. Which of the two was worse was no longer a simple problem to Var, who had grown much older in the last days. "Wait," he commanded. While she waited he spun a dream, attaching it to the crags that loomed over the pass, and to the frozen ground underfoot. It was black night, as it would really be when Groz and his henchmen reached this place; lurid fire spewed from the Ryzga mountain, and strange lights dipped above it; and for good measure there was an avalanche in the dream, and hideous beasts rushed snapping and ravening from the crevices of the rock. "Oh!" cried Neena in involuntary alarm. Var sighed, shaking his head. "It won't hold them for long, but it's the best I can do now. Come on." There was no path. Now they were descending the steeper face of the sierra, and the way led over bottomless crevasses, sheer drops and sheer ascents, sheets of traitorous glare ice. Place after place had to be crossed on the air, and both grew weary with the effort such crossings cost. They hoarded their strength, helping one another; one alone might never have won through. It was starry night already when they saw the light from the Watcher's cave. The light shone watery and dim from beneath the hoary back of the glacier, and as they came nearer they saw why: the cave entrance was sealed by a sheet of ice, a frozen waterfall that fell motionless from the rocks above. They heard no sound. The two young people stared for a long minute, intrigued and fearful. Both had heard of this place, and the ancient who lived there to keep watch on the Ryzga mountain, as a part of the oldest legends of their childhood; but neither had been here before. But this was no time for shyness. Var eyed the ice-curtain closely to make sure that it was real, not dream-stuff; then he struck it boldly with his fist. It shattered and fell in a rain of splinters, sparkling in the light that poured from within. They felt the Watcher rouse, heard his footsteps, and finally saw him—a shrunken old man, white-haired, with a lined beardless face. The sight of him, more marred by age than anyone they had ever seen before, was disappointing. They had expected something more—an ancient giant, a tower of wisdom and strength. The Watcher was four hundred years old; beside him even Groz, who had always seemed so ancient, was like a boy. The Watcher peered at them in turn. "Welcome," he said in a cracked voice. He did not speak again; the rest of his conversation was in thought only. "Welcome indeed. I am too much alone here." "You were asleep!" said Var. Shock made his thought accusing, though he had not meant to be. The old man grinned toothlessly. "Never fear. Asleep or awake, I watch. Come in! You're letting in the wind." Inside the cave it was warm as summer. Var saw with some surprise that all the walls were sheathed in ice—warm to the touch, bound fast against melting by the Watcher's will. Light blazed in reflections from the ice walls, till there was no shadow in the place. Behind them began a tinkling of falling water, thawed from the glacial ridges above to descend sheet-wise over the cave mouth, freezing as it fell into lengthening icicles. The old man gazed at his work for a moment, then turned questioningly to the young pair. "We need a little rest out of the cold," said Var. "And food, if you can spare it. We're pursued." "Yes, yes. You shall have what I can give you. Make yourselves comfortable, and in one minute.... Pursued, eh? A pity. I see the world is as bad as it was when I was last in it." Hot food and drink were before them almost at once. The Watcher regarded them with compassion as their eyes brightened and some of the shadow of weariness lifted from them. "You have stolen your enemy's daughter, no doubt, young man? Such things happened when I was young." Warming to the old man now, Var sketched his and Neena's history briefly. "We should have been safe among my people by now. And before very long, I'm sure, I would have performed some deed which Groz would recognize as a worthy exploit, and would thus have healed the feud between our families. But our flight was found out too soon. They cut us off and forced us into the mountains, and now they are only a few hours behind us." "A pity, indeed. I would like to help you—but, you understand, I am the Mountain Watcher. I must be above feuds and families." Var nodded somberly, thinking that an old recluse would in any case be able to do little for them against Groz and his violent kinsfolk. "And what will you do now?" Var grinned mirthlessly. "We haven't much choice, since they're overtaking us. I have only one idea left: we can go where Groz may fear to follow us." "To the mountain, you mean." "And into it, if need be." The Watcher was broodingly silent; his eyes shifted to Neena, where she nestled by Var's side. He asked, "And you—are you willing to follow your lover in this?" Neena returned his gaze without flinching; then she looked sidelong at Var, and her lips curled with a proud and tender mockery. "Follow? Why, I will lead, if his courage should fail him." The old man said, "It is no part of my duty to dissuade you from this thing. You are free persons. But I must be sure that you know what you are doing. That is the second part of the law the First Watcher made: to guard lest the unwary and the ignorant should bring harm on themselves and on all men." "We know the stories," Var said brusquely. "In the hollow heart of their mountain the Ryzgas sleep, as they chose to do when their world crumbled. But if they are wakened, the mountain will tremble, and the Ryzgas will come forth." "Do you believe that?" "As one believes stories." "It is true," said the Watcher heavily. "In my youth I penetrated farther into the mountain than anyone before, farther even than did the First Watcher. I did not see the sleepers, nor will any man until they come again, but I met their sentries, the sentinel machines that guard them now as they have for two thousand years. When I had gone that far, the mountain began to shake, the force that is in the Earth rumbled below, and I returned in time." Now for the first time Var sensed the power in the old man's look, the power of four hundred years' wisdom. Var stared down at his hands. "The Ryzgas also were men," said the Watcher. "But they were such a race as the world has not seen before or since. There were tyrannies before the Ryzgas, there was lust for power, and atrocious cruelty; but such tyranny, power, and cruelty as theirs, had never been known. They ruled the Earth for four generations, and the Earth was too little for them. They laid the world waste, stripped it of metals and fuels and bored to its heart for energy, poisoned its seas and its air with the fume of their works, wrung its peoples dry for their labor ... and in each of those four generations they launched a ship of space. They were great and evil as no other people has been, because they wanted the stars. "Because of them we must build with dreams instead of iron, and our only fire is that of the Sun, and even now, two thousand years later, the Earth is still slowly recovering from the pangs and poison of that age. If you turn up the sod in the plain where the wild herds graze, you will find numberless fragments of rusted or corroded metal, bits of glass and strange plastic substances, debris of artifacts still showing the marks of their shaping—the scattered wreckage of the things they made. And we—we too are a remnant, the descendants of the few out of all humanity that survived when the Ryzgas' world went down in flame and thunder. "In the last generation of their power the Ryzgas knew by their science that the race of man would endure them no longer. They made ready their weapons, they mined the cities and the factories for destruction, making sure that their works and their knowledge would perish with them. Meanwhile they redoubled the yoke and the punishments, hastening the completion of the last of the starships. "From the memories that the old Watchers have left here, and from the memories of dead men that still echo in the air, I have gathered a picture of that world's end. I will show it to you...." Var and Neena stared, unstirring, with wide vacant eyes, while the old man wove a dream around them, and the bright ice-cave faded from their vision, and they saw— Black starless night, a sky of rolling smoke above the greatest city that was ever built. Only the angry light of fires relieved the city's darkness—that, and the blue-white lightning flashes that silhouetted the naked skeletons of buildings and were followed by thunder and a shaking of the earth. Along lightless streets, half choked with rubble and with the dead, poured a mad, hating horde. The recurrent flashes lit scarred faces, naked bodies blackened and maimed from the hell of the workshops where the Ryzgas' might had been forged, eyes that stared white and half sightless from the glare of the furnaces, gnarled hands that now at long last clutched the weapons of the last rebellion—a rebellion without hope of new life on a world gutted and smoldering from the fulfilment of the Ryzgas' dream, without slogans other than a cry for blood. Before them death waited around the citadel where the masters still fought. All round, from the lowest and most poisonous levels of the shattered city, the slaves swarmed up in their millions. And the lightning blazed, and the city howled and screamed and burned. Then, unbelievably, the thunder fell silent, and the silence swept outward like a wave, from ruined street to street. The mouths that had shouted their wrath were speechless, and the rage-blinded eyes were lifted in sudden awe. From the center, over the citadel, an immense white globe soared upward, rising swiftly without sound. They had never seen its like, but they knew. It was the last starship, and it was leaving. It poised motionless. For an instant the burning city lay mute; then the millions found voice. Some roared ferocious threats and curses; others cried desolately— wait! Then the whole city, the dark tumuli of its buildings and its leaping fires and tormented faces, and the black sky over it, seemed to twist and swim, like a scene under water when a great fish sweeps past, and the ship was gone. The stunned paralysis fell apart in fury. Flame towered over the citadel. The hordes ran and shrieked again toward the central inferno, and the city burned and burned.... Var blinked dazedly in the shadowless glow of the ice-cave. His arm tightened about Neena till she gasped. He was momentarily uncertain that he and she were real and here, such had been the force of the dream, a vision of such scope and reality as Var had never seen—no, lived through—before. With deep respect now he gazed upon the bent old man who was the Mountain Watcher. "Some of the Ryzgas took flight to the stars, and some perished on Earth. But there was a group of them who believed that their time to rule would come again. These raised a black mountain from the Earth's heart, and in hollows within it cast themselves into deathless sleep, their deathless and lifeless sentinels round them, to wait till someone dare arouse them, or until their chosen time—no one knows surely. "I have told you the story you know, and have shown you a glimpse of the old time, because I must make sure that you do not approach the mountain in ignorance. Our world is unwise and sometimes evil, full of arrogance, folly, and passion that are in the nature of man. Yet it is a happy world, compared to that the Ryzgas made and will make again." The Watcher eyed them speculatively. "Before all," he said finally, "this is a world where you are free to risk wakening the old tyrants, if in your own judgment your great need renders the chance worth taking." Neena pressed her face against Var's shoulder, hiding her eyes. In her mind as it groped for his there was a confusion of horror and pity. Var looked grimly at the Watcher, and would have spoken; but the Watcher seemed suddenly a very long way off, and Var could no longer feel his own limbs, his face was a numb mask. Dully he heard the old man say, "You are tired. Best sleep until morning." Var strove to cry out that there was no time, that Groz was near and that sleep was for infants and the aged, but his intention sank and drowned under wave upon wave of unconquerable languor. The bright cave swam and dissolved; his eyelids closed. Var woke. Daylight glimmered through the ice of the cave mouth. He had been unconscious, helpless, for hours! At the thought of that, panic gripped him. He had not slept since childhood, and he had forgotten how it was. He came to his feet in one quick movement, realizing in that action that sleep had refreshed his mind and body—realizing also that a footstep had wakened him. Across the cave he faced a young man who watched him coolly with dark piercing eyes that were familiar though he did not know the face. Neena sat up and stifled a cry of fright. Var growled, "Who are you? Where's the Watcher?" The other flashed white teeth in a smile. "I'm the Watcher," he answered. "Often I become a youth at morning, and relax into age as the day passes. A foolish amusement, no doubt, but amusements are few here." "You made us fall asleep. Groz will be on us—" "Groz and his people could not detect your thoughts as you slept. They were all night chasing elusive dreams on the high ridges, miles away." Var passed a hand across bewildered eyes. Neena said softly, "Thank you, Watcher." "Don't thank me. I take no sides in your valley feuds. But now you are rested, your minds are clear. Do you still mean to go on to the Ryzga mountain?" Not looking at the Watcher, Var muttered unsteadily, "We have no alternative." There was a liquid tinkling as the ice-curtain collapsed; the fresh breeze of morning swept into the cave. The youth beckoned to them, and they followed him outside. The glacial slope on which the cavern opened faced toward the mountain. It rose black and forbidding in the dawn as it had by sunset. To right and left of it, the grand cliffs, ocher and red, were lit splendidly by the morning sun, but the mountain of the Ryzgas drank in the light and gave nothing back. Below their feet the slope fell away into an opaque sea of fog, filling a mile-wide gorge. There was a sound of turbulent water, of a river dashed from rock to rock in its struggle toward the plain, but the curling fog hid everything. "You have an alternative," said the Watcher crisply. The two took their eyes from the black mountain and gazed at him in sudden hope, but his face was unsmiling. "It is this. You, Var, can flee up the canyon to the north, by a way I will show you, disguising your thoughts and masking your presence as well as you are able, while the girl goes in the other direction, southward, without seeking to conceal herself. Your pursuers will be deceived and follow her, and by the time they catch her it will be too late for them to overtake Var." That possibility had not occurred to them at all. Var and Neena looked at one another. Then by common consent they blended their minds into one. They thought, in the warm intimacy of unreserved understanding: " It would work: I-you would make the sacrifice of shame and mockery—yet these can be borne—that I-you might be saved from death—which is alone irreparable.... But to become I and you again—that cannot be borne. " They said in unison, "No. Not that." The Watcher's face did not change. He said gravely, "Very well. I will give you what knowledge I have that may help you when you enter the Ryzga mountain." Quickly, he impressed on them what he had learned of the structure of the mountain and of its guardian machines. Var closed his eyes, a little dizzied by the rapid flood of detail. "You are ready to go," said the Watcher. He spoke aloud, and his voice was cracked and harsh. Var opened his eyes in surprise, and saw that the Watcher had become again the hoary ancient of last night. Var felt a twinge of unfamiliar emotion; only by its echo in Neena's mind did he recognize it as a sense of guilt. He said stiffly, "You don't blame us?" "You have taken life in your own hands," rasped the Watcher. "Who does that needs no blessing and feels no curse. Go!" They groped through the fog above blank abysses that hid the snarling river, crept hand in hand, sharing their strength, across unstable dream bridges from crag to crag. Groz and his pack, in their numbers, would cross the gorge more surely and swiftly. When Var and Neena set foot at last on the cindery slope of the great volcanic cone, they sensed that the pursuit already halved their lead. They stood high on the side of the Ryzga mountain, and gazed at the doorway. It was an opaque yet penetrable well of darkness, opening into the face of a lava cliff, closed only by an intangible curtain—so little had the Ryzgas feared those who might assail them in their sleep. Var sent his thoughts probing beyond the curtain, listened intently, head thrown back, to their echoes that returned. The tunnel beyond slanted steeply downward. Var's hands moved, molding a radiant globe from the feeble sunshine that straggled through the fog-bank. With an abrupt motion he hurled it. The sun-globe vanished, as if the darkness had drunk it up, but though sight did not serve they both sensed that it had passed through to light up the depths beyond. For within the mountain something snapped suddenly alert—something alive yet not living, seeing yet blind. They felt light-sensitive cells tingle in response, felt electric currents sting along buried, long-idle circuits.... The two stood shivering together. The morning wind stirred, freshening, the fog lifted a little, and they heard a great voice crying, "There they are!" Var and Neena turned. Far out in the sea of fog, on a dream bridge that they could not see, stood Groz. He shook the staff he carried. It was too far to discern the rage that must contort his features, but the thought he hurled at them was a soundless bellow: "Young fools! I've caught you now!" Behind Groz the figures of his followers loomed up as striding shadows. Neena's hand tightened on Var's. Var sent a thought of defiance: "Go back! Or you'll drive us to enter the mountain!" Groz seemed to hesitate. Then he swung his staff up like a weapon, and for the two on the mountainside the world turned upside down, the mountain's black shoulder hung inverted above them and the dizzy gulf of sky was beneath. Var fought for footing with his balance gone, feeling Neena reel against him until, summoning all his strength, he broke the grip of the illusion and the world seemed to right itself. The mist billowed again and Groz was out of sight, but they could hear him exhorting his men to haste. Neena's face was deadly pale and her lips trembled, but her urgent whisper said, "Come on!" Together they plunged into the curtain of darkness. At Var's thought command Neena froze instantly. "Feel that!" he muttered, and she, listening, sensed it too: the infinitesimal trickle of currents behind what appeared to be a blank tunnel wall, a rising potential that seemed to whisper Ready ... ready.... The sun-globe floated behind them, casting light before them down the featureless tunnel that sloped always toward the mountain's heart. Var summoned it, and it drifted ahead, a dozen feet, a little more— Between wall and wall a blinding spindle of flame sprang into being, pulsed briefly with radiant energy that pained the eyes, and went out. The immaterial globe of light danced on before them. "Forward, before the charge builds up again!" said Var. A few feet further on, they stumbled over a pile of charred bones. Someone else had made it only this far. It was farther than the Watcher had gone into these uncharted regions, and only the utmost alertness of mind and sense had saved them from death in traps like this. But as yet the way was not blocked.... Then they felt the mountain begin to tremble. A very faint and remote vibration at first, then an increasingly potent shuddering of the floor under their feet and the walls around them. Somewhere far below immense energies were stirring for the first time in centuries. The power that was in the Earth was rising; great wheels commenced to turn, the mechanical servitors of the Ryzgas woke one by one and began to make ready, while their masters yet slept, for the moment of rebirth that might be near at hand. From behind, up the tunnel, came a clear involuntary thought of dismay, then a directed thought, echoing and ghostly in the confinement of the dark burrow: " Stop! —before you go too far!" Var faced that way and thought coldly: "Only if you return and let us go free." In the black reaches of the shaft his will groped for and locked with that of Groz, like the grip of two strong wrestlers. In that grip each knew with finality that the other's stubbornness matched his own—that neither would yield, though the mountain above them and the world outside should crumble to ruin around them. "Follow us, then!" They plunged deeper into the mountain. And the shaking of the mountain increased with every step, its vibrations became sound, and its sound was like that of the terrible city which they had seen in the dream. Through the slow-rolling thunder of the hidden machines seemed to echo the death-cries of a billion slaves, the despair of all flesh and blood before their monstrous and inhuman power. Without warning, lights went on. Blinking in their glare, Var and Neena saw that fifty paces before them the way opened out into a great rounded room that was likewise ablaze with light. Cautiously they crept forward to the threshold of that chamber at the mountain's heart. Its roof was vaulted; its circular walls were lined with panels studded with gleaming control buttons, levers, colored lights. As they watched light flicked on and off in changing patterns, registering the progressive changes in the vast complex of mechanisms for which this must be the central control station. Behind those boards circuits opened and closed in bewildering confusion; the two invaders felt the rapid shifting of magnetic fields, the fury of electrons boiling in vacuum.... For long moments they forgot the pursuit, forgot everything in wonder at this place whose remotest like they had never seen in the simplicity of their machineless culture. In all the brilliant space there was no life. They looked at one another, the same thought coming to both at once: perhaps, after two thousand years, the masters were dead after all, and only the machines remained? As if irresistibly drawn, they stepped over the threshold. There was a clang of metal like a signal. Halfway up the wall opposite, above a narrow ramp that descended between the instrument panels, a massive doorway swung wide, and in its opening a figure stood. Var and Neena huddled frozenly, half expecting each instant to be their last. And the Ryzga too stood motionless, looking down at them. He was a man of middle height and stocky build, clad in a garment of changing colors, of fabric delicate as dream-stuff. In his right hand, with the care one uses with a weapon, he grasped a gleaming metal tube; his other hand rested as for support against the frame of the doorway. That, and his movements when he came slowly down the ramp toward them, conveyed a queer suggestion of weariness or weakness, as if he were yet not wholly roused from his two millenia of slumber. But the Ryzga's manner and his mind radiated a consciousness of power, a pride and assurance of self that smote them like a numbing blow. With a new shock, Var realized that the Ryzga's thoughts were quite open. They had a terse, disconnected quality that was strange and unsettling, and in part they were couched in alien and unintelligible symbols. But there was no block. Apparently the Ryzga felt no need to close his mind in the presence of inferior creatures.... He paused with his back to the central control panel, and studied the interlopers with the dispassionate gaze of a scientist examining a new, but not novel, species of insect. His thoughts seemed to click, like metal parts of a mechanism falling into places prepared for them. The image occurred oddly to Var, to whom such a comparison would ordinarily have been totally strange. "Culture: late barbarism. Handwork of high quality—good. Physically excellent stock...." There was a complicated and incomprehensible schemata of numbers and abstract forms. "The time: two thousand years—more progress might have been expected, if any survivors at all initially postulated; but this will do. The pessimists were mistaken. We can begin again." Then, startlingly super-imposed on the cool progression of logical thought, came a wave of raw emotion, devastating in its force. It was a lustful image of a world once more obedient, crawling, laboring to do the Ryzgas' will— toward the stars, the stars! The icy calculation resumed: "Immobilize these and the ones indicated in the passage above. Then wake the rest...." Var was staring in fascination at the Ryzga's face. It was a face formed by the custom of unquestioned command; yet it was lined by a deeply ingrained weariness, the signs of premature age—denied, overridden by the driving will they had sensed a moment earlier. It was a sick man's face. The Ryzga's final thought clicked into place: Decision! He turned toward the switchboard behind him, reaching with practised certainty for one spot upon it. Neena screamed. Between the Ryzga and the control panel a nightmare shape reared up seven feet tall, flapping black amorphous limbs and flashing red eyes and white fangs. The Ryzga recoiled, and the weapon in his hand came up. There was an instantaneous glare like heat lightning, and the monster crumpled in on itself, twitched briefly and vanished. But in that moment a light of inspiration had flashed upon Var, and it remained. As the Ryzga stretched out his hand again, Var acted. The Ryzga froze, teetering off balance and almost falling, as a numbing grip closed down on all his motor nerves. Holding that grip, Var strode across the floor and looked straight into the Ryzga's frantic eyes. They glared back at him with such hatred and such evil that for an instant he almost faltered. But the Ryzga's efforts, as he strove to free himself from the neural hold, were as misdirected and unavailing as those of a child who has not learned to wrestle with the mind. Var had guessed right. When Neena in her terror had flung a dream monster into the Ryzga's way—a mere child's bogey out of a fairy tale—the Ryzga had not recognized it as such, but had taken it for a real being. Var laughed aloud, and with great care, as one communicates with an infant, he projected his thoughts into the other's mind. "There will be no new beginning for you in our world, Ryzga! In two thousand years, we've learned some new things. Now at last I understand why you built so many machines, such complicated arrangements of matter and energy to do simple tasks—it was because you knew no other way." Behind the hate-filled eyes the cold brain tried to reason still. "Barbarians...? Our party was wrong after all. After us the machine civilization could never rise again, because it was a fire that consumed its fuel. After us man could not survive on the Earth, because the conditions that made him great were gone. The survivors must be something else—capacities undeveloped by our science—after us the end of man, the beginning.... But those of us who chose to die were right." The tide of hate and sick desire rose up to drown all coherence. The Ryzga made a savage, wholly futile effort to lift the weapon in his paralyzed hand. Then his eyes rolled upward, and abruptly he went limp and fell in a heap, like a mechanical doll whose motive power has failed. Var felt Neena beside him, and drew her close. As she sobbed her relief, he continued to look down absently at the dead man. When at last he raised his head, he saw that the drama's end had had a further audience. In the outer doorway, backed by his clansmen, stood Groz, gazing first in stupefaction at the fallen Ryzga, then with something like awe at Var. Var eyed him for a long moment; then he smiled, and asked, "Well, Groz? Is our feud finished, or does your ambition for a worthy son-in-law go beyond the conqueror of the Ryzgas?"
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A. Someone who has been granted the honor of watching over the mountain region
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What is the significance of the story's title?
A. It hints at the extra costs for less natural things
B. It marks the setting for the story
C. It hints at Linton's constant desire for sweet things
D. It shows Linton's goal for the story
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FEBRUARY STRAWBERRIES By JIM HARMON How much is the impossible worth? [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, March 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Linton lay down his steel fork beside the massively solid transparency of the restaurant water glass. "Isn't that Rogers Snead at that table?" he heard himself say stupidly. Howell, the man across the table from him, looked embarrassed without looking. "Not at all. Somebody who looks like him. Twin brother. You know how it is. Snead's dead, don't you remember?" Linton remembered. Howell had to know that he would remember. What were they trying to pull on him? "The man who isn't Snead is leaving," Linton said, describing the scene over Howell's shoulder. "If that's Snead's brother, I might catch him to pay my respects." "No," Howell said, "I wouldn't do that." "Snead came to Greta's funeral. It's the least I could do." "I wouldn't. Probably no relation to Snead at all. Somebody who looks like him." "He's practically running," Linton said. "He almost ran out of the restaurant." "Who? Oh, the man who looked like Snead, you mean." "Yes," Linton said. A thick-bodied man at the next table leaned his groaning chair back intimately against Linton's own chair. "That fellow who just left looked like a friend of yours, huh?" the thick man said. "Couldn't have been him, though," Linton answered automatically. "My friend's dead." The thick man rocked forward and came down on all six feet. He threw paper money on the table as if he were disgusted with it. He plodded out of the place quickly. Howell breathed in deeply and sucked back Linton's attention. "Now you've probably got old Snead into trouble." "Snead's dead," Linton said. "Oh, well, 'dead,'" Howell replied. "What do you say it like that for?" Linton demanded angrily. "The man's dead. Plain dead. He's not Sherlock Holmes or the Frankenstein Monster—there's no doubt or semantic leeway to the thing." "You know how it is," Howell said. Linton had thought he had known how death was. He had buried his wife, or rather he had watched the two workmen scoop and shove dirt in on the sawdust-fresh pine box that held the coffin. He had known what he sincerely felt to be a genuine affection for Greta. Even after they had let him out of the asylum as cured, he still secretly believed he had known a genuine affection for her. But it didn't seem he knew about death at all. Linton felt that his silence was asking Howell by this time. "I don't know, mind you," Howell said, puffing out tobacco smoke, "but I suppose he might have been resurrected." "Who by?" Linton asked, thinking: God? "The Mafia, I guess. Who knows who runs it?" "You mean, somebody has invented a way to bring dead people back to life?" Linton said. He knew, of course, that Howell did not mean that. Howell meant that some people had a system of making it appear that a person had died in order to gain some illegal advantage. But by saying something so patently ridiculous, Linton hoped to bring the contradicting truth to the surface immediately. "An invention? I guess that's how it is," Howell agreed. "I don't know much about people like that. I'm an honest businessman." "But it's wonderful," Linton said, thinking his immediate thoughts. "Wonderful! Why should a thing like that be illegal? Why don't I know about it?" "Sh-h," Howell said uneasily. "This is a public place." "I don't understand," Linton said helplessly. "Look, Frank, you can't legalize a thing like resurrection," Howell said with feigned patience. "There are strong religious convictions to consider. The undertakers have a lobby. I've heard they got spies right in the White House, ready to assassinate if they have to. Death is their whole life. You got to realize that." "That's not enough. Not nearly enough." "Think of all the problems it would cause. Insurance, for one thing. Overpopulation. Birth control is a touchy subject. They'd have to take it up if everybody got resurrected when they died, wouldn't they?" "But what do they do about it? Against it?" "There are a lot of fakes and quacks in the resurrection business. When the cops find out about a place, they break in, smash all the equipment and arrest everybody in sight. That's about all they can do. The charges, if any, come under general vice classification." "I don't understand," Linton complained. "Why haven't I heard about it?" "They didn't talk much about white slavery in Victorian England. I read an article in Time the other day that said 'death' was our dirty word, not sex. You want to shock somebody, you tell him, 'You're going to be dead someday,' not anything sexual. You know how it is. The opposite of 'live' these days is 'video-taped.'" "I see," Linton said. He tried to assimilate it. Of course he had, he reminded himself, been out of touch for some time. It might be true. Then again, they might be trying to trick him. They used to do that to see if he was really well. But the temptation was too strong. "Tell me, Howell, where could I find a resurrectionist?" Howell looked away. "Frank, I don't have anything to do with that kind of people and if you're smart, you'll not either." Linton's fingers imprinted the linen. "Damn you, Howell, you tell me!" Howell climbed to his feet hurriedly. "I take you out to dinner to console you over the loss of your wife a half a year ago, and to make you feel welcome back to the society of your fellows after being in the hospital for a nervous breakdown. I do all that, and for thanks, you yell at me and curse me. You kooks are all alike!" Howell threw money on the table with the same kind of disinterest as the thick-set man and stalked out. I've got to hurry too, Linton thought. It's Resurrection Day! The doctor fluttered his hands and chirped about the office. "Well, well, Mr. Linton, we understand you've been causing disturbances." "Not really," Linton said modestly. "Come, come," the doctor chided. "You started riots in two places, attempted to bribe an officer. That's disturbing, Mr. Linton, very disturbing." "I was only trying to find out something," Linton maintained. "They could have told me. Everybody seems to know but me." The doctor clucked his tongue. "Let's not think any such thing. People don't know more than you do." Linton rubbed his shoulder. "That cop knew more about Judo holds than I did." "A few specific people know a few specific things you don't. But let me ask you, Mr. Linton, could Einstein bake a pie?" "I don't know. Who the hell ever wasted Einstein's time asking him a thing like that?" "People who want to know the answers to questions have to ask them. You can find out anything by asking the right questions of the right person at the right time." Linton stared suspiciously. "Do you know where I can find a resurrectionist?" "I am a resurrectionist." "But the policeman brought me to you!" "Well, that's what you paid him to do, wasn't it? Did you think a policeman would just steal your money? Cynics—all you young people are cynics." Linton scooted forward on the insultingly cold metal chair and really looked at the doctor for the first time. "Doctor, can you really resurrect the dead?" "Will you stop being cynical? Of course I can!" "Doctor, I'm beginning to believe in you," Linton said, "but tell me, can you resurrect the long dead?" "Size has nothing to do with it." "No, my wife has been dead a long time. Months." "Months?" The doctor snapped those weeks away with his fingers. "It could be years. Centuries. It's all mathematics, my boy. I need only one fragment of the body and my computers can compute what the rest of it was like and recreate it. It's infallible. Naturally there is a degree of risk involved." "Infallible risk, yes," Linton murmured. "Could you go to work right away?" "First, I must follow an ancient medical practice. I must bleed you." Linton grasped the situation immediately. "You mean you want money. You realize I've just got out of an institution...." "I've often been in institutions myself, for alcoholism, narcotics addiction and more." "What a wonderful professional career," Linton said, when he couldn't care less. "Oh, yes—yes, indeed. But I didn't come out broke." "Neither did I," Linton said hastily. "I invested in shifty stocks, faltering bonds, and while I was away they sank to rock bottom." "Then—" "When they hit rock bottom, they bounced up. If I hadn't found you, I would have been secure for the rest of my lonely, miserable life." "All that's ended now," the doctor assured him. "Now we must go dig up the corpse. The female corpse, eh?" Resurrection Day! "Doctor," Linton whispered, "my mind is singing with battalions of choirs. I hope that doesn't sound irreverent to you." The doctor stroked his oily palms together. "Oh, but it does. Beautifully." The certificate to allow reburial in Virginia hadn't been impossible to obtain. The doctor had taken the body and Linton's fortune and fed them both into the maw of his calculators, and by means of the secret, smuggled formulae, Greta would be cybernetically reborn. Linton shook his head. It seemed impossible. But Greta opened the olive-drab slab of metal of the door to the doctor's inner-inner sanctum and walked out into the medicinal cold fluorescent lighting. It wasn't fair at all, Linton thought. He should have had some time to prepare himself. Greta lifted her arms, stretching the white smock over the lines of her body. "Darling!" she said. "Greta!" he said, feeling a slight revulsion but repressing it. No doubt he would be able to adjust to her once having been dead the same way he had learned to accept the, to him, distasteful duty of kissing her ears the way she enjoyed. Greta swirled across the room and folded her arms across his shoulders. She kissed his cheek. "It's so wonderful to be back. This calls for a celebration. We must see Nancy, Oscar, Johnny, all our old friends." "Yes," he said, his heart lurching for her sad ignorance. "But tell me—how was it being away ?" The curves and angles of her flesh changed their positions against his Ivy dacron. Her attitude altered. "I can't remember," she said. "I can't really remember anything. Not really. My memories are ghosts...." "Now, now," Linton said, "we mustn't get excited. You've been through a trial." She accepted the verdict. She pulled away and touched at her hair. It was the same hair, black as evil, contrasting with her inner purity. Of course it would be; it hadn't changed even in the grave. He remembered the snaky tendrils of it growing out of the water-logged casket. "I must see all our old friends," Greta persisted. "Helen and Johnny...." "My darling," he said gently, "about Johnny—" Her fine black brows made Gothic arches. "Yes? What about Johnny?" "It was a terrible accident right after—that is, about five months ago. He was killed." "Killed?" Greta repeated blankly. "Johnny Gorman was killed?" "Traffic accident. Killed instantly." "But Johnny was your friend, your best friend. Why didn't you have him resurrected the same way you did me?" "Darling, resurrection is a risky business and an expensive one. You have to pay premium prices for strawberries in February. I no longer have the money to pay for a resurrection of Johnny." Greta turned her back to him. "It's just as well. You shouldn't bring back Johnny to this dream of life, give him a ghost of mind and the photograph of a soul. It's monstrous. No one should do that. No one. But you're sure you haven't the money to do it?" "No," Linton said. "I'm sold out. I've borrowed on my insurance to the hilt. It won't pay any more until I'm buried, and then, of course, you can resurrect me." "Of course," Greta said. She sighed. "Poor Johnny. He was such a good friend of yours. You must miss him. I'm so sorry for you." "I have you," he said with great simplicity. "Frank," she said, "you should see that place in there. There are foaming acid baths, great whale-toothed disposals, barrels of chemicals to quench death and smother decay. It's perfect ." "It sounds carnal," he said uneasily. "No, dear, it's perfect for some things that have to be done." Her eyes flashed around the doctor's office and settled somewhere, on something. Linton followed the direction of Greta's gaze and found only an ashtray stand, looking vaguely like a fanatic's idol to a heathen religion on a pedestal. Greta pounced on the stand, hefted it at the base and ran toward him with it over her head. Linton leaped aside and Greta hit the edge of the desk instead of him. Brain damage, he concluded nervously. Cell deterioration. Greta raised it again and he caught her wrists high over her head. She writhed against him provocatively. "Frank, I'm sorry, dear, but I have to have that insurance money. It's hell!" Linton understood immediately. He felt foolish, humiliated. All that money! He had resurrected a gold ring that had turned his knuckles green. No one must ever know. Linton twisted the stand away from his wife and watched her face in some appalled form of satisfaction as it registered horror and acceptance of the crumpled metal disk falling toward it. He split her head open and watched her float to the floor. Linton was surprised at the fine wire mesh just below the skin and those shiny little tabs that looked like pictures of transistors in institutional advertising. He knelt beside the body and poked into the bleeding, smoldering wreckage. Yes, it seemed they had to automate and modify the bodies somewhat in resurrection. They couldn't chemically revive the old corpse like pouring water on a wilted geranium. Or— Did they use the old bodies at all? What were all those acid baths for if the bodies were used? Didn't the resurrectionists just destroy the old corpses and make androids, synthetic creatures, to take their place? But it didn't matter. Not a bit. She had thought she was his wife, sharing her viewpoint down to the finest detail, and he had thought she was his wife. It was what you thought was real that made it so, not the other way around. "I've killed my wife!" Linton called, rising from his knees, stretching his hands out to something. The pain stung him to sleep—a pain in his neck like a needle that left a hole big enough for a camel to pass through and big enough for him to follow the camel in his turn. He opened his eyes to the doctor's spotless, well-ordered office. The doctor looked down at him consolingly. "You'll have to go back, Mr. Linton. But they'll cure you. You'll be cured of ever thinking your wife was brought back to life and that you killed her all over again." "Do you really think so, Doctor?" Linton asked hopefully.
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A. It hints at the extra costs for less natural things
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Why has UFC moved to smaller locations over the course of its history?
A. Over time, popularity decreased enough that these are the only places fights can happen
B. This way, UFC fits in with public perception driven by movies like Fight Club, which is more true to its roots
C. The fans are dedicated to their small local stadiums prefer to not have matches televised
D. It is now illegal to have UFC matches in large stadiums for safety reasons
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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.
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A. Over time, popularity decreased enough that these are the only places fights can happen
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Who would the gambling industry least want to hear speak at their meeting?
A. a Nevada senator
B. a "narrow"
C. a Latina housekeeper
D. a union representative
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Is <A NAME= Gambling's would-be federal regulators--the National Gambling Impact Study Commission--went to Las Vegas this week to hold hearings. In today's dispatch, we learn how gambling's foes seek to demonize wagering as a pernicious tobaccolike vice. In yesterday's dispatch, gambling's foes learn the folly of having brought their anti-sin crusade to an adult Disneyland. Tuesday's overpowering show of force by the Nevada gambling aristocracy has had at least one audible effect on the National Gambling Impact Study Commission. Wednesday, even commission Chair Kay Coles James, a gambling skeptic, succumbs to the hideous Vegas euphemism: She begins referring to the "gaming industry." After Tuesday's casino triumphalism, Wednesday is a comedown, eight hours of policy panels on teen gambling, compulsive gambling, gambling regulation, gambling marketing, and gambling credit practices. It is tough slogging, but for the first time I sense that this commission--though divided, underfunded, timid, and without any power beyond exhortation--isn't entirely useless. It may finally settle this question: Is gambling Hollywood or tobacco? Entertainment or vice? The sleek Vegas types, whose Strip palaces scramble casinos, theaters, restaurants, arcades, discos, cabarets, theme parks, concert halls, sports arenas, and museums into one giant orgy of amusement, have been selling the idea that gambling is just entertainment--Disney in the desert. This effort has largely succeeded, because Vegas is still the dominant image of American gambling, if not the dominant reality. The antis, meanwhile, cry that gambling is like cigarettes: unsafe for kids, viciously addictive, deceptively marketed, unhealthy, expensive, and unacceptable unless mightily regulated. Judging by today's hearings and by conversations with most of the commissioners, the tobacco model is winning. Today's panelists tell the commission that kids are starting to gamble too young and are getting addicted too easily, that compulsive gambling appears to be increasing as gambling spreads, that gambling marketing may be designed to addict customers, and that the industry exploits problem gamblers by allowing them to draw repeated credit card advances from ATMs on casino floors. The testimony clearly impresses the commissioners and seems especially to impress the three nonaligned commissioners who will be the swing votes on the June 1999 report. It is starting to become clear what that report will say. The commission won't (and can't) take any grand stand against gambling. Instead it will opt for small, targeted policies, concentrating on compulsive gambling. It will probably propose that casinos and state lotteries fund gambling-addiction research and that casinos take much stronger measures to bar problem gamblers from wagering. The commission may recommend that gaming taxes be used to underwrite treatment of pathological gamblers and that insurance companies be encouraged to cover gambling addiction. Similarly, the commission will try to reduce gamblers' access to cash by limiting the size of ATM advances and prodding casinos to remove the machines from their floors. The commission will also push the industry to do more to prevent kids from gambling. It will call for heavier regulation of Indian gambling and will probably try to ban or severely regulate Internet gambling, perhaps by forbidding gambling companies from running online casinos. It will rebuke state lotteries for their deceptive marketing and will try to force them to post odds and stop targeting the poor. In short, it will treat gambling as a tobaccolike vice. If the comments of the pro-industry commissioners can be believed, the industry will happily endorse such a report. Gamblers don't quite accept the cigarette analogy--though commission member Bill Bible, a former chief of the Nevada Gaming Commission, did concede that gambling was like alcohol--but they're happy to sign on to the specific measures. The casino industry is even trying to get ahead of the commission. It has already established a (mostly) independent center to fund research into pathological gambling. I suspect that the industry will not only agree to the commission's recommendations but will become their strongest advocate. Casino owners will avidly lobby Congress and state legislatures to enact the recommendations into law. Why should the pro-gamblers cooperate with a critical study? Because it provides superb cover for them. It medicalizes the problem of compulsive gambling, blaming it on psychological abnormality rather than industry machination. Likewise, cracking down on compulsives is also politically cost-effective. In exchange for losing a few compulsive gamblers, the casinos will (falsely) appear more concerned with the health of their customers than with profits. The cigarette agenda will also distract the commission and the public from the true reasons for worry. A few years ago, gambling was confined to Las Vegas and Atlantic City. It is now thriving in 48 states, and there is no sign that anyone can stop it. In this election, gambling interests dropped $100 million on a single California ballot initiative, toppled governors in two states, and bought senators and representatives by the crate. What the commission ought to be investigating is whether the gambling industry has become so powerful that it's politically untouchable. But it can't, because the gambling industry has become so powerful that it's politically untouchable. The antis can call gambling "tobacco." They can call it "vice." They can call it "a big red balloon" for all that the industry cares. As long as the commission just nibbles around the edges, the casino operators and state lotteries will be happy to indulge it. The pro-gambling folks will win credit for cooperating, without having to do anything that really hurts. The last national gambling commission was in the mid-1970s. If the gamblers play along with this commission's timid recommendations, they'll be safe for another 20 years. An Apology I owe an apology to Nevada Sen. Richard Bryan, whom I criticized yesterday for using the term "Indian country" during a speech critical of Indian casinos. As several readers pointed out to me, "Indian country" is a common phrase in the West and has no derogatory connotations. I'm sorry, Senator. Talk about quick defeats: The first sign I see outside the MGM Grand ballroom all but declares that the National Gambling Impact Study Commission has already lost. The sign reads: "National Gaming Impact Study Commission." "Gaming"? In Las Vegas, the euphemizers reign. Once upon a time, the casino owners decided that "gambling" was too crude, too avaricious, to describe their fair business. So "gambling" disappeared in Las Vegas, and "gaming" has risen in its place. He who controls language controls ideas, and at today's commission hearing, it is perfectly clear who controls the language. Video slot machines crammed into convenience stores--perhaps the most pernicious form of legal gambling there is--are called "retail gaming." People who own casinos are not "casino owners," they are "gaming visionaries." Pathological gamblers are "problem gamers"--as if they're having trouble mastering the rules of Monopoly. And the National Gambling Impact Study Commission is reborn as the National Gaming Impact Study Commission. The gambling industry did everything in its power to stop the establishment of this commission two years ago, but Congress and a fervent grassroots anti-gambling group eventually foisted it on the industry. The nine member blue-ribbon panel was charged with assessing the social and economic impact of gambling, and it will issue a final report to Congress and the president in June 1999. Even though the panel was carefully balanced between pro- and anti-gambling leaders, it was supposed to be Vegas' nemesis. The industry and Las Vegas' pro-gambling media quaked in anticipation of the onerous regulations and taxes the commission might recommend. But they quake no more. Whatever national momentum the anti-gamblers had dissolved in last week's elections. The industry routed opponents in state after state. Missouri voters passed a ballot initiative to allow boat casinos. Californians voted to expand Indian casinos. In South Carolina and Alabama, voters expelled anti-lottery, anti-gambling Republican governors and replaced them with pro-lottery Democrats. The gambling industry spent more than $100 million on political contributions and issue ads. It has never been fatter, happier, or more secure. "My goodness, no politician can withstand their resources," Focus on the Family's James Dobson, the commission's leading gambling opponent, tells me. The industry's political clout has emasculated the commission, Dobson continues: "Our report won't be acted on by the president or Congress. They are too heavily influenced by gambling money. Almost all the leaders of Congress are on the dole." It has also become obvious that the commission has too many pro-gambling members to produce a report that recommends taxes or other real penalties on the industry. So the commission's two day visit to Gomorrah has been transformed from a charged political event to a kind of victory lap for gaming. Nevada Gov. Bob Miller and the "gaming visionaries" have been planning for these hearings for months, hoping to use them to demonstrate the might and sanctity and goodness of the Nevada gambling industry. The MGM Grand, which is run by commission member Terrence Lanni, is itself the first exhibit of the Vegas triumphalists. It is gaudy testimony that consumers, at least, have no problem with this business. The MGM Grand, a k a "The City of Entertainment," has 5,000 rooms--the corridor outside my room is 200 yards long, so long I can't see its end--to feed the endless supply of slot machines, craps tables, and roulette wheels. David Cassidy performs here every night--twice! A few steps outside on the Strip is still more overwhelming evidence that Las Vegas has won the popular vote. New York, New York is just across the street, the $1.6 billion Bellagio is one door down, and a half-scale Eiffel Tower is going up next door. The setting has, as the pro-gambling folks no doubt hoped, stunned some of the gambling opponents. I asked one anti-gambling activist who had never before been to Vegas what she thinks of it. She could only blurt out "Wow." The hearings, too, reinforce the Glorious Las Vegas theme. Frank Fahrenkopf, the industry's top lobbyist (who is paid so much he can afford monogrammed shirt cuffs --I saw them), holds forth cheerfully outside the ballroom, celebrating the electoral triumph of freedom over religious moralist tyranny. Inside, the room is packed with more than 600 people in neon lime green T-shirts that read "Unions and Gaming: Together for a Better Life." They are members of the major casino union, here to cheer on their employers and their union. (Most of them, it must be said, are getting paid to do this.) Chairwoman Kay Coles James, a Christian conservative and skeptic of gambling, opens the hearing by assuring the crowd that the committee is toothless: "We're not here to take anyone's job. ... We have no power to do anything except make recommendations." This sets the mood for most of the day: Vegas is great, so you'd better leave it alone! The local government, by all appearances a wholly owned subsidiary of the casinos, puts on a bravura performance. Gov. Miller opens the show with a 15 minute hymn to Las Vegas. It is the first of many statistical barrages about Nevada's one-ders: No. 1 in job growth, No. 1 in population growth, and No. 1 on planet Earth in per capita Girl Scout troops--and Boy Scout troops! Later in the day, Nevada's senators and both its congressmen appear to chew out the commission for even thinking that Nevada might have a dark side. They pay tribute to Nevada's sophisticated gambling industry, especially its regulation (much stricter than other gambling states) and its use of gambling taxes to fund state services. It is one of the ironies of Nevada politics that its Republican congressmen (Jim Gibbons and John Ensign) end up crediting their state's success to government regulation and corporate taxation. There are also a fair share of gleeful gambling regulators, bookmakers, and casino employees among the panels of expert witnesses the commission hears from. Critics who gripe about the perils of sports gambling and the evils of convenience store slot machines leaven the pro-gambling folks. Everyone, including the gambling industry shills, agrees that Internet gambling is evil and should be destroyed. Everyone agrees to this because no one in Las Vegas is making any money off Internet gambling. If they were, you can be sure they would explain why it's as American as nickel slots and scratch-off games. Pro-Vegas forces are also perfectly happy to take shots at Indian gambling, the chief economic threat to Nevada's prosperity. The expansion of Indian casinos resulting from last week's California voter initiative will slam Las Vegas, cutting its gambling revenues by $400 million a year. So the Vegans repeatedly swing at casinos in "Indian country" (that's Nevada Sen. Richard Bryan's term--I'm not joking) for being insufficiently regulated and taxed. One tribal chief I spoke to calls this "red baiting." (Pause for an aesthetic observation: I am sitting right behind the witnesses, and after a while I begin to separate them into the Wides and the Narrows. The Wides are men in suits with enormous backs and enormous bellies, men who eat and eat and used to play football. They all testify to their love of gambling. The Narrows are thin and generally disapprove of it. I begin to wonder whether fondness for gambling correlates with general indulgence, and dislike correlates with asceticism, and decide that they probably do.) During the last hour of the day, the public comment period, the union sends a parade of casino employees to the microphone to hallelujah the gaming industry. Housekeepers, cooks, and slot change girls, almost all black or Latina, tell the same story: I was working a dead-end job in another state, "then I heard about Las Vegas, where there's opportunity!" I moved here, landed a job at a union casino with high pay, free medical insurance, a pension, and "now I am buying a house." The stories are intensely moving, by far the most persuasive tribute to the Strip that I've ever heard. Still, for all the Vegan triumphalism in the air, it's impossible not to be charmed by the chief gambling opponent, the Rev. Tom Grey. Grey is utterly irrepressible. A Vietnam rifleman turned Methodist minister, Grey has spent the last eight years evangelizing against gambling. He founded the National Coalition Against Legalized Gambling, the primary force behind the commission's creation. (Grey, in a rare acknowledgement of defeat, has just renamed it the National Coalition Against Gambling Expansion, tacitly recognizing that gambling is here to stay.) He is a genial motormouth and shameless promoter of the cause. He wears a gigantic "CasiNO" button in the casino. He posed for People in a shepherd's robe. He says "I would do anything short of lighting myself on fire in the Capitol rotunda to stop gambling." He is so excitable that I have to yank him out of the way of an oncoming car when he gets too wrapped up in one of his soliloquies. He and his Las Vegas allies, a former Las Vegas city councilman named Steve Miller and an inner city venture capitalist named Otis Harris, invite me on a tour of Las Vegas. "Behind the Mirage," they call it. For two hours, we cruise the streets behind the casinos. They show me all the evidence of gambling blight you'd never want to see, from a youth-center-turned-crack-house to pawn shops to sex shops to down at heels casinos to quickie motels. All the while, they keep up a patter about how terrible a neighbor the casino industry is and how superficial Las Vegas' prosperity is. It's very grim and mostly persuasive. Still, when we turn back on to the Strip, and pass the jaw-dropping Stratosphere and Circus Circus and Bellagio and the MGM Grand--a 30 story tower bathed in fabulous emerald light, I realize why Grey's task is hopeless here. He is committing the cardinal sin of Vegas. All he wants to do is talk about losers. In Las Vegas, under the thrilling lights of the Strip, no one wants to hear about losers. In the land of gaming, not gambling, everyone is sure he's a winner.
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B. a "narrow"
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Which of these sets of descriptions best describes Peggy?
A. She's dedicated, bold, and pretty
B. She's talented, unwise, and creative
C. She's reserved, strong, and caring
D. She's reasonable, unobservant, and bold
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PEGGY FINDS THE THEATER I Dramatic Dialogue “Of course, this is no surprise to us,” Thomas Lane said to his daughter Peggy, who perched tensely on the edge of a kitchen stool. “We could hardly have helped knowing that you’ve wanted to be an actress since you were out of your cradle. It’s just that decisions like this can’t be made quickly.” “But, Dad!” Peggy almost wailed. “You just finished saying yourself that I’ve been thinking about this and wanting it for years! You can’t follow that by calling it a quick decision!” She turned to her mother, her hazel eyes flashing under a mass of dark chestnut curls. “Mother, you understand, don’t you?” Mrs. Lane smiled gently and placed her soft white hand on her daughter’s lean brown one. “Of course I understand, Margaret, and so does your father. We both want to do what’s best for you, not to stand in your way. The only question is whether the time is right, or if you should wait longer.” 2 “Wait! Mother—Dad—I’m years behind already! The theater is full of beginners a year and even two years younger than I am, and girls of my age have lots of acting credits already. Besides, what is there to wait for?” Peggy’s father put down his coffee cup and leaned back in the kitchen chair until it tilted on two legs against the wall behind him. He took his time before answering. When he finally spoke, his voice was warm and slow. “Peg, I don’t want to hold up your career. I don’t have any objections to your wanting to act. I think—judging from the plays I’ve seen you in at high school and college—that you have a real talent. But I thought that if you would go on with college for three more years and get your degree, you would gain so much worth-while knowledge that you’d use and enjoy for the rest of your life—” “But not acting knowledge!” Peggy cried. “There’s more to life than that,” her father put in. “There’s history and literature and foreign languages and mathematics and sciences and music and art and philosophy and a lot more—all of them fascinating and all important.” “None of them is as fascinating as acting to me,” Peggy replied, “and none of them is nearly as important to my life.” 3 Mrs. Lane nodded. “Of course, dear. I know just how you feel about it,” she said. “I would have answered just the same way when I was your age, except that for me it was singing instead of acting. But—” and here her pleasant face betrayed a trace of sadness—“but I was never able to be a singer. I guess I wasn’t quite good enough or else I didn’t really want it hard enough—to go on with all the study and practice it needed.” She paused and looked thoughtfully at her daughter’s intense expression, then took a deep breath before going on. “What you must realize, Margaret, is that you may not quite make the grade. We think you’re wonderful, but the theater is full of young girls whose parents thought they were the most talented things alive; girls who won all kinds of applause in high-school and college plays; girls who have everything except luck. You may be one of these girls, and if you are, we want you to be prepared for it. We want you to have something to fall back on, just in case you ever need it.” Mr. Lane, seeing Peggy’s hurt look, was quick to step in with reassurance. “We don’t think you’re going to fail, Peg. We have every confidence in you and your talents. I don’t see how you could miss being the biggest success ever—but I’m your father, not a Broadway critic or a play producer, and I could be wrong. And if I am wrong, I don’t want you to be hurt. All I ask is that you finish college and get a teacher’s certificate so that you can always find useful work if you have to. Then you can try your luck in the theater. Doesn’t that make sense?” 4 Peggy stared at the faded linoleum on the floor for a few moments before answering. Then, looking first at her mother and then at her father, she replied firmly, “No, it doesn’t! It might make sense if we were talking about anything else but acting, but we’re not. If I’m ever going to try, I’ll have a better chance now than I will in three years. But I can see your point of view, Dad, and I’ll tell you what—I’ll make a bargain with you.” “What sort of bargain, Peg?” her father asked curiously. “If you let me go to New York now, and if I can get into a good drama school there, I’ll study and try to find acting jobs at the same time. That way I’ll still be going to school and I’ll be giving myself a chance. And if I’m not started in a career in one year, I’ll go back to college and get my teacher’s certificate before I try the theater again. How does that sound to you?” “It sounds fair enough,” Tom Lane admitted, “but are you so confident that you’ll see results in one year? After all, some of our top stars worked many times that long before getting any recognition.” “I don’t expect recognition in one year, Dad,” Peggy said. “I’m not that conceited or that silly. All I hope is that I’ll be able to get a part in that time, and maybe be able to make a living out of acting. And that’s probably asking too much. If I have to, I’ll make a living at something else, maybe working in an office or something, while I wait for parts. What I want to prove in this year is that I can act. If I can’t, I’ll come home.” 5 “It seems to me, Tom, that Margaret has a pretty good idea of what she’s doing,” Mrs. Lane said. “She sounds sensible and practical. If she were all starry-eyed and expected to see her name in lights in a few weeks, I’d vote against her going, but I’m beginning to think that maybe she’s right about this being the best time.” “Oh, Mother!” Peggy shouted, jumping down from the stool and throwing her arms about her mother’s neck. “I knew you’d understand! And you understand too, don’t you, Dad?” she appealed. Her father replied in little puffs as he drew on his pipe to get it started. “I ... never said ... I didn’t ... understand you ... did I?” His pipe satisfactorily sending up thick clouds of fragrant smoke, he took it out of his mouth before continuing more evenly. “Peg, your mother and I are cautious only because we love you so much and want what’s going to make you happy. At the same time, we want to spare you any unnecessary unhappiness along the way. Remember, I’m not a complete stranger to show business. Before I came out here to Rockport to edit the Eagle , I worked as a reporter on one of the best papers in New York. I saw a lot ... I met a lot of actors and actresses ... and I know how hard the city often was for them. But I don’t want to protect you from life. That’s no good either. Just let me think about it a little longer and let me talk to your mother some more.” 6 Mrs. Lane patted Peggy’s arm and said, “We won’t keep you in suspense long, dear. Why don’t you go out for a walk for a while and let us go over the situation quietly? We’ll decide before bedtime.” Peggy nodded silently and walked to the kitchen door, where she paused to say, “I’m just going out to the barn to see if Socks is all right for the night. Then maybe I’ll go down to Jean’s for a while.” As she stepped out into the soft summer dusk she turned to look back just in time to see her mother throw her a comically exaggerated wink of assurance. Feeling much better, Peggy shut the screen door behind her and started for the barn. Ever since she had been a little girl, the barn had been Peggy’s favorite place to go to be by herself and think. Its musty but clean scent of straw and horses and leather made her feel calm and alive. Breathing in its odor gratefully, she walked into the half-dark to Socks’s stall. As the little bay horse heard her coming, she stamped one foot and softly whinnied a greeting. Peggy stopped first at the bag that hung on the wall among the bridles and halters and took out a lump of sugar as a present. Then, after stroking Socks’s silky nose, she held out her palm with the sugar cube. Socks took it eagerly and pushed her nose against Peggy’s hand in appreciation. As Peggy mixed some oats and barley for her pet and checked to see that there was enough straw in the stall, she thought about her life in Rockport and the new life that she might soon be going to. 7 Rockport, Wisconsin, was a fine place, as pretty a small town as any girl could ask to grow up in. And not too small, either, Peggy thought. Its 16,500 people supported good schools, an excellent library, and two good movie houses. What’s more, the Rockport Community College attracted theater groups and concert artists, so that life in the town had always been stimulating. And of course, all of this was in addition to the usual growing-up pleasures of swimming and sailing, movie dates, and formal dances—everything that a girl could want. Peggy had lived all her life here, knew every tree-shaded street, every country road, field, lake, and stream. All of her friends were here, friends she had known since her earliest baby days. It would be hard to leave them, she knew, but there was no doubt in her mind that she was going to do so. If not now, then as soon as she possibly could. It was not any dissatisfaction with her life, her friends, or her home that made Peggy want to leave Rockport. She was not running away from anything, she reminded herself; she was running to something. To what? To the bright lights, speeding taxis, glittering towers of a make-believe movie-set New York? Would it really be like that? Or would it be something different, something like the dreary side-street world of failure and defeat that she had also seen in movies? 8 Seeing the image of herself hungry and tired, going from office to office looking for a part in a play, Peggy suddenly laughed aloud and brought herself back to reality, to the warm barn smell and the big, soft-eyed gaze of Socks. She threw her arm around the smooth bay neck and laid her face next to the horse’s cheek. “Socks,” she murmured, “I need some of your horse sense if I’m going to go out on my own! We’ll go for a fast run in the morning and see if some fresh air won’t clear my silly mind!” With a final pat, she left the stall and the barn behind, stepping out into the deepening dusk. It was still too early to go back to the house to see if her parents had reached a decision about her future. Fighting down an impulse to rush right into the kitchen to see how they were coming along, Peggy continued down the driveway and turned left on the slate sidewalk past the front porch of her family’s old farmhouse and down the street toward Jean Wilson’s house at the end of the block. As she walked by her own home, she noticed with a familiar tug at her heart how the lilac bushes on the front lawn broke up the light from the windows behind them into a pattern of leafy lace. For a moment, or maybe a little more, she wondered why she wanted to leave this. What for? What could ever be better? 9 II Dramatic Decision Upstairs at the Wilsons’, Peggy found Jean swathed in bath towels, washing her long, straight red hair, which was now white with lather and piled up in a high, soapy knot. “You just washed it yesterday!” Peggy said. “Are you doing it again—or still?” Jean grinned, her eyes shut tight against the soapsuds. “Again, I’m afraid,” she answered. “Maybe it’s a nervous habit!” “It’s a wonder you’re not bald, with all the rubbing you give your hair,” Peggy said with a laugh. “Well, if I do go bald, at least it will be with a clean scalp!” Jean answered with a humorous crinkle of her freckled nose. Taking a deep breath and puffing out her cheeks comically, she plunged her head into the basin and rinsed off the soap with a shampoo hose. When she came up at last, dripping-wet hair was tightly plastered to the back of her head. “There!” she announced. “Don’t I look beautiful?” 10 After a brisk rubdown with one towel, Jean rolled another dry towel around her head like an Indian turban. Then, having wrapped herself in an ancient, tattered, plaid bathrobe, she led Peggy out of the steamy room and into her cozy, if somewhat cluttered, bedroom. When they had made themselves comfortable on the pillow-strewn daybeds, Jean came straight to the point. “So the grand debate is still going on, is it? When do you think they’ll make up their minds?” she asked. “How do you know they haven’t decided anything yet?” Peggy said, in a puzzled tone. “Oh, that didn’t take much deduction, my dear Watson,” Jean laughed. “If they had decided against the New York trip, your face would be as long as Socks’s nose, and it’s not half that long. And if the answer was yes, I wouldn’t have to wait to hear about it! You would have been flying around the room and talking a mile a minute. So I figured that nothing was decided yet.” “You know, if I were as smart as you,” Peggy said thoughtfully, “I would have figured out a way to convince Mother and Dad by now.” “Oh, don’t feel bad about being dumb,” Jean said in mock tones of comfort. “If I were as pretty and talented as you are, I wouldn’t need brains, either!” With a hoot of laughter, she rolled quickly aside on the couch to avoid the pillow that Peggy threw at her. A short, breathless pillow fight followed, leaving the girls limp with laughter and with Jean having to retie her towel turban. From her new position, flat on the floor, Peggy looked up at her friend with a rueful smile. 11 “You know, I sometimes think that we haven’t grown up at all!” she said. “I can hardly blame my parents for thinking twice—and a lot more—before treating me like an adult.” “Nonsense!” Jean replied firmly. “Your parents know a lot better than to confuse being stuffy with being grown-up and responsible. And, besides, I know that they’re not the least bit worried about your being able to take care of yourself. I heard them talking with my folks last night, and they haven’t got a doubt in the world about you. But they know how hard it can be to get a start as an actress, and they want to be sure that you have a profession in case you don’t get a break in show business.” “I know,” Peggy answered. “We had a long talk about it this evening after dinner.” Then she told her friend about the conversation and her proposed “bargain” with her parents. “They both seemed to think it was fair,” she concluded, “and when I went out, they were talking it over. They promised me an answer by bedtime, and I’m over here waiting until the jury comes in with its decision. You know,” she said suddenly, sitting up on the floor and crossing her legs under her, “I bet they wouldn’t hesitate a minute if you would only change your mind and decide to come with me and try it too!” 12 After a moment’s thoughtful silence, Jean answered slowly, “No, Peg. I’ve thought this all out before, and I know it would be as wrong for me as it is right for you. I know we had a lot of fun in the dramatic groups, and I guess I was pretty good as a comedienne in a couple of the plays, but I know I haven’t got the real professional thing—and I know that you have. In fact, the only professional talent I think I do have for the theater is the ability to recognize talent when I see it—and to recognize that it’s not there when it isn’t!” “But, Jean,” Peggy protested, “you can handle comedy and character lines as well as anyone I know!” Jean nodded, accepting the compliment and seeming at the same time to brush it off. “That doesn’t matter. You know even better than I that there’s a lot more to being an actress—a successful one—than reading lines well. There’s the ability to make the audience sit up and notice you the minute you walk on, whether you have lines or not. And that’s something you can’t learn; you either have it, or you don’t. It’s like being double-jointed. I can make an audience laugh when I have good lines, but you can make them look at you and respond to you and be with you all the way, even with bad lines. That’s why you’re going to go to New York and be an actress. And that’s why I’m not.” “But, Jean—” Peggy began. 13 “No buts!” Jean cut in. “We’ve talked about this enough before, and I’m not going to change my mind. I’m as sure about what I want as you are about what you want. I’m going to finish college and get my certificate as an English teacher.” “And what about acting? Can you get it out of your mind as easily as all that?” Peggy asked. “That’s the dark and devious part of my plan,” Jean answered with a mysterious laugh that ended in a comic witch’s cackle and an unconvincing witch-look that was completely out of place on her round, freckled face. “Once I get into a high school as an English teacher, I’m going to try to teach a special course in the literature of the theater and maybe another one in stagecraft. I’m going to work with the high-school drama group and put on plays. That way, I’ll be in a spot where I can use my special talent of recognizing talent. And that way,” she added, becoming much more serious, “I have a chance really to do something for the theater. If I can help and encourage one or two people with real talent like yours, then I’ll feel that I’ve really done something worth while.” Peggy nodded silently, not trusting herself to speak for fear of saying something foolishly sentimental, or even of crying. Her friend’s earnestness about the importance of her work and her faith in Peggy’s talent had touched her more than she could say. 14 The silence lasted what seemed a terribly long time, until Jean broke it by suddenly jumping up and flinging a last pillow which she had been hiding behind her back. Running out of the bedroom, she called, “Come on! I’ll race you down to the kitchen for cocoa! By the time we’re finished, it’ll be about time for your big Hour of Decision scene!” It was nearly ten o’clock when Peggy finally felt that her parents had had enough time to talk things out. Leaving the Wilson house, she walked slowly despite her eagerness, trying in all fairness to give her mother and father every minute she could. Reaching her home, she cut across the lawn behind the lilac bushes, to the steps up to the broad porch that fronted the house. As she climbed the steps, she heard her father’s voice raised a little above its normal soft, deep tone, but she could not make out the words. Crossing the porch, she caught sight of him through the window. He was speaking on the telephone, and now she caught his words. “Fine. Yes.... Yes—I think we can. Very well, day after tomorrow, then. That’s right—all three of us. And, May—it’ll be good to see you again, after all these years! Good-by.” As Peggy entered the room, her father put down the phone and turned to Mrs. Lane. “Well, Betty,” he said, “it’s all set.” “What’s all set, Dad?” Peggy said, breaking into a run to her father’s side. 15 “Everything’s all set, Peg,” her father said with a grin. “And it’s set just the way you wanted it! There’s not a man in the world who can hold out against two determined women.” He leaned back against the fireplace mantel, waiting for the explosion he felt sure was to follow his announcement. But Peggy just stood, hardly moving a muscle. Then she walked carefully, as if she were on the deck of a rolling ship, to the big easy chair and slowly sat down. “Well, for goodness’ sake!” her mother cried. “Where’s the enthusiasm?” Peggy swallowed hard before answering. When her voice came, it sounded strange, about two tones higher than usual. “I ... I’m trying to be sedate ... and poised ... and very grown-up,” she said. “But it’s not easy. All I want to do is to—” and she jumped out of the chair—“to yell whoopee !” She yelled at the top of her lungs. After the kisses, the hugs, and the first excitement, Peggy and her parents adjourned to the kitchen, the favorite household conference room, for cookies and milk and more talk. “Now, tell me, Dad,” Peggy asked, her mouth full of oatmeal cookies, no longer “sedate” or “poised,” but her natural, bubbling self. “Who was that on the phone, and where are the three of us going, and what’s all set?” 16 “One thing at a time,” her father said. “To begin with, we decided almost as soon as you left that we were going to let you go to New York to try a year’s experience in the theater. But then we had to decide just where you would live, and where you should study, and how much money you would need, and a whole lot of other things. So I called New York to talk to an old friend of mine who I felt would be able to give us some help. Her name is May Berriman, and she’s spent all her life in the theater. In fact, she was a very successful actress. Now she’s been retired for some years, but I thought she might give us some good advice.” “And did she?” Peggy asked. “We were luckier than I would have thought possible,” Mrs. Lane put in. “It seems that May bought a big, old-fashioned town house and converted it into a rooming house especially for young actresses. She always wanted a house of her own with a garden in back, but felt it was foolish for a woman living alone. This way, she can afford to run a big place and at the same time not be alone. And best of all, she says she has a room that you can have!” “Oh, Mother! It sounds wonderful!” Peggy exulted. “I’ll be with other girls my own age who are actresses, and living with an experienced actress! I’ll bet she can teach me loads!” “I’m sure she can,” her father said. “And so can the New York Dramatic Academy.” “Dad!” Peggy shouted, almost choking on a cooky. “Don’t tell me you’ve managed to get me accepted there! That’s the best dramatic school in the country! How—?” 17 “Don’t get too excited, Peg,” Mr. Lane interrupted. “You’re not accepted anywhere yet, but May Berriman told me that the Academy is the best place to study acting, and she said she would set up an audition for you in two days. The term starts in a couple of weeks, so there isn’t much time to lose.” “Two days! Do you mean we’ll be going to New York day after tomorrow, just like that?” “Oh, no,” her mother answered calmly. “We’re going to New York tomorrow on the first plane that we can get seats on. Your father doesn’t believe in wasting time, once his mind is made up.” “Tomorrow?” Peggy repeated, almost unable to believe what she had heard. “What are we sitting here talking for, then? I’ve got a million things to do! I’ve got to get packed ... I’ve got to think of what to read for the audition! I can study on the plane, I guess, but ... oh! I’ll be terrible in a reading unless I can have more time! Oh, Mother, what parts will I do? Where’s the Shakespeare? Where’s—” “Whoa!” Mr. Lane said, catching Peggy’s arm to prevent her from rushing out of the kitchen. “Not now, young lady! We’ll pack in the morning, talk about what you should read, and take an afternoon plane to New York. But tonight, you’d better think of nothing more than getting to bed. This is going to be a busy time for all of us.” Reluctantly, Peggy agreed, recognizing the sense of what her father said. She finished her milk and cookies, kissed her parents good night and went upstairs to bed. But it was one thing to go to bed and another to go to sleep. 18 Peggy lay on her back, staring at the ceiling and the patterns of light and shade cast by the street lamp outside as it shone through the leaves of the big maple tree. As she watched the shifting shadows, she reviewed the roles she had played since her first time in a high-school play. Which should she refresh herself on? Which ones would she do best? And which ones were most suited to her now? She recognized that she had grown and developed past some of the roles which had once seemed perfectly suited to her talent and her appearance. But both had changed. She was certainly not a mature actress yet, from any point of view, but neither was she a schoolgirl. Her trim figure was well formed; her face had lost the undefined, simple cuteness of the early teens, and had gained character. She didn’t think she should read a young romantic part like Juliet. Not that she couldn’t do it, but perhaps something sharper was called for. Perhaps Viola in Twelfth Night ? Or perhaps not Shakespeare at all. Maybe the people at the Academy would think she was too arty or too pretentious? Maybe she should do something dramatic and full of stormy emotion, like Blanche in A Streetcar Named Desire ? Or, better for her development and age, a light, brittle, comedy role...? 19 Nothing seemed quite right. Peggy’s thoughts shifted with the shadows overhead. All the plays she had ever seen or read or acted in melted together in a blur, until the characters from one seemed to be talking with the characters from another and moving about in an enormous set made of pieces from two or three different plays. More actors kept coming on in a fantastic assortment of costumes until the stage was full. Then the stage lights dimmed, the actors joined hands across the stage to bow, the curtain slowly descended, the lights went out—and Peggy was fast asleep.
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A. She's dedicated, bold, and pretty
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Which word would least describe the character, Elizabeth?
A. strong
B. intelligent
C. uncompromising
D. feminine
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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?
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D. feminine
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What statement would many of the chess players at the tournament NOT agree with?
A. The Machine is impossible to win against.
B. There comes pride in winning against the Machine.
C. Chess tournaments are serious competitions.
D. Chess is a tedious game.
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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?"
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A. The Machine is impossible to win against.
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What they use as a metric of finding hot spots in meeting?
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### Introduction and Prior Work
A definition of the meeting “hot spots” was first introduced in BIBREF2, where it was investigated whether human annotators could reliably identify regions in which participants are “highly involved in the discussion”. The motivation was that meetings generally have low information density and are tedious to review verbatim after the fact. An automatic system that could detect regions of high interest (as indicated by the involvement of the participants during the meeting) would thus be useful. Relatedly, automatic meeting summarization could also benefit from such information to give extra weight to hot spot regions in selecting or abstracting material for inclusion in the summary. Later work on the relationship between involvement and summarization BIBREF3 defined a different approach: hot spots are those regions chosen for inclusion in a summary by human annotators (“summarization hot spots”). In the present work we stick with the original “involvement hot spot” notion, and refer to such regions simply as “hot spots”, regardless of their possible role in summarization. We note that high involvement may be triggered both by a meeting's content (“what is being talked about”, and “what may be included in a textual summary”), as well as behavioral and social factors, such as a desire to participate, to stake out a position, or to oppose another participant. As related notion in dialog system research is “level of interest” BIBREF4. The initial research on hot spots focused on the reliability of human annotators and correlations with certain low-level acoustic features, such as pitch BIBREF2. Also investigated were the correlation between hot spots and dialog acts BIBREF5 and hot spots and speaker overlap BIBREF6, without however conducting experiments in automatic hot spot prediction using machine learning techniques. Laskowski BIBREF7 redefined the hot spot annotations in terms of time-based windows over meetings, and investigated various classifier models to detect “hotness” (i.e., elevated involvement). However, that work focused on only two types of speech features: presence of laughter and the temporal patterns of speech activity across the various participants, both of which were found to be predictive of involvement. For the related problem of level-of-interest prediction in dialog systems BIBREF8, it was found that content-based classification can also be effective, using both a discriminative TF-IDF model and lexical affect scores, as well as prosodic features. In line with the earlier hot spot research on interaction patterns and speaker overlap, turn-taking features were shown to be helpful for spotting summarization hot spots, in BIBREF3, and even more so than the human involvement annotations. The latter result confirms our intuition that summarization-worthiness and involvement are different notions of “hotness”. In this paper, following Laskowski, we focus on the automatic prediction of the speakers' involvement in sliding-time windows/segments. We evaluate machine learning models based on a range of features that can be extracted automatically from audio recordings, either directly via signal processing or via the use of automatic transcriptions (ASR outputs). In particular, we investigate the relative contributions of three classes of information: low-level acoustic-prosodic features, such as those commonly used in other paralinguistic tasks, such as sentiment analysis (extracted using openSMILE BIBREF0); spoken word content, as encoded with a state-of-the-art lexical embedding approach such as BERT BIBREF1; speaker interaction, based on speech activity over time and across different speakers. We attach lower importance to laughter, even though it was found to be highly predictive of involvement in the ICSI corpus, partly because we believe it would not transfer well to more general types of (e.g., business) meetings, and partly because laughter detection is still a hard problem in itself BIBREF9. Generation of speaker-attributed meeting transcriptions, on the other hand, has seen remarkable progress BIBREF10 and could support the features we focus on here. ### Data
The ICSI Meeting Corpus BIBREF11 is a collection of meeting recordings that has been thoroughly annotated, including annotations for involvement hot spots BIBREF12, linguistic utterance units, and word time boundaries based on forced alignment. The dataset is comprised of 75 meetings and about 70 hours of real-time audio duration, with 6 speakers per meeting on average. Most of the participants are well-acquainted and friendly with each other. Hot spots were originally annotated with 8 levels and degrees, ranging from `not hot' to `luke warm' to `hot +'. Every utterance was labeled with one of these discrete labels by a single annotator. Hightened involvement is rare, being marked on only 1% of utterances. Due to the severe imbalance in the label distribution, Laskowski BIBREF13 proposed extending the involvement, or hotness, labels to sliding time windows. In our implementation (details below), this resulted in 21.7% of samples (windows) being labeled as “involved”. We split the corpus into three subsets: training, development, and evaluation, keeping meetings intact. Table TABREF4 gives statistics of these partitions. We were concerned with the relatively small number of meetings in the test sets, and repeated several of our experiments with a (jackknifing) cross-validation setup over the training set. The results obtained were very similar to those with the fixed train/test split results that we report here. ### Data ::: Time Windowing
As stated above, the corpus was originally labeled for hot spots at the utterance level, where involvement was marked by either a `b' or a `b+' label. Training and test samples for our experiments correspond to 60 s-long sliding windows, with a 15 s step size. If a certain window, e.g., a segment spanning the times 15 s ...75 s, overlaps with any involved speech utterance, then we label that whole window as `hot'. Fig. FIGREF6 gives a visual representation. ### Data ::: Metric
In spite of the windowing approach, the class distribution is still skewed, and an accuracy metric would reflect the particular class distribution in our data set. Therefore, we adopt the unweighted average recall (UAR) metric commonly used in emotion classification research. UAR is a reweighted accuracy where the samples of both classes are weighted equally in aggregate. UAR thus simulates a uniform class distribution. To match the objective, our classifiers are trained on appropriately weighted training data. Note that chance performance for UAR is by definition 50%, making results more comparable across different data sets. ### Feature Description ::: Acoustic-Prosodic Features
Prosody encompasses pitch, energy, and durational features of speech. Prosody is thought to convey emphasis, sentiment, and emotion, all of which are presumably correlated with expressions of involvement. We used the openSMILE toolkit BIBREF0 to compute 988 features as defined by the emobase988 configuration file, operating on the close-talking meeting recordings. This feature set consists of low-level descriptors such as intensity, loudness, Mel-frequency cepstral coefficients, and pitch. For each low-level descriptor, functionals such as max/min value, mean, standard deviation, kurtosis, and skewness are computed. Finally, global mean and variance normalization are applied to each feature, using training set statistics. The feature vector thus captures acoustic-prosodic features aggregated over what are typically utterances. We tried extracting openSMILE features directly from 60 s windows, but found better results by extracting subwindows of 5 s, followed by pooling over the longer 60 s duration. We attribute this to the fact that emobase features are designed to operate on individual utterances, which have durations closer to 5 s than 60 s. ### Feature Description ::: Word-Based Features ::: Bag of words with TF-IDF
Initially, we investigated a simple bag-of-words model including all unigrams, bigrams, and trigrams found in the training set. Occurrences of the top 10,000 n-grams were encoded to form a 10,000-dimensional vector, with values weighted according to TD-IDF. TF-IDF weights n-grams according to both their frequency (TF) and their salience (inverse document frequency, IDF) in the data, where each utterance was treated as a separate document. The resulting feature vectors are very sparse. ### Feature Description ::: Word-Based Features ::: Embeddings
The ICSI dataset is too small to train a neural embedding model from scratch. Therefore, it is convenient to use the pre-trained BERT embedding architecture BIBREF1 to create an utterance-level embedding vector for each region of interest. Having been trained on a large text corpus, the resulting embeddings encode semantic similarities among utterances, and would enable generalization from word patterns seen in the ICSI training data to those that have not been observed on that limited corpus. We had previously also created an adapted version of the BERT model, tuned to to perform utterance-level sentiment classification, on a separate dataset BIBREF14. As proposed in BIBREF1, we fine-tuned all layers of the pre-trained BERT model by adding a single fully-connected layer and classifying using only the embedding corresponding to the classification ([CLS]) token prepended to each utterance. The difference in UAR between the hot spot classifiers using the pre-trained embeddings and those using the sentiment-adapted embeddings is small. Since the classifier using embeddings extracted by the sentiment-adapted model yielded slightly better performance, we report all results using these as input. To obtain a single embedding for each 60 s window, we experimented with various approaches of pooling the token and utterance-level embeddings. For our first approach, we ignored the ground-truth utterance segmentation and speaker information. We merged all words spoken within a particular window into a single contiguous span. Following BIBREF1, we added the appropriate classification and separation tokens to the text and selected the embedding corresponding to the [CLS] token as the window-level embedding. Our second approach used the ground-truth segmentation of the dialogue. Each speaker turn was independently modeled, and utterance-level embeddings were extracted using the representation corresponding to the [CLS] token. Utterances that cross window boundaries are truncated using the word timestamps, so only words spoken within the given time window are considered. For all reported experiments, we use L2-norm pooling to form the window-level embeddings for the final classifier, as this performed better than either mean or max pooling. ### Feature Description ::: Speaker Activity Features
These features were a compilation of three different feature types: Speaker overlap percentages: Based on the available word-level times, we computed a 6-dimensional feature vector, where the $i$th index indicates the fraction of time that $i$ or more speakers are talking within a given window. This can be expressed by $\frac{t_i}{60}$ with $t_i$ indicating the time in seconds that $i$ or more people were speaking at the same time. Unique speaker count: Counts the unique speakers within a window, as a useful metric to track the diversity of participation within a certain window. Turn switch count: Counts the number of times a speaker begins talking within a window. This is a similar metric to the number of utterances. However, unlike utterance count, turn switches can be computed entirely from speech activity, without requiring a linguistic segmentation. ### Feature Description ::: Laughter Count
Laskowski found that laughter is highly predictive of involvement in the ICSI data. Laughter is annotated on an utterance level and falls into two categories: laughter solely on its own (no words) or laughter contained within an utterance (i.e. during speech). The feature is a simple tally of the number of times people laughed within a window. We include it in some of our experiments for comparison purposes, though we do not trust it as general feature. (The participants in the ICSI meetings are far too familiar and at ease with each other to be representative with regards to laughter.) ### Modeling ::: Non-Neural Models
In preliminary experiments, we compared several non-neural classifiers, including logistic regression (LR), random forests, linear support vector machines, and multinomial naive Bayes. Logistic regression gave the best results all around, and we used it exclusively for the results shown here, unless neural networks are used instead. ### Modeling ::: Feed-Forward Neural Networks ::: Pooling Techniques
For BERT and openSMILE vector classification, we designed two different feed-forward neural network architectures. The sentiment-adapted embeddings described in Section SECREF3 produce one 1024-dimensional vector per utterance. Since all classification operates on time windows, we had to pool over all utterances falling withing a given window, taking care to truncate words falling outside the window. We tested four pooling methods: L2-norm, mean, max, and min, with L2-norm giving the best results. As for the prosodic model, each vector extracted from openSMILE represents a 5 s interval. Since there was both a channel/speaker-axis and a time-axis, we needed to pool over both dimensions in order to have a single vector representing the prosodic features of a 60 s window. The second to last layer is the pooling layer, max-pooling across all the channels, and then mean-pooling over time. The output of the pooling layer is directly fed into the classifier. ### Modeling ::: Feed-Forward Neural Networks ::: Hyperparameters
The hyperparameters of the neural networks (hidden layer number and sizes) were also tuned in preliminary experiments. Details are given in Section SECREF5. ### Modeling ::: Model Fusion
Fig. FIGREF19 depicts the way features from multiple categories are combined. Speech activity and word features are fed directly into a final LR step. Acoustic-prosodic features are first combined in a feed-forward neural classifier, whose output log posteriors are in turn fed into the LR step for fusion. (When using only prosodic features, the ANN outputs are used directly.) ### Experiments
We group experiments by the type of feaures they are based on: acoustic-prosodic, word-based, and speech activity, evaluating each group first by itself, and then in combination with others. ### Experiments ::: Speech Feature Results
As discussed in Section SECREF3, a multitude of input features were investigated, with some being more discriminative. The most useful speech activity features were speaker overlap percentage, number of unique speakers, and number of turn switches, giving evaluation set UARs of 63.5%, 63.9%, and 66.6%, respectively. When combined the UAR improved to 68.0%, showing that these features are partly complementary. ### Experiments ::: Word-Based Results
The TF-IDF model alone gave a UAR of 59.8%. A drastic increase in performance to 70.5% was found when using the BERT embeddings instead. Therefore we adopted embeddings for all further experiments based on word information. Three different types of embeddings were investigated, i.e. sentiment-adapted embeddings at an utterance-level, unadapted embeddings at the utterance-level, and unadapted embeddings over time windows. The adapted embeddings (on utterances) performed best, indicating that adaptation to sentiment task is useful for involvement classification. It is important to note, however, that the utterance-level embeddings are larger than the window-level embeddings. This is due to there being more utterances than windows in the meeting corpus. The best neural architecture we found for these embeddings is a 5-layer neural network with sizes 1024-64-32-12-2. Other hyperparameters for this model are dropout rate = 0.4, learning rate = $10^{-7}$ and activation function “tanh”. The UAR on the evaluation set with just BERT embeddings as input is 65.2%. Interestingly, the neural model was outperformed by a LR directly on the embedding vectors. Perhaps the neural network requires further fine-tuning, or the neural model is too prone to overfitting, given the small training corpus. In any case, we use LR on embeddings for all subsequent results. ### Experiments ::: Acoustic-Prosodic Feature Results
Our prosodic model is a 5-layer ANN, as described in Section SECREF15. The architecture is: 988-512-128-16-Pool-2. The hyperparameters are: dropout rate = 0.4, learning rate = $10^{-7}$, activation = “tanh". The UAR on the evaluation set with just openSMILE features is 62.0%. ### Experiments ::: Fusion Results and Discussion
Table TABREF24 gives the UAR for each feature subset individually, for all features combined, and for a combination in which one feature subset in turn is left out. The one-feature-set-at-time results suggest that prosody, speech activity and words are of increasing importance in that order. The leave-one-out analysis agrees that the words are the most important (largest drop in accuracy when removed), but on that criterion the prosodic features are more important than speech-activity. The combination of all features is 0.4% absolute better than any other subset, showing that all feature subsets are partly complementary. Fig. FIGREF25 shows the same results in histogram form, but also add those with laughter information. Laughter count by itself is the strongest cue to involvement, as Laskowski BIBREF7 had found. However, even given the strong individual laughter feature, the other features add information, pushing the UAR from from 75.1% to 77.5%. ### Conclusion
We studied detection of areas of high involvement, or “hot spots”, within meetings using the ICSI corpus. The features that yielded the best results are in line with our intuitions. Word embeddings, speech activity features such a number of turn changes, and prosodic features are all plausible indicators of high involvement. Furthermore, the feature sets are partly complementary and yield best results when combined using a simple logistic regression model. The combined model achieves 72.6% UAR, or 77.5% with laughter feature. For future work, we would want to see a validation on an independent meeting collection, such as business meetings. Some features, in particular laughter, are bound not be as useful in this case. More data could also enable the training of joint models that perform an early fusion of the different feature types. Also, the present study still relied on human transcripts, and it would be important to know how much UAR suffers with a realistic amount of speech recognition error. Transcription errors are expected to boost the importance of the features types that do not rely on words. ### Acknowledgments
We thank Britta Wrede, Elizabeth Shriberg and Kornel Laskowski for explanations concerning the details of the data. Fig. 1. Visualization of the sliding window defining data points. Area bounded by red box indicates labeled involvement, causing 4 windows to be marked as ‘hot’. Table 1. Partitions of the ICSI dataset Fig. 2. Overview of fusion model Fig. 3. Graph of different combinations of features. Green rectangles indicate models using laughter. Prosody = openSMILE features with NN, Words = embeddings, Spch-act = speech activity, Laugh = laughter count. Combination was done using LR. Table 2. Hot spot classification results with individual feature subsets, all features, and with individual feature sets left out.
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unweighted average recall (UAR) metric
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How long ago had it been since Ben had first encountered the dead man?
A. 3 weeks
B. 1 month
C. 3 months
D. 1 week
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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.
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D. 1 week
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What baseline function is used in REINFORCE algorithm?
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### Introduction
Self-Critical Sequence Training(SCST), upon its release, has been a popular way to train sequence generation models. While originally proposed for image captioning task, SCST not only has become the new standard for training captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, but also has been applied to many other tasks, like video captioningBIBREF10, BIBREF11, BIBREF12, reading comprehensionBIBREF13, summarizationBIBREF14, BIBREF15, BIBREF16, BIBREF17, image paragraph generationBIBREF18, speech recognitionBIBREF19. SCST is used to optimize generated sequences over a non-differentiable objective, usually the evaluation metrics, for example, CIDEr for captioning, ROUGE for summarization. To optimize such objective, SCST adopts REINFORCE with baseline BIBREF20, where a “Self-Critical” baseline is used; specifically, the score of the greedy decoding output is used as the baseline. This is proved to be better than learned baseline function which is more commonly used in Reinforcement Learning literature. In this work, we present a different baseline choice which was first proposed in BIBREF21, to the best of our knowledge. With more elaboration in Sec. SECREF3, this baseline can be described as a variant of “Self-Critical”. This method is simple, but also faster and more effective compared to the greedy decoding baseline used in SCST. ### Recap for SCST
MIXER BIBREF22 is the first to use REINFORCE algorithm for sequence generation training. They use a learned function approximator to get the baseline. SCST inherits the REINFORCE algorithm from MIXER, but discards the learned baseline function. Instead, SCST uses the reward of the greedy decoding result as the baseline, achieving better captioning performance and lower gradient variance. ### Recap for SCST ::: Math formulation
The goal of SCST, for example in captioning, is to maximize the expected CIDEr score of generated captions. where ${\hat{c}}$ is a sampled caption; $I$ is the image; $p_{\theta }(c|I)$ is the captioning model parameterized by $\theta $, and $R(\cdot )$ is the CIDEr score. Since this objective is not non-differentiable with respect to $\theta $, back propagation is not feasible. To optimize it, a policy gradient method, specifically REINFORCE with baseline BIBREF20 is used. The policy gradient method allows estimating the gradient from individual samples (the right-hand side) and applying gradient ascent. To reduce the variance of the estimation, a baseline $b$ is needed, and $b$ has to be independent of $\hat{c}$. In SCST, the baseline is set to be the CIDEr score of the greedy decoding caption, denoted as $c^*$. Thus, we have ### The Better SCST
The success of SCST comes from better gradient variance reduction introduced by the greedy decoding baseline. In our variant, we use the baseline proposed in BIBREF21 to achieve even better variance reduction. Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\hat{c}}_1 \ldots {\hat{c}}_K$, ${\hat{c}}_k \sim p_{\theta }(c|I)$, The baseline for each sampled caption is defined as the average reward of the rest samples. That is, for caption $\hat{c}_k$, its baseline is Since each sample is independently drawn, $b_k$ is a valid baseline. The final gradient estimation is Note that, $b_k$ is an estimation of expected reward, which is similar to the learning objective of value functions in other Reinforcement Learning algorithms. The expected reward is usually a good baseline choice in that it can effectively reduce gradient variance. In Sec. SECREF4, we show that our gradient variance is lower than SCST empirically. It is still a “Self-Critical” baseline because the critic is still from itself: its other sampling results, instead of the greedy decoding result. ### Experiments
For all models, we first pretrain them using standard cross-entropy loss and then switch to Self-Critical training. For a fair comparison, during Self-Critical stage, we always sample 5 captions for each image, same for both SCST and our variant. All the experiments are done on COCO captioning dataset BIBREF23. The scores are obtained on Karparthy test split BIBREF24 with beam search of beam size 5 if not explicitly noted. ### Experiments ::: Speed
Since no extra greedy decoding is needed, our method is slightly faster than SCST. ### Experiments ::: Performance on different model architectures
We experiment with four different architectures. FC and Att2in are from SCSTBIBREF25. UpDown is from BIBREF26. Transformer is adapted from BIBREF27 for captioning task. Table TABREF6 shows that our variant is better than SCST on all architectures, especially on Transformer. ### Experiments ::: Different training hyperparameters
Here we adopt a different training setting (`Long') for UpDown model. The `Long' setting (from https://github.com/yangxuntu/SGAE) uses a larger batch size and a longer training time. Table TABREF8 shows that there is always a gap between our method and SCST which cannot be closed by longer training or a larger batch size. ### Experiments ::: Multiple runs
Table TABREF10 shows that our variant is consistently better than SCST with different random seeds. All the models use `Long' setting with UpDown model. Specifically, we pretrain 5 models using cross-entropy loss, and then apply SCST and our method respectively. The same $RS*$ means they share the same pretrained model. ### Experiments ::: Training curves
Figure FIGREF12 shows the model performance on the validation set during training, after entering Self-Critical stage. The scores are averaged over the 5 UpDown(Long) models above. ### Experiments ::: Is greedy decoding necessary for SCST
We also experiment with a variant of SCST, by replacing the greedy decoding output with a sampled output. (This is similar to our method with $K=2$.) Table TABREF14 shows that one sample baseline is worse than greedy decoding. This is as expected, because using one sample to estimate the expected reward is too noisy, resulting in larger gradient variance, while the reward of greedy decoding output may be biased but more stable. It also shows that it is important to use sufficiently large $K$ to have a better estimation of expected reward. ### Experiments ::: Variance reduction
As stated in Sec. SECREF3, the motivation of using the average reward baseline is for better variance reduction. Here we show it indeed is better in practice. The gradient variance is calculated as follows. At the end of each epoch, we take the saved model and run through the training set. We get the gradients from each training batch and calculate the variance for each parameter gradient across batches. To get a single value, we take the average of all the parameters. A mathematic expression of this process is: where $i$ is the index of each parameter; $b$ is the index of each batch; $\theta $ is the network parameters; $\text{grad}_{\theta _i}^b$ is the gradient of $\theta _i$ at batch $b$. As shown in Fig. FIGREF16, our method is always getting lower variance than SCST. ### Code release
Code has been released at https://github.com/ruotianluo/self-critical.pytorch. More instructions of using this method are at https://github.com/ruotianluo/self-critical.pytorch/tree/master/projects/NewSelfCritical ### Conclusion
We propose a variant of popular SCST, which can work as a drop-in replacement for SCST. This variant reduces the gradient variance when applying REINFORCE by modifying the baseline function. We show that this method is effective on Image Captioning task, and we believe it should benefit other tasks as well. Table 1: The performance of our method on different model architectures. The numbers are from authors’ own implementation. Table 2: The performance of UpDown model with SCST/Ours under two different hyperparameter settings. Table 3: Within the first 5 block, the models share the same cross-entropy pretrained model (RS stands for random seed). The last block shows the average score of 5 models. Table 4: Replacing the greedy decoding output c∗ in SCST with a separately drawn sample ĉ′. Figure 1: Performance on validation set during training. (With UpDown(Long) + greedy decoding) Figure 2: The gradient variance on training set.(Model: UpDown)
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baseline for each sampled caption is defined as the average reward of the rest samples
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What does the older man plan to do after this event?
A. retire
B. grow his company and make more money
C. go to another dimension
D. travel back in time again
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... and it comes out here By LESTER DEL REY Illustrated by DON SIBLEY [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There is one fact no sane man can quarrel with ... everything has a beginning and an end. But some men aren't sane; thus it isn't always so! No, you're wrong. I'm not your father's ghost, even if I do look a bit like him. But it's a longish story, and you might as well let me in. You will, you know, so why quibble about it? At least, you always have ... or do ... or will. I don't know, verbs get all mixed up. We don't have the right attitude toward tenses for a situation like this. Anyhow, you'll let me in. I did, so you will. Thanks. You think you're crazy, of course, but you'll find out you aren't. It's just that things are a bit confused. And don't look at the machine out there too long—until you get used to it, you'll find it's hard on the eyes, trying to follow where the vanes go. You'll get used to it, of course, but it will take about thirty years. You're wondering whether to give me a drink, as I remember it. Why not? And naturally, since we have the same tastes, you can make the same for me as you're having. Of course we have the same tastes—we're the same person. I'm you thirty years from now, or you're me. I remember just how you feel; I felt the same way when he—that is, of course, I or we—came back to tell me about it, thirty years ago. Here, have one of these. You'll get to like them in a couple more years. And you can look at the revenue stamp date, if you still doubt my story. You'll believe it eventually, though, so it doesn't matter. Right now, you're shocked. It's a real wrench when a man meets himself for the first time. Some kind of telepathy seems to work between two of the same people. You sense things. So I'll simply go ahead talking for half an hour or so, until you get over it. After that you'll come along with me. You know, I could try to change things around by telling what happened to me; but he—I—told me what I was going to do, so I might as well do the same. I probably couldn't help telling you the same thing in the same words, even if I tried—and I don't intend to try. I've gotten past that stage in worrying about all this. So let's begin when you get up in half an hour and come out with me. You'll take a closer look at the machine, then. Yes, it'll be pretty obvious it must be a time machine. You'll sense that, too. You've seen it, just a small little cage with two seats, a luggage compartment, and a few buttons on a dash. You'll be puzzling over what I'll tell you, and you'll be getting used to the idea that you are the man who makes atomic power practical. Jerome Boell, just a plain engineer, the man who put atomic power in every home. You won't exactly believe it, but you'll want to go along. I'll be tired of talking by then, and in a hurry to get going. So I cut off your questions, and get you inside. I snap on a green button, and everything seems to cut off around us. You can see a sort of foggy nothing surrounding the cockpit; it is probably the field that prevents passage through time from affecting us. The luggage section isn't protected, though. You start to say something, but by then I'm pressing a black button, and everything outside will disappear. You look for your house, but it isn't there. There is exactly nothing there—in fact, there is no there . You are completely outside of time and space, as best you can guess how things are. You can't feel any motion, of course. You try to reach a hand out through the field into the nothing around you and your hand goes out, all right, but nothing happens. Where the screen ends, your hand just turns over and pokes back at you. Doesn't hurt, and when you pull your arm back, you're still sound and uninjured. But it looks frightening and you don't try it again. Then it comes to you slowly that you're actually traveling in time. You turn to me, getting used to the idea. "So this is the fourth dimension?" you ask. Then you feel silly, because you'll remember that I said you'd ask that. Well, I asked it after I was told, then I came back and told it to you, and I still can't help answering when you speak. "Not exactly," I try to explain. "Maybe it's no dimension—or it might be the fifth; if you're going to skip over the so-called fourth without traveling along it, you'd need a fifth. Don't ask me. I didn't invent the machine and I don't understand it." "But...." I let it go, and so do you. If you don't, it's a good way of going crazy. You'll see later why I couldn't have invented the machine. Of course, there may have been a start for all this once. There may have been a time when you did invent the machine—the atomic motor first, then the time-machine. And when you closed the loop by going back and saving yourself the trouble, it got all tangled up. I figured out once that such a universe would need some seven or eight time and space dimensions. It's simpler just to figure that this is the way time got bent back on itself. Maybe there is no machine, and it's just easier for us to imagine it. When you spend thirty years thinking about it, as I did—and you will—you get further and further from an answer. Anyhow, you sit there, watching nothing all around you, and no time, apparently, though there is a time effect back in the luggage space. You look at your watch and it's still running. That means you either carry a small time field with you, or you are catching a small increment of time from the main field. I don't know, and you won't think about that then, either. I'm smoking, and so are you, and the air in the machine is getting a bit stale. You suddenly realize that everything in the machine is wide open, yet you haven't seen any effects of air loss. "Where are we getting our air?" you ask. "Or why don't we lose it?" "No place for it to go," I explain. There isn't. Out there is neither time nor space, apparently. How could the air leak out? You still feel gravity, but I can't explain that, either. Maybe the machine has a gravity field built in, or maybe the time that makes your watch run is responsible for gravity. In spite of Einstein, you have always had the idea that time is an effect of gravity, and I sort of agree, still. Then the machine stops—at least, the field around us cuts off. You feel a dankish sort of air replace the stale air, and you breathe easier, though we're in complete darkness, except for the weak light in the machine, which always burns, and a few feet of rough dirty cement floor around. You take another cigaret from me and you get out of the machine, just as I do. I've got a bundle of clothes and I start changing. It's a sort of simple, short-limbed, one-piece affair I put on, but it feels comfortable. "I'm staying here," I tell you. "This is like the things they wear in this century, as near as I can remember it, and I should be able to pass fairly well. I've had all my fortune—the one you make on that atomic generator—invested in such a way I can get it on using some identification I've got with me, so I'll do all right. I know they still use some kind of money, you'll see evidence of that. And it's a pretty easygoing civilization, from what I could see. We'll go up and I'll leave you. I like the looks of things here, so I won't be coming back with you." You nod, remembering I've told you about it. "What century is this, anyway?" I'd told you that, too, but you've forgotten. "As near as I can guess, it's about 2150. He told me, just as I'm telling you, that it's an interstellar civilization." You take another cigaret from me, and follow me. I've got a small flashlight and we grope through a pile of rubbish, out into a corridor. This is a sub-sub-sub-basement. We have to walk up a flight of stairs, and there is an elevator waiting, fortunately with the door open. "What about the time machine?" you ask. "Since nobody ever stole it, it's safe." We get in the elevator, and I say "first" to it. It gives out a coughing noise and the basement openings begin to click by us. There's no feeling of acceleration—some kind of false gravity they use in the future. Then the door opens, and the elevator says "first" back at us. It's obviously a service elevator and we're in a dim corridor, with nobody around. I grab your hand and shake it. "You go that way. Don't worry about getting lost; you never did, so you can't. Find the museum, grab the motor, and get out. And good luck to you." You act as if you're dreaming, though you can't believe it's a dream. You nod at me and I move out into the main corridor. A second later, you see me going by, mixed into a crowd that is loafing along toward a restaurant, or something like it, that is just opening. I'm asking questions of a man, who points, and I turn and move off. You come out of the side corridor and go down a hall, away from the restaurant. There are quiet little signs along the hall. You look at them, realizing for the first time that things have changed. Steij:neri, Faunten, Z:rgat Dispenseri. The signs are very quiet and dignified. Some of them can be decoded to stationery shops, fountains, and the like. What a zergot is, you don't know. You stop at a sign that announces: Trav:l Biwrou—F:rst-Clas Twrz—Marz, Viin*s, and x: Trouj:n Planets. Spej:l reits tu aol s*nz wixin 60 lyt iirz! But there is only a single picture of a dull-looking metal sphere, with passengers moving up a ramp, and the office is closed. You begin to get the hang of the spelling they use, though. Now there are people around you, but nobody pays much attention to you. Why should they? You wouldn't care if you saw a man in a leopard-skin suit; you'd figure it was some part in a play and let it go. Well, people don't change much. You get up your courage and go up to a boy selling something that might be papers on tapes. "Where can I find the Museum of Science?" "Downayer rien turn lefa the sign. Stoo bloss," he tells you. Around you, you hear some pretty normal English, but there are others using stuff as garbled as his. The educated and uneducated? I don't know. You go right until you find a big sign built into the rubbery surface of the walk: Miuzi:m *v Syens . There's an arrow pointing and you turn left. Ahead of you, two blocks on, you can see a pink building, with faint aqua trimming, bigger than most of the others. They are building lower than they used to, apparently. Twenty floors up seems about the maximum. You head for it, and find the sidewalk is marked with the information that it is the museum. You go up the steps, but you see that it seems to be closed. You hesitate for a moment, then. You're beginning to think the whole affair is complete nonsense, and you should get back to the time machine and go home. But then a guard comes to the gate. Except for the short legs in his suit and the friendly grin on his face, he looks like any other guard. What's more, he speaks pretty clearly. Everyone says things in a sort of drawl, with softer vowels and slurred consonants, but it's rather pleasant. "Help you, sir? Oh, of course. You must be playing in 'Atoms and Axioms.' The museum's closed, but I'll be glad to let you study whatever you need for realism in your role. Nice show. I saw it twice." "Thanks," you mutter, wondering what kind of civilization can produce guards as polite as that. "I—I'm told I should investigate your display of atomic generators." He beams at that. "Of course." The gate is swung to behind you, but obviously he isn't locking it. In fact, there doesn't seem to be a lock. "Must be a new part. You go down that corridor, up one flight of stairs and left. Finest display in all the known worlds. We've got the original of the first thirteen models. Professor Jonas was using them to check his latest theory of how they work. Too bad he could not explain the principle, either. Someone will, some day, though. Lord, the genius of that twentieth century inventor! It's quite a hobby with me, sir. I've read everything I could get on the period. Oh—congratulations on your pronunciation. Sounds just like some of our oldest tapes." You get away from him, finally, after some polite thanks. The building seems deserted and you wander up the stairs. There's a room on your right filled with something that proclaims itself the first truly plastic diamond former, and you go up to it. As you come near, it goes through a crazy wiggle inside, stops turning out a continual row of what seem to be bearings, and slips something the size of a penny toward you. "Souvenir," it announces in a well-modulated voice. "This is a typical gem of the twentieth century, properly cut to 58 facets, known technically as a Jaegger diamond, and approximately twenty carats in size. You can have it made into a ring on the third floor during morning hours for one-tenth credit. If you have more than one child, press the red button for the number of stones you desire." You put it in your pocket, gulping a little, and get back to the corridor. You turn left and go past a big room in which models of spaceships—from the original thing that looks like a V-2, and is labeled first Lunar rocket, to a ten-foot globe, complete with miniature manikins—are sailing about in some kind of orbits. Then there is one labeled Wep:nz , filled with everything from a crossbow to a tiny rod four inches long and half the thickness of a pencil, marked Fynal Hand Arm . Beyond is the end of the corridor, and a big place that bears a sign, Mad:lz *v Atamic Pau:r Sorsez . By that time, you're almost convinced. And you've been doing a lot of thinking about what you can do. The story I'm telling has been sinking in, but you aren't completely willing to accept it. You notice that the models are all mounted on tables and that they're a lot smaller than you thought. They seem to be in chronological order, and the latest one, marked 2147—Rincs Dyn*pat: , is about the size of a desk telephone. The earlier ones are larger, of course, clumsier, but with variations, probably depending on the power output. A big sign on the ceiling gives a lot of dope on atomic generators, explaining that this is the first invention which leaped full blown into basically final form. You study it, but it mentions casually the inventor, without giving his name. Either they don't know it, or they take it for granted that everyone does, which seems more probable. They call attention to the fact that they have the original model of the first atomic generator built, complete with design drawings, original manuscript on operation, and full patent application. They state that it has all major refinements, operating on any fuel, producing electricity at any desired voltage up to five million, any chosen cyclic rate from direct current to one thousand megacycles, and any amperage up to one thousand, its maximum power output being fifty kilowatts, limited by the current-carrying capacity of the outputs. They also mention that the operating principle is still being investigated, and that only such refinements as better alloys and the addition of magnetric and nucleatric current outlets have been added since the original. So you go to the end and look over the thing. It's simply a square box with a huge plug on each side, and a set of vernier controls on top, plus a little hole marked, in old-style spelling, Drop BBs or wire here . Apparently that's the way it's fueled. It's about one foot on each side. "Nice," the guard says over your shoulder. "It finally wore out one of the cathogrids and we had to replace that, but otherwise it's exactly as the great inventor made it. And it still operates as well as ever. Like to have me tell you about it?" "Not particularly," you begin, and then realize bad manners might be conspicuous here. While you're searching for an answer, the guard pulls something out of his pocket and stares at it. "Fine, fine. The mayor of Altasecarba—Centaurian, you know—is arriving, but I'll be back in about ten minutes. He wants to examine some of the weapons for a monograph on Centaurian primitives compared to nineteenth century man. You'll pardon me?" You pardon him pretty eagerly and he wanders off happily. You go up to the head of the line, to that Rinks Dynapattuh, or whatever it transliterates to. That's small and you can carry it. But the darned thing is absolutely fixed. You can't see any bolts, but you can't budge it, either. You work down the line. It'd be foolish to take the early model if you can get one with built-in magnetic current terminals—Ehrenhaft or some other principle?—and nuclear binding-force energy terminals. But they're all held down by the same whatchamaycallem effect. And, finally, you're right back beside the original first model. It's probably bolted down, too, but you try it tentatively and you find it moves. There's a little sign under it, indicating you shouldn't touch it, since the gravostatic plate is being renewed. Well, you won't be able to change the time cycle by doing anything I haven't told you, but a working model such as that is a handy thing. You lift it; it only weighs about fifty pounds! Naturally, it can be carried. You expect a warning bell, but nothing happens. As a matter of fact, if you'd stop drinking so much of that scotch and staring at the time machine out there now, you'd hear what I'm saying and know what will happen to you. But of course, just as I did, you're going to miss a lot of what I say from now on, and have to find out for yourself. But maybe some of it helps. I've tried to remember how much I remembered, after he told me, but I can't be sure. So I'll keep on talking. I probably can't help it, anyhow. Pre-set, you might say. Well, you stagger down the corridor, looking out for the guard, but all seems clear. Then you hear his voice from the weapons room. You bend down and try to scurry past, but you know you're in full view. Nothing happens, though. You stumble down the stairs, feeling all the futuristic rays in the world on your back, and still nothing happens. Ahead of you, the gate is closed. You reach it and it opens obligingly by itself. You breathe a quick sigh of relief and start out onto the street. Then there's a yell behind you. You don't wait. You put one leg in front of the other and you begin racing down the walk, ducking past people, who stare at you with expressions you haven't time to see. There's another yell behind you. Something goes over your head and drops on the sidewalk just in front of your feet, with a sudden ringing sound. You don't wait to find out about that, either. Somebody reaches out a hand to catch you and you dart past. The street is pretty clear now and you jolt along, with your arms seeming to come out of the sockets, and that atomic generator getting heavier at every step. Out of nowhere, something in a blue uniform about six feet tall and on the beefy side appears—and the badge hasn't changed much. The cop catches your arm and you know you're not going to get away, so you stop. "You can't exert yourself that hard in this heat, fellow," the cop says. "There are laws against that, without a yellow sticker. Here, let me grab you a taxi." Reaction sets in a bit and your knees begin to buckle, but you shake your head and come up for air. "I—I left my money home," you begin. The cop nods. "Oh, that explains it. Fine, I won't have to give you an appearance schedule. But you should have come to me." He reaches out and taps a pedestrian lightly on the shoulder. "Sir, an emergency request. Would you help this gentleman?" The pedestrian grins, looks at his watch, and nods. "How far?" You did notice the name of the building from which you came and you mutter it. The stranger nods again, reaches out and picks up the other side of the generator, blowing a little whistle the cop hands him. Pedestrians begin to move aside, and you and the stranger jog down the street at a trot, with a nice clear path, while the cop stands beaming at you both. That way, it isn't so bad. And you begin to see why I decided I might like to stay in the future. But all the same, the organized cooperation here doesn't look too good. The guard can get the same and be there before you. And he is. He stands just inside the door of the building as you reach it. The stranger lifts an eyebrow and goes off at once when you nod at him, not waiting for thanks. And the guard comes up, holding some dinkus in his hand, about the size of a big folding camera and not too dissimilar in other ways. He snaps it open and you get set to duck. "You forgot the prints, monograph, and patent applications," he says. "They go with the generator—we don't like to have them separated. A good thing I knew the production office of 'Atoms and Axioms' was in this building. Just let us know when you're finished with the model and we'll pick it up." You swallow several sets of tonsils you had removed years before, and take the bundle of papers he hands you out of the little case. He pumps you for some more information, which you give him at random. It seems to satisfy your amiable guard friend. He finally smiles in satisfaction and heads back to the museum. You still don't believe it, but you pick up the atomic generator and the information sheets, and you head down toward the service elevator. There is no button on it. In fact, there's no door there. You start looking for other doors or corridors, but you know this is right. The signs along the halls are the same as they were. Then there's a sort of cough and something dilates in the wall. It forms a perfect door and the elevator stands there waiting. You get in, gulping out something about going all the way down, and then wonder how a machine geared for voice operation can make anything of that. What the deuce would that lowest basement be called? But the elevator has closed and is moving downward in a hurry. It coughs again and you're at the original level. You get out—and realize you don't have a light. You'll never know what you stumbled over, but, somehow, you move back in the direction of the time machine, bumping against boxes, staggering here and there, and trying to find the right place by sheer feel. Then a shred of dim light appears; it's the weak light in the time machine. You've located it. You put the atomic generator in the luggage space, throw the papers down beside it, and climb into the cockpit, sweating and mumbling. You reach forward toward the green button and hesitate. There's a red one beside it and you finally decide on that. Suddenly, there's a confused yell from the direction of the elevator and a beam of light strikes against your eyes, with a shout punctuating it. Your finger touches the red button. You'll never know what the shouting was about—whether they finally doped out the fact that they'd been robbed, or whether they were trying to help you. You don't care which it is. The field springs up around you and the next button you touch—the one on the board that hasn't been used so far—sends you off into nothingness. There is no beam of light, you can't hear a thing, and you're safe. It isn't much of a trip back. You sit there smoking and letting your nerves settle back to normal. You notice a third set of buttons, with some pencil marks over them—"Press these to return to yourself 30 years"—and you begin waiting for the air to get stale. It doesn't because there is only one of you this time. Instead, everything flashes off and you're sitting in the machine in your own back yard. You'll figure out the cycle in more details later. You get into the machine in front of your house, go to the future in the sub-basement, land in your back yard, and then hop back thirty years to pick up yourself, landing in front of your house. Just that. But right then, you don't care. You jump out and start pulling out that atomic generator and taking it inside. It isn't hard to disassemble, but you don't learn a thing; just some plates of metal, some spiral coils, and a few odds and ends—all things that can be made easily enough, all obviously of common metals. But when you put it together again, about an hour later, you notice something. Everything in it is brand-new and there's one set of copper wires missing! It won't work. You put some #12 house wire in, exactly like the set on the other side, drop in some iron filings, and try it again. And with the controls set at 120 volts, 60 cycles and 15 amperes, you get just that. You don't need the power company any more. And you feel a little happier when you realize that the luggage space wasn't insulated from time effects by a field, so the motor has moved backward in time, somehow, and is back to its original youth—minus the replaced wires the guard mentioned—which probably wore out because of the makeshift job you've just done. But you begin getting more of a jolt when you find that the papers are all in your own writing, that your name is down as the inventor, and that the date of the patent application is 1951. It will begin to soak in, then. You pick up an atomic generator in the future and bring it back to the past—your present—so that it can be put in the museum with you as the inventor so you can steal it to be the inventor. And you do it in a time machine which you bring back to yourself to take yourself into the future to return to take back to yourself.... Who invented what? And who built which? Before long, your riches from the generator are piling in. Little kids from school are coming around to stare at the man who changed history and made atomic power so common that no nation could hope to be anything but a democracy and a peaceful one—after some of the worst times in history for a few years. Your name eventually becomes as common as Ampere, or Faraday, or any other spelled without a capital letter. But you're thinking of the puzzle. You can't find any answer. One day you come across an old poem—something about some folks calling it evolution and others calling it God. You go out, make a few provisions for the future, and come back to climb into the time machine that's waiting in the building you had put around it. Then you'll be knocking on your own door, thirty years back—or right now, from your view—and telling your younger self all these things I'm telling you. But now.... Well, the drinks are finished. You're woozy enough to go along with me without protest, and I want to find out just why those people up there came looking for you and shouting, before the time machine left. Let's go.
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A. retire
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What does 14 mention that no other critiques mention?
A. parents may be disappointed by the child born
B. people aren't thinking about the long-term
C. the children born may not be beautiful
D. this may have a negative impact on the children
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eBabe This week, soft-porn entrepreneur Ron Harris began auctioning the eggs of fashion models on the Internet. His site, ronsangels.com (named after the 1970s' babe show Charlie's Angels ), invites visitors to "bid on eggs from beautiful, healthy and intelligent women." Like Dr. Richard Seed, who recently declared his intention to clone human beings, Harris has attracted the attention of the media and politicians who are "looking into" whether he can be stopped. Most people agree that Harris is a creep and that his site is an outrage. What they don't agree on is why. Here's what the critics have to say about the auction--and each other. 1. Egg auctions will produce designer babies. Harris cites his experience as a horse breeder and asks, "We bid for everything else in this society--why not eggs?" Alarmists, agreeing that Harris "can put you into your own designer baby by selling eggs," predict that his success will steer "the future of human breeding" toward "genetic engineering." 2. Egg auctions will fail to produce designer babies. While fretting about what will happen if Harris succeeds, fertility experts simultaneously debunk that scenario. "Not only is it ethically ludicrous, but the fact is, no kid's going to look like the model's picture," observes ethicist George Annas. The experts give four reasons. First, the child of an ugly man and a pretty woman is just as likely to be ugly as to be pretty. Second, everyone carries "recessive" genes, which are invisible in this generation but may become visible in the next. A model with a small nose can pass on genes for a big nose. Third, even if both parents are attractive, a child can combine their features unattractively. For example, a girl can inherit her mother's weak nose and her father's strong brow. 3. Egg auctions will promote the survival of the fittest. Doomsayers predict that once "beautiful eggs are available strictly to people who are willing to spend an ungodly sum for them," the rich will transform themselves into a "super-race" reminiscent of the Nazis. To this, Harris replies, "It is not our intention to suggest that we make a super society of only beautiful people. This site simply mirrors our current society, in that beauty usually goes to the highest bidder." But this reply only fuels concern that gradually, society will separate into "genetic haves and have nots." 4. Egg auctions will promote the survival of the unfittest. Harris writes that only men with "substantial financial resources" are fit to give his models' offspring "a financially secure and stable life." But skeptics wonder whether women who sell their eggs to the highest bidder--and men who buy these eggs for the sole purpose of spawning good-looking children--may produce children just as dysfunctional as themselves. As Calgary Sun columnist Sydney Sharpe put it, "Any woman ... who enters into this mephistophelian pact has a few screws loose. Maybe her kid will, too. Not to mention the buyers who sign her up." 5. Egg auctions will fail to promote the survival of the unfittest. Many models, if not most, have had cosmetic surgery. A model who is perfectly ruthless will conceal this fact when selling her eggs. (One of Harris' "angels" has already been caught lying about her age.) How does Harris know whether his models have had collagen injections and nose jobs? "There's no way to know that. You can ask the girl and hope she tells you the truth," he says. Annas concludes that since there's "no way to know how much of their beauty is a product of their genes, plastic surgery, a makeup artist, or exercise," only a "naive" person would buy their eggs on the basis of the photographs displayed on the site. "You don't want to see the models," he points out. "You want to see pictures of their parents." On this theory, children produced by the egg auction are likely to be the offspring of liars on one side and fools on the other. 6. Beauty doesn't convey health. Harris casually asserts that beauty "shows healthiness and longevity." On his site, he writes, " 'Natural Selection' is choosing genes that are healthy and beautiful." Skeptics question this assumed equivalence, noting that traits men find attractive in women these days--thinness, for example--are often unhealthy. When asked on the Today show how much "medical screening" he has given his egg donors, Harris answered, "None." 7. Beauty is less meaningful than intelligence. Harris says he's not the first person to market good genes. Others, he notes, have sold sperm and solicited eggs on the basis of the donor's intelligence. Harris' detractors reply that beauty is "superficial" and conveys a "harmful preoccupation with exterior appearances over intelligence and content of character." This critique is usually offered by a blow-dried TV interviewer who, after thanking Harris for his time, urges viewers to stay tuned for the movie starlet who will join the program after a brief commercial break. 8. Beauty is less useful than intelligence. Harris advertises beauty not as an end but as a means to "success," since people who are physically desirable get more attention, power, and favorable treatment. Having chided Harris for exalting social advantage over "character," critics turn around and adopt his ruthless logic. While conceding that beauty is useful, they argue that intelligence is a better weapon in today's meritocratic information economy--and that although Harris claims his models are "beautiful, healthy and intelligent," he offers no evidence of brains, such as IQ or SAT scores. London's Independent envisions "Bimbo births." A fertility expert shrugs, "If people want to spend $150,000 for the eggs of a gorgeous woman who has an IQ of 68, let them." 9. The auction exploits desperate buyers. Harris preaches pure capitalism, saying it's "unfair to put a limit on a girl's ability to make money" by auctioning her eggs. In turn, fertility clinic operators accuse Harris of "taking advantage of couples trying to conceive" and exploiting "desperate people ... susceptible to the dreams he is trying to sell." USA Today laments, "This is about human need. And human greed." 10. The auction exploits desperate sellers. By late Monday, Harris had only a handful of bids, and only one was verified as legitimate. On the other hand, 50 women had asked him to put their eggs up for auction. Gradually, the media concluded that the donors were the true victims. USA Today described the models as "struggling actresses," reported that they were unaware of the health risks of donating eggs, and quoted one as saying, "I'd rather do this than do Playboy or Penthouse ." Harris' sole verified bidder told the paper that selling eggs was "better than prostitution." Harris constantly refers to the donors as his "girls" and describes them like cattle--"We have a legitimate bid of $42,000 on one of the girls." He gets a 20 percent commission on each winning bid, though he takes no responsibility for executing financial transactions or medical procedures. "We have no control over the quality, safety or legality of the items advertised, the truth or accuracy of the listings, the ability of sellers to sell items or the ability of buyers to buy items," he stipulates. His role, he explains, is simply to "find beautiful girls, take beautiful photographs of them, [and] put them up on the Web." To some critics, the mystery isn't, as Harris suggests, how women throughout history have exploited their sexual power over men, but how pimps like him have come away with the profit. 11. The auction exploits voyeurs. The Washington Post thinks Harris isn't targeting either buyers or sellers. He's not serious about selling eggs, says the Post . He's just using the sex appeal of his models and the intriguing perversity of a human egg auction to drum up publicity and attract Internet traffic to his site, from which he can sell advertising and subscriptions ($24.95 a month to view profiles of the models), hawk his forthcoming book ( Naked Power ), and direct prurient visitors to his various porn sites. A spokesman for fertility doctors suspects that ronsangels.com is really aimed at "adolescent boys." 12. The Internet facilitates monstrous purchases. Technology watchdogs call the egg auction another chapter in the cultural slide marked by Jennycam (a Web site featuring live video of a young woman undressing and doing other normal activities in her apartment), the promised Webcast of a man and woman losing their virginity together (which turned out to be a hoax), and a human kidney auction that was conducted and aborted on eBay last month. "Ever since the Internet, it seems to snowball more rapidly, this depersonalization of people and selling of eggs," one fertility expert complains to the New York Times . USA Today says the egg auction "just might force an Internet-obsessed society to finally sit down and ask itself: Where is the Internet taking us?" 13. The Internet cheats people of their monstrous purchases. The only thing worse than buying human eggs on the Internet, according to the critics, is not getting the eggs you paid for. "When you have large transactions of this kind conducted over the Internet, there may be fraud," a computer crime expert warns USA Today . Lori Andrews, a reproductive technology lawyer, warns CNN viewers that "there's very little that you can do to prove that these eggs actually came from the donors that were expected," and "the Internet just adds ... a layer that it makes it even more difficult to scrutinize where the eggs are coming from." 14. Egg buyers will reap unintended consequences. Sophisticated skeptics point out that Harris' application of Darwin's theories to human professional success overlooks the interaction of genetics and human psychology. To begin with, if a child produced by Harris' auction fails to turn out as pretty as the buyer expected, the buyer may shun the child, or the child may grow to hate herself for disappointing her parents. (On the Today show, Harris said of this theory, "That's a pretty cynical view of human nature.") Second, if the child turns out pretty but doesn't want to be a beauty queen, her parents may force her in that direction anyway, thereby stifling her true talents and preventing her from becoming successful. Third, the child's good looks may attract too much attention of the wrong kind, eventually destroying her. Critics cite Elvis Presley and Marilyn Monroe as examples. 15. Other people's eggs don't pass on your genes. In defense of his auction, Harris quotes author Helen Fisher's statement that "having sex is the most important act of your life. This is how we get our genes to the next generation." But Harris seems to have overlooked the crucial words: "our genes." "The drive to send your own genes into tomorrow is much stronger than the [drive] to pick out of a sperm bank or egg site," Fisher observes. This consideration may not affect single men, but it can be a decisive turnoff for couples. On this view, Harris' mistake is not that he focuses too much on selfishness, but that he neglects it. He forgets that you don't care about reproducing unless what you're reproducing is yourself. 16. The power of beauty should be transcended, not exploited. Harris preaches that the world rewards beauty because it's human nature to favor those who are pleasant to look at, and therefore the way to have successful children is to make sure they're attractive. The most ambitious response is to attack the whole "prejudice" in favor of beauty. "The standards of beauty do vary with the culture. And they are social facts, not really genetics facts," says Hastings Center ethicist Bruce Jennings. Therefore, "we should think about" whether to "accept the existing prejudices and then try to eugenically manipulate them" or to transcend those prejudices. This critique challenges two precepts of Harris' worldview. First, while pretending to accept human nature as a given, he violates it by peddling strangers' eggs and encouraging the production of children who will probably never know their mothers. Family association, loyalty, and love are among the best parts of human nature. Slavish catering to physically attractive strangers is among the worst. If we're going to challenge human nature, the critics ask, why not start with the latter rather than the former? Second, Harris assumes that the perfection parents want in their children coincides with Darwinian perfection. "Every organism is trying to evolve to its most perfect state," he writes. What he doesn't seem to understand is that human beings aren't quite like other animals, just as the rest of the world isn't exactly like the modeling and soft-porn industries of Southern California. Humans have evolved to a stage at which our ideas about virtue, perfection, and success have become more than material. At least, most of us have.
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D. this may have a negative impact on the children
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How much does Pfizer expect to pay to spin off Upjohn in the future in USD million?
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Evidence 0:
We expect to incur costs of approximately $700 million in connection with separating Upjohn, of which approximately 90% has been incurred since inception
and through the second quarter of 2023. These charges include costs and expenses related to separation of legal entities and transaction costs.
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77.78
|
What is the difference between speaker-open and speaker-closed setting?
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### Introduction
Automatic speech recognition (ASR) technology has been made a dramatic progress and is currently brought to a pratical levels of performance assisted by large speech corpora and the introduction of deep learning techniques. However, this is not the case for low-resource languages which do not have large corpora like English and Japanese have. There are about 5,000 languages in the world over half of which are faced with the danger of extinction. Therefore, constructing ASR systems for these endangered languages is an important issue. The Ainu are an indigenous people of northern Japan and Sakhakin in Russia, but their language has been fading away ever since the Meiji Restoration and Modernization. On the other hand, active efforts to preserve their culture have been initiated by the Government of Japan, and exceptionally large oral recordings have been made. Nevertheless, a majority of the recordings have not been transcribed and utilized effectively. Since transcribing them requires expertise in the Ainu language, not so many people are able to work on this task. Hence, there is a strong demand for an ASR system for the Ainu language. We started a project of Ainu ASR and this article is the first report of this project. We have built an Ainu speech corpus based on data provided by the Ainu Museum and the Nibutani Ainu Culture Museum. The oral recordings in this data consist of folklore and folk songs, and we chose the former to construct the ASR model. The end-to-end method of speech recognition has been proposed recently and has achieved performance comparable to that of the conventional DNN-HMM hybrid modeling BIBREF0, BIBREF1, BIBREF2. End-to-end systems do not have a complex hierarchical structure and do not require expertise in target languages such as their phonology and morphology. In this study we adopt the attention mechanism BIBREF3, BIBREF4 and combine it with Connectionist Temporal Classification (CTC) BIBREF5, BIBREF6. In this work, we investigate the modeling unit and utilization of corpora of other languages. ### Overview of the Ainu Language
This section briefly overviews the background of the data collection, the Ainu language, and its writing system. After that, we describe how Ainu recordings are classified and review previous works dealing with the Ainu language. ### Overview of the Ainu Language ::: Background
The Ainu people had total population of about 20,000 in the mid-19th century BIBREF7 and they used to live widely distributed in the area that includes Hokkaido, Sakhalin, and the Kuril Islands. The number of native speakers, however, rapidly decreased through the assimilation policy after late 19th century. At present, there are only less than 10 native speakers, and UNESCO listed their language as critically endangered in 2009 BIBREF8. In response to this situation, Ainu folklore and songs have been actively recorded since the late 20th century in efforts initiated by the Government of Japan. For example, the Ainu Museum started audio recording of Ainu folklore in 1976 with the cooperation of a few Ainu elders which resulted in the collection of speech data with the total duration of roughly 700 hours. This kind of data should be a key to the understanding of Ainu culture, but most of it is not transcribed and fully studied yet. ### Overview of the Ainu Language ::: The Ainu Language and its Writing System
The Ainu language is an agglutinative language and has some similarities to Japanese. However, its genealogical relationship with other languages has not been clearly understood yet. Among its features such as closed syllables and personal verbal affixes, one important feature is that there are many compound words. For example, a word atuykorkamuy (means “a sea turtle”) can be disassembled into atuy (“the sea”), kor (“to have”), and kamuy (“god”). Although the Ainu people did not traditionally have a writing system, the Ainu language is currently written following the examples in a reference book “Akor itak” BIBREF9. With this writing system, it is transcribed with sixteen Roman letters {a, c, e, h, i, k, m, n, o, p, r, s, t, u, w, y}. Since each of these letters correspond to a unique pronunciation, we call them “phones” for convenience. In addition, the symbol {=} is used for connecting a verb and a personal affix and { ' } is used to represent the pharyngeal stop. For the purpose of transcribing recordings, consonant symbols {b, d, g, z} are additionally used to transcribe Japanese sounds the speakers utter. The symbols { _ , __ } are used to transcribe drops and liaisons of phones. An example is shown below. ### Overview of the Ainu Language ::: Types of Ainu Recordings
The Ainu oral traditions are classified into three types: “yukar” (heroic epics), “kamuy yukar” (mythic epics), and “uwepeker” (prose tales). Yukar and kamuy yukar are recited in the rhythm while uwepeker is not. In this study we focus on the the prose tales as the first step. ### Overview of the Ainu Language ::: Previous Work
There have so far been a few studies dealing with the Ainu language. ainulrec built a dependency tree bank in the scheme of Universal Dependencies. postag developed tools for part-of-speech (POS) tagging and word segmentation. Ainu speech recognition was tried by ainutrans with 2.5 hours of Ainu folklore data even though the Ainu language was not their main target. Their phone error rare was about 40% which is not an accuracy level for practical use yet. It appears that there has not been a substantial Ainu speech recognition study yet that utilizes corpora of a reasonable size. Therefore, our first step was to build a speech corpus for ASR based on the data sets provided by the Ainu Museum and the Nibutani Ainu Culture Museum. ### Ainu Speech Corpus
In this section we explain the content of the data sets and how we modified it for our ASR corpus. ### Ainu Speech Corpus ::: Numbers of Speakers and Episodes
The corpus we have prepared for ASR in this study is composed of text and speech. Table 1 shows the number of episodes and the total speech duration for each speaker. Among the total of eight speakers, the data of the speakers KM and UT is from the Ainu Museum, and the rest is from Nibutani Ainu Culture Museum. All speakers are female. The length of the recording for a speaker varies depending on the circumstances at the recording times. A sample text and its English translation are shown in Table 2. ### Ainu Speech Corpus ::: Data Annotation
For efficient training of ASR model, we have made some modifications to the provided data. First, from the transcripts explained in Section 2.1, the symbols {_ , __ , '} have been removed as seen in the example below. Though the equal symbol (`=') does not represent a sound, we keep it because it is used in almost all of the Ainu documents and provides grammatical information. To train an ASR system, the speech data needs to be segmented into a set of manageable chunks. For the ease of automatic processing, we chose to segment speech into inter-pausal units (IPUs) BIBREF10which is a stretch of speech bounded by pauses. The number of IPUs for each speaker is shown in Table 1. ### End-to-end Speech Recognition
In this section, the two approaches to end-to-end speech recognition that we adopt in this work are summarized. Then, we introduce four modeling units we explained, i.e., phone, syllable, word piece, and word. We also discuss multilingual training that we adopt for tackling the low resource problem. ### End-to-end Speech Recognition ::: End-to-end Modeling
End-to-end models have an architecture much simpler than that of conventional DNN-HMM hybrid models. Since they predict character or word symbols directly from acoustic features, pronunciation dictionaries and language modeling are not required explicitly. In this paper, we utilize two kinds of end-to-end models, namely, Connectionist Temporal Classification (CTC) and the attention-based encoder-decoder model. CTC augments the output symbol set with the “blank” symbol `$\phi $'. It outputs symbols by contracting frame-wise outputs from recurrent neural networks (RNNs). This is done by first collapsed repeating symbols and then removing all blank symbols as in the following example: The probability of an output sequence $\mathbf {L}$ for an input acoustic feature sequence $\mathbf {X}$, where $|\mathbf {L}| < |\mathbf {X}|$, is defined as follows. $\mathcal {B}$ is a function to contract the outputs of RNNs, so $\mathcal {B}^{-1}(\mathbf {L})$ means the set of symbol sequences which is reduced to $\mathbf {L}$. The model is trained to maximize (1). The attention-based encoder-decoder model is another method for mapping between two sequences with different lengths. It has two RNNs called the “encoder” and the “decoder”. In naive encoder-decoder model, the encoder converts the input sequence into a single context vector which is the last hidden state of the encoder RNN from which the decoder infers output symbols. In an attention-based model, the context vector $\mathbf {c}_l$ at $l$-th decoding step is the sum of the product of all encoder outputs $h_1, ... , h_\mathrm {T}$ and the $l$-th attention weight $\alpha _{1,l}, ... , \alpha _{\mathrm {T},l}$ as shown in (2). Here, $\mathrm {T}$ is the length of the encoder output. The attention weights $\alpha _{1,l}, ... , \alpha _{\mathrm {T},l}$ indicates the relative importances of the encoder output frames for the $l$-th decoding step and the model parameters to generate these weights are determined in an end-to-end training. In our model, the attention-based model and the CTC share the encoder and are optimized simultaneously as shown in Figure 1.BIBREF11 Long Short-Term Memory (LSTM) BIBREF12 is used for RNNs in the encoder and the decoder. ### End-to-end Speech Recognition ::: Modeling Units
In the conventional DNN-HMM hybrid modeling, the acoustic model outputs probabilities triphone states from each acoustic feature which is converted into the most likely word sequence. An end-to-end model, on the other hand, has some degree of freedom in the modeling unit other than phones, and there are some studies that use characters or words as a unit BIBREF13, BIBREF14. A word unit based end-to-end model can take long context into consideration at the inference time, but it has the data sparsity problem due to its large vocabulary size. Though phone unit based model does not have such a problem, it cannot grasp so long context. It depends on the size of available corpora to decide which to adopt. In addition to these both models, a word piece unit, which is defined by automatically dividing a word into frequent parts, has been proposed BIBREF15, BIBREF16, and its vocabulary size can be determined almost freely. In this paper, we investigate the modeling unit for the end-to-end Ainu speech recognition since the optimal unit for this size of corpus is not obvious. BIBREF17 It is presupposed that all units can be converted into word units automatically. The candidates are phone, syllable, word piece (WP), and word. Examples of them are shown in Table 3 and the details of each unit are described below. ### End-to-end Speech Recognition ::: Modeling Units ::: Phone
As mentioned in Section 2.1, we regard the Roman letters as phones. `=' and the special symbol `$\langle $wb$\rangle $', which means a word boundary, are added to make it possible to convert the output into a sequence of words like the `original' in Table 3. ### End-to-end Speech Recognition ::: Modeling Units ::: Syllable
A syllable of the Ainu language takes the form of either V, CV, VC, or CVC, where `C' and `V' mean consonant and vowel, respectively. The phones {a, e, i, o, u} are vowels and the rest of the Roman letters in Section 2.2 are consonants. In this work, every word is divided into syllables by the following procedure. A word with a single letter is unchanged. Two consecutive Cs and Vs are given a syllable boundary between them. R$^*${CC, VV}R$^*$$\rightarrow $ R$^*${C-C, V-V}R$^*$ (R $$ {C, V}) Put a syllable boundary after the segment-initial V if it is following by at least two phones. VCR$^+$$\rightarrow $ V-CR$^+$ Put a syllable boundary after CV repeatedly from left to right until only CV or CVC is left. (CV)$^*${CV, CVC} $\rightarrow $ (CV-)$^*${CV, CVC} In addition, `=' and `$\langle $wb$\rangle $' are added as explained in Section 4.2.1. through the model training process. This procedure does not always generate a morphologically relevant syllable segmentation. For example, a word isermakus (meaning “(for a god) to protect from behind”) is divided as i-ser-ma-kus, but the right syllabification is i-ser-mak-us. ### End-to-end Speech Recognition ::: Modeling Units ::: Word Piece
The byte pair encoding (BPE) BIBREF18 and the unigram language modeling BIBREF19 are alternative methods for dividing a word into word pieces. The former repeatedly replaces the most common character pair with a new single symbol until the vocabulary becomes the intended size. The latter decides the segmentation to maximize the likelihood of occurrence of the sequence. We adopt the latter and use the open-source software SentencePiece BIBREF20. With this tool, `$\langle $wb$\rangle $' and other units are often merged to constitute a single piece as seen in Table 3. ### End-to-end Speech Recognition ::: Modeling Units ::: Word
The original text can be segmented into words separated by spaces. To make the vocabulary smaller for the ease of training, `=' is treated as a word and infrequent words are replaced with a special label `$\langle $unk$\rangle $'. As seen in Table 3, `a=saha' is dealt with as three words (`a', `=', `saha') and the word `kokopan' is replaced with `$\langle $unk$\rangle $'. ### End-to-end Speech Recognition ::: Multilingual Training
When an enough amount of data is not available for the target languages, the ASR model training can be enhanced by taking advantage of data from other languages BIBREF21, BIBREF22. There are some similarities between Ainu and Japanese language BIBREF23. For instance, both have almost the same set of vowels and do not have consonant clusters (like `str' of `strike' in English). Hence, the multilingual training with a Japanese corpus is expected to be effective. In addition, an English corpus is used for the purpose of comparison. The corpora used are the JNAS corpus BIBREF24 (in Japanese) and the WSJ corpus BIBREF25 (in English). JNAS comprises roughly 80 hours from 320 speakers, and WSJ has about 70 hours of speech from 280 speakers. In the multilingual training, the encoder and the attention module are shared among the Ainu ASR model and the models for other languages, and they are trained using data for all languages. Figure 2 shows the architecture for the multilingual learning with two corpora. When the input acoustic features are from the Ainu ASR corpus, they go through the shared encoder and attention module and are delivered into the decoder on the left side in Figure 2 as a context vector. In this case, the right-side decoder is not trained. ### Experimental Evaluation
In this section the setting and results of ASR experiments are described and the results are discussed. ### Experimental Evaluation ::: Data Setup
The ASR experiments were performed in speaker-open condition as well as speaker-closed condition. In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets. Thereafter, the total sizes of the development and test sets turns out to be 1585 IPUs spanning 2 hours 23 minutes and 1841 IPUs spanning 2 hours and 48 minutes respectively. The ASR model is trained with the rest data. In the speaker-open condition, all the data except for the test speaker's were used for training As it would be difficult to train the model if all of the data of speaker KM or UT were removed, experiments using their speaker-open conditions were not conducted. ### Experimental Evaluation ::: Experimental Setting
The input acoustic features were 120-dimensional vectors made by frame stacking BIBREF26 three 40-dimensional log-mel filter banks features at contiguous time frames. The window length and the frame shift were set to be 25ms and 10ms. The encoder was composed of five BiLSTM layers and the attention-based decoder had a single layer of LSTM. Each LSTM had 320 cells and their weights were randomly initialized using a uniform distribution DBLP:journals/corr/HeZR015 with biases of zero. The fully connected layers were initialized following $\mathcal {U}{(-0.1, 0.1)}$. The weight decay BIBREF27 whose rate was $10^{-5}$ and the dropout BIBREF28 following $\mathcal {B}e(0.2)$ were used to alleviate overfitting. The parameters were optimized with Adam BIBREF29. The learning rate was $10^{-3}$ at first and was multiplied by $10^{-1}$ at the beginning of 31st and 36th epoch BIBREF30. The mini-batch size was 30 and the utterances (IPUs) were sorted in an ascending order of length. To stabilize the training, we removed utterances longer than 12 seconds. The loss function of the model was a linear sum of the loss from CTC and the attention-based decoder, where $\lambda $ was set to be 0.5. Through all experiments, the phone labels are used to train the auxiliary CTC task because it is reported that the hierarchical architecture, using few and general labels in the auxiliary task, improves the performance BIBREF31. Strictly speaking, the number of each modeling units depends on the training set, but there are roughly 25-phone, 500-syllable, and 5,000-word units including special symbols that represent the start and end of a sentence. The words occurring less than twice were replaced with `$\langle $unk$\rangle $'. The vocabulary size for word piece modeling was set to be 500. These settings were based on the results of preliminary experiments with the development set. For the multilingual training, we made three training scripts by concatenating the script of Ainu and other languages (JNAS, WSJ, JNAS and WSJ). The model was trained by these scripts until 30th epoch. From 31$^{\rm {st}}$ and 40th epoch, the model was fine-turned by the Ainu script. Phone units are used for JNAS and WSJ throughout the experiments. ### Experimental Evaluation ::: Results
Table 4 shows the phone error rates (PERs) and word error rates (WERs) for the speaker-closed and speaker-open settings. The `average' is weighted by the numbers of tokens in the ground truth transcriptions for speaker-wise evaluation sets. The word recognition accuracy reached about 80% in the speaker-closed setting. In the speaker-open setting it was 60% on average and varied greatly from speaker to speaker (from 50% to 70%). The best phone accuracies in the speaker-closed and speaker-open settings were about 94% and 86%. Regardless of the settings, the syllable-based modeling yielded the best WER and PER. This suggests that syllables provide reasonable coverage and constraints for the Ainu language in a corpus of this size. The PERs of the word unit model were larger than those of other units. This is because the word model often outputs the `$\langle $unk$\rangle $' symbols while other unit models are able to output symbols similar in sound as below. In this example, the PER of the syllable model is 5% and that of the word model is 30% even though the WERs are the same. (The output of the syllable model is rewritten into words using the `$\langle $wb$\rangle $' symbol.) WERs are generally much larger than PERs and it is further aggravated with the Ainu language. This is because, as mentioned in Section 2.1, the Ainu language has a lot of compound words and the model may be confused about whether the output is multiple words or a single compound word. The actual outputs frequently contain errors as below. The WER of this example is 57% though the PER is zero. The results of multilingual training in which the modeling unit is syllables are presented in Table 5. All error rates are the weighted averages of all evaluated speakers. Here, `+ both' represents the result of training with both JNAS and WSJ corpora. The multilingual training is effective in the speaker-open setting, providing a relative WER improvement of 10%. The JNAS corpus was more helpful than the WSJ corpus because of the similarities between Ainu and Japanese language. ### Summary
In this study, we first developed a speech corpus for Ainu ASR and then, using the end-to-end model with CTC and the attention mechanism, compared four modeling units: phones, syllables, word pieces, and words. The best performance was obtained with the syllable unit, with which WERs in the speaker-closed and speaker-open settings were respectively about 20% and 40% while PERs were about 6% and 14%. Multilingual training using the JNAS improved the performance in the speaker-open setting. Future tasks include reducing the between-speaker performance differences by using speaker adaptation techniques. ### Acknowledgement
The data sets used in this study are provided by the Ainu Museum and Nibutani Ainu Culture Museum. The authors would like to thank Prof. Osami Okuda of Sapporo Gakuin University for his useful advices on the Ainu language. Table 1: Speaker-wise details of the corpus Table 2: Text excerpted from the prose tale ‘The Boy Who Became Porosir God’ spoken by KM. Figure 1: The attention model with CTC auxiliary task. Table 3: Examples of four modeling units. Figure 2: The architecture of the multilingual learning with two corpora. ‘FC’ and ‘CE’ means ‘fully connected’ and ‘cross-entropy’ respectively. Table 4: ASR performance for each speaker and modeling unit. The lowest error rates for each unit are highlighted. Table 5: Results of multilingual training.
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In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets., In the speaker-open condition, all the data except for the test speaker's were used for training
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What word best describes Commander Curtis?
A. Collaborative
B. Egotistical
C. Authoritative
D. Fierce
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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.
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C. Authoritative
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Why do they need an incubator?
A. The baby is one month early.
B. The baby is three months early.
C. The baby is two months early.
D. The baby is four months early.
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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.
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C. The baby is two months early.
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What metric was used in the evaluation step?
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### Introduction
The goal of text summarization is to condense a piece of text into a shorter version that contains the salient information. Due to the prevalence of news articles and the need to provide succinct summaries for readers, a majority of existing datasets for summarization come from the news domain BIBREF0, BIBREF1, BIBREF2. However, according to journalistic conventions, the most important information in a news report usually appears near the beginning of the article BIBREF3. While it facilitates faster and easier understanding of the news for readers, this lead bias causes undesirable consequences for summarization models. The output of these models is inevitably affected by the positional information of sentences. Furthermore, the simple baseline of using the top few sentences as summary can achieve a stronger performance than many sophisticated models BIBREF4. It can take a lot of effort for models to overcome the lead bias BIBREF3. Additionally, most existing summarization models are fully supervised and require time and labor-intensive annotations to feed their insatiable appetite for labeled data. For example, the New York Times Annotated Corpus BIBREF1 contains 1.8 million news articles, with 650,000 summaries written by library scientists. Therefore, some recent work BIBREF5 explores the effect of domain transfer to utilize datasets other than the target one. But this method may be affected by the domain drift problem and still suffers from the lack of labelled data. The recent promising trend of pretraining models BIBREF6, BIBREF7 proves that a large quantity of data can be used to boost NLP models' performance. Therefore, we put forward a novel method to leverage the lead bias of news articles in our favor to conduct large-scale pretraining of summarization models. The idea is to leverage the top few sentences of a news article as the target summary and use the rest as the content. The goal of our pretrained model is to generate an abstractive summary given the content. Coupled with careful data filtering and cleaning, the lead bias can provide a delegate summary of sufficiently good quality, and it immediately renders the large quantity of unlabeled news articles corpus available for training news summarization models. We employ this pretraining idea on a three-year collection of online news articles. We conduct thorough data cleaning and filtering. For example, to maintain a quality assurance bar for using leading sentences as the summary, we compute the ratio of overlapping non-stopping words between the top 3 sentences and the rest of the article. As a higher ratio implies a closer semantic connection, we only keep articles for which this ratio is higher than a threshold. We end up with 21.4M articles based on which we pretrain a transformer-based encoder-decoder summarization model. We conduct thorough evaluation of our models on five benchmark news summarization datasets. Our pretrained model achieves a remarkable performance on various target datasets without any finetuning. This shows the effectiveness of leveraging the lead bias to pretrain on large-scale news data. We further finetune the model on target datasets and achieve better results than a number of strong baseline models. For example, the pretrained model without finetuning obtains state-of-the-art results on DUC-2003 and DUC-2004. The finetuned model obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset BIBREF2. Human evaluation results also show that our models outperform existing baselines like pointer-generator network. The rest of paper is organized as follows. We introduce related work in news summarization and pretraining in Sec:rw. We describe the details of pretraining using lead bias in Sec:pre. We introduce the transformer-based summarization model in Sec:model. We show the experimental results in Sec:exp and conclude the paper in Sec:conclusion. ### Related work ::: Document Summarization
End-to-end abstractive text summarization has been intensively studied in recent literature. To generate summary tokens, most architectures take the encoder-decoder approach BIBREF8. BIBREF9 first introduces an attention-based seq2seq model to the abstractive sentence summarization task. However, its output summary degenerates as document length increases, and out-of-vocabulary (OOV) words cannot be efficiently handled. To tackle these challenges, BIBREF4 proposes a pointer-generator network that can both produce words from the vocabulary via a generator and copy words from the source article via a pointer. BIBREF10 utilizes reinforcement learning to improve the result. BIBREF11 uses a content selector to over-determine phrases in source documents that helps constrain the model to likely phrases. BIBREF12 adds Gaussian focal bias and a salience-selection network to the transformer encoder-decoder structure BIBREF13 for abstractive summarization. BIBREF14 randomly reshuffles the sentences in news articles to reduce the effect of lead bias in extractive summarization. ### Related work ::: Pretraining
In recent years, pretraining language models have proved to be quite helpful in NLP tasks. The state-of-the-art pretrained models include ELMo BIBREF15, GPT BIBREF7, BERT BIBREF6 and UniLM BIBREF16. Built upon large-scale corpora, these pretrained models learn effective representations for various semantic structures and linguistic relationships. As a result, pretrained models have been widely used with considerable success in applications such as question answering BIBREF17, sentiment analysis BIBREF15 and passage reranking BIBREF18. Furthermore, UniLM BIBREF16 leverages its sequence-to-sequence capability for abstractive summarization; the BERT model has been employed as an encoder in BERTSUM BIBREF19 for extractive/abstractive summarization. Compared to our work, UniLM BIBREF16 is a general language model framework and does not take advantage of the special semantic structure of news articles. Similarly, BERTSUM BIBREF19 directly copies the pretrained BERT structure into its encoder and finetunes on labelled data instead of pretraining with the large quantity of unlabeled news corpus available. Recently, PEGASUS BIBREF20 leverages a similar idea of summarization pretraining, but they require finetuning with data from target domains, whereas our model has a remarkable performance without any finetuning. ### Pretraining with Leading Sentences
News articles usually follow the convention of placing the most important information early in the content, forming an inverted pyramid structure. This lead bias has been discovered in a number of studies BIBREF3, BIBREF14. One of the consequences is that the lead baseline, which simply takes the top few sentences as the summary, can achieve a rather strong performance in news summarization. For instance, in the CNN/Daily Mail dataset BIBREF0, using the top three sentences as summaries can get a higher ROUGE score than many deep learning based models. This positional bias brings lots of difficulty for models to extract salient information from the article and generate high-quality summaries. For instance, BIBREF14 discovers that most models' performances drop significantly when a random sentence is inserted in the leading position, or when the sentences in a news article are shuffled. On the other hand, news summarization, just like many other supervised learning tasks, suffers from the scarcity of labelled training data. Abstractive summarization is especially data-hungry since the efficacy of models depends on high-quality handcrafted summaries. We propose that the lead bias in news articles can be leveraged in our favor to train an abstractive summarization model without human labels. Given a news article, we treat the top three sentences, denoted by Lead-3, as the target summary, and use the rest of the article as news content. The goal of the summarization model is to produce Lead-3 using the following content, as illustrated in fig:top3. The benefit of this approach is that the model can leverage the large number of unlabeled news articles for pretraining. In the experiment, we find that the pretrained model alone can have a strong performance on various news summarization datasets, without any further training. We also finetune the pretrained model on downstream datasets with labelled summaries. The model can quickly adapt to the target domain and further increase its performance. It is worth noting that this idea of utilizing structural bias for large-scale summarization pretraining is not limited to specific types of models, and it can be applied to other types of text as well: academic papers with abstracts, novels with editor's notes, books with tables of contents. However, one should carefully examine and clean the source data to take advantage of lead bias, as the top three sentences may not always form a good summary. We provide more details in the experiments about the data filtering and cleaning mechanism we apply. ### Model
In this section, we introduce our abstractive summarization model, which has a transformer-based encoder-decoder structure. We first formulate the supervised summarization problem and then present the network architecture. ### Model ::: Problem formulation
We formalize the problem of supervised abstractive summarization as follows. The input consists of $a$ pairs of articles and summaries: $\lbrace (X_1, Y_1), (X_2, Y_2), ..., (X_a, Y_a)\rbrace $. Each article and summary are tokenized: $X_i=(x_1,...,x_{L_i})$ and $Y_i=(y_1,...,y_{N_i})$. In abstractive summarization, the summary tokens need not be from the article. For simplicity, we will drop the data index subscript. The goal of the system is to generate summary $Y=(y_1,...,y_m)$ given the transcript $X=\lbrace x_1, ..., x_n\rbrace $. ### Model ::: Network Structure
We utilize a transformer-based encoder-decoder structure that maximizes the conditional probability of the summary: $P(Y|X, \theta )$, where $\theta $ represents the parameters. ### Model ::: Network Structure ::: Encoder
The encoder maps each token into a fixed-length vector using a trainable dictionary $\mathcal {D}$ randomly initialized using a normal distribution with zero mean and a standard deviation of 0.02. Each transformer block conducts multi-head self-attention. And we use sinusoidal positional embedding in order to process arbitrarily long input. In the end, the output of the encoder is a set of contextualized vectors: ### Model ::: Network Structure ::: Decoder
The decoder is a transformer that generates the summary tokens one at a time, based on the input and previously generated summary tokens. Each token is projected onto a vector using the same dictionary $\mathcal {D}$ as the encoder. The decoder transformer block includes an additional cross-attention layer to fuse in information from the encoder. The output of the decoder transformer is denoted as: To predict the next token $w_{k}$, we reuse the weights of dictionary $\mathcal {D}$ as the final linear layer to decode $u^D_{k-1}$ into a probability distribution over the vocabulary: $P(w_k|w_{<k},u^E_{1:m})=( \mathcal {D}u^D_{k-1})$. Training. During training, we seek to minimize the cross-entropy loss: We use teacher-forcing in decoder training, i.e. the decoder takes ground-truth summary tokens as input. The model has 10 layers of 8-headed transformer blocks in both its encoder and decoder, with 154.4M parameters. Inference. During inference, we employ beam search to select the best candidate. The search starts with the special token $\langle \mbox{BEGIN}\rangle $. We ignore any candidate word which results in duplicate trigrams. We select the summary with the highest average log-likelihood per token. ### Experiments ::: Datasets
We evaluate our model on five benchmark summarization datasets: the New York Times Annotated Corpus (NYT) BIBREF1, XSum BIBREF2, the CNN/DailyMail dataset BIBREF0, DUC-2003 and DUC-2004 BIBREF21. These datasets contain 104K, 227K, 312K, 624 and 500 news articles and human-edited summaries respectively, covering different topics and various summarization styles. For NYT dataset, we use the same train/val/test split and filtering methods following BIBREF22. As DUC-2003/2004 datasets are very small, we follow BIBREF23 to employ them as test set only. ### Experiments ::: Implementation Details
We use SentencePiece BIBREF24 for tokenization, which segments any sentence into subwords. We train the SentencePiece model on pretrained data to generate a vocabulary of size 32K and of dimension 720. The vocabulary stays fixed during pretraining and finetuning. Pretraining. We collect three years of online news articles from June 2016 to June 2019. We filter out articles overlapping with the evaluation data on media domain and time range. We then conduct several data cleaning strategies. First, many news articles begin with reporter names, media agencies, dates or other contents irrelevant to the content, e.g. “New York (CNN) –”, “Jones Smith, May 10th, 2018:”. We therefore apply simple regular expressions to remove these prefixes. Second, to ensure that the summary is concise and the article contains enough salient information, we only keep articles with 10-150 words in the top three sentences and 150-1200 words in the rest, and that contain at least 6 sentences in total. In this way, we filter out i) articles with excessively long content to reduce memory consumption; ii) very short leading sentences with little information which are unlikely to be a good summary. To encourage the model to generate abstrative summaries, we also remove articles where any of the top three sentences is exactly repeated in the rest of the article. Third, we try to remove articles whose top three sentences may not form a relevant summary. For this purpose, we utilize a simple metric: overlapping words. We compute the portion of non-stopping words in the top three sentences that are also in the rest of an article. A higher portion implies that the summary is representative and has a higher chance of being inferred by the model using the rest of the article. To verify, we compute the overlapping ratio of non-stopping words between human-edited summary and the article in CNN/DailyMail dataset, which has a median value of 0.87. Therefore, in pretraining, we keep articles with an overlapping word ratio higher than 0.65. These filters rule out around 95% of the raw data and we end up with 21.4M news articles, 12,000 of which are randomly sampled for validation. We pretrain the model for 10 epochs and evaluate its performance on the validation set at the end of each epoch. The model with the highest ROUGE-L score is selected. During pretraining, we use a dropout rate of 0.3 for all inputs to transformer layers. The batch size is 1,920. We use RAdam BIBREF25 as the optimizer, with a learning rate of $10^{-4}$. Also, due to the different numerical scales of the positional embedding and initialized sentence piece embeddings, we divide the positional embedding by 100 before feeding it into the transformer. The beam width is set to 5 during inference. Finetuning. During finetuning, we keep the optimizer, learning rate and dropout rate unchanged as in pretraining. The batch size is 32 for all datasets. We pick the model with the highest ROUGE-L score on the validation set and report its performance on the test set. Our strategy of Pretraining with unlabeled Lead-3 summaries is called PL. We denote the pretrained model with finetuning on target datasets as PL-FT. The model with only pretraining and no finetuning is denoted as PL-NoFT, which is the same model for all datasets. ### Experiments ::: Baseline
To compare with our model, we select a number of strong summarization models as baseline systems. $\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. The value of $X$ is 3 for NYT and CNN/DailyMail and 1 for XSum to accommodate the nature of summary length. $\textsc {PTGen}$ BIBREF4 is the pointer-generator network. $\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\textsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries. ### Experiments ::: Metrics
We employ the standard ROUGE-1, ROUGE-2 and ROUGE-L metrics BIBREF29 to evaluate all summarization models. These three metrics respectively evaluate the accuracy on unigrams, bigrams and longest common subsequence. ROUGE metrics have been shown to highly correlate with the human judgment BIBREF29. Following BIBREF22, BIBREF23, we use F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC. In NYT, the prediction is truncated to the length of the ground-truth summaries; in DUC, the prediction is truncated to 75 characters. ### Experiments ::: Results
The results are displayed in tab:nyt, tab:xsumresults, tab:cnndaily and tab:duc. As shown, on both NYT and XSum dataset, PL-FT outperforms all baseline models by a large margin. For instance, PL-FT obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset. We conduct statistical test and found that the results are all significant with p-value smaller than 0.05 (marked by *) or 0.01 (marked by **), compared with previous best scores. On CNN/DailyMail dataset, PL-FT outperforms all baseline models except BottomUp BIBREF11. PL-NoFT, the pretrained model without any finetuning, also gets remarkable results. On XSum dataset, PL-NoFT is almost 8% higher than Lead-1 in ROUGE-1 and ROUGE-L. On CNN/DailyMail dataset, PL-NoFT significantly outperforms unsupervised models SEQ$^3$ and GPT-2, and even surpasses the supervised pointer-generator network. PL-NoFT also achieves state-of-the-art results on DUC-2003 and DUC-2004 among unsupervised models (except ROUGE-1 on DUC-2004), outperforming other carefully designed unsupervised summarization models. It's worth noting that PL-NoFT is the same model for all experiments, which proves that our pretrain strategy is effective across different news corpus. ### Experiments ::: Abstractiveness Analysis
We measure the abstractiveness of our model via the ratio of novel n-grams in summaries, i.e. the percentage of n-grams in the summary that are not present in the article. fig:novel shows this ratio in summaries from reference and generated by PL-NoFT and PL-FT in NYT dataset. Both PL-NoFT and PL-FT yield more novel 1-grams in summary than the reference. And PL-NoFT has similar novelty ratio with the reference in other n-gram categories. Also, we observe that the novelty ratio drops after finetuning. We attribute this to the strong lead bias in the NYT dataset which affects models trained on it. ### Experiments ::: Human Evaluation
We conduct human evaluation of the generated summaries from our models and the pointer generator network with coverage. We randomly sample 100 articles from the CNN/DailyMail test set and ask 3 human labelers from Amazon Mechanical Turk to assess the quality of summaries with a score from 1 to 5 (5 means perfect quality. The labelers need to judge whether the summary can express the salient information from the article in a concise form of fluent language. The evaluation guidelines are given in Table TABREF23. To reduce bias, we randomly shuffle summaries from different sources for each article. As shown in Table TABREF23, both of our models PL-NoFT and PL-FT outperform the pointer generator network (PTGen+Cov), and PL-FT's advantage over PTGen+Cov is statistically significant. This shows the effectiveness of both our pretraining and finetuning strategy. To evaluate the inter-annotator agreement, we compute the kappa statistics among the labels and the score is 0.34. ### Conclusions
In this paper, we propose a simple and effective pretraining method for news summarization. By employing the leading sentences from a news article as its target summary, we turn the problematic lead bias for news summarization in our favor. Based on this strategy, we conduct pretraining for abstractive summarization in a large-scale news corpus. We conduct thorough empirical tests on five benchmark news summarization datasets, including both automatic and human evaluations. Results show that the same pretrained model without any finetuning can achieve state-of-the-art results among unsupervised methods over various news summarization datasets. And finetuning on target domains can further improve the model's performance. We argue that this pretraining method can be applied in more scenarios where structural bias exists. Figure 1: Using Lead-3 summary as target in pretraining. Figure 2: Ratio of novel n-grams in summaries from reference, PL-NoFT and PL-FT models in NYT test set. Table 1: ROUGE recall scores on NYT test set. Table 2: ROUGE F1 results on XSum test set. Table 3: ROUGE F1 results on CNN/DailyMail test set. Table 4: Scoring criteria for human evaluation of summaries. Table 5: Average and standard deviations of human evaluation scores for summaries on CNN/DailyMail test set. Scores range from 1 to 5 with 5 being perfect. Each summary is judged by 3 human evaluators. PL-FT’s result is statistically significant compared with pointer-generator network with coverage with a p-value less than 10−7.
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ROUGE-1, ROUGE-2 and ROUGE-L, F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC
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How big is Switchboard-300 database?
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### Introduction
Powerful neural networks have enabled the use of “end-to-end” speech recognition models that directly map a sequence of acoustic features to a sequence of words without conditional independence assumptions. Typical examples are attention based encoder-decoder BIBREF0 and recurrent neural network transducer models BIBREF1. Due to training on full sequences, an utterance corresponds to a single observation from the view point of these models; thus, data sparsity is a general challenge for such approaches, and it is believed that these models are effective only when sufficient training data is available. Indeed, many end-to-end speech recognition papers focus on LibriSpeech, which has 960 hours of training audio. Nevertheless, the best performing systems follow the traditional hybrid approach BIBREF2, outperforming attention based encoder-decoder models BIBREF3, BIBREF4, BIBREF5, BIBREF6, and when less training data is used, the gap between “end-to-end” and hybrid models is more prominent BIBREF3, BIBREF7. Several methods have been proposed to tackle data sparsity and overfitting problems; a detailed list can be found in Sec. SECREF2. Recently, increasingly complex attention mechanisms have been proposed to improve seq2seq model performance, including stacking self and regular attention layers and using multiple attention heads in the encoder and decoder BIBREF4, BIBREF8. We show that consistent application of various regularization techniques brings a simple, single-head LSTM attention based encoder-decoder model to state-of-the-art performance on Switchboard-300, a task where data sparsity is more severe than LibriSpeech. We also note that remarkable performance has been achieved with single-head LSTM models in a recent study on language modeling BIBREF9. ### Methods to improve seq2seq models
In contrast to traditional hybrid models, where even recurrent networks are trained on randomized, aligned chunks of labels and features BIBREF10, BIBREF11, whole sequence models are more prone to memorizing the training samples. In order to improve generalization, many of the methods we investigate introduce additional noise, either directly or indirectly, to stochastic gradient descent (SGD) training to avoid narrow, local optima. The other techniques we study address the highly non-convex nature of training neural networks, ease the optimization process, and speed up convergence. Weight decay adds the $l_2$ norm of the trainable parameters to the loss function, which encourages the weights to stay small unless necessary, and is one of the oldest techniques to improve neural network generalization. As shown in BIBREF12, weight decay can improve generalization by suppressing some of the effects of static noise on the targets. Dropout randomly deactivates neurons with a predefined probability in every training step BIBREF13 to reduce co-adaptation of neurons. DropConnect, which is similar in spirit to dropout, randomly deactivates connections between neurons by temporarily zeroing out weights BIBREF14. Zoneout, which is also inspired by dropout and was especially developed for recurrent models BIBREF15, stochastically forces some hidden units to maintain their previous values. In LSTMs, the method is applied on the cell state or on the recurrent feedback of the output. Label smoothing interpolates the hard label targets with a uniform distribution over targets, and improves generalization in many classification tasks BIBREF16. Batch normalization (BN) accelerates training by standardizing the distribution of each layer's input BIBREF17. In order to reduce the normalization mismatch between training and testing, we modify the original approach by freezing the batch normalization layers in the middle of the training when the magnitude of parameter updates is small. After freezing, the running statistics are not updated, batch statistics are ignored, and BN layers approximately operate as global normalization. Scheduled sampling stochastically uses the token produced by a sequence model instead of the true previous token during training to mitigate the effects of exposure bias BIBREF18. Residual networks address the problem of vanishing and exploding gradients by including skip connections BIBREF19 in the model that force the neural network to learn a residual mapping function using a stack of layers. Optimization of this residual mapping is easier, allowing the use of much deeper structures. Curriculum learning simplifies deep neural network training by presenting training examples in a meaningful order, usually by increasing order of difficulty BIBREF20. In seq2seq models, the input acoustic sequences are frequently sorted in order of increasing length BIBREF21. Speed and tempo perturbation changes the rate of speech, typically by $\pm $10%, with or without altering the pitch and timbre of the speech signal BIBREF22, BIBREF23. The goal of these methods is to increase the amount of training data for the model. Sequence noise injection adds structured sequence level noise generated from speech utterances to training examples to improve the generalization of seq2seq models BIBREF24. As previously shown, input noise during neural network training encourages convergence to a local optimum with lower curvature, which indicates better generalization BIBREF25. Weight noise adds noise directly to the network parameters to improve generalization BIBREF26. This form of noise can be interpreted as a simplified form of Bayesian inference that optimizes a minimum description length loss BIBREF27. SpecAugment masks blocks of frequency channels and blocks of time steps BIBREF3 and also warps the spectrogram along the time axis to perform data augmentation. It is closely related to BIBREF28. ### Experimental setup
This study focuses on Switchboard-300, a standard 300-hour English conversational speech recognition task. Our acoustic and text data preparation follows the Kaldi BIBREF29 s5c recipe. Our attention based seq2seq model is similar to BIBREF30, BIBREF31 and follows the structure of BIBREF32. We extract 80-dimensional log-Mel filterbank features over 25ms frames every 10ms from the input speech signal. The input audio is speed and/or tempo perturbed with 56 probability. Following BIBREF24, sequence noise mixed from up to 4 utterances is injected with 40% probability and 0.3 weight. The filterbank output is mean-and-variance normalized at the speaker level, and first ($\Delta $) and second ($\Delta \Delta $) derivatives are also calculated. The final features presented to the network are also processed through a SpecAugment block that uses the SM policy BIBREF3 with $p=0.3$ and no time warping. The encoder network comprises 8 bidirectional LSTM layers with 1536 nodes per direction per layer BIBREF33, BIBREF34. As shown in Fig. FIGREF1, each LSTM block in the encoder includes a residual connection with a linear transformation that bypasses the LSTM, a 1024-dimensional linear reduction layer on the LSTM output, and batch-normalization (BN) of the block output. A pyramidal structure BIBREF31 in the first two LSTM layers reduces the frame rate by a factor of 4. The final dimension of the encoder output is 256, enforced by a linear bottleneck. We apply 30% dropout to the LSTM outputs and 30% drop-connect to the hidden-to-hidden matrices BIBREF14, BIBREF35. As suggested by BIBREF36, the weight dropout is fixed for a batch of sequences. The attention based decoder model is illustrated in Fig. FIGREF1. The decoder models the sequence of 600 BPE units estimated on characters BIBREF37, where the BPE units are embedded in 256 dimensions. We use additive, location aware attention, without key/value transformations, and the attention is smoothed by 256, 5-dimensional kernels BIBREF38. The decoder block consists of 2 unidirectional LSTM layers: one is a dedicated language-model-like component with 512 nodes that operates only on the embedded predicted symbol sequence, and the other is a 768 unit layer processing acoustic and symbol information. The output of both LSTMs is reduced to 256 dimensions by a linear bottleneck BIBREF39. Fixed sequence-level weight dropout of 15% is applied in the decoder LSTMs, a dropout of 5% is applied to the embeddings, and a dropout of 15% is applied to the decoder LSTM outputs. The second LSTM in the decoder also uses zoneout, where the cell state update is deactivated with 15% probability and the recurrent feedback from the output maintains its previous value with 5% probability. Overall, the model has 280M parameters, of which only 5.4M are in the decoder. Aiming at the best word error rate, this design choice is based on our observation that an external language model has significantly larger effect if the decoder is not over-parametrized BIBREF32. The model is trained for 250 epochs on 32 P100 GPUs in less than 4 days using a PyTorch BIBREF40 implementation of distributed synchronous SGD with up to 32 sequences per GPU per batch. Training uses a learning rate of 0.03 and Nesterov momentum BIBREF41 of 0.9. The weight decay parameter is 4e-6, the label smoothing parameter is 0.35, and teacher forcing is fixed to 0.8 throughout training. In the first 3 epochs the learning rate is warmed up and batch size is gradually increased from 8 to 32 BIBREF42. In the first 35 epochs, the neural network is trained on sequences sorted in ascending order of length of the input. Afterwards, batches are randomized within length buckets, ensuring that a batch always contains sequences with similar length. Weight noise from a normal distribution with mean 0.0 and variance 0.015 is switched on after 70 epochs. After 110 epochs, the updates of sufficient statistics in the batch-normalization layers are turned off, converting them into fixed affine transformations. The learning rate is annealed by 0.9 per epoch after 180 epochs of training, and simultaneously label smoothing is also switched off. The external language model (LM) is built on the BPE segmentation of 24M words from the Switchboard and Fisher corpora. It is trained for 40 epochs using label smoothing of 0.15 in the first 20 epochs. The baseline LM has 57M parameters and consists of 2 unidirectional LSTM layers with 2048 nodes BIBREF43 trained with drop-connect and dropout probabilities of 15%. The embedding layer has 512 nodes, and the output of the last LSTM is projected to 128 dimensions. When the LM is trained and evaluated across utterances, consecutive segments of a single-channel recording are grouped together up to 40 seconds. Perplexities (PPL) are measured at the word level on the concatenation of ground truth transcripts, while the WER is obtained by retaining the LM state of the single-best hypothesis of the preceding utterance. Decoding uses simple beam search with a beam width of 60 hypotheses and no lexical prefix tree constraint BIBREF44. The search performs shallow fusion of the encoder-decoder score, the external language model score, a length normalization term, and a coverage term BIBREF45, BIBREF46, BIBREF47. For more details, please refer to BIBREF32. Hub5'00 is used as a development set to optimize decoding hyperparameters, while Hub5'01 and RT03 are used as final test sets. ### Experimental results
Our current setup is the result of incremental development. Keeping in mind that several other equally powerful setups probably exist, the focus of the following experiments is to investigate ours around the current optimum. ### Experimental results ::: Effect of data preparation
We first investigate the importance of different data processing steps. The s5c Kaldi recipe includes a duplicate filtering step, in which the maximum number of occurrences of utterances with the same content is limited. We measure the impact of duplicate filtering and also the effect of filtering out word fragments and noise tokens from the training transcripts. Since the LM is trained on identically filtered transcripts from Fisher+Switchboard data, word fragment and noise token filters were applied consistently. The results are summarized in Table TABREF5. Deactivating the duplicate filter is never harmful when an external LM is used, and the gains on CallHome can be substantial. Considering performance on the complete Hub5'00 data, the best systems either explicitly handle both word fragments and noise tokens or filter them all out. When an external LM is used, the best results are obtained when word fragment and noise token filters are activated and the duplicate filter is deactivated. This setting is also appealing in cases where the external LM may be trained on text data that will not contain word fragments or noise; thus, the remaining experiments are carried out with this system setting. ### Experimental results ::: Ablation study
In a second set of experiments, we characterize the importance of each of the regularization methods described in Sec. SECREF2 for our model performance by switching off one training method at a time without re-optimizing the remaining settings. In these experiments, decoding is performed without an external language model. Curriculum learning is evaluated by either switching to randomized batches after 35 epochs or leaving the sorting on throughout training. We also test the importance of $\Delta $ and $\Delta \Delta $ features BIBREF48. Sorting the results by decreasing number of absolute errors on Hub5'00, Table TABREF7 indicates that each regularization method contributes to the improved WER. SpecAugment is by far the most important method, while using $\Delta $ and $\Delta \Delta $ features or switching off the curriculum learning in the later stage of training have marginal but positive effects. Other direct input level perturbation steps (speed/tempo perturbation and sequence noise injection) are also key techniques that can be found in the upper half of the table. If we compare the worst and baseline models, we find that the relative performance difference between them is nearly unchanged by including the external LM in decoding. Without the LM, the gap is 18% relative, while with the LM the gap is 17% relative. This clearly underlines the importance of the regularization techniques. ### Experimental results ::: Optimizing the language model
The following experiments summarize our optimization of the LM. Compared to our previous LM BIBREF24, we measure better perplexity and WER if no bottleneck is used before the softmax layer (rows 1 and 3 in Table TABREF9). Increasing the model capacity to 122M parameters results in a significant gain in PPL only after the dropout rates are tuned (rows 3, 5 and 6). Similar to BIBREF49, BIBREF50, significant PPL gain is observed if the LM was trained across utterances. However, this PPL improvement does not translate into reduced WER with a bigger model when cross utterance modeling is used (rows 4 and 7). Thus, in all other experiments we use the smaller, 57M-parameter model. ### Experimental results ::: Effect of beam size and number of parameters
A 280M-parameter model may be larger than is practical in many applications. Thus, we also conduct experiments to see if this model size is necessary for reasonable ASR performance. Models are trained without changing the training configuration, except that the size or number of LSTM layers is reduced. As Table TABREF11 shows, although our smallest attention based model achieves reasonable results on this task, a significant loss is indeed observed with decreasing model size, especially on CallHome. Nevertheless, an external language model reduces the performance gap. A small, 57M-parameter model together with a similar size language model is only 5% relative worse than our largest model. We note that this model already outperforms the best published attention based seq2seq model BIBREF3, with roughly 66% fewer parameters. Additional experiments are carried out to characterize the search and modeling errors in decoding. The results of tuning the beam size and keeping the other search hyperparameters unchanged are shown in Fig. FIGREF12. “Small” denotes the 57M model, while “large” denotes the 280M model. When greedy search (beam 1) is used, the external language model increases WER, an effect that might be mitigated with re-optimized hyperparameters. Nevertheless, if a beam of at least 2 hypotheses is used, the positive effect of the language model is clear. We also observe that without the language model the search saturates much earlier, around beam 8, fluctuating within only a few absolute errors afterwards. On the contrary, decoding with the language model, we measure consistent but small gains with larger beams. The minimum number of word errors was measured with a relatively large beam of 240. The figure also shows that the effect of a cross-utterance language model grows with larger beams. Lastly, if the model is trained on 2000 hours of speech data (see next section), the extremely fast greedy decoding gives remarkably good performance. Although the importance of beam search decreases with an increased amount of training data, we still measure 10% relative degradation compared to a system with a cross-utterance LM and wide (240) beam search. ### Experimental results ::: Experiments on Switchboard-2000
As a contrast to our best results on Switchboard-300, we also train a seq2seq model on the 2000-hour Switchboard+Fisher data. This model consists of 10 encoder layers, and is trained for only 50 epochs. Our overall results on the Hub5'00 and other evaluation sets are summarized in Table TABREF14. The results in Fig. FIGREF12 and Table TABREF14 show that adding more training data greatly improves the system, by around 30% relative in some cases. For comparison with others, the 2000-hour system reaches 8.7% and 7.4% WER on rt02 and rt04. We observe that the regularization techniques, which are extremely important on the 300h setup, are still beneficial but have a significantly smaller effect. ### Comparison with the literature
For comparison with results in the literature we refer to the Switchboard-300 results in BIBREF3, BIBREF7, BIBREF51, BIBREF52 and the Switchboard-2000 results in BIBREF50, BIBREF51, BIBREF53, BIBREF54, BIBREF55, BIBREF56. Our 300-hour model not only outperforms the previous best attention based encoder-decoder model BIBREF3 by a large margin, it also surpasses the best hybrid systems with multiple LMs BIBREF7. Our result on Switchboard-2000 is also better than any single system results reported to date, and reaches the performance of the best system combinations. ### Conclusions
We presented an attention based encoder-decoder setup which achieves state-of-the-art performance on Switchboard-300. A rather simple model built from LSTM layers and a decoder with a single-headed attention mechanism outperforms the standard hybrid approach. This is particularly remarkable given that in our model neither a pronunciation lexicon nor a speech model with explicit hidden state representations is needed. We also demonstrated that excellent results are possible with smaller models and with practically search-free, greedy decoding. The best results were achieved with a speaker independent model in a single decoding pass, using a minimalistic search algorithm, and without any attention mechanism in the language model. Thus, we believe that further improvements are still possible if we apply a more complicated sequence-level training criterion and speaker adaptation. Figure 1: (a) Building block of the encoder; (b) attention based decoder network used in the experiments. Table 1: Effect of data preparation steps on WER [%] measured on Hub5’00. The second row corresponds to the Kaldi s5c recipe. Table 2: Ablation study on the final training recipe. Table 3: Optimizing dropout (dropo.), DropConnect (dropc.), layer and bottleneck (bn) size for LSTM LM, optionally modeling across utterances (x-utt.) Table 5: Detailed results with the best performing systems. Table 4: Reducing the model size
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300-hour English conversational speech
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How would one best describe the lifestyle discussed in the article?
A. Excessive
B. Confidential
C. Exhausting
D. Competitive
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Let Si Get This During a typical lunch time at the Royalton Hotel restaurant in midtown Manhattan, The New Yorker 's Tina Brown might be installed at her usual table, and Vogue 's Anna Wintour might be at her usual table (chewing on her usual meal--a $25 hamburger). Vanity Fair 's Graydon Carter might be there too, although he has transferred his main allegiance to a place called Patroon. Filling out the room are other editors, publicists, and writers from these magazines and GQ and House & Garden and so on. And one man, who probably isn't there himself, picks up every tab. Some of the lesser fry may even utter the Condé Nast mantra--though it is hardly necessary at the Royalton--as they grab for the check: "Let Si get this." S.I. "Si" Newhouse Jr. and his younger brother, Donald, control Advance Publications, one of America's largest privately held companies. (Estimate of their combined wealth: $13 billion.) Donald tends to Advance's hugely profitable newspaper, radio, and TV holdings. Si runs the less profitable but more glamorous properties. These are the 15 Condé Nast magazines, including (in descending order of fabulousness) Vogue , Vanity Fair , GQ , Condé Nast Traveler , House & Garden , Allure , Details , Self , Mademoiselle , and Glamour ; ; and Random House. The expense-account lunch is a hallowed journalistic tradition. But consider a day in the life of an editor working for Si Newhouse. (Donald's editors are a different story, as they will be happy to tell you.) It's a closed economy where almost all human needs and desires can be gratified with a miraculous, unlimited currency called the Si. A Lincoln Town Car is waiting outside your door in the morning to take you to work. The car, which costs $50 an hour, is written into your contract. First stop, breakfast with a writer at the Four Seasons. The check may be as little as $40. When you reach the office, you realize you're out of cigarettes. No problem--you send your assistant to buy a pack for you. She gets reimbursed from petty cash ($3). (Could be worse for the assistant: She could be forced to pick up her boss's birth-control pills, or her boss's pet from the vet, or presents for her boss's children--regular duties for Condé Nast underlings.) You've forgotten to return the video your kids watched yesterday, so you have a messenger take it back to Blockbuster. Si spends $20; you save a $1.50 late fee. Then there's lunch. The magazines account for more than a quarter of daytime revenues at the Four Seasons and the Royalton. A modest lunch for two at the Royalton (no fancy wine or anything) might cost $80. But Si's generosity extends to even assistants and sub-sub-editors, dining on sushi at their desks. If you spend $10 or less on lunch, and claim you were working, Si pays. At Vogue and Vanity Fair , almost everyone has a "working lunch" every day . An editor at Allure says that "working lunches" there are limited to 10 a month. Back at the office, you hear that a friend at another Newhouse magazine has been promoted, so you send flowers. The tab: $100. Si pays. (One of my favorite Condé Nast stories is of an editor who had just been promoted to an extremely senior job. His office was jammed with congratulatory flowers and cards. All had been sent by fellow Condé Nast staffers. All had been billed to the company.) Four o'clock, and it's snack time. Your assistant joins the mob in the lobby newsstand. She bills your candy bar, juice, and cigarettes (as well as her own candy bar, juice, and cigarettes) to the magazine ($15). After all, it's a "working snack." Later, there's a birthday party for your assistant. You order champagne and a cake--on the company, of course, and present her with your gift--a Prada wallet ($200). Later, she submits the expense sheet for it. Finally, after a Random House book party at Le Cirque 2000 (estimated cost to Si: $35,000), your car ferries you home. Newhouse expense stories are a staple of New York literary-journalistic conversation. Stories about the $10,000 in expenses that a New Yorker editor billed for a single month. About the interior-decorating costs for the fashion-magazine editor who likes to have her office photographs rearranged every few months. About the hotel tab for the big-name New York writer who spent three weeks in Washington's Hay-Adams (basic room: $285 a night) researching a Vanity Fair story that will never run. About the Vogue editor who has furnished her summer house from items purchased for fashion shoots--beautiful furniture, designer pillows, coffee-table books. Vogue assistants have nicknamed the house "Petty Cash Junction." None of the 39 past and present Newhouse employees I spoke to for this story would talk on the record, for . And the nature of the subject makes it hard to separate apocrypha from the truth. Did Condé Nast pay, as sources insist it did, hundreds of thousands of dollars in back taxes on behalf of an editor who didn't bother to file tax forms? Did an editor really expense $20,000 in a weeklong trip to Paris? The people who pay the bills are not talking. But every example of excess cited here was told to me by at least one source (and usually more than one) in a position to know. Need a facial? Treat yourself and bill it to Si. This is what is called "scouting." It is also a great way to get free haircuts. To be fair, Si doesn't pay for all such treats. There is also a much-honored tradition of accepting tribute from companies that Condé Nast magazines cover. One magazine exec reportedly got so much loot last Christmas--Cuban cigars, "crates of wine," designer suits ("It was like a Spanish galleon")--that he needed three cars to cart it home. At yuletide, even midlevel fashion-mag writers and editors are inundated with "cashmere sweaters, Versace pillows, coats ..." recalls one ex- Vogue staffer wistfully. At the top of the masthead, the perks are perkier. His Si-ness (their joke, not mine) does not expect his editors in chief to actually live on their million-dollar salaries. He also gives them clothing allowances (up to $50,000 a year). He buys them cars of their choice and hires chauffeurs to drive them. He offers them low- or no-interest home loans. GQ editor Art Cooper reportedly received two $1-million loans, one for a Manhattan apartment, the other for a Connecticut farm. Tina Brown and her husband, Harold Evans, former president of Random House, reportedly just took a $2-million boost to buy a $3.7-million Manhattan house. Si's favorite courtiers lead lives of jaw-dropping privilege. When she was editor of British Vogue , Wintour commuted between London and New York--on the Concorde. Another Si confidant decided his office didn't feel right, so he hired one of the grandmasters of feng shui to rearrange it. Some editors prepare for trips by Federal Expressing their luggage to their destination. Why? "So you don't have to carry your bags. No one would be caught dead carrying a bag." Condé Nast has also created a class of mandarin journalists, writers who live much better than they ever could if they wrote only for normal magazines. One free-lancer tells of building much of a summer traveling with her husband in the West and Europe around a couple of Condé Nast assignments. Last summer, The New Yorker sent a staffer to Venice to cover the Venice Film Festival. The weeklong trip, which must have cost thousands, resulted in a short piece. Writers, of course, are nowhere near as profligate as photographers. Stories of wasteful shoots abound: the matching seaweed that had to be flown from California to the Caribbean for a fashion photo; the Annie Liebovitz Vanity Fair cover shot of Arnold Schwarzenegger that reportedly cost $100,000; the Vogue shoot in Africa in which, an ex- Vogue editor claims, the photographer and his huge entourage wined and dined to the tune of "hundreds of thousands of dollars." And then there are the parties. Last month The New Yorker spent--and this is not a joke--$500,000 on a two-day "Next Conference" at the Disney Institute in Florida, in connection with a special issue on the same theme. In order to get Vice President Gore, who was traveling in California at the time, The New Yorker paid for him and his entourage to fly Air Force Two from California to Florida and back. And vice presidents are not the only things that Condé Nast flies in for parties. The New Yorker once shipped silverware from New York to Chicago for a dinner. ("What, they don't have silverware in Chicago?" asks a New Yorker staffer.) Vanity Fair toted food from New York to Washington for this year's party on the night of the White House Correspondents Dinner. (What, they don't have food in Washington?) That annual Washington do has grown from an after-dinner gathering for drinks at a contributor's apartment to two huge blasts--before and after the dinner itself--at a rented embassy. VF 's annual Oscar-night party has become a similar institution in Hollywood. In addition to the parties themselves, Si also naturally pays to fly in VF staffers and to put them up at top hotels. (What, they don't have editors in Washington or L.A.?) Some Condé Nast parties are so ridiculous that even other Condé Nasties make fun of them. This week's New Yorker , for example, mocks a recent Vogue party in honor of food writer Jeffrey Steingarten. According to The New Yorker , Wintour so detested the carpet at Le Cirque 2000 that she ordered the florist to cover it with autumn leaves (handpicked, of course). The apogee of party absurdity is Vanity Fair 's sponsorship of an annual London dinner for the Serpentine Museum in Hyde Park. As one observer puts it, "Vanity Fair , an American magazine, pays more than $100,000 to a British art museum solely so that it can sponsor a dinner where Graydon Carter gets to sit next to Princess Diana." The princess was the museum's patron. Actually, paying $100,000 for face time with Princess Di may not have been a foolish investment for a magazine so dependent on peddling her image. And Condé Nast's excess has other plausible justifications as well. Some top editors may earn their perks. Vogue and GQ make millions, according to industry analysts. Vanity Fair is enjoying banner years, and while it probably hasn't made back the millions Newhouse lost in starting it up, it is certainly in the black. The New Yorker loses money--how much may even surpass perks as a topic of Newhouse gossip and speculation. On the other hand, The New Yorker is the most talked-about magazine in America, and Tina Brown is the most talked-about editor. That is worth something. Public media companies such as Time Warner (or, for that matter, Microsoft) can entice and hold journalists with stock options. Advance is private, so Newhouse uses other golden handcuffs. He runs a lifestyle prison. Top editors stay because they could never afford to live in a house as nice as the one Si's interest-free loan bought them, or to host parties as nice as the ones Si's party planners throw for them. Condé Nast's magazines are all about glamour, wealth, prestige. To uphold that image, magazine editors need to circulate at the top of New York society. But the top of New York society consists of people who make far more money than magazine editors do--investment bankers, corporate chieftains, and fashion designers. Million-dollar salaries aren't enough to mix as equals with the Trumps and Karans. Si's perks are equalizers. And they say it's not as good as it used to be. In 1992, according to Thomas Maier's biography of Newhouse, the editor of Self held a birthday party for Si Newhouse's dog . (Owners ate caviar; dogs drank Evian.) The lowliest assistants used to take car services home. But new Condé Nast CEO Steve Florio has restricted cars and catering. Editors who used to fly the Concorde now fly first-class; those who used to fly first-class now fly business. Expense accounts are scrutinized. Even so, today's Condé Nast is economical only by Condé Nast standards. The belt is tighter, but it's still hand-tooled, hand-tanned, and fashioned from the finest Italian leather.
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A. Excessive
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How is neural planning component trained?
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### 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).
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plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller
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Which subtasks do they evaluate on?
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### Introduction
Semantic parsing aims to solve the problem of canonicalizing language and representing its meaning: given an input sentence, it aims to extract a semantic representation of that sentence. Abstract meaning representation BIBREF0 , or AMR for short, allows us to do that with the inclusion of most of the shallow-semantic natural language processing (NLP) tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and co-reference resolution. AMR is partially motivated by the need to provide the NLP community with a single dataset that includes basic disambiguation information, instead of having to rely on different datasets for each disambiguation problem. The annotation process is straightforward, enabling the development of large datasets. Alternative semantic representations have been developed and studied, such as CCG BIBREF1 , BIBREF2 and UCCA BIBREF3 . Several parsers for AMR have been recently developed BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . This line of research is new and current results suggest a large room for improvement. Greedy transition-based methods BIBREF14 are one of the most popular choices for dependency parsing, because of their good balance between efficiency and accuracy. These methods seem promising also for AMR, due to the similarity between dependency trees and AMR structures, i.e., both representations use graphs with nodes that have lexical content and edges that represent linguistic relations. A transition system is an abstract machine characterized by a set of configurations and transitions between them. The basic components of a configuration are a stack of partially processed words and a buffer of unseen input words. Starting from an initial configuration, the system applies transitions until a terminal configuration is reached. The sentence is scanned left to right, with linear time complexity for dependency parsing. This is made possible by the use of a greedy classifier that chooses the transition to be applied at each step. In this paper we introduce a parser for AMR that is inspired by the ArcEager dependency transition system of nivre2004. The main difference between our system and ArcEager is that we need to account for the mapping from word tokens to AMR nodes, non-projectivity of AMR structures and reentrant nodes (multiple incoming edges). Our AMR parser brings closer dependency parsing and AMR parsing by showing that dependency parsing algorithms, with some modifications, can be used for AMR. Key properties such as working left-to-right, incrementality and linear complexity further strengthen its relevance. The AMR parser of wang2boosting, called CAMR, also defines a transition system. It differs from ours because we process the sentence left-to-right while they first acquire the entire dependency tree and then process it bottom-up. More recently emnlp2016 presented a non-greedy transition system for AMR parsing, based on ArcStandard BIBREF15 . Our transition system is also related to an adaptation of ArcEager for directed acyclic graphs (DAGs), introduced by sagae2008shift. This is also the basis for ribeyre2015because, a transition system used to parse dependency graphs. Similarly, du2014peking also address dependency graph parsing by means of transition systems. Analogously to dependency trees, dependency graphs have the property that their nodes consist of the word tokens, which is not true for AMR. As such, these transition systems are more closely related to traditional transition systems for dependency parsing. Our contributions in this paper are as follows: ### Transition-Based AMR Parsing
Similarly to dependency parsing, AMR parsing is partially based on the identification of predicate-argument structures. Much of the dependency parsing literature focuses on transition-based dependency parsing—an approach to parsing that scans the sentence from left to right in linear time and updates an intermediate structure that eventually ends up being a dependency tree. The two most common transition systems for greedy dependency parsing are ArcStandard and ArcEager. With ArcStandard, a stack is maintained along with a buffer on which the left-to-right scan is performed. At each step, the parser chooses to scan a word in the buffer and shift it onto the stack, or else to create an arc between the two top-most elements in the stack and pop the dependent. ArcStandard parses a sentence in a pure bottom-up, left-to-right fashion (similarly to shift-reduce context-free grammar parsers), and must delay the construction of right arcs until all the dependent node has been completed. This imposes strong limitations on the degree of incrementality of the parser. The ArcEager system was designed to improve on ArcStandard by mixing bottom up and top-down strategies. More precisely, in the ArcEager parser left arcs are constructed bottom-up and right arcs are constructed top-down, so that right dependents can be attached to their heads even if some of their own dependents are not identified yet. In this way arcs are constructed as soon as the head and the dependent are available in the stack. Because of the similarity of AMR structures to dependency structures, transition systems are also helpful for AMR parsing. Starting from the ArcEager system, we develop here a novel transition system, called AmrEager that parses sentences into AMR structures. There are three key differences between AMRs and dependency trees that require further adjustments for dependency parsers to be used with AMRs. A key difference between English dependency trees and AMR structures is projectivity. Dependency trees in English are usually projective, roughly meaning that there are no crossing arcs if the edges are drawn in the semi-plane above the words. While this restriction is empirically motivated in syntactic theories for English, it is no longer motivated for AMR structures. The notion of projectivity can be generalized to AMR graphs as follows. The intuition is that we can use the alignment INLINEFORM0 to map AMR edges back to the sentence INLINEFORM1 , and test whether there exist pairs of crossing edges. Figure FIGREF13 shows this mapping for the AMR of Figure FIGREF7 , where the edge connecting excuse to I crosses another edge. More formally, consider an AMR edge INLINEFORM2 . Let INLINEFORM3 and INLINEFORM4 , so that INLINEFORM5 is aligned with INLINEFORM6 and INLINEFORM7 is aligned with INLINEFORM8 . The spanning set for INLINEFORM9 , written INLINEFORM10 , is the set of all nodes INLINEFORM11 such that INLINEFORM12 and INLINEFORM13 if INLINEFORM14 or INLINEFORM15 if INLINEFORM16 . We say that INLINEFORM17 is projective if, for every node INLINEFORM18 , all of its parent and child nodes are in INLINEFORM19 ; otherwise, we say that INLINEFORM20 is non-projective. An AMR is projective if all of its edges are projective, and is non-projective otherwise. This corresponds to the intuitive definition of projectivity for DAGs introduced in sagae2008shift and is closely related to the definition of non-crossing graphs of kuhlmann2015parsing. Table TABREF15 demonstrates that a relatively small percentage of all AMR edges are non-projective. Yet, 35% of the sentences contain at least one non-projective edge. https://github.com/jflanigan/jamr/blob/master/docs/Hand_Alignments.md AMRs are graphs rather than trees because they can have nodes with multiple parents, called reentrant nodes, as in the node you for the AMR of Figure FIGREF7 . There are two phenomena that cause reentrancies in AMR: control, where a reentrant edge appears between siblings of a control verb, and co-reference, where multiple mentions correspond to the same concept. In contrast, dependency trees do not have nodes with multiple parents. Therefore, when creating a new arc, transition systems for dependency parsing check that the dependent does not already have a head node, preventing the node from having additional parents. To handle reentrancy, which is not uncommon in AMR structures as shown in Table TABREF15 , we drop this constraint. Another main difference with dependency parsing is that in AMR there is no straightforward mapping between a word in the sentence and a node in the graph: words may generate no nodes, one node or multiple nodes. In addition, the labels at the nodes are often not easily determined by the word in the sentence. For instance expectation translates to expect-01 and teacher translates to the two nodes teach-01 and person, connected through an :ARG0 edge, expressing that a teacher is a person who teaches. A mechanism of concept identification is therefore required to map each token INLINEFORM0 to a subgraph with the correct labels at its nodes and edges: if INLINEFORM1 is the gold alignment, this should be the subgraph INLINEFORM2 defined in Equation ( EQREF11 ). To obtain alignments between the tokens in the sentence and the nodes in the AMR graph of our training data, we run the JAMR aligner. ### Transition system for AMR Parsing
A stack INLINEFORM0 is a list of nodes of the partially constructed AMR graph, with the top element INLINEFORM1 at the right. We use the symbol ` INLINEFORM2 ' as the concatenation operator. A buffer INLINEFORM3 is a list of indices from INLINEFORM4 , with the first element INLINEFORM5 at the left, representing the word tokens from the input still to be processed. A configuration of our parser is a triple INLINEFORM6 , where INLINEFORM7 is the set of AMR edges that have been constructed up to this point. In order to introduce the transition actions of our parser we need some additional notation. We use a function INLINEFORM0 that maps indices from INLINEFORM1 to AMR graph fragments. For each INLINEFORM2 , INLINEFORM3 is a graph INLINEFORM4 , with single root INLINEFORM5 , representing the semantic contribution of word INLINEFORM6 to the AMR for INLINEFORM7 . As already mentioned, INLINEFORM8 can have a single node representing the concept associated with INLINEFORM9 , or it can have several nodes in case INLINEFORM10 denotes a complex concept, or it can be empty. The transition Shift is used to decide if and what to push on the stack after consuming a token from the buffer. Intuitively, the graph fragment INLINEFORM0 obtained from the token INLINEFORM1 , if not empty, is “merged” with the graph we have constructed so far. We then push onto the stack the node INLINEFORM2 for further processing. LArc INLINEFORM3 creates an edge with label INLINEFORM4 between the top-most node and the second top-most node in the stack, and pops the latter. RArc INLINEFORM5 is the symmetric operation, but does not pop any node from the stack. Finally, Reduce pops the top-most node from the stack, and it also recovers reentrant edges between its sibling nodes, capturing for instance several control verb patterns. To accomplish this, Reduce decides whether to create an additional edge between the node being removed and the previously created sibling in the partial graph. This way of handling control verbs is similar to the REENTRANCE transition of wang2boosting. The choice of popping the dependent in the LArc transition is inspired by ArcEager, where left-arcs are constructed bottom-up to increase the incrementality of the transition system BIBREF15 . This affects our ability to recover some reentrant edges: consider a node INLINEFORM0 with two parents INLINEFORM1 and INLINEFORM2 , where the arc INLINEFORM3 is a left-arc and INLINEFORM4 is any arc. If the first arc to be processed is INLINEFORM5 , we use LArc that pops INLINEFORM6 , hence making it impossible to create the second arc INLINEFORM7 . Nevertheless, we discovered that this approach works better than a completely unrestricted allowance of reentrancy. The reason is that if we do not remove dependents at all when first attached to a node, the stack becomes larger, and nodes which should be connected end up being distant from each other, and as such, are never connected. The initial configuration of the system has a INLINEFORM0 node (representing the root) in the stack and the entire sentence in the buffer. The terminal configuration consists of an empty buffer and a stack with only the INLINEFORM1 node. The transitions required to parse the sentence The boy and the girl are shown in Table TABREF20 , where the first line shows the initial configuration and the last line shows the terminal configuration. Similarly to the transitions of the ArcEager, the above transitions construct edges as soon as the head and the dependent are available in the stack, with the aim of maximizing the parser incrementality. We now show that our greedy transition-based AMR parser is linear-time in INLINEFORM0 , the length of the input sentence INLINEFORM1 . We first claim that the output graph has size INLINEFORM2 . Each token in INLINEFORM3 is mapped to a constant number of nodes in the graph by Shift. Thus the number of nodes is INLINEFORM4 . Furthermore, each node can have at most three parent nodes, created by transitions RArc, LArc and Reduce, respectively. Thus the number of edges is also INLINEFORM5 . It is possible to bound the maximum number of transitions required to parse INLINEFORM6 : the number of Shift is bounded by INLINEFORM7 , and the number of Reduce, LArc and RArc is bounded by the size of the graph, which is INLINEFORM8 . Since each transition can be carried out in constant time, we conclude that our parser runs in linear time. ### Training the System
Several components have to be learned: (1) a transition classifier that predicts the next transition given the current configuration, (2) a binary classifier that decides whether or not to create a reentrancy after a Reduce, (3) a concept identification step for each Shift to compute INLINEFORM0 , and 3) another classifier to label edges after each LArc or RArc. ### Oracle
Training our system from data requires an oracle—an algorithm that given a gold-standard AMR graph and a sentence returns transition sequences that maximize the overlap between the gold-standard graph and the graph dictated by the sequence of transitions. We adopt a shortest stack, static oracle similar to manningfast. Informally, static means that if the actual configuration of the parser has no mistakes, the oracle provides a transition that does not introduce any mistake. Shortest stack means that the oracle prefers transitions where the number of items in the stack is minimized. Given the current configuration INLINEFORM0 and the gold-standard graph INLINEFORM1 , the oracle is defined as follows, where we test the conditions in the given order and apply the action associated with the first match: if INLINEFORM0 then LArc( INLINEFORM1 ); if INLINEFORM0 then RArc( INLINEFORM1 ); if INLINEFORM0 then Reduce; Shift otherwise. The oracle first checks whether some gold-standard edge can be constructed from the two elements at the top of the stack (conditions 1 and 2). If LArc or RArc are not possible, the oracle checks whether all possible edges in the gold graph involving INLINEFORM0 have already been processed, in which case it chooses Reduce (conditions 3). To this end, it suffices to check the buffer, since LArc and RArc have already been excluded and elements in the stack deeper than position two can no longer be accessed by the parser. If Reduce is not possible, Shift is chosen. Besides deciding on the next transition, the oracle also needs the alignments, which we generate with JAMR, in order to know how to map the next token in the sentence to its AMR subgraph INLINEFORM0 defined in ( EQREF11 ). ### Transition Classifier
Like all other transition systems of this kind, our transition system has a “controller” that predicts a transition given the current configuration (among Shift, LArc, RArc and Reduce). The examples from which we learn this controller are based on features extracted from the oracle transition sequences, where the oracle is applied on the training data. As a classifier, we use a feed-forward neural network with two hidden layers of 200 tanh units and learning rate set to 0.1, with linear decaying. The input to the network consists of the concatenation of embeddings for words, POS tags and Stanford parser dependencies, one-hot vectors for named entities and additional sparse features, extracted from the current configuration of the transition system; this is reported in more details in Table TABREF27 . The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia. For lexical information, we also extract the leftmost (in the order of the aligned words) child (c), leftmost parent (p) and leftmost grandchild (cc). Leftmost and rightmost items are common features for transition-based parsers BIBREF17 , BIBREF18 but we found only leftmost to be helpful in our case. All POS tags, dependencies and named entities are generated using Stanford CoreNLP BIBREF19 . The accuracy of this classifier on the development set is 84%. Similarly, we train a binary classifier for deciding whether or not to create a reentrant edge after a Reduce: in this case we use word and POS embeddings for the two nodes being connected and their parent as well as dependency label embeddings for the arcs between them. ### Concept Identification
This routine is called every time the transition classifier decides to do a Shift; it is denoted by INLINEFORM0 in § SECREF3 . This component could be learned in a supervised manner, but we were not able to improve on a simple heuristic, which works as follows: during training, for each Shift decided by the oracle, we store the pair INLINEFORM1 in a phrase-table. During parsing, the most frequent graph INLINEFORM2 for the given token is then chosen. In other words, INLINEFORM3 approximates INLINEFORM4 by means of the graph most frequently seen among all occurrences of token INLINEFORM5 in the training set. An obvious problem with the phrase-table approach is that it does not generalize to unseen words. In addition, our heuristic relies on the fact that the mappings observed in the data are correct, which is not the case when the JAMR-generated alignments contain a mistake. In order to alleviate this problem we observe that there are classes of words such as named entities and numeric quantities that can be disambiguated in a deterministic manner. We therefore implement a set of “hooks” that are triggered by the named entity tag of the next token in the sentence. These hooks override the normal Shift mechanism and apply a fixed rule instead. For instance, when we see the token New York (the two tokens are collapsed in a single one at preprocessing) we generate the subgraph of Figure FIGREF30 and push its root onto the stack. Similar subgraphs are generated for all states, cities, countries and people. We also use hooks for ordinal numbers, percentages, money and dates. ### Edge Labeling
Edge labeling determines the labels for the edges being created. Every time the transition classifier decides to take an LArc or RArc operation, the edge labeler needs to decide on a label for it. There are more than 100 possible labels such as :ARG0, :ARG0-of, :ARG1, :location, :time and :polarity. We use a feed-forward neural network similar to the one we trained for the transition classier, with features shown in Table TABREF32 . The accuracy of this classifier on the development set is 77%. We constrain the labels predicted by the neural network in order to satisfy requirements of AMR. For instance, the label :top can only be applied when the node from which the edge starts is the special INLINEFORM0 node. Other constraints are used for the :polarity label and for edges attaching to numeric quantities. Sometimes the label predicted by the neural network is not a label that satisfies the requirements of AMR. For instance, the label :top can only be applied when the node from which the edge starts is the special INLINEFORM0 node. In order to avoid generating such erroneous labels, we use a set of rules, shown in Table TABREF34 . These rules determine which labels are allowed for the newly created edge so that we only consider those during prediction. Also ARG roles cannot always be applied: each Propbank frame allows a limited number of arguments. For example, while add-01 and add-02 allow for :ARG1 and :ARG2 (and their inverse :ARG1-of and :ARG2-of), add-03 and add-04 only allow :ARG2 (and :ARG2-of). ### Fine-grained Evaluation
Until now, AMR parsers were evaluated using the Smatch score. Given the candidate graphs and the gold graphs in the form of AMR annotations, Smatch first tries to find the best alignments between the variable names for each pair of graphs and it then computes precision, recall and F1 of the concepts and relations. We note that the Smatch score has two flaws: (1) while AMR parsing involves a large number of subtasks, the Smatch score consists of a single number that does not assess the quality of each subtasks separately; (2) the Smatch score weighs different types of errors in a way which is not necessarily useful for solving a specific NLP problem. For example, for a specific problem concept detection might be deemed more important than edge detection, or guessing the wrong sense for a concept might be considered less severe than guessing the wrong verb altogether. Consider the two parses for the sentence Silvio Berlusconi gave Lucio Stanca his current role of modernizing Italy's bureaucracy in Figure FIGREF36 . At the top, we show the output of a parser (Parse 1) that is not able to deal with named entities. At the bottom, we show the output of a parser (Parse 2) which, except for :name, :op and :wiki, always uses the edge label :ARG0. The Smatch scores for the two parses are 56 and 78 respectively. Both parses make obvious mistakes but the three named entity errors in Parse 1 are considered more important than the six wrong labels in Parse 2. However, without further analysis, it is not advisable to conclude that Parse 2 is better than Parse 1. In order to better understand the limitations of the different parsers, find their strengths and gain insight in which downstream tasks they may be helpful, we compute a set of metrics on the test set. Unlabeled is the Smatch score computed on the predicted graphs after removing all edge labels. In this way, we only assess the node labels and the graph topology, which may be enough to benefit several NLP tasks because it identifies basic predicate-argument structure. For instance, we may be interested in knowing whether two events or entities are related to each other, while not being concerned with the precise type of relation holding between them. No WSD gives a score that does not take into account word sense disambiguation errors. By ignoring the sense specified by the Propbank frame used (e.g., duck-01 vs duck-02) we have a score that does not take into account this additional complexity in the parsing procedure. To compute this score, we simply strip off the suffixes from all Propbank frames and calculate the Smatch score. Following sawai, we also evaluate the parsers using the Smatch score on noun phrases only (NP-only), by extracting from the AMR dataset all noun phrases that do not include further NPs. As we previously discussed, reentrancy is a very important characteristic of AMR graphs and it is not trivial to handle. We therefore implement a test for it (Reentrancy), where we compute the Smatch score only on reentrant edges. Concept identification is another critical component of the parsing process and we therefore compute the F-score on the list of predicted concepts (Concepts) too. Identifying the correct concepts is fundamental: if a concept is not identified, it will not be possible to retrieve any edge involving that concept, with likely significant consequences on accuracy. This metric is therefore quite important to score highly on. Similarly to our score for concepts, we further compute an F-score on the named entities (Named Ent.) and wiki roles for named entities (Wikification) that consider edges labeled with :name and :wiki respectively. These two metrics are strictly related to the concept score. However, since named entity recognition is the focus of dedicated research, we believe it is important to define a metric that specifically assesses this problem. Negation detection is another task which has received some attention. An F-score for this (Negations) is also defined, where we find all negated concepts by looking for the :polarity role. The reason we can compute a simple F-score instead of using Smatch for these metrics is that there are no variable names involved. Finally we compute the Smatch score on :ARG edges only, in order to have a score for semantic role labeling (SRL), which is another extremely important subtask of AMR, as it is based on the identification of predicate-argument structures. Using this evaluation suite we can evaluate AMRs on a wide range of metrics that can help us find strengths and weakness of each parser, hence speeding up the research in this area. Table TABREF37 reports the scores for the two parses of Figure FIGREF36 , where we see that Parse 1 gets a high score for semantic role labeling while Parse 2 is optimal for named entity recognition. Moreover, we can make additional observations such as that Parse 2 is optimal with respect to unlabeled score and that Parse 1 recovers more reentrancies. ### Experiments
We compare our parser against two available parsers: JAMR BIBREF4 and CAMR BIBREF20 , BIBREF5 , using the LDC2015E86 dataset for evaluation. Both parsers are available online and were recently updated for SemEval-2016 Task 8 BIBREF21 , BIBREF22 . However, CAMR's SemEval system, which reports a Smatch score of 67, is not publicly available. CAMR has a quadratic worst-case complexity (although linear in practice). In JAMR, the concept identification step is quadratic and the relation identification step is INLINEFORM0 , with INLINEFORM1 being the set of nodes in the AMR graph. Table TABREF40 shows the results obtained by the parsers on all metrics previously introduced. On Smatch, our system does not give state-of-the-art results. However, we do obtain the best results for Unlabeled and Concept and outperform the other parses for Named Ent. and Negations. Our score of Reentrancy is also close the best scoring system, which is particularly relevant given the importance of reentrancies in AMR. The use of the Reduce transition, which targets reentrancies caused by control verbs, is critical in order to achieve this result. The relatively high results we obtain for the unlabeled case suggests that our parser has difficulty in labeling the arcs. Our score for concept identification, which is on par with the best result from the other parsers, demonstrates that there is a relatively low level of token ambiguity. State-of-the-art results for this problem can be obtained by choosing the most frequent subgraph for a given token based on a phrase-table constructed from JAMR alignments on the training data. The scores for named entities and wikification are heavily dependent on the hooks mentioned in § SECREF29 , which in turn relies on the named entity recognizer to make the correct predictions. In order to alleviate the problem of wrong automatic alignments with respect to polarity and better detect negation, we performed a post-processing step on the aligner output where we align the AMR constant - (minus) with words bearing negative polarity such as not, illegitimate and asymmetry. Our experiments demonstrate that there is no parser for AMR yet that conclusively does better than all other parsers on all metrics. Advantages of our parser are the worst-case linear complexity and the fact that is possible to perform incremental AMR parsing, which is both helpful for real-time applications and to investigate how meaning of English sentences can be built incrementally left-to-right. ### Related Work
The first data-driven AMR parser is due to carbonell2014discriminative. The problem is addressed in two separate stages: concept identification and relation identification. They use a sequence labeling algorithm to identify concepts and frame the relation prediction task as a constrained combinatorial optimization problem. werling2015robust notice that the difficult bit is the concept identification and propose a better way to handle that task: an action classifier to generate concepts by applying predetermined actions. Other proposals involve a synchronous hyperedge replacement grammar solution BIBREF6 , a syntax-based machine translation approach BIBREF7 where a grammar of string-to-tree rules is created after reducing AMR graphs to trees by removing all reentrancies, a CCG system that first parses sentences into lambda-calculus representations BIBREF11 . A systematic translation from AMR to first order logic formulas, with a special treatment for quantification, reentrancy and negation, is discussed in bos2016expressive. In microsoft, a pre-existing logical form parser is used and the output is then converted into AMR graphs. Yet another solution is proposed by searnamr who discuss a parser that uses SEARN BIBREF23 , a “learning to search” algorithm. Transition-based algorithms for AMR parsing are compelling because traditional graph-based techniques are computationally expensive. wang and wang2boosting propose a framework that parses a sentence into its AMR structure through a two-stage process: a dependency tree is generated from the input sentence through a transition-based parser and then another transition-based parser is used to generate the AMR. The main benefit of this approach is that the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm. Others further built on this parser: goodman2016noise use imitation learning to alleviate the probem of error propagation in the greedy parser, while barzdins2016riga create a wrapper around it to fix frequent mistakes and investigate ensembles with a character level neural parser. More recently emnlp2016 presented a non-greedy transition system for AMR parsing, based on ArcStandard BIBREF15 . AMR parsing as a whole is a complex task because it involves many subtasks including named entity recognition, co-reference resolution and semantic role labeling. sawai do not attempt at parsing AMR graphs for entire sentences but they instead handle simple noun phrases (NPs). They extract NPs from the AMR dataset only when they do not include further NPs, do not include pronouns nor named entities. Due to these restrictions, the AMRs are mostly trees and easier to handle than the original AMR graphs. They approach this task using a transition based system inspired by ArcStandard. AMR is not the only way to represent meaning in natural language sentences. Alternative semantic representations have been developed and studied, such as Boxer BIBREF24 , CCG BIBREF1 , BIBREF2 and UCCA BIBREF3 . ### Conclusion
We presented a transition system that builds AMR graphs in linear time by processing the sentences left-to-right, trained with feed-forward neural networks. The parser demonstrates that it is possible to perform AMR parsing using techniques inspired by dependency parsing. We also noted that it is less informative to evaluate the entire parsing process with Smatch than to use a collection of metrics aimed at evaluating the various subproblems in the parsing process. We further showed that our left-to-right transition system is competitive with publicly available state-of-the-art parsers. Although we do not outperform the best baseline in terms of Smatch score, we show on par or better results for several of the metrics proposed. We hope that moving away from a single-metric evaluation will further speed up progress in AMR parsing. ### Acknowledgments
The authors would like to thank the three anonymous reviewers and Sameer Bansal, Jeff Flanigan, Sorcha Gilroy, Adam Lopez, Nikos Papasarantopoulos, Nathan Schneider, Mark Steedman, Sam Thomson, Clara Vania and Chuan Wang for their help and comments. This research was supported by a grant from Bloomberg and by the H2020 project SUMMA, under grant agreement 688139. Figure 1: Annotation for the sentence “I beg that you will excuse me.” In this AMR graph, variables are denoted in boldface and concepts and edge labels are denoted in italics. Figure 2: AMR graph for the sentence “I beg that you will excuse me.” Table 1: Statistics for non-projectivity and re-entrancies in 200 AMR manually aligned with the associated sentences.2 Table 2: Parsing steps for the sentence “The boy and the girl.” Table 4: Confusion matrix for the neural network transition classifier on the development set. Table 3: Features used in transition classifier. Stack and buffer elements are denoted by σi and βi, respectively, for i ∈ {0, 3}. The function d maps a stack element to the depth of the associated graph fragment. The functions #c and #p count the number of children and parents, respectively, of a stack element. The function w maps a stack/buffer element to the word embedding for the associated word in the sentence. The function p gives the leftmost (according to the alignment) parent of a stack element, the function c the leftmost child and the function cc the leftmost grandchild. The function s maps a stack/buffer element to the part-of-speech embedding for the associated word. The function e maps a stack/buffer element to its entity. Finally, the function ℓ maps a pair of symbols to the dependency label embedding, according to the edge (or lack of) in the dependency tree for the two words these symbols are mapped to. Figure 3: Subgraph for “New York”. Similar subgraphs are generated for all states, city, countries and people. Table 5: Features used in edge labeling. See Table 3 for a legend of symbols. Table 6: Labeling rules: For each edge label, we provide regular expressions that must hold on the labels at the start node (start) and the end node (end) of the edge. Ex. indicates when the rule is exclusive, d-ent is the AMR concept date-entity, inter. is the AMR constant interrogative, expr. is the AMR constant expressive, imp. is the AMR constant imperative. Table 7: Domain datasets and their sizes, taken from LDC2015E86. # sent. denotes the number of sentences, # tokens denotes the number of tokens and # nodes denotes the total number of nodes in all graphs. Table 9: Results on test split of LDC2015E86 for JAMR, CAMR and our AMREAGER. Best systems are in bold. Table 8: Development set Smatch scores for JAMR (J), CAMR (C) and our parser (E) on different domains. Best systems are in bold.
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entity recognition, semantic role labeling and co-reference resolution
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What is the central purpose of the article?
A. To advocate support for expansion of Creative Commons licenses
B. To inform the readership of current problems in the photography industry
C. To illustrate how photographers go about their creative processes
D. To praise a fellow photographer and writer for his recent contributions
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Just another free soul In his foreword to the book, Lessig writes that you understand your subjects “by learning to see them in a certain way.” What is that certain way? I think I’m trying to get a mental image of a person, certain expressions, or what I think that person is about. I’m trying to capture what I think they look like, which is many times a minority of their typical expressions, or their typical stance. So, if I’m taking pictures of Larry [Lessig], I want to have his signature hand gestures, and not just random ones. I think I’m trying to capture pictures of people that help others see what they’re about. Some photographers will make someone look the way the photographer wants them to look, and not the way they appear, so they’ll pick the one picture out of 100 where the guy looks more egotistical than he really is. Some photographers are almost medical, and are going after a perfect portrait. I’m somewhere in between. It’s amazing how many people will upload snapshots of people where the pictures don’t look like them at all. To me, uploading a picture that is not an easily recognizable picture of that person defeats the point, which I’m working toward, to try to express who they are. On the other hand, professional photographers usually have a subject whom they don’t know personally, so they end up having to try to capture an image that they’ve created based on who they think the person is or how they want that person to appear. You know how sculptors often say that they’re just freeing an image from a block? What I’m trying to do is free someone’s soul from his or her image. There are a lot of things that make this hard. A lot of people are uncomfortable in front of a camera, or might make expressions that aren’t very natural for them. And if the person is nervous, it’s very difficult to try to see what it is that you’re trying to capture. A lot of what I’m doing is, I just start shooting photos. After half an hour of having their picture taken, people start to ignore you. Or I’ll take pictures when I’m talking to people about what they’re doing, so after a while they get distracted by the conversation and forget about the camera. That’s something that I’m not perfect at, but I’m getting better. I think good photographers are also able to disarm people through conversation, but still, it’s difficult to have a disarming conversation with somebody you don’t know, or to make them laugh. Many times people make a face for me that they wouldn’t make for a professional photographer. For instance, a board meeting picture, like the one with Eric Saltzman: that was during a very tense discussion. I’ve found that people are at their most animated at these kinds of meetings, and look the most alive when they are under a lot of pressure, and super- focused. But usually if an outsider is in the room, they won’t get into that. I mean, it would be difficult for a cameraman to be in a room where a board is having a heated debate. But those are the things that I’m trying to capture, because most people don’t get to see that. At the Creative Commons board meeting, Larry asked me to put the camera away after awhile [laughs] because it was distracting. We were having a very heated discussion and I was taking all of these pictures. But he credited me later because afterward those pictures turned out the best. In your mind, what is a ‘Freesoul’ ? A freesoul is somewhat of a pun. On the one hand it means you are free, liberated. You, as a human spirit, are open. And then, it also has the meaning that you are unencumbered legally, that you are free, as in ‘free software.’ There’s a paradox: with many people’s Wikipedia articles to which I’ve contributed, when it comes to the picture, many of these people don’t have any free photos of themselves on the web, so while they are “notable” on Wikipedia, their images aren’t free of the copyright of the photographer, or the institution who hired the photographer to take the picture. Often, even the subject of the article can’t make an image available to the Wikimedia/Wikipedia community. This means that a lot of people who have a Net presence have a legally encumbered Net presence. People who are invited to conferences get asked all the time, “By the way, do you have a photo that we can use?” But they don’t. By making these pictures available under a Creative Commons license, now they do. This is solving the issue of legal freedom. The third part of the pun is that, since I’m asking for a model release from the subjects, I’m asking everyone to be much more open and giving about their image than most people typically are. I’m giving, you’re giving, we’re all giving to participate and to try to create this wonderful work, and allow others to create derivative works. Of course people can abuse that, just like they can abuse anything. But I want people to see the value in sharing over the fear in sharing. The fact is, it’s much more likely that somebody is going to use these pictures for something positive, rather than for something negative. The benefits greatly outweigh the risks. I think we spend way too much of our lives worrying about the risks, at the cost of a lot of the benefits. This is a celebration of all of the people who are willing to give. In a way, giving up your image and allowing anyone to use it: it’s the ultimate gift. In one way it’s kind of vain. [laughs] But in another way it’s wonderful. A Wikipedia article on some person but with no picture is sad. Besides Wikipedia, how do you imagine these photos being used? They can be used in textbooks and in mainstream media articles about the person. Now they can get a picture that represents the person, at least from my perspective. That said, I shouldn’t be the only person doing this. More people should do the same, and make the photographs available freely. For one, I feel that “free” CC licensed photos have a much higher chance of not disappearing. But I don’t know exactly how these photos are going to be used, so in a sense I’m curious. For example, recently I received the Harvard Berkman Center pamphlet. It was a report of what they’re doing, and they also had a bunch of my pictures in there. They all had attribution, and it made me feel really good. There were pictures of different Berkman Center members that I had taken in various places all over the world. I think that the subject is probably happy with this, and I’m happy, and the Berkman Center’s happy because they’re not all pictures of people sitting at desks in the Berkman Center. There’s one more important thing: Creative Commons is great for original creative works or derivative creative works, but when it involves human images, it gets very complicated. We all know the Virgin Mobile case, where Virgin used CC licensed images in an advertisement without getting permission from the models, and got in trouble. What we’re trying to do here is to expand beyond just copyright, to make it more thorough from a legal perspective. It’s also an important educational point, so people understand that, in addition to the Creative Commons licenses, we need people to provide other rights in cases where the law requires such rights to be cleared before reuse. What have you learned about the people in these networks, just in the past year? That’s a good question. I think that at least Creative Commons has become much more mainstream. Creative Commons has moved from a fringy academic discussion to a boardroom discussion. Yahoo announced that it will be using Creative Commons for all of their basic infrastructure, and integrating it all. Google has CC search in their advanced search. Microsoft is working with CC as well and have a plug-in. Nine Inch Nails released their album, Ghost, under a Creative Commons license. The list goes on. Many people are asking: can you make money and share? The answer is, yes. CC is becoming an important part of the business discussion. But one thing that happens when a movement like CC becomes a business thing, is that a lot of the pioneers fade into the background, and it becomes a part of industry. This happened to the Internet. And so while you still have the core people who still remember and hold the torch for the philosophical side, the Internet has become much more of a business. Now, when you go to many Internet conferences, it’s mostly salesmen in attendance. I believe that the success of the Internet has two parts. The first part is the market- driven business side, which has made the Internet affordable and ubiquitous. The second part is the strong movement of participants who fight to keep the Internet open and try to prevent the business side from corrupting the fundamental elements that make the Internet great. The Net Neutrality or Open Network discussion going on right now is a good example of the importance of continuing to balance these principles with business interests. Similarly, I think that business interests can help make Creative Commons ubiquitous and more easily accessible to everyone. However, I think it’s important to remember to keep pushing to make content more “free” and not allow businesses to use Creative Commons in exploitive or destructive ways. In addition to the business side, Creative Commons is being used by educators to create open courseware around the world and in the area of science and technology to promote sharing in research. And as of now, we have the license ported to at least 44 jurisdictions, and the number of countries with projects continues to grow. In many ways, the movement outside of the United States has become much bigger than the movement in the United States. Although the United States is still slightly farther ahead in terms of commercialization, the size of the whole free culture movement outside of the United States is huge now. The CC China Photo exhibit was just amazing. There were some great images, and a lot of the photographers were professionals. This is beyond what anybody has done in the US. A lot of the progress that we’re making is international. What are your personal realizations or experiences? Well, we’re all getting old, if you look at these pictures. But there’s another thing, though, about this book: the number of professional-quality amateurs has increased significantly due to the importance of digital in both professional and high-end amateur photography I hate to say it, a lot of people love the darkroom, but it really feels like the death of the darkroom with this year. With new 22 megapixel cameras coming in under $10,000, and Lightroom and some of this software at a couple hundred dollars, it doesn’t really make sense, except for particularly fussy artists, to do wet-work anymore. If you’re a commercial photographer or a high-end amateur, you can do anything you used to do in the darkroom. I think it has really lowered the bar. I don’t know how that affects the industry directly, but for me, it bridged a huge gap. I used to be darkroom geek. I loved my darkroom, and even when I didn’t have my darkroom anymore, I still was shooting 6x6 Hasselblad 120 film and processing it in a special lab, and then digitizing it. For me, that film was it. You could never get as good as medium-format film or large-format film At the time, the digital Hasselblad backs were too expensive, and were still not as good as 8x10 film. So there was this whole period where the darkroom was not all that exciting, but the digital wasn’t perfect. I went through a limbo period. I had invested so much in my Hasselblad system, and my Leica M6 set. I had bought the Leica R8, but I was kicking myself because it was terrible. But then the Leica M8 came out, and I bought one at the beginning of 2007. The M8 really got me to where I could use my old gear, and it had enough megapixels to be as good as some film. Another way of saying it was that there was a gear breakthrough at the beginning of last year. Okay, that’s pretty materialistic! So there was a technology breakthrough, let’s call it that, that allowed me to switch completely away from film, and I think this happened to a lot of photographers. It caused an explosion of content and an increase in the quality of content on sites like Flickr. It has allowed amateurs to create a business model with professionals. Interestingly, I think these new high-end amateurs are buying more photography books and photographs and are probably providing an increasing revenue stream for professional photographers. I think most amateurs, including myself, are paying homage to the professionals and not trying to “compete” with them. Despite the existence of social software, what is still important about meeting people face-to-face? For me, the right way to use a lot of the new social software is by making it easier to spend more physical time with the people you like best. Dopplr is a great example. When I visit a city, I will see all of the people who are in the city at the same time. When I went to London awhile ago, there were 47 people I knew in London, and a huge percentage of those people don’t live there. I would bet that more than half of the photos in this book are pictures of friends, and they’re not in their hometown. That’s the really interesting thing that is happening right now: it’s really increasing your ability to spend quality time with, actually, a smaller number of people. It allows you to actively filter. Your meetings don’t have to be random. If I look at the list of people in this book, although there are some obvious people missing whom I didn’t see last year, probably met more of my friends last year, my real friends, than I’ve met in any other year. I know my travels were crazy, but I think that the online world has allowed me to do that. What’s great about photography is that it captures the moment that I was sharing with that person. It’s not just a connection on a social network online, which is really pretty binary. I can look at all these photos and remember exactly what we were doing, what we were eating, what we were drinking, what we were talking about, and to me that’s a much more rich experience. It’s the combination of social software and photography. For me, reality is “the present” plus what you remember from the past. I think this project is really sharing memories with people. Blog posts contribute as well, but to me photography is a really good way of doing that. When I look at the expressions, I remember the moment and get a sense of presence. I think the main problem for me is the environmental impact of flying around. Just as I never believed that we would have a paperless office, being able to connect with people through social software mostly increases your travel, it doesn’t decrease it. It is great because you get to meet all these people. But it is bad for the environment, and bad for our jet lag. How would you characterize your contributions to free culture? I think it’s mostly incremental. I think there is very little we actually do all by ourselves, and I hate saying, “I did this” or “I did that.” I think that in most cases, focusing on individual contributions or achievements undervalues the importance of everyone else involved. Having said that, I think my main contribution is probably in supporting Creative Commons as a fan, board member, chairman of the board and now CEO. I think CC has a significant role, and helping to keep it on track and growing is probably the single most important role that I have in Free Culture. Specifically, I think that trying to keep an international focus and a balance between business and the non-business elements of the movement is essential. My job is to keep that focus and maintain that balance. Also, CC needs to run smoothly as an organization and there is a lot of operational work that we all need to do. My photography is a way for me to participate in a small measure on the creative side of the Free Culture movement, and helps me see things from that perspective as well. However, I believe in emergent democracy and the importance of trying to celebrate the community more than the heroes. Of course, I’m a huge fan of Larry’s and I have great respect for the leaders of our movement. But more than anything, I’m thankful for and respectful of all of the participants who aren’t so well known and who are essential to moving everything forward. Personally, I don’t think it’s ultimately meaningful to talk about one individual’s personal contribution to any movement. The real meaning is in the whole movement. I’m just one participant. Just another free soul.
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A. To advocate support for expansion of Creative Commons licenses
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Which term best describes how the author characterizes the home in the beginning of the story?
A. neoclassical
B. industrial
C. eclectic
D. gothic
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JUDAS RAM BY SAM MERWIN, Jr. Illustrated by JAMES VINCENT [Transcriber's Note: This etext was produced from Galaxy Science Fiction December 1950. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] The house was furnished with all luxuries, including women. If it only had a lease that could be broken— Roger Tennant, crossing the lawn, could see two of the three wings of the house, which radiated spoke-like from its heptagonal central portion. The wing on the left was white, with slim square pillars, reminiscent of scores of movie sets of the Deep South. That on the right was sundeck solar-house living-machine modern, something like a montage of shoeboxes. The wing hidden by the rest of the house was, he knew, spired, gabled and multicolored, like an ancient building in pre-Hitler Cracow. Dana was lying under a tree near the door, stretched out on a sort of deck chair with her eyes closed. She wore a golden gown, long and close-fitting and slit up the leg like the gown of a Chinese woman. Above it her comely face was sullen beneath its sleek cocoon of auburn hair. She opened her eyes at his approach and regarded him with nothing like favor. Involuntarily he glanced down at the tartan shorts that were his only garment to make sure that they were on properly. They were. He had thought them up in a moment of utter boredom and they were extremely comfortable. However, the near-Buchanan tartan did not crease or even wrinkle when he moved. Their captors had no idea of how a woven design should behave. "Waiting for me?" Tennant asked the girl. She said, "I'd rather be dead. Maybe I am. Maybe we're all dead and this is Hell." He stood over her and looked down until she turned away her reddening face. He said, "So it's going to be you again, Dana. You'll be the first to come back for a second run." "Don't flatter yourself," she replied angrily. She sat up, pushed back her hair, got to her feet a trifle awkwardly because of the tight-fitting tubular gown. "If I could do anything about it...." "But you can't," he told her. "They're too clever." "Is this crop rotation or did you send for me?" she asked cynically. "If you did, I wish you hadn't. You haven't asked about your son." "I don't even want to think about him," said Tennant. "Let's get on with it." He could sense the restless stirring of the woman within Dana, just as he could feel the stirring toward her within himself—desire that both of them loathed because it was implanted within them by their captors. They walked toward the house. It didn't look like a prison—or a cage. Within the dome of the barrier, it looked more like a well-kept if bizarre little country estate. There was clipped lawn, a scattering of trees, even a clear little brook that chattered unending annoyance at the small stones which impeded its flow. But the lawn was not of grass—it was of a bright green substance that might have been cellophane but wasn't, and it sprouted from a fabric that might have been canvas but was something else. The trees looked like trees, only their trunks were bark all the way through—except that it was not bark. The brook was practically water, but the small stones over which it flowed were of no earthly mineral. They entered the house, which had no roof, continued to move beneath a sky that glowed with light which did not come from a sun or moon. It might have been a well-kept if bizarre little country estate, but it wasn't. It was a prison, a cage. The other two women were sitting in the heptagonal central hall. Eudalia, who had borne twin girls recently, was lying back, newly thin and dark of skin and hair, smoking a scentless cigarette. A tall woman, thirtyish, she wore a sort of shimmering green strapless evening gown. Tennant wondered how she maintained it in place, for despite her recent double motherhood, she was almost flat of bosom. He asked her how she was feeling. "Okay, I guess," she said. "The way they manage it, there's nothing to it." She had a flat, potentially raucous voice. Eudalia had been a female foreman in a garment-cutting shop before being captured and brought through. "Good," he said. "Glad to hear it." He felt oddly embarrassed. He turned to Olga, broad, blonde and curiously vital, who sat perfectly still, regarding him over the pregnant swell of her dirndl-clad waist. Olga had been a waitress in a mining town hash-house near Scranton. Tennant wanted to put an encouraging hand on her shoulder, to say something that might cheer her up, for she was by far the youngest of the three female captives, barely nineteen. But with the eyes of the other two, especially Dana, upon him, he could not. "I guess I wasn't cut out to be a Turk," he said. "I don't feel at ease in a harem, even when it's supposedly my own." "You're not doing so badly," Dana replied acidly. "Lay off—he can't help it," said Eudalia unexpectedly. "He doesn't like it any better than we do." "But he doesn't have to—have them," objected Olga. She had a trace of Polish accent that was not unpleasant. In fact, Tennant thought, only her laughter was unpleasant, a shrill, uncontrolled burst of staccato sound that jarred him to his heels. Olga had not laughed of late, however. She was too frightened. "Let's get the meal ordered," said Dana and they were all silent, thinking of what they wanted to eat but would not enjoy when it came. Tennant finished with his order, then got busy with his surprise. It arrived before the meal, materializing against one of the seven walls of the roofless chamber. It was a large cabinet on slender straight legs that resembled dark polished wood. Tennant went to it, opened a hingeless door and pushed a knob on the inner surface. At once the air was hideous with the acerate harmony of a singing commercial.... ... so go soak your head, be it gold, brown or red, in Any-tone Shampoo! A disc jockey's buoyant tones cut in quickly as the final ooooo faded. "This is Grady Martin, your old night-owl, coming to you with your requests over Station WZZX, Manhattan. Here's a wire from Theresa McManus and the girls in the family entrance of Conaghan's Bar and Grill on West...." Tennant watched the girls as a sweet-voiced crooner began to ply an unfamiliar love lyric to a melody whose similarity to a thousand predecessors doomed it to instant success. Olga sat up straight, her pale blue eyes round with utter disbelief. She looked at the radio, at Tennant, at the other two women, then back at the machine. She murmured something in Polish that was inaudible, but her expression showed that it must have been wistful. Eudalia grinned at Tennant and, rising, did a sort of tap dance to the music, then whirled back into her chair, green dress ashimmer, and sank into it just to listen. Dana stood almost in the center of the room, carmine-tipped fingers clasped beneath the swell of her breasts. She might have been listening to Brahms or Debussy. Her eyes glowed with the salty brilliance of emotion and she was almost beautiful. " Rog! " she cried softly when the music stopped. "A radio and WZZX! Is it—are they—real?" "As real as you or I," he told her. "It took quite a bit of doing, getting them to put a set together. And I wasn't sure that radio would get through. TV doesn't seem to. Somehow it brings things closer...." Olga got up quite suddenly, went to the machine and, after frowning at it for a moment, tuned in another station from which a Polish-speaking announcer was followed by polka music. She leaned against the wall, resting one smooth forearm on the top of the machine. Her eyes closed and she swayed a little in time to the polka beat. Tennant caught Dana looking at him and there was near approval in her expression—approval that faded quickly as soon as she caught his gaze upon her. The food arrived then and they sat down at the round table to eat it. Tennant's meat looked like steak, it felt like steak, but, lacking the aroma of steak, it was almost tasteless. This was so with all of their foods, with their cigarettes, with everything in their prison—or their cage. Their captors were utterly without a human conception of smell, living, apparently, in a world without odor at all. Dana said suddenly, "I named the boy Tom, after somebody I hate almost as much as I hate you." Eudalia laid down her fork with a clatter and regarded Dana disapprovingly. "Why take it out on Rog?" she asked bluntly. "He didn't ask to come here any more than we did. He's got a wife back home. Maybe you want him to fall in love with you? Maybe you're jealous because he doesn't? Well, maybe he can't! And maybe it wouldn't work, the way things are arranged here." "Thanks, Eudalia," said Tennant. "I think I can defend myself. But she's right, Dana. We're as helpless as—laboratory animals. They have the means to make us do whatever they want." "Rog," said Dana, looking suddenly scared, "I'm sorry I snapped at you. I know it's not your fault. I'm— changing ." He shook his head. "No, Dana, you're not changing. You're adapting. We all are. We seem to be in a universe of different properties as well as different dimensions. We're adjusting. I can do a thing or two myself that seem absolutely impossible." "Are we really in the fourth dimension?" Dana asked. Of the three of them, she alone had more than a high-school education. "We may be in the eleventh for all I know," he told her. "But I'll settle for the fourth—a fourth dimension in space, if that makes scientific sense, because we don't seem to have moved in time. I wasn't sure of that, though, till we got the radio." "Why haven't they brought more of us through?" Eudalia asked, tamping out ashes in a tray that might have been silver. "I'm not sure," he said thoughtfully. "I think it's hard for them. They have a hell of a time bringing anyone through alive, and lately they haven't brought anyone through—not alive." "Why do they do it—the other way, I mean?" asked Dana. Tennant shrugged. "I don't know. I've been thinking about it. I suppose it's because they're pretty human." " Human! " Dana was outraged. "Do you call it human to—" "Hold on," he said. "They pass through their gateway to Earth at considerable danger and, probably, expense of some kind. Some of them don't come back. They kill those of us who put up a fight. Those who don't—or can't—they bring back with them. Live or dead, we're just laboratory specimens." "Maybe," Eudalia conceded doubtfully. Then her eyes blazed. "But the things they do—stuffing people, mounting their heads, keeping them on display in their—their whatever they live in. You call that human, Rog?" "Were you ever in a big-game hunter's trophy room?" Tennant asked quietly. "Or in a Museum of Natural History? A zoo? A naturalist's lab? Or even, maybe, photographed as a baby on a bear-skin rug?" "I was," said Olga. "But that's not the same thing." "Of course not," he agreed. "In the one instance, we're the hunters, the breeders, the trophy collectors. In the other"—he shrugged—"we're the trophies." There was a long silence. They finished eating and then Dana stood up and said, "I'm going out on the lawn for a while." She unzipped her golden gown, stepped out of it to reveal a pair of tartan shorts that matched his, and a narrow halter. "You thought those up while we ate," he said. It annoyed him to be copied, though he did not know why. She laughed at him silently, tossed her auburn hair back from her face and went out of the roofless house, holding the gold dress casually over her bare arm. Eudalia took him to the nursery. He was irritated now in another, angrier way. The infants, protected by cellophane-like coverlets, were asleep. "They never cry," the thin woman told him. "But they grow—God, how they grow!" "Good," said Tennant, fighting down his anger. He kissed her, held her close, although neither of them felt desire at the moment. Their captors had seen to that; it wasn't Eudalia's turn. Tennant said, "I wish I could do something about this. I hate seeing Dana so bitter and Olga so scared. It isn't their fault." "And it's not yours," insisted Eudalia. "Don't let them make you think it is." "I'll try not to," he said and stopped, realizing the family party was over. He had felt the inner tug of command, said good-by to the women and returned to his smaller compound within its own barrier dome. Then came the invisible aura of strain in the air, the shimmering illusion of heat that was not heat, that was prelude to his teleportation ... if that were the word. It was neither pleasant nor unpleasant; it was , that was all. He called it the training hall, not because it looked like a training hall but because that was its function. It didn't actually look like anything save some half-nourished dream a surrealist might have discarded as too nightmarish for belief. As in all of this strange universe, excepting the dome-cages in which the captives were held, the training hall followed no rules of three-dimensional space. One wall looked normal for perhaps a third of its length, then it simply wasn't for a bit. It came back farther on at an impossible angle. Yet, walking along it, touching it, it felt perfectly smooth and continuously straight. The opposite wall resembled a diagonal cross-section of an asymmetrical dumbbell—that was the closest Tennant could come to it in words. And it, too, felt straight. The floor looked like crystal smashed by some cosmic impact, yet it had reason. He knew this even though no reason was apparent to his three-dimensional vision. The ceiling, where he could see it, was beyond description. The captor Tennant called Opal came in through a far corner of the ceiling. He—if it was a he—was not large, although this, Tennant knew, meant nothing; Opal might extend thousands of yards in some unseen direction. He had no regular shape and much of him was iridescent and shot with constantly changing colors. Hence the name Opal. Communication was telepathic. Tennant could have yodeled or yelled or sung Mississippi Mud and Opal would have shown no reaction. Yet Tennant suspected that the captors could hear somewhere along the auditory scale, just as perhaps they could smell, although not in any human sense. You will approach without use of your appendages. The command was as clear as if it had been spoken aloud. Tennant took a deep breath. He thought of the space beside Opal. It took about three seconds and he was there, having spanned a distance of some ninety feet. He was getting good at it. Dog does trick, he thought. He went through the entire routine at Opal's bidding. When at last he was allowed to relax, he wondered, not for the first time, if he weren't mastering some of the alleged Guru arts. At once he felt probing investigation. Opal, like the rest of the captors, was as curious as a cat—or a human being. Tennant sat against a wall, drenched with sweat. There would be endless repetition before his workout was done. On Earth, dogs were said to be intellectually two-dimensional creatures. He wondered if they felt this helpless futility when their masters taught them to heel, to point, to retrieve. Some days later, the training routine was broken. He felt a sudden stir of near-sick excitement as he received the thought: Now you are ready. We are going through at last. Opal was nervous, so much so that he revealed more than he intended. Or perhaps that was his intent; Tennant could never be sure. They were going through to Tennant's own dimension. He wondered briefly just what his role was to be. He had little time to speculate before Opal seemed to envelop him. There was the blurring wrench of forced teleportation and they were in another room, a room which ended in a huge irregular passage that might have been the interior of a giant concertina—or an old-fashioned kodak. He stood before a kidney-shaped object over whose jagged surface colors played constantly. From Opal's thoughts it appeared to be some sort of ultradimensional television set, but to Tennant it was as incomprehensible as an oil painting to an animal. Opal was annoyed that Tennant could make nothing of it. Then came the thought: What cover must your body have not to be conspicuous? Tennant wondered, cynically, what would happen if he were to demand a costume of mediaeval motley, complete with Pied Piper's flute. He received quick reproof that made his head ring as from a blow. He asked Opal where and when they were going, was informed that he would soon emerge on Earth where he had left it. That told him everything but the date and season. Opal, like the rest of the captors, seemed to have no understanding of time in a human sense. Waiting, Tennant tried not to think of his wife, of the fact that he hadn't seen her in—was it more than a year and a half on Earth? He could have controlled his heartbeat with one of his new powers, but that might have made Opal suspicious. He should be somewhat excited. He allowed himself to be, though he obscured the reasons. He was going to see his wife again ... and maybe he could trick his way into not returning. The maid who opened the door for him was new, although her eyes were old. But she recognized him and stood aside to let him enter. There must, he thought, still be pictures of him around. He wondered how Agatha could afford a servant. "Is Mrs. Tennant in?" he asked. She shook her head and fright made twin stoplights of the rouge on her cheeks as she shut the door behind him. He went into the living room, directly to the long silver cigarette box on the coffee table. It was proof of homecoming to fill his lungs with smoke he could smell . He took another drag, saw the maid still in the doorway, staring. "There's no need for fright," he told her. "I believe I still own this house." Then, "When do you expect Mrs. Tennant?" "She just called. She's on her way home from the club." Still looking frightened, she departed for the rear of the house. Tennant stared after her puzzledly until the kitchen door swung shut behind her. The club? What club? He shrugged, returned to the feeling of comfort that came from being back here, about to see Agatha again, hold her close in no more than a few minutes. And stay, his mind began to add eagerly, but he pushed the thought down where Opal could not detect it. He took another deep, lung-filling drag on his cigarette, looked around the room that was so important a part of his life. The three women back there would be in a ghastly spot. He felt like a heel for wanting to leave them there, then knew that he would try somehow to get them out. Not, of course, anything that would endanger his remaining with Agatha; the only way his captors would get him back would be as a taxidermist's specimen. He realized, shocked and scared, that his thoughts of escape had slipped past his mental censor, and he waited apprehensively for Opal to strike. Nothing happened and he warily relaxed. Opal wasn't tapping his thoughts. Because he felt sure of his captive ... or because he couldn't on Earth? It was like being let out of a cage. Tennant grinned at the bookcase; the ebony-and-ivory elephants that Agatha had never liked were gone, but he'd get them back or another pair. The credenza had been replaced by a huge and ugly television console. That, he resolved, would go down in the cellar rumpus room, where its bleached modernity wouldn't clash with the casual antiquity of the living room. Agatha would complain, naturally, but his being back would make up for any amount of furniture shifting. He imagined her standing close to him, her lovely face lifted to be kissed, and his heart lurched like an adolescent's. This hunger was real, not implanted. Everything would be real ... his love for her, the food he ate, the things he touched, his house, his life.... Your wife and a man are approaching the house. The thought message from Opal crumbled his illusion of freedom. He sank down in a chair, trying to refuse to listen to the rest of the command: You are to bring the man through the gateway with you. We want another live male. Tennant shook his head, stiff and defiant in his chair. The punishment, when it came, was more humiliating than a slap across a dog's snout. Opal had been too interested in the next lab specimen to bother about his thoughts—that was why he had been free to think of escape. Tennant closed his eyes, willed himself to the front window. Now that he had mastered teleportation, it was incredible how much easier it was in his own world. He had covered the two miles from the gateway to the house in a mere seven jumps, the distance to the window in an instant. But there was no pleasure in it, only a confirmation of his captor's power over him. He was not free of them. He understood all too well what they wanted him to do; he was to play the Judas goat ... or rather the Judas ram, leading another victim to the fourth-dimensional pen. Grim, he watched the swoop of headlights in the driveway and returned to the coffee table, lit a fresh cigarette. The front door was flung open and his diaphragm tightened at the remembered sound of Agatha's throaty laugh ... and tightened further when it was followed by a deeper rumbling laugh. Sudden fear made the cigarette shake in his fingers. "... Don't be such a stuffed-shirt, darling." Agatha's mocking sweetness rang alarm-gongs in Tennant's memory. "Charley wasn't making a grab for me . He'd had one too many and only wanted a little fun. Really, darling, you seem to think that a girl...." Her voice faded out as she saw Tennant standing there. She was wearing a white strapless gown, had a blue-red-and-gold Mandarin jacket slung hussar-fashion over her left shoulder. She looked even sleeker, better groomed, more assured than his memory of her. "I'm no stuffed-shirt and you know it." Cass' tone was peevish. "But your idea of fun, Agatha, is pretty damn...." It was his turn to freeze. Unbelieving, Tennant studied his successor. Cass Gordon—the man , the ex-halfback whose bulk was beginning to get out of hand, but whose inherent aggressive grace had not yet deserted him. The man , that was all—unless one threw in the little black mustache and the smooth salesman's manner. "You know, Cass," Tennant said quietly, "I never for a moment dreamed it would be you." " Roger! " Agatha found her voice. "You're alive !" "Roger," repeated Tennant viciously. He felt sick with disgust. Maybe he should have expected a triangle, but somehow he hadn't. And here it was, with all of them going through their paces like a trio of tent-show actors. He said, "For God's sake, sit down." Agatha did so hesitantly. Her huge dark eyes, invariably clear and limpid no matter how much she had drunk, flickered toward him furtively. She said defensively, "I had detectives looking for you for six months. Where have you been, Rog? Smashing up the car like that and—disappearing! I've been out of my mind." "Sorry," said Tennant. "I've had my troubles, too." Agatha was scared stiff—of him. Probably with reason. He looked again at Cass Gordon and found that he suddenly didn't care. She couldn't say it was loneliness. Women have waited longer than eighteen months. He would have if his captors had let him. "Where in hell have you been, Rog?" Gordon's tone was almost parental. "I don't suppose it's news to you, but there was a lot of suspicion directed your way while that crazy killer was operating around here. Agatha and I managed to clear you." "Decent of you," said Tennant. He got up, crossed to the cabinet that served as a bar. It was fully equipped—with more expensive liquor, he noticed, than he had ever been able to afford. He poured a drink of brandy, waited for the others to fill their glasses. Agatha looked at him over the rim of hers. "Tell us, Rog. We have a right to know. I do, anyway." "One question first," he said. "What about those killings? Have there been any lately?" "Not for over a year," Cass told him. "They never did get the devil who skinned those bodies and removed the heads." So, Tennant thought, they hadn't used the gateway. Not since they had brought the four of them through, not since they had begun to train him for his Judas ram duties. Agatha was asking him if he had been abroad. "In a way," he replied unemotionally. "Sorry if I've worried you, Agatha, but my life has been rather—indefinite, since I—left." He was standing no more than four inches from this woman he had desired desperately for six years, and he no longer wanted her. He was acutely conscious of her perfume. It wrapped them both like an exotic blanket, and it repelled him. He studied the firm clear flesh of her cheek and chin, the arch of nostril, the carmine fullness of lower lip, the swell of bosom above low-cut gown. And he no longer wanted any of it or of her. Cass Gordon— It didn't have to be anybody at all. For it to be Cass Gordon was revolting. "Rog," she said and her voice trembled, "what are we going to do? What do you want to do?" Take her back? He smiled ironically; she wouldn't know what that meant. It would serve her right, but maybe there was another way. "I don't know about you," he said, "but I suspect we're in the same boat. I also have other interests." "You louse!" said Cass Gordon, arching rib cage and nostrils. "If you try to make trouble for Agatha, I can promise...." " What can you promise?" demanded Tennant. When Gordon's onset subsided in mumbles, he added, "Actually, I don't think I'm capable of making more than a fraction of the trouble for either of you that you both are qualified to make for yourselves." He lit a cigarette, inhaled. "Relax. I'm not planning revenge. After this evening, I plan to vanish for good. Of course, Agatha, that offers you a minor nuisance. You will have to wait six years to marry Cass—seven years if the maid who let me in tonight talks. That's the law, isn't it, Cass? You probably had it all figured out." "You bastard," said Cass. "You dirty bastard! You know what a wait like that could do to us." "Tristan and Isolde," said Tennant, grinning almost happily. "Well, I've had my little say. Now I'm off again. Cass, would you give me a lift? I have a conveyance of sorts a couple of miles down the road." He needed no telepathic powers to read the thoughts around him then. He heard Agatha's quick intake of breath, saw the split-second look she exchanged with Cass. He turned away, knowing that she was imploring her lover to do something, anything , as long as it was safe. Deliberately, Tennant poured himself a second drink. This might be easier and pleasanter than he had expected. They deserved some of the suffering he had had and there was a chance that they might get it. Tennant knew now why he was the only male human the captors had been able to take alive. Apparently, thanks to the rain-slick road, he had run the sedan into a tree at the foot of the hill beyond the river. He had been sitting there, unconscious, ripe fruit on their doorstep. They had simply picked him up. Otherwise, apparently, men were next to impossible for them to capture. All they could do was kill them and bring back their heads and hides as trophies. With women it was different—perhaps the captors' weapons, whatever they were, worked more efficiently on females. A difference in body chemistry or psychology, perhaps. More than once, during his long training with Opal, Tennant had sent questing thoughts toward his captor, asking why they didn't simply set up the gateway in some town or city and take as many humans as they wanted. Surprisingly there had been a definite fear reaction. As nearly as he could understand, it had been like asking an African pygmy, armed with a blowgun, to set up shop in the midst of a herd of wild elephants. It simply wasn't feasible—and furthermore he derived an impression of the tenuosity as well as the immovability of the gateway itself. They could be hurt, even killed by humans in a three-dimensional world. How? Tennant did not know. Perhaps as a man can cut finger or even throat on the edge of a near-two-dimensional piece of paper. It took valor for them to hunt men in the world of men. In that fact lay a key to their character—if such utterly alien creatures could be said to have character.
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C. eclectic
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What does it mean to be Cured?
A. Cured humans are genetically superior to Normal humans
B. Cured humans have received an intervention for their phobia
C. Cured humans are allowed to reproduce while Normals aren't
D. Cured humans are fearless while Normals live their lives in fear
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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.
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B. Cured humans have received an intervention for their phobia
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What did Casey probably learn from this experience?
A. Never give up on your friends
B. Never trust a crook
C. Always listen carefully to instructions
D. Don't judge others by how they look
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JUPITER'S JOKE By A. L. HALEY Casey Ritter, the guy who never turned down a dare, breathed a prayer to the gods of idiots and spacemen, and headed in toward the great red spot of terrible Jupiter. [Transcriber's Note: This etext was produced from Planet Stories Fall 1954. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Those methane and ammonia planets, take it from me, they're the dead-end of creation, and why the Old Man ever thought them up I'll never know. I never thought I'd mess around any of them, but things can sure happen. A man can get himself backed into a corner in this little old solar system. It just ain't big enough for a gent of scope and talent; and the day the Solar System Customs caught me red-handed smuggling Kooleen crystals in from Mars, I knew I was in that corner, and sewed up tight. Sure, the crystals are deadly, but I was smuggling them legitimately, in a manner of speaking, for this doctor to experiment with. He wasn't going to sell them for dope. But—and this was the 'but' that was likely to deprive the System of my activities—even experimenting with them was illegal even if it needed to be done; also, I had promised not to rat on him before taking the job. Well, Casey Ritter may be a lot of things we won't mention, but he doesn't rat on his clients. So there I was, closeted with the ten members of the S.S. Customs Court, getting set to hear the gavel fall and the head man intone the sentence that would take me out of circulation for a long, long time. And instead, blast me, if they didn't foul me with this trip to good old Jupiter. I didn't get it at first. I'd argued with 'em, but inside I'd been all set for the sentence, and even sort of reconciled to it. I could even hear the words in my mind. But they didn't match what the judge was saying. I stood there gaping like a beached fish while I sorted it out. Then I croaked, "Jupiter! What for? Are you running outa space in stir? Want to choke me to death in chlorine instead?" Being civil to the court didn't seem important just then. Jupiter was worse than the pen, a lot worse. Jupiter was a death sentence. The senior judge rapped sharply with his gavel. He frowned me down and then nodded at the judge on his right. This bird, a little old hank of dried-up straw, joined his fingertips carefully, cleared his scrawny throat, and told me what for. "You've no doubt heard tales of the strange population of Jupiter," he said. "Every spaceman has, I am sure. Insect-like creatures who manifestly migrated there from some other system and who inhabit the Red Spot of the planet, floating in some kind of artificial anti-gravity field in the gaseous portion of the atmosphere—" I snorted. "Aw, hell, judge, that's just one of those screwy fairy tales! How could any—" The senior judge rapped ferociously, and I skidded to a halt. Our little story teller patiently cleared his skinny throat again. "I assure you it is no fairy tale. We possess well-authenticated photographs of these inhabitants, and if you are prepared to visit them and in some way worm from them the secret of their anti-gravity field, the government stands ready to issue you a full pardon as well as a substantial monetary reward. Your talents, Mr. Ritter, seem, shall we say, eminently suited to the task." He beamed at me. I looked around. They were all beaming. At me! Suddenly I smelled a rat as big as an elephant. That whole Kooleen caper: Had it been just a trap to lead me straight to this? I hadn't been able to figure how they'd cracked my setup.... At the thought my larynx froze up tight. This was worse than I'd thought. Government men trapping me and then beaming at me. And a full pardon. And a reward. Oh, no! I told myself, it wasn't possible. Not when I already had more counts against me than a cur has fleas. Not unless it was a straight suicide mission! I feebly massaged my throat. "Pictures?" I whispered. "Show me 'em." Crude, but it was all I could squeeze out. I squeezed out more when I saw those pictures, though. Those inhabitants were charming, just charming if you like scorpions. Well, a cross between a scorpion and a grasshopper, to be accurate. Floating among that red stuff, they showed up a kind of sickly purple turning to gangrene around the edges. The bleat of anguish that accompanied my first view of those beauties had taken my voice again. "How big?" I whispered. He shrugged, trying for nonchalance. "About the size of a man, I believe." I raised my shrinking head. "Take me to jail!" I said firmly, and collapsed onto my chair. A crafty-eyed buzzard across the table leaned toward me. "So this is the great Casey Ritter, daredevil of the Solar System!" he sneered. "Never loses a bet, never turns down a dare!" I shuddered. "You're telling that one! And besides, a man's got to draw the line somewhere. And I'm drawing it right here. Take me to jail!" They were really stumped. They hadn't expected me to take this attitude at all. No doubt they had it figured that I'd gratefully throw myself into a sea of ammonia among man-size scorpions just for the hell of it. Nuts! After all, in the pen a man can eat and breathe, and a guard won't reach in and nip off an arm or leg while he's got his back turned. How stupid could they get? When I finally wore them down and got to my little cell, I looked around it with a feeling of real coziness. I even patted the walls chummily and snapped a salute at the guard. It makes me grind my molars now to think of it. The way that bunch of stuffed shirts in the S.S.C. made a gold-barred chimpanzee out of me has broken my spirit and turned me into an honest trader. Me, Casey Ritter, slickest slicker in the Solar System, led like a precious infant right where I'd flatly refused to go! In plain English, I underestimated the enemy. Feeling safe and secure in the grip of the good old Iron College, I relaxed. At this strategic point, the enemy planted a stoolie on me. Not in my cell block. They were too smart for that. But we met at recreation, and his mug seemed familiar, like a wisp of smoke where no smoke has got a right to be; and after awhile I braced him. I was right. I'd met the shrimp before when I was wound up in an asteroid real estate racket. Pard Hoskins was his alias, and he had the tag of being a real slick operator. We swapped yarns for about a week when we met, and then I asked him what's his rap this trip. "Oh, a pretty good jolt if they can keep hold of me," he says. "I just made a pass at the Killicut Emeralds, that's all, and got nabbed." "Oh, no!" I moaned. "What were you trying to do, start a feud between us and Mars?" He shrugged, but his little black-currant eyes began to sparkle with real passion, the high voltage kind that only a woman in a million, or a million in a bank, can kindle in a guy. "Buddy," he said reverently, "I'd start more than that just to get me mitts on them stones again! Why, you ain't never seen jools till you've seen them! Big as hen's eggs, an even dozen of 'em; and flawless, I'm a-shoutin', not a flaw!" His eyes watered at the memory, yearning like a hound-dog's over a fresh scent. I couldn't believe it. Those emeralds were in the inner shrine of the super-sacred, super-secret temple of the cavern-dwelling tribe of Killicuts on Mars—the real aborigines. Bleachies, we call them, sort of contemptuously; but those Bleachies are a rough lot when they're mad, and if Pard had really got near those emeralds, he should be nothing but a heap of cleaned bones by now. Either he was the world's champion liar or its bravest son, and either way I took my hat off to him. "How'd you make the getaway?" I asked, taking him at his word. He looked loftily past me. "Sorry. Gotta keep that a secret. Likewise where I cached 'em." "Cached what?" "The rocks, stupe." I hardly heard the cut. "You mean you really did get away with them?" My jaw must've been hanging down a foot, because I'd just been playing along with him, not really believing him, and now all of a sudden I somehow knew that he'd really lifted those emeralds. But how? It was impossible. I'd investigated once myself. He nodded and then moved casually away. I looked up and saw a guard coming. That night I turned on my hard prison cot until my bones were so much jelly, trying to figure that steal. The next morning I got up burning with this fever for information, only to find that Pard had got himself put in solitary for mugging a guard, and that really put the heat on me. I chewed my fingernails down to the quick by the time he got out a week later. By that time he really had me hooked. I'd of sworn he was leveling with me. But he wouldn't tell me how he'd worked the steal. Instead, he opened up on the trade he'd booked for the string. He said, "When I chisel me way outa this squirrel cage, I'm gonna hit fer good old Jupe and sell 'em to Akroida. She's nuts about jools. What that old girl won't give me fer 'em—" He whistled appreciatively, thinking about it. "Jupiter!" I goggled at him. "Akroida! Who's she?" He looked at me as if I hadn't yet got out from under the rock where he was sure I'd been born. "Don't you know nothin', butterhead?" From him I took it. I even waited patiently till the master spoke again. The memory still makes me fry. "Akroida," he explained in his own sweet time, "is the queen-scorp of them idiotic scorpions that lives on Jupiter. I sold her the Halcyon Diamond that disappeared from the World Museum five years ago, remember?" He winked broadly. "It come from Mars in the first place, you know. Mars! What a place fer jools! Damn desert's lousy with 'em, if it wasn't so much trouble to dig 'em out—" He went off into a dream about the rocks on Mars but I jerked him back. "You mean those scorpions have really got brains?" "Brains!" he snorted. "Have they got brains! Why, they're smarter than people! And not ferocious, neither, in spite of how they look, if you just leave 'em alone. That's all they want, just to be left alone. Peace an' quiet, and lots of methane and ammonia and arsenic, that's fer them. Besides, the space suit rig you got to wear, they can't bite you. Akroida's not a bad old girl. Partial to arsenic on her lettuce, so I brought her a hundred pounds of the stuff, an' she went fer that almost like it was diamonds, too. Did I rate around there fer awhile!" He sighed regretfully. "But then I went and made her mad, an' I'm kinda persona non grata there right now. By the time I gnaw outa this here cheese trap, though, I figger she'll be all cooled off and ready fer them emeralds." I went back to my cot that night, and this time instead of biting my nails, I bit myself. So I faced it. Casey Ritter lost his nerve, and along with it, the chance of a lifetime. A better man than me had already penetrated the Great Red Spot of old Jupiter and come out alive. That thought ate me to the quick, and I began to wonder if it was too late, after all. I could hardly wait for morning to come, so that I could pry more information out of Pard Hoskins. But I didn't see Pard for a few days. And then, a week later, a group of lifers made a break that didn't jell, and the whole bunch was locked up in the blockhouse, the special building reserved for escapees. Pard Hoskins was in the bunch. He'd never get out of there, and he knew it. So did I. For three more days I worked down my knuckles, my nails being gone, while I sat around all hunched up, wondering feverishly if Pard would make a deal about those emeralds. Then I broke down and sent out a letter to the S.S.C. The Big Sneer of the conference table promptly dropped in on me, friendly as a bottle of strychnine. But for a lad headed for Jupiter that was good training, so I sneered right back at him, explained the caper, and we both paid a visit to Pard. In two days the deal was made and the caper set up. There were a few bits of info that Pard had to shell out, like where the emeralds were, and how to communicate with those scorpions, and how he'd made Akroida mad. "I put on a yeller slicker," he confessed sadly. "That there ammonia mist was eatin' into the finish on my spacesuit, so I draped this here slicker around me to sorta fancy up the rig before goin' in to an audience with the old rip." He shook his head slowly. "The kid that took me in was colorblind, so I didn't have no warning at all. I found out that them scorpions can't stand yeller. It just plain drives them nuts! Thought they'd chaw me up and spit me out into the chlorine before I could get outa the damn thing. If my colorblind pal hadn't helped me, they'd of done it, too. And Akroida claimed I done it a-purpose to upset her." Then he winked at me. "But then I got off in a corner and cooked up some perfume that drives them nuts the other way; sorta frantic with ecstasy, like the book says. Didn't have a chance to try it on Akroida, though. She wouldn't give me another audience. It's in the stuff they cleaned outa me room: a poiple bottle with a bright green stopper." He ruminated a few minutes. "Tell you what, chump. Make them shell out with a green an' poiple spacesuit—them's the real Jupiter colors—an' put just a touch o' that there perfume on the outside of it. Akroida'll do anything fer you if she just gets a whiff. Just anything! But remember, don't use but a drop. It's real powerful." II Real powerful, said the man. What an understatement! But the day I was set adrift in that sea of frozen ammonia clouds mixed with nice cozy methane gas I sure prayed for it to be powerful, and I clutched that tiny bottle like that boy Aladdin clutching his little old lamp. I'd had a lot of cooperation getting that far. An Earth patrol had slipped down onto the Red Desert of Mars and picked up the Killicut Emeralds from where Pard Hoskins had cached them; and safe out in space again, we had pored over that string of green headlights practically slobbering. But the Big Sneer of the S.S.C., the fellow that had got me into this caper, was right there to take the joy out of it all and to remind me that this was public service, strictly. "These—" he had proclaimed with a disdainful flourish, like a placer miner pointing to a batch of fool's gold—"These jewels are as nothing, Ritter, compared with the value of the secret you are to buy with them. And be assured that if you're man enough to effect the trade—" He paused, his long nose twitching cynically—"IF you succeed, your reward will be triple what you could get for them in any market. Added to which, IF you succeed, you will be a free man." That twitch of the nose riled me no little. "I ain't failed yet!" I snarled at him. "Just you wait till I do, feller!" I slipped the string of emeralds back into its little safe. "Instead of sniping at me, why don't you get that brain busy and set our rendezvous?" With that we got down to business and fixed a meeting point out on Jupiter's farthest moon; then they took me in to the edge of Jupiter's ice-cloud and turned me loose in a peanut of a space boat with old Jupe looming ahead bigger than all outdoors and the Red Spot dead ahead. I patted my pretty enameled suit, which was a study in paris green and passionate purple. I patted the three hundred pounds of arsenic crystals for Akroida and anyone else I might have to bribe. I anxiously examined my suit's air and water containers and the heating unit that would keep them in their proper state. I had already gone over the space boat. Yeah, I was as nervous as a cat with new kittens. Feeling again for my little bottle of horrid stench, I breathed a prayer to the god of idiots and spacemen, and headed in. The big ship was long gone, and I felt like a mighty small and naked microbe diving into the Pacific Ocean. That famous Red Spot was that big, too. It kept expanding until the whole universe was a fierce, raw luminous red. Out beyond it at first there had been fringes of snow-white frozen ammonia, but now it was all dyed redder than Mars. Then I took the plunge right into it. Surprise! The stuff was plants! Plants as big as meadows, bright red, floating around in those clouds of frozen ammonia like seaweed! Then I noticed that the ammonia around them wasn't frozen any more and peeked at the outside thermometer I couldn't believe it. It was above zero. Then I forgot about the temperature because it dawned on me that I was lost. I couldn't see a thing but drifting ammonia fog and those tangles of red floating plants like little islands all around. Cutting down the motor, I eased along. But my green boat must have showed up like a lighthouse in all that red, because it wasn't long until I spotted a purple and green hopper-scorp traveling straight toward me, sort of rowing along with a pair of stubby wings. He didn't seem to be making much effort, even though he was climbing vertically up from the planet. In fact, he didn't seem to be climbing at all but just going along horizontally. There just wasn't any up or down in that crazy place. It must be that anti-grav field, I concluded. The air was getting different, too, now that I was further in. I'm no chemist, and I couldn't have gotten out there to experiment if I had been, but those plants were certainly doing something to that ammonia and methane. The fog thinned, for one thing, and the temperature rose to nearly forty. Meanwhile the hopper-scorp reached the ship. Hastily I squirted some of my Scorpion-Come-Hither lure on the chest of my spacesuit, opened the lock, and popped out, brave as could be. Face to face with that thing, though, I nearly lost my grip on the handle. In fact, I'd have fainted dead away right there if Pard Hoskins hadn't been there already and lived. If that little shrimp could do it, I could, too. I braced up and tapped out the greeting Pard had taught me. My fiendish-looking opponent tapped right back, inquiring why the hell I was back so soon when I knew that Akroida was all set to carve me into steaks for just any meal. But the tone was friendly and even intimate—or rather, the taps were. There was even a rather warm expression discernible in the thing's eyes, so I took heart and decided to ignore the ferocious features surrounding those eyes. After all, the poor sinner's map was made of shell, and he wasn't responsible for its expression. I tapped back very politely that he must be mistaking me for someone else. "I've never been here before, and so I've never met the charming lady," I informed him. "However, I have something very special in the way of jewels—not with me, naturally—and the rumor is that she might be interested." He reared back at that, and reaching up, plucked his right eye out of the socket and reeled it out to the end of a two-foot tentacle, and then he examined me with it just like an old-time earl with one of those things they called monocles. Pard hadn't warned me about those removable eyes, for reasons best known to himself. I still wake up screaming.... Anyway, when that thing pulled out its eye and held it toward me, I backed up against the side of the ship like I'd been half-electrocuted. Then I gagged. But I could still remember that I had to live in that suit for awhile, so I held on. Then that monstrosity reeled in the eye, and I gagged again. My actions didn't bother him a bit. "Jewels, did you say?" he tapped out thoughtfully, just like an ordinary business man, and I managed to tap out yes. He drifted closer; close enough to get a whiff.... A shudder of ecstasy stiffened him. His head and eyes rolled with it, and he wafted closer still. Right there I began to harbor a premonition that there might be such a thing as being too popular in Scorpdom, but I thrust this sneak-thief idea back into limbo. Taking advantage of his condition, I boldly tapped out, "How's about taking me on a guided tour through this red spinach patch to Akroida, old pal?" Or words to that effect. He lolled his hideous cranium practically on my shoulder. "Anything! Just anything you desire, my dearest friend." I tried to back off from him a bit, but the ship stopped me. "I'm Casey Ritter. What's your label, chum?" "Attaboy," he ticked coyly. "Attaboy?" Things blurred around me. It couldn't be. It was just plain nuts. Then I got a glimmer through my paralyzed gray matter. "Who named you that?" He simpered. "My dear friend, Pard Hoskins." I breathed again. How simple could I get? He'd already mistaken me for Pard, hadn't he? Then I remembered something else. "How come you aren't mad at him? Don't you hate yellow, too?" He hung his silly head. "I fear I am colorblind," he confessed sadly. Right there I forgave him for pulling that eye on me. He was the guide I needed, the one who had got Pard out alive. I almost hugged him. "Lead off, old pal," I sang out, and then had to tap it. "I'll follow in my boat." Well, I'd met the first of the brood and was still alive. Not only alive but loved and cherished, thanks to Pard's inventiveness and to a kindly fate which had sent Pard's old pal my way. A great man, Pard Hoskins. How had he made friends with the brute in the first place? Being once more inside my spaceboat, I raised my helmet, which was like one of those head-pieces they used to put on suits of armor instead of the usual plastic bubble. And it was rigged out with phony antennae and mandibles and other embellishments calculated to interest my hosts. Whether it interested them or not, it was plenty uncomfortable for me. Peeking out the porthole I saw that my guide was fidgeting and looking over his shoulder at my ship, so I eased in the controls and edge after him. To my surprise a vapor shot out of a box that I had taken for a natural lump on his back, and he darted away from me. I opened the throttle and tore after him among the immense red blobs that were now beginning to be patterned with dozens of green-and-purple scorpions, all busy filling huge baskets with buds and tendrils, no doubt. Other scorpions oared and floated about in twos and threes in a free and peaceable manner that almost made me forget that I was scared to death of them, and they stared at my boat with only a mild interest that would have taught manners to most of my fellow citizens of Earth. It wasn't until we had covered some two hundred miles of this that something began to loom out of the mist, and I forgot the playboys and the field workers. It loomed higher and higher. Then we burst out into a clearing several miles in diameter, and I saw the structure clearly. It was red, like everything else in this screwy place, and could only have been built out of compressed blocks of the red plant. In shape it was a perfect octagon. It hung poised in the center of the cleared space, suspended on nothing. It had to be at least a mile in diameter, and its sides were pierced with thousands of openings through which its nightmare occupants appeared and disappeared, drifting in and out like they had all the time in the world. I stared until my eyeballs felt paralyzed. Pard was right again. These critters had brains. And my S.S.C. persecutor was right, too. That anti-grav secret was worth more than any string of rocks in the system, including the Killicut Emeralds. Then I swallowed hard. Attaboy was leading me straight across to a window. Closing my helmet, my fingers fumbled badly. My brain was fumbling, too. "Zero hour, chump!" it told me, and I shuddered. Picking up the first hundred pounds of the arsenic, I wobbled over to the airlock. III That palace was like nothing on earth. Naturally, you'll say, it's on Jupiter. But I mean it was even queerer than that. It was like no building on any planet at all. And, in fact, it wasn't on a planet; it was floating up there only two hundred miles in from the raw edge of space. In that building everything stayed right where it was put. If it was put twelve or fifty feet up off the floor, it stayed there. Not that there wasn't gravity. There was plenty of gravity to suit me—just right, in fact—and still they had furniture sitting around in the air as solid as if on a floor. Which was fine for flying hopper-scorps, but what about Casey Ritter, who hadn't cultivated even a feather? Attaboy, however, had the answers for everything. Towing me from the airlock to the window ledge, he again sniffed that delectable odor on my chest, caressed me with his front pair of legs while I manfully endured, and then without warning tossed me onto his back above the little box and flew off with me along a tunnel with luminous red walls. We finally came to the central hall of the palace, and at the sight of all that space dropping away, I clutched at his shell and nearly dropped the arsenic. But he didn't have any brakes I could grab, so he just flew out into mid-air in a room that could have swallowed a city block, skyscrapers and all. It was like a mammoth red cavern, and it glowed like the inside of a red light. No wonder those scorpions like green and purple. What a relief from all that red! A patch in the middle of the hall became a floating platform holding up a divan twenty feet square covered with stuff as green as new spring grass, and in the center of this reclined Akroida. It had to be. Who else could look like that? No one, believe me, boys and girls, no one! Our little Akroida was a pure and peculiarly violent purple—not a green edge anywhere. She was even more purple than my fancy enameled space suit, and she was big enough to comfortably fill most of that twenty-foot couch. To my shrinking eyes right then she looked as big as a ten-ton cannon and twice as mean and dangerous. She was idly nipping here and there as though she was just itching to take a hunk out of somebody, and the way the servants were edging away out around her, I could see they didn't want to get in range. I didn't blame them a bit. Under the vicious sag of her Roman nose, her mandibles kept grinding, shaking the jewels that were hung all over her repulsive carcass, and making the Halcyon Diamond on her chest blaze like a bonfire. Attaboy dumped me onto a floating cushion where I lay clutching and shuddering away from her and from the void all around me, and went across to her alone with the arsenic. Akroida rose up sort of languidly on an elbow that was all stripped bone and sharp as a needle. She pulled an eyeball out about a yard and scanned Attaboy and the box. He closed in to the couch all hunched over, ducked his head humbly half-a-dozen times, and pushed the box over beside her. Akroida eased her eyeball back, opened the box and sniffed, and then turned to Attaboy with a full-blown Satanic grin. I could hear her question reverberate away over where I was. "Who from?" asked Akroida. That conversation was telegraphed to me blow by blow by the actions of those hopper-scorps. I didn't need their particular brand of Morse Code at all. "Who from?" Attaboy cringed lower and blushed a purple all-over blush. "Dear lady, it is from an interspace trader who possesses some truly remarkable jewels," he confessed coyly. Akroida toyed with the Halcyon Diamond and ignored the bait. "His name?" she demanded. And when he told her, with a bad stutter in his code, she reared up higher on her skinny elbow and glared in my direction. "Casey Ritter? Never heard of him. Where's he from?" Well, after all, she wasn't blind. He had to confess. "I—uh—the stones were so amazing, Royal Akroida, that I didn't pay much attention to the—uh—trader. He does seem to resemble an—ah—earthman." He ducked his head and fearfully waited. A sort of jerking quiver ran through Akroida. She reared up even higher. Her mean Roman nose twitched. "An earthman? Like Pard Hoskins?" Attaboy shrank smaller and smaller. He could only nod dumbly. The storm broke, all right. That old dame let out a scream like a maddened stallion and began to thrash around and flail her couch with that dragon's tail of hers.
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B. Never trust a crook
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What was on exhibit in the Groacian parade?
A. Groacian government officials
B. people they had taken as prisoners
C. animals from all over the galaxy
D. people visiting from Earth
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THE MADMAN FROM EARTH BY KEITH LAUMER You don't have to be crazy to be an earth diplomat—but on Groac it sure helps! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, March 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I "The Consul for the Terrestrial States," Retief said, "presents his compliments, et cetera, to the Ministry of Culture of the Groacian Autonomy, and with reference to the Ministry's invitation to attend a recital of interpretive grimacing, has the honor to express regret that he will be unable—" "You can't turn this invitation down," Administrative Assistant Meuhl said flatly. "I'll make that 'accepts with pleasure'." Retief exhaled a plume of cigar smoke. "Miss Meuhl," he said, "in the past couple of weeks I've sat through six light-concerts, four attempts at chamber music, and god knows how many assorted folk-art festivals. I've been tied up every off-duty hour since I got here—" "You can't offend the Groaci," Miss Meuhl said sharply. "Consul Whaffle would never have been so rude." "Whaffle left here three months ago," Retief said, "leaving me in charge." "Well," Miss Meuhl said, snapping off the dictyper. "I'm sure I don't know what excuse I can give the Minister." "Never mind the excuses," Retief said. "Just tell him I won't be there." He stood up. "Are you leaving the office?" Miss Meuhl adjusted her glasses. "I have some important letters here for your signature." "I don't recall dictating any letters today, Miss Meuhl," Retief said, pulling on a light cape. "I wrote them for you. They're just as Consul Whaffle would have wanted them." "Did you write all Whaffle's letters for him, Miss Meuhl?" "Consul Whaffle was an extremely busy man," Miss Meuhl said stiffly. "He had complete confidence in me." "Since I'm cutting out the culture from now on," Retief said, "I won't be so busy." "Well!" Miss Meuhl said. "May I ask where you'll be if something comes up?" "I'm going over to the Foreign Office Archives." Miss Meuhl blinked behind thick lenses. "Whatever for?" Retief looked thoughtfully at Miss Meuhl. "You've been here on Groac for four years, Miss Meuhl. What was behind the coup d'etat that put the present government in power?" "I'm sure I haven't pried into—" "What about that Terrestrial cruiser? The one that disappeared out this way about ten years back?" "Mr. Retief, those are just the sort of questions we avoid with the Groaci. I certainly hope you're not thinking of openly intruding—" "Why?" "The Groaci are a very sensitive race. They don't welcome outworlders raking up things. They've been gracious enough to let us live down the fact that Terrestrials subjected them to deep humiliation on one occasion." "You mean when they came looking for the cruiser?" "I, for one, am ashamed of the high-handed tactics that were employed, grilling these innocent people as though they were criminals. We try never to reopen that wound, Mr. Retief." "They never found the cruiser, did they?" "Certainly not on Groac." Retief nodded. "Thanks, Miss Meuhl," he said. "I'll be back before you close the office." Miss Meuhl's face was set in lines of grim disapproval as he closed the door. The pale-featured Groacian vibrated his throat-bladder in a distressed bleat. "Not to enter the Archives," he said in his faint voice. "The denial of permission. The deep regret of the Archivist." "The importance of my task here," Retief said, enunciating the glottal dialect with difficulty. "My interest in local history." "The impossibility of access to outworlders. To depart quietly." "The necessity that I enter." "The specific instructions of the Archivist." The Groacian's voice rose to a whisper. "To insist no longer. To give up this idea!" "OK, Skinny, I know when I'm licked," Retief said in Terran. "To keep your nose clean." Outside, Retief stood for a moment looking across at the deeply carved windowless stucco facades lining the street, then started off in the direction of the Terrestrial Consulate General. The few Groacians on the street eyed him furtively, veered to avoid him as he passed. Flimsy high-wheeled ground cars puffed silently along the resilient pavement. The air was clean and cool. At the office, Miss Meuhl would be waiting with another list of complaints. Retief studied the carving over the open doorways along the street. An elaborate one picked out in pinkish paint seemed to indicate the Groacian equivalent of a bar. Retief went in. A Groacian bartender was dispensing clay pots of alcoholic drink from the bar-pit at the center of the room. He looked at Retief and froze in mid-motion, a metal tube poised over a waiting pot. "To enjoy a cooling drink," Retief said in Groacian, squatting down at the edge of the pit. "To sample a true Groacian beverage." "To not enjoy my poor offerings," the Groacian mumbled. "A pain in the digestive sacs; to express regret." "To not worry," Retief said, irritated. "To pour it out and let me decide whether I like it." "To be grappled in by peace-keepers for poisoning of—foreigners." The barkeep looked around for support, found none. The Groaci customers, eyes elsewhere, were drifting away. "To get the lead out," Retief said, placing a thick gold-piece in the dish provided. "To shake a tentacle." "The procuring of a cage," a thin voice called from the sidelines. "The displaying of a freak." Retief turned. A tall Groacian vibrated his mandibles in a gesture of contempt. From his bluish throat coloration, it was apparent the creature was drunk. "To choke in your upper sac," the bartender hissed, extending his eyes toward the drunk. "To keep silent, litter-mate of drones." "To swallow your own poison, dispenser of vileness," the drunk whispered. "To find a proper cage for this zoo-piece." He wavered toward Retief. "To show this one in the streets, like all freaks." "Seen a lot of freaks like me, have you?" Retief asked, interestedly. "To speak intelligibly, malodorous outworlder," the drunk said. The barkeep whispered something, and two customers came up to the drunk, took his arms and helped him to the door. "To get a cage!" the drunk shrilled. "To keep the animals in their own stinking place." "I've changed my mind," Retief said to the bartender. "To be grateful as hell, but to have to hurry off now." He followed the drunk out the door. The other Groaci released him, hurried back inside. Retief looked at the weaving alien. "To begone, freak," the Groacian whispered. "To be pals," Retief said. "To be kind to dumb animals." "To have you hauled away to a stockyard, ill-odored foreign livestock." "To not be angry, fragrant native," Retief said. "To permit me to chum with you." "To flee before I take a cane to you!" "To have a drink together—" "To not endure such insolence!" The Groacian advanced toward Retief. Retief backed away. "To hold hands," Retief said. "To be palsy-walsy—" The Groacian reached for him, missed. A passer-by stepped around him, head down, scuttled away. Retief backed into the opening to a narrow crossway and offered further verbal familiarities to the drunken local, who followed, furious. Retief backed, rounded a corner into a narrow alley-like passage, deserted, silent ... except for the following Groacian. Retief stepped around him, seized his collar and yanked. The Groacian fell on his back. Retief stood over him. The downed native half-rose; Retief put a foot against his chest and pushed. "To not be going anywhere for a few minutes," Retief said. "To stay right here and have a nice long talk." II "There you are!" Miss Meuhl said, eyeing Retief over her lenses. "There are two gentlemen waiting to see you. Groacian gentlemen." "Government men, I imagine. Word travels fast." Retief pulled off his cape. "This saves me the trouble of paying another call at the Foreign Ministry." "What have you been doing? They seem very upset, I don't mind telling you." "I'm sure you don't. Come along. And bring an official recorder." Two Groaci wearing heavy eye-shields and elaborate crest ornaments indicative of rank rose as Retief entered the room. Neither offered a courteous snap of the mandibles, Retief noted. They were mad, all right. "I am Fith, of the Terrestrial Desk, Ministry of Foreign Affairs, Mr. Consul," the taller Groacian said, in lisping Terran. "May I present Shluh, of the Internal Police?" "Sit down, gentlemen," Retief said. They resumed their seats. Miss Meuhl hovered nervously, then sat on the edge of a comfortless chair. "Oh, it's such a pleasure—" she began. "Never mind that," Retief said. "These gentlemen didn't come here to sip tea today." "So true," Fith said. "Frankly, I have had a most disturbing report, Mr. Consul. I shall ask Shluh to recount it." He nodded to the police chief. "One hour ago," The Groacian said, "a Groacian national was brought to hospital suffering from serious contusions. Questioning of this individual revealed that he had been set upon and beaten by a foreigner. A Terrestrial, to be precise. Investigation by my department indicates that the description of the culprit closely matches that of the Terrestrial Consul." Miss Meuhl gasped audibly. "Have you ever heard," Retief said, looking steadily at Fith, "of a Terrestrial cruiser, the ISV Terrific , which dropped from sight in this sector nine years ago?" "Really!" Miss Meuhl exclaimed, rising. "I wash my hands—" "Just keep that recorder going," Retief snapped. "I'll not be a party—" "You'll do as you're told, Miss Meuhl," Retief said quietly. "I'm telling you to make an official sealed record of this conversation." Miss Meuhl sat down. Fith puffed out his throat indignantly. "You reopen an old wound, Mr. Consul. It reminds us of certain illegal treatment at Terrestrial hands—" "Hogwash," Retief said. "That tune went over with my predecessors, but it hits a sour note with me." "All our efforts," Miss Meuhl said, "to live down that terrible episode! And you—" "Terrible? I understand that a Terrestrial task force stood off Groac and sent a delegation down to ask questions. They got some funny answers, and stayed on to dig around a little. After a week they left. Somewhat annoying to the Groaci, maybe—at the most. If they were innocent." "IF!" Miss Meuhl burst out. "If, indeed!" Fith said, his weak voice trembling. "I must protest your—" "Save the protests, Fith. You have some explaining to do. And I don't think your story will be good enough." "It is for you to explain! This person who was beaten—" "Not beaten. Just rapped a few times to loosen his memory." "Then you admit—" "It worked, too. He remembered lots of things, once he put his mind to it." Fith rose; Shluh followed suit. "I shall ask for your immediate recall, Mr. Consul. Were it not for your diplomatic immunity, I should do more—" "Why did the government fall, Fith? It was just after the task force paid its visit, and before the arrival of the first Terrestrial diplomatic mission." "This is an internal matter!" Fith cried, in his faint Groacian voice. "The new regime has shown itself most amiable to you Terrestrials. It has outdone itself—" "—to keep the Terrestrial consul and his staff in the dark," Retief said. "And the same goes for the few terrestrial businessmen you've visaed. This continual round of culture; no social contacts outside the diplomatic circle; no travel permits to visit out-lying districts, or your satellite—" "Enough!" Fith's mandibles quivered in distress. "I can talk no more of this matter—" "You'll talk to me, or there'll be a task force here in five days to do the talking," Retief said. "You can't!" Miss Meuhl gasped. Retief turned a steady look on Miss Meuhl. She closed her mouth. The Groaci sat down. "Answer me this one," Retief said, looking at Shluh. "A few years back—about nine, I think—there was a little parade held here. Some curious looking creatures were captured. After being securely caged, they were exhibited to the gentle Groaci public. Hauled through the streets. Very educational, no doubt. A highly cultural show. "Funny thing about these animals. They wore clothes. They seemed to communicate with each other. Altogether it was a very amusing exhibit. "Tell me, Shluh, what happened to those six Terrestrials after the parade was over?" Fith made a choked noise and spoke rapidly to Shluh in Groacian. Shluh retracted his eyes, shrank down in his chair. Miss Meuhl opened her mouth, closed it and blinked rapidly. "How did they die?" Retief snapped. "Did you murder them, cut their throats, shoot them or bury them alive? What amusing end did you figure out for them? Research, maybe? Cut them open to see what made them yell...." "No!" Fith gasped. "I must correct this terrible false impression at once." "False impression, hell," Retief said. "They were Terrans! A simple narco-interrogation would get that out of any Groacian who saw the parade." "Yes," Fith said weakly. "It is true, they were Terrestrials. But there was no killing." "They're alive?" "Alas, no. They ... died." Miss Meuhl yelped faintly. "I see," Retief said. "They died." "We tried to keep them alive, of course. But we did not know what foods—" "Didn't take the trouble to find out, either, did you?" "They fell ill," Fith said. "One by one...." "We'll deal with that question later," Retief said. "Right now, I want more information. Where did you get them? Where did you hide the ship? What happened to the rest of the crew? Did they 'fall ill' before the big parade?" "There were no more! Absolutely, I assure you!" "Killed in the crash landing?" "No crash landing. The ship descended intact, east of the city. The ... Terrestrials ... were unharmed. Naturally, we feared them. They were strange to us. We had never before seen such beings." "Stepped off the ship with guns blazing, did they?" "Guns? No, no guns—" "They raised their hands, didn't they? Asked for help. You helped them; helped them to death." "How could we know?" Fith moaned. "How could you know a flotilla would show up in a few months looking for them, you mean? That was a shock, wasn't it? I'll bet you had a brisk time of it hiding the ship, and shutting everybody up. A close call, eh?" "We were afraid," Shluh said. "We are a simple people. We feared the strange creatures from the alien craft. We did not kill them, but we felt it was as well they ... did not survive. Then, when the warships came, we realized our error. But we feared to speak. We purged our guilty leaders, concealed what had happened, and ... offered our friendship. We invited the opening of diplomatic relations. We made a blunder, it is true, a great blunder. But we have tried to make amends...." "Where is the ship?" "The ship?" "What did you do with it? It was too big to just walk off and forget. Where is it?" The two Groacians exchanged looks. "We wish to show our contrition," Fith said. "We will show you the ship." "Miss Meuhl," Retief said. "If I don't come back in a reasonable length of time, transmit that recording to Regional Headquarters, sealed." He stood, looked at the Groaci. "Let's go," he said. Retief stooped under the heavy timbers shoring the entry to the cavern. He peered into the gloom at the curving flank of the space-burned hull. "Any lights in here?" he asked. A Groacian threw a switch. A weak bluish glow sprang up. Retief walked along the raised wooden catwalk, studying the ship. Empty emplacements gaped below lensless scanner eyes. Littered decking was visible within the half-open entry port. Near the bow the words 'IVS Terrific B7 New Terra' were lettered in bright chrome duralloy. "How did you get it in here?" Retief asked. "It was hauled here from the landing point, some nine miles distant," Fith said, his voice thinner than ever. "This is a natural crevasse. The vessel was lowered into it and roofed over." "How did you shield it so the detectors didn't pick it up?" "All here is high-grade iron ore," Fith said, waving a member. "Great veins of almost pure metal." Retief grunted. "Let's go inside." Shluh came forward with a hand-lamp. The party entered the ship. Retief clambered up a narrow companionway, glanced around the interior of the control compartment. Dust was thick on the deck, the stanchions where acceleration couches had been mounted, the empty instrument panels, the litter of sheared bolts, scraps of wire and paper. A thin frosting of rust dulled the exposed metal where cutting torches had sliced away heavy shielding. There was a faint odor of stale bedding. "The cargo compartment—" Shluh began. "I've seen enough," Retief said. Silently, the Groacians led the way back out through the tunnel and into the late afternoon sunshine. As they climbed the slope to the steam car, Fith came to Retief's side. "Indeed, I hope that this will be the end of this unfortunate affair," he said. "Now that all has been fully and honestly shown—" "You can skip all that," Retief said. "You're nine years late. The crew was still alive when the task force called, I imagine. You killed them—or let them die—rather than take the chance of admitting what you'd done." "We were at fault," Fith said abjectly. "Now we wish only friendship." "The Terrific was a heavy cruiser, about twenty thousand tons." Retief looked grimly at the slender Foreign Office official. "Where is she, Fith? I won't settle for a hundred-ton lifeboat." Fith erected his eye stalks so violently that one eye-shield fell off. "I know nothing of ... of...." He stopped. His throat vibrated rapidly as he struggled for calm. "My government can entertain no further accusations, Mr. Consul," he said at last. "I have been completely candid with you, I have overlooked your probing into matters not properly within your sphere of responsibility. My patience is at an end." "Where is that ship?" Retief rapped out. "You never learn, do you? You're still convinced you can hide the whole thing and forget it. I'm telling you you can't." "We return to the city now," Fith said. "I can do no more." "You can and you will, Fith," Retief said. "I intend to get to the truth of this matter." Fith spoke to Shluh in rapid Groacian. The police chief gestured to his four armed constables. They moved to ring Retief in. Retief eyed Fith. "Don't try it," he said. "You'll just get yourself in deeper." Fith clacked his mandibles angrily, eye stalks canted aggressively toward the Terrestrial. "Out of deference to your diplomatic status, Terrestrial, I shall ignore your insulting remarks," Fith said in his reedy voice. "Let us now return to the city." Retief looked at the four policemen. "I see your point," he said. Fith followed him into the car, sat rigidly at the far end of the seat. "I advise you to remain very close to your consulate," Fith said. "I advise you to dismiss these fancies from your mind, and to enjoy the cultural aspects of life at Groac. Especially, I should not venture out of the city, or appear overly curious about matters of concern only to the Groacian government." In the front seat, Shluh looked straight ahead. The loosely-sprung vehicle bobbed and swayed along the narrow highway. Retief listened to the rhythmic puffing of the motor and said nothing. III "Miss Meuhl," Retief said, "I want you to listen carefully to what I'm going to tell you. I have to move rapidly now, to catch the Groaci off guard." "I'm sure I don't know what you're talking about," Miss Meuhl snapped, her eyes sharp behind the heavy lenses. "If you'll listen, you may find out," Retief said. "I have no time to waste, Miss Meuhl. They won't be expecting an immediate move—I hope—and that may give me the latitude I need." "You're still determined to make an issue of that incident!" Miss Meuhl snorted. "I really can hardly blame the Groaci. They are not a sophisticated race; they had never before met aliens." "You're ready to forgive a great deal, Miss Meuhl. But it's not what happened nine years ago I'm concerned with. It's what's happening now. I've told you that it was only a lifeboat the Groaci have hidden out. Don't you understand the implication? That vessel couldn't have come far. The cruiser itself must be somewhere near by. I want to know where!" "The Groaci don't know. They're a very cultured, gentle people. You can do irreparable harm to the reputation of Terrestrials if you insist—" "That's my decision," Retief said. "I have a job to do and we're wasting time." He crossed the room to his desk, opened a drawer and took out a slim-barreled needler. "This office is being watched. Not very efficiently, if I know the Groaci. I think I can get past them all right." "Where are you going with ... that?" Miss Meuhl stared at the needler. "What in the world—" "The Groaci won't waste any time destroying every piece of paper in their files relating to this thing. I have to get what I need before it's too late. If I wait for an official Inquiry Commission, they'll find nothing but blank smiles." "You're out of your mind!" Miss Meuhl stood up, quivering with indignation. "You're like a ... a...." "You and I are in a tight spot, Miss Meuhl. The logical next move for the Groaci is to dispose of both of us. We're the only ones who know what happened. Fith almost did the job this afternoon, but I bluffed him out—for the moment." Miss Meuhl emitted a shrill laugh. "Your fantasies are getting the better of you," she gasped. "In danger, indeed! Disposing of me! I've never heard anything so ridiculous." "Stay in this office. Close and safe-lock the door. You've got food and water in the dispenser. I suggest you stock up, before they shut the supply down. Don't let anyone in, on any pretext whatever. I'll keep in touch with you via hand-phone." "What are you planning to do?" "If I don't make it back here, transmit the sealed record of this afternoon's conversation, along with the information I've given you. Beam it through on a mayday priority. Then tell the Groaci what you've done and sit tight. I think you'll be all right. It won't be easy to blast in here and anyway, they won't make things worse by killing you. A force can be here in a week." "I'll do nothing of the sort! The Groaci are very fond of me! You ... Johnny-come-lately! Roughneck! Setting out to destroy—" "Blame it on me if it will make you feel any better," Retief said, "but don't be fool enough to trust them." He pulled on a cape, opened the door. "I'll be back in a couple of hours," he said. Miss Meuhl stared after him silently as he closed the door. It was an hour before dawn when Retief keyed the combination to the safe-lock and stepped into the darkened consular office. He looked tired. Miss Meuhl, dozing in a chair, awoke with a start. She looked at Retief, rose and snapped on a light, turned to stare. "What in the world—Where have you been? What's happened to your clothing?" "I got a little dirty. Don't worry about it." Retief went to his desk, opened a drawer and replaced the needler. "Where have you been?" Miss Meuhl demanded. "I stayed here—" "I'm glad you did," Retief said. "I hope you piled up a supply of food and water from the dispenser, too. We'll be holed up here for a week, at least." He jotted figures on a pad. "Warm up the official sender. I have a long transmission for Regional Headquarters." "Are you going to tell me where you've been?" "I have a message to get off first, Miss Meuhl," Retief said sharply. "I've been to the Foreign Ministry," he added. "I'll tell you all about it later." "At this hour? There's no one there...." "Exactly." Miss Meuhl gasped. "You mean you broke in? You burgled the Foreign Office?" "That's right," Retief said calmly. "Now—" "This is absolutely the end!" Miss Meuhl said. "Thank heaven I've already—" "Get that sender going, woman!" Retief snapped. "This is important." "I've already done so, Mr. Retief!" Miss Meuhl said harshly. "I've been waiting for you to come back here...." She turned to the communicator, flipped levers. The screen snapped aglow, and a wavering long-distance image appeared. "He's here now," Miss Meuhl said to the screen. She looked at Retief triumphantly. "That's good," Retief said. "I don't think the Groaci can knock us off the air, but—" "I have done my duty, Mr. Retief," Miss Meuhl said. "I made a full report to Regional Headquarters last night, as soon as you left this office. Any doubts I may have had as to the rightness of that decision have been completely dispelled by what you've just told me." Retief looked at her levelly. "You've been a busy girl, Miss Meuhl. Did you mention the six Terrestrials who were killed here?" "That had no bearing on the matter of your wild behavior! I must say, in all my years in the Corps, I've never encountered a personality less suited to diplomatic work." The screen crackled, the ten-second transmission lag having elapsed. "Mr. Retief," the face on the screen said, "I am Counsellor Pardy, DSO-1, Deputy Under-secretary for the region. I have received a report on your conduct which makes it mandatory for me to relieve you administratively, vice Miss Yolanda Meuhl, DAO-9. Pending the findings of a Board of Inquiry, you will—" Retief reached out and snapped off the communicator. The triumphant look faded from Miss Meuhl's face. "Why, what is the meaning—" "If I'd listened any longer, I might have heard something I couldn't ignore. I can't afford that, at this moment. Listen, Miss Meuhl," Retief went on earnestly, "I've found the missing cruiser." "You heard him relieve you!" "I heard him say he was going to, Miss Meuhl. But until I've heard and acknowledged a verbal order, it has no force. If I'm wrong, he'll get my resignation. If I'm right, that suspension would be embarrassing all around." "You're defying lawful authority! I'm in charge here now." Miss Meuhl stepped to the local communicator. "I'm going to report this terrible thing to the Groaci at once, and offer my profound—" "Don't touch that screen," Retief said. "You go sit in that corner where I can keep an eye on you. I'm going to make a sealed tape for transmission to Headquarters, along with a call for an armed task force. Then we'll settle down to wait." Retief ignored Miss Meuhl's fury as he spoke into the recorder. The local communicator chimed. Miss Meuhl jumped up, staring at it. "Go ahead," Retief said. "Answer it." A Groacian official appeared on the screen. "Yolanda Meuhl," he said without preamble, "for the Foreign Minister of the Groacian Autonomy, I herewith accredit you as Terrestrial Consul to Groac, in accordance with the advices transmitted to my government direct from the Terrestrial Headquarters. As consul, you are requested to make available for questioning Mr. J. Retief, former consul, in connection with the assault on two peace keepers and illegal entry into the offices of the Ministry for Foreign Affairs." "Why, why," Miss Meuhl stammered. "Yes, of course. And I do want to express my deepest regrets—" Retief rose, went to the communicator, assisted Miss Meuhl aside. "Listen carefully, Fith," he said. "Your bluff has been called. You don't come in and we don't come out. Your camouflage worked for nine years, but it's all over now. I suggest you keep your heads and resist the temptation to make matters worse than they are." "Miss Meuhl," Fith said, "a peace squad waits outside your consulate. It is clear you are in the hands of a dangerous lunatic. As always, the Groaci wish only friendship with the Terrestrials, but—" "Don't bother," Retief said. "You know what was in those files I looked over this morning." Retief turned at a sound behind him. Miss Meuhl was at the door, reaching for the safe-lock release.... "Don't!" Retief jumped—too late. The door burst inward. A crowd of crested Groaci pressed into the room, pushed Miss Meuhl back, aimed scatter guns at Retief. Police Chief Shluh pushed forward. "Attempt no violence, Terrestrial," he said. "I cannot promise to restrain my men." "You're violating Terrestrial territory, Shluh," Retief said steadily. "I suggest you move back out the same way you came in." "I invited them here," Miss Meuhl spoke up. "They are here at my express wish." "Are they? Are you sure you meant to go this far, Miss Meuhl? A squad of armed Groaci in the consulate?" "You are the consul, Miss Yolanda Meuhl," Shluh said. "Would it not be best if we removed this deranged person to a place of safety?" "You're making a serious mistake, Shluh," Retief said. "Yes," Miss Meuhl said. "You're quite right, Mr. Shluh. Please escort Mr. Retief to his quarters in this building—" "I don't advise you to violate my diplomatic immunity, Fith," Retief said. "As chief of mission," Miss Meuhl said quickly, "I hereby waive immunity in the case of Mr. Retief." Shluh produced a hand recorder. "Kindly repeat your statement, Madam, officially," he said. "I wish no question to arise later." "Don't be a fool, woman," Retief said. "Don't you see what you're letting yourself in for? This would be a hell of a good time for you to figure out whose side you're on." "I'm on the side of common decency!" "You've been taken in. These people are concealing—" "You think all women are fools, don't you, Mr. Retief?" She turned to the police chief and spoke into the microphone he held up. "That's an illegal waiver," Retief said. "I'm consul here, whatever rumors you've heard. This thing's coming out into the open, whatever you do. Don't add violation of the Consulate to the list of Groacian atrocities." "Take the man," Shluh said.
|
B. people they had taken as prisoners
|
What are the different bilingual models employed?
|
### Introduction
The Cambridge Handbook of Endangered Languages BIBREF3 estimates that at least half of the 7,000 languages currently spoken worldwide will no longer exist by the end of this century. For these endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. This transcription bottleneck problem can be handled by translating into a widely spoken language to ensure subsequent interpretability of the collected recordings, and such parallel corpora have been recently created by aligning the collected audio with translations in a well-resourced language BIBREF1, BIBREF2, BIBREF4. Moreover, some linguists suggested that more than one translation should be collected to capture deeper layers of meaning BIBREF5. This work is a contribution to the Computational Language Documentation (CLD) research field, that aims to replace part of the manual steps performed by linguists during language documentation initiatives by automatic approaches. Here we investigate the unsupervised word discovery and segmentation task, using the bilingual-rooted approach from BIBREF6. There, words in the well-resourced language are aligned to unsegmented phonemes in the endangered language in order to identify group of phonemes, and to cluster them into word-like units. We experiment with the Mboshi-French parallel corpus, translating the French text into four other well-resourced languages in order to investigate language impact in this CLD approach. Our results hint that this language impact exists, and that models based on different languages will output different word-like units. ### Methodology ::: The Multilingual Mboshi Parallel Corpus:
In this work we extend the bilingual Mboshi-French parallel corpus BIBREF2, fruit of the documentation process of Mboshi (Bantu C25), an endangered language spoken in Congo-Brazzaville. The corpus contains 5,130 utterances, for which it provides audio, transcriptions and translations in French. We translate the French into four other well-resourced languages through the use of the $DeepL$ translator. The languages added to the dataset are: English, German, Portuguese and Spanish. Table shows some statistics for the produced Multilingual Mboshi parallel corpus. ### Methodology ::: Bilingual Unsupervised Word Segmentation/Discovery Approach:
We use the bilingual neural-based Unsupervised Word Segmentation (UWS) approach from BIBREF6 to discover words in Mboshi. In this approach, Neural Machine Translation (NMT) models are trained between language pairs, using as source language the translation (word-level) and as target, the language to document (unsegmented phonemic sequence). Due to the attention mechanism present in these networks BIBREF7, posterior to training, it is possible to retrieve soft-alignment probability matrices between source and target sequences. These matrices give us sentence-level source-to-target alignment information, and by using it for clustering neighbor phonemes aligned to the same translation word, we are able to create segmentation in the target side. The product of this approach is a set of (discovered-units, translation words) pairs. ### Methodology ::: Multilingual Leveraging:
In this work we apply two simple methods for including multilingual information into the bilingual models from BIBREF6. The first one, Multilingual Voting, consists of merging the information learned by models trained with different language pairs by performing a voting over the final discovered boundaries. The voting is performed by applying an agreement threshold $T$ over the output boundaries. This threshold balances between accepting all boundaries from all the bilingual models (zero agreement) and accepting only input boundaries discovered by all these models (total agreement). The second method is ANE Selection. For every language pair and aligned sentence in the dataset, a soft-alignment probability matrix is generated. We use Average Normalized Entropy (ANE) BIBREF8 computed over these matrices for selecting the most confident one for segmenting each phoneme sequence. This exploits the idea that models trained on different language pairs will have language-related behavior, thus differing on the resulting alignment and segmentation over the same phoneme sequence. ### Experiments
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset. Languages closely related to French (Spanish and Portuguese) ranked better, while our worst result used German. English also performs notably well in our experiments. We believe this is due to the statistics features of the resulting text. We observe in Table that the English portion of the dataset contains the smallest vocabulary among all languages. Since we train our systems in very low-resource settings, vocabulary-related features can impact greatly the system's capacity to language-model, and consequently the final quality of the produced alignments. Even in high-resource settings, it was already attested that some languages are more difficult to model than others BIBREF9. For the multilingual selection experiments, we experimented combining the languages from top to bottom as they appear Table (ranked by performance; e.g. 1-3 means the combination of FR(1), EN(2) and PT(3)). We observe that the performance improvement is smaller than the one observed in previous work BIBREF10, which we attribute to the fact that our dataset was artificially augmented. This could result in the available multilingual form of supervision not being as rich as in a manually generated dataset. Finally, the best boundary segmentation result is obtained by performing multilingual voting with all the languages and an agreement of 50%, which indicates that the information learned by different languages will provide additional complementary evidence. Lastly, following the methodology from BIBREF8, we extract the most confident alignments (in terms of ANE) discovered by the bilingual models. Table presents the top 10 most confident (discovered type, translation) pairs. Looking at the pairs the bilingual models are most confident about, we observe there are some types discovered by all the bilingual models (e.g. Mboshi word itua, and the concatenation oboá+ngá). However, the models still differ for most of their alignments in the table. This hints that while a portion of the lexicon might be captured independently of the language used, other structures might be more dependent of the chosen language. On this note, BIBREF11 suggests the notion of word cannot always be meaningfully defined cross-linguistically. ### Conclusion
In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language. Lastly, we extend the bilingual Mboshi-French parallel corpus, creating a multilingual corpus for the endangered language Mboshi that we make available to the community. Table 2: From left to right, results for: bilingual UWS, multilingual leveraging by voting, ANE selection. Table 1: Statistics for the Multilingual Mboshi parallel corpus. The French text is used for generating translation in the four other languages present in the right side of the table. Table 3: Top 10 confident (discovered type, translation) pairs for the five bilingual models. The “+” mark means the discovered type is a concatenation of two existing true types.
|
Neural Machine Translation (NMT) models are trained between language pairs, using as source language the translation (word-level) and as target
|
What is specific about the specific embeddings?
|
### Introduction
Recent studies in information extraction domain (but also in other natural language processing fields) show that deep learning models produce state-of-the-art results BIBREF0 . Deep architectures employ multiple layers to learn hierarchical representations of the input data. In the last few years, neural networks based on dense vector representations provided the best results in various NLP tasks, including named entities recognition BIBREF1 , semantic role labelling BIBREF2 , question answering BIBREF3 and multitask learning BIBREF4 . The core element of most deep learning solutions is the dense distributed semantic representation of words, often called word embeddings. Distributional vectors follow the distributional hypothesis that words with a similar meaning tend to appear in similar contexts. Word embeddings capture the similarity between words and are often used as the first layer in deep learning models. Two of the most common and very efficient methods to produce word embeddings are Continuous Bag-of-Words (CBOW) and Skip-gram (SG), which produce distributed representations of words in a vector space, grouping them by similarity BIBREF5 , BIBREF6 . With the progress of machine learning techniques, it is possible to train such models on much larger data sets, and these often outperform the simple ones. It is possible to use a set of text documents containing even billions of words as training data. Both architectures (CBOW and SG) describe how the neural network learns the vector word representations for each word. In CBOW architecture the task is predicting the word given its context and in SG the task in predicting the context given the word. Due to a significant increase of quality using deep learning methods together with word embeddings as the input layer for neural networks, many word vector sets have been created, using different corpora. The widest range of available word embeddings is available for English BIBREF7 and there were not so many options for less popular languages, e.g. Polish. There was a definite need within CLARIN-PL project and Sentimenti to increase the quality of NLP methods for Polish which were utilising available Polish word vectors BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 but only FastText modification of Skip-gram BIBREF9 was able to produce vectors for unknown words, based on character n-grams. The observation was that even using a sophisticated deep neural structure, the result strongly depends on the initial distributional representation. There was a need to build a massive corpus of Polish and create high-quality word vectors from that corpus. This work describes how we extended KGR7 1G corpus to become KGR10 with 4 billion words. Next, we present the different variants of word embeddings produced using this corpus. In the article about the recognition of named entities for Polish from the previous year, these embeddings were used in one of the three voting models to obtain the best results and the final system PolDeepNer BIBREF12 took the second place in PolEval2018 Task 2 BIBREF13 . In this article, we evaluated KGR10 FastText word embeddings in recognition of timexes. ### Available word embeddings
At the time we were testing word embeddings for different applications, there were 2 most popular sources of word vectors. The first one, called IPIPAN, is the result of the project Compositional distributional semantic models for identification, discrimination and disambiguation of senses in Polish texts, the process of creating word embeddings is described in article BIBREF10 and corpora used were National Corpus of Polish (NKJP) BIBREF14 and Wikipedia (Wiki). The second one, called FASTTEXT, is original FastText word embeddings set, created for 157 languages (including Polish). Authors used Wikipedia and Common Crawl as the linguistic data source. Table TABREF6 shows the number of tokens in each corpus and the name of the institution which prepared it. There is also information about the public availability of the resource. Table TABREF7 presents the most commonly used word embeddings in CLARIN-PL before the creation of our embeddings. ### Building a larger corpus
KGR7 corpus (also called plWordNet Corpus 7.0, PLWNC 7.0) BIBREF15 , BIBREF16 was created at the Wroclaw University of Science and Technology by G4.19 Group. Due to the licences of documents in this corpus, this resource is not publicly available. Table TABREF8 contains KGR7 subcorpora and statistics BIBREF17 . One of the subcorpora in KGR7 is KIPI (the IPI PAN Corpus) BIBREF18 . KGR7 covers texts from a wide range of domains like: blogs, science, stenographic recordings, news, journalism, books and parliamentary transcripts. All texts come from the second half of the 20th century and represent the modern Polish language. ### plWordNet Corpus 10.0 (KGR10)
KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish. In order to significantly expand the set of documents in KGR7, we utilised DMOZ (short for directory.mozilla.org) – a multilingual open content directory of World Wide Web links, also known as Open Directory Project (ODP). The website with directory was closed in 2017, but the database still can be found on the web. Polish part of this directory contains more than 30,000 links to Polish websites. We used these links as root URLs for CorpoGrabber, and we downloaded more than 7TB of HTML web pages. After the extraction of text from HTML pages, deduplication of documents (including texts from KGR7) and removing bad quality documents (containing more than 30% of words outside the Morfeusz BIBREF23 dictionary) the result is KGR10 corpus, which contains 4,015,569,051 tokens and 18,084,712 unique words. Due to component licenses, KGR10 corpus is not publicly available. ### KGR10 word embeddings
We created a new Polish word embeddings models using the KGR10 corpus. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool BIBREF9 . These models are available under an open license in the CLARIN-PL project DSpace repository. The internal encoding solution based on embeddings of n-grams composing each word makes it possible to obtain FastText vector representations, also for words which were not processed during the creation of the model. A vector representation is associated with character n-gram and each word is represented as the sum of its n-gram vector representations. Previous solutions ignored the morphology of words and were assigning a distinct vector to each word. This is a limitation for languages with large vocabularies and many rare words, like Turkish, Finnish or Polish BIBREF9 . Authors observed that using word representations trained with subword information outperformed the plain Skip-gram model and the improvement was most significant for morphologically rich Slavic languages such as Czech (8% reduction of perplexity over SG) and Russian (13% reduction) BIBREF9 . We expected that word embeddings created that way for Polish should also provide such improvements. There were also previous attempts to build KGR10 word vectors with other methods (including FastText), and the results are presented in the article BIBREF8 . We selected the best models from that article – with embedding ID prefix EP (embeddings, previous) in Table TABREF13 – to compare with new models, marked as embedding ID prefix EC in Table TABREF13 ). The word embeddings models used in PolDeepNer for recognition of timexes and named entities were EE1, . It was built on a plain KGR10. The dimension of word embedding is 300, the method of constructing vectors was Skip-gram BIBREF9 , and the number of negative samples for each positive example was 10. ### Temporal expressions
Temporal expressions (henceforth timexes) tell us when something happens, how long something lasts, or how often something occurs. The correct interpretation of a timex often involves knowing the context. Usually, a person is aware of their location in time, i.e., they know what day, month and year it is, and whether it is the beginning or the end of week or month. Therefore, they refer to specific dates, using incomplete expressions such as 12 November, Thursday, the following week, after three days. The temporal context is often necessary to determine to which specific date and time timexes refer. These examples do not exhaust the complexity of the problem of recognising timexes. TimeML BIBREF24 is a markup language for describing timexes that has been adapted to many languages. One of the best-known methods of recognition of timexes called HeidelTime BIBREF25 , which uses the TIMEX3 annotation standard, currently supports 13 languages (with the use of hand-crafted resources). PLIMEX is a specification for the description of Polish timexes. It is based on TIMEX3 used in TimeML. Classes proposed in TimeML are adapted, namely: date, time, duration, set. ### Recognition of timexes
There are many methods for recognising timexes that are widely used in natural language engineering. For English (but not exclusively), in approaches based on supervised learning, sequence labelling methods are often used, especially Conditional Random Fields BIBREF26 . A review of the methods in the article BIBREF27 about the recognition of timexes for English and Spanish has shown a certain shift within the most popular solutions. As with the normalisation of timexes, the best results are still achieved with rule-based methods, many new solutions have been introduced in the area of recognition. The best systems listed in BIBREF27 , called TIPSem BIBREF28 and ClearTK BIBREF29 , use CRFs for recognition, so initially, we decided to apply the CRF-based approach for this task. The results were described in BIBREF30 , BIBREF31 . In recent years, solutions based on deep neural networks, using word representation in the form of word embeddings, created with the use of large linguistic corpus, have begun to dominate in the field of recognition of word expressions. The most popular solutions include bidirectional long short-term memory neural networks (henceforth Bi-LSTM), often in combination with conditional random fields, as presented in the paper BIBREF32 dedicated to the recognition of proper names. For the Polish language, deep networks have also recently been used to recognise word expressions. In the issue of recognition of timexes, a bidirectional gated recurrent unit network (GRU) has been used BIBREF33 , BIBREF34 . GRU network is described in detail in the article BIBREF35 . In case of recognition of event descriptions using Bi-LSTM and Bi-GRU, where most of the Liner2 features were included in the input feature vector, better results were obtained BIBREF36 than for the Liner2 method (but without taking into account domain dictionaries). In last year's publication on the issue of named entities recognition using BiLSTM+CRF (together with G4.19 Group members), we received a statistically significant improvement in the quality of recognition compared to a solution using CRF only. The solution has been called PolDeepNer BIBREF12 . ### Experiments and Results
Experiments were carried out by the method proposed in BIBREF27 . The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We trained the final models using the train set and we evaluated it using the test set, which was the reproduction of analysis performed in articles BIBREF37 , BIBREF38 . The division is presented in Table TABREF16 . We used BiLSTM+CRF classifier as in previous work BIBREF12 . We used precision, recall and F1 metrics from the classic NER task BIBREF12 , where true positive system answer has the same boundaries and type as annotation in gold data set. We evaluated all 17 word embeddings models using these metrics. The results are presented in Tables TABREF17 , TABREF18 and TABREF19 . We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . The following measures were used to evaluate the quality of boundaries and class recognition, so-called strict match: strict precision (Str.P), strict recall (Str.R) and strict F1-score (Str.F1). A relaxed match (Rel.P, Rel.R, Rel.F1) evaluation has also been carried out to determine whether there is an overlap between the system entity and gold entity, e.g. [Sunday] and [Sunday morning] BIBREF27 . If there was an overlap, a relaxed type F1-score (Type.F1) was calculated BIBREF27 . The results are presented in Table TABREF20 . ### Conclusions
The analysis of results from Tables TABREF17 , TABREF18 and TABREF19 show that 12 of 15 best results were obtained using new word embeddings. The evaluation results presented in Table TABREF20 (the chosen best embeddings models from Table TABREF19 ) prove that the best group of word embeddings is EC. The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information, with vector dimension equal to 300 and negative sampling equal to 10. The ability of the model to provide vector representation for the unknown words seems to be the most important. Also, previous models built using KGR10 (EP) are probably less accurate due to an incorrect tokenisation of the corpus. We used WCRFT tagger BIBREF22 , which utilises Toki BIBREF21 to tokenise the input text before the creation of the embeddings model. The comparison of EC1 with previous results obtained using only CRF BIBREF38 show the significant improvement across all the tested metrics: 3.6pp increase in strict F1-score, 1.36pp increase in relaxed precision, 5.61pp increase in relaxed recall and 3.51pp increase in relaxed F1-score. ### Acknowledgements
Work co-financed as part of the investment in the CLARIN-PL research infrastructure funded by the Polish Ministry of Science and Higher Education and in part by the National Centre for Research and Development, Poland, under grant no POIR.01.01.01-00-0472/16. Table 1: Informations about corpora used to prepare embeddings by FASTTEXT and IPIPAN: corpus ID, number of tokens, number of unique words, the name of the institution and the availability of the resource. Table 2: Available word embeddings (external, EE – created outside Wroclaw University of Technology, G4.19 Group) with the information about embedding ID, linguistic sources used to create embedding, original embedding name, method of creation, vector dimension, format and the institution which prepared the resource. Original file names are: cc.pl.300 – cc.pl.300.bin, NWfa-1-s-n – nkjp+wiki-forms-all-100-skipg-ns.vec, NWfa-3-s-n – nkjp+wiki-forms-all-300-skipg-ns.vec Table 3: Names and the number of tokens in KGR7 subcorpora. Table 4: KGR10 word embeddings created at WUST, G4.19, with the information about embedding ID (EP – previous, EC – current), original embedding name, dimension, binary format, method of creation (Skipgram, CBOW), softmax approximation method (hs – hierarchical softmax, ns – negative sampling). Table 5: Evaluation data sets (source: KPWr). Table 6: Evaluation results (precision) for 17 word embeddings models for each TIMEX3 class (date, time, duration and set). Table 7: Evaluation results (recall) for 17 word embeddings models for each TIMEX3 class (date, time, duration and set). Table 8: Evaluation results (F1-score) for 17 word embeddings models for each TIMEX3 class (date, time, duration and set). Table 9: Evaluation results for all TIMEX3 classes (total) for 9 word embeddings models (3 best models from each embeddings group: EE, EP, EC from Table 8) using the following measures from [35]: strict precision, strict recall, strict F1-score, relaxed precision, relaxed recall, relaxed F1-score, type F1-score.
|
predicting the word given its context
|
Based on the findings from the CT scan of the chest and abdomen performed on 09/28/21, which of the following statements is TRUE for Mrs. Anderson?
Choose the correct answer from the following options:
A. There was evidence of distant metastases.
B. The mass in the pancreatic head was in contact with the SMA (>180°).
C. Suspicious regional lymph nodes were particularly noted in the anterocaval region.
D. The liver showed signs of fatty infiltration.
E. The spleen's vein was involved.
|
### 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.
|
The liver showed signs of fatty infiltration.
|
What is the baseline machine learning prediction approach?
|
### INTRODUCTION
In a world where traditional financial information is ubiquitous and the financial models are largely homogeneous, finding hidden information that has not been priced in from alternative data is critical. The recent development in Natural Language Processing provides such opportunities to look into text data in addition to numerical data. When the market sets the stock price, it is not uncommon that the expectation of the company growth outweighs the company fundamentals. Twitter, a online news and social network where the users post and interact with messages to express views about certain topics, contains valuable information on the public mood and sentiment. A collection of research BIBREF0 BIBREF1 have shown that there is a positive correlation between the "public mood" and the "market mood". Other research BIBREF2 also shows that significant correlation exists between the Twitter sentiment and the abnormal return during the peaks of the Twitter volume during a major event. Once a signal that has predicting power on the stock market return is constructed, a trading strategy to express the view of the signal is needed. Traditionally, the quantitative finance industry relies on backtest, a process where the trading strategies are tuned during the simulations or optimizations. Reinforcement learning provides a way to find the optimal policy by maximizing the expected future utility. There are recent attempts from the Artificial Intelligence community to apply reinforcement learning to asset allocation BIBREF3 , algorithmic trading BIBREF4 BIBREF5 , and portfolio management BIBREF6 . The contribution of this paper is two-fold: First, the predicting power of Twitter sentiment is evaluated. Our results show sentiment is more suitable to construct alpha signals rather than total return signals and shows predicting power especially when the Twitter volume is high. Second, we proposed a trading strategy based on reinforcement learning (Q-learning) that takes the sentiment features as part of its states. The paper is constructed as follows: In the second section, scraping Tweets from Twitter website and preprocessing the data are described in details. In the third section, assigning sentiment scores to the text data is discussed. In the fourth section, feature engineering and prediction based on the sentiment score is discussed. In the fifth section, how the reinforcement learning is applied to generate the optimal trading strategy is described. ### Twitter Data Scraping and Preprocessing
There are two options of getting the Tweets. First, Twitter provides an API to download the Tweets. However, rate limit and history limit make it not an option for this paper. Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded. The predicting power of Twitter sentiment varies from stock to stock. For stocks that are mostly driven by the company fundamentals and hold by the institutional investors, the predicting power of the Twitter sentiment is limited. For stocks that are priced by the public expectation on the company's future growth, Twitter sentiment describes the confidence and expectation level of the investors. For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price. Tesla is an electronic car company that shows consecutive negative operating cash flow and net income but carries very high expectation from the public. Ford, is a traditional auto maker whose stock prices has been stabilized to represent the company fundamentals. To investigate how different key words impact the predicting power of the sentiment score, two Tweet sets, a ticker set and a product set, are prepared for each stock. The first set of Tweets are searched strictly according to the stock ticker. The second set of Tweets are searched according to the company's products and news. The keywords for the second dataset are defined according to the top twenty related keywords of the stock ticker according to Google Trend, a web facility shows how often a certain word is searched relative to Google's total search volume. For example, "Elon Musk" is among the set of keywords that retrieve the second tweets set for Tesla. Tweets contain irregular symbols, url and emoji etc which has to be preprocessed so that the NLP algorithm can extract the relevant information efficiently. Examples of preprocessing are described as below: ### Sentiment Score
To translate each tweet into a sentiment score, the Stanford coreNLP software was used. Stanford CoreNLP is designed to make linguistic analysis accessible to the general public. It provides named Entity Recognition, co-reference and basic dependencies and many other text understanding applications. An example that illustrate the basic functionality of Stanford coreNLP is shown in Figure. FIGREF5 coreNLP can compute a sentiment score for each sentence with value ranging from 0 to 4 , where 0 stands for negative, and 4 stands for very positive. For tweets with multiple sentences, the average of the sentiment scores of all sentences is used as the sentiment score of the Tweets. The number of Tweets varies everyday from a couple of hundreds to over ten thousands, depends on if the company has a major event that attracts the public attention. The sentiment scores are normalized between 0 to 1, and features based on the sentiment score is constructed and normalized. Figure FIGREF6 shows the relationship between Tesla stock return and stock sentiment score. According the distribution of the sentiment score, the sentiment on Tesla is slightly skewed towards positive during the testing period. The price has been increased significantly during the testing period, which reflected the positive sentiment. The predicting power of sentiment score is more significant when the sentiment is more extreme and less so when the sentiment is neutral. ### Feature Engineering
Feature engineering is the process to extract meaningful information from the raw data in order to improve the performance of machine learning mode. Domain knowledge and intuition are often applied to keep the number of the features reasonable relative to the training data size. Two categories of features are defines: technical features and sentiment features. The technical features include previous day's return and volume, price momentum and volatility. The sentiment features include number of Tweets, daily average sentiment score, cross-section sentiment volatility, sentiment momentum and reversal. ### Machine Learning Prediction Model
The logistic regression with L1 regularization and RBF-kernel SVM are applied to predict a binary outcome, i.e. whether the stock return will be positive or negative in the next day. Both technical and sentiment-based features carry important information about the stock price and therefore are provided as the model inputs. Half of the dataset is used for training and the rest is used for testing. The 3 fold cross validation is applied to learn the model hyper-parameters. Specifically, the hyper-parameters C of both models and γ of RBF-kernel SVM are learned such that the dev set accuracy is maximized. The hyper-parameter C in logistic regression determines the degree of regularization. Smaller C means more regularization, i.e. high bias and low variance. RBF-kernel SVM has two hyper-parameters, C and γ. C controls the width of soft margin, smaller C allows placing more samples on the wrong side of the margin. γ is a parameter in RBF kernel. A larger γ means a Gaussian with smaller variance and thus less influence of support vectors. Typically, small C and large γ lead to high bias and low variance. To evaluate if the sentiment feature improves the prediction accuracy, a baseline model is defined. The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model. ### Predicting using ticker dataset and product dataset
The predicting power for the ticker dataset and product dataset are compared. The ticker dataset contains tweets that searched strictly according to the stock ticker. The product dataset is searched using keywords that related to the company's product and other related topic(see session II for more details). The former dataset represents the investors' sentiment, while the latter dataset represents customers’ sentiment. In the Tesla case, using product tweets consistently outperforms using the ticker tweets(accuracy 0.6 vs 0.5), it is less so in the Ford case(0.58 vs 0.55). The result is displayed in Figure FIGREF9 First, this is because Tesla's stock price is driven more by the sentiment on its product instead of the stock itself. For Ford, not many people actually express their opinion about Ford's product via Twitter. Secondly, Tesla has many more product tweets than ticker tweets, but Ford is opposite. ### Predicting using logistic regression and SVM
In most cases, SVM performs only slightly better than logistic regression in validation set, although much better in testing set. This may be because the dataset is not large enough to prevent SVM overfitting. The comparision between the logistic regression and the SVM is displayed in Figure FIGREF9 ### Predicting Total Return vs Alpha
It is important to identify which is a better target for the prediction. Two targets, predicting "alpha or predicting "total return" are compared. "Alpha" defines as the excess stock return over its sector ETF. "Total return" is the absolution stock return. Predicting "alpha" achieves better performance than predicting total return. This is because the sentiment is more related to stock’s idiosyncratic. Good sentiments towards a specific company or its stock won’t override the overall stock market or sector’s impact on the stock return. ### Tesla vs Ford
The prediction accuracy on Tesla is higher than Ford according to Figure FIGREF9 . The reason is because Tesla's stock price largely reflects the sentiment and confidence level of the public. The company has consecutive negative cash flow and net income, making prediction based on its fundamental information unrealistic. On the other hand, the stock price of Ford, which is a traditional automaker, is not that related to the public sentiment. ### Feature selection and overfitting
To improve the model accuracy, more features were constructed. However, more features do not result in better accuracy. For example, in Figure FIGREF15 , adding more features improve the training accuracy but deteriorates out-of-sample accuracy due to overfitting. The recursive feature elimination and cross validation (RFECV) for feature selection is experimented during the feature selection phase. However, only similar or even slightly worse performance was achieved by RFECV than selecting features according to domain knowledge and intuition. This is because recursive feature elimination is a greedy algorithm and thus doesn’t guarantee optimal solution. ### Q-learning
Q-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal policy given a Markov Decision Process(MDP). Instead of learning the transition probability, Q-learning directly learns the expected utility of taking an action from a certain state. By maximizing the expected utility of the certain state, the optimal policy is found. Traditionally, quants propose trading strategies according to backtest, where the optimal parameters are tuned by maximizing the objective function based on historical data. However, this common practice adopted by the investment industry has drawbacks. First, it over-fits the historical data and doesn't generalize to out of sample data. In addition, the model need to be recalibrated periodically due to the economic regime change. A strategy significantly outperforms in a high volatility environment might suffer significantly in a low volatility environment. The Q-learning, in the opposite, learns from the feedback from the market, and generates the optimal trading strategy according to past experience, and automatically adapts to the new market regime. In this paper, the Q-learning algorithm is applied to generate the optimal trading strategy. The market is modeled as a Markov Decision Process where the outcomes are random and not under the control of the decision maker. The states contain information of three categories: technical indicators, sentiment features and portfolio information. The actions contains buy, sell and hold. The reward is the next day market return. The limit of leverage and the loss-cutting threshold are implemented in the relation ship of successor state and action. For example, if the leverage constrain has been met, the actions that valid for this state are only "hold" or "sell". If the loss cutting threshold has been triggered, say the portfolio lost half of the capital and this is the maximum tolerance of loss, only the action that exit current position is valid. ### Learning
Formally, the learning process defines as below. In Q-learning the optimal expected utility of a (state, action) pair INLINEFORM0 is updated with the rewards INLINEFORM1 and the expected utility of the subsequent state INLINEFORM2 after taking the action INLINEFORM3 . DISPLAYFORM0 DISPLAYFORM1 The optimal policy is proposed by Q-learning as DISPLAYFORM0 ### Function Approximation
Function approximation refers to the method to generalize unseen states by applying machine learning methods. The Q-table stores the expected utility for each (state,action) pair that has been explored. When predicting the expected utility for a certain (state, action) pair, we will look up the Q-table. When the MDP has many states and actions, it is very likely that a (state, action) pair has not been explored yet so the estimate is not accurate. It is too slow to look up a gigantic table and most likely there is not enough training data to learn each of the state individually. Function approximation uses features to capture the characteristics of the states and applies stochastic gradient descent to update the weights on each feature. More specifically, below equation is applied to generalize the unseen state in this paper. Define features INLINEFORM0 and weights INLINEFORM1 , then DISPLAYFORM0 For each INLINEFORM0 , apply stochastic gradient descent to update the weights. DISPLAYFORM0 where INLINEFORM0 is the learning rate, INLINEFORM1 is the reward and INLINEFORM2 is the discount factor. ### Exploration and Exploitation
It is necessary to balance the exploration and exploitation. One might suggest naively to take action only according to the optimal policy estimated by maximizing INLINEFORM0 . However, this greedy strategy is equivalent to stay in the comfortable zone all the time in life, without gaining new experience and unable to give reasonable prediction when encounters unseen situations. Another extreme is to always explore by choosing an action randomly. Without applying the hard lesson learned and obtaining the rewards, the algorithm can lead to unsatisfiable utility at the end. Therefore, in this paper the Epsilon-greedy strategy is applied for exploration. For a certain probability, the algorithm acts randomly(exploration), for the rest the algorithm acts optimally(exploitation). ### Result and Discussion
Figure FIGREF25 shows the cumulative return over 1 year period. The strategy trades daily. The Q-learning states include portfolio position, sentiment features and technical indicators such as price momentum. The machine learning strategy predicts the binary movement (+ or -) of next trading day price based on sentiment features and technical indicators. The backtest rule based on the machine learning prediction is to long the stock if the prediction is +, short the stock if -. The baseline is the same with machine learning except only the technical indicator was used as the feature. The oracle model of this project is a trader who has insider information about the stock and be able to bet and act correctly on every single day of the testing period. The oracle model is able to achieve 6 times of the initial capital at the end of testing period. There are observations that worth a discussion. At the beginning of the testing period, the Q-learning has not learnt how to estimate the expected utility of a certain action yet. The performance of the initial period is more unstable than later. Q-learning does better when the state is more common because it accumulates more experience about the situation but might not take the best action when a outlier state is presented. The performance of the q-learning varies during different batch due to the random nature of exploitation and exploration. In general Q-learning is able to deliver better performance than using the binary prediction from the machine learning models. Both of the Q-learning and machine learning model outperform the baseline model. ### Future work
There are many areas that can be improved given more resource and data. Below is a list of the improvement that could make this idea more robust. ### CONCLUSIONS
The paper explores the possibility to predict stock price using text data and reinforcement learning technique. Predicting stock price direction using Twitter sentiment is challenging but promising. Which stock and what to predict is more important than how to predict. For example, Tesla, a company driven by the expectation of the company's growth is a better target than Ford, a traditional auto maker. Reinforcement learning is applied to find the optimal trading policy by learning the feedbacks from the market. The Q-learning is able to adapt automatically if the market regime shifts and avoid backtesting, a process applied by investment industry that often overfit the historical data. Both of the machine learning model and the Q-learning model outperforms the baseline model, which is a logistic regression without sentiment features. ### ACKNOWLEDGMENT
We would like to thank Anna Wang, who is the project mentor, gives very practical suggestions and guidance. We would like to thank Standford University for the very challenging and exciting CS221 course materials and Prof. Percy Liang who did such a great job getting us interested in sentiment analysis and reinforcement learning Fig. 2. Histogram Fig. 1. Demo of the functionalities provided by Stanford CoreNLP Fig. 3. The chart displays the accuracy on predicting the ”alpha”, which defines as the return of the stock minus the return of its sector ETF. Fig. 4. The chart shows an example of overfitting in the SVM model. The overfitting is caused by adding too many features to the model inputs but not providing enough data for the model to generalize. Different lines shows the SVM performance under different γ parameter. None of the parameter achieves better accuracy than a restricted set of features. Fig. 5. The chart shows the trading strategy derived from Q-learning(in blue) outperform the backtest result using machine learning features(in red). Both of Q-learning strategy and machine learning strategy outperform the baseline(in green).
|
linear logistic regression to a set of stock technical signals
|
How was the previous dataset annotated?
|
### Introduction
Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural language questions, while arguments represent their answers, making the annotations intuitive, semantically rich, and easily attainable by laymen. For example, in Table TABREF4, the question Who cut something captures the traditional “agent” role. Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability. Naturally, employing crowd workers raises challenges when annotating semantic structures like SRL. As BIBREF5 acknowledged, the main shortage of the large-scale 2018 dataset is the lack of recall, estimated by experts to be in the lower 70s. In light of this and other annotation inconsistencies, we propose an improved QA-SRL crowdsourcing protocol for high-quality annotation, allowing for substantially more reliable performance evaluation of QA-SRL parsers. To address worker quality, we systematically screen workers, provide concise yet effective guidelines, and perform a short training procedure, all within a crowd-sourcing platform. To address coverage, we employ two independent workers plus an additional one for consolidation — similar to conventional expert-annotation practices. In addition to yielding 25% more roles, our coverage gain is demonstrated by evaluating against expertly annotated data and comparison with PropBank (Section SECREF4). To foster future research, we release an assessed high-quality gold dataset along with our reproducible protocol and evaluation scheme, and report the performance of the existing parser BIBREF5 as a baseline. ### Background — QA-SRL ::: Specifications
In QA-SRL, a role question adheres to a 7-slot template, with slots corresponding to a WH-word, the verb, auxiliaries, argument placeholders (SUBJ, OBJ), and prepositions, where some slots are optional BIBREF4 (see appendix for examples). Such question captures the corresponding semantic role with a natural easily understood expression. The set of all non-overlapping answers for the question is then considered as the set of arguments associated with that role. This broad question-based definition of roles captures traditional cases of syntactically-linked arguments, but also additional semantic arguments clearly implied by the sentence meaning (see example (2) in Table TABREF4). ### Background — QA-SRL ::: Corpora
The original 2015 QA-SRL dataset BIBREF4 was annotated by non-expert workers after completing a brief training procedure. They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. In subsequent work, BIBREF5 constructed a large-scale corpus and used it to train a parser. They crowdsourced 133K verbs with 2.0 QA pairs per verb on average. Since crowd-workers had no prior training, quality was established using an additional validation step, where workers had to ascertain the validity of the question, but not of its answers. Instead, the validator provided additional answers, independent of the other annotators. Each verb in the corpus was annotated by a single QA-generating worker and validated by two others. In a reserved part of the corpus (Dense), targeted for parser evaluation, verbs were densely validated with 5 workers, approving questions judged as valid by at least 4/5 validators. Notably, adding validators to the Dense annotation pipeline accounts mostly for precision errors, while role coverage solely relies upon the single generator's set of questions. As both 2015 and 2018 datasets use a single question generator, both struggle with maintaining coverage. Also noteworthy, is that while traditional SRL annotations contain a single authoritative and non-redundant annotation, the 2018 dataset provides the raw annotations of all annotators. These include many overlapping or noisy answers, without settling on consolidation procedures to provide a single gold reference. We found that these characteristics of the dataset impede its utility for future development of parsers. ### Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Screening and Training
Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 1 out of 3 participants were selected after exhibiting good performance, tested against expert annotations. ### Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Annotation
We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. In this consolidation task, the worker validates questions, merges, splits or modifies answers for the same role according to guidelines, and removes redundant roles by picking the more naturally phrased questions. For example, in Table TABREF4 ex. 1, one worker could have chosen “47 people”, while another chose “the councillor”; in this case the consolidator would include both of those answers. In Section SECREF4, we show that this process yields better coverage. For example annotations, please refer to the appendix. ### Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Guidelines Refinements
We refine the previous guidelines by emphasizing several semantic features: correctly using modal verbs and negations in the question, and choosing answers that coincide with a single entity (example 1 in Table TABREF4). ### Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Data & Cost
We annotated a sample taken from the Dense set on Wikinews and Wikipedia domains, each with 1000 sentences, equally divided between development and test. QA generating annotators are paid the same as in fitz2018qasrl, while the consolidator is rewarded 5¢ per verb and 3¢ per question. Per predicate, on average, our cost is 54.2¢, yielding 2.9 roles, compared to reported 2.3 valid roles with an approximated cost of 51¢ per predicate for Dense. ### Annotation and Evaluation Methods ::: Evaluation Metrics
Evaluation in QA-SRL involves aligning predicted and ground truth argument spans and evaluating role label equivalence. Since detecting question paraphrases is still an open challenge, we propose both unlabeled and labeled evaluation metrics. Unlabeled Argument Detection (UA) Inspired by the method presented in BIBREF5, arguments are matched using a span matching criterion of intersection over union $\ge 0.5$ . To credit each argument only once, we employ maximal bipartite matching between the two sets of arguments, drawing an edge for each pair that passes the above mentioned criterion. The resulting maximal matching determines the true-positive set, while remaining non-aligned arguments become false-positives or false-negatives. Labeled Argument Detection (LA) All aligned arguments from the previous step are inspected for label equivalence, similar to the joint evaluation reported in BIBREF5. There may be many correct questions for a role. For example, What was given to someone? and What has been given by someone? both refer to the same semantic role but diverge in grammatical tense, voice, and presence of a syntactical object or subject. Aiming to avoid judging non-equivalent roles as equivalent, we propose Strict-Match to be an equivalence on the following template slots: WH, SUBJ, OBJ, as well as on negation, voice, and modality extracted from the question. Final reported numbers on labelled argument detection rates are based on bipartite aligned arguments passing Strict-Match. We later manually estimate the rate of correct equivalences missed by this conservative method. As we will see, our evaluation heuristics, adapted from those in BIBREF5, significantly underestimate agreement between annotations, hence reflecting performance lower bounds. Devising more tight evaluation measures remains a challenge for future research. ### Annotation and Evaluation Methods ::: Evaluation Metrics ::: Evaluating Redundant Annotations
We extend our metric for evaluating manual or automatic redundant annotations, like the Dense dataset or the parser in BIBREF5, which predicts argument spans independently of each other. To that end, we ignore predicted arguments that match ground-truth but are not selected by the bipartite matching due to redundancy. After connecting unmatched predicted arguments that overlap, we count one false positive for every connected component to avoid penalizing precision too harshly when predictions are redundant. ### Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Individual worker-vs-worker agreement yields 79.8 F1 over 10 experiments with 150 predicates, indicating high consistency across our annotators, inline with results by other structured semantic annotations (e.g. BIBREF6). Overall consistency of the dataset is assessed by measuring agreement between different consolidated annotations, obtained by disjoint triplets of workers, which achieves F1 of 84.1 over 4 experiments, each with 35 distinct predicates. Notably, consolidation boosts agreement, suggesting it is a necessity for semantic annotation consistency. ### Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. To construct the expert set, we blindly merged the Dense set with our worker annotations and manually corrected them. We further corrected the evaluation decisions, accounting for some automatic evaluation mistakes introduced by the span-matching and question paraphrasing criteria. As seen in Table TABREF19, our gold set yields comparable precision with significantly higher recall, which is in line with our 25% higher yield. Examining disagreements between our gold and Dense, we observe that our workers successfully produced more roles, both implied and explicit. To a lesser extent, they split more arguments into independent answers, as emphasized by our guidelines, an issue which was left under-specified in the previous annotation guidelines. ### Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. In Table TABREF22, we replicate the experiments in BIBREF4 for both our gold set and theirs, over a sample of 200 sentences from Wall Street Journal (agreement evaluation is automatic and the metric is somewhat similar to our UA). We report macro-averaged (over predicates) precision and recall for all roles, including core and adjuncts, while considering the PropBank data as the reference set. Our recall of the PropBank roles is notably high, reconfirming the coverage obtained by our annotation protocol. The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not covered in PropBank. To examine this, we analyzed 100 false positive arguments. Only 32 of those were due to wrong or incomplete QA annotations in our gold, while most others were outside of PropBank's scope, capturing either implied arguments or roles not covered in PropBank. Extrapolating from this manual analysis estimates our true precision (on all roles) to be about 91%, which is consistent with the 88% precision figure in Table TABREF19. Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. Overall, the comparison to PropBank reinforces the quality of our gold dataset and shows its better coverage relative to the 2015 dataset. ### Baseline Parser Evaluation
To illustrate the effectiveness of our new gold-standard, we use its Wikinews development set to evaluate the currently available parser from BIBREF5. For each predicate, the parser classifies every span for being an argument, independently of the other spans. Unlike many other SRL systems, this policy often produces outputs with redundant arguments (see appendix for examples). Results for 1200 predicates are reported in Table TABREF23, demonstrating reasonable performance along with substantial room for improvement, especially with respect to coverage. As expected, the parser's recall against our gold is substantially lower than the 84.2 recall reported in BIBREF5 against Dense, due to the limited recall of Dense relative to our gold set. ### Baseline Parser Evaluation ::: Error Analysis
We sample and evaluate 50 predicates to detect correct argument and paraphrase pairs that are skipped by the IOU and Strict-Match criteria. Based on this inspection, the parser completely misses 23% of the 154 roles present in the gold-data, out of which, 17% are implied. While the parser correctly predicts 82% of non-implied roles, it skips half of the implied ones. ### Conclusion
We introduced a refined crowdsourcing pipeline and a corresponding evaluation methodology for QA-SRL. It enabled us to release a new gold standard for evaluations, notably of much higher coverage of core and implied roles than the previous Dense evaluation dataset. We believe that our annotation methodology and dataset would facilitate future research on natural semantic annotations and QA-SRL parsing. ### Supplemental Material ::: The Question Template
For completeness, we include several examples with some questions restructured into its 7 template slots in Table TABREF26 ### Supplemental Material ::: Annotation Pipeline
As described in section 3 The consolidator receives two sets of QA annotations and merges them according to the guidelines to produce an exhaustive and consistent QA set. See Table TABREF28 for examples. ### Supplemental Material ::: Redundant Parser Output
As mentioned in the paper body, the Fitzgerald et al. parser generates redundant role questions and answers. The first two rows in Table TABREF30 illustrate different, partly redundant, argument spans for the same question. The next two rows illustrate two paraphrased questions for the same role. Generating such redundant output might complicate downstream use of the parser output as well as evaluation methodology. Table 1: Running examples of QA-SRL annotations; this set is a sample of the possible questions that can be asked. The bar (|) separates multiple selected answers. Table 2: Automatic and manually-corrected evaluation of our gold standard and Dense (Fitzgerald et al., 2018) against the expert annotated sample. Table 3: Performance analysis against PropBank. Precision, recall and F1 for all roles, core roles, and adjuncts. Table 4: Automatic and manual parser evaluation against 500 Wikinews sentences from the gold dataset. Manual is evaluated on 50 sampled predicates. Table 6: The consolidation task – A1, A2 refer to the original annotator QAs, C refers to the consolidator selected question and corrected answers. Table 7: The parser generates redundant arguments with different paraphrased questions.
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the annotation machinery of BIBREF5
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What risk, according to the author, is increased by practitioners who are wary of performing C-sections?
A. They could be sued for malpractice if the fetus does not survive childbirth
B. They could be sued for malpractice if the mother does not survive childbirth
C. They could increase the prevalence of impaction and, therefore, challenging births
D. They could accidentally make the incision in the wrong location, necessitating further costly surgeries
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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.
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C. They could increase the prevalence of impaction and, therefore, challenging births
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Why is there no feeling of acceleration in the elevator in the future?
A. The force is too fast to be felt.
B. The elevator doesn't actually move, only the scenery does.
C. It's moving slower in opposition to the gravity.
D. The false gravity used in the interstellar civilization.
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... and it comes out here By LESTER DEL REY Illustrated by DON SIBLEY [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] There is one fact no sane man can quarrel with ... everything has a beginning and an end. But some men aren't sane; thus it isn't always so! No, you're wrong. I'm not your father's ghost, even if I do look a bit like him. But it's a longish story, and you might as well let me in. You will, you know, so why quibble about it? At least, you always have ... or do ... or will. I don't know, verbs get all mixed up. We don't have the right attitude toward tenses for a situation like this. Anyhow, you'll let me in. I did, so you will. Thanks. You think you're crazy, of course, but you'll find out you aren't. It's just that things are a bit confused. And don't look at the machine out there too long—until you get used to it, you'll find it's hard on the eyes, trying to follow where the vanes go. You'll get used to it, of course, but it will take about thirty years. You're wondering whether to give me a drink, as I remember it. Why not? And naturally, since we have the same tastes, you can make the same for me as you're having. Of course we have the same tastes—we're the same person. I'm you thirty years from now, or you're me. I remember just how you feel; I felt the same way when he—that is, of course, I or we—came back to tell me about it, thirty years ago. Here, have one of these. You'll get to like them in a couple more years. And you can look at the revenue stamp date, if you still doubt my story. You'll believe it eventually, though, so it doesn't matter. Right now, you're shocked. It's a real wrench when a man meets himself for the first time. Some kind of telepathy seems to work between two of the same people. You sense things. So I'll simply go ahead talking for half an hour or so, until you get over it. After that you'll come along with me. You know, I could try to change things around by telling what happened to me; but he—I—told me what I was going to do, so I might as well do the same. I probably couldn't help telling you the same thing in the same words, even if I tried—and I don't intend to try. I've gotten past that stage in worrying about all this. So let's begin when you get up in half an hour and come out with me. You'll take a closer look at the machine, then. Yes, it'll be pretty obvious it must be a time machine. You'll sense that, too. You've seen it, just a small little cage with two seats, a luggage compartment, and a few buttons on a dash. You'll be puzzling over what I'll tell you, and you'll be getting used to the idea that you are the man who makes atomic power practical. Jerome Boell, just a plain engineer, the man who put atomic power in every home. You won't exactly believe it, but you'll want to go along. I'll be tired of talking by then, and in a hurry to get going. So I cut off your questions, and get you inside. I snap on a green button, and everything seems to cut off around us. You can see a sort of foggy nothing surrounding the cockpit; it is probably the field that prevents passage through time from affecting us. The luggage section isn't protected, though. You start to say something, but by then I'm pressing a black button, and everything outside will disappear. You look for your house, but it isn't there. There is exactly nothing there—in fact, there is no there . You are completely outside of time and space, as best you can guess how things are. You can't feel any motion, of course. You try to reach a hand out through the field into the nothing around you and your hand goes out, all right, but nothing happens. Where the screen ends, your hand just turns over and pokes back at you. Doesn't hurt, and when you pull your arm back, you're still sound and uninjured. But it looks frightening and you don't try it again. Then it comes to you slowly that you're actually traveling in time. You turn to me, getting used to the idea. "So this is the fourth dimension?" you ask. Then you feel silly, because you'll remember that I said you'd ask that. Well, I asked it after I was told, then I came back and told it to you, and I still can't help answering when you speak. "Not exactly," I try to explain. "Maybe it's no dimension—or it might be the fifth; if you're going to skip over the so-called fourth without traveling along it, you'd need a fifth. Don't ask me. I didn't invent the machine and I don't understand it." "But...." I let it go, and so do you. If you don't, it's a good way of going crazy. You'll see later why I couldn't have invented the machine. Of course, there may have been a start for all this once. There may have been a time when you did invent the machine—the atomic motor first, then the time-machine. And when you closed the loop by going back and saving yourself the trouble, it got all tangled up. I figured out once that such a universe would need some seven or eight time and space dimensions. It's simpler just to figure that this is the way time got bent back on itself. Maybe there is no machine, and it's just easier for us to imagine it. When you spend thirty years thinking about it, as I did—and you will—you get further and further from an answer. Anyhow, you sit there, watching nothing all around you, and no time, apparently, though there is a time effect back in the luggage space. You look at your watch and it's still running. That means you either carry a small time field with you, or you are catching a small increment of time from the main field. I don't know, and you won't think about that then, either. I'm smoking, and so are you, and the air in the machine is getting a bit stale. You suddenly realize that everything in the machine is wide open, yet you haven't seen any effects of air loss. "Where are we getting our air?" you ask. "Or why don't we lose it?" "No place for it to go," I explain. There isn't. Out there is neither time nor space, apparently. How could the air leak out? You still feel gravity, but I can't explain that, either. Maybe the machine has a gravity field built in, or maybe the time that makes your watch run is responsible for gravity. In spite of Einstein, you have always had the idea that time is an effect of gravity, and I sort of agree, still. Then the machine stops—at least, the field around us cuts off. You feel a dankish sort of air replace the stale air, and you breathe easier, though we're in complete darkness, except for the weak light in the machine, which always burns, and a few feet of rough dirty cement floor around. You take another cigaret from me and you get out of the machine, just as I do. I've got a bundle of clothes and I start changing. It's a sort of simple, short-limbed, one-piece affair I put on, but it feels comfortable. "I'm staying here," I tell you. "This is like the things they wear in this century, as near as I can remember it, and I should be able to pass fairly well. I've had all my fortune—the one you make on that atomic generator—invested in such a way I can get it on using some identification I've got with me, so I'll do all right. I know they still use some kind of money, you'll see evidence of that. And it's a pretty easygoing civilization, from what I could see. We'll go up and I'll leave you. I like the looks of things here, so I won't be coming back with you." You nod, remembering I've told you about it. "What century is this, anyway?" I'd told you that, too, but you've forgotten. "As near as I can guess, it's about 2150. He told me, just as I'm telling you, that it's an interstellar civilization." You take another cigaret from me, and follow me. I've got a small flashlight and we grope through a pile of rubbish, out into a corridor. This is a sub-sub-sub-basement. We have to walk up a flight of stairs, and there is an elevator waiting, fortunately with the door open. "What about the time machine?" you ask. "Since nobody ever stole it, it's safe." We get in the elevator, and I say "first" to it. It gives out a coughing noise and the basement openings begin to click by us. There's no feeling of acceleration—some kind of false gravity they use in the future. Then the door opens, and the elevator says "first" back at us. It's obviously a service elevator and we're in a dim corridor, with nobody around. I grab your hand and shake it. "You go that way. Don't worry about getting lost; you never did, so you can't. Find the museum, grab the motor, and get out. And good luck to you." You act as if you're dreaming, though you can't believe it's a dream. You nod at me and I move out into the main corridor. A second later, you see me going by, mixed into a crowd that is loafing along toward a restaurant, or something like it, that is just opening. I'm asking questions of a man, who points, and I turn and move off. You come out of the side corridor and go down a hall, away from the restaurant. There are quiet little signs along the hall. You look at them, realizing for the first time that things have changed. Steij:neri, Faunten, Z:rgat Dispenseri. The signs are very quiet and dignified. Some of them can be decoded to stationery shops, fountains, and the like. What a zergot is, you don't know. You stop at a sign that announces: Trav:l Biwrou—F:rst-Clas Twrz—Marz, Viin*s, and x: Trouj:n Planets. Spej:l reits tu aol s*nz wixin 60 lyt iirz! But there is only a single picture of a dull-looking metal sphere, with passengers moving up a ramp, and the office is closed. You begin to get the hang of the spelling they use, though. Now there are people around you, but nobody pays much attention to you. Why should they? You wouldn't care if you saw a man in a leopard-skin suit; you'd figure it was some part in a play and let it go. Well, people don't change much. You get up your courage and go up to a boy selling something that might be papers on tapes. "Where can I find the Museum of Science?" "Downayer rien turn lefa the sign. Stoo bloss," he tells you. Around you, you hear some pretty normal English, but there are others using stuff as garbled as his. The educated and uneducated? I don't know. You go right until you find a big sign built into the rubbery surface of the walk: Miuzi:m *v Syens . There's an arrow pointing and you turn left. Ahead of you, two blocks on, you can see a pink building, with faint aqua trimming, bigger than most of the others. They are building lower than they used to, apparently. Twenty floors up seems about the maximum. You head for it, and find the sidewalk is marked with the information that it is the museum. You go up the steps, but you see that it seems to be closed. You hesitate for a moment, then. You're beginning to think the whole affair is complete nonsense, and you should get back to the time machine and go home. But then a guard comes to the gate. Except for the short legs in his suit and the friendly grin on his face, he looks like any other guard. What's more, he speaks pretty clearly. Everyone says things in a sort of drawl, with softer vowels and slurred consonants, but it's rather pleasant. "Help you, sir? Oh, of course. You must be playing in 'Atoms and Axioms.' The museum's closed, but I'll be glad to let you study whatever you need for realism in your role. Nice show. I saw it twice." "Thanks," you mutter, wondering what kind of civilization can produce guards as polite as that. "I—I'm told I should investigate your display of atomic generators." He beams at that. "Of course." The gate is swung to behind you, but obviously he isn't locking it. In fact, there doesn't seem to be a lock. "Must be a new part. You go down that corridor, up one flight of stairs and left. Finest display in all the known worlds. We've got the original of the first thirteen models. Professor Jonas was using them to check his latest theory of how they work. Too bad he could not explain the principle, either. Someone will, some day, though. Lord, the genius of that twentieth century inventor! It's quite a hobby with me, sir. I've read everything I could get on the period. Oh—congratulations on your pronunciation. Sounds just like some of our oldest tapes." You get away from him, finally, after some polite thanks. The building seems deserted and you wander up the stairs. There's a room on your right filled with something that proclaims itself the first truly plastic diamond former, and you go up to it. As you come near, it goes through a crazy wiggle inside, stops turning out a continual row of what seem to be bearings, and slips something the size of a penny toward you. "Souvenir," it announces in a well-modulated voice. "This is a typical gem of the twentieth century, properly cut to 58 facets, known technically as a Jaegger diamond, and approximately twenty carats in size. You can have it made into a ring on the third floor during morning hours for one-tenth credit. If you have more than one child, press the red button for the number of stones you desire." You put it in your pocket, gulping a little, and get back to the corridor. You turn left and go past a big room in which models of spaceships—from the original thing that looks like a V-2, and is labeled first Lunar rocket, to a ten-foot globe, complete with miniature manikins—are sailing about in some kind of orbits. Then there is one labeled Wep:nz , filled with everything from a crossbow to a tiny rod four inches long and half the thickness of a pencil, marked Fynal Hand Arm . Beyond is the end of the corridor, and a big place that bears a sign, Mad:lz *v Atamic Pau:r Sorsez . By that time, you're almost convinced. And you've been doing a lot of thinking about what you can do. The story I'm telling has been sinking in, but you aren't completely willing to accept it. You notice that the models are all mounted on tables and that they're a lot smaller than you thought. They seem to be in chronological order, and the latest one, marked 2147—Rincs Dyn*pat: , is about the size of a desk telephone. The earlier ones are larger, of course, clumsier, but with variations, probably depending on the power output. A big sign on the ceiling gives a lot of dope on atomic generators, explaining that this is the first invention which leaped full blown into basically final form. You study it, but it mentions casually the inventor, without giving his name. Either they don't know it, or they take it for granted that everyone does, which seems more probable. They call attention to the fact that they have the original model of the first atomic generator built, complete with design drawings, original manuscript on operation, and full patent application. They state that it has all major refinements, operating on any fuel, producing electricity at any desired voltage up to five million, any chosen cyclic rate from direct current to one thousand megacycles, and any amperage up to one thousand, its maximum power output being fifty kilowatts, limited by the current-carrying capacity of the outputs. They also mention that the operating principle is still being investigated, and that only such refinements as better alloys and the addition of magnetric and nucleatric current outlets have been added since the original. So you go to the end and look over the thing. It's simply a square box with a huge plug on each side, and a set of vernier controls on top, plus a little hole marked, in old-style spelling, Drop BBs or wire here . Apparently that's the way it's fueled. It's about one foot on each side. "Nice," the guard says over your shoulder. "It finally wore out one of the cathogrids and we had to replace that, but otherwise it's exactly as the great inventor made it. And it still operates as well as ever. Like to have me tell you about it?" "Not particularly," you begin, and then realize bad manners might be conspicuous here. While you're searching for an answer, the guard pulls something out of his pocket and stares at it. "Fine, fine. The mayor of Altasecarba—Centaurian, you know—is arriving, but I'll be back in about ten minutes. He wants to examine some of the weapons for a monograph on Centaurian primitives compared to nineteenth century man. You'll pardon me?" You pardon him pretty eagerly and he wanders off happily. You go up to the head of the line, to that Rinks Dynapattuh, or whatever it transliterates to. That's small and you can carry it. But the darned thing is absolutely fixed. You can't see any bolts, but you can't budge it, either. You work down the line. It'd be foolish to take the early model if you can get one with built-in magnetic current terminals—Ehrenhaft or some other principle?—and nuclear binding-force energy terminals. But they're all held down by the same whatchamaycallem effect. And, finally, you're right back beside the original first model. It's probably bolted down, too, but you try it tentatively and you find it moves. There's a little sign under it, indicating you shouldn't touch it, since the gravostatic plate is being renewed. Well, you won't be able to change the time cycle by doing anything I haven't told you, but a working model such as that is a handy thing. You lift it; it only weighs about fifty pounds! Naturally, it can be carried. You expect a warning bell, but nothing happens. As a matter of fact, if you'd stop drinking so much of that scotch and staring at the time machine out there now, you'd hear what I'm saying and know what will happen to you. But of course, just as I did, you're going to miss a lot of what I say from now on, and have to find out for yourself. But maybe some of it helps. I've tried to remember how much I remembered, after he told me, but I can't be sure. So I'll keep on talking. I probably can't help it, anyhow. Pre-set, you might say. Well, you stagger down the corridor, looking out for the guard, but all seems clear. Then you hear his voice from the weapons room. You bend down and try to scurry past, but you know you're in full view. Nothing happens, though. You stumble down the stairs, feeling all the futuristic rays in the world on your back, and still nothing happens. Ahead of you, the gate is closed. You reach it and it opens obligingly by itself. You breathe a quick sigh of relief and start out onto the street. Then there's a yell behind you. You don't wait. You put one leg in front of the other and you begin racing down the walk, ducking past people, who stare at you with expressions you haven't time to see. There's another yell behind you. Something goes over your head and drops on the sidewalk just in front of your feet, with a sudden ringing sound. You don't wait to find out about that, either. Somebody reaches out a hand to catch you and you dart past. The street is pretty clear now and you jolt along, with your arms seeming to come out of the sockets, and that atomic generator getting heavier at every step. Out of nowhere, something in a blue uniform about six feet tall and on the beefy side appears—and the badge hasn't changed much. The cop catches your arm and you know you're not going to get away, so you stop. "You can't exert yourself that hard in this heat, fellow," the cop says. "There are laws against that, without a yellow sticker. Here, let me grab you a taxi." Reaction sets in a bit and your knees begin to buckle, but you shake your head and come up for air. "I—I left my money home," you begin. The cop nods. "Oh, that explains it. Fine, I won't have to give you an appearance schedule. But you should have come to me." He reaches out and taps a pedestrian lightly on the shoulder. "Sir, an emergency request. Would you help this gentleman?" The pedestrian grins, looks at his watch, and nods. "How far?" You did notice the name of the building from which you came and you mutter it. The stranger nods again, reaches out and picks up the other side of the generator, blowing a little whistle the cop hands him. Pedestrians begin to move aside, and you and the stranger jog down the street at a trot, with a nice clear path, while the cop stands beaming at you both. That way, it isn't so bad. And you begin to see why I decided I might like to stay in the future. But all the same, the organized cooperation here doesn't look too good. The guard can get the same and be there before you. And he is. He stands just inside the door of the building as you reach it. The stranger lifts an eyebrow and goes off at once when you nod at him, not waiting for thanks. And the guard comes up, holding some dinkus in his hand, about the size of a big folding camera and not too dissimilar in other ways. He snaps it open and you get set to duck. "You forgot the prints, monograph, and patent applications," he says. "They go with the generator—we don't like to have them separated. A good thing I knew the production office of 'Atoms and Axioms' was in this building. Just let us know when you're finished with the model and we'll pick it up." You swallow several sets of tonsils you had removed years before, and take the bundle of papers he hands you out of the little case. He pumps you for some more information, which you give him at random. It seems to satisfy your amiable guard friend. He finally smiles in satisfaction and heads back to the museum. You still don't believe it, but you pick up the atomic generator and the information sheets, and you head down toward the service elevator. There is no button on it. In fact, there's no door there. You start looking for other doors or corridors, but you know this is right. The signs along the halls are the same as they were. Then there's a sort of cough and something dilates in the wall. It forms a perfect door and the elevator stands there waiting. You get in, gulping out something about going all the way down, and then wonder how a machine geared for voice operation can make anything of that. What the deuce would that lowest basement be called? But the elevator has closed and is moving downward in a hurry. It coughs again and you're at the original level. You get out—and realize you don't have a light. You'll never know what you stumbled over, but, somehow, you move back in the direction of the time machine, bumping against boxes, staggering here and there, and trying to find the right place by sheer feel. Then a shred of dim light appears; it's the weak light in the time machine. You've located it. You put the atomic generator in the luggage space, throw the papers down beside it, and climb into the cockpit, sweating and mumbling. You reach forward toward the green button and hesitate. There's a red one beside it and you finally decide on that. Suddenly, there's a confused yell from the direction of the elevator and a beam of light strikes against your eyes, with a shout punctuating it. Your finger touches the red button. You'll never know what the shouting was about—whether they finally doped out the fact that they'd been robbed, or whether they were trying to help you. You don't care which it is. The field springs up around you and the next button you touch—the one on the board that hasn't been used so far—sends you off into nothingness. There is no beam of light, you can't hear a thing, and you're safe. It isn't much of a trip back. You sit there smoking and letting your nerves settle back to normal. You notice a third set of buttons, with some pencil marks over them—"Press these to return to yourself 30 years"—and you begin waiting for the air to get stale. It doesn't because there is only one of you this time. Instead, everything flashes off and you're sitting in the machine in your own back yard. You'll figure out the cycle in more details later. You get into the machine in front of your house, go to the future in the sub-basement, land in your back yard, and then hop back thirty years to pick up yourself, landing in front of your house. Just that. But right then, you don't care. You jump out and start pulling out that atomic generator and taking it inside. It isn't hard to disassemble, but you don't learn a thing; just some plates of metal, some spiral coils, and a few odds and ends—all things that can be made easily enough, all obviously of common metals. But when you put it together again, about an hour later, you notice something. Everything in it is brand-new and there's one set of copper wires missing! It won't work. You put some #12 house wire in, exactly like the set on the other side, drop in some iron filings, and try it again. And with the controls set at 120 volts, 60 cycles and 15 amperes, you get just that. You don't need the power company any more. And you feel a little happier when you realize that the luggage space wasn't insulated from time effects by a field, so the motor has moved backward in time, somehow, and is back to its original youth—minus the replaced wires the guard mentioned—which probably wore out because of the makeshift job you've just done. But you begin getting more of a jolt when you find that the papers are all in your own writing, that your name is down as the inventor, and that the date of the patent application is 1951. It will begin to soak in, then. You pick up an atomic generator in the future and bring it back to the past—your present—so that it can be put in the museum with you as the inventor so you can steal it to be the inventor. And you do it in a time machine which you bring back to yourself to take yourself into the future to return to take back to yourself.... Who invented what? And who built which? Before long, your riches from the generator are piling in. Little kids from school are coming around to stare at the man who changed history and made atomic power so common that no nation could hope to be anything but a democracy and a peaceful one—after some of the worst times in history for a few years. Your name eventually becomes as common as Ampere, or Faraday, or any other spelled without a capital letter. But you're thinking of the puzzle. You can't find any answer. One day you come across an old poem—something about some folks calling it evolution and others calling it God. You go out, make a few provisions for the future, and come back to climb into the time machine that's waiting in the building you had put around it. Then you'll be knocking on your own door, thirty years back—or right now, from your view—and telling your younger self all these things I'm telling you. But now.... Well, the drinks are finished. You're woozy enough to go along with me without protest, and I want to find out just why those people up there came looking for you and shouting, before the time machine left. Let's go.
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D. The false gravity used in the interstellar civilization.
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What statement would the narrator most likely agree with?
A. He does not fully understand how or why he is Yandro.
B. The inhabitants of Dondromogon are unwelcoming no matter his status.
C. The inhabitants of Dondromogon are playing a joke on him.
D. He has been mistakenly selected by the people of Dondromogon.
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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.
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A. He does not fully understand how or why he is Yandro.
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How do they determine similarity between predicted word and topics?
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### Introduction
The web has provided researchers with vast amounts of unlabeled text data, and enabled the development of increasingly sophisticated language models which can achieve state of the art performance despite having no task specific training BIBREF0, BIBREF1, BIBREF2. It is desirable to adapt these models for bespoke tasks such as short text classification. Short-text is nuanced, difficult to model statistically, and sparse in features, hindering traditional analysis BIBREF3. These difficulties become further compounded when training is limited, as is the case for many practical applications. This paper provides a method to expand short-text with additional keywords, generated using a pre-trained language model. The method takes advantage of general language understanding to suggest contextually relevant new words, without necessitating additional domain data. The method can form both derivatives of the input vocabulary, and entirely new words arising from contextualised word interactions and is ideally suited for applications where data volume is limited. figureBinary Classification of short headlines into 'WorldPost' or 'Crime' categories, shows improved performance with extended pseudo headlines when the training set is small. Using: Random forest classifier, 1000 test examples, 10-fold cross validation. ### Literature Review
Document expansion methods have typically focused on creating new features with the help of custom models. Word co-occurrence models BIBREF4, topic modeling BIBREF5, latent concept expansion BIBREF6, and word embedding clustering BIBREF7, are all examples of document expansion methods that must first be trained using either the original dataset or an external dataset from within the same domain. The expansion models may therefore only be used when there is a sufficiently large training set. Transfer learning was developed as a method of reducing the need for training data by adapting models trained mostly from external data BIBREF8. Transfer learning can be an effective method for short-text classification and requires little domain specific training data BIBREF9, BIBREF10, however it demands training a new model for every new classification task and does not offer a general solution to sparse data enrichment. Recently, multi-task language models have been developed and trained using ultra-large online datasets without being confined to any narrow applications BIBREF0, BIBREF1, BIBREF2. It is now possible to benefit from the information these models contain by adapting them to the task of text expansion and text classification. This paper is a novel approach which combines the advantages of document expansion, transfer learning, and multitask modeling. It expends documents with new and relevant keywords by using the BERT pre-trained learning model, thus taking advantage of transfer learning acquired during BERT's pretraining. It is also unsupervised and requires no task specific training, thus allowing the same model to be applied to many different tasks or domains. ### Procedures ::: Dataset
The News Category Dataset BIBREF11 is a collection of headlines published by HuffPost BIBREF12 between 2012 and 2018, and was obtained online from Kaggle BIBREF13. The full dataset contains 200k news headlines with category labels, publication dates, and short text descriptions. For this analysis, a sample of roughly 33k headlines spanning 23 categories was used. Further analysis can be found in table SECREF12 in the appendix. ### Procedures ::: Word Generation
Words were generated using the BERT pre-trained model developed and trained by Google AI Language BIBREF0. BERT creates contextualized word embedding by passing a list of word tokens through 12 hidden transformer layers and generating encoded word vectors. To generate extended text, an original short-text document was passed to pre-trained BERT. At each transformer layer a new word embedding was formed and saved. BERT's vector decoder was then used to convert hidden word vectors to candidate words, the top three candidate words at each encoder layer were kept. Each input word produced 48 candidate words, however many were duplicates. Examples of generated words per layer can be found in table SECREF12 and SECREF12 in the appendix. The generated words were sorted based on frequency, duplicate words from the original input were removed, as were stop-words, punctuation, and incomplete words. The generated words were then appended to the original document to create extended pseudo documents, the extended document was limited to 120 words in order to normalize each feature set. Further analysis can be found in table SECREF12 in the appendix. figureThe proposed method uses the BERT pre-trained word embedding model to generate new words which are appended to the orignal text creating extended pseudo documents. ### Procedures ::: Topic Evaluation
To test the proposed methods ability to generate unsupervised words, it was necessary to devise a method of measuring word relevance. Topic modeling was used based on the assumption that words found in the same topic are more relevant to one another then words from different topics BIBREF14. The complete 200k headline dataset BIBREF11 was modeled using a Naïve Bayes Algorithm BIBREF15 to create a word-category co-occurrence model. The top 200 most relevant words were then found for each category and used to create the topic table SECREF12. It was assumed that each category represented its own unique topic. The number of relevant output words as a function of the headline’s category label were measured, and can be found in figure SECREF4. The results demonstrate that the proposed method could correctly identify new words relevant to the input topic at a signal to noise ratio of 4 to 1. figureThe number of generated words within each topic was counted, topics which matched the original headline label were considered 'on target'. Results indicate that the unsupervised generation method produced far more words relating to the label category then to other topics. Tested on 7600 examples spanning 23 topics. ### Procedures ::: Binary and Multi-class Classification Experiments
Three datasets were formed by taking equal length samples from each category label. The new datastes are ‘Worldpost vs Crime’, ‘Politics vs Entertainment’, and ‘Sports vs Comedy’, a fourth multiclass dataset was formed by combining the three above sets. For each example three feature options were created by extending every headline by 0, 15 and 120 words. Before every run, a test set was removed and held aside. The remaining data was sampled based on the desired training size. Each feature option was one-hot encoded using a unique tfidf-vectorizer BIBREF16 and used to train a random-forest classifier BIBREF17 with 300-estimators for binary predictions and 900-estimators for multiclass. Random forest was chosen since it performs well on small datasets and is resistant to overfitting BIBREF18. Each feature option was evaluated against its corresponding test set. 10 runs were completed for each dataset. ### Results and Analysis ::: Evaluating word relevance
It is desirable to generate new words which are relevant to the target topics and increase predictive signal, while avoiding words which are irrelevant, add noise, and mislead predictions. The strategy, described in section SECREF4, was created to measure word relevance and quantify the unsupervised model performance. It can be seen from fig SECREF4 and SECREF12 in the appendix that the proposed expansion method is effective at generating words which relate to topics of the input sentence, even from very little data. From the context of just a single word, the method can generate 3 new relevant words, and can generate as many as 10 new relevant words from sentences which contain 5 topic related words SECREF12. While the method is susceptible to noise, producing on average 1 word related to each irrelevant topic, the number of correct predictions statistically exceed the noise. Furthermore, because the proposed method does not have any prior knowledge of its target topics, it remains completely domain agnostic, and can be applied generally for short text of any topic. ### Results and Analysis ::: Evaluating word relevance ::: Binary Classification
Comparing the performance of extended pseudo documents on three separate binary classification datasets shows significant improvement from baseline in the sparse data region of 100 to 1000 training examples. The ‘Worldpost vs Crime’ dataset showed the most improvement as seen in figure SECREF1. Within the sparse data region the extended pseudo documents could achieve similar performance as original headlines with only half the data, and improve F1 score between 13.9% and 1.7% The ‘Comedy vs Sports’ dataset, seen in figure SECREF11, showed an average improvement of 2% within the sparse region. The ‘Politics vs Entertainment’ dataset, figure SECREF11, was unique. It is the only dataset for which a 15-word extended feature set surpassed the 120-words feature set. It demonstrates that the length of the extended pseudo documents can behave like a hyper parameter for certain datasets, and should be tuned according to the train-size. ### Results and Analysis ::: Evaluating word relevance ::: Multiclass Classification
The Extended pseudo documents improved multiclass performance by 4.6% on average, in the region of 100 to 3000 training examples, as seen in figure SECREF11. The results indicate the effectiveness of the proposed method at suggesting relevant words within a narrow topic domain, even without any previous domain knowledge. In each instance it was found that the extended pseudo documents only improved performance on small training sizes. This demonstrates that while the extended pseudo docs are effective at generating artificial data, they also produce a lot of noise. Once the training size exceeds a certain threshold, it becomes no longer necessary to create additional data, and using extended documents simply adds noise to an otherwise well trained model. figureBinary Classification of 'Politics' or 'Entertainment' demonstrates that the number of added words can behave like a hyper paremeter and should be tuned based on training size. Tested on 1000 examples with 10-fold cross validation figureBinary Classification of 'Politics' vs 'Sports' has less improvement compared to other datasets which indicates that the proposed method, while constructed to be domain agnostic, shows better performance towards certain topics. Tested on 1000 examples with 10-fold cross validation. figureAdded Words improve Multiclass Classification between 1.5% and 13% in the range of 150 to 2000 training examples. Tests were conducted using equal size samples of Headlines categorized into 'World-Post', 'Crime', 'Politics', 'Entertainment', 'Sports' or 'Comedy'. A 900 Estimator Random Forest classifier was trained for each each data point, tested using 2000 examples, and averaged using 10-fold cross validation. 2 ### Discussion
Generating new words based solely on ultra small prompts of 10 words or fewer is a major challenge. A short sentence is often characterized by a just a single keyword, and modeling topics from such little data is difficult. Any method of keyword generation that overly relies on the individual words will lack context and fail to add new information, while attempting to freely form new words without any prior domain knowledge is uncertain and leads to misleading suggestions. This method attempts to find balance between synonym and free-form word generation, by constraining words to fit the original sentence while still allowing for word-word and word-sentence interactions to create novel outputs. The word vectors must move through the transformer layers together and therefore maintain the same token order and semantic meaning, however they also receive new input from the surrounding words at each layer. The result, as can be seen from table SECREF12 and SECREF12 in the appendix, is that the first few transformer layers are mostly synonyms of the input sentence since the word vectors have not been greatly modified. The central transformer layers are relevant and novel, since they are still slightly constrained but also have been greatly influenced by sentence context. And the final transformer layers are mostly non-sensical, since they have been completely altered from their original state and lost their ability to retrieve real words. This method is unique since it avoids needing a prior dataset by using the information found within the weights of a general language model. Word embedding models, and BERT in particular, contain vast amounts of information collected through the course of their training. BERT Base for instance, has 110 Million parameters and was trained on both Wikipedea Corpus and BooksCorpus BIBREF0, a combined collection of over 3 Billion words. The full potential of such vastly trained general language models is still unfolding. This paper demonstrates that by carefully prompting and analysing these models, it is possible to extract new information from them, and extend short-text analysis beyond the limitations posed by word count. ### Appendix ::: Additional Tables and Figures
figureA Topic table, created from the category labels of the complete headline dataset, can be used to measure the relevance of generated words. An original headline was analyzed by counting the number of words which related to each topic. The generated words were then analyzed in the same way. The change in word count between input topics and output topics was measured and plotted as seen in figure SECREF12. figureBox plot of the number of generated words within a topic as a function of the number of input words within the same topic. Results indicate that additional related words can be generated by increasing the signal of the input prompt. Tested on 7600 examples spanning 23 topics. figureInformation regarding the original headlines, and generated words used to create extended pseudo headlines. figureTop 3 guesses for each token position at each later of a BERT pretrained embedding model. Given the input sentence '2 peoplpe injured in Indiana school shooting', the full list of generated words can be obtainedfrom the values in the table. figureTop 3 guesses for each token position at each later of a BERT pretrained embedding model. Figure 1: Binary Classification of short headlines into ’WorldPost’ or ’Crime’ categories, shows improved performance with extended pseudo headlines when the training set is small. Using: Random forest classifier, 1000 test examples, 10-fold cross validation. Figure 2: The proposed method uses the BERT pre-trained word embedding model to generate new words which are appended to the orignal text creating extended pseudo documents. Figure 3: The number of generated words within each topic was counted, topics which matched the original headline label were considered ’on target’. Results indicate that the unsupervised generation method produced far more words relating to the label category then to other topics. Tested on 7600 examples spanning 23 topics. Figure 4: Binary Classification of ’Politics’ or ’Entertainment’ demonstrates that the number of added words can behave like a hyper paremeter and should be tuned based on training size. Tested on 1000 examples with 10-fold cross validation Figure 5: Binary Classification of ’Politics’ vs ’Sports’ has less improvement compared to other datasets which indicates that the proposed method, while constructed to be domain agnostic, shows better performance towards certain topics. Tested on 1000 examples with 10-fold cross validation. Figure 6: Added Words improve Multiclass Classification between 1.5% and 13% in the range of 150 to 2000 training examples. Tests were conducted using equal size samples of Headlines categorized into ’World-Post’, ’Crime’, ’Politics’, ’Entertainment’, ’Sports’ or ’Comedy’. A 900 Estimator Random Forest classifier was trained for each each data point, tested using 2000 examples, and averaged using 10-fold cross validation. Figure 7: A Topic table, created from the category labels of the complete headline dataset, can be used to measure the relevance of generated words. Figure 8: Box plot of the number of generated words within a topic as a function of the number of input words within the same topic. Results indicate that additional related words can be generated by increasing the signal of the input prompt. Tested on 7600 examples spanning 23 topics. Figure 9: Information regarding the original headlines, and generated words used to create extended pseudo headlines. Figure 10: Top 3 guesses for each token position at each later of a BERT pretrained embedding model. Given the input sentence ’2 peoplpe injured in Indiana school shooting’, the full list of generated words can be obtainedfrom the values in the table. Figure 11: Top 3 guesses for each token position at each later of a BERT pretrained embedding model.
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number of relevant output words as a function of the headline’s category label
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Why did Jaro ask to meet Miss Webb?
A. He doesn't have anyone else to talk to
B. He wants to know what's really going on
C. He wants her to be an assassin
D. He found her attractive
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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."
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B. He wants to know what's really going on
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Which term best describes the author's tone toward delivering a 'baby' by C-section for the first time?
A. befuddled
B. petrified
C. apprehensive
D. confident
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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. apprehensive
|
Which complication did Mr. Chapman NOT experience during his ICU stay?
Choose the correct answer from the following options:
A. Rhabdomyolysis
B. Diabetes insipidus
C. Malresorptive hydrocephalus
D. Infarct areas in the MCA-ACA border zones
E. Subarachnoid hemorrhage
|
### Patient Report 0
**Dear colleague, **
We are reporting on our shared patient, Mr. John Chapman, born on
11/16/1994, who received emergency treatment at our clinic on
04/03/2017.
**Diagnoses**:
- Severe open traumatic brain injury with fractures of the cranial
vault, mastoid, and skull base
- Dissection of the distal internal carotid artery on both sides
- Subarachnoid hemorrhage involving both hemispheres and extending
into the basal cisterns
- Aspiration pneumonia
**Other Diagnoses: **
- Status post rib fracture 2005
- Status post appendectomy 2006
- Status post distal radius fracture 2008
- Status post elbow fracture 20010
**Procedure**: External ventricular drain (EVD) placement.
**Medical History:** Admission through the emergency department as a
polytrauma alert. The patient was involved in a motocross accident,
where he jumped, fell, and landed face-first. He was intubated at the
scene, and either during or before intubation, aspiration occurred. No
issues with airway, breathing, or circulation (A, B, or C problems) were
noted. A CT scan performed in the emergency department revealed an open
traumatic brain injury with fractures of the cranial vault, mastoid, and
skull base, as well as dissection of both carotid arteries. Upon
admission, we encountered an intubated and sedated patient with a
Richmond Agitation-Sedation Scale (RASS) score of -4. He was
hemodynamically stable at all times.
**Current Recommendations:**
- Regular checks of vigilance, laboratory values and microbiological
findings.
- Careful balancing
### Patient Report 1
**Dear colleague, **
We report on Mr. John Chapman, born on 11/16/1994, who was admitted to
our Intensive Care Unit from 04/03/2017 to 05/01/2017.
**Diagnoses:**
- Open severe traumatic brain injury with fractures of the skull
vault, mastoid, and skull base
- Dissection of the distal ACI on both sides
- Subarachnoid hemorrhage involving both hemispheres and extending
into basal cisterns
- Infarct areas in the border zone between MCA-ACA on the right
frontal and left parietal sides
- Malresorptive hydrocephalus
<!-- -->
- Rhabdomyolysis
- Aspiration pneumonia
**Other Diagnoses: **
- Status post rib fracture in 2005
- Status post appendectomy in 2006
- Status post distal radius fracture in 2008
- Status post elbow fracture in 20010
**Surgical Procedures:**
- 04/03/2017: Placement of external ventricular drain
- 04/08/2017: Placement of an intracranial pressure monitoring
catheter
- 04/13/2017: Surgical tracheostomy
- 05/01/2017: Left ventriculoperitoneal shunt placement
**Medical History:** The patient was admitted through the emergency
department as a polytrauma alert. The patient had fallen while riding a
motocross bike, landing face-first after jumping. He was intubated at
the scene. Aspiration occurred either during or before intubation. No
problems with breathing or circulation were noted. The CT performed in
the emergency department showed an open traumatic brain injury with
fractures of the skull vault, mastoid, and skull base, as well as
dissection of the carotid arteries on both sides and bilateral
subarachnoid hemorrhage.
Upon admission, the patient was sedated and intubated, with a Richmond
Agitation-Sedation Scale (RASS) score of -4, and was hemodynamically
stable under controlled ventilation.
**Therapy and Progression:**
[Neurology]{.underline}: Following the patient\'s admission, an external
ventricular drain was placed. Reduction of sedation had to be
discontinued due to increased intracranial pressure. A right pupil size
greater than the left showed no intracranial correlate. With
persistently elevated intracranial pressure, intensive intracranial
pressure therapy was initiated using deeper sedation, administration of
hyperosmolar sodium, and cerebrospinal fluid drainage, which normalized
intracranial pressure. Intermittently, there were recurrent intracranial
pressure peaks, which could be treated conservatively. Transcranial
Doppler examinations showed normal flow velocities. Microbiological
samples from cerebrospinal fluid were obtained when the patient had
elevated temperatures, but no bacterial growth was observed. Due to the
inability to adequately monitor intracranial pressure via the external
ventricular drain, an intracranial pressure monitoring catheter was
placed to facilitate adequate intracranial pressure monitoring. In the
perfusion computed tomography, progressive edema with increasingly
obstructed external ventricular spaces and previously known infarcts in
the border zone area were observed. To ensure appropriate intracranial
pressure monitoring, a Tuohy drain was inserted due to cerebrospinal
fluid buildup on 04/21/2017. After the initiation of antibiotic therapy
for suspected ventriculitis, the intracranial pressure monitoring
catheter was removed on 04/20/2017. Subsequently, a liquorrhea
developed, leading to the placement of a Tuohy drain. After successful
antibiotic treatment of ventriculitis, a ventriculoperitoneal shunt was
placed on 05/01/2017 without complications, and the Tuohy drain was
removed. Radiological control confirmed the correct positioning. The
patient gradually became more alert. Both pupils were isochoric and
reacted to light. All extremities showed movement, although the patient
only intermittently responded to commands. On 05/01/2017, a VP shunt was
placed on the left side without complications. Currently, the patient is
sedated with continuous clonidine at 60µg/h.
**Hemodynamics**: To maintain cerebral perfusion pressure in the
presence of increased intracranial pressure, circulatory support with
vasopressors was necessary. Echocardiography revealed preserved cardiac
function without wall motion abnormalities or right heart strain,
despite the increasing need for noradrenaline support. As the patient
had bilateral carotid dissection, a therapy with Aspirin 100mg was
initiated. On 04/16/2017, clinical examination revealed right\>left leg
circumference difference and redness of the right leg. Utrasound
revealed a long-segment deep vein thrombosis in the right leg, extending
from the pelvis (proximal end of the thrombus not clearly delineated) to
the lower leg. Therefore, Heparin was increased to a therapeutic dose.
Heparin therapy was paused on postoperative day 1, and prophylactic
anticoagulation started, followed by therapeutic anticoagulation on
postoperative day 2. The patient was switched to subcutaneous Lovenox.
**Pulmonary**: Due to the history of aspiration in the prehospital
setting, a bronchoscopy was performed, revealing a moderately obstructed
bronchial system with several clots. As prolonged sedation was
necessary, a surgical tracheostomy was performed without complications
on 04/13/2017. Subsequently, we initiated weaning from mechanical
ventilation. The current weaning strategy includes 12 hours of
synchronized intermittent mandatory ventilation (SIMV) during the night,
with nighttime pressure support ventilation (DuoPAP: Ti high 1.3s,
respiratory rate 11/min, Phigh 11 mbar, PEEP 5 mbar, Psupport 5 mbar,
trigger 4l, ramp 50 ms, expiratory trigger sensitivity 25%).
**Abdomen**: FAST examinations did not reveal any signs of
intra-abdominal trauma. Enteral feeding was initiated via a gastric
tube, along with supportive parenteral nutrition. With forced bowel
movement measures, the patient had regular bowel movements. On
04/17/2017, a complication-free PEG (percutaneous endoscopic
gastrostomy) placement was performed due to the potential long-term need
for enteral nutrition. The PEG tube is currently being fed with tube
feed nutrition, with no bowel movement for the past four days.
Additionally, supportive parenteral nutrition is being provided.
**Kidney**: Initially, the patient had polyuria without confirming
diabetes insipidus, and subsequently, adequate diuresis developed.
Retention parameters were within the normal range. As crush parameters
increased, a therapy involving forced diuresis was initiated, resulting
in a significant reduction of crush parameters.
**Infection Course:** Upon admission, with elevated infection parameters
and intermittently febrile temperatures, empirical antibiotic therapy
was initiated for suspected pneumonia using Piperacillin/Tazobactam.
Staphylococcus capitis was identified in blood cultures, and
Staphylococcus aureus was found in bronchial lavage. Both microbes were
sensitive to the current antibiotic therapy, so treatment with
Piperacillin/Tazobactam continued. Additionally, Enterobacter cloacae
was identified in tracheobronchial secretions during the course, also
sensitive to the ongoing antibiotic therapy. On 05/17, the patient
experienced another fever episode with elevated infection parameters and
right lower lobe infiltrates in the chest X-ray. After obtaining
microbiological samples, antibiotic therapy was switched to Meropenem
for suspected pneumonia. Microbiological findings from cerebrospinal
fluid indicated gram-negative rods. Therefore, antibiotic therapy was
adjusted to Ciprofloxacin in accordance with susceptibility testing due
to suspected ventriculitis, and the Meropenem dose was increased. This
led to a reduction in infection parameters. Finally, microbiological
examination of cerebrospinal fluid, blood cultures, and urine revealed
no pathological findings. Infection parameters decreased. We recommend
continuing antibiotic therapy until 05/02/2017.
**Anti-Infective Course: **
- Piperacillin/Tazobactam 04/03/2017-04/16/2017: Staph. Capitis in
Blood Culture Staph. Aureus in Bronchial Lavage
- Meropenem 04/16/2017-present (increased dose since 04/18) CSF:
gram-negative rods in Blood Culture: Pseudomonas aeruginosa
Acinetobacter radioresistens
- Ciprofloxacin 04/18/2017-present CSF: gram-negative rods in Blood
Culture: Pseudomonas aeruginosa, Acinetobacter radioresistens
**Weaning Settings:** Weaning Stage 6: 12-hour synchronized intermittent
mandatory ventilation (SIMV) with DuoPAP during the night (Thigh 1.3s,
respiratory rate 11/min, Phigh 11 mbar, PEEP 5 mbar, Psupport 5 mbar,
trigger 4l, ramp 50 ms, expiratory trigger sensitivity 25%).
**Status at transfer:** Currently, Mr. Chapman is monosedated with
Clonidine. He spontaneously opens both eyes and spontaneously moves all
four extremities. Pupils are bilaterally moderately dilated, round and
sensitive to light. There is bulbar divergence. Circulation is stable
without catecholamine therapy. He is in the process of weaning,
currently spontaneous breathing with intermittent CPAP. Renal function
is sufficient, enteral nutrition via PEG with supportive parenteral
nutrition is successful.
**Current Medication:**
**Medication** **Dosage** **Frequency**
------------------------------------ ---------------- ---------------
Bisoprolol (Zebeta) 2.5 mg 1-0-0
Ciprofloxacin (Cipro) 400 mg 1-1-1
Meropenem (Merrem) 4 g Every 4 hours
Morphine Hydrochloride (MS Contin) 10 mg 1-1-1-1-1-1
Polyethylene Glycol 3350 (MiraLAX) 13.1 g 1-1-1
Acetaminophen (Tylenol) 1000 mg 1-1-1-1
Aspirin 100 mg 1-0-0
Enoxaparin (Lovenox) 30 mg (0.3 mL) 0-0-1
Enoxaparin (Lovenox) 70 mg (0.7 mL) 1-0-1
**Lab results:**
**Parameter** **Results** **Reference Range**
-------------------- ------------- ---------------------
Creatinine (Jaffé) 0.42 mg/dL 0.70-1.20 mg/dL
Urea 31 mg/dL 17-48 mg/dL
Total Bilirubin 0.35 mg/dL \< 1.20 mg/dL
Hemoglobin 7.6 g/dL 13.5-17.0 g/dL
Hematocrit 28% 39.5-50.5%
Red Blood Cells 3.5 M/uL 4.3-5.8 M/uL
White Blood Cells 10.35 K/uL 3.90-10.50 K/uL
Platelets 379 K/uL 150-370 K/uL
MCV 77.2 fL 80.0-99.0 fL
MCH 24.1 pg 27.0-33.5 pg
MCHC 32.5 g/dL 31.5-36.0 g/dL
MPV 11.3 fL 7.0-12.0 fL
RDW-CV 17.7% 11.5-15.0%
Quick 54% 78-123%
INR 1.36 0.90-1.25
aPTT 32.8 sec 25.0-38.0 sec
**Addition: Radiological Findings**
[Clinical Information and Justification:]{.underline} Suspected deep
vein thrombosis (DVT) on the right leg.
[Special Notes:]{.underline} Examination at the bedside in the intensive
care unit, no digital image archiving available.
[Findings]{.underline}: Confirmation of a long-segment deep venous
thrombosis in the right leg, starting in the pelvis (proximal end not
clearly delineated) and extending to the lower leg.
Visible Inferior Vena Cava without evidence of thrombosis.
The findings were communicated to the treating physician.
**Full-Body Trauma CT on 04/03/2017:**
[Clinical Information and Justification:]{.underline} Motocross
accident. Polytrauma alert. Consequences of trauma? Informed consent:
Emergency indication. Recommended monitoring of kidney and thyroid
laboratory parameters.
**Findings**: CCT: Dissection of the distal internal carotid artery on
both sides (left 2-fold).
Signs of generalized elevated intracranial pressure.
Open skull-brain trauma with intracranial air inclusions and skull base
fracture at the level of the roof of the ethmoidal/sphenoidal sinuses
and clivus (in a close relationship to the bilateral internal carotid
arteries) and the temporal
**CT Head on 04/16/2017:**
[Clinical Information and Justification:]{.underline} History of skull
fracture, removal of EVD (External Ventricular Drain). Inquiry about the
course.
[Findings]{.underline}: Regression of ventricular system width (distance
of SVVH currently 41 mm, previously 46 mm) with residual liquor caps,
indicative of regressed hydrocephalus. Interhemispheric fissure in the
midline. No herniation.
Complete regression of subdural hematoma on the left, tentorial region.
Known defect areas on the right frontal lobe where previous catheters
were inserted.
Progression of a newly hypodense demarcated cortical infarct on the
left, postcentral.
Known bilateral skull base fractures involving the petrous bone, with
secretion retention in the mastoid air cells bilaterally. Minimal
secretion also in the sphenoid sinuses.
Postoperative bone fragments dislocated intracranially after right
frontal trepanation.
**Chest X-ray on 04/24/2017.**
[Clinical Information and Justification:]{.underline} Mechanically
ventilated patient. Suspected pneumonia. Question about infiltrates.
[Findings]{.underline}: Several previous images for comparison, last one
from 08/20/2021.
Persistence of infiltrates in the right lower lobe. No evidence of new
infiltrates. Removal of the tracheal tube and central venous catheter
with a newly inserted tracheal cannula. No evidence of pleural effusion
or pneumothorax.
**CT Head on 04/25/2017:**
[Clinical Information and Justification:]{.underline} Severe traumatic
brain injury with brain edema, one External Ventricular Drain removed,
one parenchymal catheter removed; Follow-up.
[Findings]{.underline}: Previous images available, CT last performed on
04/09/17, and MRI on 04/16/17.
Massive cerebrospinal fluid (CSF) stasis supra- and infratentorially
with CSF pressure caps at the ventricular and cisternal levels with
completely depleted external CSF spaces, differential diagnosis:
malresorptive hydrocephalus. The EVD and parenchymal catheter have been
completely removed.
No evidence of fresh intracranial hemorrhage. Residual subdural hematoma
on the left, tentorial. Slight regression of the cerebellar tonsils.
Increasing hypodensity of the known defect zone on the right frontal
region, differential diagnosis: CSF diapedesis. Otherwise, the status is
the same as for the other defects.
Secretion in the sphenoid sinus and mastoid cells bilaterally, known
bilateral skull base fractures.
**Bedside Chest X-ray on 04/262017:**
[Clinical Information and Justification]{.underline}: Respiratory
insufficiency. Inquiry about cardiorespiratory status.
[Findings]{.underline}: Previous image from 08/17/2021.
Left Central Venous Catheter and gastric tube in unchanged position.
Persistent consolidation in the right para-hilar region, differential
diagnosis: contusion or partial atelectasis. No evidence of new
pulmonary infiltrates. No pleural effusion. No pneumothorax. No
pulmonary congestion.
**Brain MRI on 04/26/2017:**
[Clinical Information and Justification:]{.underline} Severe skull-brain
trauma with skull calvarium, mastoid, and skull base fractures.
Assessment of infarct areas/edema for rehabilitation planning.
[Findings:]{.underline} Several previous examinations available.
Persistent small sulcal hemorrhages in both hemispheres (left \> right)
and parenchymal hemorrhage on the left frontal with minimal perifocal
edema.
Narrow subdural hematoma on the left occipital extending tentorially (up
to 2 mm).
No current signs of hypoxic brain damage. No evidence of fresh ischemia.
Slightly regressed ventricular size. No herniation. Unchanged placement
of catheters on the right frontal side. Mastoid air cells blocked
bilaterally due to known bilateral skull base fractures, mucosal
swelling in the sphenoid and ethmoid sinuses. Polypous mucosal swelling
in the left maxillary sinus. Other involved paranasal sinuses and
mastoids are clear.
**Bedside Chest X-ray on 04/27/2017:**
[Clinical Information and Justification:]{.underline} Tracheal cannula
placement. Inquiry about the position.
[Findings]{.underline}: Images from 04/03/2017 for comparison.
Tracheal cannula with tip projecting onto the trachea. No pneumothorax.
Regressing infiltrate in the right lower lung field. No leaking pleural
effusions.
Left ubclavian central venous catheter with tip projecting onto the
superior vena cava. Gastric tube in situ.
**CT Head on 04/28/2017:**
[Clinical Information and Justification:]{.underline} Open head injury,
bilateral subarachnoid hemorrhage (SAH), EVD placement. Inquiry about
herniation.
[Findings]{.underline}: Comparison with the last prior examination from
the previous day.
Generalized signs of cerebral edema remain constant, slightly
progressing with a somewhat increasing blurred cortical border,
particularly high frontal.
Essentially constant transtentorial herniation of the midbrain and low
position of the cerebellar tonsils. Marked reduction of inner CSF spaces
and depleted external CSF spaces, unchanged position of the ventricular
drainage catheter with the tip in the left lateral ventricle.
Constant small parenchymal hemorrhage on the left frontal and constant
SDH at the tentorial edge on both sides. No evidence of new intracranial
space-occupying hemorrhage.
Slightly less distinct demarcation of the demarcated infarcts/defect
zones, e.g., on the right frontal region, differential diagnosis:
fogging.
**CT Head Angiography with Perfusion on 04/28/2017:**
[Clinical Information and Justification]{.underline}: Post-traumatic
head injury, rising intracranial pressure, bilateral internal carotid
artery dissection. Inquiry about intracranial bleeding, edema course,
herniation, brain perfusion.
[Emergency indication:]{.underline} Vital indication. Recommended
monitoring of kidney and thyroid laboratory parameters. Consultation
with the attending physician from and the neuroradiology service was
conducted.
[Technique]{.underline}: Native moderately of the neurocranium. CT
angiography of brain-supplying cervical intracranial vessels during
arterial contrast agent phase and perfusion imaging of the neurocranium
after intravenous injection of a total of 140 ml of Xenetix-350. DLP
Head 502.4 mGy*cm. DLP Body 597.4 mGy*cm.
[Findings]{.underline}: Previous images from 08/11/2021 and the last CTA
of the head/neck from 04/03/2017 for comparison.
[Brain]{.underline}: Constant bihemispheric and cerebellar brain edema
with a slit-like appearance of the internal and completely compressed
external ventricular spaces. Constant compression of the midbrain with
transtentorial herniation and a constant tonsillar descent.
Increasing demarcation of infarct areas in the border zone of MCA-ACA on
the right frontal, possibly also on the left frontal. Predominantly
preserved cortex-gray matter contrast, sometimes discontinuous on both
frontal sides, differential diagnosis: artifact-related, differential
diagnosis: disseminated infarct demarcations/contusions.
Unchanged placement of the ventricular drainage from the right frontal
with the catheter tip in the left lateral ventricle anterior horn.
Constant subdural hematoma tentorial and posterior falx. Increasingly
vague delineation of the small frontal parenchymal hemorrhage. No new
space-occupying intracranial bleeding.
No evidence of secondary dislocation of the skull base fracture with
constant fluid collections in the paranasal sinuses and mastoid air
cells. Hematoma possible, cerebrospinal fluid leakage possible.
[CT Angiography Head/Neck]{.underline}: Constant presentation of
bilateral internal carotid artery dissection.
No evidence of higher-grade vessel stenosis or occlusion of the
brain-supplying intracranial arteries.
Moderately dilated venous collateral circuits in the cranial soft
tissues on both sides, right \> left. Moderately dilated ophthalmic
veins on both sides, right \> left.
No evidence of sinus or cerebral venous thrombosis. Slight perfusion
deficits in the area of the described infarct areas and contusions.
No evidence of perfusion mismatches in the perfusion imaging.
Unchanged presentation of the other documented skeletal segments.
Additional Note: Discussion of findings with the responsible medical
colleagues on-site and by telephone, as well as with the neuroradiology
service by telephone, was conducted.
**CT Head on 04/30/2017:**
[Clinical Information and Justification]{.underline}: Open head injury
following a motorcycle accident.. Inquiry about rebleeding, edema, EVD
displacement.
[Findings and Assessment:]{.underline} CT last performed on 04/05/2017
for comparison.
Constant narrow subdural hematoma on both sides, tentorial and posterior
parasagittal. Constant small parenchymal hemorrhage on the left frontal.
No new intracranial bleeding.
Progressively demarcated infarcts on the right frontal and left
parietal.
Slightly progressive compression of the narrow ventricles as an
indication of progressive edema. Completely depleted external CSF spaces
with the ventricular drain catheter in the left lateral ventricle.
Increasing compression of the midbrain due to transtentorial herniation,
progressive tonsillar descent of 6 mm.
Fracture of the skull base and the petrous part of the temporal bone on
both sides without significant displacement. Hematoma in the mastoid and
sphenoid sinuses and the maxillary sinus.
**CT Head on 05/01/2017:**
[Clinical Information and Justification:]{.underline} Open skull-brain
trauma. Inquiry about CSF stasis, bleeding, edema.
[Findings]{.underline}: CT last performed on 04/05/17 for comparison.
Completely regressed subarachnoid hemorrhages on both sides. Minimal SDH
components on the tentorial edges bilaterally (left more than right,
with a 3 mm margin width). No new intracranial bleeding. Continuously
narrow inner ventricular system and narrow basal cisterns. The fourth
ventricle is unfolded. Narrow external CSF spaces and consistently
swollen gyration with global cerebral edema.
Better demarcated circumscribed hypodensity in the centrum semiovale on
the right (Series 3, Image 176) and left (Series 3, Image 203);
Differential diagnosis: fresh infarcts due to distal ACI dissections.
Consider repeat vascular imaging. No midline shift. No herniation.
Regressing intracranial air inclusions. Fracture of the skull base and
the petrous part of the temporal bone on both sides without significant
displacement. Hematoma in the maxillary, sphenoidal, and ethmoidal
sinuses.
**Consultation Reports:**
**1) Consultation with Ophthalmology on 04/03/2017**
[Patient Information:]{.underline}
- Motorbike accident, heavily contaminated eyes.
- Request for assessment.
**Diagnosis:** Motorbike accident
**Findings:** Patient intubated, unresponsive. In cranial CT, the
eyeball appears intact, no retrobulbar hematoma. Intraocular pressure:
Right/left within the normal range. Eyelid margins of both eyes crusty
with sand, inferiorly in the lower lid sac, and on the upper lid with
sand. Lower lid somewhat chemotic. Slight temporal hyperemia in the left
eyelid angle. Both eyes have erosions, small, multiple, superficial.
Lower conjunctival sac clean. Round pupils, anisocoria right larger than
left. Left iris hyperemia, no iris defects in the direct light. Lens
unremarkable. Reduced view of the optic nerve head due to miosis,
somewhat pale, rather sharp-edged, central neuroretinal rim present,
central vessels normal. Left eye, due to narrow pupil, limited view,
optic nerve head not visible, central vessels normal, no retinal
hemorrhages.
**Assessment:** Eyelid and conjunctival foreign bodies removed. Mild
erosions in the lower conjunctival sac. Right optic nerve head somewhat
pale, rather sharp-edged.
**Current Recommendations:**
- Antibiotic eye drops three times a day for both eyes.
- Ensure complete eyelid closure.
**2) Consultation with Craniomaxillofacial (CMF) Surgery on 04/05/2017**
**Patient Information:**
- Motorbike accident with severe open traumatic brain injury with
fractures of the cranial vault, mastoid, and skull base
<!-- -->
- Request for assessment.
- Patient with maxillary fracture.
**Findings:** According to the responsible attending physician,
\"minimal handling in case of decompensating intracranial pressure\" is
indicated. Therefore, currently, a cautious approach is suggested
regarding surgical intervention for the radiologically hardly displaced
maxillary fracture. Re-consultation is possible if there are changes in
the clinical outcome.
**Assessment:** Awaiting developments.
**3) Consultation with Neurology on 04/06/2017**
**Patient Information:**
- Brain edema following a severe open traumatic brain injury with
fractures of the cranial vault, mastoid, and skull base
<!-- -->
- Request for assessment.
- Traumatic subarachnoid hemorrhage, intracranial artery dissection,
and various other injuries.
**Findings:** Patient comatose, intubated, sedated. Isocoric pupils. No
light reaction in either eye. No reaction to pain stimuli for
vestibulo-ocular reflex and oculomotor responses. Babinski reflex
negative.
**Assessment:** Long-term ventilation due to a history of intracerebral
bleeding and skull base fracture. No response to pain stimuli or light
reactions in the eyes.
**Procedure/Therapy Suggestion:** Monitoring of patient condition.
**4) Consultation with ENT on 04/16/2017**
**Patient Information:** Tracheostomy tube change.
**Findings:** Tracheostomy tube change performed. Stoma unremarkable.
Trachea clear up to the bifurcation. Sutures in place.
**Assessment:** Re-consultation on 08/27/2021 for suture removal.
**5) Consultation with Neurology on 04/22/2017**
**Patient Information:** Adduction deficit., Request for assessment.
**Findings:** Long-term ventilation due to a history of intracerebral
bleeding and skull base fracture. Adduction deficit in the right eye and
horizontal nystagmus.
**Assessment:** Suspected mesencephalic lesion due to horizontal
nystagmus, but no diagnostic or therapeutic action required.
**6) Consultation with ENT on 04/23/2017**
**Patient Information:** Suture removal. Request for assessment.
**Findings:** Tracheostomy site unremarkable. Sutures trimmed, and skin
sutures removed.
**Assessment:** Procedure completed successfully.
Please note that some information is clinical and may not include
specific dates or recommendations for further treatment.
**Antibiogram:**
**Antibiotic** **Organism 1 (Pseudomonas aeruginosa)** **Organism 2 (Acinetobacter radioresistens)**
------------------------- ----------------------------------------- -----------------------------------------------
Aztreonam I (4.0) \-
Cefepime I (2.0) \-
Cefotaxime \- \-
Amikacin S (\<=2.0) S (4.0)
Ampicillin \- \-
Piperacillin I (\<=4.0) \-
Piperacillin/Tazobactam I (8.0) \-
Imipenem I (2.0) S (\<=0.25)
Meropenem S (\<=0.25) S (\<=0.25)
Ceftriaxone \- \-
Ceftazidime I (4.0) \-
Gentamicin . (\<=1.0) S (\<=1.0)
Tobramycin S (\<=1.0) S (\<=1.0)
Cotrimoxazole \- S (\<=20.0)
Ciprofloxacin I (\<=0.25) I (0.5)
Moxifloxacin \- \-
Fosfomycin \- \-
Tigecyclin \- \-
\"S\" means Susceptible
\"I\" means Intermediate
\".\" indicates not specified
\"-\" means Resistant
### Patient Report 2
**Dear colleague, **
We are reporting on our mutual patient, Mr. John Chapman, born on
11/16/1994, who presented himself to our Outpatient Clinic from
08/08/2018.
**Diagnoses**:
- Right abducens Nerve Palsy and Facial Nerve Palsy
- Lagophthalmos with corneal opacities due to eyelid closure deficit
- Left Abducens Nerve Palsy with slight compensatory head leftward
rotation and preferred leftward gaze
- Bilateral disc swelling
- Suspected left cavernous internal carotid artery aneurysm following
traumatic ICA dissection
- History of shunt explantation due to dysfunction and right-sided
re-implantation (Codman, current pressure setting 12 cm H2O)
- History of left VP shunt placement (programmable
ventriculoperitoneal shunt, initial pressure setting 5/25 cm H2O,
adjusted to 3 cm H2O before discharge)
- Malresorptive hydrocephalus
- History of severe open head injury in a motocross accident with
multiple skull fractures and distal dissection
**Procedure**: We conducted the following preoperative assessment:
- Visual acuity: Distant vision: Right eye: 0.5, Left eye: 0.8p
- Eye position: Fusion/Normal with significant esotropia in the right
eye; no fusion reflex observed
- Ocular deviation: After CT, at distance, esodeviation simulating
alternating 100 prism diopters (overcorrection); at near,
esodeviation simulating alternating 90 prism diopters
- Head posture: Fusion/Normal with leftward head turn of 5-10 degrees
- Correspondence: Bagolini test shows suppression at both distance and
near fixation
- Motility: Right eye abduction limited to 25 degrees from the
midline, abduction in up and down gaze limited to 30 degrees from
midline; left eye abduction limited to 30 degrees
- Binocular functions: Bagolini test shows suppression in the right
eye at both distance and near fixation; Lang I negative
**Current Presentation:** Mr. Chapman presented himself today in our
neurovascular clinic, providing an MRI of the head.
**Medical History:** The patient is known to have a pseudoaneurysm of
the cavernous left internal carotid artery following traumatic carotid
dissection in 04/2017, along with ipsilateral abducens nerve palsy.
**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 pseudoaneurysm has shown slight
enlargement in the recent follow-up imaging and remains partially
thrombosed. The findings were discussed on during a neurovascular board
meeting, where a recommendation for endovascular treatment was made,
which the patient has not yet pursued. Since Mr. Chapman has not been
able to decide on treatment thus far, it is advisable to further
evaluate this still asymptomatic condition through a diagnostic
angiography. This examination would also help in better planning any
potential intervention. Mr. Chapman agreed to this course of action, and
we will provide him with a timely appointment for the angiography.
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
-------------------- ------------- ---------------------
Creatinine (Jaffé) 0.44 mg/dL 0.70-1.20 mg/dL
Urea 31 mg/dL 17-48 mg/dL
Total Bilirubin 0.35 mg/dL \< 1.20 mg/dL
Hemoglobin 7.8 g/dL 13.5-17.0 g/dL
Hematocrit 28% 39.5-50.5%
Red Blood Cells 3.5 M/uL 4.3-5.8 M/uL
White Blood Cells 10.35 K/uL 3.90-10.50 K/uL
Platelets 379 K/uL 150-370 K/uL
MCV 77.2 fL 80.0-99.0 fL
MCH 24.1 pg 27.0-33.5 pg
MCHC 32.5 g/dL 31.5-36.0 g/dL
MPV 11.3 fL 7.0-12.0 fL
RDW-CV 17.7% 11.5-15.0%
Quick 54% 78-123%
INR 1.36 0.90-1.25
aPTT 32.8 sec 25.0-38.0 sec
### Patient Report 3
**Dear colleague, **
We are reporting on our patient, Mr. John Chapman, born on 11/16/1994,
who was under our inpatient care from 05/25/2019 to 05/26/2019.
**Diagnoses: **
- Pseudoaneurysm of the cavernous left internal carotid artery
following traumatic carotid dissection
- Abducens nerve palsy.
- History of severe open head trauma with fractures of the cranial
vault, mastoid, and skull base. Distal ICA dissection bilaterally.
Bilateral hemispheric subarachnoid hemorrhage extending into the
basal cisterns.mInfarct areas in the MCA-ACA border zones, right
frontal, and left parietal. Malresorptive hydrocephalus.
<!-- -->
- Rhabdomyolysis.
- History of aspiration pneumonia.
- Suspected Propofol infusion syndrome.
**Current Presentation:** For cerebral digital subtraction angiography
of the intracranial vessels. The patient presented with stable
cardiopulmonary conditions.
**Medical History**: The patient was admitted for the evaluation of a
pseudoaneurysm of the supra-aortic vessels. Further medical history can
be assumed to be known.
**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.
**Supra-aortic angiography on 05/25/2019:**
[Clinical context, question, justifying indication:]{.underline}
Pseudoaneurysm of the left ICA. Written consent was obtained for the
procedure. Anesthesia, Medications: Procedure performed under local
anesthesia. Medications: 500 IU Heparin in 500 mL NaCl for flushing.
[Methodology]{.underline}: Puncture of the right common femoral artery
under local anesthesia. 4F sheath, 4F vertebral catheter. Serial
angiographies after selective catheterization of the internal carotid
arteries. Uncomplicated manual intra-arterial contrast medium injection
with a total of 50 mL of Iomeron 300. Post-interventional closure of the
puncture site by manual compression. Subsequent application of a
circular pressure bandage.
[Technique]{.underline}: Biplanar imaging technique, area dose product
1330 cGy x cm², fluoroscopy time 3:43 minutes.
[Findings]{.underline}: The perfused portion of the partially thrombosed
cavernous aneurysm of the left internal carotid artery measures 4 x 2
mm. No evidence of other vascular pathologies in the anterior
circulation.
[Recommendation]{.underline}: In case of post-procedural bleeding,
immediate manual compression of the puncture site and notification of
the on-call neuroradiologist are advised.
- Pressure bandage to be kept until 2:30 PM. Bed rest until 6:30 PM.
- Follow-up in our Neurovascular Clinic
**Addition: Doppler ultrasound of the right groin on 05/26/2019:**
[Clinical context, question, justifying indication:]{.underline} Free
fluid? Hematoma?
[Findings]{.underline}: A CT scan from 04/05/2017 is available for
comparison. No evidence of a significant hematoma or an aneurysm in the
right groin puncture site. No evidence of an arteriovenous fistula.
Normal flow profiles of the femoral artery and vein. No evidence of
thrombosis.
**Treatment and Progression:** Pre-admission occurred on 05/24/2019 due
to a medically justified increase in risk for DSA of intracranial
vessels. After appropriate preparation, the angiography was performed on
05/25/2019. The puncture site was managed with a pressure bandage. In
the color Doppler sonographic control the following day, neither a
puncture aneurysm nor an arteriovenous fistula was detected. On
05/25/2019, we discharged the patient in good subjective condition for
your outpatient follow-up care.
**Current Recommendations:** Outpatient follow-up
**Lab results:**
**Parameter** **Reference Range** **Result**
----------------------- --------------------- -------------
Sodium 136-145 mEq/L 141 mEq/L
Potassium 3.5-4.5 mEq/L 4.9 mEq/L
Chloride 98-107 mEq/L 100 mEq/L
Osmolality 280-300 mOsm/kg 290 mOsm/kg
Glucose in Fluoride 60-110 mg/dL 76 mg/dL
Creatinine (Jaffé) 0.70-1.20 mg/dL 0.98 mg/dL
CRP \< 5.0 mg/L 4.5 mg/L
Triglycerides \< 150 mg/dL 119 mg/dL
Creatine Kinase \< 190 U/L 142 U/L
Free Triiodothyronine 2.00-4.40 ng/L 3.25 ng/L
Free Thyroxine 9.30-17.00 ng/L 14.12 ng/L
TSH Basal 0.27-4.20 mU/L 1.65 mU/L
Hemoglobin 13.5-17.0 g/dL 14.3 g/dL
Hematocrit 39.5-50.5% 43.4%
Erythrocytes 4.3-5.8 M/uL 5.6 M/uL
Leukocytes 3.90-10.50 K/uL 10.25 K/uL
Platelets 150-370 K/uL 198 K/uL
MCV 80.0-99.0 fL 83.2 fL
MCH 27.0-33.5 pg 28.1 pg
MCHC 31.5-36.0 g/dL 33.4 g/dL
MPV 7.0-12.0 fL 11.6 fL
RDW-CV 11.5-15.0% 13.5%
Quick \> 78% 90%
INR \< 1.25 1.07
aPTT 25.0-38.0 sec 36.1 sec
|
Diabetes insipidus
|
Which are the state-of-the-art models?
|
### Introduction
It is a common habit for people to keep several versions of documents, which creates duplicate data. A scholarly article is normally revised several times before being published. An academic paper may be listed on personal websites, digital conference libraries, Google Scholar, etc. In major corporations, a document typically goes through several revisions involving multiple editors and authors. Users would benefit from visualizing the entire history of a document. It is worthwhile to develop a system that is able to intelligently identify, manage and represent revisions. Given a collection of text documents, our study identifies revision relationships in a completely unsupervised way. For each document in a corpus we only use its content and the last modified timestamp. We assume that a document can be revised by many users, but that the documents are not merged together. We consider collaborative editing as revising documents one by one. The two research problems that are most relevant to document revision detection are plagiarism detection and revision provenance. In a plagiarism detection system, every incoming document is compared with all registered non-plagiarized documents BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . The system returns true if an original copy is found in the database; otherwise, the system returns false and adds the document to the database. Thus, it is a 1-to-n problem. Revision provenance is a 1-to-1 problem as it keeps track of detailed updates of one document BIBREF4 , BIBREF5 . Real-world applications include GitHub, version control in Microsoft Word and Wikipedia version trees BIBREF6 . In contrast, our system solves an n-to-n problem on a large scale. Our potential target data sources, such as the entire web or internal corpora in corporations, contain numerous original documents and their revisions. The aim is to find all revision document pairs within a reasonable time. Document revision detection, plagiarism detection and revision provenance all rely on comparing the content of two documents and assessing a distance/similarity score. The classic document similarity measure, especially for plagiarism detection, is fingerprinting BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . Fixed-length fingerprints are created using hash functions to represent document features and are then used to measure document similarities. However, the main purpose of fingerprinting is to reduce computation instead of improving accuracy, and it cannot capture word semantics. Another widely used approach is computing the sentence-to-sentence Levenshtein distance and assigning an overall score for every document pair BIBREF13 . Nevertheless, due to the large number of existing documents, as well as the large number of sentences in each document, the Levenshtein distance is not computation-friendly. Although alternatives such as the vector space model (VSM) can largely reduce the computation time, their effectiveness is low. More importantly, none of the above approaches can capture semantic meanings of words, which heavily limits the performances of these approaches. For instance, from a semantic perspective, “I went to the bank" is expected to be similar to “I withdrew some money" rather than “I went hiking." Our document distance measures are inspired by the weaknesses of current document distance/similarity measures and recently proposed models for word representations such as word2vec BIBREF14 and Paragraph Vector (PV) BIBREF15 . Replacing words with distributed vector embeddings makes it feasible to measure semantic distances using advanced algorithms, e.g., Dynamic Time Warping (DTW) BIBREF16 , BIBREF17 , BIBREF18 and Tree Edit Distance (TED) BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 . Although calculating text distance using DTW BIBREF27 , TED BIBREF28 or Word Mover's Distance (WMV) BIBREF29 has been attempted in the past, these measures are not ideal for large-scale document distance calculation. The first two algorithms were designed for sentence distances instead of document distances. The third measure computes the distance of two documents by solving a transshipment problem between words in the two documents and uses word2vec embeddings to calculate semantic distances of words. The biggest limitation of WMV is its long computation time. We show in Section SECREF54 that our wDTW and wTED measures yield more precise distance scores with much shorter running time than WMV. We recast the problem of detecting document revisions as a network optimization problem (see Section SECREF2 ) and consequently as a set of document distance problems (see Section SECREF4 ). We use trained word vectors to represent words, concatenate the word vectors to represent documents and combine word2vec with DTW or TED. Meanwhile, in order to guarantee reasonable computation time in large data sets, we calculate document distances at the paragraph level with Apache Spark. A distance score is computed by feeding paragraph representations to DTW or TED. Our code and data are publicly available. The primary contributions of this work are as follows. The rest of this paper is organized in five parts. In Section 2, we clarify related terms and explain the methodology for document revision detection. In Section 3, we provide a brief background on existing document similarity measures and present our wDTW and wTED algorithms as well as the overall process flow. In Section 4, we demonstrate our revision detection results on Wikipedia revision dumps and six simulated data sets. Finally, in Section 5, we summarize some concluding remarks and discuss avenues for future work and improvements. ### Revision Network
The two requirements for a document INLINEFORM0 being a revision of another document INLINEFORM1 are that INLINEFORM2 has been created later than INLINEFORM3 and that the content of INLINEFORM4 is similar to (has been modified from) that of INLINEFORM5 . More specifically, given a corpus INLINEFORM6 , for any two documents INLINEFORM7 , we want to find out the yes/no revision relationship of INLINEFORM8 and INLINEFORM9 , and then output all such revision pairs. We assume that each document has a creation date (the last modified timestamp) which is readily available from the meta data of the document. In this section we also assume that we have a INLINEFORM0 method and a cut-off threshold INLINEFORM1 . We represent a corpus as network INLINEFORM2 , for example Figure FIGREF5 , in which a vertex corresponds to a document. There is an arc INLINEFORM3 if and only if INLINEFORM4 and the creation date of INLINEFORM5 is before the creation date of INLINEFORM6 . In other words, INLINEFORM7 is a revision candidate for INLINEFORM8 . By construction, INLINEFORM9 is acyclic. For instance, INLINEFORM10 is a revision candidate for INLINEFORM11 and INLINEFORM12 . Note that we allow one document to be the original document of several revised documents. As we only need to focus on revision candidates, we reduce INLINEFORM13 to INLINEFORM14 , shown in Figure FIGREF5 , by removing isolated vertices. We define the weight of an arc as the distance score between the two vertices. Recall the assumption that a document can be a revision of at most one document. In other words, documents cannot be merged. Due to this assumption, all revision pairs form a branching in INLINEFORM15 . (A branching is a subgraph where each vertex has an in-degree of at most 1.) The document revision problem is to find a minimum cost branching in INLINEFORM16 (see Fig FIGREF5 ). The minimum branching problem was earlier solved by BIBREF30 edmonds1967optimum and BIBREF31 velardi2013ontolearn. The details of his algorithm are as follows. In our case, INLINEFORM0 is acyclic and, therefore, the second step never occurs. For this reason, Algorithm SECREF2 solves the document revision problem. Find minimum branching INLINEFORM0 for network INLINEFORM1 [1] Input: INLINEFORM0 INLINEFORM1 every vertex INLINEFORM0 Set INLINEFORM1 to correspond to all arcs with head INLINEFORM2 Select INLINEFORM3 such that INLINEFORM4 is minimum INLINEFORM5 Output: INLINEFORM0 The essential part of determining the minimum branching INLINEFORM0 is extracting arcs with the lowest distance scores. This is equivalent to finding the most similar document from the revision candidates for every original document. ### Distance/similarity Measures
In this section, we first introduce the classic VSM model, the word2vec model, DTW and TED. We next demonstrate how to combine the above components to construct our semantic document distance measures: wDTW and wTED. We also discuss the implementation of our revision detection system. ### Background
VSM represents a set of documents as vectors of identifiers. The identifier of a word used in this work is the tf-idf weight. We represent documents as tf-idf vectors, and thus the similarity of two documents can be described by the cosine distance between their vectors. VSM has low algorithm complexity but cannot represent the semantics of words since it is based on the bag-of-words assumption. Word2vec produces semantic embeddings for words using a two-layer neural network. Specifically, word2vec relies on a skip-gram model that uses the current word to predict context words in a surrounding window to maximize the average log probability. Words with similar meanings tend to have similar embeddings. DTW was developed originally for speech recognition in time series analysis and has been widely used to measure the distance between two sequences of vectors. Given two sequences of feature vectors: INLINEFORM0 and INLINEFORM1 , DTW finds the optimal alignment for INLINEFORM2 and INLINEFORM3 by first constructing an INLINEFORM4 matrix in which the INLINEFORM5 element is the alignment cost of INLINEFORM6 and INLINEFORM7 , and then retrieving the path from one corner to the diagonal one through the matrix that has the minimal cumulative distance. This algorithm is described by the following formula. DISPLAYFORM0 TED was initially defined to calculate the minimal cost of node edit operations for transforming one labeled tree into another. The node edit operations are defined as follows. Deletion Delete a node and connect its children to its parent maintaining the order. Insertion Insert a node between an existing node and a subsequence of consecutive children of this node. Substitution Rename the label of a node. Let INLINEFORM0 and INLINEFORM1 be two labeled trees, and INLINEFORM2 be the INLINEFORM3 node in INLINEFORM4 . INLINEFORM5 corresponds to a mapping from INLINEFORM6 to INLINEFORM7 . TED finds mapping INLINEFORM8 with the minimal edit cost based on INLINEFORM9 where INLINEFORM0 means transferring INLINEFORM1 to INLINEFORM2 based on INLINEFORM3 , and INLINEFORM4 represents an empty node. ### Semantic Distance between Paragraphs
According to the description of DTW in Section UID14 , the distance between two documents can be calculated using DTW by replacing each element in the feature vectors INLINEFORM0 and INLINEFORM1 with a word vector. However, computing the DTW distance between two documents at the word level is basically as expensive as calculating the Levenshtein distance. Thus in this section we propose an improved algorithm that is more appropriate for document distance calculation. In order to receive semantic representations for documents and maintain a reasonable algorithm complexity, we use word2vec to train word vectors and represent each paragraph as a sequence of vectors. Note that in both wDTW and wTED we take document titles and section titles as paragraphs. Although a more recently proposed model PV can directly train vector representations for short texts such as movie reviews BIBREF15 , our experiments in Section SECREF54 show that PV is not appropriate for standard paragraphs in general documents. Therefore, we use word2vec in our work. Algorithm SECREF20 describes how we compute the distance between two paragraphs based on DTW and word vectors. The distance between one paragraph in a document and one paragraph in another document can be pre-calculated in parallel using Spark to provide faster computation for wDTW and wTED. DistPara [h] Replace the words in paragraphs INLINEFORM0 and INLINEFORM1 with word2vec embeddings: INLINEFORM2 and INLINEFORM3 Input: INLINEFORM4 and INLINEFORM5 Initialize the first row and the first column of INLINEFORM6 matrix INLINEFORM7 INLINEFORM8 INLINEFORM9 INLINEFORM10 in range INLINEFORM11 INLINEFORM12 in range INLINEFORM13 INLINEFORM14 calculate INLINEFORM15 Return: INLINEFORM16 ### Word Vector-based Dynamic Time Warping
As a document can be considered as a sequence of paragraphs, wDTW returns the distance between two documents by applying another DTW on top of paragraphs. The cost function is exactly the DistPara distance of two paragraphs given in Algorithm SECREF20 . Algorithm SECREF21 and Figure FIGREF22 describe our wDTW measure. wDTW observes semantic information from word vectors, which is fundamentally different from the word distance calculated from hierarchies among words in the algorithm proposed by BIBREF27 liu2007sentence. The shortcomings of their work are that it is difficult to learn semantic taxonomy of all words and that their DTW algorithm can only be applied to sentences not documents. wDTW [h] Represent documents INLINEFORM0 and INLINEFORM1 with vectors of paragraphs: INLINEFORM2 and INLINEFORM3 Input: INLINEFORM4 and INLINEFORM5 Initialize the first row and the first column of INLINEFORM6 matrix INLINEFORM7 INLINEFORM8 INLINEFORM9 INLINEFORM10 in range INLINEFORM11 INLINEFORM12 in range INLINEFORM13 INLINEFORM14 DistPara INLINEFORM15 calculate INLINEFORM16 Return: INLINEFORM17 ### Word Vector-based Tree Edit Distance
TED is reasonable for measuring document distances as documents can be easily transformed to tree structures visualized in Figure FIGREF24 . The document tree concept was originally proposed by BIBREF0 si1997check. A document can be viewed at multiple abstraction levels that include the document title, its sections, subsections, etc. Thus for each document we can build a tree-like structure with title INLINEFORM0 sections INLINEFORM1 subsections INLINEFORM2 ... INLINEFORM3 paragraphs being paths from the root to leaves. Child nodes are ordered from left to right as they appear in the document. We represent labels in a document tree as the vector sequences of titles, sections, subsections and paragraphs with word2vec embeddings. wTED converts documents to tree structures and then uses DistPara distances. More formally, the distance between two nodes is computed as follows. The cost of substitution is the DistPara value of the two nodes. The cost of insertion is the DistPara value of an empty sequence and the label of the inserted node. This essentially means that the cost is the sum of the L2-norms of the word vectors in that node. The cost of deletion is the same as the cost of insertion. Compared to the algorithm proposed by BIBREF28 sidorov2015computing, wTED provides different edit cost functions and uses document tree structures instead of syntactic n-grams, and thus wTED yields more meaningful distance scores for long documents. Algorithm SECREF23 and Figure FIGREF28 describe how we calculate the edit cost between two document trees. wTED [1] Convert documents INLINEFORM0 and INLINEFORM1 to trees INLINEFORM2 and INLINEFORM3 Input: INLINEFORM4 and INLINEFORM5 Initialize tree edit distance INLINEFORM0 node label INLINEFORM1 node label INLINEFORM2 Update TED mapping cost INLINEFORM3 using INLINEFORM4 DistPara INLINEFORM5 INLINEFORM6 DistPara INLINEFORM7 INLINEFORM8 DistPara INLINEFORM9 Return: INLINEFORM0 ### Process Flow
Our system is a boosting learner that is composed of four modules: weak filter, strong filter, revision network and optimal subnetwork. First of all, we sort all documents by timestamps and pair up documents so that we only compare each document with documents that have been created earlier. In the first module, we calculate the VSM similarity scores for all pairs and eliminate those with scores that are lower than an empirical threshold ( INLINEFORM0 ). This is what we call the weak filter. After that, we apply the strong filter wDTW or wTED on the available pairs and filter out document pairs having distances higher than a threshold INLINEFORM1 . For VSM in Section SECREF32 , we directly filter out document pairs having similarity scores lower than a threshold INLINEFORM2 . The cut-off threshold estimation is explained in Section SECREF30 . The remaining document pairs from the strong filter are then sent to the revision network module. In the end, we output the optimal revision pairs following the minimum branching strategy. ### Estimating the Cut-off Threshold
Hyperprameter INLINEFORM0 is calibrated by calculating the absolute extreme based on an initial set of documents, i.e., all processed documents since the moment the system was put in use. Based on this set, we calculate all distance/similarity scores and create a histogram, see Figure FIGREF31 . The figure shows the correlation between the number of document pairs and the similarity scores in the training process of one simulated corpus using VSM. The optimal INLINEFORM1 in this example is around 0.6 where the number of document pairs noticeably drops. As the system continues running, new documents become available and INLINEFORM0 can be periodically updated by using the same method. ### Numerical Experiments
This section reports the results of the experiments conducted on two data sets for evaluating the performances of wDTW and wTED against other baseline methods. ### Distance/Similarity Measures
We denote the following distance/similarity measures. wDTW: Our semantic distance measure explained in Section SECREF21 . wTED: Our semantic distance measure explained in Section SECREF23 . WMD: The Word Mover's Distance introduced in Section SECREF1 . WMD adapts the earth mover's distance to the space of documents. VSM: The similarity measure introduced in Section UID12 . PV-DTW: PV-DTW is the same as Algorithm SECREF21 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 where INLINEFORM1 is the PV embedding of paragraph INLINEFORM2 . PV-TED: PV-TED is the same as Algorithm SECREF23 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 . Our experiments were conducted on an Apache Spark cluster with 32 cores and 320 GB total memory. We implemented wDTW, wTED, WMD, VSM, PV-DTW and PV-TED in Java Spark. The paragraph vectors for PV-DTW and PV-TED were trained by gensim. ### Data Sets
In this section, we introduce the two data sets we used for our revision detection experiments: Wikipedia revision dumps and a document revision data set generated by a computer simulation. The two data sets differ in that the Wikipedia revision dumps only contain linear revision chains, while the simulated data sets also contains tree-structured revision chains, which can be very common in real-world data. The Wikipedia revision dumps that were previously introduced by Leskovec et al. leskovec2010governance contain eight GB (compressed size) revision edits with meta data. We pre-processed the Wikipedia revision dumps using the JWPL Revision Machine BIBREF32 and produced a data set that contains 62,234 documents with 46,354 revisions. As we noticed that short documents just contributed to noise (graffiti) in the data, we eliminated documents that have fewer than three paragraphs and fewer than 300 words. We removed empty lines in the documents and trained word2vec embeddings on the entire corpus. We used the documents occurring in the first INLINEFORM0 of the revision period for INLINEFORM1 calibration, and the remaining documents for test. The generation process of the simulated data sets is designed to mimic the real world. Users open some existing documents in a file system, make some changes (e.g. addition, deletion or replacement), and save them as separate documents. These documents become revisions of the original documents. We started from an initial corpus that did not have revisions, and kept adding new documents and revising existing documents. Similar to a file system, at any moment new documents could be added and/or some of the current documents could be revised. The revision operations we used were deletion, addition and replacement of words, sentences, paragraphs, section names and document titles. The addition of words, ..., section names, and new documents were pulled from the Wikipedia abstracts. This corpus generation process had five time periods INLINEFORM0 . Figure FIGREF42 illustrates this simulation. We set a Poisson distribution with rate INLINEFORM1 (the number of documents in the initial corpus) to control the number of new documents added in each time period, and a Poisson distribution with rate INLINEFORM2 to control the number of documents revised in each time period. We generated six data sets using different random seeds, and each data set contained six corpora (Corpus 0 - 5). Table TABREF48 summarizes the first data set. In each data set, we name the initial corpus Corpus 0, and define INLINEFORM0 as the timestamp when we started this simulation process. We set INLINEFORM1 , INLINEFORM2 . Corpus INLINEFORM3 corresponds to documents generated before timestamp INLINEFORM4 . We extracted document revisions from Corpus INLINEFORM5 and compared the revisions generated in (Corpus INLINEFORM6 - Corpus INLINEFORM7 ) with the ground truths in Table TABREF48 . Hence, we ran four experiments on this data set in total. In every experiment, INLINEFORM8 is calibrated based on Corpus INLINEFORM9 . For instance, the training set of the first experiment was Corpus 1. We trained INLINEFORM10 from Corpus 1. We extracted all revisions in Corpus 2, and compared revisions generated in the test set (Corpus 2 - Corpus 1) with the ground truth: 258 revised documents. The word2vec model shared in the four experiments was trained on Corpus 5. ### Results
We use precision, recall and F-measure to evaluate the detected revisions. A true positive case is a correctly identified revision. A false positive case is an incorrectly identified revision. A false negative case is a missed revision record. We illustrate the performances of wDTW, wTED, WMD, VSM, PV-DTW and PV-TED on the Wikipedia revision dumps in Figure FIGREF43 . wDTW and wTED have the highest F-measure scores compared to the rest of four measures, and wDTW also have the highest precision and recall scores. Figure FIGREF49 shows the average evaluation results on the simulated data sets. From left to right, the corpus size increases and the revision chains become longer, thus it becomes more challenging to detect document revisions. Overall, wDTW consistently performs the best. WMD is slightly better than wTED. In particular, when the corpus size increases, the performances of WMD, VSM, PV-DTW and PV-TED drop faster than wDTW and wTED. Because the revision operations were randomly selected in each corpus, it is possible that there are non-monotone points in the series. wDTW and wTED perform better than WMD especially when the corpus is large, because they use dynamic programming to find the global optimal alignment for documents. In contrast, WMD relies on a greedy algorithm that sums up the minimal cost for every word. wDTW and wTED perform better than PV-DTW and PV-TED, which indicates that our DistPara distance in Algorithm SECREF20 is more accurate than the Euclidian distance between paragraph vectors trained by PV. We show in Table TABREF53 the average running time of the six distance/similarity measures. In all the experiments, VSM is the fastest, wTED is faster than wDTW, and WMD is the slowest. Running WMD is extremely expensive because WMD needs to solve an INLINEFORM0 sequential transshipment problem for every two documents where INLINEFORM1 is the average number of words in a document. In contrast, by splitting this heavy computation into several smaller problems (finding the distance between any two paragraphs), which can be run in parallel, wDTW and wTED scale much better. Combining Figure FIGREF43 , Figure FIGREF49 and Table TABREF53 we conclude that wDTW yields the most accurate results using marginally more time than VSM, PV-TED and PV-DTW, but much less running time than WMD. wTED returns satisfactory results using shorter time than wDTW. ### Conclusion
This paper has explored how DTW and TED can be extended with word2vec to construct semantic document distance measures: wDTW and wTED. By representing paragraphs with concatenations of word vectors, wDTW and wTED are able to capture the semantics of the words and thus give more accurate distance scores. In order to detect revisions, we have used minimum branching on an appropriately developed network with document distance scores serving as arc weights. We have also assessed the efficiency of the method of retrieving an optimal revision subnetwork by finding the minimum branching. Furthermore, we have compared wDTW and wTED with several distance measures for revision detection tasks. Our results demonstrate the effectiveness and robustness of wDTW and wTED in the Wikipedia revision dumps and our simulated data sets. In order to reduce the computation time, we have computed document distances at the paragraph level and implemented a boosting learning system using Apache Spark. Although we have demonstrated the superiority of our semantic measures only in the revision detection experiments, wDTW and wTED can also be used as semantic distance measures in many clustering, classification tasks. Our revision detection system can be enhanced with richer features such as author information and writing styles, and exact changes in revision pairs. Another interesting aspect we would like to explore in the future is reducing the complexities of calculating the distance between two paragraphs. ### Acknowledgments
This work was supported in part by Intel Corporation, Semiconductor Research Corporation (SRC). Figure 1: Revision network visualization Figure 2: Setting τ Figure 3: Corpora simulation Figure 4: Precision, recall and F-measure on the Wikipedia revision dumps Table 1: A simulated data set Figure 5: Average precision, recall and F-measure on the simulated data sets Table 2: Running time of VSM, PV-TED, PV-DTW, wTED, wDTW and WMD Figure 1: wDTW visualization Figure 2: wTED visualization
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WMD, VSM, PV-DTW, PV-TED
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How will Read likely be remembered by the UN Corps?
A. As a cowardly man who always played it safe.
B. As Sergeant Rashid's second-in-command.
C. As a man who made the arrest of Umluana possible.
D. As a man that cared more about his uniform that his team.
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Transcriber's Note: This etext was produced from Analog, January 1961. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. THE GREEN BERET By TOM PURDOM It's not so much the decisions a man does make that mark him as a Man—but the ones he refrains from making. Like the decision "I've had enough!" Illustrated by Schoenherr Read locked the door and drew his pistol. Sergeant Rashid handed Premier Umluana the warrant. "We're from the UN Inspector Corps," Sergeant Rashid said. "I'm very sorry, but we have to arrest you and bring you in for trial by the World Court." If Umluana noticed Read's gun, he didn't show it. He read the warrant carefully. When he finished, he said something in Dutch. "I don't know your language," Rashid said. "Then I'll speak English." Umluana was a small man with wrinkled brow, glasses and a mustache. His skin was a shade lighter than Read's. "The Inspector General doesn't have the power to arrest a head of state—especially the Premier of Belderkan. Now, if you'll excuse me, I must return to my party." In the other room people laughed and talked. Glasses clinked in the late afternoon. Read knew two armed men stood just outside the door. "If you leave, Premier, I'll have to shoot you." "I don't think so," Umluana said. "No, if you kill me, all Africa will rise against the world. You don't want me dead. You want me in court." Read clicked off the safety. "Corporal Read is very young," Rashid said, "but he's a crack shot. That's why I brought him with me. I think he likes to shoot, too." Umluana turned back to Rashid a second too soon. He saw the sergeant's upraised hand before it collided with his neck. "Help! Kidnap. " Rashid judo chopped him and swung the inert body over his shoulders. Read pulled a flat grenade from his vest pocket. He dropped it and yellow psycho gas hissed from the valve. "Let's be off," Rashid said. The door lock snapped as they went out the window. Two men with rifles plunged into the gas; sighing, they fell to the floor in a catatonic trance. A little car skimmed across the lawn. Bearing the Scourge of Africa, Rashid struggled toward it. Read walked backward, covering their retreat. The car stopped, whirling blades holding it a few inches off the lawn. They climbed in. "How did it go?" The driver and another inspector occupied the front seat. "They'll be after us in half a minute." The other inspector carried a light machine gun and a box of grenades. "I better cover," he said. "Thanks," Rashid said. The inspector slid out of the car and ran to a clump of bushes. The driver pushed in the accelerator. As they swerved toward the south, Read saw a dozen armed men run out of the house. A grenade arced from the bushes and the pursuers recoiled from the cloud that rose before them. "Is he all right?" the driver asked. "I don't think I hurt him." Rashid took a syrette from his vest pocket. "Well, Read, it looks like we're in for a fight. In a few minutes Miaka Station will know we're coming. And God knows what will happen at the Game Preserve." Read wanted to jump out of the car. He could die any minute. But he had set his life on a well-oiled track and he couldn't get off until they reached Geneva. "They don't know who's coming," he said. "They don't make them tough enough to stop this boy." Staring straight ahead, he didn't see the sergeant smile. Two types of recruits are accepted by the UN Inspector Corps: those with a fanatic loyalty to the ideals of peace and world order, and those who are loyal to nothing but themselves. Read was the second type. A tall, lanky Negro he had spent his school days in one of the drab suburbs that ring every prosperous American city. It was the home of factory workers, clerks, semiskilled technicians, all who do the drudge work of civilization and know they will never do more. The adults spent their days with television, alcohol and drugs; the young spent their days with gangs, sex, television and alcohol. What else was there? Those who could have told him neither studied nor taught at his schools. What he saw on the concrete fields between the tall apartment houses marked the limits of life's possibilities. He had belonged to a gang called The Golden Spacemen. "Nobody fools with me," he bragged. "When Harry Read's out, there's a tiger running loose." No one knew how many times he nearly ran from other clubs, how carefully he picked the safest spot on the battle line. "A man ought to be a man," he once told a girl. "He ought to do a man's work. Did you ever notice how our fathers look, how they sleep so much? I don't want to be like that. I want to be something proud." He joined the UN Inspector Corps at eighteen, in 1978. The international cops wore green berets, high buttonless boots, bush jackets. They were very special men. For the first time in his life, his father said something about his ambitions. "Don't you like America, Harry? Do you want to be without a country? This is the best country in the world. All my life I've made a good living. Haven't you had everything you ever wanted? I've been a king compared to people overseas. Why, you stay here and go to trade school and in two years you'll be living just like me." "I don't want that," Read said. "What do you mean, you don't want that?" "You could join the American Army," his mother said. "That's as good as a trade school. If you have to be a soldier." "I want to be a UN man. I've already enlisted. I'm in! What do you care what I do?" The UN Inspector Corps had been founded to enforce the Nuclear Disarmament Treaty of 1966. Through the years it had acquired other jobs. UN men no longer went unarmed. Trained to use small arms and gas weapons, they guarded certain borders, bodyguarded diplomats and UN officials, even put down riots that threatened international peace. As the UN evolved into a strong world government, the UN Inspector Corps steadily acquired new powers. Read went through six months training on Madagascar. Twice he nearly got expelled for picking fights with smaller men. Rather than resign, he accepted punishment which assigned him to weeks of dull, filthy extra labor. He hated the restrictions and the iron fence of regulations. He hated boredom, loneliness and isolation. And yet he responded with enthusiasm. They had given him a job. A job many people considered important. He took his turn guarding the still disputed borders of Korea. He served on the rescue teams that patrol the busy Polar routes. He mounted guard at the 1980 World's Fair in Rangoon. "I liked Rangoon," he even told a friend. "I even liked Korea. But I think I liked the Pole job best. You sit around playing cards and shooting the bull and then there's a plane crash or something and you go out and win a medal. That's great for me. I'm lazy and I like excitement." One power implied in the UN Charter no Secretary General or Inspector General had ever tried to use. The power to arrest any head of state whose country violated international law. Could the World Court try and imprison a politician who had conspired to attack another nation? For years Africa had been called "The South America of the Old World." Revolution followed revolution. Colonies became democracies. Democracies became dictatorships or dissolved in civil war. Men planted bases on the moon and in four years, 1978-82, ringed the world with matter transmitters; but the black population of Africa still struggled toward political equality. Umluana took control of Belderkan in 1979. The tiny, former Dutch colony, had been a tottering democracy for ten years. The very day he took control the new dictator and his African party began to build up the Belderkan Army. For years he had preached a new Africa, united, free of white masters, the home of a vigorous and perfect Negro society. His critics called him a hypocritical racist, an opportunist using the desires of the African people to build himself an empire. He began a propaganda war against neighboring South Africa, promising the liberation of that strife-torn land. Most Negro leaders, having just won representation in the South African Parliament, told him to liberate his own country. They believed they could use their first small voice in the government to win true freedom for their people. But the radio assault and the arms buildup continued. Early in 1982, South Africa claimed the Belderkan Army exceeded the size agreed to in the Disarmament Treaty. The European countries and some African nations joined in the accusation. China called the uproar a vicious slur on a new African nation. The United States and Russia, trying not to get entangled, asked for more investigation by the UN. But the evidence was clear. Umluana was defying world law. If he got away with it, some larger and more dangerous nation might follow his precedent. And the arms race would begin again. The Inspector General decided. They would enter Belderkan, arrest Umluana and try him by due process before the World Court. If the plan succeeded, mankind would be a long step farther from nuclear war. Read didn't know much about the complicated political reasons for the arrest. He liked the Corp and he liked being in the Corp. He went where they sent him and did what they told him to do. The car skimmed above the tree-tops. The driver and his two passengers scanned the sky. A plane would have been a faster way to get out of the country. But then they would have spent hours flying over Africa, with Belderkan fighters in hot pursuit, other nations joining the chase and the world uproar gaining volume. By transmitter, if all went well, they could have Umluana in Geneva in an hour. They were racing toward Miaka, a branch transmitter station. From Miaka they would transmit to the Belderkan Preserve, a famous tourist attraction whose station could transmit to any point on the globe. Even now a dozen inspectors were taking over the Game Preserve station and manning its controls. They had made no plans to take over Miaka. They planned to get there before it could be defended. "There's no military base near Miaka," Rashid said. "We might get there before the Belderkans." "Here comes our escort," Read said. A big car rose from the jungle. This one had a recoilless rifle mounted on the roof. The driver and the gunner waved and fell in behind them. "One thing," Read said, "I don't think they'll shoot at us while he's in the car." "Don't be certain, corporal. All these strong-arm movements are alike. I'll bet Umluana's lieutenants are hoping he'll become a dead legend. Then they can become live conquerors." Sergeant Rashid came from Cairo. He had degrees in science and history from Cambridge but only the Corp gave him work that satisfied his conscience. He hated war. It was that simple. Read looked back. He saw three spots of sunlight about two hundred feet up and a good mile behind. "Here they come, Sarge." Rashid turned his head. He waved frantically. The two men in the other car waved back. "Shall I duck under the trees?" the driver asked. "Not yet. Not until we have to." Read fingered the machine gun he had picked up when he got in the car. He had never been shot at. Twice he had faced an unarmed mob, but a few shots had sent them running. Birds flew screaming from their nests. Monkeys screeched and threw things at the noisy, speeding cars. A little cloud of birds surrounded each vehicle. The escort car made a sharp turn and charged their pursuers. The big rifle fired twice. Read saw the Belderkan cars scatter. Suddenly machine-gun bullets cracked and whined beside him. "Evade," Rashid said. "Don't go down." Without losing any forward speed, the driver took them straight up. Read's stomach bounced. A shell exploded above them. The car rocked. He raised his eyes and saw a long crack in the roof. "Hit the floor," Rashid said. They knelt on the cramped floor. Rashid put on his gas mask and Read copied him. Umluana breathed like a furnace, still unconscious from the injection Rashid had given him. I can't do anything , Read thought. They're too far away to shoot back. All we can do is run. The sky was clear and blue. The jungle was a noisy bazaar of color. In the distance guns crashed. He listened to shells whistle by and the whipcrack of machine-gun bullets. The car roller-coastered up and down. Every time a shell passed, he crawled in waves down his own back. Another explosion, this time very loud. Rashid raised his eyes above the seat and looked out the rear window. "Two left. Keep down, Read." "Can't we go down?" Read said. "They'll get to Miaka before us." He shut his eyes when he heard another loud explosion. Sergeant Rashid looked out the window again. He swore bitterly in English and Egyptian. Read raised his head. The two cars behind them weren't fighting each other. A long way back the tree-tops burned. "How much farther?" Rashid said. The masks muffled their voices. "There it is now. Shall I take us right in?" "I think you'd better." The station was a glass diamond in a small clearing. The driver slowed down, then crashed through the glass walls and hovered by the transmitter booth. Rashid opened the door and threw out two grenades. Read jumped out and the two of them struggled toward the booth with Umluana. The driver, pistol in hand, ran for the control panel. There were three technicians in the station and no passengers. All three panicked when the psycho gas enveloped them. They ran howling for the jungle. Through the window of his mask, Read saw their pursuers land in the clearing. Machine-gun bullets raked the building. They got Umluana in the booth and hit the floor. Read took aim and opened fire on the largest car. "Now, I can shoot back," he said. "Now we'll see what they do." "Are you ready, Rashid?" yelled the driver. "Man, get us out of here!" The booth door shut. When it opened, they were at the Game Preserve. The station jutted from the side of a hill. A glass-walled waiting room surrounded the bank of transmitter booths. Read looked out the door and saw his first battlefield. Directly in front of him, his head shattered by a bullet, a dead inspector lay behind an overturned couch. Read had seen dozens of training films taken during actual battles or after atomic attacks. He had laughed when other recruits complained. "That's the way this world is. You people with the weak stomachs better get used to it." Now he slid against the rear wall of the transmitter booth. A wounded inspector crawled across the floor to the booth. Read couldn't see his wound, only the pain scratched on his face and the blood he deposited on the floor. "Did you get Umluana?" he asked Sergeant Rashid. "He's in the booth. What's going on?" Rashid's Middle East Oxford seemed more clipped than ever. "They hit us with two companies of troops a few minutes ago. I think half our men are wounded." "Can we get out of here?" "They machine-gunned the controls." Rashid swore. "You heard him, Read! Get out there and help those men." He heard the screams of the wounded, the crack of rifles and machine guns, all the terrifying noise of war. But since his eighteenth year he had done everything his superiors told him to do. He started crawling toward an easy-chair that looked like good cover. A bullet cracked above his head, so close he felt the shock wave. He got up, ran panicky, crouched, and dove behind the chair. An inspector cracked the valve on a smoke grenade. A white fog spread through the building. They could see anyone who tried to rush them but the besiegers couldn't pick out targets. Above the noise, he heard Rashid. "I'm calling South Africa Station for a copter. It's the only way out of here. Until it comes, we've got to hold them back." Read thought of the green beret he had stuffed in his pocket that morning. He stuck it on his head and cocked it. He didn't need plain clothes anymore and he wanted to wear at least a part of his uniform. Bullets had completely shattered the wall in front of him. He stared through the murk, across the broken glass. He was Corporal Harry Read, UN Inspector Corps—a very special man. If he didn't do a good job here, he wasn't the man he claimed to be. This might be the only real test he would ever face. He heard a shout in rapid French. He turned to his right. Men in red loincloths ran zigzagging toward the station. They carried light automatic rifles. Half of them wore gas masks. "Shoot the masks," he yelled. "Aim for the masks." The machine gun kicked and chattered on his shoulder. He picked a target and squeezed off a burst. Tensely, he hunted for another mask. Three grenades arced through the air and yellow gas spread across the battlefield. The attackers ran through it. A few yards beyond the gas, some of them turned and ran for their own lines. In a moment only half a dozen masked men still advanced. The inspectors fired a long, noisy volley. When they stopped only four attackers remained on their feet. And they were running for cover. The attackers had come straight up a road that led from the Game Preserve to the station. They had not expected any resistance. The UN men had already taken over the station, chased out the passengers and technicians and taken up defense positions; they had met the Belderkans with a dozen grenades and sent them scurrying for cover. The fight so far had been vicious but disorganized. But the Belderkans had a few hundred men and knew they had wrecked the transmitter controls. The first direct attack had been repulsed. They could attack many more times and continue to spray the building with bullets. They could also try to go around the hill and attack the station from above; if they did, the inspectors had a good view of the hill and should see them going up. The inspectors had taken up good defensive positions. In spite of their losses, they still had enough firepower to cover the area surrounding the station. Read surveyed his sector of fire. About two hundred yards to his left, he saw the top of a small ditch. Using the ditch for cover, the Belderkans could sneak to the top of the hill. Gas grenades are only three inches long. They hold cubic yards of gas under high pressure. Read unclipped a telescoping rod from his vest pocket. He opened it and a pair of sights flipped up. A thin track ran down one side. He had about a dozen grenades left, three self-propelling. He slid an SP grenade into the rod's track and estimated windage and range. Sighting carefully, not breathing, muscles relaxed, the rod rock steady, he fired and lobbed the little grenade into the ditch. He dropped another grenade beside it. The heavy gas would lie there for hours. Sergeant Rashid ran crouched from man to man. He did what he could to shield the wounded. "Well, corporal, how are you?" "Not too bad, sergeant. See that ditch out there? I put a little gas in it." "Good work. How's your ammunition?" "A dozen grenades. Half a barrel of shells." "The copter will be here in half an hour. We'll put Umluana on, then try to save ourselves. Once he's gone, I think we ought to surrender." "How do you think they'll treat us?" "That we'll have to see." An occasional bullet cracked and whined through the misty room. Near him a man gasped frantically for air. On the sunny field a wounded man screamed for help. "There's a garage downstairs," Rashid said. "In case the copter doesn't get here on time, I've got a man filling wine bottles with gasoline." "We'll stop them, Sarge. Don't worry." Rashid ran off. Read stared across the green land and listened to the pound of his heart. What were the Belderkans planning? A mass frontal attack? To sneak in over the top of the hill? He didn't think, anymore than a rabbit thinks when it lies hiding from the fox or a panther thinks when it crouches on a branch above the trail. His skin tightened and relaxed on his body. "Listen," said a German. Far down the hill he heard the deep-throated rumble of a big motor. "Armor," the German said. The earth shook. The tank rounded the bend. Read watched the squat, angular monster until its stubby gun pointed at the station. It stopped less than two hundred yards away. A loud-speaker blared. ATTENTION UN SOLDIERS. ATTENTION UN SOLDIERS. YOU MAY THINK US SAVAGES BUT WE HAVE MODERN WEAPONS. WE HAVE ATOMIC WARHEADS, ALL GASES, ROCKETS AND FLAME THROWERS. IF YOU DO NOT SURRENDER OUR PREMIER, WE WILL DESTROY YOU. "They know we don't have any big weapons," Read said. "They know we have only gas grenades and small arms." He looked nervously from side to side. They couldn't bring the copter in with that thing squatting out there. A few feet away, sprawled behind a barricade of tables, lay a man in advanced shock. His deadly white skin shone like ivory. They wouldn't even look like that. One nuclear shell from that gun and they'd be vaporized. Or perhaps the tank had sonic projectors; then the skin would peel off their bones. Or they might be burned, or cut up by shrapnel, or gassed with some new mist their masks couldn't filter. Read shut his eyes. All around him he heard heavy breathing, mumbled comments, curses. Clothes rustled as men moved restlessly. But already the voice of Sergeant Rashid resounded in the murky room. "We've got to knock that thing out before the copter comes. Otherwise, he can't land. I have six Molotov cocktails here. Who wants to go hunting with me?" For two years Read had served under Sergeant Rashid. To him, the sergeant was everything a UN inspector should be. Rashid's devotion to peace had no limits. Read's psych tests said pride alone drove him on. That was good enough for the UN; they only rejected men whose loyalties might conflict with their duties. But an assault on the tank required something more than a hunger for self-respect. Read had seen the inspector who covered their getaway. He had watched their escort charge three-to-one odds. He had seen another inspector stay behind at Miaka Station. And here, in this building, lay battered men and dead men. All UN inspectors. All part of his life. And he was part of their life. Their blood, their sacrifice, and pain, had become a part of him. "I'll take a cocktail, Sarge." "Is that Read?" "Who else did you expect?" "Nobody. Anybody else?" "I'll go," the Frenchman said. "Three should be enough. Give us a good smoke screen." Rashid snapped orders. He put the German inspector in charge of Umluana. Read, the Frenchman and himself, he stationed at thirty-foot intervals along the floor. "Remember," Rashid said. "We have to knock out that gun." Read had given away his machine gun. He held a gas-filled bottle in each hand. His automatic nestled in its shoulder holster. Rashid whistled. Dozens of smoke grenades tumbled through the air. Thick mist engulfed the tank. Read stood up and ran forward. He crouched but didn't zigzag. Speed counted most here. Gunfire shook the hill. The Belderkans couldn't see them but they knew what was going on and they fired systematically into the smoke. Bullets ploughed the ground beside him. He raised his head and found the dim silhouette of the tank. He tried not to think about bullets ploughing through his flesh. A bullet slammed into his hip. He fell on his back, screaming. "Sarge. Sarge. " "I'm hit, too," Rashid said. "Don't stop if you can move." Listen to him. What's he got, a sprained ankle? But he didn't feel any pain. He closed his eyes and threw himself onto his stomach. And nearly fainted from pain. He screamed and quivered. The pain stopped. He stretched out his hands, gripping the wine bottles, and inched forward. Pain stabbed him from stomach to knee. "I can't move, Sarge." "Read, you've got to. I think you're the only—" "What?" Guns clattered. Bullets cracked. "Sergeant Rashid! Answer me." He heard nothing but the lonely passage of the bullets in the mist. "I'm a UN man," he mumbled. "You people up there know what a UN man is? You know what happens when you meet one?" When he reached the tank, he had another bullet in his right arm. But they didn't know he was coming and when you get within ten feet of a tank, the men inside can't see you. He just had to stand up and drop the bottle down the gun barrel. That was all—with a broken hip and a wounded right arm. He knew they would see him when he stood up but he didn't think about that. He didn't think about Sergeant Rashid, about the complicated politics of Africa, about crowded market streets. He had to kill the tank. That was all he thought about. He had decided something in the world was more important than himself, but he didn't know it or realize the psychologists would be surprised to see him do this. He had made many decisions in the last few minutes. He had ceased to think about them or anything else. With his cigarette lighter, he lit the rag stuffed in the end of the bottle. Biting his tongue, he pulled himself up the front of the tank. His long arm stretched for the muzzle of the gun. He tossed the bottle down the dark throat. As he fell, the machine-gun bullets hit him in the chest, then in the neck. He didn't feel them. He had fainted the moment he felt the bottle leave his hand. The copter landed ten minutes later. Umluana left in a shower of bullets. A Russian private, the ranking man alive in the station, surrendered the survivors to the Belderkans. His mother hung the Global Medal above the television set. "He must have been brave," she said. "We had a fine son." "He was our only son," her husband said. "What did he volunteer for? Couldn't somebody else have done it?" His wife started to cry. Awkwardly, he embraced her. He wondered what his son had wanted that he couldn't get at home. THE END
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C. As a man who made the arrest of Umluana possible.
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In the beginning, how does the author try to make you feel about this world?
A. skeptical but optimistic
B. curious and interested
C. like it's uninhabited and scary
D. like it's a place unworthy of going to
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Spawning Ground By LESTER DEL REY They weren't human. They were something more—and something less—they were, in short, humanity's hopes for survival! [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 Starship Pandora creaked and groaned as her landing pads settled unevenly in the mucky surface of the ugly world outside. She seemed to be restless to end her fool's errand here, two hundred light years from the waiting hordes on Earth. Straining metal plates twanged and echoed through her hallways. Captain Gwayne cursed and rolled over, reaching for his boots. He was a big, rawboned man, barely forty; but ten years of responsibility had pressed down his shoulders and put age-feigning hollows under his reddened eyes. The starlanes between Earth and her potential colonies were rough on the men who traveled them now. He shuffled toward the control room, grumbling at the heavy gravity. Lieutenant Jane Corey looked up, nodding a blonde head at him as he moved toward the ever-waiting pot of murky coffee. "Morning, Bob. You need a shave." "Yeah." He swallowed the hot coffee without tasting it, then ran a hand across the dark stubble on his chin. It could wait. "Anything new during the night?" "About a dozen blobs held something like a convention a little ways north of us. They broke up about an hour ago and streaked off into the clouds." The blobs were a peculiarity of this planet about which nobody knew anything. They looked like overgrown fireballs, but seemed to have an almost sentient curiosity about anything moving on the ground. "And our two cadets sneaked out again. Barker followed them, but lost them in the murk. I've kept a signal going to guide them back." Gwayne swore softly to himself. Earth couldn't turn out enough starmen in the schools, so promising kids were being shipped out for training as cadets on their twelfth birthday. The two he'd drawn, Kaufman and Pinelli, seemed to be totally devoid of any sense of caution. Of course there was no obvious need for caution here. The blobs hadn't seemed dangerous, and the local animals were apparently all herbivorous and harmless. They were ugly enough, looking like insects in spite of their internal skeletons, with anywhere from four to twelve legs each on their segmented bodies. None acted like dangerous beasts. But something had happened to the exploration party fifteen years back, and to the more recent ship under Hennessy that was sent to check up. He turned to the port to stare out at the planet. The Sol-type sun must be rising, since there was a dim light. But the thick clouds that wrapped the entire world diffused its rays into a haze. For a change, it wasn't raining, though the ground was covered by thick swirls of fog. In the distance, the tops of shrubs that made a scrub forest glowed yellow-green. Motions around them suggested a herd of feeding animals. Details were impossible to see through the haze. Even the deep gorge where they'd found Hennessy's carefully buried ship was completely hidden by the fog. There were three of the blobs dancing about over the grazing animals now, as they often seemed to do. Gwayne stared at them for a minute, trying to read sense into the things. If he had time to study them.... But there was no time. Earth had ordered him to detour here, after leaving his load of deep-sleep stored colonists on Official World 71, to check on any sign of Hennessy. He'd been here a week longer than he should have stayed already. If there was no sign in another day or so of what had happened to the men who'd deserted their ship and its equipment, he'd have to report back. He would have left before, if a recent landslip hadn't exposed enough of the buried ship for his metal locators to spot from the air by luck. It had obviously been hidden deep enough to foil the detectors originally. "Bob!" Jane Corey's voice cut through his pondering. "Bob, there are the kids!" Before he could swing to follow her pointing finger, movement caught his eye. The blobs had left the herd. Now the three were streaking at fantastic speed to a spot near the ship, to hover excitedly above something that moved there. He saw the two cadets then, heading back to the waiting ship, just beyond the movement he'd seen through the mist. Whatever was making the fog swirl must have reached higher ground. Something began to heave upwards. It was too far to see clearly, but Gwayne grabbed the microphone, yelling into the radio toward the cadets. They must have seen whatever it was just as the call reached them. Young Kaufman grabbed at Pinelli, and they swung around together. Then the mists cleared. Under the dancing blobs, a horde of things was heading for the cadets. Shaggy heads, brute bodies vaguely man-like! One seemed to be almost eight feet tall, leading the others directly toward the spacesuited cadets. Some of the horde were carrying spears or sticks. There was a momentary halt, and then the leader lifted one arm, as if motioning the others forward. "Get the jeeps out!" Gwayne yelled at Jane. He yanked the door of the little officers' lift open and jabbed the down button. It was agonizingly slow, but faster than climbing down. He ripped the door back at the exit deck. Men were dashing in, stumbling around in confusion. But someone was taking over now—one of the crew women. The jeeps were lining up. One, at the front, was stuttering into life, and Gwayne dashed for it as the exit port slid back. There was no time for suits or helmets. The air on the planet was irritating and vile smelling, but it could be breathed. He leaped to the seat, to see that the driver was Doctor Barker. At a gesture, the jeep rolled down the ramp, grinding its gears into second as it picked up speed. The other two followed. There was no sign of the cadets at first. Then Gwayne spotted them; surrounded by the menacing horde. Seen from here, the things looked horrible in a travesty of manhood. The huge leader suddenly waved and pointed toward the jeeps that were racing toward him. He made a fantastic leap backwards. Others swung about, two of them grabbing up the cadets. The jeep was doing twenty miles an hour now, but the horde began to increase the distance, in spite of the load of the two struggling boys! The creatures dived downward into lower ground, beginning to disappear into the mists. "Follow the blobs," Gwayne yelled. He realized now he'd been a fool to leave his suit; the radio would have let him keep in contact with the kids. But it was too late to go back. The blobs danced after the horde. Barker bounced the jeep downward into a gorge. Somewhere the man had learned to drive superlatively; but he had to slow as the fog thickened lower down. Then it cleared to show the mob of creatures doubling back on their own trail to confuse the pursuers. There was no time to stop. The jeep plowed through them. Gwayne had a glimpse of five-foot bodies tumbling out of the way. Monstrously coarse faces were half hidden by thick hair. A spear crunched against the windshield from behind, and Gwayne caught it before it could foul the steering wheel. It had a wickedly beautiful point of stone. The creatures vanished as Barker fought to turn to follow them. The other jeeps were coming up, by the sound of their motors, but too late to help. They'd have to get to the group with the cadets in a hurry or the horde would all vanish in the uneven ground, hidden by the fog. A blob dropped down, almost touching Gwayne. He threw up an instinctive hand. There was a tingling as the creature seemed to pass around it. It lifted a few inches and drifted off. Abruptly, Barker's foot ground at the brake. Gwayne jolted forward against the windshield, just as he made out the form of the eight-foot leader. The thing was standing directly ahead of him, a cadet on each shoulder. The wheels locked and the jeep slid protestingly forward. The creature leaped back. But Gwayne was out of the jeep before it stopped, diving for the figure. It dropped the boys with a surprised grunt. The arms were thin and grotesque below the massively distorted shoulders, but amazingly strong. Gwayne felt them wrench at him as his hands locked on the thick throat. A stench of alien flesh was in his nose as the thing fell backwards. Doc Barker had hit it seconds after the captain's attack. Its head hit rocky ground with a dull, heavy sound, and it collapsed. Gwayne eased back slowly, but it made no further move, though it was still breathing. Another jeep had drawn up, and men were examining the cadets. Pinelli was either laughing or crying, and Kaufman was trying to break free to kick at the monster. But neither had been harmed. The two were loaded onto a jeep while men helped Barker and Gwayne stow the bound monster on another before heading back. "No sign of skull fracture. My God, what a tough brute!" Barker shook his own head, as if feeling the shock of the monster's landing. "I hope so," Gwayne told him. "I want that thing to live—and you're detailed to save it and revive it. Find out if it can make sign language or draw pictures. I want to know what happened to Hennessy and why that ship was buried against detection. This thing may be the answer." Barker nodded grimly. "I'll try, though I can't risk drugs on an alien metabolism." He sucked in on the cigarette he'd dug out, then spat sickly. Smoke and this air made a foul combination. "Bob, it still makes no sense. We've scoured this planet by infra-red, and there was no sign of native villages or culture. We should have found some." "Troglodytes, maybe," Gwayne guessed. "Anyhow, send for me when you get anything. I've got to get this ship back to Earth. We're overstaying our time here already." The reports from the cadets were satisfactory enough. They'd been picked up and carried, but no harm had been done them. Now they were busy being little heroes. Gwayne sentenced them to quarters as soon as he could, knowing their stories would only get wilder and less informative with retelling. If they could get any story from the captured creature, they might save time and be better off than trying to dig through Hennessy's ship. That was almost certainly spoorless by now. The only possible answer seemed to be that the exploring expedition and Hennessy's rescue group had been overcome by the aliens. It was an answer, but it left a lot of questions. How could the primitives have gotten to the men inside Hennessy's ship? Why was its fuel dumped? Only men would have known how to do that. And who told these creatures that a space ship's metal finders could be fooled by a little more than a hundred feet of solid rock? They'd buried the ship cunningly, and only the accidental slippage had undone their work. Maybe there would never be a full answer, but he had to find something—and find it fast. Earth needed every world she could make remotely habitable, or mankind was probably doomed to extinction. The race had blundered safely through its discovery of atomic weapons into a peace that had lasted two hundred years. It had managed to prevent an interplanetary war with the Venus colonists. It had found a drive that led to the stars, and hadn't even found intelligent life there to be dangerous on the few worlds that had cultures of their own. But forty years ago, observations from beyond the Solar System had finally proved that the sun was going to go nova. It wouldn't be much of an explosion, as such things go—but it would render the whole Solar System uninhabitable for millenia. To survive, man had to colonize. And there were no worlds perfect for him, as Earth had been. The explorers went out in desperation to find what they could; the terraforming teams did what they could. And then the big starships began filling worlds with colonists, carried in deep sleep to conserve space. Almost eighty worlds. The nearest a four month journey from Earth and four more months back. In another ten years, the sun would explode, leaving man only on the footholds he was trying to dig among other solar systems. Maybe some of the strange worlds would let men spread his seed again. Maybe none would be spawning grounds for mankind in spite of the efforts. Each was precious as a haven for the race. If this world could be used, it would be nearer than most. If not, as it now seemed, no more time could be wasted here. Primitives could be overcome, maybe. It would be ruthless and unfair to strip them of their world, but the first law was survival. But how could primitives do what these must have done? He studied the spear he had salvaged. It was on a staff made of cemented bits of smaller wood from the scrub growth, skillfully laminated. The point was of delicately chipped flint, done as no human hand had been able to do for centuries. "Beautiful primitive work," he muttered. Jane pulled the coffee cup away from her lips and snorted. "You can see a lot more of it out there," she suggested. He went to the port and glanced out. About sixty of the things were squatting in the clearing fog, holding lances and staring at the ship. They were perhaps a thousand yards away, waiting patiently. For what? For the return of their leader—or for something that would give the ship to them? Gwayne grabbed the phone and called Barker. "How's the captive coming?" Barker's voice sounded odd. "Physically fine. You can see him. But—" Gwayne dropped the phone and headed for the little sick bay. He swore at Doc for not calling him at once, and then at himself for not checking up sooner. Then he stopped at the sound of voices. There was the end of a question from Barker and a thick, harsh growling sound that lifted the hair along the nape of Gwayne's neck. Barker seemed to understand, and was making a comment as the captain dashed in. The captive was sitting on the bunk, unbound and oddly unmenacing. The thick features were relaxed and yet somehow intent. He seemed to make some kind of a salute as he saw Gwayne enter, and his eyes burned up unerringly toward the device on the officer's cap. "Haarroo, Cabbaan!" the thing said. "Captain Gwayne, may I present your former friend, Captain Hennessy?" Barker said. There was a grin on the doctor's lips, but his face was taut with strain. The creature nodded slowly and drew something from the thick hair on its head. It was the golden comet of a captain. "He never meant to hurt the kids—just to talk to them," Barker cut in quickly. "I've got some of the story. He's changed. He can't talk very well. Says they've had to change the language around to make the sounds fit, and he's forgotten how to use what normal English he can. But it gets easier as you listen. It's Hennessy, all right. I'm certain." Gwayne had his own ideas on that. It was easy for an alien to seize on the gold ornament of a captive earthman, even to learn a little English, maybe. But Hennessy had been his friend. "How many barmaids in the Cheshire Cat? How many pups did your oldest kid's dog have? How many were brown?" The lips contorted into something vaguely like a smile, and the curiously shaped fingers that could handle no human-designed equipment spread out. Three. Seven. Zero. The answers were right. By the time the session was over, Gwayne had begun to understand the twisted speech from inhuman vocal cords better. But the story took a long time telling. When it was finished, Gwayne and Barker sat for long minutes in silence. Finally Gwayne drew a shuddering breath and stood up. "Is it possible, Doc?" "No," Barker said flatly. He spread his hands and grimaced. "No. Not by what I know. But it happened. I've looked at a few tissues under the microscope. The changes are there. It's hard to believe about their kids. Adults in eight years, but they stay shorter. It can't be a hereditary change—the things that affect the body don't change the germ plasm. But in this case, what changed Hennessy is real, so maybe the fact that the change is passed on is as real as he claims." Gwayne led the former Hennessy to the exit. The waiting blobs dropped down to touch the monstrous man, then leaped up again. The crowd of monsters began moving forward toward their leader. A few were almost as tall as Hennessy, but most were not more than five feet high. The kids of the exploring party.... Back in the control room, Gwayne found the emergency release levers, set the combinations and pressed the studs. There was a hiss and gurgle as the great tanks of fuel discharged their contents out onto the ground where no ingenuity could ever recover it to bring life to the ship again. He'd have to tell the men and women of the crew later, after he'd had time to organize things and present it all in a way they could accept, however much they might hate it at first. But there was no putting off giving the gist of it to Jane. "It was the blobs," he summarized it. "They seem to be amused by men. They don't require anything from us, but they like us around. Hennessy doesn't know why. They can change our cells, adapt us. Before men came, all life here had twelve legs. Now they're changing that, as we've seen. "And they don't have to be close to do it. We've all been outside the hull. It doesn't show yet—but we're changed. In another month, Earth food would kill us. We've got to stay here. We'll bury the ships deeper this time, and Earth won't find us. They can't risk trying a colony where three ships vanish, so we'll just disappear. And they'll never know." Nobody would know. Their children—odd children who matured in eight years—would be primitive savages in three generations. The Earth tools would be useless, impossible for the hands so radically changed. Nothing from the ship would last. Books could never be read by the new eyes. And in time, Earth wouldn't even be a memory to this world. She was silent a long time, staring out of the port toward what must now be her home. Then she sighed. "You'll need practice, but the others don't know you as well as I do, Bob. I guess we can fix it so they'll believe it all. And it's too late now. But we haven't really been changed yet, have we?" "No," he admitted. Damn his voice! He'd never been good at lying. "No. They have to touch us. I've been touched, but the rest could go back." She nodded. He waited for the condemnation, but there was only puzzlement in her face. "Why?" And then, before he could answer, her own intelligence gave her the same answer he had found for himself. "The spawning ground!" It was the only thing they could do. Earth needed a place to plant her seed, but no world other than Earth could ever be trusted to preserve that seed for generation after generation. Some worlds already were becoming uncertain. Here, though, the blobs had adapted men to the alien world instead of men having to adapt the whole planet to their needs. Here, the strange children of man's race could grow, develop and begin the long trek back to civilization. The gadgets would be lost for a time. But perhaps some of the attitudes of civilized man would remain to make the next rise to culture a better one. "We're needed here," he told her, his voice pleading for the understanding he couldn't yet fully give himself. "These people need as rich a set of bloodlines as possible to give the new race strength. The fifty men and women on this ship will be needed to start them with a decent chance. We can't go to Earth, where nobody would believe or accept the idea—or even let us come back. We have to stay here." She smiled then and moved toward him, groping for his strength. "Be fruitful," she whispered. "Be fruitful and spawn and replenish an earth." "No," he told her. "Replenish the stars." But she was no longer listening, and that part of his idea could wait. Some day, though, their children would find a way to the starlanes again, looking for other worlds. With the blobs to help them, they could adapt to most worlds. The unchanged spirit would lead them through all space, and the changing bodies would claim worlds beyond numbering. Some day, the whole universe would be a spawning ground for the children of men!
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D. like it's a place unworthy of going to
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What is the purpose of paraoxynebutal?
A. It relaxes the sympathetic nervous system
B. It puts a human to sleep for up to 12 days
C. It helps people adjust to changes in gravity
D. It opens the airways to allow for easier breathing
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SEA LEGS By FRANK QUATTROCCHI Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction November 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Rootless and footloose, a man in space can't help but dream of coming home. But something nobody should do is bet on the validity of a homesick dream! Flight Officer Robert Craig surrendered the tube containing his service record tapes and stood waiting while the bored process clerk examined the seal. "Your clearance," said the clerk. Craig handed him a battered punch card and watched the man insert it in the reproducer. He felt anxiety as the much-handled card refused for a time to match the instrument's metal contact points. The line of men behind Craig fidgeted. "You got to get this punched by Territorial," said the clerk. "Take it back to your unit's clearance office." "Look again, Sergeant," Craig said, repressing his irritation. "It ain't notched." "The hell it isn't." The man examined the card with squinting care and nodded finally. "It's so damn notched," he complained. "You ought to take care of that card; can't get on without one." Craig hesitated before moving. "Next," said the clerk, "What you waiting for?" "Don't I take my 201 file?" "We send it on ahead. Go to Grav 1 desk." A murmur greeted the order. Craig experienced the thrill of knowing the envy of the others. Grav 1—that meant Terra. He crossed the long, dreary room, knowing the eyes of the other men were upon him. "Your service tapes," the next noncom said. "Where you going?" "Grav 1—Terra," fumbled Craig. "Los Angeles." "Los Angeles, eh? Where in Los Angeles?" "I—I—" Craig muttered, fumbling in his pockets. "No specific destination," supplied the man as he punched a key on a small instrument, "Air-lock ahead and to your right. Strip and follow the robot's orders. Any metal?" "Metal?" asked Craig. "You know, metal ." "Well, my identification key." "Here," commanded the clerk, extending a plastic envelope. Craig moved in the direction indicated. He fought the irrational fear that he had missed an important step in the complicated clerical process. He cursed the grudging attitude of the headquarters satellite personnel and felt the impotence of a spaceman who had long forgotten the bureaucracy of a rear area base. The knowledge that much of it was motivated by envy soothed him as he clumsily let himself into the lock. "Place your clothing in the receptacle provided and assume a stationary position on the raised podium in the center of the lock." Craig obeyed the robot voice and began reluctantly to remove his flight jacket. Its incredibly fine-grained leather would carry none of the strange, foreign associations for the base station clerk who would appropriate it. He would never know the beautiful, gentle beast that supplied this skin. "You are retarding the progress of others. Please respond more quickly to your orders." Craig quickly removed the last of his clothing. It was impossible to hate a robot, but one could certainly hate those who set it into operation. "You will find a red button at your feet. Lower your head and depress that button." Stepping on the button with his bare foot produced an instant of brilliant blue illumination. A small scratch on his arm stung briefly and he was somewhat blinded by the flash even through his eyelids, but that was all there was to the sterilizing process. "Your clothing and effects will be in the dressing room immediately beyond the locked door." He found his clothing cleanly and neatly hung on plastic hangers just inside the door to the dressing room. The few personal items he carried in his pockets were still there. The Schtann flight jacket was actually there, looking like new, its space-blue unfaded and as wonderfully pliant as before. "Insert your right arm into the instrument on the central table," commanded the same voice he had heard before. "Turn your arm until the scratch is in contact with the metal plate. There will be a slight pain, but it is necessary to treat the small injury you have been disregarding." Craig obeyed and clenched his teeth against a sharp stinging. His respect for the robot-controlled equipment of bases had risen. When he withdrew his arm, the scratch was neatly coated with a layer of flesh-colored plastic material. He dressed quickly and was on the verge of asking the robot for instructions, when a man appeared in the open doorway. "I am Captain Wyandotte," said the man in a pleasant voice. "Well, what's next?" asked Craig somewhat more belligerently than he had intended. The man smiled. "Your reaction is quite natural. You are somewhat aggressive after Clerical, eh?" "I'm a little anxious to get home, I suppose," said Craig defensively. "By 'home' you mean Terra. But you've never been there, have you?" "No, but my father—" "Your parents left Terra during the Second Colonization of Cassiopeia II, didn't they?" "Yes," Craig said. He was uncomfortable; Wyandotte seemed to know all about him. "We might say you've been away quite a while, eh?" "I was entered as a spaceman when I was 16," Craig said. "I've never been down for any period as yet." "You mean you haven't been in a gravity system?" "Oh, I've landed a few times, even walked around for a while...." "With the help of paraoxylnebutal," supplied the captain. "Well, sure." "Mr. Craig, I suppose you've guessed that the next step in our little torture system here is psych." "So I gathered." The captain laughed reassuringly. "No, don't put up your guard again. The worst is over. Short of Gravitational conditioning, there is nothing to stop you from going to Terra." "Sorry, I guess I'm a little touchy. This is my first time...." "Quite natural. But it being your first time—in quite a number of ways, I might add—it will be necessary for you to undergo some conditioning." "Conditioning?" asked Craig. "Yes. You have spent eleven years in space. Your body is conditioned to a normal state of free fall, or at best to a state of acceleration." "Yeah, I know. Once on Gerymeade...." "You were ill, couldn't keep your balance, felt dizzy. That is why all spacemen carry PON, paraoxylnebutal, with them. It helps suppress certain physiological reactions to an entirely new set of conditions. Channels of the ear, for example. They play an important part in our awareness of balance. They operate on a simple gravity principle. Without gravity they act up for a time, then gradually lose function. Returning to gravity is rather frightening at first." "I know all about this, Captain." "You've undoubtedly read popularizations in tapezines. But you have experienced it briefly." "I expect to have some trouble at first." Craig was disturbed by the wordy psychologist. What was the man actually saying? "Do you know what sailors of ancient times meant by 'sea legs?'" asked Wyandotte. "Men on a rolling ocean acclimated themselves to a rolling horizontal. They had trouble when they went ashore and the horizontal didn't roll any more. "It meant more than that. There were excellent psychological reasons for the old stereotype, the 'drunken sailor.' A port city was a frightening thing to an old sailor—but let's begin our little job at the beginning. I'll turn you over to psychometry for the usual tests and pick you up tomorrow morning at, say, 0900." During the days that followed, the psychologist seemed to Craig to become progressively more didactic. He would deliver long speeches about the "freedom of open space." He spoke repetitiously of the "growing complexity of Terran society." And yet the man could not be pinned down to any specific condition the spaceman would find intolerable. Craig began to hate the delay that kept him from Terra. Through the ports of the headquarters base satellite, he scanned the constellations for the scores of worlds he had visited during his eleven years in space. They were incredibly varied, even those that supported life. He had weathered difficult landings on worlds with rip-tide gravities, had felt the pull of the incredible star-tides imparted by twin and even triple star systems. He had been on Einstein IV, the planet of eight moons, and had felt the pulse of all eight of the satellites at once that no PON could completely nullify. But even if he could accept the psychologist's authority for the cumulative effect of a gravity system, he could not understand the unspoken warning he felt underlying all that the man said. "Of course it has changed," Craig was protesting. "Anyway, I never really knew very much about Terra. So what? I know it won't be as it was in tapezines either." "Yet you are so completely sure you will want to live out your life there, that you are willing to give up space service for it." "We've gone through this time and time again," Craig said wearily. "I gave you my reasons for quitting space. We analyzed them. You agreed that you could not decide that for me and that my decision is logical. You tell me spacemen don't settle down on Terra. Yet you won't—or can't—tell me why. I've got a damned good job there—" "You may find that 'damned good jobs' become boring." "So I'll transfer. I don't know what you're trying to get at, Captain, but you're not talking me out of going back. If the service needs men so badly, let them get somebody else. I've put in my time." "Do you really think that's my reason?" "Sure. What else can it be?" "Mr. Craig," the psychologist said slowly, "you have my authorization for you to return to Terra as a private citizen of that planet. You will be given a very liberal supply of PON—which you will definitely need. Good luck. You'll need that too." On the eighth day, two attendants, who showed the effects of massive doses of PON to protect themselves from the centrifugal force, had to carry a man out of the tank. Many others asked to be removed, begged to be allowed to withdraw their resignations. "The twelfth day is the worst," a grizzled spaceman told Craig. "That's when the best of 'em want out." Craig clenched the iron rung of his bed and struggled to bring the old man's face into focus. "How ... how do they know when you ought ... to come out?" he asked between waves of nausea. "Blood pressure. They get you just before you go into shock." "How can they tell?" Craig fought down his growing panic. "I can't." "That strap around your belly. You mean you ain't noticed it?" "Haven't noticed much of anything." "Well, it's keyed to give them some kind of signal." The old man lapsed into silence. Craig wished him to continue. He desperately wanted something to distract his mind from the ghastly conditioning process. Slowly at first, the lines formed by seams in the metal ceiling began to bend. Here it came again! "Old man!" shouted Craig. "Yeah, son. They've dropped it down a notch." "Dropped ... it ... down?" "Maybe that ain't scientific, but it's the way I always think of it." "Can't they ... drop it down continuously?" "They tried that a few times—once when I was aboard. You wouldn't like it, kid. You wouldn't like it at all." "How ... many times ... do they drop it?" "Four times during the day, three at night. Twenty days." A nightmare of visual sensations ebbed into Craig's mind. He was vaguely aware of the moans of other men in the vaultlike room. Wave upon wave of nausea swept him as he watched the seam lines bend and warp fantastically. He snapped his eyelids shut, only to begin feeling the nightmarish bodily sensations once more. He felt the cot slowly rise longitudinally, felt himself upside down, then the snap of turning right side up once more—and he knew that neither he nor the cot had moved so much as an inch. Craig heard the voices around him, muffled, as though talking through wadding. "... got it bad." "We better take him out." "... pretty bad." "He'll go into shock." "... never make it the twelfth." "We better yank him." "I'm ... all right," Craig mumbled at the voices. He struggled with the bonds of his cot. With terrible effort he forced his eyes open. Two white-clad figures, ridiculously out of proportion, hovered wraithlike over him. Four elongated eyes peered at him. Attendants coming for to take me home.... "Touch me and I'll kick your teeth in!" he yelled. "I'm going to Terra. Wish you were going to Terra?" Then it was better. Oddly, he passed the twelfth day easily. By the fourteenth day, Craig knew he could stand Grav 1. The whine of the centrifuge's motors had diminished to a low hum. Either that or they had begun to produce ultra-sonic waves. Craig was not sure. Most of the men had passed through the torments of gravitational conditioning. The huge headquarters base centrifuge aboard the man-made satellite had gradually caused their bodies to respond once more to a single source of pull. They were now ready to become inhabitants of planets again, instead of free-falling ships. On the eighteenth day, automatic machinery freed them from their imprisoning cots. Clumsily and awkwardly at first, the men began to walk, to hold their heads and arms in proper attitudes. They laughed and joked about it and kidded those who were slow at adjusting. Then they again began taking paraoxylnebutal in preparation for the free-fall flight to Terra. Only one of the score of men in the centrifuge tank remained voluntarily in his cot. "Space article violator," the old man informed Craig. "Psycho, I think. Went amuck with some extraterritorials. Killed a dozen." "What will they do, exile him?" "Not to Chociante, if that's what you mean. They just jerked his space card and gave him a one-way ticket to Terra." "For twelve murders?" asked Craig incredulously. "That's enough, son." The old man eyed Craig for an instant before looking away. "Pick something to talk about. What do you figure on doing when you get to Terra, for instance?" "I'm going into Import. My father was in it for twenty years." "Sure," said the old spaceman, watching a group of young crewmen engaged in an animated conversation. "It's a good job. There's a future to it." "Yeah." Why did he have to explain anything at all to the old space tramp? "Once I get set up, I'll probably try to open my own business." "And spend your weekends on Luna." Craig half rose from his cot, jarred into anger. But the old spaceman turned, smiling wryly. "Don't get hot, kid. I guess I spent too long in Zone V." He paused to examine his wrinkled hands. They were indelibly marked with lever callouses. "You get to thinking anyone who stays closer'n eighty light years from Terra is a land-lubber." Craig relaxed, realizing he had acted childishly. "Used to think the same. Then I took the exam and got this job." "Whereabouts?" "Los Angeles." The old man looked up at Craig. "You don't know much about Terra, do you, son?" "Not much." "Yeah. Well, I hope you ain't disappointed." "My father was born there, but I never saw it. Never hit the Solar System, matter of fact. Never saw much of anything close up. I stood it a long time, old man, this hitting atmospheres all over the Universe." But the spaceman seemed to have lost interest. He was unpacking some personal belongings from a kit. "What are you doing in Grav 1?" Craig asked. The old man's face clouded for an instant. "In the old days, they used to say us old-timers acted like clocks. They used to say we just ran down. Now they got some fancy psychology name for it." Craig regretted his question. He would have muttered some word of apology, but the old man continued. "Maybe you've read some of the old sea stories, or more'n likely had 'em read to you. Sailors could go to sea until they just sort of dried up. The sea tanned their skins and stiffened their bones, but it never stiffened their hearts. When they got old, it just pulled them in. "But space is different. Space is raw and new. It tugs at your guts. It sends the blood rushing through your veins. It's like loving. You don't become a part of space the way you do the old sea, though. It leaves you strictly alone. Except that it sucks you dry, takes all the soup out of you, leaves you brittle and old—old as a dehydrated piece of split leather. "Then one day it shoots a spurt of blood around in one of your old veins. Something gives. Space is through with you then. And if you can stand this whirligig conditioning, you're through with space." " You can't figure it. Some of 'em urp all over and turn six shades of green. " " You got to watch the ones that don't. " " Yeah, you got to watch the ones that don't. Especially the old ones. " " He's old. You think it was his heart? " " Who knows? " " They'll dump him, won't they? " " After a tracer is sent through. But it won't do any good. " " He probably outlived everybody that ever knew him. " " Wouldn't be surprised. Here, grab his leg. " Robert Craig folded the flight jacket tightly and stuffed it into the cylindrical carton. A sleeve unwound just as he did so, making it difficult to fit into the place he had made for it. Exasperated, he refolded it and jammed it in place. Smaller rolls of underclothing were then fitted in. When he was satisfied with the layer, he tossed in a small handful of crystals and began to fill the next layer. After the carton was completely filled, he ignited the sealing strip and watched as the plastic melted into a single, seamless whole. It was ready for irradiation. Probably in another ten years his son-to-be would put it on and play spaceman. But Craig swore he'd make sure that the kid knew what a stinking life it was. At 1300 hours, the ferry bumped heavily alongside the starboard lock. It was the signal for relief in the passengers' quarters; many were beginning to feel a reaction to the short free-fall flight from the headquarters satellite. The audio called out: "Flight Officer Robert Craig. Flight Officer Robert Craig. Report to Orderly 12. Report to Orderly 12 through the aft door." With pangs of anxiety he could not completely suppress, Craig obeyed. Orderly 12 handed him a message container. "Who's it from? Somebody on Terra?" "From a private spaceman named Morgan Brockman." " Brockman? " "He was with you in the grav tank." "The old man!" The message container produced a battered punch card. Craig straightened it and was about to reach into his pocket for a hand transcriber. But then he noticed the card bore only a few irregular punches and was covered with rough hand printing. Son, when the flunkies get around to giving you this, they'll have shot me out the tube. How do I know? Same way you know when your turbos are going to throw a blade. It's good this way. There's something you can do for me if you want to. Way back, some fifty years ago, there was a woman. She was my wife. It's a long story I won't bother you with. Anyway, I left her. Wanted to take her along with me, but she wouldn't go. Earth was a lot different then than it is now. They don't have to tell me; I know. I saw it coming and so did Ethel. We talked about it and I knew I had to go. She wouldn't or couldn't go. Wanted me to stay, but I couldn't. I tried to send her some units once in a while. Don't know if she ever got them. Sometimes I forgot to send them at all. You know, you're way out across the Galaxy, while she's home. Go see her if you can, son. Will you? Make sure she gets the unit transfer I made out. It isn't much out of seventy years of living, but she may need it. And maybe you can tell her a little bit about what it means to be out there. Tell her it's open and free and when you got hold of those levers and you're trying for an orbit on something big and new and green.... Hell, you remember. You know how to tell her. Her name is Ethel Brockman. I know she'll still use my name. Her address is or was East 71, North 101, Number 4. You can trace her easy if she moved. Women don't generally shove off and not leave a forwarding address. Not Ethel, at least. Craig put the battered card in his pocket and walked back through the door to the passenger room. How did you explain to an old woman why her husband deserted her fifty years before? Some kind of story about one's duty to the Universe? No, the old man had not been in Intergalactic. He had been a tramp spaceman. Well, why had he left? Fifty years in space. Fifty years! Zone V had been beyond anybody's imagination that long ago. He must have been in on the first Cetusian flights and shot the early landings in Cetus II. God only knew how many times he had battled Zone 111b pirates.... Damn the old man! How did one explain? Craig descended the ramp from the huge jet and concentrated on his impressions. One day he would recall this moment, his first on the planet Terra. He tried to recall his first thrill at seeing Los Angeles, 1500 square miles of it, from the ship as it entered the atmosphere. He was about to step off the last step when a man appeared hurriedly. A rather plump man, he displayed a toothy smile on his puffy red face. "A moment, sir. Just a little greeting from the Terra. You understand, of course. Purely routine." Craig remained on the final step of the ramp, puzzled. The man turned to a companion at his right. "We can see that this gentleman has come from a long, long way off, can't we?" The other man did not look up. He was peering into what seemed to Craig to be a kind of camera. "We can allow the gentlemen to continue now, can't we? It wasn't that we believed for a minute, you understand ... purely routine." Both men were gone in an instant, leaving Craig completely bewildered. "You goin' to move on, buddy, or you want to go back?" Craig turned to face a line of his fellow passengers up the ramp behind him. "Who was that?" Craig asked. "Customs. Bet you never got such a smooth screening before, eh?" "You mean he screened me? What for?" "Hard to say," the other passenger said. "You'll get used to this. They get it over with quick." Craig made his way toward the spaceport administration building. His first physical contact with Terra had passed unnoticed. "Sir! Sir!" cried a voice behind him. He wheeled to see a man walking briskly toward him. "You dropped this, sir. Quite by accident, of course." Craig examined the small object the man had given him before rushing off toward an exit. It was an empty PON tube he had just discarded. He couldn't understand why the man had bothered until he realized that the plastaloid floor of the lobby displayed not the faintest scrap of paper nor trace of dirt. The Import personnel man was toying with a small chip of gleaming metal. He did not look directly at Craig for more than an instant at a time, and commented on Craig's description of his trip through the city only very briefly between questions. "It's a good deal bigger than I imagined," Craig was saying. "Haven't seen much of it, of course. Thought I'd check in here with you first." "Yes, naturally." "Thought you could give me some idea of conditions...." "Conditions?" "For instance, what part of the city I should live in. That is, what part is closest to where I'll work." "I see," said the man noncommittally. It seemed to Craig that he was about to add something. He did not, however, but instead rose from his chair and walked to the large window overlooking an enormous section of the city far below. He stared out the window for a time, leaving Craig seated uncomfortably in the silent room. There was a distracted quality about him, Craig thought. "You are the first man we have had from the Intergalactic Service," the personnel man said finally. "That so?" "Yes." He turned to face Craig briefly before continuing. "You must find it very strange here." "Well, I've never seen a city so big." "Yes, so big. And also...." He seemed to consider many words before completing the sentence. "And also different." "I haven't been here very long," said Craig. "Matter of fact, I haven't been anywhere very long. This is my first real experience with life on a planet. As an adult, anyway." The personnel man seated himself once more and pressed a button on a small instrument. A secretary entered the office from a door to Craig's left. "Miss Wendel, this is Mr. Craig. Mr. Craig, my secretary. Mr. Craig will enter Minerals and Metals, Zone V." They exchanged formal greetings. She was a moderately pretty girl of medium height and, to Craig, a pleasantly rounded figure. He would have attempted to catch her eye had she not immediately occupied herself with unfolding the legs of a small instrument she was carrying. "This is Mr. Craig's first landing on Terra, Miss Wendel," the personnel man continued. "Actually, we shall have to consider him in much the same way we would an extraterrestrial." The girl glanced at Craig, casting him a cool, impersonal smile. "He was formerly a flight officer in the Intergalactic Space Service." The statement was delivered in an almost exaggeratedly casual tone. The girl glanced at him once more, this time with a definite quizzical look in her brown eyes. "Three complete tours of duty, I believe." "Four," corrected Craig. "Four tours of three years each, minus a year's terminal leave." "I take it you have no identification card?" the man asked. "The one I held in the service. It's pretty comprehensive." The other turned to the secretary. "You'll see that he is assisted in filing his application, won't you? A provisional Code II. That will enable you to enter all Import offices freely, Mr. Craig." "Will he need a food and—clothing ration also?" asked the girl, without looking at Craig. "Yes." The man laughed. "You'll excuse us, Mr. Craig. We realize that you couldn't be expected to be familiar with Terra's fashions. In your present outfit you would certainly be typed as a ... well, you'd be made uncomfortable." Craig reddened in spite of himself. He had bought the suit on Ghandii. "A hick," he supplied. "I wouldn't go that far, but some people might." Craig noted the pleasant way the girl filled her trim, rather severe business suit. He amused himself by calculating stress patterns in its plain woven material as she assembled the forms for him. "Here, Mr. Craig. I believe these are complete." "They look pretty complicated." "Not at all. The questions are quite explicit." Craig looked them over quickly. "I guess so. Say, Miss Wendel, I was wondering—I don't know the city at all. Maybe you could go with me to have dinner. It must be almost dinnertime now. You could sort of check me out on some...." "I'm afraid that would be quite impossible. You couldn't gain admittance to any office you need to visit tonight. Therefore, it is impossible for me to be of any assistance to you." "Oh, come now, Miss Wendel. There are women aboard spaceships. I'm not a starved wolf." "Certainly you are not, Mr. Craig. But it is not possible for me...." "You said that already, but you can have dinner with me. Just company." "I'm afraid I don't understand." The Galactic hotel strove to preserve an archaic tone of hospitality. It advertised "a night's lodgings" and it possessed a bellboy. The bellboy actually carried Craig's plasticarton and large file of punch cards and forms to his room. Tired from the long, confusing day, Craig was not impressed. He vaguely wondered if the little drama of the hotel carried so far as a small fee to be paid the bellboy, and he hoped he would have the right size of Terran units in his wallet. Outside the door to the room, the bellboy stopped and turned to Craig. "For five I'll tell you where it is," he said in a subdued tone. "Tell me where what is?" "You know, the mike." "Mike?" "All right, mister, three units, then. I wasn't trying to hold you up." "You mean a microphone?" asked Craig, mechanically fishing for his wallet. "Sure, they don't put in screens here. Wanted to, but the boss convinced 'em there aren't any Freedomites ever stay here." "Where is the microphone?" Craig asked as he found a ten unit note. He was too puzzled to wonder what he was expected to do with the information. "It's in the bed illuminator. You can short it out with a razor blade. Or I'll do it for another two." "Never mind," Craig said wearily. He waited while the bellboy inserted a key into the door and opened it for him. "I can get you a sensatia-tape," whispered the boy when they had entered. He nudged Craig wickedly. "You know what they're like?" "Yeah," Craig said disgustedly. Traffic in the illicit mental-image tapes was known as far into space as lonely men had penetrated. Intergalactic considered them as great a menace to mental and moral stability as the hectopiates. Craig wearily got the man out of the room, took a PON pill, and eased himself into the bed. It had been a weird day and he had not liked it. There was no telling how long it would take him to shake his—sea legs, the psychologist had called it. One thing was sure: Terra aggressively went after its strangers.
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C. It helps people adjust to changes in gravity
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According to Fiss, free speech issues should be thought of as a conflict between...?
A. Individual liberty and the right to social equality
B. Two kinds of equality: individual and social
C. Two kinds of liberty: individual and social
D. Liberty and equality
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Shut Up, He Explained Owen Fiss is a professor at the Yale Law School and a highly regarded scholar of constitutional law. The subject of this short book is the present direction of the law governing the freedom of speech. What Professor Fiss has to say about it is worth attending to not merely because of his prominence in the field but because his argument is planted in the common assumptive ground of a lot of contemporary academic thought about the bankruptcy of individualism. The thesis of the book is Fiss', but the wisdom is conventional. Professor Fiss thinks the present direction of First Amendment law is a bad one, and he has an idea about how we might improve it. The short way to put his argument (though it is not quite the way he puts it) is to say that our approach to speech has become increasingly permissive. Courts have become more and more reluctant to allow the state to interfere with the rights of individual speakers to say what they wish, and it is time to roll back that permissiveness and to embark on a new approach that would permit the state to silence some speakers and promote others, but still, Fiss argues, in the name of freedom of speech. This is what Fiss means by the "irony" in his title: that true freedom of speech for all requires suppressing the speech of some. This is not, technically, an irony. It is a paradox. An irony would be the observation that an attempt to increase freedom for all often entails, despite our best efforts, a decrease in freedom for a few. If Fiss had addressed the subject of free speech in this spirit, as an irony, he would undoubtedly have had some interesting things to say, for he is a learned and temperate writer. But he has, instead, chosen to address the issue as an advocate for specific groups he regards as politically disadvantaged--women, gays, victims of racial-hate speech, the poor (or, at least, the not-rich), and people who are critical of market capitalism--and to design a constitutional theory that will enable those groups to enlist the state in efforts either to suppress speech they dislike or to subsidize speech they do like, without running afoul of the First Amendment. Embarked on this task, the most learned and temperate writer in the world would have a hard time avoiding tendentiousness. Fiss does not avoid it. The Irony of Free Speech is a discussion of several speech issues: campaign-finance laws, state funding for the arts, pornography, speech codes, and equal time. These discussions are not doctrinaire, but their general inclination is to favor state intervention, on political grounds, in each of those areas--that is, to favor restrictions on campaign spending, greater regulation of pornography, and so on. Fiss' analyses of specific cases are presented against a lightly sketched historical argument. Light though the sketching is, the historical argument is almost the most objectionable thing about the book, since it involves a distortion of the history of First Amendment law that is fairly plain even to someone who is not a professor at Yale Law School. The argument is that "the liberalism of the nineteenth century was defined by the claims of individual liberty and resulted in an unequivocal demand for liberal government, [while] the liberalism of today embraces the value of equality as well as liberty." The constitutional law of free speech, says Fiss, was shaped by the earlier type of liberalism--he calls it "libertarian"--which regarded free speech as a right of individual self-expression; it is now used to foil efforts to regulate speech in the name of the newer liberal value, equality. Contemporary liberals, inheriting both these traditions, find themselves in a bind. They want, let's say, black students to be free from harassment at institutions where they are, racially, in a minority, since liberals worry that black students cannot be "equal" if they feel intimidated. But those same liberals get upset at the thought of outlawing hate speech, since that would mean infringing upon the right of individuals to express themselves. Fiss' suggestion--this is the chief theoretical proposal of his book--is that liberals should stop thinking about this as a conflict between liberty and equality and start thinking about it as a conflict between two kinds of liberty: social vs. individual. The First Amendment, he says, was intended to foster (in William Brennan's words) "uninhibited, robust, and wide-open" debate in society as a whole; speech that inhibits or monopolizes that debate should therefore fall outside the protection of the law. We can maximize the total freedom of speech by silencing people who prevent others from speaking--when they utter racial epithets, represent women in degrading ways, use their wealth to dominate the press and the political process, or block the funding of unorthodox art. The historical part of this analysis rests on a canard, which is the assertion that the constitutional law of free speech emerged from 19 th -century classical laissez-faire liberalism. It did not. It emerged at the time of World War I, and the principal figures in its creation--Learned Hand, Oliver Wendell Holmes Jr., and Louis Brandeis--were not classical liberals; they were progressives. They abhorred the doctrine of natural rights because, in their time, that doctrine was construed to cover not the right to "self-expression" but the "right to property." Turn-of-the-century courts did not display a libertarian attitude toward civil rights; they displayed a libertarian attitude toward economic rights, tending to throw out legislation aimed at regulating industry and protecting workers on the grounds that people had a constitutional right to enter into contracts and to use their own property as they saw fit. Holmes, Brandeis, and their disciples consistently supported state intervention in economic affairs--the passage of health and safety regulations, the protection of unions, the imposition of taxes, and so on. The post-New Deal liberals whom Fiss associates with the value of equality are their heirs. The heirs of the19 th -century classical liberals are Jack Kemp and Newt Gingrich. Fiss' two "liberalisms" are, in fact, almost entirely different political philosophies. Hand, Holmes, and Brandeis based their First Amendment opinions not on some putative right to individual self-expression (an idea Holmes referred to as "the right of the donkey to drool") but on a democratic need for full and open political debate. First Amendment law since their time has performed its balancing acts on precisely that social value--the very value Fiss now proposes we need to insert into First Amendment jurisprudence. We don't need to insert it, because it was there from the start. Why does Fiss portray the history of First Amendment jurisprudence in this perverted way? Because he wants to line up his own free-speech argument within the conventional academic view that our problems are mostly the consequences of an antiquated and discreditable ideology of liberal individualism, and that they can mostly be solved by adopting a social-constructionist, or communitarian, or "intersubjective" view of human nature instead. The merits of liberal individualism vs. communitarianism can await another occasion to be debated. For since the law governing the freedom of speech does not emerge out of libertarianism, the matter does not boil down to replacing an obsolete belief in "self-expression" with a more up-to-date belief in "robust debate," as Fiss would like to think it does. What it boils down to is whether we need to replace the Hand-Holmes-Brandeis way of maximizing the benefits of free speech in a democratic society, which tries to push the state as far out of the picture as possible, with a different way, which tries to get the state farther into the picture. Here, assuming we want to try the interventionist approach, it is hard to see how a one-size theory can possibly fit all cases. The issues underlying pornography, hate speech, arts grants, campaign finance, and equal-time provisions are all different. The ideological impetus behind judicial developments in the last two areas, campaign finance and equal-time provisions, is related less to speech, except as a kind of constitutional cover, than to a revival of the old "right to property"--that is, the Supreme Court tends to disapprove of legislative and administrative efforts to require broadcasters to carry "opposing viewpoints" on the grounds that since it's their property, owners of television stations should be able to broadcast what they like. Fiss believes that the need for equal-time laws is as urgent today as it was in the 1970s, which is peculiar in light of the proliferation of media outlets. But the state does arguably have an interest, compatible with the First Amendment, in stipulating the way those media are used, and Fiss' discussion of those issues is the least aggravating in his book. Still, that discussion, like his discussions of the other issues, rests on a claim long associated with the left--the claim, in a phrase, that the minority is really the majority. In the case of speech, Fiss appears to believe that the reason the American public is less enlightened than he would wish it to be concerning matters such as feminism, the rights of homosexuals, and regulation of industry is that people are denied access to the opinions and information that would enlighten them. The public is denied this access because the state, in thrall to the ideology of individualism, refuses either to interfere with speech bullies--such as pornographers--who "silence" women, or to subsidize the speech of the unorthodox, such as Robert Mapplethorpe. Fiss' analysis of the Mapplethorpe case offers a good example of the perils of his interventionist approach. Arts policy is, unquestionably, a mess. The solution usually proposed is divorce: Either get the state out of the business altogether or invent some ironclad process for distributing the money using strictly artistic criteria. Fiss rejects both solutions; he wants the criteria to be political. He thinks the NEA should subsidize art that will enhance the "robustness" of the debate and should therefore prefer unorthodox art--though only, of course, if it represents a viewpoint the endowment considers, by virtue of social need and a prior history of exclusion, worthy of its megaphone. (No Nazi art, in other words.) Mapplethorpe's photographs seem to Fiss to qualify under these guidelines, since, he says, "in the late 1980s the AIDS crisis confronted America in the starkest fashion and provoked urgent questions regarding the scope and direction of publicly funded medical research. To address those issues the public--represented by the casual museum visitor--needed an understanding of the lives and practices of the gay community, so long hidden from view." This seems completely wrongheaded. People (for the most part) didn't find Mapplethorpe's X Portfolio photographs objectionable because they depicted homosexuality. They found them objectionable because they depicted sadomasochism. The notion that it was what Fiss calls a "source of empowerment for the members of the gay community" to have homosexuality associated with snarling guys prancing around in leather jockstraps, using bullwhips as sex toys, and pissing in each other's mouths, at a time when AIDS had become a national health problem and the issue of gays in the military was about to arise, is ludicrous. Any NEA chairperson who had the interests of the gay community at heart would have rushed to defund the exhibit. Jesse Helms could not have demonized homosexuality more effectively--which, of course, is why he was pleased to draw public attention to the pictures. Now that is what we call an irony of free speech. Awarding funding to the work of a gay artist because gay Americans need more political clout is an effort at cultural engineering, and the problem with cultural engineering is the problem with social engineering raised to a higher power. We have a hard enough time calculating the effects of the redistribution of wealth in our society. How can we possibly calculate the effects of redistributing the right to speak--of taking it away from people Professor Fiss feels have spoken long enough and mandating it for people he feels have not been adequately heard? One thing that is plain from the brief unhappy history of campus speech codes is that you automatically raise the value of the speech you punish and depress the value of the speech you sponsor. There are indeed many ironies here. Maybe someone will write a book about them.
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C. Two kinds of liberty: individual and social
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