question
stringlengths 12
1.77k
| context
stringlengths 79
71.7k
| answer
stringlengths 1
1.63k
|
|---|---|---|
What is the central irony of Quest's last words in the story?
A. He claims that "androids are made" to justify his human status, disregarding the impact of his father's programming efforts
B. He declares that "androids don't grow up," when in reality, his father programmed him to appear to (physically) age
C. He states that he "remembers his boyhood on Jupiter," when in reality, he is still a boy
D. He says he "remembers his boyhood on Jupiter," when in reality, his memories were programmed into his brain
| Transcriber's Note: Every effort has been made to replicate this text as faithfully as possible; changes (corrections of spelling and punctuation) made to the original text are marked like this . The original text appears when hovering the cursor over the marked text. This e-text was produced from Amazing Science Fiction Stories March 1959. Extensive research did not uncover any evidence that the U. S. copyright on this publication was renewed. 50 THE JUPITER WEAPON By CHARLES L. FONTENAY He was a living weapon of destruction— immeasurably powerful, utterly invulnerable. There was only one question: Was he human? Trella feared she was in for trouble even before Motwick's head dropped forward on his arms in a drunken stupor. The two evil-looking men at the table nearby had been watching her surreptitiously, and now they shifted restlessly in their chairs. Trella had not wanted to come to the Golden Satellite. It was a squalid saloon in the rougher section of Jupiter's View, the terrestrial dome-colony on Ganymede. Motwick, already drunk, had insisted. A woman could not possibly make her way through these streets alone to the better section of town, especially one clad in a silvery evening dress. Her only hope was that this place had a telephone. Perhaps she could call one of Motwick's friends; she had no one on Ganymede she could call a real friend herself. Tentatively, she pushed her chair back from the table and arose. She had to brush close by the other table to get to the bar. As she did, the dark, slick-haired man reached out and grabbed her around the waist with a steely arm. Trella swung with her whole body, and slapped him so hard he nearly fell from his chair. As she walked swiftly toward the bar, he leaped up to follow her. There were only two other people in the Golden Satellite: the fat, mustached bartender and a short, square-built man at the bar. The latter swung around at the pistol-like report of her slap, and she saw that, though no more than four and a half feet tall, he was as heavily muscled as a lion. 51 His face was clean and open, with close-cropped blond hair and honest blue eyes. She ran to him. “Help me!” she cried. “Please help me!” He began to back away from her. “I can't,” he muttered in a deep voice. “I can't help you. I can't do anything.” The dark man was at her heels. In desperation, she dodged around the short man and took refuge behind him. Her protector was obviously unwilling, but the dark man, faced with his massiveness, took no chances. He stopped and shouted: “Kregg!” The other man at the table arose, ponderously, and lumbered toward them. He was immense, at least six and a half feet tall, with a brutal, vacant face. Evading her attempts to stay behind him, the squat man began to move down the bar away from the approaching Kregg. The dark man moved in on Trella again as Kregg overtook his quarry and swung a huge fist like a sledgehammer. Exactly what happened, Trella wasn't sure. She had the impression that Kregg's fist connected squarely with the short man's chin before he dodged to one side in a movement so fast it was a blur. But that couldn't have been, because the short man wasn't moved by that blow that would have felled a steer, and Kregg roared in pain, grabbing his injured fist. “The bar!” yelled Kregg. “I hit the damn bar!” At this juncture, the bartender took a hand. Leaning far over the bar, he swung a full bottle in a complete arc. It smashed on Kregg's head, splashing the floor with liquor, and Kregg sank stunned to his knees. The dark man, who had grabbed Trella's arm, released her and ran for the door. Moving agilely around the end of the bar, the bartender stood over Kregg, holding the jagged-edged bottleneck in his hand menacingly. “Get out!” rumbled the bartender. “I'll have no coppers raiding my place for the likes of you!” Kregg stumbled to his feet and staggered out. Trella ran to the unconscious Motwick's side. “That means you, too, lady,” said the bartender beside her. “You and your boy friend get out of here. You oughtn't to have come here in the first place.” “May I help you, Miss?” asked a deep, resonant voice behind her. She straightened from her anxious examination of Motwick. The squat man was standing there, an apologetic look on his face. She looked contemptuously at the massive muscles whose help had been denied her. Her arm ached where the dark man had grasped it. The broad face before 52 her was not unhandsome, and the blue eyes were disconcertingly direct, but she despised him for a coward. “I'm sorry I couldn't fight those men for you, Miss, but I just couldn't,” he said miserably, as though reading her thoughts. “But no one will bother you on the street if I'm with you.” “A lot of protection you'd be if they did!” she snapped. “But I'm desperate. You can carry him to the Stellar Hotel for me.” The gravity of Ganymede was hardly more than that of Earth's moon, but the way the man picked up the limp Motwick with one hand and tossed him over a shoulder was startling: as though he lifted a feather pillow. He followed Trella out the door of the Golden Satellite and fell in step beside her. Immediately she was grateful for his presence. The dimly lighted street was not crowded, but she didn't like the looks of the men she saw. The transparent dome of Jupiter's View was faintly visible in the reflected night lights of the colonial city, but the lights were overwhelmed by the giant, vari-colored disc of Jupiter itself, riding high in the sky. “I'm Quest Mansard, Miss,” said her companion. “I'm just in from Jupiter.” “I'm Trella Nuspar,” she said, favoring him with a green-eyed glance. “You mean Io, don't you—or Moon Five?” “No,” he said, grinning at her. He had an engaging grin, with even white teeth. “I meant Jupiter.” “You're lying,” she said flatly. “No one has ever landed on Jupiter. It would be impossible to blast off again.” “My parents landed on Jupiter, and I blasted off from it,” he said soberly. “I was born there. Have you ever heard of Dr. Eriklund Mansard?” “I certainly have,” she said, her interest taking a sudden upward turn. “He developed the surgiscope, didn't he? But his ship was drawn into Jupiter and lost.” “It was drawn into Jupiter, but he landed it successfully,” said Quest. “He and my mother lived on Jupiter until the oxygen equipment wore out at last. I was born and brought up there, and I was finally able to build a small rocket with a powerful enough drive to clear the planet.” She looked at him. He was short, half a head shorter than she, but broad and powerful as a man might be who had grown up in heavy gravity. He trod the street with a light, controlled step, seeming to deliberately hold himself down. “If Dr. Mansard succeeded in landing on Jupiter, why didn't anyone ever hear from him again?” she demanded. “Because,” said Quest, “his radio was sabotaged, just as his ship's drive was.” “Jupiter strength,” she murmured, looking him over coolly. 53 “You wear Motwick on your shoulder like a scarf. But you couldn't bring yourself to help a woman against two thugs.” He flushed. “I'm sorry,” he said. “That's something I couldn't help.” “Why not?” “I don't know. It's not that I'm afraid, but there's something in me that makes me back away from the prospect of fighting anyone.” Trella sighed. Cowardice was a state of mind. It was peculiarly inappropriate, but not unbelievable, that the strongest and most agile man on Ganymede should be a coward. Well, she thought with a rush of sympathy, he couldn't help being what he was. They had reached the more brightly lighted section of the city now. Trella could get a cab from here, but the Stellar Hotel wasn't far. They walked on. Trella had the desk clerk call a cab to deliver the unconscious Motwick to his home. She and Quest had a late sandwich in the coffee shop. “I landed here only a week ago,” he told her, his eyes frankly admiring her honey-colored hair and comely face. “I'm heading for Earth on the next spaceship.” “We'll be traveling companions, then,” she said. “I'm going back on that ship, too.” For some reason she decided against telling him that the assignment on which she had come to the Jupiter system was to gather his own father's notebooks and take them back to Earth. Motwick was an irresponsible playboy whom Trella had known briefly on Earth, and Trella was glad to dispense with his company for the remaining three weeks before the spaceship blasted off. She found herself enjoying the steadier companionship of Quest. As a matter of fact, she found herself enjoying his companionship more than she intended to. She found herself falling in love with him. Now this did not suit her at all. Trella had always liked her men tall and dark. She had determined that when she married it would be to a curly-haired six-footer. She was not at all happy about being so strongly attracted to a man several inches shorter than she. She was particularly unhappy about feeling drawn to a man who was a coward. The ship that they boarded on Moon Nine was one of the newer ships that could attain a hundred-mile-per-second velocity and take a hyperbolic path to Earth, but it would still require fifty-four days to make the trip. So Trella was delighted to find that the ship was the Cometfire and its skipper was her old friend, dark-eyed, curly-haired Jakdane Gille. “Jakdane,” she said, flirting with him with her eyes as in 54 days gone by, “I need a chaperon this trip, and you're ideal for the job.” “I never thought of myself in quite that light, but maybe I'm getting old,” he answered, laughing. “What's your trouble, Trella?” “I'm in love with that huge chunk of man who came aboard with me, and I'm not sure I ought to be,” she confessed. “I may need protection against myself till we get to Earth.” “If it's to keep you out of another fellow's clutches, I'm your man,” agreed Jakdane heartily. “I always had a mind to save you for myself. I'll guarantee you won't have a moment alone with him the whole trip.” “You don't have to be that thorough about it,” she protested hastily. “I want to get a little enjoyment out of being in love. But if I feel myself weakening too much, I'll holler for help.” The Cometfire swung around great Jupiter in an opening arc and plummeted ever more swiftly toward the tight circles of the inner planets. There were four crew members and three passengers aboard the ship's tiny personnel sphere, and Trella was thrown with Quest almost constantly. She enjoyed every minute of it. She told him only that she was a messenger, sent out to Ganymede to pick up some important papers and take them back to Earth. She was tempted to tell him what the papers were. Her employer had impressed upon her that her mission was confidential, but surely Dom Blessing could not object to Dr. Mansard's son knowing about it. All these things had happened before she was born, and she did not know what Dom Blessing's relation to Dr. Mansard had been, but it must have been very close. She knew that Dr. Mansard had invented the surgiscope. This was an instrument with a three-dimensional screen as its heart. The screen was a cubical frame in which an apparently solid image was built up of an object under an electron microscope. The actual cutting instrument of the surgiscope was an ion stream. By operating a tool in the three-dimensional screen, corresponding movements were made by the ion stream on the object under the microscope. The principle was the same as that used in operation of remote control “hands” in atomic laboratories to handle hot material, and with the surgiscope very delicate operations could be performed at the cellular level. Dr. Mansard and his wife had disappeared into the turbulent atmosphere of Jupiter just after his invention of the surgiscope, and it had been developed by Dom Blessing. Its success had built Spaceway Instruments, Incorporated, which Blessing headed. Through all these years since Dr. Mansard's disappearance, 55 Blessing had been searching the Jovian moons for a second, hidden laboratory of Dr. Mansard. When it was found at last, he sent Trella, his most trusted secretary, to Ganymede to bring back to him the notebooks found there. Blessing would, of course, be happy to learn that a son of Dr. Mansard lived, and would see that he received his rightful share of the inheritance. Because of this, Trella was tempted to tell Quest the good news herself; but she decided against it. It was Blessing's privilege to do this his own way, and he might not appreciate her meddling. At midtrip, Trella made a rueful confession to Jakdane. “It seems I was taking unnecessary precautions when I asked you to be a chaperon,” she said. “I kept waiting for Quest to do something, and when he didn't I told him I loved him.” “What did he say?” “It's very peculiar,” she said unhappily. “He said he can't love me. He said he wants to love me and he feels that he should, but there's something in him that refuses to permit it.” She expected Jakdane to salve her wounded feelings with a sympathetic pleasantry, but he did not. Instead, he just looked at her very thoughtfully and said no more about the matter. He explained his attitude after Asrange ran amuck. Asrange was the third passenger. He was a lean, saturnine individual who said little and kept to himself as much as possible. He was distantly polite in his relations with both crew and other passengers, and never showed the slightest spark of emotion … until the day Quest squirted coffee on him. It was one of those accidents that can occur easily in space. The passengers and the two crewmen on that particular waking shift (including Jakdane) were eating lunch on the center-deck. Quest picked up his bulb of coffee, but inadvertently pressed it before he got it to his lips. The coffee squirted all over the front of Asrange's clean white tunic. “I'm sorry!” exclaimed Quest in distress. The man's eyes went wide and he snarled. So quickly it seemed impossible, he had unbuckled himself from his seat and hurled himself backward from the table with an incoherent cry. He seized the first object his hand touched—it happened to be a heavy wooden cane leaning against Jakdane's bunk—propelled himself like a projectile at Quest. Quest rose from the table in a sudden uncoiling of movement. He did not unbuckle his safety belt—he rose and it snapped like a string. For a moment Trella thought he was going to meet Asrange's assault. But he fled in a long leap toward the companionway leading to the astrogation deck 56 above. Landing feet-first in the middle of the table and rebounding, Asrange pursued with the stick upraised. In his haste, Quest missed the companionway in his leap and was cornered against one of the bunks. Asrange descended on him like an avenging angel and, holding onto the bunk with one hand, rained savage blows on his head and shoulders with the heavy stick. Quest made no effort to retaliate. He cowered under the attack, holding his hands in front of him as if to ward it off. In a moment, Jakdane and the other crewman had reached Asrange and pulled him off. When they had Asrange in irons, Jakdane turned to Quest, who was now sitting unhappily at the table. “Take it easy,” he advised. “I'll wake the psychosurgeon and have him look you over. Just stay there.” Quest shook his head. “Don't bother him,” he said. “It's nothing but a few bruises.” “Bruises? Man, that club could have broken your skull! Or a couple of ribs, at the very least.” “I'm all right,” insisted Quest; and when the skeptical Jakdane insisted on examining him carefully, he had to admit it. There was hardly a mark on him from the blows. “If it didn't hurt you any more than that, why didn't you take that stick away from him?” demanded Jakdane. “You could have, easily.” “I couldn't,” said Quest miserably, and turned his face away. Later, alone with Trella on the control deck, Jakdane gave her some sober advice. “If you think you're in love with Quest, forget it,” he said. “Why? Because he's a coward? I know that ought to make me despise him, but it doesn't any more.” “Not because he's a coward. Because he's an android!” “What? Jakdane, you can't be serious!” “I am. I say he's an android, an artificial imitation of a man. It all figures. “Look, Trella, he said he was born on Jupiter. A human could stand the gravity of Jupiter, inside a dome or a ship, but what human could stand the rocket acceleration necessary to break free of Jupiter? Here's a man strong enough to break a spaceship safety belt just by getting up out of his chair against it, tough enough to take a beating with a heavy stick without being injured. How can you believe he's really human?” Trella remembered the thug Kregg striking Quest in the face and then crying that he had injured his hand on the bar. “But he said Dr. Mansard was his father,” protested Trella. “Robots and androids frequently look on their makers as their parents,” said Jakdane. “Quest may not even know he's 57 artificial. Do you know how Mansard died?” “The oxygen equipment failed, Quest said.” “Yes. Do you know when?” “No. Quest never did tell me, that I remember.” “He told me: a year before Quest made his rocket flight to Ganymede! If the oxygen equipment failed, how do you think Quest lived in the poisonous atmosphere of Jupiter, if he's human?” Trella was silent. “For the protection of humans, there are two psychological traits built into every robot and android,” said Jakdane gently. “The first is that they can never, under any circumstances, attack a human being, even in self defense. The second is that, while they may understand sexual desire objectively, they can never experience it themselves. “Those characteristics fit your man Quest to a T, Trella. There is no other explanation for him: he must be an android.” Trella did not want to believe Jakdane was right, but his reasoning was unassailable. Looking upon Quest as an android, many things were explained: his great strength, his short, broad build, his immunity to injury, his refusal to defend himself against a human, his inability to return Trella's love for him. It was not inconceivable that she should have unknowingly fallen in love with an android. Humans could love androids, with real affection, even knowing that they were artificial. There were instances of android nursemaids who were virtually members of the families owning them. She was glad now that she had not told Quest of her mission to Ganymede. He thought he was Dr. Mansard's son, but an android had no legal right of inheritance from his owner. She would leave it to Dom Blessing to decide what to do about Quest. Thus she did not, as she had intended originally, speak to Quest about seeing him again after she had completed her assignment. Even if Jakdane was wrong and Quest was human—as now seemed unlikely—Quest had told her he could not love her. Her best course was to try to forget him. Nor did Quest try to arrange with her for a later meeting. “It has been pleasant knowing you, Trella,” he said when they left the G-boat at White Sands. A faraway look came into his blue eyes, and he added: “I'm sorry things couldn't have been different, somehow.” “Let's don't be sorry for what we can't help,” she said gently, taking his hand in farewell. Trella took a fast plane from White Sands, and twenty-four hours later walked up the front steps of the familiar brownstone house on the outskirts of Washington. Dom Blessing himself met her at the door, a stooped, graying 58 man who peered at her over his spectacles. “You have the papers, eh?” he said, spying the brief case. “Good, good. Come in and we'll see what we have, eh?” She accompanied him through the bare, windowless anteroom which had always seemed to her such a strange feature of this luxurious house, and they entered the big living room. They sat before a fire in the old-fashioned fireplace and Blessing opened the brief case with trembling hands. “There are things here,” he said, his eyes sparkling as he glanced through the notebooks. “Yes, there are things here. We shall make something of these, Miss Trella, eh?” “I'm glad they're something you can use, Mr. Blessing,” she said. “There's something else I found on my trip, that I think I should tell you about.” She told him about Quest. “He thinks he's the son of Dr. Mansard,” she finished, “but apparently he is, without knowing it, an android Dr. Mansard built on Jupiter.” “He came back to Earth with you, eh?” asked Blessing intently. “Yes. I'm afraid it's your decision whether to let him go on living as a man or to tell him he's an android and claim ownership as Dr. Mansard's heir.” Trella planned to spend a few days resting in her employer's spacious home, and then to take a short vacation before resuming her duties as his confidential secretary. The next morning when she came down from her room, a change had been made. Two armed men were with Dom Blessing at breakfast and accompanied him wherever he went. She discovered that two more men with guns were stationed in the bare anteroom and a guard was stationed at every entrance to the house. “Why all the protection?” she asked Blessing. “A wealthy man must be careful,” said Blessing cheerfully. “When we don't understand all the implications of new circumstances, we must be prepared for anything, eh?” There was only one new circumstance Trella could think of. Without actually intending to, she exclaimed: “You aren't afraid of Quest? Why, an android can't hurt a human!” Blessing peered at her over his spectacles. “And what if he isn't an android, eh? And if he is—what if old Mansard didn't build in the prohibition against harming humans that's required by law? What about that, eh?” Trella was silent, shocked. There was something here she hadn't known about, hadn't even suspected. For some reason, Dom Blessing feared Dr. Eriklund Mansard … or his heir … or his mechanical servant. She was sure that Blessing was wrong, that Quest, whether man or android, intended no 59 harm to him. Surely, Quest would have said something of such bitterness during their long time together on Ganymede and aspace, since he did not know of Trella's connection with Blessing. But, since this was to be the atmosphere of Blessing's house, she was glad that he decided to assign her to take the Mansard papers to the New York laboratory. Quest came the day before she was scheduled to leave. Trella was in the living room with Blessing, discussing the instructions she was to give to the laboratory officials in New York. The two bodyguards were with them. The other guards were at their posts. Trella heard the doorbell ring. The heavy oaken front door was kept locked now, and the guards in the anteroom examined callers through a tiny window. Suddenly alarm bells rang all over the house. There was a terrific crash outside the room as the front door splintered. There were shouts and the sound of a shot. “The steel doors!” cried Blessing, turning white. “Let's get out of here.” He and his bodyguards ran through the back of the house out of the garage. Blessing, ahead of the rest, leaped into one of the cars and started the engine. The door from the house shattered and Quest burst through. The two guards turned and fired together. He could be hurt by bullets. He was staggered momentarily. Then, in a blur of motion, he sprang forward and swept the guards aside with one hand with such force that they skidded across the floor and lay in an unconscious heap against the rear of the garage. Trella had opened the door of the car, but it was wrenched from her hand as Blessing stepped on the accelerator and it leaped into the driveway with spinning wheels. Quest was after it, like a chunky deer, running faster than Trella had ever seen a man run before. Blessing slowed for the turn at the end of the driveway and glanced back over his shoulder. Seeing Quest almost upon him, he slammed down the accelerator and twisted the wheel hard. The car whipped into the street, careened, and rolled over and over, bringing up against a tree on the other side in a twisted tangle of wreckage. With a horrified gasp, Trella ran down the driveway toward the smoking heap of metal. Quest was already beside it, probing it. As she reached his side, he lifted the torn body of Dom Blessing. Blessing was dead. “I'm lucky,” said Quest soberly. “I would have murdered him.” “But why, Quest? I knew he was afraid of you, but he didn't tell me why.” “It was conditioned into me,” answered Quest “I didn't know 60 it until just now, when it ended, but my father conditioned me psychologically from my birth to the task of hunting down Dom Blessing and killing him. It was an unconscious drive in me that wouldn't release me until the task was finished. “You see, Blessing was my father's assistant on Ganymede. Right after my father completed development of the surgiscope, he and my mother blasted off for Io. Blessing wanted the valuable rights to the surgiscope, and he sabotaged the ship's drive so it would fall into Jupiter. “But my father was able to control it in the heavy atmosphere of Jupiter, and landed it successfully. I was born there, and he conditioned me to come to Earth and track down Blessing. I know now that it was part of the conditioning that I was unable to fight any other man until my task was finished: it might have gotten me in trouble and diverted me from that purpose.” More gently than Trella would have believed possible for his Jupiter-strong muscles, Quest took her in his arms. “Now I can say I love you,” he said. “That was part of the conditioning too: I couldn't love any woman until my job was done.” Trella disengaged herself. “I'm sorry,” she said. “Don't you know this, too, now: that you're not a man, but an android?” He looked at her in astonishment, stunned by her words. “What in space makes you think that?” he demanded. “Why, Quest, it's obvious,” she cried, tears in her eyes. “Everything about you … your build, suited for Jupiter's gravity … your strength … the fact that you were able to live in Jupiter's atmosphere after the oxygen equipment failed. I know you think Dr. Mansard was your father, but androids often believe that.” He grinned at her. “I'm no android,” he said confidently. “Do you forget my father was inventor of the surgiscope? He knew I'd have to grow up on Jupiter, and he operated on the genes before I was born. He altered my inherited characteristics to adapt me to the climate of Jupiter … even to being able to breathe a chlorine atmosphere as well as an oxygen atmosphere.” Trella looked at him. He was not badly hurt, any more than an elephant would have been, but his tunic was stained with red blood where the bullets had struck him. Normal android blood was green. “How can you be sure?” she asked doubtfully. “Androids are made,” he answered with a laugh. “They don't grow up. And I remember my boyhood on Jupiter very well.” He took her in his arms again, and this time she did not resist. His lips were very human. THE END | A. He claims that "androids are made" to justify his human status, disregarding the impact of his father's programming efforts |
Why couldn't they find a volunteer to man the big ship?
A. the ship wasn't going to be ready for a long time
B. there was no proof that it was safe for humans
C. the rats didn't survive, so people probably wouldn't
D. no one wanted to spend that much time on the ship
| The Dwindling Years He didn’t expect to be last—but neither did he anticipate the horror of being the first! By LESTER DEL REY Illustrated by JOHNS NEARLY TWO hundred years of habit carried the chairman of Exodus Corporation through the morning ritual of crossing the executive floor. Giles made the expected comments, smiled the proper smiles and greeted his staff by the right names, but it was purely automatic. Somehow, thinking had grown difficult in the mornings recently. Inside his private office, he dropped all pretense and slumped into the padding of his chair, gasping for breath and feeling his heart hammering in his chest. He’d been a fool to come to work, he realized. But with the Procyon shuttle arriving yesterday, there was no telling what might turn up. Besides, that fool of a medicist had sworn the shot would cure any allergy or asthma. Giles heard his secretary come in, but it wasn’t until the smell of the coffee reached his nose that he looked up. She handed him a filled cup and set the carafe down on the age-polished surface of the big desk. She watched solicitously as he drank. “That bad, Arthur?” she asked. “Just a little tired,” he told her, refilling the cup. She’d made the coffee stronger than usual and it seemed to cut through some of the thickness in his head. “I guess I’m getting old, Amanda.” She smiled dutifully at the time-worn joke, but he knew she wasn’t fooled. She’d cycled to middle age four times in her job and she probably knew him better than he knew himself—which wouldn’t be hard, he thought. He’d hardly recognized the stranger in the mirror as he tried to shave. His normal thinness had looked almost gaunt and there were hollows in his face and circles under his eyes. Even his hair had seemed thinner, though that, of course, was impossible. “Anything urgent on the Procyon shuttle?” he asked as she continue staring at him with worried eyes. SHE JERKED her gaze away guiltily and turned to the incoming basket. “Mostly drugs for experimenting. A personal letter for you, relayed from some place I never heard of. And one of the super-light missiles! They found it drifting half a light-year out and captured it. Jordan’s got a report on it and he’s going crazy. But if you don’t feel well—” “I’m all right!” he told her sharply. Then he steadied himself and managed to smile. “Thanks for the coffee, Amanda.” She accepted dismissal reluctantly. When she was gone, he sat gazing at the report from Jordan at Research. For eighty years now, they’d been sending out the little ships that vanished at greater than the speed of light, equipped with every conceivable device to make them return automatically after taking pictures of wherever they arrived. So far, none had ever returned or been located. This was the first hope they’d found that the century-long trips between stars in the ponderous shuttles might be ended and he should have been filled with excitement at Jordan’s hasty preliminary report. He leafed through it. The little ship apparently had been picked up by accident when it almost collided with a Sirius-local ship. Scientists there had puzzled over it, reset it and sent it back. The two white rats on it had still been alive. Giles dropped the report wearily and picked up the personal message that had come on the shuttle. He fingered the microstrip inside while he drank another coffee, and finally pulled out the microviewer. There were three frames to the message, he saw with some surprise. He didn’t need to see the signature on the first projection. Only his youngest son would have sent an elaborate tercentenary greeting verse—one that would arrive ninety years too late! Harry had been born just before Earth passed the drastic birth limitation act and his mother had spoiled him. He’d even tried to avoid the compulsory emigration draft and stay on with his mother. It had been the bitter quarrels over that which had finally broken Giles’ fifth marriage. Oddly enough, the message in the next frame showed none of that. Harry had nothing but praise for the solar system where he’d been sent. He barely mentioned being married on the way or his dozen children, but filled most of the frame with glowing description and a plea for his father to join him there! GILES SNORTED and turned to the third frame, which showed a group picture of the family in some sort of vehicle, against the background of an alien but attractive world. He had no desire to spend ninety years cooped up with a bunch of callow young emigrants, even in one of the improved Exodus shuttles. And even if Exodus ever got the super-light drive working, there was no reason he should give up his work. The discovery that men could live practically forever had put an end to most family ties; sentiment wore thin in half a century—which wasn’t much time now, though it had once seemed long enough. Strange how the years seemed to get shorter as their number increased. There’d been a song once—something about the years dwindling down. He groped for the lines and couldn’t remember. Drat it! Now he’d probably lie awake most of the night again, trying to recall them. The outside line buzzed musically, flashing Research’s number. Giles grunted in irritation. He wasn’t ready to face Jordan yet. But he shrugged and pressed the button. The intense face that looked from the screen was frowning as Jordan’s eyes seemed to sweep around the room. He was still young—one of the few under a hundred who’d escaped deportation because of special ability—and patience was still foreign to him. Then the frown vanished as an expression of shock replaced it, and Giles felt a sinking sensation. If he looked that bad— But Jordan wasn’t looking at him; the man’s interest lay in the projected picture from Harry, across the desk from the communicator. “Antigravity!” His voice was unbelieving as he turned his head to face the older man. “What world is that?” Giles forced his attention on the picture again and this time he noticed the vehicle shown. It was enough like an old model Earth conveyance to pass casual inspection, but it floated wheellessly above the ground. Faint blur lines indicated it had been moving when the picture was taken. “One of my sons—” Giles started to answer. “I could find the star’s designation....” Jordan cursed harshly. “So we can send a message on the shuttle, begging for their secret in a couple of hundred years! While a hundred other worlds make a thousand major discoveries they don’t bother reporting! Can’t the Council see anything ?” Giles had heard it all before. Earth was becoming a backwater world; no real progress had been made in two centuries; the young men were sent out as soon as their first fifty years of education were finished, and the older men were too conservative for really new thinking. There was a measure of truth in it, unfortunately. “They’ll slow up when their populations fill,” Giles repeated his old answers. “We’re still ahead in medicine and we’ll get the other discoveries eventually, without interrupting the work of making the Earth fit for our longevity. We can wait. We’ll have to.” THE YOUNGER man stared at him with the strange puzzled look Giles had seen too often lately. “Damn it, haven’t you read my report? We know the super-light drive works! That missile reached Sirius in less than ten days. We can have the secret of this antigravity in less than a year! We—” “Wait a minute.” Giles felt the thickness pushing back at his mind and tried to fight it off. He’d only skimmed the report, but this made no sense. “You mean you can calibrate your guiding devices accurately enough to get a missile where you want it and back?” “ What? ” Jordan’s voice rattled the speaker. “Of course not! It took two accidents to get the thing back to us—and with a half-light-year miss that delayed it about twenty years before the Procyon shuttle heard its signal. Pre-setting a course may take centuries, if we can ever master it. Even with Sirius expecting the missiles and ready to cooperate. I mean the big ship. We’ve had it drafted for building long enough; now we can finish it in three months. We know the drive works. We know it’s fast enough to reach Procyon in two weeks. We even know life can stand the trip. The rats were unharmed.” Giles shook his head at what the other was proposing, only partly believing it. “Rats don’t have minds that could show any real damage such as the loss of power to rejuvenate. We can’t put human pilots into a ship with our drive until we’ve tested it more thoroughly, Bill, even if they could correct for errors on arrival. Maybe if we put in stronger signaling transmitters....” “Yeah. Maybe in two centuries we’d have a through route charted to Sirius. And we still wouldn’t have proved it safe for human pilots. Mr. Giles, we’ve got to have the big ship. All we need is one volunteer!” It occurred to Giles then that the man had been too fired with the idea to think. He leaned back, shaking his head again wearily. “All right, Bill. Find me one volunteer. Or how about you? Do you really want to risk losing the rest of your life rather than waiting a couple more centuries until we know it’s safe? If you do, I’ll order the big ship.” Jordan opened his mouth and for a second Giles’ heart caught in a flux of emotions as the man’s offer hovered on his lips. Then the engineer shut his mouth slowly. The belligerence ran out of him. He looked sick, for he had no answer. NO SANE man would risk a chance for near eternity against such a relatively short wait. Heroism had belonged to those who knew their days were numbered, anyhow. “Forget it, Bill,” Giles advised. “It may take longer, but eventually we’ll find a way. With time enough, we’re bound to. And when we do, the ship will be ready.” The engineer nodded miserably and clicked off. Giles turned from the blank screen to stare out of the windows, while his hand came up to twist at the lock of hair over his forehead. Eternity! They had to plan and build for it. They couldn’t risk that plan for short-term benefits. Usually it was too easy to realize that, and the sight of the solid, time-enduring buildings outside should have given him a sense of security. Today, though, nothing seemed to help. He felt choked, imprisoned, somehow lost; the city beyond the window blurred as he studied it, and he swung the chair back so violently that his hand jerked painfully on the forelock he’d been twisting. Then he was staring unbelievingly at the single white hair that was twisted with the dark ones between his fingers. Like an automaton, he bent forward, his other hand groping for the mirror that should be in one of the drawers. The dull pain in his chest sharpened and his breath was hoarse in his throat, but he hardly noticed as he found the mirror and brought it up. His eyes focused reluctantly. There were other white strands in his dark hair. The mirror crashed to the floor as he staggered out of the office. It was only two blocks to Giles’ residence club, but he had to stop twice to catch his breath and fight against the pain that clawed at his chest. When he reached the wood-paneled lobby, he was barely able to stand. Dubbins was at his side almost at once, with a hand under his arm to guide him toward his suite. “Let me help you, sir,” Dubbins suggested, in the tones Giles hadn’t heard since the man had been his valet, back when it was still possible to find personal servants. Now he managed the club on a level of quasi-equality with the members. For the moment, though, he’d slipped back into the old ways. GILES FOUND himself lying on his couch, partially undressed, with the pillows just right and a long drink in his hand. The alcohol combined with the reaction from his panic to leave him almost himself again. After all, there was nothing to worry about; Earth’s doctors could cure anything. “I guess you’d better call Dr. Vincenti,” he decided. Vincenti was a member and would probably be the quickest to get. Dubbins shook his head. “Dr. Vincenti isn’t with us, sir. He left a year ago to visit a son in the Centauri system. There’s a Dr. Cobb whose reputation is very good, sir.” Giles puzzled over it doubtfully. Vincenti had been an oddly morose man the last few times he’d seen him, but that could hardly explain his taking a twenty-year shuttle trip for such a slim reason. It was no concern of his, though. “Dr. Cobb, then,” he said. Giles heard the other man’s voice on the study phone, too low for the words to be distinguishable. He finished the drink, feeling still better, and was sitting up when Dubbins came back. “Dr. Cobb wants you to come to his office at once, sir,” he said, dropping to his knee to help Giles with his shoes. “I’d be pleased to drive you there.” Giles frowned. He’d expected Cobb to come to him. Then he grimaced at his own thoughts. Dubbins’ manners must have carried him back into the past; doctors didn’t go in for home visits now—they preferred to see their patients in the laboratories that housed their offices. If this kept on, he’d be missing the old days when he’d had a mansion and counted his wealth in possessions, instead of the treasures he could build inside himself for the future ahead. He was getting positively childish! Yet he relished the feeling of having Dubbins drive his car. More than anything else, he’d loved being driven. Even after chauffeurs were a thing of the past, Harry had driven him around. Now he’d taken to walking, as so many others had, for even with modern safety measures so strict, there was always a small chance of some accident and nobody had any desire to spend the long future as a cripple. “I’ll wait for you, sir,” Dubbins offered as they stopped beside the low, massive medical building. It was almost too much consideration. Giles nodded, got out and headed down the hall uncertainly. Just how bad did he look? Well, he’d soon find out. He located the directory and finally found the right office, its reception room wall covered with all the degrees Dr. Cobb had picked up in some three hundred years of practice. Giles felt better, realizing it wouldn’t be one of the younger men. COBB APPEARED himself, before the nurse could take over, and led Giles into a room with an old-fashioned desk and chairs that almost concealed the cabinets of equipment beyond. He listened as Giles stumbled out his story. Halfway through, the nurse took a blood sample with one of the little mosquito needles and the machinery behind the doctor began working on it. “Your friend told me about the gray hair, of course,” Cobb said. At Giles’ look, he smiled faintly. “Surely you didn’t think people could miss that in this day and age? Let’s see it.” He inspected it and began making tests. Some were older than Giles could remember—knee reflex, blood pressure, pulse and fluoroscope. Others involved complicated little gadgets that ran over his body, while meters bobbed and wiggled. The blood check came through and Cobb studied it, to go back and make further inspections of his own. At last he nodded slowly. “Hyper-catabolism, of course. I thought it might be. How long since you had your last rejuvenation? And who gave it?” “About ten years ago,” Giles answered. He found his identity card and passed it over, while the doctor studied it. “My sixteenth.” It wasn’t going right. He could feel it. Some of the panic symptoms were returning; the pulse in his neck was pounding and his breath was growing difficult. Sweat ran down his sides from his armpit and he wiped his palms against his coat. “Any particular emotional strain when you were treated—some major upset in your life?” Cobb asked. Giles thought as carefully as he could, but he remembered nothing like that. “You mean—it didn’t take? But I never had any trouble, Doctor. I was one of the first million cases, when a lot of people couldn’t rejuvenate at all, and I had no trouble even then.” Cobb considered it, hesitated as if making up his mind to be frank against his better judgment. “I can’t see any other explanation. You’ve got a slight case of angina—nothing serious, but quite definite—as well as other signs of aging. I’m afraid the treatment didn’t take fully. It might have been some unconscious block on your part, some infection not diagnosed at the time, or even a fault in the treatment. That’s pretty rare, but we can’t neglect the possibility.” HE STUDIED his charts again and then smiled. “So we’ll give you another treatment. Any reason you can’t begin immediately?” Giles remembered that Dubbins was waiting for him, but this was more important. It hadn’t been a joke about his growing old, after all. But now, in a few days, he’d be his old—no, of course not—his young self again! They went down the hall to another office, where Giles waited outside while Cobb conferred with another doctor and technician, with much waving of charts. He resented every second of it. It was as if the almost forgotten specter of age stood beside him, counting the seconds. But at last they were through and he was led into the quiet rejuvenation room, where the clamps were adjusted about his head and the earpieces were fitted. The drugs were shot painlessly into his arm and the light-pulser was adjusted to his brain-wave pattern. It had been nothing like this his first time. Then it had required months of mental training, followed by crude mechanical and drug hypnosis for other months. Somewhere in every human brain lay the memory of what his cells had been like when he was young. Or perhaps it lay in the cells themselves, with the brain as only a linkage to it. They’d discovered that, and the fact that the mind could effect physical changes in the body. Even such things as cancer could be willed out of existence—provided the brain could be reached far below the conscious level and forced to operate. There had been impossible faith cures for millenia—cataracts removed from blinded eyes within minutes, even—but finding the mechanism in the brain that worked those miracles had taken an incredible amount of study and finding a means of bringing it under control had taken even longer. Now they did it with dozens of mechanical aids in addition to the hypnotic instructions—and did it usually in a single sitting, with the full transformation of the body taking less than a week after the treatment! But with all the equipment, it wasn’t impossible for a mistake to happen. It had been no fault of his ... he was sure of that ... his mind was easy to reach ... he could relax so easily.... He came out of it without even a headache, while they were removing the probes, but the fatigue on the operator’s face told him it had been a long and difficult job. He stretched experimentally, with the eternal unconscious expectation that he would find himself suddenly young again. But that, of course, was ridiculous. It took days for the mind to work on all the cells and to repair the damage of time. COBB LED him back to the first office, where he was given an injection of some kind and another sample of his blood was taken, while the earlier tests were repeated. But finally the doctor nodded. “That’s all for now, Mr. Giles. You might drop in tomorrow morning, after I’ve had a chance to complete my study of all this. We’ll know by then whether you’ll need more treatment. Ten o’clock okay?” “But I’ll be all right?” Cobb smiled the automatic reassurance of his profession. “We haven’t lost a patient in two hundred years, to my knowledge.” “Thanks,” said Giles. “Ten o’clock is fine.” Dubbins was still waiting, reading a paper whose headlined feature carried a glowing account of the discovery of the super-light missile and what it might mean. He took a quick look at Giles and pointed to it. “Great work, Mr. Giles. Maybe we’ll all get to see some of those other worlds yet.” Then he studied Giles more carefully. “Everything’s in good shape now, sir?” “The doctor says everything’s going to be fine,” Giles answered. It was then he realized for the first time that Cobb had said no such thing. A statement that lightning had never struck a house was no guarantee that it never would. It was an evasion meant to give such an impression. The worry nagged at him all the way back. Word had already gone around the club that he’d had some kind of attack and there were endless questions that kept it on his mind. And even when it had been covered and recovered, he could still sense the glances of the others, as if he were Vincenti in one of the man’s more morose moods. He found a single table in the dining room and picked his way through the meal, listening to the conversation about him only when it was necessary because someone called across to him. Ordinarily, he was quick to support the idea of clubs in place of private families. A man here could choose his group and grow into them. Yet he wasn’t swallowed by them, as he might be by a family. Giles had been living here for nearly a century now and he’d never regretted it. But tonight his own group irritated him. He puzzled over it, finding no real reason. Certainly they weren’t forcing themselves on him. He remembered once when he’d had a cold, before they finally licked that; Harry had been a complete nuisance, running around with various nostrums, giving him no peace. Constant questions about how he felt, constant little looks of worry—until he’d been ready to yell at the boy. In fact, he had. Funny, he couldn’t picture really losing his temper here. Families did odd things to a man. HE LISTENED to a few of the discussions after the dinner, but he’d heard them all before, except for one about the super-speed drive, and there he had no wish to talk until he could study the final report. He gave up at last and went to his own suite. What he needed was a good night’s sleep after a little relaxation. Even that failed him, though. He’d developed one of the finest chess collections in the world, but tonight it held no interest. And when he drew out his tools and tried working on the delicate, lovely jade for the set he was carving his hands seemed to be all thumbs. None of the other interests he’d developed through the years helped to add to the richness of living now. He gave it up and went to bed—to have the fragment of that song pop into his head. Now there was no escaping it. Something about the years—or was it days—dwindling down to something or other. Could they really dwindle down? Suppose he couldn’t rejuvenate all the way? He knew that there were some people who didn’t respond as well as others. Sol Graves, for instance. He’d been fifty when he finally learned how to work with the doctors and they could only bring him back to about thirty, instead of the normal early twenties. Would that reduce the slice of eternity that rejuvenation meant? And what had happened to Sol? Or suppose it wasn’t rejuvenation, after all; suppose something had gone wrong with him permanently? He fought that off, but he couldn’t escape the nagging doubts at the doctor’s words. He got up once to stare at himself in the mirror. Ten hours had gone by and there should have been some signs of improvement. He couldn’t be sure, though, whether there were or not. He looked no better the next morning when he finally dragged himself up from the little sleep he’d managed to get. The hollows were still there and the circles under his eyes. He searched for the gray in his hair, but the traitorous strands had been removed at the doctor’s office and he could find no new ones. He looked into the dining room and then went by hastily. He wanted no solicitous glances this morning. Drat it, maybe he should move out. Maybe trying family life again would give him some new interests. Amanda probably would be willing to marry him; she’d hinted at a date once. He stopped, shocked by the awareness that he hadn’t been out with a woman for.... He couldn’t remember how long it had been. Nor why. “In the spring, a young man’s fancy,” he quoted to himself, and then shuddered. It hadn’t been that kind of spring for him—not this rejuvenation nor the last, nor the one before that. GILES TRIED to stop scaring himself and partially succeeded, until he reached the doctor’s office. Then it was no longer necessary to frighten himself. The wrongness was too strong, no matter how professional Cobb’s smile! He didn’t hear the preliminary words. He watched the smile vanish as the stack of reports came out. There was no nurse here now. The machines were quiet—and all the doors were shut. Giles shook his head, interrupting the doctor’s technical jargon. Now that he knew there was reason for his fear, it seemed to vanish, leaving a coldness that numbed him. “I’d rather know the whole truth,” he said. His voice sounded dead in his ears. “The worst first. The rejuvenation...?” Cobb sighed and yet seemed relieved. “Failed.” He stopped, and his hands touched the reports on his desk. “Completely,” he added in a low, defeated tone. “But I thought that was impossible!” “So did I. I wouldn’t believe it even yet—but now I find it isn’t the first case. I spent the night at Medical Center going up the ranks until I found men who really know about it. And now I wish I hadn’t.” His voice ran down and he gathered himself together by an effort. “It’s a shock to me, too, Mr. Giles. But—well, to simplify it, no memory is perfect—even cellular memory. It loses a little each time. And the effect is cumulative. It’s like an asymptotic curve—the further it goes, the steeper the curve. And—well, you’ve passed too far.” He faced away from Giles, dropping the reports into a drawer and locking it. “I wasn’t supposed to tell you, of course. It’s going to be tough enough when they’re ready to let people know. But you aren’t the first and you won’t be the last, if that’s any consolation. We’ve got a longer time scale than we used to have—but it’s in centuries, not in eons. For everybody, not just you.” It was no consolation. Giles nodded mechanically. “I won’t talk, of course. How—how long?” Cobb spread his hands unhappily. “Thirty years, maybe. But we can make them better. Geriatric knowledge is still on record. We can fix the heart and all the rest. You’ll be in good physical condition, better than your grandfather—” “And then....” Giles couldn’t pronounce the words. He’d grown old and he’d grow older. And eventually he’d die! An immortal man had suddenly found death hovering on his trail. The years had dwindled and gone, and only a few were left. He stood up, holding out his hand. “Thank you, Doctor,” he said, and was surprised to find he meant it. The man had done all he could and had at least saved him the suspense of growing doubt and horrible eventual discovery. OUTSIDE ON the street, he looked up at the Sun and then at the buildings built to last for thousands of years. Their eternity was no longer a part of him. Even his car would outlast him. He climbed into it, still partly numbed, and began driving mechanically, no longer wondering about the dangers that might possibly arise. Those wouldn’t matter much now. For a man who had thought of living almost forever, thirty years was too short a time to count. He was passing near the club and started to slow. Then he went on without stopping. He wanted no chance to have them asking questions he couldn’t answer. It was none of their business. Dubbins had been kind—but now Giles wanted no kindness. The street led to the office and he drove on. What else was there for him? There, at least, he could still fill his time with work—work that might even be useful. In the future, men would need the super-light drive if they were to span much more of the Universe than now. And he could speed up the work in some ways still, even if he could never see its finish. It would be cold comfort but it was something. And he might keep busy enough to forget sometimes that the years were gone for him. Automatic habit carried him through the office again, to Amanda’s desk, where her worry was still riding her. He managed a grin and somehow the right words came to his lips. “I saw the doctor, Amanda, so you can stop figuring ways to get me there.” She smiled back suddenly, without feigning it. “Then you’re all right?” “As all right as I’ll ever be,” he told her. “They tell me I’m just growing old.” This time her laugh was heartier. He caught himself before he could echo her mirth in a different voice and went inside where she had the coffee waiting for him. Oddly, it still tasted good to him. The projection was off, he saw, wondering whether he’d left it on or not. He snapped the switch and saw the screen light up, with the people still in the odd, wheelless vehicle on the alien planet. FOR A long moment, he stared at the picture without thinking, and then bent closer. Harry’s face hadn’t changed much. Giles had almost forgotten it, but there was still the same grin there. And his grandchildren had a touch of it, too. And of their grandfather’s nose, he thought. Funny, he’d never seen even pictures of his other grandchildren. Family ties melted away too fast for interstellar travel. Yet there seemed to be no slackening of them in Harry’s case, and somehow it looked like a family, rather than a mere group. A very pleasant family in a very pleasant world. He read Harry’s note again, with its praise for the planet and its invitation. He wondered if Dr. Vincenti had received an invitation like that, before he left. Or had he even been one of those to whom the same report had been delivered by some doctor? It didn’t matter, but it would explain things, at least. Twenty years to Centaurus, while the years dwindled down— Then abruptly the line finished itself. “The years dwindle down to a precious few....” he remembered. “A precious few.” Those dwindling years had been precious once. He unexpectedly recalled his own grandfather holding him on an old knee and slipping him candy that was forbidden. The years seemed precious to the old man then. Amanda’s voice came abruptly over the intercom. “Jordan wants to talk to you,” she said, and the irritation was sharp in her voice. “He won’t take no!” Giles shrugged and reached for the projector, to cut it off. Then, on impulse, he set it back to the picture, studying the group again as he switched on Jordan’s wire. But he didn’t wait for the hot words about whatever was the trouble. “Bill,” he said, “start getting the big ship into production. I’ve found a volunteer.” He’d been driven to it, he knew, as he watched the man’s amazed face snap from the screen. From the first suspicion of his trouble, something inside him had been forcing him to make this decision. And maybe it would do no good. Maybe the ship would fail. But thirty years was a number a man could risk. If he made it, though.... Well, he’d see those grandchildren of his this year—and Harry. Maybe he’d even tell Harry the truth, once they got done celebrating the reunion. And there’d be other grandchildren. With the ship, he’d have time enough to look them up. Plenty of time! Thirty years was a long time, when he stopped to think of it. —LESTER DEL REY | B. there was no proof that it was safe for humans |
What was the problem with having the fifty-five gallon barrell in the dome?
A. It would be impossible to get out once it was inside the dome.
B. It took up too much room in an already crowded area.
C. It had a terribly overpowering smell.
D. It weighed too much to be supported by the dome.
| The Winning of the Moon BY KRIS NEVILLE The enemy was friendly enough. Trouble was—their friendship was as dangerous as their hate! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, September 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] General Finogenov notified Major Winship that the underground blast was scheduled for the following morning. Major Winship, after receiving the message, discussed precautions with the three other Americans. Next morning, before the sunlight exploded, the four of them donned their space suits and went and sat outside the dome, waiting. The sun rose with its bright, silent clap of radiance. Black pools of shadows lay in harsh contrast, their edges drawn with geometric precision. Major Winship attempted unsuccessfully to communicate with Base Gagarin. "Will you please request the general to keep us informed on the progress of the countdown?" "Is Pinov," came the reply. "Help?" " Nyet ," said Major Winship, exhausting his Russian. "Count down. Progress. When—boom?" "Is Pinov," came the reply. "Boom! Boom!" said Major Winship in exasperation. "Boom!" said Pinov happily. "When?" "Boom—boom!" said Pinov. "Oh, nuts." Major Winship cut out the circuit. "They've got Pinov on emergency watch this morning," he explained to the other Americans. "The one that doesn't speak English." "He's done it deliberately," said Capt. Wilkins, the eldest of the four Americans. "How are we going to know when it's over?" No one bothered to respond. They sat for a while in silence while the shadows evaporated. One by one they clicked on their cooling systems. Ultimately, Lt. Chandler said, "This is a little ridiculous. I'm going to switch over to their channel. Rap if you want me." He sat transfixed for several minutes. "Ah, it's all Russian. Jabbering away. I can't tell a thing that's going on." In the airless void of the moon, the blast itself would be silent. A moth's wing of dust would, perhaps, rise and settle beyond the horizon: no more. "Static?" "Nope." "We'll get static on these things." A small infinity seemed to pass very slowly. Major Winship shifted restlessly. "My reefer's gone on the fritz." Perspiration was trickling down his face. "Let's all go in," said the fourth American, Capt. Lawler. "It's probably over by now." "I'll try again," Major Winship said and switched to the emergency channel. "Base Gagarin? Base Gagarin?" "Is Pinov. Help?" " Nyet. " "Pinov's still there," Major Winship said. "Tell him, 'Help'," said Capt. Wilkins, "so he'll get somebody we can talk to." "I'll see them all in hell, first," Major Winship said. Five minutes later, the perspiration was rivers across his face. "This is it," he said. "I'm going in." "Let's all—" "No. I've got to cool off." "Hell, Charlie, I feel stupid sitting out here," Capt. Lawler said. "The shot probably went off an hour ago." "The static level hasn't gone up much, if at all." "Maybe," Lt. Chandler said, "it's buried too deep." "Maybe so," Major Winship said. "But we can't have the dome fall down around all our ears." He stood. "Whew! You guys stay put." He crossed with the floating moon-motion to the airlock and entered, closing the door behind him. The darkness slowly filled with air, and the temperature inside the suit declined steadily. At the proper moment of pressure, the inner lock slid open and Major Winship stepped into the illuminated central area. His foot was lifted for the second step when the floor beneath him rose and fell gently, pitching him forward, off balance. He stumbled against the table and ended up seated beside the radio equipment. The ground moved again. "Charlie! Charlie!" "I'm okay," Major Winship answered. "Okay! Okay!" "It's—" There was additional surface movement. The movement ceased. "Hey, Les, how's it look?" Capt. Wilkins asked. "Okay from this side. Charlie, you still okay?" "Okay," Major Winship said. "We told them this might happen," he added bitterly. There was a wait during which everyone seemed to be holding their breath. "I guess it's over," said Major Winship, getting to his feet. "Wait a bit more, there may be an after-shock." He switched once again to the emergency channel. "Is Pinov," came the supremely relaxed voice. "Help?" Major Winship whinnied in disgust. " Nyet! " he snarled. To the other Americans: "Our comrades seem unconcerned." "Tough." They began to get the static for the first time. It crackled and snapped in their speakers. They made sounds of disapproval at each other. For a minute or two, static blanked out the communications completely. It then abated to something in excess of normal. "Well," Lt. Chandler commented, "even though we didn't build this thing to withstand a moonquake, it seems to have stood up all right." "I guess I was just—" Major Winship began. "Oh, hell! We're losing pressure. Where's the markers?" "By the lug cabinet." "Got 'em," Major Winship said a moment later. He peeled back a marker and let it fall. Air currents whisked it away and plastered it against a riveted seam of the dome. It pulsed as though it were breathing and then it ruptured. Major Winship moved quickly to cut out the emergency air supply which had cut in automatically with the pressure drop. "You guys wait. It's on your right side, midway up. I'll try to sheet it." He moved for the plastic sheeting. "We've lost about three feet of calk out here," Capt. Lawler said. "I can see more ripping loose. You're losing pressure fast at this rate." Major Winship pressed the sheeting over the leak. "How's that?" "Not yet." "I don't think I've got enough pressure left to hold it, now. It's sprung a little, and I can't get it to conform over the rivet heads." There was a splatter of static. "Damn!" Major Winship said, "they should have made these things more flexible." "Still coming out." "Best I can do." Major Winship stepped back. The sheet began slowly to slide downward, then it fell away completely and lay limply on the floor. "Come on in," he said dryly. With the four of them inside, it was somewhat cramped. Most of the five hundred square feet was filled with equipment. Electrical cables trailed loosely along the walls and were festooned from the ceiling, radiating from the connections to the outside solar cells. The living space was more restricted than in a submarine, with the bunks jutting out from the walls about six feet from the floor. Lt. Chandler mounted one of the bunks to give them more room. "Well," he said wryly, "it doesn't smell as bad now." "Oops," said Major Winship. "Just a second. They're coming in." He switched over to the emergency channel. It was General Finogenov. "Major Winship! Hello! Hello, hello, hello. You A Okay?" "This is Major Winship." "Oh! Excellent, very good. Any damage, Major?" "Little leak. You?" "Came through without damage." General Finogenov paused a moment. When no comment was forthcoming, he continued: "Perhaps we built a bit more strongly, Major." "You did this deliberately," Major Winship said testily. "No, no. Oh, no, no, no, no. Major Winship, please believe me. I very much regret this. Very much so. I am very distressed. Depressed. After repeatedly assuring you there was no danger of a quake—and then to have something like this happen. Oh, this is very embarrassing to me. Is there anything at all we can do?" "Just leave us alone, thank you," Major Winship said and cut off the communication. "What'd they say?" Capt. Wilkins asked. "Larry, General Finogenov said he was very embarrassed by this." "That's nice," Lt. Chandler said. "I'll be damned surprised," Major Winship said, "if they got any seismic data out of that shot.... Well, to hell with them, let's get this leak fixed. Skip, can you get the calking compound?" "Larry, where's the inventory?" "Les has got it." Lt. Chandler got down from the bunk and Capt. Wilkins mounted. "Larry," Major Winship said, "why don't you get Earth?" "Okay." Capt. Wilkins got down from the bunk and Capt. Lawler ascended. "Got the inventory sheet, Les?" "Right here." Squeezed in front of the massive transmitter, Capt. Wilkins had energized the circuits. There was a puzzled look on his face. He leaned his helmet against the speaker and then shook his head sadly. "We can't hear anything without any air." Major Winship looked at the microphone. "Well, I'll just report and—" He started to pick up the microphone and reconsidered. "Yes," he said. "That's right, isn't it." Capt. Wilkins flicked off the transmitter. "Some days you don't mine at all," he said. "Les, have you found it?" "It's around here somewhere. Supposed to be back here." "Well, find it." Lt. Chandler began moving boxes. "I saw it—" "Skip, help look." Capt. Lawler got down from the bunk and Major Winship mounted. "We haven't got all day." A few minutes later, Lt. Chandler issued the triumphant cry. "Here it is! Dozen tubes. Squeeze tubes. It's the new stuff." Major Winship got down and Capt. Wilkins got up. "Marker showed it over here," Major Winship said, inching over to the wall. He traced the leak with a metallic finger. "How does this stuff work?" Capt. Lawler asked. They huddled over the instruction sheet. "Let's see. Squeeze the tube until the diaphragm at the nozzle ruptures. Extrude paste into seam. Allow to harden one hour before service." Major Winship said dryly, "Never mind. I notice it hardens on contact with air." Capt. Wilkins lay back on the bunk and stared upward. He said, "Now that makes a weird kind of sense, doesn't it?" "How do they possibly think—?" "Gentlemen! It doesn't make any difference," Lt. Chandler said. "Some air must already have leaked into this one. It's hard as a rock. A gorilla couldn't extrude it." "How're the other ones?" asked Major Winship. Lt. Chandler turned and made a quick examination. "Oh, they're all hard, too." "Who was supposed to check?" demanded Capt. Wilkins in exasperation. "The only way you can check is to extrude it," Lt. Chandler said, "and if it does extrude, you've ruined it." "That's that," Major Winship said. "There's nothing for it but to yell help." II Capt. Lawler and Lt. Chandler took the land car to Base Gagarin. The Soviet base was situated some ten miles toward sunset at the bottom of a natural fold in the surface. The route was moderately direct to the tip of the gently rolling ridge. At that point, the best pathway angled left and made an S-shaped descent to the basin. It was a one-way trip of approximately thirty exhausting minutes. Major Winship, with his deficient reefer, remained behind. Capt. Wilkins stayed for company. "I want a cigarette in the worst way," Capt. Wilkins said. "So do I, Larry. Shouldn't be more than a couple of hours. Unless something else goes wrong." "As long as they'll loan us the calking compound," Capt. Wilkins said. "Yeah, yeah," Major Winship said. "Let's eat." "You got any concentrate? I'm empty." "I'll load you," Capt. Wilkins volunteered wearily. It was an awkward operation that took several minutes. Capt. Wilkins cursed twice during the operation. "I'd hate to live in this thing for any period." "I think these suits are one thing we've got over the Russians," Major Winship said. "I don't see how they can manipulate those bulky pieces of junk around." They ate. "Really horrible stuff." "Nutritious." After the meal, Major Winship said reflectively, "Now I'd like a cup of hot tea. I'm cooled off." Capt. Wilkins raised eyebrows. "What brought this on?" "I was just thinking.... They really got it made, Larry. They've got better than three thousand square feet in the main dome and better than twelve hundred square feet in each of the two little ones. And there's only seven of them right now. That's living." "They've been here six years longer, after all." "Finogenov had a clay samovar sent up. Lemon and nutmeg, too. Real, by God, fresh lemons for the tea, the last time I was there. His own office is about ten by ten. Think of that. One hundred square feet. And a wooden desk. A wooden desk. And a chair. A wooden chair. Everything big and heavy. Everything. Weight, hell. Fifty pounds more or less—" "They've got the power-plants for it." "Do you think he did that deliberately?" Major Winship asked. "I think he's trying to force us off. I think he hoped for the quake. Gagarin's built to take it, I'll say that. Looks like it, anyhow. You don't suppose they planned this all along? Even if they didn't, they sure got the jump on us again, didn't they? I told you what he told me?" "You told me," Capt. Wilkins said. After a moment, Major Winship said bitterly, "To hell with the Russian engineer." "If you've got all that power...." "That's the thing. That's the thing that gripes me, know what I mean? It's just insane to send up a heavy wooden desk. That's showing off. Like a little kid." "Maybe they don't make aluminum desks." "They've—got—aluminum. Half of everything on the whole planet is aluminum. You know they're just showing off." "Let me wire you up," Capt. Wilkins said. "We ought to report." "That's going to take awhile." "It's something to do while we wait." "I guess we ought to." Major Winship came down from the bunk and sat with his back toward the transmitter. Capt. Wilkins slewed the equipment around until the emergency jacks were accessible. He unearthed the appropriate cable and began unscrewing the exterior plate to the small transmitter-receiver set on Major Winship's back. Eventually, trailing wires, Major Winship was coupled into the network. "Okay?" "Okay," Major Winship gestured. They roused Earth. "This is Major Charles Winship, Commanding Officer, Freedom 19, the American moonbase." At this point, Major Winship observed for the first time that he was now on emergency air. He started to ask Capt. Wilkins to change his air bottle, but then he realized his communications were cut off. He reached over and rapped Capt. Wilkins' helmet. "This is the Cape. Come in, Major Winship." "Just a moment." "Is everything all right?" Major Winship was squirming nervously, obviously perturbed. "A-Okay," he said. "Just a moment." "What's wrong?" came the worried question. In the background, he heard someone say, "I think there's something wrong." Capt. Wilkins peered intently. Major Winship contorted his face in a savage grimace. Capt. Wilkins raised his eyebrows in alarm. They were face to face through their helmets, close together. Each face appeared monstrously large to the other. Major Winship made a strangling motion and reached for his throat. One arm tangled a cable and jerked the speaker jack loose. Major Winship could no longer hear the alarmed expressions from the Cape. The effort was not entirely subvocal, since he emitted a little gasping cry in involuntary realism. This, in the course of some 90 seconds, was transmitted to Earth. Capt. Wilkins's lips were desperately forming the word "Leak?" Air, Major Winship said silently. Leak? Bottle! Bottle! Bottle! It was a frog-like, unvocal expletive. Comprehension dawned. Capt. Wilkins nodded and started to turn away. Major Winship caught his arm and nodded his head toward the loose jack. Oh. Capt. Wilkins nodded and smiled. He reached across and plugged the speaker in again. "... Freedom 19! Hello, Freedom 19! Come in!" "We're here," Major Winship said. "All right? Are you all right?" "We're all right. A-Okay." Major Winship, mindful of the extent of his potential audience, took a deep breath. "Earlier this morning, the Soviet Union fired an underground atomic device for the ostensible purpose of investigating the composition of the lunar mass by means of seismic analysis of the resultant shock waves. This was done in spite of American warnings that such a disturbance might release accumulated stresses in the long undisturbed satellite, and was done in the face of vigorous American protests." Capt. Wilkins tapped his helmet and gestured for him to swivel around. The turn was uncomfortably tight and complicated by the restraining cables. Capt. Wilkins began replacement of the air bottle. "These protests have proved well founded," Major Winship continued. "Immediately following the detonation, Freedom 19 was called on to withstand a moderately severe shifting of the Lunar surface. No personnel were injured and there was no equipment damage." Capt. Wilkins tapped his shoulder to indicate the new air bottle was being inserted. Another tap indicated it was seated. Major Winship flicked the appropriate chest button and nodded in appreciation. "However," he continued, "we did experience a minor leak in the dome, which is presently being repaired." "The Soviet Union," came the reply, "has reported the disturbance and has tendered their official apology. You want it?" "It can wait until later. Send it by mail for all I care. Vacuum has destroyed our organic air reconditioner. We have approximately three weeks of emergency air. However, Base Gagarin reports no damage, so that, in the event we exhaust our air, we will be able to obtain the necessary replacement." The wait of a little better than three seconds for the response gave the conversation a tone of deliberation. A new voice came on. "We tried to contact you earlier, Major. We will be able to deliver replacements in about ten days." "I will forward a coded report on the occurrence," Major Winship said. "Let us hear from you again in ... about three hours. Is the leak repaired?" "The leak has not yet been repaired. Over and out." He nodded to Capt. Wilkins and leaned back. Methodically, Capt. Wilkins set about disconnecting the major from the transmitter. "Wow!" said Major Winship when he was once more in communication. "For a moment there, I thought...." "What?" Capt. Wilkins asked with interest. "I could see myself asking them to ask the Russians to ask Finogenov to get on the emergency channel to ask you to charge the air bottle. I never felt so ... idiotic is not quite strong enough ... there for a minute in my whole life. I didn't know how much emergency air was left, and I thought, my God, I'll never live this down. All the hams in the world listening, while I try to explain the situation. I could see the nickname being entered in my files: aka. The Airless Idiot. I tell you, that was rough." III Capt. Lawler and Lt. Chandler returned with the calking compound. It occupied the rear section of the land car. Lt. Chandler sat atop it. It was a fifty-five gallon drum. The airlock to Freedom 19 was open. "What is that ?" asked Major Winship, squinting out into the glaring sunlight. "That," said Capt. Lawler, "is the calking compound." "You're kidding," said Capt. Wilkins. "I am not kidding." Capt. Lawler and Lt. Chandler came inside. Capt. Wilkins mounted a bunk. "Why didn't you just borrow a cupful?" Major Winship said sarcastically. "It's this way," Lt. Chandler said. "They didn't have anything but 55-gallon drums of it." "Oh, my," said Capt. Wilkins. "I suppose it's a steel drum. Those things must weigh...." "Actually, I think you guys have got the general wrong," Capt. Lawler said. "He was out, himself, to greet us. I think he was really quite upset by the quake. Probably because his people had misfigured so bad." "He's too damned suspicious," Major Winship said. "You know and I know why they set that blast off. I tried to tell him. Hell. He looks at me like an emasculated owl and wants to know our ulterior motive in trying to prevent a purely scientific experiment, the results of which will be published in the technical press for the good of everybody. I'll bet!" "About this drum," Capt. Wilkins said. "Well, like I said, it's this way," Lt. Chandler resumed. "I told him we needed about a pint. Maybe a quart. But this stuff you have to mix up. He only had these drums. There's two parts to it, and you have to combine them in just the right proportion. He told me to take a little scale—" "A little scale?" asked Capt. Wilkins, rolling his eyes at the dome. "That's what I told him. We don't have any little scale." "Yeah," said Captain Lawler, "and he looked at us with that mute, surprised look, like everybody, everywhere has dozens of little scales." "Well, anyway," Lt. Chandler continued, "he told us just to mix up the whole fifty-five gallon drum. There's a little bucket of stuff that goes in, and it's measured just right. We can throw away what we don't need." "Somehow, that sounds like him," Major Winship said. "He had five or six of them." "Jesus!" said Capt. Wilkins. "That must be three thousand pounds of calking compound. Those people are insane." "The question is," Capt. Lawler said, "'How are we going to mix it?' It's supposed to be mixed thoroughly." They thought over the problem for a while. "That will be a man-sized job," Major Winship said. "Let's see, Charlie. Maybe not too bad," said Capt. Wilkins. "If I took the compressor motor, we could make up a shaft and ... let's see ... if we could...." It took the better part of an hour to rig up the electric mixer. Capt. Wilkins was profusely congratulated. "Now," Major Winship said, "we can either bring the drum inside or take the mixer out there." "We're going to have to bring the drum in," Capt. Wilkins said. "Well," said Capt. Lawler, "that will make it nice and cozy." It took the four of them to roll the drum inside, rocking it back and forth through the airlock. At that time, it was apparent the table was interposing itself. Lt. Chandler tried to dismantle the table. "Damn these suits," he said. "You've got it stuck between the bunk post." "I know that." "I don't think this is the way to do it," Major Winship said. "Let's back the drum out." Reluctantly, they backed the drum out and deposited it. With the aid of Capt. Lawler, Lt. Chandler got the table unstuck. They passed it over to Major Winship, who handed it out to Capt. Wilkins. Captain Wilkins carried it around the drum of calking compound and set it down. It rested uneasily on the uneven surface. "Now, let's go," said Major Winship. Eventually, they accomplished the moving. They wedged the drum between the main air-supply tank and the transmitter. They were all perspiring. "It's not the weight, it's the mass," said Capt. Wilkins brightly. "The hell it isn't the weight," said Lt. Chandler. "That's heavy." "With my reefer out," said Major Winship, "I'm the one it's rough on." He shook perspiration out of his eyes. "They should figure a way to get a mop in here, or a towel, or a sponge, or something. I'll bet you've forgotten how much sweat stings in the eyes." "It's the salt." "Speaking of salt. I wish I had some salt tablets," Major Winship said. "I've never sweat so much since basic." "Want to bet Finogenov hasn't got a bushel of them?" "No!" Major Winship snapped. With the drum of calking compound inside, both Capt. Lawler and Lt. Chandler retreated to the bunks. Capt. Wilkins maneuvered the mixing attachment. "I feel crowded," he said. "Cozy's the word." "Watch it! Watch it! You almost hit me in the face plate with that!" "Sorry." At length the mixer was in operation in the drum. "Works perfectly," said Capt. Wilkins proudly. "Now what, Skip? The instructions aren't in English." "You're supposed to dump the bucket of stuff in. Then clean the area thoroughly around the leak." "With what?" asked Major Winship. "Sandpaper, I guess." "With sandpaper?" Major Winship said, emptying the bucket of fluid into the drum. "We don't have any sandpaper." "It's been a long day," Capt. Wilkins said. "Mix it thoroughly," Lt. Chandler mused. "I guess that means let it mix for about ten minutes or so. Then you apply it. It sets for service in just a little bit, Finogenov said. An hour or so, maybe." "I hope this doesn't set on exposure to air." "No," Capt. Lawler said. "It sets by some kind of chemical action. General Finogenov wasn't sure of the English name for it. Some kind of plastic." "Let's come back to how we're going to clean around the leak," Major Winship said. "Say, I—" interrupted Capt. Wilkins. There was a trace of concern in his voice. "This is a hell of a time for this to occur to me. I just wasn't thinking, before. You don't suppose it's a room-temperature-curing epoxy resin, do you? " "Larry," said Major Winship, "I wouldn't know a room-temperature-curing epoxy resin from—" "Hey!" exclaimed Capt. Wilkins. "The mixer's stopped." He bent forward and touched the drum. He jerked back. "Ye Gods! that's hot! And it's harder than a rock! It is an epoxy! Let's get out of here." "Huh?" "Out! Out!" Major Winship, Lt. Chandler, and Capt. Lawler, recognizing the sense of urgency, simultaneously glanced at the drum. It was glowing cherry red. "Let's go!" Capt. Wilkins said. He and the Major reached the airlock at the same time and became temporarily engaged with each other. Movement was somewhat ungainly in the space suits under the best of conditions, and now, with the necessity for speed, was doubly so. The other two crashed into them from behind, and they spewed forth from the dome in a tangle of arms and legs. At the table, they separated, two going to the left, two to the right. The table remained untouched. When they halted, Capt. Wilkins said, "Get to one side, it may go off like shrapnel." They obeyed. "What—what—what?" Capt. Lawler stuttered. They were still separated, two on one side of the airlock, two on the other. "I'm going to try to look," Capt. Wilkins said. "Let me go." He lumbered directly away from the dome for a distance of about fifteen feet, then turned and positioned himself, some five feet behind the table, on a line of sight with the airlock. "I can see it," he said. "It's getting redder. It's ... it's ... melting, yes. Melting down at the bottom a little. Now it's falling over to one side and laying on the air tank. The air tank is getting red, too. I'm afraid ... it's weakening it.... Redder. Oh, oh." "What?" said Capt. Lawler. "Watch out! There. There! " Capt. Wilkins leaped from his position. He was still floating toward the ground when there was an incredibly bright flare from inside the dome, and a great, silent tongue of flame lashed through the airlock and rolled across the lunar surface. The table was sent tumbling. The flame was gone almost instantly. "There went the air," Capt. Lawler commented. "We got T-Trouble," said Lt. Chandler. | B. It took up too much room in an already crowded area. |
What is the year end FY2019 total amount of inventories for Best Buy? Answer in USD millions. Base your judgments on the information provided primarily in the balance sheet. | Evidence 0:
Table of Contents
Consolidated Balance Sheets
$ in millions, except per share and share amounts
February 2, 2019
February 3, 2018
Assets
Current assets
Cashandcashequivalents
$
1,980 $
1,101
Short-terminvestments
2,032
Receivables,net
1,015
1,049
Merchandiseinventories
5,409
5,209
Othercurrentassets
466
438
Totalcurrentassets
8,870
9,829
Property and equipment
Landandbuildings
637
623
Leaseholdimprovements
2,119
2,327
Fixturesandequipment
5,865
5,410
Propertyundercapitalandfinancingleases
579
340
Grosspropertyandequipment
9,200
8,700
Lessaccumulateddepreciation
6,690
6,279
Netpropertyandequipment
2,510
2,421
Goodwill
915
425
Other assets
606
374
Total assets
$
12,901 $
13,049
Liabilities and equity
Current liabilities
Accountspayable
$
5,257 $
4,873
Unredeemedgiftcardliabilities
290
385
Deferredrevenue
446
453
Accruedcompensationandrelatedexpenses
482
561
Accruedliabilities
982
1,001
Currentportionoflong-termdebt
56
544
Totalcurrentliabilities
7,513
7,817
Long-term liabilities
750
809
Long-term debt
1,332
811
Contingencies and commitments (Note 13)
Equity
BestBuyCo.,Inc.Shareholders'Equity
Preferredstock,$1.00parvalue:Authorized400,000shares;Issuedandoutstandingnone
Commonstock,$0.10parvalue:Authorized1.0billionshares;Issuedandoutstanding265,703,000and
282,988,000shares,respectively
27
28
Additionalpaid-incapital
Retainedearnings
2,985
3,270
Accumulatedothercomprehensiveincome
294
314
Totalequity
3,306
3,612
Total liabilities and equity
$
12,901 $
13,049
SeeNotestoConsolidatedFinancialStatements.
50 | $5409.00 |
What is "La-anago Yergis"?
A. It's a panacea that can cure any ailment.
B. It's medicine. It's a cure for "asteroid fever."
C. It's purified water.
D. It's a placebo. It's not real medicine.
| GRIFTERS' ASTEROID By H. L. GOLD Harvey and Joe were the slickest con-men ever to gyp a space-lane sucker. Or so they thought! Angus Johnson knew differently. He charged them five buckos for a glass of water—and got it! [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.] Characteristically, Harvey Ellsworth tried to maintain his dignity, though his parched tongue was almost hanging out. But Joe Mallon, with no dignity to maintain, lurched across the rubbish-strewn patch of land that had been termed a spaceport. When Harvey staggered pontifically into the battered metalloy saloon—the only one on Planetoid 42—his tall, gangling partner was already stumbling out, mouthing something incoherent. They met in the doorway, violently. "We're delirious!" Joe cried. "It's a mirage!" "What is?" asked Harvey through a mouthful of cotton. Joe reeled aside, and Harvey saw what had upset his partner. He stared, speechless for once. In their hectic voyages from planet to planet, the pair of panacea purveyors had encountered the usual strange life-forms. But never had they seen anything like the amazing creature in that colonial saloon. Paying no attention to them, it was carrying a case of liquor in two hands, six siphons in two others, and a broom and dustpan in the remaining pair. The bartender, a big man resembling the plumpish Harvey in build, was leaning negligently on the counter, ordering this impossible being to fill the partly-emptied bottles, squeeze fruit juice and sweep the floor, all of which the native did simultaneously. "Nonsense," Harvey croaked uncertainly. "We have seen enough queer things to know there are always more." He led the way inside. Through thirst-cracked lips he rasped: "Water—quick!" Without a word, the bartender reached under the counter, brought out two glasses of water. The interplanetary con-men drank noisily, asked for more, until they had drunk eight glasses. Meanwhile, the bartender had taken out eight jiggers and filled them with whiskey. Harvey and Joe were breathing hard from having gulped the water so fast, but they were beginning to revive. They noticed the bartender's impersonal eyes studying them shrewdly. "Strangers, eh?" he asked at last. "Solar salesmen, my colonial friend," Harvey answered in his usual lush manner. "We purvey that renowned Martian remedy, La-anago Yergis , the formula for which was recently discovered by ourselves in the ancient ruined city of La-anago. Medical science is unanimous in proclaiming this magic medicine the sole panacea in the entire history of therapeutics." "Yeah?" said the bartender disinterestedly, polishing the chaser glasses without washing them. "Where you heading?" "Out of Mars for Ganymede. Our condenser broke down, and we've gone without water for five ghastly days." "Got a mechanic around this dumping ground you call a port?" Joe asked. "We did. He came near starving and moved on to Titan. Ships don't land here unless they're in trouble." "Then where's the water lead-in? We'll fill up and push off." "Mayor takes care of that," replied the saloon owner. "If you gents're finished at the bar, your drinks'll be forty buckos." Harvey grinned puzzledly. "We didn't take any whiskey." "Might as well. Water's five buckos a glass. Liquor's free with every chaser." Harvey's eyes bulged. Joe gulped. "That—that's robbery!" the lanky man managed to get out in a thin quaver. The barkeeper shrugged. "When there ain't many customers, you gotta make more on each one. Besides—" "Besides nothing!" Joe roared, finding his voice again. "You dirty crook—robbing poor spacemen! You—" "You dirty crook!" Joe roared. "Robbing honest spacemen!" Harvey nudged him warningly. "Easy, my boy, easy." He turned to the bartender apologetically. "Don't mind my friend. His adrenal glands are sometimes overactive. You were going to say—?" The round face of the barkeeper had assumed an aggrieved expression. "Folks are always thinkin' the other feller's out to do 'em," he said, shaking his head. "Lemme explain about the water here. It's bitter as some kinds of sin before it's purified. Have to bring it in with buckets and make it sweet. That takes time and labor. Waddya think—I was chargin' feller critters for water just out of devilment? I charge because I gotta." "Friend," said Harvey, taking out a wallet and counting off eight five-bucko bills, "here is your money. What's fair is fair, and you have put a different complexion on what seemed at first to be an unconscionable interjection of a middleman between Nature and man's thirst." The saloon man removed his dirty apron and came around the bar. "If that's an apology, I accept it. Now the mayor'll discuss filling your tanks. That's me. I'm also justice of the peace, official recorder, fire chief...." "And chief of police, no doubt," said Harvey jocosely. "Nope. That's my son, Jed. Angus Johnson's my name. Folks here just call me Chief. I run this town, and run it right. How much water will you need?" Joe estimated quickly. "About seventy-five liters, if we go on half rations," he answered. He waited apprehensively. "Let's say ten buckos a liter," the mayor said. "On account of the quantity, I'm able to quote a bargain price. Shucks, boys, it hurts me more to charge for water than it does for you to pay. I just got to, that's all." The mayor gestured to the native, who shuffled out to the tanks with them. The planetoid man worked the pump while the mayor intently watched the crude level-gauge, crying "Stop!" when it registered the proper amount. Then Johnson rubbed his thumb on his index finger and wetted his lips expectantly. Harvey bravely counted off the bills. He asked: "But what are we to do about replenishing our battery fluid? Ten buckos a liter would be preposterous. We simply can't afford it." Johnson's response almost floored them. "Who said anything about charging you for battery water? You can have all you want for nothing. It's just the purified stuff that comes so high." After giving them directions that would take them to the free-water pool, the ponderous factotum of Planetoid 42 shook hands and headed back to the saloon. His six-armed assistant followed him inside. "Now do you see, my hot-tempered colleague?" said Harvey as he and Joe picked up buckets that hung on the tank. "Johnson, as I saw instantly, is the victim of a difficult environment, and must charge accordingly." "Just the same," Joe griped, "paying for water isn't something you can get used to in ten minutes." In the fragile forest, they soon came across a stream that sprang from the igneous soil and splashed into the small pond whose contents, according to the mayor, was theirs for the asking. They filled their buckets and hauled them to the ship, then returned for more. It was on the sixth trip that Joe caught a glimpse of Jupiter-shine on a bright surface off to the left. The figure, 750, with the bucko sign in front of it, was still doing acrobatics inside his skull and keeping a faint suspicion alive in him. So he called Harvey and they went to investigate. Among the skimpy ground-crawling vines, they saw a long slender mound that was unmistakably a buried pipe. "What's this doing here?" Harvey asked, puzzled. "I thought Johnson had to transport water in pails." "Wonder where it leads to," Joe said uneasily. "It leads to the saloon," said Harvey, his eyes rapidly tracing the pipe back toward the spaceport. "What I am concerned with is where it leads from ." Five minutes later, panting heavily from the unaccustomed exertion of scrambling through the tangle of planetorial undergrowth, they burst into the open—before a clear, sparkling pool. Mutely, Harvey pointed out a pipe-end jutting under the water. "I am growing suspicious," he said in a rigidly controlled voice. But Joe was already on his knees, scooping up a handful of water and tasting it. "Sweet!" he snarled. They rushed back to the first pool, where Joe again tasted a sample. His mouth went wry. "Bitter! He uses only one pool, the sweet one! The only thing that needs purifying around here is that blasted mayor's conscience." "The asteroidal Poobah has tricked us with a slick come-on," said Harvey slowly. His eyes grew cold. "Joseph, the good-natured artist in me has become a hard and merciless avenger. I shall not rest until we have had the best of this colonial con-man! Watch your cues from this point hence." Fists clenched, the two returned to the saloon. But at the door they stopped and their fists unclenched. "Thought you gents were leaving," the mayor called out, seeing them frozen in the doorway. "Glad you didn't. Now you can meet my son, Jed. Him and me are the whole Earthman population of Johnson City." "You don't need any more," said Harvey, dismayed. Johnson's eight-foot son, topped by a massive roof of sun-bleached hair and held up by a foundation that seemed immovable, had obviously been born and raised in low gravity. For any decent-sized world would have kept him down near the general dimensions of a man. He held out an acre of palm. Harvey studied it worriedly, put his own hand somewhere on it, swallowed as it closed, then breathed again when his fingers were released in five units instead of a single compressed one. "Pleased to meet you," piped a voice that had never known a dense atmosphere. The pursuit of vengeance, Harvey realized, had taken a quick and unpleasant turn. Something shrewd was called for.... "Joseph!" he exclaimed, looking at his partner in alarm. "Don't you feel well?" Even before the others could turn to him, Joe's practiced eyes were gently crossing. He sagged against the door frame, all his features drooping like a bloodhound's. "Bring him in here!" Johnson cried. "I mean, get him away! He's coming down with asteroid fever!" "Of course," replied Harvey calmly. "Any fool knows the first symptoms of the disease that once scourged the universe." "What do you mean, once ?" demanded Johnson. "I come down with it every year, and I ain't hankering to have it in an off-season. Get him out of here!" "In good time. He can't be moved immediately." "Then he'll be here for months!" Harvey helped Joe to the counter and lifted him up on it. The mayor and his gigantic offspring were cowering across the room, trying to breathe in tiny, uncontaminating gasps. "You'll find everything you want in the back room," Johnson said frantically, "sulfopyridine, mustard plasters, rubs, inhalers, suction cups—" "Relics of the past," Harvey stated. "One medication is all modern man requires to combat the dread menace, asteroid fever." "What's that?" asked the mayor without conviction. Instead of replying, Harvey hurried outside to the ungainly second-hand rocket ship in the center of the shabby spaceport. He returned within a few minutes, carrying a bottle. Joe was still stretched out on the bar, panting, his eyes slowly crossing and uncrossing. Harvey lifted the patient's head tenderly, put the bottle to his lips and tilted it until he was forced to drink. When Joe tried to pull away, Harvey was inexorable. He made his partner drink until most of the liquid was gone. Then he stepped back and waited for the inevitable result. Joe's performance was better than ever. He lay supine for several moments, his face twisted into an expression that seemed doomed to perpetual wryness. Slowly, however, he sat up and his features straightened out. "Are—are you all right?" asked the mayor anxiously. "Much better," said Joe in a weak voice. "Maybe you need another dose," Harvey suggested. Joe recoiled. "I'm fine now!" he cried, and sprang off the bar to prove it. Astonished, Johnson and his son drew closer. They searched Joe's face, and then the mayor timidly felt his pulse. "Well, I'll be hanged!" Johnson ejaculated. " La-anago Yergis never fails, my friend," Harvey explained. "By actual test, it conquers asteroid fever in from four to twenty-three minutes, depending on the severity of the attack. Luckily, we caught this one before it grew formidable." The mayor's eyes became clouded mirrors of an inward conflict. "If you don't charge too much," he said warily, "I might think of buying some." "We do not sell this unbelievable remedy," Harvey replied with dignity. "It sells itself." "'Course, I'd expect a considerable reduction if I bought a whole case," said Johnson. "That would be the smallest investment you could make, compared with the vast loss of time and strength the fever involves." "How much?" asked the mayor unhappily. "For you, since you have taken us in so hospitably, a mere five hundred buckos." Johnson did not actually stagger back, but he gave the impression of doing so. "F-four hundred," he offered. "Not a red cent less than four seventy-five," Harvey said flatly. "Make it four fifty," quavered Johnson. "I dislike haggling," said Harvey. The final price, however, was four hundred and sixty-nine buckos and fifty redsents. Magnanimously, Harvey added: "And we will include, gratis , an elegant bottle-opener, a superb product of Mercurian handicraftsmanship." Johnson stabbed out a warning finger. "No tricks now. I want a taste of that stuff. You're not switching some worthless junk on me." Harvey took a glass from the bar and poured him a generous sample. The mayor sniffed it, grimaced, then threw it down his gullet. The ensuing minute saw a grim battle between a man and his stomach, a battle which the man gradually won. "There ain't no words for that taste," he gulped when it was safe to talk again. "Medicine," Harvey propounded, "should taste like medicine." To Joe he said: "Come, my esteemed colleague. We must perform the sacred task to which we have dedicated ourselves." With Joe stumbling along behind, he left the saloon, crossed the clearing and entered the ship. As soon as they were inside, Joe dropped his murderous silence and cried: "What kind of a dirty trick was that, giving me poison instead of that snake oil?" "That was not poison," Harvey contradicted quietly. "It was La-anago Yergis extract, plus." "Plus what—arsenic?" "Now, Joseph! Consider my quandary when I came back here to manufacture our specific for all known ailments, with the intention of selling yonder asteroidal tin-horn a bill of medical goods—an entire case, mind you. Was I to mix the extract with the water for which we had been swindled to the tune of ten buckos a liter? Where would our profit have been, then? No; I had to use the bitter free water, of course." "But why use it on me?" Joe demanded furiously. Harvey looked reprovingly at his gangling partner. "Did Johnson ask to taste it, or did he not? One must look ahead, Joseph. I had to produce the same medicine that we will now manufacture. Thus, you were a guinea pig for a splendid cause." "Okay, okay," Joe said. "But you shoulda charged him more." "Joseph, I promise you that we shall get back every redsent of which that swindler cheated us, besides whatever other funds or valuables he possesses. We could not be content with less." "Well, we're starting all right," admitted Joe. "How about that thing with six arms? He looks like a valuable. Can't we grab him off?" Harvey stopped filling bottles and looked up pensively. "I have every hope of luring away the profitable monstrosity. Apparently you have also surmised the fortune we could make with him. At first I purpose to exhibit him on our interplanetary tours with our streamlined panacea; he would be a spectacular attraction for bucolic suckers. Later, a brief period of demonstrating his abilities on the audio-visiphone. Then our triumph—we shall sell him at a stupendous figure to the zoo!" Joe was still dazed by that monetary vista when he and Harvey carried the case of medicine to the saloon. The mayor had already cleared a place of honor in the cluttered back room, where he told them to put it down carefully. Then he took the elaborate bottle-opener Harvey gave him, reverently uncorked a bottle and sampled it. It must have been at least as good as the first; he gagged. "That's the stuff, all right," he said, swallowing hard. He counted out the money into Harvey's hand, at a moderate rate that precariously balanced between his pleasure at getting the fever remedy and his pain at paying for it. Then he glanced out to see the position of Jupiter, and asked: "You gents eaten yet? The restaurant's open now." Harvey and Joe looked at each other. They hadn't been thinking about food at all, but suddenly they realized that they were hungry. "It's only water we were short of," Harvey said apprehensively. "We've got rations back at the ship." " H-mph! " the mayor grunted. "Powdered concentrates. Compressed pap. Suit yourselves. We treat our stomachs better here. And you're welcome to our hospitality." "Your hospitality," said Harvey, "depends on the prices you charge." "Well, if that's what's worrying you, you can stop worrying," answered the mayor promptly. "What's more, the kind of dinner I serve here you can't get anywhere else for any price." Swiftly, Harvey conned the possibilities of being bilked again. He saw none. "Let's take a look at the menu, anyhow, Joe," he said guardedly. Johnson immediately fell into the role of "mine host." "Come right in, gents," he invited. "Right into the dining room." He seated them at a table, which a rope tied between posts made more or less private, though nobody else was in the saloon and there was little chance of company. Genius, the six-armed native, appeared from the dingy kitchen with two menus in one hand, two glasses of water in another, plus napkins, silverware, a pitcher, plates, saucers, cups, and their cocktails, which were on the house. Then he stood by for orders. Harvey and Joe studied the menu critically. The prices were phenomenally low. When they glanced up at Johnson in perplexity, he grinned, bowed and asked: "Everything satisfactory, gents?" "Quite," said Harvey. "We shall order." For an hour they were served amazing dishes, both fresh and canned, the culinary wealth of this planetoid and all the system. And the service was as extraordinary as the meal itself. With four hands, Genius played deftly upon a pair of mellow Venusian viotars , using his other two hands for waiting on the table. "We absolutely must purchase this incredible specimen," Harvey whispered excitedly when Johnson and the native were both in the kitchen, attending to the next course. "He would make any society hostess's season a riotous success, which should be worth a great sum to women like Mrs. van Schuyler-Morgan, merely for his hire." "Think of a fast one fast," Joe agreed. "You're right." "But I dislike having to revise my opinion of a man so often," complained Harvey. "I wish Johnson would stay either swindler or honest merchant. This dinner is worth as least twenty buckos, yet I estimate our check at a mere bucko twenty redsents." The mayor's appearance prevented them from continuing the discussion. "It's been a great honor, gents," he said. "Ain't often I have visitors, and I like the best, like you two gents." As if on cue, Genius came out and put the check down between Joe and Harvey. Harvey picked it up negligently, but his casual air vanished in a yelp of horror. "What the devil is this?" he shouted.—"How do you arrive at this fantastic, idiotic figure— three hundred and twenty-eight buckos !" Johnson didn't answer. Neither did Genius; he simply put on the table, not a fingerbowl, but a magnifying glass. With one of his thirty fingers he pointed politely to the bottom of the menu. Harvey focused on the microscopic print, and his face went pasty with rage. The minute note read: "Services and entertainment, 327 buckos 80 redsents." "You can go to hell!" Joe growled. "We won't pay it!" Johnson sighed ponderously. "I was afraid you'd act like that," he said with regret. He pulled a tin badge out of his rear pocket, pinned it on his vest, and twisted his holstered gun into view. "Afraid I'll have to ask the sheriff to take over." Johnson, the "sheriff," collected the money, and Johnson, the "restaurateur," pocketed it. Meanwhile, Harvey tipped Joe the sign to remain calm. "My friend," he said to the mayor, and his tones took on a schoolmasterish severity, "your long absence from Earth has perhaps made you forget those elements of human wisdom that have entered the folk-lore of your native planet. Such as, for example: 'It is folly to kill a goose that lays golden eggs,' and 'Penny wise is pound foolish.'" "I don't get the connection," objected Johnson. "Well, by obliging us to pay such a high price for your dinner, you put out of your reach the chance of profiting from a really substantial deal. My partner and I were prepared to make you a sizable offer for the peculiar creature you call Genius. But by reducing our funds the way you have—" "Who said I wanted to sell him?" the mayor interrupted. He rubbed his fingers together and asked disinterestedly: "What were you going to offer, anyhow?" "It doesn't matter any longer," Harvey said with elaborate carelessness. "Perhaps you wouldn't have accepted it, anyway." "That's right," Johnson came back emphatically. "But what would your offer have been which I would have turned down?" "Which one? The one we were going to make, or the one we can make now?" "Either one. It don't make no difference. Genius is too valuable to sell." "Oh, come now, Mr. Johnson. Don't tell me no amount of money would tempt you!" "Nope. But how much did you say?" "Ah, then you will consider releasing Genius!" "Well, I'll tell you something," said the mayor confidentially. "When you've got one thing, you've got one thing. But when you've got money, it's the same as having a lot of things. Because, if you've got money, you can buy this and that and this and that and—" "This and that," concluded Joe. "We'll give you five hundred buckos." "Now, gents!" Johnson remonstrated. "Why, six hundred would hardly—" "You haven't left us much money," Harvey put in. The mayor frowned. "All right, we'll split the difference. Make it five-fifty." Harvey was quick to pay out, for this was a genuine windfall. Then he stood up and admired the astonishing possession he had so inexpensively acquired. "I really hate to deprive you of this unique creature," he said to Johnson. "I should imagine you will be rather lonely, with only your filial mammoth to keep you company." "I sure will," Johnson confessed glumly. "I got pretty attached to Genius, and I'm going to miss him something awful." Harvey forcibly removed his eyes from the native, who was clearing off the table almost all at once. "My friend," he said, "we take your only solace, it is true, but in his place we can offer something no less amazing and instructive." The mayor's hand went protectively to his pocket. "What is it?" he asked with the suspicion of a man who has seen human nature at its worst and expects nothing better. "Joseph, get our most prized belonging from the communications room of the ship," Harvey instructed. To Johnson he explained: "You must see the wondrous instrument before its value can be appreciated. My partner will soon have it here for your astonishment." Joe's face grew as glum as Johnson's had been. "Aw, Harv," he protested, "do we have to sell it? And right when I thought we were getting the key!" "We must not be selfish, my boy," Harvey said nobly. "We have had our chance; now we must relinquish Fate to the hands of a man who might have more success than we. Go, Joseph. Bring it here." Unwillingly, Joe turned and shuffled out. On a larger and heavier world than Planetoid 42, Johnson's curiosity would probably have had weight and mass. He was bursting with questions, but he was obviously afraid they would cost him money. For his part, Harvey allowed that curiosity to grow like a Venusian amoeba until Joe came in, lugging a radio. "Is that what you were talking about?" the mayor snorted. "What makes you think I want a radio? I came here to get away from singers and political speech-makers." "Do not jump to hasty conclusions," Harvey cautioned. "Another word, and I shall refuse you the greatest opportunity any man has ever had, with the sole exceptions of Joseph, myself and the unfortunate inventor of this absolutely awe-inspiring device." "I ain't in the market for a radio," Johnson said stubbornly. Harvey nodded in relief. "We have attempted to repay our host, Joseph. He has spurned our generosity. We have now the chance to continue our study, which I am positive will soon reward us with the key to an enormous fortune." "Well, that's no plating off our bow," Joe grunted. "I'm glad he did turn it down. I hated to give it up after working on it for three whole years." He picked up the radio and began walking toward the door. "Now, hold on!" the mayor cried. "I ain't saying I'll buy, but what is it I'm turning down?" Joe returned and set the instrument down on the bar. His face sorrowful, Harvey fondly stroked the scarred plasticoid cabinet. "To make a long story, Mr. Johnson," he said, "Joseph and I were among the chosen few who knew the famous Doctor Dean intimately. Just before his tragic death, you will recall, Dean allegedly went insane." He banged his fist on the bar. "I have said it before, and I repeat again, that was a malicious lie, spread by the doctor's enemies to discredit his greatest invention—this fourth dimensional radio!" "This what?" Johnson blurted out. "In simple terms," clarified Harvey, "the ingenious doctor discovered that the yawning chasm between the dimensions could be bridged by energy of all quanta. There has never been any question that the inhabitants of the super-dimension would be far more civilized than ourselves. Consequently, the man who could tap their knowledge would find himself in possession of a powerful, undreamt-of science!" The mayor looked respectfully at the silent box on the bar. "And this thing gets broadcasts from the fourth dimension?" "It does, Mr. Johnson! Only charlatans like those who envied Doctor Dean's magnificent accomplishments could deny that fact." The mayor put his hands in his pockets, unswiveled one hip and stared thoughtfully at the battered cabinet. "Well, let's say it picks up fourth dimensional broadcasts," he conceded. "But how could you understand what they're saying? Folks up there wouldn't talk our language." Again Harvey smashed his fist down. "Do you dare to repeat the scurvy lie that broke Dean's spirit and drove him to suicide?" Johnson recoiled. "No—no, of course not . I mean, being up here, I naturally couldn't get all the details." "Naturally," Harvey agreed, mollified. "I'm sorry I lost my temper. But it is a matter of record that the doctor proved the broadcasts emanating from the super-dimension were in English! Why should that be so difficult to believe? Is it impossible that at one time there was communication between the dimensions, that the super-beings admired our language and adopted it in all its beauty, adding to it their own hyper-scientific trimmings?" "Why, I don't know," Johnson said in confusion. "For three years, Joseph and I lost sleep and hair, trying to detect the simple key that would translate the somewhat metamorphosed broadcasts into our primitive English. It eluded us. Even the doctor failed. But that was understandable; a sensitive soul like his could stand only so much. And the combination of ridicule and failure to solve the mystery caused him to take his own life." Johnson winced. "Is that what you want to unload on me?" "For a very good reason, sir. Patience is the virtue that will be rewarded with the key to these fourth dimensional broadcasts. A man who could devote his life to improving this lonely worldlet is obviously a person with unusual patience." "Yeah," the mayor said grudgingly, "I ain't exactly flighty." "Therefore, you are the man who could unravel the problem!" Johnson asked skeptically: "How about a sample first?" | D. It's a placebo. It's not real medicine. |
What is the narrator's profession?
A. astronaut
B. doctor
C. sailor
D. cook
| GOURMET By ALLEN KIM LANG [Transcriber's Note: This etext was produced from Galaxy Magazine April 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] This was the endless problem of all spaceship cooks: He had to feed the men tomorrow on what they had eaten today! Unable to get out to the ballgame and a long way off from the girls, men on ships think about, talk about, bitch about their food. It's true that Woman remains a topic of thoughtful study, but discussion can never replace practice in an art. Food, on the other hand, is a challenge shipmen face three times a day, so central to their thoughts that a history of sea-faring can be read from a commissary list. In the days when salt-sea sailors were charting islands and spearing seals, for example, the fo'c's'le hands called themselves Lobscousers, celebrating the liquid hash then prominent in the marine menu. The Limey sailor got the name of the anti-scorbutic citrus squeezed into his diet, a fruit known to us mariners of a more sophisticated age only as garnish for our groundside gin-and-tonic. And today we Marsmen are called Slimeheads, honoring in our title the Chlorella and Scenedesmus algae that, by filling up the spaces within, open the road to the larger Space without. Should any groundsman dispute the importance of belly-furniture in history—whether it be exterminating whales, or introducing syphilis to the Fiji Islanders, or settling the Australian littoral with cross-coves from Middlesex and Hampshire—he is referred to the hundred-and-first chapter of Moby Dick , a book spooled in the amusement tanks of all but the smallest spacers. I trust, however, that no Marsman will undertake to review this inventory of refreshment more than a week from groundfall. A catalogue of sides of beef and heads of Leyden cheese and ankers of good Geneva would prove heavy reading for a man condemned to snack on the Chlorella-spawn of cis-Martian space. The Pequod's crew ate wormy biscuit and salt beef. Nimitz's men won their war on canned pork and beans. The Triton made her underwater periplus of Earth with a galley stocked with frozen pizza and concentrated apple-juice. But then, when sailors left the seas for the skies, a decline set in. The first amenity of groundside existence to be abandoned was decent food. The earliest men into the vacuum swallowed protein squeezings from aluminum tubes, and were glad enough to drop back to the groundsman's diet of steak and fried potatoes. Long before I was a boy in Med School, itching to look at black sky through a view-port, galley science had fulfilled the disgusting exordium of Isaiah 36:12, to feed the Slimeheads for breakfast today what was day-before-yesterday's table-scraps and jakes-water. The Ship's Cook, the man who accomplishes the daily miracle of turning offal into eatables, is in many ways the most vital man aboard a spacer. He can make morale or foment a mutiny. His power is paramount. Slimeheads remember the H. M. S. Ajax fiasco, for example, in which a galleyman leveled his Chlorella tanks with heavy water from the ship's shielding. Four officers and twenty-one Other Ranks were rescued from the Ajax in deep space, half dead from deuterium poisoning. We think of the Benjo Maru incident, too, caused by a Ship's Cook who allowed his algaeal staff-of-life to become contaminated with a fast-growing Saccharomycodes yeast. The Japanese vessel staggered to her pad at Piano West after a twenty-week drunk: the alien yeast had got into the stomach of every man aboard, where it fermented each subsequent bite he ate to a superior grade of sake . And for a third footnote to the ancient observation, "God sends food, and the Devil sends cooks," Marsmen will recall what happened aboard my ship the Charles Partlow Sale . The Sale blasted off from Brady Station in the middle of August, due in at Piano West in early May. In no special hurry, we were taking the low-energy route to Mars, a pathway about as long in time as the human period of gestation. Our cargo consisted mostly of Tien-Shen fir seedlings and some tons of an arctic grass-seed—these to be planted in the maria to squeeze out the native blue bugberry vines. We had aboard the Registry minimum of six men and three officers. Ship's Surgeon was myself, Paul Vilanova. Our Captain was Willy Winkelmann, the hardest man in space and very likely the fattest. Ship's Cook was Robert Bailey. Cooking aboard a spacer is a job combining the more frustrating tensions of biochemistry, applied mycology, high-speed farming, dietetics and sewage engineering. It's the Cook's responsibility to see that each man aboard gets each day no less than five pounds of water, two pounds of oxygen, and one-and-a-half pounds of dry food. This isn't just a paragraph from the Spacer Union Contract. It's a statement of the least fuel a man can run on. Twelve tons of water, oxygen, and food would have filled the cargo compartments to bursting, and left a small ship like the C. P. Sale no reason to reach for Mars. By allowing a colony of Chlorella algae to work over our used air, water and other effluvia, though, three tons of metabolites would see us through from Brady Station to Piano West and back. Recycling was the answer. The molecule of carbohydrate, fat, protein or mineral that didn't feed the crew fed the algae. And the algae fed us. All waste was used to fertilize our liquid fields. Even the stubble from our 2,680 shaves and the clippings from our 666 haircuts en route and back would be fed into the Chlorella tanks. Human hair is rich in essential amino acids. The algae—dried by the Cook, bleached with methyl alcohol to kill the smell and make the residue more digestible, disguised and seasoned in a hundred ways—served as a sort of meat-and-potatoes that never quite wore out. Our air and water were equally immortal. Each molecule of oxygen would be conversant with the alveoli of every man aboard by the end of our trip. Every drop of water would have been intimate with the glomeruli of each kidney on the ship before we grounded in. Groundling politicians are right enough when they say that we spacers are a breed apart. We're the one race of men who can't afford the luxury of squeamishness. Though I'm signed aboard as Ship's Surgeon, I seldom lift a knife in space. My employment is more in the nature of TS-card-puncher extraordinary. My duties are to serve as wailing-wall, morale officer, guardian of the medicinal whiskey and frustrator of mutual murder. Generally the man aboard who'd serve as the most popular murder-victim is the Cook. This trip, the-man-you-love-to-hate was our Captain. If the Cook hadn't problems enough with the chemical and psychic duties of his office, Winkelmann supplied the want. Captain Willy Winkelmann was the sort of man who, if he had to go into space at all, had best do so alone. If the Prussians had a Marine Corps, Winkelmann would have done splendidly as Drill Instructor for their boot camp. His heart was a chip of helium ice, his voice dripped sarcastic acid. The planet Earth was hardly large enough to accommodate a wart as annoying as Willy Winkelmann. Cheek-by-jowl every day in a nacelle the size of a Pullman car, our Captain quickly established himself as a major social hemorrhoid. The Captain's particular patsy was, of course, young Bailey the Cook. It was Winkelmann who saw humorous possibilities in the entry, "Bailey, Robert," on Ship's Articles. He at once renamed our unfortunate shipmate "Belly-Robber." It was Winkelmann who discussed haut cuisine and the properties of the nobler wines while we munched our algaeburgers and sipped coffee that tasted of utility water. And it was Captain Willy Winkelmann who never referred to the ship's head by any other name than The Kitchen Cabinet. Bailey tried to feed us by groundside standards. He hid the taste of synthetic methionine—an essential amino acid not synthesized by Chlorella—by seasoning our algaeal repasts with pinches of oregano and thyme. He tinted the pale-green dollops of pressed Chlorella pink, textured the mass to the consistency of hamburger and toasted the slabs to a delicate brown in a forlorn attempt to make mock-meat. For dessert, he served a fudge compounded from the dextrose-paste of the carbohydrate recycler. The crew thanked him. The Captain did not. "Belly-Robber," he said, his tone icy as winter wind off the North Sea, "you had best cycle this mess through the tanks again. There is a pun in my home country: Mensch ist was er isst. It means, you are what you eat. I think you are impertinent to suggest I should become this Schweinerei you are feeding me." Captain Winkelmann blotted his chin with his napkin, heaved his bulk up from the table, and climbed up the ladder from the dining-cubby. "Doc, do you like Winkelmann?" the Cook asked me. "Not much," I said. "I suspect that the finest gift our Captain can give his mother is to be absent from her on Mother's Day. But we've got to live with him. He's a good man at driving a ship." "I wish he'd leave off driving this Cook," Bailey said. "The fat swine!" "His plumpness is an unwitting tribute to your cooking, Bailey," I said. "He eats well. We all do. I've dined aboard a lot of spacers in my time, and I'll testify that you set a table second to none." Bailey took a handful of dried Chlorella from a bin and fingered it. It was green, smelled of swamp, and looked appetizing as a bedsore. "This is what I have to work with," he said. He tossed the stuff back into its bin. "In Ohio, which is my home country, in the presence of ladies, we'd call such garbage Horse-Leavings." "You'll never make Winkelmann happy," I said. "Even the simultaneous death of all other human beings could hardly make him smile. Keep up the good work, though, and you'll keep our Captain fat." Bailey nodded from his one-man cloud of gloom. I got a bottle of rye from Medical Stores and offered him a therapeutic draught. The Cook waved my gift aside. "Not now, Doc," he said. "I'm thinking about tomorrow's menu." The product of Bailey's cerebrations was on the mess table at noon the next day. We were each served an individual head of lettuce, dressed with something very like vinegar and oil, spiced with tiny leaves of burnet. How Bailey had constructed those synthetic lettuces I can only guess: the hours spent preparing a green Chlorella paste, rolling and drying and shaping each artificial leaf, the fitting together of nine heads like crisp, three-dimensional jigsaw puzzles. The pièce de résistance was again a "hamburger steak;" but this time the algaeal mass that made it up was buried in a rich, meaty gravy that was only faintly green. The essence-of-steak used in these Chlorella cutlets had been sprinkled with a lavish hand. Garlic was richly in evidence. "It's so tender," the radioman joked, "that I can hardly believe it's really steak." Bailey stared across the dining-cubby toward Winkelmann, silently imploring the Captain's ratification of his masterpiece. The big man's pink cheeks bulged and jumped with his chewing. He swallowed. "Belly-Robber," Winkelmann said, "I had almost rather you served me this pond-scum raw than have it all mucked-up with synthetic onions and cycler-salt." "You seem able enough to choke down Bailey's chow, Captain," I said. I gazed at Winkelmann's form, bulbous from a lifetime of surfeit feeding. "Yes, I eat it," the Captain said, taking and talking through another bite. "But I eat only as a man in the desert will eat worms and grasshoppers, to stay alive." "Sir, what in heaven's name do you expect from me?" Bailey pleaded. "Only good food," Winkelmann mumbled through his mouthful of disguised algae. He tapped his head with a finger. "This—the brain that guides the ship—cannot be coaxed to work on hog-slop. You understand me, Belly-Robber?" Bailey, his hands fisted at his sides, nodded. "Yes, sir. But I really don't know what I can do to please you." "You are a spacer and a Ship's Cook, not a suburban Hausfrau with the vapors," Winkelmann said. "I do not expect from you hysterics, tantrums or weeping. Only—can you understand this, so simple?—food that will keep my belly content and my brain alive." "Yes, sir," Bailey said, his face a picture of that offense the British term Dumb Insolence. Winkelmann got up and climbed the ladder to the pilot-cubicle. I followed him. "Captain," I said, "you're driving Bailey too hard. You're asking him to make bricks without straw." Winkelmann regarded me with his pale-blue stare. "You think, Doctor, that my cruelty to the Belly-Robber is the biliousness of a middle-aged man?" "Frankly, I can't understand your attitude at all," I said. "You accuse me of driving a man to make bricks without straw," Winkelmann said. "Very well, Doctor. It is my belief that if the Pharaoh's taskmaster had had my firmness of purpose, the Children of Israel would have made bricks with stubble. Necessity, Doctor, is the mother of invention. I am Bailey's necessity. My unkindnesses make him uncomfortable, I doubt that not. But I am forcing him to experiment, to improvise, to widen the horizons of his ingenuity. He will learn somehow to bring good food from Chlorella tanks." "You're driving him too hard, Sir," I said. "He'll crack." "Bailey will have some fifty thousand dollars' salary waiting when we ground at Brady Station," Captain Winkelmann said. "So much money buys many discomforts. That will be all, Doctor Vilanova." "Crew morale on the ship...." I began. "That will be all, Doctor Vilanova," Captain Winkelmann repeated. Bailey grew more silent as we threaded our way along the elliptical path to Mars. Each meal he prepared was a fresh attempt to propitiate the appetite of our splenetic Captain. Each such offering was condemned by that heartless man. Bailey began to try avoiding the Captain at mealtimes, but was frustrated by Winkelmann's orders. "Convey my compliments to the Chef, please," the Captain would instruct one of the crew, "and ask him to step down here a moment." And the Cook would cheerlessly appear in the dining-cubby, to have his culinary genius acidly called in question again. I myself do not doubt that Bailey was the finest Cook ever to go into Hohmann orbit. His every meal established a higher benchmark in brilliant galleymanship. We were served, for instance, an ersatz hot turkey supreme. The cheese-sauce was almost believable, the Chlorella turkey-flesh was white and tender. Bailey served with this delicacy a grainy and delicious "cornbread," and had extracted from his algae a lipid butter-substitute that soaked into the hot "bread" with a genuinely dairy smell. "Splendid, Bailey," I said. "We are not amused," said Captain Winkelmann, accepting a second helping of the pseudo-turkey. "You are improving, Belly-Robber, but only arithmetically. Your first efforts were so hideous as to require a geometric progression of improving excellence to raise them to mere edibility. By the time we are halfway 'round the Sun, I trust you will have learned to cook with the competence of a freshman Home Economics student. That will be all, Bailey." The crew and my fellow-officers were amused by Winkelmann's riding of Bailey; they were in addition gratified that the battle between their Captain and their Cook served to feed them so well. Most spacers embark on an outward voyage somewhat plump, having eaten enough on their last few days aground to smuggle several hundred calories of fat and many memories of good food aboard with them. This trip, none of the men had lost weight during the first four months in space. Winkelmann, indeed, seemed to have gained. His uniform was taut over his plump backside, and he puffed a bit up the ladders. I was considering suggesting to our Captain that he curtail his diet for reasons of health, a bit of advice that would have stood unique in the annals of space medicine, when Winkelmann produced his supreme insult to our Cook. Each man aboard a spacer is allowed ten kilograms of personal effects besides his uniforms, these being considered Ship's Furnishing. As his rank and responsibility merit, the Captain is allowed double this ration. He may thus bring aboard with him some forty-five pounds of books, playing-cards, knitting-wool, whiskey or what have you to help him while away the hours between the planets. Bailey, I knew for a fact, had used up his weight-allowance in bringing aboard a case of spices: marjoram and mint, costmary, file powder, basil and allspice, and a dozen others. Captain Winkelmann was not a reader, and had brought no books. Cards interested him not at all, as card-playing implies a sociability alien to his nature. He never drank aboard ship. I had supposed that he'd exercised his option of returning his personal-effects weight allowance to the owners for the consideration of one hundred dollars a kilogram. To collect the maximum allowance, spacers have been known to come aboard their ship mother-naked. But this was not the case with Winkelmann. His personal-effects baggage, an unlabeled cardboard box, appeared under the table at noon mess some hundred days out from Piano West. Winkelmann rested his feet on the mysterious box as he sat to eat. "What disgusting form does the ship's garbage appear in today, Belly-Robber?" he asked the Cook. Bailey frowned, but kept his temper, an asceticism in which by now he'd had much practice. "I've been working on the problem of steak, Sir," he said. "I think I've whipped the taste; what was left was to get the texture steak-like. Do you understand, Sir?" "I understand," Winkelmann growled. "You intend that your latest mess should feel like steak to the mouth, and not like baby-food. Right?" "Yes, Sir," Bailey said. "Well, I squeezed the steak-substrate—Chlorella, of course, with all sorts of special seasonings—through a sieve, and blanched the strands in hot algaeal oil. Then I chopped those strands to bits and rolled them out. Voila! I had something very close in texture to the muscle-fibers of genuine meat." "Remarkable, Bailey," I said. "It rather throws me off my appetite to hear how you muddle about with our food," the Captain said, his jowls settling into an expression of distaste. "It's quite all right to eat lobster, for example, but I never cared to see the ugly beast boiled before my eyes. Detail spoils the meal." Bailey lifted the cover off the electric warming-pan at the center of the table and tenderly lifted a small "steak" onto each of our plates. "Try it," he urged the Captain. Captain Winkelmann sliced off a corner of his algaeal steak. The color was an excellent medium-rare, the odor was the rich smell of fresh-broiled beef. Winkelmann bit down, chewed, swallowed. "Not too bad, Belly-Robber," he said, nodding. Bailey grinned and bobbed his head, his hands folded before him in an ecstasy of pleasure. A kind word from the Captain bettered the ruffles-and-flourishes of a more reasonable man. "But it still needs something ... something," Winkelmann went on, slicing off another portion of the tasty Chlorella. "Aha! I have it!" "Yes, Sir?" Bailey asked. "This, Belly-Robber!" Winkelmann reached beneath the mess-table and ripped open his cardboard carton. He brought out a bottle and unscrewed the cap. "Ketchup," he said, splattering the red juice over Bailey's masterpiece. "The scarlet burial-shroud for the failures of Cooks." Lifting a hunk of the "steak," streaming ketchup, to his mouth, Winkelmann chewed. "Just the thing," he smiled. "Damn you!" Bailey shouted. Winkelmann's smile flicked off, and his blue eyes pierced the Cook. "... Sir," Bailey added. "That's better," Winkelmann said, and took another bite. He said meditatively, "Used with caution, and only by myself, I believe I have sufficient ketchup here to see me through to Mars. Please keep a bottle on the table for all my future meals, Belly-Robber." "But, Sir...." Bailey began. "You must realize, Belly-Robber, that a dyspeptic Captain is a threat to the welfare of his ship. Were I to continue eating your surrealistic slops for another hundred days, without the small consolation of this sauce I had the foresight to bring with me, I'd likely be in no condition to jet us safely down to the Piano West pad. Do you understand, Belly-Robber?" he demanded. "I understand that you're an ungrateful, impossible, square-headed, slave-driving...." "Watch your noun," Winkelmann cautioned the Cook. "Your adjectives are insubordinate; your noun might prove mutinous." "Captain, you've gone too far," I said. Bailey, his fists knotted, was scarlet, his chest heaving with emotion. "Doctor, I must point out to you that it ill behooves the Ship's Surgeon to side with the Cook against the Captain," Winkelmann said. "Sir, Bailey has tried hard to please you," I said. "The other officers and the men have been more than satisfied with his work." "That only suggests atrophy of their taste buds," Winkelmann said. "Doctor, you are excused. As are you, Belly-Robber," he added. Bailey and I climbed from the mess compartment together. I steered him to my quarters, where the medical supplies were stored. He sat on my bunk and exploded into weeping, banging his fists against the metal bulkhead. "You'll have that drink now," I said. "No, dammit!" he shouted. "Orders," I said. I poured us each some fifty cc's of rye. "This is therapy, Bailey," I told him. He poured the fiery stuff down his throat like water and silently held out his glass for a second. I provided it. After a few minutes Bailey's sobbing ceased. "Sorry, Doc," he said. "You've taken more pressure than most men would," I said. "Nothing to be ashamed of." "He's crazy. What sane man would expect me to dip Wiener schnitzel and sauerkraut and Backhahndl nach suddeutscher Art out of an algae tank? I've got nothing but microscopic weeds to cook for him! Worn-out molecules reclaimed from the head; packaged amino acid additives. And he expects meals that would take the blue ribbon at the annual banquet of the Friends of Escoffier!" "Yours is an ancient plaint, Bailey," I said. "You've worked your fingers to the bone, slaving over a hot stove, and you're not appreciated. But you're not married to Winkelmann, remember. A year from now you'll be home in Ohio, fifty grand richer, set to start that restaurant of yours and forget about our fat Flying Dutchman." "I hate him," Bailey said with the simplicity of true emotion. He reached for the bottle. I let him have it. Sometimes alcohol can be an apt confederate of vis medicatrix naturae , the healing power of nature. Half an hour later I strapped Bailey into his bunk to sleep it off. That therapeutic drunk seemed to be just what he'd needed. For morning mess the next day we had a broth remarkable in horribleness, a pottage or boiled Chlorella vulgaris that looked and tasted like the vomit of some bottom-feeding sea-beast. Bailey, red-eyed and a-tremble, made no apology, and stared at Winkelmann as though daring him to comment. The Captain lifted a spoonful of the disgusting stuff to his lips, smacked and said, "Belly-Robber, you're improving a little at last." Bailey nodded and smiled. "Thank you, Sir," he said. I smiled, too. Bailey had conquered himself. His psychic defenses were now strong enough to withstand the Captain's fiercest assaults of irony. Our food would likely be bad the rest of this trip, but that was a price I was willing to pay for seeing destroyed the Willy Winkelmann theory of forcing a Cook to make bricks without straw. The Captain had pushed too hard. He'd need that ketchup for the meals to come, I thought. Noon mess was nearly as awful as breakfast had been. The coffee tasted of salt, and went largely undrunk. The men in the mess compartment were vehement in their protests, blaming the Captain, in his absence, for the decline in culinary standards. Bailey seemed not to care. He served the algaeburgers with half a mind, and hurried back into his galley oblivious of the taunts of his crewmates. There being only three seats in the Sale's mess compartment, we ate our meals in three shifts. That evening, going down the ladder to supper, my nose was met with a spine-tingling barbecue tang, a smell to make a man think of gray charcoal glowing in a picnic brazier, of cicadas chirping and green grass underfoot, of the pop and hiss of canned beer being church-keyed. "He's done it, Doc!" one of the first-shift diners said. "It actually tastes of food!" "Then he's beat the Captain at his game," I said. "The Dutchman won't want to mess ketchup on these steaks," the crewman said. I sat, unfolded my napkin, and looked with hope to the electric warming-pan at the center of the table. Bailey served the three of us with the small "steaks." Each contained about a pound of dried Chlorella, I judged, teasing mine with my fork. But they were drenched in a gravy rich as the stuff grandma used to make in her black iron skillet, peppery and seasoned with courageous bits of garlic. I cut a bit from my steak and chewed it. Too tender, of course; there are limits to art. But the pond-scum taste was gone. Bailey appeared in the galley door. I gestured for him to join me. "You've done it, Bailey," I said. "Every Slimehead in orbit will thank you for this. This is actually good ." "Thanks, Doc," Bailey said. I smiled and took another bite. "You may not realize it, Bailey; but this is a victory for the Captain, too. He drove you to this triumph; you couldn't have done it without him." "You mean he was just whipping me on, trying to make me do better?" Bailey asked. "He was driving you to do the impossible," I said; "and you did it. Our Captain may be a hard man, Bailey; but he did know how to coax maximum performance out of his Ship's Cook." Bailey stood up. "Do you like Captain Winkelmann, Doctor?" he asked. I thought about his question a moment. Winkelmann was good at his job. He persuaded his men by foul means, true; but it was all for the good of the ship and his crew. "Do I like Captain Winkelmann?" I asked, spearing another piece of my artificial steak. "Bailey, I'm afraid I'll have to admit that I do." Bailey smiled and lifted a second steak from the warming-pan onto my plate. "Then have another piece," he said. | B. doctor |
What does the author view as the purpose of AI
A. To eliminate natural selection
B. To achieve ultimate convenience
C. To amplify social improvement
D. To mitigate climate threats
| AI: what's the worst that could happen? The Centre for the Future of Intelligence is seeking to investigate the implications of artificial intelligence for humanity, and make sure humans take advantage of the opportunities while dodging the risks. It launched at the University of Cambridge last October, and is a collaboration between four universities and colleges – Cambridge, Oxford, Imperial and Berkeley – backed with a 10-year, £10m grant from the Leverhulme Trust. Because no single discipline is ideally suited to this task, the centre emphasises the importance of interdisciplinary knowledge-sharing and collaboration. It is bringing together a diverse community of some of the world's best researchers, philosophers, psychologists, lawyers and computer scientists. Executive director of the centre is Stephen Cave, a writer, philosopher and former diplomat. Harry Armstrong, head of futures at Nesta, which publishes The Long + Short, spoke with Cave about the impact of AI. Their conversation has been edited. Harry Armstrong: Do you see the interdisciplinary nature of the centre as one of its key values and one of the key impacts you hope it will have on the field? Stephen Cave: Thinking about the impact of AI is not something that any one discipline owns or does in any very systematic way. So if academia is going to rise to the challenge and provide thought leadership on this hugely important issue, then we’re going to need to do it by breaking down current disciplinary boundaries and bringing people with very different expertise together. That means bringing together the technologists and the experts at developing these algorithms together with social scientists, philosophers, legal scholars and so forth. I think there are many areas of science where more interdisciplinary engagement would be valuable. Biotech’s another example. In that sense AI isn’t unique, but I think because thinking about AI is still in very early stages, we have an opportunity to shape the way in which we think about it, and build that community. We want to create a space where many different disciplines can come together and develop a shared language, learn from each other’s approaches, and hopefully very quickly move to be able to actually develop new ideas, new conclusions, together. But the first step is learning how to talk to each other. At a recent talk, Naomi Klein said that addressing the challenge of climate change could not have come at a worse time. The current dominant political and economic ideologies, along with growing isolationist sentiment, runs contrary to the bipartisan, collaborative approaches needed to solve global issues like climate change. Do you see the same issues hampering a global effort to respond to the challenges AI raises? Climate change suffers from the problem that the costs are not incurred in any direct way by the industrialists who own the technology and are profiting from it. With AI, that has been the case so far; although not on the same scale. There has been disruption but so far, compared to industrialisation, the impact has been fairly small. That will probably change. AI companies, and in particular the big tech companies, are very concerned that this won't go like climate change, but rather it will go like GMOs: that people will have a gut reaction to this technology as soon as the first great swathe of job losses take hold. People speculate that 50m jobs could be lost in the US if trucking is automated, which is conceivable within 10 years. You could imagine a populist US government therefore simply banning driverless cars. So I think there is anxiety in the tech industry that there could be a serious reaction against this technology at any point. And so my impression is that there is a feeling within these companies that these ethical and social implications need to be taken very seriously, now. And that a broad buy-in by society into some kind of vision of the future in which this technology plays a role is required, if a dangerous – or to them dangerous – counteraction is to be avoided. My personal experience working with these tech companies is that they are concerned for their businesses and genuinely want to do the right thing. Of course there are intellectual challenges and there is money to be made, but equally they are people who don't think when they get up in the morning that they're going to put people out of jobs or bring about the downfall of humanity. As the industry matures it's developing a sense of responsibility. So I think we've got a real opportunity, despite the general climate, and in some ways because of it. There's a great opportunity to bring industry on board to make sure the technology is developed in the right way. One of the dominant narratives around not only AI but technology and automation more generally is that we, as humans, are at the mercy of technological progress. If you try and push against this idea you can be labelled as being anti-progress and stuck in the past. But we do have a lot more control than we give ourselves credit for. For example, routineness and susceptibility to automation are not inevitable features of occupations, job design is hugely important. How do we design jobs? How do we create jobs that allow people to do the kind of work they want to do? There can be a bit of a conflict between being impacted by what's happening and having some sort of control over what we want to happen. Certainly, we encounter technological determinism a lot. And it's understandable. For us as individuals, of course it does feel like it always is happening and we just have to cope. No one individual can do much about it, other than adapt. But that's different when we consider ourselves at a level of a society, as a polis [city state], or as an international community. I think we can shape the way in which technology develops. We have various tools. In any given country, we have regulations. There's a possibility of international regulation. Technology is emerging from a certain legal, political, normative, cultural, and social framework. It's coming from a certain place. And it is shaped by all of those things. And I think the more we understand a technology's relationship with those things, and the more we then consciously try to shape those things, the more we are going to influence the technology. So, for example, developing a culture of responsible innovation. For example, a kind of Hippocratic oath for AI developers. These things are within the realms of what is feasible, and I think will help to shape the future. One of the problems with intervention, generally, is that we cannot control the course of events. We can attempt to, but we don't know how things are going to evolve. The reality is, societies are much too complex for us to be able to shape them in any very specific way, as plenty of ideologies and political movements have found to their cost. There are often unforeseen consequences that can derail a project. I think, nonetheless, there are things we can do. We can try to imagine how things might go very badly wrong, and then work hard to develop systems that will stop that from happening. We can also try collectively to imagine how things could go very right. The kind of society that we actually want to live in that uses this technology. And I'm sure that will be skewed in all sorts of ways, and we might imagine things that seem wonderful and actually have terrible by-products. This conversation cannot be in the hands of any one group. It oughtn't be in the hands of Silicon Valley billionaires alone. They've got their role to play, but this is a conversation we need to be having as widely as possible. The centre is developing some really interesting projects but perhaps one of the most interesting is the discussion of what intelligence might be. Could you go into a bit more detail about the kinds of questions you are trying to explore in this area? You mean kinds of intelligence? Yeah. I think this is very important because historically, we've had an overwhelming tendency to anthropomorphise. We define what intelligence is, historically, as being human-like. And then within that, being like certain humans. And it's taken a very long time for the academic community to accept that there could be such a thing as non-human intelligence at all. We know that crows, for example, who have had a completely different evolutionary history, or octopuses, who have an even more different evolutionary history, might have a kind of intelligence that's very different to ours. That in some ways rivals our own, and so forth. But luckily, we have got to that point in recent years of accepting that we are not the only form of intelligence. But now, AI is challenging that from a different direction. Just as we are accepting that the natural world offers this enormous range of different intelligences, we are at the same time inventing new intelligences that are radically different to humans. And I think, still, this anthropomorphic picture of the kind of humanoid android, the robot, dominates our idea of what AI is too much. And too many people, and the industry as well, talk about human-level artificial intelligence as a goal, or general AI, which basically means like a human. But actually what we're building is nothing like a human. When the first pocket calculator was made, it didn't do maths like a human. It was vastly better. It didn't make the occasional mistake. When we set about creating these artificial agents to solve these problems, because they have a completely different evolutionary history to humans, they solve problems in very different ways. And until now, people have been fairly shy about describing them as intelligent. Or rather, in the history of AIs, we think solving a particular problem would require intelligence. Then we solve it. And then that's no longer intelligence, because we've solved it. Chess is a good example. But the reality is, we are creating a whole new world of different artificial agents. And we need to understand that world. We need to understand all the different ways of being clever, if you like. How you can be extremely sophisticated at some particular rational process, and yet extremely bad at another one in a way that bears no relation to the way humans are on these axes. And this is important, partly because we need to expand our sense of what is intelligent, like we have done with the natural world. Because lots of things follow from saying something is intelligent. Historically, we have a long tradition in Western philosophy of saying those who are intelligent should rule. So if intelligence equates to power, then obviously we need to think about what we mean by intelligence. Who has it and who doesn't. Or how it equates to rights and responsibilities. It certainly is a very ambitious project to create the atlas of intelligence. There was a point I read in something you wrote on our ideas of intelligence that I thought was very interesting. We actually tend to think of intelligence at the societal level when we think about human ability, rather than at the individual level but in the end conflate the two. I think that's a very good point, when we think about our capabilities, we think about what we can achieve as a whole, not individually. But when we talk about AI, we tend to think about that individual piece of technology, or that individual system. So for example if we think about the internet of things and AI, we should discuss intelligence as something encompassed by the whole. Yeah, absolutely. Yes, right now, perhaps it is a product of our anthropomorphising bias. But there is a tendency to see a narrative of AI versus humanity, as if it's one or the other. And yet, obviously, there are risks in this technology long before it acquires any kind of manipulative agency. Robotic technology is dangerous. Or potentially dangerous. But at the same time, most of what we're using technology for is to enhance ourselves, to increase our capacities. And a lot of what AI is going to be doing is augmenting us – we're going to be working as teams, AI-human teams. Where do you think this AI-human conflict, or concept of a conflict, comes from? Do you think that's just a reflection of historical conversations we've had about automation, or do you think it is a deeper fear? I do think it comes both from some biases that might well be innate, such as anthropomorphism, or our human tendency to ascribe agency to other objects, particularly moving ones, is well-established and probably has sound evolutionary roots. If it moves, it's probably wise to start asking yourself questions like, "What is it? What might it want? Where might it be going? Might it be hungry? Do I look like food to it?" I think it makes sense, it's natural for us to think in terms of agency. And when we do, it's natural for us to project our own ways of being and acting. And we, as primates, are profoundly co-operative. But at the same time, we're competitive and murderous. We have a strong sense of in-group versus out-group, which is responsible for both a great deal of cooperation, within the in-group, but also terrible crimes. Murder, rape, pillage, genocide; and they're pointed at the out-group. And so I think it's very natural for us to see AIs in terms of agents. We anthropomorphise them as these kind of android robots. And then we think about, well, you know, are they part of our in-group, or are they some other group? If they're some other group, it's us against them. Who's going to win? Well, let's see. So I think that's very natural, I think that's very human. There is this long tradition, in Western culture in particular, with associating intelligence and dominance and power. It's interesting to speculate about how, and I wish I knew more about it, and I'd like to see more research on this, about how different cultures perceive AI. It's well known that Japan is very accepting of technology and robots, for example. You can think, well, we in the West have long been justifying power relations of a certain kind on the basis that we're 'cleverer'. That's why men get to vote and women don't, or whatever. In a culture where power is not based on intelligence but, say, on a caste system, which is purely hereditary, we’d build an AI, and it would just tune in, drop out, attain enlightenment, just sit in the corner. Or we beg it to come back and help us find enlightenment. It might be that we find a completely different narrative to the one that's dominant in the West. One of the projects the centre is running is looking into what kind of AI breakthroughs may come, when and what the social consequences could be. What do you think the future holds? What are your fears – what do you think could go right and wrong in the short, medium and long term? That's a big question. Certainly I don't lie awake at night worried that robots are going to knock the door down and come in with a machine gun. If the robots take over the world, it won't be by knocking the door down. At the moment, I think it's certainly as big a risk that we have a GMO moment, and there's a powerful reaction against the technology which prevents us from reaping the benefits, which are enormous. I think that's as big a risk as the risks from the technologies themselves. I think one worry that we haven't talked about is that we've become extremely dependent upon this technology. And that we essentially become deskilled. There's an extent to which the history of civilisation is the history of the domestication of the human species sort of by ourselves, and also by our technology, to some extent. And AI certainly allows for that to reach a whole new level. Just think about GPs with diagnostic tools. Even now, my GP consults the computer fairly regularly. But as diagnostic tools get better, what are they going to be doing other than just typing something into the computer and reading out what comes back? At which point, you might as well do away with the GP. But then, who does know about medicine? And so we do need to worry about deskilling and about becoming dependent. And it is entirely possible that you can imagine a society in which we're all sort of prosperous, in a sense. Our basic bodily needs are provided for, perhaps, in a way, to an extent that we've never before even dreamed of. Unprecedented in human history. And yet, we're stripped of any kind of meaningful work. We have no purpose. We're escaping to virtual reality. And then you could imagine all sorts of worrying countercultures or Luddite movements or what have you. I guess that's the kind of scenario that – I haven't sketched it terribly well – but that's the kind of thing that worries me more than missile-toting giant robots. As to utopian, yes, that's interesting. I certainly mentioned a couple of things. One thing that I hope is that this new technological revolution enables us to undo some of the damage of the last one. That's a very utopian thought and not terribly realistic, but we use fossil fuels so incredibly efficiently. The idea that driverless cars that are shared, basically a kind of shared service located off a Brownfield site does away with 95 per cent of all cars, freeing up a huge amount of space in the city to be greener, many fewer cars need to be produced, they would be on the road much less, there'd be fewer traffic jams. It's just one example, but the idea that we can live much more resource-efficiently, because we are living more intelligently through using these tools. And therefore can undo some of the damage of the last Industrial Revolution. That's my main utopian hope, I guess. Vintage toy robot image by josefkubes/Shutterstock This article was originally published on TheLong+Short. Read the original article. | C. To amplify social improvement |
Who is the bucolic person and what do they want from MUDDLE?
A. Hank Arapoulous. He wants Magnan to help him find men to pick his crops in time to pay back Croanie.
B. Hank Arapoulous. He wants Retief to help him find men to fight the Croanie invasion.
C. Hank Arapoulous. He wants Retief to help him find men to pick his crops in time to pay back Croanie.
D. Hank Arapoulous. He wants Retief to help him find able bodied college students to help out on Lovenbroy.
| CULTURAL EXCHANGE BY KEITH LAUMER It was a simple student exchange—but Retief gave them more of an education than they expected! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, September 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I Second Secretary Magnan took his green-lined cape and orange-feathered beret from the clothes tree. "I'm off now, Retief," he said. "I hope you'll manage the administrative routine during my absence without any unfortunate incidents." "That seems a modest enough hope," Retief said. "I'll try to live up to it." "I don't appreciate frivolity with reference to this Division," Magnan said testily. "When I first came here, the Manpower Utilization Directorate, Division of Libraries and Education was a shambles. I fancy I've made MUDDLE what it is today. Frankly, I question the wisdom of placing you in charge of such a sensitive desk, even for two weeks. But remember. Yours is purely a rubber-stamp function." "In that case, let's leave it to Miss Furkle. I'll take a couple of weeks off myself. With her poundage, she could bring plenty of pressure to bear." "I assume you jest, Retief," Magnan said sadly. "I should expect even you to appreciate that Bogan participation in the Exchange Program may be the first step toward sublimation of their aggressions into more cultivated channels." "I see they're sending two thousand students to d'Land," Retief said, glancing at the Memo for Record. "That's a sizable sublimation." Magnan nodded. "The Bogans have launched no less than four military campaigns in the last two decades. They're known as the Hoodlums of the Nicodemean Cluster. Now, perhaps, we shall see them breaking that precedent and entering into the cultural life of the Galaxy." "Breaking and entering," Retief said. "You may have something there. But I'm wondering what they'll study on d'Land. That's an industrial world of the poor but honest variety." "Academic details are the affair of the students and their professors," Magnan said. "Our function is merely to bring them together. See that you don't antagonize the Bogan representative. This will be an excellent opportunity for you to practice your diplomatic restraint—not your strong point, I'm sure you'll agree." A buzzer sounded. Retief punched a button. "What is it, Miss Furkle?" "That—bucolic person from Lovenbroy is here again." On the small desk screen, Miss Furkle's meaty features were compressed in disapproval. "This fellow's a confounded pest. I'll leave him to you, Retief," Magnan said. "Tell him something. Get rid of him. And remember: here at Corps HQ, all eyes are upon you." "If I'd thought of that, I'd have worn my other suit," Retief said. Magnan snorted and passed from view. Retief punched Miss Furkle's button. "Send the bucolic person in." A tall broad man with bronze skin and gray hair, wearing tight trousers of heavy cloth, a loose shirt open at the neck and a short jacket, stepped into the room. He had a bundle under his arm. He paused at sight of Retief, looked him over momentarily, then advanced and held out his hand. Retief took it. For a moment the two big men stood, face to face. The newcomer's jaw muscles knotted. Then he winced. Retief dropped his hand and motioned to a chair. "That's nice knuckle work, mister," the stranger said, massaging his hand. "First time anybody ever did that to me. My fault though. I started it, I guess." He grinned and sat down. "What can I do for you?" Retief said. "You work for this Culture bunch, do you? Funny. I thought they were all ribbon-counter boys. Never mind. I'm Hank Arapoulous. I'm a farmer. What I wanted to see you about was—" He shifted in his chair. "Well, out on Lovenbroy we've got a serious problem. The wine crop is just about ready. We start picking in another two, three months. Now I don't know if you're familiar with the Bacchus vines we grow...?" "No," Retief said. "Have a cigar?" He pushed a box across the desk. Arapoulous took one. "Bacchus vines are an unusual crop," he said, puffing the cigar alight. "Only mature every twelve years. In between, the vines don't need a lot of attention, so our time's mostly our own. We like to farm, though. Spend a lot of time developing new forms. Apples the size of a melon—and sweet—" "Sounds very pleasant," Retief said. "Where does the Libraries and Education Division come in?" Arapoulous leaned forward. "We go in pretty heavy for the arts. Folks can't spend all their time hybridizing plants. We've turned all the land area we've got into parks and farms. Course, we left some sizable forest areas for hunting and such. Lovenbroy's a nice place, Mr. Retief." "It sounds like it, Mr. Arapoulous. Just what—" "Call me Hank. We've got long seasons back home. Five of 'em. Our year's about eighteen Terry months. Cold as hell in winter; eccentric orbit, you know. Blue-black sky, stars visible all day. We do mostly painting and sculpture in the winter. Then Spring; still plenty cold. Lots of skiing, bob-sledding, ice skating; and it's the season for woodworkers. Our furniture—" "I've seen some of your furniture," Retief said. "Beautiful work." Arapoulous nodded. "All local timbers too. Lots of metals in our soil and those sulphates give the woods some color, I'll tell you. Then comes the Monsoon. Rain—it comes down in sheets. But the sun's getting closer. Shines all the time. Ever seen it pouring rain in the sunshine? That's the music-writing season. Then summer. Summer's hot. We stay inside in the daytime and have beach parties all night. Lots of beach on Lovenbroy; we're mostly islands. That's the drama and symphony time. The theatres are set up on the sand, or anchored off-shore. You have the music and the surf and the bonfires and stars—we're close to the center of a globular cluster, you know...." "You say it's time now for the wine crop?" "That's right. Autumn's our harvest season. Most years we have just the ordinary crops. Fruit, grain, that kind of thing; getting it in doesn't take long. We spend most of the time on architecture, getting new places ready for the winter or remodeling the older ones. We spend a lot of time in our houses. We like to have them comfortable. But this year's different. This is Wine Year." Arapoulous puffed on his cigar, looked worriedly at Retief. "Our wine crop is our big money crop," he said. "We make enough to keep us going. But this year...." "The crop isn't panning out?" "Oh, the crop's fine. One of the best I can remember. Course, I'm only twenty-eight; I can't remember but two other harvests. The problem's not the crop." "Have you lost your markets? That sounds like a matter for the Commercial—" "Lost our markets? Mister, nobody that ever tasted our wines ever settled for anything else!" "It sounds like I've been missing something," said Retief. "I'll have to try them some time." Arapoulous put his bundle on the desk, pulled off the wrappings. "No time like the present," he said. Retief looked at the two squat bottles, one green, one amber, both dusty, with faded labels, and blackened corks secured by wire. "Drinking on duty is frowned on in the Corps, Mr. Arapoulous," he said. "This isn't drinking . It's just wine." Arapoulous pulled the wire retainer loose, thumbed the cork. It rose slowly, then popped in the air. Arapoulous caught it. Aromatic fumes wafted from the bottle. "Besides, my feelings would be hurt if you didn't join me." He winked. Retief took two thin-walled glasses from a table beside the desk. "Come to think of it, we also have to be careful about violating quaint native customs." Arapoulous filled the glasses. Retief picked one up, sniffed the deep rust-colored fluid, tasted it, then took a healthy swallow. He looked at Arapoulous thoughtfully. "Hmmm. It tastes like salted pecans, with an undercurrent of crusted port." "Don't try to describe it, Mr. Retief," Arapoulous said. He took a mouthful of wine, swished it around his teeth, swallowed. "It's Bacchus wine, that's all. Nothing like it in the Galaxy." He pushed the second bottle toward Retief. "The custom back home is to alternate red wine and black." Retief put aside his cigar, pulled the wires loose, nudged the cork, caught it as it popped up. "Bad luck if you miss the cork," Arapoulous said, nodding. "You probably never heard about the trouble we had on Lovenbroy a few years back?" "Can't say that I did, Hank." Retief poured the black wine into two fresh glasses. "Here's to the harvest." "We've got plenty of minerals on Lovenbroy," Arapoulous said, swallowing wine. "But we don't plan to wreck the landscape mining 'em. We like to farm. About ten years back some neighbors of ours landed a force. They figured they knew better what to do with our minerals than we did. Wanted to strip-mine, smelt ore. We convinced 'em otherwise. But it took a year, and we lost a lot of men." "That's too bad," Retief said. "I'd say this one tastes more like roast beef and popcorn over a Riesling base." "It put us in a bad spot," Arapoulous went on. "We had to borrow money from a world called Croanie. Mortgaged our crops. Had to start exporting art work too. Plenty of buyers, but it's not the same when you're doing it for strangers." "Say, this business of alternating drinks is the real McCoy," Retief said. "What's the problem? Croanie about to foreclose?" "Well, the loan's due. The wine crop would put us in the clear. But we need harvest hands. Picking Bacchus grapes isn't a job you can turn over to machinery—and anyway we wouldn't if we could. Vintage season is the high point of living on Lovenbroy. Everybody joins in. First, there's the picking in the fields. Miles and miles of vineyards covering the mountain sides, and crowding the river banks, with gardens here and there. Big vines, eight feet high, loaded with fruit, and deep grass growing between. The wine-carriers keep on the run, bringing wine to the pickers. There's prizes for the biggest day's output, bets on who can fill the most baskets in an hour.... The sun's high and bright, and it's just cool enough to give you plenty of energy. Come nightfall, the tables are set up in the garden plots, and the feast is laid on: roast turkeys, beef, hams, all kinds of fowl. Big salads. Plenty of fruit. Fresh-baked bread ... and wine, plenty of wine. The cooking's done by a different crew each night in each garden, and there's prizes for the best crews. "Then the wine-making. We still tramp out the vintage. That's mostly for the young folks but anybody's welcome. That's when things start to get loosened up. Matter of fact, pretty near half our young-uns are born after a vintage. All bets are off then. It keeps a fellow on his toes though. Ever tried to hold onto a gal wearing nothing but a layer of grape juice?" "Never did," Retief said. "You say most of the children are born after a vintage. That would make them only twelve years old by the time—" "Oh, that's Lovenbroy years; they'd be eighteen, Terry reckoning." "I was thinking you looked a little mature for twenty-eight," Retief said. "Forty-two, Terry years," Arapoulous said. "But this year it looks bad. We've got a bumper crop—and we're short-handed. If we don't get a big vintage, Croanie steps in. Lord knows what they'll do to the land. Then next vintage time, with them holding half our grape acreage—" "You hocked the vineyards?" "Yep. Pretty dumb, huh? But we figured twelve years was a long time." "On the whole," Retief said, "I think I prefer the black. But the red is hard to beat...." "What we figured was, maybe you Culture boys could help us out. A loan to see us through the vintage, enough to hire extra hands. Then we'd repay it in sculpture, painting, furniture—" "Sorry, Hank. All we do here is work out itineraries for traveling side-shows, that kind of thing. Now, if you needed a troop of Groaci nose-flute players—" "Can they pick grapes?" "Nope. Anyway, they can't stand the daylight. Have you talked this over with the Labor Office?" "Sure did. They said they'd fix us up with all the electronics specialists and computer programmers we wanted—but no field hands. Said it was what they classified as menial drudgery; you'd have thought I was trying to buy slaves." The buzzer sounded. Miss Furkle's features appeared on the desk screen. "You're due at the Intergroup Council in five minutes," she said. "Then afterwards, there are the Bogan students to meet." "Thanks." Retief finished his glass, stood. "I have to run, Hank," he said. "Let me think this over. Maybe I can come up with something. Check with me day after tomorrow. And you'd better leave the bottles here. Cultural exhibits, you know." II As the council meeting broke up, Retief caught the eye of a colleague across the table. "Mr. Whaffle, you mentioned a shipment going to a place called Croanie. What are they getting?" Whaffle blinked. "You're the fellow who's filling in for Magnan, over at MUDDLE," he said. "Properly speaking, equipment grants are the sole concern of the Motorized Equipment Depot, Division of Loans and Exchanges." He pursed his lips. "However, I suppose there's no harm in telling you. They'll be receiving heavy mining equipment." "Drill rigs, that sort of thing?" "Strip mining gear." Whaffle took a slip of paper from a breast pocket, blinked at it. "Bolo Model WV/1 tractors, to be specific. Why is MUDDLE interested in MEDDLE's activities?" "Forgive my curiosity, Mr. Whaffle. It's just that Croanie cropped up earlier today. It seems she holds a mortgage on some vineyards over on—" "That's not MEDDLE's affair, sir," Whaffle cut in. "I have sufficient problems as Chief of MEDDLE without probing into MUDDLE'S business." "Speaking of tractors," another man put in, "we over at the Special Committee for Rehabilitation and Overhaul of Under-developed Nations' General Economies have been trying for months to get a request for mining equipment for d'Land through MEDDLE—" "SCROUNGE was late on the scene," Whaffle said. "First come, first served. That's our policy at MEDDLE. Good day, gentlemen." He strode off, briefcase under his arm. "That's the trouble with peaceful worlds," the SCROUNGE committeeman said. "Boge is a troublemaker, so every agency in the Corps is out to pacify her. While my chance to make a record—that is, assist peace-loving d'Land—comes to naught." He shook his head. "What kind of university do they have on d'Land?" asked Retief. "We're sending them two thousand exchange students. It must be quite an institution." "University? D'Land has one under-endowed technical college." "Will all the exchange students be studying at the Technical College?" "Two thousand students? Hah! Two hundred students would overtax the facilities of the college." "I wonder if the Bogans know that?" "The Bogans? Why, most of d'Land's difficulties are due to the unwise trade agreement she entered into with Boge. Two thousand students indeed!" He snorted and walked away. Retief stopped by the office to pick up a short cape, then rode the elevator to the roof of the 230-story Corps HQ building and hailed a cab to the port. The Bogan students had arrived early. Retief saw them lined up on the ramp waiting to go through customs. It would be half an hour before they were cleared through. He turned into the bar and ordered a beer. A tall young fellow on the next stool raised his glass. "Happy days," he said. "And nights to match." "You said it." He gulped half his beer. "My name's Karsh. Mr. Karsh. Yep, Mr. Karsh. Boy, this is a drag, sitting around this place waiting...." "You meeting somebody?" "Yeah. Bunch of babies. Kids. How they expect—Never mind. Have one on me." "Thanks. You a Scoutmaster?" "I'll tell you what I am. I'm a cradle-robber. You know—" he turned to Retief—"not one of those kids is over eighteen." He hiccupped. "Students, you know. Never saw a student with a beard, did you?" "Lots of times. You're meeting the students, are you?" The young fellow blinked at Retief. "Oh, you know about it, huh?" "I represent MUDDLE." Karsh finished his beer, ordered another. "I came on ahead. Sort of an advance guard for the kids. I trained 'em myself. Treated it like a game, but they can handle a CSU. Don't know how they'll act under pressure. If I had my old platoon—" He looked at his beer glass, pushed it back. "Had enough," he said. "So long, friend. Or are you coming along?" Retief nodded. "Might as well." At the exit to the Customs enclosure, Retief watched as the first of the Bogan students came through, caught sight of Karsh and snapped to attention, his chest out. "Drop that, mister," Karsh snapped. "Is that any way for a student to act?" The youth, a round-faced lad with broad shoulders, grinned. "Heck, no," he said. "Say, uh, Mr. Karsh, are we gonna get to go to town? We fellas were thinking—" "You were, hah? You act like a bunch of school kids! I mean ... no! Now line up!" "We have quarters ready for the students," Retief said. "If you'd like to bring them around to the west side, I have a couple of copters laid on." "Thanks," said Karsh. "They'll stay here until take-off time. Can't have the little dears wandering around loose. Might get ideas about going over the hill." He hiccupped. "I mean they might play hookey." "We've scheduled your re-embarkation for noon tomorrow. That's a long wait. MUDDLE's arranged theater tickets and a dinner." "Sorry," Karsh said. "As soon as the baggage gets here, we're off." He hiccupped again. "Can't travel without our baggage, y'know." "Suit yourself," Retief said. "Where's the baggage now?" "Coming in aboard a Croanie lighter." "Maybe you'd like to arrange for a meal for the students here." "Sure," Karsh said. "That's a good idea. Why don't you join us?" Karsh winked. "And bring a few beers." "Not this time," Retief said. He watched the students, still emerging from Customs. "They seem to be all boys," he commented. "No female students?" "Maybe later," Karsh said. "You know, after we see how the first bunch is received." Back at the MUDDLE office, Retief buzzed Miss Furkle. "Do you know the name of the institution these Bogan students are bound for?" "Why, the University at d'Land, of course." "Would that be the Technical College?" Miss Furkle's mouth puckered. "I'm sure I've never pried into these details." "Where does doing your job stop and prying begin, Miss Furkle?" Retief said. "Personally, I'm curious as to just what it is these students are travelling so far to study—at Corps expense." "Mr. Magnan never—" "For the present. Miss Furkle, Mr. Magnan is vacationing. That leaves me with the question of two thousand young male students headed for a world with no classrooms for them ... a world in need of tractors. But the tractors are on their way to Croanie, a world under obligation to Boge. And Croanie holds a mortgage on the best grape acreage on Lovenbroy." "Well!" Miss Furkle snapped, small eyes glaring under unplucked brows. "I hope you're not questioning Mr. Magnan's wisdom!" "About Mr. Magnan's wisdom there can be no question," Retief said. "But never mind. I'd like you to look up an item for me. How many tractors will Croanie be getting under the MEDDLE program?" "Why, that's entirely MEDDLE business," Miss Furkle said. "Mr. Magnan always—" "I'm sure he did. Let me know about the tractors as soon as you can." Miss Furkle sniffed and disappeared from the screen. Retief left the office, descended forty-one stories, followed a corridor to the Corps Library. In the stacks he thumbed through catalogues, pored over indices. "Can I help you?" someone chirped. A tiny librarian stood at his elbow. "Thank you, ma'am," Retief said. "I'm looking for information on a mining rig. A Bolo model WV tractor." "You won't find it in the industrial section," the librarian said. "Come along." Retief followed her along the stacks to a well-lit section lettered ARMAMENTS. She took a tape from the shelf, plugged it into the viewer, flipped through and stopped at a squat armored vehicle. "That's the model WV," she said. "It's what is known as a continental siege unit. It carries four men, with a half-megaton/second firepower." "There must be an error somewhere," Retief said. "The Bolo model I want is a tractor. Model WV M-1—" "Oh, the modification was the addition of a bulldozer blade for demolition work. That must be what confused you." "Probably—among other things. Thank you." Miss Furkle was waiting at the office. "I have the information you wanted," she said. "I've had it for over ten minutes. I was under the impression you needed it urgently, and I went to great lengths—" "Sure," Retief said. "Shoot. How many tractors?" "Five hundred." "Are you sure?" Miss Furkle's chins quivered. "Well! If you feel I'm incompetent—" "Just questioning the possibility of a mistake, Miss Furkle. Five hundred tractors is a lot of equipment." "Was there anything further?" Miss Furkle inquired frigidly. "I sincerely hope not," Retief said. III Leaning back in Magnan's padded chair with power swivel and hip-u-matic concontour, Retief leafed through a folder labelled "CERP 7-602-Ba; CROANIE (general)." He paused at a page headed Industry. Still reading, he opened the desk drawer, took out the two bottles of Bacchus wine and two glasses. He poured an inch of wine into each and sipped the black wine meditatively. It would be a pity, he reflected, if anything should interfere with the production of such vintages.... Half an hour later he laid the folder aside, keyed the phone and put through a call to the Croanie Legation. He asked for the Commercial Attache. "Retief here, Corps HQ," he said airily. "About the MEDDLE shipment, the tractors. I'm wondering if there's been a slip up. My records show we're shipping five hundred units...." "That's correct. Five hundred." Retief waited. "Ah ... are you there, Retief?" "I'm still here. And I'm still wondering about the five hundred tractors." "It's perfectly in order. I thought it was all settled. Mr. Whaffle—" "One unit would require a good-sized plant to handle its output," Retief said. "Now Croanie subsists on her fisheries. She has perhaps half a dozen pint-sized processing plants. Maybe, in a bind, they could handle the ore ten WV's could scrape up ... if Croanie had any ore. It doesn't. By the way, isn't a WV a poor choice as a mining outfit? I should think—" "See here, Retief! Why all this interest in a few surplus tractors? And in any event, what business is it of yours how we plan to use the equipment? That's an internal affair of my government. Mr. Whaffle—" "I'm not Mr. Whaffle. What are you going to do with the other four hundred and ninety tractors?" "I understood the grant was to be with no strings attached!" "I know it's bad manners to ask questions. It's an old diplomatic tradition that any time you can get anybody to accept anything as a gift, you've scored points in the game. But if Croanie has some scheme cooking—" "Nothing like that, Retief. It's a mere business transaction." "What kind of business do you do with a Bolo WV? With or without a blade attached, it's what's known as a continental siege unit." "Great Heavens, Retief! Don't jump to conclusions! Would you have us branded as warmongers? Frankly—is this a closed line?" "Certainly. You may speak freely." "The tractors are for transshipment. We've gotten ourselves into a difficult situation, balance-of-payments-wise. This is an accommodation to a group with which we have rather strong business ties." "I understand you hold a mortgage on the best land on Lovenbroy," Retief said. "Any connection?" "Why ... ah ... no. Of course not, ha ha." "Who gets the tractors eventually?" "Retief, this is unwarranted interference!" "Who gets them?" "They happen to be going to Lovenbroy. But I scarcely see—" "And who's the friend you're helping out with an unauthorized transshipment of grant material?" "Why ... ah ... I've been working with a Mr. Gulver, a Bogan representative." "And when will they be shipped?" "Why, they went out a week ago. They'll be half way there by now. But look here, Retief, this isn't what you're thinking!" "How do you know what I'm thinking? I don't know myself." Retief rang off, buzzed the secretary. "Miss Furkle, I'd like to be notified immediately of any new applications that might come in from the Bogan Consulate for placement of students." "Well, it happens, by coincidence, that I have an application here now. Mr. Gulver of the Consulate brought it in." "Is Mr. Gulver in the office? I'd like to see him." "I'll ask him if he has time." "Great. Thanks." It was half a minute before a thick-necked red-faced man in a tight hat walked in. He wore an old-fashioned suit, a drab shirt, shiny shoes with round toes and an ill-tempered expression. "What is it you wish?" he barked. "I understood in my discussions with the other ... ah ... civilian there'd be no further need for these irritating conferences." "I've just learned you're placing more students abroad, Mr. Gulver. How many this time?" "Two thousand." "And where will they be going?" "Croanie. It's all in the application form I've handed in. Your job is to provide transportation." "Will there be any other students embarking this season?" "Why ... perhaps. That's Boge's business." Gulver looked at Retief with pursed lips. "As a matter of fact, we had in mind dispatching another two thousand to Featherweight." "Another under-populated world—and in the same cluster, I believe," Retief said. "Your people must be unusually interested in that region of space." "If that's all you wanted to know, I'll be on my way. I have matters of importance to see to." After Gulver left, Retief called Miss Furkle in. "I'd like to have a break-out of all the student movements that have been planned under the present program," he said. "And see if you can get a summary of what MEDDLE has been shipping lately." Miss Furkle compressed her lips. "If Mr. Magnan were here, I'm sure he wouldn't dream of interfering in the work of other departments. I ... overheard your conversation with the gentleman from the Croanie Legation—" "The lists, Miss Furkle." "I'm not accustomed," Miss Furkle said, "to intruding in matters outside our interest cluster." "That's worse than listening in on phone conversations, eh? But never mind. I need the information, Miss Furkle." "Loyalty to my Chief—" "Loyalty to your pay-check should send you scuttling for the material I've asked for," Retief said. "I'm taking full responsibility. Now scat." The buzzer sounded. Retief flipped a key. "MUDDLE, Retief speaking...." Arapoulous's brown face appeared on the desk screen. "How-do, Retief. Okay if I come up?" "Sure, Hank. I want to talk to you." In the office, Arapoulous took a chair. "Sorry if I'm rushing you, Retief," he said. "But have you got anything for me?" Retief waved at the wine bottles. "What do you know about Croanie?" "Croanie? Not much of a place. Mostly ocean. All right if you like fish, I guess. We import our seafood from there. Nice prawns in monsoon time. Over a foot long." "You on good terms with them?" "Sure, I guess so. Course, they're pretty thick with Boge." "So?" "Didn't I tell you? Boge was the bunch that tried to take us over here a dozen years back. They'd've made it too, if they hadn't had a lot of bad luck. Their armor went in the drink, and without armor they're easy game." Miss Furkle buzzed. "I have your lists," she said shortly. "Bring them in, please." The secretary placed the papers on the desk. Arapoulous caught her eye and grinned. She sniffed and marched from the room. "What that gal needs is a slippery time in the grape mash," Arapoulous observed. Retief thumbed through the papers, pausing to read from time to time. He finished and looked at Arapoulous. "How many men do you need for the harvest, Hank?" Retief inquired. Arapoulous sniffed his wine glass and looked thoughtful. "A hundred would help," he said. "A thousand would be better. Cheers." "What would you say to two thousand?" "Two thousand? Retief, you're not fooling?" "I hope not." He picked up the phone, called the Port Authority, asked for the dispatch clerk. "Hello, Jim. Say, I have a favor to ask of you. You know that contingent of Bogan students. They're traveling aboard the two CDT transports. I'm interested in the baggage that goes with the students. Has it arrived yet? Okay, I'll wait." Jim came back to the phone. "Yeah, Retief, it's here. Just arrived. But there's a funny thing. It's not consigned to d'Land. It's ticketed clear through to Lovenbroy." "Listen, Jim," Retief said. "I want you to go over to the warehouse and take a look at that baggage for me." Retief waited while the dispatch clerk carried out the errand. The level in the two bottles had gone down an inch when Jim returned to the phone. "Hey, I took a look at that baggage, Retief. Something funny going on. Guns. 2mm needlers, Mark XII hand blasters, power pistols—" "It's okay, Jim. Nothing to worry about. Just a mix-up. Now, Jim, I'm going to ask you to do something more for me. I'm covering for a friend. It seems he slipped up. I wouldn't want word to get out, you understand. I'll send along a written change order in the morning that will cover you officially. Meanwhile, here's what I want you to do...." Retief gave instructions, then rang off and turned to Arapoulous. "As soon as I get off a couple of TWX's, I think we'd better get down to the port, Hank. I think I'd like to see the students off personally." | C. Hank Arapoulous. He wants Retief to help him find men to pick his crops in time to pay back Croanie. |
Given d'Land's lack of a successful college, what can you best infer about the society there?
A. It is not an intellectual society.
B. It is a society that despises education.
C. It is a society lacking sufficient leadership to establish better education sources.
D. It is a society that has found it is more prosperous without high-level education.
| CULTURAL EXCHANGE BY KEITH LAUMER It was a simple student exchange—but Retief gave them more of an education than they expected! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, September 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I Second Secretary Magnan took his green-lined cape and orange-feathered beret from the clothes tree. "I'm off now, Retief," he said. "I hope you'll manage the administrative routine during my absence without any unfortunate incidents." "That seems a modest enough hope," Retief said. "I'll try to live up to it." "I don't appreciate frivolity with reference to this Division," Magnan said testily. "When I first came here, the Manpower Utilization Directorate, Division of Libraries and Education was a shambles. I fancy I've made MUDDLE what it is today. Frankly, I question the wisdom of placing you in charge of such a sensitive desk, even for two weeks. But remember. Yours is purely a rubber-stamp function." "In that case, let's leave it to Miss Furkle. I'll take a couple of weeks off myself. With her poundage, she could bring plenty of pressure to bear." "I assume you jest, Retief," Magnan said sadly. "I should expect even you to appreciate that Bogan participation in the Exchange Program may be the first step toward sublimation of their aggressions into more cultivated channels." "I see they're sending two thousand students to d'Land," Retief said, glancing at the Memo for Record. "That's a sizable sublimation." Magnan nodded. "The Bogans have launched no less than four military campaigns in the last two decades. They're known as the Hoodlums of the Nicodemean Cluster. Now, perhaps, we shall see them breaking that precedent and entering into the cultural life of the Galaxy." "Breaking and entering," Retief said. "You may have something there. But I'm wondering what they'll study on d'Land. That's an industrial world of the poor but honest variety." "Academic details are the affair of the students and their professors," Magnan said. "Our function is merely to bring them together. See that you don't antagonize the Bogan representative. This will be an excellent opportunity for you to practice your diplomatic restraint—not your strong point, I'm sure you'll agree." A buzzer sounded. Retief punched a button. "What is it, Miss Furkle?" "That—bucolic person from Lovenbroy is here again." On the small desk screen, Miss Furkle's meaty features were compressed in disapproval. "This fellow's a confounded pest. I'll leave him to you, Retief," Magnan said. "Tell him something. Get rid of him. And remember: here at Corps HQ, all eyes are upon you." "If I'd thought of that, I'd have worn my other suit," Retief said. Magnan snorted and passed from view. Retief punched Miss Furkle's button. "Send the bucolic person in." A tall broad man with bronze skin and gray hair, wearing tight trousers of heavy cloth, a loose shirt open at the neck and a short jacket, stepped into the room. He had a bundle under his arm. He paused at sight of Retief, looked him over momentarily, then advanced and held out his hand. Retief took it. For a moment the two big men stood, face to face. The newcomer's jaw muscles knotted. Then he winced. Retief dropped his hand and motioned to a chair. "That's nice knuckle work, mister," the stranger said, massaging his hand. "First time anybody ever did that to me. My fault though. I started it, I guess." He grinned and sat down. "What can I do for you?" Retief said. "You work for this Culture bunch, do you? Funny. I thought they were all ribbon-counter boys. Never mind. I'm Hank Arapoulous. I'm a farmer. What I wanted to see you about was—" He shifted in his chair. "Well, out on Lovenbroy we've got a serious problem. The wine crop is just about ready. We start picking in another two, three months. Now I don't know if you're familiar with the Bacchus vines we grow...?" "No," Retief said. "Have a cigar?" He pushed a box across the desk. Arapoulous took one. "Bacchus vines are an unusual crop," he said, puffing the cigar alight. "Only mature every twelve years. In between, the vines don't need a lot of attention, so our time's mostly our own. We like to farm, though. Spend a lot of time developing new forms. Apples the size of a melon—and sweet—" "Sounds very pleasant," Retief said. "Where does the Libraries and Education Division come in?" Arapoulous leaned forward. "We go in pretty heavy for the arts. Folks can't spend all their time hybridizing plants. We've turned all the land area we've got into parks and farms. Course, we left some sizable forest areas for hunting and such. Lovenbroy's a nice place, Mr. Retief." "It sounds like it, Mr. Arapoulous. Just what—" "Call me Hank. We've got long seasons back home. Five of 'em. Our year's about eighteen Terry months. Cold as hell in winter; eccentric orbit, you know. Blue-black sky, stars visible all day. We do mostly painting and sculpture in the winter. Then Spring; still plenty cold. Lots of skiing, bob-sledding, ice skating; and it's the season for woodworkers. Our furniture—" "I've seen some of your furniture," Retief said. "Beautiful work." Arapoulous nodded. "All local timbers too. Lots of metals in our soil and those sulphates give the woods some color, I'll tell you. Then comes the Monsoon. Rain—it comes down in sheets. But the sun's getting closer. Shines all the time. Ever seen it pouring rain in the sunshine? That's the music-writing season. Then summer. Summer's hot. We stay inside in the daytime and have beach parties all night. Lots of beach on Lovenbroy; we're mostly islands. That's the drama and symphony time. The theatres are set up on the sand, or anchored off-shore. You have the music and the surf and the bonfires and stars—we're close to the center of a globular cluster, you know...." "You say it's time now for the wine crop?" "That's right. Autumn's our harvest season. Most years we have just the ordinary crops. Fruit, grain, that kind of thing; getting it in doesn't take long. We spend most of the time on architecture, getting new places ready for the winter or remodeling the older ones. We spend a lot of time in our houses. We like to have them comfortable. But this year's different. This is Wine Year." Arapoulous puffed on his cigar, looked worriedly at Retief. "Our wine crop is our big money crop," he said. "We make enough to keep us going. But this year...." "The crop isn't panning out?" "Oh, the crop's fine. One of the best I can remember. Course, I'm only twenty-eight; I can't remember but two other harvests. The problem's not the crop." "Have you lost your markets? That sounds like a matter for the Commercial—" "Lost our markets? Mister, nobody that ever tasted our wines ever settled for anything else!" "It sounds like I've been missing something," said Retief. "I'll have to try them some time." Arapoulous put his bundle on the desk, pulled off the wrappings. "No time like the present," he said. Retief looked at the two squat bottles, one green, one amber, both dusty, with faded labels, and blackened corks secured by wire. "Drinking on duty is frowned on in the Corps, Mr. Arapoulous," he said. "This isn't drinking . It's just wine." Arapoulous pulled the wire retainer loose, thumbed the cork. It rose slowly, then popped in the air. Arapoulous caught it. Aromatic fumes wafted from the bottle. "Besides, my feelings would be hurt if you didn't join me." He winked. Retief took two thin-walled glasses from a table beside the desk. "Come to think of it, we also have to be careful about violating quaint native customs." Arapoulous filled the glasses. Retief picked one up, sniffed the deep rust-colored fluid, tasted it, then took a healthy swallow. He looked at Arapoulous thoughtfully. "Hmmm. It tastes like salted pecans, with an undercurrent of crusted port." "Don't try to describe it, Mr. Retief," Arapoulous said. He took a mouthful of wine, swished it around his teeth, swallowed. "It's Bacchus wine, that's all. Nothing like it in the Galaxy." He pushed the second bottle toward Retief. "The custom back home is to alternate red wine and black." Retief put aside his cigar, pulled the wires loose, nudged the cork, caught it as it popped up. "Bad luck if you miss the cork," Arapoulous said, nodding. "You probably never heard about the trouble we had on Lovenbroy a few years back?" "Can't say that I did, Hank." Retief poured the black wine into two fresh glasses. "Here's to the harvest." "We've got plenty of minerals on Lovenbroy," Arapoulous said, swallowing wine. "But we don't plan to wreck the landscape mining 'em. We like to farm. About ten years back some neighbors of ours landed a force. They figured they knew better what to do with our minerals than we did. Wanted to strip-mine, smelt ore. We convinced 'em otherwise. But it took a year, and we lost a lot of men." "That's too bad," Retief said. "I'd say this one tastes more like roast beef and popcorn over a Riesling base." "It put us in a bad spot," Arapoulous went on. "We had to borrow money from a world called Croanie. Mortgaged our crops. Had to start exporting art work too. Plenty of buyers, but it's not the same when you're doing it for strangers." "Say, this business of alternating drinks is the real McCoy," Retief said. "What's the problem? Croanie about to foreclose?" "Well, the loan's due. The wine crop would put us in the clear. But we need harvest hands. Picking Bacchus grapes isn't a job you can turn over to machinery—and anyway we wouldn't if we could. Vintage season is the high point of living on Lovenbroy. Everybody joins in. First, there's the picking in the fields. Miles and miles of vineyards covering the mountain sides, and crowding the river banks, with gardens here and there. Big vines, eight feet high, loaded with fruit, and deep grass growing between. The wine-carriers keep on the run, bringing wine to the pickers. There's prizes for the biggest day's output, bets on who can fill the most baskets in an hour.... The sun's high and bright, and it's just cool enough to give you plenty of energy. Come nightfall, the tables are set up in the garden plots, and the feast is laid on: roast turkeys, beef, hams, all kinds of fowl. Big salads. Plenty of fruit. Fresh-baked bread ... and wine, plenty of wine. The cooking's done by a different crew each night in each garden, and there's prizes for the best crews. "Then the wine-making. We still tramp out the vintage. That's mostly for the young folks but anybody's welcome. That's when things start to get loosened up. Matter of fact, pretty near half our young-uns are born after a vintage. All bets are off then. It keeps a fellow on his toes though. Ever tried to hold onto a gal wearing nothing but a layer of grape juice?" "Never did," Retief said. "You say most of the children are born after a vintage. That would make them only twelve years old by the time—" "Oh, that's Lovenbroy years; they'd be eighteen, Terry reckoning." "I was thinking you looked a little mature for twenty-eight," Retief said. "Forty-two, Terry years," Arapoulous said. "But this year it looks bad. We've got a bumper crop—and we're short-handed. If we don't get a big vintage, Croanie steps in. Lord knows what they'll do to the land. Then next vintage time, with them holding half our grape acreage—" "You hocked the vineyards?" "Yep. Pretty dumb, huh? But we figured twelve years was a long time." "On the whole," Retief said, "I think I prefer the black. But the red is hard to beat...." "What we figured was, maybe you Culture boys could help us out. A loan to see us through the vintage, enough to hire extra hands. Then we'd repay it in sculpture, painting, furniture—" "Sorry, Hank. All we do here is work out itineraries for traveling side-shows, that kind of thing. Now, if you needed a troop of Groaci nose-flute players—" "Can they pick grapes?" "Nope. Anyway, they can't stand the daylight. Have you talked this over with the Labor Office?" "Sure did. They said they'd fix us up with all the electronics specialists and computer programmers we wanted—but no field hands. Said it was what they classified as menial drudgery; you'd have thought I was trying to buy slaves." The buzzer sounded. Miss Furkle's features appeared on the desk screen. "You're due at the Intergroup Council in five minutes," she said. "Then afterwards, there are the Bogan students to meet." "Thanks." Retief finished his glass, stood. "I have to run, Hank," he said. "Let me think this over. Maybe I can come up with something. Check with me day after tomorrow. And you'd better leave the bottles here. Cultural exhibits, you know." II As the council meeting broke up, Retief caught the eye of a colleague across the table. "Mr. Whaffle, you mentioned a shipment going to a place called Croanie. What are they getting?" Whaffle blinked. "You're the fellow who's filling in for Magnan, over at MUDDLE," he said. "Properly speaking, equipment grants are the sole concern of the Motorized Equipment Depot, Division of Loans and Exchanges." He pursed his lips. "However, I suppose there's no harm in telling you. They'll be receiving heavy mining equipment." "Drill rigs, that sort of thing?" "Strip mining gear." Whaffle took a slip of paper from a breast pocket, blinked at it. "Bolo Model WV/1 tractors, to be specific. Why is MUDDLE interested in MEDDLE's activities?" "Forgive my curiosity, Mr. Whaffle. It's just that Croanie cropped up earlier today. It seems she holds a mortgage on some vineyards over on—" "That's not MEDDLE's affair, sir," Whaffle cut in. "I have sufficient problems as Chief of MEDDLE without probing into MUDDLE'S business." "Speaking of tractors," another man put in, "we over at the Special Committee for Rehabilitation and Overhaul of Under-developed Nations' General Economies have been trying for months to get a request for mining equipment for d'Land through MEDDLE—" "SCROUNGE was late on the scene," Whaffle said. "First come, first served. That's our policy at MEDDLE. Good day, gentlemen." He strode off, briefcase under his arm. "That's the trouble with peaceful worlds," the SCROUNGE committeeman said. "Boge is a troublemaker, so every agency in the Corps is out to pacify her. While my chance to make a record—that is, assist peace-loving d'Land—comes to naught." He shook his head. "What kind of university do they have on d'Land?" asked Retief. "We're sending them two thousand exchange students. It must be quite an institution." "University? D'Land has one under-endowed technical college." "Will all the exchange students be studying at the Technical College?" "Two thousand students? Hah! Two hundred students would overtax the facilities of the college." "I wonder if the Bogans know that?" "The Bogans? Why, most of d'Land's difficulties are due to the unwise trade agreement she entered into with Boge. Two thousand students indeed!" He snorted and walked away. Retief stopped by the office to pick up a short cape, then rode the elevator to the roof of the 230-story Corps HQ building and hailed a cab to the port. The Bogan students had arrived early. Retief saw them lined up on the ramp waiting to go through customs. It would be half an hour before they were cleared through. He turned into the bar and ordered a beer. A tall young fellow on the next stool raised his glass. "Happy days," he said. "And nights to match." "You said it." He gulped half his beer. "My name's Karsh. Mr. Karsh. Yep, Mr. Karsh. Boy, this is a drag, sitting around this place waiting...." "You meeting somebody?" "Yeah. Bunch of babies. Kids. How they expect—Never mind. Have one on me." "Thanks. You a Scoutmaster?" "I'll tell you what I am. I'm a cradle-robber. You know—" he turned to Retief—"not one of those kids is over eighteen." He hiccupped. "Students, you know. Never saw a student with a beard, did you?" "Lots of times. You're meeting the students, are you?" The young fellow blinked at Retief. "Oh, you know about it, huh?" "I represent MUDDLE." Karsh finished his beer, ordered another. "I came on ahead. Sort of an advance guard for the kids. I trained 'em myself. Treated it like a game, but they can handle a CSU. Don't know how they'll act under pressure. If I had my old platoon—" He looked at his beer glass, pushed it back. "Had enough," he said. "So long, friend. Or are you coming along?" Retief nodded. "Might as well." At the exit to the Customs enclosure, Retief watched as the first of the Bogan students came through, caught sight of Karsh and snapped to attention, his chest out. "Drop that, mister," Karsh snapped. "Is that any way for a student to act?" The youth, a round-faced lad with broad shoulders, grinned. "Heck, no," he said. "Say, uh, Mr. Karsh, are we gonna get to go to town? We fellas were thinking—" "You were, hah? You act like a bunch of school kids! I mean ... no! Now line up!" "We have quarters ready for the students," Retief said. "If you'd like to bring them around to the west side, I have a couple of copters laid on." "Thanks," said Karsh. "They'll stay here until take-off time. Can't have the little dears wandering around loose. Might get ideas about going over the hill." He hiccupped. "I mean they might play hookey." "We've scheduled your re-embarkation for noon tomorrow. That's a long wait. MUDDLE's arranged theater tickets and a dinner." "Sorry," Karsh said. "As soon as the baggage gets here, we're off." He hiccupped again. "Can't travel without our baggage, y'know." "Suit yourself," Retief said. "Where's the baggage now?" "Coming in aboard a Croanie lighter." "Maybe you'd like to arrange for a meal for the students here." "Sure," Karsh said. "That's a good idea. Why don't you join us?" Karsh winked. "And bring a few beers." "Not this time," Retief said. He watched the students, still emerging from Customs. "They seem to be all boys," he commented. "No female students?" "Maybe later," Karsh said. "You know, after we see how the first bunch is received." Back at the MUDDLE office, Retief buzzed Miss Furkle. "Do you know the name of the institution these Bogan students are bound for?" "Why, the University at d'Land, of course." "Would that be the Technical College?" Miss Furkle's mouth puckered. "I'm sure I've never pried into these details." "Where does doing your job stop and prying begin, Miss Furkle?" Retief said. "Personally, I'm curious as to just what it is these students are travelling so far to study—at Corps expense." "Mr. Magnan never—" "For the present. Miss Furkle, Mr. Magnan is vacationing. That leaves me with the question of two thousand young male students headed for a world with no classrooms for them ... a world in need of tractors. But the tractors are on their way to Croanie, a world under obligation to Boge. And Croanie holds a mortgage on the best grape acreage on Lovenbroy." "Well!" Miss Furkle snapped, small eyes glaring under unplucked brows. "I hope you're not questioning Mr. Magnan's wisdom!" "About Mr. Magnan's wisdom there can be no question," Retief said. "But never mind. I'd like you to look up an item for me. How many tractors will Croanie be getting under the MEDDLE program?" "Why, that's entirely MEDDLE business," Miss Furkle said. "Mr. Magnan always—" "I'm sure he did. Let me know about the tractors as soon as you can." Miss Furkle sniffed and disappeared from the screen. Retief left the office, descended forty-one stories, followed a corridor to the Corps Library. In the stacks he thumbed through catalogues, pored over indices. "Can I help you?" someone chirped. A tiny librarian stood at his elbow. "Thank you, ma'am," Retief said. "I'm looking for information on a mining rig. A Bolo model WV tractor." "You won't find it in the industrial section," the librarian said. "Come along." Retief followed her along the stacks to a well-lit section lettered ARMAMENTS. She took a tape from the shelf, plugged it into the viewer, flipped through and stopped at a squat armored vehicle. "That's the model WV," she said. "It's what is known as a continental siege unit. It carries four men, with a half-megaton/second firepower." "There must be an error somewhere," Retief said. "The Bolo model I want is a tractor. Model WV M-1—" "Oh, the modification was the addition of a bulldozer blade for demolition work. That must be what confused you." "Probably—among other things. Thank you." Miss Furkle was waiting at the office. "I have the information you wanted," she said. "I've had it for over ten minutes. I was under the impression you needed it urgently, and I went to great lengths—" "Sure," Retief said. "Shoot. How many tractors?" "Five hundred." "Are you sure?" Miss Furkle's chins quivered. "Well! If you feel I'm incompetent—" "Just questioning the possibility of a mistake, Miss Furkle. Five hundred tractors is a lot of equipment." "Was there anything further?" Miss Furkle inquired frigidly. "I sincerely hope not," Retief said. III Leaning back in Magnan's padded chair with power swivel and hip-u-matic concontour, Retief leafed through a folder labelled "CERP 7-602-Ba; CROANIE (general)." He paused at a page headed Industry. Still reading, he opened the desk drawer, took out the two bottles of Bacchus wine and two glasses. He poured an inch of wine into each and sipped the black wine meditatively. It would be a pity, he reflected, if anything should interfere with the production of such vintages.... Half an hour later he laid the folder aside, keyed the phone and put through a call to the Croanie Legation. He asked for the Commercial Attache. "Retief here, Corps HQ," he said airily. "About the MEDDLE shipment, the tractors. I'm wondering if there's been a slip up. My records show we're shipping five hundred units...." "That's correct. Five hundred." Retief waited. "Ah ... are you there, Retief?" "I'm still here. And I'm still wondering about the five hundred tractors." "It's perfectly in order. I thought it was all settled. Mr. Whaffle—" "One unit would require a good-sized plant to handle its output," Retief said. "Now Croanie subsists on her fisheries. She has perhaps half a dozen pint-sized processing plants. Maybe, in a bind, they could handle the ore ten WV's could scrape up ... if Croanie had any ore. It doesn't. By the way, isn't a WV a poor choice as a mining outfit? I should think—" "See here, Retief! Why all this interest in a few surplus tractors? And in any event, what business is it of yours how we plan to use the equipment? That's an internal affair of my government. Mr. Whaffle—" "I'm not Mr. Whaffle. What are you going to do with the other four hundred and ninety tractors?" "I understood the grant was to be with no strings attached!" "I know it's bad manners to ask questions. It's an old diplomatic tradition that any time you can get anybody to accept anything as a gift, you've scored points in the game. But if Croanie has some scheme cooking—" "Nothing like that, Retief. It's a mere business transaction." "What kind of business do you do with a Bolo WV? With or without a blade attached, it's what's known as a continental siege unit." "Great Heavens, Retief! Don't jump to conclusions! Would you have us branded as warmongers? Frankly—is this a closed line?" "Certainly. You may speak freely." "The tractors are for transshipment. We've gotten ourselves into a difficult situation, balance-of-payments-wise. This is an accommodation to a group with which we have rather strong business ties." "I understand you hold a mortgage on the best land on Lovenbroy," Retief said. "Any connection?" "Why ... ah ... no. Of course not, ha ha." "Who gets the tractors eventually?" "Retief, this is unwarranted interference!" "Who gets them?" "They happen to be going to Lovenbroy. But I scarcely see—" "And who's the friend you're helping out with an unauthorized transshipment of grant material?" "Why ... ah ... I've been working with a Mr. Gulver, a Bogan representative." "And when will they be shipped?" "Why, they went out a week ago. They'll be half way there by now. But look here, Retief, this isn't what you're thinking!" "How do you know what I'm thinking? I don't know myself." Retief rang off, buzzed the secretary. "Miss Furkle, I'd like to be notified immediately of any new applications that might come in from the Bogan Consulate for placement of students." "Well, it happens, by coincidence, that I have an application here now. Mr. Gulver of the Consulate brought it in." "Is Mr. Gulver in the office? I'd like to see him." "I'll ask him if he has time." "Great. Thanks." It was half a minute before a thick-necked red-faced man in a tight hat walked in. He wore an old-fashioned suit, a drab shirt, shiny shoes with round toes and an ill-tempered expression. "What is it you wish?" he barked. "I understood in my discussions with the other ... ah ... civilian there'd be no further need for these irritating conferences." "I've just learned you're placing more students abroad, Mr. Gulver. How many this time?" "Two thousand." "And where will they be going?" "Croanie. It's all in the application form I've handed in. Your job is to provide transportation." "Will there be any other students embarking this season?" "Why ... perhaps. That's Boge's business." Gulver looked at Retief with pursed lips. "As a matter of fact, we had in mind dispatching another two thousand to Featherweight." "Another under-populated world—and in the same cluster, I believe," Retief said. "Your people must be unusually interested in that region of space." "If that's all you wanted to know, I'll be on my way. I have matters of importance to see to." After Gulver left, Retief called Miss Furkle in. "I'd like to have a break-out of all the student movements that have been planned under the present program," he said. "And see if you can get a summary of what MEDDLE has been shipping lately." Miss Furkle compressed her lips. "If Mr. Magnan were here, I'm sure he wouldn't dream of interfering in the work of other departments. I ... overheard your conversation with the gentleman from the Croanie Legation—" "The lists, Miss Furkle." "I'm not accustomed," Miss Furkle said, "to intruding in matters outside our interest cluster." "That's worse than listening in on phone conversations, eh? But never mind. I need the information, Miss Furkle." "Loyalty to my Chief—" "Loyalty to your pay-check should send you scuttling for the material I've asked for," Retief said. "I'm taking full responsibility. Now scat." The buzzer sounded. Retief flipped a key. "MUDDLE, Retief speaking...." Arapoulous's brown face appeared on the desk screen. "How-do, Retief. Okay if I come up?" "Sure, Hank. I want to talk to you." In the office, Arapoulous took a chair. "Sorry if I'm rushing you, Retief," he said. "But have you got anything for me?" Retief waved at the wine bottles. "What do you know about Croanie?" "Croanie? Not much of a place. Mostly ocean. All right if you like fish, I guess. We import our seafood from there. Nice prawns in monsoon time. Over a foot long." "You on good terms with them?" "Sure, I guess so. Course, they're pretty thick with Boge." "So?" "Didn't I tell you? Boge was the bunch that tried to take us over here a dozen years back. They'd've made it too, if they hadn't had a lot of bad luck. Their armor went in the drink, and without armor they're easy game." Miss Furkle buzzed. "I have your lists," she said shortly. "Bring them in, please." The secretary placed the papers on the desk. Arapoulous caught her eye and grinned. She sniffed and marched from the room. "What that gal needs is a slippery time in the grape mash," Arapoulous observed. Retief thumbed through the papers, pausing to read from time to time. He finished and looked at Arapoulous. "How many men do you need for the harvest, Hank?" Retief inquired. Arapoulous sniffed his wine glass and looked thoughtful. "A hundred would help," he said. "A thousand would be better. Cheers." "What would you say to two thousand?" "Two thousand? Retief, you're not fooling?" "I hope not." He picked up the phone, called the Port Authority, asked for the dispatch clerk. "Hello, Jim. Say, I have a favor to ask of you. You know that contingent of Bogan students. They're traveling aboard the two CDT transports. I'm interested in the baggage that goes with the students. Has it arrived yet? Okay, I'll wait." Jim came back to the phone. "Yeah, Retief, it's here. Just arrived. But there's a funny thing. It's not consigned to d'Land. It's ticketed clear through to Lovenbroy." "Listen, Jim," Retief said. "I want you to go over to the warehouse and take a look at that baggage for me." Retief waited while the dispatch clerk carried out the errand. The level in the two bottles had gone down an inch when Jim returned to the phone. "Hey, I took a look at that baggage, Retief. Something funny going on. Guns. 2mm needlers, Mark XII hand blasters, power pistols—" "It's okay, Jim. Nothing to worry about. Just a mix-up. Now, Jim, I'm going to ask you to do something more for me. I'm covering for a friend. It seems he slipped up. I wouldn't want word to get out, you understand. I'll send along a written change order in the morning that will cover you officially. Meanwhile, here's what I want you to do...." Retief gave instructions, then rang off and turned to Arapoulous. "As soon as I get off a couple of TWX's, I think we'd better get down to the port, Hank. I think I'd like to see the students off personally." | A. It is not an intellectual society. |
How did Templin find about about Pendleton's death?
A. He was told by Nayova
B. He received a formal letter from the captain.
C. He received a letter from Pendleton himself.
D. He was told by Eckert.
| 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." | D. He was told by Eckert. |
Where does the data in CoLA come from? | ### Introduction
The effectiveness and ubiquity of pretrained sentence embeddings for natural language understanding has grown dramatically in recent years. Recent sentence encoders like OpenAI's Generative Pretrained Transformer BIBREF3 and BERT BIBREF2 achieve the state of the art on the GLUE benchmark BIBREF4 . Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability. Our goal in this paper is to develop an evaluation dataset that can locate which syntactic features that a model successfully learns by identifying the syntactic domains of CoLA in which it performs the best. Using this evaluation set, we compare the syntactic knowledge of GPT and BERT in detail, and investigate the strengths of these models over the baseline BiLSTM model published by warstadt2018neural. The analysis set includes expert annotations labeling the entire CoLA development set for the presence of 63 fine-grained syntactic features. We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations. ### Analysis Set
We introduce a grammatically annotated version of the entire CoLA development set to facilitate detailed error analysis of acceptability classifiers. These 1043 sentences are expert-labeled for the presence of 63 minor grammatical features organized into 15 major features. Each minor feature belongs to a single major feature. A sentence belongs to a major feature if it belongs to one or more of the relevant minor features. The Appendix includes descriptions of each feature along with examples and the criteria used for annotation. The 63 minor features and 15 major features are illustrated in Table TABREF5 . Considering minor features, an average of 4.31 features is present per sentence (SD=2.59). The average feature is present in 71.3 sentences (SD=54.7). Turning to major features, the average sentence belongs to 3.22 major features (SD=1.66), and the average major feature is present in 224 sentences (SD=112). Every sentence is labeled with at least one feature. ### Annotation
The sentences were annotated manually by one of the authors, who is a PhD student with extensive training in formal linguistics. The features were developed in a trial stage, in which the annotator performed a similar annotation with different annotation schema for several hundred sentences from CoLA not belonging to the development set. ### Feature Descriptions
Here we briefly summarize the feature set in order of the major features. Many of these constructions are well-studied in syntax, and further background can be found in textbooks such as adger2003core and sportiche2013introduction. This major feature contains only one minor feature, simple, including sentences with a syntactically simplex subject and predicate. These three features correspond to predicative phrases, including copular constructions, small clauses (I saw Bo jump), and resultatives/depictives (Bo wiped the table clean). These six features mark various kinds of optional modifiers. This includes modifiers of NPs (The boy with blue eyes gasped) or VPs (The cat meowed all morning), and temporal (Bo swam yesterday) or locative (Bo jumped on the bed). These five features identify syntactically selected arguments, differentiating, for example, obliques (I gave a book to Bo), PP arguments of NPs and VPs (Bo voted for Jones), and expletives (It seems that Bo left). These four features mark VPs with unusual argument structures, including added arguments (I baked Bo a cake) or dropped arguments (Bo knows), and the passive (I was applauded). This contains only one feature for imperative clauses (Stop it!). These are two minor features, one for bound reflexives (Bo loves himself), and one for other bound pronouns (Bo thinks he won). These five features apply to sentences with question-like properties. They mark whether the interrogative is an embedded clause (I know who you are), a matrix clause (Who are you?), or a relative clause (Bo saw the guy who left); whether it contains an island out of which extraction is unacceptable (*What was a picture of hanging on the wall?); or whether there is pied-piping or a multi-word wh-expressions (With whom did you eat?). These six features apply to various complement clauses (CPs), including subject CPs (That Bo won is odd); CP arguments of VPs or NPs/APs (The fact that Bo won); CPs missing a complementizer (I think Bo's crazy); or non-finite CPs (This is ready for you to eat). These four minor features mark the presence of auxiliary or modal verbs (I can win), negation, or “pseudo-auxiliaries” (I have to win). These five features mark various infinitival embedded VPs, including control VPs (Bo wants to win); raising VPs (Bo seemed to fly); VP arguments of NPs or APs (Bo is eager to eat); and VPs with extraction (e.g. This is easy to read ts ). These seven features mark complex NPs and APs, including ones with PP arguments (Bo is fond of Mo), or CP/VP arguments; noun-noun compounds (Bo ate mud pie); modified NPs, and NPs derived from verbs (Baking is fun). These seven features mark various unrelated syntactic constructions, including dislocated phrases (The boy left who was here earlier); movement related to focus or information structure (This I've gotta see this); coordination, subordinate clauses, and ellipsis (I can't); or sentence-level adjuncts (Apparently, it's raining). These four features mark various determiners, including quantifiers, partitives (two of the boys), negative polarity items (I *do/don't have any pie), and comparative constructions. These three features apply only to unacceptable sentences, and only ones which are ungrammatical due to a semantic or morphological violation, or the presence or absence of a single salient word. ### Correlations
We wish to emphasize that these features are overlapping and in many cases are correlated, thus not all results from using this analysis set will be independent. We analyzed the pairwise Matthews Correlation Coefficient BIBREF17 of the 63 minor features (giving 1953 pairs), and of the 15 major features (giving 105 pairs). MCC is a special case of Pearson's INLINEFORM0 for Boolean variables. These results are summarized in Table TABREF25 . Regarding the minor features, 60 pairs had a correlation of 0.2 or greater, 17 had a correlation of 0.4 or greater, and 6 had a correlation of 0.6 or greater. None had an anti-correlation of greater magnitude than -0.17. Turning to the major features, 6 pairs had a correlation of 0.2 or greater, and 2 had an anti-correlation of greater magnitude than -0.2. We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who). Finally, two strongest anti-correlations between major features are between simple and the two features related to argument structure, argument types and arg altern. This follows from the definition of simple, which excludes any sentence containing a large number or unusual configuration of arguments. ### Models Evaluated
We train MLP acceptability classifiers for CoLA on top of three sentence encoders: (1) the CoLA baseline encoder with ELMo-style embeddings, (2) OpenAI GPT, and (3) BERT. We use publicly available sentence encoders with pretrained weights. ### Overall CoLA Results
The overall performance of the three sentence encoders is shown in Table TABREF33 . Performance on CoLA is measured using MCC BIBREF14 . We present the best single restart for each encoder, the mean over restarts for an encoder, and the result of ensembling the restarts for a given encoder, i.e. taking the majority classification for a given sentence, or the majority label of acceptable if tied. For BERT results, we exclude 5 out of the 20 restarts because they were degenerate (MCC=0). Across the board, BERT outperforms GPT, which outperforms the CoLA baseline. However, BERT and GPT are much closer in performance than they are to CoLA baseline. While ensemble performance exceeded the average for BERT and GPT, it did not outperform the best single model. ### Analysis Set Results
The results for the major features and minor features are shown in Figures FIGREF26 and FIGREF35 , respectively. For each feature, we measure the MCC of the sentences including that feature. We plot the mean of these results across the different restarts for each model, and error bars mark the mean INLINEFORM0 standard deviation. For the Violations features, MCC is technically undefined because these features only contain unacceptable sentences. We report MCC in these cases by including for each feature a single acceptable example that is correctly classified by all models. Comparison across features reveals that the presence of certain features has a large effect on performance, and we comment on some overall patterns below. Within a given feature, the effect of model type is overwhelmingly stable, and resembles the overall difference in performance. However, we observe several interactions, i.e. specific features where the relative performance of models does not track their overall relative performance. Among the major features (Figure FIGREF26 ), performance is universally highest on the simple sentences, and is higher than each model's overall performance. Though these sentences are simple, we notice that the proportion of ungrammatical ones is on par with the entire dataset. Otherwise we find that a model's performance on sentences of a given feature is on par with or lower than its overall performance, reflecting the fact that features mark the presence of unusual or complex syntactic structure. Performance is also high (and close to overall performance) on sentences with marked argument structures (Argument Types and Arg(ument) Alt(ernation)). While these models are still worse than human (overall) performance on these sentences, this result indicates that argument structure is relatively easy to learn. Comparing different kinds of embedded content, we observe higher performance on sentences with embedded clauses (major feature=Comp Clause) embedded VPs (major feature=to-VP) than on sentences with embedded interrogatives (minor features=Emb-Q, Rel Clause). An exception to this trend is the minor feature No C-izer, which labels complement clauses without a complementizer (e.g. I think that you're crazy). Low performance on these sentences compared to most other features in Comp Clause might indicate that complementizers are an important syntactic cue for these models. As the major feature Question shows, the difficulty of sentences with question-like syntax applies beyond just embedded questions. Excluding polar questions, sentences with question-like syntax almost always involve extraction of a wh-word, creating a long-distance dependency between the wh-word and its extraction site, which may be difficult for models to recognize. The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions. Finally, unusual performance on some features is due to small samples, and have a high standard deviation, suggesting the result is unreliable. This includes CP Subj, Frag/Paren, imperative, NPI/FCI, and Comparative. Comparing within-feature performance of the three encoders to their overall performance, we find they have differing strengths and weaknesses. BERT stands out over other models in Deep Embed, which includes challenging sentences with doubly-embedded, as well as in several features involving extraction (i.e. long-distance dependencies) such as VP+Extract and Info-Struc. The transformer models show evidence of learning long-distance dependencies better than the CoLA baseline. They outperform the CoLA baseline by an especially wide margin on Bind:Refl, which all involves establishing a dependency between a reflexive and its antecedent (Bo tries to love himself). They also have a large advantage in dislocation, in which expressions are separated from their dependents (Bo practiced on the train an important presentation). The advantage of BERT and GPT may be due in part to their use of the transformer architecture. Unlike the BiLSTM used by the CoLA baseline, the transformer uses a self-attention mechanism that associates all pairs of words regardless of distance. In some cases models showed surprisingly good or bad performance, revealing possible idiosyncrasies of the sentence embeddings they output. For instance, the CoLA baseline performs on par with the others on the major feature adjunct, especially considering the minor feature Particle (Bo looked the word up). Furthermore, all models struggle equally with sentences in Violation, indicating that the advantages of the transformer models over the CoLA baseline does not extend to the detection of morphological violations (Infl/Agr Violation) or single word anomalies (Extra/Missing Expr). ### Length Analysis
For comparison, we analyze the effect of sentence length on acceptability classifier performance. The results are shown in Figure FIGREF39 . The results for the CoLA baseline are inconsistent, but do drop off as sentence length increases. For BERT and GPT, performance decreases very steadily with length. Exceptions are extremely short sentences (length 1-3), which may be challenging due to insufficient information; and extremely long sentences, where we see a small (but somewhat unreliable) boost in BERT's performance. BERT and GPT are generally quite close in performance, except on the longest sentences, where BERT's performance is considerably better. ### Conclusion
Using a new grammatically annotated analysis set, we identify several syntactic phenomena that are predictive of good or bad performance of current state of the art sentence encoders on CoLA. We also use these results to develop hypotheses about why BERT is successful, and why transformer models outperform sequence models. Our findings can guide future work on sentence embeddings. A current weakness of all sentence encoders we investigate, including BERT, is the identification of morphological violations. Future engineering work should investigate whether switching to a character-level model can mitigate this problem. Additionally, transformer models appear to have an advantage over sequence models with long-distance dependencies, but still struggle with these constructions relative to more local phenomena. It stands to reason that this performance gap might be widened by training larger or deeper transformer models, or training on longer or more complex sentences. This analysis set can be used by engineers interested in evaluating the syntactic knowledge of their encoders. Finally, these findings suggest possible controlled experiments that could confirm whether there is a causal relation between the presence of the syntactic features we single out as interesting and model performance. Our results are purely correlational, and do not mark whether a particular construction is crucial for the acceptability of the sentence. Future experiments following ettinger2018assessing and kann2019verb can semi-automatically generate datasets manipulating, for example, length of long-distance dependencies, inflectional violations, or the presence of interrogatives, while controlling for factors like sentence length and word choice, in order determine the extent to which these features impact the quality of sentence embeddings. ### Acknowledgments
We would like to thank Jason Phang and Thibault Févry for sharing GPT and BERT model predictions on CoLA, and Alex Wang for feedback. ### Simple
These are sentences with transitive or intransitive verbs appearing with their default syntax and argument structure. All arguments are noun phrases (DPs), and there are no modifiers or adjuncts on DPs or the VP. . Included J̇ohn owns the book. (37) Park Square has a festive air. (131) *Herself likes Mary's mother. (456) . Excluded Ḃill has eaten cake. I gave Joe a book. ### Pred (Predicates)
These are sentences including the verb be used predicatively. Also, sentences where the object of the verb is itself a predicate, which applies to the subject. Not included are auxiliary uses of be or other predicate phrases that are not linked to a subject by a verb. . Included J̇ohn is eager. (27) He turned into a frog. (150) To please John is easy. (315) . Excluded Ṫhere is a bench to sit on. (309) John broke the geode open. The cake was eaten. These sentences involve predication of a non-subject argument by another non-subject argument, without the presence of a copula. Some of these cases may be analyzed as small clauses. BIBREF35 . Included J̇ohn called the president a fool. (234) John considers himself proud of Mary. (464) They want them arrested. (856) the election of John president surprised me. (1001) Modifiers that act as predicates of an argument. Resultatives express a resulting state of that argument, and depictives describe that argument during the matrix event. See BIBREF24 . . Included Ṙesultative Ṭhe table was wiped by John clean. (625) The horse kicked me black and blue. (898) . Depictive J̇ohn left singing. (971) In which car was the man seen? (398) . Excluded Ḣe turned into a frog. (150) ### Adjunct
Particles are lone prepositions associated with verbs. When they appear with transitive verbs they may immediately follow the verb or the object. Verb-particle pairs may have a non-compositional (idiomatic) meaning. See [pp. 69-70]carnie2013syntax and [pp. 16-17]kim2008syntax. . Included Ṭhe argument was summed by the coach up. (615) Some sentences go on and on and on. (785) *He let the cats which were whining out. (71) Adjuncts modifying verb phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. See BIBREF33 . . Included ṖP-adjuncts, e.g. locative, temporal, instrumental, beneficiary Ṅobody who hates to eat anything should work in a delicatessen. (121) Felicia kicked the ball off the bench. (127) . Adverbs Ṁary beautifully plays the violin. (40) John often meets Mary. (65) . Purpose VPs Ẇe need another run to win. (769) . 0.5em. Excluded ṖP arguments Ṣue gave to Bill a book. (42) Everything you like is on the table. (736) . S-adjuncts J̇ohn lost the race, unfortunately. These are adjuncts modifying noun phrases. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. Single-word prenominal adjectives are excluded, as are relative clauses (this has another category). . Included ṖP-adjuncts Ṭom's dog with one eye attacked Frank's with three legs. (676) They were going to meet sometime on Sunday, but the faculty didn't know when. (565) . Phrasal adjectives Ȧs a statesman, scarcely could he do anything worth mentioning. (292) . Verbal modifiers Ṫhe horse raced past the barn fell. (900) . Excluded Ṗrenominal Adjectives İt was the policeman met that several young students in the park last night. (227) . Relative Clauses NP arguments These are adjuncts of VPs and NPs that specify a time or modify tense or aspect or frequency of an event. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included Ṡhort adverbials (never, today, now, always) Ẉhich hat did Mike quip that she never wore? (95) . PPs Ḟiona might be here by 5 o'clock. (426) . When İ inquired when could we leave. (520) These are adjuncts of VPs and NPs that specify a location of an event or a part of an event, or of an individual. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included Ṡhort adverbials PPs Ṫhe bed was slept in. (298) *Anson demonized up the Khyber (479) Some people consider dogs in my neighborhood dangerous. (802) Mary saw the boy walking toward the railroad station. (73) . Where İ found the place where we can relax. (307) . Excluded Ŀocative arguments Ṣam gave the ball out of the basket. (129) Jessica loaded boxes on the wagon. (164) I went to Rome. These are adjuncts of VPs and NPs not described by some other category (with the exception of (6-7)), i.e. not temporal, locative, or relative clauses. Adjuncts are (usually) optional, and they do not change the category of the expression they modify. . Included Ḃeneficiary Ị know which book José didn't read for class, and which book Lilly did it for him. (58) . Instrument Ŀee saw the student with a telescope. (770) . Comitative J̇oan ate dinner with someone but I don't know who. (544) . VP adjuncts Ẇhich article did Terry file papers without reading? (431) . Purpose Ẇe need another run to win. (769) ### Argument Types
Oblique arguments of verbs are individual-denoting arguments (DPs or PPs) which act as the third argument of verb, i.e. not a subject or (direct) object. They may or may not be marked by a preposition. Obliques are only found in VPs that have three or more individual arguments. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. See [p.40]kim2008syntax. . Included Ṗrepositional Ṣue gave to Bill a book. (42) Mary has always preferred lemons to limes. (70) *Janet broke Bill on the finger. (141) . Benefactives Ṁartha carved the baby a toy out of wood. (139) . Double object Ṡusan told her a story. (875) Locative arguments Ȧnn may spend her vacation in Italy. (289) . High-arity Passives Ṃary was given by John the book. (626) . Excluded Ṅon-DP arguments Ẇe want John to win (28) . 3rd argments where not all three arguments are DPs Ẇe want John to win (28) Prepositional Phrase arguments of VPs are individual-denoting arguments of a verb which are marked by a proposition. They may or may not be obliques. Arguments are selected for by the verb, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. . Included Ḋative Ṣue gave to Bill a book. (42) . Conative (at) C̣arla slid at the book. (179) . Idiosyncratic prepositional verbs İ wonder who to place my trust in. (711) She voted for herself. (743) . Locative J̇ohn was found in the office. (283) . PP predicates Ėverything you like is on the table. (736) . Excluded ṖP adjuncts Particles Arguments of deverbal expressions ṭhe putter of books left. (892) . By-phrase Ṫed was bitten by the spider. (613) Prepositional Phrase arguments of NPs or APs are individual-denoting arguments of a noun or adjective which are marked by a proposition. Arguments are selected for by the head, and they are (generally) not optional, though in some cases they may be omitted where they are understood or implicitly existentially quantified over. . Included Ṙelational adjectives Ṁany people were fond of Pat. (936) *I was already aware of fact. (824) . Relational nouns Ẇe admired the pictures of us in the album. (759) They found the book on the atom. (780) . Arguments of deverbal nouns ṭhe putter of books left. (892) Prepositional arguments introduced with by. Usually, this is the (semantic) subject of a passive verb, but in rare cases it may be the subject of a nominalized verb. Arguments are usually selected for by the head, and they are generally not optional. In this case, the argument introduced with by is semantically selected for by the verb, but it is syntactically optional. See [p.190]adger2003core and []collins2005smuggling. . Included Ṗassives Ṫed was bitten by the spider. (613) . Subjects of deverbal nouns ṫhe attempt by John to leave surprised me. (1003) Expletives, or “dummy” arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax. . Included Ṫhere—inserted, existential Ṭhere loved Sandy. (939) There is a nurse available. (466) . It—cleft, inserted İt was a brand new car that he bought. (347) It bothers me that John coughs. (314) It is nice to go abroad. (47) . Environmental it K̇erry remarked it was late. (821) Poor Bill, it had started to rain and he had no umbrella. (116) You've really lived it up. (160) . Excluded J̇ohn counted on Bill to get there on time. (996) I bought it to read. (1026) ### Arg Altern (Argument Alternations)
These are verbs with 3 or more arguments of any kind. Arity refers to the number of arguments that a head (or function) selects for. Arguments are usually selected for by the head, and they are generally not optional. They may be DPs, PPs, CPs, VPs, APs or other categories. . Included Ḋitransitive [̣Sue] gave [to Bill] [a book]. (42) [Martha] carved [the baby] [a toy] out of wood. (139) . VP arguments [̣We] believed [John] [to be a fountain in the park]. (274) [We] made [them] [be rude]. (260) . Particles He] let [the cats which were whining] [out]. (71) . Passives with by-phrase [̣A good friend] is remained [to me] [by him]. (237) . Expletives [̣We] expect [there] [to will rain]. (282) [There] is [a seat] [available]. (934) [It] bothers [me] [that he is here]. (1009) . Small clause John] considers [Bill] [silly]. (1039) . Excluded Ṙesults, depictives John] broke [the geode] [open]. These are VPs where a canonical argument of the verb is missing. This can be difficult to determine, but in many cases the missing argument is understood with existential quantification or generically, or contextually salient. See [p.106-109]sportiche2013introduction. . Included Ṁiddle voice/causative inchoative Ṭhe problem perceives easily. (66) . Passive Ṫhe car was driven. (296) . Null complement anaphora J̇ean persuaded Robert. (380) Nobody told Susan. (883) . Dropped argument Ḳim put in the box. (253) The guests dined. (835) I wrote to Bill. (1030) . Transitive adjective J̇ohn is eager. (27) We pulled free. (144) . Transitive noun İ sensed his eagerness. (155) . Expletive insertion Ịt loved Sandy. (949) . Excluded Ṫed was bitten by the spider. (613) These are VPs in which a non-canonical argument of the verb has been added. These cases are clearer to identify where the additional argument is a DP. In general, PPs which mark locations, times, beneficiaries, or purposes should be analyzed as adjuncts, while PPs marking causes can be considered arguments. See []pylkkanen2008introducing. . Included Ėxtra argument Ḷinda winked her lip. (202) Sharon fainted from hunger. (204) I shaved myself. (526) . Causative Ị squeaked the door. (207) . Expletive insertion Ṫhere is a monster in Loch Ness. (928) It annoys people that dogs bark. (943) . Benefactive Ṁartha carved the baby a toy out of wood. (139) The passive voice is marked by the demotion of the subject (either complete omission or to a by-phrase) and the verb appearing as a past participle. In the stereotypical construction there is an auxiliary be verb, though this may be absent. See [p.175-190]kim2008syntax, collins2005smuggling, and [p.311-333]sag2003syntactic. . Included V̇erbs Ṫhe earth was believed to be round. (157) . Psuedopassive Ṫhe bed was slept in. (298) . Past participle adjuncts Ṫhe horse raced past the barn fell. (900) ### Imperative
The imperative mood is marked by the absence of the a subject and the bare form of the verb, and expresses a command, request, or other directive speech act. . Included Ẉash you! (224) Somebody just left - guess who. (528) ### Binding
These are cases in which a reflexive (non-possessive) pronoun, usually bound by an antecedent. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic. . Included Ọurselves like ourselves. (742) Which pictures of himself does John like? (386) These are cases in which a non-reflexive pronoun appears along with its antecedent. This includes donkey anaphora, quantificational binding, and bound possessives, among other bound pronouns. See [p.163-186]sportiche2013introduction and [p.203-226]sag2003syntactic. . Included Ḃound possessor Ṫhe children admire their mother. (382) . Quantificational binding Ėverybody gets on well with a certain relative, but often only his therapist knows which one. (562) . Bound pronoun Ẉe gave us to the cause. (747) ### Question
These are sentences in which the matrix clause is interrogative (either a wh- or polar question). See [pp.282-213]adger2003core, [pp.193-222]kim2008syntax, and [p.315-350]carnie2013syntax. . Included Ẇh-question Ẇho always drinks milk? (684) . Polar question Ḋid Athena help us? (486) These are embedded interrogative clauses appearing as arguments of verbs, nouns, and adjectives. Not including relative clauses and free relatives. See [p.297]adger2003core. . Included U̇nder VP İ forgot how good beer tastes. (235) *What did you ask who saw? (508) . Under NP Ṫhat is the reason why he resigned. (313) . Under AP Ṫhey claimed they had settled on something, but it wasn't clear what they had settled on. (529) . Free relative Ẇhat the water did to the bottle was fill it. (33) . Excluded Relative clauses, free relatives These are phrasal Wh-phrases, in which the wh-word moves along with other expressions, including prepositions (pied-piping) or nouns in the case of determiner wh-words such as how many and which. . Included Ṗied-piping Ṭhe ship sank, but I don't know with what. (541) . Other phrasal wh-phrases İ know which book Mag read, and which book Bob read my report that you hadn't. (61) How sane is Peter? (88) Relative clauses are noun modifiers appearing with a relativizer (either that or a wh-word) and an associated gap. See [p.223-244]kim2008syntax. . Included Ṫhough he may hate those that criticize Carter, it doesn't matter. (332) *The book what inspired them was very long. (686) Everything you like is on the table. (736) . Excluded Ṭhe more you would want, the less you would eat. (6) This is wh-movement out of an extraction island, or near-island. Islands include, for example, complex NPs, adjuncts, embedded questions, coordination. A near-island is an extraction that closely resembles an island violation, such as extraction out of an embedded clause, or across-the-board extraction. See [pp.323-333]adger2003core and [pp.332-334]carnie2013syntax. . Included Ėmbedded question *What did you ask who Medea gave? (493) . Adjunct Ẉhat did you leave before they did? (598) . Parasitic gaps Ẇhich topic did you choose without getting his approval? (311) . Complex NP Ẇho did you get an accurate description of? (483) ### Comp Clause (Complement Clauses)
These are complement clauses acting as the (syntactic) subject of verbs. See [pp.90-91]kim2008syntax. . Included Ṫhat dogs bark annoys people. (942) The socks are ready for for you to put on to be planned. (112) . Excluded Ėxpletive insertion İt bothers me that John coughs. (314) These are complement clauses acting as (non-subject) arguments of verbs. See [pp.84-90]kim2008syntax. . Included İ can't believe Fred won't, either. (50) I saw that gas can explode. (222) It bothers me that John coughs. (314) Clefts İt was a brand new car that he bought. (347) These are complement clauses acting as an argument of a noun or adjective. See [pp.91-94]kim2008syntax. . Included U̇nder NP Ḋo you believe the claim that somebody was looking for something? (99) . Under AP Ṭhe children are fond that they have ice cream. (842) These are complement clauses with a non-finite matrix verb. Often, the complementizer is for, or there is no complementizer. See [pp.252-253,256-260]adger2003core. . Included Ḟor complementizer İ would prefer for John to leave. (990) . No Complementizer Ṁary intended John to go abroad. (48) . Ungrammatical Ḣeidi thinks that Andy to eat salmon flavored candy bars. (363) . V-ing Ȯnly Churchill remembered Churchill giving the Blood, Sweat and Tears speech. (469) These are complement clauses with no overt complementizer. . Included Ċomplement clause İ'm sure we even got these tickets! (325) He announced he would marry the woman he loved most, but none of his relatives could figure out who. (572) . Relative clause Ṫhe Peter we all like was at the party (484) These are sentences with three or nested verbs, where VP is not an aux or modal, i.e. with the following syntax: [S ...[ VP ...[ VP ...[ VP ...] ...] ...] ...] . Included Ėmbedded VPs Ṁax seemed to be trying to force Ted to leave the room, and Walt, Ira. (657) . Embedded clauses İ threw away a book that Sandy thought we had read. (713) ### Aux (Auxiliaries)
Any occurrence of negation in a sentence, including sentential negation, negative quantifiers, and negative adverbs. . Included Ṡentential İ can't remember the name of somebody who had misgivings. (123) . Quantifier Ṅo writer, and no playwright, meets in Vienna. (124) . Adverb Ṫhey realised that never had Sir Thomas been so offended. (409) Modal verbs (may, might, can, could, will, would, shall, should, must). See [pp.152-155]kim2008syntax. . Included J̇ohn can kick the ball. (280) As a statesman, scarcely could he do anything worth mentioning. (292) . Excluded Ṗseudo-modals Ṡandy was trying to work out which students would be able to solve a certain problem. (600) Auxiliary verbs (e.g. be, have, do). See [pp.149-174]kim2008syntax. . Included Ṫhey love to play golf, but I do not. (290) The car was driven. (296) he had spent five thousand dollars. (301) . Excluded Ṗseudo-auxiliaries Ṣally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) These are predicates acting as near-auxiliary (e.g. get-passive) or near-modals (e.g. willing) . Included Ṅear-auxiliaries Ṃary came to be introduced by the bartender and I also came to be. (55) *Sally asked if somebody was going to fail math class, but I can't remember who. (589) The cat got bitten. (926) . Near-modals Ċlinton is anxious to find out which budget dilemmas Panetta would be willing to tackle in a certain way, but he won't say in which. (593) Sandy was trying to work out which students would be able to solve a certain problem. (600) ### to-VP (Infinitival VPs)
These are VPs with control verbs, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. See [pp.252,266-291]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax. . Included İntransitive subject control Ịt tries to leave the country. (275) . Transitive subject control J̇ohn promised Bill to leave. (977) . Transitive object control İ want her to dance. (379) John considers Bill to be silly. (1040) . Excluded V̇P args of NP/AP Ṫhis violin is difficult to play sonatas on. (114) . Purpose Ṫhere is a bench to sit on. (309) . Subject VPs Ṫo please John is easy. (315) . Argument present participles Ṁedea denied poisoning the phoenix. (490) . Raising Ȧnson believed himself to be handsome. (499) These are VPs with raising predicates, where one argument is a non-finite to-VP without a covert subject co-indexed with an argument of the matrix verb. Unlike control verbs, the coindexed argument is not a semantic argument of the raising predicate. See [pp.260-266]adger2003core, [pp.203-222]sportiche2013introduction, and [pp.125-148]kim2008syntax. . Included Ṡubject raising U̇nder the bed seems to be a fun place to hide. (277) . Object raising Ȧnson believed himself to be handsome. (499) . Raising adjective J̇ohn is likely to leave. (370) These are embedded infinitival VPs containing a (non-subject) gap that is filled by an argument in the upper clause. Examples are purpose-VPs and tough-movement. See [pp.246-252]kim2008syntax. . Included Ṫough-movement Ḍrowning cats, which is against the law, are hard to rescue. (79) . Infinitival relatives F̣ed knows which politician her to vote for. (302) . Purpose ṫhe one with a red cover takes a very long time to read. (352) . Other non-finite VPs with extraction Ȧs a statesman, scarcely could he do anything worth mentioning. (292) These are non-finite VP arguments of nouns and adjectives. . Included Ṙaising adjectives J̇ohn is likely to leave. (370) . Control adjectives Ṫhe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) . Control nouns Ȧs a teacher, you have to deal simultaneously with the administration's pressure on you to succeed, and the children's to be a nice guy. (673) . Purpose VPs ṫhere is nothing to do. (983) These are miscellaneous non-finite VPs. . Included İ saw that gas can explode. (222) Gerunds/Present participles Ṣtudents studying English reads Conrad's Heart of Darkness while at university. (262) Knowing the country well, he took a short cut. (411) John became deadly afraid of flying. (440) . Subject VPs Ṫo please John is easy. (315) . Nominalized VPs Ẉhat Mary did Bill was give a book. (473) . Excluded ṫo-VPs acting as complements or modifiers of verbs, nouns, or adjectives ### N, Adj (Nouns and Adjectives)
These are nouns and adjectives derived from verbs. . Included Ḋeverbal nouns ṭhe election of John president surprised me. (1001) . “Light” verbs Ṫhe birds give the worm a tug. (815) . Gerunds İf only Superman would stop flying planes! (773) . Event-wh Ẇhat the water did to the bottle was fill it. (33) . Deverbal adjectives Ḣis or her least known work. (95) Relational nouns are NPs with an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of NP and the argument. The argument must be a DP possessor or a PP. See [pp.82-83]kim2008syntax. . Included Ṅouns with of-arguments J̇ohn has a fear of dogs. (353) . Nouns with other PP-arguments Ḣenri wants to buy which books about cooking? (442) . Measure nouns İ bought three quarts of wine and two of Clorox. (667) . Possessed relational nouns J̣ohn's mother likes himself. (484) . Excluded Ṅouns with PP modifiers Ṡome people consider dogs in my neighborhood dangerous. (802) Transitive (non-relational) nouns take a VP or CP argument. See [pp.82-83]kim2008syntax. . Included V̇P argument ṫhe attempt by John to leave surprised me. (1003) . CP argument Ẉhich report that John was incompetent did he submit? (69) . QP argument Ṫhat is the reason why he resigned. (313) These are complex NPs, including coordinated nouns and nouns with modifiers (excluding prenominal adjectives). . Included Ṁodified NPs Ṭhe madrigals which Henry plays the lute and sings sound lousy. (84) John bought a book on the table. (233) . NPs with coordination Ṭhe soundly and furry cat slept. (871) The love of my life and mother of my children would never do such a thing. (806) Noun-noun compounds are NPs consisting of two constituent nouns. . Included İt was the peasant girl who got it. (320) A felon was elected to the city council. (938) These are adjectives that take an obligatory (or existentially closed) argument. A particular relation holds between the members of the extension of the modified NP and the argument. The argument must be a DP or PP. See [pp.80-82]kim2008syntax. . Included Ȯf-arguments Ṫhe chickens seem fond of the farmer. (254) . Other PP arguments Ṫhis week will be a difficult one for us. (241) John made Bill mad at himself. (1035) A transitive (non-relational) adjective. I.e. an adjectives that takes a VP or CP argument. See [pp.80-82]kim2008syntax. . Included V̇P argument J̇ohn is likely to leave. (370) . CP argument J̇ohn is aware of it that Bill is here. (1013) . QP argument Ṫhe administration has issued a statement that it is willing to meet a student group, but I'm not sure which one. (604) ### S-Syntax (Sentence-Level Syntax)
These are expressions with non-canonical word order. See, for example, [p.76]sportiche2013introduction. . Includes Ṗarticle shift Ṃickey looked up it. (24) . Preposed modifiers Ȯut of the box jumped a little white rabbit. (215) *Because she's so pleasant, as for Mary I really like her. (331) . Quantifier float Ṫhe men will all leave. (43) . Preposed argument Ẇith no job would John be happy. (333) . Relative clause extraposition Ẇhich book's, author did you meet who you liked? (731) . Misplaced phrases Ṁary was given by John the book. (626) This includes topicalization and focus constructions. See [pp.258-269]kim2008syntax and [pp.68-75]sportiche2013introduction. . Included Ṫopicalization Ṁost elections are quickly forgotten, but the election of 2000, everyone will remember for a long time. (807) . Clefts İt was a brand new car that he bought. (347) . Pseudo-clefts Ẇhat John promised is to be gentle. (441) . Excluded Ṫhere-insertion Passive These are parentheticals or fragmentary expressions. . Included Ṗarenthetical Ṁary asked me if, in St. Louis, John could rent a house cheap. (704) . Fragments Ṫhe soup cooks, thickens. (448) . Tag question Ġeorge has spent a lot of money, hasn't he? (291) Coordinations and disjunctions are expressions joined with and, but, or, etc. See [pp.61-68]sportiche2013introduction. . Included ḊP coordination Ḋave, Dan, Erin, Jaime, and Alina left. (341) . Right Node Raising K̇im gave a dollar to Bobbie and a dime to Jean. (435) . Clausal coordination Ṡhe talked to Harry, but I don't know who else. (575) . Or, nor Ṇo writer, nor any playwright, meets in Vienna. (125) . Pseudo-coordination İ want to try and buy some whiskey. (432) . Juxtaposed clauses Ŀights go out at ten. There will be no talking afterwards. (779) This includes subordinate clauses, especially with subordinating conjunctions, and conditionals. . Included Ċonditional İf I can, I will work on it. (56) . Subordinate clause Ẉhat did you leave before they did? (598) *Because Steve's of a spider's eye had been stolen, I borrowed Fred's diagram of a snake's fang. (677) . Correlative Ạs you eat the most, you want the least. (5) This includes VP or NP ellipsis, or anaphora standing for VPs or NPs (not DPs). See [pp.55-61]sportiche2013introduction. . Included V̇P Ellipsis İf I can, I will work on it. (56) Mary likes to tour art galleries, but Bill hates to. (287) . VP Anaphor İ saw Bill while you did so Mary. (472) . NP Ellipsis Ṫom's dog with one eye attacked Fred's. (679) . NP anaphor ṫhe one with a red cover takes a very long time to read. (352) . Sluicing Ṁost columnists claim that a senior White House official has been briefing them, and the newspaper today reveals which one. (557) . Gapping Ḃill ate the peaches, but Harry the grapes. (646) These are adjuncts modifying sentences, sentence-level adverbs, subordinate clauses. . Included Ṡentence-level adverbs Ṡuddenly, there arrived two inspectors from the INS. (447) . Subordinate clauses Ṫhe storm arrived while we ate lunch. (852) ### Determiner
These are quantificational DPs, i.e. the determiner is a quantifier. . Included Q̇uantifiers Ẹvery student, and he wears socks, is a swinger. (118) We need another run to win. (769) . Partitive Ṇeither of students failed. (265) These are quantifiers that take PP arguments, and measure nouns. See [pp.109-118]kim2008syntax. . Included Q̇uantifiers with PP arguments Ṇeither of students failed. (265) . Numerals Ȯne of Korea's most famous poets wrote these lines. (294) . Measure nouns İ bought three quarts of wine and two of Clorox. (667) These are negative polarity items (any, ever, etc.) and free choice items (any). See kadmon1993any. . Included ṄPI Ėverybody around here who ever buys anything on credit talks in his sleep. (122) I didn't have a red cent. (350) . FCI Ȧny owl hunts mice. (387) These are comparative constructions. See BIBREF22 . . Included Ċorrelative Ṫhe angrier Mary got, the more she looked at pictures. (9) They may grow as high as bamboo. (337) I know you like the back of my hand. (775) ### Violations
These are sentences that include a semantic violation, including type mismatches, violations of selectional restrictions, polarity violations, definiteness violations. . Included V̇olation of selectional restrictions ṃany information was provided. (218) *It tries to leave the country. (275) . Aspectual violations J̣ohn is tall on several occasions. (540) . Definiteness violations Ịt is the problem that he is here. (1018) . Polarity violations Ȧny man didn't eat dinner. (388) These are sentences that include a violation in inflectional morphology, including tense-aspect marking, or agreement. . Included Ċase Ụs love they. (46) . Agreement Ṣtudents studying English reads Conrad's Heart of Darkness while at university. (262) . Gender Ṣally kissed himself. (339) . Tense/Aspect Ḳim alienated cats and beating his dog. (429) These are sentences with a violation that can be identified with the presence or absence of a single word. . Included Ṁissing word J̣ohn put under the bathtub. (247) *I noticed the. (788) . Extra word Ẹveryone hopes everyone to sleep. (467) *He can will go (510) Table 1: A random sample of sentences from the CoLA development set, shown with their original acceptability labels (3= acceptable, *=unacceptable) and with a subset of our new phenomenon-level annotations. Table 2: Major features and their associated minor features (with number of occurrences n). Table 3: Correlation (MCC) of features in the annotated analysis set. We display only the correlations with the greatest magnitude. Figure 1: Performance (MCC) on CoLA analysis set by major feature. Dashed lines show mean performance on all of CoLA. Table 4: Performance (MCC) on the CoLA test set, including mean over restarts of a given model with standard deviation, max over restarts, and majority prediction over restarts. Human performance is measured by Warstadt et al. Figure 2: Performance (MCC) on CoLA analysis set by minor feature. Dashed lines show mean performance on all of CoLA. Figure 3: Performance (MCC) on the CoLA analysis set by sentence length. | CoLA contains example sentences from linguistics publications labeled by experts |
Did William kill the man?
A. no - he watched the men do it, but William thought it was the spirits
B. yes, though he doesn't remember
C. yes - he used the pipe and killed the man
D. no - the two men did it when William's back was turned
| Charity Case By JIM HARMON Illustrated by DICK FRANCIS [Transcriber's Note: This etext was produced from Galaxy Science Fiction December 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Certainly I see things that aren't there and don't say what my voice says—but how can I prove that I don't have my health? When he began his talk with "You got your health, don't you?" it touched those spots inside me. That was when I did it. Why couldn't what he said have been "The best things in life are free, buddy" or "Every dog has his day, fellow" or "If at first you don't succeed, man"? No, he had to use that one line. You wouldn't blame me. Not if you believe me. The first thing I can remember, the start of all this, was when I was four or five somebody was soiling my bed for me. I absolutely was not doing it. I took long naps morning and evening so I could lie awake all night to see that it wouldn't happen. It couldn't happen. But in the morning the bed would sit there dispassionately soiled and convict me on circumstantial evidence. My punishment was as sure as the tide. Dad was a compact man, small eyes, small mouth, tight clothes. He was narrow but not mean. For punishment, he locked me in a windowless room and told me to sit still until he came back. It wasn't so bad a punishment, except that when Dad closed the door, the light turned off and I was left there in the dark. Being four or five, I didn't know any better, so I thought Dad made it dark to add to my punishment. But I learned he didn't know the light went out. It came back on when he unlocked the door. Every time I told him about the light as soon as I could talk again, but he said I was lying. One day, to prove me a liar, he opened and closed the door a few times from outside. The light winked off and on, off and on, always shining when Dad stuck his head inside. He tried using the door from the inside, and the light stayed on, no matter how hard he slammed the door. I stayed in the dark longer for lying about the light. Alone in the dark, I wouldn't have had it so bad if it wasn't for the things that came to me. They were real to me. They never touched me, but they had a little boy. He looked the way I did in the mirror. They did unpleasant things to him. Because they were real, I talked about them as if they were real, and I almost earned a bunk in the home for retarded children until I got smart enough to keep the beasts to myself. My mother hated me. I loved her, of course. I remember her smell mixed up with flowers and cookies and winter fires. I remember she hugged me on my ninth birthday. The trouble came from the notes written in my awkward hand that she found, calling her names I didn't understand. Sometimes there were drawings. I didn't write those notes or make those drawings. My mother and father must have been glad when I was sent away to reform school after my thirteenth birthday party, the one no one came to. The reform school was nicer. There were others there who'd had it about like me. We got along. I didn't watch their shifty eyes too much, or ask them what they shifted to see. They didn't talk about my screams at night. It was home. My trouble there was that I was always being framed for stealing. I didn't take any of those things they located in my bunk. Stealing wasn't in my line. If you believe any of this at all, you'll see why it couldn't be me who did the stealing. There was reason for me to steal, if I could have got away with it. The others got money from home to buy the things they needed—razor blades, candy, sticks of tea. I got a letter from Mom or Dad every now and then before they were killed, saying they had sent money or that it was enclosed, but somehow I never got a dime of it. When I was expelled from reform school, I left with just one idea in mind—to get all the money I could ever use for the things I needed and the things I wanted. It was two or three years later that I skulked into Brother Partridge's mission on Durbin Street. The preacher and half a dozen men were singing Onward Christian Soldiers in the meeting room. It was a drafty hall with varnished camp chairs. I shuffled in at the back with my suitcoat collar turned up around my stubbled jaw. I made my hand shaky as I ran it through my knotted hair. Partridge was supposed to think I was just a bum. As an inspiration, I hugged my chest to make him think I was some wino nursing a flask full of Sneaky Pete. All I had there was a piece of copper alloy tubing inside a slice of plastic hose for taking care of myself, rolling sailors and the like. Who had the price of a bottle? Partridge didn't seem to notice me, but I knew that was an act. I knew people were always watching every move I made. He braced his red-furred hands on the sides of his auctioneer's stand and leaned his splotched eagle beak toward us. "Brothers, this being Thanksgiving, I pray the good Lord that we all are truly thankful for all that we have received. Amen." Some skin-and-bones character I didn't know struggled out of his seat, amening. I could see he had a lot to be thankful for—somewhere he had received a fix. "Brothers," Partridge went on after enjoying the interruption with a beaming smile, "you shall all be entitled to a bowl of turkey soup prepared by Sister Partridge, a generous supply of sweet rolls and dinner rolls contributed by the Early Morning Bakery of this city, and all the coffee you can drink. Let us march out to The Stars and Stripes Forever , John Philip Sousa's grand old patriotic song." I had to laugh at all those bums clattering the chairs in front of me, scampering after water soup and stale bread. As soon as I got cleaned up, I was going to have dinner in a good restaurant, and I was going to order such expensive food and leave such a large tip for the waiter and send one to the chef that they were going to think I was rich, and some executive with some brokerage firm would see me and say to himself, "Hmm, executive material. Just the type we need. I beg your pardon, sir—" just like the razor-blade comic-strip ads in the old magazines that Frankie the Pig sells three for a quarter. I was marching. Man, was I ever marching, but the secret of it was I was only marking time the way we did in fire drills at the school. They passed me, every one of them, and marched out of the meeting room into the kitchen. Even Partridge made his way down from the auctioneer's stand like a vulture with a busted wing and darted through his private door. I was alone, marking time behind the closed half of double doors. One good breath and I raced past the open door and flattened myself to the wall. Crockery was ringing and men were slurping inside. No one had paid any attention to me. That was pretty odd. People usually watch my every move, but a man's luck has to change sometime, doesn't it? Following the wallboard, I went down the side of the room and behind the last row of chairs, closer, closer, and halfway up the room again to the entrance—the entrance and the little wooden box fastened to the wall beside it. The box was old and made out of some varnished wood. There was a slot in the top. There wasn't any sign anywhere around it, but you knew it wasn't a mailbox. My hand went flat on the top of the box. One finger at a time drew up and slipped into the slot. Index, fore, third, little. I put my thumb in my palm and shoved. My hand went in. There were coins inside. I scooped them up with two fingers and held them fast with the other two. Once I dropped a dime—not a penny, milled edge—and I started to reach for it. No, don't be greedy. I knew I would probably lose my hold on all the coins if I tried for that one. I had all the rest. It felt like about two dollars, or close to it. Then I found the bill. A neatly folded bill in the box. Somehow I knew all along it would be there. I tried to read the numbers on the bill with my fingertips, but I couldn't. It had to be a one. Who drops anything but a one into a Skid Row collection box? But still there were tourists, slummers. They might leave a fifty or even a hundred. A hundred! Yes, it felt new, crisp. It had to be a hundred. A single would be creased or worn. I pulled my hand out of the box. I tried to pull my hand out of the box. I knew what the trouble was, of course. I was in a monkey trap. The monkey reaches through the hole for the bait, and when he gets it in his hot little fist, he can't get his hand out. He's too greedy to let go, so he stays there, caught as securely as if he were caged. I was a man, not a monkey. I knew why I couldn't get my hand out. But I couldn't lose that money, especially that century bill. Calm, I ordered myself. Calm. The box was fastened to the vertical tongue-and-groove laths of the woodwork, not the wall. It was old lumber, stiffened by a hundred layers of paint since 1908. The paint was as thick and strong as the boards. The box was fastened fast. Six-inch spike nails, I guessed. Calmly, I flung my whole weight away from the wall. My wrist almost cracked, but there wasn't even a bend in the box. Carefully, I tried to jerk my fist straight up, to pry off the top of the box. It was as if the box had been carved out of one solid piece of timber. It wouldn't go up, down, left or right. But I kept trying. While keeping a lookout for Partridge and somebody stepping out of the kitchen for a pull on a bottle, I spotted the clock for the first time, a Western Union clock high up at the back of the hall. Just as I seen it for the first time, the electricity wound the spring motor inside like a chicken having its neck wrung. The next time I glanced at the clock, it said ten minutes had gone by. My hand still wasn't free and I hadn't budged the box. "This," Brother Partridge said, "is one of the most profound experiences of my life." My head hinged until it lined my eyes up with Brother Partridge. The pipe hung heavy in my pocket, but he was too far from me. "A vision of you at the box projected itself on the crest of my soup," the preacher explained in wonderment. I nodded. "Swimming right in there with the dead duck." "Cold turkey," he corrected. "Are you scoffing at a miracle?" "People are always watching me, Brother," I said. "So now they do it even when they aren't around. I should have known it would come to that." The pipe was suddenly a weight I wanted off me. I would try robbing a collection box, knowing positively that I would get caught, but I wasn't dumb enough to murder. Somebody, somewhere, would be a witness to it. I had never got away with anything in my life. I was too smart to even try anything but the little things. "I may be able to help you," Brother Partridge said, "if you have faith and a conscience." "I've got something better than a conscience," I told him. Brother Partridge regarded me solemnly. "There must be something special about you, for your apprehension to come through miraculous intervention. But I can't imagine what." "I always get apprehended somehow, Brother," I said. "I'm pretty special." "Your name?" "William Hagle." No sense lying. I had been booked and printed before. Partridge prodded me with his bony fingers as if making sure I was substantial. "Come. Let's sit down, if you can remove your fist from the money box." I opened up my fingers and let the coins ring inside the box and I drew out my hand. The bill stuck to the sweat on my fingers and slid out along with the digits. A one, I decided. I had got into trouble for a grubby single. It wasn't any century. I had been kidding myself. I unfolded the note. Sure enough, it wasn't a hundred-dollar bill, but it was a twenty, and that was almost the same thing to me. I creased it and put it back into the slot. As long as it stalled off the cops, I'd talk to Partridge. We took a couple of camp chairs and I told him the story of my life, or most of it. It was hard work on an empty stomach; I wished I'd had some of that turkey soup. Then again I was glad I hadn't. Something always happened to me when I thought back over my life. The same thing. The men filed out of the kitchen, wiping their chins, and I went right on talking. After some time Sister Partridge bustled in and snapped on the overhead lights and I kept talking. The brother still hadn't used the phone to call the cops. "Remarkable," Partridge finally said when I got so hoarse I had to take a break. "One is almost— almost —reminded of Job. William, you are being punished for some great sin. Of that, I'm sure." "Punished for a sin? But, Brother, I've always had it like this, as long as I can remember. What kind of a sin could I have committed when I was fresh out of my crib?" "William, all I can tell you is that time means nothing in Heaven. Do you deny the transmigration of souls?" "Well," I said, "I've had no personal experience—" "Of course you have, William! Say you don't remember. Say you don't want to remember. But don't say you have no personal experience!" "And you think I'm being punished for something I did in a previous life?" He looked at me in disbelief. "What else could it be?" "I don't know," I confessed. "I certainly haven't done anything that bad in this life." "William, if you atone for this sin, perhaps the horde of locusts will lift from you." It wasn't much of a chance, but I was unused to having any at all. I shook off the dizziness of it. "By the Lord Harry, Brother, I'm going to give it a try!" I cried. "I believe you," Partridge said, surprised at himself. He ambled over to the money box on the wall. He tapped the bottom lightly and a box with no top slid out of the slightly larger box. He reached in, fished out the bill and presented it to me. "Perhaps this will help in your atonement," he said. I crumpled it into my pocket fast. Not meaning to sound ungrateful, I'm pretty sure he hadn't noticed it was a twenty. And then the bill seemed to lie there, heavy, a lead weight. It would have been different if I had managed to get it out of the box myself. You know how it is. Money you haven't earned doesn't seem real to you. There was something I forgot to mention so far. During the year between when I got out of the reformatory and the one when I tried to steal Brother Partridge's money, I killed a man. It was all an accident, but killing somebody is reason enough to get punished. It didn't have to be a sin in some previous life, you see. I had gotten my first job in too long, stacking boxes at the freight door of Baysinger's. The drivers unloaded the stuff, but they just dumped it off the truck. An empty rear end was all they wanted. The freight boss told me to stack the boxes inside, neat and not too close together. I stacked boxes the first day. I stacked more the second. The third day I went outside with my baloney and crackers. It was warm enough even for November. Two of them, dressed like Harvard seniors, caps and striped duffer jackets, came up to the crate I was dining off. "Work inside, Jack?" the taller one asked. "Yeah," I said, chewing. "What do you do, Jack?" the fatter one asked. "Stack boxes." "Got a union card?" I shook my head. "Application?" "No," I said. "I'm just helping out during Christmas." "You're a scab, buddy," Long-legs said. "Don't you read the papers?" "I don't like comic strips," I said. They sighed. I think they hated to do it, but I was bucking the system. Fats hit me high. Long-legs hit me low. I blew cracker crumbs into their faces. After that, I just let them go. I know how to take a beating. That's one thing I knew. Then lying there, bleeding to myself, I heard them talking. I heard noises like make an example of him and do something permanent and I squirmed away across the rubbish like a polite mouse. I made it around a corner of brick and stood up, hurting my knee on a piece of brown-splotched pipe. There were noises on the other angle of the corner and so I tested if the pipe was loose and it was. I closed my eyes and brought the pipe up and then down. It felt as if I connected, but I was so numb, I wasn't sure until I unscrewed my eyes. There was a big man in a heavy wool overcoat and gray homburg spread on a damp centerfold from the News . There was a pick-up slip from the warehouse under the fingers of one hand, and somebody had beaten his brains out. The police figured it was part of some labor dispute, I guess, and they never got to me. I suppose I was to blame anyway. If I hadn't been alive, if I hadn't been there to get beaten up, it wouldn't have happened. I could see the point in making me suffer for it. There was a lot to be said for looking at it like that. But there was nothing to be said for telling Brother Partridge about the accident, or murder, or whatever had happened that day. Searching myself after I left Brother Partridge, I finally found a strip of gray adhesive tape on my side, out of the fuzzy area. Making the twenty the size of a thick postage stamp, I peeled back the tape and put the folded bill on the white skin and smoothed the tape back. There was only one place for me to go now. I headed for the public library. It was only about twenty blocks, but not having had anything to eat since the day before, it enervated me. The downstairs washroom was where I went first. There was nobody there but an old guy talking urgently to a kid with thick glasses, and somebody building a fix in one of the booths. I could see charred matches dropping down on the floor next to his tennis shoes, and even a few grains of white stuff. But he managed to hold still enough to keep from spilling more from the spoon. I washed my hands and face, smoothed my hair down, combing it with my fingers. Going over my suit with damp toweling got off a lot of the dirt. I put my collar on the outside of my jacket and creased the wings with my thumbnail so it would look more like a sports shirt. It didn't really. I still looked like a bum, but sort of a neat, non-objectionable bum. The librarian at the main desk looked sympathetically hostile, or hostilely sympathetic. "I'd like to get into the stacks, miss," I said, "and see some of the old newspapers." "Which newspapers?" the old girl asked stiffly. I thought back. I couldn't remember the exact date. "Ones for the first week in November last year." "We have the Times microfilmed. I would have to project them for you." "I didn't want to see the Times ," I said, fast. "Don't you have any newspapers on paper?" I didn't want her to see what I wanted to read up on. "We have the News , bound, for last year." I nodded. "That's the one I wanted to see." She sniffed and told me to follow her. I didn't rate a cart to my table, I guess, or else the bound papers weren't supposed to come out of the stacks. The cases of books, row after row, smelled good. Like old leather and good pipe tobacco. I had been here before. In this world, it's the man with education who makes the money. I had been reading the Funk & Wagnalls Encyclopedia. So far I knew a lot about Mark Antony, Atomic Energy, Boron, Brussels, Catapults, Demons, and Divans. I guess I had stopped to look around at some of the titles, because the busy librarian said sharply, "Follow me." I heard my voice say, "A pleasure. What about after work?" I didn't say it, but I was used to my voice independently saying things. Her neck got to flaming, but she walked stiffly ahead. She didn't say anything. She must be awful mad, I decided. But then I got the idea she was flushed with pleasure. I'm pretty ugly and I looked like a bum, but I was young. You had to grant me that. She waved a hand at the rows of bound News and left me alone with them. I wasn't sure if I was allowed to hunt up a table to lay the books on or not, so I took the volume for last year and laid it on the floor. That was the cleanest floor I ever saw. It didn't take me long to find the story. The victim was a big man, because the story was on the second page of the Nov. 4 edition. I started to tear the page out, then only memorized the name and home address. Somebody was sure to see me and I couldn't risk trouble just now. I stuck the book back in line and left by the side door. I went to a dry-cleaner, not the cheapest place I knew, because I wouldn't be safe with the change from a twenty in that neighborhood. My suit was cleaned while I waited. I paid a little extra and had it mended. Funny thing about a suit—it's almost never completely shot unless you just have it ripped off you or burned up. It wasn't exactly in style, but some rich executives wore suits out of style that they had paid a lot of money for. I remembered Fredric March's double-breasted in Executive Suite while Walter Pidgeon and the rest wore Ivy Leagues. Maybe I would look like an eccentric executive. I bought a new shirt, a good used pair of shoes, and a dime pack of single-edged razor blades. I didn't have a razor, but anybody with nerve can shave with a single-edge blade and soap and water. The clerk took my two bucks in advance and I went up to my room. I washed out my socks and underwear, took a bath, shaved and trimmed my hair and nails with the razor blade. With some soap on my finger, I scrubbed my teeth. Finally I got dressed. Everything was all right except that I didn't have a tie. They had them, a quarter a piece, where I got the shoes. It was only six blocks—I could go back. But I didn't want to wait. I wanted to complete the picture. The razor blade sliced through the pink bath towel evenly. I cut out a nice modern-style tie, narrow, with some horizontal stripes down at the bottom. I made a tight, thin knot. It looked pretty good. I was ready to leave, so I started for the door. I went back. I had almost forgotten my luggage. The box still had three unwrapped blades in it. I pocketed it. I hefted the used blade, dulled by all the work it had done. You can run being economical into stinginess. I tossed it into the wastebasket. I had five hamburgers and five cups of coffee. I couldn't finish all of the French fries. "Mac," I said to the fat counterman, who looked like all fat countermen, "give me a Milwaukee beer." He stopped polishing the counter in front of his friend. "Milwaukee, Wisconsin, or Milwaukee, Oregon?" "Wisconsin." He didn't argue. It was cold and bitter. All beer is bitter, no matter what they say on TV. I like beer. I like the bitterness of it. It felt like another, but I checked myself. I needed a clear head. I thought about going back to the hotel for some sleep; I still had the key in my pocket (I wasn't trusting it to any clerk). No, I had had sleep on Thanksgiving, bracing up for trying the lift at Brother Partridge's. Let's see, it was daylight outside again, so this was the day after Thanksgiving. But it had only been sixteen or twenty hours since I had slept. That was enough. I left the money on the counter for the hamburgers and coffee and the beer. There was $7.68 left. As I passed the counterman's friend on his stool, my voice said, "I think you're yellow." He turned slowly, his jaw moving further away from his brain. I winked. "It was just a bet for me to say that to you. I won two bucks. Half of it is yours." I held out the bill to him. His paw closed over the money and punched me on the biceps. Too hard. He winked back. "It's okay." I rubbed my shoulder, marching off fast, and I counted my money. With my luck, I might have given the counterman's friend the five instead of one of the singles. But I hadn't. I now had $6.68 left. "I still think you're yellow," my voice said. It was my voice, but it didn't come from me. There were no words, no feeling of words in my throat. It just came out of the air the way it always did. I ran. Harold R. Thompkins, 49, vice-president of Baysinger's, was found dead behind the store last night. His skull had been crushed by a vicious beating with a heavy implement, Coroner McClain announced in preliminary verdict. Tompkins, who resided at 1467 Claremont, Edgeway, had been active in seeking labor-management peace in the recent difficulties.... I had read that a year before. The car cards on the clanking subway and the rumbling bus didn't seem nearly so interesting to me. Outside the van, a tasteful sign announced the limits of the village of Edgeway, and back inside, the monsters of my boyhood went bloomp at me. I hadn't seen anything like them in years. The slimy, scaly beasts were slithering over the newspaper holders, the ad card readers, the girl watchers as the neat little carbon-copy modern homes breezed past the windows. I ignored the devils and concentrated on reading the withered, washed-out political posters on the telephone poles. My neck ached from holding it so stiff, staring out through the glass. More than that, I could feel the jabberwocks staring at me. You know how it is. You can feel a stare with the back of your neck and between your eyes. They got one brush of a gaze out of me. The things abruptly started their business, trying to act casually as if they hadn't been waiting for me to look at them at all. They had a little human being of some sort. It was the size of a small boy, like the small boy who looked like me that they used to destroy when I was locked up with them in the dark. Except this was a man, scaled down to child's size. He had sort of an ugly, worried, tired, stupid look and he wore a shiny suit with a piece of a welcome mat or something for a necktie. Yeah, it was me. I really knew it all the time. They began doing things to the midget me. I didn't even lift an eyebrow. They couldn't do anything worse to the small man than they had done to the young boy. It was sort of nostalgic watching them, but I really got bored with all that violence and killing and killing the same kill over and over. Like watching the Saturday night string of westerns in a bar. The sunlight through the window was yellow and hot. After a time, I began to dose. The shrieks woke me up. For the first time, I could hear the shrieks of the monster's victim and listen to their obscene droolings. For the very first time in my life. Always before it had been all pantomime, like Charlie Chaplin. Now I heard the sounds of it all. They say it's a bad sign when you start hearing voices. I nearly panicked, but I held myself in the seat and forced myself to be rational about it. My own voice was always saying things everybody could hear but which I didn't say. It wasn't any worse to be the only one who could hear other things I never said. I was as sane as I ever was. There was no doubt about that. But a new thought suddenly impressed itself on me. Whatever was punishing me for my sin was determined that I turn back before reaching 1467 Claremont. | C. yes - he used the pipe and killed the man |
What exactly is new about this stochastic gradient descent algorithm? | ### Introduction
Emergency events such as natural or man-made disasters bring unique challenges for humanitarian response organizations. Particularly, sudden-onset crisis situations demand officials to make fast decisions based on minimum information available to deploy rapid crisis response. However, information scarcity during time-critical situations hinders decision-making processes and delays response efforts BIBREF0 , BIBREF1 . During crises, people post updates regarding their statuses, ask for help and other useful information, report infrastructure damages, injured people, etc., on social media platforms like Twitter BIBREF2 . Humanitarian organizations can use this citizen-generated information to provide relief if critical information is easily available in a timely fashion. In this paper, we consider the classification of the social media posts into different humanitarian categories to fulfill different information needs of humanitarian organizations. Specifically, we address two types of information needs described as follows: Informativeness of social media posts: Information posted on social networks during crises vary greatly in value. Most messages contain irrelevant information not useful for disaster response and management. Humanitarian organizations do not want a deluge of noisy messages that are of a personal nature or those that do not contain any useful information. They want clean data that consists of messages containing potentially useful information. They can then use this information for various purposes such as situational awareness. In order to assist humanitarian organizations, we perform binary classification. That is, we aim to classify each message into one of the two classes i.e. “informative" vs. “not informative". Information types of social media posts Furthermore, humanitarian organizations are interested in sorting social media posts into different categories. Identifying social media posts by category assists humanitarian organizations in coordinating their response. Categories such as infrastructure damage, reports of deceased or injured, urgent need for shelter, food and water, or donations of goods or services could therefore be directed to different relief functions. In this work, we show how we can classify tweets into multiple classes. Automatic classification of short crisis-related messages such as tweets is a challenging task due to a number of reasons. Tweets are short (only 140 characters), informal, often contain abbreviations, spelling variations and mistakes, and, therefore, they are hard to understand without enough context. Despite advances in natural language processing (NLP), interpreting the semantics of short informal texts automatically remains a hard problem. Traditional classification approaches rely on manually engineered features like cue words and TF-IDF vectors for learning BIBREF1 . Due to the high variability of the data during a crisis, adapting the model to changes in features and their importance manually is undesirable (and often infeasible). To overcome these issues, we use Deep Neural Networks (DNNs) to classify the tweets. DNNs are usually trained using online learning and have the flexibility to adaptively learn the model parameters as new batches of labeled data arrive, without requiring to retrain the model from scratch. DNNs use distributed condensed representation of words and learn the representation as well as higher level abstract features automatically for the classification task. Distributed representation (as opposed to sparse discrete representation) generalizes well. This can be a crucial advantage at the beginning of a new disaster, when there is not enough event-specific labeled data. We can train a reasonably good DNN model using previously labeled data from other events, and then the model is fine-tuned adaptively as newly labeled data arrives in small batches. In this paper, we use Deep Neural Network (DNN) to address two types of information needs of response organizations: identifying informative tweets and classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. Moreover, we make our source code publicly available for crisis computing community for further research at: https://github.com/CrisisNLP/deep-learning-for-big-crisis-data In the next section, we provide details regarding DNNs we use and the online learning algorithm. Section "Dataset and Experimental Settings" describes datasets and online learning settings. In Section "Results" , we describe results of our models. Section "Related work" presents related-work and we conclude our paper in Section "Conclusions" . ### Deep Neural Network
As argued before, deep neural networks (DNNs) can be quite effective in classifying tweets during a disaster situation because of their distributed representation of words and automatic feature learning capabilities. Furthermore, DNNs are usually trained using online algorithms, which nicely suits the needs of a crisis response situation. Our main hypothesis is that in order to effectively classify tweets, which are short and informal, a classification model should learn the key features at different levels of abstraction. To this end, we use a Convolutional Neural Network (CNN), which has been shown to be effective for sentence-level classification tasks BIBREF3 . ### Convolutional Neural Network
Figure 1 demonstrates how a CNN works with an example tweet. Each word in the vocabulary $V$ is represented by a $D$ dimensional vector in a shared look-up table $L$ $\in $ $^{|V| \times D}$ . $L$ is considered a model parameter to be learned. We can initialize $L$ randomly or using pretrained word embedding vectors like word2vec BIBREF4 . Given an input tweet $\mathbf {s} = (w_1, \cdots , w_T)$ , we first transform it into a feature sequence by mapping each word token $w_t \in \mathbf {s}$ to an index in $L$ . The look-up layer then creates an input vector $\mathbf {x_t}\in ^{D}$ for each token $w_t$ , which are passed through a sequence of convolution and pooling operations to learn high-level abstract features. A convolution operation involves applying a filter $\mathbf {u} \in ^{L.D}$ to a window of $L$ words to produce a new feature $$h_t = f(\mathbf {u} . \mathbf {x}_{t:t+L-1} + b_t)$$ (Eq. 5) where $\mathbf {x}_{t:t+L-1}$ denotes the concatenation of $L$ input vectors, $b_t$ is a bias term, and $f$ is a nonlinear activation function (e.g., $, \tanh $ ). A filter is also known as a kernel or a feature detector. We apply this filter to each possible $L$ -word window in the tweet to generate a feature map $\mathbf {h}_i = [h_1, \cdots , h_{T+L-1}]$ . We repeat this process $N$ times with $N$ different filters to get $N$ different feature maps. We use a wide convolution BIBREF5 (as opposed to narrow), which ensures that the filters reach the entire sentence, including the boundary words. This is done by performing zero-padding, where out-of-range (i.e., $L$0 $L$1 1 or $L$2 $L$3 $L$4 ) vectors are assumed to be zero. After the convolution, we apply a max-pooling operation to each feature map. $$\mathbf {m} = [\mu _p(\mathbf {h}_1), \cdots , \mu _p(\mathbf {h}_N)] $$ (Eq. 6) where $\mu _p(\mathbf {h}_i)$ refers to the $\max $ operation applied to each window of $p$ features in the feature map $\mathbf {h}_i$ . For instance, with $p=2$ , this pooling gives the same number of features as in the feature map (because of the zero-padding). Intuitively, the filters compose local $n$ -grams into higher-level representations in the feature maps, and max-pooling reduces the output dimensionality while keeping the most important aspects from each feature map. Since each convolution-pooling operation is performed independently, the features extracted become invariant in locations (i.e., where they occur in the tweet), thus acting like bag-of- $n$ -grams. However, keeping the order information could be important for modeling sentences. In order to model interactions between the features picked up by the filters and the pooling, we include a dense layer of hidden nodes on top of the pooling layer $$\mathbf {z} = f(V\mathbf {m} + \mathbf {b_h}) $$ (Eq. 7) where $V$ is the weight matrix, $\mathbf {b_h}$ is a bias vector, and $f$ is a non-linear activation. The dense layer naturally deals with variable sentence lengths by producing fixed size output vectors $\mathbf {z}$ , which are fed to the output layer for classification. Depending on the classification tasks, the output layer defines a probability distribution. For binary classification tasks, it defines a Bernoulli distribution: $$p(y|\mathbf {s}, \theta )= (y| (\mathbf {w^T} \mathbf {z} + b )) $$ (Eq. 8) where $$ refers to the sigmoid function, and $\mathbf {w}$ are the weights from the dense layer to the output layer and $b$ is a bias term. For multi-class classification the output layer uses a softmax function. Formally, the probability of $k$ -th label in the output for classification into $K$ classes: $$P(y = k|\mathbf {s}, \theta ) = \frac{exp~(\mathbf {w}_k^T\mathbf {z} + b_k)}{\sum _{j=1}^{K} exp~({\mathbf {w}_j^T\mathbf {z} + b_j)}} $$ (Eq. 9) where, $\mathbf {w}_k$ are the weights associated with class $k$ in the output layer. We fit the models by minimizing the cross-entropy between the predicted distributions $\hat{y}_{n\theta } = p(y_n|\mathbf {s}_n, \theta )$ and the target distributions $y_n$ (i.e., the gold labels). The objective function $f(\theta )$ can be written as: $$f (\theta ) = \sum _{n=1}^{N} \sum _{k=1}^{K} y_{nk}~log~P(y_n = k|\mathbf {s}_n, \theta ) $$ (Eq. 11) where, $N$ is the number of training examples and $y_{nk}$ $=$ $I(y_n = k)$ is an indicator variable to encode the gold labels, i.e., $y_{tk}=1$ if the gold label $y_t=k$ , otherwise 0. ### Online Learning
DNNs are usually trained with first-order online methods like stochastic gradient descent (SGD). This method yields a crucial advantage in crisis situations, where retraining the whole model each time a small batch of labeled data arrives is impractical. Algorithm "Online Learning" demonstrates how our CNN model can be trained in a purely online setting. We first initialize the model parameters $\theta _0$ (line 1), which can be a trained model from other disaster events or it can be initialized randomly to start from scratch. As a new batch of labeled tweets $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ arrives, we first compute the log-loss (cross entropy) in Equation 11 for $B_t$ with respect to the current parameters $\theta _t$ (line 2a). Then, we use backpropagation to compute the gradients $f^{\prime }(\theta _{t})$ of the loss with respect to the current parameters (line 2b). Finally, we update the parameters with the learning rate $\eta _t$ and the mean of the gradients (line 2c). We take the mean of the gradients to deal with minibatches of different sizes. Notice that we take only the current minibatch into account to get an updated model. Choosing a proper learning rate $\eta _t$ can be difficult in practice. Several adaptive methods such as ADADELTA BIBREF6 , ADAM BIBREF7 , etc., have been proposed to overcome this issue. In our model, we use ADADELTA. [t] 1. Initialize the model parameters $\theta _0$ ; 2. a minibatch $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ at time $t$ a. Compute the loss $f(\theta _{t})$ in Equation 11 ; b. Compute gradients of the loss $f^{\prime }(\theta _{t})$ using backpropagation; c. Update: $\theta _{t+1} = \theta _{t} - \eta _t \frac{1}{n} f^{\prime }(\theta _{t})$ ; Online learning of CNN ### Word Embedding and Fine-tuning
As mentioned before, we can initialize the word embeddings $L$ randomly, and learn them as part of model parameters by backpropagating the errors to the look-up layer. Random initialization may lead the training algorithm to get stuck in a local minima. One can plug the readily available embeddings from external sources (e.g., Google embeddings BIBREF4 ) in the neural network model and use them as features without further task-specific tuning. However, the latter approach does not exploit the automatic feature learning capability of DNN models, which is one of the main motivations of using them. In our work, we use pre-trained word embeddings (see below) to better initialize our models, and we fine-tune them for our task, which turns out to be beneficial. Mikolov et al. BIBREF4 propose two log-linear models for computing word embeddings from large (unlabeled) corpuses efficiently: a bag-of-words model CBOW that predicts the current word based on the context words, and a skip-gram model that predicts surrounding words given the current word. They released their pre-trained 300-dimensional word embeddings trained by the skip-gram model on a Google news dataset. Since we work on disaster related tweets, which are quite different from news, we have trained domain-specific embeddings of 300-dimensions (vocabulary size 20 million) using the Skip-gram model of word2vec tool BIBREF8 from a large corpus of disaster related tweets. The corpus contains $57,908$ tweets and $9.4$ million tokens. ### Dataset and Experimental Settings
In this section, we describe the datasets used for the classification tasks and the settings for CNN and online learning. ### Dataset and Preprocessing
We use CrisisNLP BIBREF9 labeled datasets. The CNN models were trained online using a labeled dataset related to the 2015 Nepal Earthquake and the rest of the datasets are used to train an initial model ( $\theta _0$ in Algorithm "Online Learning" ) upon which the online learning is performed. The Nepal earthquake dataset consists of approximately 12k labeled tweets collected from Twitter during the event using different keywords like NepalEarthquake. Of all the labeled tweets, 9k are labeled by trained volunteers during the actual event using the AIDR platform BIBREF10 and the remaining 3k tweets are labeled using the Crowdflower crowdsourcing platform. The dataset is labeled into different informative classes (e.g., affected individuals, infrastructure damage, donations etc.) and one “not-related” or “irrelevant” class. Table 1 provides a one line description of each class and also the total number of labels in each class. Other useful information and Not related or irrelevant are the most frequent classes in the dataset. Data Preprocessing: We normalize all characters to their lower-cased forms, truncate elongations to two characters, spell out every digit to D, all twitter usernames to userID, and all URLs to HTTP. We remove all punctuation marks except periods, semicolons, question and exclamation marks. We further tokenize the tweets using the CMU TweetNLP tool BIBREF11 . ### Online Training Settings
Before performing the online learning, we assume that an initial model $\theta _0$ exists. In our case, we train the initial model using all the datasets from CrisisNLP except the Nepal earthquake. For online training, we sort the Nepal labeled data based on the time stamp of the tweets. This brings the tweets in their posting order. Next, the dataset $D$ is divided at each time interval $d_t$ in which case $D$ is defined as: D = $\sum _{t=1}^T d_t$ where $d_t= 200$ . For each time interval $t$ , we divide the available labeled dataset into a train set (70%), dev set (10%), and a test set (20%) using ski-learn toolkit's module BIBREF12 , which ensured that the class distribution remains reasonably balanced in each subset. Based on the data splitting strategy mentioned above, we start online learning to train a binary and a multi-class classifier. For the binary classifier training, we merge all the informative classes to create one general Informative class. We train CNN models by optimizing the cross entropy in Equation 8 using the gradient-based online learning algorithm ADADELTA BIBREF6 . The learning rate and the parameters were set to the values as suggested by the authors. The maximum number of epochs was set to 25. To avoid overfitting, we use dropout BIBREF13 of hidden units and early stopping based on the accuracy on the validation set. We experimented with $\lbrace 0.0, 0.2, 0.4, 0.5\rbrace $ dropout rates and $\lbrace 32, 64, 128\rbrace $ minibatch sizes. We limit the vocabulary ( $V$ ) to the most frequent $P\%$ ( $P\in \lbrace 80, 85, 90\rbrace $ ) words in the training corpus. The word vectors in $L$ were initialized with the pre-trained embeddings. We use rectified linear units (ReLU) for the activation functions ( $f$ ), $\lbrace 100, 150, 200\rbrace $ filters each having window size ( $L$ ) of $\lbrace 2, 3, 4\rbrace $ , pooling length ( $p$ ) of $\lbrace 2,3, 4\rbrace $ , and $\lbrace 100, 150, 200\rbrace $ dense layer units. All the hyperparameters are tuned on the development set. ### Results
In this section, we present our results for binary and multi-class classification tasks. ### Binary Classification
Figure 2 shows the results for the “informative" vs. “not informative" binary classification task using online learning. The performance of the model is quite inconsistent as the size of the in-event training data varies. We observe an improvement in performance initially. However, the results dropped when the training size is between 2200 to 3900 tweets. We investigated this strange result and found that this could be due to the inconsistencies in the annotation procedure and the data sources. In our in-event (Nepal Earthquake) training data, first 3000 tweets are from CrowdFlower and the rest are from AIDR. Tweets in CrowdFlower were annotated by paid workers, where AIDR tweets are annotated by volunteers. We speculate these inconsistencies can affect the performance at the beginning, but as the model sees more AIDR data (4000+), the performance stabilizes. ### Multi-Class Classification
Figure 3 summarizes the results of online training for the multi-class classification task. Since multi-class classification is a harder task than binary classification, the first training run provides very low accuracy and the results continue to drop until a good number of training examples are available, which in this case is approximately 2200 labeled tweets. As in the binary classification case, after the initial dip in performance, once over 3000 tweets are available, the performance of the classifier improves and remains stable after that. The benefit of using online learning methods like CNN compared to offline learning methods used in classifiers like SVM, Naive Bayes, and Logistic Regression is online training. The labeled data comes in batches and retraining a model on the complete data every time with the addition of newly labeled data is an expensive task. Online training methods learn in small batches, which suits the situation in hand perfectly. Another advantage of neural network methods is automatic feature extraction that does not require any manual feature engineering. The models take labeled tweets as input and automatically learn features based on distributed representation of words. ### Discussion
Rapid analysis of social media posts during time-critical situations is important for humanitarian response organization to take timely decisions and to launch relief efforts. This work proposes solutions to two main challenges that humanitarian organizations face while incorporating social media data into crisis response. First, how to filter-out noisy and irrelevant messages from big crisis data and second, categorization of the informative messages into different classes of interest. By utilizing labeled data from past crises, we show the performance of DNNs trained using the proposed online learning algorithm for binary and multi-class classification tasks. We observe that past labeled data helps when no event-specific data is available in the early hours of a crisis. However, labeled data from event always help improve the classification accuracy. ### Related work
Recent studies have shown the usefulness of crisis-related data on social media for disaster response and management BIBREF14 , BIBREF15 , BIBREF16 . A number of systems have been developed to classify, extract, and summarize BIBREF17 crisis-relevant information from social media; for a detailed survey see BIBREF1 . Cameron, et al., describe a platform for emergency situation awareness BIBREF18 . They classify interesting tweets using an SVM classifier. Verma, et al., use Naive Bayes and MaxEnt classifiers to find situational awareness tweets from several crises BIBREF19 . Imran, et al., implemented AIDR to classify a Twitter data stream during crises BIBREF10 . They use a random forest classifier in an offline setting. After receiving every mini-batch of 50 training examples, they replace the older model with a new one. In BIBREF20 , the authors show the performance of a number of non-neural network classifiers trained on labeled data from past crisis events. However, they do not use DNNs in their comparison. DNNs and word embeddings have been applied successfully to address NLP problems BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 . The emergence of tools such as word2vec BIBREF8 and GloVe BIBREF26 have enabled NLP researchers to learn word embeddings efficiently and use them to train better models. Collobert, et al. BIBREF21 presented a unified DNN architecture for solving various NLP tasks including part-of-speech tagging, chunking, named entity recognition and semantic role labeling. They showed that DNNs outperform traditional models in most of these tasks. They also proposed a multi-task learning framework for solving the tasks jointly. Kim BIBREF3 and Kalchbrenner et al. BIBREF5 used convolutional neural networks (CNN) for sentence-level classification tasks (e.g., sentiment/polarity classification, question classification) and showed that CNNs outperform traditional methods (e.g., SVMs, MaxEnts). Caragea, Silvescu, and Tapia used CNNs to identify informative messages during disasters BIBREF22 . However, to the best of our knowledge, no previous research has shown the efficacy of CNNs to both the binary classification and the multi-class classification problems using online learning. ### Conclusions
We presented an online learning model namely Convolutional Neural Network for the purpose of classifying tweets in a disaster response scenario. We proposed a new online learning algorithm for training CNNs in online fashion. We showed that online training of the model perfectly suits the disaster response situation. We assume that a base model trained on past crisis labeled data exists and the event-specific labeled data arrive in small batches which are used to perform online learning. The neural network models bring an additive advantage of automatic feature extraction which eases the training process when compared with offline learning methods like SVM, logistic regression. The model uses only labeled tweets for training and automatically learns features from them. We reported the results of two classification tasks (i.e. binary and multi-class). Moreover, we also provide source code for the online learning of CNN models to research community for further extensions. Figure 1: Convolutional neural network on a sample tweet: “guys if know any medical emergency around balaju area you can reach umesh HTTP doctor at HTTP HTTP”. Table 1: Description of the classes in the dataset. Column Labels shows the total number of labeled examples in each class Figure 2: Binary classification: Performance of the CNN model with varying size of the training data Figure 3: Multi-class classification: Performance of the CNN model with various sizes of training data. | CNN model can be trained in a purely online setting. We first initialize the model parameters $\theta _0$ (line 1), which can be a trained model from other disaster events or it can be initialized randomly to start from scratch.
As a new batch of labeled tweets $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ arrives, we first compute the log-loss (cross entropy) in Equation 11 for $B_t$ with respect to the current parameters $\theta _t$ (line 2a). Then, we use backpropagation to compute the gradients $f^{\prime }(\theta _{t})$ of the loss with respect to the current parameters (line 2b). Finally, we update the parameters with the learning rate $\eta _t$ and the mean of the gradients (line 2c). We take the mean of the gradients to deal with minibatches of different sizes. Notice that we take only the current minibatch into account to get an updated model. |
What is the best description of what the article is doing with Fiss’s book?
A. Taking a neutral approach in order to summarize the book.
B. Challenging Fiss’s points while unpacking what the book has to say on the whole.
C. Challenging Fiss’s points while offering better stats and better solutions.
D. Taking a supportive approach and demonstrating how and where Fiss is especially effective.
| 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. | B. Challenging Fiss’s points while unpacking what the book has to say on the whole. |
What is not something the article mentioned?
A. internet gambling is something the commission may regulate heavier
B. the gambling industry is funding political campaigns
C. states are allowing more methods of gambling to happen
D. the commission's research on the benefits of gambling taxes
| 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. | D. the commission's research on the benefits of gambling taxes |
What is Mia’s relationship to Jimmy D. and how does it develop throughout the story?
A. Jimmy D. is Mia’s fellow scout. At first, Mia describes their relationship as turbulent, complaining that Jimmy always asks her to be his partner even though she’s already partners with Venie Morlock. However, when Jimmy is arrested during their mission on Tintera, Mia agrees to be his partner out of pity.
B. Jimmy D. is Mia’s partner. At first, Mia describes their relationship as efficient and workable. But when competition around being the best colony scout come up, things start to change. Their partnership falls apart during their scout mission to Tintera, when Jimmy is arrested and jailed.
C. Jimmy D. is Mia’s soon to be partner. At first, Mia describes Jimmy as “a meatball,” suggesting that Jimmy is goofy and won’t prove to be a satisfactory partner. However, when Jimmy shows his smarts by saveing Mia from Horst and his grizzly gang, Mia realizes he will be a good partner after all.
D. Jimmy D. is Mia’s fellow scout. At first, Mia describes how they butt heads a lot due to differences in their personalities. But as Mia begins to face the trials of her mission, she comes to miss Jimmy, wishing that Jimmy could be there with her and provide a little help.
| DOWN TO THE WORLDS OF MEN BY ALEXEI PANSHIN The ancient rule was sink or swim—swim in the miasma of a planet without spaceflight, or sink to utter destruction! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, July 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I The horses and packs were loaded before we went aboard the scoutship. The scout bay is no more than a great oversized airlock with a dozen small ships squatting over their tubes, but it was the last of the Ship that I might ever see, so I took a long final look from the top of the ramp. There were sixteen of us girls and thirteen boys. We took our places in the seats in the center of the scout. Riggy Allen made a joke that nobody bothered to laugh at, and then we were all silent. I was feeling lost and just beginning to enjoy it when Jimmy Dentremont came over to me. He's red-headed and has a face that makes him look about ten. An intelligent runt like me. He said what I expected. "Mia, do you want to go partners if we can get together when we get down?" I guess he thought that because we were always matched on study I liked him. Well, I did when I wasn't mad at him, but now I had that crack he'd made about being a snob in mind, so I said, "Not likely. I want to come back alive." It wasn't fair, but it was a good crack and he went back to his place without saying anything. My name is Mia Havero. I'm fourteen, of course, or I wouldn't be telling this. I'm short, dark and scrawny, though I don't expect that scrawniness to last much longer. Mother is very good looking. In the meantime, I've got brains as a consolation. After we were all settled, George Fuhonin, the pilot, raised the ramps. We sat there for five minutes while they bled air out of our tube and then we just ... dropped. My stomach turned flips. We didn't have to leave that way, but George thinks it's fun to be a hot pilot. Thinking it over, I was almost sorry I'd been stinking to Jimmy D. He's the only competition I have my own age. The trouble is, you don't go partners with the competition, do you? Besides, there was still that crack about being a snob. The planet chosen for our Trial was called Tintera. The last contact the Ship had had with it—and we were the ones who dropped them—was almost 150 years ago. No contact since. That had made the Council debate a little before they dropped us there, but they decided it was all right in the end. It didn't make any practical difference to us kids because they never tell you anything about the place they're going to drop you. All I knew was the name. I wouldn't have known that much if Daddy weren't Chairman of the Council. I felt like crawling in a corner of the ship and crying, but nobody else was breaking down, so I didn't. I did feel miserable. I cried when I said good-by to Mother and Daddy—a real emotional scene—but that wasn't in public. It wasn't the chance of not coming back that bothered me really, because I never believed that I wouldn't. The thought that made me unhappy was that I would have to be on a planet for a whole month. Planets make me feel wretched. The gravity is always wrong, for one thing. Either your arches and calves ache or every time you step you think you're going to trip on a piece of fluff and break your neck. There are vegetables everywhere and little grubby things just looking for you to crawl on. If you can think of anything creepier than that, you've got a real nasty imagination. Worst of all, planets stink. Every single one smells—I've been on enough to know that. A planet is all right for a Mud-eater, but not for me. We have a place in the Ship like that—the Third Level—but it's only a thousand square miles and any time it gets on your nerves you can go up a level or down a level and be back in civilization. When we reached Tintera, they started dropping us. We swung over the sea from the morning side and then dropped low over gray-green forested hills. Finally George spotted a clear area and dropped into it. They don't care what order you go in, so Jimmy D. jumped up, grabbed his gear and then led his horse down the ramp. I think he was still smarting from the slap I'd given him. In a minute we were airborne again. I wondered if I would ever see Jimmy—if he would get back alive. It's no game we play. When we turn fourteen, they drop us on the nearest colonized planet and come back one month later. That may sound like fun to you, but a lot of us never come back alive. Don't think I was helpless. I'm hell on wheels. They don't let us grow for fourteen years and then kick us out to die. They prepare us. They do figure, though, that if you can't keep yourself alive by the time you're fourteen, you're too stupid, foolish or unlucky to be any use to the Ship. There's sense behind it. It means that everybody on the Ship is a person who can take care of himself if he has to. Daddy says that something has to be done in a closed society to keep the population from decaying mentally and physically, and this is it. And it helps to keep the population steady. I began to check my gear out—sonic pistol, pickup signal so I could be found at the end of the month, saddle and cinches, food and clothes. Venie Morlock has got a crush on Jimmy D., and when she saw me start getting ready to go, she began to check her gear, too. At our next landing, I grabbed Ninc's reins and cut Venie out smoothly. It didn't have anything to do with Jimmy. I just couldn't stand to put off the bad moment any longer. The ship lifted impersonally away from Ninc and me like a rising bird, and in just a moment it was gone. Its gray-blue color was almost the color of the half-overcast sky, so I was never sure when I saw it last. II The first night was hell, I guess because I'm not used to having the lights out. That's when you really start to feel lonely, being alone in the dark. When the sun disappears, somehow you wonder in your stomach if it's really going to come back. But I lived through it—one day in thirty gone. I rode in a spiral search pattern during the next two days. I had three things in mind—stay alive, find people and find some of the others. The first was automatic. The second was to find out if there was a slot I could fit into for a month. If not, I would have to find a place to camp out, as nasty as that would be. The third was to join forces, though not with that meatball Jimmy D. No, he isn't really a meatball. The trouble is that I don't take nothing from nobody, especially him, and he doesn't take nothing from nobody, especially me. So we do a lot of fighting. I had a good month for Trial. My birthday is in November—too close to Year End Holiday for my taste, but this year it was all right. It was spring on Tintera, but it was December in the Ship, and after we got back we had five days of Holiday to celebrate. It gave me something to look forward to. In two days of riding, I ran onto nothing but a few odd-looking animals. I shot one small one and ate it. It turned out to taste pretty good, though not as good as a slice from Hambone No. 4, to my mind the best meat vat on the Ship. I've eaten things so gruey-looking that I wondered that anybody had the guts to try them in the first place and they've turned out to taste good. And I've seen things that looked good that I couldn't keep on my stomach. So I guess I was lucky. On the third day, I found the road. I brought Ninc down off the hillside, losing sight of the road in the trees, and then reaching it in the level below. It was narrow and made of sand spread over a hard base. Out of the marks in the sand, I could pick out the tracks of horses and both narrow and wide wheels. Other tracks I couldn't identify. One of the smartest moves in history was to include horses when they dropped the colonies. I say "they" because, while we did the actual dropping, the idea originated with the whole evac plan back on Earth. Considering how short a time it was in which the colonies were established, there was not time to set up industry, so they had to have draft animals. The first of the Great Ships was finished in 2025. One of the eight, as well as the two that were being built then, went up with everything else in the Solar System in 2041. In that sixteen years 112 colonies were planted. I don't know how many of those planets had animals that could have been substituted but, even if they had, they would have had to be domesticated from scratch. That would have been stupid. I'll bet that half the colonies would have failed if they hadn't had horses. We'd come in from the west over the ocean, so I traveled east on the road. That much water makes me nervous, and roads have to go somewhere. I came on my first travelers three hours later. I rounded a tree-lined bend, ducking an overhanging branch, and pulled Ninc to a stop. There were five men on horseback herding a bunch of the ugliest creatures alive. They were green and grotesque. They had squat bodies, long limbs and knobby bulges at their joints. They had square, flat animal masks for faces. But they walked on their hind legs and they had paws that were almost hands, and that was enough to make them seem almost human. They made a wordless, chilling, lowing sound as they milled and plodded along. I started Ninc up again and moved slowly to catch up with them. All the men on horseback had guns in saddle boots. They looked as nervous as cats with kittens. One of them had a string of packhorses on a line and he saw me and called to another who seemed to be the leader. That one wheeled his black horse and rode back toward me. He was a middle-aged man, maybe as old as my Daddy. He was large and he had a hard face. Normal enough, but hard. He pulled to a halt when we reached each other, but I kept going. He had to come around and follow me. I believe in judging a person by his face. A man can't help the face he owns, but he can help the expression he wears on it. If a man looks mean, I generally believe that he is. This one looked mean. That was why I kept riding. He said, "What be you doing out here, boy? Be you out of your head? There be escaped Losels in these woods." I told you I hadn't finished filling out yet, but I hadn't thought it was that bad. I wasn't ready to make a fight over the point, though. Generally, I can't keep my bloody mouth shut, but now I didn't say anything. It seemed smart. "Where be you from?" he asked. I pointed to the road behind us. "And where be you going?" I pointed ahead. No other way to go. He seemed exasperated. I have that effect sometimes. Even on Mother and Daddy, who should know better. We were coming up on the others now, and the man said, "Maybe you'd better ride on from here with us. For protection." He had an odd way of twisting his sounds, almost as though he had a mouthful of mush. I wondered whether he were just an oddball or whether everybody here spoke the same way. I'd never heard International English spoken any way but one, even on the planet Daddy made me visit with him. One of the other outriders came easing by then. I suppose they'd been watching us all the while. He called to the hard man. "He be awfully small, Horst. I doubt me a Losel'd even notice him at all. We mought as well throw him back again." The rider looked at me. When I didn't dissolve in terror as he expected, he shrugged and one of the other men laughed. The hard man said to the others, "This boy will be riding along with us to Forton for protection." I looked down at the plodding, unhappy creatures they were driving along and one looked back at me with dull, expressionless golden eyes. I felt uncomfortable. I said, "I don't think so." What the man did then surprised me. He said, "I do think so," and reached for the rifle in his saddle boot. I whipped my sonic pistol out so fast that he was caught leaning over with the rifle half out. His jaw dropped. He knew what I held and he didn't want to be fried. I said, "Ease your rifles out and drop them gently to the ground." They did, watching me all the while with wary expressions. When all the rifles were on the ground, I said, "All right, let's go." They didn't want to move. They didn't want to leave the rifles. I could see that. Horst didn't say anything. He just watched me with narrowed eyes. But one of the others held up a hand and in wheedling tones said, "Look here, kid...." "Shut up," I said, in as mean a voice as I could muster, and he did. It surprised me. I didn't think I sounded that mean. I decided he just didn't trust the crazy kid not to shoot. After twenty minutes of easy riding for us and hard walking for the creatures, I said, "If you want your rifles, you can go back and get them now." I dug my heels into Ninc's sides and rode on. At the next bend I looked back and saw four of them holding their packhorses and the creatures still while one beat a dust-raising retreat down the road. I put this episode in the "file and hold for analysis" section in my mind and rode on, feeling good. I think I even giggled once. Sometimes I even convince myself that I'm hell on wheels. III When I was nine, my Daddy gave me a painted wooden doll that my great-grandmother brought from Earth. The thing is that inside it, nestled one in another, are eleven more dolls, each one smaller than the last. I like to watch people when they open it for the first time. My face must have been like that as I rode along the road. The country leveled into a great rolling valley and the trees gave way to great farms and fields. In the fields, working, were some of the green creatures, which surprised me since the ones I'd seen before hadn't seemed smart enough to count to one, let alone do any work. But it relieved me. I thought they might have been eating them or something. I passed two crossroads and started to meet more people, but nobody questioned me. I met people on horseback, and twice I met trucks moving silently past. And I overtook a wagon driven by the oldest man I've seen in my life. He waved to me, and I waved back. Near the end of the afternoon I came to the town, and there I received a jolt that sickened me. By the time I came out on the other side, I was sick. My hands were cold and sweaty and my head was spinning, and I wanted to kick Ninc to a gallop. I rode slowly in, looking all around, missing nothing. The town was all stone, wood and brick. Out of date. Out of time, really. There were no machines more complicated than the trucks I'd seen earlier. At the edge of town, I passed a newspaper office with a headline pasted in the window—INVASION! I remember that. I wondered about it. But I looked most closely at the people. In all that town, I didn't see one girl over ten years old and no grown-up women at all. There were little kids, there were boys and there were men, but no girls. All the boys and men wore pants, and so did I, which must have been why Horst and his buddies assumed I was a boy. It wasn't flattering; but I decided I'd not tell anybody different until I found what made the clocks tick on this planet. But that wasn't what bothered me. It was the kids. My God! They swarmed. I saw a family come out of a house—a father and four children. It was the most foul thing I've ever seen. It struck me then—these people were Free Birthers! I felt a wave of nausea and I closed my eyes until it passed. The first thing you learn in school is that if it weren't for idiot and criminal people like these, Earth would never have been destroyed. The evacuation would never have had to take place, and eight billion people wouldn't have died. There wouldn't have been eight billion people. But, no. They bred and they spread and they devoured everything in their path like a cancer. They gobbled up all the resources that Earth had and crowded and shoved one another until the final war came. I am lucky. My great-great-grandparents were among those who had enough foresight to see what was coming. If it hadn't been for them and some others like them, there wouldn't be any humans left anywhere. And I wouldn't be here. That may not scare you, but it scares me. What happened before, when people didn't use their heads and wound up blowing the Solar System apart, is something nobody should forget. The older people don't let us forget. But these people had, and that the Council should know. For the first time since I landed on Tintera, I felt really frightened. There was too much going on that I didn't understand. I felt a blind urge to get away, and when I reached the edge of town, I whomped Ninc a good one and gave him his head. I let him run for almost a mile before I pulled him down to a walk again. I couldn't help wishing for Jimmy D. Whatever else he is, he's smart and brains I needed. How do you find out what's going on? Eavesdrop? That's a lousy method. For one thing, people can't be depended on to talk about the things you want to hear. For another, you're likely to get caught. Ask somebody? Who? Make the mistake of bracing a fellow like Horst and you might wind up with a sore head and an empty pocket. The best thing I could think of was to find a library, but that might be a job. I'd had two bad shocks on this day, but they weren't the last. In the late afternoon, when the sun was starting to sink and a cool wind was starting to ripple the tree leaves, I saw the scoutship high in the sky. The dying sun colored it a deep red. Back again? I wondered what had gone wrong. I reached down into my saddlebag and brought out my contact signal. The scoutship swung up in the sky in a familiar movement calculated to drop the stomach out of everybody aboard. George Fuhonin's style. I triggered the signal, my heart turning flips all the while. I didn't know why he was back, but I wasn't really sorry. The ship swung around until it was coming back on a path almost over my head, going in the same direction. Then it went into a slip and started bucking so hard that I knew this wasn't hot piloting at all, just plain idiot stutter-fingered stupidity at the controls. As it skidded by me overhead, I got a good look at it and knew that it wasn't one of ours. Not too different, but not ours. One more enigma. Where was it from? Not here. Even if you know how, and we wouldn't tell these Mud-eaters how, a scoutship is something that takes an advanced technology to build. I felt defeated and tired. Not much farther along the road, I came to a campsite with two wagons pulled in for the night, and I couldn't help but pull in myself. The campsite was large and had two permanent buildings on it. One was a well enclosure and the other was little more than a high-walled pen. It didn't even have a roof. I set up camp and ate my dinner. In the wagon closest to me were a man, his wife and their three children. The kids were running around and playing, and one of them ran close to the high-walled pen. His father came and pulled him away. The kids weren't to blame for their parents, but when one of them said hello to me, I didn't even answer. I know how lousy I would feel if I had two or three brothers and sisters, but it didn't strike me until that moment that it wouldn't even seem out of the ordinary to these kids. Isn't that horrible? About the time I finished eating, and before it grew dark, the old man I had seen earlier in the day drove his wagon in. He fascinated me. He had white hair, something I had read about in stories but had never seen before. When nightfall came, they started a large fire. Everybody gathered around. There was singing for awhile, and then the father of the children tried to pack them off to bed. But they weren't ready to go, so the old man started telling them a story. In the old man's odd accent, and sitting there in the campfire light surrounded by darkness, it seemed just right. It was about an old witch named Baba Yaga who lived in the forest in a house that stood on chicken legs. She was the nasty stepmother of a nice little girl, and to get rid of the kid, she sent her on a phony errand into the deep dark woods at nightfall. I could appreciate the poor girl's position. All the little girl had to help her were the handkerchief, the comb and the pearl that she had inherited from her dear dead mother. But, as it turned out, they were just enough to defeat nasty old Baba Yaga and bring the girl safely home. I wished for the same for myself. The old man had just finished and they were starting to drag the kids off to bed when there was a commotion on the road at the edge of the camp. I looked but my eyes were adjusted to the light of the fire and I couldn't see far into the dark. A voice there said, "I'll be damned if I'll take another day like this one, Horst. We should have been here hours ago. It be your fault we're not." Horst growled a retort. I decided that it was time for me to leave the campfire. I got up and eased away as Horst and his men came up to the fire, and cut back to where Ninc was parked. I grabbed up my blankets and mattress and started to roll them up. I had a pretty good idea now what they used the high-walled pen for. I should have known that they would have to pen the animals up for the night. I should have used my head. I hadn't and now it was time to take leave. I never got the chance. I was just heaving the saddle up on Ninc when I felt a hand on my shoulder and I was swung around. "Well, well. Horst, look who we have here," he called. It was the one who'd made the joke about me being beneath the notice of a Losel. He was alone with me now, but with that call the others would be up fast. I brought the saddle around as hard as I could and then up, and he went down. He started to get up again, so I dropped the saddle on him and reached inside my jacket for my gun. Somebody grabbed me then from behind and pinned my arms to my side. I opened my mouth to scream—I have a good scream—but a rough smelly hand clamped down over it before I had a chance to get more than a lungful of air. I bit down hard—5000 lbs. psi, I'm told—but he didn't let me go. I started to kick, but Horst jerked me off my feet and dragged me off. When we were behind the pen and out of earshot of the fire, he stopped dragging me and dropped me in a heap. "Make any noise," he said, "and I'll hurt you." That was a silly way to put it, but somehow it said more than if he'd threatened to break my arm or my head. It left him a latitude of things to do if he pleased. He examined his hand. There was enough moonlight for that. "I ought to club you anyway," he said. The one I'd dropped the saddle on came up then. The others were putting the animals in the pen. He started to kick me, but Horst stopped him. "No," he said. "Look through the kid's gear, bring the horse and what we can use." The other one didn't move. "Get going, Jack," Horst said in a menacing tone and they stood toe to toe for a long moment before Jack finally backed down. It seemed to me that Horst wasn't so much objecting to me being kicked, but was rather establishing who did the kicking in his bunch. But I wasn't done yet. I was scared, but I still had the pistol under my jacket. Horst turned back to me and I said, "You can't do this and get away with it." He said, "Look, boy. You may not know it, but you be in a lot of trouble. So don't give me a hard time." He still thought I was a boy. It was not time to correct him, but I didn't like to see the point go unchallenged. It was unflattering. "The courts won't let you get away with this," I said. I'd passed a courthouse in the town with a carved motto over the doors: EQUAL JUSTICE UNDER THE LAW or TRUTH OUR SHIELD AND JUSTICE OUR SWORD or something stuffy like that. He laughed, not a phony, villian-type laugh, but a real laugh, so I knew I'd goofed. "Boy, boy. Don't talk about the courts. I be doing you a favor. I be taking what I can use of your gear, but I be letting you go. You go to court and they'll take everything and lock you up besides. I be leaving you your freedom." "Why would they be doing that?" I asked. I slipped my hand under my jacket. "Every time you open your mouth you shout that you be off one of the Ships," Horst said. "That be enough. They already have one of you brats in jail in Forton." I was about to bring my gun out when up came Jack leading Ninc, with all my stuff loaded on. I mentally thanked him. He said, "The kid's got some good equipment. But I can't make out what this be for." He held out my pickup signal. Horst looked at it, then handed it back. "Throw it away," he said. I leveled my gun at them—Hell on Wheels strikes again! I said, "Hand that over to me." Horst made a disgusted sound. "Don't make any noise," I said, "or you'll fry. Now hand it over." I stowed it away, then paused with one hand on the leather horn of the saddle. "What's the name of the kid in jail in Forton." "I can't remember," he said. "But it be coming to me. Hold on." I waited. Then suddenly my arm was hit a numbing blow from behind and the gun went flying. Jack pounced after it and Horst said, "Good enough," to the others who'd come up behind me. I felt like a fool. Horst stalked over and got the signal. He dropped it on the ground and said in a voice far colder than mine could ever be, because it was natural and mine wasn't, "The piece be yours." Then he tromped on it until it cracked and fell apart. Then he said, "Pull a gun on me twice. Twice." He slapped me so hard that my ears rang. "You dirty little punk." I said calmly, "You big louse." It was a time I would have done better to keep my mouth shut. All I can remember is a flash of pain as his fist crunched against the side of my face and then nothing. Brains are no good if you don't use them. | D. Jimmy D. is Mia’s fellow scout. At first, Mia describes how they butt heads a lot due to differences in their personalities. But as Mia begins to face the trials of her mission, she comes to miss Jimmy, wishing that Jimmy could be there with her and provide a little help. |
How does the multi-turn dialog system learns? | ### Introduction
Recent development in neural language modeling has generated significant excitement in the open-domain dialog generation community. The success of sequence-to-sequence learning BIBREF0, BIBREF1 in the field of neural machine translation has inspired researchers to apply the recurrent neural network (RNN) encoder-decoder structure to response generation BIBREF2. Specifically, the encoder RNN reads the input message, encodes it into a fixed context vector, and the decoder RNN uses it to generate the response. Shang et al. BIBREF3 applied the same structure combined with attention mechanism BIBREF4 on Twitter-style microblogging data. Following the vanilla sequence-to-sequence structure, various improvements have been made on the neural conversation model—for example, increasing the diversity of the response BIBREF5, BIBREF6, modeling personalities of the speakers BIBREF7, and developing topic aware dialog systems BIBREF8. Some of the recent work aims at incorporating affect information into neural conversational models. While making the responses emotionally richer, existing approaches either explicitly require an emotion label as input BIBREF9, or rely on hand-crafted rules to determine the desired emotion responses BIBREF10, BIBREF11, ignoring the subtle emotional interactions captured in multi-turn conversations, which we believe to be an important aspect of human dialogs. For example, Gottman BIBREF12 found that couples are likely to practice the so called emotional reciprocity. When an argument starts, one partner's angry and aggressive utterance is often met with equally furious and negative utterance, resulting in more heated exchanges. On the other hand, responding with complementary emotions (such as reassurance and sympathy) is more likely to lead to a successful relationship. However, to the best of our knowledge, the psychology and social science literature does not offer clear rules for emotional interaction. It seems such social and emotional intelligence is captured in our conversations. This is why we believe that the data driven approach will have an advantage. In this paper, we propose an end-to-end data driven multi-turn dialog system capable of learning and generating emotionally appropriate and human-like responses with the ultimate goal of reproducing social behaviors that are habitual in human-human conversations. We chose the multi-turn setting because only in such cases is the emotion appropriateness most necessary. To this end, we employ the latest multi-turn dialog model by Xing et al. BIBREF13, but we add an additional emotion RNN to process the emotional information in each history utterance. By leveraging an external text analysis program, we encode the emotion aspects of each utterance into a fixed-sized one-zero vector. This emotion RNN reads and encodes the input affect information, and then uses the final hidden state as the emotion representation vector for the context. When decoding, at each time step, this emotion vector is concatenated with the hidden state of the decoder and passed to the softmax layer to produce the probability distribution over the vocabulary. Thereby, our contributions are threefold. (1) We propose a novel emotion-tracking dialog generation model that learns the emotional interactions directly from the data. This approach is free of human-defined heuristic rules, and hence, is more robust and fundamental than those described in existing work BIBREF9, BIBREF10, BIBREF11. (2) We apply the emotion-tracking mechanism to multi-turn dialogs, which has never been attempted before. Human evaluation shows that our model produces responses that are emotionally more appropriate than the baselines, while slightly improving the language fluency. (3) We illustrate a human-evaluation approach for judging machine-produced emotional dialogs. We consider factors such as the balance of positive and negative sentiments in test dialogs, a well-chosen range of topics, and dialogs that our human evaluators can relate. It is the first time such an approach is designed with consideration for the human judges. Our main goal is to increase the objectivity of the results and reduce judges' mistakes due to out-of-context dialogs they have to evaluate. The rest of the paper unfolds as follows. Section SECREF2 discusses some related work. In Section SECREF3, we give detailed description of the methodology. We present experimental results and some analysis in Section SECREF4. The paper is concluded in Section SECREF5, followed by some future work we plan to do. ### Related Work
Many early open-domain dialog systems are rule-based and often require expert knowledge to develop. More recent work in response generation seeks data-driven solutions, leveraging on machine learning techniques and the availability of data. Ritter et al. BIBREF14 first applied statistical machine translation (SMT) methods to this area. However, it turns out that bilingual translation and response generation are different. The source and target sentences in translation share the same meaning; thus the words in the two sentences tend to align well with each other. However, for response generation, one could have many equally good responses for a single input. Later studies use the sequence-to-sequence neural framework to model dialogs, followed by various improving work on the quality of the responses, especially the emotional aspects of the conversations. The vanilla RNN encoder-decoder is usually applied to single-turn response generation, where the response is generated based on one single input message. In multi-turn settings, where a context with multiple history utterances is given, the same structure often ignores the hierarchical characteristic of the context. Some recent work addresses this problem by adopting a hierarchical recurrent encoder-decoder (HRED) structure BIBREF15, BIBREF16, BIBREF17. To give attention to different parts of the context while generating responses, Xing et al. BIBREF13 proposed the hierarchical recurrent attention network (HRAN) that uses a hierarchical attention mechanism. However, these multi-turn dialog models do not take into account the turn-taking emotional changes of the dialog. Recent work on incorporating affect information into natural language processing tasks, such as building emotional dialog systems and affect language models, has inspired our current work. For example, the Emotional Chatting Machine (ECM) BIBREF9 takes as input a post and a specified emotional category and generates a response that belongs to the pre-defined emotion category. The main idea is to use an internal memory module to capture the emotion dynamics during decoding, and an external memory module to model emotional expressions explicitly by assigning different probability values to emotional words as opposed to regular words. However, the problem setting requires an emotional label as an input, which might be unpractical in real scenarios. Asghar et al. BIBREF10 proposed to augment the word embeddings with a VAD (valence, arousal, and dominance) affective space by using an external dictionary, and designed three affect-related loss functions, namely minimizing affective dissonance, maximizing affective dissonance, and maximizing affective content. The paper also proposed the affectively diverse beam search during decoding, so that the generated candidate responses are as affectively diverse as possible. However, literature in affective science does not necessarily validate such rules. In fact, the best strategy to speak to an angry customer is the de-escalation strategy (using neutral words to validate anger) rather than employing equally emotional words (minimizing affect dissonance) or words that convey happiness (maximizing affect dissonance). Zhong et al. BIBREF11 proposed a biased attention mechanism on affect-rich words in the input message, also by taking advantage of the VAD embeddings. The model is trained with a weighted cross-entropy loss function, which encourages the generation of emotional words. However, these models only deal with single-turn conversations. More importantly, they all adopt hand-coded emotion responding mechanisms. To our knowledge, we are the first to consider modeling the emotional flow and its appropriateness in a multi-turn dialog system by learning from humans. ### Model
In this paper, we consider the problem of generating response $\mathbf {y}$ given a context $\mathbf {X}$ consisting of multiple previous utterances by estimating the probability distribution $p(\mathbf {y}\,|\,\mathbf {X})$ from a data set $\mathcal {D}=\lbrace (\mathbf {X}^{(i)},\mathbf {y}^{(i)})\rbrace _{i=1}^N$ containing $N$ context-response pairs. Here is a sequence of $m_i$ utterances, and is a sequence of $n_{ij}$ words. Similarly, is the response with $T_i$ words. Usually the probability distribution $p(\mathbf {y}\,|\,\mathbf {X})$ can be modeled by an RNN language model conditioned on $\mathbf {X}$. When generating the word $y_t$ at time step $t$, the context $\mathbf {X}$ is encoded into a fixed-sized dialog context vector $\mathbf {c}_t$ by following the hierarchical attention structure in HRAN BIBREF13. Additionally, we extract the emotion information from the utterances in $\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\mathbf {e}$, which is combined with $\mathbf {c}_t$ to produce the distribution. The overall architecture of the model is depicted in Figure FIGREF4. We are going to elaborate on how to obtain $\mathbf {c}_t$ and $\mathbf {e}$, and how they are combined in the decoding part. ### Model ::: Hierarchical Attention
The hierarchical attention structure involves two encoders to produce the dialog context vector $\mathbf {c}_t$, namely the word-level encoder and the utterance-level encoder. The word-level encoder is essentially a bidirectional RNN with gated recurrent units (GRU) BIBREF1. For utterance $\mathbf {x}_j$ in $\mathbf {X}$ ($j=1,2,\dots ,m$), the bidirectional encoder produces two hidden states at each word position $k$, the forward hidden state $\mathbf {h}^\mathrm {f}_{jk}$ and the backward hidden state $\mathbf {h}^\mathrm {b}_{jk}$. The final hidden state $\mathbf {h}_{jk}$ is then obtained by concatenating the two, The utterance-level encoder is a unidirectional RNN with GRU that goes from the last utterance in the context to the first, with its input at each step as the summary of the corresponding utterance, which is obtained by applying a Bahdanau-style attention mechanism BIBREF4 on the word-level encoder output. More specifically, at decoding step $t$, the summary of utterance $\mathbf {x}_j$ is a linear combination of $\mathbf {h}_{jk}$, for $k=1,2,\dots ,n_j$, Here $\alpha _{jk}^t$ is the word-level attention score placed on $\mathbf {h}_{jk}$, and can be calculated as where $\mathbf {s}_{t-1}$ is the previous hidden state of the decoder, $\mathbf {\ell }_{j+1}^t$ is the previous hidden state of the utterance-level encoder, and $\mathbf {v}_a$, $\mathbf {U}_a$, $\mathbf {V}_a$ and $\mathbf {W}_a$ are word-level attention parameters. The final dialog context vector $\mathbf {c}_t$ is then obtained as another linear combination of the outputs of the utterance-level encoder $\mathbf {\ell }_{j}^t$, for $j=1,2,\dots ,m$, Here $\beta _{j}^t$ is the utterance-level attention score placed on $\mathbf {\ell }_{j}^t$, and can be calculated as where $\mathbf {s}_{t-1}$ is the previous hidden state of the decoder, and $\mathbf {v}_b$, $\mathbf {U}_b$ and $\mathbf {W}_b$ are utterance-level attention parameters. ### Model ::: Emotion Encoder
In order to capture the emotion information carried in the context $\mathbf {X}$, we utilize an external text analysis program called the Linguistic Inquiry and Word Count (LIWC) BIBREF18. LIWC accepts text files as input, and then compares each word in the input with a user-defined dictionary, assigning it to one or more of the pre-defined psychologically-relevant categories. We make use of five of these categories, related to emotion, namely positive emotion, negative emotion, anxious, angry, and sad. Using the newest version of the program LIWC2015, we are able to map each utterance $\mathbf {x}_j$ in the context to a six-dimensional indicator vector ${1}(\mathbf {x}_j)$, with the first five entries corresponding to the five emotion categories, and the last one corresponding to neutral. If any word in $\mathbf {x}_j$ belongs to one of the five categories, then the corresponding entry in ${1}(\mathbf {x}_j)$ is set to 1; otherwise, $\mathbf {x}_j$ is treated as neutral, with the last entry of ${1}(\mathbf {x}_j)$ set to 1. For example, assuming $\mathbf {x}_j=$ “he is worried about me”, then since the word “worried” is assigned to both negative emotion and anxious. We apply a dense layer with sigmoid activation function on top of ${1}(\mathbf {x}_j)$ to embed the emotion indicator vector into a continuous space, where $\mathbf {W}_e$ and $\mathbf {b}_e$ are trainable parameters. The emotion flow of the context $\mathbf {X}$ is then modeled by an unidirectional RNN with GRU going from the first utterance in the context to the last, with its input being $\mathbf {a}_j$ at each step. The final emotion context vector $\mathbf {e}$ is obtained as the last hidden state of this emotion encoding RNN. ### Model ::: Decoding
The probability distribution $p(\mathbf {y}\,|\,\mathbf {X})$ can be written as We model the probability distribution using an RNN language model along with the emotion context vector $\mathbf {e}$. Specifically, at time step $t$, the hidden state of the decoder $\mathbf {s}_t$ is obtained by applying the GRU function, where $\mathbf {w}_{y_{t-1}}$ is the word embedding of $y_{t-1}$. Similar to Affect-LM BIBREF19, we then define a new feature vector $\mathbf {o}_t$ by concatenating $\mathbf {s}_t$ with the emotion context vector $\mathbf {e}$, on which we apply a softmax layer to obtain a probability distribution over the vocabulary, Each term in Equation (DISPLAY_FORM16) is then given by We use the cross-entropy loss as our objective function ### Evaluation
We trained our model using two different datasets and compared its performance with HRAN as well as the basic sequence-to-sequence model by performing both offline and online testings. ### Evaluation ::: Datasets
We use two different dialog corpora to train our model—the Cornell Movie Dialogs Corpus BIBREF20 and the DailyDialog dataset BIBREF21. Cornell Movie Dialogs Corpus. The dataset contains 83,097 dialogs (220,579 conversational exchanges) extracted from raw movie scripts. In total there are 304,713 utterances. DailyDialog. The dataset is developed by crawling raw data from websites used for language learners to learn English dialogs in daily life. It contains 13,118 dialogs in total. We summarize some of the basic information regarding the two datasets in Table TABREF25. In our experiments, the models are first trained on the Cornell Movie Dialogs Corpus, and then fine-tuned on the DailyDialog dataset. We adopted this training pattern because the Cornell dataset is bigger but noisier, while DailyDialog is smaller but more daily-based. To create a training set and a validation set for each of the two datasets, we take segments of each dialog with number of turns no more than six, to serve as the training/validation examples. Specifically, for each dialog $\mathbf {D}=(\mathbf {x}_1,\mathbf {x}_2,\dots ,\mathbf {x}_M)$, we create $M-1$ context-response pairs, namely $\mathbf {U}_i=(\mathbf {x}_{s_i},\dots ,\mathbf {x}_i)$ and $\mathbf {y}_i=\mathbf {x}_{i+1}$, for $i=1,2,\dots ,M-1$, where $s_i=\max (1,i-4)$. We filter out those pairs that have at least one utterance with length greater than 30. We also reduce the frequency of those pairs whose responses appear too many times (the threshold is set to 10 for Cornell, and 5 for DailyDialog), to prevent them from dominating the learning procedure. See Table TABREF25 for the sizes of the training and validation sets. The test set consists of 100 dialogs with four turns. We give more detailed description of how we create the test set in Section SECREF31. ### Evaluation ::: Baselines and Implementation
We compared our multi-turn emotionally engaging dialog model (denoted as MEED) with two baselines—the vanilla sequence-to-sequence model (denoted as S2S) and HRAN. We chose S2S and HRAN as baselines because we would like to evaluate our model's capability to keep track of the multi-turn context and to produce emotionally more appropriate responses, respectively. In order to adapt S2S to the multi-turn setting, we concatenate all the history utterances in the context into one. For all the models, the vocabulary consists of 20,000 most frequent words in the Cornell and DailyDialog datasets, plus three extra tokens: <unk> for words that do not exist in the vocabulary, <go> indicating the begin of an utterance, and <eos> indicating the end of an utterance. Here we summarize the configurations and parameters of our experiments: We set the word embedding size to 256. We initialized the word embeddings in the models with word2vec BIBREF22 vectors first trained on Cornell and then fine-tuned on DailyDialog, consistent with the training procedure of the models. We set the number of hidden units of each RNN to 256, the word-level attention depth to 256, and utterance-level 128. The output size of the emotion embedding layer is 256. We optimized the objective function using the Adam optimizer BIBREF23 with an initial learning rate of 0.001. We stopped training the models when the lowest perplexity on the validation sets was achieved. For prediction, we used beam search BIBREF24 with a beam width of 256. ### Evaluation ::: Evaluation Metrics
The evaluation of chatbots remains an open problem in the field. Recent work BIBREF25 has shown that the automatic evaluation metrics borrowed from machine translation such as BLEU score BIBREF26 tend to align poorly with human judgement. Therefore, in this paper, we mainly adopt human evaluation, along with perplexity, following the existing work. ### Evaluation ::: Evaluation Metrics ::: Human evaluation setup
To develop a test set for human evaluation, we first selected the emotionally colored dialogs with exactly four turns from the DailyDialog dataset. In the dataset each dialog turn is annotated with a corresponding emotional category, including the neutral one. For our purposes we filtered out only those dialogs where more than a half of utterances have non-neutral emotional labels. This gave us 78 emotionally positive dialogs and 14 emotionally negative dialogs. In order to have a balanced test set with equal number of positive and negative dialogs, we recruited two English-speaking students from our university without any relationship to the authors' lab and instructed them to create five negative dialogs with four turns, as if they were interacting with another human, according to each of the following topics: relationships, entertainment, service, work and study, and everyday situations. Thus each person produced 25 dialogs, and in total we obtained 50 emotionally negative daily dialogs in addition to the 14 already available. To form the test set, we randomly selected 50 emotionally positive and 50 emotionally negative dialogs from the two pools of dialogs described above (78 positive dialogs from DailyDialog, 64 negative dialogs from DailyDialog and human-generated). For human evaluation of the models, we recruited another four English-speaking students from our university without any relationship to the authors' lab to rate the responses generated by the models. Specifically, we randomly shuffled the 100 dialogs in the test set, then we used the first three utterances of each dialog as the input to the three models being compared and let them generate the responses. According to the context given, the raters were instructed to evaluate the quality of the responses based on three criteria: (1) grammatical correctness—whether or not the response is fluent and free of grammatical mistakes; (2) contextual coherence—whether or not the response is context sensitive to the previous dialog history; (3) emotional appropriateness—whether or not the response conveys the right emotion and feels as if it had been produced by a human. For each criterion, the raters gave scores of either 0, 1 or 2, where 0 means bad, 2 means good, and 1 indicates neutral. ### Evaluation ::: Results
Table TABREF34 gives the perplexity scores obtained by the three models on the two validation sets and the test set. As shown in the table, MEED achieves the lowest perplexity score on all three sets. We also conducted t-test on the perplexity obtained, and results show significant improvements (with $p$-value $<0.05$). Table TABREF34, TABREF35 and TABREF35 summarize the human evaluation results on the responses' grammatical correctness, contextual coherence, and emotional appropriateness, respectively. In the tables, we give the percentage of votes each model received for the three scores, the average score obtained with improvements over S2S, and the agreement score among the raters. Note that we report Fleiss' $\kappa $ score BIBREF27 for contextual coherence and emotional appropriateness, and Finn's $r$ score BIBREF28 for grammatical correctness. We did not use Fleiss' $\kappa $ score for grammatical correctness. As agreement is extremely high, this can make Fleiss' $\kappa $ very sensitive to prevalence BIBREF29. On the contrary, we did not use Finn's $r$ score for contextual coherence and emotional appropriateness because it is only reasonable when the observed variance is significantly less than the chance variance BIBREF30, which did not apply to these two criteria. As shown in the tables, we got high agreement among the raters for grammatical correctness, and fair agreement among the raters for contextual coherence and emotional appropriateness. For grammatical correctness, all three models achieved high scores, which means all models are capable of generating fluent utterances that make sense. For contextual coherence and emotional appropriateness, MEED achieved higher average scores than S2S and HRAN, which means MEED keeps better track of the context and can generate responses that are emotionally more appropriate and natural. We conducted Friedman test BIBREF31 on the human evaluation results, showing the improvements of MEED are significant (with $p$-value $<0.01$). ### Evaluation ::: Results ::: Case Study
We present four sample dialogs in Table TABREF36, along with the responses generated by the three models. Dialog 1 and 2 are emotionally positive and dialog 3 and 4 are negative. For the first two examples, we can see that MEED is able to generate more emotional content (like “fun” and “congratulations”) that is appropriate according to the context. For dialog 4, MEED responds in sympathy to the other speaker, which is consistent with the second utterance in the context. On the contrary, HRAN poses a question in reply, contradicting the dialog history. ### Conclusion and Future Work
According to the Media Equation Theory BIBREF32, people respond to computers socially. This means humans expect talking to computers as they talk to other human beings. This is why we believe reproducing social and conversational intelligence will make social chatbots more believable and socially engaging. In this paper, we propose a multi-turn dialog system capable of generating emotionally appropriate responses, which is the first step toward such a goal. We have demonstrated how to do so by (1) modeling utterances with extra affect vectors, (2) creating an emotional encoding mechanism that learns emotion exchanges in the dataset, (3) curating a multi-turn dialog dataset, and (4) evaluating the model with offline and online experiments. As future work, we would like to investigate the diversity issue of the responses generated, possibly by extending the mutual information objective function BIBREF5 to multi-turn settings. We would also like to evaluate our model on a larger dataset, for example by extracting multi-turn dialogs from the OpenSubtitles corpus BIBREF33. Figure 1: The overall architecture of our model. Table 1: Statistics of the two datasets. Table 2: Perplexity scores achieved by the models. Validation set 1 comes from the Cornell dataset, while validation set 2 comes from the DailyDialog dataset. Table 5: Human evaluation results on emotional appropriateness. Table 4: Human evaluation results on contextual coherence. Table 6: Sample responses for the three models. | we extract the emotion information from the utterances in $\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\mathbf {e}$, which is combined with $\mathbf {c}_t$ to produce the distribution |
Why did Lt. Britton laugh at Major Banes' anxiety?
A. The British accent he used as a coping mechanism sounded ridiculous
B. He had forgotten to order the simplest of supplies
C. She didn't think his worries were an actual problem
D. She already had the equipment he thought he was missing
| 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. | C. She didn't think his worries were an actual problem |
How did the dosage of Aspirin change from Mr. Nilsson's medication regimen in 2005 to 2008?
Choose the correct answer from the following options:
A. Increased from 10 mg to 25 mg.
B. Decreased from 25 mg to 10 mg.
C. Remained constant at 10 mg.
D. Initially excluded, then introduced at 25 mg.
E. Changed from 1-0-1 to 1-1-1.
| ### Patient Report 0
**Dear colleague, **
We are reporting on the inpatient stay of our patient, Emil Nilsson,
born on 12/04/2004, who was under our inpatient care from 01/26/05 to
02/02/05.
**Diagnoses:**
- Upper respiratory tract infection
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
**Medical History:** Emil has a Hypoplastic Left Heart Syndrome. The
corrective procedure, including the Damus-Kaye-Stansel and
Blalock-Taussig Anastomosis, took place three months ago. Under the
current medication, the cardiac situation has been stable. He has shown
satisfactory weight gain. Emil is the first child of parents with
healthy hearts. An external nursing service provides home care every two
days. The parents feel confident in the daily care of the child,
including the placement of gastric tubes.
**Current Presentation:** Since the evening before admission, Emil had
elevated temperatures up to 40°C with a slight runny nose. No coughing,
no diarrhea, no vomiting. After an outpatient visit to the treating
pediatrician, Emil was referred to our hospital due to the complex
cardiac history. Admission for the Glenn procedure is scheduled for
01/20/05.
**Physical Examination:** Stable appearance and condition. Pinkish skin
color, good skin turgor.
- Cardiovascular: Rhythmic, 3/6 systolic murmur auscultated on the
left parasternal side, radiating to the back.
- Respiratory: Bilateral vesicular breath sounds, no rales.
- Abdomen: Soft and unremarkable, no hepatosplenomegaly, no
pathological resistances.
- ENT exam, except for runny nose, unremarkable.
- Good spontaneous motor skills with cautious head control.
- Current Weight: 4830 g; Current Length: 634cm. Transcutaneous Oxygen
Saturation: 78%.
- Blood Pressure Measurement (mmHg): Left Upper Arm 89/56 (66), Right
Upper Arm 90/45
**Current Medication:**
**Medication** **Dosage** **Frequency**
--------------------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Furosemide (Lasix) 4 mg 1-1-1-1
Spironolactone (Aldactone) 10 mg 1-0-0-0
Hydrochlorothiazide (Microzide) 2 mg 1-0-1
Aspirin 10 mg 1-0-0
Omeprazole (Prilosec) 2.5 mg 1-0-1
Vitamin D (Drisdol) 500 IU Once daily
**ECG on 01/27/2006:** Sinus rhythm, heart rate 83/min, sagittal type.
P: 60 ms, PQ: 100 ms, QRS: 80 ms, QT: 260 ms. T-wave negative in V1 and
V2, biphasic in V3, positive from V4 onward, no arrhythmias. Signs of
right ventricular hypertrophy.
**Echocardiography on 01/27/2006:** Satisfactory function of the
morphological right ventricle, small hypoplastic left ventricle with
minimal contractility. Hypoplastic mitral and original aortic valve
barely opening. Regular flow profile in the neoaorta. Aortic arch and
Blalock-Taussig shunt not optimally visible due to restlessness. Trivial
tricuspid valve insufficiency.
**Chest X-ray on 01/28/2006:** Widened heart shadow, cardiothoracic
ratio 0.5. Slight diffuse increase in markings on the right lung, no
signs of pulmonary congestion. Hilum delicate. Recesses visible, no
effusion. No localized infiltrations. No pneumothorax.
**Therapy and Progression:** Based on the clinical and paraclinical
picture of a pulmonary infection, we treated Emil with intravenous
Cefuroxime for five days, along with daily physical therapy. Under this
treatment, Emil's condition improved rapidly, with no auscultatory lung
abnormalities. CRP and leukocyte count reduced. No fever. In the course
of treatment, Emil had temporary diarrhea, which was well managed with
adequate fluid substitution.
We were able to discharge Emil in a significantly improved and stable
general condition on the fifth day of treatment, with a weight of 5060
g. Transcutaneous oxygen saturations were consistently between 70%
(during infection) and 85%.
Three days later, the mother presented the child again at the emergency
department due to vomiting after each meal and diarrhea. After changing
the gastric tube and readmission here, there was no more vomiting, and
feeding was feasible. Three to four stools of adequate consistency
occurred daily. Cardiac medication remained unchanged.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
--------------------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-0-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Furosemide (Lasix) 4 mg 1-1-1-1
Spironolactone (Aldactone) 10 mg 1-0-0-0
Hydrochlorothiazide (Microzide) 2 mg 1-0-1
Aspirin 10 mg 1-0-0
Omeprazole (Prilosec) 2.5 mg 1-0-1
Vitamin D (Drisdol) 500 IU Once daily
### Patient Report 1
**Dear colleague, **
We are reporting on the inpatient stay of your patient Emil Nilsson,
born on 12/04/2004, who received inpatient care from 01/20/2005 to
01/27/2005.
**Diagnoses:**
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel Procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
**Current Presentation:** Bidirectional Glenn Anastomosis, enlargement
of the pulmonary trunk, and closure of BT shunt
**Medical History:** We kindly assume that you are familiar with the
detailed medical history.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
--------------------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-0-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Furosemide (Lasix) 4 mg 1-1-1-1
Spironolactone (Aldactone) 10 mg 1-0-0-0
Hydrochlorothiazide (Microzide) 2 mg 1-0-1
Aspirin 10 mg 1-0-0
Omeprazole (Prilosec) 2.5 mg 1-0-1
Vitamin D 500 IU Once daily
**Physical Examination:** Stable general condition, no fever. Gastric
tube.
Unremarkable sternotomy scar, dry. Drains in situ, unremarkable.
[Heart]{.underline}: Rhythmic heart action, 2/6 systolic murmur audible
left parasternal.
[Lungs]{.underline}: Bilateral vesicular breath sounds, no additional
sounds.
[Abdomen]{.underline}: Soft liver 1.5 cm below the costal margin. No
pathological resistances.
Pulses palpable on all sides.
[Current weight:]{.underline} 4765 g; current length: 62 cm; head
circumference: 37 cm.
Transcutaneous oxygen saturation: 85%.
[Blood pressure (mmHg):]{.underline} Left arm 91/65 (72), right arm
72/55 (63).
**Echocardiography on 01/21/2005 and 01/27/2005:**
Global mildly impaired function of the morphologically right systemic
ventricle with satisfactory contractility. Minimal tricuspid
insufficiency with two small jets (central and septal), Inflow merged
Vmax 0.9 m/s. DKS anastomosis well visible, aortic VTI 14-15 cm. Free
flow in Glenn with breath-variable flow pattern, Vmax 0.5 m/s. No
pleural effusions, good diaphragmatic mobility bilaterally, no
pericardial effusion. Isthmus optically free with Vmax 1.8 m/s.
**Speech Therapy Consultation on 01/23/2005:**
No significant orofacial disorders. Observation of drinking behavior
recommended initially. Stimulation of sucking with various pacifiers.
Instruction given to the father.
**Therapy and Progression:** On 02/15/2006, the BT shunt was severed and
a bidirectional Glenn Anastomosis was created, along with an enlargement
of the pulmonary artery. The course was uncomplicated with swift
extubation and transfer to the intermediate care unit on the second
postoperative day. Timely removal of drains and pacemaker wires. The
child remained clinically stable throughout the stay. The child\'s own
drinking performance is satisfactory, with varying amounts of fluid
intake between 60 and 100 ml per meal. The tube feeding is well
tolerated, no vomiting, and discharged without a tube. Stool normal. IV
antibiotics were continued until 01/22/2005. Transition from
heparinization to daily Aspirin. Inhalation was also stopped during the
course with a stable clinical condition.
Due to persistently elevated mean pressures of 70 to 80 mmHg and limited
global contractility of the morphologically right systemic ventricle, we
increased both Carvedilol and Captopril medication. Blood pressures have
changed only slightly. Therefore, we request an outpatient long-term
blood pressure measurement and, if necessary, further medication
optimization. Echocardiographically, we observed impaired but
satisfactory contractility of the right systemic ventricle with only
minimal tricuspid valve insufficiency, as well as a well-functioning
Glenn Anastomosis. No insufficiency of the neoaortic valve with a VTI of
15 cm. No pericardial effusion or pleural effusions upon discharge.
A copy of the summary has been sent to the involved external home care
service for further outpatient care.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
---------------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-0-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Spironolactone (Aldactone) 10 mg 1-0-0-0
Iron Supplement 4 drops 1-0-1
Omeprazole (Prilosec) 2.5 mg 1-0-1
Vitamin D 500 IU Once daily
Aspirin 10 mg 1-0-0
### Patient Report 2
**Dear colleague, **
We are reporting to you about the inpatient stay of our patient, Emil
Nilsson, born on 12/04/2004. He was admitted to our ward from 03/01/2008
to 03/10/2008.
**Diagnoses:**
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel Procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
**Current Presentation:** Inpatient admission for dental rehabilitation
under intubation anesthesia
**Medical History:** We may kindly assume that you are familiar with the
medical history. Prior to the planned Fontan completion, dental
rehabilitation under intubation anesthesia was required due to the
patient\'s carious dental status, which led to the scheduled inpatient
admission.
**Physical Examination:** Friendly toddler in stable general condition,
pale skin color, central cyanosis, no edema.
- ENT unremarkable, large tonsils, no cervical lymphadenopathy.
- Heart: Heart sounds clear, rhythmic, 1/6 systolic murmur with a
point of maximal intensity over the 3rd intercostal space on the
left.
- Lungs: Bilateral equal ventilation, vesicular breath sounds.
- Initial neurological examination unremarkable.
- Current weight: 12.4 kg; current body length: 93 cm.
- Percutaneous oxygen saturation: 76%.
- Blood pressure (mmHg): Right upper arm 117/50, left upper arm
110/57, right lower leg 134/55, left lower leg 146/71.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
--------------------- ------------ -----------------------------------------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Aspirin 10 mg 1-0-0 (discontinued 10 days before admission)
**ECG at Admission:** Sinus rhythm, heart rate 84/min, sagittal type. P
wave 50 ms, PQ interval 120 ms, QRS duration 80 ms, QT interval 360 ms,
QTc interval 440 ms, R/S transition in V4, T wave positive in V3 to V6.
Persistent S wave in V4 to V6 -1.1 mV, no extrasystoles in the rhythm
strip.
**Consultation with Maxillofacial Surgery on 02/03/2008:**
Timely wound conditions, clot at positions 55, 65, 84 in situ, Aspirin
may be resumed today, further treatment by the Southern Dental Clinic.
**Treatment and Progression:** Upon admission, the necessary
pre-interventional diagnostics were performed. Dental rehabilitation
(extraction and fillings) was performed without complications under
intubation anesthesia on 03/02/2008. After anesthesia, the child
experienced pronounced restlessness, requiring a single sedation with
intravenous Midazolam. The child\'s behavior improved over time, and the
wound conditions were unremarkable. Discharge on 03/03/2008 after
consultation with our maxillofacial surgeon into outpatient follow-up
care. We request pediatric cardiology and dental follow-up checks.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
--------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Aspirin 25 mg 1-0-0
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
----------------------------------------------- --------------- ---------------------
Calcium 2.33 mEq/L 2.10-2.55 mEq/L
Phosphorus 1.12 mEq/L 0.84-1.45 mEq/L
Osmolality 286 mOsm/kg 280-300 mOsm/kg
Iron 20.4 µg/dL 4.8-24.7 µg/dL
Transferrin Saturation 28.3% 16.0-45.0%
Magnesium 1.84 mg/dL 1.5-2.3 mg/dL
Creatinine 0.84 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 132 mL/min
Estimated GFR (eGFR Cystatin) \>90.0 mL/min
Blood Urea Nitrogen (BUN) 29 mg/dL 18-45 mg/dL
Total Bilirubin 0.97 mg/dL \<1.20 mg/dL
Direct Bilirubin 0.34 mg/dL \<0.30 mg/dL
Immunoglobulin G 11.42 g/L 5.49-15.84 g/L
Immunoglobulin A 1.94 g/L 0.61-3.48 g/L
Immunoglobulin M 0.65 g/L 0.50-1.90 g/L
Cystatin C 0.93 mg/L 0.50-1.00 mg/L
Transferrin 2.89 g/L
Ferritin 54.2 ng/mL 14.0-152.0 ng/mL
Total Cholesterol 110 mg/dL 82-192 mg/dL
Triglycerides 64 mg/dL
Apolipoprotein A1 0.91 g/L 1.04-2.02 g/L
ALT 37 U/L \<41 U/L
AST 33 U/L \<50 U/L
Alkaline Phosphatase 138 U/L 55-149 U/L
Butyrylcholinesterase (Pseudo-Cholinesterase) 5.62 kU/L 5.32-12.92 kU/L
GLDH 3.1 U/L \<6.4 U/L
Gamma-GT 96 U/L 8-61 U/L
LDH 184 U/L 135-250 U/L
Parathyroid Hormone 55.0 pg/mL 15.0-65.0 pg/mL
25-OH-Vitamin D3 10.9 ng/mL 20.0-50.0 ng/mL
Free Thyroxine 17.90 ng/dL 9.50-16.40 ng/dL
TSH 3.56 mIU/mL 0.50-4.30 mIU/mL
### Patient Report 3
**Dear colleague, **
We are reporting about the inpatient stay of our patient, Emil Nilsson,
born on 12/04/2004. He was admitted to our ward from 07/02/2008 to
07/23/2008.
**Diagnoses:**
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel Procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
**Current Presentation:** Planned admission for Fontan Procedure
**Medical History:** We may assume that you are familiar with the
detailed medical history.
**Physical Examination:** Friendly toddler in stable general condition,
pale skin color, central cyanosis, no edema.
- ENT unremarkable, large tonsils, no cervical lymphadenopathy.
- Heart: Heart sounds clear, rhythmic, 1/6 systolic murmur with a
point of maximal intensity over the 3rd intercostal space on the
left.
- Lungs: Bilateral equal ventilation, vesicular breath sounds. Initial
neurological examination unremarkable.
- Percutaneous oxygen saturation: 77%.
- Blood pressure (mmHg): Right upper arm 124/60, left upper arm
112/59, right lower leg 134/55, left lower leg 146/71.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
---------------------- ------------ ---------------
Captopril (Capoten®) 2 mg 1-1-1
Carvedilol (Coreg®) 0.2 mg 1-0-1
Aspirin 25 mg 1-0-0
**Surgical Report:**
Median Sternotomy, dissection of adhesions to access the anterior aspect
of the heart, cannulation for extracorporeal circulation with bicaval
cannulation. Further preparation of the heart, followed by clamping of
the inferior vena cava towards the heart. Cutting the vessel, suturing
the cardiac end, and then anastomosis of the inferior vena cava with an
18mm Gore-Tex prosthesis, which is subsequently tapered and sutured to
the central pulmonary artery in an open anastomosis technique.
Resumption of ventilation, smooth termination of extracorporeal
circulation. Placement of 2 drains. Layered wound closure.
Transesophageal Echocardiogram shows good biventricular function. The
patient is transferred back to the ward with ongoing catecholamine
support.
**ECG on 07/02/2008:** Sinus rhythm, heart rate 76/min, steep type, PQ
interval 140 ms, QRS duration 110 ms, QT interval 340 ms, QTc 385 mmHg.
ST depression, descending in V2+V3. T-wave positivity from V2. No
extrasystoles. No pauses.
**Therapy and Progression:**
The patient was admitted for a planned Fontan procedure on 07/02/2008.
The procedure was performed without complications. An extracardiac
conduit without overflow was created. Postoperatively, there was a rapid
recovery. Extubation took place 2 hours after the procedure. Peri- and
postoperative antibiotic treatment with Cefuroxim was administered.
Bilateral pleural effusions were drained using thoracic drains, which
were subsequently changed to pigtail drains after transfer to the
general ward. Daily aspiration of the pleural effusions was performed.
These effusions decreased over time, and the drains were removed on
07/14/2008. No further pleural effusions occurred. A minimal pericardial
effusion and ascites were still present. Diuretic therapy was initially
continued but could be significantly reduced by the time of discharge.
Echocardiography showed a favorable postoperative result. Monitoring of
vital signs and consciousness did not reveal any abnormalities. However,
the ECG showed occasional idioventricular rhythms during bradycardia.
Oxygen saturation ranged between 95% and 100%. Scarring revealed a
dehiscence in the middle third and apical region. Regular dressing
changes and disinfection of the affected wound area were performed.
After consulting with our pediatric surgical colleagues, glucose was
locally applied. There was no fever. Antibiotic treatment was
discontinued after the removal of the pigtail drain, and the
postoperatively increased inflammatory parameters had already returned
to normal. The patient received physiotherapy, and their general
condition improved daily. We were thus able to discharge Emil on
07/23/2008.
**Current Recommendations:**
- We recommend regular wound care with Octinisept.
- Follow-up in the pediatric cardiology outpatient clinic.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
--------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Aspirin 25 mg 1-0-0
**Lab results upon Discharge:**
**Parameter** **Result** **Reference Range**
------------------------------- --------------- ---------------------
Calcium 2.54 mEq/L 2.10-2.55 mEq/L
Phosphate 1.42 mEq/L 0.84-1.45 mEq/L
Osmolality 298 mOsm/kg 280-300 mOsm/kg
Iron 20.6 µmol/L 4.8-24.7 µmol/L
Transferrin Saturation 34 % 16.0-45.0 %
Magnesium 0.61 mEq/L 0.62-0.91 mEq/L
Creatinine 0.84 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 132 mL/min
Estimated GFR (eGFR Cystatin) \>90.0 mL/min
Urea 29 mg/dL 18-45 mg/dL
Total Bilirubin 0.97 mg/dL \<1.20 mg/dL
Direct Bilirubin 0.34 mg/dL \<0.30 mg/dL
Immunoglobulin G 11.42 g/L 5.49-15.84 g/L
Immunoglobulin A 1.94 g/L 0.61-3.48 g/L
Immunoglobulin M 0.65 g/L 0.50-1.90 g/L
Cystatin C 0.93 mg/L 0.50-1.00 mg/L
Transferrin 2.89 g/L
Ferritin 54.2 µg/L 14.0-152.0 µg/L
Total Cholesterol 110 mg/dL 82-192 mg/dL
Apolipoprotein A1 0.91 g/L 1.04-2.02 g/L
ALT 37 U/L \<41 U/L
AST 33 U/L \<50 U/L
Alkaline Phosphatase 139 U/L 55-149 U/L
GLDH 3.5 U/L \<6.4 U/L
Gamma-GT 24 U/L 8-61 U/L
LDH 145 U/L 135-250 U/L
Parathyroid Hormone 57.2 ng/L 15.0-65.0 ng/L
25-OH-Vitamin D3 34.2 nmol/L 50.0-150.0 nmol/L
### Patient Report 4
**Dear colleague, **
We are reporting to you about the inpatient stay of our patient, Emil
Nilsson, born on 12/04/2004, who was admitted to our clinic from
10/20/2021 to 10/22/2021.
**Diagnoses:**
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel Procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
- Status post Glenn procedure
- Fontan conduit retrocardial narrowing, extended hepatic vein
window/VCI
- Chronic liver congestion with mild fibrosis (sonography)
**Procedures**: Diagnostic cardiac catheterization in analgosedation on
10/20/2021.
**Medical History:** We kindly assume that the detailed medical history
is known to you and refer to previous medical reports from our clinic.
The current admission is based on a referral from the outpatient
pediatric cardiologist for a diagnostic cardiac catheterization to
evaluate Fontan hemodynamics in the context of desaturation during a
stress test. Emil reports feeling subjectively well, but during school
sports, he can only run briefly before experiencing palpitations and
dyspnea. Emil attends a special needs school. He is currently free from
infection and fever.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
--------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Aspirin 25 mg 1-0-0
**Physical Examination:** Emil is in good general condition and slim
build, with no signs of infection.
- Cardiac status: Rhythmic heart action, 2/6 systolic murmur.
- Pulse status: Normal.
- Lungs: Bilateral equal ventilation, vesicular breath sounds, no
rales.
- Abdomen: Soft, no hepatosplenomegaly. Unremarkable sternal scars. No
signs of cardiopulmonary decompensation.
- Current weight: 47 kg; current height: 169 cm.
- Pulse oximetry oxygen saturation: 95%.
- Blood pressure (mmHg): Right upper arm 132/94, left upper arm
121/98, right lower leg 158/94, left lower leg 156/94.
**Lab results:**
**Parameter** **Result** **Reference Range**
------------------------------- --------------- ---------------------
Calcium 2.38 mEq/L 2.10-2.55 mEq/L
Phosphate 1.19 mEq/L 0.84-1.45 mEq/L
Osmolality 282 mOsm/Kg 280-300 mOsm/Kg
Iron 20.0 µg/dL 4.8-24.7 µg/dL
Transferrin Saturation 28.1 % 16.0-45.0 %
Magnesium 0.79 mEq/L 0.62-0.91 mEq/L
Creatinine 0.81 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 131 mL/min
Estimated GFR (eGFR Cystatin) \>90.0 mL/min
Urea (BUN) 27 mg/dL 18-45 mg/dL
Total Bilirubin 0.92 mg/dL \<1.20 mg/dL
Direct Bilirubin 0.38 mg/dL \<0.30 mg/dL
Immunoglobulin G 11.47 g/L 5.49-15.84 g/L
Immunoglobulin A 1.99 g/L 0.61-3.48 g/L
Immunoglobulin M 0.61 g/L 0.50-1.90 g/L
Cystatin C 0.95 mg/L 0.50-1.00 mg/L
Transferrin 2.83 g/L
Ferritin 54.5 µg/L 14.0-152.0 µg/L
Total Cholesterol 110 mg/dL 82-192 mg/dL
Triglycerides 62 mg/dL
Apolipoprotein A1 0.94 g/L 1.04-2.02 g/L
ALT (GPT) 35 U/L \<41 U/L
AST (GOT) 32 U/L \<50 U/L
Alkaline Phosphatase 135 U/L 55-149 U/L
Pseudo-Cholinesterase 5.65 kU/L 5.32-12.92 kU/L
GLDH 3.7 U/L \<6.4 U/L
Gamma-GT 89 U/L 8-61 U/L
LDH 184 U/L 135-250 U/L
Parathyroid Hormone 55.0 pg/mL 15.0-65.0 pg/mL
25-OH-Vitamin D3 10.9 ng/mL 50.0-150.0 ng/mL
Free Thyroxine 17.90 ng/dL 9.50-16.40 ng/dL
TSH 3.56 mIU/L 0.50-4.30 mIU/L
**ECG on 10/20/21:** Sinus rhythm, heart rate 79/min, steep type, PQ
interval 140 ms, QRS duration 110 ms, QT interval 340 ms, QTc 385 mmHg.
ST depression, descending in V2+V3. T-wave positivity from V2. No
extrasystoles. No pauses.
**ECG on 11/20/2021:** Sinus rhythm, heart rate 70/min, left type,
inverted RS wave in lead I, PQ 160, QRS 100 ms, QT 340 ms, QTc 390 ms.
ST depression, descending in V1+V2, T-wave positivity from V2,
isoelectric in V5/V6, S-wave persistence until V6. Intraventricular
conduction disorder. No extrasystoles. No pauses.
**Holter monitor from 11/21/2021:** Normal heart rate spectrum, min 64
bpm, median 81 bpm, max 102 bpm, no intolerable bradycardia or pauses,
monomorphic ventricular extrasystole in 0.5% of QRS complexes, no
couplets or salvos.
**Echocardiography on 10/20/2021:** Poor ultrasound conditions, TI I+°,
good RV function, no LV cavity, aortic arch normal. No pulmonary
embolism after catheterization.
**Abdominal Ultrasound on 10/20/2021:** Borderline enlarged liver with
extremely hypoechoic basic structure, wide hepatic veins extending into
second-order branches, and a barely compressible wide inferior vena
cava. The basic architecture is preserved, the ventral contour is
smooth, no nodularity. No suspicious focal lesions, no portal vein
thrombosis, no ascites, no splenomegaly.
[Measurement values as follows:]{.underline}
ATI damping coefficient (as always in congestion livers) very low,
sometimes below 0.45 dB/cm/MHz, thus certainly no steatosis.
Elastography with good measurement quality (IQR=0.22) with 1.9 m/s or
10.9 kPa with significantly elevated values (attributed to all
conventional elastography, including Fibroscan, measurement error in
congestion livers).
Dispersion measurement (parametrized not for fibrosis, but for
viscosity, here therefore the congestion component) in line with the
images at 18 (m/s)/kHz, significantly elevated, thus corroborating that
the elastography values are too high.
In the synopsis of the different parameterizations as well as the
overall image, mild fibrosis at a low F2 level.
[Other Status]{.underline}:
No enlargement of intra- and extrahepatic bile ducts. Normal-sized
gallbladder with echo-free lumen and delicate wall. The pancreas is well
defined, with homogeneous parenchyma; no pancreatic duct dilation, no
focal lesions. The spleen is homogeneous and not enlarged. Both kidneys
are orthotopic and normal in size. The parenchymal rim is not narrowed.
The non-bridging bile duct is closed, no evidence of stones. The
moderately filled bladder is unremarkable. No pathological findings in
the pelvis. No enlarged lymph nodes along the large vessels, no free
fluid.
[Result:]{.underline} Morphologically and parametrically (after
downgrading the significantly elevated elastography value due to
congestion), there is evidence of chronic congestive liver with mild
fibrosis (low F2 level).
Otherwise, an unremarkable abdominal overview.
**Cardiac Angiography and Catheterization on 10/20/2021:**
[X-ray data]{.underline}: 5.50 min / 298.00 cGy\*cm²
[Medication]{.underline}: 4 mg Acetaminophen (5 mg/5 mL, 5 mL/amp); 4000
IU Heparin RATIO (25000 IU/5 ml, 5 mL/IJF); 156 mg Propofol 1% MCT (200
mg/20 mL, 20 mL/amp); 5 mg/ml, 5 mL/vial)
[Contrast agent:]{.underline} 105 ml Iomeron 350
[Puncture site]{.underline}: Right femoral vein (Terumo Pediatric Sheath
5F 7 cm).
Right femoral artery (Terumo Pediatric Sheath 5F 7 cm).
[Vital Parameters:]{.underline}
- Height: 169.0 cm
- Weight: 47.00 kg
- Body surface area: 1.44 m²
-
[Catheter course]{.underline}**:** Puncture of the above-mentioned
vessels under analgosedation and local anesthesia. Performance of
oximetry, pressure measurements, and angiographies. After completing the
examination, removal of the sheaths, Angioseal 6F AFC right, manual
compression until hemostasis, and application of a pressure bandage.
Transfer of the patient in a cardiopulmonary stable condition to the
post-interventional intensive care unit 24i for heparinization and
monitor monitoring.
[Pressure values (mmHg):]{.underline}
- VCI: 8 mmHg
- VCS: 9 mmHg
- RV: 103/0-8 syst/diast-edP mmHg
- RPA: 8 syst/diast mmHg
- LPA: 8 syst/diast mmHg
- AoAsc 103/63 (82) syst/diast mmHg
- AoDesc 103/61 (81) syst/diast mmHg
- PCW left: 6 mmHg
- PCW right: 6 mmHg
[Summary]{.underline}**:** Uncomplicated arterial and venous puncture,
5F right femoral arterial sheath, cannulation of VCI, VCS up to V.
anonyma, LPA and RPA with 5F wedge and 5F pigtail catheters. Retrograde
aorta to atretic AoV and via Neo-AoV (PV) into RV. Low pressures, Fontan
8 mmHg, TPG 2 mmHg with wedge 6 mmHg, max. RVedP 8 mmHg. No shunt
oximetrically, CI 2.7 l/min/m2. No gradient across Neo-AoV and arch.
Angiographically no veno-venous collaterals, no MAPCA. Glenn wide, LPA
and RPA stenosis-free, well-developed, rapid capillary phase and
pulmonary vein return to LA/RA. Fontan tunnel centrally constricted to
12.5 mm, to VCI 18 mm. Satisfactory function of the hypertrophic right
systemic ventricle, mild TI. No Neo-AI, native AoV without flow, normal
coronary arteries, wide DKS, aortic arch without any stenosis.
**Abdominal Ultrasound on 10/21/2022: **
[Clinical Information, Question, Justification:]{.underline} Post-Fontan
procedure. Evaluation for chronic congestive liver.
[Findings]{.underline}: Moderately enlarged liver with an extremely
hypoechoic texture, which is typical for congestive livers. There are
dilated liver veins extending into the second-order branches and a
barely compressible wide inferior vena cava. The basic architecture of
the liver is preserved, and the contour is smooth without nodularity. On
the high-frequency scan, there are subtle but significant periportal
cuffing enhancements throughout the liver, consistent with mild
fibrosis. No suspicious focal lesions, no portal vein thrombosis, no
ascites, and no splenomegaly are observed. Measurement values as
follows: ATI damping coefficient (as usual in congestive livers) is very
low, sometimes less than 0.45 dB/cm/MHz, indicating no steatosis. Shear
wave elastography with good measurement quality (IQR=0.22) shows a
velocity of 1.9 m/s or 10.9 kPa, which are significantly higher values
(attributable to measurement errors inherent in all conventional
elastography techniques, including Fibroscan, in congestive livers).
Dispersion measurement (parameters not indicating fibrosis but
viscosity, which in this case represents congestion) corresponds to the
images, with a significantly high 18 (m/s)/kHz, thus supporting that the
shear wave elastography values are too high (and should be lower).
Overall, a mild fibrosis at a low F2 level is evident based on the
synopsis of various parameterizations and the overall image impression.
[Other findings:]{.underline} No dilation of intrahepatic and
extrahepatic bile ducts. The gallbladder is of normal size with anechoic
lumen and a delicate wall. The pancreas is well-defined with homogeneous
parenchyma, no dilation of the pancreatic duct, and no focal lesions.
The spleen is homogeneous and not enlarged. Both kidneys are and of
normal size. The parenchymal rim is not narrowed. No evidence of stones
in the renal collecting system. The moderately filled bladder is
unremarkable. No pathological findings in the small pelvis. No enlarged
lymph nodes along major vessels, and no free fluid. Conclusion:
Morphologically and parametrically (after downgrading the significantly
elevated elastography values due to congestion), the findings are
consistent with chronic congestive liver with mild fibrosis. Otherwise,
the abdominal overview is unremarkable.
[Assessment]{.underline}: Very good findings after Norwood I-III, no
current need for intervention. In the long term, there may be an
indication for BAP/stent expansion of the central conduit constriction.
The routine blood test for Fontan patients showed no abnormalities;
vitamin D supplementation may be recommended in case of low levels. A
cardiac MRI with flow measurement in the Fontan tunnel is initially
recommended, followed by a decision on intervention in that area.
We kindly remind you of the unchanged necessity of endocarditis
prophylaxis in case of all bacteremias and dental restorations. An
appropriate certificate is available for Emil, and the family is
well-informed about the indication and the existence of the certificate.
A LIMAX examination can only be performed in an inpatient setting, which
was not possible during this stay due to organizational reasons. This
should be done in the next inpatient stay.
**Summary**: We are discharging Emil in good general condition and slim
build, with no signs of infection. Puncture site is unremarkable.
Cardiac status: Rhythmic heart action, no pathological heart sounds.
Pulse status is normal. Lungs: Clear. Abdomen: Soft.
Pulse oximetry oxygen saturation: 93%
Blood pressure measurement (mmHg): 117/74
**Current Recommendations:**
- Cardiac MRI in follow-up, appointment will be communicated, possibly
including LIMAX
- Vitamin D supplementation
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
--------------------- ------------ ---------------
Captopril (Capoten) 2 mg 1-1-1
Carvedilol (Coreg) 0.2 mg 1-0-1
Aspirin 25 mg 1-0-0
**Lab results upon Discharge:**
**Parameter** **Result** **Reference Range**
------------------------ -------------- ---------------------
Calcium 2.34 mEq/L 2.10-2.55 mEq/L
Phosphate 1.20 mEq/L 0.84-1.45 mEq/L
Osmolality 285 mosmo/Kg 280-300 mosmo/Kg
Iron 20.0 µmol/L 4.8-24.7 µmol/L
Transferrin Saturation 28.1% 16.0-45.0%
Magnesium 0.77 mEq/L 0.62-0.91 mEq/L
Creatinine (Jaffé) 0.85 mg/dL 0.70-1.20 mg/dL
Urea 26 mg/dL 18-45 mg/dL
Total Bilirubin 0.97 mg/dL \<1.20 mg/dL
Direct Bilirubin 0.33 mg/dL \<0.30 mg/dL
Immunoglobulin G 11.44 g/L 5.49-15.84 g/L
Immunoglobulin A 1.95 g/L 0.61-3.48 g/L
Immunoglobulin M 0.62 g/L 0.50-1.90 g/L
Cystatin C 0.96 mg/L 0.50-1.00 mg/L
Transferrin 2.87 g/L \-
Ferritin 54.5 µg/L 14.0-152.0 µg/L
Total Cholesterol 110 mg/dL 82-192 mg/dL
Triglycerides 64 mg/dL \-
Apolipoprotein A1 0.96 g/L 1.04-2.02 g/L
GPT 36 U/L \<41 U/L
GOT 35 U/L \<50 U/L
Alkaline Phosphatase 135 U/L 55-149 U/L
Pseudo-Cholinesterase 5.64 kU/L 5.32-12.92 kU/L
GLDH 3.2 U/L \<6.4 U/L
Gamma-GT 92 U/L 8-61 U/L
LDH 180 U/L 135-250 U/L
Parathyroid Hormone 55.0 ng/L 15.0-65.0 ng/L
25-OH-Vitamin D3 10.9 nmol/L 50.0-150.0 nmol/L
Free Thyroxine 17.90 ng/L 9.50-16.40 ng/L
TSH 3.56 mU/L 0.50-4.30 mU/L
### Patient Report 5
**Dear colleague, **
We are reporting about the examination of our patient, Emil Nilsson,
born on 12/04/2004, who presented to our outpatient clinic on
12/10/2021.
**Diagnoses:**
- Hypoplastic Left Heart Syndrome
- Persistent foramen ovale
- Persistent ductus arteriosus botalli (under Prostaglandin E1
Infusion)
- Dysplasia of the mitral valve
- Damus-Kaye-Stansel Procedure and aortopulmonary anastomosis on the
right (modified BT-Shunt)
- Secondary thoracic closure
- Tricuspid valve insufficiency
- Mild aortic valve insufficiency
- Status post Glenn procedure
- Fontan conduit retrocardial narrowing, extended hepatic vein
window/VCI
- Chronic liver congestion with mild fibrosis (sonography)
**Procedures**: Cardiac MRI.
**Medical History:** We kindly assume that the detailed medical history
is known to you and refer to previous medical reports from our clinic.
The current presentation is based on a referral from the outpatient
pediatric cardiologist for a Cardiac MRI. Emil reports feeling
subjectively well.
**Physical Examination:** Emil is in good general condition and slim
build, with no signs of infection.
- Cardiac status: Rhythmic heart action, 2/6 systolic murmur.
- Pulse status: Normal.
- Lungs: Bilateral equal ventilation, vesicular breath sounds, no
rales.
- Abdomen: Soft, no hepatosplenomegaly. Unremarkable sternal scars. No
signs of cardiopulmonary decompensation.
- Current weight: 47 kg; current height: 169 cm.
- Pulse oximetry oxygen saturation: 95%.
- Blood pressure (mmHg): Right upper arm 132/94, left upper arm
121/98, right lower leg 158/94, left lower leg 156/94.
**Cardiac MRI on 03/02/2022:**
[Clinical Information, Question, Justification:]{.underline} Hypoplastic
Left Heart Syndrome, Fontan procedure, congestive liver, retrocardiac
Fontan tunnel narrowing, VCI dilation, Fontan tunnel flow pathology?
[Technique]{.underline}: 1.5 Tesla MRI. Localization scan.
Transverse/coronal T2 HASTE. Cine Fast Imaging with Steady-State
Precession functional assessment in short-axis view, two-chamber view,
four-chamber view, and three-chamber view. Flow quantifications of the
right and left pulmonary arteries, main pulmonary artery, superior vena
cava, and inferior vena cava using through-plane phase-contrast
gradient-echo measurement. Contrast-enhanced MR angiography.
[Findings]{.underline}: No prior images for comparison available.
Anatomy: Hypoplastic left heart with DKS (Damus-Kaye-Stansel)
anastomosis, dilated and hypertrophied right ventricle, broad ASD. No
focal wall thinning or outpouchings. No intracavitary thrombi detected.
No pericardial effusion. Descending aorta on the left side. Status post
total cavopulmonary anastomosis with slight tapering between the LPA and
the anastomosis at 7 mm, LPA 11 mm, RPA 14 mm. No pleural effusions. No
evidence of confluent pulmonary infiltrates in the imaged lung regions.
Congestive liver. Cine MRI: The 3D volumetry shows a normal global RVEF
in the setting of Fontan procedure. No regional wall motion
abnormalities. Mild tricuspid valve prolapse with minor regurgitation
jet.
**Volumetry: **
[1) Left Ventricle:]{.underline}
- Left Ventricle Absolute Normalized LV-EF: 29 %
LV-EDV: 6 ml 4.2 mL/m²
<!-- -->
- LV-ESV: 4 ml 3 mL/m²
- LV-SV: 2 ml 1 mL/m²
- Cardiac Output: 0.1 L/min 0.1 L/min*m² *
[2) Right Ventricle:]{.underline}
- Right Ventricle maximum flow velocity: 109 cm/s
- Antegrade volume 50 mL
- Retrograde volume 2 mL
- Regurgitation fraction 4 %
[3) Right Pulmonary Artery: ]{.underline}
- Right Pulmonary Artery maximum flow velocity: 27 cm/s
<!-- -->
- Antegrade volume: 14 mL
- Retrograde volume: 0 mL
- Regurgitation fraction: 0 %
- CAVE: Right upper pulmonary artery not captured
[4) Left Pulmonary Artery:]{.underline}
- Maximum flow velocity: 33 cm/s
- Antegrade volume: 18 mL
- Retrograde volume: 0 mL
- Regurgitation fraction: 0 %
[5) Inferior Vena Cava:]{.underline}
- Maximum flow velocity: 38 cm/s
- Antegrade volume: 30 mL
- Retrograde volume: 0 mL
- Regurgitation fraction: 0 %
[6) Fontan Tunnel:]{.underline}
- Maximum flow velocity: 53 cm/s
- Antegrade volume 31: mL
- Retrograde volume: 0 mL
- Regurgitation fraction: 0 %
[7) Superior Vena Cava]{.underline}:
- Maximum flow velocity: 23 cm/s
- Antegrade volume: 16 mL
- Retrograde volume: 0 mL
- Regurgitation fraction: 0 %
[Assessment:]{.underline} In the setting of status post Total
Cavopulmonary Anastomosis with DKS anastomosis for hypoplastic left
heart, there is good right ventricular systolic function with only
minimal ejection above the aortic valve.
Slight tapering of the baffles up to 13 mm compared to VCI up to 21 mm
without evidence of stenosis or major baffle leakage.
Morphologically, slight tapering between the LPA and the anastomosis
with essentially balanced flow between the LPA and RPA.
Mild tricuspid valve prolapse with discrete insufficiency.
Hepatomegaly with signs of chronic congestion. | Increased from 10 mg to 25 mg. |
Why does Quest say he is lucky?
A. Quest considers himself lucky that Trella is in love with him.
B. Quest considers himself lucky that he is not actually an android.
C. Quest considers himself lucky that Asrange did not kill him.
D. Quest considers himself lucky that he did not commit murder. He is not a murderer at heart.
| Transcriber's Note: Every effort has been made to replicate this text as faithfully as possible; changes (corrections of spelling and punctuation) made to the original text are marked like this . The original text appears when hovering the cursor over the marked text. This e-text was produced from Amazing Science Fiction Stories March 1959. Extensive research did not uncover any evidence that the U. S. copyright on this publication was renewed. 50 THE JUPITER WEAPON By CHARLES L. FONTENAY He was a living weapon of destruction— immeasurably powerful, utterly invulnerable. There was only one question: Was he human? Trella feared she was in for trouble even before Motwick's head dropped forward on his arms in a drunken stupor. The two evil-looking men at the table nearby had been watching her surreptitiously, and now they shifted restlessly in their chairs. Trella had not wanted to come to the Golden Satellite. It was a squalid saloon in the rougher section of Jupiter's View, the terrestrial dome-colony on Ganymede. Motwick, already drunk, had insisted. A woman could not possibly make her way through these streets alone to the better section of town, especially one clad in a silvery evening dress. Her only hope was that this place had a telephone. Perhaps she could call one of Motwick's friends; she had no one on Ganymede she could call a real friend herself. Tentatively, she pushed her chair back from the table and arose. She had to brush close by the other table to get to the bar. As she did, the dark, slick-haired man reached out and grabbed her around the waist with a steely arm. Trella swung with her whole body, and slapped him so hard he nearly fell from his chair. As she walked swiftly toward the bar, he leaped up to follow her. There were only two other people in the Golden Satellite: the fat, mustached bartender and a short, square-built man at the bar. The latter swung around at the pistol-like report of her slap, and she saw that, though no more than four and a half feet tall, he was as heavily muscled as a lion. 51 His face was clean and open, with close-cropped blond hair and honest blue eyes. She ran to him. “Help me!” she cried. “Please help me!” He began to back away from her. “I can't,” he muttered in a deep voice. “I can't help you. I can't do anything.” The dark man was at her heels. In desperation, she dodged around the short man and took refuge behind him. Her protector was obviously unwilling, but the dark man, faced with his massiveness, took no chances. He stopped and shouted: “Kregg!” The other man at the table arose, ponderously, and lumbered toward them. He was immense, at least six and a half feet tall, with a brutal, vacant face. Evading her attempts to stay behind him, the squat man began to move down the bar away from the approaching Kregg. The dark man moved in on Trella again as Kregg overtook his quarry and swung a huge fist like a sledgehammer. Exactly what happened, Trella wasn't sure. She had the impression that Kregg's fist connected squarely with the short man's chin before he dodged to one side in a movement so fast it was a blur. But that couldn't have been, because the short man wasn't moved by that blow that would have felled a steer, and Kregg roared in pain, grabbing his injured fist. “The bar!” yelled Kregg. “I hit the damn bar!” At this juncture, the bartender took a hand. Leaning far over the bar, he swung a full bottle in a complete arc. It smashed on Kregg's head, splashing the floor with liquor, and Kregg sank stunned to his knees. The dark man, who had grabbed Trella's arm, released her and ran for the door. Moving agilely around the end of the bar, the bartender stood over Kregg, holding the jagged-edged bottleneck in his hand menacingly. “Get out!” rumbled the bartender. “I'll have no coppers raiding my place for the likes of you!” Kregg stumbled to his feet and staggered out. Trella ran to the unconscious Motwick's side. “That means you, too, lady,” said the bartender beside her. “You and your boy friend get out of here. You oughtn't to have come here in the first place.” “May I help you, Miss?” asked a deep, resonant voice behind her. She straightened from her anxious examination of Motwick. The squat man was standing there, an apologetic look on his face. She looked contemptuously at the massive muscles whose help had been denied her. Her arm ached where the dark man had grasped it. The broad face before 52 her was not unhandsome, and the blue eyes were disconcertingly direct, but she despised him for a coward. “I'm sorry I couldn't fight those men for you, Miss, but I just couldn't,” he said miserably, as though reading her thoughts. “But no one will bother you on the street if I'm with you.” “A lot of protection you'd be if they did!” she snapped. “But I'm desperate. You can carry him to the Stellar Hotel for me.” The gravity of Ganymede was hardly more than that of Earth's moon, but the way the man picked up the limp Motwick with one hand and tossed him over a shoulder was startling: as though he lifted a feather pillow. He followed Trella out the door of the Golden Satellite and fell in step beside her. Immediately she was grateful for his presence. The dimly lighted street was not crowded, but she didn't like the looks of the men she saw. The transparent dome of Jupiter's View was faintly visible in the reflected night lights of the colonial city, but the lights were overwhelmed by the giant, vari-colored disc of Jupiter itself, riding high in the sky. “I'm Quest Mansard, Miss,” said her companion. “I'm just in from Jupiter.” “I'm Trella Nuspar,” she said, favoring him with a green-eyed glance. “You mean Io, don't you—or Moon Five?” “No,” he said, grinning at her. He had an engaging grin, with even white teeth. “I meant Jupiter.” “You're lying,” she said flatly. “No one has ever landed on Jupiter. It would be impossible to blast off again.” “My parents landed on Jupiter, and I blasted off from it,” he said soberly. “I was born there. Have you ever heard of Dr. Eriklund Mansard?” “I certainly have,” she said, her interest taking a sudden upward turn. “He developed the surgiscope, didn't he? But his ship was drawn into Jupiter and lost.” “It was drawn into Jupiter, but he landed it successfully,” said Quest. “He and my mother lived on Jupiter until the oxygen equipment wore out at last. I was born and brought up there, and I was finally able to build a small rocket with a powerful enough drive to clear the planet.” She looked at him. He was short, half a head shorter than she, but broad and powerful as a man might be who had grown up in heavy gravity. He trod the street with a light, controlled step, seeming to deliberately hold himself down. “If Dr. Mansard succeeded in landing on Jupiter, why didn't anyone ever hear from him again?” she demanded. “Because,” said Quest, “his radio was sabotaged, just as his ship's drive was.” “Jupiter strength,” she murmured, looking him over coolly. 53 “You wear Motwick on your shoulder like a scarf. But you couldn't bring yourself to help a woman against two thugs.” He flushed. “I'm sorry,” he said. “That's something I couldn't help.” “Why not?” “I don't know. It's not that I'm afraid, but there's something in me that makes me back away from the prospect of fighting anyone.” Trella sighed. Cowardice was a state of mind. It was peculiarly inappropriate, but not unbelievable, that the strongest and most agile man on Ganymede should be a coward. Well, she thought with a rush of sympathy, he couldn't help being what he was. They had reached the more brightly lighted section of the city now. Trella could get a cab from here, but the Stellar Hotel wasn't far. They walked on. Trella had the desk clerk call a cab to deliver the unconscious Motwick to his home. She and Quest had a late sandwich in the coffee shop. “I landed here only a week ago,” he told her, his eyes frankly admiring her honey-colored hair and comely face. “I'm heading for Earth on the next spaceship.” “We'll be traveling companions, then,” she said. “I'm going back on that ship, too.” For some reason she decided against telling him that the assignment on which she had come to the Jupiter system was to gather his own father's notebooks and take them back to Earth. Motwick was an irresponsible playboy whom Trella had known briefly on Earth, and Trella was glad to dispense with his company for the remaining three weeks before the spaceship blasted off. She found herself enjoying the steadier companionship of Quest. As a matter of fact, she found herself enjoying his companionship more than she intended to. She found herself falling in love with him. Now this did not suit her at all. Trella had always liked her men tall and dark. She had determined that when she married it would be to a curly-haired six-footer. She was not at all happy about being so strongly attracted to a man several inches shorter than she. She was particularly unhappy about feeling drawn to a man who was a coward. The ship that they boarded on Moon Nine was one of the newer ships that could attain a hundred-mile-per-second velocity and take a hyperbolic path to Earth, but it would still require fifty-four days to make the trip. So Trella was delighted to find that the ship was the Cometfire and its skipper was her old friend, dark-eyed, curly-haired Jakdane Gille. “Jakdane,” she said, flirting with him with her eyes as in 54 days gone by, “I need a chaperon this trip, and you're ideal for the job.” “I never thought of myself in quite that light, but maybe I'm getting old,” he answered, laughing. “What's your trouble, Trella?” “I'm in love with that huge chunk of man who came aboard with me, and I'm not sure I ought to be,” she confessed. “I may need protection against myself till we get to Earth.” “If it's to keep you out of another fellow's clutches, I'm your man,” agreed Jakdane heartily. “I always had a mind to save you for myself. I'll guarantee you won't have a moment alone with him the whole trip.” “You don't have to be that thorough about it,” she protested hastily. “I want to get a little enjoyment out of being in love. But if I feel myself weakening too much, I'll holler for help.” The Cometfire swung around great Jupiter in an opening arc and plummeted ever more swiftly toward the tight circles of the inner planets. There were four crew members and three passengers aboard the ship's tiny personnel sphere, and Trella was thrown with Quest almost constantly. She enjoyed every minute of it. She told him only that she was a messenger, sent out to Ganymede to pick up some important papers and take them back to Earth. She was tempted to tell him what the papers were. Her employer had impressed upon her that her mission was confidential, but surely Dom Blessing could not object to Dr. Mansard's son knowing about it. All these things had happened before she was born, and she did not know what Dom Blessing's relation to Dr. Mansard had been, but it must have been very close. She knew that Dr. Mansard had invented the surgiscope. This was an instrument with a three-dimensional screen as its heart. The screen was a cubical frame in which an apparently solid image was built up of an object under an electron microscope. The actual cutting instrument of the surgiscope was an ion stream. By operating a tool in the three-dimensional screen, corresponding movements were made by the ion stream on the object under the microscope. The principle was the same as that used in operation of remote control “hands” in atomic laboratories to handle hot material, and with the surgiscope very delicate operations could be performed at the cellular level. Dr. Mansard and his wife had disappeared into the turbulent atmosphere of Jupiter just after his invention of the surgiscope, and it had been developed by Dom Blessing. Its success had built Spaceway Instruments, Incorporated, which Blessing headed. Through all these years since Dr. Mansard's disappearance, 55 Blessing had been searching the Jovian moons for a second, hidden laboratory of Dr. Mansard. When it was found at last, he sent Trella, his most trusted secretary, to Ganymede to bring back to him the notebooks found there. Blessing would, of course, be happy to learn that a son of Dr. Mansard lived, and would see that he received his rightful share of the inheritance. Because of this, Trella was tempted to tell Quest the good news herself; but she decided against it. It was Blessing's privilege to do this his own way, and he might not appreciate her meddling. At midtrip, Trella made a rueful confession to Jakdane. “It seems I was taking unnecessary precautions when I asked you to be a chaperon,” she said. “I kept waiting for Quest to do something, and when he didn't I told him I loved him.” “What did he say?” “It's very peculiar,” she said unhappily. “He said he can't love me. He said he wants to love me and he feels that he should, but there's something in him that refuses to permit it.” She expected Jakdane to salve her wounded feelings with a sympathetic pleasantry, but he did not. Instead, he just looked at her very thoughtfully and said no more about the matter. He explained his attitude after Asrange ran amuck. Asrange was the third passenger. He was a lean, saturnine individual who said little and kept to himself as much as possible. He was distantly polite in his relations with both crew and other passengers, and never showed the slightest spark of emotion … until the day Quest squirted coffee on him. It was one of those accidents that can occur easily in space. The passengers and the two crewmen on that particular waking shift (including Jakdane) were eating lunch on the center-deck. Quest picked up his bulb of coffee, but inadvertently pressed it before he got it to his lips. The coffee squirted all over the front of Asrange's clean white tunic. “I'm sorry!” exclaimed Quest in distress. The man's eyes went wide and he snarled. So quickly it seemed impossible, he had unbuckled himself from his seat and hurled himself backward from the table with an incoherent cry. He seized the first object his hand touched—it happened to be a heavy wooden cane leaning against Jakdane's bunk—propelled himself like a projectile at Quest. Quest rose from the table in a sudden uncoiling of movement. He did not unbuckle his safety belt—he rose and it snapped like a string. For a moment Trella thought he was going to meet Asrange's assault. But he fled in a long leap toward the companionway leading to the astrogation deck 56 above. Landing feet-first in the middle of the table and rebounding, Asrange pursued with the stick upraised. In his haste, Quest missed the companionway in his leap and was cornered against one of the bunks. Asrange descended on him like an avenging angel and, holding onto the bunk with one hand, rained savage blows on his head and shoulders with the heavy stick. Quest made no effort to retaliate. He cowered under the attack, holding his hands in front of him as if to ward it off. In a moment, Jakdane and the other crewman had reached Asrange and pulled him off. When they had Asrange in irons, Jakdane turned to Quest, who was now sitting unhappily at the table. “Take it easy,” he advised. “I'll wake the psychosurgeon and have him look you over. Just stay there.” Quest shook his head. “Don't bother him,” he said. “It's nothing but a few bruises.” “Bruises? Man, that club could have broken your skull! Or a couple of ribs, at the very least.” “I'm all right,” insisted Quest; and when the skeptical Jakdane insisted on examining him carefully, he had to admit it. There was hardly a mark on him from the blows. “If it didn't hurt you any more than that, why didn't you take that stick away from him?” demanded Jakdane. “You could have, easily.” “I couldn't,” said Quest miserably, and turned his face away. Later, alone with Trella on the control deck, Jakdane gave her some sober advice. “If you think you're in love with Quest, forget it,” he said. “Why? Because he's a coward? I know that ought to make me despise him, but it doesn't any more.” “Not because he's a coward. Because he's an android!” “What? Jakdane, you can't be serious!” “I am. I say he's an android, an artificial imitation of a man. It all figures. “Look, Trella, he said he was born on Jupiter. A human could stand the gravity of Jupiter, inside a dome or a ship, but what human could stand the rocket acceleration necessary to break free of Jupiter? Here's a man strong enough to break a spaceship safety belt just by getting up out of his chair against it, tough enough to take a beating with a heavy stick without being injured. How can you believe he's really human?” Trella remembered the thug Kregg striking Quest in the face and then crying that he had injured his hand on the bar. “But he said Dr. Mansard was his father,” protested Trella. “Robots and androids frequently look on their makers as their parents,” said Jakdane. “Quest may not even know he's 57 artificial. Do you know how Mansard died?” “The oxygen equipment failed, Quest said.” “Yes. Do you know when?” “No. Quest never did tell me, that I remember.” “He told me: a year before Quest made his rocket flight to Ganymede! If the oxygen equipment failed, how do you think Quest lived in the poisonous atmosphere of Jupiter, if he's human?” Trella was silent. “For the protection of humans, there are two psychological traits built into every robot and android,” said Jakdane gently. “The first is that they can never, under any circumstances, attack a human being, even in self defense. The second is that, while they may understand sexual desire objectively, they can never experience it themselves. “Those characteristics fit your man Quest to a T, Trella. There is no other explanation for him: he must be an android.” Trella did not want to believe Jakdane was right, but his reasoning was unassailable. Looking upon Quest as an android, many things were explained: his great strength, his short, broad build, his immunity to injury, his refusal to defend himself against a human, his inability to return Trella's love for him. It was not inconceivable that she should have unknowingly fallen in love with an android. Humans could love androids, with real affection, even knowing that they were artificial. There were instances of android nursemaids who were virtually members of the families owning them. She was glad now that she had not told Quest of her mission to Ganymede. He thought he was Dr. Mansard's son, but an android had no legal right of inheritance from his owner. She would leave it to Dom Blessing to decide what to do about Quest. Thus she did not, as she had intended originally, speak to Quest about seeing him again after she had completed her assignment. Even if Jakdane was wrong and Quest was human—as now seemed unlikely—Quest had told her he could not love her. Her best course was to try to forget him. Nor did Quest try to arrange with her for a later meeting. “It has been pleasant knowing you, Trella,” he said when they left the G-boat at White Sands. A faraway look came into his blue eyes, and he added: “I'm sorry things couldn't have been different, somehow.” “Let's don't be sorry for what we can't help,” she said gently, taking his hand in farewell. Trella took a fast plane from White Sands, and twenty-four hours later walked up the front steps of the familiar brownstone house on the outskirts of Washington. Dom Blessing himself met her at the door, a stooped, graying 58 man who peered at her over his spectacles. “You have the papers, eh?” he said, spying the brief case. “Good, good. Come in and we'll see what we have, eh?” She accompanied him through the bare, windowless anteroom which had always seemed to her such a strange feature of this luxurious house, and they entered the big living room. They sat before a fire in the old-fashioned fireplace and Blessing opened the brief case with trembling hands. “There are things here,” he said, his eyes sparkling as he glanced through the notebooks. “Yes, there are things here. We shall make something of these, Miss Trella, eh?” “I'm glad they're something you can use, Mr. Blessing,” she said. “There's something else I found on my trip, that I think I should tell you about.” She told him about Quest. “He thinks he's the son of Dr. Mansard,” she finished, “but apparently he is, without knowing it, an android Dr. Mansard built on Jupiter.” “He came back to Earth with you, eh?” asked Blessing intently. “Yes. I'm afraid it's your decision whether to let him go on living as a man or to tell him he's an android and claim ownership as Dr. Mansard's heir.” Trella planned to spend a few days resting in her employer's spacious home, and then to take a short vacation before resuming her duties as his confidential secretary. The next morning when she came down from her room, a change had been made. Two armed men were with Dom Blessing at breakfast and accompanied him wherever he went. She discovered that two more men with guns were stationed in the bare anteroom and a guard was stationed at every entrance to the house. “Why all the protection?” she asked Blessing. “A wealthy man must be careful,” said Blessing cheerfully. “When we don't understand all the implications of new circumstances, we must be prepared for anything, eh?” There was only one new circumstance Trella could think of. Without actually intending to, she exclaimed: “You aren't afraid of Quest? Why, an android can't hurt a human!” Blessing peered at her over his spectacles. “And what if he isn't an android, eh? And if he is—what if old Mansard didn't build in the prohibition against harming humans that's required by law? What about that, eh?” Trella was silent, shocked. There was something here she hadn't known about, hadn't even suspected. For some reason, Dom Blessing feared Dr. Eriklund Mansard … or his heir … or his mechanical servant. She was sure that Blessing was wrong, that Quest, whether man or android, intended no 59 harm to him. Surely, Quest would have said something of such bitterness during their long time together on Ganymede and aspace, since he did not know of Trella's connection with Blessing. But, since this was to be the atmosphere of Blessing's house, she was glad that he decided to assign her to take the Mansard papers to the New York laboratory. Quest came the day before she was scheduled to leave. Trella was in the living room with Blessing, discussing the instructions she was to give to the laboratory officials in New York. The two bodyguards were with them. The other guards were at their posts. Trella heard the doorbell ring. The heavy oaken front door was kept locked now, and the guards in the anteroom examined callers through a tiny window. Suddenly alarm bells rang all over the house. There was a terrific crash outside the room as the front door splintered. There were shouts and the sound of a shot. “The steel doors!” cried Blessing, turning white. “Let's get out of here.” He and his bodyguards ran through the back of the house out of the garage. Blessing, ahead of the rest, leaped into one of the cars and started the engine. The door from the house shattered and Quest burst through. The two guards turned and fired together. He could be hurt by bullets. He was staggered momentarily. Then, in a blur of motion, he sprang forward and swept the guards aside with one hand with such force that they skidded across the floor and lay in an unconscious heap against the rear of the garage. Trella had opened the door of the car, but it was wrenched from her hand as Blessing stepped on the accelerator and it leaped into the driveway with spinning wheels. Quest was after it, like a chunky deer, running faster than Trella had ever seen a man run before. Blessing slowed for the turn at the end of the driveway and glanced back over his shoulder. Seeing Quest almost upon him, he slammed down the accelerator and twisted the wheel hard. The car whipped into the street, careened, and rolled over and over, bringing up against a tree on the other side in a twisted tangle of wreckage. With a horrified gasp, Trella ran down the driveway toward the smoking heap of metal. Quest was already beside it, probing it. As she reached his side, he lifted the torn body of Dom Blessing. Blessing was dead. “I'm lucky,” said Quest soberly. “I would have murdered him.” “But why, Quest? I knew he was afraid of you, but he didn't tell me why.” “It was conditioned into me,” answered Quest “I didn't know 60 it until just now, when it ended, but my father conditioned me psychologically from my birth to the task of hunting down Dom Blessing and killing him. It was an unconscious drive in me that wouldn't release me until the task was finished. “You see, Blessing was my father's assistant on Ganymede. Right after my father completed development of the surgiscope, he and my mother blasted off for Io. Blessing wanted the valuable rights to the surgiscope, and he sabotaged the ship's drive so it would fall into Jupiter. “But my father was able to control it in the heavy atmosphere of Jupiter, and landed it successfully. I was born there, and he conditioned me to come to Earth and track down Blessing. I know now that it was part of the conditioning that I was unable to fight any other man until my task was finished: it might have gotten me in trouble and diverted me from that purpose.” More gently than Trella would have believed possible for his Jupiter-strong muscles, Quest took her in his arms. “Now I can say I love you,” he said. “That was part of the conditioning too: I couldn't love any woman until my job was done.” Trella disengaged herself. “I'm sorry,” she said. “Don't you know this, too, now: that you're not a man, but an android?” He looked at her in astonishment, stunned by her words. “What in space makes you think that?” he demanded. “Why, Quest, it's obvious,” she cried, tears in her eyes. “Everything about you … your build, suited for Jupiter's gravity … your strength … the fact that you were able to live in Jupiter's atmosphere after the oxygen equipment failed. I know you think Dr. Mansard was your father, but androids often believe that.” He grinned at her. “I'm no android,” he said confidently. “Do you forget my father was inventor of the surgiscope? He knew I'd have to grow up on Jupiter, and he operated on the genes before I was born. He altered my inherited characteristics to adapt me to the climate of Jupiter … even to being able to breathe a chlorine atmosphere as well as an oxygen atmosphere.” Trella looked at him. He was not badly hurt, any more than an elephant would have been, but his tunic was stained with red blood where the bullets had struck him. Normal android blood was green. “How can you be sure?” she asked doubtfully. “Androids are made,” he answered with a laugh. “They don't grow up. And I remember my boyhood on Jupiter very well.” He took her in his arms again, and this time she did not resist. His lips were very human. THE END | D. Quest considers himself lucky that he did not commit murder. He is not a murderer at heart. |
What is implied about the beautiful woman when Charles leaves her apartment?
A. She is sleeping soundly, which means she’s unaware that Charles is sneaking out.
B. She is dead.
C. She is more in love with Charles than he is with her.
D. She is frustrated at Charles for being the last man on Earth.
| "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! | B. She is dead. |
Despite what they told Johnson, what can be determined as Harvey and Joe's true occupation?
A. Sales men
B. space-side mechanics
C. Traveling gamblers
D. Con artists
| GRIFTERS' ASTEROID By H. L. GOLD Harvey and Joe were the slickest con-men ever to gyp a space-lane sucker. Or so they thought! Angus Johnson knew differently. He charged them five buckos for a glass of water—and got it! [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.] Characteristically, Harvey Ellsworth tried to maintain his dignity, though his parched tongue was almost hanging out. But Joe Mallon, with no dignity to maintain, lurched across the rubbish-strewn patch of land that had been termed a spaceport. When Harvey staggered pontifically into the battered metalloy saloon—the only one on Planetoid 42—his tall, gangling partner was already stumbling out, mouthing something incoherent. They met in the doorway, violently. "We're delirious!" Joe cried. "It's a mirage!" "What is?" asked Harvey through a mouthful of cotton. Joe reeled aside, and Harvey saw what had upset his partner. He stared, speechless for once. In their hectic voyages from planet to planet, the pair of panacea purveyors had encountered the usual strange life-forms. But never had they seen anything like the amazing creature in that colonial saloon. Paying no attention to them, it was carrying a case of liquor in two hands, six siphons in two others, and a broom and dustpan in the remaining pair. The bartender, a big man resembling the plumpish Harvey in build, was leaning negligently on the counter, ordering this impossible being to fill the partly-emptied bottles, squeeze fruit juice and sweep the floor, all of which the native did simultaneously. "Nonsense," Harvey croaked uncertainly. "We have seen enough queer things to know there are always more." He led the way inside. Through thirst-cracked lips he rasped: "Water—quick!" Without a word, the bartender reached under the counter, brought out two glasses of water. The interplanetary con-men drank noisily, asked for more, until they had drunk eight glasses. Meanwhile, the bartender had taken out eight jiggers and filled them with whiskey. Harvey and Joe were breathing hard from having gulped the water so fast, but they were beginning to revive. They noticed the bartender's impersonal eyes studying them shrewdly. "Strangers, eh?" he asked at last. "Solar salesmen, my colonial friend," Harvey answered in his usual lush manner. "We purvey that renowned Martian remedy, La-anago Yergis , the formula for which was recently discovered by ourselves in the ancient ruined city of La-anago. Medical science is unanimous in proclaiming this magic medicine the sole panacea in the entire history of therapeutics." "Yeah?" said the bartender disinterestedly, polishing the chaser glasses without washing them. "Where you heading?" "Out of Mars for Ganymede. Our condenser broke down, and we've gone without water for five ghastly days." "Got a mechanic around this dumping ground you call a port?" Joe asked. "We did. He came near starving and moved on to Titan. Ships don't land here unless they're in trouble." "Then where's the water lead-in? We'll fill up and push off." "Mayor takes care of that," replied the saloon owner. "If you gents're finished at the bar, your drinks'll be forty buckos." Harvey grinned puzzledly. "We didn't take any whiskey." "Might as well. Water's five buckos a glass. Liquor's free with every chaser." Harvey's eyes bulged. Joe gulped. "That—that's robbery!" the lanky man managed to get out in a thin quaver. The barkeeper shrugged. "When there ain't many customers, you gotta make more on each one. Besides—" "Besides nothing!" Joe roared, finding his voice again. "You dirty crook—robbing poor spacemen! You—" "You dirty crook!" Joe roared. "Robbing honest spacemen!" Harvey nudged him warningly. "Easy, my boy, easy." He turned to the bartender apologetically. "Don't mind my friend. His adrenal glands are sometimes overactive. You were going to say—?" The round face of the barkeeper had assumed an aggrieved expression. "Folks are always thinkin' the other feller's out to do 'em," he said, shaking his head. "Lemme explain about the water here. It's bitter as some kinds of sin before it's purified. Have to bring it in with buckets and make it sweet. That takes time and labor. Waddya think—I was chargin' feller critters for water just out of devilment? I charge because I gotta." "Friend," said Harvey, taking out a wallet and counting off eight five-bucko bills, "here is your money. What's fair is fair, and you have put a different complexion on what seemed at first to be an unconscionable interjection of a middleman between Nature and man's thirst." The saloon man removed his dirty apron and came around the bar. "If that's an apology, I accept it. Now the mayor'll discuss filling your tanks. That's me. I'm also justice of the peace, official recorder, fire chief...." "And chief of police, no doubt," said Harvey jocosely. "Nope. That's my son, Jed. Angus Johnson's my name. Folks here just call me Chief. I run this town, and run it right. How much water will you need?" Joe estimated quickly. "About seventy-five liters, if we go on half rations," he answered. He waited apprehensively. "Let's say ten buckos a liter," the mayor said. "On account of the quantity, I'm able to quote a bargain price. Shucks, boys, it hurts me more to charge for water than it does for you to pay. I just got to, that's all." The mayor gestured to the native, who shuffled out to the tanks with them. The planetoid man worked the pump while the mayor intently watched the crude level-gauge, crying "Stop!" when it registered the proper amount. Then Johnson rubbed his thumb on his index finger and wetted his lips expectantly. Harvey bravely counted off the bills. He asked: "But what are we to do about replenishing our battery fluid? Ten buckos a liter would be preposterous. We simply can't afford it." Johnson's response almost floored them. "Who said anything about charging you for battery water? You can have all you want for nothing. It's just the purified stuff that comes so high." After giving them directions that would take them to the free-water pool, the ponderous factotum of Planetoid 42 shook hands and headed back to the saloon. His six-armed assistant followed him inside. "Now do you see, my hot-tempered colleague?" said Harvey as he and Joe picked up buckets that hung on the tank. "Johnson, as I saw instantly, is the victim of a difficult environment, and must charge accordingly." "Just the same," Joe griped, "paying for water isn't something you can get used to in ten minutes." In the fragile forest, they soon came across a stream that sprang from the igneous soil and splashed into the small pond whose contents, according to the mayor, was theirs for the asking. They filled their buckets and hauled them to the ship, then returned for more. It was on the sixth trip that Joe caught a glimpse of Jupiter-shine on a bright surface off to the left. The figure, 750, with the bucko sign in front of it, was still doing acrobatics inside his skull and keeping a faint suspicion alive in him. So he called Harvey and they went to investigate. Among the skimpy ground-crawling vines, they saw a long slender mound that was unmistakably a buried pipe. "What's this doing here?" Harvey asked, puzzled. "I thought Johnson had to transport water in pails." "Wonder where it leads to," Joe said uneasily. "It leads to the saloon," said Harvey, his eyes rapidly tracing the pipe back toward the spaceport. "What I am concerned with is where it leads from ." Five minutes later, panting heavily from the unaccustomed exertion of scrambling through the tangle of planetorial undergrowth, they burst into the open—before a clear, sparkling pool. Mutely, Harvey pointed out a pipe-end jutting under the water. "I am growing suspicious," he said in a rigidly controlled voice. But Joe was already on his knees, scooping up a handful of water and tasting it. "Sweet!" he snarled. They rushed back to the first pool, where Joe again tasted a sample. His mouth went wry. "Bitter! He uses only one pool, the sweet one! The only thing that needs purifying around here is that blasted mayor's conscience." "The asteroidal Poobah has tricked us with a slick come-on," said Harvey slowly. His eyes grew cold. "Joseph, the good-natured artist in me has become a hard and merciless avenger. I shall not rest until we have had the best of this colonial con-man! Watch your cues from this point hence." Fists clenched, the two returned to the saloon. But at the door they stopped and their fists unclenched. "Thought you gents were leaving," the mayor called out, seeing them frozen in the doorway. "Glad you didn't. Now you can meet my son, Jed. Him and me are the whole Earthman population of Johnson City." "You don't need any more," said Harvey, dismayed. Johnson's eight-foot son, topped by a massive roof of sun-bleached hair and held up by a foundation that seemed immovable, had obviously been born and raised in low gravity. For any decent-sized world would have kept him down near the general dimensions of a man. He held out an acre of palm. Harvey studied it worriedly, put his own hand somewhere on it, swallowed as it closed, then breathed again when his fingers were released in five units instead of a single compressed one. "Pleased to meet you," piped a voice that had never known a dense atmosphere. The pursuit of vengeance, Harvey realized, had taken a quick and unpleasant turn. Something shrewd was called for.... "Joseph!" he exclaimed, looking at his partner in alarm. "Don't you feel well?" Even before the others could turn to him, Joe's practiced eyes were gently crossing. He sagged against the door frame, all his features drooping like a bloodhound's. "Bring him in here!" Johnson cried. "I mean, get him away! He's coming down with asteroid fever!" "Of course," replied Harvey calmly. "Any fool knows the first symptoms of the disease that once scourged the universe." "What do you mean, once ?" demanded Johnson. "I come down with it every year, and I ain't hankering to have it in an off-season. Get him out of here!" "In good time. He can't be moved immediately." "Then he'll be here for months!" Harvey helped Joe to the counter and lifted him up on it. The mayor and his gigantic offspring were cowering across the room, trying to breathe in tiny, uncontaminating gasps. "You'll find everything you want in the back room," Johnson said frantically, "sulfopyridine, mustard plasters, rubs, inhalers, suction cups—" "Relics of the past," Harvey stated. "One medication is all modern man requires to combat the dread menace, asteroid fever." "What's that?" asked the mayor without conviction. Instead of replying, Harvey hurried outside to the ungainly second-hand rocket ship in the center of the shabby spaceport. He returned within a few minutes, carrying a bottle. Joe was still stretched out on the bar, panting, his eyes slowly crossing and uncrossing. Harvey lifted the patient's head tenderly, put the bottle to his lips and tilted it until he was forced to drink. When Joe tried to pull away, Harvey was inexorable. He made his partner drink until most of the liquid was gone. Then he stepped back and waited for the inevitable result. Joe's performance was better than ever. He lay supine for several moments, his face twisted into an expression that seemed doomed to perpetual wryness. Slowly, however, he sat up and his features straightened out. "Are—are you all right?" asked the mayor anxiously. "Much better," said Joe in a weak voice. "Maybe you need another dose," Harvey suggested. Joe recoiled. "I'm fine now!" he cried, and sprang off the bar to prove it. Astonished, Johnson and his son drew closer. They searched Joe's face, and then the mayor timidly felt his pulse. "Well, I'll be hanged!" Johnson ejaculated. " La-anago Yergis never fails, my friend," Harvey explained. "By actual test, it conquers asteroid fever in from four to twenty-three minutes, depending on the severity of the attack. Luckily, we caught this one before it grew formidable." The mayor's eyes became clouded mirrors of an inward conflict. "If you don't charge too much," he said warily, "I might think of buying some." "We do not sell this unbelievable remedy," Harvey replied with dignity. "It sells itself." "'Course, I'd expect a considerable reduction if I bought a whole case," said Johnson. "That would be the smallest investment you could make, compared with the vast loss of time and strength the fever involves." "How much?" asked the mayor unhappily. "For you, since you have taken us in so hospitably, a mere five hundred buckos." Johnson did not actually stagger back, but he gave the impression of doing so. "F-four hundred," he offered. "Not a red cent less than four seventy-five," Harvey said flatly. "Make it four fifty," quavered Johnson. "I dislike haggling," said Harvey. The final price, however, was four hundred and sixty-nine buckos and fifty redsents. Magnanimously, Harvey added: "And we will include, gratis , an elegant bottle-opener, a superb product of Mercurian handicraftsmanship." Johnson stabbed out a warning finger. "No tricks now. I want a taste of that stuff. You're not switching some worthless junk on me." Harvey took a glass from the bar and poured him a generous sample. The mayor sniffed it, grimaced, then threw it down his gullet. The ensuing minute saw a grim battle between a man and his stomach, a battle which the man gradually won. "There ain't no words for that taste," he gulped when it was safe to talk again. "Medicine," Harvey propounded, "should taste like medicine." To Joe he said: "Come, my esteemed colleague. We must perform the sacred task to which we have dedicated ourselves." With Joe stumbling along behind, he left the saloon, crossed the clearing and entered the ship. As soon as they were inside, Joe dropped his murderous silence and cried: "What kind of a dirty trick was that, giving me poison instead of that snake oil?" "That was not poison," Harvey contradicted quietly. "It was La-anago Yergis extract, plus." "Plus what—arsenic?" "Now, Joseph! Consider my quandary when I came back here to manufacture our specific for all known ailments, with the intention of selling yonder asteroidal tin-horn a bill of medical goods—an entire case, mind you. Was I to mix the extract with the water for which we had been swindled to the tune of ten buckos a liter? Where would our profit have been, then? No; I had to use the bitter free water, of course." "But why use it on me?" Joe demanded furiously. Harvey looked reprovingly at his gangling partner. "Did Johnson ask to taste it, or did he not? One must look ahead, Joseph. I had to produce the same medicine that we will now manufacture. Thus, you were a guinea pig for a splendid cause." "Okay, okay," Joe said. "But you shoulda charged him more." "Joseph, I promise you that we shall get back every redsent of which that swindler cheated us, besides whatever other funds or valuables he possesses. We could not be content with less." "Well, we're starting all right," admitted Joe. "How about that thing with six arms? He looks like a valuable. Can't we grab him off?" Harvey stopped filling bottles and looked up pensively. "I have every hope of luring away the profitable monstrosity. Apparently you have also surmised the fortune we could make with him. At first I purpose to exhibit him on our interplanetary tours with our streamlined panacea; he would be a spectacular attraction for bucolic suckers. Later, a brief period of demonstrating his abilities on the audio-visiphone. Then our triumph—we shall sell him at a stupendous figure to the zoo!" Joe was still dazed by that monetary vista when he and Harvey carried the case of medicine to the saloon. The mayor had already cleared a place of honor in the cluttered back room, where he told them to put it down carefully. Then he took the elaborate bottle-opener Harvey gave him, reverently uncorked a bottle and sampled it. It must have been at least as good as the first; he gagged. "That's the stuff, all right," he said, swallowing hard. He counted out the money into Harvey's hand, at a moderate rate that precariously balanced between his pleasure at getting the fever remedy and his pain at paying for it. Then he glanced out to see the position of Jupiter, and asked: "You gents eaten yet? The restaurant's open now." Harvey and Joe looked at each other. They hadn't been thinking about food at all, but suddenly they realized that they were hungry. "It's only water we were short of," Harvey said apprehensively. "We've got rations back at the ship." " H-mph! " the mayor grunted. "Powdered concentrates. Compressed pap. Suit yourselves. We treat our stomachs better here. And you're welcome to our hospitality." "Your hospitality," said Harvey, "depends on the prices you charge." "Well, if that's what's worrying you, you can stop worrying," answered the mayor promptly. "What's more, the kind of dinner I serve here you can't get anywhere else for any price." Swiftly, Harvey conned the possibilities of being bilked again. He saw none. "Let's take a look at the menu, anyhow, Joe," he said guardedly. Johnson immediately fell into the role of "mine host." "Come right in, gents," he invited. "Right into the dining room." He seated them at a table, which a rope tied between posts made more or less private, though nobody else was in the saloon and there was little chance of company. Genius, the six-armed native, appeared from the dingy kitchen with two menus in one hand, two glasses of water in another, plus napkins, silverware, a pitcher, plates, saucers, cups, and their cocktails, which were on the house. Then he stood by for orders. Harvey and Joe studied the menu critically. The prices were phenomenally low. When they glanced up at Johnson in perplexity, he grinned, bowed and asked: "Everything satisfactory, gents?" "Quite," said Harvey. "We shall order." For an hour they were served amazing dishes, both fresh and canned, the culinary wealth of this planetoid and all the system. And the service was as extraordinary as the meal itself. With four hands, Genius played deftly upon a pair of mellow Venusian viotars , using his other two hands for waiting on the table. "We absolutely must purchase this incredible specimen," Harvey whispered excitedly when Johnson and the native were both in the kitchen, attending to the next course. "He would make any society hostess's season a riotous success, which should be worth a great sum to women like Mrs. van Schuyler-Morgan, merely for his hire." "Think of a fast one fast," Joe agreed. "You're right." "But I dislike having to revise my opinion of a man so often," complained Harvey. "I wish Johnson would stay either swindler or honest merchant. This dinner is worth as least twenty buckos, yet I estimate our check at a mere bucko twenty redsents." The mayor's appearance prevented them from continuing the discussion. "It's been a great honor, gents," he said. "Ain't often I have visitors, and I like the best, like you two gents." As if on cue, Genius came out and put the check down between Joe and Harvey. Harvey picked it up negligently, but his casual air vanished in a yelp of horror. "What the devil is this?" he shouted.—"How do you arrive at this fantastic, idiotic figure— three hundred and twenty-eight buckos !" Johnson didn't answer. Neither did Genius; he simply put on the table, not a fingerbowl, but a magnifying glass. With one of his thirty fingers he pointed politely to the bottom of the menu. Harvey focused on the microscopic print, and his face went pasty with rage. The minute note read: "Services and entertainment, 327 buckos 80 redsents." "You can go to hell!" Joe growled. "We won't pay it!" Johnson sighed ponderously. "I was afraid you'd act like that," he said with regret. He pulled a tin badge out of his rear pocket, pinned it on his vest, and twisted his holstered gun into view. "Afraid I'll have to ask the sheriff to take over." Johnson, the "sheriff," collected the money, and Johnson, the "restaurateur," pocketed it. Meanwhile, Harvey tipped Joe the sign to remain calm. "My friend," he said to the mayor, and his tones took on a schoolmasterish severity, "your long absence from Earth has perhaps made you forget those elements of human wisdom that have entered the folk-lore of your native planet. Such as, for example: 'It is folly to kill a goose that lays golden eggs,' and 'Penny wise is pound foolish.'" "I don't get the connection," objected Johnson. "Well, by obliging us to pay such a high price for your dinner, you put out of your reach the chance of profiting from a really substantial deal. My partner and I were prepared to make you a sizable offer for the peculiar creature you call Genius. But by reducing our funds the way you have—" "Who said I wanted to sell him?" the mayor interrupted. He rubbed his fingers together and asked disinterestedly: "What were you going to offer, anyhow?" "It doesn't matter any longer," Harvey said with elaborate carelessness. "Perhaps you wouldn't have accepted it, anyway." "That's right," Johnson came back emphatically. "But what would your offer have been which I would have turned down?" "Which one? The one we were going to make, or the one we can make now?" "Either one. It don't make no difference. Genius is too valuable to sell." "Oh, come now, Mr. Johnson. Don't tell me no amount of money would tempt you!" "Nope. But how much did you say?" "Ah, then you will consider releasing Genius!" "Well, I'll tell you something," said the mayor confidentially. "When you've got one thing, you've got one thing. But when you've got money, it's the same as having a lot of things. Because, if you've got money, you can buy this and that and this and that and—" "This and that," concluded Joe. "We'll give you five hundred buckos." "Now, gents!" Johnson remonstrated. "Why, six hundred would hardly—" "You haven't left us much money," Harvey put in. The mayor frowned. "All right, we'll split the difference. Make it five-fifty." Harvey was quick to pay out, for this was a genuine windfall. Then he stood up and admired the astonishing possession he had so inexpensively acquired. "I really hate to deprive you of this unique creature," he said to Johnson. "I should imagine you will be rather lonely, with only your filial mammoth to keep you company." "I sure will," Johnson confessed glumly. "I got pretty attached to Genius, and I'm going to miss him something awful." Harvey forcibly removed his eyes from the native, who was clearing off the table almost all at once. "My friend," he said, "we take your only solace, it is true, but in his place we can offer something no less amazing and instructive." The mayor's hand went protectively to his pocket. "What is it?" he asked with the suspicion of a man who has seen human nature at its worst and expects nothing better. "Joseph, get our most prized belonging from the communications room of the ship," Harvey instructed. To Johnson he explained: "You must see the wondrous instrument before its value can be appreciated. My partner will soon have it here for your astonishment." Joe's face grew as glum as Johnson's had been. "Aw, Harv," he protested, "do we have to sell it? And right when I thought we were getting the key!" "We must not be selfish, my boy," Harvey said nobly. "We have had our chance; now we must relinquish Fate to the hands of a man who might have more success than we. Go, Joseph. Bring it here." Unwillingly, Joe turned and shuffled out. On a larger and heavier world than Planetoid 42, Johnson's curiosity would probably have had weight and mass. He was bursting with questions, but he was obviously afraid they would cost him money. For his part, Harvey allowed that curiosity to grow like a Venusian amoeba until Joe came in, lugging a radio. "Is that what you were talking about?" the mayor snorted. "What makes you think I want a radio? I came here to get away from singers and political speech-makers." "Do not jump to hasty conclusions," Harvey cautioned. "Another word, and I shall refuse you the greatest opportunity any man has ever had, with the sole exceptions of Joseph, myself and the unfortunate inventor of this absolutely awe-inspiring device." "I ain't in the market for a radio," Johnson said stubbornly. Harvey nodded in relief. "We have attempted to repay our host, Joseph. He has spurned our generosity. We have now the chance to continue our study, which I am positive will soon reward us with the key to an enormous fortune." "Well, that's no plating off our bow," Joe grunted. "I'm glad he did turn it down. I hated to give it up after working on it for three whole years." He picked up the radio and began walking toward the door. "Now, hold on!" the mayor cried. "I ain't saying I'll buy, but what is it I'm turning down?" Joe returned and set the instrument down on the bar. His face sorrowful, Harvey fondly stroked the scarred plasticoid cabinet. "To make a long story, Mr. Johnson," he said, "Joseph and I were among the chosen few who knew the famous Doctor Dean intimately. Just before his tragic death, you will recall, Dean allegedly went insane." He banged his fist on the bar. "I have said it before, and I repeat again, that was a malicious lie, spread by the doctor's enemies to discredit his greatest invention—this fourth dimensional radio!" "This what?" Johnson blurted out. "In simple terms," clarified Harvey, "the ingenious doctor discovered that the yawning chasm between the dimensions could be bridged by energy of all quanta. There has never been any question that the inhabitants of the super-dimension would be far more civilized than ourselves. Consequently, the man who could tap their knowledge would find himself in possession of a powerful, undreamt-of science!" The mayor looked respectfully at the silent box on the bar. "And this thing gets broadcasts from the fourth dimension?" "It does, Mr. Johnson! Only charlatans like those who envied Doctor Dean's magnificent accomplishments could deny that fact." The mayor put his hands in his pockets, unswiveled one hip and stared thoughtfully at the battered cabinet. "Well, let's say it picks up fourth dimensional broadcasts," he conceded. "But how could you understand what they're saying? Folks up there wouldn't talk our language." Again Harvey smashed his fist down. "Do you dare to repeat the scurvy lie that broke Dean's spirit and drove him to suicide?" Johnson recoiled. "No—no, of course not . I mean, being up here, I naturally couldn't get all the details." "Naturally," Harvey agreed, mollified. "I'm sorry I lost my temper. But it is a matter of record that the doctor proved the broadcasts emanating from the super-dimension were in English! Why should that be so difficult to believe? Is it impossible that at one time there was communication between the dimensions, that the super-beings admired our language and adopted it in all its beauty, adding to it their own hyper-scientific trimmings?" "Why, I don't know," Johnson said in confusion. "For three years, Joseph and I lost sleep and hair, trying to detect the simple key that would translate the somewhat metamorphosed broadcasts into our primitive English. It eluded us. Even the doctor failed. But that was understandable; a sensitive soul like his could stand only so much. And the combination of ridicule and failure to solve the mystery caused him to take his own life." Johnson winced. "Is that what you want to unload on me?" "For a very good reason, sir. Patience is the virtue that will be rewarded with the key to these fourth dimensional broadcasts. A man who could devote his life to improving this lonely worldlet is obviously a person with unusual patience." "Yeah," the mayor said grudgingly, "I ain't exactly flighty." "Therefore, you are the man who could unravel the problem!" Johnson asked skeptically: "How about a sample first?" | D. Con artists |
What are the datasets used for training? | ### Introduction
The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Their compact nature allows them to be transmitted efficiently and assimilated easily. These short messages can shape people's thought and opinion. This makes them an interesting and important area of study. Tweets are not only important for an individual but also for the companies, political parties or any organization. Companies can use tweets to gauge the performance of their products and predict market trends BIBREF0. The public opinion is particularly interesting for political parties as it gives them an idea of voter's inclination and their support. Sentiment and emotion analysis can help to gauge product perception, predict stock prices and model public opinions BIBREF1. Sentiment analysis BIBREF2 is an important area of research in natural language processing (NLP) where we automatically determine the sentiments (positive, negative, neutral). Emotion analysis focuses on the extraction of predefined emotion from documents. Discrete emotions BIBREF3, BIBREF4 are often classified into anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Sentiments and emotions are subjective and hence they are understood similarly and often used interchangeably. This is also mostly because both emotions and sentiments refer to experiences that result from the combined influences of the biological, the cognitive, and the social BIBREF5. However, emotions are brief episodes and are shorter in length BIBREF6, whereas sentiments are formed and retained for a longer period. Moreover, emotions are not always target-centric whereas sentiments are directed. Another difference between emotion and sentiment is that a sentence or a document may contain multiple emotions but a single overall sentiment. Prior studies show that sentiment and emotion are generally tackled as two separate problems. Although sentiment and emotion are not exactly the same, they are closely related. Emotions, like joy and trust, intrinsically have an association with a positive sentiment. Similarly, anger, disgust, fear and sadness have a negative tone. Moreover, sentiment analysis alone is insufficient at times in imparting complete information. A negative sentiment can arise due to anger, disgust, fear, sadness or a combination of these. Information about emotion along with sentiment helps to better understand the state of the person or object. The close association of emotion with sentiment motivates us to build a system for sentiment analysis using the information obtained from emotion analysis. In this paper, we put forward a robust two-layered multi-task attention based neural network which performs sentiment analysis and emotion analysis simultaneously. The model uses two levels of attention - the first primary attention builds the best representation for each word using Distributional Thesaurus and the secondary attention mechanism creates the final sentence level representation. The system builds the representation hierarchically which gives it a good intuitive working insight. We perform several experiments to evaluate the usefulness of primary attention mechanism. Experimental results show that the two-layered multi-task system for sentiment analysis which uses emotion analysis as an auxiliary task improves over the existing state-of-the-art system of SemEval 2016 Task 6 BIBREF7. The main contributions of the current work are two-fold: a) We propose a novel two-layered multi-task attention based system for joint sentiment and emotion analysis. This system has two levels of attention which builds a hierarchical representation. This provides an intuitive explanation of its working; b) We empirically show that emotion analysis is relevant and useful in sentiment analysis. The multi-task system utilizing fine-grained information of emotion analysis performs better than the single task system of sentiment analysis. ### Related Work
A survey of related literature reveals the use of both classical and deep-learning approaches for sentiment and emotion analysis. The system proposed in BIBREF8 relied on supervised statistical text classification which leveraged a variety of surface form, semantic, and sentiment features for short informal texts. A Support Vector Machine (SVM) based system for sentiment analysis was used in BIBREF9, whereas an ensemble of four different sub-systems for sentiment analysis was proposed in BIBREF10. It comprised of Long Short-Term Memory (LSTM) BIBREF11, Gated Recurrent Unit (GRU) BIBREF12, Convolutional Neural Network (CNN) BIBREF13 and Support Vector Regression (SVR) BIBREF14. BIBREF15 reported the results for emotion analysis using SVR, LSTM, CNN and Bi-directional LSTM (Bi-LSTM) BIBREF16. BIBREF17 proposed a lexicon based feature extraction for emotion text classification. A rule-based approach was adopted by BIBREF18 to extract emotion-specific semantics. BIBREF19 used a high-order Hidden Markov Model (HMM) for emotion detection. BIBREF20 explored deep learning techniques for end-to-end trainable emotion recognition. BIBREF21 proposed a multi-task learning model for fine-grained sentiment analysis. They used ternary sentiment classification (negative, neutral, positive) as an auxiliary task for fine-grained sentiment analysis (very-negative, negative, neutral, positive, very-positive). A CNN based system was proposed by BIBREF22 for three phase joint multi-task training. BIBREF23 presented a multi-task learning based model for joint sentiment analysis and semantic embedding learning tasks. BIBREF24 proposed a multi-task setting for emotion analysis based on a vector-valued Gaussian Process (GP) approach known as coregionalisation BIBREF25. A hierarchical document classification system based on sentence and document representation was proposed by BIBREF26. An attention framework for sentiment regression is described in BIBREF27. BIBREF28 proposed a DeepEmoji system based on transfer learning for sentiment, emotion and sarcasm detection through emoji prediction. However, the DeepEmoji system treats these independently, one at a time. Our proposed system differs from the above works in the sense that none of these works addresses the problem of sentiment and emotion analysis concurrently. Our empirical analysis shows that performance of sentiment analysis is boosted significantly when this is jointly performed with emotion analysis. This may be because of the fine-grained characteristics of emotion analysis that provides useful evidences for sentiment analysis. ### Proposed Methodology
We propose a novel two-layered multi-task attention based neural network for sentiment analysis where emotion analysis is utilized to improve its efficiency. Figure FIGREF1 illustrates the overall architecture of the proposed multi-task system. The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks. The BiLSTM encodes the word representation of each word. This representation is shared between the subsystems of sentiment and emotion analysis. Each of the shared representations is then fed to the primary attention mechanism of both the subsystems. The primary attention mechanism finds the best representation for each word for each task. The secondary attention mechanism acts on top of the primary attention to extract the best sentence representation by focusing on the suitable context for each task. Finally, the representations of both the tasks are fed to two different feed-forward neural networks to produce two outputs - one for sentiment analysis and one for emotion analysis. Each component is explained in the subsequent subsections. ### Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: BiLSTM based word encoder
Recurrent Neural Networks (RNN) are a class of networks which take sequential input and computes a hidden state vector for each time step. The current hidden state vector depends on the current input and the previous hidden state vector. This makes them good for handling sequential data. However, they suffer from a vanishing or exploding gradient problem when presented with long sequences. The gradient for back-propagating error either reduces to a very small number or increases to a very high value which hinders the learning process. Long Short Term Memory (LSTM) BIBREF11, a variant of RNN solves this problem by the gating mechanisms. The input, forget and output gates control the information flow. BiLSTM is a special type of LSTM which takes into account the output of two LSTMs - one working in the forward direction and one working in the backward direction. The presence of contextual information for both past and future helps the BiLSTM to make an informed decision. The concatenation of a hidden state vectors $\overrightarrow{h_t}$ of the forward LSTM and $\overleftarrow{h_t}$ of the backward LSTM at any time step t provides the complete information. Therefore, the output of the BiLSTM at any time step t is $h_t$ = [$\overrightarrow{h_t}$, $\overleftarrow{h_t}$]. The output of the BiLSTM is shared between the main task (Sentiment Analysis) and the auxiliary task (Emotion Analysis). ### Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Word Attention
The word level attention (primary attention) mechanism gives the model a flexibility to represent each word for each task differently. This improves the word representation as the model chooses the best representation for each word for each task. A Distributional Thesaurus (DT) identifies words that are semantically similar, based on whether they tend to occur in a similar context. It provides a word expansion list for words based on their contextual similarity. We use the top-4 words for each word as their candidate terms. We only use the top-4 words for each word as we observed that the expansion list with more words started to contain the antonyms of the current word which empirically reduced the system performance. Word embeddings of these four candidate terms and the hidden state vector $h_t$ of the input word are fed to the primary attention mechanism. The primary attention mechanism finds the best attention coefficient for each candidate term. At each time step $t$ we get V($x_t$) candidate terms for each input $x_t$ with $v_i$ being the embedding for each term (Distributional Thesaurus and word embeddings are described in the next section). The primary attention mechanism assigns an attention coefficient to each of the candidate terms having the index $i$ $\in $ V($x_t$): where $W_w$ and $b_{w}$ are jointly learned parameters. Each embedding of the candidate term is weighted with the attention score $\alpha _{ti}$ and then summed up. This produces $m_{t}$, the representation for the current input $x_{t}$ obtained from the Distributional Thesaurus using the candidate terms. Finally, $m_{t}$ and $h_{t}$ are concatenated to get $\widehat{h_{t}}$, the final output of the primary attention mechanism. ### Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Sentence Attention
The sentence attention (secondary attention) part focuses on each word of the sentence and assigns the attention coefficients. The attention coefficients are assigned on the basis of words' importance and their contextual relevance. This helps the model to build the overall sentence representation by capturing the context while weighing different word representations individually. The final sentence representation is obtained by multiplying each word vector representation with their attention coefficient and summing them over. The attention coefficient $\alpha _t$ for each word vector representation and the sentence representation $\widehat{H}$ are calculated as: where $W_s$ and $b_{s}$ are parameters to be learned. $\widehat{H}$ denotes the sentence representation for sentiment analysis. Similarly, we calculate $\bar{H}$ which represents the sentence for emotion classification. The system has the flexibility to compute different representations for sentiment and emotion analysis both. ### Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Final Output
The final outputs for both sentiment and emotion analysis are computed by feeding $\widehat{H}$ and $\bar{H}$ to two different one-layer feed forward neural networks. For our task, the feed forward network for sentiment analysis has two output units, whereas the feed forward network for emotion analysis has eight output nodes performing multi-label classification. ### Proposed Methodology ::: Distributional Thesaurus
Distributional Thesaurus (DT) BIBREF31 ranks words according to their semantic similarity. It is a resource which produces a list of words in decreasing order of their similarity for each word. We use the DT to expand each word of the sentence. The top-4 words serve as the candidate terms for each word. For example, the candidate terms for the word good are: great, nice awesome and superb. DT offers the primary attention mechanism external knowledge in the form of candidate terms. It assists the system to perform better when presented with unseen words during testing as the unseen words could have been a part of the DT expansion list. For example, the system may not come across the word superb during training but it can appear in the test set. Since the system has already seen the word superb in the DT expansion list of the word good, it can handle this case efficiently. This fact is established by our evaluation results as the model performs better when the DT expansion and primary attentions are a part of the final multi-task system. ### Proposed Methodology ::: Word Embeddings
Word embeddings represent words in a low-dimensional numerical form. They are useful for solving many NLP problems. We use the pre-trained 300 dimensional Google Word2Vec BIBREF32 embeddings. The word embedding for each word in the sentence is fed to the BiLSTM network to get the current hidden state. Moreover, the primary attention mechanism is also applied to the word embeddings of the candidate terms for the current word. ### Datasets, Experiments and Analysis
In this section we present the details of the datasets used for the experiments, results that we obtain and the necessary analysis. ### Datasets, Experiments and Analysis ::: Datasets
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively. ### Datasets, Experiments and Analysis ::: Preprocessing
The SemEval 2016 task 6 corpus contains tweets from Twitter. Since the tweets are derived from an environment with the constraint on the number of characters, there is an inherent problem of word concatenation, contractions and use of hashtags. Example: #BeautifulDay, we've, etc. Usernames and URLs do not impart any sentiment and emotion information (e.g. @John). We use the Python package ekphrasis BIBREF33 for handling these situations. Ekphrasis helps to split the concatenated words into individual words and expand the contractions. For example, #BeautifulDay to # Beautiful Day and we've to we have. We replace usernames with $<$user$>$, number with $<number>$ and URLs with $<$url$>$ token. ### Datasets, Experiments and Analysis ::: Implementation Details
We implement our model in Python using Tensorflow on a single GPU. We experiment with six different BiLSTM based architectures. The three architectures correspond to BiLSTM based systems without primary attention i.e. only with secondary attention for sentiment analysis (S1), emotion analysis (E1) and the multi-task system (M1) for joint sentiment and emotion analysis. The remaining three architectures correspond to the systems for sentiment analysis (S2), emotion analysis (E2) and multi-task system (M2), with both primary and secondary attention. The weight matrices were initialized randomly using numbers form a truncated normal distribution. The batch size was 64 and the dropout BIBREF34 was 0.6 with the Adam optimizer BIBREF35. The hidden state vectors of both the forward and backward LSTM were 300-dimensional, whereas the context vector was 150-dimensional. Relu BIBREF36 was used as the activation for the hidden layers, whereas in the output layer we used sigmoid as the activation function. Sigmoid cross-entropy was used as the loss function. F1-score was reported for the sentiment analysis BIBREF7 and precision, recall and F1-score were used as the evaluation metric for emotion analysis BIBREF15. Therefore, we report the F1-score for sentiment and precision, recall and F1-score for emotion analysis. ### Datasets, Experiments and Analysis ::: Results and Analysis
We compare the performance of our proposed system with the state-of-the-art systems of SemEval 2016 Task 6 and the systems of BIBREF15. Experimental results show that the proposed system improves the existing state-of-the-art systems for sentiment and emotion analysis. We summarize the results of evaluation in Table TABREF18. The primary attention mechanism plays a key role in the overall system as it improves the score of both sentiment and emotion analysis in both single task as well as multi-task systems. The use of primary attention improves the performance of single task systems for sentiment and emotion analysis by 2.21 and 1.72 points, respectively.Similarly, when sentiment and emotion analysis are jointly performed the primary attention mechanism improves the score by 0.93 and 2.42 points for sentiment and emotion task, respectively. To further measure the usefulness of the primary attention mechanism and the Distributional Thesaurus, we remove it from the systems S2, E2, and M2 to get the systems S1, E1, and M1. In all the cases, with the removal of primary attention mechanism, the performance drops. This is clearly illustrated in Figure FIGREF21. These observations indicate that the primary attention mechanism is an important component of the two-layered multi-task attention based network for sentiment analysis. We also perform t-test BIBREF40 for computing statistical significance of the obtained results from the final two-layered multi-task system M2 for sentiment analysis by calculating the p-values and observe that the performance gain over M1 is significant with p-value = 0.001495. Similarly, we perform the statistical significance test for each emotion class. The p-values for anger, anticipation, fear, disgust, joy, sadness, surprise and trust are 0.000002, 0.000143, 0.00403, 0.000015, 0.004607, 0.069, 0.000001 and 0.000001, respectively. These results provide a good indication of statistical significance. Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis. We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15. Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. The findings of BIBREF15 also support this behavior (i.e. worst result for the surprise class). This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise. Experimental results indicate that the multi-task system which uses fine-grained information of emotion analysis helps to boost the performance of sentiment analysis. The system M1 comprises of the system S1 performing the main task (sentiment analysis) with E1 undertaking the auxiliary task (emotion analysis). Similarly, the system M2 is made up of S2 and E2 where S2 performs the main task (sentiment analysis) and E2 commits to the auxiliary task (emotion analysis). We observe that in both the situations, the auxiliary task, i.e. emotional information increases the performance of the main task, i.e. sentiment analysis when these two are jointly performed. Experimental results help us to establish the fact that emotion analysis benefits sentiment analysis. The implicit sentiment attached to the emotion words assists the multi-task system. Emotion such as joy and trust are inherently associated with a positive sentiment whereas, anger, disgust, fear and sadness bear a negative sentiment. Figure FIGREF21 illustrates the performance of various models for sentiment analysis. As a concrete example which justifies the utility of emotion analysis in sentiment analysis is shown below. @realMessi he is a real sportsman and deserves to be the skipper. The gold labels for the example are anticipation, joy and trust emotion with a positive sentiment. Our system S2 (single task system for sentiment analysis with primary and secondary attention) had incorrectly labeled this example with a negative sentiment and the E2 system (single task system with both primary and secondary attention for emotion analysis) had tagged it with anticipation and joy only. However, M2 i.e. the multi-task system for joint sentiment and emotion analysis had correctly classified the sentiment as positive and assigned all the correct emotion tags. It predicted the trust emotion tag, in addition to anticipation and joy (which were predicted earlier by E2). This helped M2 to correctly identify the positive sentiment of the example. The presence of emotional information helped the system to alter its sentiment decision (negative by S2) as it had better understanding of the text. A sentiment directly does not invoke a particular emotion always and a sentiment can be associated with more than one emotion. However, emotions like joy and trust are associated with positive sentiment mostly whereas, anger, disgust and sadness are associated with negative sentiment particularly. This might be the reason of the extra sentiment information not helping the multi-task system for emotion analysis and hence, a decreased performance for emotion analysis in the multi-task setting. ### Datasets, Experiments and Analysis ::: Error Analysis
We perform quantitative error analysis for both sentiment and emotion for the M2 model. Table TABREF23 shows the confusion matrix for sentiment analysis. anger,anticipation,fear,disgust,joy,sadness,surprise,trust consist of the confusion matrices for anger, anticipation, fear, disgust, joy, sadness, surprise and trust. We observe from Table TABREF23 that the system fails to label many instances with the emotion surprise. This may be due to the reason that this particular class is the most underrepresented in the training set. A similar trend can also be observed for the emotion fear and trust in Table TABREF23 and Table TABREF23, respectively. These three emotions have the least share of training instances, making the system less confident towards these emotions. Moreover, we closely analyze the outputs to understand the kind of errors that our proposed model faces. We observe that the system faces difficulties at times and wrongly predicts the sentiment class in the following scenarios: $\bullet $ Often real-world phrases/sentences have emotions of conflicting nature. These conflicting nature of emotions are directly not evident from the surface form and are left unsaid as these are implicitly understood by humans. The system gets confused when presented with such instances. Text: When you become a father you realize that you are not the most important person in the room anymore... Your child is! Actual Sentiment: positive Actual Emotion: anticipation, joy, surprise, trust Predicted Sentiment: negative Predicted Emotion: anger, anticipation, sadness The realization of not being the most important person in a room invokes anger, anticipation and sadness emotions, and a negative sentiment. However, it is a natural feeling of overwhelmingly positive sentiment when you understand that your own child is the most significant part of your life. $\bullet $ Occasionally, the system focuses on the less significant part of the sentences. Due to this the system might miss crucial information which can influence and even change the final sentiment or emotion. This sometimes lead to the incorrect prediction of the overall sentiment and emotion. Text: I've been called many things, quitter is not one of them... Actual Sentiment: positive Actual Emotion: anticipation, joy, trust Predicted Sentiment: negative Predicted Emotion: anticipation, sadness Here, the system focuses on the first part of the sentence where the speaker was called many things which denotes a negative sentiment. Hence, the system predicts a negative sentiment and, anticipation and sadness emotions. However, the speaker in the second part uplifts the overall tone by justifying that s/he has never been called a quitter. This changes the negative sentiment to a positive sentiment and the overall emotion. ### Conclusion
In this paper, we have presented a novel two-layered multi-task attention based neural network which performs sentiment analysis through emotion analysis. The primary attention mechanism of the two-layered multi-task system relies on Distributional Thesaurus which acts as a source of external knowledge. The system hierarchically builds the final representation from the word level to the sentence level. This provides a working insight to the system and its ability to handle the unseen words. Evaluation on the benchmark dataset suggests an improvement of 3.2 F-score point for sentiment analysis and an overall performance boost of 5 F-score points for emotion analysis over the existing state-of-the-art systems. The system empirically establishes the fact that emotion analysis is both useful and relevant to sentiment analysis. The proposed system does not rely on any language dependent features or lexicons. This makes it extensible to other languages as well. In future, we would like to extend the two-layered multi-task attention based neural network to other languages. ### Acknowledgements
Asif Ekbal acknowledges the Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia). Fig. 1. Two-layered multi-task attention based network TABLE I DATASET STATISTICS OF SEMEVAL 2016 TASK 6 AND SSEC USED FOR SENTIMENT AND EMOTION ANALYSIS, RESPECTIVELY. TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET. TABLE III COMPARISON WITH THE STATE-OF-THE-ART SYSTEMS OF SEMEVAL 2016 TASK 6 ON SENTIMENT DATASET. Fig. 2. Comparison of various models (S1, S2, M1, M2) w.r.t different hidden state vector sizes of BiLSTM for sentiment analysis. Y-axis denotes the Fscores. TABLE IV COMPARISON WITH THE STATE-OF-THE-ART SYSTEMS PROPOSED BY [16] ON EMOTION DATASET. THE METRICS P, R AND F STAND FOR PRECISION, RECALL AND F1-SCORE. TABLE XI CONFUSION MATRIX FOR sadness | SemEval 2016 Task 6 BIBREF7, Stance Sentiment Emotion Corpus (SSEC) BIBREF15 |
On which date was Mr. Romero's myocardial biopsy performed that revealed a positive detection of HCV-specific RNA?
Choose the correct answer from the following options:
A. 01/27/2014
B. 03/26/2016
C. 06/15/2016
D. 09/23/2022
E. 05/20/2023
| ### Patient Report 0
**Dear colleague, **
We would like to report to you about our patient, Mr. David Romero, born
on 02/16/1942, who was under our inpatient care from 03/25/2016 to
03/30/2016.
**Diagnoses:**
- Suspected myocarditis
- Uncomplicated biopsy, pending results
- LifeVest has been adjusted
- Left ventricular ejection fraction of 28%
- Chronic hepatitis C
- Status post hepatitis A
- Post-antiviral therapy
- Exclusion of relevant coronary artery disease
**Medical History:** The patient was admitted with suspected myocarditis
due to a significantly impaired pump function noticed during outpatient
visits. Anamnestically, the patient reported experiencing fatigue and
exertional dyspnea since mid-December, with no recollection of a
preceding infection. Antiviral therapy with Interferon/Ribavirin for
chronic Hepatitis C had been ongoing since November. An outpatient
evaluation had excluded relevant coronary artery disease.
**Current Presentation:** Suspected inflammatory/dilated cardiomyopathy,
Indication for biopsy
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without guarding, spleen
and liver not palpable. Normal bowel sounds.
**Coronary Angiography**: Globally significantly impaired left
ventricular function (EF: 28%)
[Myocardial biopsy:]{.underline} Uncomplicated retrieval of LV
endomyocardial biopsies
[Recommendation]{.underline}: A conservative medical approach is
recommended, and further therapeutic decisions will depend on the
histological, immunohistological, and molecular biological examination
results of the now-retrieved myocardial biopsies.
[Procedure]{.underline}: Femoral closure system is applied, 6 hours of
bed rest, administration of 100 mg/day of Aspirin for 4 weeks following
left ventricular heart biopsy.
**Echocardiography before Heart Catheterization**:
Performed in sinus rhythm. Satisfactory ultrasound condition.
[Findings]{.underline}: Moderately dilated left ventricle (LVDd 64mm).
Markedly reduced systolic LV function (EF 28%). Global longitudinal
strain (2D speckle tracking): -8.6%.
Regional wall motion abnormalities: despite global hypokinesia, the
posterolateral wall (basal) contracts best. Diastolic dysfunction Grade
1 (LV relaxation disorder) (E/A 0.7) (E/E\' mean 13.8). No LV
hypertrophy. Morphologically age-appropriate heart valves. Moderately
dilated left atrium (LA Vol. 71ml). Mild mitral valve insufficiency
(Grade 1 on a 3-grade scale). Normal-sized right ventricle. Moderately
reduced RV function Normal-sized right atrium. Minimal tricuspid valve
insufficiency (Grade 0-1 on a 3-grade scale). Systolic pulmonary artery
pressure in the normal range (systolic PAP 27mmHg).
No thrombus detected. Minimal pericardial effusion, circular, maximum
2mm, no hemodynamic relevance.
**Echocardiography after Heart Catheterization:**
[Indication]{.underline}: Follow-up on pericardial effusion.
[Examination]{.underline}: TTE at rest, including duplex and
quantitative determination of parameters. [Echocardiographic
Finding:]{.underline} Regarding pericardial effusion, the status is the
same. Circular effusion, maximum 2mm.
**ECG after Heart Catheterization:**
76/min, sinus rhythm, complete left bundle branch block.
**Summary:** On 03/26/2016, biopsy and left heart catheterization were
successfully performed without complications. Here, too, the patient
exhibited a significantly impaired pump function, currently at 28%.
**Therapy and Progression:**
Throughout the inpatient stay, the patient remained cardiorespiratorily
stable at all times. Malignant arrhythmias were ruled out via telemetry.
After the intervention, echocardiography showed no pericardial effusion.
The results of the endomyocardial biopsies are still pending. An
appointment for results discussion and evaluation of further procedures
at our facility should be scheduled in 3 weeks. Following the biopsy,
Aspirin 100 as specified should be given for 4 weeks. We intensified the
ongoing heart failure therapy and added Spironolactone to the
medication, recommending further escalation based on hemodynamic
tolerability.
**Current Recommendations:** Close cardiological follow-up examinations,
electrolyte monitoring, and echocardiography are advised. Depending on
the left ventricular ejection fraction\'s course, the implantation of an
ICD or ICD/CRT system should be considered after 3 months. On the day of
discharge, we initiated the adjustment of a Life Vest, allowing the
patient to return home in good general condition.
**Medication upon Discharge: **
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torasemide (Torem) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
------------------------ ------------- ---------------------
Absolute Erythroblasts 0.01/nL \< 0.01/nL
Sodium 134 mEq/L 136-145 mEq/L
Potassium 4.5 mEq/L 3.5-4.5 mEq/L
Creatinine (Jaffé) 1.25 mg/dL 0.70-1.20 mg/dL
Urea 50 mg/dL 17-48 mg/dL
Total Bilirubin 1.9 mg/dL \< 1.20 mg/dL
CRP 4.1 mg/L \< 5.0 mg/L
Troponin-T 78 ng/L \< 14 ng/L
ALT 67 U/L \< 41 U/L
AST 78 U/L \< 50 U/L
Alkaline Phosphatase 151 U/L 40-130 U/L
gamma-GT 200 U/L 8-61 U/L
Free Triiodothyronine 2.3 ng/L 2.00-4.40 ng/L
Free Thyroxine 14.2 ng/L 9.30-17.00 ng/L
TSH 4.1 mU/L 0.27-4.20 mU/L
Hemoglobin 11.6 g/dL 13.5-17.0 g/dL
Hematocrit 34.5% 39.5-50.5%
Erythrocytes 3.7 /pL 4.3-5.8/pL
Leukocytes 9.56/nL 3.90-10.50/nL
MCV 92.5 fL 80.0-99.0 fL
MCH 31.1 pg 27.0-33.5 pg
MCHC 33.6 g/dL 31.5-36.0 g/dL
MPV 8.9 fL 7.0-12.0 fL
RDW-CV 14.0% 11.5-15.0%
Quick 89% 78-123%
INR 1.09 0.90-1.25
PTT Actin-FS 25.3 sec. 22.0-29.0 sec.
### Patient Report 1
**Dear colleague, **
We are reporting on the pending findings of the myocardial biopsies
taken from Mr. David Romero, born on 02/16/1942 on 03/26/2016 due to the
deterioration of LV function from 40% to 28% after interferon therapy
for HCV infection.
**Diagnoses:**
- Suspected myocarditis
- LifeVest
- Left ventricular ejection fraction of 28%
- Chronic hepatitis C
- Status post hepatitis A
- Post-antiviral therapy
- Exclusion of relevant coronary artery disease
**Current Medication:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torasemide (Torem) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
**Myocardial Biopsy on 01/27/2014:**
[Molecular Biology:]{.underline}
PCR examinations performed under the question of myocardial infection
with cardiotropic pathogens yielded a positive detection of HCV-specific
RNA in myocardial tissue without quantification possibility
(methodically determined). Otherwise, there was no evidence of
myocardial infection with enteroviruses, adenoviruses, Epstein-Barr
virus, Human Herpes Virus Type 6 A/B, or Erythrovirus genotypes 1/2 in
the myocardium.
[Assessment]{.underline}: Positive HCV-mRNA detection in myocardial
tissue. This positive test result does not unequivocally prove an
infection of myocardial cells, as contamination of the tissue sample
with HCV-infected peripheral blood cells cannot be ruled out in chronic
hepatitis.
**Histology and Immunohistochemistry**:
Unremarkable endocardium, normal cell content of the interstitium with
only isolated lymphocytes and histiocytes in the histologically examined
samples. Quantitatively, immunohistochemically examined native
preparations showed borderline high CD3-positive lymphocytes with a
diffuse distribution pattern at 10.2 cells/mm2. No increased
perforin-positive cytotoxic T cells. The expression of cell adhesion
molecules is discreetly elevated. Otherwise, only slight perivascular
but no interstitial fibrosis. Cardiomyocytes are properly arranged and
slightly hypertrophied (average diameter around 23 µm), the surrounding
capillaries are unremarkable. No evidence of acute
inflammation-associated myocardial cell necrosis (no active myocarditis)
and no interstitial scars from previous myocyte loss. No lipomatosis.
[Assessment:]{.underline} Based on the myocardial biopsy findings, there
is positive detection of HCV-RNA in the myocardial tissue samples, with
the possibility of tissue contamination with HCV-infected peripheral
blood cells. Significant myocardial inflammatory reaction cannot be
documented histologically and immunohistochemically. In the endocardial
samples, apart from mild hypertrophy of properly arranged
cardiomyocytes, there are no significant signs of myocardial damage
(interstitial fibrosis or scars from previous myocyte loss). Therefore,
the present findings do not indicate the need for specific further
antiviral or anti-inflammatory therapy, and the existing heart failure
medication can be continued unchanged. If LV function impairment
persists for an extended period, there is an indication for
antiarrhythmic protection of the patient using an ICD.
### Patient Report 2
**Dear colleague, **
We thank you for referring your patient Mr. David Romero, born on
02/16/1942, to us for echocardiographic follow-up on 05/04/2016.
**Diagnoses:**
- Dilatated cardiomyopathy
- LifeVest
- Left ventricular ejection fraction of 28%
- Chronic Hepatitis C
- Status post Hepatitis A
- Post-antiviral therapy
- Exclusion of relevant coronary artery disease
- Type 2 diabetes mellitus
- Hypothyroidism
**Current Medication:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torem (Torasemide) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without pressure pain,
spleen and liver not palpable. Normal bowel sounds.
**Echocardiography: M-mode and 2-dimensional.**
The left ventricle measures approximately 65/56 mm (normal up to 56 mm).
The right atrium and right ventricle are of normal dimensions.
Global progressive reduction in contractility, morphologically
unremarkable.
In Doppler echocardiography, normal heart valves are observed.
Mitral valve insufficiency Grade I.
[Assessment]{.underline}: Dilated cardiomyopathy with slightly reduced
left ventricular function. MI I TII °, PAP 23 mm Hg + CVP. No more
pulmonary embolism detectable.
**Summary:**
Currently, the cardiac situation is stable, LVEDD slightly decreasing.
### Patient Report 3
**Dear colleague, **
We thank you for referring your patient, Mr. David Romero, born on
02/16/1942 to us for echocardiographic follow-up on 06/15/2016.
**Diagnoses:**
- Dilatated cardiomyopathy
- LifeVest
- Left ventricular ejection fraction of 28%
- Chronic Hepatitis C
- Status post Hepatitis A
- Post-antiviral therapy
- Exclusion of relevant coronary artery disease
- Type 2 diabetes mellitus
- Hypothyroidism
**Medication upon Admission:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torasemide (Torem) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without guarding, spleen
and liver not palpable. Normal bowel sounds.
**Echocardiography from 06/15/2016**: Good ultrasound conditions.
The left ventricle is dilated to approximately 65/57 mm (normal up to 56
mm). The left atrium is dilated to 48 mm. Normal thickness of the left
ventricular myocardium. Ejection fraction is around 28%. Heart valves
show normal flow velocities.
**Summary:**
Currently, the cardiac situation is stable, LVEDD slightly decreasing,
potassium and creatinine levels were obtained. If EF remains this low,
an ICD may be indicated.
**Lab results from 06/15/2016:**
**Parameter** **Result** **Reference Range**
----------------------------------- ------------ ---------------------
Reticulocytes 0.01/nL \< 0.01/nL
Sodium 135 mEq/L 136-145 mEq/L
Potassium 4.8 mEq/L 3.5-4.5 mEq/L
Creatinine 1.34 mg/dL 0.70-1.20 mg/dL
BUN 49 mg/dL 17-48 mg/dL
Total Bilirubin 1.9 mg/dL \< 1.20 mg/dL
C-reactive Protein 4.1 mg/L \< 5.0 mg/L
Troponin-T 78 ng/L \< 14 ng/L
ALT 67 U/L \< 41 U/L
AST 78 U/L \< 50 U/L
Alkaline Phosphatase 151 U/L 40-130 U/L
gamma-GT 200 U/L 8-61 U/L
Free Triiodothyronine (T3) 2.3 ng/L 2.00-4.40 ng/L
Free Thyroxine (T4) 14.2 ng/L 9.30-17.00 ng/L
Thyroid Stimulating Hormone (TSH) 4.1 mU/L 0.27-4.20 mU/L
Hemoglobin 11.6 g/dL 13.5-17.0 g/dL
Hematocrit 34.5% 39.5-50.5%
Red Blood Cell Count 3.7 M/µL 4.3-5.8 M/µL
White Blood Cell Count 9.56 K/µL 3.90-10.50 K/µL
Platelet Count 280 K/µL 150-370 K/µL
MC 92.5 fL 80.0-99.0 fL
MCH 31.1 pg 27.0-33.5 pg
MCHC 33.6 g/dL 31.5-36.0 g/dL
MPV 8.9 fL 7.0-12.0 fL
RDW-CV 14.0% 11.5-15.0%
Quick 89% 78-123%
INR 1.09 0.90-1.25
Partial Thromboplastin Time 25.3 sec. 22.0-29.0 sec.
### Patient Report 4
**Dear colleague, **
We are reporting to you about Mr. David Romero, born on 02/16/1942, who
presented himself at our Cardiology University Outpatient Clinic on
06/30/2016.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function (ejection fraction
around 30%)
- LifeVest
- Planned CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Current Medication:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ---------------- ---------------
Aspirin 100 mg/tablet 1-0-0
Ramipril (Altace) 2.5 mg/tablet 1-0-1
Carvedilol (Coreg) 12.5 mg/tablet 1-0-1
Torasemide (Torem) 5 mg/tablet 1-0-0
Spironolactone (Aldactone) 25 mg/tablet 1-0-0
L-Thyroxine (Synthroid) 50 µg/tablet 1-0-0
**Echocardiography on 06/30/2016:** In sinus rhythm. Adequate ultrasound
window.
Moderately dilated left ventricle (LVDd 63mm). Significantly reduced
systolic LV function (EF biplane 29%). No LV hypertrophy.
**ECG on 06/30/2016:** Sinus rhythm, regular tracing, heart rate 69/min,
complete left bundle branch block, QRS 135 ms, ERBS with left bundle
branch block.
**Assessment**: Mr. Romero presents himself for the follow-up assessment
of known dilated cardiomyopathy. He currently reports minimal dyspnea.
Coronary heart disease has been ruled out. No virus was detected
bioptically. However, the recent echocardiography still shows severely
impaired LV function.
**Current Recommendations:** Given the presence of left bundle branch
block, there is an indication for CRT-D implantation. For this purpose,
we have scheduled a pre-admission appointment, with the implantation
planned for 07/04/2016. We kindly request a referral letter. The
LifeVest should continue to be worn until the implantation, despite the
pressure sores on the thorax.
### Patient Report 5
**Dear colleague, **
We would like to report to you about our patient, Mr. David Romero, born
on 02/16/1942, who was in our inpatient care from 07/04/2016 to
07/06/2016.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function (ejection fraction
around 30%)
- LifeVest
- Planned CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Medication upon Admission:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torem (Torasemide) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
Sitagliptin (Januvia) 100 mg 1-0-0
Insulin glargine (Lantus) 0-0-20IE
**Current Presentation:** The current admission was elective for CRT-D
implantation in dilated cardiomyopathy with severely impaired LV
function despite full heart failure medication and complete left bundle
branch block. Please refer to previous medical records for a detailed
history. On 07/05/2016, a CRT-ICD system was successfully implanted. The
peri- and post-interventional course was uncomplicated. Pneumothorax was
ruled out post-interventionally. The wound conditions are
irritation-free. The ICD card was given to the patient. We request
outpatient follow-up on the above-mentioned date for wound inspection
and CRT follow-up. Please adjust the known cardiovascular risk factors.
**Findings:**
**ECG upon Admission:** Sinus rhythm 66/min, PQ 176ms, QRS 126ms, QTc
432ms, Complete left bundle branch block with corresponding excitation
regression disorder.
**Procedure**: Implantation of a CRT-D with left ventricular multipoint
pacing left pectoral. Smooth triple puncture of the lateral left
subclavian vein and implantation of an active single-coil electrode in
the RV apex with very good electrical values. Trouble-free probing of
the CS and direct venography using a balloon occlusion catheter.
Identification of a suitable lateral vein and implantation of a
quadripolar electrode (Quartet, St. Jude Medical) with very good
electrical values. No phrenic stimulation up to 10 volts in all
polarities. Finally, implantation of an active P/S electrode in the
right atrial roof with equally very good electrical values. Connection
to the device and submuscular implantation. Wound irrigation and layered
wound closure with absorbable suture material. Finally, extensive
testing of all polarities of the LV electrode and activation of
multipoint pacing. Final setting of the ICD.
**Chest X-ray on 07/05/2016:**
[Clinical status, question, justifying indication:]{.underline} History
of CRT-D implantation. Question about lead position, pneumothorax?
**Findings**: New CRT-D unit left pectoral with leads projected onto the
right ventricle, the right atrium, and the sinus coronarius. No
pneumothorax.
Normal heart size. No pulmonary congestion. No diffuse infiltrates. No
pleural effusions.
**ECG at Discharge:** Continuous ventricular PM stimulation, HR: 66/min.
**Current Recommendations:**
- We request a follow-up appointment in our Pacemaker Clinic. Please
provide a referral slip.
- We ask for the protection of the left arm and avoidance of
elevations \> 90 degrees. Self-absorbing sutures have been used.
- We request regular wound checks.
### Patient Report 6
**Dear colleague, **
We thank you for referring your patient, Mr. David Romero, born on
02/16/1942, who presented to our Cardiological University Outpatient
Clinic on 08/26/2016.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function
- LifeVest
- CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Current Medication:**
**Medication ** **Dosage ** **Frequency**
---------------------------- ------------- ---------------
Aspirin 100 mg 1-0-0
Ramipril (Altace) 2.5 mg 1-0-1
Carvedilol (Coreg) 12.5 mg 1-0-1
Torem (Torasemide) 5 mg 1-0-0
Spironolactone (Aldactone) 25 mg 1-0-0
L-Thyroxine (Synthroid) 50 µg 1-0-0
Sitagliptin (Januvia) 100 mg 1-0-0
Insulin glargine (Lantus) 0-0-20IE
**Current Presentation**: Slightly increasing exertional dyspnea, no
coronary heart disease.
**Cardiovascular Risk Factors:**
- Family history: No
- Smoking: No
- Hypertension: No
- Diabetes: Yes
- Dyslipidemia: Yes
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without pressure pain,
spleen and liver not palpable. Normal bowel sounds.
**Findings**:
**Resting ECG:** Sinus rhythm, 83 bpm. Blood pressure: 120/70 mmHg.
**Echocardiography: M-mode and 2-dimensional**
Left ventricle dimensions: Approximately 57/45 mm (normal up to 56 mm),
moderately dilated
- Right atrium and right ventricle: Normal dimensions
- Normal thickness of left ventricular muscle
- Globally, mild reduction in contractility
- Heart valves: Morphologically normal
- Doppler-Echocardiography: No significant valve regurgitation
**Assessment**: Mildly dilated cardiomyopathy with slightly reduced left
ventricular function. Ejection fraction at 45 - 50%. Mild diastolic
dysfunction. Mild tricuspid regurgitation, pulmonary artery pressure 22
mm Hg, and left ventricular filling pressure slightly increased.
**Stress Echocardiography: Stress echocardiography with exercise test**
- Stress test protocol: Treadmill exercise test
- Reason for stress test: Exertional dyspnea
- Quality of the ultrasound: Good
- Initial workload: 50 watts
- Maximum workload achieved: 150 Watt
- Blood pressure response: Systolic BP increased from 112/80 mmHg to
175/90 mmHg
- Heart rate response: Increased from 71bpm to 124bpm
- Exercise terminated due to leg pain
**Resting ECG:** Sinus rhythm**.** No significant changes during
exercise
**Echocardiography at rest:** Normokinesis of all left ventricular
segments EF: 45 - 50%
**Echocardiography during exercise:** Increased contractility and wall
thickening of all segments
[Summary]{.underline}: No dynamic wall motion abnormalities. No evidence
of exercise-induced myocardial ischemia
**Carotid Doppler Ultrasound:** Both common carotid arteries are
smooth-walled**.** Intima-media thickness: 0.8 mm**.** Small plaque in
the carotid bulb on both sides**.** Normal flow in the internal and
external carotid arteries**.** Normal dimensions and flow in the
vertebral arteries
**Summary:** Non-obstructive carotid plaques**.** Indicated to lower LDL
to below 1.8 mmol/L
**Summary:**
- Stress echocardiography shows no evidence of ischemia, EF \>45-50%
- Carotid duplex shows minimal non-obstructive plaques
- Increase Simvastatin to 20 mg, target LDL-C \< 1.8 mmol/L
### Patient Report 7
**Dear colleague, **
We would like to inform you about the results of the cardiac
catheterization of Mr. David Romero, born on 02/16/1942 performed by us
on 08/10/2022.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function
- LifeVest
- CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Procedure:** Right femoral artery puncture. Left ventriculography with
a 5F pigtail catheter in the right anterior oblique projection. Coronary
angiography with 5F JL4.0 and 5F JR 4.0 catheters. End-diastolic
pressure in the left ventricle within the normal range, measured in
mmHg. No pathological pressure gradient across the aortic valve.
**Coronary angiography:**
- Unremarkable left main stem.
- The left anterior descending (LAD) artery shows mild wall changes,
with a maximum stenosis of 20-\<30%.
- The robust right coronary artery (RCA) is stenosed proximally by
30-40%, subsequently ectatic and then stenosed to 40-\<50% distally.
Slow contrast clearance. The right coronary artery is also stenosed
up to 30%.
- Left-dominant coronary circulation.
**Assessment**: Diffuse coronary atherosclerosis with less than 50%
stenosis in the RCA and evidence of endothelial dysfunction.
**Current Recommendations:**
- Initiation of Ranolazine
- Additional stress myocardial perfusion scintigraphy
### Patient Report 8
**Dear colleague, **
We would like to inform you about the results of the Myocardial
Perfusion Scintigraphy performed on our patient, Mr. David Romero, born
on 02/16/1942, on 09/23/2022.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function (ejection fraction
around 30%)
- LifeVest
- CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without guarding, spleen
and liver not palpable. Normal bowel sounds.
**Myocardial Perfusion Scintigraphy:**
The myocardial perfusion scintigraphy was conducted using 365 MBq of
99m-Technetium MIBI during pharmacological stress and 383 MBq of
99m-Technetium MIBI at rest.
[Technique]{.underline}: Initially, the patient was pharmacologically
stressed with the intravenous administration of 400 µg of Regadenoson
over 20 seconds, accompanied by ergometer exercise at 50 W.
Subsequently, the intravenous injection of the radiopharmaceutical was
performed. The maximum blood pressure achieved during the stress phase
was 143/84 mm Hg, and the maximum heart rate reached was 102 beats per
minute.
Approximately 60 minutes later, ECG-triggered acquisition of a
360-degree SPECT study was conducted with reconstructions of short and
long-axis slices.
Due to inhomogeneities in the myocardial wall segments during stress,
rest images were acquired on another examination day. Following the
intravenous injection of the radiopharmaceutical, ECG-triggered
acquisition of a 360-degree SPECT study was performed, including
short-axis and long-axis slices, approximately 60 minutes later.
[Clinical Information:]{.underline} Known coronary heart disease (RCA
50%). ICD/CRT pacemaker.
[Findings]{.underline}: No clear perfusion defects are seen in the
scintigraphic images acquired after pharmacologic exposure to
Regadenoson. This finding remains unchanged in the scintigraphic images
acquired at rest.
Quantitative analysis shows a normal-sized ventricle with a normal left
ventricular ejection fraction (LVEF) of 53% under exercise conditions
and 47% at rest (EDV 81 mL). There are no clear wall motion
abnormalities. In the gated SPECT analysis, there are no definite wall
motion abnormalities observed in both stress and rest conditions.
**Quantitative Scoring:**
- SSS (Summed Stress Score): 3 (4.4%)
- SRS (Summed Rest Score): 0 (0.0%)
- SDS (Summed Difference Score): 3 (4.4%)
**Assessment**: No evidence of myocardial perfusion defects with
Regadenoson stress or at rest. Normal ventricular size and function with
no significant wall motion abnormalities.
### Patient Report 9
**Dear colleague, **
We would like to report on our patient, Mr. David Romero, born on
02/16/1942, who was under our inpatient care from 05/20/2023 to
05/21/2023.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function
- LifeVest
- CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Medical History:** The patient was admitted for device replacement due
and upgrading to a CRT-P pacemaker. At admission, the patient reported
no complaints of fever, cough, dyspnea, chest pain, or melena.
**Physical Examination:** The patient is fully oriented with good
general condition and normal mental state. Dry skin and mucous
membranes, normal breathing, no cyanosis. Cranial nerves are grossly
intact, no focal neurological deficits, good muscle strength and
sensitivity all around. Clear, rhythmic heart sounds, with a 2/6
systolic murmur at the apex. Lungs are evenly ventilated without rales.
Resonant percussion. Soft and supple abdomen without guarding, spleen
and liver not palpable. Normal bowel sounds.
**Medication upon Admission**
**Medication** **Dosage** **Frequency**
--------------------------- -------------- ----------------------
Insulin glargine (Lantus) 450 E/1.5 ml 0-0-0-6-8 IU
Insulin lispro (Humalog) 300 E/3 ml 5-8 IU-5-8 IU-5-8 IU
Levothyroxine (Synthroid) 100 mcg 1-0-0-0
Colecalciferol 12.5 mcg 2-0-0-0
Atorvastatin (Lipitor) 21.7 mg 0-0-1-0
Amlodipine (Norvasc) 6.94 mg 1-0-0-0
Ramipril (Altace) 5 mg 1-0-0-0
Torasemide (Torem) 5 mg 0-0-0.5-0
Carvedilol (Coreg) 25 mg 0.5-0-0.5-0
Simvastatin (Zocor) 40 mg 0-0-0.5-0
Aspirin 100 mg 1-0-0-0
**Therapy and Progression:** The patient\'s current admission was
elective for the implantation of a 3-chamber CRT-D device due to device
depletion. The procedure was performed without complications on
05/20/2023. The post-interventional course was uneventful. The
implantation site showed no irritation or significant hematoma at the
time of discharge, and no pneumothorax was detected on X-ray.
To protect the surgical wound, we request dry wound dressing for the
next 10 days and clinical wound checks. Suture removal is not necessary
with absorbable suture material. We advise against arm elevation for the
next 4 weeks, avoiding heavy lifting on the side of the device pocket
and gradual, pain-adapted full range of motion after 4 weeks.
**Current Recommendations:** We kindly request an outpatient follow-up
appointment in our Pacemaker Clinic.
**Medication upon Discharge:**
**Medication ** **Dosage ** **Frequency**
----------------------------- --------------- -----------------------
Insulin glargine (Lantus) 450 E./1.5 ml 0-0-0-/6-8 IU
Insulin lispro (Humalog) 300 E./3 ml 5-8 IU/-5-8 IU/5-8 IU
Levothyroxine (Synthroid) 100 µg 1-0-0-0
Colecalciferol (Vitamin D3) 12.5 µg 2-0-0-0
Atorvastatin (Lipitor) 21.7 mg 0-0-1-0
Amlodipine (Norvasc) 6.94 mg 1-0-0-0
Ramipril (Altace) 5 mg 1-0-0-0
Torasemide (Torem) 5 mg 0-0-0.5-0
Carvedilol (Coreg) 25 mg 0.5-0-0.5-0
Simvastatin (Zocor) 40 mg 0-0-0.5-0
Aspirin 100 mg 1-0-0-0
Colecalciferol 12.5 µg 2-0-0-0
**Addition: Findings:**
**ECG at Discharge:** Sinus rhythm, ventricular pacing, QRS 122ms, QTc
472ms
**Rhythm Examination on 05/20/2023:**
[Results:]{.underline} Replacement of a 3-chamber CRT-D device (new:
SJM/Abbott Quadra Assura) due to impending battery depletion:
Uncomplicated replacement. Tedious freeing of the submuscular device and
proximal lead portions using a plasma blade. Extraction of the old
device. Connection to the new device. Avoidance of device fixation in
the submuscular position. Hemostasis by electrocauterization. Layered
wound closure. Skin closure with absorbable intracutaneous sutures. End
adjustment of the CRT-D device is complete. [Procedure]{.underline}:
Compression of the wound with a sandbag and local cooling. First
outpatient follow-up in 8 weeks through our pacemaker clinic (please
schedule an appointment before discharge). Postoperative chest X-ray is
not necessary. Cefuroxime 1.5 mg again tonight.
**Transthoracic Echocardiography on 05/18/2023**
**Results:** Globally mildly impaired systolic LV function. Diastolic
dysfunction Grade 1 (LV relaxation disorder).
- Right Ventricle: Normal-sized right ventricle. Normal RV function.
Pulmonary arterial pressure is normal.
- Left Atrium: Slightly dilated left atrium.
- Right Atrium: Normal-sized right atrium.
- Mitral Valve: Morphologically unremarkable. Minimal mitral valve
regurgitation.
- Aortic Valve: Mildly sclerotic aortic valve cusps. No aortic valve
insufficiency. No aortic valve stenosis (AV PGmax 7 mmHg).
- Tricuspid Valve: Delicate tricuspid valve leaflets. Minimal
tricuspid valve regurgitation (TR Pmax 26 mmHg).
- Pulmonary Valve: No pulmonary valve insufficiency. Pericardium: No
pericardial effusion.
**Assessment**: Examination in sinus rhythm with bundle branch block.
Moderate ultrasound windows. Normal-sized left ventricle (LVED 54 mm)
with mildly reduced systolic LV function (EF biplan 55%) with mildly
reduced contractility without regional emphasis. Mild LV hypertrophy,
predominantly septal, without obstruction. Diastolic dysfunction Grade 1
(E/A 0.47) with a normal LV filling index (E/E\' mean 3.5). Slightly
sclerotic aortic valve without stenosis, no AI. Slightly dilated left
atrium (LAVI 31 ml/m²). Minimal MI. Normal-sized right ventricle with
normal function. Normal-sized right atrium (RAVI 21 ml/m²). Minimal TI.
As far as assessable, systolic PA pressure is within the normal range.
The IVC cannot be viewed from the subcostal angle. No thrombi are
visible. As far as assessable, no pericardial effusion is visible.
**Chest X-ray in two planes on 05/20/2023: **
[Clinical Information, Question, Justification:]{.underline} Post CRT
device replacement. Inquiry about position, pneumothorax.
[Findings]{.underline}: No pneumothorax following CRT device
replacement.
### Patient Report 0
**Dear colleague, **
We are writing to provide an update on Mr. David Romero, born on
02/16/1942, who presented at our Rhythm Clinic on 09/29/2023.
**Diagnoses:**
- Dilated cardiomyopathy
- Exclusion of coronary heart diseases
- Myocardial biopsy showed no inflammation
- Left bundle branch block
- Severely impaired left ventricular (LV) function
- LifeVest
- CRT-D implantation
- Chronic Hepatitis C
- Type 2 diabetes
**Current Medication:**
**Medication** **Dosage** **Frequency**
----------------------------- ------------------ ---------------
Lantus (Insulin glargine) 450 Units/1.5 mL 0-0-0-/6-8
Humalog (Insulin lispro) 300 Units/3 mL 5-8/0/5-8/5-8
Levothyroxine (Synthroid) 100 mcg 1-0-0-0
Vitamin D3 (Colecalciferol) 12.5 mcg 2-0-0-0
Lipitor (Atorvastatin) 21.7 mg 0-0-1-0
Norvasc (Amlodipine) 6.94 mg 1-0-0-0
Altace (Ramipril) 5 mg 1-0-0-0
Demadex (Torasemide) 5 mg 0-0-0.5-0
Coreg (Carvedilol) 25 mg 0.5-0-0.5-0
Zocor (Simvastatin) 40 mg 0-0-0.5-0
Aspirin 100 mg 1-0-0-0
Vitamin D3 (Colecalciferol) 12.5 mcg 2-0-0-0
**Measurement Results:**
Battery/Capacitor: Status: OK, Voltage: 8.4V
- Right Atrial: 375 Ohms 3.80 mV 0.375 V 0.50 ms
- Right Ventricular: 388 Ohms 11.80 mV 0.750 V 0.50 ms
- Left Ventricular: 350 Ohms 0.625 V 0.50 ms
- Defibrillation Impedance: Right Ventricular: 48 Ohms
**Implant Settings:**
- Bradycardia Setting: Mode: DDD
- Tachycardia Settings: Zone Detection Interval (ms) Detection Beats
ATP Shocks Details Status
- VFVF 260 ms 30 /
- VTVT1 330 ms 55 /
<!-- -->
- Probe Settings: Lead Sensitivity Sensing Polarity/Vector
Amplification/Pulse Width Stimulation Polarity/Vector Auto Amplitude
Control
- Right Atrial: 0.30 mV Bipolar/ 1.375 V/0.50 ms Bipolar/
- Right Ventricular: Bipolar/ 2.000 V/0.50 ms Bipolar/
- Left Ventricular: 2.000 V/0.50 ms tip 1 - RV Coil
**Assessment:**
- Routine visit with normal device function.
- Normal sinus rhythm with a heart rate of 65/min.
- Balanced heart rate histogram with a plateau at 60-70 bpm.
- Wound conditions are unremarkable.
- Battery status: OK.
- Atrial probe: Intact
- Right ventricular probe: Intact
- Left ventricular probe: Intact
- A follow-up appointment for the patient is requested in 6 months.
**Lab results:**
**Parameter** **Result** **Reference Range**
----------------------------------- ------------ ---------------------
Reticulocytes 0.01/nL \< 0.01/nL
Sodium 137 mEq/L 136-145 mEq/L
Potassium 4.2 mEq/L 3.5-4.5 mEq/L
Creatinine 1.34 mg/dL 0.70-1.20 mg/dL
BUN 49 mg/dL 17-48 mg/dL
Total Bilirubin 1.8 mg/dL \< 1.20 mg/dL
C-reactive Protein 5.9 mg/L \< 5.0 mg/L
ALT 67 U/L \< 41 U/L
AST 78 U/L \< 50 U/L
Alkaline Phosphatase 151 U/L 40-130 U/L
Gamma-Glutamyl Transferase 200 U/L 8-61 U/L
Free Triiodothyronine (T3) 2.3 ng/L 2.00-4.40 ng/L
Free Thyroxine (T4) 14.2 ng/L 9.30-17.00 ng/L
Thyroid Stimulating Hormone (TSH) 4.1 mU/L 0.27-4.20 mU/L
Hemoglobin 11.6 g/dL 13.5-17.0 g/dL
Hematocrit 34.5% 39.5-50.5%
Red Blood Cell Count 3.7 M/µL 4.3-5.8 M/µL
White Blood Cell Count 9.56 K/µL 3.90-10.50 K/µL
MCV 92.7 fL 80.0-99.0 fL
MCH 31.8 pg 27.0-33.5 pg
MCHC 33.9 g/dL 31.5-36.0 g/dL
MPV 8.9 fL 7.0-12.0 fL
RDW-CV 14.2% 11.5-15.0%
Quick 89% 78-123%
INR 1.09 0.90-1.25
Partial Thromboplastin Time 25.3 sec. 22.0-29.0 sec. | 01/27/2014 |
How does the author characterize the mood of the pre-launch location, prior to Phil's arrival?
A. Apprehensive
B. Monotonous
C. Frightening
D. Energized
| Transcriber's Note: This etext was produced from Astounding Science Fiction December 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. BREAKAWAY BY STANLEY GIMBLE Illustrated by Freas She surely got her wish ... but there was some question about getting what she wanted. Phil Conover pulled the zipper of his flight suit up the front of his long, thin body and came into the living room. His face, usually serious and quietly handsome, had an alive, excited look. And the faint lines around his dark, deep-set eyes were accentuated when he smiled at his wife. "All set, honey. How do I look in my monkey suit?" His wife was sitting stiffly on the flowered couch that was still not theirs completely. In her fingers she held a cigarette burned down too far. She said, "You look fine, Phil. You look just right." She managed a smile. Then she leaned forward and crushed the cigarette in the ash tray on the maple coffee table and took another from the pack. He came to her and touched his hands to her soft blond hair, raising her face until she was looking into his eyes. "You're the most beautiful girl I know. Did I ever tell you that?" "Yes, I think so. Yes, I'm sure you did," she said, finishing the ritual; but her voice broke, and she turned her head away. Phil sat beside her and put his arm around her small shoulders. He had stopped smiling. "Honey, look at me," he said. "It isn't going to be bad. Honestly it isn't. We know exactly how it will be. If anything could go wrong, they wouldn't be sending me; you know that. I told you that we've sent five un-manned ships up and everyone came back without a hitch." She turned, facing him. There were tears starting in the corners of her wide, brown eyes, and she brushed them away with her hand. "Phil, don't go. Please don't. They can send Sammy. Sammy doesn't have a wife. Can't he go? They'd understand, Phil. Please!" She was holding his arms tightly with her hands, and the color had drained from her cheeks. "Mary, you know I can't back out now. How could I? It's been three years. You know how much I've wanted to be the first man to go. Nothing would ever be right with me again if I didn't go. Please don't make it hard." He stopped talking and held her to him and stroked the back of her head. He could feel her shoulders shaking with quiet sobs. He released her and stood up. "I've got to get started, Mary. Will you come to the field with me?" "Yes, I'll come to say good-by." She paused and dropped her eyes. "Phil, if you go, I won't be here when you get back—if you get back. I won't be here because I won't be the wife of a space pilot for the rest of my life. It isn't the kind of life I bargained for. No matter how much I love you, I just couldn't take that, Phil. I'm sorry. I guess I'm not the noble sort of wife." She finished and took another cigarette from the pack on the coffee table and put it to her lips. Her hand was trembling as she touched the lighter to the end of the cigarette and drew deeply. Phil stood watching her, the excitement completely gone from his eyes. "I wish you had told me this a long time ago, Mary," Phil said. His voice was dry and low. "I didn't know you felt this way about it." "Yes, you did. I told you how I felt. I told you I could never be the wife of a space pilot. But I don't think I ever really believed it was possible—not until this morning when you said tonight was the take-off. It's so stupid to jeopardize everything we've got for a ridiculous dream!" He sat down on the edge of the couch and took her hands between his. "Mary, listen to me," he said. "It isn't a dream. It's real. There's nothing means anything more to me than you do—you know that. But no man ever had the chance to do what I'm going to do tonight—no man ever. If I backed out now for any reason, I'd never be able to look at the sky again. I'd be through." She looked at him without seeing him, and there was nothing at all in her eyes. "Let's go, if you're still going," she finally said. They drove through the streets of the small town with its small bungalows, each alike. There were no trees and very little grass. It was a new town, a government built town, and it had no personality yet. It existed only because of the huge ship standing poised in the take-off zone five miles away in the desert. Its future as a town rested with the ship, and the town seemed to feel the uncertainty of its future, seemed ready to stop existing as a town and to give itself back to the desert, if such was its destiny. Phil turned the car off the highway onto the rutted dirt road that led across the sand to the field where the ship waited. In the distance they could see the beams of the searchlights as they played across the take-off zone and swept along the top of the high wire fence stretching out of sight to right and left. At the gate they were stopped by the guard. He read Phil's pass, shined his flashlight in their faces, and then saluted. "Good luck, colonel," he said, and shook Phil's hand. "Thanks, sergeant. I'll be seeing you next week," Phil said, and smiled. They drove between the rows of wooden buildings that lined the field, and he parked near the low barbed fence ringing the take-off zone. He turned off the ignition, and sat quietly for a moment before lighting a cigarette. Then he looked at his wife. She was staring through the windshield at the rocket two hundred yards away. Its smooth polished surface gleamed in the spotlight glare, and it sloped up and up until the eye lost the tip against the stars. "She's beautiful, Mary. You've never seen her before, have you?" "No, I've never seen her before," she said. "Hadn't you better go?" Her voice was strained and she held her hands closed tightly in her lap. "Please go now, Phil," she said. He leaned toward her and touched her cheek. Then she was in his arms, her head buried against his shoulder. "Good-by, darling," she said. "Wish me luck, Mary?" he asked. "Yes, good luck, Phil," she said. He opened the car door and got out. The noise of men and machines scurrying around the ship broke the spell of the rocket waiting silently for flight. "Mary, I—" he began, and then turned and strode toward the administration building without looking back. Inside the building it was like a locker room before the big game. The tension stood alone, and each man had the same happy, excited look that Phil had worn earlier. When he came into the room, the noise and bustle stopped. They turned as one man toward him, and General Small came up to him and took his hand. "Hello, Phil. We were beginning to think you weren't coming. You all set, son?" "Yes, sir, I'm all set, I guess," Phil said. "I'd like you to meet the Secretary of Defense, Phil. He's over here by the radar." As they crossed the room, familiar faces smiled, and each man shook his hand or touched his arm. He saw Sammy, alone, by the coffee urn. Sammy waved to him, but he didn't smile. Phil wanted to talk to him, to say something; but there was nothing to be said now. Sammy's turn would come later. "Mr. Secretary," the general said, "this is Colonel Conover. He'll be the first man in history to see the other side of the Moon. Colonel—the Secretary of Defense." "How do you do, sir. I'm very proud to meet you," Phil said. "On the contrary, colonel. I'm very proud to meet you. I've been looking at that ship out there and wondering. I almost wish I were a young man again. I'd like to be going. It's a thrilling thought—man's first adventure into the universe. You're lighting a new dawn of history, colonel. It's a privilege few men have ever had; and those who have had it didn't realize it at the time. Good luck, and God be with you." "Thank you, sir. I'm aware of all you say. It frightens me a little." The general took Phil's arm and they walked to the briefing room. There were chairs set up for the scientists and Air Force officers directly connected with the take-off. They were seated now in a semicircle in front of a huge chart of the solar system. Phil took his seat, and the last minute briefing began. It was a routine he knew by heart. He had gone over and over it a thousand times, and he only half listened now. He kept thinking of Mary outside, alone by the fence. The voice of the briefing officer was a dull hum in his ears. "... And orbit at 18,000-mph. You will then accelerate for the breakaway to 24,900-mph for five minutes and then free-coast for 116 hours until—" Phil asked a few questions about weather and solar conditions. And then the session was done. They rose and looked at each other, the same unanswered questions on each man's face. There were forced smiles and handshakes. They were ready now. "Phil," the general said, and took him aside. "Sir?" "Phil, you're ... you feel all right, don't you, son?" "Yes, sir. I feel fine. Why?" "Phil, I've spent nearly every day with you for three years. I know you better than I know myself in many ways. And I've studied the psychologist's reports on you carefully. Maybe it's just nervousness, Phil, but I think there's something wrong. Is there?" "No, sir. There's nothing wrong," Phil said, but his voice didn't carry conviction. He reached for a cigarette. "Phil, if there is anything—anything at all—you know what it might mean. You've got to be in the best mental and physical condition of your life tonight. You know better than any man here what that means to our success. I think there is something more than just natural apprehension wrong with you. Want to tell me?" Outside, the take-off zone crawled with men and machines at the base of the rocket. For ten hours, the final check-outs had been in progress; and now the men were checking again, on their own time. The thing they had worked toward for six years was ready to happen, and each one felt that he was sending just a little bit of himself into the sky. Beyond the ring of lights and moving men, on the edge of the field, Mary stood. Her hands moved slowly over the top of the fence, twisting the barbs of wire. But her eyes were on the ship. And then they were ready. A small group of excited men came out from the administration building and moved forward. The check-out crews climbed into their machines and drove back outside the take-off zone. And, alone, one man climbed the steel ladder up the side of the rocket—ninety feet into the air. At the top he waved to the men on the ground and then disappeared through a small port. Mary waved to him. "Good-by," she said to herself, but the words stuck tight in her throat. The small group at the base of the ship turned and walked back to the fence. And for an eternity the great ship stood alone, waiting. Then, from deep inside, a rumble came, increasing in volume to a gigantic roar that shook the earth and tore at the ears. Slowly, the first manned rocket to the Moon lifted up and up to the sky. For a long time after the rocket had become a tiny speck of light in the heavens, she stood holding her face in her hands and crying softly to herself. And then she felt the touch of a hand on her arm. She turned. "Phil! Oh, Phil." She held tightly to him and repeated his name over and over. "They wouldn't let me go, Mary," he said finally. "The general would not let me go." She looked at him. His face was drawn tight, and there were tears on his cheeks. "Thank, God," she said. "It doesn't matter, darling. The only thing that matters is you didn't go." "You're right, Mary," he said. His voice was low—so low she could hardly hear him. "It doesn't matter. Nothing matters now." He stood with his hands at his sides, watching her. And then turned away and walked toward the car. THE END | D. Energized |
How are the various local currencies connected?
A. They are independnet systems but can sometimes be traded for currency in a town where there is an existing partnership
B. They are developed entirely independently from one another
C. They are all developed by the same national organization, adapting to the needs of specific areas
D. They are independently developed but there are groups dedicated to sharing information about the various systems
| New money: Do local currencies actually work? It's lunchtime at Glasgow Chambers in late November, and Councillor George Redmond is getting worked up at the prospect a Glasgow Pound. "We would be Glasgow-centric about it," he says conspiratorially, as though there is any other way to be. "Can you imagine having the face of Billy Connolly on our local currency? Or Alex Ferguson, or Kenny Dalglish?" Inventing an alternative to sterling might sound far-fetched, even illegal. But it's not that strange. In the UK we think of the pound like fish think about water, which is to say not at all. It might never have occurred to many of us that there are other types of exchange that can stand in for ragged bank notes tucked away in pockets, or other objects that can stand in for those notes. Not every country is so lucky. In crisis-hit Greece, where the euro can be hard to come by, businesses and citizens have turned to bartering using a points system where goods like pianos, pot and pans can be exchanged for security services or loaned farming equipment. In India last year, desperate people burned sacks of illegal cash after the government withdrew two high-denomination notes as part of a crackdown on corruption. Hoarders woke up to discover the banknotes under their mattresses were suddenly worthless. The pound has been trading at its lowest level since 1985 since the UK voted to leave the European Union and there are fears that it could dip further as Brexit ensues. Timebanks, local exchange trading systems (LETS) and digital inventions like bitcoin can provide alternative ways for people to pay for goods and services when mainstream currencies hit crises. But they will only work if Britons are ready to accept that they have the power to invent their own currency. "At the moment, if the pound stops working for us, the whole economy grinds to a halt because there aren't alternatives," Duncan McCann, a researcher at the New Economics Foundation, tells those gathered in a gilded room at Glasgow Chambers to discuss the Glasgow Pound. McCann is a long-time advocate of alternative means of exchange. He is behind the ScotPound, a proposal for a new national currency for Scotland that emerged after the referendum on Scottish independence. It's an idea he no longer thinks will work, because the debate, since Brexit, has shifted from the currency issue back to ideas about Scottish independence. Today, he's preaching to the converted. Alex Walker, the chairman of the 250-person Ekopia community in Northern Scotland, listens at the back. The Eko has been the main means of buying everything from beer to bananas in Ekopia since Walker founded it 20 years ago. On an adjacent table, Tracy Duff, a community learning and development worker from Clackmannanshire Council, digs out some papers. She runs the Clacks Youth Timebank, a scheme where 12- to 15-year-olds can earn credit for volunteering. Taking notes up front is Ailie Rutherford, one of the people who organised the meeting. Rutherford runs the People's Bank of Govanhill, a currency that changes value depending on the income of the user. "I don't see any reason why we shouldn't invent our own currency and play with it," she says. Everyone has gathered to decide what a Glasgow Pound might look like at a time when many are asking if local currencies can work at all. Councillor Redmond says Glasgow has been closely watching existing alternative currencies like the Brixton Pound in London, which was introduced in 2011. The founders of the Brixton Pound wanted to do something to stop 80p of every £1 spent locally from leaking out of the area into the pockets of corporations, at the expense of small local traders. So they printed a currency that would have the same value as the pound, but could only be traded in independent Brixton shops, where the shopkeeper would also have to spend it locally. This year the Brixton Pound got its own cashpoint, from where people can withdraw local banknotes bearing colourful images of local heroes, like David Bowie and secret Agent Violette Szabo, to spend in over 150 local shops. It can also be used by residents to pay council tax and by employers to pay wages. No two local currencies are exactly the same. But the Brixton Pound and other recent schemes follow the example ten years ago of the Totnes Pound, a 'complementary currency': that is, one supplementing the national currency. As fears for financial stability took hold during the recession, complementary currencies grew in popularity. The Bank of England does not consider these forms of currency legal tender, but the notes hold value in the same way as a gift-card from a department store, with the same kind of restrictions about where they can be spent. Proponents say complementary currencies boost spending in smaller geographical areas, which can have environmental benefits as businesses cut transport distances to deal with local suppliers. Detractors say they have no real economic impact and work only as a game for the middle classes, who can afford to buy from independent shops rather than chains. In Britain, there are now schemes in Totnes, Lewes, Brixton, Bristol and Exeter. Hull has its own local digital currency that can be earned from volunteering and used to pay council tax. Kingston, Birmingham and Liverpool have schemes underway. Glasgow could be next. But the working group has some serious questions to answer first, not least: do complementary currencies actually work? "People don't understand money," Molly Scott Cato, Green MEP for the South West of England and Gibraltar, says over the phone. Scott Cato says the fish-in-water problem – the idea that sterling is so ubiquitous, it is never questioned – is the biggest challenge for complementary currencies. She knows all about it as a founder of the Stroud Pound in 2010, a currency that has since gone out of circulation. "[People] think they put money into a bank and someone else takes it out. What they don't understand is that banks have the power to create money. We've given the power to create money to private corporations and people don't understand that we can have it back," she says. In Stroud, suspicion of the local currency among local businesses became a barrier to success. Scott-Cato said traders refused to join the scheme because they were "running a business", as though putting the community first and placing the needs of others as equivalent to their own was in itself bad business practice, or as though they were somehow being disloyal to sterling. The Bristol Pound (£B) entered into circulation in September 2012. By June 2015, 1m £B had been issued, with £B700,000 of that still in circulation. In a population of some 450,000 people, that's the equivalent of each Bristolian carrying less than £B2 in change in their pocket. "The small scale is a problem and a strength," says Stephen Clarke, chief financial officer of the Bristol Pound. "The benefit comes from the fact that local currencies are trusted organisations: we're a Community Interest Company limited by guarantee." That means assets owned by the the Bristol Pound have to be used for the good of the community, rather than purely for profit. Without enough currency in circulation, it ceases to work. Scott-Cato says Stroud's size meant meant the Stroud Pound was never viable: "We couldn't get the velocity of circulation right, which contrasts with the Bristol Pound." Clarke also says the small scale of local currencies means they are "always scrabbling around looking for money". One way founders of the Bristol Pound have addressed his is by setting up an umbrella organisation, the Guild of Independent Currencies, to share information between local currencies in the UK and help new organisations. "At the moment we're all reinventing the wheel every time," Clarke says. Technology might also have a solution. Peter Ferry, a commercial director, travels to Glasgow to tell those working on the Glasgow Pound that that his company Wallet has come up with a way to use the blockchain, the technology behind bitcoin, to make it easier for people to use multiple types of currency. "There might be many currencies around the country that people want to use. We need to make it simple for them to do that and also to make it simple to earn these currencies in many ways," he says. Size doesn't always matter. Sometimes, the smallest places – like Totnes and the Ekopia community – are best able to support complementary currencies because the people who live there are engaged with their local economy in a meaningful way. "Bristol is seen as a quirky, individualistic kind of place," Clarke says. "When we first produced the Bristol Pound note, people were really proud of it. It got through to people not just sat around coffee shops. I'm not sure a London Pound would work, because people identify with their local area in London rather than the city as a whole." Bristol Pound users don't have high incomes necessarily, but surveys show they are engaged with their local community and they have a higher educational attainment than average. In the years since the financial crisis, as local authority budgets have shrunk, some areas have relied heavily on engaged communities to fill in gaps in public services. By contrast, deprived areas where people cannot afford time and money to put into their community have become more deprived, making them even harder for local currencies to reach. "It is difficult to get into more disadvantaged areas," Stephen Clarke says. "We have a ten-year life expectancy gap between different parts of the city. When you go to disadvantaged areas with the Bristol Pound hat on you realise there aren't independent shops there, there's an Aldi and Lidl and that's it." More than a third of children grow up in poverty in Glasgow. A Glasgow Pound might struggle to get poorer families to buy into a local currency that ties them to shopping at more expensive, independent shops, rather than getting deals at big supermarket chains. When Scott-Cato and her colleagues wrote about the experience of setting up the Stroud Pound, they said it was telling that complementary currencies have been accused of being a game for middle-class people, rather than a genuine economic solution. Perhaps for that reason, experts like Duncan McCann have stopped thinking of complementary currencies as a one-size-fits-all solution. He said they can function as a kind of 'gateway drug' to introduce people to a new way of thinking about money. "That is especially for those who use it, but also for those who just become aware of it," he says. Ciaran Mundy, CEO of the Bristol Pound, says it is important to think of the systemic impact rather than looking for targeted treatment of symptoms of economic deprivation. "Poverty has many causes," he says. "One of these is how the economy is structured in terms of how money flows out of poor areas due to high dependence on larger national and international companies paying lower wages and using offshore accounts to hide the money from the tax man." Nothing is tying Glasgow to existing models for complementary currencies. But during the first meeting about setting up the Glasgow Pound, the workshop shows just how hard it would be to invent a new system that works for everyone. Each table is handed a wad of Post-it notes and a piece of white paper. A table leader asks everyone to write on the Post-its what they want the Glasgow Pound to achieve. Elbowing teacups out the way, people get to work. They scrawl a dizzying number of proposals, from keeping more wealth in the local area to empowering people who feel cut out of the national economy, or to moving towards land reform and saving the environment. Team leaders try to assemble these ideas in themes to report back to the room. On one table, Duncan McCann encourages people to urge businesses to do things they have never done before. "One of the goals should be to move businesses from where they are today into the future," he says. After years of researc,h McCann believes the only way complementary currencies can create real value for local economies is if they make transactions happen that wouldn't otherwise have taken place. "They need to create additional spending power. This is this what the local currencies, despite all their good points, fail to do," McCann says. Every time a Brixton Pound transaction is made, 1.5 per cent goes into a Brixton Fund. This is used to give micro-grants of between a few hundred and £2000 to local projects and community groups. "We aim to target projects that aren't large enough to apply for more formal grant funding," says Lucy Çava, project manager at the Brixton Pound. "We see this as part of community building – linking the Brixton Pound user with community groups, so both groups become more visible to each other through the currency and fund. This is particularly important in Brixton because of the gentrification debates which are very salient round there," Çava says. Meanwhile, the people behind the Bristol Pound are readying a mutual credit network called Bristol Prospects. Through this network, businesses in Bristol can exchange credit in the form of loans that are neutralised within the network, helping one another to grow without relying on the high rates of commercial lenders. Once operational, loans offered through the Prospects network will have negative interest, so that businesses are encouraged to pass credit on as quickly as possible. "That's the plan," says Clarke, "because it's rather like a hot potato: people will want to pass it on." "We know from research that a number of small businesses in Bristol are struggling to get money on reasonable terms," says Clarke, "and that banks are not interested in smaller loans to businesses. So we think there is a strength in the Bristol Pound network to start something like this that is linked, but separate." Duncan McCann, with all his experience, knows that challenge is worthwhile. "As people we have a right to make credit and loan money. We mustn't forget that. We mustn't leave that to corporations and the state," he says. This article is part of a series on local economies Hazel is documenting at farnearer.org, with funding from the Friends Provident Foundation Illustration by PureSolution/Shutterstock This article was originally published on TheLong+Short. Read the original article. | D. They are independently developed but there are groups dedicated to sharing information about the various systems |
How many tweets were manually labelled? | ### Abstract
Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD). The Twitter stance is compared to the actual currency rates by applying the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders. Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline. ### Introduction
Foreign exchange market (Forex) is a global decentralized market for trading with currencies. The daily trading volume exceeds 5 trillion USD, thus making it the largest market in the world. In this paper we analyze three sources of data, over a period of three years (from January 2014 to December 2016) BIBREF0 : We focus on potential missinformation spreading and manipulations on Twitter. The main issue is: What is the ground truth? We address this problem by moving out of the social network system and by observing another, financial market system. Actual financial gains in the market provide clues to potential manipulations in the social network. We relate both systems by applying and adapting the “event study” methodology BIBREF1 . The currency announcements are events which are expected to influence the EUR-USD exchange rate. If the event signal (buy, hold, or sell) is properly recognized then some actual financial returns can be made in the hours (or days) after the event. In contrast to classical event studies, we categorize events on the basis of sentiment (properly called “stance”) of relevant Twitter users. In our previous work, we already analyzed the effects of Twitter stance on stock prices (30 stocks from the Dow Jones index) BIBREF2 , BIBREF3 . We showed that the peaks of Twitter activity and their polarity are significantly correlated with stock returns. In this paper, we show that, for certain classes of Twitter users, returns after the events are statistically significant (albeit small). And we can also identify differences in returns after the potential manipulations of Twitter feed. The paper is organized as follows. In section SECREF2 we specify how the Forex tweets were collected, a subset manually annotated, and a stance classification model constructed. Section SECREF3 provides simple rules to identify different classes of Twitter users (such as trading robots, spammers, and actual traders). We show that there are large differences in Twitter stance between these users. Section SECREF4 describes the event study methodology in some detail, as needed to understand the subsequent results. We show significant differences in cumulative abnormal returns between the different user groups. In section SECREF5 we address potential manipulations of the user Twitter feed with a tentative goal to improve her/his reputation and visibility. We focus on the tweets that were deleted after we originally collected them, and analyze different reasons for this post festum deletions. We conclude with the ideas for further work and enhancements of the preliminary, but promising, results presented so far. ### Twitter stance model
Tweets related to Forex, specifically to EUR and USD, were acquired through the Twitter search API with the following query: “EURUSD”, “USDEUR”, “EUR”, or “USD”. In the period of three years (January 2014 to December 2016) almost 15 million tweets were collected. A subset of them (44,000 tweets) was manually labeled by knowledgeable students of finance. The label captures the leaning or stance of the Twitter user with respect to the anticipated move of one currency w.r.t. the other. The stance is represented by three values: buy (EUR vs. USD), hold, or sell. The tweets were collected, labeled and provided to us by the Sowa Labs company (http://www.sowalabs.com). The labeled tweets were generalized into a Twitter stance model. For supervised learning, variants of SVM BIBREF4 are often used, because they are well suited for large scale text categorization, are robust, and perform well. For Forex tweets, we constructed a two plane SVM classifier BIBREF5 , BIBREF6 . The two plane SVM assumes the ordering of stance values and implements ordinal classification. It consists of two SVM classifiers: One classifier is trained to separate the `buy' tweets from the `hold-or-sell' tweets; the other separates the `sell' tweets from the `buy-or-hold' tweets. The result is a classifier with two hyperplanes that partitions the vector space into three subspaces: buy, hold, or sell. During classification, the distances from both hyperplanes determine the predicted stance value. The stance classifier was evaluated by 10-fold blocked cross-validation. Since tweets are time-ordered, they should not be randomly selected into individual folds, but retained in blocks of consecutive tweets BIBREF7 . The results of performance evaluation are in Table TABREF5 . Note that the F INLINEFORM0 measure considers just the `buy' and `sell' classes, as is common in the three-valued sentiment classification evaluations BIBREF5 . ### Twitter user groups
Different types of Twitter users have very different intentions regarding their impact and message they want to spread. In recent years, specially automated robots became increasingly influential. To properly estimate the relation between the Forex market and tweetosphere, it is important to focus on relevant Twitter users, i.e., Forex trading companies and individual traders. In related work, it was already shown that bots exercise a profound impact on content popularity and activity on Twitter. For example, Gilani et al. BIBREF8 implemented a simple bot detection mechanism based on click frequency and user agent strings. To classify users into three categories (organizations, journalists/media bloggers, and individuals), De Choudhury et al. BIBREF9 trained an automatic classifier. An alternative approach is to detect communities in a retweet network, e.g., BIBREF10 , BIBREF11 . It turns out that it is easy to identify Forex trading robots. Their tweets ( INLINEFORM0 ) all start with one of the eighth patterns (such as “Closed Buy”, “Sell stop”, ...). The Forex Twitter users can then be classified into one of the four groups by the following simple rules: where INLINEFORM0 indicates the daily activity of the user, and INLINEFORM1 is the proportion of the user tweets that were retweeted by others. Figure FIGREF10 shows the proportions of different Twitter user groups and their tweets in our dataset. We can see that more than half of the users are individuals, but that the trading robots produce by far the largest fraction of Forex tweets. There are also considerable differences in the stance between different user groups. Figure FIGREF11 shows that trading robots produce almost exclusively polarized tweets (no `hold' tweets). On the other hand, spammers (without robots) are predominantly neutral (relatively few `buy' or 'sell' tweets). The groups we focus on, trading companies and individuals, are more opinionated than spammers. It is interesting that in their tweets the `sell' signal is prevailing, probably due to the downward trend of EUR vs. USD in the last three years. ### Event study
An event study captures the impact of external events on the market returns. External events that we consider here are the currency related announcements by the central banks (FED and ECB) and governments (around 750 in the three years). In an event study, Cumulative Abnormal Return (CAR) is defined as a measure of return which exceed the overall market return. Specifically: The other essential component of an event study is determining the type of event in terms of its expected impact on the price. In stock market, typically Earnings Announcements are studied. If an announcement exceeds prior expectations of analysts, it is classified as positive, and stock prices are expected to rise. An event study combines announcements about several stocks, over longer period of time, and computes the average CARs in the days or hours after the announcements. In our case, we do not consider expectation of the analysts, but instead use the stance of the Forex Twitter users regarding the EUR vs. USD exchange rate. We consider all tweets in one hour after the announcement, and aggregate their stance to categorize the event. Then we compute the CARs for up to one day after the event, at one minute resolution. If Twitter stance correctly predicts the exchange rate movement then there should be some tangible returns (CARs) in the hours after the event. Figure FIGREF15 shows returns, aggregated over all 750 events, for different Twitter user groups. The expected result is visible for trading companies (bottom-left chart). For `buy' events (we buy EUR at time 0) CARs are positive (return is around 0.1%, small but significant), for `sell' events (we sell EUR at time 0) CARs are negative , and for `hold' events (no transaction) CARs are around zero. Similar results are obtained for individual traders (bottom-right chart), but the separation of events is not as clear as for trading companies. On the other hand, trading robots and spam users (top two charts in Figure FIGREF15 ) show no useful correlation between the Twitter stance and CARs. As a consequence, we conclude that it is important to properly identify them and eliminate their tweets from any trading strategy based on Twitter. ### Reputation manipulation
Here we focus on another aspect of Twitter misuse for potential manipulation: post festum deletion of tweets by the Twitter user. What are the reasons for users to delete their tweets? Previous research addressed prediction of malicious or deleted tweets BIBREF12 , BIBREF13 , BIBREF14 , and identification of deleted and suspicious accounts BIBREF15 . On one hand, some authors show that typos and rephrasing are among the major causes for deleting tweets BIBREF13 . On the other hand, other authors found that in deleted tweets, a significantly higher fraction of the vocabulary consists of swear words, and markers that indicate anger, anxiety, and sadness BIBREF16 . We verified which of the tweets that were collected during the three years in near real time, still exist. It turns out that in our dataset, 4.7% (689,658) posts were post festum deleted by the users. Different user groups exhibit different patterns of deletion. A histogram in Figure FIGREF16 shows fractions of tweets deleted by different user groups. The majority of users do not delete their own tweets at all (peak at 0). At the other extreme (100), there is about 5% of the users who deleted their accounts and all their tweets. But the really interesting are the trading companies, where only one third of them does not delete tweets, and more than half of them delete up to 10% of their tweets. We focus on the deleted tweets by trading companies and individual traders and search for signs of reputation manipulations. A breakdown of deleted tweets for both groups in terms of different stances is in Table TABREF17 . ### Deleting tweets to increase CARs
One reason for companies and individuals to delete their tweets might be to create an image of their capabilities to predict the market. For example, one can post two contradictory tweets at the same time: EUR will go up, and EUR will go down. After the market shows the actual EUR move, the incorrect prediction is deleted, and the user's timeline shows his forecasting insight. We compare the results of the event study before and after the tweets were deleted. Figure FIGREF19 shows CARs for trading companies and individual traders after removing their deleted tweets. At this point, we can report only negative results, i.e., there is no increase of CARs, and the `hold' events are further away from the zero line than in Figure FIGREF15 . ### Analyzing trading companies
We analyze deleted tweets of 189 (out of 195) Twitter users categorized as trading companies that have active Twitter accounts (by deleting an account, all the tweets from that account are also deleted). The 189 companies deleted 3,741 tweets. Among them, four deleted all Forex related tweets from their profile while the accounts are still active, 8 users deleted between 10% and 40% of their tweets, 33 users deleted between 1% and 5% of their tweets, and only 68 did not delete any tweets. The deleting behaviour of trading companies is shown in Figure FIGREF21 . Note that the majority (76% of the trading companies) deleted less than 1% of their tweets. Note also that there are no trading companies that delete between 5 and 10% of their tweets. We analyze the deleted tweets and focus on criteria that might indicate reputation manipulation. Out of the 3,741 deleted tweets, 3,611 are unique (same author and identical text) while 130 tweets are deleted more than once. An extreme case is a tweet (advertising easy and safe profit) which is deleted 46 times (same author and identical text). The deleting and reposting of identical tweets is one form of increasing visibility without tainting the author's Twitter timeline. A tweet that is deleted and posted again appears several times in the user's followers feed while it appears just once in the authors timeline. This can be therefore considered a kind of reputation manipulation. Out of the 93 tweets that were deleted and reposted, 50 were deleted and reposted once while the rest were deleted and reposted several times. The 746 `recommendation' tweets that were deleted afterward point to a potential reputation manipulation by deleting the bad recommendations. The breakdown of deleted tweets is shown in Figure FIGREF22 . One of the major reasons to delete tweets are typos and rephrasing BIBREF13 . In these cases, a very similar tweet to the deleted tweet is posted again. We check for each of the 3,575 tweets that were deleted once and not reposted, if they were deleted due to a typo. We define typo as a reason of tweet deletion if the tweet is: posted by the same author, within the three next tweets after the deleted one, with a very similar text ( INLINEFORM0 Levenshtein distance INLINEFORM1 ), and the difference is not in the URLs present in the tweet. We found that 122 deleted tweets were reposted with changes so small that indicate typos. Another category of deleted tweets are retweets. If retweets are deleted, it is usually because the original tweets were deleted. In our dataset, 406 retweets are deleted. We check the remaining 3,437 tweets for the use of vocabulary specific for trading: long, short, bear, bull, bearish, bullish, resistance, support, buy, sell, close. We identify 746 tweets that are recommendations for trading (manually confirmed). This is another kind of possible reputation manipulation: a tweet with recommendation is posted and afterwards, if the recommendation turns out to be spurious, the tweet is deleted. The author's Twitter timeline then falsely appears as if following his recommendations would yield profit. We inspect a specific Twitter account from the category trading companies that posted more than 500 tweets and deleted between 10% and 40% of them. The identity of the account cannot be revealed due to the privacy issues. The tweets deleted fall into the following categories: Reposts: 91, 60 of them are advertisements (e.g., subscribe for analysis), Links (to recommendations): 17, Recommendations: 11, Retweet: 1 (if the original tweet is deleted, retweets are also deleted). We manually checked each of the 11 recommendations that were deleted. In all the cases, the recommendations turned out to be bad, i.e., an investor would loose money. An (anonymized) example of a bad recommendation post is the following: "@user_mention while daily candle is above 1.xyz we are bullish on $EURUSD." while in the actual Forex market, EUR went down. This user used both types of reputation manipulation: deleting poor recommendations, and deleting/reposting of identical tweets to increase their visibility. The percentage of deleted poor predictions is small compared to all the deleted tweets and compared to all the posted tweets. We speculate that the manipulation by tweet deletion needs to be subtle to go unnoticed by the users' followers. However, even a subtle reputation burst in a domain as competitive as Forex trading can bring major benefits to the deceptive user. ### Conclusions
This is an initial study of potential misuses of Twitter to influence the public interested in Forex trading. We identify different types of Twitter accounts that are posting tweets related to the EUR-USD currency exchange. We show that there are considerable differences between them in terms of Twitter stance distribution and CARs. If we eliminate trading robots and spam, we find significant correlations between the Twitter stance and CARs (the returns are small, but the Forex market has very low trading costs). The remaining posts come from the Forex trading companies and individual traders. We further analyze the reasons for post festum deleting of tweets. Some reasons are harmless (such as correcting typos), but some show indications of reputation busting. We consider this a promising direction for further, more in-depth analysis. ### Acknowledgements
The authors acknowledge financial support from the H2020 FET project DOLFINS (grant no. 640772), and the Slovenian Research Agency (research core funding no. P2-103). Table 1. Evaluation results of the Twitter stance model. Fig 1. Proportions of Twitter accounts and tweets for different user groups. Fig 2. Twitter stance distribution of different user groups (bars show the proportion of tweets). Trading robots produce almost exclusively polarized tweets while spammers are predominantly neutral. Fig 3. Cumulative abnormal returns (CARs) for different user groups. The events are classified as ‘buy’, ‘hold’, or ‘sell’ according to the cumulative Twitter stance in one hour after the event. The event is announced at lag = 0. CARs are computed at one minute resolution, for up to one day (1440 minutes) after the event. Fig 4. Fractions of tweets deleted for different user groups. Table 2. The number of deleted tweets of different stance. Fig 5. Cumulative abnormal returns (CARs) for trading companies and individual traders, after removing the tweets that were post festum deleted by the user. Fig 6. The fractions of deleted tweets (altogether 3,741 tweets) for the 189 trading companies. Fig 7. A breakdown of deleted tweets by trading companies. | 44,000 tweets |
Would Sandra consistently consider herself a skilled journalist?
A. Yes, and the way she was able to easily journal about the chess competition shows her competency.
B. No, because she usually knows very little about what she will be journaling about.
C. No, she has her doubts that her skills are not what makes her successful at interviewing people.
D. Yes, because she considers herself a very experienced talker.
| 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?" | C. No, she has her doubts that her skills are not what makes her successful at interviewing people. |
How do they measure the quality of detection? | ### Introduction
Keyword detection is like searching for a needle in a haystack: the detector must listen to continuously streaming audio, ignoring nearly all of it, yet still triggering correctly and instantly. In the last few years, with the advent of voice assistants, keyword spotting has become a common way to initiate a conversation with them (e.g. "Ok Google", "Alexa", or "Hey Siri"). As the assistant use cases spread through a variety of devices, from mobile phones to home appliances and further into the internet-of-things (IoT) –many of them battery powered or with restricted computational capacity, it is important for the keyword spotting system to be both high-quality as well as computationally efficient. Neural networks are core to the state of-the-art keyword spotting systems. These solutions, however, are not developed as a single deep neural network (DNN). Instead, they are traditionally comprised of different subsystems, independently trained, and/or manually designed. For example, a typical system is composed by three main components: 1) a signal processing frontend, 2) an acoustic encoder, and 3) a separate decoder. Of those components, it is the last two that make use of DNNs along with a wide variety of decoding implementations. They range from traditional approaches that make use of a Hidden Markov Model (HMM) to characterize acoustic features from a DNN into both "keyword" and "background" (i.e. non-keyword speech and noise) classes BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . Simpler derivatives of that approach perform a temporal integration computation that verifies the outputs of the acoustic model are high in the right sequence for the target keyword in order to produce a single detection likelyhood score BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . Other recent systems make use of CTC-trained DNNs –typically recurrent neural networks (RNNs) BIBREF10 , or even sequence-to-sequence trained models that rely on beam search decoding BIBREF11 . This last family of systems is the closest to be considered end-to-end, however they are generally too computationally complex for many embedded applications. Optimizing independent components, however, creates added complexities and is suboptimal in quality compared to doing it jointly. Deployment also suffers due to the extra complexity, making it harder to optimize resources (e.g. processing power and memory consumption). The system described in this paper addresses those concerns by learning both the encoder and decoder components into a single deep neural network, jointly optimizing to directly produce the detection likelyhood score. This system could be trained to subsume the signal processing frontend as well as in BIBREF2 , BIBREF12 , but it is computationally costlier to replace highly optimized fast fourier transform implementations with a neural network of equivalent quality. However, it is something we consider exploring in the future. Overall, we find this system provides state of the art quality across a number of audio and speech conditions compared to a traditional, non end-to-end baseline system described in BIBREF13 . Moreover, the proposed system significantly reduces the resource requirements for deployment by cutting computation and size over five times compared to the baseline system. The rest of the paper is organized as follows. In Section SECREF2 we present the architecture of the keyword spotting system; in particular the two main contributions of this work: the neural network topology, and the end-to-end training methodology. Next, in Section SECREF3 we describe the experimental setup, and the results of our evaluations in Section SECREF4 , where we compare against the baseline approach of BIBREF13 . Finally, we conclude with a discussion of our findings in Section SECREF5 . ### End-to-End system
This paper proposes a new end-to-end keyword spotting system that by subsuming both the encoding and decoding components into a single neural network can be trained to produce directly an estimation (i.e. score) of the presence of a keyword in streaming audio. The following two sections cover the efficient memoized neural network topology being utilized, as well as the method to train the end-to-end neural network to directly produce the keyword spotting score. ### Efficient memoized neural network topology
We make use of a type of neural network layer topology called SVDF (single value decomposition filter), originally introduced in BIBREF14 to approximate a fully connected layer with a low rank approximation. As proposed in BIBREF14 and depicted in equation EQREF2 , the activation INLINEFORM0 for each node INLINEFORM1 in the rank-1 SVDF layer at a given inference step INLINEFORM2 can be interpreted as performing a mix of selectivity in time ( INLINEFORM3 ) with selectivity in the feature space ( INLINEFORM4 ) over a sequence of input vectors INLINEFORM5 of size INLINEFORM6 . DISPLAYFORM0 This is equivalent to performing, on an SVDF layer of INLINEFORM0 nodes, INLINEFORM1 1-D convolutions of the feature filters INLINEFORM2 (by "sliding" each of the INLINEFORM3 filters on the input feature frames, with a stride of INLINEFORM4 ), and then filtering each of INLINEFORM5 output vectors (of size INLINEFORM6 ) with the time filters INLINEFORM7 . A more general and efficient interpretation, depicted in Figure FIGREF3 , is that the layer is just processing a single input vector INLINEFORM0 at a time. Thus for each node INLINEFORM1 , the input INLINEFORM2 goes through the feature filter INLINEFORM3 , and the resulting scalar output gets concatenated to those INLINEFORM4 computed in previous inference steps. The memory is either initialized to zeros during training for the first INLINEFORM5 inferences. Finally the time filter INLINEFORM6 is applied to them. This is how stateful networks work, where the layer is able to memorize the past within its state. Different from typical recurrent approaches though, and other types of stateful layers BIBREF15 , the SVDF does not recur the outputs into the state (memory), nor rewrites the entirety of the state with each iteration. Instead, the memory keeps each inference's state isolated from subsequent runs, just pushing new entries and popping old ones based on the memory size INLINEFORM7 configured for the layer. This also means that by stacking SVDF layers we are extending the receptive field of the network. For example, a DNN with INLINEFORM8 stacked layers, each with a memory of INLINEFORM9 , means that the DNN is taking into account inputs as old as INLINEFORM10 . This approach works very well for streaming execution, like in speech, text, and other sequential processing, where we constantly process new inputs from a large, possibly infinite sequence but do not want to attend to all of it. An implementation is available at BIBREF16 . This layer topology offers a number of benefits over other approaches. Compared with the convolutions use in BIBREF13 , it allows finer-grained control of the number of parameters and computation, given that the SVDF is composed by several relatively small filters. This is useful when selecting a tradeoff between quality, size and computation. Moreover, because of this characteristic, the SVDF allows creating very small networks that outperform other topologies which operate at larger granularity (e.g. our first stage, always-on network has about 13K parameters BIBREF7 ). The SVDF also pairs very well with linear “bottleneck” layers to significantly reduce the parameter count as in BIBREF17 , BIBREF18 , and more recently in BIBREF9 . And because it allows for creating evenly sized deep networks, we can insert them throughout the network as in Figure FIGREF8 . Another benefit is that due to the explicit sizing of the receptive field it allows for a fine grained control over how much to remember from the past. This has resulted in SVDF outperforming RNN-LSTMs, which do not benefit from, and are potentially hurt by, paying attention to theoretically infinite past. It also avoids having complex logic to reset the state every few seconds as in BIBREF11 . ### Method to train the end-to-end neural network
The goal of our end-to-end training is to optimize the network to produce the likelihood score, and to do so as precisely as possible. This means have a high score right at the place where the last bit of the keyword is present in the streaming audio, and not before and particularly not much after (i.e. a "spiky" behaviour is desirable). This is important since the system is bound to an operating point defined by a threshold (between 0 and 1) that is choosen to strike a balance between false-accepts and false-rejects, and a smooth likelyhood curve would add variability to the firing point. Moreover, any time between the true end of the keyword and the point where the score meets the threshold will become latency in the system (e.g. the "assistant" will be slow to respond). A common drawback of CTC-trained RNNs BIBREF19 we aim to avoid. We generate input sequences composed of pairs < INLINEFORM0 , INLINEFORM1 >. Where INLINEFORM2 is a 1D tensor corresponding to log-mel filter-bank energies produced by a front-end as in BIBREF5 , BIBREF14 , BIBREF13 , and INLINEFORM3 is the class label (one of INLINEFORM4 ). Each tensor INLINEFORM5 is first force-aligned from annotated audio utterances, using a large LVCSR system, to break up the components of the keyword BIBREF20 . For example, "ok google" is broken into: "ou", "k", "eI", "<silence>", "g", "u", "g", "@", "l". Then we assign labels of 1 to all sequence entries, part of a true keyword utterance, that correspond to the last component of the keyword ("l" in our "ok google" example). All other entries are assigned a label of 0, including those that are part of the keyword but that are not its last component. See Figure FIGREF6 . Additionally, we tweak the label generation by adding a fixed amount of entries with a label of 1, starting from the first vector INLINEFORM6 corresponding to the final keyword component. This is with the intetion of balancing the amount of negative and positive examples, in the same spirit as BIBREF0 . This proved important to make training stable, as otherwise the amount of negative examples overpowered the positive ones. The end-to-end training uses a simple frame-level cross-entropy (CE) loss that for the feature vector INLINEFORM0 is defined by INLINEFORM1 , where INLINEFORM2 are the parameters of the network, INLINEFORM3 the INLINEFORM4 th output of the final softmax. Our training recipe uses asynchronous stochastic gradient descent (ASGD) to produce a single neural network that can be fed streaming input features and produce a detection score. We propose two options to this recipe: Encoder+decoder. A two stage training procedure where we first train an acoustic encoder, as in BIBREF5 , BIBREF14 , BIBREF13 , and then a decoder from the outputs of the encoder (rather than filterbank energies) and the labels from SECREF5 . We do this in a single DNN by creating a final topology that is composed of the encoder and its pre-trained parameters (including the softmax), followed by the decoder. See Figure FIGREF8 . During the second stage of training the encoder parameters are frozen, such that only the decoder is trained. This recipe useful on models that tend to overfit to parts of the training set. End-to-end. In this option, we train the DNN end-to-end directly, with the sequences from SECREF5 . The DNN may use any topology, but we use that of the encoder+decoder, except for the intermediate encoder softmax. See Figure FIGREF8 . Similar to the encoder+decoder recipe, we can also initialize the encoder part with a pre-trained model, and use an adaptation rate INLINEFORM0 to tune how much the encoder part is being adjusted (e.g. a rate of 0 is equivalent to the encoder+decoder recipe). This end-to-end pipeline, where the entirety of the topology's parameters are adjusted, tends to outperform the encoder+decoder one, particularly in smaller sized models which do not tend to overfit. ### Experimental setup
In order to determine the effectiveness of our approach, we compare against a known keyword spotting system proposed in BIBREF13 . This section describes the setups used in the results section. ### Front-end
Both setups use the same front-end, which generates 40-dimensional log-mel filter-bank energies out of 30ms windows of streaming audio, with overlaps of 10ms. The front-end can be queried to produce a sequence of contiguous frames centered around the current frame INLINEFORM0 . Older frames are said to form the left context INLINEFORM1 , and newer frames form the right context INLINEFORM2 . Additionally, the sequences can be requested with a given stride INLINEFORM3 . ### Baseline model setup
Our baseline system (Baseline_1850K) is taken from BIBREF13 . It consists of a DNN trained to predict subword targets within the keywords. The input to the DNN consists of a sequence with INLINEFORM0 frames of left and INLINEFORM1 frames of right context; each with a stride of INLINEFORM2 . The topology consists of a 1-D convolutional layer with 92 filters (of shape 8x8 and stride 8x8), followed by 3 fully-connected layers with 512 nodes and a rectified linear unit activation each. A final softmax output predicts the 7 subword targets, obtained from the same forced alignment process described in SECREF5 . This results in the baseline DNN containing 1.7M parameters, and performing 1.8M multiply-accumulate operations per inference (every 30ms of streaming audio). A keyword spotting score between 0 and 1 is computed by first smoothing the posterior values, averaging them over a sliding window of the previous 100 frames with respect to the current INLINEFORM3 ; the score is then defined as the largest product of the smoothed posteriors in the sliding window as originally proposed in BIBREF6 . ### End-to-end model setup
The end-to-end system (prefix E2E) uses the DNN topology depicted in Figure FIGREF8 . We present results with 3 distinct size configurations (infixes 700K, 318K, and 40K) each representing the number of approximate parameters, and 2 types of training recipes (suffixes 1stage and 2stage) corresponding to end-to-end and encoder+decoder respectively, as described in UID7 . The input to all DNNs consist of a sequence with INLINEFORM0 frames of left and INLINEFORM1 frames of right context; each with a stride of INLINEFORM2 . More specifically, the E2E_700K model uses INLINEFORM3 nodes in the first 4 SVDF layers, each with a memory INLINEFORM4 , with intermediate bottleneck layers each of size 64; the following 3 SVDF layers have INLINEFORM5 nodes, each with a memory INLINEFORM6 . This model performs 350K multiply-accumulate operations per inference (every 20ms of streaming audio). The E2E_318K model uses INLINEFORM7 nodes in the first 4 SVDF layers, each with a memory INLINEFORM8 , with intermediate bottleneck layers each of size 64; the remainder layers are the same as E2E_700K. This model performs 159K multiply-accumulate operations per inference. Finally, the E2E_40K model uses INLINEFORM9 nodes in the first 4 SVDF layers, each with a memory INLINEFORM10 , with intermediate bottleneck layers each of size 32; the remainder layers are the same as the other two models. This model performs 20K multiply-accumulate operations per inference. ### Dataset
The training data for all experiments consists of 1 million anonymized hand-transcribed utterances of the keywords "Ok Google" and "Hey Google", with an even distribution. To improve robustness, we create "multi-style" training data by synthetically distorting the utterances, simulating the effect of background noise and reverberation. 8 distorted utterances are created for each original utterance; noise samples used in this process are extracted from environmental recordings of everyday events, music, and Youtube videos. Results are reported on four sets representative of various environmental conditions: Clean non-accented contains 170K non-accented english utterances of the keywords in "clean" conditions, plus 64K samples without the keywords (1K hours); Clean accented has 153K english utterances of the keywords with Australian, British, and Indian accents (also in "clean" conditions), plus 64K samples without the keywords (1K hours); High pitched has 1K high pitched utterances of the keywords, and 64K samples without them (1K hours); Query logs contains 110K keyword and 21K non-keyword utterances, collected from anonymized voice search queries. This last set contains background noises from real living conditions. ### Results
Our goal is to compare the efectiviness of the proposed approach against the baseline system described in BIBREF13 . We evaluate the false-reject (FR) and false-accept (FA) tradeoff across several end-to-end models of distinct sizes and computational complexities. As can be seen in the Receiver Operating Characteristic (ROC) curves in Figure FIGREF14 , the 2 largest end-to-end models, with 2-stage training, significantly outperform the recognition quality of the much larger and complex Baseline_1850K system. More specifically, E2E_318K_2stage and E2E_700K_2stage show up to 60% relative FR rate reduction over Baseline_1850K in most test conditions. Moreover, E2E_318K_2stage uses only about 26% of the computations that Baseline_1850K uses (once normalizing their execution rates over time), but still shows significant improvements. We also explore end-to-end models at a size that, as described in BIBREF7 , is small enough, in both size and computation, to be executed continuously with very little power consumption. These 2 models, E2E_40K_1stage and E2E_40K_2stage, also explore the capacity of end-to-end training (1stage) versus encoder+decoder training (2stage). As can be appreciated in the ROC curves, 1stage training outperforms 2stage training on all conditions, but particularly on both "clean" environments where it gets fairly close to the performance of the baseline setup. That is a significant achievement considering E2E_40K_1stage has 2.3% the parameters and performs 3.2% the computations of Baseline_1850K. Table TABREF13 compares the recognition quality of all setups by fixing on a very low false-accept rate of 0.1 FA per hour on a dataset containing only negative (i.e. non-keyword) utterances. Thus the table shows the false-reject rates at that operating point. Here we can appreciate similar trends as those described above: the 2 largest end-to-end models outperforms the baseline across all datasets, reducing FR rate about 40% on the clean conditions and 40%-20% on the other 2 sets depending on the model size. This table also shows how 1stage outperforms 2stage for small size models, and presents similar FR rates as Baseline_1850K on clean conditions. ### Conclusion
We presented a system for keyword spotting that by combining an efficient topology and two types of end-to-end training can significantly ourperform previous appraoches, at a much lower cost of size and computation. We specifically show how it beats the performance of a setup taken from BIBREF13 with models over 5 times smaller, and even get close to the same performance with a model over 40 times smaller. Our approach provides further benefits of not requiring anything other than a front-end and a neural network to perform the detection, and thus it is easier to extend to newer keywords and/or fine-tune with new training data. Future work includes exploring other loss-functions, as well as generalizing multi-channel support. Fig. 2. Input sequence generation for “Ok google”. Fig. 1. A single node (m) in the SVDF layer. Fig. 3. End-to-end topology trained to predict the keyword likelihood score. Bottleneck layers reduce parameters and computation. The intermediate softmax is used in encoder+decoder training only. Table 1. FR rate over 4 test conditions at 0.1 FAh level. Fig. 4. ROC curves under different conditions. | We evaluate the false-reject (FR) and false-accept (FA) tradeoff across several end-to-end models of distinct sizes and computational complexities. |
What genetic analysis was planned for Mr. Harder for the end of January according to the report from the outpatient clinic visit on 1/8/2023?
Choose the correct answer from the following options:
A. Whole-genome sequencing
B. Re-genetic sequencing of a subdiaphragmatic lymph node metastasis
C. Genetic testing for BRCA mutations
D. Genetic sequencing for DNA repair gene alterations
E. PSMA-related gene sequencing
| ### Patient Report 0
**Dear colleague, **
We are reporting on Mr. Ben Harder, born on 08/02/1940, who was admitted
to our hospital from 12/17/2015 to 12/27/2015.
**Diagnoses:**
- Prostate carcinoma pT3b pN1 R1 L0 V0, Gleason: 4 + 5 = 9
- Urine extravasation
- Persistent lymphatic leakage
**Other Diagnoses:**
- Arterial hypertension
- Status post excision on the nose with suspicion of basal cell
carcinoma
- Status post laparoscopic cholecystectomy
- Retropubic radical prostatectomy without nerve preservation and with
bilateral pelvic lymphadenectomy was performed on 12/17/2015.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
------------------------- ------------- ---------------------------------------------------
Valsartan (Diovan) 160 mg 1-0-1
Aspirin 100 mg 1-0-1
Simvastatin (Zocor) 15 mg 0-0-1
Doxazosin (Cardura) 1 mg 0-0-1
Enoxaparin (Lovenox) 0.6 mL s.c. Administer subcutaneously for a total of 4 weeks.
Acetaminophen (Tylenol) 500 mg 1-1-1 (for 4 days)
**Histopathology:**
1. 2 adenocarcinoma metastases in 2 out of 4 lymph nodes, left external
iliac region.
2. 3 adenocarcinoma metastases in 3 out of 6 lymph nodes, right pelvic
region.
3. 7 adenocarcinoma metastases in 7 out of 7 lymph nodes, right lumbar
para-aortic region.
4. Acinar adenocarcinoma of the prostate is observed bilaterally, with
a Gleason score of 4 (70%) + 5 (25%) = 9 and a tertiary Gleason
grade of 3 (5%) according to the modified Gleason grading of the
ISUP 2005. The tumor is multifocal and encompasses the entire
prostate with a maximum extrapolated tumor extension of 60 mm. There
is extracapsular tumor growth, with focal involvement at the dorsal
right base. Vascular invasion is not noted, but perineural invasion
is extensive. Both seminal vesicles are heavily infiltrated, and the
resection margin of the left seminal vesicle is involved. Before
tissue embedding, the margins of the specimen show focal
infiltration by the tumor, in the right anterior region near the
base (section 7) with a total contact area of 2 mm wide, and the
primary Gleason grade at the positive margin is 4. In addition to
the carcinoma, other prostatic tissue shows features of myoglandular
hyperplasia and high-grade prostatic intraepithelial neoplasia
(HGPIN). The prostatic urethra is free of tumors or dysplasia.
5. **Tumor classification:** pT3bpN1(12/17), R1L0V0, Gleason: 4 + 5 = 9
**Medical History:** Mr. Harder was admitted for open prostatectomy due
to biopsy-confirmed prostate carcinoma.
Initial PSA value: 5.42 ng/ml. Gleason score of biopsy: 4+5=9 in 11 out
of 12 biopsy samples. Clinical tumor stage: cT2c PSMA PET-CT + MRI from
12/23/2015: Left capsular penetration without rectal infiltration.
Seminal vesicles are infiltrated on both sides. Evidence of multiple
lymph nodes; intrapelvic locoregional and two lymph nodes on the right
parailiacal and lumbar interaortocaval region.
TRUS: 88 cc Digital Rectal Examination: Abnormal findings on the left
side
**Physical Examination:** The patient is in good general condition, has
a lean nutritional status, and reports feeling well. The abdomen is
soft, with no tenderness, masses, resistance, or guarding, and the
external genitalia are unremarkable.
**Ultrasound upon admission:** Both kidneys are not dilated and show no
space-occupying lesions. The bladder is minimally filled and appears
unremarkable to the extent assessable.
**Pretherapeutic Tumor Conference:** The findings were discussed
interdisciplinary, and the possible treatment options were explained.
The patient opted for radical prostatectomy.
**Therapy and Progression:** The above-mentioned procedure was performed
without complications. The postoperative course was uneventful. Blood
transfusions were not required. Unfortunately, a cystogram on revealed
extravasation, requiring the indwelling catheter to be retained. The
wound drain was lifted once with serum-identical creatinine values and
retained with persistent output (approximately 400 ml daily). Both
kidneys were not dilated, and there were no signs of lymphocele or
hematoma in the pelvic region on ultrasound. A follow-up rehabilitation
treatment has been organized through our social services. We discharged
the patient with absorbable intracutaneous sutures for further
outpatient care.
**Current Recommendations:** The patient was discharged with a permanent
catheter and will present on 01/03/2016 for cystogram and possibly
catheter removal. If catheter removal is indicated, we recommend
considering a trial of voiding with subsequent admission if the
cystogram is normal. The wound drain was also retained, and we request
documentation of output. If output regresses and remains persistently \<
30-40 ml, and there is no ultrasound evidence of lymphocele, it could be
removed on an outpatient basis or during the follow-up appointment.
We recommend the first PSA check 6 -- 8 weeks postoperatively, followed
by quarterly intervals. If the PSA level does not reach the zero range
or rises again from the zero range, the patient can be offered
radiotherapy of the prostatic bed and lymphatic drainage pathways in
combination with a 2-year hormonal ablative therapy as an individual
therapeutic trial. Alternatively, primary hormonal ablative therapy is
an option. If the PSA level reaches the zero range, the patient may be
offered adjuvant hormonal ablative therapy for 2 years, possibly
combined with radiotherapy. Additional findings will be discussed in our
post-therapeutic conference. In case of changes in the recommended
procedure mentioned above, we will inform you again.
**Course of the lab results:**
**Parameter** **12/18/15** **12/19/15** **12/20/15** **12/23/15** **Reference Range**
--------------------------- --------------- ---------------- --------------- -------------- ---------------------
Sodium 135 mEq/L 138 mEq/L 136-145 mEq/L
Potassium 5.1 mEq/L 4.4 mEq/L 3.4-4.5 mEq/L
Creatinine (Jaffe method) 0.93 mg/dL 1.05 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR) 83 65
Hemoglobin 11.4 g/dL 12.4 g/dL 12.5-17.2 g/dL
Hematocrit 0.323 L/L 0.361 L/L 0.370-0.490 L/L
Red Blood Cells 3.8 x10\^12/L 4.3 x10\^12/L 4.2 x10\^12/L 4.0-5.7 x10\^12/L
White Blood Cells 9.57 x10\^9/L 11.51 x10\^9/L 9.65 x10\^9/L 3.90-10.50 x10\^9/L
Platelets 216 x10\^9/L 239 x10\^9/L 285 x10\^9/L 150-370 x10\^9/L
MCV 88.1 fL 86.0 fL 88.3 fL 80.0-101.0 fL
MCH 30.2 pg 30.1 pg 29.5 pg 27.0-34.0 pg
MCHC 34.5 g/dL 35.3 g/dL 33.8 g/dL 31.5-36.0 g/dL
MPV 10.2 fL 10.4 fL 10.3 fL 7.0-12.0 fL
RDW-CV 12.1% 12.2% 12.8% 11.6-14.4%
### Patient Report 1
**Dear colleague, **
We report to you about Mr. Ben Harder, born on 08/02/1940 who received
inpatient treatment from 01/13/2016 to 01/19/2016.
**Diagnosis:** Urinary Tract Infection in Patient with indwelling
catheter
**Other Diagnoses:**
- Prostate carcinoma pT3b pN1 R1 L0 V0, Gleason: 4 + 5 = 9
- Urine extravasation
- Persistent lymphatic leakage
- Arterial hypertension
- History of excision on the nose with suspicion of basal cell
carcinoma
- History of laparoscopic cholecystectomy
**Medication upon Admission: **
**Medication (Brand)** **Dosage** **Frequency**
------------------------------ ------------ ---------------
Aspirin 100 mg 1-0-0-0
Candesartan (Atacand) 16 mg 1-0-1-0
Chlorthalidone (Hygroton) 25 mg 0.5-0-0-0
Multivitamin \- 1-1-0-0
Hawthorn Herb 450 mg 1-1-1-0
Selenium 999 mcg 0-0-1-0
Zinc 157 mg 0-1-0-0
Vitamin D3 (Cholecalciferol) 20 mg 0-1-0-0
Vitamin B complex 0.5 mg 1-0-0-0
Vitamin E 200 IU 1-0-1-0
Vitamin A \- 0-2-0-0
Lercanidipine 10 mg 0.5-0-0.5-0
Thiamine 200 mg 1x/Week
Pyridoxine 25 mg 2-3x/Week
**Current presentation:** Mr. Harder returned to our clinic on
01/13/2016, complaining of new-onset symptoms including increased
urgency and frequency of urination, discomfort, lower abdominal pain,
and fever. Given his recent surgery and indwelling catheter, concerns
were raised about a possible urinary tract infection.
**Clinical Examination:** On physical examination, Mr. Harder appeared
unwell. He had a temperature of 38.8°C, elevated heart rate
(tachycardia), and mild lower abdominal tenderness on palpation. The
indwelling urinary catheter was in situ, and no signs of catheter
dislodgment or leakage were observed.
**Ultrasound of the Abdomen upon admission:** Bilateral, no urinary
transport obstruction, approximately 4x2 cm-sized fluid collection noted
in the right inguinal area, suggestive of possible lymphocele.
**CT Scan Abdomen/Pelvic from 01/13/2016:** The liver displays a smooth
contour, with homogeneous parenchymal contrast enhancement, and no
evidence of focal intrahepatic lesions. There is no indication of
intrahepatic or extrahepatic cholestasis. History of previous
cholecystectomy with an accentuated common hepatic duct. Spleen,
pancreas, and adrenal glands appear unremarkable. Both orthotopically
located kidneys exhibit simultaneous and equal contrast enhancement. No
intrarenal structural abnormalities or signs of urinary obstruction are
observed. The colonic frame and small intestine show adequate perfusion,
without focal wall thickening. The stomach is distended. The urinary
bladder contains a catheter. Two intraluminal air pockets are seen.
Known circumferential, uniform bladder wall thickening from previous
examinations. No free intrabdominal air is detected. No evidence of
ascites. Bilateral iliac and inguinal operative clips from prior
lymphadenectomy. Right iliac region shows a serous fluid collection
measuring approximately 3 x 2 cm. Para-aortic lymph nodes, up to 14 mm
in size, are consistent with findings from previous evaluations. No
suspicious malignancy-related bone destruction is noted. A drainage tube
has been placed through the right lower abdominal wall, with its tip
located in the left pelvic area.
**Assessment:** No evidence of abscess formation. A lymphocele measuring
approximately 3 x 2 cm is noted in the right iliac region, without signs
of acute inflammation.
**Microbiological Examination**
**Material**: Catheter Urine Examination Request: Identification of
Pathogens and Resistance Results: Organism 1: Growth of 100,000 CFU/mL
Enterococcus faecalis Possible ICD-10 Coding Suggestion: Enterococci as
Pathogen.
- Acute Cystitis
- Pyelonephritis
- Urinary Tract Infection related to Catheter/Implant
- Urinary Tract Infection, Unspecified Location
**Antibiogram**
- Gentamicin HL: S
- Levofloxacin: R 1
- Teicoplanin: S \<=0.5
- Ampicillin: S \<=2
- Piperacillin: S
- Ampicillin/Sulbactam: S \<=2
- Piperacillin/Tazobactam: S
- Imipenem: S \<=1
- Cefuroxim: R \>=64
- Gentamicin: R
- Cotrimoxazole: R \<=10
- Ciprofloxacin: R \<=0.5
- Vancomycin: S 2
- Linezolid: S 2
- Tigecyclin: S \<=0.12
**Therapy and Progression:** After CT morphological exclusion of an
abscess formation or retention, the wound drainage was removed under
antibiotic coverage. Initially, empirical antibiotic therapy with
Cefuroxim was administered, followed by targeted treatment with oral
Unacid based on resistance testing. The drainage insertion site healed
primarily. The bladder catheter was removed, after which urination was
free of residual urine. The patient has primary continence. We
discharged the patient to further outpatient treatment, with the patient
reporting subjective well-being.
Mr. Harder showed gradual clinical improvement after initiating
antibiotic therapy. His fever subsided, and lower abdominal tenderness
diminished. The IV fluids were discontinued, and he remained on oral
antibiotics.
**Urine Culture Results:** The urine culture results returned positive
for Escherichia coli (E. coli), a common uropathogen. The sensitivity
profile indicated susceptibility to ciprofloxacin.
**Follow-Up:** Mr. Harder was closely monitored for the duration of his
antibiotic course. He was advised to complete the full course of
antibiotics and maintain adequate hydration. The urinary catheter was
removed on the fifth day of hospitalization after demonstrating improved
urine output and resolution of symptoms. No further complications
related to the catheter removal were observed.
**Current Recommendations:**
1. Please refer to the previous discharge letter for the procedure
regarding prostate cancer.
2. He was educated on the signs and symptoms of UTIs and instructed to
seek prompt medical attention if symptoms recurred.
**\
**
### Patient Report 2
**Dear colleague, **
Thank you for assigning Mr. Ben Harder, born on 08/02/1940 to the PET/CT
combination scanner examination on 12/11/2022.
**Diagnoses:**
- Initial diagnosis of prostate cancer in December 2015, confirmed by
prostate biopsy.
- Tumor detected in 11 out of 12 biopsy samples
- Maximum Gleason score of 9
- Preoperative PSA level: 5.42 ng/ml
- In the initial PET/CT examination (preoperative) on 12/23/2015
evidence of prostate cancer extending beyond the capsule in both
prostatic lobes was found.
- Infiltration of the left seminal vesicles and beginning infiltration
of the right seminal vesicles
- Multiple retroperitoneal lymph node metastases and bilateral pelvic
lymph nodes.
- Radical prostatectomy
- Current PSA level: 1.23 ng/ml
- Radiation therapy planned at our facility
**Technique**: To expedite renal-urinary activity elimination, the
patient was adequately hydrated. The examination was conducted using the
PET/CT combination scanner BIOGRAPH 64 with CT parameters set at 120 kV
and 1 mm slice thickness. PET emission data were acquired with 5 bed
positions on a radiation therapy-compatible table for whole-body
examination in the caudocranial direction with transverse slices at 3.0
mm intervals over the same axial range as the CT scan. Iterative
reconstruction was performed. A whole-body scan was conducted 90 minutes
after the administration of 278 MBq Ga-68-PSMA (prostate-specific
membrane antigen). Transmissions-corrected and non-corrected PET scans,
CT scans, fusion images, and the determination of the SUV value
(standard uptake value, a measure of activity uptake per volume) were
used for evaluation.
**PET Findings:** The 3D whole-body images documented in 3 planes using
PET and PET/CT technology showed the following changes compared to the
prior examination on 12/23/2015:
- Post-radical prostatectomy, there is diffuse activity accumulation
in the region dorsal to the bladder, on the left side.
- Regarding the known retroperitoneal lymph node metastases, the
following changes were observed: New retrocrural nodule on the
right. SUV 6.6, diameter: 6 mm.
- Known interaortocaval lymph node, dorsally at the level of L3/4,
showed increased metabolic activity from SUV 4.7 to 11.5. Slightly
increased in size, measuring 7 to 8 mm. Additionally, two new
metabolically active nodules cranially, up to the level of L2/3.
- Slightly increased metabolic activity in the known right iliaca
communis lymph node, located between the fifth lumbar vertebra and
the psoas muscle, from 4.0 to 4.6, with the same size of 5 mm.
- Progressive enlargement of the known confluent lymph nodes on the
right parailiacal externa proximal side, now having a combined size
of 10 x 26 mm (width x height), previously individual nodules of 10
and 12 mm. SUV 18.2, previously max 11.5.
- New paraaortic lymph nodes on the left, mostly small, SUV 11.2.
- Newly added lymph nodes in both biiliac communal areas. Maximum size
on the left is 15 mm, SUV 17.2.
- New retrocrural lymph node on the right, measuring 6 mm, SUV 6.6.
- Known lymph node on the right perirectal, slightly progressive from
8 to 10 mm. SUV 6.9, previously 6.4.
- Known lymph node on the left iliaca externa not currently
verifiable, possibly postoperative scarring.
- Newly added focus in the bone at the level of the spinous
process/dorsal arch of the fifth lumbar vertebra. In CT, a 7 mm
focal sclerosis is noted. Normal activity accumulation in the soft
tissues of the neck, axillae, and chest. Physiological accumulation
in the parenchymal upper abdominal organs. Kidneys and urinary tract
appear functionally normal. Whole-body CT following bolus-like
peripheral venous machine injection of 100 ml of Optiray 350: No
suspicious lymph nodes in the cervical, axillary, or mediastinal
regions. Normal-sized thyroid gland. No pleuropulmonary infiltrates
or round lesions. Scarred changes in the left lower lobe.
Normal-sized liver without focal lesions. Spleen, pancreas, adrenal
glands, and kidneys appear regular. No urinary obstruction..
**Results**: In the postoperative PET/CT compared to the preoperative
examination on, there is now malignancy-typical PSMA receptor binding in
the former prostate lodge, indicating a local recurrence. Progression of
retroperitoneal lymph node metastases, with further extension cranially,
extending to the interaortocaval region up to the level of L2/3. Newly
added metastases on the left paraaortic and biiliac communal areas.
Progression of known right iliaca externa lymph node metastases. The
left iliaca externa nodule is not verifiable, likely removed. New small
retrocrural nodule on the right. New osteosclerotic metastasis in the
dorsal arch of LWK 5. Minimal activity accumulation in the 8th rib on
the right lateral aspect. A developing metastasis cannot be conclusively
ruled out here. We kindly request information on the patient\'s further
clinical course (submission of medical reports, etc.).
**Lab results**
**Parameter** **Results** **Reference Range**
---------------------- ------------- ---------------------
Glucose (Plasma) 91 mg/dL 55-100 mg/dL
Alkaline Phosphatase 93 U/L \< 130 U/L
Total Cholesterol 152 mg/dL \< 200 mg/dL
LDL-Cholesterol 89 mg/dL \< 130 mg/dL
HDL-Cholesterol 50 mg/dL 40-60 mg/dL
Non-HDL Cholesterol 101.8 mg/dL \< 200 mg/dL
Triglycerides 64 mg/dL \< 150 mg/dL
White Blood Cells 4.1 K/uL 4.5-11 K/uL
Red Blood Cells 4.68 M/uL 4.0-5.5 M/uL
Hemoglobin 13.6 g/dL 12.5-17.2 g/dL
Hematocrit 39.5% 37.0-49.0%
MCH 29.1 pg 27.0-34.0 pg
MCHC 34.4 g/dL 31.5-36.0 g/dL
MCV 84.4 fL 80.0-101.0 fL
RDW 13.0% 11.6-14.4%
Platelets 238 K/uL 150-370 K/uL
**\
**
### Patient Report 3
**Dear colleague, **
We are writing to provide an update on Mr. Ben Harder, born on
08/02/1940, who received inpatient treatment at our facility from
06/23/2023 to 06/26/2023.
**Diagnosis:**
Prostate Cancer pT3b pN1 R1 L0 V0 Gleason Score: 4 + 5 = 9 (Initial
diagnosis in December 2015)
- History of Retropubic Radical Prostatectomy without nerve
preservation and with bilateral pelvic lymph node dissection on
November 16, 2015. Currently, lymph node and bone metastasis
**Other Diagnoses:**
- History of Retropubic Radical Prostatectomy without nerve
preservation and with bilateral pelvic lymph node dissection on
November 16, 2015.
- Prostate Cancer pT3b pN1 R1 L0 V0, Gleason Score: 4 + 5 = 9
- Initial PSA (Prostate-Specific Antigen) level of 4.8 ng/ml
- Subsequent treatments included Docetaxel, Cabazitaxel, and 4 cycles
of Lutetium-Radioligand Therapy.
- 12/2022, a subdiaphragmatic lymph node was punctured at Sea Clinic,
followed by radiation therapy of the lymph node metastasis.
Radiation was discontinued after 11 sessions due to dyspnea and
Grade 3 esophagitis.
- Notable PSA levels include 5.42 ng/ml in 10/2015, PSA undetectable
in 07/2019 (PSA 0.01 ng/ml, Testosterone 0.00 ng/ml), PSA rising in
11/2019 (PSA \> 0.03 ng/ml), PSA 0.16 ng/ml in 01/2020, PSA 0.06
ng/ml in 02/ 2020 (with undetectable Testosterone and Ostease 31),
and various other PSA values during the course of treatment.
- Imaging studies confirmed bone metastasis in the ilium and sacrum in
03/2020. A CT scan of the pelvis revealed these metastases, as well
as sclerosis of the sacrum and dorsal vertebral arches of L5.
- Further treatments included Zometa, Trenantone, and radiotherapy.
- An MRI of the lumbar spine in 02/2021 showed intraspinal soft tissue
structures with compression of the dural sac, along with extensive
predominantly sclerotic bone metastasis from L4 to S1.
- Surgical intervention included a decompressive hemilaminectomy with
microsurgical tumor resection from the epidural space in 02/2021,
followed by postoperative radiation therapy to the lumbar spine in
04/2021.
- Cabazitaxel therapy commenced in 07/2021, and a CT scan in 09/2021
showed morphologically progressive bone metastasis in the lumbar
spine.
- The patient received Lutetium PSMA-Therapy cycles in 04/2022,
06/2022, 08/2022, and 10/2022. A PSMA-PET-CT scan in 11/2022
indicated a very good partial remission in bone metastases but
progressive mediastinal lymph node metastasis.
- Radiotherapy was administered to the mediastinal lymph nodes but
discontinued after 11 sessions due to side effects
- In 04/2023, the patient underwent a re-challenge with Cabazitaxel
for one cycle but had to discontinue chemotherapy due to
polyneuropathy and cramps.
- A CT scan of the chest and abdomen in 04/2023 showed similar
findings, including two new sclerosis sites in the thoracic spine
(thora 11 and 12) with possible post-radiation changes.
- PSA level in 05/2023 was 0.48 ng/ml.
- Genetic sequencing revealed no therapeutic consequences.
- A PSMA-PET-CT in 06/2023 scan indicated new extensive metastasis in
the sacrum and diffuse lung metastases, accompanied by a PSA level
of 1.35 ng/ml.
- Arterial Hypertension
- Chronic Kidney Insufficiency
**Medications on Admission:**
**Medication ** **Dosage** **Frequency**
---------------------------- ------------ ---------------
Candesartan (Atacand) 16 mg 1-0-1-0
Aspirin 100 mg 1-0-0-0
Chlorthalidone (Thalitone) 25 mg 1-0-0-0
**Physical Examination:** The patient was in good general condition and
had normal orientation to all qualities. There were no edemas, dyspnea,
fever, or cough.
**Medical History:** Mr. Hader presents himself for the 1st cycle of RLT
with Ac-225-PSMA/Lu-177-I&T-PSMA for lymph node and bone metastatic
prostate cancer on an inpatient basis. In the presence of progressive
imaging findings under guideline-compliant therapy, the indication for
RLT tandem therapy was confirmed according to the tumor conference on.
Upon admission, the patient reports feeling well, denies any B-symptoms.
There is no fever or nausea, and the weight is currently stable. There
has been a tendency to fall for some time. The rest of the medical
history is assumed to be known.
**Neurological Consultation on 06/25/2023: **
Clinically neurological examination revealed a polyneuropathy syndrome
of the lower extremities, predominantly on the right side, as well as a
known right-sided foot drop. In summary, we consider the falls to be
multifactorial due to foot weakness as well as polyneuropathy syndrome
with impaired proprioception as the cause of the balance disorder.
Recommended further procedure: In the presence of a known PNP syndrome
that has occurred during chemotherapy, consider outpatient neurological
evaluation and objectification by means of Electromyography and
polyneuropathy laboratory tests.
**Salivary Gland Scintigraphy on 06/26/2023**
**Assessment**: Normal function of the submandibular and parotid glands
bilaterally.
Post-therapeutic imaging with Lu-177-PSMA imaging using
SPECT/low-dose-CT
**Assessment:** Consistent with the PET-CT, there is no tracer uptake in
the area of the prostate. Intensive accumulation of the therapeutic
agent in the area of lymph node metastases, especially mediastinal.
Corresponding to the PET/CT, there are clear focal tracer accumulations
in the left upper lobe of the lung in the area of nodular or diffuse
tissue condensations, possibly metastases or, secondarily,
post-inflammatory. Intensive tracer uptake in the area of known bone
metastases from the previous examination. No newly appearing
tracer-enhancing lesions. In addition, physiological accumulation in the
organ systems involved in tracer metabolism and excretion. NB: Small
pleural effusions on both sides. Known pronounced peribronchial cuffs in
the upper lobes on both sides, possibly scarred, or indicative of
pulmonary venous congestion. Known atrophic kidney on the right.
**Current Recommendations:**
- Blood count checks and determination of kidney and liver parameters
1, 2, 4, and 8 weeks after therapy
- Outpatient neurologic assessment for the evaluation of
polyneuropathy
- PSA determination 6-8 weeks after therapy
- Appointment for a 2nd cycle of radioligand therapy
(Ac-225-/Lu-177-PSMA)
**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 2.31 mEq/L 2.20-2.55 mEq/L
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
C-reactive Protein 0.8 mg/L \< 5.0 mg/L
PSA 2.31 µg/L \< 4.40 µg/L
ALT 12 U/L \< 41 U/L
AST 38 U/L \< 50 U/L
Alkaline Phosphatase 115 U/L 40-130 U/L
Gamma-GT 20 U/L 8-61 U/L
LDH 335 U/L 135-250 U/L
Testosterone \<0.03 µg/L 1.32-8.92 µg/L
TSH 1.42 mU/L 0.27-4.20 mU/L
Hemoglobin 10.1 g/dL 12.5-17.2 g/dL
Hematocrit 0.285 L/L 0.370-0.490 L/L
RBC 3.3 /pL 4.0-5.6 /pL
WBC 4.98 /nL 3.90-10.50 /nL
Platelets 281 /nL 150-370 /nL
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 %
Neutrophils (Absolute) 3.59 /nL 1.50-7.70 /nL
Immature Granulocytes (Absolute) 0.010 /nL \< 0.050 /nL
Lymphocytes (Absolute) 0.43 /nL 1.10-4.50 /nL
Monocytes (Absolute) 0.58 /nL 0.10-0.90 /nL
Eosinophils (Absolute) 0.30 /nL 0.02-0.50 /nL
Basophils (Absolute) 0.07 /nL 0.00-0.20 /nL
Reticulocytes 31.3 /nL 25.0-105.0 /nL
Reticulocyte (%) 0.94 % 0.50-2.00 %
Reticulocyte Production Index 0.3 \-
Ret-Hb 33.9 pg 28.5-34.
### Patient Report 4
**Dear colleague, **
We would like to report on our mutual patient, Mr. Ben Harder, born on
08/02/1940, who presented himself to our outpatient clinic on 1/8/2023.
**Diagnoses:**
- Prostate cancer pT3b pN1 R1 L0 V0, Gleason: 4 + 5 = 9 (initial
diagnosis in 11/2015)
- History of retropubic radical prostatectomy without nerve
preservation and with pelvic lymphadenectomy bilaterally on
11/16/2015
- Currently, there are lymph node and bone metastases
- History of retropubic radical prostatectomy without nerve
preservation and with pelvic lymphadenectomy bilaterally
- Prostate cancer pT3b pN1 R1 L0 V0, Gleason: 4 + 5 = 9
- Initial PSA level was 4.8 ng/ml
- History of Docetaxel therapy
- History of Cabazitaxel therapy
- History of 4 cycles of Lutetium-Radioligand therapy
- Subsequently, radiation therapy was initiated for the lymph node
metastasis but discontinued after 11 sessions due to dyspnea and G3
esophagitis.
- Arterial hypertension
- Chronic renal insufficiency
- Type 2 diabetes mellitus
**Treatment and Progression:** The patient presents for a second opinion
on his prostate cancer, which has metastasized to the bones and lymph
nodes and has become castration-resistant. He recently received
Lutetium-Radioligand therapy. Genetic sequencing from the tissue biopsy
did not reveal any significant gene mutations. The patient wishes to
undergo further evaluation for the diagnosis of relevant genetic
mutations. A previously punctured subdiaphragmatic lymph node metastasis
has not yet undergone genetic testing, which may be justified based on
the available data and publications in specific cases. A chemotherapy
session with Cabazitaxel is planned for the end of January in the
treating urological practice. In cases of DNA repair gene alterations, a
platinum combination could also be considered. Further possible
diagnostic and therapeutic steps were discussed with the patient. An
application for a repeat genetic sequencing will be submitted by our
colleagues from the genetics department.
**Current Recommendations:**
- Application for genetic sequencing for the punctured lymph node
metastasis through the genetics department and DNA-med
- Subsequent re-genetic sequencing of the subdiaphragmatic lymph node
metastasis for relevant mutations
- After receiving the results, a follow-up appointment can be
scheduled in our uro-oncology outpatient clinic.
.
**\
**
### Patient Report 5
**Dear colleague, **
We are reporting to you regarding the inpatient stay of our patient Mr.
Ben Harder, born on 08/02/1940. He was under our care from 09/16/2023 to
09/23/2023.
**Diagnosis**: Prostate Cancer pT3b pN1 R1 L0 V0
- Gleason Score: 4 + 5 = 9
- Postoperative status following retropubic radical prostatectomy
without nerve preservation and with pelvic lymphadenectomy.
- Currently presenting with lymph node and bone metastases, mCRPC
(metastatic castration-resistant prostate cancer)
- Initial PSA level: 4.8 ng/ml
**Previous Treatment and Course:**
- History of Retropubic Radical Prostatectomy without nerve
preservation and with bilateral pelvic lymph node dissection on
- Prostate Cancer pT3b pN1 R1 L0 V0, Gleason Score: 4 + 5 = 9
- Initial PSA (Prostate-Specific Antigen) level of 4.8 ng/ml
- Subsequent treatments included Docetaxel, Cabazitaxel, and 4 cycles
of Lutetium-Radioligand Therapy.
- 12/2022, a subdiaphragmatic lymph node was punctured at Sea Clinic,
followed by radiation therapy of the lymph node metastasis.
Radiation was discontinued after 11 sessions due to dyspnea and
Grade 3 esophagitis.
- Notable PSA levels include 5.42 ng/ml in 10/2015, PSA undetectable
in 07/2019 (PSA 0.01 ng/ml, Testosterone 0.00 ng/ml), PSA rising in
11/2019 (PSA \> 0.03 ng/ml), PSA 0.16 ng/ml in 01/2020, PSA 0.06
ng/ml in 02/ 2020 (with undetectable Testosterone and Ostease 31),
and various other PSA values during the course of treatment.
- Imaging studies confirmed bone metastasis in the ilium and sacrum in
03/2020. A CT scan of the pelvis revealed these metastases, as well
as sclerosis of the sacrum and dorsal vertebral arches of L5.
- Further treatments included Zometa, Trenantone, and radiotherapy.
- An MRI of the lumbar spine in 02/2021 showed intraspinal soft tissue
structures with compression of the dural sac, along with extensive
predominantly sclerotic bone metastasis from L4 to S1.
- Surgical intervention included a decompressive hemilaminectomy with
microsurgical tumor resection from the epidural space in 02/2021,
followed by postoperative radiation therapy to the lumbar spine in
04/2021.
- Cabazitaxel therapy commenced in 07/2021, and a CT scan in 09/2021
showed morphologically progressive bone metastasis in the lumbar
spine.
- The patient received Lutetium PSMA-Therapy cycles in 04/2022,
06/2022, 08/2022, and 10/2022. A PSMA-PET-CT scan in 11/2022
indicated a very good partial remission in bone metastases but
progressive mediastinal lymph node metastasis.
- Radiotherapy was administered to the mediastinal lymph nodes but
discontinued after 11 sessions due to side effects in 01/2023.
- In 04/2023, the patient underwent a re-challenge with Cabazitaxel
for one cycle but had to discontinue chemotherapy due to
polyneuropathy and cramps.
- A CT scan of the chest and abdomen in 04/2023 showed similar
findings, including two new sclerosis sites in the thoracic spine
with possible post-radiation changes.
- PSA level in 05/2023 was 0.48 ng/ml.
- Genetic sequencing revealed no therapeutic consequences.
- A PSMA-PET-CT scan indicated new extensive metastasis in the sacrum
and diffuse lung metastases, accompanied by a PSA level of 1.35
ng/ml.
- Current PET-CT not available. Recommendations for further treatment
options are as follows, based on externally described
image-morphological progression in the recent CT:
1. Actinium-225-PSMA Therapy (Lu-177 Tandem Therapy), provided that
all vital metastases are PSMA-positive (mandatory exclusion of
post-renal urinary flow obstruction)
2. Alternatively, consider initiating therapy with Abiraterone +
Prednisolone or a Cabazitaxel re-challenge (if there was a
favorable response to the last 2 cycles of Cabazitaxel
3. Evaluation of pre-screening for CAR-T cell studies in oncology
at CBF (contact will be made)
**Other Diagnoses:**
- Arterial Hypertension
- Chronic Kidney Insufficiency
- Type 2 Diabetes Mellitus
**Current Presentation:** Mr. Ben Harder is presenting for his 2nd cycle
of Radioligand Therapy (RLT) with Ac-225-PSMA/Lu-177-I&T-PSMA for lymph
node and bone metastatic prostate cancer. In light of progressive
image-morphological findings despite guideline-compliant treatment, the
indication for RLT tandem therapy was determined in the tumor
conference.
**Medical History:** Mr. Harder reports that after the last treatment
cycle, he experienced pronounced fatigue symptoms. He particularly
struggled with climbing stairs and walking longer distances. However, he
managed to fully recover from these symptoms through targeted training.
Additionally, he developed pain in the area of the right ribcage
following the last treatment cycle. The pain occurs intermittently and
is accompanied by increased salivation and nausea, sometimes leading to
vomiting. Mr. Ben Harder also reports newly developed swallowing
difficulties. He feels that food gets stuck in his throat after
swallowing.
**Therapy and Progression:**
In the case of Mr. Ben Harder, due to metastatic prostate cancer with
radiographic progression despite previous guideline-recommended therapy,
according to the recommendations, there was an indication for the 2nd
radioligand therapy with Ac-225-PSMA/Lu-177-I&T-PSMA. The
post-therapeutic imaging showed targeted accumulation of the therapeutic
agent within the tumor. The therapy was administered due to elevated
renal retention parameters with reduced activity of Lu-177-PSMA. The
course of therapy and the entire hospital stay were uncomplicated, so we
can now transition the patient to your outpatient care. We recommend
close laboratory monitoring (blood count, liver and kidney parameters)
at 1, 2, 4, and 8 weeks, as well as a PSA determination 6-8 weeks after
therapy.
In the case of significant fatigue symptoms after the 1st cycle of
tandem RLT, if there are blood count changes indicating a decrease in
hemoglobin levels and recurrent fatigue symptoms, the administration of
erythropoietin or the indication for blood product transfusion should be
considered. Depending on the PSA value 6 weeks post-therapy and the
findings of the PSMA-PET/CT, the further course of action will be
determined in the interdisciplinary Tumor Conference.
If the patient desires, they can seek a second opinion on further
therapeutic procedures in the specialized clinic of the Uro-Oncology
colleagues. In case of pronounced rib pain, if requested by the patient,
the possibility of undergoing radiation therapy can be evaluated. To do
so, Mr. Harder can schedule an appointment at the Radio-Oncology clinic.
Psycho-oncological counseling has been offered to the patient.
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
-------------------------------- ------------ ---------------
Aspirin 100 mg 1-0-0-0
Candesartan Cilexetil (Atacan) 16 mg 1-0-1-0
Chlorthalidone (Hygroton) 25 mg 0.5-0-0-0
Multi-Vitamin \- 1-1-0-0
Hawthorn Herb 450 mg 1-1-1-0
Sodium Selenite 999 µg 0-0-1-0
Zinc 157 mg 0-1-0-0
Vitamin D3 (Cholecalciferol) 20 mg 0-1-0-0
Vitamin B Complex 0.5 mg 1-0-0-0
Vitamin E 200 IU 1-0-1-0
Vitamin A \- 0-2-0-0
Lercanidipine 10 mg 0.5-0-0.5-0
Vitamin B1 200 mg 1x/Week
Vitamin B6 25 mg 2-3x/Week
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
-------------------------------- -------------------- ---------------------
Neutrophils % 80.3 % 42.0-77.0 %
Lymphocytes % 6.7 % 20.0-44.0 %
Monocytes % 8.9 % 2.0-9.5 %
Basophils % 1.3 % 0.0-1.8 %
Eosinophils % 2.4 % 0.5-5.5 %
Immature Granulocytes % 0.4 % 0.0-1.0 %
I:T Ratio 0.005
HFLC Absolute 0.0 /µL
Sodium 140 mEq/L 136-145 mEq/L
Potassium 3.9 mEq/L 3.5-4.5 mEq/L
Calcium 9.36 mg/dL 8.8-10.2 mg/dL
Chloride 102 mEq/L 98-107 mEq/L
Creatinine 1.25 P+ mg/dL 0.70-1.20 mg/dL
Estimated GFR 52 mL/min/1.73m\^2
BUN (Urea) 44 mg/dL 17-48 mg/dL
Uric Acid 3.8 mg/dL 3.6-8.2 mg/dL
CRP 1.3 mg/L \< 5.0 mg/L
PSA 2.98 ng/mL \< 4.4 ng/mL
ALT (GPT) 22 U/L \< 41 U/L
AST (GOT) 49 U/L \< 50 U/L
Alkaline Phosphatase 114 U/L 40-130 U/L
Gamma-GT 19 U/L 8-61 U/L
LDH 404 P+ U/L 135-250 U/L
Testosterone \<0.03 P- ng/mL 1.32-8.92 ng/mL
TSH 1.14 mIU/L 0.27-4.20 mIU/L
Complete Blood Count
Differential Count
Hemoglobin 10.6 g/dL 12.5-17.2 g/dL
Hematocrit 30.5 % 37.0-49.0 %
RBC 3.4 M/µL 4.0-5.6 M/µL
WBC 5.49 K/µL 3.90-10.50 K/µL
Platelets 279 K/µL 150-370 K/µL
MCV 88.7 fL 80.0-101.0 fL
MCH 30.8 pg 27.0-34.0 pg
MCHC 34.8 g/dL 31.5-36.0 g/dL
MPV 10.1 fL 7.0-12.0 fL
RDW-CV 14.1 % 11.5-15.0 %
Absolute Neutrophils 4.41 K/µL 1.50-7.70 K/µL
Absolute Immature Granulocytes 0.020 K/µL \< 0.050 K/µL
Absolute Lymphocytes 0.37 K/µL 1.10-4.50 K/µL
Absolute Monocytes 0.49 K/µL 0.10-0.90 K/µL
Absolute Eosinophils 0.13 K/µL 0.02-0.50 K/µL
Absolute Basophils 0.07 K/µL 0.00-0.20 K/µL
Reticulocytes 37.8 K/µL 25.0-105.0 K/µL
Reticulocyte Percentage 1.10 % 0.50-2.00 %
Reticulocyte Production Index 0.4
Ret-Hb 35.0 pg 28.5-34.5 pg
Prothrombin Time 117 % \> 78 %
INR 0.94 \< 1.25
aPTT 30.2 sec 25.0-38.0 sec | Re-genetic sequencing of a subdiaphragmatic lymph node metastasis |
Who are the animals that Purnie plays with?
A. They are three-legged ostriches.
B. They are a flock of spora.
C. They are mannikins.
D. They are humans.
| 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. | D. They are humans. |
What are the baselines model? | ### Introduction
With more than one hundred thousand new scholarly articles being published each year, there is a rapid growth in the number of citations for the relevant scientific articles. In this context, we highlight the following interesting facts about the process of citing scientific articles: (i) the most commonly cited paper by Gerard Salton, titled “A Vector Space Model for Information Retrieval” (alleged to have been published in 1975) does not actually exist in reality BIBREF0 , (ii) the scientific authors read only 20% of the works they cite BIBREF1 , (iii) one third of the references in a paper are redundant and 40% are perfunctory BIBREF2 , (iv) 62.7% of the references could not be attributed a specific function (definition, tool etc.) BIBREF3 . Despite these facts, the existing bibliographic metrics consider that all citations are equally significant. In this paper, we would emphasize the fact that all the references of a paper are not equally influential. For instance, we believe that for our current paper, BIBREF4 is more influential reference than BIBREF5 , although the former has received lower citations (9) than the latter (1650) so far. Therefore the influence of a cited paper completely depends upon the context of the citing paper, not the overall citation count of the cited paper. We further took the opinion of the original authors of few selective papers and realized that around 16% of the references in a paper are highly influential, and the rest are trivial (Section SECREF4 ). This motivates us to design a prediction model, GraLap to automatically label the influence of a cited paper with respect to a citing paper. Here, we label paper-reference pairs rather than references alone, because a reference that is influential for one citing paper may not be influential with equal extent for another citing paper. We experiment with ACL Anthology Network (AAN) dataset and show that GraLap along with the novel feature set, quite efficiently, predicts the intensity of references of papers, which achieves (Pearson) correlation of INLINEFORM0 with the human annotations. Finally, we present four interesting applications to show the efficacy of considering unequal intensity of references, compared to the uniform intensity. The contributions of the paper are four-fold: (i) we acquire a rich annotated dataset where paper-reference pairs are labeled based on the influence scores (Section SECREF4 ), which is perhaps the first gold-standard for this kind of task; (ii) we propose a graph-based label propagation model GraLap for semi-supervised learning which has tremendous potential for any task where the training set is less in number and labels are non-uniformly distributed (Section SECREF3 ); (iii) we propose a diverse set of features (Section SECREF10 ); most of them turn out to be quite effective to fit into the prediction model and yield improved results (Section SECREF5 ); (iv) we present four applications to show how incorporating the reference intensity enhances the performance of several state-of-the-art systems (Section SECREF6 ). ### Defining Intensity of References
All the references of a paper usually do not carry equal intensity/strength with respect to the citing paper because some papers have influenced the research more than others. To pin down this intuition, here we discretize the reference intensity by numerical values within the range of 1 to 5, (5: most influential, 1: least influential). The appropriate definitions of different labels of reference intensity are presented in Figure FIGREF2 , which are also the basis of building the annotated dataset (see Section SECREF4 ): Note that “reference intensity” and “reference similarity” are two different aspects. It might happen that two similar reference are used with different intensity levels in a citing paper – while one is just mentioned somewhere in the paper and other is used as a baseline. Here, we address the former problem as a semi-supervised learning problem with clues taken from content of the citing and cited papers. ### Reference Intensity Prediction Model
In this section, we formally define the problem and introduce our prediction model. ### Problem Definition
We are given a set of papers INLINEFORM0 and a sets of references INLINEFORM1 , where INLINEFORM2 corresponds to the set of references (or cited papers) of INLINEFORM3 . There is a set of papers INLINEFORM4 whose references INLINEFORM5 are already labeled by INLINEFORM6 (each reference is labeled with exactly one value). Our objective is to define a predictive function INLINEFORM7 that labels the references INLINEFORM8 of the papers INLINEFORM9 whose reference intensities are unknown, i.e., INLINEFORM10 . Since the size of the annotated (labeled) data is much smaller than unlabeled data ( INLINEFORM0 ), we consider it as a semi-supervised learning problem. Definition 1 (Semi-supervised Learning) Given a set of entries INLINEFORM0 and a set of possible labels INLINEFORM1 , let us assume that ( INLINEFORM2 ), ( INLINEFORM3 ),..., ( INLINEFORM4 ) be the set of labeled data where INLINEFORM5 is a data point and INLINEFORM6 is its corresponding label. We assume that at least one instance of each class label is present in the labeled dataset. Let ( INLINEFORM7 ), ( INLINEFORM8 ),..., ( INLINEFORM9 ) be the unlabeled data points where INLINEFORM10 are unknown. Each entry INLINEFORM11 is represented by a set of features INLINEFORM12 . The problem is to determine the unknown labels using INLINEFORM13 and INLINEFORM14 . ### GraLap: A Prediction Model
We propose GraLap, a variant of label propagation (LP) model proposed by BIBREF9 where a node in the graph propagates its associated label to its neighbors based on the proximity. We intend to assign same label to the vertices which are closely connected. However unlike the traditional LP model where the original values of the labels continue to fade as the algorithm progresses, we systematically handle this problem in GraLap. Additionally, we follow a post-processing in order to handle “class-imbalance problem”. Graph Creation. The algorithm starts with the creation of a fully connected weighted graph INLINEFORM0 where nodes are data points and the weight INLINEFORM1 of each edge INLINEFORM2 is determined by the radial basis function as follows: DISPLAYFORM0 The weight is controlled by a parameter INLINEFORM0 . Later in this section, we shall discuss how INLINEFORM1 is selected. Each node is allowed to propagate its label to its neighbors through edges (the more the edge weight, the easy to propagate). Transition Matrix. We create a probabilistic transition matrix INLINEFORM0 , where each entry INLINEFORM1 indicates the probability of jumping from INLINEFORM2 to INLINEFORM3 based on the following: INLINEFORM4 . Label Matrix. Here, we allow a soft label (interpreted as a distribution of labels) to be associated with each node. We then define a label matrix INLINEFORM0 , where INLINEFORM1 th row indicates the label distribution for node INLINEFORM2 . Initially, INLINEFORM3 contains only the values of the labeled data; others are zero. Label Propagation Algorithm. This algorithm works as follows: After initializing INLINEFORM0 and INLINEFORM1 , the algorithm starts by disseminating the label from one node to its neighbors (including self-loop) in one step (Step 3). Then we normalize each entry of INLINEFORM2 by the sum of its corresponding row in order to maintain the interpretation of label probability (Step 4). Step 5 is crucial; here we want the labeled sources INLINEFORM3 to be persistent. During the iterations, the initial labeled nodes INLINEFORM4 may fade away with other labels. Therefore we forcefully restore their actual label by setting INLINEFORM5 (if INLINEFORM6 is originally labeled as INLINEFORM7 ), and other entries ( INLINEFORM8 ) by zero. We keep on “pushing” the labels from the labeled data points which in turn pushes the class boundary through high density data points and settles in low density space. In this way, our approach intelligently uses the unlabeled data in the intermediate steps of the learning. Assigning Final Labels. Once INLINEFORM0 is computed, one may take the most likely label from the label distribution for each unlabeled data. However, this approach does not guarantee the label proportion observed in the annotated data (which in this case is not well-separated as shown in Section SECREF4 ). Therefore, we adopt a label-based normalization technique. Assume that the label proportions in the labeled data are INLINEFORM1 (s.t. INLINEFORM2 . In case of INLINEFORM3 , we try to balance the label proportion observed in the ground-truth. The label mass is the column sum of INLINEFORM4 , denoted by INLINEFORM5 , each of which is scaled in such a way that INLINEFORM6 . The label of an unlabeled data point is finalized as the label with maximum value in the row of INLINEFORM7 . Convergence. Here we briefly show that our algorithm is guaranteed to converge. Let us combine Steps 3 and 4 as INLINEFORM0 , where INLINEFORM1 . INLINEFORM2 is composed of INLINEFORM3 and INLINEFORM4 , where INLINEFORM5 never changes because of the reassignment. We can split INLINEFORM6 at the boundary of labeled and unlabeled data as follows: INLINEFORM0 Therefore, INLINEFORM0 , which can lead to INLINEFORM1 , where INLINEFORM2 is the shape of INLINEFORM3 at iteration 0. We need to show INLINEFORM4 . By construction, INLINEFORM5 , and since INLINEFORM6 is row-normalized, and INLINEFORM7 is a part of INLINEFORM8 , it leads to the following condition: INLINEFORM9 . So, DISPLAYFORM0 Therefore, the sum of each row in INLINEFORM0 converges to zero, which indicates INLINEFORM1 . Selection of INLINEFORM0 . Assuming a spatial representation of data points, we construct a minimum spanning tree using Kruskal's algorithm BIBREF10 with distance between two nodes measured by Euclidean distance. Initially, no nodes are connected. We keep on adding edges in increasing order of distance. We choose the distance (say, INLINEFORM1 ) of the first edge which connects two components with different labeled points in them. We consider INLINEFORM2 as a heuristic to the minimum distance between two classes, and arbitrarily set INLINEFORM3 , following INLINEFORM4 rule of normal distribution BIBREF11 . ### Features for Learning Model
We use a wide range of features that suitably represent a paper-reference pair ( INLINEFORM0 ), indicating INLINEFORM1 refers to INLINEFORM2 through reference INLINEFORM3 . These features can be grouped into six general classes. The “reference context” of INLINEFORM0 in INLINEFORM1 is defined by three-sentence window (sentence where INLINEFORM2 occurs and its immediate previous and next sentences). For multiple occurrences, we calculate its average score. We refer to “reference sentence” to indicate the sentence where INLINEFORM3 appears. (i) CF:Alone. It indicates whether INLINEFORM0 is mentioned alone in the reference context or together with other references. (ii) CF:First. When INLINEFORM0 is grouped with others, this feature indicates whether it is mentioned first (e.g., “[2]” is first in “[2,4,6]”). Next four features are based on the occurrence of words in the corresponding lists created manually (see Table TABREF9 ) to understand different aspects. (iii) CF:Relevant. It indicates whether INLINEFORM0 is explicitly mentioned as relevant in the reference context (Rel in Table TABREF9 ). (iv) CF:Recent. It tells whether the reference context indicates that INLINEFORM0 is new (Rec in Table TABREF9 ). (v) CF:Extreme. It implies that INLINEFORM0 is extreme in some way (Ext in Table TABREF9 ). (vi) CF:Comp. It indicates whether the reference context makes some kind of comparison with INLINEFORM0 (Comp in Table TABREF9 ). Note we do not consider any sentiment-based features as suggested by BIBREF6 . It is natural that the high degree of semantic similarity between the contents of INLINEFORM0 and INLINEFORM1 indicates the influence of INLINEFORM2 in INLINEFORM3 . We assume that although the full text of INLINEFORM4 is given, we do not have access to the full text of INLINEFORM5 (may be due to the subscription charge or the unavailability of the older papers). Therefore, we consider only the title of INLINEFORM6 as a proxy of its full text. Then we calculate the cosine-similarity between the title (T) of INLINEFORM7 and (i) SF:TTitle. the title, (ii) SF:TAbs. the abstract, SF:TIntro. the introduction, (iv) SF:TConcl. the conclusion, and (v) SF:TRest. the rest of the sections (sections other than abstract, introduction and conclusion) of INLINEFORM8 . We further assume that the “reference context” (RC) of INLINEFORM0 in INLINEFORM1 might provide an alternate way of summarizing the usage of the reference. Therefore, we take the same similarity based approach mentioned above, but replace the title of INLINEFORM2 with its RC and obtain five more features: (vi) SF:RCTitle, (vii) SF:RCAbs, (viii) SF:RCIntro, (ix) SF:RCConcl and (x) SF:RCRest. If a reference appears multiple times in a citing paper, we consider the aggregation of all INLINEFORM3 s together. The underlying assumption of these features is that a reference which occurs more frequently in a citing paper is more influential than a single occurrence BIBREF8 . We count the frequency of INLINEFORM0 in (i) FF:Whole. the entire content, (ii) FF:Intro. the introduction, (iii) FF:Rel. the related work, (iv) FF:Rest. the rest of the sections (as mentioned in Section UID12 ) of INLINEFORM1 . We also introduce (v) FF:Sec. to measure the fraction of different sections of INLINEFORM2 where INLINEFORM3 occurs (assuming that appearance of INLINEFORM4 in different sections is more influential). These features are further normalized using the number of sentences in INLINEFORM5 in order to avoid unnecessary bias on the size of the paper. Position of a reference in a paper might be a predictive clue to measure the influence BIBREF6 . Intuitively, the earlier the reference appears in the paper, the more important it seems to us. For the first two features, we divide the entire paper into two parts equally based on the sentence count and then see whether INLINEFORM0 appears (i) PF:Begin. in the beginning or (ii) PF:End. in the end of INLINEFORM1 . Importantly, if INLINEFORM2 appears multiple times in INLINEFORM3 , we consider the fraction of times it occurs in each part. For the other two features, we take the entire paper, consider sentences as atomic units, and measure position of the sentences where INLINEFORM0 appears, including (iii) PF:Mean. mean position of appearance, (iv) PF:Std. standard deviation of different appearances. These features are normalized by the total length (number of sentences) of INLINEFORM1 . , thus ranging from 0 (indicating beginning of INLINEFORM2 ) to 1 (indicating the end of INLINEFORM3 ). The linguistic evidences around the context of INLINEFORM0 sometimes provide clues to understand the intrinsic influence of INLINEFORM1 on INLINEFORM2 . Here we consider word level and structural features. (i) LF:NGram. Different levels of INLINEFORM0 -grams (1-grams, 2-grams and 3-grams) are extracted from the reference context to see the effect of different word combination BIBREF13 . (ii) LF:POS. Part-of-speech (POS) tags of the words in the reference sentence are used as features BIBREF14 . (iii) LF:Tense. The main verb of the reference sentence is used as a feature BIBREF3 . (iv) LF:Modal. The presence of modal verbs (e.g., “can”, “may”) often indicates the strength of the claims. Hence, we check the presence of the modal verbs in the reference sentence. (v) LF:MainV. We use the main-verb of the reference sentence as a direct feature in the model. (vi) LF:hasBut. We check the presence of conjunction “but”, which is another clue to show less confidence on the cited paper. (vii) LF:DepRel. Following BIBREF13 we use all the dependencies present in the reference context, as given by the dependency parser BIBREF15 . (viii) LF:POSP. BIBREF16 use seven regular expression patterns of POS tags to capture syntactic information; then seven boolean features mark the presence of these patterns. We also utilize the same regular expressions as shown below with the examples (the empty parenthesis in each example indicates the presence of a reference token INLINEFORM0 in the corresponding sentence; while few examples are complete sentences, few are not): “.*\\(\\) VV[DPZN].*”: Chen () showed that cohesion is held in the vast majority of cases for English-French. “.*(VHP|VHZ) VV.*”: while Cherry and Lin () have shown it to be a strong feature for word alignment... “.*VH(D|G|N|P|Z) (RB )*VBN.*”: Inducing features for taggers by clustering has been tried by several researchers (). “.*MD (RB )*VB(RB )* VVN.*”: For example, the likelihood of those generative procedures can be accumulated to get the likelihood of the phrase pair (). “[∧ IW.]*VB(D|P|Z) (RB )*VV[ND].*”: Our experimental set-up is modeled after the human evaluation presented in (). “(RB )*PP (RB )*V.*”: We use CRF () to perform this tagging. “.*VVG (NP )*(CC )*(NP ).*”: Following (), we provide the annotators with only short sentences: those with source sentences between 10 and 25 tokens long. These are all considered as Boolean features. For each feature, we take all the possible evidences from all paper-reference pairs and prepare a vector. Then for each pair, we check the presence (absence) of tokens for the corresponding feature and mark the vector accordingly (which in turn produces a set of Boolean features). This group provides other factors to explain why is a paper being cited. (i) MS:GCount. To answer whether a highly-cited paper has more academic influence on the citing paper than the one which is less cited, we measure the number of other papers (except INLINEFORM0 ) citing INLINEFORM1 . (ii) MS:SelfC. To see the effect of self-citation, we check whether at least one author is common in both INLINEFORM0 and INLINEFORM1 . (iii) MG:Time. The fact that older papers are rarely cited, may not stipulate that these are less influential. Therefore, we measure the difference of the publication years of INLINEFORM0 and INLINEFORM1 . (iv) MG:CoCite. It measures the co-citation counts of INLINEFORM0 and INLINEFORM1 defined by INLINEFORM2 , which in turn answers the significance of reference-based similarity driving the academic influence BIBREF18 . Following BIBREF19 , we further make one step normalization and divide each feature by its maximum value in all the entires. ### Dataset and Annotation
We use the AAN dataset BIBREF20 which is an assemblage of papers included in ACL related venues. The texts are preprocessed where sentences, paragraphs and sections are properly separated using different markers. The filtered dataset contains 12,843 papers (on average 6.21 references per paper) and 11,092 unique authors. Next we use Parscit BIBREF21 to identify the reference contexts from the dataset and then extract the section headings from all the papers. Then each section heading is mapped into one of the following broad categories using the method proposed by BIBREF22 : Abstract, Introduction, Related Work, Conclusion and Rest. Dataset Labeling. The hardest challenge in this task is that there is no publicly available dataset where references are annotated with the intensity value. Therefore, we constructed our own annotated dataset in two different ways. (i) Expert Annotation: we requested members of our research group to participate in this survey. To facilitate the labeling process, we designed a portal where all the papers present in our dataset are enlisted in a drop-down menu. Upon selecting a paper, its corresponding references were shown with five possible intensity values. The citing and cited papers are also linked to the original texts so that the annotators can read the original papers. A total of 20 researchers participated and they were asked to label as many paper-reference pairs as they could based on the definitions of the intensity provided in Section SECREF2 . The annotation process went on for one month. Out of total 1640 pairs annotated, 1270 pairs were taken such that each pair was annotated by at least two annotators, and the final intensity value of the pair was considered to be the average of the scores. The Pearson correlation and Kendell's INLINEFORM0 among the annotators are INLINEFORM1 and INLINEFORM2 respectively. (ii) Author Annotation: we believe that the authors of a paper are the best experts to judge the intensity of references present in the paper. With this intension, we launched a survey where we requested the authors whose papers are present in our dataset with significant numbers. We designed a web portal in similar fashion mentioned earlier; but each author was only shown her own papers in the drop-down menu. Out of 35 requests, 22 authors responded and total 196 pairs are annotated. This time we made sure that each paper-reference pair was annotated by only one author. The percentages of labels in the overall annotated dataset are as follows: 1: 9%, 2: 74%, 3: 9%, 4: 3%, 5: 4%. ### Experimental Results
In this section, we start with analyzing the importance of the feature sets in predicting the reference intensity, followed by the detailed results. Feature Analysis. In order to determine which features highly determine the gold-standard labeling, we measure the Pearson correlation between various features and the ground-truth labels. Figure FIGREF27 (a) shows the average correlation for each feature group, and in each group the rank of features based on the correlation is shown in Figure FIGREF27 (b). Frequency-based features (FF) turn out to be the best, among which FF:Rest is mostly correlated. This set of features is convenient and can be easily computed. Both CF and LF seem to be equally important. However, INLINEFORM0 tends to be less important in this task. Results of Predictive Models. For the purpose of evaluation, we report the average results after 10-fold cross-validation. Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 . Three metrics are used to compare the results of the competing models with the annotated labels: Root Mean Square Error (RMSE), Pearson's correlation coefficient ( INLINEFORM0 ), and coefficient of determination ( INLINEFORM1 ). Table TABREF28 shows the performance of the competing models. We incrementally include each feature set into GraLap greedily on the basis of ranking shown in Figure FIGREF27 (a). We observe that GraLap with only FF outperforms SVR+O with 41% improvement of INLINEFORM0 . As expected, the inclusion of PF into the model improves the model marginally. However, the overall performance of GraLap is significantly higher than any of the baselines ( INLINEFORM1 ). ### Applications of Reference Intensity
In this section, we provide four different applications to show the use of measuring the intensity of references. To this end, we consider all the labeled entries for training and run GraLap to predict the intensity of rest of the paper-reference pairs. ### Discovering Influential Articles
Influential papers in a particular area are often discovered by considering equal weights to all the citations of a paper. We anticipate that considering the reference intensity would perhaps return more meaningful results. To show this, Here we use the following measures individually to compute the influence of a paper: (i) RawCite: total number of citations per paper, (ii) RawPR: we construct a citation network (nodes: papers, links: citations), and measure PageRank BIBREF24 of each node INLINEFORM0 : INLINEFORM1 ; where, INLINEFORM2 , the damping factor, is set to 0.85, INLINEFORM3 is the total number of nodes, INLINEFORM4 is the set of nodes that have edges to INLINEFORM5 , and INLINEFORM6 is the set of nodes that INLINEFORM7 has an edge to, (iii) InfCite: the weighted version of RawCite, measured by the sum of intensities of all citations of a paper, (iv) InfPR: the weighted version of RawPR: INLINEFORM8 , where INLINEFORM9 indicates the influence of a reference. We rank all the articles based on these four measures separately. Table TABREF32 (a) shows the Spearman's rank correlation between pair-wise measures. As expected, (i) and (ii) have high correlation (same for (iii) and (iv)), whereas across two types of measures the correlation is less. Further, in order to know which measure is more relevant, we conduct a subjective study where we select top ten papers from each measure and invite the experts (not authors) who annotated the dataset, to make a binary decision whether a recommended paper is relevant. . The average pair-wise inter-annotator's agreement (based on Cohen's kappa BIBREF25 ) is INLINEFORM10 . Table TABREF32 (b) presents that out of 10 recommendations of InfPR, 7 (5) papers are marked as influential by majority (all) of the annotators, which is followed by InfCite. These results indeed show the utility of measuring reference intensity for discovering influential papers. Top three papers based on InfPR from the entire dataset are shown in Table TABREF33 . ### Identifying Influential Authors
H-index, a measure of impact/influence of an author, considers each citation with equal weight BIBREF29 . Here we incorporate the notion of reference intensity into it and define hif-index. Definition 2 An author INLINEFORM0 with a set of papers INLINEFORM1 has an hif-index equals to INLINEFORM2 , if INLINEFORM3 is the largest value such that INLINEFORM4 ; where INLINEFORM5 is the sum of intensities of all citations of INLINEFORM6 . We consider 37 ACL fellows as the list of gold-standard influential authors. For comparative evaluation, we consider the total number of papers (TotP), total number of citations (TotC) and average citations per paper (AvgC) as three competing measures along with h-index and hif-index. We arrange all the authors in our dataset in decreasing order of each measure. Figure FIGREF36 (a) shows the Spearman's rank correlation among the common elements across pair-wise rankings. Figure FIGREF36 (b) shows the INLINEFORM0 for five competing measures at identifying ACL fellows. We observe that hif-index performs significantly well with an overall precision of INLINEFORM1 , followed by AvgC ( INLINEFORM2 ), h-index ( INLINEFORM3 ), TotC ( INLINEFORM4 ) and TotP ( INLINEFORM5 ). This result is an encouraging evidence that the reference-intensity could improve the identification of the influential authors. Top three authors based on hif-index are shown in Table TABREF33 . ### Effect on Recommendation System
Here we show the effectiveness of reference-intensity by applying it to a real paper recommendation system. To this end, we consider FeRoSA BIBREF30 , a new (probably the first) framework of faceted recommendation for scientific articles, where given a query it provides facet-wise recommendations with each facet representing the purpose of recommendation BIBREF30 . The methodology is based on random walk with restarts (RWR) initiated from a query paper. The model is built on AAN dataset and considers both the citation links and the content information to produce the most relevant results. Instead of using the unweighted citation network, here we use the weighted network with each edge labeled by the intensity score. The final recommendation of FeRoSA is obtained by performing RWR with the transition probability proportional to the edge-weight (we call it Inf-FeRoSA). We observe that Inf-FeRoSA achieves an average precision of INLINEFORM0 at top 10 recommendations, which is 14% higher then FeRoSA while considering the flat version and 12.34% higher than FeRoSA while considering the faceted version. ### Detecting Citation Stacking
Recently, Thomson Reuters began screening for journals that exchange large number of anomalous citations with other journals in a cartel-like arrangement, often known as “citation stacking” BIBREF31 , BIBREF32 . This sort of citation stacking is much more pernicious and difficult to detect. We anticipate that this behavior can be detected by the reference intensity. Since the AAN dataset does not have journal information, we use DBLP dataset BIBREF8 where the complete metadata information (along with reference contexts and abstract) is available, except the full content of the paper (559,338 papers and 681 journals; more details in BIBREF33 ). From this dataset, we extract all the features mentioned in Section SECREF10 except the ones that require full text, and run our model using the existing annotated dataset as training instances. We measure the traditional impact factor ( INLINEFORM0 ) of the journals and impact factor after considering the reference intensity ( INLINEFORM1 ). Figure FIGREF39 (a) shows that there are few journals whose INLINEFORM2 significantly deviates (3 INLINEFORM3 from the mean) from INLINEFORM4 ; out of the suspected journals 70% suffer from the effect of self-journal citations as well (shown in Figure FIGREF39 (b)), example including Expert Systems with Applications (current INLINEFORM5 of INLINEFORM6 ). One of the future work directions would be to predict such journals as early as possible after their first appearance. ### Related Work
Although the citation count based metrics are widely accepted BIBREF5 , BIBREF34 , the belief that mere counting of citations is dubious has also been a subject of study BIBREF35 . BIBREF36 was the first who explained the reasons of citing a paper. BIBREF37 introduced a method for the rapid development of complex rule bases for classifying text segments. BIBREF16 focused on a less manual approach by learning domain-insensitive features from textual, physical, and syntactic aspects To address concerns about h-index, different alternative measures are proposed BIBREF38 . However they too could benefit from filtering or weighting references with a model of influence. Several research have been proposed to weight citations based on factors such as the prestige of the citing journal BIBREF39 , BIBREF40 , prestige of an author BIBREF41 , frequency of citations in citing papers BIBREF42 . Recently, BIBREF4 proposed a SVR based approach to measure the intensity of citations. Our methodology differs from this approach in at lease four significant ways: (i) they used six very shallow level features; whereas we consider features from different dimensions, (ii) they labeled the dataset by the help of independent annotators; here we additionally ask the authors of the citing papers to identify the influential references which is very realistic BIBREF43 ; (iii) they adopted SVR for labeling, which does not perform well for small training instances; here we propose GraLap , designed specifically for small training instances; (iv) four applications of reference intensity mentioned here are completely new and can trigger further to reassessing the existing bibliometrics. ### Conclusion
We argued that the equal weight of all references might not be a good idea not only to gauge success of a research, but also to track follow-up work or recommending research papers. The annotated dataset would have tremendous potential to be utilized for other research. Moreover, GraLap can be used for any semi-supervised learning problem. Each application mentioned here needs separate attention. In future, we shall look into more linguistic evidences to improve our model. Figure 2: Pearson correlation coefficient between the features and the gold-standard annotations. (a) Group-wise average correlation, and (b) ranking of features in each group based on the correlation. Table 2: Performance of the competing models. The features are added greedily into the GraLap model. Figure 3: (a) Sprearman’s rank correlation among pair-wise ranks, and (b) the performance of all the measures. Table 3: (a) Spearman’s rank correlation among influence measures and (b) expert evaluation of the ranked results (for top 10 recommendations). Figure 4: Correlation between (a) IF and IFif and (b) number of citations before and after removing self-journal citations. | (i) Uniform, (ii) SVR+W, (iii) SVR+O, (iv) C4.5SSL, (v) GLM |
How do the crewmen view the tension between Winkelmann and Bailey?
A. They are repulsed by the Captain's condescending remarks
B. They are thankful that the Captain's cruelty influences Bailey to create more palatable food
C. They are determined to stay out of the conflict, for fear of being punished by the Captain
D. They are concerned that Bailey will mutiny by refusing to fulfill his job responsibilities
| GOURMET By ALLEN KIM LANG [Transcriber's Note: This etext was produced from Galaxy Magazine April 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] This was the endless problem of all spaceship cooks: He had to feed the men tomorrow on what they had eaten today! Unable to get out to the ballgame and a long way off from the girls, men on ships think about, talk about, bitch about their food. It's true that Woman remains a topic of thoughtful study, but discussion can never replace practice in an art. Food, on the other hand, is a challenge shipmen face three times a day, so central to their thoughts that a history of sea-faring can be read from a commissary list. In the days when salt-sea sailors were charting islands and spearing seals, for example, the fo'c's'le hands called themselves Lobscousers, celebrating the liquid hash then prominent in the marine menu. The Limey sailor got the name of the anti-scorbutic citrus squeezed into his diet, a fruit known to us mariners of a more sophisticated age only as garnish for our groundside gin-and-tonic. And today we Marsmen are called Slimeheads, honoring in our title the Chlorella and Scenedesmus algae that, by filling up the spaces within, open the road to the larger Space without. Should any groundsman dispute the importance of belly-furniture in history—whether it be exterminating whales, or introducing syphilis to the Fiji Islanders, or settling the Australian littoral with cross-coves from Middlesex and Hampshire—he is referred to the hundred-and-first chapter of Moby Dick , a book spooled in the amusement tanks of all but the smallest spacers. I trust, however, that no Marsman will undertake to review this inventory of refreshment more than a week from groundfall. A catalogue of sides of beef and heads of Leyden cheese and ankers of good Geneva would prove heavy reading for a man condemned to snack on the Chlorella-spawn of cis-Martian space. The Pequod's crew ate wormy biscuit and salt beef. Nimitz's men won their war on canned pork and beans. The Triton made her underwater periplus of Earth with a galley stocked with frozen pizza and concentrated apple-juice. But then, when sailors left the seas for the skies, a decline set in. The first amenity of groundside existence to be abandoned was decent food. The earliest men into the vacuum swallowed protein squeezings from aluminum tubes, and were glad enough to drop back to the groundsman's diet of steak and fried potatoes. Long before I was a boy in Med School, itching to look at black sky through a view-port, galley science had fulfilled the disgusting exordium of Isaiah 36:12, to feed the Slimeheads for breakfast today what was day-before-yesterday's table-scraps and jakes-water. The Ship's Cook, the man who accomplishes the daily miracle of turning offal into eatables, is in many ways the most vital man aboard a spacer. He can make morale or foment a mutiny. His power is paramount. Slimeheads remember the H. M. S. Ajax fiasco, for example, in which a galleyman leveled his Chlorella tanks with heavy water from the ship's shielding. Four officers and twenty-one Other Ranks were rescued from the Ajax in deep space, half dead from deuterium poisoning. We think of the Benjo Maru incident, too, caused by a Ship's Cook who allowed his algaeal staff-of-life to become contaminated with a fast-growing Saccharomycodes yeast. The Japanese vessel staggered to her pad at Piano West after a twenty-week drunk: the alien yeast had got into the stomach of every man aboard, where it fermented each subsequent bite he ate to a superior grade of sake . And for a third footnote to the ancient observation, "God sends food, and the Devil sends cooks," Marsmen will recall what happened aboard my ship the Charles Partlow Sale . The Sale blasted off from Brady Station in the middle of August, due in at Piano West in early May. In no special hurry, we were taking the low-energy route to Mars, a pathway about as long in time as the human period of gestation. Our cargo consisted mostly of Tien-Shen fir seedlings and some tons of an arctic grass-seed—these to be planted in the maria to squeeze out the native blue bugberry vines. We had aboard the Registry minimum of six men and three officers. Ship's Surgeon was myself, Paul Vilanova. Our Captain was Willy Winkelmann, the hardest man in space and very likely the fattest. Ship's Cook was Robert Bailey. Cooking aboard a spacer is a job combining the more frustrating tensions of biochemistry, applied mycology, high-speed farming, dietetics and sewage engineering. It's the Cook's responsibility to see that each man aboard gets each day no less than five pounds of water, two pounds of oxygen, and one-and-a-half pounds of dry food. This isn't just a paragraph from the Spacer Union Contract. It's a statement of the least fuel a man can run on. Twelve tons of water, oxygen, and food would have filled the cargo compartments to bursting, and left a small ship like the C. P. Sale no reason to reach for Mars. By allowing a colony of Chlorella algae to work over our used air, water and other effluvia, though, three tons of metabolites would see us through from Brady Station to Piano West and back. Recycling was the answer. The molecule of carbohydrate, fat, protein or mineral that didn't feed the crew fed the algae. And the algae fed us. All waste was used to fertilize our liquid fields. Even the stubble from our 2,680 shaves and the clippings from our 666 haircuts en route and back would be fed into the Chlorella tanks. Human hair is rich in essential amino acids. The algae—dried by the Cook, bleached with methyl alcohol to kill the smell and make the residue more digestible, disguised and seasoned in a hundred ways—served as a sort of meat-and-potatoes that never quite wore out. Our air and water were equally immortal. Each molecule of oxygen would be conversant with the alveoli of every man aboard by the end of our trip. Every drop of water would have been intimate with the glomeruli of each kidney on the ship before we grounded in. Groundling politicians are right enough when they say that we spacers are a breed apart. We're the one race of men who can't afford the luxury of squeamishness. Though I'm signed aboard as Ship's Surgeon, I seldom lift a knife in space. My employment is more in the nature of TS-card-puncher extraordinary. My duties are to serve as wailing-wall, morale officer, guardian of the medicinal whiskey and frustrator of mutual murder. Generally the man aboard who'd serve as the most popular murder-victim is the Cook. This trip, the-man-you-love-to-hate was our Captain. If the Cook hadn't problems enough with the chemical and psychic duties of his office, Winkelmann supplied the want. Captain Willy Winkelmann was the sort of man who, if he had to go into space at all, had best do so alone. If the Prussians had a Marine Corps, Winkelmann would have done splendidly as Drill Instructor for their boot camp. His heart was a chip of helium ice, his voice dripped sarcastic acid. The planet Earth was hardly large enough to accommodate a wart as annoying as Willy Winkelmann. Cheek-by-jowl every day in a nacelle the size of a Pullman car, our Captain quickly established himself as a major social hemorrhoid. The Captain's particular patsy was, of course, young Bailey the Cook. It was Winkelmann who saw humorous possibilities in the entry, "Bailey, Robert," on Ship's Articles. He at once renamed our unfortunate shipmate "Belly-Robber." It was Winkelmann who discussed haut cuisine and the properties of the nobler wines while we munched our algaeburgers and sipped coffee that tasted of utility water. And it was Captain Willy Winkelmann who never referred to the ship's head by any other name than The Kitchen Cabinet. Bailey tried to feed us by groundside standards. He hid the taste of synthetic methionine—an essential amino acid not synthesized by Chlorella—by seasoning our algaeal repasts with pinches of oregano and thyme. He tinted the pale-green dollops of pressed Chlorella pink, textured the mass to the consistency of hamburger and toasted the slabs to a delicate brown in a forlorn attempt to make mock-meat. For dessert, he served a fudge compounded from the dextrose-paste of the carbohydrate recycler. The crew thanked him. The Captain did not. "Belly-Robber," he said, his tone icy as winter wind off the North Sea, "you had best cycle this mess through the tanks again. There is a pun in my home country: Mensch ist was er isst. It means, you are what you eat. I think you are impertinent to suggest I should become this Schweinerei you are feeding me." Captain Winkelmann blotted his chin with his napkin, heaved his bulk up from the table, and climbed up the ladder from the dining-cubby. "Doc, do you like Winkelmann?" the Cook asked me. "Not much," I said. "I suspect that the finest gift our Captain can give his mother is to be absent from her on Mother's Day. But we've got to live with him. He's a good man at driving a ship." "I wish he'd leave off driving this Cook," Bailey said. "The fat swine!" "His plumpness is an unwitting tribute to your cooking, Bailey," I said. "He eats well. We all do. I've dined aboard a lot of spacers in my time, and I'll testify that you set a table second to none." Bailey took a handful of dried Chlorella from a bin and fingered it. It was green, smelled of swamp, and looked appetizing as a bedsore. "This is what I have to work with," he said. He tossed the stuff back into its bin. "In Ohio, which is my home country, in the presence of ladies, we'd call such garbage Horse-Leavings." "You'll never make Winkelmann happy," I said. "Even the simultaneous death of all other human beings could hardly make him smile. Keep up the good work, though, and you'll keep our Captain fat." Bailey nodded from his one-man cloud of gloom. I got a bottle of rye from Medical Stores and offered him a therapeutic draught. The Cook waved my gift aside. "Not now, Doc," he said. "I'm thinking about tomorrow's menu." The product of Bailey's cerebrations was on the mess table at noon the next day. We were each served an individual head of lettuce, dressed with something very like vinegar and oil, spiced with tiny leaves of burnet. How Bailey had constructed those synthetic lettuces I can only guess: the hours spent preparing a green Chlorella paste, rolling and drying and shaping each artificial leaf, the fitting together of nine heads like crisp, three-dimensional jigsaw puzzles. The pièce de résistance was again a "hamburger steak;" but this time the algaeal mass that made it up was buried in a rich, meaty gravy that was only faintly green. The essence-of-steak used in these Chlorella cutlets had been sprinkled with a lavish hand. Garlic was richly in evidence. "It's so tender," the radioman joked, "that I can hardly believe it's really steak." Bailey stared across the dining-cubby toward Winkelmann, silently imploring the Captain's ratification of his masterpiece. The big man's pink cheeks bulged and jumped with his chewing. He swallowed. "Belly-Robber," Winkelmann said, "I had almost rather you served me this pond-scum raw than have it all mucked-up with synthetic onions and cycler-salt." "You seem able enough to choke down Bailey's chow, Captain," I said. I gazed at Winkelmann's form, bulbous from a lifetime of surfeit feeding. "Yes, I eat it," the Captain said, taking and talking through another bite. "But I eat only as a man in the desert will eat worms and grasshoppers, to stay alive." "Sir, what in heaven's name do you expect from me?" Bailey pleaded. "Only good food," Winkelmann mumbled through his mouthful of disguised algae. He tapped his head with a finger. "This—the brain that guides the ship—cannot be coaxed to work on hog-slop. You understand me, Belly-Robber?" Bailey, his hands fisted at his sides, nodded. "Yes, sir. But I really don't know what I can do to please you." "You are a spacer and a Ship's Cook, not a suburban Hausfrau with the vapors," Winkelmann said. "I do not expect from you hysterics, tantrums or weeping. Only—can you understand this, so simple?—food that will keep my belly content and my brain alive." "Yes, sir," Bailey said, his face a picture of that offense the British term Dumb Insolence. Winkelmann got up and climbed the ladder to the pilot-cubicle. I followed him. "Captain," I said, "you're driving Bailey too hard. You're asking him to make bricks without straw." Winkelmann regarded me with his pale-blue stare. "You think, Doctor, that my cruelty to the Belly-Robber is the biliousness of a middle-aged man?" "Frankly, I can't understand your attitude at all," I said. "You accuse me of driving a man to make bricks without straw," Winkelmann said. "Very well, Doctor. It is my belief that if the Pharaoh's taskmaster had had my firmness of purpose, the Children of Israel would have made bricks with stubble. Necessity, Doctor, is the mother of invention. I am Bailey's necessity. My unkindnesses make him uncomfortable, I doubt that not. But I am forcing him to experiment, to improvise, to widen the horizons of his ingenuity. He will learn somehow to bring good food from Chlorella tanks." "You're driving him too hard, Sir," I said. "He'll crack." "Bailey will have some fifty thousand dollars' salary waiting when we ground at Brady Station," Captain Winkelmann said. "So much money buys many discomforts. That will be all, Doctor Vilanova." "Crew morale on the ship...." I began. "That will be all, Doctor Vilanova," Captain Winkelmann repeated. Bailey grew more silent as we threaded our way along the elliptical path to Mars. Each meal he prepared was a fresh attempt to propitiate the appetite of our splenetic Captain. Each such offering was condemned by that heartless man. Bailey began to try avoiding the Captain at mealtimes, but was frustrated by Winkelmann's orders. "Convey my compliments to the Chef, please," the Captain would instruct one of the crew, "and ask him to step down here a moment." And the Cook would cheerlessly appear in the dining-cubby, to have his culinary genius acidly called in question again. I myself do not doubt that Bailey was the finest Cook ever to go into Hohmann orbit. His every meal established a higher benchmark in brilliant galleymanship. We were served, for instance, an ersatz hot turkey supreme. The cheese-sauce was almost believable, the Chlorella turkey-flesh was white and tender. Bailey served with this delicacy a grainy and delicious "cornbread," and had extracted from his algae a lipid butter-substitute that soaked into the hot "bread" with a genuinely dairy smell. "Splendid, Bailey," I said. "We are not amused," said Captain Winkelmann, accepting a second helping of the pseudo-turkey. "You are improving, Belly-Robber, but only arithmetically. Your first efforts were so hideous as to require a geometric progression of improving excellence to raise them to mere edibility. By the time we are halfway 'round the Sun, I trust you will have learned to cook with the competence of a freshman Home Economics student. That will be all, Bailey." The crew and my fellow-officers were amused by Winkelmann's riding of Bailey; they were in addition gratified that the battle between their Captain and their Cook served to feed them so well. Most spacers embark on an outward voyage somewhat plump, having eaten enough on their last few days aground to smuggle several hundred calories of fat and many memories of good food aboard with them. This trip, none of the men had lost weight during the first four months in space. Winkelmann, indeed, seemed to have gained. His uniform was taut over his plump backside, and he puffed a bit up the ladders. I was considering suggesting to our Captain that he curtail his diet for reasons of health, a bit of advice that would have stood unique in the annals of space medicine, when Winkelmann produced his supreme insult to our Cook. Each man aboard a spacer is allowed ten kilograms of personal effects besides his uniforms, these being considered Ship's Furnishing. As his rank and responsibility merit, the Captain is allowed double this ration. He may thus bring aboard with him some forty-five pounds of books, playing-cards, knitting-wool, whiskey or what have you to help him while away the hours between the planets. Bailey, I knew for a fact, had used up his weight-allowance in bringing aboard a case of spices: marjoram and mint, costmary, file powder, basil and allspice, and a dozen others. Captain Winkelmann was not a reader, and had brought no books. Cards interested him not at all, as card-playing implies a sociability alien to his nature. He never drank aboard ship. I had supposed that he'd exercised his option of returning his personal-effects weight allowance to the owners for the consideration of one hundred dollars a kilogram. To collect the maximum allowance, spacers have been known to come aboard their ship mother-naked. But this was not the case with Winkelmann. His personal-effects baggage, an unlabeled cardboard box, appeared under the table at noon mess some hundred days out from Piano West. Winkelmann rested his feet on the mysterious box as he sat to eat. "What disgusting form does the ship's garbage appear in today, Belly-Robber?" he asked the Cook. Bailey frowned, but kept his temper, an asceticism in which by now he'd had much practice. "I've been working on the problem of steak, Sir," he said. "I think I've whipped the taste; what was left was to get the texture steak-like. Do you understand, Sir?" "I understand," Winkelmann growled. "You intend that your latest mess should feel like steak to the mouth, and not like baby-food. Right?" "Yes, Sir," Bailey said. "Well, I squeezed the steak-substrate—Chlorella, of course, with all sorts of special seasonings—through a sieve, and blanched the strands in hot algaeal oil. Then I chopped those strands to bits and rolled them out. Voila! I had something very close in texture to the muscle-fibers of genuine meat." "Remarkable, Bailey," I said. "It rather throws me off my appetite to hear how you muddle about with our food," the Captain said, his jowls settling into an expression of distaste. "It's quite all right to eat lobster, for example, but I never cared to see the ugly beast boiled before my eyes. Detail spoils the meal." Bailey lifted the cover off the electric warming-pan at the center of the table and tenderly lifted a small "steak" onto each of our plates. "Try it," he urged the Captain. Captain Winkelmann sliced off a corner of his algaeal steak. The color was an excellent medium-rare, the odor was the rich smell of fresh-broiled beef. Winkelmann bit down, chewed, swallowed. "Not too bad, Belly-Robber," he said, nodding. Bailey grinned and bobbed his head, his hands folded before him in an ecstasy of pleasure. A kind word from the Captain bettered the ruffles-and-flourishes of a more reasonable man. "But it still needs something ... something," Winkelmann went on, slicing off another portion of the tasty Chlorella. "Aha! I have it!" "Yes, Sir?" Bailey asked. "This, Belly-Robber!" Winkelmann reached beneath the mess-table and ripped open his cardboard carton. He brought out a bottle and unscrewed the cap. "Ketchup," he said, splattering the red juice over Bailey's masterpiece. "The scarlet burial-shroud for the failures of Cooks." Lifting a hunk of the "steak," streaming ketchup, to his mouth, Winkelmann chewed. "Just the thing," he smiled. "Damn you!" Bailey shouted. Winkelmann's smile flicked off, and his blue eyes pierced the Cook. "... Sir," Bailey added. "That's better," Winkelmann said, and took another bite. He said meditatively, "Used with caution, and only by myself, I believe I have sufficient ketchup here to see me through to Mars. Please keep a bottle on the table for all my future meals, Belly-Robber." "But, Sir...." Bailey began. "You must realize, Belly-Robber, that a dyspeptic Captain is a threat to the welfare of his ship. Were I to continue eating your surrealistic slops for another hundred days, without the small consolation of this sauce I had the foresight to bring with me, I'd likely be in no condition to jet us safely down to the Piano West pad. Do you understand, Belly-Robber?" he demanded. "I understand that you're an ungrateful, impossible, square-headed, slave-driving...." "Watch your noun," Winkelmann cautioned the Cook. "Your adjectives are insubordinate; your noun might prove mutinous." "Captain, you've gone too far," I said. Bailey, his fists knotted, was scarlet, his chest heaving with emotion. "Doctor, I must point out to you that it ill behooves the Ship's Surgeon to side with the Cook against the Captain," Winkelmann said. "Sir, Bailey has tried hard to please you," I said. "The other officers and the men have been more than satisfied with his work." "That only suggests atrophy of their taste buds," Winkelmann said. "Doctor, you are excused. As are you, Belly-Robber," he added. Bailey and I climbed from the mess compartment together. I steered him to my quarters, where the medical supplies were stored. He sat on my bunk and exploded into weeping, banging his fists against the metal bulkhead. "You'll have that drink now," I said. "No, dammit!" he shouted. "Orders," I said. I poured us each some fifty cc's of rye. "This is therapy, Bailey," I told him. He poured the fiery stuff down his throat like water and silently held out his glass for a second. I provided it. After a few minutes Bailey's sobbing ceased. "Sorry, Doc," he said. "You've taken more pressure than most men would," I said. "Nothing to be ashamed of." "He's crazy. What sane man would expect me to dip Wiener schnitzel and sauerkraut and Backhahndl nach suddeutscher Art out of an algae tank? I've got nothing but microscopic weeds to cook for him! Worn-out molecules reclaimed from the head; packaged amino acid additives. And he expects meals that would take the blue ribbon at the annual banquet of the Friends of Escoffier!" "Yours is an ancient plaint, Bailey," I said. "You've worked your fingers to the bone, slaving over a hot stove, and you're not appreciated. But you're not married to Winkelmann, remember. A year from now you'll be home in Ohio, fifty grand richer, set to start that restaurant of yours and forget about our fat Flying Dutchman." "I hate him," Bailey said with the simplicity of true emotion. He reached for the bottle. I let him have it. Sometimes alcohol can be an apt confederate of vis medicatrix naturae , the healing power of nature. Half an hour later I strapped Bailey into his bunk to sleep it off. That therapeutic drunk seemed to be just what he'd needed. For morning mess the next day we had a broth remarkable in horribleness, a pottage or boiled Chlorella vulgaris that looked and tasted like the vomit of some bottom-feeding sea-beast. Bailey, red-eyed and a-tremble, made no apology, and stared at Winkelmann as though daring him to comment. The Captain lifted a spoonful of the disgusting stuff to his lips, smacked and said, "Belly-Robber, you're improving a little at last." Bailey nodded and smiled. "Thank you, Sir," he said. I smiled, too. Bailey had conquered himself. His psychic defenses were now strong enough to withstand the Captain's fiercest assaults of irony. Our food would likely be bad the rest of this trip, but that was a price I was willing to pay for seeing destroyed the Willy Winkelmann theory of forcing a Cook to make bricks without straw. The Captain had pushed too hard. He'd need that ketchup for the meals to come, I thought. Noon mess was nearly as awful as breakfast had been. The coffee tasted of salt, and went largely undrunk. The men in the mess compartment were vehement in their protests, blaming the Captain, in his absence, for the decline in culinary standards. Bailey seemed not to care. He served the algaeburgers with half a mind, and hurried back into his galley oblivious of the taunts of his crewmates. There being only three seats in the Sale's mess compartment, we ate our meals in three shifts. That evening, going down the ladder to supper, my nose was met with a spine-tingling barbecue tang, a smell to make a man think of gray charcoal glowing in a picnic brazier, of cicadas chirping and green grass underfoot, of the pop and hiss of canned beer being church-keyed. "He's done it, Doc!" one of the first-shift diners said. "It actually tastes of food!" "Then he's beat the Captain at his game," I said. "The Dutchman won't want to mess ketchup on these steaks," the crewman said. I sat, unfolded my napkin, and looked with hope to the electric warming-pan at the center of the table. Bailey served the three of us with the small "steaks." Each contained about a pound of dried Chlorella, I judged, teasing mine with my fork. But they were drenched in a gravy rich as the stuff grandma used to make in her black iron skillet, peppery and seasoned with courageous bits of garlic. I cut a bit from my steak and chewed it. Too tender, of course; there are limits to art. But the pond-scum taste was gone. Bailey appeared in the galley door. I gestured for him to join me. "You've done it, Bailey," I said. "Every Slimehead in orbit will thank you for this. This is actually good ." "Thanks, Doc," Bailey said. I smiled and took another bite. "You may not realize it, Bailey; but this is a victory for the Captain, too. He drove you to this triumph; you couldn't have done it without him." "You mean he was just whipping me on, trying to make me do better?" Bailey asked. "He was driving you to do the impossible," I said; "and you did it. Our Captain may be a hard man, Bailey; but he did know how to coax maximum performance out of his Ship's Cook." Bailey stood up. "Do you like Captain Winkelmann, Doctor?" he asked. I thought about his question a moment. Winkelmann was good at his job. He persuaded his men by foul means, true; but it was all for the good of the ship and his crew. "Do I like Captain Winkelmann?" I asked, spearing another piece of my artificial steak. "Bailey, I'm afraid I'll have to admit that I do." Bailey smiled and lifted a second steak from the warming-pan onto my plate. "Then have another piece," he said. | B. They are thankful that the Captain's cruelty influences Bailey to create more palatable food |
What Was AMCOR's Adjusted Non GAAP EBITDA for FY 2023 | Evidence 0:
Twelve Months Ended June 30, 2022
Twelve Months Ended June 30, 2023
($ million)
EBITDA
EBIT
Net
Income
EPS
(Diluted
US
cents)(1)
EBITDA
EBIT
Net
Income
EPS
(Diluted
US
cents)(1)
Net income attributable to Amcor
805
805
805
52.9
1,048
1,048
1,048
70.5
Net income attributable to non-controlling
interests
10
10
10
10
Tax expense
300
300
193
193
Interest expense, net
135
135
259
259
Depreciation and amortization
579
569
EBITDA, EBIT, Net income and EPS
1,829
1,250
805
52.9
2,080
1,510
1,048
70.5
2019 Bemis Integration Plan
37
37
37
2.5
Net loss on disposals(2)
10
10
10
0.7
Impact of hyperinflation
16
16
16
1.0
24
24
24
1.9
Property and other losses, net(3)
13
13
13
0.8
2
2
2
0.1
Russia-Ukraine conflict impacts(4)
200
200
200
13.2
(90)
(90)
(90)
(6.0)
Pension settlements
8
8
8
0.5
5
5
5
0.3
Other
4
4
4
0.3
(3)
(3)
(3)
(0.3)
Amortization of acquired intangibles (5)
163
163
10.7
160
160
10.8
Tax effect of above items
(32)
(2.1)
(57)
(4.0)
Adjusted EBITDA, EBIT, Net income and EPS
2,117
1,701
1,224
80.5
2,018
1,608
1,089
73.3 | AMCOR's Adj. EBITDA was $2,018mn in FY 2023 |
The author believes that a/an ________ audience will enjoy the film "Unmade Beds."
A. voyeuristic
B. insensitive
C. crude
D. empathetic
| Dirty Laundry Now and then, a documentary film comes along that makes us re-examine the rules that unofficially govern the genre: Can there be a middle ground between fiction and fact? Can a documentary use scripted scenes and yet remain ontologically authentic? How much can you stylize material before you alter the reality that you're striving, at least in theory, to capture? Unmade Beds , Nicholas Barker's " 'real life' feature film," has proudly worn its mongrel status as a "directed" documentary of single life in the big city, employing, in the face of criticism, what amounts to a cackling-punk defiance. The movie tracks four aging New Yorkers--two men, two women--through their lonely dating rituals, in the process depicting a universe of lusty, coupled-up haves and downcast, excluded have-nots, all viewed Rear Window -style through rectangular openings in the massive apartment houses in which they reside. This is not cinema vérité , and nothing has been left to chance. The director selected his four subjects from many hundreds of potential candidates, followed them around for months, and then scripted their monologues and dialogues to reflect what he says he saw. Calling his own film "an exercise in mendacity," Barker goes on, "I'm quite happy to tell lies about my characters and even collude with their self-delusions if it enables me to communicate larger dramatic truths." Spurned by U.S. distributors, Unmade Beds opened two weeks ago in a small screening room in downtown Manhattan, where it proceeded to set box office records and generate lots of (largely favorable) press. In part due to smart publicity, which has bannered some of the bad reviews and commentary ("I have to tell you that this film upset me so much that I really don't want to have anything to do with it"--a New York publicist), it threatens to become a cause célèbre --and to be coming soon to a theater near you. It's always nice to see distributors proved wrong about the merits of "difficult" films, but in this case I think they did the decent thing. Unmade Beds isn't just bad--it's obnoxiously, noxiously bad, a freak show for the empathetically challenged. The outrage it has prompted isn't the Puritan kind; it's more like legitimate revulsion at watching a blowhard pervert people's lives in the name of "larger dramatic truths." Those truths are large, all right. Take Michael, the 40-year-old, 5 foot 4 inch lonely guy who has been looking for a wife for almost two decades. If you were to walk past him on the street, you might think that a man of his small stature might have some trouble getting dates and be rather bitter about it. The larger dramatic truth is that Michael has lots of trouble getting dates and is very bitter about it. Just in case you feel too sorry for him, however, Barker is careful to include a homophobic monologue in which Michael complains about young women who waste their lives hanging out with effeminate males. Michael turns out to be the film's most sympathetic subject--by a wide margin. At least he's not Mikey, a paunchy 54-year-old who writes but can't sell screenplays and who always flees blind dates, because the women he gets fixed up with are "mutts." Sounding like one of the low-level gangsters who posture like kingpins in Donnie Brasco , Mikey talks a lot about mutts. He also reminisces about that 24 hour period in the '70s when he managed to sleep with three different beautiful women, whose pictures he shows off. These days, all he meets are mutts. He comes off as a pathetic little loser--a mutt. Aimee, on the other hand, is a pathetic big loser, weighing in at 225 pounds. Determined to get married before she turns 30, she generally is filmed beside bags of groceries and assorted junk foods. She cries about her situation to her thin friend, Laurie, who, in one scene, gently mentions Aimee's weight. Clearly the scene is scripted, but Aimee does a good job acting taken aback. She has always been fat--and she's "OK with it," and a man just has to accept it. This is followed by more talk about how you attract men. Will they respect you if you call them back? If you express too much interest? "Or," the viewer thinks, "if you're 225 pounds?" The only natural performer here is Brenda, a garrulous exhibitionist who blossoms with the camera on her--she could have a career as a Penny Marshall-style character actress. Divorced and aging, Brenda needs money and is willing to charge for her sexual services. It shouldn't be too difficult, because men are always showing her their dicks ("I'm up to two dicks a day"). They meet her and, a few minutes later, they show her their dicks. Weird, huh? What Barker leaves out (it's in a New York Observer article) is that Brenda, a former lap dancer, works in marketing at a strip joint. Presumably, men standing next to her in line at McDonald's don't show her their dicks. Nor, presumably, does she show them her breasts--although she bares them for Barker's camera, jabbering about her body while she doffs her clothes and steps into the shower and soaps up. Barker might have crafted his subjects' monologues from their own words, but he has robbed them of their spontaneity--and, thus, of their essence. They aren't thinking or trying to come to grips with their situations in front of your eyes, because they already know what they're going to say: They've been fixed like butterflies on the ends of pins and held up for voyeuristic inspection. The scenes with friends and confidantes have a crude, programmatic purpose. You can imagine the director composing a shot (the shots are tightly composed and elaborately lighted) and reminding them, "In this scene she points out that you should lose weight and you get shocked and defensive. Ready ... Action." Call me square, but I find this antithetical to the documentary spirit. An Englishman who trained as an anthropologist before going to work for BBC Television, Barker clearly made up his mind about his material before his cameras began to roll--so it's no surprise that it feels prechewed and predigested. When reality interfered (Brenda apparently did not go through with a marriage to an immigrant in search of a green card for $10,000, as she does on-screen), Barker brushed the truth aside as immaterial, following her up the steps of City Hall in her wedding dress because it was "true to her character." But what separates documentary from fiction is that real people are often more complicated, and more conflicted, than finished characters--as Brenda proved to be more (or, at least, other) than the sum of her parts. That's the kind of truth that reveals itself to documentary filmmakers after the fact, when they go over footage and discover unexpected patterns, dissonances, glimmers of a universe that's richer and messier than the one they set out to portray. So what are Barker's "larger dramatic truths"? Single people in big cities can be desperate. Single people fear they're going to die alone--unloved and unloving. People are judged and, in turn, judge others by how they look. Big news. One could argue, charitably, that the movie is meant to be prescriptive, that Barker intends for us to regard the ways in which his subjects delude themselves and thereby learn to see through our own self-delusions. But Barker hasn't concocted a larger dramatic structure that would hold those larger dramatic truths together and help us comprehend where these people went wrong. He dramatizes right up to the point where a dramatist would be expected to provide some insight--and then, hey, he's a documentarian. Unmade Beds might make a good date movie. There's little to argue about in its subjects' personalities--both males and females will find them repulsive--and the picture the film paints of single life in the big city is so bleak that you'll probably want to jump into bed with whoever is sitting next to you. Anything to keep from turning into one of those people. The Slums of Beverly Hills also walks a line between two genres, in this case coming-of-age sex comedy and autobiographical monologue. Tamara Jenkins, the writer and first-time director, has an eye for absurd juxtapositions that was obviously sharpened by the pain of her nomadic upbringing. Her protagonist (Natasha Lyonne) spends her teen-age years being shuttled with her two brothers from one cheap dive to another in the 90210 ZIP code, all because her egregiously unsuccessful father (Alan Arkin) wants them to be educated in the best schools. ("Furniture's temporary; education is permanent.") It's a major omission, then, that we never see those schools or the kids' interaction with their stable, well-to-do Beverly Hills counterparts. We can't tell if the father is, on some weird level, justified in his fervor, or whether he's screwing up his children--subjecting them to humiliation and robbing them of a sense of permanence--for no reason. Jenkins hasn't quite figured out how to shape her narrative, which is full of episodes that are there because they actually happened but that don't have a payoff. I almost wish she'd included more voice-over narration, more commentary on the things that, as a filmmaker, she hasn't learned to bring out. The Slums of Beverly Hills never gels, but it has a likable spirit, and it's exceedingly easy on the eye, with lots of pretty girls and wry evocations of '70s fashions and decor. The father, to obtain financial support from his wealthy brother (Carl Reiner), volunteers to take in his vaguely schizzy, dipsomaniacal niece (Marisa Tomei). She and her cousin compare breasts, play with vibrators, and talk in pig Latinish gibberish, but Jenkins never lets the proceedings get too sentimental: The whimsy is always cut with an acidic awareness of the family's desperation. "Are we middle-class now?" ask the children, hopefully, before another crisis sends them back into their van, cruising past the movie stars' mansions, in the mean streets of Beverly Hills. Grading on the steep curve established by summer blockbuster seasons past, these have turned out to be a pretty good few months at the movies. Even the commercial swill ( Deep Impact , Armageddon , The Mask of Zorro , Small Soldiers , Snake Eyes , Halloween: H20 ) has been of a high grade, and Saving Private Ryan and Return to Paradise were Vitalis slaps in the kisser for people woozy from all the warm weather escapism. Out of Sight was tender and charming, as was, in its gross-out way, There's Something About Mary . And, on the indie front, The Opposite of Sex , Buffalo 66 , and Pi have proved that there's still commercial life after Sundance. Sure, we had stinkers, but even Godzilla was fun to jeer at. And there's something reassuring about the fact that The Avengers is so rotten: proof yet again that people with piles of money can hire wizard production designers but can't fake class. I don't know who the credited screenwriter, Don MacPherson, is, but it's unlikely that he has ever seen an episode of the old Avengers , let alone sussed out the source of its appeal. Opening with a slapstick sequence of agent John Steed (Ralph Fiennes) doing kung fu, the film shifts to a scene in which he meets Mrs. Peel (Uma Thurman) while sitting naked in a sauna with only a newspaper to cover his private parts. The series was erotic in a way only prim English humor can be: The Old Boy Steed was capable of throwing a punch and bonking someone with his bowler, but he left the karate kicking to his liberated, leather-suited distaff associate. Here their roles have been witlessly muddled, and MacPherson's idea of banter is to have the pair complete each other's clichés. Whereas the original Steed, Patrick Macnee, was to the English Men's Club born, Fiennes is an eternal caddie. The willowy Thurman looks great in her outfits, but it's ever more apparent that she isn't much of an actress--at least, not a trained one--and her attempts at insouciance are embarrassingly arch. As the eccentric master villain who controls the weather, even Sean Connery is flat-out terrible, acting high on the hog. To think Connery once found the Bond films so far beneath him! When he sputters lines like "Time to die!" one imagines Dr. No, Goldfinger, and Blofeld snickering in the wings. | A. voyeuristic |
What are results of public code repository study? | ### Introduction
Reading and writing comments, method names and variable names is a crucial part of software engineering and as such, programs have both a human language, the language of identifiers and comments, in addition to the source-code language (eg Java or Python). This has meant that non-English speakers are often second class citizens when learning to program BIBREF0. In this paper we present a tool for translating a program from one human-language to another to assist in code education, which could reduce the barrier to computer science education for non-English speakers. The main contributions presented in this paper are: Analysis of 1.1M non-English code projects on GitHub CodeInternational: A tool which can translate code between human languages, powered by Google Translate. Validation of CodeInternational by evaluating the translation of 1,000 randomly chosen projects from GitHub. Use of CodeInternational to automatically translate the popular Karel textbook into 100+ languages. We further extend the textbook to parse and run KarelJava code in any language; we report adoption by classrooms around the world. Our human-language code translator was inspired by a desire to make programming more accessible BIBREF1. An accurate and useful translator would enable faster localization of instruction materials and it would allow learners (as well as practitioners) to translate code that they are working with. As programming becomes more of a requisite common knowledge skill, we expect coding education to become open-access to everyone. One barrier to this goal is human language. English is currently the modal language of programming instruction perhaps given that the keywords of most of the popular languages, Java, JavaScript etc, are in English (even including Python and Lua, invented in the Netherlands and Brasil respectively). However, a majority of the world, estimated in 2008 at 80%, can't “use" English for communication and substantially more don't speak English as their L1 language (the technical term for one's arterial language, aka, mother tongue) BIBREF2. Should the more than 6 billion non-English speakers learn to program in their native language or in English? This question is debated, which we address in the discussion. We take the position that whether or not code instruction is in English, if students do not speak English as their L1 language, their code education would benefit from the ability to translate Code between their preferred language and English. ### Introduction ::: Related Work
To the best of our knowledge, automatic translation of code between human languages, did not appear in literature, making us hypothesize: it is either difficult, or had remained ignored. Nonetheless, we summarize related work that motivate our contribution. Translation of Text automatic translation of natural language has recently achieved high accuracy and is used in highly sensitive contexts BIBREF3, BIBREF4, BIBREF5. At the time of writing this article, Google Translate uses Neural Machine Translation BIBREF6 to translate pairwise between languages and has become incredibly accurate, at least for languages common on the web BIBREF7. Further research has been done on transliterating text BIBREF8, BIBREF9. However, current state-of-the-art methods for text translation fail at translating code. Directly running a translation algorithm on code would fail to distinguish between code syntax and identifiers, would not recognize terms embedded in identifiers e.g. with camel case getElementAt, and could produce code with one identifier name having different translations on separate lines. As such, current automatic text translation, if ran directly on code, would produce malfunctional code. Code Instruction in Non-English In 2017, Dasgupta and Hill published seminal work outlining the importance of learning to code in one's own language. They conclude that "novice users who code with their programming language keywords and environment localized into their home countries' primary language demonstrate new programming concepts at a faster rate than users from the same countries whose interface is in English" BIBREF10. Since then, there has been a large set of papers expanding on the barriers for non-native English speakers. Guo et al survey over 800 non-English students learning who report on the many challenges that come with not understanding English while coding. BIBREF11 reinforced by BIBREF12, BIBREF13. This has led to preliminary work into translating compiler errors BIBREF14 and advocation for language-free block free programming BIBREF15. However, while language-free programming is a great step forward for younger students, it doesn't address the needs of CS1 students who program in common programming languages like Python or Java. While all of this work motivates our contribution, none has attempted an automatic solution to the problem, making crowd-translation a viable alternative BIBREF16. Mining Github To understand the patterns of code that students and practitioners use, we analyze public repositories on GitHub. Other researchers also analyzed GitHub, sometimes via the dataset and tools provided by BIBREF17, including work on social diversity of teams BIBREF18 and affiliation influence on code popularity BIBREF19. This has led to a set of best practices for navigating the promises and perils of mining GitHub BIBREF20. A growing number of students are using GitHub in software engineering courses BIBREF21 which makes it a valuable resource for understanding code of the general population, including students. Code Conversion There is a rich literature of work to translate code between programming languages, such as C or C++ to Java BIBREF22, BIBREF23, or even from English to code BIBREF24. However, the emphasis is often on maintaining efficiency, not on making code readable for students. We focus on translating the human language of code. Byckling et al BIBREF25 analyze naming conventions of identifiers based their function (fixed, iterators, transformers, etc), and correlate the naming consistency with the students' learning experience. This motivates aspects of our translation. See Section SECREF22. ### Human Languages on GitHub
How do non-English speakers program in a language like Java, where the keywords and core libraries are written in English? We employ a data driven approach to tell the story of non-English code and inform the decisions we made in our auto-translator. We analyzed Java repositories on GitHub, the largest host of source code in the world, where 1.1 million unique users host 2.9 million public Java projects. We downloaded and analyzed the human language used for writing comments (in Java code), naming identifiers (method and variable names), and writing git commit messages. We focused on Java code as it is both one of the most popular source-code languages on GitHub and in the classroom. A selection of results from this study are that: Non-English code is a large-scale phenomena. Transliteration is common in identifiers for all languages. Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration. Non-latin script users write comments in their L1 script but write identifiers in English. Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code. This is, to the best of our knowledge, the first analysis of the human languages on GitHub. See Figure FIGREF6 for an overview. Users on GitHub do not state their L1 (arterial) language. While a subset of users optionally state their country this is neither common nor reliable. To estimate a user's preferred language we use the language that they use in the git commit message. To find subsets of users who speak a given language, we search for all users who write git commits in that language. We observe that, especially in personal projects, users write commit messages in their L1 language at a higher rate than comments or identifiers. To identify languages we use Google Language Detect which is highly accurate (more so for common internet languages) and can identify languages with non-Roman Alphabet text which has been transliterated, for example it can detect bothUTF8gbsn算法 the Chinese characters for “algorithm" and "suanfa", the Mandarin transliteration, as Chinese. Of the 1.1 million GitHub users, 12.7% wrote commit messages in non-English languages. Of the non-English languages Chinese was the most common (28.6% of non-English committers), followed by Spanish, Portuguese, French, and Japanese. More than 100 languages were detected in commit messages on public Java projects. Figure FIGREF6 contains breakdowns and the appendix contains the full list. This does not match the distribution of non-English in web content (55% English) with both major and minor languages underrepresented. For example the prevalence of Spanish on GitHub (2.1%) is about half of webcontent (5.1% BIBREF26) and further trails native speakers (7.8% of the worlds population BIBREF27). Github does not present a random sample of programs written in the world, and we consider the relevant confounds this introduces. To that point, we believe the under-representation of certain languages is a form of Survivorship Bias. It suggests that users have found barriers to entry towards joining the GitHub community. Those barriers could derive from the English dominance of programming languages, code instruction, or the github interface. ### Human Languages on GitHub ::: Non-English in Java
The use of non-English in identifiers and comments is large for the population of users who we define as non-English "speakers" (those who use non-English in their git-commit messages). 90% of users who use a non-English language in the commit messages also use that language in their comments or as identifiers. We note that, in Java, identifiers can be written in any script. Surprisingly, the patterns of non-English usage differs substantially when we condition on users "speaking" different languages. For example, among the detected Spanish speakers, 87.2% percent of users write identifiers in Spanish. On the other hand, among Chinese users only 23.3% of users write code with Chinese identifiers (either in Chinese script or ASCII). Figure 1b shows coding patterns conditioned on users speaking different languages. For each language we plot the percent of projects with identifiers in the language against the percent of projects with comments in the language. Languages naturally cluster into three categories: (1) Major-Euro-Latin: languages with high use of non-English identifier including Spanish, German and French (2) Non-Latin: languages in non-latin scripts including Russian and Chinese which have low use of non-English identifiers and (3) English-Comment: Programmers write their comments in English (> 70% of projects only have English comments). This group contains many smaller and non-European languages like Dutch and Bahasa Indonesia. 0% of projects in this group still uses their L1 language in identifiers. The use of identifiers in local language (as opposed to English) is very clearly split on whether languages use the Latin alphabet. On average 82% of projects from users speak languages with different scripts like Chinese, Korean, or Russian have only English identifiers, compared to 12% of projects from Latin alphabet users ($p < 0.0001$). The percentage of projects with only English comments is roughly correlated to the English Proficiency Index BIBREF28 of the corresponding countries ($\rho = 0.42$ $p < 0.01$). ### Human Languages on GitHub ::: Transliteration on GitHub
Transliteration is the process of transferring a word from the alphabet of one language to another (eg -> namaste duniya). We observed that most Java code with human languages that have non-ascii scripts like Kanji, Devanagari, or even Spanish accents like ñ, will have been "transliterated" into ascii. The Java Language Specification states that, "letters and digits (in identifiers) may be drawn from the entire Unicode character set, which supports most writing scripts". This specification is not widely known, and even if Java supports non-ascii , there can be complexities of file encodings across different operating systems. We find that regardless of L1 language most users transliterate identifiers: among L1 Chinese speakers, 93% of projects have identifiers which are only written in ASCII. Similarly in Spanish 88% of projects have only ASCII identifiers. As a concrete example, in GitHub Java code "numero" is 3.8x more common than "número". Among comments languages differ greatly: 99% of Chinese projects have non ASCII comments compared to only 53% of Spanish. As an example a comment above a method specifies in script that it is calculating the Fibonacci sequence however the method name (an identifier) is transliterated "//UTF8gbsn斐波那契" however the code uses a transliteration of the phonemes in the script "public int feibonaqie(int n)". This is a common pattern: Within comments, UTF8gbsn计数 chinese for count), is 4.0x more common than jishu, the transliteration. However in identifiers jishu is 4.8x more common. The difference in transliteration patterns between Chinese and Spanish suggests a different intent: in Spanish transliteration is used to avoid file encoding errors, in Chinese it is to prevent a mix of scripts among identifiers. ### Human Languages on GitHub ::: Right-to-Left Languages on GitHub
One question that we did not have a solid pre-conception for was: How do Java users who speak languages with right-to-left (RTL) scripts like Arabic, Urdu or Hebrew, write code? 18,961 users on GitHub report their country as one where a RTL script (Arabic or Hebrew) is the primary script. Those users have 8,060 public Java repositories of which only 50 repositories (0.6%) have Arabic or Hebrew script (excluding string literals). Of those repositories, only a single Java file had a single identifier written in Arabic and none in Hebrew. It is extremely rare for methods or identifiers to be a mix of RTL and LTR. ### Code International
The GitHub analysis is coherent with the contemporary narrative: there are perhaps hundreds of millions of learners who will not speak English as their L1 language. For those learners, teachers need a tool to translate code so they can give examples with less congitive load. Similarly students need a tool to understand the non-English code they encounter. Finally, to a growing extent English speakers will begin to interact with code written in other languages. To adress this need, we designed a tool to help programmers, regardless of their spoken language, access code in many languages. The tool, which we call CodeInternational, takes in code written in either Java or Python with comments and identifiers written in a human-language and translates the comments and identifiers into another human-language. It supports the growing set of human languages covered by Google Translate and is adaptive to the particular context of source-code. To translate code, it first parses the code and extracts four types of tokens: [leftmargin=3mm] Comments: inline or multi-line comments. Their purpose is for the programmer to communicate to programmers (including herself) on the purpose of code sections. Immutable: consisting of language keywords (while, void, etc), and identifiers imported from libraries that are external to the code being translated (e.g. FileReader of java.io). By default this group is not translated. Target identifiers: including variable and function names that are defined in the code base undergoing translation. String literals: In some cases a user may want String literals to be translated, other times they should be unchanged. Our translation algorithm is as follows. We (1) collect all of the target identifiers defined in the codebase and (2) translate them (enforcing that if two identifiers have the same name, they are given the same translation). Once the identifiers are translated we (3) translate the comments preserving structure and references to identifiers. (4) Finally string literals are optionally translated. See Figure FIGREF20 for a highlevel depiction and Figure FIGREF23 for a concrete example. Each of these steps has surprising challenges. In this section we cover the corresponding solutions we developed. The mapping of identifier translations that the tool decides on is preserved to assist any external source which needs to refer to the newly translated identifiers (such as text in a text-book or code in a related project). CodeInternational is implemented in Python. Tokenization is performed using a modified version of "Javalang" (for Java) and the "Parser" library (for Python). Supporting other programming languages requires a small amount of extra work. ### Code International ::: Translating Identifiers
In order to properly translate identifiers, we consider the following: Identifier segmentation: Translating an identifier using a tool like Google Translate does not work by default as identifiers are often composed of unsegmented words. For example: getFavoriteNumber is readable to a human as "get favorite number" but is not parsable by an online translator. We segment identifiers using naming conventions (e.g. camelCaseVariable, PascalCaseClass, UPPERCASE_CONSTANT). We thus segment identifiers into phrases which we feed into an automatic translator. We then recombine the translated phrase using the original casing convention. For example, to translate the method name identifier "turnAround" into Spanish: "turnAround" is segmented into "turn around" which is translated into "media vuelta" which is formatted into the original camelCase "mediaVuelta". Advances in artificial intelligence for word segmentation enable a future version of this tool to break up words without a given segmentation (eg "turnaround"). Verb prior: The correct translation for a phrase can be ambiguous, especially without context. As an example the method "move" translated into Spanish could be translated into a noun ("movimiento", movement) or a verb ("moverse"). For method identifiers there is an implicit context that an action is being performed. We incorporate this context by placing a prior on the first word being a verb. Thus, for example, when we translate "move()" into Spanish we chose "moverse()" instead of "movimiento()", the noun movement, as Google suggests. In addition to knowing the translations of methods should start with verbs, we also have a select number of reasonable tenses for the verb: infinitive (eg "toMove"), third person present (eg "moves" as in "he moves") and imperative (eg "move"). In most languages, including English, we translate verbs with a prior that they be the imperative tense. In English you would expect a method to be "getObject()" the imperative. However some languages, especially Romance languages, use the infinitive of the verb: as an example, "obtener" the infinitive of "obtain" is 200x more common on GitHub then "obtenga" the imperative. Translating short identifiers: Short variable names that are used for mathematical symbols or as iterators should not be translated. This is especially important to pay attention to for the cannonical for loop identifier "i". For example translating the code "for(int i = 0; i < 10; i++)" into Spanish should not produce "for(int yo = 0; yo < 10; yo++)" even though "yo" is the translation of the pronoun "I". We only translate identifiers which are at least two characters long. This exception has its own edge-case: CJK (Chinese, Japanese Korean) identifiers can be non-mathematical names even if only a character long. ### Code International ::: Translating comments
Once we have finished translating identifiers, we translate the comments in a program. Translating comments has two complexities: (1) we would like to maintain the comment structure, eg if it is a block javadoc comment, we would like to reserve the column of '*'s on the left margin of the comment and (2) we want references to identifiers to be translated exactly as they were in the code. To translate a comment we classify the structure (eg JavaDoc, BlockComment PythonDocString). We then strip the text out, translate it, and reformat it back into the same structure. For multi-line comments we are conscious not to increase the maximum length of a line, taking into account the wider width of CJK characters. ### Code International ::: Translating Right-to-Left languages
Arabic, Hebrew, Farsi, and Urdu are popular right-to-left (RTL) natural languages. When translating code to RTL languages, comment can be translated (mixing RTL within the left-to-right syntax) and optionally transliterated (keeping left-to-right flow). Some of the difficulty in RTL transliteration is in distinguishing between short- and long-vowels. Further, these languages contains consonant that cannot be described using Latin alphabets, which are generally represented with numbers in the transliteration culture – e.g. 7 for utf8 ح > , which is closest to Latin alphabet “h” e.g. in “Ahmad”. When translating non-Latin scripts which are LTR we give the user the option to transliterate identifiers and separately, to transliterate comments or not. Transliteration is currently supported in Arabic, Chinese, Hebrew, Japanese, Korean, and Russian. ### Code International ::: Prior and posterior translations
Translations of code need to be coherent with respect to other translations of written text (or other files) that refers to the code. To that end our translator takes in, and uses, a preset identifier translation map and returns the translations it made. This system enables having humans override translations, translating text-books with text that references embedded code and more. ### Translating Github
How good is a translation of source-code from one human language to another? Evaluating quality of a translation is hard without a large collection of native speakers and since we are powered by Google, evaluation can devolve into evaluating how accurate Google Translation is. Such an evaluation is a moving target: Google Translation is perennially improving. To evaluate out translator we randomly selected 1,000 (1k) single file projects from public GitHub Java and translated them into the languages: Chinese, Spanish and Arabic. We measure (1) how often the translated code still compiles and (2) what percent of identifiers that we attempt to translate are translatable. Of the 1k projects 100% maintained their ability to be compiled regardless of whether we translated or transliterated the comments or identifiers. From the 1k projects 91% of the identifiers were able to be translated. The nine percent that were not able to be translated were mainly abbreviations (such as users who named a variable frac instead of fraction or pct instead of percent). This is an opportunity for future work. Overall the results paint the picture of a functioning tool which is ready for use. ### International Karel
Our motivation for developing an automatic human-language code translation tool was to support education for non-English speakers. To that end we used CodeInternational to translate a web-version of the popular Karel the Robot learns Java reader by Eric Robers BIBREF29 a textbook for a Karel the Robot, a grid world robot invented by Richard Pattis BIBREF30 to help CS1 students learn to program. Karel has been the inspiration for assignments on platforms such as Code.org and CodeHS and is a staple of the first weeks of CS1 BIBREF31. We translated a Karel reader in Python and Java to 100 languages. The translated web-reader is free to use, and is hosted at [redacted]. At time of publication the reader has been public (without advertising) and has already been used by over 3,000 people from 50 countries. With permission from Eric Roberts, we first made an eBook version of his Karel reader and simplified the English used BIBREF32. The reader merges text and code in a seemless fashion. Then, for each language: we (a) translated code on each chapter using CodeInternational and (b) translated the reader text such that any reference to identifiers in the example code would use the same translations. In order to have text which is consistent with the corresponding code we heavily rely on the "Posterior identifier translation map" from CodeInternational's translations. ### International Karel ::: Line-highlighting in any language
To make the Karel reader a fantastic learning experience we made it so that each code-snippet is runnable. When run, the program executes the code and highlights the corresponding lines as the program is run, regardless of the complexity of the program's control flow. In order to line-highlight we parse and compile the Python-Karel or Java-Karel programs using an engine written in JavaScript. Our line-highlighter builds upon the compiler described in Informatics Education using Nothing but a Browser BIBREF33. Our Karel reader can run and line-highlight in any human-language that we translate into. For example our compiler can execute and line-highlight the command "moverse()" if the code is written in Spanish, "UTF8gbsn移动()" if the program is written in Chinese, "emshi()" if the program is written in Arabic, or "move()" if the Karel program is written in English. We chose to only transliterate commands for RTL scripts. Figure FIGREF27 shows three screenshots from the international Karel reader, though of course a PDF is unable to capture the ability of the reader to line-highlight code. ### International Karel ::: Usage in Classrooms
We know of four classes where the internationalized Karel eReader has been used. These classes are around the world in: Istanbul, Bogota, Prague and [Redacted]. The eReader has been visited by >1k users in 3 months and both the English and the non-English version of the website have a high average session duration (9.7 min and 10.1 min respectively). Moreover, the tool has been used to translate the CSBridge curriculum website and assignments; HTML that mixes code and description (used by 400 students / year). ### Discussion
Whether English should be used as the sole language of instruction has been debated. Case for code instruction in English: In order to program professionally, one will have to interact with keywords and libraries that are written for English speakers. English is the language of code, and it is practically required from anyone who wants to interact globally: correspond via email, read stack-overflow, watch educational videos, travel, etc. For classrooms where English is the main form of instruction, but students are not yet fluent, CodeInternational can be used to assist learning English and learning to program. Students could improve their English through coding, e.g. by placing English code against their L1 code, side-by-side. Case for instruction on transl(iter)ated code: On the other hand, people argue that it is beneficial for students to have much of their coding instruction in their L1 language, and doing so benefits access to CS. The primary reason for this intuitive: the cognitive-load of learning to program is already high. Moreover, if students learn coding using their L1 language and enjoy it, they become intrinsically motivated to learn English, knowing that English would broaden their access to learning material (learning earning a language, with no short-term motives, could be dull especially for young students). In this context CodeInternational can help students who are interacting with libraries in English. Perhaps more importantly our tool can help teachers rapidly develop localized content that builds off English content. The alternative: manual-translation of API, code-examples and website text, can be a huge barrier to translating material. Finally, our tool builds off GoogleTranslate, which is high accurate, but charges $1 per 50,000 characters. A free version would have a huge impact on utility. We call for future work from tool experts, for extending popular code-editors (e.g. vim, XCode, Visual Studio, Eclipse) to integrate with our APIs for back-and-forth translation and side-by-side display. Optionally, integrating with automatic text-to-speech (e.g. BIBREF34) could allow students learn English pronunciation of code components. Moreover, one remaining feature in automatic human-translation of code is identifier consistency: if two identifiers have specific terms in common, eg getHeight, setHeight, we would like the translation of "height" to be consistent. While they are often consistent in our work, it is not enforced. Full consistency is hard, but not impossible, with modern neural machine translation. ### Conclusion
We analyze millions of non-English Java programs on GitHub to inform our understanding of patterns of human-language and make some surprising observations. We build CodeInternational, an open-source tool which can translate Java or Python code between human languages. We evaluate our tool and use it to make an internationalized Karel eReader (with runnable code) in 100+ languages. Our tool is already being used in classrooms around the world, a trend we hope to continue supporting. ### Conclusion ::: Acknowledgements
We would like to graciously thank XXX and YYY for contributing code to this translation project. We would also like to thank ZZZ for her inspiration. We also thank the WWW teachers for educating students around the world in their local language. Figure 1: (a): The four most popular non-Eng languages for Java GitHub commits. (b) Java non-Eng example methods. (c) Use of local language in identifiers and comments conditioned on users speaking different languages. (d) Proportion of non-English projects with script vs transliteration Figure 2: High-level of how CodeInternational work Figure 3: An example of using CodeInternational to translate a simple Java program from English to Chinese. Figure 4: Three screenshots from the Karel eReader, translated both into 100+ languages for Java and Python: Left: intro page in Hindi; Middle: code translated into Arabic with transliterated identifiers; Right: reference in Spanish. | Non-English code is a large-scale phenomena., Transliteration is common in identifiers for all languages., Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration., Non-latin script users write comments in their L1 script but write identifiers in English., Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers |
By how much do they outperform basic greedy and cross-entropy beam decoding? | ### Introduction
[t] Standard Beam Search [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 t = 0 to T i = 1 to k INLINEFORM4 INLINEFORM5 INLINEFORM6 is the local output scoring function INLINEFORM7 top-k-max INLINEFORM8 Top k values of the input matrix INLINEFORM9 top-k-argmax INLINEFORM10 Top INLINEFORM11 argmax index pairs of the input matrix i = 1 to k INLINEFORM12 embedding( INLINEFORM13 ) INLINEFORM14 INLINEFORM15 is a nonlinear recurrent function that returns state at next step INLINEFORM16 INLINEFORM17 follow-backpointer( INLINEFORM18 ) INLINEFORM19 Sequence-to-sequence (seq2seq) models have been successfully used for many sequential decision tasks such as machine translation BIBREF0 , BIBREF1 , parsing BIBREF2 , BIBREF3 , summarization BIBREF4 , dialog generation BIBREF5 , and image captioning BIBREF6 . Beam search is a desirable choice of test-time decoding algorithm for such models because it potentially avoids search errors made by simpler greedy methods. However, the typical approach to training neural sequence models is to use a locally normalized maximum likelihood objective (cross-entropy training) BIBREF0 . This objective does not directly reason about the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding BIBREF7 , BIBREF8 , BIBREF9 . These negative results are not unexpected. The training procedure was not search-aware: it was not able to consider the effect that changing the model's scores might have on the ease of search while using a beam decoding, greedy decoding, or otherwise. We hypothesize that the under-performance of beam search in certain scenarios can be resolved by using a better designed training objective. Because beam search potentially offers more accurate search when compared to greedy decoding, we hope that appropriately trained models should be able to leverage beam search to improve performance. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined and a valid training criterion, this “direct loss” objective is discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross-entropy trained greedy decoding and cross-entropy trained beam decoding baselines. Several related methods, including reinforcement learning BIBREF10 , BIBREF11 , imitation learning BIBREF12 , BIBREF13 , BIBREF14 , and discrete search based methods BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , have also been proposed to make training search-aware. These methods include approaches that forgo direct optimization of a global training objective, instead incorporating credit assignment for search errors by using methods like early updates BIBREF19 that explicitly track the reachability of the gold target sequence during the search procedure. While addressing a related problem – credit assignment for search errors during training – in this paper, we propose an approach with a novel property: we directly optimize a continuous and global training objective using backpropagation. As a result, in our approach, credit assignment is handled directly via gradient optimization in an end-to-end computation graph. The most closely related work to our own approach was proposed by Goyal et al. BIBREF20 . They do not consider beam search, but develop a continuous approximation of greedy decoding for scheduled sampling objectives. Other related work involves training a generator with a Gumbel reparamterized sampling module to more reliably find the MAP sequences at decode-time BIBREF21 , and constructing surrogate loss functions BIBREF22 that are close to task losses. ### Model
We denote the seq2seq model parameterized by INLINEFORM0 as INLINEFORM1 . We denote the input sequence as INLINEFORM2 , the gold output sequence as INLINEFORM3 and the result of beam search over INLINEFORM4 as INLINEFORM5 . Ideally, we would like to directly minimize a final evaluation loss, INLINEFORM6 , evaluated on the result of running beam search with input INLINEFORM7 and model INLINEFORM8 . Throughout this paper we assume that the evaluation loss decomposes over time steps INLINEFORM9 as: INLINEFORM10 . We refer to this idealized training objective that directly evaluates prediction loss as the “direct loss” objective and define it as: DISPLAYFORM0 Unfortunately, optimizing this objective using gradient methods is difficult because the objective is discontinuous. The two sources of discontinuity are: We introduce a surrogate training objective that avoids these problems and as a result is fully continuous. In order to accomplish this, we propose a continuous relaxation to the composition of our final loss metric, INLINEFORM0 , and our decoder function, INLINEFORM1 : INLINEFORM2 Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . By introducing this new module, we are now able to construct our surrogate training objective: DISPLAYFORM0 Specified in more detail in Section SECREF9 , our surrogate objective in Equation 2 will additionally take a hyperparameter INLINEFORM0 that trades approximation quality for smoothness of the objective. Under certain conditions, Equation 2 converges to the objective in Equation 1 as INLINEFORM1 is increased. We first describe the standard discontinuous beam search procedure and then our training approach (Equation 2) involving a continuous relaxation of beam search. ### Discontinuity in Beam Search
[t] continuous-top-k-argmax [1] INLINEFORM0 INLINEFORM1 , s.t. INLINEFORM2 INLINEFORM3 INLINEFORM4 = 1 to k peaked-softmax will be dominated by scores closer to INLINEFORM5 INLINEFORM6 The square operation is element-wise Formally, beam search is a procedure with hyperparameter INLINEFORM7 that maintains a beam of INLINEFORM8 elements at each time step and expands each of the INLINEFORM9 elements to find the INLINEFORM10 -best candidates for the next time step. The procedure finds an approximate argmax of a scoring function defined on output sequences. We describe beam search in the context of seq2seq models in Algorithm SECREF1 – more specifically, for an encoder-decoder BIBREF0 model with a nonlinear auto-regressive decoder (e.g. an LSTM BIBREF23 ). We define the global model score of a sequence INLINEFORM0 with length INLINEFORM1 to be the sum of local output scores at each time step of the seq2seq model: INLINEFORM2 . In neural models, the function INLINEFORM3 is implemented as a differentiable mapping, INLINEFORM4 , which yields scores for vocabulary elements using the recurrent hidden states at corresponding time steps. In our notation, INLINEFORM5 is the hidden state of the decoder at time step INLINEFORM6 for beam element INLINEFORM7 , INLINEFORM8 is the embedding of the output symbol at time-step INLINEFORM9 for beam element INLINEFORM10 , and INLINEFORM11 is the cumulative model score at step INLINEFORM12 for beam element INLINEFORM13 . In Algorithm SECREF1 , we denote by INLINEFORM14 the cumulative candidate score matrix which represents the model score of each successor candidate in the vocabulary for each beam element. This score is obtained by adding the local output score (computed as INLINEFORM15 ) to the running total of the score for the candidate. The function INLINEFORM16 in Algorithms SECREF1 and SECREF7 yields successive hidden states in recurrent neural models like RNNs, LSTMs etc. The INLINEFORM17 operation maps a word in the vocabulary INLINEFORM18 , to a continuous embedding vector. Finally, backpointers at each time step to the beam elements at the previous time step are also stored for identifying the best sequence INLINEFORM19 , at the conclusion of the search procedure. A backpointer at time step INLINEFORM20 for a beam element INLINEFORM21 is denoted by INLINEFORM22 which points to one of the INLINEFORM23 elements at the previous beam. We denote a vector of backpointers for all the beam elements by INLINEFORM24 . The INLINEFORM25 operation takes as input backpointers ( INLINEFORM26 ) and candidates ( INLINEFORM27 ) for all the beam elements at each time step and traverses the sequence in reverse (from time-step INLINEFORM28 through 1) following backpointers at each time step and identifying candidate words associated with each backpointer that results in a sequence INLINEFORM29 , of length INLINEFORM30 . The procedure described in Algorithm SECREF1 is discontinuous because of the top-k-argmax procedure that returns a pair of vectors corresponding to the INLINEFORM0 highest-scoring indices for backpointers and vocabulary items from the score matrix INLINEFORM1 . This index selection results in hard backpointers at each time step which restrict the gradient flow during backpropagation. In the next section, we describe a continuous relaxation to the top-k-argmax procedure which forms the crux of our approach. ### Continuous Approximation to top-k-argmax
[t] Continuous relaxation to beam search [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 t = 0 to T INLINEFORM5 i=1 to k INLINEFORM6 INLINEFORM7 is a local output scoring function INLINEFORM8 INLINEFORM9 is used to compute INLINEFORM10 INLINEFORM11 Call Algorithm 2 i = 1 to k INLINEFORM12 Soft back pointer computation INLINEFORM13 Contribution from vocabulary items INLINEFORM14 Peaked distribution over the candidates to compute INLINEFORM15 INLINEFORM16 INLINEFORM17 INLINEFORM18 j = 1 to k Get contributions from soft backpointers for each beam element INLINEFORM19 INLINEFORM20 INLINEFORM21 INLINEFORM22 is a nonlinear recurrent function that returns state at next step INLINEFORM23 Pick the loss for the sequence with highest model score on the beam in a soft manner. The key property that we use in our approximation is that for a real valued vector INLINEFORM0 , the argmax with respect to a vector of scores, INLINEFORM1 , can be approximated by a temperature controlled softmax operation. The argmax operation can be represented as: INLINEFORM2 which can be relaxed by replacing the indicator function with a peaked-softmax operation with hyperparameter INLINEFORM0 : INLINEFORM1 As INLINEFORM0 , INLINEFORM1 so long as there is only one maximum value in the vector INLINEFORM2 . This peaked-softmax operation has been shown to be effective in recent work BIBREF24 , BIBREF25 , BIBREF20 involving continuous relaxation to the argmax operation, although to our knowledge, this is the first work to apply it to approximate the beam search procedure. Using this peaked-softmax operation, we propose an iterative algorithm for computing a continuous relaxation to the top-k-argmax procedure in Algorithm SECREF6 which takes as input a score matrix of size INLINEFORM0 and returns INLINEFORM1 peaked matrices INLINEFORM2 of size INLINEFORM3 . Each matrix INLINEFORM4 represents the index of INLINEFORM5 -th max. For example, INLINEFORM6 will have most of its mass concentrated on the index in the matrix that corresponds to the argmax, while INLINEFORM7 will have most of its mass concentrated on the index of the 2nd-highest scoring element. Specifically, we obtain matrix INLINEFORM8 by computing the squared difference between the INLINEFORM9 -highest score and all the scores in the matrix and then using the peaked-softmax operation over the negative squared differences. This results in scores closer to the INLINEFORM10 -highest score to have a higher mass than scores far away from the INLINEFORM11 -highest score. Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation. As INLINEFORM0 , each INLINEFORM1 vector converges to hard index pairs representing hard backpointers and successor candidates described in Algorithm SECREF1 . For finite INLINEFORM2 , we introduce a notion of a soft backpointer, represented as a vector INLINEFORM3 in the INLINEFORM4 -probability simplex, which represents the contribution of each beam element from the previous time step to a beam element at current time step. This is obtained by a row-wise sum over INLINEFORM5 to get INLINEFORM6 values representing soft backpointers. ### Training with Continuous Relaxation of Beam Search
We describe our approach in detail in Algorithm 3 and illustrate the soft beam recurrence step in Figure 1. For composing the loss function and the beam search function for our optimization as proposed in Equation 2, we make use of decomposability of the loss function across time-steps. Thus for a sequence y, the total loss is: INLINEFORM0 . In our experiments, INLINEFORM1 is the Hamming loss which can be easily computed at each time-step by simply comparing gold INLINEFORM2 with INLINEFORM3 . While exact computation of INLINEFORM4 will vary according to the loss, our proposed procedure will be applicable as long as the total loss is decomposable across time-steps. While decomposability of loss is a strong assumption, existing literature on structured prediction BIBREF26 , BIBREF27 has made due with this assumption, often using decomposable losses as surrogates for non-decomposable ones. We detail the continuous relaxation to beam search in Algorithm SECREF7 with INLINEFORM5 being the cumulative loss of beam element INLINEFORM6 at time step INLINEFORM7 and INLINEFORM8 being the embedding matrix of the target vocabulary which is of size INLINEFORM9 where INLINEFORM10 is the size of the embedding vector. In Algorithm SECREF7 , all the discrete selection functions have been replaced by their soft, continuous counterparts which can be backpropagated through. This results in all the operations being matrix and vector operations which is ideal for a GPU implementation. An important aspect of this algorithm is that we no longer rely on exactly identifying a discrete search prediction INLINEFORM0 since we are only interested in a continuous approximation to the direct loss INLINEFORM1 (line 18 of Algorithm SECREF7 ), and all the computation is expressed via the soft beam search formulation which eliminates all the sources of discontinuities associated with the training objective in Equation 1. The computational complexity of our approach for training scales linearly with the beam size and hence is roughly INLINEFORM2 times slower than standard CE training for beam size INLINEFORM3 . Since we have established the pointwise convergence of peaked-softmax to argmax as INLINEFORM4 for all vectors that have a unique maximum value, we can establish pointwise convergence of objective in Equation 2 to objective in Equation 1 as INLINEFORM5 , as long as there are no ties among the top-k scores of the beam expansion candidates at any time step. We posit that absolute ties are unlikely due to random initialization of weights and the domain of the scores being INLINEFORM6 . Empirically, we did not observe any noticeable impact of potential ties on the training procedure and our approach performed well on the tasks as discussed in Section SECREF4 . DISPLAYFORM0 We experimented with different annealing schedules for INLINEFORM0 starting with non-peaked softmax moving toward peaked-softmax across epochs so that learning is stable with informative gradients. This is important because cost functions like Hamming distance with very high INLINEFORM1 tend to be non-smooth and are generally flat in regions far away from changepoints and have a very large gradient near the changepoints which makes optimization difficult. ### Decoding
The motivation behind our approach is to make the optimization aware of beam search decoding while maintaining the continuity of the objective. However, since our approach doesn't introduce any new model parameters and optimization is agnostic to the architecture of the seq2seq model, we were able to experiment with various decoding schemes like locally normalized greedy decoding, and hard beam search, once the model has been trained. However, to reduce the gap between the training procedure and test procedure, we also experimented with soft beam search decoding. This decoding approach closely follows Algorithm SECREF7 , but along with soft back pointers, we also compute hard back pointers at each time step. After computing all the relevant quantities like model score, loss etc., we follow the hard backpointers to obtain the best sequence INLINEFORM0 . This is very different from hard beam decoding because at each time step, the selection decisions are made via our soft continuous relaxation which influences the scores, LSTM hidden states and input embeddings at subsequent time-steps. The hard backpointers are essentially the MAP estimate of the soft backpointers at each step. With small, finite INLINEFORM1 , we observe differences between soft beam search and hard beam search decoding in our experiments. ### Comparison with Max-Margin Objectives
Max-margin based objectives are typically motivated as another kind of surrogate training objective which avoid the discontinuities associated with direct loss optimization. Hinge loss for structured prediction typically takes the form: INLINEFORM0 where INLINEFORM0 is the input sequence, INLINEFORM1 is the gold target sequence, INLINEFORM2 is the output search space and INLINEFORM3 is the discontinuous cost function which we assume is decomposable across the time-steps of a sequence. Finding the cost augmented maximum score is generally difficult in large structured models and often involves searching over the output space and computing the approximate cost augmented maximal output sequence and the score associated with it via beam search. This procedure introduces discontinuities in the training procedure of structured max-margin objectives and renders it non amenable to training via backpropagation. Related work BIBREF15 on incorporating beam search into the training of neural sequence models does involve cost-augmented max-margin loss but it relies on discontinuous beam search forward passes and an explicit mechanism to ensure that the gold sequence stays in the beam during training, and hence does not involve back propagation through the beam search procedure itself. Our continuous approximation to beam search can very easily be modified to compute an approximation to the structured hinge loss so that it can be trained via backpropagation if the cost function is decomposable across time-steps. In Algorithm SECREF7 , we only need to modify line 5 as: INLINEFORM0 and instead of computing INLINEFORM0 in Algorithm SECREF7 , we first compute the cost augmented maximum score as: INLINEFORM1 and also compute the target score INLINEFORM0 by simply running the forward pass of the LSTM decoder over the gold target sequence. The continuous approximation to the hinge loss to be optimized is then: INLINEFORM1 . We empirically compare this approach with the proposed approach to optimize direct loss in experiments. ### Experimental Setup
Since our goal is to investigate the efficacy of our approach for training generic seq2seq models, we perform experiments on two NLP tagging tasks with very different characteristics and output search spaces: Named Entity Recognition (NER) and CCG supertagging. While seq2seq models are appropriate for CCG supertagging task because of the long-range correlations between the sequential output elements and a large search space, they are not ideal for NER which has a considerably smaller search space and weaker correlations between predictions at subsequent time steps. In our experiments, we observe improvements from our approach on both of the tasks. We use a seq2seq model with a bi-directional LSTM encoder (1 layer with tanh activation function) for the input sequence INLINEFORM0 , and an LSTM decoder (1 layer with tanh activation function) with a fixed attention mechanism that deterministically attends to the INLINEFORM1 -th input token when decoding the INLINEFORM2 -th output, and hence does not involve learning of any attention parameters. Since, computational complexity of our approach for optimization scales linearly with beam size for each instance, it is impractical to use very large beam sizes for training. Hence, beam size for all the beam search based experiments was set to 3 which resulted in improvements on both the tasks as discussed in the results. For both tasks, the direct loss function was the Hamming distance cost which aims to maximize word level accuracy. ### Named Entity Recognition
For named entity recognition, we use the CONLL 2003 shared task data BIBREF28 for German language and use the provided data splits. We perform no preprocessing on the data. The output vocabulary length (label space) is 10. A peculiar characteristic of this problem is that the training data is naturally skewed toward one default label (`O') because sentences typically do not contain many named entities and the evaluation focuses on the performance recognizing entities. Therefore, we modify the Hamming cost such that incorrect prediction of `O' is doubly penalized compared to other incorrect predictions. We use the hidden layers of size 64 and label embeddings of size 8. As mentioned earlier, seq2seq models are not an ideal choice for NER (tag-level correlations are short-ranged in NER – the unnecessary expressivity of full seq2seq models over simple encoder-classifier neural models makes training harder). However, we wanted to evaluate the effectiveness of our approach on different instantiations of seq2seq models. ### CCG Supertagging
We used the standard splits of CCG bank BIBREF29 for training, development, and testing. The label space of supertags is 1,284 which is much larger than NER. The distribution of supertags in the training data exhibits a long tail because these supertags encode specific syntactic information about the words' usage. The supertag labels are correlated with each other and many tags encode similar information about the syntax. Moreover, this task is sensitive to the long range sequential decisions and search effects because of how it holistically encodes the syntax of the entire sentence. We perform minor preprocessing on the data similar to the preprocessing in BIBREF30 . For this task, we used hidden layers of size 512 and the supertag label embeddings were also of size 512. The standard evaluation metric for this task is the word level label accuracy which directly corresponds to Hamming loss. ### Hyperparameter tuning
For tuning all the hyperparameters related to optimization we trained our models for 50 epochs and picked the models with the best performance on the development set. We also ran multiple random restarts for all the systems evaluated to account for performance variance across randomly started runs. We pretrained all our models with standard cross entropy training which was important for stable optimization of the non convex neural objective with a large parameter search space. This warm starting is a common practice in prior work on complex neural models BIBREF10 , BIBREF4 , BIBREF14 . ### Comparison
We report performance on validation and test sets for both the tasks in Tables 1 and 2. The baseline model is a cross entropy trained seq2seq model (Baseline CE) which is also used to warm start the the proposed optimization procedures in this paper. This baseline has been compared against the approximate direct loss training objective (Section SECREF9 ), referred to as INLINEFORM0 in the tables, and the approximate max-margin training objective (Section SECREF12 ), referred to as INLINEFORM1 in the tables. Results are reported for models when trained with annealing INLINEFORM2 , and also with a constant setting of INLINEFORM3 which is a very smooth but inaccurate approximation of the original direct loss that we aim to optimize. Comparisons have been made on the basis of performance of the models under different decoding paradigms (represented as different column in the tables): locally normalized decoding (CE greedy), hard beam search decoding and soft beam search decoding described in Section SECREF11 . ### Results
As shown in Tables 1 and 2, our approach INLINEFORM0 shows significant improvements over the locally normalized CE baseline with greedy decoding for both the tasks (+5.5 accuracy points gain for supertagging and +1.5 F1 points for NER). The improvement is more pronounced on the supertagging task, which is not surprising because: (i) the evaluation metric is tag-level accuracy which is congruent with the Hamming loss that INLINEFORM1 directly optimizes and (ii) the supertagging task itself is very sensitive to the search procedure because tags across time-steps tend to exhibit long range dependencies as they encode specialized syntactic information about word usage in the sentence. Another common trend to observe is that annealing INLINEFORM0 always results in better performance than training with a constant INLINEFORM1 for both INLINEFORM2 (Section SECREF9 ) and INLINEFORM3 (Section SECREF12 ). This shows that a stable training scheme that smoothly approaches minimizing the actual direct loss is important for our proposed approach. Additionally, we did not observe a large difference when our soft approximation is used for decoding (Section SECREF11 ) compared to hard beam search decoding, which suggests that our approximation to the hard beam search is as effective as its discrete counterpart. For supertagging, we observe that the baseline cross entropy trained model improves its predictions with beam search decoding compared to greedy decoding by 2 accuracy points, which suggests that beam search is already helpful for this task, even without search-aware training. Both the optimization schemes proposed in this paper improve upon the baseline with soft direct loss optimization ( INLINEFORM0 ), performing better than the approximate max-margin approach. For NER, we observe that optimizing INLINEFORM0 outperforms all the other approaches but we also observe interesting behaviour of beam search decoding and the approximate max-margin objective for this task. The pretrained CE baseline model yields worse performance when beam search is done instead of greedy locally normalized decoding. This is because the training data is heavily skewed toward the `O' label and hence the absolute score resolution between different tags at each time-step during decoding isn't enough to avoid leading beam search toward a wrong hypothesis path. We observed in our experiments that hard beam search resulted in predicting more `O's which also hurt the prediction of tags at future time steps and hurt precision as well as recall. Encouragingly, INLINEFORM1 optimization, even though warm started with a CE trained model that performs worse with beam search, led to the NER model becoming more search aware, which resulted in superior performance. However, we also observe that the approximate max-margin approach ( INLINEFORM2 ) performs poorly here. We attribute this to a deficiency in the max-margin objective when coupled with approximate search methods like beam search that do not provide guarantees on finding the supremum: one way to drive this objective down is to learn model scores such that the search for the best hypothesis is difficult, so that the value of the loss augmented decode is low, while the gold sequence maintains higher model score. Because we also warm started with a pre-trained model that results in a worse performance with beam search decode than with greedy decode, we observe the adverse effect of this deficiency. The result is a model that scores the gold hypothesis highly, but yields poor decoding outputs. This observation indicates that using max-margin based objectives with beam search during training actually may achieve the opposite of our original intent: the objective can be driven down by introducing search errors. The observation that our optimization method led to improvements on both the tasks–even on NER for which hard beam search during decoding on a CE trained model hurt the performance–by making the optimization more search aware, indicates the effectiveness of our approach for training seq2seq models. ### Conclusion
While beam search is a method of choice for performing search in neural sequence models, as our experiments confirm, it is not necessarily guaranteed to improve accuracy when applied to cross-entropy-trained models. In this paper, we propose a novel method for optimizing model parameters that directly takes into account the process of beam search itself through a continuous, end-to-end sub-differentiable relaxation of beam search composed with the final evaluation loss. Experiments demonstrate that our method is able to improve overall test-time results for models using beam search as a test-time inference method, leading to substantial improvements in accuracy. Figure 1: Illustration of our approximate continuous beam search (Algorithm 3) module to obtain hidden states for beam elements at the next time step (ht+1,∗), starting from the hidden states corresponding to beam elements are current time step (ht,∗) with beam size of 2. ‘Beam recurrence’ module has been expanded for ht+1,2 and similar procedure is carried out for ht+1,1. Table 1: Results on CCG Supertagging. Tag-level accuracy is reported in this table which is a standard evaluation metric for supertagging. Table 2: Results on Named Entity Recognition. Macro F1 over the prediction of different named entities is reported that is a standard evaluation metric for this task. | 2 accuracy points |
What is author's opinion on why current multimodal models cannot outperform models analyzing only text? | ### Introduction
Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease. In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech. Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K. The contributions of this work are as follows: [noitemsep,leftmargin=*] We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset. We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models. We study the challenges of the proposed task, and open the field for future research. ### Related Work ::: Hate Speech Detection
The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets: [noitemsep,leftmargin=*] RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate. DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate. WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets. Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner. Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%. ### Related Work ::: Visual and Textual Data Fusion
A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information –which they call question-guided kernels– and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection. ### The MMHS150K dataset
Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps. ### The MMHS150K dataset ::: Tweets Gathering
We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter. ### The MMHS150K dataset ::: Textual Image Filtering
We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\%$ of the collected tweets. ### The MMHS150K dataset ::: Annotation
We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers. We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision. We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training. We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research. As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords. ### Methodology ::: Unimodal Treatment ::: Images.
All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection. ### Methodology ::: Unimodal Treatment ::: Tweet Text.
We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word. ### Methodology ::: Unimodal Treatment ::: Image Text.
The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word. ### Methodology ::: Multimodal Architectures
The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any). ### Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)
The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26. ### Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)
Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\times 8\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer. ### Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)
The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\times 8\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\times 1\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\times 8\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29. ### Methodology ::: Multimodal Architectures ::: Training
We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text. ### Results
Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models. First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts. The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words. Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data: [noitemsep,leftmargin=*] Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection. Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network. Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate. ### Conclusions
In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in . Figure 1. Tweets from MMHS150K where the visual information adds relevant context for the hate speech detection task. Figure 2. Percentage of tweets per class in MMHS150K. Figure 3. Percentage of hate and not hate tweets for top keywords of MMHS150K. Figure 4. FCM architecture. Image and text representations are concatenated and processed by a set of fully connected layers. Figure 5. TKM architecture. Textual kernels are learnt from the text representations, and convolved with the image representation. Table 1. Performance of the proposed models, the LSTM and random scores. The Inputs column indicate which inputs are available at training and testing time. Figure 7. Top scored examples for hate (top) and for not hate (bottom) for the FCM model trained only with images. Figure 6. Precision vs Recall (left) and ROC curve (True Positive Rate vs False Positive Rate) (right) plots of the proposed models trained with the different inputs, the LSTM and random scores. | Noisy data, Complexity and diversity of multimodal relations, Small set of multimodal examples |
How did Read's parents feel about his work with the UN?
A. They were thankful he finally did something important with his life.
B. They were thankful he did not go to trade school.
C. They were upset he wanted to leave the United States for work.
D. They were surprised by his choice but did not keep them from going.
| 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 | C. They were upset he wanted to leave the United States for work. |
What didn't happen because of the original Super-Opener?
A. Feetch became famous
B. Feetch got a raise
C. People had to begin wearing hats and helmets
D. Piltdon made a lot of money
| THE SUPER OPENER BY MICHAEL ZUROY Here's why you should ask for a "Feetch M-D" next time you get a can opener! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, August 1958. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] "Feetch!" grated Ogden Piltdon, president of the Piltdon Opener Company, slamming the drafting board with his hairy fist, "I want results!" Heads lifted over boards. Kalvin Feetch shrunk visibly. "As chief engineer you're not carrying the ball," Piltdon went on savagely. "The Piltdon Can-Opener is trailing the competition. Advertising and Sales are breaking their necks. It's Engineering that's missing the boat!" "But Mr. Piltdon," remonstrated Feetch unsteadily under his employer's glare, "don't you remember? I tried to...." "For two years there hasn't been one lousy improvement in the Piltdon Can-Opener!" roared Mr. Piltdon. "Look at our competitors. The International rips apart cans in three and three-tenths seconds. Universal does it in four." "But Mr. Piltdon—" "The Minerva Mighty Midget does it in four point two two and plays Home Sweet Home in chimes. Our own Piltdon opener barely manages to open a can in eight point nine without chimes. Is this what I'm paying you for?" Feetch adjusted his spectacles with shaking hands. "But Mr. Piltdon, our opener still has stability, solidity. It is built to last. It has dignity...." "Dignity," pronounced Piltdon, "is for museums. Four months, Feetch! In four months I want a new can-opener that will be faster, lighter, stronger, flashier and more musical than any other on the market. I want it completely developed, engineered and tooled-up, ready for production. Otherwise, Feetch—" Feetch's body twitched. "But Mr. Piltdon, four months is hardly time enough for development, even with an adequate staff. I've been trying to tell you for years that we're bound to fall behind because we don't have enough personnel to conduct research. Our men can barely keep up with production and maintenance. If you would let me put on a few draftsmen and...." "Excuses," sneered Mr. Piltdon. "Your staff is more than adequate. I will not allow you to throw out my money. Four months, Feetch, no more!" Piltdon trudged out of the room, leaving behind him an oppressive silence. How could you set a time limit on research and development? A designer had to dream at his board, investigate, search, build, test, compare, discard. He had always wanted to devote all his time to research, but Piltdon Opener had not given him that opportunity. Twenty-five years! thought Feetch. Twenty-five years of close supervision, dead-lines, production headaches, inadequate facilities and assistance. What had happened, to the proud dream he once had, the dream of exploring uncharted engineering regions, of unlimited time to investigate and develop? Ah, well, thought Feetch straightening his thin shoulders, he had managed somehow to design a few good things during his twenty-five years with Piltdon. That was some satisfaction. What now? He had to hang on to his job. Technical work was scarce. Since the early 1980's the schools had been turning out more technicians than industry could absorb. He was too old to compete in the employment market. He couldn't afford to lose any money. Jenny wasn't well. How to meet this four month dead-line? He would get right on it himself, of course; Hanson—good man—could work with him. He shook his head despairingly. Something would be sure to blow up. Well, he had to start— "Chief," said Hanson a few weeks later as they entered the lab, "I'm beginning to wonder if the answer is in the hand mechanical type at all." "Got to be," answered Feetch tiredly. "We must work along classical can-opener lines. Departures, such as the thermal or motor-driven types, would be too expensive for mass production." Three new models and a group of cans were waiting for them on the bench. They began testing, Hanson operating the openers and Feetch clocking. "Four point four," announced Feetch after the last test. "Good, but not good enough. Too bulky. Appearance unsatisfactory. Chimes tinny. We've made progress, but we've a long way to go." The problem was tricky. It might seem that use of the proper gear ratios would give the required velocity, but there were too many other factors that negated this direct approach. The mechanism had to be compact and streamlined. Gear sizes had to be kept down. Can-top resistance, internal resistance, cutting tooth performance, handle size and moment, the minimum strength of a woman's hand were some of the variables that had to be balanced within rigid limits. Sector type cutters, traversing several arcs at the same time, had seemed to offer the answer for a while, but the adjusting mechanism necessary to compensate for variable can sizes had been too complex to be practical. There was the ever-present limit to production cost. Hanson's eyes were upon him. "Chief," he said, "it's a rotten shame. Twenty-five years of your life you put in with Piltdon, and he'd fire you just like that if you don't do the impossible. The Piltdon Company is built upon your designs and you get handed this deal!" "Well, well," said Feetch. "I drew my pay every week so I suppose I have no complaints. Although," a wistful note crept into his voice "I would have liked a little recognition. Piltdon is a household word, but who has heard of Feetch? Well,"—Feetch blew his nose—"how do we stand, Hanson?" Hanson's bull-dog features drew into a scowl. "Piltdon ought to be rayed," he growled. "O.K., Chief. Eleven experimental models designed to date. Two more on the boards. Nine completed and tested, two in work. Best performance, four point four, but model otherwise unsatisfactory." "Hello," said Feetch as an aproned machinist entered carrying a glistening mechanism. "Here's another model. Let's try it." The machinist departed and Hanson locked the opener on a can. "I hope——" he turned the handle, and stopped abruptly, staring down open-mouthed. A cylinder of close-packed beans rested on the bench under the opener. The can itself had disappeared. "Chief," said Hanson. "Chief." "Yes," said Feetch. "I see it too. Try another can." "Vegetable soup or spinach?" inquired Hanson dreamily. "Spinach, I think," said Feetch. "Where did the can go, do you suppose?" The spinach can disappeared. Likewise several corn cans, sweet potato cans and corned-beef hash cans, leaving their contents intact. It was rather disconcerting. "Dear, dear," said Feetch, regarding the piles of food on the bench. "There must be some explanation. I designed this opener with sixteen degree, twenty-two minute pressure angle modified involute gear teeth, seven degree, nineteen minute front clearance cutter angle and thirty-six degree, twelve minute back rake angle. I expected that such departures from the norm might achieve unconventional performance, but this—Dear, dear. Where do the cans go, I wonder?" "What's the difference? Don't you see what you've got here? It's the answer! It's more than the answer! We can put this right into work and beat the dead-line." Feetch shook his head. "No, Hanson. We're producing something we don't understand. What forces have we uncovered here? Where do the cans go? What makes them disappear? Are we dealing with a kinetic or a kinematic effect? What motions can we plot in the area of disappearance and what are their analytical mathematical formulae? What masses may be critical here? What transformations of energy are involved? No, Hanson, we must learn a lot more." "But Chief, your job." "I'll risk that. Not a word to Piltdon." Several days later, however, Piltdon himself charged into the drawing room and slapped Feetch heartily on the back, causing him to break a pencil point. "Feetch!" roared Piltdon. "Is this talk that's going around the plant true? Why didn't you tell me? Let's see it." After Piltdon had seen it his eyes took on a feverish glint. "This," he exulted, "will make can-opener history. Instantaneous opening! Automatic disposal! Wait until Advertising and Sales get hold of this! We'll throttle our competitors! The Piltdon Super-Opener we'll call it." "Mr. Piltdon—" said Feetch shakily. Piltdon stared at his chief engineer sharply. "What's the matter, Feetch? The thing can be duplicated, can't it?" "Yes, sir. I've just finished checking that. But I'm in the midst of further investigation of the effect. There's more here than just a new type can-opener, sir. A whole new field of physics. New principles. This is big, Mr. Piltdon. I recommend that we delay production until further research can be completed. Hire a few top scientists and engineers. Find out where the cans go. Put out a scientific paper on the effect." "Feetch," bit out Piltdon, his face growing hard. "Stow this hooey. I don't give a damn where the cans go. May I remind you that under our standard patent agreement, all rights to your invention belong to the company? As well as anything you may produce in the field within a year after leaving our employ? We have a good thing here, and I don't want you holding it back. We're going into production immediately." Close, thought Feetch, wearily. It had been a man-killing job, and it had been close, but he'd made it. Beat the time limit by a half-day. The first tentative shipments of Piltdon Super-Openers had gone to distributors along the Eastern seaboard. The first advertisements blazed in selected media. The first reorders came back, and then: "It's a sell-out!" crowed Piltdon, waving a sheaf of telegrams. "Step up production! Let 'er rip!" The Super-Openers rolled over the country. In a remarkably short time they appeared in millions of kitchens from coast-to-coast. Sales climbed to hundreds of thousands per day. Piltdon Opener went into peak production in three shifts, but was still unable to keep up with the demand. Construction was begun on a new plant, and additional plants were planned. Long lines waited in front of houseware stores. Department stores, lucky enough to have Super-Openers on hand, limited sales to one to a customer. Piltdon cancelled his advertising program. Newspapers, magazines, radio, television and word-of-mouth spread the fame of the opener so that advertising was unnecessary. Meanwhile, of course, government scientists, research foundations, universities and independent investigators began to look into this new phenomonen. Receiving no satisfactory explanation from Piltdon, they set up their own research. Far into the night burned the lights of countless laboratories. Noted physicists probed, measured, weighed, traced, X-rayed, dissolved, spun, peered at, photographed, magnetized, exploded, shattered and analyzed Super-Openers without achieving the glimmer of a satisfactory explanation. Competitors found the patent impossible to circumvent, for any departure from its exact specifications nullified the effect. Piltdon, genial these days with success and acclaim, roared at Feetch: "I'm putting you in for a raise. Yes sir! To reward you for assisting me with my invention I'm raising your pay two hundred dollars a year. That's almost four dollars a week, man." "Thank you, Mr. Piltdon." And still, thought Feetch wryly, he received no recognition. His name did not even appear on the patent. Well, well, that was the way it went. He must find his satisfaction in his work. And it had been interesting lately, the work he had been doing nights at home investigating what had been named the Piltdon Effect. It had been difficult, working alone and buying his own equipment. The oscillator and ultra microwave tracking unit had been particularly expensive. He was a fool, he supposed, to try independent research when so many huge scientific organizations were working on it. But he could no more keep away from it than he could stop eating. He still didn't know where the cans went, but somehow he felt that he was close to the answer. When he finally found the answer, it was too late. The Borenchuck incident was only hours away. As soon as he could get hold of Piltdon, Feetch said trembling, "Sir, I think I know where those cans are going. I recommend—" "Are you still worrying about that?" Piltdon roared jovially. "Leave that to the long-hairs. We're making money, that's all that counts, eh Feetch?" That night, at six-ten p.m., the Borenchuck family of Selby, South Dakota, sat down to their evening meal. Just as they started in on the soup, a rain of empty tin cans clattered down, splashed into the soup, raised a welt on the forehead of Borenchuck senior, settled down to a gentle, steady klunk! klunk! klunk! and inexorably began to pile up on the dining-room floor. They seemed to materialize from a plane just below the ceiling. The police called the fire department and the fire department stared helplessly and recommended the sanitation department. The incident made headlines in the local papers. The next day other local papers in widely scattered locations reported similar incidents. The following day, cans began falling on Chicago. St. Louis was next, and then over the entire nation the cans began to rain down. They fell outdoors and indoors, usually materializing at heights that were not dangerous. The deluge followed no pattern. Sometimes it would slacken, sometimes it would stop, sometimes begin heavily again. It fell in homes, on the streets, in theatres, trains, ships, universities and dog-food factories. No place was immune. People took to wearing hats indoors and out, and the sale of helmets boomed. All activity was seriously curtailed. A state of national emergency was declared. Government investigators went to work and soon confirmed what was generally suspected: these were the same cans that had been opened by the Piltdon Super-Opener. Statisticians and mathematicians calculated the mean rate of can precipitation and estimated that if all the cans opened by Piltdon openers were to come back, the deluge should be over in fifteen point twenty-nine days. Super-Opener sales of course immediately plummeted to zero and stayed there. Anti-Piltdon editorials appeared in the papers. Commentators accused Piltdon of deliberately hoaxing the public for his own gain. A Congressional investigation was demanded. Piltdon received threats of bodily injury. Lawsuits were filed against him. He barricaded himself in the plant, surrounded by bodyguards. Livid with fury and apprehension, he screamed at Feetch, "This is your doing, you vandal! I'm a ruined man!" A falling can caught him neatly on the tip of his nose. "But sir," trembled Feetch, dodging three spaghetti cans, "I tried to warn you." "You're through, Feetch!" raved Piltdon. "Fired! Get out! But before you go, I want you to know that I've directed the blame where it belongs. I've just released to the press the truth about who created the Super-Opener. Now, get out!" "Yes, sir," said Feetch paling. "Then you don't want to hear about my discovery of a way to prevent the cans from coming back?" Klunk! A barrage of cans hit the floor, and both men took refuge under Piltdon's huge desk. "No!" yelled Piltdon at Feetch's face which was inches away. "No, I——What did you say?" "A small design improvement sir, and the cans would disappear forever." Klunk! "Forever, Feetch?" "Yes sir." Klunk! Klunk! "You're positive, Feetch?" Piltdon's eyes glared into Feetch's. "Sir, I never make careless claims." "That's true," said Piltdon. His eyes grew dreamy. "It can be done," he mused. "The New Type Super-Opener. Free exchanges for the old. Cash guarantee that empty cans will never bother you. Take a licking at first, but then monopolize the market. All right, Feetch, I'll give you another chance. You'll turn over all the details to me. The patent on the improvement will naturally be mine. I'll get the credit for rectifying your blunder. Fine, fine. We'll work it out. Hop on production, at once, Feetch." Feetch felt himself sag inwardly. "Mr. Piltdon," he said. "I'm asking only one favor. Let me work full time on research and development, especially on the Piltdon effect. Hire a couple of extra men to help with production. I assure you the company will benefit in the end." "Damn it, no!" roared Piltdon. "How many times must I tell you? You got your job back, didn't you?" The prospect of long years of heavy production schedules, restricted engineering and tight supervision suddenly made Kalvin Feetch feel very tired. Research, he thought. Development. What he had always wanted. Over the years he had waited, thinking that there would be opportunities later. But now he was growing older, and he felt that there might not be a later. Somehow he would manage to get along. Perhaps someone would give him a job working in the new field he had pioneered. With a sense of relief he realized that he had made his decision. "Mr. Piltdon," Feetch said. "I—" klunk!—"resign." Piltdon started, extreme astonishment crossing his face. "No use," said Feetch. "Nothing you can say—" klunk! klunk! klunk!—"will make any difference now." "But see here, the New Type Super-Opener...!" "Will remain my secret. Good day." "Feetch!" howled Piltdon. "I order you to remain!" Feetch almost submitted from force of habit. He hesitated for a moment, then turned abruptly. "Good-day," said Feetch firmly, sprinting through the falling cans to the door. Money, Feetch decided after a while, was a good thing to have. His supply was running pretty low. He was not having any luck finding another job. Although the cans had stopped falling on the fifteenth day, as predicted by the statisticians, industry would not soon forget the inconvenience and losses caused by the deluge. It was not anxious to hire the man it regarded as responsible for the whole thing. "Feetch," the personnel man would read. "Kalvin Feetch." Then, looking up, "Not the Kalvin Feetch who—" "Yes," Feetch would admit miserably. "I am sorry, but—" He did no better with research organizations. Typical was a letter from the Van Terrel Foundation: "—cannot accept your application inasmuch as we feel your premature application of your discovery to profit-making denotes a lack of scientific responsibility and ethics not desirable in a member of our organization—former employer states the decision was yours entirely. Unfavorable reference—" Piltdon, Feetch thought, feeling a strange sensation deep within his chest that he had not the experience to recognize as the beginning of a slow anger, Piltdon was hitting low and getting away with it. Of course, if he were to agree to reveal his latest discoveries to a research organization, he would undoubtedly get an appointment. But how could he? Everything patentable in his work would automatically revert to Piltdon under the one year clause in the company patent agreement. No, Feetch told himself, he was revealing nothing that Piltdon might grab. The anger began to mount. But he was beginning to need money desperately. Jenny wasn't getting any better and medical bills were running high. The phone rang. Feetch seized it and said to the image: "Absolutely not." "I'll go up another ten dollars," grated the little Piltdon image. "Do you realize, man, this is the fourteenth raise I've offered you? A total increase of one hundred and twenty-six dollars? Be sensible, Feetch. I know you can't find work anywhere else." "Thanks to you. Mr. Piltdon, I wouldn't work for you if—" A barrage of rocks crashed against the heavy steel screening of the window. "What's going on!" yelled Piltdon. "Oh, I see. People throwing rocks at your house again? Oh, I know all about that, Feetch. I know that you're probably the most unpopular man alive to-day. I know about the rocks, the tomatoes, the rotten eggs, the sneaking out at night, the disguises you've had to use. Why don't you come back to us and change all that, Feetch? We'll put out the New Type Super-Opener and the world will soon forget about the old one." "No," said Feetch. "People will forget anyway—I hope." "If you won't think of yourself, at least think of your fellow workmen," begged Piltdon, his voice going blurry. "Do you realize that Piltdon Opener will soon be forced to close down, throwing all your former associates out of work? Think of Hanson, Sanchez, Forbes. They have families too. Think of the men in the shop, the girls in the office, the salesmen on the road. All, all unemployed because of you. Think of that, Feetch." Feetch blinked. This had not occurred to him. Piltdon eyed him sharply, then smiled with a hint of triumph. "Think it over, Feetch." Feetch sat, thinking it over. Was it right to let all these people lose their jobs? Frowning, he dialed Hanson's number. "Chief," said Hanson, "Forget it. The boys are behind you one hundred per cent. We'll make out." "But that's the trouble. I thought you'd feel like this, and I can't let you." "You're beginning to weaken. Don't. Think, chief, think. The brain that figured the Super-Opener can solve this." Feetch hung up. A glow of anger that had been building up in his chest grew warmer. He began pacing the floor. How he hated to do it. Think, Hanson had said. But he had. He's considered every angle, and there was no solution. Feetch walked into the kitchen and carefully poured himself a drink of water. He drank the water slowly and placed the glass on the washstand with a tiny click. It was the tiny click that did it. Something about it touched off the growing rage. If Piltdon were there he would have punched him in the nose. The twenty-five years. The tricks. The threats. Think? He'd figured the solution long ago, only he hadn't allowed himself to see it. Not lack of brains, lack of guts. Well, he thought grimly, dialing Piltdon's number, he was going through with it now. "Piltdon!" he barked. "Three p.m. tomorrow. My place. Be here. That's all." He hung up. In the same grim mood the following morning, he placed a few more calls. In the same mood that afternoon he stood in the middle of his living-room and looked at his visitors: Piltdon, Williams, the Government man; Billings from the Van Terrel Foundation; Steiner of Westchester University; the members of the press. "Gentlemen," he said. "I'll make it brief." He waved the papers in his hand. "Here is everything I know about what I call the Feetch Effect, including plans and specifications for the New Type Super-Opener. All of you have special reasons for being keenly interested in this information. I am now going to give a copy to each of you, providing one condition is met by Mr. Piltdon." He stared at Piltdon. "In short, I want fifty-one per cent of the stock of Piltdon Opener." Piltdon leaped from his chair. "Outrageous!" He roared. "Ridiculous!" "Fifty-one percent," said Feetch firmly. "Don't bother with any counterproposals or the interview is at an end." "Gentlemen!" squawked Piltdon, "I appeal to you—" "Stop bluffing," said Feetch coldly. "There's no other way out for you. Otherwise you're ruined. Here, sign this agreement." Piltdon threw the paper to the floor and screamed: "Gentlemen, will you be a party to this?" "Well," murmured the Government man, "I never did think Feetch got a fair shake." "This information is important to science," said the Van Terrel man. After Piltdon had signed, the papers were distributed. Published in the newspapers the following day, Feetch's statement read, in part: "The motion in space and time of the singular curvilinear proportions of the original Super-Opener combined with the capacitor effect built up as it increased its frictional electro-static charge in inverse proportion to the cube root of the tolerance between the involute teeth caused an instantaneous disruption of what I call the Alpha multi-dimensional screen. The can, being metallic, dropped through, leaving its non-metallic contents behind. The disruption was instantly repaired by the stable nature of the screen. "Beyond the screen is what I call Alpha space, a space apparently quite as extensive as our own universe. Unfortunately, as my investigations indicated, Alpha space seems to be thickly inhabited. These inhabitants, the nature of whom I have not yet ascertained, obviously resented the intrusion of the cans, developed a method of disrupting the screen from their side, and hurled the cans back at us. "However, I have established the existence of other spaces up to Mu space, and suspect that others exist beyond that. Beta space, which is also adjacent to our own space, is devoid of any form of life. The New Type Super-Opener is designed to pass cans through the Beta screen. Beta space will safely absorb an infinite number of cans. "I sincerely and humbly venture the opinion that we are on the threshold of tremendous and mighty discoveries. It is my belief that possibly an infinite number of universes exist in a type of laminated block separated by screens. "Therefore, might it not be that an infinite number of laminated blocks exist—?" "Mr Feetch—" said Piltdon. Feetch looked up from his desk in the newly constructed Feetch Multi-Dimensional Development Division of the Piltdon Opener Company. "Piltdon, don't bother me about production. Production is your problem." "But Mr. Feetch—" "Get out," said Feetch. Piltdon blanched and left. "As I was saying, Hanson—" continued Feetch. | A. Feetch became famous |
Why did the robot at the stream fall into the mud?
A. Alan managed to knock it down with his blaster.
B. It fell while trying to chase Alan, who managed to confuse it.
C. The mud blocked its sensors and it did not know how to move properly.
D. Its signals were disrupted and it malfunctioned.
| 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. | B. It fell while trying to chase Alan, who managed to confuse it. |
What is Bergstrom's relationship with Johnson?
A. Johnson is the dictator of St. Martin's where Bergstrom lives.
B. Johnson is the client paying Bergstrom to retrieve
Zarwell's memories.
C. Johnson is the man Bergstrom wants Zarwell to help overthrow the dictator.
D. Johnson is Bergstrom's boss.
| 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 | C. Johnson is the man Bergstrom wants Zarwell to help overthrow the dictator. |
Which two datasets is the system tested on? | ### None
[block] 1 5mm * 2pt*22pt [block] 2.1 5mm [block] 2.1.1 5mm ### Introduction
Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BIBREF0 Their ubiquity have given researchers a treasure trove of new data, especially in the realm of unstructured textual data. However, this new data source comes with usage restrictions in order to preserve the privacy of individual patients as mandated by the Health Insurance Portability and Accountability Act (HIPAA). HIPAA demands any researcher using this sensitive data to first strip the medical records of any protected health information (PHI), a process known as de-identification. HIPAA allows for two methods for de-identifying PHIs: the “Expert Determination” method in which an expert certifies that the information is rendered not individually identifiable, and the “Safe Harbor” method in which 18 identifiers are removed or replaced with random data in order for the data to be considered not individually identifiable. Our research pertains to the second method (a list of the relevant identifiers can be seen in Table TABREF4 ). The process of de-identification has been largely a manual and labor intensive task due to both the sensitive nature of the data and the limited availability of software to automate the task. This has led to a relatively small number of open health data sets available for public use. Recently, there have been two well-known de-identification challenges organized by Informatics for Integrating Biology and the Bedside (i2b2) to encourage innovation in the field of de-identification. In this paper, we build on the recent advances in natural language processing, especially with regards to word embeddings, by incorporating deep contextualized word embeddings developed by Peters et al. BIBREF1 into a deep learning architecture. More precisely, we present a deep learning architecture that differs from current architectures in literature by using bi-directional long short-term memory networks (Bi-LSTMs) with variational dropouts and deep contextualized word embeddings while also using components already present in other systems such traditional word embeddings, character LSTM embeddings and conditional random fields. We test this architecture on two gold standard data sets, the 2014 i2b2 de-identification Track 1 data set BIBREF2 and the nursing notes corpus BIBREF3 . The architecture achieves state-of-the-art performance on both data sets while also achieving faster convergence without the use of dictionaries (or gazetteers) or other rule-based methods that are typically used in other de-identification systems. The paper is organized as follows: In Section SECREF4 , we review the latest literature around techniques for de-identification with an emphasis on related work using deep learning techniques. In Section SECREF5 , we detail our deep learning architecture and also describe how we use the deep contextualized word embeddings method to improve our results. Section SECREF6 describes the two data sets we will use to evaluate our method and our evaluation metrics. Section SECREF7 presents the performance of our architecture on the data sets. In Section SECREF8 , we discuss the results and provide an analysis of the errors. Finally, in Section SECREF9 , we summarize our contributions while also discussing possible future research. ### Background and Related Work
The task of automatic de-identification has been heavily studied recently, in part due to two main challenges organized by i2b2 in 2006 and in 2014. The task of de-identification can be classified as a named entity recognition (NER) problem which has been extensively studied in machine learning literature. Automated de-identification systems can be roughly broken down into four main categories: ### Rule-based Systems
Rule-based systems make heavy use of pattern matching such as dictionaries (or gazetteers), regular expressions and other patterns. BIBREF2 Systems such as the ones described in BIBREF5 , BIBREF6 do not require the use any labeled data. Hence, they are considered as unsupervised learning systems. Advantages of such systems include their ease of use, ease of adding new patterns and easy interpretability. However, these methods suffer from a lack of robustness with regards to the input. For example, different casings of the same word could be misinterpreted as an unknown word. Furthermore, typographical errors are almost always present in most documents and rule-based systems often cannot correctly handle these types of inaccuracies present in the data. Critically, these systems cannot handle context which could render a medical text unreadable. For example, a diagnosis of “Lou Gehring disease” could be misidentified by such a system as a PHI of type Name. The system might replace the tokens “Lou” and “Gehring” with randomized names rendering the text meaningless if enough of these tokens were replaced. ### Machine Learning Systems
The drawbacks of such rule-based systems led researchers to adopt a machine learning approach. A comprehensive review of such systems can be found in BIBREF7 , BIBREF8 . In machine learning systems, given a sequence of input vectors INLINEFORM0 , a machine learning algorithm outputs label predictions INLINEFORM1 . Since the task of de-identification is a classification task, traditional classification algorithms such as support vector machines, conditional random fields (CRFs) and decision trees BIBREF9 have been used for building de-identification systems. These machine learning-based systems have the advantage of being able to recognize complex patterns that are not as readily evident to the naked eye. However, the drawback of such ML-based systems is that since classification is a supervised learning task, most of the common classification algorithms require a large labeled data set for robust models. Furthermore, since most of the algorithms described in the last paragraph maximize the likelihood of a label INLINEFORM0 given an vector of inputs INLINEFORM1 , rare patterns that might not occur in the training data set would be misclassified as not being a PHI label. Furthermore, these models might not be generalizable to other text corpora that contain significantly different patterns such as sentence structures and uses of different abbreviated words found commonly in medical notes than the training data set. ### Hybrid Systems
With both the advantages and disadvantages of stand alone rule-based and ML-based systems well-documented, systems such as the ones detailed in BIBREF2 combined both ML and rule-based systems to achieve impressive results. Systems such as the ones presented for 2014 i2b2 challenge by Yang et al. BIBREF10 and Liu et al. BIBREF11 used dictionary look-ups, regular expressions and CRFs to achieve accuracies of well over 90% in identifying PHIs. It is important to note that such hybrid systems rely heavily on feature engineering, a process that manufactures new features from the data that are not present in the raw text. Most machine learning techniques, for example, cannot take text as an input. They require the text to be represented as a vector of numbers. An example of such features can be seen in the system that won the 2014 i2b2 de-identification challenge by Yang et al. BIBREF10 . Their system uses token features such as part-of-speech tagging and chunking, contextual features such as word lemma and POS tags of neighboring words, orthographic features such as capitalization and punctuation marks and task-specific features such as building a list that included all the full names, acronyms of US states and collecting TF-IDF-statistics. Although such hybrid systems achieve impressive results, the task of feature engineering is a time-intensive task that might not be generalizable to other text corpora. ### Deep Learning Systems
With the disadvantages of the past three approaches to building a de-identification system in mind, the current state-of-the-art systems employ deep learning techniques to achieve better results than the best hybrid systems while also not requiring the time-consuming process of feature engineering. Deep learning is a subset of machine learning that uses multiple layers of Artificial Neural Networks (ANNs), which has been very succesful at most Natural Language Processing (NLP) tasks. Recent advances in the field of deep learning and NLP especially in regards to named entity recognition have allowed systems such as the one by Dernoncourt et al. BIBREF9 to achieve better results on the 2014 i2b2 de-identification challenge data set than the winning hybrid system proposed by Yang et al. BIBREF10 . The advances in NLP and deep learning which have allowed for this performance are detailed below. ANNs cannot take words as inputs and require numeric inputs, therefore, past approaches to using ANNs for NLP have been to employ a bag-of-words (BoW) representation of words where a dictionary is built of all known words and each word in a sentence is assigned a unique vector that is inputted into the ANN. A drawback of such a technique is such that words that have similar meanings are represented completely different. As a solution to this problem, a technique called word embeddings have been used. Word embeddings gained popularity when Mikolov et al. BIBREF12 used ANNs to generate a distributed vector representation of a word based on the usage of the word in a text corpus. This way of representing words allowed for similar words to be represented using vectors of similar values while also allowing for complex operations such as the famous example: INLINEFORM0 , where INLINEFORM1 represents a vector for a particular word. While pre-trained word embeddings such as the widely used GloVe BIBREF12 embeddings are revolutionary and powerful, such representations only capture one context representation, namely the one of the training corpus they were derived from. This shortcoming has led to the very recent development of context-dependent representations such as the ones developed by BIBREF1 , BIBREF13 , which can capture different features of a word. The Embeddings from Language Models (ELMo) from the system by Peters et al. BIBREF1 are used by the architecture in this paper to achieve state-of-the-art results. The ELMo representations, learned by combining Bi-LSTMs with a language modeling objective, captures context-depended aspects at the higher-level LSTM while the lower-level LSTM captures aspects of syntax. Moreover, the outputs of the different layers of the system can be used independently or averaged to output embeddings that significantly improve some existing models for solving NLP problems. These results drive our motivation to include the ELMo representations in our architecture. The use of ANNs for many machine learning tasks has gained popularity in recent years. Recently, a variant of recurrent neural networks (RNN) called Bi-directional Long Short-Term Memory (Bi-LSTM) networks has been successfully employed especially in the realm of NER. In fact, several Bi-LSTM architectures have been proposed to tackle the problem of NER: LSTM-CRF, LSTM-CNNs-CRF and LSTM-CNNs BIBREF9 . The current best performing system on the i2b2 dataset is in fact a system based on LSTM-CRF BIBREF9 . ### Method
Our architecture incorporates most of the recent advances in NLP and NER while also differing from other architectures described in the previous section by use of deep contextualized word embeddings, Bi-LSTMs with a variational dropout and the use of the Adam optimizer. Our architecture can be broken down into four distinct layers: pre-processing, embeddings, Bi-LSTM and CRF classifier. A graphical illustration of the architecture can be seen in Figure FIGREF16 while a summary of the parameters for our architecture can be found in Table TABREF17 . ### Pre-processing Layer
For a given document INLINEFORM0 , we first break down the document into sentences INLINEFORM1 , tokens INLINEFORM2 and characters INLINEFORM3 where INLINEFORM4 represents the document number, INLINEFORM5 represents the sentence number, INLINEFORM6 represents the token number, and INLINEFORM7 represents the character number. For example, INLINEFORM8 Patient, where the token: “Patient” represents the 3rd token of the 2nd sentence of the 1st document. After parsing the tokens, we use a widely used and readily available Python toolkit called Natural Langauge ToolKit (NLTK) to generate a part-of-speech (POS) tag for each token. This generates a POS feature for each token which we will transform into a 20-dimensional one-hot-encoded input vector, INLINEFORM0 , then feed into the main LSTM layer. For the data labels, since the data labels can be made up of multiple tokens, we formatted the labels to the BIO scheme. The BIO scheme tags the beginning of a PHI with a B-, the rest of the same PHI tokens as I- and the rest of the tokens not associated with a PHI as O. For example, the sentence, “ INLINEFORM0 ”, would have the corresponding labels, “ INLINEFORM1 ”. ### Embedding Layer
For the embedding layer, we use three main types of embeddings to represent our input text: traditional word embeddings, ELMo embeddings and character-level LSTM embeddings. The traditional word embeddings use the latest GloVe 3 BIBREF12 pre-trained word vectors that were trained on the Common Crawl with about 840 billion tokens. For every token input, INLINEFORM0 , the GloVe system outputs INLINEFORM1 , a dense 300-dimensional word vector representation of that same token. We also experimented with other word embeddings by using the bio-medical corpus trained word embeddings BIBREF14 to see if having word embeddings trained on medical texts will have an impact on our results. As mentioned in previous sections, we also incorporate the powerful ELMo representations as a feature to our Bi-LSTMs. The specifics of the ELMo representations are detailed in BIBREF1 . In short, we compute an ELMo representation by passing a token input INLINEFORM0 to the ELMo network and averaging the the layers of the network to produce an 1024-dimensional ELMo vector, INLINEFORM1 . Character-level information can capture some information about the token itself while also mitigating issues such as unseen words and misspellings. While lemmatizing (i.e., the act of turning inflected forms of a word to their base or dictionary form) of a token can solve these issues, tokens such as the ones found in medical texts could have important distinctions between, for example, the grammar form of the token. As such, Ma et al. BIBREF15 have used Convolutional Neural Networks (CNN) while Lample et al. BIBREF16 have used Bi-LSTMs to produce character-enhanced representations of each unique token. We have utilized the latter approach of using Bi-LSTMs for produce a character-enhanced embedding for each unique word in our data set. Our parameters for the forward and backward LSTMs are 25 each and the maximum character length is 25, which results in an 50-dimensional embedding vector, INLINEFORM0 , for each token. After creating the three embeddings for each token, INLINEFORM0 , we concatenate the GloVe and ELMo representations to produce a single 1324-dimensional word input vector, INLINEFORM1 . The concatenated word vector is then further concatenated with the character embedding vector, INLINEFORM2 , POS one-hot-encoded vector, INLINEFORM3 , and the casing embedded vector, INLINEFORM4 , to produce a single 1394-dimensional input vector, INLINEFORM5 , that we feed into our Bi-LSTM layer. ### Bi-LSTM Layer
The Bi-LSTM layer is composed of two LSTM layers, which are a variant of the Bidirectional RNNs. In short, the Bi-LSTM layer contains two independent LSTMs in which one network is fed input in the normal time direction while the other network is fed input in the reverse time direction. The outputs of the two networks can then be combined using either summation, multiplication, concatenation or averaging. Our architecture uses simple concatenation to combine the outputs of the two networks. Our architecture for the Bi-LSTM layer is similar to the ones used by BIBREF16 , BIBREF17 , BIBREF18 with each LSTM containing 100 hidden units. To ensure that the neural networks do not overfit, we use a variant of the popular dropout technique called variational dropout BIBREF19 to regularize our neural networks. Variational dropout differs from the traditional naïve dropout technique by having the same dropout mask for the inputs, outputs and the recurrent layers BIBREF19 . This is in contrast to the traditional technique of applying a different dropout mask for each of the input and output layers. BIBREF20 shows that variational dropout applied to the output and recurrent units performs significantly better than naïve dropout or no dropout for the NER tasks. As such, we apply a dropout probability of 0.5 for both the output and the recurrent units in our architecture. ### CRF layer
As a final step, the outputs of the Bi-LSTM layer are inputted into a linear-chain CRF classifier, which maximizes the label probabilities of the entire input sentence. This approach is identical to the Bi-LSTM-CRF model by Huang et al. BIBREF21 CRFs have been incorporated in numerous state-of-the-art models BIBREF16 , BIBREF18 , BIBREF3 because of their ability to incorporate tag information at the sentence level. While the Bi-LSTM layer takes information from the context into account when generating its label predictions, each decision is independent from the other labels in the sentence. The CRF allows us to find the labeling sequence in a sentence with the highest probability. This way, both previous and subsequent label information is used in determining the label of a given token. As a sequence model, the CRF posits a probability model for the label sequence of the tokens in a sentence, conditional on the word sequence and the output scores from the Bi-LTSM model for the given sentence. In doing so, the CRF models the conditional distribution of the label sequence instead of a joint distribution with the words and output scores. Thus, it does not assume independent features, while at the same time not making strong distributional assumptions about the relationship between the features and sequence labels. ### Data and Evaluation Metrics
The two main data sets that we will use to evaluate our architecture are the 2014 i2b2 de-identification challenge data set BIBREF2 and the nursing notes corpus BIBREF3 . The i2b2 corpus was used by all tracks of the 2014 i2b2 challenge. It consists of 1,304 patient progress notes for 296 diabetic patients. All the PHIs were removed and replaced with random replacements. The PHIs in this data set were broken down first into the HIPAA categories and then into the i2b2-PHI categories as shown in Table TABREF23 . Overall, the data set contains 56,348 sentences with 984,723 separate tokens of which 41,355 are separate PHI tokens, which represent 28,867 separate PHI instances. For our test-train-valid split, we chose 10% of the training sentences to serve as our validation set, which represents 3,381 sentences while a separately held-out official test data set was specified by the competition. This test data set contains 22,541 sentences including 15,275 separate PHI tokens. The nursing notes were originally collected by Neamatullah et al. BIBREF3 . The data set contains 2,434 notes of which there are 1,724 separate PHI instances. A summary of the breakdown of the PHI categories of this nursing corpora can be seen in Table TABREF23 . ### Evaluation Metrics
For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. We will compute both the binary INLINEFORM1 score and the three metrics for each PHI type for both data sets. Note that binary INLINEFORM2 score calculates whether or not a token was identified as a PHI as opposed to correctly predicting the right PHI type. For de-identification, we place more importance on identifying if a token was a PHI instance with correctly predicting the right PHI type as a secondary objective. INLINEFORM3 INLINEFORM4 Notice that a high recall is paramount given the risk of accidentally disclosing sensitive patient information if not all PHI are detected and removed from the document or replaced by fake data. A high precision is also desired to preserve the integrity of the documents, as a large number of false positives might obscure the meaning of the text or even distort it. As the harmonic mean of precision and recall, the INLINEFORM0 score gives an overall measure for model performance that is frequently employed in the NLP literature. As a benchmark, we will use the results of the systems by Burckhardt et al. BIBREF22 , Liu et al. BIBREF18 , Dernoncourt et al. BIBREF9 and Yang et al. BIBREF10 on the i2b2 dataset and the performance of Burckhardt et al. on the nursing corpus. Note that Burckhardt et al. used the entire data set for their results as it is an unsupervised learning system while we had to split our data set into 60% training data and 40% testing data. ### Results
We evaluated the architecture on both the i2b2-PHI categories and the HIPAA-PHI categories for the i2b2 data set based on token-level labels. Note that the HIPAA categories are a super set of the i2b2-PHI categories. We also ran the analysis 5+ times to give us a range of maximum scores for the different data sets. Table TABREF25 gives us a summary of how our architecture performed against other systems on the binary INLINEFORM0 score metrics while Table TABREF26 and Table TABREF27 summarizes the performance of our architecture against other systems on HIPAA-PHI categories and i2b2-PHI categories respectively. Table TABREF28 presents a summary of the performance on the nursing note corpus while also contrasting the performances achieved by the deidentify system. ### Discussion and Error Analysis
As we can see in Table TABREF26 , with the exception of ID, our architecture performs considerably better than systems by Liu et al. and Yang et al. Dernoncourt et al. did not provide exact figures for the HIPAA-PHI categories so we have excluded them from our analysis. Furthermore, Table TABREF25 shows that our architecture performs similarly to the best scores achieved by Dernoncourt et al., with our architecture slightly edging out Dernoncourt et al. on the precision metric. For the nursing corpus, our system, while not performing as well as the performances on i2b2 data set, managed to best the scores achieved by the deidentify system while also achieving a binary INLINEFORM0 score of over 0.812. It is important to note that deidentify was a unsupervised learning system, it did not require the use of a train-valid-test split and therefore, used the whole data set for their performance numbers. The results of our architecture is assessed using a 60%/40% train/test split. Our architecture noticeably converges faster than the NeuroNER, which was trained for 100 epochs and the system by Liu et al. BIBREF18 which was trained for 80 epochs. Different runs of training our architecture on the i2b2 dataset converge at around 23 INLINEFORM0 4 epochs. A possible explanation for this is due to our architecture using the Adam optimizer, whereas the NeuroNER system use the Stochastic Gradient Descent (SGD) optimizer. In fact, Reimers et al. BIBREF20 show that the SGD optimizer performed considerably worse than the Adam optimizer for different NLP tasks. Furthermore, we also do not see any noticeable improvements from using the PubMed database trained word embeddings BIBREF14 instead of the general text trained GloVe word embeddings. In fact, we consistently saw better INLINEFORM0 scores using the GloVe embeddings. This could be due to the fact that our use case was for identifying general labels such as Names, Phones, Locations etc. instead of bio-medical specific terms such as diseases which are far better represented in the PubMed corpus. ### Error Analysis
We will mainly focus on the two PHI categories: Profession and ID for our error analysis on the i2b2 data set. It is interesting to note that the best performing models on the i2b2 data set by Dernoncourt et al. BIBREF9 experienced similar lower performances on the same two categories. However, we note the performances by Dernoncourt et al. were achieved using a “combination of n-gram, morphological, orthographic and gazetteer features” BIBREF9 while our architecture uses only POS tagging as an external feature. Dernoncourt et al. posits that the lower performance on the Profession category might be due to the close embeddings of the Profession tokens to other PHI tokens which we can confirm on our architecture as well. Furthermore, our experiments show that the Profession PHI performs considerably better with the PubMed embedded model than GloVe embedded model. This could be due to the fact that PubMed embeddings were trained on the PubMed database, which is a database of medical literature. GloVe on the other hand was trained on a general database, which means the PubMed embeddings for Profession tokens might not be as close to other tokens as is the case for the GloVe embeddings. For the ID PHI, our analysis shows that some of the errors were due to tokenization errors. For example, a “:” was counted as PHI token which our architecture correctly predicted as not a PHI token. Since our architecture is not custom tailored to detect sophisticated ID patterns such as the systems in BIBREF9 , BIBREF10 , we have failed to detect some ID PHIs such as “265-01-73”, a medical record number, which our architecture predicted as a phone number due to the format of the number. Such errors could easily be mitigated by the use of simple regular expressions. We can see that our architecture outperforms the deidentify system by a considerable margin on most categories as measured by the INLINEFORM0 score. For example, the authors of deidentify note that Date PHIs have considerably low precision values while our architecture achieve a precision value of greater than 0.915% for the Date PHI. However, Burckhardt et al. BIBREF22 achieve an impressive precision of 0.899 and recall of 1.0 for the Phone PHI while our architecture only manages 0.778 and 0.583 respectively. Our analysis of this category shows that this is mainly due a difference in tokenization, stand alone number are being classified as not a PHI. We tried to use the model that we trained on the i2b2 data set to predict the categories of the nursing data set. However, due to difference in the text structure, the actual text and the format, we achieved less than random performance on the nursing data set. This brings up an important point about the transferability of such models. ### Ablation Analysis
Our ablation analysis shows us that the layers of our models adds to the overall performance. Figure FIGREF33 shows the binary INLINEFORM0 scores on the i2b2 data set with each bar being a feature toggled off. For example, the “No Char Embd” bar shows the performance of the model with no character embeddings and everything else the same as our best model. We can see a noticeable change in the performance if we do not include the ELMo embeddings versus no GloVe embeddings. The slight decrease in performance when we use no GloVe embeddings shows us that this is a feature we might choose to exclude if computation time is limited. Furthermore, we can see the impact of having no variational dropout and only using a naïve dropout, it shows that variational dropout is better at regularizing our neural network. ### Conclusion
In this study, we show that our deep learning architecture, which incorporates the latest developments in contextual word embeddings and NLP, achieves state-of-the-art performance on two widely available gold standard de-identification data sets while also achieving similar performance as the best system available in less epochs. Our architecture also significantly improves over the performance of the hybrid system deidentify on the nursing data set. This architecture could be integrated into a client-ready system such as the deidentify system. However, as mentioned in Section SECREF8 , the use of a dictionary (or gazetter) might help improve the model even further specially with regards to the Location and Profession PHI types. Such a hybrid system would be highly beneficial to practitioners that needs to de-identify patient data on a daily basis. Table 1: Protected Health Information Types [5] Table 2: Summary of architecture parameters Figure 1: Deep Learning Architecture Table 3: PHI Categories Breakdown between HIPPA and i2b2 Table 4: Binary F-1 scores comparison Table 5: HIPAA-PHI result Table 6: Scores broken down by i2b2 PHI categories Table 7: Nursing Dataset performance Figure 2: Ablation Analysis | 2014 i2b2 de-identification challenge data set BIBREF2, nursing notes corpus BIBREF3 |
What datasets used for evaluation? | ### Introduction
Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually ambiguous, i.e., different named entities may share the same surface form and the same entity may have multiple aliases. EL is key for information retrieval (IE) and has many applications, such as knowledge base population (KBP), question answering (QA), etc. Existing EL methods can be divided into two categories: local model and global model. Local models concern mainly on contextual words surrounding the mentions, where mentions are disambiguated independently. These methods are not work well when the context information is not rich enough. Global models take into account the topical coherence among the referred entities within the same document, where mentions are disambiguated jointly. Most of previous global models BIBREF0 , BIBREF1 , BIBREF2 calculate the pairwise scores between all candidate entities and select the most relevant group of entities. However, the consistency among wrong entities as well as that among right ones are involved, which not only increases the model complexity but also introduces some noises. For example, in Figure 1, there are three mentions "France", "Croatia" and "2018 World Cup", and each mention has three candidate entities. Here, "France" may refer to French Republic, France national basketball team or France national football team in KB. It is difficult to disambiguate using local models, due to the scarce common information in the contextual words of "France" and the descriptions of its candidate entities. Besides, the topical coherence among the wrong entities related to basketball team (linked by an orange dashed line) may make the global models mistakenly refer "France" to France national basketball team. So, how to solve these problems? We note that, mentions in text usually have different disambiguation difficulty according to the quality of contextual information and the topical coherence. Intuitively, if we start with mentions that are easier to disambiguate and gain correct results, it will be effective to utilize information provided by previously referred entities to disambiguate subsequent mentions. In the above example, it is much easier to map "2018 World Cup" to 2018 FIFA World Cup based on their common contextual words "France", "Croatia", "4-2". Then, it is obvious that "France" and "Croatia" should be referred to the national football team because football-related terms are mentioned many times in the description of 2018 FIFA World Cup. Inspired by this intuition, we design the solution with three principles: (i) utilizing local features to rank the mentions in text and deal with them in a sequence manner; (ii) utilizing the information of previously referred entities for the subsequent entity disambiguation; (iii) making decisions from a global perspective to avoid the error propagation if the previous decision is wrong. In order to achieve these aims, we consider global EL as a sequence decision problem and proposed a deep reinforcement learning (RL) based model, RLEL for short, which consists of three modules: Local Encoder, Global Encoder and Entity Selector. For each mention and its candidate entities, Local Encoder encodes the local features to obtain their latent vector representations. Then, the mentions are ranked according to their disambiguation difficulty, which is measured by the learned vector representations. In order to enforce global coherence between mentions, Global Encoder encodes the local representations of mention-entity pairs in a sequential manner via a LSTM network, which maintains a long-term memory on features of entities which has been selected in previous states. Entity Selector uses a policy network to choose the target entities from the candidate set. For a single disambiguation decision, the policy network not only considers the pairs of current mention-entity representations, but also concerns the features of referred entities in the previous states which is pursued by the Global Encoder. In this way, Entity Selector is able to take actions based on the current state and previous ones. When eliminating the ambiguity of all mentions in the sequence, delayed rewards are used to adjust its policy in order to gain an optimized global decision. Deep RL model, which learns to directly optimize the overall evaluation metrics, works much better than models which learn with loss functions that just evaluate a particular single decision. By this property, RL has been successfully used in many NLP tasks, such as information retrieval BIBREF3 , dialogue system BIBREF4 and relation classification BIBREF5 , etc. To the best of our knowledge, we are the first to design a RL model for global entity linking. And in this paper, our RL model is able to produce more accurate results by exploring the long-term influence of independent decisions and encoding the entities disambiguated in previous states. In summary, the main contributions of our paper mainly include following aspects: ### Methodology
The overall structure of our RLEL model is shown in Figure 2. The proposed framework mainly includes three parts: Local Encoder which encodes local features of mentions and their candidate entities, Global Encoder which encodes the global coherence of mentions in a sequence manner and Entity Selector which selects an entity from the candidate set. As the Entity Selector and the Global Encoder are correlated mutually, we train them jointly. Moreover, the Local Encoder as the basis of the entire framework will be independently trained before the joint training process starts. In the following, we will introduce the technical details of these modules. ### Preliminaries
Before introducing our model, we firstly define the entity linking task. Formally, given a document $D$ with a set of mentions $M = \lbrace m_1, m_2,...,m_k\rbrace $ , each mention $ m_t \in D$ has a set of candidate entities $C_{m_t} = \lbrace e_{t}^1, e_{t}^2,..., e_{t}^n\rbrace $ . The task of entity linking is to map each mention $m_t$ to its corresponding correct target entity $e_{t}^+$ or return "NIL" if there is not correct target entity in the knowledge base. Before selecting the target entity, we need to generate a certain number of candidate entities for model selection. Inspired by the previous works BIBREF6 , BIBREF7 , BIBREF8 , we use the mention's redirect and disambiguation pages in Wikipedia to generate candidate sets. For those mentions without corresponding disambiguation pages, we use its n-grams to retrieve the candidates BIBREF8 . In most cases, the disambiguation page contains many entities, sometimes even hundreds. To optimize the model's memory and avoid unnecessary calculations, the candidate sets need to be filtered BIBREF9 , BIBREF0 , BIBREF1 . Here we utilize the XGBoost model BIBREF10 as an entity ranker to reduce the size of candidate set. The features used in XGBoost can be divided into two aspects, the one is string similarity like the Jaro-Winkler distance between the entity title and the mention, the other is semantic similarity like the cosine distance between the mention context representation and the entity embedding. Furthermore, we also use the statistical features based on the pageview and hyperlinks in Wikipedia. Empirically, we get the pageview of the entity from the Wikipedia Tool Labs which counts the number of visits on each entity page in Wikipedia. After ranking the candidate sets based on the above features, we take the top k scored entities as final candidate set for each mention. ### Local Encoder
Given a mention $m_t$ and the corresponding candidate set $\lbrace e_t^1, e_t^2,..., \\ e_t^k\rbrace $ , we aim to get their local representation based on the mention context and the candidate entity description. For each mention, we firstly select its $n$ surrounding words, and represent them as word embedding using a pre-trained lookup table BIBREF11 . Then, we use Long Short-Term Memory (LSTM) networks to encode the contextual word sequence $\lbrace w_c^1, w_c^2,..., w_c^n\rbrace $ as a fixed-size vector $V_{m_t}$ . The description of entity is encoded as $D_{e_t^i}$ in the same way. Apart from the description of entity, there are many other valuable information in the knowledge base. To make full use of these information, many researchers trained entity embeddings by combining the description, category, and relationship of entities. As shown in BIBREF0 , entity embeddings compress the semantic meaning of entities and drastically reduce the need for manually designed features or co-occurrence statistics. Therefore, we use the pre-trained entity embedding $E_{e_t^i}$ and concatenate it with the description vector $D_{e_t^i}$ to enrich the entity representation. The concatenation result is denoted by $V_{e_t^i}$ . After getting $V_{e_t^i}$ , we concatenate it with $V_{m_t}$ and then pass the concatenation result to a multilayer perceptron (MLP). The MLP outputs a scalar to represent the local similarity between the mention $m_t$ and the candidate entity $e_t^i$ . The local similarity is calculated by the following equations: $$\Psi (m_t, e_t^i) = MLP(V_{m_t}\oplus {V_{e_t^i}})$$ (Eq. 9) Where $\oplus $ indicates vector concatenation. With the purpose of distinguishing the correct target entity and wrong candidate entities when training the local encoder model, we utilize a hinge loss that ranks ground truth higher than others. The rank loss function is defined as follows: $$L_{local} = max(0, \gamma -\Psi (m_t, e_t^+)+\Psi (m_t, e_t^-))$$ (Eq. 10) When optimizing the objective function, we minimize the rank loss similar to BIBREF0 , BIBREF1 . In this ranking model, a training instance is constructed by pairing a positive target entity $e_t^+$ with a negative entity $e_t^-$ . Where $\gamma > 0$ is a margin parameter and our purpose is to make the score of the positive target entity $e_t^+$ is at least a margin $\gamma $ higher than that of negative candidate entity $e_t^-$ . With the local encoder, we obtain the representation of mention context and candidate entities, which will be used as the input into the global encoder and entity selector. In addition, the similarity scores calculated by MLP will be utilized for ranking mentions in the global encoder. ### Global Encoder
In the global encoder module, we aim to enforce the topical coherence among the mentions and their target entities. So, we use an LSTM network which is capable of maintaining the long-term memory to encode the ranked mention sequence. What we need to emphasize is that our global encoder just encode the mentions that have been disambiguated by the entity selector which is denoted as $V_{a_t}$ . As mentioned above, the mentions should be sorted according to their contextual information and topical coherence. So, we firstly divide the adjacent mentions into a segment by the order they appear in the document based on the observation that the topical consistency attenuates along with the distance between the mentions. Then, we sort mentions in a segment based on the local similarity and place the mention that has a higher similarity value in the front of the sequence. In Equation 1, we define the local similarity of $m_i$ and its corresponding candidate entity $e_t^i$ . On this basis, we define $\Psi _{max}(m_i, e_i^a)$ as the the maximum local similarity between the $m_i$ and its candidate set $C_{m_i} = \lbrace e_i^1, e_i^2,..., e_i^n\rbrace $ . We use $\Psi _{max}(m_i, e_i^a)$ as criterion when sorting mentions. For instance, if $\Psi _{max}(m_i, e_i^a) > \Psi _{max}(m_j, e_j^b)$ then we place $m_i$ before $m_j$ . Under this circumstances, the mentions in the front positions may not be able to make better use of global consistency, but their target entities have a high degree of similarity to the context words, which allows them to be disambiguated without relying on additional information. In the end, previous selected target entity information is encoded by global encoder and the encoding result will be served as input to the entity selector. Before using entity selector to choose target entities, we pre-trained the global LSTM network. During the training process, we input not only positive samples but also negative ones to the LSTM. By doing this, we can enhance the robustness of the network. In the global encoder module, we adopt the following cross entropy loss function to train the model. $$L_{global} = -\frac{1}{n}\sum _x{\left[y\ln {y^{^{\prime }}} + (1-y)\ln (1-y^{^{\prime }})\right]}$$ (Eq. 12) Where $y\in \lbrace 0,1\rbrace $ represents the label of the candidate entity. If the candidate entity is correct $y=1$ , otherwise $y=0$ . $y^{^{\prime }}\in (0,1)$ indicates the output of our model. After pre-training the global encoder, we start using the entity selector to choose the target entity for each mention and encode these selections. ### Entity Selector
In the entity selector module, we choose the target entity from candidate set based on the results of local and global encoder. In the process of sequence disambiguation, each selection result will have an impact on subsequent decisions. Therefore, we transform the choice of the target entity into a reinforcement learning problem and view the entity selector as an agent. In particular, the agent is designed as a policy network which can learn a stochastic policy and prevents the agent from getting stuck at an intermediate state BIBREF12 . Under the guidance of policy, the agent can decide which action (choosing the target entity from the candidate set)should be taken at each state, and receive a delay reward when all the selections are made. In the following part, we first describe the state, action and reward. Then, we detail how to select target entity via a policy network. The result of entity selection is based on the current state information. For time $t$ , the state vector $S_t$ is generated as follows: $$S_t = V_{m_i}^t\oplus {V_{e_i}^t}\oplus {V_{feature}^t}\oplus {V_{e^*}^{t-1}}$$ (Eq. 15) Where $\oplus $ indicates vector concatenation. The $V_{m_i}^t$ and $V_{e_i}^t$ respectively denote the vector of $m_i$ and $e_i$ at time $t$ . For each mention, there are multiple candidate entities correspond to it. With the purpose of comparing the semantic relevance between the mention and each candidate entity at the same time, we copy multiple copies of the mention vector. Formally, we extend $V_{m_i}^t \in \mathbb {R}^{1\times {n}}$ to $V_{m_i}^t{^{\prime }} \in \mathbb {R}^{k\times {n}}$ and then combine it with $V_{e_i}^t \in \mathbb {R}^{k\times {n}}$ . Since $V_{m_i}^t$ and $V_{m_i}^t$0 are mainly to represent semantic information, we add feature vector $V_{m_i}^t$1 to enrich lexical and statistical features. These features mainly include the popularity of the entity, the edit distance between the entity description and the mention context, the number of identical words in the entity description and the mention context etc. After getting these feature values, we combine them into a vector and add it to the current state. In addition, the global vector $V_{m_i}^t$2 is also added to $V_{m_i}^t$3 . As mentioned in global encoder module, $V_{m_i}^t$4 is the output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7 . Thus, the state $V_{m_i}^t$8 contains current information and previous decisions, while also covering the semantic representations and a variety of statistical features. Next, the concatenated vector will be fed into the policy network to generate action. According to the status at each time step, we take corresponding action. Specifically, we define the action at time step $t$ is to select the target entity $e_t^*$ for $m_t$ . The size of action space is the number of candidate entities for each mention, where $a_i \in \lbrace 0,1,2...k\rbrace $ indicates the position of the selected entity in the candidate entity list. Clearly, each action is a direct indicator of target entity selection in our model. After completing all the actions in the sequence we will get a delayed reward. The agent takes the reward value as the feedback of its action and learns the policy based on it. Since current selection result has a long-term impact on subsequent decisions, we don't give an immediate reward when taking an action. Instead, a delay reward is given by follows, which can reflect whether the action improves the overall performance or not. $$R(a_t) = p(a_t)\sum _{j=t}^{T}p(a_j) + (1 - p(a_t))(\sum _{j=t}^{T}p(a_j) + t - T)$$ (Eq. 16) where $p(a_t)\in \lbrace 0,1\rbrace $ indicates whether the current action is correct or not. When the action is correct $p(a_t)=1$ otherwise $p(a_t)=0$ . Hence $\sum _{j=t}^{T}p(a_j)$ and $\sum _{j=t}^{T}p(a_j) + t - T$ respectively represent the number of correct and wrong actions from time t to the end of episode. Based on the above definition, our delayed reward can be used to guide the learning of the policy for entity linking. After defining the state, action, and reward, our main challenge becomes to choose an action from the action space. To solve this problem, we sample the value of each action by a policy network $\pi _{\Theta }(a|s)$ . The structure of the policy network is shown in Figure 3. The input of the network is the current state, including the mention context representation, candidate entity representation, feature representation, and encoding of the previous decisions. We concatenate these representations and fed them into a multilayer perceptron, for each hidden layer, we generate the output by: $$h_i(S_t) = Relu(W_i*h_{i-1}(S_t) + b_i)$$ (Eq. 17) Where $W_i$ and $ b_i$ are the parameters of the $i$ th hidden layer, through the $relu$ activation function we get the $h_i(S_t)$ . After getting the output of the last hidden layer, we feed it into a softmax layer which generates the probability distribution of actions. The probability distribution is generated as follows: $$\pi (a|s) = Softmax(W * h_l(S) + b)$$ (Eq. 18) Where the $W$ and $b$ are the parameters of the softmax layer. For each mention in the sequence, we will take action to select the target entity from its candidate set. After completing all decisions in the episode, each action will get an expected reward and our goal is to maximize the expected total rewards. Formally, the objective function is defined as: $$\begin{split}
J(\Theta ) &= \mathbb {E}_{(s_t, a_t){\sim }P_\Theta {(s_t, a_t)}}R(s_1{a_1}...s_L{a_L}) \\
&=\sum _{t}\sum _{a}\pi _{\Theta }(a|s)R(a_t)
\end{split}$$ (Eq. 19) Where $P_\Theta {(s_t, a_t)}$ is the state transfer function, $\pi _{\Theta }(a|s)$ indicates the probability of taking action $a$ under the state $s$ , $R(a_t)$ is the expected reward of action $a$ at time step $t$ . According to REINFORCE policy gradient algorithm BIBREF13 , we update the policy gradient by the way of equation 9. $$\Theta \leftarrow \Theta + \alpha \sum _{t}R(a_t)\nabla _{\Theta }\log \pi _{\Theta }(a|s)$$ (Eq. 20) As the global encoder and the entity selector are correlated mutually, we train them jointly after pre-training the two networks. The details of the joint learning are presented in Algorithm 1. [t] The Policy Learning for Entity Selector [1] Training data include multiple documents $D = \lbrace D_1, D_2, ..., D_N\rbrace $ The target entity for mentions $\Gamma = \lbrace T_1, T_2, ..., T_N\rbrace $ Initialize the policy network parameter $\Theta $ , global LSTM network parameter $\Phi $ ; $D_k$ in $D$ Generate the candidate set for each mention Divide the mentions in $D_k$ into multiple sequences $S = \lbrace S_1, S_2, ..., S_N\rbrace $ ; $S_k$ in $S$ Rank the mentions $M = \lbrace m_1, m_2, ..., m_n\rbrace $ in $S_k$ based on the local similarity; $\Phi $0 in $\Phi $1 Sample the target entity $\Phi $2 for $\Phi $3 with $\Phi $4 ; Input the $\Phi $5 and $\Phi $6 to global LSTM network; $\Phi $7 End of sampling, update parameters Compute delayed reward $\Phi $8 for each action; Update the parameter $\Phi $9 of policy network: $\Theta \leftarrow \Theta + \alpha \sum _{t}R(a_t)\nabla _{\Theta }\log \pi _{\Theta }(a|s)$ Update the parameter $\Phi $ in the global LSTM network ### Experiment
In order to evaluate the effectiveness of our method, we train the RLEL model and validate it on a series of popular datasets that are also used by BIBREF0 , BIBREF1 . To avoid overfitting with one dataset, we use both AIDA-Train and Wikipedia data in the training set. Furthermore, we compare the RLEL with some baseline methods, where our model achieves the state-of-the-art results. We implement our models in Tensorflow and run experiments on 4 Tesla V100 GPU. ### Experiment Setup
We conduct experiments on several different types of public datasets including news and encyclopedia corpus. The training set is AIDA-Train and Wikipedia datasets, where AIDA-Train contains 18448 mentions and Wikipedia contains 25995 mentions. In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets. These datasets are well-known and have been used for the evaluation of most entity linking systems. The statistics of the datasets are shown in Table 1. AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets. ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents. MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.) AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press. WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset. WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation. OURSELF-WIKI is crawled by ourselves from Wikipedia pages. During the training of our RLEL model, we select top K candidate entities for each mention to optimize the memory and run time. In the top K candidate list, we define the recall of correct target entity is $R_t$ . According to our statistics, when K is set to 1, $R_t$ is 0.853, when K is 5, $R_t$ is 0.977, when K increases to 10, $R_t$ is 0.993. Empirically, we choose top 5 candidate entities as the input of our RLEL model. For the entity description, there are lots of redundant information in the wikipedia page, to reduce the impact of noise data, we use TextRank algorithm BIBREF19 to select 15 keywords as description of the entity. Simultaneously, we choose 15 words around mention as its context. In the global LSTM network, when the number of mentions does not reach the set length, we adopt the mention padding strategy. In short, we copy the last mention in the sequence until the number of mentions reaches the set length. We set the dimensions of word embedding and entity embedding to 300, where the word embedding and entity embedding are released by BIBREF20 and BIBREF0 respectively. For parameters of the local LSTM network, the number of LSTM cell units is set to 512, the batch size is 64, and the rank margin $\gamma $ is 0.1. Similarly, in global LSTM network, the number of LSTM cell units is 700 and the batch size is 16. In the above two LSTM networks, the learning rate is set to 1e-3, the probability of dropout is set to 0.8, and the Adam is utilized as optimizer. In addition, we set the number of MLP layers to 4 and extend the priori feature dimension to 50 in the policy network. ### Comparing with Previous Work
We compare RLEL with a series of EL systems which report state-of-the-art results on the test datasets. There are various methods including classification model BIBREF17 , rank model BIBREF21 , BIBREF15 and probability graph model BIBREF18 , BIBREF14 , BIBREF22 , BIBREF0 , BIBREF1 . Except that, Cheng $et$ $al.$ BIBREF23 formulate their global decision problem as an Integer Linear Program (ILP) which incorporates the entity-relation inference. Globerson $et$ $al.$ BIBREF24 introduce a multi-focal attention model which allows each candidate to focus on limited mentions, Yamada $et$ $al.$ BIBREF25 propose a word and entity embedding model specifically designed for EL. We use the standard Accuracy, Precision, Recall and F1 at mention level (Micro) as the evaluation metrics: $$Accuracy = \frac{|M \cap M^*|}{|M \cup M^*|}$$ (Eq. 31) $$Precision = \frac{|M \cap M^*|}{|M|}$$ (Eq. 32) where $M^*$ is the golden standard set of the linked name mentions, $M$ is the set of linked name mentions outputted by an EL method. Same as previous work, we use in-KB accuracy and micro F1 to evaluate our method. We first test the model on the AIDA-B dataset. From Table 2, we can observe that our model achieves the best result. Previous best results on this dataset are generated by BIBREF0 , BIBREF1 which both built CRF models. They calculate the pairwise scores between all candidate entities. Differently, our model only considers the consistency of the target entities and ignores the relationship between incorrect candidates. The experimental results show that our model can reduce the impact of noise data and improve the accuracy of disambiguation. Apart from experimenting on AIDA-B, we also conduct experiments on several different datasets to verify the generalization performance of our model. From Table 3, we can see that RLEL has achieved relatively good performances on ACE2004, CWEB and WIKI. At the same time, previous models BIBREF0 , BIBREF1 , BIBREF23 achieve better performances on the news datasets such as MSNBC and AQUINT, but their results on encyclopedia datasets such as WIKI are relatively poor. To avoid overfitting with some datasets and improve the robustness of our model, we not only use AIDA-Train but also add Wikipedia data to the training set. In the end, our model achieve the best overall performance. For most existing EL systems, entities with lower frequency are difficult to disambiguate. To gain further insight, we analyze the accuracy of the AIDA-B dataset for situations where gold entities have low popularity. We divide the gold entities according to their pageviews in wikipedia, the statistical disambiguation results are shown in Table 4. Since some pageviews can not be obtained, we only count part of gold entities. The result indicates that our model is still able to work well for low-frequency entities. But for medium-frequency gold entities, our model doesn't work well enough. The most important reason is that other candidate entities corresponding to these medium-frequency gold entities have higher pageviews and local similarities, which makes the model difficult to distinguish. ### Discussion on different RLEL variants
To demonstrate the effects of RLEL, we evaluate our model under different conditions. First, we evaluate the effect of sequence length on global decision making. Second, we assess whether sorting the mentions have a positive effect on the results. Third, we analysis the results of not adding globally encoding during entity selection. Last, we compare our RL selection strategy with the greedy choice. A document may contain multiple topics, so we do not add all mentions to a single sequence. In practice, we add some adjacent mentions to the sequence and use reinforcement learning to select entities from beginning to end. To analysis the impact of the number of mentions on joint disambiguation, we experiment with sequences on different lengths. The results on AIDA-B are shown in Figure 4. We can see that when the sequence is too short or too long, the disambiguation results are both very poor. When the sequence length is less than 3, delay reward can't work in reinforcement learning, and when the sequence length reaches 5 or more, noise data may be added. Finally, we choose the 4 adjacent mentions to form a sequence. In this section, we test whether ranking mentions is helpful for entity selections. At first, we directly input them into the global encoder by the order they appear in the text. We record the disambiguation results and compare them with the method which adopts ranking mentions. As shown in Figure 5a, the model with ranking mentions has achieved better performances on most of datasets, indicating that it is effective to place the mention that with a higher local similarity in front of the sequence. It is worth noting that the effect of ranking mentions is not obvious on the MSNBC dataset, the reason is that most of mentions in MSNBC have similar local similarities, the order of disambiguation has little effect on the final result. Most of previous methods mainly use the similarities between entities to correlate each other, but our model associates them by encoding the selected entity information. To assess whether the global encoding contributes to disambiguation rather than add noise, we compare the performance with and without adding the global information. When the global encoding is not added, the current state only contains the mention context representation, candidate entity representation and feature representation, notably, the selected target entity information is not taken into account. From the results in Figure 5b, we can see that the model with global encoding achieves an improvement of 4% accuracy over the method that without global encoding. To illustrate the necessity for adopting the reinforcement learning for entity selection, we compare two entity selection strategies like BIBREF5 . Specifically, we perform entity selection respectively with reinforcement learning and greedy choice. The greedy choice is to select the entity with largest local similarity from candidate set. But the reinforcement learning selection is guided by delay reward, which has a global perspective. In the comparative experiment, we keep the other conditions consistent, just replace the RL selection with a greedy choice. Based on the results in Figure 5c, we can draw a conclusion that our entity selector perform much better than greedy strategies. ### Case Study
Table 5 shows two entity selection examples by our RLEL model. For multiple mentions appearing in the document, we first sort them according to their local similarities, and select the target entities in order by the reinforcement learning model. From the results of sorting and disambiguation, we can see that our model is able to utilize the topical consistency between mentions and make full use of the selected target entity information. ### Related Work
The related work can be roughly divided into two groups: entity linking and reinforcement learning. ### Entity Linking
Entity linking falls broadly into two major approaches: local and global disambiguation. Early studies use local models to resolve mentions independently, they usually disambiguate mentions based on lexical matching between the mention's surrounding words and the entity profile in the reference KB. Various methods have been proposed to model mention's local context ranging from binary classification BIBREF17 to rank models BIBREF26 , BIBREF27 . In these methods, a large number of hand-designed features are applied. For some marginal mentions that are difficult to extract features, researchers also exploit the data retrieved by search engines BIBREF28 , BIBREF29 or Wikipedia sentences BIBREF30 . However, the feature engineering and search engine methods are both time-consuming and laborious. Recently, with the popularity of deep learning models, representation learning is utilized to automatically find semantic features BIBREF31 , BIBREF32 . The learned entity representations which by jointly modeling textual contexts and knowledge base are effective in combining multiple sources of information. To make full use of the information contained in representations, we also utilize the pre-trained entity embeddings in our model. In recent years, with the assumption that the target entities of all mentions in a document shall be related, many novel global models for joint linking are proposed. Assuming the topical coherence among mentions, authors in BIBREF33 , BIBREF34 construct factor graph models, which represent the mention and candidate entities as variable nodes, and exploit factor nodes to denote a series of features. Two recent studies BIBREF0 , BIBREF1 use fully-connected pairwise Conditional Random Field(CRF) model and exploit loopy belief propagation to estimate the max-marginal probability. Moreover, PageRank or Random Walk BIBREF35 , BIBREF18 , BIBREF7 are utilized to select the target entity for each mention. The above probabilistic models usually need to predefine a lot of features and are difficult to calculate the max-marginal probability as the number of nodes increases. In order to automatically learn features from the data, Cao et al. BIBREF9 applies Graph Convolutional Network to flexibly encode entity graphs. However, the graph-based methods are computationally expensive because there are lots of candidate entity nodes in the graph. To reduce the calculation between candidate entity pairs, Globerson et al. BIBREF24 introduce a coherence model with an attention mechanism, where each mention only focus on a fixed number of mentions. Unfortunately, choosing the number of attention mentions is not easy in practice. Two recent studies BIBREF8 , BIBREF36 finish linking all mentions by scanning the pairs of mentions at most once, they assume each mention only needs to be consistent with one another mention in the document. The limitation of their method is that the consistency information is too sparse, resulting in low confidence. Similar to us, Guo et al. BIBREF18 also sort mentions according to the difficulty of disambiguation, but they did not make full use of the information of previously referred entities for the subsequent entity disambiguation. Nguyen et al. BIBREF2 use the sequence model, but they simply encode the results of the greedy choice, and measure the similarities between the global encoding and the candidate entity representations. Their model does not consider the long-term impact of current decisions on subsequent choices, nor does they add the selected target entity information to the current state to help disambiguation. ### Reinforcement Learning
In the last few years, reinforcement learning has emerged as a powerful tool for solving complex sequential decision-making problems. It is well known for its great success in the game field, such as Go BIBREF37 and Atari games BIBREF38 . Recently, reinforcement learning has also been successfully applied to many natural language processing tasks and achieved good performance BIBREF12 , BIBREF39 , BIBREF5 . Feng et al. BIBREF5 used reinforcement learning for relation classification task by filtering out the noisy data from the sentence bag and they achieved huge improvements compared with traditional classifiers. Zhang et al. BIBREF40 applied the reinforcement learning on sentence representation by automatically discovering task-relevant structures. To automatic taxonomy induction from a set of terms, Han et al. BIBREF41 designed an end-to-end reinforcement learning model to determine which term to select and where to place it on the taxonomy, which effectively reduced the error propagation between two phases. Inspired by the above works, we also add reinforcement learning to our framework. ### Conclusions
In this paper we consider entity linking as a sequence decision problem and present a reinforcement learning based model. Our model learns the policy on selecting target entities in a sequential manner and makes decisions based on current state and previous ones. By utilizing the information of previously referred entities, we can take advantage of global consistency to disambiguate mentions. For each selection result in the current state, it also has a long-term impact on subsequent decisions, which allows learned policy strategy has a global view. In experiments, we evaluate our method on AIDA-B and other well-known datasets, the results show that our system outperforms state-of-the-art solutions. In the future, we would like to use reinforcement learning to detect mentions and determine which mention should be firstly disambiguated in the document. This research is supported by the GS501100001809National Key Research and Development Program of China (No. GS5011000018092018YFB1004703), GS501100001809the Beijing Municipal Science and Technology Project under grant (No. GS501100001809 Z181100002718004), and GS501100001809the National Natural Science Foundation of China grants(No. GS50110000180961602466). Figure 1: Illustration of mentions in the free text and their candidate entities in the knowledge base. Solid black lines point to the correct target entities corresponding to the mentions and to the descriptions of these correct target entities. Solid red lines indicate the consistency between correct target entities and the orange dashed lines denote the consistency between wrong candidate entities. Figure 2: The overall structure of our RLEL model. It contains three parts: Local Encoder, Global Encoder and Entity Selector. In this framework, (Vmt ,Vekt ) denotes the concatenation of the mention context vector Vmt and one candidate entity vector Vekt . The policy network selects one entity from the candidate set, and Vat denotes the concatenation of the mention context vector Vmt and the selected entity vector Ve∗t . ht represents the hidden status of Vat , and it will be fed into St+1. Figure 3: The architecture of policy network. It is a feedforward neural network and the input consists of four parts: mention context representation, candidate entity representation, feature representation, and encoding of the previous decisions. Table 1: Statistics of document and mention numbers on experimental datasets. Table 2: In-KB accuracy result on AIDA-B dataset. Table 3: Compare our model with other baseline methods on different types of datasets. The evaluation metric is micro F1. Figure 4: The performance of models with different sequence lengths on AIDA-B dataset. Table 4: The micro F1 of gold entities with different pageviews on part of AIDA-B dataset. Figure 5: The comparative experiments of RLEL model. Table 5: Entity selection examples by our RLEL model. | AIDA-CoNLL, ACE2004, MSNBC, AQUAINT, WNED-CWEB, WNED-WIKI, OURSELF-WIKI |
What or who was a determining factor in prompting the beginning of UFC's decline in popularity?
A. The UFC's grotesque use of a chain-link fence surrounding the octagon.
B. The UFC's lack of boxing gloves.
C. Senator McCain.
D. The cable TV industry.
| 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. | C. Senator McCain. |
What was the primary purpose of Mr. Hurley's admission for allogeneic stem cell transplantation?
Choose the correct answer from the following options:
A. Treatment of refractory tuberculosis
B. Management of acute pancreatitis
C. Treatment of secondary acute myeloid leukemia
D. Intervention for EBV reactivation
E. Correction of immune deficiency
| ### Patient Report 0
**Dear colleague, **
We are reporting on Mr. Bruno Hurley, born on 12/24/1965, who has been
under our outpatient treatment since 02/11/2020.
**Diagnoses:**
- Refractory tuberculosis
- Manifestations: Open pulmonary tuberculosis, lymph node tuberculosis
(cervical, hilar, mediastinal), liver tuberculosis
**Imaging:**
- 11/01/19 Chest CT: Mediastinal lymph node conglomerate centrally
with poststenotic infiltrates on both sides. Splenomegaly.
- 11/04/19 Bronchoscopy: Large mediastinal and right hilar lymphomas.
Subcritical constriction of right segmental bronchi. EBUS-TBNA LK4R
and 10/11R.
**Microbiology:**
- 11/04/19 Tracheobronchial Secretions: Microscopic detection of
acid-fast rods, cultural detection of Mycobacterium tuberculosis,
phenotypically no evidence of resistance.
**Therapy:**
- Initial omission of pyrazinamide due to pancytopenia.
- Moxifloxacin: 11/10/19-11/20/19
- Pyrazinamide: 11/20/19-02/11/20
- Ethambutol: 11/08/19-02/11/20
- Rifampicin since: 11/08/19
- Isoniazid since: 11/08/19
- Levofloxacin since: 02/11/20
- Immunomodulatory therapy for low basal interferon / interferon
levels (ACTIMMUNE®)
**Microbiology:**
- 01/20/20 Sputum: Cultural detection of Mycobacterium tuberculosis:
Phenotypically no evidence of resistance.
- 01/02/20 Sputum: Last cultural detection of Mycobacterium
tuberculosis.
- 06/15/20 BAL: Occasional acid-fast rods, 16S-rRNA-PCR: M.
tuberculosis complex, no cultural evidence of Mycobacteria.
- 06/15/20 Lung biopsy: Occasional acid-fast rods, no cultural
evidence of Mycobacteria.
- 03/12/21 Sputum: first sputum without acid-fast rods, consistently
microscopically negative sputum samples since then.
**Histology:**
- 07/16/21: Mediastinal lymph node biopsy: Histologically no evidence
of malignancy/lymphoma.
**Other Diagnoses: **
- Secondary Acute Myeloid Leukemia with Myelodysplastic Syndrome
- Blood count at initial diagnosis: 15% blasts, erythrocyte
substitution required.
**Therapy:**
- 12/20-03/21 TB therapy
- 02/20-01/21 TB therapy: RMP + INH + FQ
- 01/21-04/21 RMP + INH + FQ + Actimmune® 04/22 CT: Regressive
findings of pulmonary TB changes, regressive cervical lymph nodes,
mediastinal LAP, and liver lesions size-stable; Sputum: No acid-fast
rods detected for the first time since 03/21.
- BM aspiration: Secondary AML.
**Current Presentation:** Admission for allogeneic stem cell
transplantation
Pathogen Location / Material of Detection or Infection Month/Year or
Last Detection
- HIV Serology: Negative - 11/19
- Mycobacterium tuberculosis Complex: Bronchoalveolar Lavage,
Tracheobronchial Secretion, Sputum - 11/19
**Medical History:** We took over Mr. Hurley for the continuation of TB
therapy on 11/02/20. His hospital admission took place at the end of
October 2019 due to neutropenic fever. The patient reported temperatures
up to 39°C for the past 3 days. Since 08/19, the patient has been
receiving hematological-oncological treatment for MDS. The colleagues
from hematology performed a repeat bone marrow aspiration before
transferring to Station 12. The blast percentage was significantly
reduced. HLA typing of the brother for allogeneic stem cell
transplantation planning had already been done in the summer of 2019.
After a chest CT revealed extensive mediastinal lymphomas with
compression of the bronchial tree bilaterally and post-stenotic
infiltrates, a bronchoscopy was performed. M. tuberculosis was cultured
from sputum and TBS. An EBUS-guided lymph node biopsy was histologically
processed, revealing granulomatous inflammation and molecular evidence
of the M. tuberculosis complex. On 11/08/19, a four-drug
anti-tuberculosis therapy was initiated, initially with Moxifloxacin
instead of Pyrazinamide due to pancytopenia. Moxifloxacin was replaced
by Pyrazinamide on 11/20/19. The four-drug therapy was continued for a
total of 3 months due to prolonged microscopic evidence of acid-fast
rods in follow-up sputum samples. Isoniazid dosage was adjusted after
peak level control (450 mg q24h), as was Rifampicin dose (900 mg q24h).
On 01/02/20, Mycobacterium tuberculosis was last cultured in a sputum
sample. Nevertheless, acid-fast rods continued to be detected in the
sputum. Due to the lack of culturability of mycobacteria, Mr. Hurley was
discharged to home care after consultation with the Tuberculosis Welfare
Office.
**Allergies**: None known. Toxic Substances: Smoking: Non-smoker;
Alcohol: No; Drugs: No
**Social History:** Originally from Brazil, has been living in the US
for 8 years. Lives with his partner.
**Current lab results:**
**Parameter** **Results** **Reference Range**
---------------------------------------- -------------- ---------------------
ConA-Induced Cytokines (Th1/Th2)
Interleukin 10 10 pg/mL \< 364 pg/mL
Interferon Gamma 265-6781 pg/mL
Interleukin 2 74 pg/mL 43-374 pg/mL
Interleukin 4 13 pg/mL \< 34 pg/mL
Interleukin 5 3 pg/mL \< 55 pg/mL
Naive CD45RA+CCR7+ (% of CD8+) 6.35 % 8.22-59.58 %
TEMRA CD45RA+CCR7- (% of CD8+) 50.44 % 7.32-55.99 %
Central Memory CD45RA-CCR7+ (% of CD) 2.60 % 1.67-5.84 %
Effector Memory CD45RA-CCR7- (% of CD) 40.60 % 22.52-62.25 %
Naive CD45RA+ (% of CD4+) 26.26 % 17.46-60.24 %
TEMRA CD45RA+ CCR7- (% of CD4+) 1.26 % 2.74-15.54 %
Central Memory CD45RA-CCR7+ (% of CD) 34.21 % 16.40-33.41 %
Effector Memory CD45RA-CCR7- (% of CD) 38.28 % 17.38-40.38 %
Granulocytes 0.60 abs./nL 3.00-6.50 abs./nL
Granulocytes (relative) 45 % 50-80 %
Lymphocytes 0.57 abs./nL 1.50-3.00 abs./nL
Lymphocytes (relative) 43 % 20-40 %
Monocytes 0.13 abs./nL \<0.50 abs./nL
Monocytes (relative) 10 % 2-10 %
NK Cells 0.16 abs./nL 0.10-0.40 abs./nL
NK Cells (% of Lymphocytes) 29 5-25
γ/δ TCR+ T-Cells (relative) 2 % \< 10 %
α/β TCR+ T-Cells (relative) 98 % \>90 %
CD19+ B-Cells (% of Lymphocytes) 3 % 5-25 %
CD4/CD8 Ratio 0.9 % 1.1-3.0 %
CD8-CD4-T-Cells (% of T-Cells) 5.86 % \< 15.00 %
CD8+CD4+-T-Cells (% of T-Cells) 0.74 % \< 10.00 %
CD3+ T-Cells 0.38 abs./nL 0.90-2.20 abs./nL
**Parameter** **Results** **Reference Range**
-------------------------------------------------- ---------------- ---------------------
Complete Blood Count (EDTA)
Hemoglobin 6.6 g/dL 13.5-17.0 g/dL
Hematocrit 19.0 % 39.5-50.5 %
Erythrocytes 2.3 x 10\^6/uL 4.3-5.8 x 10\^6/uL
Platelets 61 x 10\^3/uL 150-370 x 10\^3/uL
MCV (Mean Corpuscular Volume) 81.5 fL 80.0-99.0 fL
MCH (Mean Corpuscular Hemoglobin) 28.3 pg 27.0-33.5 pg
MCHC (Mean Corpuscular Hemoglobin Concentration) 34.7 g/dL 31.5-36.0 g/dL
MPV (Mean Platelet Volume) 10.4 fL 7.0-12.0 fL
RDW-CV (Red Cell Distribution Width-CV) 12.7 % 11.5-15.0 %
**Parameter** **Results** **Reference Range**
-------------------------- ------------- ---------------------
Other Investigations
QFT-TB Gold plus TB1 0.11 IU/mL \<0.35 IU/mL
QFT-TB Gold plus TB2 0.07 IU/mL \<0.35 IU/mL
QFT-TB Gold plus Mitogen 3.38 IU/mL \>0.50 IU/mL
QFT-TB Gold plus Result Negative
**Lung Aspiration from 06/15/20:** Examination Request: Acid-fast rods
(Microscopy + Culture) **Microscopic Findings:**
- Auramine stain: Occasionally, acid-fast rods Result: No growth of
Mycobacterium sp. after 12 weeks of incubation.
2. Forceps Biopsy Exophytic Trachea: One piece of tissue. Microscopy:
HE, PAS, Giemsa, Diagnosis:
3. Predominantly blood clot and necrotic material alongside sparsely
altered lymphatic tissue due to sampling (EBUS-TBNA LK 7 as
indicated).
4. Components of a granulation tissue polyp (Forceps Biopsy Exophytic
Trachea as indicated). Comment: The finding in 1. continues to be
suspicious of a mycobacterial infection. We are conducting molecular
pathological examinations in this regard and will report again.
> [Comment]{.underline}: Detection of mycobacterial DNA of the M.
> tuberculosis complex type. No evidence of atypical mycobacteria. No
> evidence of malignancy.
**Current Medication:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**\
**
### Patient Report 1
**Dear colleague, **
We report to you about our patient Mr. Bruno Hurley, born on 12/24/1965.
Who has received inpatient treatment from 07/17/2021 to 09/03/2021.
**Diagnoses**:
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
<!-- -->
- Myelodysplastic Syndrome EB-2, diagnosed in July 2010. Blood count
at initial diagnosis: 15% blasts, erythrocyte transfusion-dependent.
Cytogenetics: 46,XY \[1\]; 47,XY,+Y,i(21)(q10)\[15\];
47,XY,+Y,trp(21)(q11q22)\[4\]. Molecular genetics: Mutations in
RUNX1, SF3B1. IPSS-R: 7 (very high risk).
- In 08/2020, diagnosed with Myelodysplastic Syndrome with ring
sideroblasts.
- Received transfusions of 2 units of red blood cells every 3-4 weeks
to maintain hemoglobin between 4-6 g/dL.
- Bone marrow biopsy showed MDS-EB2 with 14.5% blasts.
- Initiated Azacitidine treatment (2x 75 mg subcutaneously, days 1-5 +
8-9 every 4 weeks) as an outpatient.
- 10/23/2019: Hospitalized for fever during neutropenia.
- 12/06/2019: Diagnosed with tuberculosis - positive Tbc-PCR in
tracheobronchial secretions, acid-fast bacilli in tracheobronchial
secretions, histological confirmation from EBUS biopsy of a
conglomerate of melted lymph nodes from 11/03/2019.
- 01/2021: Bone marrow biopsy showed secondary AML with 26% blasts.
- 03/2021: Started Venetoclax/Vidaza.
- 05/2021: Bone marrow biopsy showed 0.8% myeloid blasts coexpressing
CD117 and CD7. Cytology showed 6% blasts.
- 05/2021: Started the 4th cycle of Vidaza/Venetoclax.
- 06/17/2022: Started the 5th cycle of Vidaza/Venetoclax.
- 07/29/2021: Underwent allogeneic stem cell transplantation from a
HLA-identical unrelated donor (10/10 antigen match) for AML-MRC in
first complete remission (CR). Conditioning regimen included
Treosulfan 12g/m2, Fludarabin 5x 30 mg/m2, ATG 3x 10 mg/kg.
**Other Diagnoses:**
- Persistent tuberculosis with lymph node swelling since June 2020.
- Open lung tuberculosis diagnosed in November 2019.
- Location: CT of the chest showed central mediastinal lymph node
conglomerate with post-stenotic infiltrates bilaterally,
splenomegaly.
- Bronchoscopy on December 5, 2020, showed large mediastinal and
right hilar lymph nodes, subcritical narrowing of right
segmental bronchi. EBUS-TBNA
- CT Chest/Neck on 02/05/2020: Regression of pulmonary
infiltrates, enlargement of necrotic lymph nodes in the upper
mediastinum and infraclavicular on the right (compressing the
internal jugular vein/esophagus).
- Culture confirmation of Mycobacterium tuberculosis,
pansensitive: Tracheobronchial secretion
- Initiated antituberculous combination therapy
**Current Presentation:** Admission for allogeneic stem cell
transplantation from a HLA-identical unrelated donor (10/10 antigen
match) for AML-MRC in first complete remission.
**Medical History:** In 2019, Mr. Hurley was diagnosed with
Myelodysplastic Syndrome EB-2. Starting from September 2019, he received
Azacitidine therapy. In December 2020, he was diagnosed with open lung
tuberculosis, which was challenging to treat due to his dysfunctional
immune system. In January 2021, his MDS progressed to AML-MRC with 26%
blasts. After treatment with Venetoclax/Vidaza, he achieved remission in
May 2021. Tuberculosis remained largely under control.
Due to AML-MRC, he was recommended for allogeneic stem cell
transplantation from a HLA-identical unrelated donor. At the time of
admission for transplantation, he was largely asymptomatic. He
occasionally experienced mild dry cough but denied fever, night sweats,
or weight loss. The admission and counseling were conducted with
translation assistance from his life partner due to limited proficiency
in English.
**Allergies**: None
**Transfusion History**: Currently requires transfusions every 14 days.
Both red blood cell and platelet transfusions have been tolerated
without problems.
**Abdominal CT from 01/20/2021:**
**Findings**: Significant peripancreatic fluid accumulation in the upper
abdominal area with a somewhat indistinct border between the pancreatic
tissue, particularly in the pancreatic head region. Evidence of
inflammation affecting the stomach and duodenum. No presence of free air
or indications of hollow organ perforation. No conclusive signs of a
well-defined abscess. Moreover, the other parenchymal abdominal organs,
especially those lacking focal abnormalities suggestive of neoplastic or
inflammatory conditions, displayed normal appearances. The gallbladder
showed no notable issues, and there were no radiopaque concretions
observed. Both the intra- and extrahepatic bile ducts appeared
adequately dilated. Abdominal hollow organs exhibited unremarkable and
normal appearances without corresponding contrast and dilation. The
appendix appeared within normal parameters. Abdominal lymph nodes showed
no unusual findings. Some degree of aortic vasosclerosis was noted. The
depiction of the included lung portions revealed no abnormalities.
**Results**: Findings indicative of acute pancreatitis, most likely of
an exudative nature. No signs of hollow organ perforation were detected,
and there was no definitive evidence of an abscess (as far as could be
determined from native imaging).
**Summary**: The patient was admitted to our hospital through the
emergency department with the symptoms described above. With typical
upper abdominal pain and significantly elevated serum lipase levels, we
diagnosed acute pancreatitis. This diagnosis was corroborated by
peripancreatic fluid and ill-defined organ involvement in the abdominal
CT scan. There were no laboratory or anamnestic indications of a biliary
origin. The patient denied excessive alcohol consumption.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
------------------------- ------------ ---------------
Rifampin (Rifadin) 600 mg 1-0-0
Isoniazid (Nydrazid) 500 mg 1-0-0
Levofloxacin (Levaquin) 450 mg 1-0-1
**Physical Examination:** General: Oriented in all qualities, in good
general condition with normal body weight (75 kg, 187 cm) Vital signs at
admission: Heart rate 63/min, Blood pressure 110/78 mmHg. Temperature at
admission 36.8 °C, Oxygen saturation 100% on room air. Skin and mucous
membranes: Dry skin, normal skin color, normal skin turgor. No scleral
icterus, non-irritated conjunctiva. Normal oral mucosa, moist tongue
without coating, no ulcers or thrush. Heart: Normal heart sounds,
rhythmic, regular rate, no pathological heart murmurs heard on
auscultation. Lungs: Resonant percussion sound, clear breath sounds
bilaterally, no wheezing, no prolonged expiration. Abdomen: Unremarkable
scar tissue, normal bowel sounds in all quadrants, soft, non-tender, no
guarding, liver and spleen not enlarged. Vascular: Central and
peripheral pulses palpable, no jugular vein distention, no peripheral
edema, extremities warm with no significant difference in size. Lymph
nodes: Palpable cervical swelling, inguinal and axillary lymph nodes
unremarkable. Neurology: Grossly neurologically unremarkable.
On 08/22/2021, a four-lumen central venous catheter was placed in the
right internal jugular vein without complications. During the
conditioning regimen, the patient received the following:
**Medication** **Dosage** **Frequency**
---------------------------------------------- ---------------------- -------------------------
Fludarabine (Fludara) 30 mg/m² (5x 57 mg) 07/23/2023 - 07/27/2023
Treosulfan (Ovastat) 12 g/m² (3x 22.9 g) 07/23/2023 - 07/25/2023
Anti-Thymocyte Globulin (ATG, Thymoglobulin) 10 mg/kg (3x 700 mg) 07/23/2023 - 07/28/2023
**Antiemetic Therapy:**
The antiemetic therapy included Ondansetron, Aprepitant, and
Dexamethasone, and the conditioning regimen was well tolerated.
**Prophylaxis of Graft-Versus-Host Disease (GvHD):**
**Substances** **Start Date** **Day -2** **Day 1**
---------------- ---------------- ------------ -----------
Cyclosporine 08/28/2022
Mycophenolate 07/30/2021
**Stem Cell Source** **Date** **CD34/kg KG** **CD45/kg KG** **CD3/kg KG** **Volume**
---------------------- ------------ ---------------- ---------------- --------------- ------------
PBSCT 07/29/2021 7.39 x10\^6 8.56 x10\^8 260.7 x10\^6 194 ml
**Summary:** Mr. Hurley was admitted for allogeneic stem cell
transplantation from a HLA-identical unrelated donor for AML-MRC. The
conditioning regimen with Treosulfan, Fludarabin, and ATG was well
tolerated, and the transplantation proceeded without complications.
**Toxicities:** There was an adverse event-related increase in bilirubin
levels, reaching a maximum of 2.68 mg/dL. Elevated ALT levels, up to a
maximum of 53 U/L, were observed.
**Acute Graft-Versus-Host Disease (GvHD):** Signs of GvHD were not
observed until the time of discharge.
**Medication upon Discharge:**Formularbeginn
**Medication ** **Dosage** **Frequency**
---------------------------------- ------------ ------------------------------------------------
Acyclovir (Zovirax) 500 mg 1-0-1-0
Entecavir (Baraclude) 0.5 mg 1-0-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 2-0-0-0
Levofloxacin (Levaquin) 500 mg 1-0-1-0
Mycophenolate Mofetil (CellCept) 500 mg 2-0-2-0
Folic Acid 5 mg 1-0-0-0
Magnesium \-- 3-3-3-0
Pantoprazole (Protonix) 40 mg 1-0-0-0 (before a meal)
Ursodeoxycholic Acid (Actigall) 250 mg 1-1-1-0
Cyclosporine (Sandimmune) 100 mg 100 mg 4-0-4-0
Cyclosporine (Sandimmune) 50 mg 50 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
Cyclosporine (Sandimmune) 10 mg 10 mg 4-0-4-0 (based on TDM, last dose 400 mg 1-0-1)
**Current Recommendations: **
1. Bone marrow puncture on Day +60, +120, and +360 post-transplantation
(including MRD and chimerism) and Day +180 depending on MRD and
chimerism progression.
2. Continuation of immunosuppressive therapy with ciclosporin adjusted
to achieve target levels of around 150 ng/ml, for a minimum of 3
months post-transplantation. Immunosuppression with mycophenolate
mofetil will be continued until Day +40.
3. Prophylaxis with Aciclovir must continue for 6 weeks after
discontinuation of immunosuppression at a dosage of 15-20 mg/kg/day
(divided into 2 doses). Dose adjustment based on renal function may
be necessary.
4. Pneumocystis pneumonia prophylaxis through monthly Pentamidine
inhalation or administration of Cotrim forte 960mg must continue at
least until immunosuppression is discontinued or until an absolute
CD4+ T-cell count exceeds \>200/µL in peripheral blood. Cotrim forte
960mg has not been started when leukocytes are \<2/nL.
5. Weekly monitoring of CMV and EBV viral loads through quantitative
PCR from EDTA blood.
6. Timing of antituberculous medication intake:
- Take Rifampicin and Isoniazid in the morning on an empty stomach, 30
minutes before breakfast.
- Take levofloxacin with a 2-hour gap from divalent cations (Mg2+,
strongly calcium-rich foods).
**Lab results upon Discharge:**
**Parameter** **Result** **Reference Range**
------------------------------ -------------------- ---------------------
Cyclosporine 127.00 ng/mL \--
Sodium 141 mEq/L 136-145 mEq/L
Potassium 4.1 mEq/L 3.5-4.5 mEq/L
Glucose 108 mg/dL 60-110 mg/dL
Creatinine 0.65 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR CKD-EPI) 111 mL/min/1.73 m² \--
Urea 26 mg/dL 17-48 mg/dL
Total Bilirubin 0.35 mg/dL \<1.20 mg/dL
**Complete Blood Count **
**Parameter** **Results** **Reference Range**
--------------- ----------------- -----------------------
Hemoglobin 9.5 g/dL 13.5-17.0 g/dL
Hematocrit 28.2% 39.5-50.5%
Erythrocytes 3.2 x 10\^6/µL 4.3-5.8 x 10\^6/µL
Leukocytes 1.47 x 10\^3/µL 3.90-10.50 x 10\^3/µL
Platelets 193 x 10\^3/µL 150-370 x 10\^3/µL
MCV 88.7 fL 80.0-99.0 fL
MCH 29.9 pg 27.0-33.5 pg
MCHC 33.7 g/dL 31.5-36.0 g/dL
MPV 9.8 fL 7.0-12.0 fL
RDW-CV 18.9% 11.5-15.0%
### Patient Report 2
**Dear colleague, **
We report on Mr. Bruno Hurley, born on 12/24/1965, who was under our
inpatient care from 2/20/2022, to 02/24/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Medical History:** The patient presented via ambulance from his
workplace. The patient reported sudden onset upper abdominal pain,
mainly in the epigastric region, accompanied by nausea and vomiting. He
also experienced watery diarrhea once today. He had lunch around noon,
consisting noodles. There was no fever, cough, sputum production,
dyspnea, or urinary abnormalities. He has been taking daily
antitubercular combination therapy, including Rifampicin, for open
tuberculosis. The patient denied alcohol consumption and weight loss.
**Medication** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg 1-0-0
Rifampin (Rifadin) 600 mg 1.5-0-0
Isoniazid/Pyridoxine (Nydrazid) 300 mg 1-0-1
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed
Prednisolone 4 mg As needed
**Allergies:** None
**Physical Exam:**
Vital Signs: Blood Pressure 178/90 mmHg, Pulse 85/min, SpO2 89%,
Temperature 36.7°C, Respiratory Rate 20/min.
Clinical Status: Upon initial examination, a reduced general condition.
Cardiovascular: Heart sounds were normal, rhythm was regular, and no
murmurs were heard.
Respiratory: Vesicular breath sounds, sonorous percussion.
Abdominal: Sluggish peristalsis, soft abdominal walls, guarding and
tenderness in the epigastrium, liver and spleen not palpable, no free
fluid.
Extremities: Minimal edema.
**ECG Findings:** ECG on admission showed normal sinus rhythm (69/min),
normal ST intervals, R/S transition in V3/V4, and no significant
abnormalities.
´
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
----------------------------------------- ------------ ----------------------------
Acyclovir (Zovirax) 400 mg 1-0-1
Entecavir (Baraclude) 0.5 mg PAUSED
Rifampin (Rifadin) 600 mg PAUSED
Isoniazid/Pyridoxine (Nydrazid) 300 mg PAUSED
Pantoprazole (Protonix) 40 mg 1-0-0
Trimethoprim/Sulfamethoxazole (Bactrim) 960 mg 1 tablet, on Mon, Wed, Fri
Methylprednisolone (Medrol) 0.79 mg As needed (as needed)
Prednisolone 4 mg As needed (as needed)
Tramadol (Ultram) 50 mg 1 tablet, every 6 hours
**Parameter** **Results** **Reference Range**
------------------------- ----------------- -----------------------
White Blood Cells (WBC) 5.0 x 10\^9/L 3.7 - 9.9 x 10\^9/L
Hemoglobin 14.0 g/dL 13.6 - 17.5 g/dL
Hematocrit 40% 40 - 53%
Red Blood Cells (RBC) 4.00 x 10\^12/L 4.4 - 5.9 x 10\^12/L
MCV 99 fL 80 - 96 fL
MCH 32.8 pg 28.3 - 33.5 pg
MCHC 33.1 g/dL 31.5 - 34.5 g/dL
Platelets 161 x 10\^9/L 146 - 328 x 10\^9/L
Absolute Neutrophils 3.7 x 10\^9/L 1.8 - 6.2 x 10\^9/L
Absolute Monocytes 0.31 x 10\^9/L 0.25 - 0.85 x 10\^9/L
Absolute Eosinophils 0.03 x 10\^9/L 0.03 - 0.44 x 10\^9/L
Absolute Basophils 0.01 x 10\^9/L 0.01 - 0.08 x 10\^9/L
Absolute Lymphocytes 0.9 x 10\^9/L 1.1 - 3.2 x 10\^9/L
Immature Granulocytes 0.0 x 10\^9/L 0.0 x 10\^9/L
### Patient Report 3
**Dear colleague, **
We are writing to inform you on our patient, Mr. Hurley, who presented
to our outpatient clinic on 07/12/2022.
**Diagnoses:**
- Acute Pancreatitis, possibly medication-related under antitubercular
therapy.
- Current medications include Entecavir, Rifampicin, and
Isoniazid/Pyridoxin, which have been paused after consultation with
the infectious disease team.
**Other Diagnoses:**
- Acute Myeloid Leukemia with Myelodysplasia-Related Changes (AML-MRC)
- Myelodysplastic Syndrome EB-2
- Allogeneic stem cell transplantation
- EBV Reactivation (Treated with immunoglobulins for 3 days)
- Persistent Tuberculosis with lymph node swelling
- Open Lung Tuberculosis - Initial Diagnosis
- Antitubercular combination therapy since (Moxifloxacin, Pyrazinamid,
Ethambutol, Rifampicin, Isoniazid).
- Rectal colonization with 4-MRGN.
**Current Presentation:** Presented with a referral from outpatient
oncologist for suspected recurrent AML, with DD GvHD ITP in the setting
of progressive pancytopenia, primarily thrombocytopenia. The patient is
in good general condition, denying acute symptoms, particularly no rash,
diarrhea, dyspnea, or fever.
**Physical Examination:** Alert, oriented, no signs of respiratory
distress, heart sounds regular, abdomen soft, no tenderness, no skin
rashes, especially no signs of GvHD, no edema.
- Heart Rate (HR): 130/85
- Temperature (Temp): 36.7°C
- Oxygen Saturation (SpO2): 97
- Respiratory Rate (AF): 12
- Pupillary Response: 15
**Imaging (CT):**
- [11/04/19 CT Chest/Abdomen/Pelvis ]{.underline}
- [01/04/20 Chest CT:]{.underline} Marked necrotic lymph nodes hilar
right with bronchus and vascular stenosis. Significant increasing
pneumonic infiltrates predominantly on the right.
- [02/05/20 Neck/Chest CT]{.underline}: Regression of pulmonary
infiltrates, but increased size of necrotic lymph nodes, especially
in the upper mediastinum and right infraclavicular with slit-like
compression of the right internal jugular vein and the esophagus.
- [06/07/20 Neck/Chest CT]{.underline}: Size-stable necrotic lymph
node conglomerate infraclavicular right, dimensioned axially up to
about 6 x 2 cm, with ongoing slit-shaped compression of the right
internal jugular vein. Hypoplastic mastoid cells left, idem.
Progressive, partly new and large-volume consolidations with
adjacent ground glass infiltrates on the right in the anterior, less
posterior upper lobe and perihilar. Inhomogeneous, partially reduced
contrast of consolidated lung parenchyma, broncho pneumogram
preserved dorsally only.
- [10/02/20 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Size-regressive
consolidating infiltrate in the right upper lobe and adjacent
central lower lobe with increasing signs of liquefaction.
Progressive right pleural effusion and progressive signs of
pulmonary volume load. Regressive cervical, mediastinal, and right
hilar lymphadenopathy. Ongoing central hilar conglomerates that
compress the central hilar structures. Partly constant, partly
regressive presentation of known tuberculosis-suspected liver
lesions.
- [12/02/20 CT Chest/Abdomen/Pelvis]{.underline}.
- [01/20/2021 Abdominal CT.]{.underline}
- [02/23/21 Neck/Chest CT:]{.underline} Slightly regressive/nodular
fibrosing infiltrate in the right upper lobe and adjacent central
lower lobe with continuing significant residual findings. Within the
infiltrate, larger poorly perfused areas with cavitations and
scarred bronchiectasis. Increasing, partly patchy densities on the
left basal region, differential diagnoses include infiltrates and
ventilation disorders. Essentially constant cervical, mediastinal,
and hilar lymphadenopathy. Constant liver lesions in the upper
abdomen, differential diagnoses include TB manifestations and cystic
changes.
- [06/12/21 Neck/Chest/Abdomen/Pelvis CT:]{.underline} Improved
ventilation with regressive necrotic TB manifestations perihilar,
now only subtotal lobar atelectasis. Essentially constant necrotic
lymph node manifestations cervical, mediastinal, and right hilar,
exemplarily suprasternal right or right paratracheal. Narrow right
pleural effusion. Medium-term constant hypodense liver lesions
(regressive).
**Patient History:** Known to have AML with myelodysplastic changes,
first diagnosed 01/2021, myelodysplastic syndrome EB-2, fist diagnoses
07/2019, and history of allogeneic stem cell transplantation.
**Treatment and Progression:** Patient is hemodynamically stable, vital
signs within normal limits, afebrile. In good general condition,
clinical examination unremarkable, especially no skin GvHD signs. Venous
blood gas: Acid-base status balanced, electrolytes within normal range.
Laboratory findings show pancytopenia, Hb 11.3 g/dL, thrombocytopenia
29/nL, leukopenia 1.8/nL, atypical lymphocytes described as \"resembling
CLL,\" no blasts noted. Consultation with Hemato/Oncology confirmed no
acute need for hospitalization. Follow-up in the Hemato-Oncological
Clinic in September.
**Imaging:**
**CT Chest/Abdomen/Pelvis on 11/04/19:**
**Assessment:** In comparison with 10/23/19: In today\'s
contrast-enhanced examination, a newly unmasked large tumor is noted in
the right pulmonary hilum with encasement of the conduits of the right
lung lobe. Differential diagnosis includes a lymph node conglomerate,
central bronchial carcinoma, or, less likely, an inflammatory lesion.
Multiple suspicious malignant enlarged mediastinal lymph nodes,
particularly on the right paratracheal and infracarinal regions.
Short-term progression of peribronchovascular consolidation in the right
upper lobe and multiple new subsolid micronodules bilaterally.
Differential diagnosis includes inflammatory lesions, especially in the
presence of known neutropenia, which could raise suspicion of fungal
infection.
Intraabdominally, there is an image suggestive of small bowel subileus
without a clearly defined mechanical obstruction.
Density-elevated and ill-defined cystic lesion in the left upper pole of
the kidney. Primary consideration is a hemorrhaged or thickened cyst,
but ultimately, the nature of the lesion remains uncertain.
**CT Chest on 01/04/20:** Significant necrotic hilar lymph nodes on the
right with bronchial and vascular stenosis. Marked progression of
pulmonary infiltrates, particularly on the right, still compatible with
superinfection in the context of known active tuberculosis
**CT Chest from 02/05/20**: Marked necrotic lymph nodes hilar right with
bronchial and vascular stenosis. Significantly increasing pneumonia-like
infiltrates, particularly on the right, still compatible with
superinfection in the context of known open tuberculosis.
**Neck/Chest CT from 06/07/20:** Size-stable necrotic lymph node
conglomerate infraclavicular right, dimensioned axially up to about 6 x
2 cm, with ongoing slit-shaped compression of the right internal jugular
vein. Hypoplastic mastoid cells left, idem. Progressive, partly new and
large-volume consolidations with adjacent ground glass infiltrates on
the right in the anterior, less posterior upper lobe and perihilar.
Inhomogeneous, partially reduced contrast of consolidated lung
parenchyma, broncho pneumogram preserved dorsally only.
**Neck Ultrasound from 08/14/2020:** Clinical History, Question,
Justifying Indication: Follow-up of cervical lymph nodes in
tuberculosis.
**Findings/Assessment:** Neck Lymph Node Ultrasound from 05/20/2020 for
comparison. As in the previous examination, evidence of two
significantly enlarged supraclavicular lymph nodes on the right, both
showing a decrease in size compared to the previous examination: The
more medial node measures 2.9 x 1.6 cm compared to the previous 3.7 x
1.7 cm, while the more laterally located lymph node measures 3.3 x 1.4
cm compared to the previous 4.2 x 1.5 cm. The more medial lymph node
appears centrally hypoechoic, indicative of partial liquefaction, while
the more lateral lymph node has a rather solid appearance. No other
pathologically enlarged lymph nodes detected in the cervical region.
**CT Neck/Chest/Abdomen/Pelvis from 10/02/2020:** Assessment: Compared
to the previous examination from 06/07/2020, there is evidence of
regression in findings: Size regression of consolidating infiltrate in
the right upper lobe and the adjacent central lower lobe, albeit with
increasing signs of cavitation. Progressive right pleural effusion and
progressive signs of pulmonary volume overload. Regression of cervical,
mediastinal, and right hilar lymphadenopathy. Persistent centrally
liquefying lymph node conglomerates in the right hilar region,
compressing central hilar structures. Some findings remain stable, while
others have regressed. No evidence of new manifestations.
**CT Chest/Abdomen/Pelvis from 12/02/20:** Assessment: Compared to
10/02/20: In today\'s contrast examination, a newly unmasked large tumor
is located right pulmonary hilar, encasing the conduits of the right
lung lobe; Differential diagnosis includes lymph node conglomerate,
central bronchial carcinoma, and a distant possibility of inflammatory
lesions. Multiple suspiciously enlarged mediastinal lymph nodes,
especially right paratracheal and infracarinal. In a short time,
progressive peribronchovascular consolidations in the right upper lobe
and multiple new subsolid micronodules bilaterally; Differential
diagnosis includes inflammatory lesions, potentially fungal in the
context of known neutropenia. Intra-abdominally, there is a picture of
small bowel subileus without discernible mechanical obstruction.
Corresponding symptoms? Densely elevated and ill-defined cystic lesion
in the upper pole of the left kidney; Differential diagnosis primarily
includes a hemorrhaged/thickened cyst, ultimately with uncertain
malignancy.
**Chest in two planes on 04/23/2021:** **Findings/Assessment:** In
comparison with the corresponding prior images, most recently on
08/14/2020. Also refer to CT Neck and Chest on 01/23/2021. The heart is
enlarged with a leftward emphasis, but there are no signs of acute
congestion. Extensive consolidation projecting onto the right mid-field,
with a long-term trend toward regression but still clearly demarcated.
No pneumothorax. No pleural effusion. Known lymph nodes in the
mediastinum/hilum. Degenerative spinal changes.
**Neck/Chest CT on 02/23/21:** Slightly regressive/nodular fibrosing
infiltrate in the right upper lobe and adjacent central lower lobe with
continuing significant residual findings. Within the infiltrate, larger
poorly perfused areas with cavitations and scarred bronchiectasis.
Increasing, partly patchy densities on the left basal region,
differential diagnoses include infiltrates and ventilation disorders.
Essentially constant cervical, mediastinal, and hilar lymphadenopathy.
Constant liver lesions in the upper abdomen, differential diagnoses
include TB manifestations and cystic changes.
**CT Neck/Chest/Abdomen/Pelvis from 06/12/2021**: CT from 02/23/2021
available for comparison. Neck/Chest: Improved right upper lobe (ROL)
ventilation with regressive necrotic TB manifestations peri-hilar, now
only with subtotal lobar atelectasis. Essentially stable necrotic lymph
node manifestations in the cervical, mediastinal, and right hilar
regions, for example, supraclavicular on the right (18 mm, previously
30.1 Im 21.2) or right paratracheal (18 mm, previously 30.1 Im 33.8).
Narrow right pleural effusion, same as before. No pneumothorax. Heart
size normal. No pericardial effusion. Abdomen: Mid-term stable hypodense
liver lesions (regressing since 07/2021).
**Treatment and Progression:** Due to the extensive findings and the
untreatable immunocompromising underlying condition, we decided to
switch from a four-drug TB therapy to a three-drug therapy after nearly
3 months. In addition to rifampicin and isoniazid, levofloxacin was
initiated. Despite very good therapy adherence, acid-fast bacilli
continued to be detected microscopically in sputum samples without
culture confirmation of mycobacteria, even after discharge. Furthermore,
the radiological findings worsened. In April 2020, liver lesions were
identified in the CT that had not been described up to that point, and
pulmonary and mediastinal changes increased. Clinically, right cervical
lymphadenopathy also progressed in size. Due to a possible immune
reaction, a therapy with prednisolone was attempted for several weeks,
which did not lead to improvement. In June 2020, Mr. Hurley was
readmitted for bronchoscopy with BAL and EBUS-guided biopsy to rule out
differential diagnoses. An NTM-NGS-PCR was performed on the BAL, which
did not detect DNA from nontuberculous mycobacteria. Histologically,
predominantly necrotic material was found in the lymph node tissue, and
molecular pathological analysis detected DNA from the M. tuberculosis
complex. There were no indications of malignancy. In addition,
whole-genome sequencing of the most recently cultured mycobacteria was
performed, and latent resistance genes were also ruled out. Other
pathogens, including fungi, were likewise not detected. Aspergillus
antigen in BAL and serum was also negative. We continued the three-drug
therapy with Rifampicin, Isoniazid, and Levofloxacin. Mr. Hurley
developed an increasing need for red blood cell transfusions due to
myelodysplastic syndrome and began receiving regular transfusions from
his outpatient hematologist-oncologist in the summer of 2020. In a
repeat CT control in October 2020, increasing necrotic breakdown of the
right upper and middle lobes was observed, as well as progressive
ipsilateral pleural effusion and persistent mediastinal lymphadenopathy
and liver lesions. Mr. Hurley was referred to the immunology colleagues
to discuss additional immunological treatment options. After extensive
immune deficiency assessment, a low basal interferon-gamma level was
noted in the setting of lymphopenia due to MDS. In an immunological
conference, the patient was thoroughly discussed, and a trial of
interferon-gamma therapy in addition to antituberculous therapy was
discussed due to a low basal interferon-gamma level and a negative
Quantiferon test. After approval of an off-label application, we began
Actimmune® injections in January 2021 after extensive patient education.
Mr. Hurley learned to self-administer the subcutaneous injections and
initially tolerated the treatment well. Due to continuous worsening of
the blood count, a bone marrow puncture was performed again on an
outpatient basis by the attending hematologist-oncologist, and secondary
AML was diagnosed. Since February 2021, Mr. Hurley has received
Azacitidine and regular red blood cell and platelet concentrates. After
3 months of Actimmune® therapy, sputum no longer showed acid-fast
bacilli in March 2021, and radiologically, the left pleural effusion had
completely regressed, and the infiltrates had decreased. Actimmune® was
discontinued after 3 months. Towards the end of Actimmune® therapy, Mr.
Hurley developed pronounced shoulder arthralgia and pain in the upper
thoracic spine. Fractures were ruled out. With pain therapy, the pain
became tolerable and gradually improved. Arthralgia and myalgia are
common side effects of interferon-gamma. Due to the demonstrable
therapeutic response, we presented Mr. Hurley, along with an
interpreter, at the Department of Hematology and Oncology to discuss
further therapeutic options for AML in the context of the hematological
and infectious disease situation. After extensive explanation of the
disease situation, the risks of aggressive AML therapy in the presence
of unresolved tuberculosis, and the consequences of palliative AML
therapy. Mr. Hurley agreed to allogeneic stem cell transplantation after
some consideration. On an outpatient basis, the cytostatic therapy with
Azacitidine was expanded to include Venetoclax. Antituberculous therapy
with rifampicin, isoniazid, and levofloxacin was continued. Regular
sputum checks remained consistently microscopically negative until
complete AML remission was achieved. Mr. will be admitted for allogeneic
stem cell transplantation in July 2021. A repeat CT in June 2021
confirmed continued regression of the tuberculosis findings.
Antituberculous therapy will be continued indefinitely.
**CT Neck/Chest/Abdomen/Pelvis on 06/12/2022:** CT for comparison.
Neck/Chest: Improved right lung upper lobe ventilation with regressing
necrotic tuberculosis manifestations, now with only subtotal lobar
atelectasis. Essentially constant necrotic lymph node manifestations in
the cervical, mediastinal, and right hilar regions, as exemplified by
the right supraclavicular (18 mm, SE 301 HU 212) or right paratracheal
(18 mm, SE 301 HU 338) nodes. Narrow pleural effusion on the right,
likewise. No pneumothorax. The heart is not enlarged. No pericardial
effusion. Abdomen: Medium-term constant hypodense liver lesions
(regressing)
**Current Recommendations:** Continue antituberculous therapy without a
defined endpoint. Sputum checks during allogeneic stem cell
transplantation every 1-2 weeks. In case of clinical signs of persistent
infection, perform early CT scans of the neck, chest, and abdomen.
Follow-up appointment in our infectious diseases outpatient clinic after
allogeneic stem cell transplantation. | Treatment of secondary acute myeloid leukemia |
What best represents the theme of the story?
A. Compromise is essential to long-lasting, happy successful relationships
B. It is better to be honest about something bothering you than to withhold it and possibly cause a shared goal to fail
C. Keeping one's family happy and intact is ultimately more important than any personal or professional goal
D. Rigid thinking and ultimatums in relationships rarely result in desired outcomes
| Transcriber's Note: This etext was produced from Astounding Science Fiction December 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. BREAKAWAY BY STANLEY GIMBLE Illustrated by Freas She surely got her wish ... but there was some question about getting what she wanted. Phil Conover pulled the zipper of his flight suit up the front of his long, thin body and came into the living room. His face, usually serious and quietly handsome, had an alive, excited look. And the faint lines around his dark, deep-set eyes were accentuated when he smiled at his wife. "All set, honey. How do I look in my monkey suit?" His wife was sitting stiffly on the flowered couch that was still not theirs completely. In her fingers she held a cigarette burned down too far. She said, "You look fine, Phil. You look just right." She managed a smile. Then she leaned forward and crushed the cigarette in the ash tray on the maple coffee table and took another from the pack. He came to her and touched his hands to her soft blond hair, raising her face until she was looking into his eyes. "You're the most beautiful girl I know. Did I ever tell you that?" "Yes, I think so. Yes, I'm sure you did," she said, finishing the ritual; but her voice broke, and she turned her head away. Phil sat beside her and put his arm around her small shoulders. He had stopped smiling. "Honey, look at me," he said. "It isn't going to be bad. Honestly it isn't. We know exactly how it will be. If anything could go wrong, they wouldn't be sending me; you know that. I told you that we've sent five un-manned ships up and everyone came back without a hitch." She turned, facing him. There were tears starting in the corners of her wide, brown eyes, and she brushed them away with her hand. "Phil, don't go. Please don't. They can send Sammy. Sammy doesn't have a wife. Can't he go? They'd understand, Phil. Please!" She was holding his arms tightly with her hands, and the color had drained from her cheeks. "Mary, you know I can't back out now. How could I? It's been three years. You know how much I've wanted to be the first man to go. Nothing would ever be right with me again if I didn't go. Please don't make it hard." He stopped talking and held her to him and stroked the back of her head. He could feel her shoulders shaking with quiet sobs. He released her and stood up. "I've got to get started, Mary. Will you come to the field with me?" "Yes, I'll come to say good-by." She paused and dropped her eyes. "Phil, if you go, I won't be here when you get back—if you get back. I won't be here because I won't be the wife of a space pilot for the rest of my life. It isn't the kind of life I bargained for. No matter how much I love you, I just couldn't take that, Phil. I'm sorry. I guess I'm not the noble sort of wife." She finished and took another cigarette from the pack on the coffee table and put it to her lips. Her hand was trembling as she touched the lighter to the end of the cigarette and drew deeply. Phil stood watching her, the excitement completely gone from his eyes. "I wish you had told me this a long time ago, Mary," Phil said. His voice was dry and low. "I didn't know you felt this way about it." "Yes, you did. I told you how I felt. I told you I could never be the wife of a space pilot. But I don't think I ever really believed it was possible—not until this morning when you said tonight was the take-off. It's so stupid to jeopardize everything we've got for a ridiculous dream!" He sat down on the edge of the couch and took her hands between his. "Mary, listen to me," he said. "It isn't a dream. It's real. There's nothing means anything more to me than you do—you know that. But no man ever had the chance to do what I'm going to do tonight—no man ever. If I backed out now for any reason, I'd never be able to look at the sky again. I'd be through." She looked at him without seeing him, and there was nothing at all in her eyes. "Let's go, if you're still going," she finally said. They drove through the streets of the small town with its small bungalows, each alike. There were no trees and very little grass. It was a new town, a government built town, and it had no personality yet. It existed only because of the huge ship standing poised in the take-off zone five miles away in the desert. Its future as a town rested with the ship, and the town seemed to feel the uncertainty of its future, seemed ready to stop existing as a town and to give itself back to the desert, if such was its destiny. Phil turned the car off the highway onto the rutted dirt road that led across the sand to the field where the ship waited. In the distance they could see the beams of the searchlights as they played across the take-off zone and swept along the top of the high wire fence stretching out of sight to right and left. At the gate they were stopped by the guard. He read Phil's pass, shined his flashlight in their faces, and then saluted. "Good luck, colonel," he said, and shook Phil's hand. "Thanks, sergeant. I'll be seeing you next week," Phil said, and smiled. They drove between the rows of wooden buildings that lined the field, and he parked near the low barbed fence ringing the take-off zone. He turned off the ignition, and sat quietly for a moment before lighting a cigarette. Then he looked at his wife. She was staring through the windshield at the rocket two hundred yards away. Its smooth polished surface gleamed in the spotlight glare, and it sloped up and up until the eye lost the tip against the stars. "She's beautiful, Mary. You've never seen her before, have you?" "No, I've never seen her before," she said. "Hadn't you better go?" Her voice was strained and she held her hands closed tightly in her lap. "Please go now, Phil," she said. He leaned toward her and touched her cheek. Then she was in his arms, her head buried against his shoulder. "Good-by, darling," she said. "Wish me luck, Mary?" he asked. "Yes, good luck, Phil," she said. He opened the car door and got out. The noise of men and machines scurrying around the ship broke the spell of the rocket waiting silently for flight. "Mary, I—" he began, and then turned and strode toward the administration building without looking back. Inside the building it was like a locker room before the big game. The tension stood alone, and each man had the same happy, excited look that Phil had worn earlier. When he came into the room, the noise and bustle stopped. They turned as one man toward him, and General Small came up to him and took his hand. "Hello, Phil. We were beginning to think you weren't coming. You all set, son?" "Yes, sir, I'm all set, I guess," Phil said. "I'd like you to meet the Secretary of Defense, Phil. He's over here by the radar." As they crossed the room, familiar faces smiled, and each man shook his hand or touched his arm. He saw Sammy, alone, by the coffee urn. Sammy waved to him, but he didn't smile. Phil wanted to talk to him, to say something; but there was nothing to be said now. Sammy's turn would come later. "Mr. Secretary," the general said, "this is Colonel Conover. He'll be the first man in history to see the other side of the Moon. Colonel—the Secretary of Defense." "How do you do, sir. I'm very proud to meet you," Phil said. "On the contrary, colonel. I'm very proud to meet you. I've been looking at that ship out there and wondering. I almost wish I were a young man again. I'd like to be going. It's a thrilling thought—man's first adventure into the universe. You're lighting a new dawn of history, colonel. It's a privilege few men have ever had; and those who have had it didn't realize it at the time. Good luck, and God be with you." "Thank you, sir. I'm aware of all you say. It frightens me a little." The general took Phil's arm and they walked to the briefing room. There were chairs set up for the scientists and Air Force officers directly connected with the take-off. They were seated now in a semicircle in front of a huge chart of the solar system. Phil took his seat, and the last minute briefing began. It was a routine he knew by heart. He had gone over and over it a thousand times, and he only half listened now. He kept thinking of Mary outside, alone by the fence. The voice of the briefing officer was a dull hum in his ears. "... And orbit at 18,000-mph. You will then accelerate for the breakaway to 24,900-mph for five minutes and then free-coast for 116 hours until—" Phil asked a few questions about weather and solar conditions. And then the session was done. They rose and looked at each other, the same unanswered questions on each man's face. There were forced smiles and handshakes. They were ready now. "Phil," the general said, and took him aside. "Sir?" "Phil, you're ... you feel all right, don't you, son?" "Yes, sir. I feel fine. Why?" "Phil, I've spent nearly every day with you for three years. I know you better than I know myself in many ways. And I've studied the psychologist's reports on you carefully. Maybe it's just nervousness, Phil, but I think there's something wrong. Is there?" "No, sir. There's nothing wrong," Phil said, but his voice didn't carry conviction. He reached for a cigarette. "Phil, if there is anything—anything at all—you know what it might mean. You've got to be in the best mental and physical condition of your life tonight. You know better than any man here what that means to our success. I think there is something more than just natural apprehension wrong with you. Want to tell me?" Outside, the take-off zone crawled with men and machines at the base of the rocket. For ten hours, the final check-outs had been in progress; and now the men were checking again, on their own time. The thing they had worked toward for six years was ready to happen, and each one felt that he was sending just a little bit of himself into the sky. Beyond the ring of lights and moving men, on the edge of the field, Mary stood. Her hands moved slowly over the top of the fence, twisting the barbs of wire. But her eyes were on the ship. And then they were ready. A small group of excited men came out from the administration building and moved forward. The check-out crews climbed into their machines and drove back outside the take-off zone. And, alone, one man climbed the steel ladder up the side of the rocket—ninety feet into the air. At the top he waved to the men on the ground and then disappeared through a small port. Mary waved to him. "Good-by," she said to herself, but the words stuck tight in her throat. The small group at the base of the ship turned and walked back to the fence. And for an eternity the great ship stood alone, waiting. Then, from deep inside, a rumble came, increasing in volume to a gigantic roar that shook the earth and tore at the ears. Slowly, the first manned rocket to the Moon lifted up and up to the sky. For a long time after the rocket had become a tiny speck of light in the heavens, she stood holding her face in her hands and crying softly to herself. And then she felt the touch of a hand on her arm. She turned. "Phil! Oh, Phil." She held tightly to him and repeated his name over and over. "They wouldn't let me go, Mary," he said finally. "The general would not let me go." She looked at him. His face was drawn tight, and there were tears on his cheeks. "Thank, God," she said. "It doesn't matter, darling. The only thing that matters is you didn't go." "You're right, Mary," he said. His voice was low—so low she could hardly hear him. "It doesn't matter. Nothing matters now." He stood with his hands at his sides, watching her. And then turned away and walked toward the car. THE END | D. Rigid thinking and ultimatums in relationships rarely result in desired outcomes |
What likely happens to Val and Ron after the story ends?
A. They stay on Mars for their contract and then move on to a different project
B. They go back to earth to make sure Ledman gets the care he needs
C. They decide to stay on Mars forever
D. They stay on Mars for a few more weeks before heading back to Earth
| THE HUNTED HEROES By ROBERT SILVERBERG The planet itself was tough enough—barren, desolate, forbidding; enough to stop the most adventurous and dedicated. But they had to run head-on against a mad genius who had a motto: Death to all Terrans! "Let's keep moving," I told Val. "The surest way to die out here on Mars is to give up." I reached over and turned up the pressure on her oxymask to make things a little easier for her. Through the glassite of the mask, I could see her face contorted in an agony of fatigue. And she probably thought the failure of the sandcat was all my fault, too. Val's usually about the best wife a guy could ask for, but when she wants to be she can be a real flying bother. It was beyond her to see that some grease monkey back at the Dome was at fault—whoever it was who had failed to fasten down the engine hood. Nothing but what had stopped us could stop a sandcat: sand in the delicate mechanism of the atomic engine. But no; she blamed it all on me somehow: So we were out walking on the spongy sand of the Martian desert. We'd been walking a good eight hours. "Can't we turn back now, Ron?" Val pleaded. "Maybe there isn't any uranium in this sector at all. I think we're crazy to keep on searching out here!" I started to tell her that the UranCo chief had assured me we'd hit something out this way, but changed my mind. When Val's tired and overwrought there's no sense in arguing with her. I stared ahead at the bleak, desolate wastes of the Martian landscape. Behind us somewhere was the comfort of the Dome, ahead nothing but the mazes and gullies of this dead world. He was a cripple in a wheelchair—helpless as a rattlesnake. "Try to keep going, Val." My gloved hand reached out and clumsily enfolded hers. "Come on, kid. Remember—we're doing this for Earth. We're heroes." She glared at me. "Heroes, hell!" she muttered. "That's the way it looked back home, but, out there it doesn't seem so glorious. And UranCo's pay is stinking." "We didn't come out here for the pay, Val." "I know, I know, but just the same—" It must have been hell for her. We had wandered fruitlessly over the red sands all day, both of us listening for the clicks of the counter. And the geigers had been obstinately hushed all day, except for their constant undercurrent of meaningless noises. Even though the Martian gravity was only a fraction of Earth's, I was starting to tire, and I knew it must have been really rough on Val with her lovely but unrugged legs. "Heroes," she said bitterly. "We're not heroes—we're suckers! Why did I ever let you volunteer for the Geig Corps and drag me along?" Which wasn't anywhere close to the truth. Now I knew she was at the breaking point, because Val didn't lie unless she was so exhausted she didn't know what she was doing. She had been just as much inflamed by the idea of coming to Mars to help in the search for uranium as I was. We knew the pay was poor, but we had felt it a sort of obligation, something we could do as individuals to keep the industries of radioactives-starved Earth going. And we'd always had a roving foot, both of us. No, we had decided together to come to Mars—the way we decided together on everything. Now she was turning against me. I tried to jolly her. "Buck up, kid," I said. I didn't dare turn up her oxy pressure any higher, but it was obvious she couldn't keep going. She was almost sleep-walking now. We pressed on over the barren terrain. The geiger kept up a fairly steady click-pattern, but never broke into that sudden explosive tumult that meant we had found pay-dirt. I started to feel tired myself, terribly tired. I longed to lie down on the soft, spongy Martian sand and bury myself. I looked at Val. She was dragging along with her eyes half-shut. I felt almost guilty for having dragged her out to Mars, until I recalled that I hadn't. In fact, she had come up with the idea before I did. I wished there was some way of turning the weary, bedraggled girl at my side back into the Val who had so enthusiastically suggested we join the Geigs. Twelve steps later, I decided this was about as far as we could go. I stopped, slipped out of the geiger harness, and lowered myself ponderously to the ground. "What'samatter, Ron?" Val asked sleepily. "Something wrong?" "No, baby," I said, putting out a hand and taking hers. "I think we ought to rest a little before we go any further. It's been a long, hard day." It didn't take much to persuade her. She slid down beside me, curled up, and in a moment she was fast asleep, sprawled out on the sands. Poor kid , I thought. Maybe we shouldn't have come to Mars after all. But, I reminded myself, someone had to do the job. A second thought appeared, but I squelched it: Why the hell me? I looked down at Valerie's sleeping form, and thought of our warm, comfortable little home on Earth. It wasn't much, but people in love don't need very fancy surroundings. I watched her, sleeping peacefully, a wayward lock of her soft blonde hair trailing down over one eyebrow, and it seemed hard to believe that we'd exchanged Earth and all it held for us for the raw, untamed struggle that was Mars. But I knew I'd do it again, if I had the chance. It's because we wanted to keep what we had. Heroes? Hell, no. We just liked our comforts, and wanted to keep them. Which took a little work. Time to get moving. But then Val stirred and rolled over in her sleep, and I didn't have the heart to wake her. I sat there, holding her, staring out over the desert, watching the wind whip the sand up into weird shapes. The Geig Corps preferred married couples, working in teams. That's what had finally decided it for us—we were a good team. We had no ties on Earth that couldn't be broken without much difficulty. So we volunteered. And here we are. Heroes. The wind blasted a mass of sand into my face, and I felt it tinkle against the oxymask. I glanced at the suit-chronometer. Getting late. I decided once again to wake Val. But she was tired. And I was tired too, tired from our wearying journey across the empty desert. I started to shake Val. But I never finished. It would be so nice just to lean back and nuzzle up to her, down in the sand. So nice. I yawned, and stretched back. I awoke with a sudden startled shiver, and realized angrily I had let myself doze off. "Come on, Val," I said savagely, and started to rise to my feet. I couldn't. I looked down. I was neatly bound in thin, tough, plastic tangle-cord, swathed from chin to boot-bottoms, my arms imprisoned, my feet caught. And tangle-cord is about as easy to get out of as a spider's web is for a trapped fly. It wasn't Martians that had done it. There weren't any Martians, hadn't been for a million years. It was some Earthman who had bound us. I rolled my eyes toward Val, and saw that she was similarly trussed in the sticky stuff. The tangle-cord was still fresh, giving off a faint, repugnant odor like that of drying fish. It had been spun on us only a short time ago, I realized. "Ron—" "Don't try to move, baby. This stuff can break your neck if you twist it wrong." She continued for a moment to struggle futilely, and I had to snap, "Lie still, Val!" "A very wise statement," said a brittle, harsh voice from above me. I looked up and saw a helmeted figure above us. He wasn't wearing the customary skin-tight pliable oxysuits we had. He wore an outmoded, bulky spacesuit and a fishbowl helmet, all but the face area opaque. The oxygen cannisters weren't attached to his back as expected, though. They were strapped to the back of the wheelchair in which he sat. Through the fishbowl I could see hard little eyes, a yellowed, parchment-like face, a grim-set jaw. I didn't recognize him, and this struck me odd. I thought I knew everyone on sparsely-settled Mars. Somehow I'd missed him. What shocked me most was that he had no legs. The spacesuit ended neatly at the thighs. He was holding in his left hand the tanglegun with which he had entrapped us, and a very efficient-looking blaster was in his right. "I didn't want to disturb your sleep," he said coldly. "So I've been waiting here for you to wake up." I could just see it. He might have been sitting there for hours, complacently waiting to see how we'd wake up. That was when I realized he must be totally insane. I could feel my stomach-muscles tighten, my throat constrict painfully. Then anger ripped through me, washing away the terror. "What's going on?" I demanded, staring at the half of a man who confronted us from the wheelchair. "Who are you?" "You'll find out soon enough," he said. "Suppose now you come with me." He reached for the tanglegun, flipped the little switch on its side to MELT, and shot a stream of watery fluid over our legs, keeping the blaster trained on us all the while. Our legs were free. "You may get up now," he said. "Slowly, without trying to make trouble." Val and I helped each other to our feet as best we could, considering our arms were still tightly bound against the sides of our oxysuits. "Walk," the stranger said, waving the tanglegun to indicate the direction. "I'll be right behind you." He holstered the tanglegun. I glimpsed the bulk of an outboard atomic rigging behind him, strapped to the back of the wheelchair. He fingered a knob on the arm of the chair and the two exhaust ducts behind the wheel-housings flamed for a moment, and the chair began to roll. Obediently, we started walking. You don't argue with a blaster, even if the man pointing it is in a wheelchair. "What's going on, Ron?" Val asked in a low voice as we walked. Behind us the wheelchair hissed steadily. "I don't quite know, Val. I've never seen this guy before, and I thought I knew everyone at the Dome." "Quiet up there!" our captor called, and we stopped talking. We trudged along together, with him following behind; I could hear the crunch-crunch of the wheelchair as its wheels chewed into the sand. I wondered where we were going, and why. I wondered why we had ever left Earth. The answer to that came to me quick enough: we had to. Earth needed radioactives, and the only way to get them was to get out and look. The great atomic wars of the late 20th Century had used up much of the supply, but the amount used to blow up half the great cities of the world hardly compared with the amount we needed to put them back together again. In three centuries the shattered world had been completely rebuilt. The wreckage of New York and Shanghai and London and all the other ruined cities had been hidden by a shining new world of gleaming towers and flying roadways. We had profited by our grandparents' mistakes. They had used their atomics to make bombs. We used ours for fuel. It was an atomic world. Everything: power drills, printing presses, typewriters, can openers, ocean liners, powered by the inexhaustible energy of the dividing atom. But though the energy is inexhaustible, the supply of nuclei isn't. After three centuries of heavy consumption, the supply failed. The mighty machine that was Earth's industry had started to slow down. And that started the chain of events that led Val and me to end up as a madman's prisoners, on Mars. With every source of uranium mined dry on Earth, we had tried other possibilities. All sorts of schemes came forth. Project Sea-Dredge was trying to get uranium from the oceans. In forty or fifty years, they'd get some results, we hoped. But there wasn't forty or fifty years' worth of raw stuff to tide us over until then. In a decade or so, our power would be just about gone. I could picture the sort of dog-eat-dog world we'd revert back to. Millions of starving, freezing humans tooth-and-clawing in it in the useless shell of a great atomic civilization. So, Mars. There's not much uranium on Mars, and it's not easy to find or any cinch to mine. But what little is there, helps. It's a stopgap effort, just to keep things moving until Project Sea-Dredge starts functioning. Enter the Geig Corps: volunteers out on the face of Mars, combing for its uranium deposits. And here we are, I thought. After we walked on a while, a Dome became visible up ahead. It slid up over the crest of a hill, set back between two hummocks on the desert. Just out of the way enough to escape observation. For a puzzled moment I thought it was our Dome, the settlement where all of UranCo's Geig Corps were located, but another look told me that this was actually quite near us and fairly small. A one-man Dome, of all things! "Welcome to my home," he said. "The name is Gregory Ledman." He herded us off to one side of the airlock, uttered a few words keyed to his voice, and motioned us inside when the door slid up. When we were inside he reached up, clumsily holding the blaster, and unscrewed the ancient spacesuit fishbowl. His face was a bitter, dried-up mask. He was a man who hated. The place was spartanly furnished. No chairs, no tape-player, no decoration of any sort. Hard bulkhead walls, rivet-studded, glared back at us. He had an automatic chef, a bed, and a writing-desk, and no other furniture. Suddenly he drew the tanglegun and sprayed our legs again. We toppled heavily to the floor. I looked up angrily. "I imagine you want to know the whole story," he said. "The others did, too." Valerie looked at me anxiously. Her pretty face was a dead white behind her oxymask. "What others?" "I never bothered to find out their names," Ledman said casually. "They were other Geigs I caught unawares, like you, out on the desert. That's the only sport I have left—Geig-hunting. Look out there." He gestured through the translucent skin of the Dome, and I felt sick. There was a little heap of bones lying there, looking oddly bright against the redness of the sands. They were the dried, parched skeletons of Earthmen. Bits of cloth and plastic, once oxymasks and suits, still clung to them. Suddenly I remembered. There had been a pattern there all the time. We didn't much talk about it; we chalked it off as occupational hazards. There had been a pattern of disappearances on the desert. I could think of six, eight names now. None of them had been particularly close friends. You don't get time to make close friends out here. But we'd vowed it wouldn't happen to us. It had. "You've been hunting Geigs?" I asked. " Why? What've they ever done to you?" He smiled, as calmly as if I'd just praised his house-keeping. "Because I hate you," he said blandly. "I intend to wipe every last one of you out, one by one." I stared at him. I'd never seen a man like this before; I thought all his kind had died at the time of the atomic wars. I heard Val sob, "He's a madman!" "No," Ledman said evenly. "I'm quite sane, believe me. But I'm determined to drive the Geigs—and UranCo—off Mars. Eventually I'll scare you all away." "Just pick us off in the desert?" "Exactly," replied Ledman. "And I have no fears of an armed attack. This place is well fortified. I've devoted years to building it. And I'm back against those hills. They couldn't pry me out." He let his pale hand run up into his gnarled hair. "I've devoted years to this. Ever since—ever since I landed here on Mars." "What are you going to do with us?" Val finally asked, after a long silence. He didn't smile this time. "Kill you," he told her. "Not your husband. I want him as an envoy, to go back and tell the others to clear off." He rocked back and forth in his wheelchair, toying with the gleaming, deadly blaster in his hand. We stared in horror. It was a nightmare—sitting there, placidly rocking back and forth, a nightmare. I found myself fervently wishing I was back out there on the infinitely safer desert. "Do I shock you?" he asked. "I shouldn't—not when you see my motives." "We don't see them," I snapped. "Well, let me show you. You're on Mars hunting uranium, right? To mine and ship the radioactives back to Earth to keep the atomic engines going. Right?" I nodded over at our geiger counters. "We volunteered to come to Mars," Val said irrelevantly. "Ah—two young heroes," Ledman said acidly. "How sad. I could almost feel sorry for you. Almost." "Just what is it you're after?" I said, stalling, stalling. "Atomics cost me my legs," he said. "You remember the Sadlerville Blast?" he asked. "Of course." And I did, too. I'd never forget it. No one would. How could I forget that great accident—killing hundreds, injuring thousands more, sterilizing forty miles of Mississippi land—when the Sadlerville pile went up? "I was there on business at the time," Ledman said. "I represented Ledman Atomics. I was there to sign a new contract for my company. You know who I am, now?" I nodded. "I was fairly well shielded when it happened. I never got the contract, but I got a good dose of radiation instead. Not enough to kill me," he said. "Just enough to necessitate the removal of—" he indicated the empty space at his thighs. "So I got off lightly." He gestured at the wheelchair blanket. I still didn't understand. "But why kill us Geigs? We had nothing to do with it." "You're just in this by accident," he said. "You see, after the explosion and the amputation, my fellow-members on the board of Ledman Atomics decided that a semi-basket case like myself was a poor risk as Head of the Board, and they took my company away. All quite legal, I assure you. They left me almost a pauper!" Then he snapped the punchline at me. "They renamed Ledman Atomics. Who did you say you worked for?" I began, "Uran—" "Don't bother. A more inventive title than Ledman Atomics, but not quite as much heart, wouldn't you say?" He grinned. "I saved for years; then I came to Mars, lost myself, built this Dome, and swore to get even. There's not a great deal of uranium on this planet, but enough to keep me in a style to which, unfortunately, I'm no longer accustomed." He consulted his wrist watch. "Time for my injection." He pulled out the tanglegun and sprayed us again, just to make doubly certain. "That's another little souvenir of Sadlerville. I'm short on red blood corpuscles." He rolled over to a wall table and fumbled in a container among a pile of hypodermics. "There are other injections, too. Adrenalin, insulin. Others. The Blast turned me into a walking pin-cushion. But I'll pay it all back," he said. He plunged the needle into his arm. My eyes widened. It was too nightmarish to be real. I wasn't seriously worried about his threat to wipe out the entire Geig Corps, since it was unlikely that one man in a wheelchair could pick us all off. No, it wasn't the threat that disturbed me, so much as the whole concept, so strange to me, that the human mind could be as warped and twisted as Ledman's. I saw the horror on Val's face, and I knew she felt the same way I did. "Do you really think you can succeed?" I taunted him. "Really think you can kill every Earthman on Mars? Of all the insane, cockeyed—" Val's quick, worried head-shake cut me off. But Ledman had felt my words, all right. "Yes! I'll get even with every one of you for taking away my legs! If we hadn't meddled with the atom in the first place, I'd be as tall and powerful as you, today—instead of a useless cripple in a wheelchair." "You're sick, Gregory Ledman," Val said quietly. "You've conceived an impossible scheme of revenge and now you're taking it out on innocent people who've done nothing, nothing at all to you. That's not sane!" His eyes blazed. "Who are you to talk of sanity?" Uneasily I caught Val's glance from a corner of my eye. Sweat was rolling down her smooth forehead faster than the auto-wiper could swab it away. "Why don't you do something? What are you waiting for, Ron?" "Easy, baby," I said. I knew what our ace in the hole was. But I had to get Ledman within reach of me first. "Enough," he said. "I'm going to turn you loose outside, right after—" " Get sick! " I hissed to Val, low. She began immediately to cough violently, emitting harsh, choking sobs. "Can't breathe!" She began to yell, writhing in her bonds. That did it. Ledman hadn't much humanity left in him, but there was a little. He lowered the blaster a bit and wheeled one-hand over to see what was wrong with Val. She continued to retch and moan most horribly. It almost convinced me. I saw Val's pale, frightened face turn to me. He approached and peered down at her. He opened his mouth to say something, and at that moment I snapped my leg up hard, tearing the tangle-cord with a snicking rasp, and kicked his wheelchair over. The blaster went off, burning a hole through the Dome roof. The automatic sealers glued-in instantly. Ledman went sprawling helplessly out into the middle of the floor, the wheelchair upended next to him, its wheels slowly revolving in the air. The blaster flew from his hands at the impact of landing and spun out near me. In one quick motion I rolled over and covered it with my body. Ledman clawed his way to me with tremendous effort and tried wildly to pry the blaster out from under me, but without success. I twisted a bit, reached out with my free leg, and booted him across the floor. He fetched up against the wall of the Dome and lay there. Val rolled over to me. "Now if I could get free of this stuff," I said, "I could get him covered before he comes to. But how?" "Teamwork," Val said. She swivelled around on the floor until her head was near my boot. "Push my oxymask off with your foot, if you can." I searched for the clamp and tried to flip it. No luck, with my heavy, clumsy boot. I tried again, and this time it snapped open. I got the tip of my boot in and pried upward. The oxymask came off, slowly, scraping a jagged red scratch up the side of Val's neck as it came. "There," she breathed. "That's that." I looked uneasily at Ledman. He was groaning and beginning to stir. Val rolled on the floor and her face lay near my right arm. I saw what she had in mind. She began to nibble the vile-tasting tangle-cord, running her teeth up and down it until it started to give. She continued unfailingly. Finally one strand snapped. Then another. At last I had enough use of my hand to reach out and grasp the blaster. Then I pulled myself across the floor to Ledman, removed the tanglegun, and melted the remaining tangle-cord off. My muscles were stiff and bunched, and rising made me wince. I turned and freed Val. Then I turned and faced Ledman. "I suppose you'll kill me now," he said. "No. That's the difference between sane people and insane," I told him. "I'm not going to kill you at all. I'm going to see to it that you're sent back to Earth." " No! " he shouted. "No! Anything but back there. I don't want to face them again—not after what they did to me—" "Not so loud," I broke in. "They'll help you on Earth. They'll take all the hatred and sickness out of you, and turn you into a useful member of society again." "I hate Earthmen," he spat out. "I hate all of them." "I know," I said sarcastically. "You're just all full of hate. You hated us so much that you couldn't bear to hang around on Earth for as much as a year after the Sadlerville Blast. You had to take right off for Mars without a moment's delay, didn't you? You hated Earth so much you had to leave." "Why are you telling all this to me?" "Because if you'd stayed long enough, you'd have used some of your pension money to buy yourself a pair of prosthetic legs, and then you wouldn't need this wheelchair." Ledman scowled, and then his face went belligerent again. "They told me I was paralyzed below the waist. That I'd never walk again, even with prosthetic legs, because I had no muscles to fit them to." "You left Earth too quickly," Val said. "It was the only way," he protested. "I had to get off—" "She's right," I told him. "The atom can take away, but it can give as well. Soon after you left they developed atomic-powered prosthetics—amazing things, virtually robot legs. All the survivors of the Sadlerville Blast were given the necessary replacement limbs free of charge. All except you. You were so sick you had to get away from the world you despised and come here." "You're lying," he said. "It's not true!" "Oh, but it is," Val smiled. I saw him wilt visibly, and for a moment I almost felt sorry for him, a pathetic legless figure propped up against the wall of the Dome at blaster-point. But then I remembered he'd killed twelve Geigs—or more—and would have added Val to the number had he had the chance. "You're a very sick man, Ledman," I said. "All this time you could have been happy, useful on Earth, instead of being holed up here nursing your hatred. You might have been useful, on Earth. But you decided to channel everything out as revenge." "I still don't believe it—those legs. I might have walked again. No—no, it's all a lie. They told me I'd never walk," he said, weakly but stubbornly still. I could see his whole structure of hate starting to topple, and I decided to give it the final push. "Haven't you wondered how I managed to break the tangle-cord when I kicked you over?" "Yes—human legs aren't strong enough to break tangle-cord that way." "Of course not," I said. I gave Val the blaster and slipped out of my oxysuit. "Look," I said. I pointed to my smooth, gleaming metal legs. The almost soundless purr of their motors was the only noise in the room. "I was in the Sadlerville Blast, too," I said. "But I didn't go crazy with hate when I lost my legs." Ledman was sobbing. "Okay, Ledman," I said. Val got him into his suit, and brought him the fishbowl helmet. "Get your helmet on and let's go. Between the psychs and the prosthetics men, you'll be a new man inside of a year." "But I'm a murderer!" "That's right. And you'll be sentenced to psych adjustment. When they're finished, Gregory Ledman the killer will be as dead as if they'd electrocuted you, but there'll be a new—and sane—Gregory Ledman." I turned to Val. "Got the geigers, honey?" For the first time since Ledman had caught us, I remembered how tired Val had been out on the desert. I realized now that I had been driving her mercilessly—me, with my chromium legs and atomic-powered muscles. No wonder she was ready to fold! And I'd been too dense to see how unfair I had been. She lifted the geiger harnesses, and I put Ledman back in his wheelchair. Val slipped her oxymask back on and fastened it shut. "Let's get back to the Dome in a hurry," I said. "We'll turn Ledman over to the authorities. Then we can catch the next ship for Earth." "Go back? Go back? If you think I'm backing down now and quitting you can find yourself another wife! After we dump this guy I'm sacking in for twenty hours, and then we're going back out there to finish that search-pattern. Earth needs uranium, honey, and I know you'd never be happy quitting in the middle like that." She smiled. "I can't wait to get out there and start listening for those tell-tale clicks." I gave a joyful whoop and swung her around. When I put her down, she squeezed my hand, hard. "Let's get moving, fellow hero," she said. I pressed the stud for the airlock, smiling. THE END Transcriber's Note: This etext was produced from Amazing Stories September 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. They stay on Mars for their contract and then move on to a different project |
Which Twitter customer service industries are investigated? | ### Introduction
The need for real-time, efficient, and reliable customer service has grown in recent years. Twitter has emerged as a popular medium for customer service dialogue, allowing customers to make inquiries and receive instant live support in the public domain. In order to provide useful information to customers, agents must first understand the requirements of the conversation, and offer customers the appropriate feedback. While this may be feasible at the level of a single conversation for a human agent, automatic analysis of conversations is essential for data-driven approaches towards the design of automated customer support agents and systems. Analyzing the dialogic structure of a conversation in terms of the "dialogue acts" used, such as statements or questions, can give important meta-information about conversation flow and content, and can be used as a first step to developing automated agents. Traditional dialogue act taxonomies used to label turns in a conversation are very generic, in order to allow for broad coverage of the majority of dialogue acts possible in a conversation BIBREF0 , BIBREF1 , BIBREF2 . However, for the purpose of understanding and analyzing customer service conversations, generic taxonomies fall short. Table TABREF1 shows a sample customer service conversation between a human agent and customer on Twitter, where the customer and agent take alternating "turns" to discuss the problem. As shown from the dialogue acts used at each turn, simply knowing that a turn is a Statement or Request, as is possible with generic taxonomies, is not enough information to allow for automated handling or response to a problem. We need more fine-grained dialogue acts, such as Informative Statement, Complaint, or Request for Information to capture the speaker's intent, and act accordingly. Likewise, turns often include multiple overlapping dialogue acts, such that a multi-label approach to classification is often more informative than a single-label approach. Dialogue act prediction can be used to guide automatic response generation, and to develop diagnostic tools for the fine-tuning of automatic agents. For example, in Table TABREF1 , the customer's first turn (Turn 1) is categorized as a Complaint, Negative Expressive Statement, and Sarcasm, and the agent's response (Turn 2) is tagged as a Request for Information, Yes-No Question, and Apology. Prediction of these dialogue acts in a real-time setting can be leveraged to generate appropriate automated agent responses to similar situations. Additionally, important patterns can emerge from analysis of the fine-grained acts in a dialogue in a post-prediction setting. For example, if an agent does not follow-up with certain actions in response to a customer's question dialogue act, this could be found to be a violation of a best practice pattern. By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. "Continuing to request information late in a conversation often leads to customer dissatisfaction." This can then be codified into a best practice pattern rules for automated systems, such as "A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation." In this work, we are motivated to predict the dialogue acts in conversations with the intent of identifying problem spots that can be addressed in real-time, and to allow for post-conversation analysis to derive rules about conversation outcomes indicating successful/unsuccessful interactions, namely, customer satisfaction, customer frustration, and problem resolution. We focus on analysis of the dialogue acts used in customer service conversations as a first step to fully automating the interaction. We address various different challenges: dialogue act annotated data is not available for customer service on Twitter, the task of dialogue act annotation is subjective, existing taxonomies do not capture the fine-grained information we believe is valuable to our task, and tweets, although concise in nature, often consist of overlapping dialogue acts to characterize their full intent. The novelty of our work comes from the development of our fine-grained dialogue act taxonomy and multi-label approach for act prediction, as well as our analysis of the customer service domain on Twitter. Our goal is to offer useful analytics to improve outcome-oriented conversational systems. We first expand upon previous work and generic dialogue act taxonomies, developing a fine-grained set of dialogue acts for customer service, and conducting a systematic user study to identify these acts in a dataset of 800 conversations from four Twitter customer service accounts (i.e. four different companies in the telecommunication, electronics, and insurance industries). We then aim to understand the conversation flow between customers and agents using our taxonomy, so we develop a real-time sequential SVM-HMM model to predict our fine-grained dialogue acts while a conversation is in progress, using a novel multi-label scheme to classify each turn. Finally, using our dialogue act predictions, we classify conversations based on the outcomes of customer satisfaction, frustration, and overall problem resolution, then provide actionable guidelines for the development of automated customer service systems and intelligent agents aimed at desired customer outcomes BIBREF3 , BIBREF4 . We begin with a discussion of related work, followed by an overview of our methodology. Next, we describe our conversation modeling framework, and explain our outcome analysis experiments, to show how we derive useful patterns for designing automated customer service agents. Finally, we present conclusions and directions for future work. ### Related Work
Developing computational speech and dialogue act models has long been a topic of interest BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , with researchers from many different backgrounds studying human conversations and developing theories around conversational analysis and interpretation on intent. Modern intelligent conversational BIBREF3 , BIBREF4 and dialogue systems draw principles from many disciplines, including philosophy, linguistics, computer science, and sociology. In this section, we describe relevant previous work on speech and dialogue act modeling, general conversation modeling on Twitter, and speech and dialogue act modeling of customer service in other data sources. Previous work has explored speech act modeling in different domains (as a predecessor to dialogue act modeling). Zhang et al. present work on recognition of speech acts on Twitter, following up with a study on scalable speech act recognition given the difficulty of obtaining labeled training data BIBREF9 . They use a simple taxonomy of four main speech acts (Statement, Question, Suggestion, Comment, and a Miscellaneous category). More recently, Vosoughi et al. develop BIBREF10 a speech act classifier for Twitter, using a modification of the taxonomy defined by Searle in 1975, including six acts they observe to commonly occur on Twitter: Assertion, Recommendation Expression, Question, Request, again plus a Miscellaneous category. They describe good features for speech act classification and the application of such a system to detect stories on social media BIBREF11 . In this work, we are interested in the dialogic characteristics of Twitter conversations, rather than speech acts in stand-alone tweets. Different dialogue act taxonomies have been developed to characterize conversational acts. Core and Allen present the Dialogue Act Marking in Several Layers (DAMSL), a standard for discourse annotation that was developed in 1997 BIBREF0 . The taxonomy contains a total of 220 tags, divided into four main categories: communicative status, information level, forward-looking function, and backward-looking function. Jurafsky, Shriberg, and Biasca develop a less fine-grained taxonomy of 42 tags based on DAMSL BIBREF1 . Stolcke et al. employ a similar set for general conversation BIBREF2 , citing that "content- and task-related distinctions will always play an important role in effective DA [Dialogue Act] labeling." Many researchers have tackled the task of developing different speech and dialogue act taxonomies and coding schemes BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 . For the purposes of our own research, we require a set of dialogue acts that is more closely representative of customer service domain interactions - thus we expand upon previously defined taxonomies and develop a more fine-grained set. Modeling general conversation on Twitter has also been a topic of interest in previous work. Honeycutt and Herring study conversation and collaboration on Twitter using individual tweets containing "@" mentions BIBREF16 . Ritter et al. explore unsupervised modeling of Twitter conversations, using clustering methods on a corpus of 1.3 million Twitter conversations to define a model of transitional flow between in a general Twitter dialogue BIBREF17 . While these approaches are relevant to understanding the nature of interactions on Twitter, we find that the customer service domain presents its own interesting characteristics that are worth exploring further. The most related previous work has explored speech and dialogue act modeling in customer service, however, no previous work has focused on Twitter as a data source. In 2005, Ivanovic uses an abridged set of 12 course-grained dialogue acts (detailed in the Taxonomy section) to describe interactions between customers and agents in instant messaging chats BIBREF18 , BIBREF19 , leading to a proposal on response suggestion using the proposed dialogue acts BIBREF20 . Follow-up work using the taxonomy selected by Ivanovic comes from Kim et al., where they focus on classifying dialogue acts in both one-on-one and multi-party live instant messaging chats BIBREF21 , BIBREF22 . These works are similar to ours in the nature of the problem addressed, but we use a much more fine-grained taxonomy to define the interactions possible in the customer service domain, and focus on Twitter conversations, which are unique in their brevity and the nature of the public interactions. The most similar work to our own is that of Herzig et al. on classifying emotions in customer support dialogues on Twitter BIBREF23 . They explore how agent responses should be tailored to the detected emotional response in customers, in order to improve the quality of service agents can provide. Rather than focusing on emotional response, we seek to model the dialogic structure and intents of the speakers using dialogue acts, with emotion included as features in our model, to characterize the emotional intent within each act. ### Methodology
The underlying goal of this work is to show how a well-defined taxonomy of dialogue acts can be used to summarize semantic information in real-time about the flow of a conversation to derive meaningful insights into the success/failure of the interaction, and then to develop actionable rules to be used in automating customer service interactions. We focus on the customer service domain on Twitter, which has not previously been explored in the context of dialogue act classification. In this new domain, we can provide meaningful recommendations about good communicative practices, based on real data. Our methodology pipeline is shown in Figure FIGREF2 . ### Taxonomy Definition
As described in the related work, the taxonomy of 12 acts to classify dialogue acts in an instant-messaging scenario, developed by Ivanovic in 2005, has been used by previous work when approaching the task of dialogue act classification for customer service BIBREF18 , BIBREF20 , BIBREF19 , BIBREF21 , BIBREF22 . The dataset used consisted of eight conversations from chat logs in the MSN Shopping Service (around 550 turns spanning around 4,500 words) BIBREF19 . The conversations were gathered by asking five volunteers to use the platform to inquire for help regarding various hypothetical situations (i.e. buying an item for someone) BIBREF19 . The process of selection of tags to develop the taxonomy, beginning with the 42 tags from the DAMSL set BIBREF0 , involved removing tags inappropriate for written text, and collapsing sets of tags into a more coarse-grained label BIBREF18 . The final taxonomy consists of the following 12 dialogue acts (sorted by frequency in the dataset): Statement (36%), Thanking (14.7%), Yes-No Question (13.9%), Response-Acknowledgement (7.2%), Request (5.9%), Open-Question (5.3%), Yes-Answer (5.1%), Conventional-Closing (2.9%), No-Answer (2.5%), Conventional-Opening (2.3%), Expressive (2.3%) and Downplayer (1.9%). For the purposes of our own research, focused on customer service on Twitter, we found that the course-grained nature of the taxonomy presented a natural shortcoming in terms of what information could be learned by performing classification at this level. We observe that while having a smaller set of dialogue acts may be helpful for achieving good agreement between annotators (Ivanovic cites kappas of 0.87 between the three expert annotators using this tag set on his data BIBREF18 ), it is unable to offer deeper semantic insight into the specific intent behind each act for many of the categories. For example, the Statement act, which comprises the largest percentage (36% of turns), is an extremely broad category that fails to provide useful information from an analytical perspective. Likewise, the Request category also does not specify any intent behind the act, and leaves much room for improvement. For this reason, and motivated by previous work seeking to develop dialogue act taxonomies appropriate for different domains BIBREF19 , BIBREF21 , we convert the list of dialogue acts presented by the literature into a hierarchical taxonomy, shown in Figure FIGREF6 . We first organize the taxonomy into six high-level dialogue acts: Greeting, Statement, Request, Question, Answer, and Social Act. Then, we update the taxonomy using two main steps: restructuring and adding additional fine-grained acts. We base our changes upon the taxonomy used by Ivanovic and Kim et al. in their work on instant messaging chat dialogues BIBREF19 , BIBREF21 , but also on general dialogue acts observed in the customer service domain, including complaints and suggestions. Our taxonomy does not make any specific restrictions on which party in the dialogue may perform each act, but we do observe that some acts are far more frequent (and sometimes non-existent) in usage, depending on whether the customer or agent is the speaker (for example, the Statement Complaint category never shows up in Agent turns). In order to account for gaps in available act selections for annotators, we include an Other act in the broadest categories. While our taxonomy fills in many gaps from previous work in our domain, we do not claim to have handled coverage of all possible acts in this domain. Our taxonomy allows us to more closely specify the intent and motivation behind each turn, and ultimately how to address different situations. ### Data Collection
Given our taxonomy of fine-grained dialogue acts that expands upon previous work, we set out to gather annotations for Twitter customer service conversations. For our data collection phase, we begin with conversations from the Twitter customer service pages of four different companies, from the electronics, telecommunications, and insurance industries. We perform several forms of pre-processing to the conversations. We filter out conversations if they contain more than one customer or agent speaker, do not have alternating customer/agent speaking turns (single turn per speaker), have less than 5 or more than 10 turns, have less than 70 words in total, and if any turn in the conversation ends in an ellipses followed by a link (indicating that the turn has been cut off due to length, and spans another tweet). Additionally, we remove any references to the company names (substituting with "Agent"), any references to customer usernames (substituting with "Customer"), and replacing and links or image references with INLINEFORM0 link INLINEFORM1 and INLINEFORM2 img INLINEFORM3 tokens. Using these filters as pre-processing methods, we end up with a set of 800 conversations, spanning 5,327 turns. We conduct our annotation study on Amazon Mechanical Turk, presenting Turkers with Human Intelligence Tasks (henceforth, HITs) consisting of a single conversation between a customer and an agent. In each HIT, we present Turkers with a definition of each dialogue act, as well as a sample annotated dialogue for reference. For each turn in the conversation, we allow Turkers to select as many labels from our taxonomy as required to fully characterize the intent of the turn. Additionally, annotators are asked three questions at the end of each conversation HIT, to which they could respond that they agreed, disagreed, or could not tell: We ask 5 Turkers to annotate each conversation HIT, and pay $0.20 per HIT. We find the list of "majority dialogue acts" for each tweet by finding any acts that have received majority-vote labels (at least 3 out of 5 judgements). It is important to note at this point that we make an important choice as to how we will handle dialogue act tagging for each turn. We note that each turn may contain more than one dialogue act vital to carry its full meaning. Thus, we choose not to carry out a specific segmentation task on our tweets, contrary to previous work BIBREF24 , BIBREF25 , opting to characterize each tweet as a single unit composed of different, often overlapping, dialogue acts. Table TABREF16 shows examples of tweets that receive majority vote on more than one label, where the act boundaries are overlapping and not necessarily distinguishable. It is clear that the lines differentiating these acts are not very well defined, and that segmentation would not necessarily aid in clearly separating out each intent. For these reasons, and due to the overall brevity of tweets in general, we choose to avoid the overhead of requiring annotators to provide segment boundaries, and instead ask for all appropriate dialogue acts. ### Annotation Results
Figure FIGREF17 shows the distribution of the number of times each dialogue act in our taxonomy is selected a majority act by the annotators (recall that each turn is annotated by 5 annotators). From the distribution, we see that the largest class is Statement Info which is part of the majority vote list for 2,152 of the 5,327 total turns, followed by Request Info, which appears in 1,088 of the total turns. Although Statement Informative comprises the largest set of majority labels in the data (as did Statement in Ivanovic's distribution), we do observe that other fine-grained categories of Statement occur in the most frequent labels as well, including Statement Complaint, Statement Expressive Negative, and Statement Suggestion – giving more useful information as to what form of statement is most frequently occurring. We find that 147 tweets receive no majority label (i.e. no single act received 3 or more votes out of 5). At the tail of the distribution, we see less frequent acts, such as Statement Sarcasm, Social Act Downplayer, Statement Promise, Greeting Closing, and Request Other. It is also interesting to note that both opening and closing greetings occur infrequently in the data – which is understandable given the nature of Twitter conversation, where formal greeting is not generally required. Table TABREF19 shows a more detailed summary of the distribution of our top 12 dialogue acts according to the annotation experiments, as presented by Ivanovic BIBREF18 . Since each turn has an overlapping set of labels, the column % of Turns (5,327) represents what fraction of the total 5,327 turns contain that dialogue act label (these values do not sum to 1, since there is overlap). To give a better sense of the percentage appearance of each dialogue act class in terms of the total number of annotated labels given, we also present column % of Annotations (10,343) (these values are percentages). We measure agreement in our annotations using a few different techniques. Since each item in our annotation experiments allows for multiple labels, we first design an agreement measure that accounts for how frequently each annotator selects the acts that agree with the majority-selected labels for the turns they annotated. To calculate this for each annotator, we find the number of majority-selected acts for each conversation they annotated (call this MAJ), and the number of subset those acts that they selected (call this SUBS), and find the ratio (SUBS/MAJ). We use this ratio to systematically fine-tune our set of annotators by running our annotation in four batches, restricting our pool of annotators to those that have above a 0.60 ratio of agreement with the majority from the previous batch, as a sort of quality assurance test. We also measure Fleiss' Kappa BIBREF26 agreement between annotators in two ways: first by normalizing our annotation results into binary-valued items indicating annotators' votes for each label contain within each turn. We find an average Fleiss- INLINEFORM0 for the full dataset, including all turn-and-label items, representing moderate agreement on the 24-label problem. We also calculate the Fleiss- INLINEFORM0 values for each label, and use the categories defined by Landis and Koch to bin our speech acts based on agreement BIBREF27 . As shown in Table TABREF18 , we find that the per-label agreement varies from "almost perfect" agreement of INLINEFORM1 for lexically defined categories such as Apology and Thanks, with only slight agreement of INLINEFORM2 for less clearly-defined categories, such as Statement (Other), Answer Response Acknowledgement and Request (Other). For the conversation-level questions, we calculate the agreement across the "Agree" label for all annotators, finding an average Fleiss- INLINEFORM3 , with question-level results of INLINEFORM4 for customer satisfaction, INLINEFORM5 for problem resolution, and INLINEFORM6 for customer frustration. These results suggest room for improvement for further development of the taxonomy, to address problem areas for annotators and remedy areas of lower agreement. ### Motivation for Multi-Label Classification
We test our hypothesis that tweet turns are often characterized by more than one distinct dialogue act label by measuring the percentage overlap between frequent pairs of labels. Of the 5,327 turns annotated, across the 800 conversations, we find that 3,593 of those turns (67.4%) contained more than one majority-act label. Table TABREF22 shows the distribution percentage of the most frequent pairs. For example, we observe that answering with informative statements is the most frequent pair, followed by complaints coupled with negative sentiment or informative statements. We also observe that requests are usually formed as questions, but also co-occur frequently with apologies. This experiment validates our intuition that the majority of turns do contain more than a single label, and motivates our use of a multi-label classification method for characterizing each turn in the conversation modeling experiments we present in the next section. ### Conversation Modeling
In this section, we describe the setup and results of our conversational modeling experiments on the data we collected using our fine-grained taxonomy of customer service dialogue acts. We begin with an overview of the features and classes used, followed by our experimental setup and results for each experiment performed. ### Features
The following list describes the set of features used for our dialogue act classification tasks: Word/Punctuation: binary bag-of-word unigrams, binary existence of a question mark, binary existence of an exclamation mark in a turn Temporal: response time of a turn (time in seconds elapsed between the posting time of the previous turn and that of the current turn) Second-Person Reference: existence of an explicit second-person reference in the turn (you, your, you're) Emotion: count of words in each of the 8 emotion classes from the NRC emotion lexicon BIBREF28 (anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, and trust) Dialogue: lexical indicators in the turn: opening greetings (hi, hello, greetings, etc), closing greetings (bye, goodbye), yes-no questions (turns with questions starting with do, did, can, could, etc), wh- questions (turns with questions starting with who, what, where, etc), thanking (thank*), apology (sorry, apolog*), yes-answer, and no-answer ### Classes
Table TABREF30 shows the division of classes we use for each of our experiments. We select our classes using the distribution of annotations we observe in our data collection phase (see Table TABREF19 ), selecting the top 12 classes as candidates. While iteratively selecting the most frequently-occurring classes helps to ensure that classes with the most data are represented in our experiments, it also introduces the problem of including classes that are very well-defined lexically, and may not require learning for classification, such as Social Act Apology and Social Act Thanking in the first 10-Class set. For this reason, we call this set 10-Class (Easy), and also experiment using a 10-Class (Hard) set, where we add in the next two less-defined and more semantically rich labels, such as Statement Offer and Question Open. When using each set of classes, a turn is either classified as one of the classes in the set, or it is classified as "other" (i.e. any of the other classes). We discuss our experiments in more detail and comment on performance differences in the experiment section. ### Experiments
Following previous work on conversation modeling BIBREF23 , we use a sequential SVM-HMM (using the INLINEFORM0 toolkit BIBREF29 ) for our conversation modeling experiments. We hypothesize that a sequential model is most suited to our dialogic data, and that we will be able to concisely capture conversational attributes such as the order in which dialogue acts often occur (i.e. some Answer act after Question a question act, or Apology acts after Complaints). We note that with default settings for a sequence of length INLINEFORM0 , an SVM-HMM model will be able to refine its answers for any turn INLINEFORM1 as information becomes available for turns INLINEFORM2 . However, we opt to design our classifier under a real-time setting, where turn-by-turn classification is required without future knowledge or adaptation of prediction at any given stage. In our setup, turns are predicted in a real-time setting to fairly model conversation available to an intelligent agent in a conversational system. At any point, a turn INLINEFORM3 is predicted using information from turns INLINEFORM4 , and where a prediction is not changed when new information is available. We test our hypothesis by comparing our real-time sequential SVM-HMM model to non-sequential baselines from the NLTK BIBREF30 and Scikit-Learn BIBREF31 toolkits. We use our selected feature set (described above) to be generic enough to apply to both our sequential and non-sequential models, in order to allow us to fairly compare performance. We shuffle and divide our data into 70% for training and development (560 conversations, using 10-fold cross-validation for parameter tuning), and hold out 30% of the data (240 conversations) for test. Motivated by the prevalent overlap of dialogue acts, we conduct our learning experiments using a multi-label setup. For each of the sets of classes, we conduct binary classification task for each label: for each INLINEFORM0 -class classification task, a turn is labeled as either belonging to the current label, or not (i.e. "other"). In this setup, each turn is assigned a binary value for each label (i.e. for the 6-class experiment, each turn receives a value of 0/1 for each indicating whether the classifier predicts it to be relevant to the each of the 6 labels). Thus, for each INLINEFORM1 -class experiment, we end up with INLINEFORM2 binary labels, for example, whether the turn is a Statement Informative or Other, Request Information or Other, etc. We aggregate the INLINEFORM3 binary predictions for each turn, then compare the resultant prediction matrix for all turns to our majority-vote ground-truth labels, where at least 3 out of 5 annotators have selected a label to be true for a given turn. The difficulty of the task increases as the number of classes INLINEFORM4 increases, as there are more classifications done for each turn (i.e., for the 6-class problem, there are 6 classification tasks per turn, while for the 8-class problem, there are 8, etc). Due to the inherent imbalance of label-distribution in the data (shown in Figure FIGREF17 ), we use weighted F-macro to calculate our final scores for each feature set (which finds the average of the metrics for each label, weighted by the number of true instances for that label) BIBREF31 . Our first experiment sets out to compare the use of a non-sequential classification algorithm versus a sequential model for dialogue act classification on our dataset. We experiment with the default Naive Bayes (NB) and Linear SVC algorithms from Scikit-Learn BIBREF31 , comparing with our sequential SVM-HMM model. We test each classifier on each of our four class sets, reporting weighted F-macro for each experiment. Figure FIGREF33 shows the results of the experiments. From this experiment, we observe that our sequential SVM-HMM outperforms each non-sequential baseline, for each of the four class sets. We select the sequential SVM-HMM model for our preferred model for subsequent experiments. We observe that while performance may be expected to drop as the number of classes increases, we instead get a spike in performance for the 10-Class (Easy) setting. This increase occurs due to the addition of the lexically well-defined classes of Statement Apology and Statement Thanks, which are much simpler for our model to predict. Their addition results in a performance boost, comparable to that of the simpler 6-Class problem. When we remove the two well-defined classes and add in the next two broader dialogue act classes of Statement Offer and Question Open (as defined by the 10-Class (Hard) set), we observe a drop in performance, and an overall result comparable to our 8-Class problem. This result is still strong, since the number of classes has increased, but the overall performance does not drop. We also observe that while NB and LinearSVC have the same performance trend for the smaller number of classes, Linear SVC rapidly improves in performance as the number of classes increases, following the same trend as SVM-HMM. The smallest margin of difference between SVM-HMM and Linear SVC also occurs at the 10-Class (Easy) setting, where the addition of highly-lexical classes makes for a more differentiable set of turns. Our next experiment tests the differences in performance when training and testing our real-time sequential SVM-HMM model using only a single type of speaker's turns (i.e. only Customer or only Agent turns). Figure FIGREF35 shows the relative performance of using only speaker-specific turns, versus our standard results using all turns. We observe that using Customer-only turns gives us lower prediction performance than using both speakers' turns, but that Agent-only turns actually gives us higher performance. Since agents are put through training on how to interact with customers (often using templates), agent behavior is significantly more predictable than customer behavior, and it is easier to predict agent turns even without utilizing any customer turn information (which is more varied, and thus more difficult to predict). We again observe a boost in performance at out 10-Class (Easy) set, due to the inclusion of lexically well-defined classes. Notably, we achieve best performance for the 10-Class (Easy) set using only agent turns, where the use of the Apology and Thanks classes are both prevalent and predictable. In our final experiment, we explore the changes in performance we get by splitting the training and test data based on company domain. We compare this performance with our standard setup for SVM-HMM from our baseline experiments (Figure FIGREF33 ), where our train-test data splitting is company-independent (i.e. all conversations are randomized, and no information is used to differentiate different companies or domains). To recap, our data consists of conversations from four companies from three different industrial domains (one from the telecommunication domain, two from the electronics domain, and one from the insurance domain). We create four different versions of our 6-class real-time sequential SVM-HMM, where we train on the data from three of the companies, and test on the remaining company. We present our findings in Table TABREF37 . From the table, we see that our real-time model achieves best prediction results when we use one of the electronics companies in the test fold, even though the number of training samples is smallest in these cases. On the other hand, when we assign insurance company in the test fold, our model's prediction performance is comparatively low. Upon further investigation, we find that customer-agent conversations in the telecommunication and electronics domains are more similar than those in the insurance domain. Our findings show that our model is robust to different domains as our test set size increases, and that our more generic, company-independent experiment gives us better performance than any domain-specific experiments. ### Conversation Outcome Analysis
Given our observation that Agent turns are more predictable, and that we achieve best performance in a company-independent setting, we question whether the training that agents receive is actually reliable in terms of resulting in overall "satisfied customers", regardless of company domain. Ultimately, our goal is to discover whether we can use the insight we derive from our predicted dialogue acts to better inform conversational systems aimed at offering customer support. Our next set of experiments aims to show the utility of our real-time dialogue act classification as a method for summarizing semantic intent in a conversation into rules that can be used to guide automated systems. ### Classifying Problem Outcomes
We conduct three supervised classification experiments to better understand full conversation outcome, using the default Linear SVC classifier in Scikit-Learn BIBREF31 (which gave us our best baseline for the dialogue classification task). Each classification experiments centers around one of three problem outcomes: customer satisfaction, problem resolution, and customer frustration. For each outcome, we remove any conversation that did not receive majority consensus for a label, or received majority vote of "can't tell". Our final conversation sets consist of 216 satisfied and 500 unsatisfied customer conversations, 271 resolved and 425 unresolved problem conversations, and 534 frustrated and 229 not frustrated customer conversations. We retain the inherent imbalance in the data to match the natural distribution observed. The clear excess of consensus of responses that indicate negative outcomes further motivates us to understand what sorts of dialogic patterns results in such outcomes. We run the experiment for each conversation outcome using 10-fold cross-validation, under each of our four class settings: 6-Class, 8-Class, 10-Class (Easy), and 10-Class (Hard). The first feature set we use is Best_Features (from the original dialogue act classification experiments), which we run as a baseline. Our second feature set is our Dialogue_Acts predictions for each turn – we choose the most probable dialogue act prediction for each turn using our dialogue act classification framework to avoid sparsity. In this way, for each class size INLINEFORM0 , each conversation is converted into a vector of INLINEFORM1 (up to 10) features that describe the most strongly associated dialogue act from the dialogue act classification experiments for each turn, and the corresponding turn number. For example, a conversation feature vector may look as follows: INLINEFORM2 Thus, our classifier can then learn patterns based on these features (for example, that specific acts appearing at the end of a conversation are strong indicators of customer satisfaction) that allow us to derive rules about successful/unsuccessful interactions. Figure FIGREF38 shows the results of our binary classification experiments for each outcome. For each experiment, the Best_Features set is constant over each class size, while the Dialogue_Act features are affected by class size (since the predicted act for each turn will change based on the set of acts available for that class size). Our first observation is that we achieve high performance on the binary classification task, reaching F-measures of 0.70, 0.65, and 0.83 for the satisfaction, resolution, and frustration outcomes, respectively. Also, we observe that the performance of our predicted dialogue act features is comparable to that of the much larger set of best features for each label (almost identical in the case of frustration). In more detail, we note interesting differences comparing the performance of the small set of dialogue act features that "summarize" the large, sparse set of best features for each label, as a form of data-driven feature selection. For satisfaction, we see that the best feature set outperforms the dialogue acts for each class set except for 10-Class (Easy), where the dialogue acts are more effective. The existence of the very lexically well-defined Social Act Thanking and Social Act Apology classes makes the dialogue acts ideal for summarization. In the case of problem resolution, we see that the performance of the dialogue acts approaches that of the best feature set as the number of classes increases, showing that the dialogue features are able to express the full intent of the turns well, even at more difficult class settings. Finally, for the frustration experiment, we observe negligible different between the best features and dialogue act features, and very high classification results overall. ### Actionable Rules for Automated Customer Support
While these experiments highlight how we can use dialogue act predictions as a means to greatly reduce feature sparsity and predict conversation outcome, our main aim is to gain good insight from the use of the dialogue acts to inform and automate customer service interactions. We conduct deeper analysis by taking a closer look at the most informative dialogue act features in each experiment. Table TABREF44 shows the most informative features and weights for each of our three conversation outcomes. To help guide our analysis, we divide the features into positions based on where they occur in the conversation: start (turns 1-3), middle (turns 4-6), and end (turns 7-10). Desirable outcomes (customers that are satisfied/not frustrated and resolved problems) are shown at the top rows of the table, and undesirable outcomes (unsatisfied/frustrated customers and unresolved problems) are shown at the bottom rows. Our analysis helps zone in on how the use of certain dialogue acts may be likely to result in different outcomes. The weights we observe vary in the amount of insight provided: for example, offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems (with ratios of above 6:1). However, some outcomes are much more subtle: for example, asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers. Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated. Likewise, requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers, with ratios of at least 4:1. By using the feature weights we derive from using our predicted dialogue acts in our outcome classification experiments, we can thus derive data-driven patterns that offer useful insight into good/bad practices. Our goal is to then use these rules as guidelines, serving as a basis for automated response planning in the customer service domain. For example, these rules can be used to recommend certain dialogue act responses given the position in a conversation, and based previous turns. This information, derived from correlation with conversation outcomes, gives a valuable addition to conversational flow for intelligent agents, and is more useful than canned responses. ### Conclusions
In this paper, we explore how we can analyze dialogic trends in customer service conversations on Twitter to offer insight into good/bad practices with respect to conversation outcomes. We design a novel taxonomy of fine-grained dialogue acts, tailored for the customer service domain, and gather annotations for 800 Twitter conversations. We show that dialogue acts are often semantically overlapping, and conduct multi-label supervised learning experiments to predict multiple appropriate dialogue act labels for each turn in real-time, under varying class sizes. We show that our sequential SVM-HMM model outperforms all non-sequential baselines, and plan to continue our exploration of other sequential models including Conditional Random Fields (CRF) BIBREF32 and Long Short-Term Memory (LSTM) BIBREF33 , as well as of dialogue modeling using different Markov Decision Process (MDP) BIBREF34 models such as the Partially-Observed MDP (POMDP) BIBREF35 . We establish that agents are more predictable than customers in terms of the dialogue acts they utilize, and set out to understand whether the conversation strategies agents employ are well-correlated with desirable conversation outcomes. We conduct binary classification experiments to analyze how our predicted dialogue acts can be used to classify conversations as ending in customer satisfaction, customer frustration, and problem resolution. We observe interesting correlations between the dialogue acts agents use and the outcomes, offering insights into good/bad practices that are more useful for creating context-aware automated customer service systems than generating canned response templates. Future directions for this work revolve around the integration of the insights derived in the design of automated customer service systems. To this end, we aim to improve the taxonomy and annotation design by consulting domain-experts and using annotator feedback and agreement information, derive more powerful features for dialogue act prediction, and automate ranking and selection of best-practice rules based on domain requirements for automated customer service system design. Table 1: Example Twitter Customer Service Conversation Figure 1: Methodology Pipeline Figure 2: Proposed Fine-Grained Dialogue Act Taxonomy for Customer Service Table 3: Dialogue Act Agreement in Fleiss-κ Bins (from Landis and Koch, 1977) Figure 3: Distribution of Annotated Dialogue Act Labels Table 4: Detailed Distribution of Top 12 Fine-Grained Dialogue Acts Derived From Annotations Table 5: Distribution of the 10 Most Frequent Dialogue Act Pairs for Turns with More Than 1 Label (3,593) Table 6: Dialogue Acts Used in Each Set of Experiments Figure 4: Plot of Non-Sequential Baselines vs. Sequential SVM-HMM Model Figure 5: Plot of Both Speaker Turns vs. Only Customer/Agent Turns for Sequential SVM-HMM Table 7: Company-Wise vs Company-Independent Evaluation for 6-Class Sequential SVM-HMM Figure 6: Plot of Dialogue Act Features vs. Best Feature Sets for Satisfaction, Resolution, and Frustration Outcomes Table 8: Most Informative Dialogue Act Features and Derivative Actionable Insights, by Conversation Outcome | four different companies in the telecommunication, electronics, and insurance industries |
What is not a theme explored in this story?
A. Change is necessary and inevitable for survival.
B. Fear is a powerful motivator.
C. Perception can often be all-encompassing.
D. Equality must be realized.
| The Sense of Wonder By MILTON LESSER Illustrated by HARRY ROSENBAUM [Transcriber's Note: This etext was produced from Galaxy Science Fiction September 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] When nobody aboard ship remembers where it's going, how can they tell when it has arrived? Every day for a week now, Rikud had come to the viewport to watch the great changeless sweep of space. He could not quite explain the feelings within him; they were so alien, so unnatural. But ever since the engines somewhere in the rear of the world had changed their tone, from the steady whining Rikud had heard all twenty-five years of his life, to the sullen roar that came to his ears now, the feelings had grown. If anyone else had noticed the change, he failed to mention it. This disturbed Rikud, although he could not tell why. And, because he had realized this odd difference in himself, he kept it locked up inside him. Today, space looked somehow different. The stars—it was a meaningless concept to Rikud, but that was what everyone called the bright pinpoints of light on the black backdrop in the viewport—were not apparent in the speckled profusion Rikud had always known. Instead, there was more of the blackness, and one very bright star set apart by itself in the middle of the viewport. If he had understood the term, Rikud would have told himself this was odd. His head ached with the half-born thought. It was—it was—what was it? Someone was clomping up the companionway behind Rikud. He turned and greeted gray-haired old Chuls. "In five more years," the older man chided, "you'll be ready to sire children. And all you can do in the meantime is gaze out at the stars." Rikud knew he should be exercising now, or bathing in the rays of the health-lamps. It had never occurred to him that he didn't feel like it; he just didn't, without comprehending. Chuls' reminder fostered uneasiness. Often Rikud had dreamed of the time he would be thirty and a father. Whom would the Calculator select as his mate? The first time this idea had occurred to him, Rikud ignored it. But it came again, and each time it left him with a feeling he could not explain. Why should he think thoughts that no other man had? Why should he think he was thinking such thoughts, when it always embroiled him in a hopeless, infinite confusion that left him with a headache? Chuls said, "It is time for my bath in the health-rays. I saw you here and knew it was your time, too...." His voice trailed off. Rikud knew that something which he could not explain had entered the elder man's head for a moment, but it had departed almost before Chuls knew of its existence. "I'll go with you," Rikud told him. A hardly perceptible purple glow pervaded the air in the room of the health-rays. Perhaps two score men lay about, naked, under the ray tubes. Chuls stripped himself and selected the space under a vacant tube. Rikud, for his part, wanted to get back to the viewport and watch the one new bright star. He had the distinct notion it was growing larger every moment. He turned to go, but the door clicked shut and a metallic voice said. "Fifteen minutes under the tubes, please." Rikud muttered to himself and undressed. The world had begun to annoy him. Now why shouldn't a man be permitted to do what he wanted, when he wanted to do it? There was a strange thought, and Rikud's brain whirled once more down the tortuous course of half-formed questions and unsatisfactory answers. He had even wondered what it was like to get hurt. No one ever got hurt. Once, here in this same ray room, he had had the impulse to hurl himself head-first against the wall, just to see what would happen. But something soft had cushioned the impact—something which had come into being just for the moment and then abruptly passed into non-being again, something which was as impalpable as air. Rikud had been stopped in this action, although there was no real authority to stop him. This puzzled him, because somehow he felt that there should have been authority. A long time ago the reading machine in the library had told him of the elders—a meaningless term—who had governed the world. They told you to do something and you did it, but that was silly, because now no one told you to do anything. You only listened to the buzzer. And Rikud could remember the rest of what the reading machine had said. There had been a revolt—again a term without any real meaning, a term that could have no reality outside of the reading machine—and the elders were overthrown. Here Rikud had been lost utterly. The people had decided that they did not know where they were going, or why, and that it was unfair that the elders alone had this authority. They were born and they lived and they died as the elders directed, like little cogs in a great machine. Much of this Rikud could not understand, but he knew enough to realize that the reading machine had sided with the people against the elders, and it said the people had won. Now in the health room, Rikud felt a warmth in the rays. Grudgingly, he had to admit to himself that it was not unpleasant. He could see the look of easy contentment on Chuls' face as the rays fanned down upon him, bathing his old body in a forgotten magic which, many generations before Rikud's time, had negated the necessity for a knowledge of medicine. But when, in another ten years, Chuls would perish of old age, the rays would no longer suffice. Nothing would, for Chuls. Rikud often thought of his own death, still seventy-five years in the future, not without a sense of alarm. Yet old Chuls seemed heedless, with only a decade to go. Under the tube at Rikud's left lay Crifer. The man was short and heavy through the shoulders and chest, and he had a lame foot. Every time Rikud looked at that foot, it was with a sense of satisfaction. True, this was the only case of its kind, the exception to the rule, but it proved the world was not perfect. Rikud was guiltily glad when he saw Crifer limp. But, if anyone else saw it, he never said a word. Not even Crifer. Now Crifer said, "I've been reading again, Rikud." "Yes?" Almost no one read any more, and the library was heavy with the smell of dust. Reading represented initiative on the part of Crifer; it meant that, in the two unoccupied hours before sleep, he went to the library and listened to the reading machine. Everyone else simply sat about and talked. That was the custom. Everyone did it. But if he wasn't reading himself, Rikud usually went to sleep. All the people ever talked about was what they had done during the day, and it was always the same. "Yes," said Crifer. "I found a book about the stars. They're also called astronomy, I think." This was a new thought to Rikud, and he propped his head up on one elbow. "What did you find out?" "That's about all. They're just called astronomy, I think." "Well, where's the book?" Rikud would read it tomorrow. "I left it in the library. You can find several of them under 'astronomy,' with a cross-reference under 'stars.' They're synonymous terms." "You know," Rikud said, sitting up now, "the stars in the viewport are changing." "Changing?" Crifer questioned the fuzzy concept as much as he questioned what it might mean in this particular case. "Yes, there are less of them, and one is bigger and brighter than the others." "Astronomy says some stars are variable," Crifer offered, but Rikud knew his lame-footed companion understood the word no better than he did. Over on Rikud's right, Chuls began to dress. "Variability," he told them, "is a contradictory term. Nothing is variable. It can't be." "I'm only saying what I read in the book," Crifer protested mildly. "Well, it's wrong. Variability and change are two words without meaning." "People grow old," Rikud suggested. A buzzer signified that his fifteen minutes under the rays were up, and Chuls said, "It's almost time for me to eat." Rikud frowned. Chuls hadn't even seen the connection between the two concepts, yet it was so clear. Or was it? He had had it a moment ago, but now it faded, and change and old were just two words. His own buzzer sounded a moment later, and it was with a strange feeling of elation that he dressed and made his way back to the viewport. When he passed the door which led to the women's half of the world, however, he paused. He wanted to open that door and see a woman. He had been told about them and he had seen pictures, and he dimly remembered his childhood among women. But his feelings had changed; this was different. Again there were inexplicable feelings—strange channelings of Rikud's energy in new and confusing directions. He shrugged and reserved the thought for later. He wanted to see the stars again. The view had changed, and the strangeness of it made Rikud's pulses leap with excitement. All the stars were paler now than before, and where Rikud had seen the one bright central star, he now saw a globe of light, white with a tinge of blue in it, and so bright that it hurt his eyes to look. Yes, hurt! Rikud looked and looked until his eyes teared and he had to turn away. Here was an unknown factor which the perfect world failed to control. But how could a star change into a blinking blue-white globe—if, indeed, that was the star Rikud had seen earlier? There was that word change again. Didn't it have something to do with age? Rikud couldn't remember, and he suddenly wished he could read Crifer's book on astronomy, which meant the same as stars. Except that it was variable, which was like change, being tied up somehow with age. Presently Rikud became aware that his eyes were not tearing any longer, and he turned to look at the viewport. What he saw now was so new that he couldn't at first accept it. Instead, he blinked and rubbed his eyes, sure that the ball of blue-white fire somehow had damaged them. But the new view persisted. Of stars there were few, and of the blackness, almost nothing. Gone, too, was the burning globe. Something loomed there in the port, so huge that it spread out over almost the entire surface. Something big and round, all grays and greens and browns, and something for which Rikud had no name. A few moments more, and Rikud no longer could see the sphere. A section of it had expanded outward and assumed the rectangular shape of the viewport, and its size as well. It seemed neatly sheered down the middle, so that on one side Rikud saw an expanse of brown and green, and on the other, blue. Startled, Rikud leaped back. The sullen roar in the rear of the world had ceased abruptly. Instead an ominous silence, broken at regular intervals by a sharp booming. Change— "Won't you eat, Rikud?" Chuls called from somewhere down below. "Damn the man," Rikud thought. Then aloud: "Yes, I'll eat. Later." "It's time...." Chuls' voice trailed off again, impotently. But Rikud forgot the old man completely. A new idea occurred to him, and for a while he struggled with it. What he saw—what he had always seen, except that now there was the added factor of change—perhaps did not exist in the viewport. Maybe it existed through the viewport. That was maddening. Rikud turned again to the port, where he could see nothing but an obscuring cloud of white vapor, murky, swirling, more confusing than ever. "Chuls," he called, remembering, "come here." "I am here," said a voice at his elbow. Rikud whirled on the little figure and pointed to the swirling cloud of vapor. "What do you see?" Chuls looked. "The viewport, of course." "What else?" "Else? Nothing." Anger welled up inside Rikud. "All right," he said, "listen. What do you hear?" "Broom, brroom, brrroom!" Chuls imitated the intermittent blasting of the engines. "I'm hungry, Rikud." The old man turned and strode off down the corridor toward the dining room, and Rikud was glad to be alone once more. Now the vapor had departed, except for a few tenuous whisps. For a moment Rikud thought he could see the gardens rearward in the world. But that was silly. What were the gardens doing in the viewport? And besides, Rikud had the distinct feeling that here was something far vaster than the gardens, although all of it existed in the viewport which was no wider than the length of his body. The gardens, moreover, did not jump and dance before his eyes the way the viewport gardens did. Nor did they spin. Nor did the trees grow larger with every jolt. Rikud sat down hard. He blinked. The world had come to rest on the garden of the viewport. For a whole week that view did not change, and Rikud had come to accept it as fact. There—through the viewport and in it—was a garden. A garden larger than the entire world, a garden of plants which Rikud had never seen before, although he had always liked to stroll through the world's garden and he had come to know every plant well. Nevertheless, it was a garden. He told Chuls, but Chuls had responded, "It is the viewport." Crifer, on the other hand, wasn't so sure. "It looks like the garden," he admitted to Rikud. "But why should the garden be in the viewport?" Somehow, Rikud knew this question for a healthy sign. But he could not tell them of his most amazing thought of all. The change in the viewport could mean only one thing. The world had been walking—the word seemed all wrong to Rikud, but he could think of no other, unless it were running. The world had been walking somewhere. That somewhere was the garden and the world had arrived. "It is an old picture of the garden," Chuls suggested, "and the plants are different." "Then they've changed?" "No, merely different." "Well, what about the viewport? It changed. Where are the stars? Where are they, Chuls, if it did not change?" "The stars come out at night." "So there is a change from day to night!" "I didn't say that. The stars simply shine at night. Why should they shine during the day when the world wants them to shine only at night?" "Once they shone all the time." "Naturally," said Crifer, becoming interested. "They are variable." Rikud regretted that he never had had the chance to read that book on astronomy. He hadn't been reading too much lately. The voice of the reading machine had begun to bore him. He said, "Well, variable or not, our whole perspective has changed." And when Chuls looked away in disinterest, Rikud became angry. If only the man would realize! If only anyone would realize! It all seemed so obvious. If he, Rikud, walked from one part of the world to another, it was with a purpose—to eat, or to sleep, or perhaps to bathe in the health-rays. Now if the world had walked from—somewhere, through the vast star-speckled darkness and to the great garden outside, this also was purposeful. The world had arrived at the garden for a reason. But if everyone lived as if the world still stood in blackness, how could they find the nature of that purpose? "I will eat," Chuls said, breaking Rikud's revery. Damn the man, all he did was eat! Yet he did have initiative after a sort. He knew when to eat. Because he was hungry. And Rikud, too, was hungry. Differently. He had long wondered about the door in the back of the library, and now, as Crifer sat cross-legged on one of the dusty tables, reading machine and book on astronomy or stars in his lap, Rikud approached the door. "What's in here?" he demanded. "It's a door, I think," said Crifer. "I know, but what's beyond it?" "Beyond it? Oh, you mean through the door." "Yes." "Well," Crifer scratched his head, "I don't think anyone ever opened it. It's only a door." "I will," said Rikud. "You will what?" "Open it. Open the door and look inside." A long pause. Then, "Can you do it?" "I think so." "You can't, probably. How can anyone go where no one has been before? There's nothing. It just isn't. It's only a door, Rikud." "No—" Rikud began, but the words faded off into a sharp intake of breath. Rikud had turned the knob and pushed. The door opened silently, and Crifer said, "Doors are variable, too, I think." Rikud saw a small room, perhaps half a dozen paces across, at the other end of which was another door, just like the first. Halfway across, Rikud heard a voice not unlike that of the reading machine. He missed the beginning, but then: —therefore, permit no unauthorized persons to go through this door. The machinery in the next room is your protection against the rigors of space. A thousand years from now, journey's end, you may have discarded it for something better—who knows? But if you have not, then here is your protection. As nearly as possible, this ship is a perfect, self-sustaining world. It is more than that: it is human-sustaining as well. Try to hurt yourself and the ship will not permit it—within limits, of course. But you can damage the ship, and to avoid any possibility of that, no unauthorized persons are to be permitted through this door— Rikud gave the voice up as hopeless. There were too many confusing words. What in the world was an unauthorized person? More interesting than that, however, was the second door. Would it lead to another voice? Rikud hoped that it wouldn't. When he opened the door a strange new noise filled his ears, a gentle humming, punctuated by a throb-throb-throb which sounded not unlike the booming of the engines last week, except that this new sound didn't blast nearly so loudly against his eardrums. And what met Rikud's eyes—he blinked and looked again, but it was still there—cogs and gears and wheels and nameless things all strange and beautiful because they shone with a luster unfamiliar to him. "Odd," Rikud said aloud. Then he thought, "Now there's a good word, but no one quite seems to know its meaning." Odder still was the third door. Rikud suddenly thought there might exist an endless succession of them, especially when the third one opened on a bare tunnel which led to yet another door. Only this one was different. In it Rikud saw the viewport. But how? The viewport stood on the other end of the world. It did seem smaller, and, although it looked out on the garden, Rikud sensed that the topography was different. Then the garden extended even farther than he had thought. It was endless, extending all the way to a ridge of mounds way off in the distance. And this door one could walk through, into the garden. Rikud put his hand on the door, all the while watching the garden through the new viewport. He began to turn the handle. Then he trembled. What would he do out in the garden? He couldn't go alone. He'd die of the strangeness. It was a silly thought; no one ever died of anything until he was a hundred. Rikud couldn't fathom the rapid thumping of his heart. And Rikud's mouth felt dry; he wanted to swallow, but couldn't. Slowly, he took his hand off the door lever. He made his way back through the tunnel and then through the room of machinery and finally through the little room with the confusing voice to Crifer. By the time he reached the lame-footed man, Rikud was running. He did not dare once to look back. He stood shaking at Crifer's side, and sweat covered him in a clammy film. He never wanted to look at the garden again. Not when he knew there was a door through which he could walk and then might find himself in the garden. It was so big. Three or four days passed before Rikud calmed himself enough to talk about his experience. When he did, only Crifer seemed at all interested, yet the lame-footed man's mind was inadequate to cope with the situation. He suggested that the viewport might also be variable and Rikud found himself wishing that his friend had never read that book on astronomy. Chuls did not believe Rikud at all. "There are not that many doors in the world," he said. "The library has a door and there is a door to the women's quarters; in five years, the Calculator will send you through that. But there are no others." Chuls smiled an indulgent smile and Rikud came nearer to him. "Now, by the world, there are two other doors!" Rikud began to shout, and everyone looked at him queerly. "What are you doing that for?" demanded Wilm, who was shorter even than Crifer, but had no lame foot. "Doing what?" "Speaking so loudly when Chuls, who is close, obviously has no trouble hearing you." "Maybe yelling will make him understand." Crifer hobbled about on his good foot, doing a meaningless little jig. "Why don't we go see?" he suggested. Then, confused, he frowned. "Well, I won't go," Chuls replied. "There's no reason to go. If Rikud has been imagining things, why should I?" "I imagined nothing. I'll show you—" "You'll show me nothing because I won't go." Rikud grabbed Chuls' blouse with his big fist. Then, startled by what he did, his hands began to tremble. But he held on, and he tugged at the blouse. "Stop that," said the older man, mildly. Crifer hopped up and down. "Look what Rikud's doing! I don't know what he's doing, but look. He's holding Chuls' blouse." "Stop that," repeated Chuls, his face reddening. "Only if you'll go with me." Rikud was panting. Chuls tugged at his wrist. By this time a crowd had gathered. Some of them watched Crifer jump up and down, but most of them watched Rikud holding Chuls' blouse. "I think I can do that," declared Wilm, clutching a fistful of Crifer's shirt. Presently, the members of the crowd had pretty well paired off, each partner grabbing for his companion's blouse. They giggled and laughed and some began to hop up and down as Crifer had done. A buzzer sounded and automatically Rikud found himself releasing Chuls. Chuls said, forgetting the incident completely, "Time to retire." In a moment, the room was cleared. Rikud stood alone. He cleared his throat and listened to the sound, all by itself in the stillness. What would have happened if they hadn't retired? But they always did things punctually like that, whenever the buzzer sounded. They ate with the buzzer, bathed in the health-rays with it, slept with it. What would they do if the buzzer stopped buzzing? This frightened Rikud, although he didn't know why. He'd like it, though. Maybe then he could take them outside with him to the big garden of the two viewports. And then he wouldn't be afraid because he could huddle close to them and he wouldn't be alone. Rikud heard the throbbing again as he stood in the room of the machinery. For a long time he watched the wheels and cogs and gears spinning and humming. He watched for he knew not how long. And then he began to wonder. If he destroyed the wheels and the cogs and the gears, would the buzzer stop? It probably would, because, as Rikud saw it, he was clearly an "unauthorized person." He had heard the voice again upon entering the room. He found a metal rod, bright and shiny, three feet long and half as wide as his arm. He tugged at it and it came loose from the wires that held it in place. He hefted it carefully for a moment, and then he swung the bar into the mass of metal. Each time he heard a grinding, crashing sound. He looked as the gears and cogs and wheels crumbled under his blows, shattered by the strength of his arm. Almost casually he strode about the room, but his blows were not casual. Soon his easy strides had given way to frenzied running. Rikud smashed everything in sight. When the lights winked out, he stopped. Anyway, by that time the room was a shambles of twisted, broken metal. He laughed, softly at first, but presently he was roaring, and the sound doubled and redoubled in his ears because now the throbbing had stopped. He opened the door and ran through the little corridor to the smaller viewport. Outside he could see the stars, and, dimly, the terrain beneath them. But everything was so dark that only the stars shone clearly. All else was bathed in a shadow of unreality. Rikud never wanted to do anything more than he wanted to open that door. But his hands trembled too much when he touched it, and once, when he pressed his face close against the viewport, there in the darkness, something bright flashed briefly through the sky and was gone. Whimpering, he fled. All around Rikud were darkness and hunger and thirst. The buzzer did not sound because Rikud had silenced it forever. And no one went to eat or drink. Rikud himself had fumbled through the blackness and the whimpering to the dining room, his tongue dry and swollen, but the smooth belt that flowed with water and with savory dishes did not run any more. The machinery, Rikud realized, also was responsible for food. Chuls said, over and over, "I'm hungry." "We will eat and we will drink when the buzzer tells us," Wilm replied confidently. "It won't any more," Rikud said. "What won't?" "The buzzer will never sound again. I broke it." Crifer growled. "I know. You shouldn't have done it. That was a bad thing you did, Rikud." "It was not bad. The world has moved through the blackness and the stars and now we should go outside to live in the big garden there beyond the viewport." "That's ridiculous," Chuls said. Even Crifer now was angry at Rikud. "He broke the buzzer and no one can eat. I hate Rikud, I think." There was a lot of noise in the darkness, and someone else said, "I hate Rikud." Then everyone was saying it. Rikud was sad. Soon he would die, because no one would go outside with him and he could not go outside alone. In five more years he would have had a woman, too. He wondered if it was dark and hungry in the women's quarters. Did women eat? Perhaps they ate plants. Once, in the garden, Rikud had broken off a frond and tasted it. It had been bitter, but not unpleasant. Maybe the plants in the viewport would even be better. "We will not be hungry if we go outside," he said. "We can eat there." "We can eat if the buzzer sounds, but it is broken," Chuls said dully. Crifer shrilled, "Maybe it is only variable and will buzz again." "No," Rikud assured him. "It won't." "Then you broke it and I hate you," said Crifer. "We should break you, too, to show you how it is to be broken." "We must go outside—through the viewport." Rikud listened to the odd gurgling sound his stomach made. A hand reached out in the darkness and grabbed at his head. He heard Crifer's voice. "I have Rikud's head." The voice was nasty, hostile. Crifer, more than anyone, had been his friend. But now that he had broken the machinery, Crifer was his enemy, because Crifer came nearer to understanding the situation than anyone except Rikud. The hand reached out again, and it struck Rikud hard across the face. "I hit him! I hit him!" Other hands reached out, and Rikud stumbled. He fell and then someone was on top of him, and he struggled. He rolled and was up again, and he did not like the sound of the angry voices. Someone said, "Let us do to Rikud what he said he did to the machinery." Rikud ran. In the darkness, his feet prodded many bodies. There were those who were too weak to rise. Rikud, too, felt a strange light-headedness and a gnawing hurt in his stomach. But it didn't matter. He heard the angry voices and the feet pounding behind him, and he wanted only to get away. It was dark and he was hungry and everyone who was strong enough to run was chasing him, but every time he thought of the garden outside, and how big it was, the darkness and the hunger and the people chasing him were unimportant. It was so big that it would swallow him up completely and positively. He became sickly giddy thinking about it. But if he didn't open the door and go into the garden outside, he would die because he had no food and no water and his stomach gurgled and grumbled and hurt. And everyone was chasing him. He stumbled through the darkness and felt his way back to the library, through the inner door and into the room with the voice—but the voice didn't speak this time—through its door and into the place of machinery. Behind him, he could hear the voices at the first door, and he thought for a moment that no one would come after him. But he heard Crifer yell something, and then feet pounding in the passage. Rikud tripped over something and sprawled awkwardly across the floor. He felt a sharp hurt in his head, and when he reached up to touch it with his hands there in the darkness, his fingers came away wet. He got up slowly and opened the next door. The voices behind him were closer now. Light streamed in through the viewport. After the darkness, it frightened Rikud and it made his eyes smart, and he could hear those behind him retreating to a safe distance. But their voices were not far away, and he knew they would come after him because they wanted to break him. Rikud looked out upon the garden and he trembled. Out there was life. The garden stretched off in unthinkable immensity to the cluster of low mounds against the bright blue which roofed the many plants. If plants could live out there as they did within the world, then so could people. Rikud and his people should . This was why the world had moved across the darkness and the stars for all Rikud's lifetime and more. But he was afraid. He reached up and grasped the handle of the door and he saw that his fingers were red with the wetness which had come from his hurt head. Slowly he slipped to the cool floor—how his head was burning!—and for a long time he lay there, thinking he would never rise again. Inside he heard the voices again, and soon a foot and then another pounded on the metal of the passage. He heard Crifer's voice louder than the rest: "There is Rikud on the floor!" Tugging at the handle of the door, Rikud pulled himself upright. Something small and brown scurried across the other side of the viewport and Rikud imagined it turned to look at him with two hideous red eyes. Rikud screamed and hurtled back through the corridor, and his face was so terrible in the light streaming in through the viewport that everyone fled before him. He stumbled again in the place of the machinery, and down on his hands and knees he fondled the bits of metal which he could see in the dim light through the open door. "Where's the buzzer?" he sobbed. "I must find the buzzer." Crifer's voice, from the darkness inside, said, "You broke it. You broke it. And now we will break you—" Rikud got up and ran. He reached the door again and then he slipped down against it, exhausted. Behind him, the voices and the footsteps came, and soon he saw Crifer's head peer in through the passageway. Then there were others, and then they were walking toward him. His head whirled and the viewport seemed to swim in a haze. Could it be variable, as Crifer had suggested? He wondered if the scurrying brown thing waited somewhere, and nausea struck at the pit of his stomach. But if the plants could live out there and the scurrying thing could live and that was why the world had moved through the blackness, then so could he live out there, and Crifer and all the others.... So tightly did he grip the handle that his fingers began to hurt. And his heart pounded hard and he felt the pulses leaping on either side of his neck. He stared out into the garden, and off into the distance, where the blue-white globe which might have been a star stood just above the row of mounds. Crifer was tugging at him, trying to pull him away from the door, and someone was grabbing at his legs, trying to make him fall. He kicked out and the hands let go, and then he turned the handle and shoved the weight of his body with all his strength against the door. It opened and he stepped outside into the warmth. The air was fresh, fresher than any air Rikud had ever breathed. He walked around aimlessly, touching the plants and bending down to feel the floor, and sometimes he looked at the blue-white globe on the horizon. It was all very beautiful. Near the ship, water that did not come from a machine gurgled across the land, and Rikud lay down and drank. It was cool and good, and when he got up, Crifer and Wilm were outside the world, and some of the others followed. They stood around for a long time before going to the water to drink. Rikud sat down and tore off a piece of a plant, munching on it. It was good. Crifer picked his head up, from the water, his chin wet. "Even feelings are variable. I don't hate you now, Rikud." Rikud smiled, staring at the ship. "People are variable, too, Crifer. That is, if those creatures coming from the ship are people." "They're women," said Crifer. They were strangely shaped in some ways, and yet in others completely human, and their voices were high, like singing. Rikud found them oddly exciting. He liked them. He liked the garden, for all its hugeness. With so many people, and especially now with women, he was not afraid. It was much better than the small world of machinery, buzzer, frightening doors and women by appointment only. Rikud felt at home. | D. Equality must be realized. |
The characters experience many emotions for the first time during the events of this story. What emotion(s) push the characters through the door.
A. Sadness
B. Hatred and anger.
C. Excitement and curiosity.
D. Pure happiness.
| The Sense of Wonder By MILTON LESSER Illustrated by HARRY ROSENBAUM [Transcriber's Note: This etext was produced from Galaxy Science Fiction September 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] When nobody aboard ship remembers where it's going, how can they tell when it has arrived? Every day for a week now, Rikud had come to the viewport to watch the great changeless sweep of space. He could not quite explain the feelings within him; they were so alien, so unnatural. But ever since the engines somewhere in the rear of the world had changed their tone, from the steady whining Rikud had heard all twenty-five years of his life, to the sullen roar that came to his ears now, the feelings had grown. If anyone else had noticed the change, he failed to mention it. This disturbed Rikud, although he could not tell why. And, because he had realized this odd difference in himself, he kept it locked up inside him. Today, space looked somehow different. The stars—it was a meaningless concept to Rikud, but that was what everyone called the bright pinpoints of light on the black backdrop in the viewport—were not apparent in the speckled profusion Rikud had always known. Instead, there was more of the blackness, and one very bright star set apart by itself in the middle of the viewport. If he had understood the term, Rikud would have told himself this was odd. His head ached with the half-born thought. It was—it was—what was it? Someone was clomping up the companionway behind Rikud. He turned and greeted gray-haired old Chuls. "In five more years," the older man chided, "you'll be ready to sire children. And all you can do in the meantime is gaze out at the stars." Rikud knew he should be exercising now, or bathing in the rays of the health-lamps. It had never occurred to him that he didn't feel like it; he just didn't, without comprehending. Chuls' reminder fostered uneasiness. Often Rikud had dreamed of the time he would be thirty and a father. Whom would the Calculator select as his mate? The first time this idea had occurred to him, Rikud ignored it. But it came again, and each time it left him with a feeling he could not explain. Why should he think thoughts that no other man had? Why should he think he was thinking such thoughts, when it always embroiled him in a hopeless, infinite confusion that left him with a headache? Chuls said, "It is time for my bath in the health-rays. I saw you here and knew it was your time, too...." His voice trailed off. Rikud knew that something which he could not explain had entered the elder man's head for a moment, but it had departed almost before Chuls knew of its existence. "I'll go with you," Rikud told him. A hardly perceptible purple glow pervaded the air in the room of the health-rays. Perhaps two score men lay about, naked, under the ray tubes. Chuls stripped himself and selected the space under a vacant tube. Rikud, for his part, wanted to get back to the viewport and watch the one new bright star. He had the distinct notion it was growing larger every moment. He turned to go, but the door clicked shut and a metallic voice said. "Fifteen minutes under the tubes, please." Rikud muttered to himself and undressed. The world had begun to annoy him. Now why shouldn't a man be permitted to do what he wanted, when he wanted to do it? There was a strange thought, and Rikud's brain whirled once more down the tortuous course of half-formed questions and unsatisfactory answers. He had even wondered what it was like to get hurt. No one ever got hurt. Once, here in this same ray room, he had had the impulse to hurl himself head-first against the wall, just to see what would happen. But something soft had cushioned the impact—something which had come into being just for the moment and then abruptly passed into non-being again, something which was as impalpable as air. Rikud had been stopped in this action, although there was no real authority to stop him. This puzzled him, because somehow he felt that there should have been authority. A long time ago the reading machine in the library had told him of the elders—a meaningless term—who had governed the world. They told you to do something and you did it, but that was silly, because now no one told you to do anything. You only listened to the buzzer. And Rikud could remember the rest of what the reading machine had said. There had been a revolt—again a term without any real meaning, a term that could have no reality outside of the reading machine—and the elders were overthrown. Here Rikud had been lost utterly. The people had decided that they did not know where they were going, or why, and that it was unfair that the elders alone had this authority. They were born and they lived and they died as the elders directed, like little cogs in a great machine. Much of this Rikud could not understand, but he knew enough to realize that the reading machine had sided with the people against the elders, and it said the people had won. Now in the health room, Rikud felt a warmth in the rays. Grudgingly, he had to admit to himself that it was not unpleasant. He could see the look of easy contentment on Chuls' face as the rays fanned down upon him, bathing his old body in a forgotten magic which, many generations before Rikud's time, had negated the necessity for a knowledge of medicine. But when, in another ten years, Chuls would perish of old age, the rays would no longer suffice. Nothing would, for Chuls. Rikud often thought of his own death, still seventy-five years in the future, not without a sense of alarm. Yet old Chuls seemed heedless, with only a decade to go. Under the tube at Rikud's left lay Crifer. The man was short and heavy through the shoulders and chest, and he had a lame foot. Every time Rikud looked at that foot, it was with a sense of satisfaction. True, this was the only case of its kind, the exception to the rule, but it proved the world was not perfect. Rikud was guiltily glad when he saw Crifer limp. But, if anyone else saw it, he never said a word. Not even Crifer. Now Crifer said, "I've been reading again, Rikud." "Yes?" Almost no one read any more, and the library was heavy with the smell of dust. Reading represented initiative on the part of Crifer; it meant that, in the two unoccupied hours before sleep, he went to the library and listened to the reading machine. Everyone else simply sat about and talked. That was the custom. Everyone did it. But if he wasn't reading himself, Rikud usually went to sleep. All the people ever talked about was what they had done during the day, and it was always the same. "Yes," said Crifer. "I found a book about the stars. They're also called astronomy, I think." This was a new thought to Rikud, and he propped his head up on one elbow. "What did you find out?" "That's about all. They're just called astronomy, I think." "Well, where's the book?" Rikud would read it tomorrow. "I left it in the library. You can find several of them under 'astronomy,' with a cross-reference under 'stars.' They're synonymous terms." "You know," Rikud said, sitting up now, "the stars in the viewport are changing." "Changing?" Crifer questioned the fuzzy concept as much as he questioned what it might mean in this particular case. "Yes, there are less of them, and one is bigger and brighter than the others." "Astronomy says some stars are variable," Crifer offered, but Rikud knew his lame-footed companion understood the word no better than he did. Over on Rikud's right, Chuls began to dress. "Variability," he told them, "is a contradictory term. Nothing is variable. It can't be." "I'm only saying what I read in the book," Crifer protested mildly. "Well, it's wrong. Variability and change are two words without meaning." "People grow old," Rikud suggested. A buzzer signified that his fifteen minutes under the rays were up, and Chuls said, "It's almost time for me to eat." Rikud frowned. Chuls hadn't even seen the connection between the two concepts, yet it was so clear. Or was it? He had had it a moment ago, but now it faded, and change and old were just two words. His own buzzer sounded a moment later, and it was with a strange feeling of elation that he dressed and made his way back to the viewport. When he passed the door which led to the women's half of the world, however, he paused. He wanted to open that door and see a woman. He had been told about them and he had seen pictures, and he dimly remembered his childhood among women. But his feelings had changed; this was different. Again there were inexplicable feelings—strange channelings of Rikud's energy in new and confusing directions. He shrugged and reserved the thought for later. He wanted to see the stars again. The view had changed, and the strangeness of it made Rikud's pulses leap with excitement. All the stars were paler now than before, and where Rikud had seen the one bright central star, he now saw a globe of light, white with a tinge of blue in it, and so bright that it hurt his eyes to look. Yes, hurt! Rikud looked and looked until his eyes teared and he had to turn away. Here was an unknown factor which the perfect world failed to control. But how could a star change into a blinking blue-white globe—if, indeed, that was the star Rikud had seen earlier? There was that word change again. Didn't it have something to do with age? Rikud couldn't remember, and he suddenly wished he could read Crifer's book on astronomy, which meant the same as stars. Except that it was variable, which was like change, being tied up somehow with age. Presently Rikud became aware that his eyes were not tearing any longer, and he turned to look at the viewport. What he saw now was so new that he couldn't at first accept it. Instead, he blinked and rubbed his eyes, sure that the ball of blue-white fire somehow had damaged them. But the new view persisted. Of stars there were few, and of the blackness, almost nothing. Gone, too, was the burning globe. Something loomed there in the port, so huge that it spread out over almost the entire surface. Something big and round, all grays and greens and browns, and something for which Rikud had no name. A few moments more, and Rikud no longer could see the sphere. A section of it had expanded outward and assumed the rectangular shape of the viewport, and its size as well. It seemed neatly sheered down the middle, so that on one side Rikud saw an expanse of brown and green, and on the other, blue. Startled, Rikud leaped back. The sullen roar in the rear of the world had ceased abruptly. Instead an ominous silence, broken at regular intervals by a sharp booming. Change— "Won't you eat, Rikud?" Chuls called from somewhere down below. "Damn the man," Rikud thought. Then aloud: "Yes, I'll eat. Later." "It's time...." Chuls' voice trailed off again, impotently. But Rikud forgot the old man completely. A new idea occurred to him, and for a while he struggled with it. What he saw—what he had always seen, except that now there was the added factor of change—perhaps did not exist in the viewport. Maybe it existed through the viewport. That was maddening. Rikud turned again to the port, where he could see nothing but an obscuring cloud of white vapor, murky, swirling, more confusing than ever. "Chuls," he called, remembering, "come here." "I am here," said a voice at his elbow. Rikud whirled on the little figure and pointed to the swirling cloud of vapor. "What do you see?" Chuls looked. "The viewport, of course." "What else?" "Else? Nothing." Anger welled up inside Rikud. "All right," he said, "listen. What do you hear?" "Broom, brroom, brrroom!" Chuls imitated the intermittent blasting of the engines. "I'm hungry, Rikud." The old man turned and strode off down the corridor toward the dining room, and Rikud was glad to be alone once more. Now the vapor had departed, except for a few tenuous whisps. For a moment Rikud thought he could see the gardens rearward in the world. But that was silly. What were the gardens doing in the viewport? And besides, Rikud had the distinct feeling that here was something far vaster than the gardens, although all of it existed in the viewport which was no wider than the length of his body. The gardens, moreover, did not jump and dance before his eyes the way the viewport gardens did. Nor did they spin. Nor did the trees grow larger with every jolt. Rikud sat down hard. He blinked. The world had come to rest on the garden of the viewport. For a whole week that view did not change, and Rikud had come to accept it as fact. There—through the viewport and in it—was a garden. A garden larger than the entire world, a garden of plants which Rikud had never seen before, although he had always liked to stroll through the world's garden and he had come to know every plant well. Nevertheless, it was a garden. He told Chuls, but Chuls had responded, "It is the viewport." Crifer, on the other hand, wasn't so sure. "It looks like the garden," he admitted to Rikud. "But why should the garden be in the viewport?" Somehow, Rikud knew this question for a healthy sign. But he could not tell them of his most amazing thought of all. The change in the viewport could mean only one thing. The world had been walking—the word seemed all wrong to Rikud, but he could think of no other, unless it were running. The world had been walking somewhere. That somewhere was the garden and the world had arrived. "It is an old picture of the garden," Chuls suggested, "and the plants are different." "Then they've changed?" "No, merely different." "Well, what about the viewport? It changed. Where are the stars? Where are they, Chuls, if it did not change?" "The stars come out at night." "So there is a change from day to night!" "I didn't say that. The stars simply shine at night. Why should they shine during the day when the world wants them to shine only at night?" "Once they shone all the time." "Naturally," said Crifer, becoming interested. "They are variable." Rikud regretted that he never had had the chance to read that book on astronomy. He hadn't been reading too much lately. The voice of the reading machine had begun to bore him. He said, "Well, variable or not, our whole perspective has changed." And when Chuls looked away in disinterest, Rikud became angry. If only the man would realize! If only anyone would realize! It all seemed so obvious. If he, Rikud, walked from one part of the world to another, it was with a purpose—to eat, or to sleep, or perhaps to bathe in the health-rays. Now if the world had walked from—somewhere, through the vast star-speckled darkness and to the great garden outside, this also was purposeful. The world had arrived at the garden for a reason. But if everyone lived as if the world still stood in blackness, how could they find the nature of that purpose? "I will eat," Chuls said, breaking Rikud's revery. Damn the man, all he did was eat! Yet he did have initiative after a sort. He knew when to eat. Because he was hungry. And Rikud, too, was hungry. Differently. He had long wondered about the door in the back of the library, and now, as Crifer sat cross-legged on one of the dusty tables, reading machine and book on astronomy or stars in his lap, Rikud approached the door. "What's in here?" he demanded. "It's a door, I think," said Crifer. "I know, but what's beyond it?" "Beyond it? Oh, you mean through the door." "Yes." "Well," Crifer scratched his head, "I don't think anyone ever opened it. It's only a door." "I will," said Rikud. "You will what?" "Open it. Open the door and look inside." A long pause. Then, "Can you do it?" "I think so." "You can't, probably. How can anyone go where no one has been before? There's nothing. It just isn't. It's only a door, Rikud." "No—" Rikud began, but the words faded off into a sharp intake of breath. Rikud had turned the knob and pushed. The door opened silently, and Crifer said, "Doors are variable, too, I think." Rikud saw a small room, perhaps half a dozen paces across, at the other end of which was another door, just like the first. Halfway across, Rikud heard a voice not unlike that of the reading machine. He missed the beginning, but then: —therefore, permit no unauthorized persons to go through this door. The machinery in the next room is your protection against the rigors of space. A thousand years from now, journey's end, you may have discarded it for something better—who knows? But if you have not, then here is your protection. As nearly as possible, this ship is a perfect, self-sustaining world. It is more than that: it is human-sustaining as well. Try to hurt yourself and the ship will not permit it—within limits, of course. But you can damage the ship, and to avoid any possibility of that, no unauthorized persons are to be permitted through this door— Rikud gave the voice up as hopeless. There were too many confusing words. What in the world was an unauthorized person? More interesting than that, however, was the second door. Would it lead to another voice? Rikud hoped that it wouldn't. When he opened the door a strange new noise filled his ears, a gentle humming, punctuated by a throb-throb-throb which sounded not unlike the booming of the engines last week, except that this new sound didn't blast nearly so loudly against his eardrums. And what met Rikud's eyes—he blinked and looked again, but it was still there—cogs and gears and wheels and nameless things all strange and beautiful because they shone with a luster unfamiliar to him. "Odd," Rikud said aloud. Then he thought, "Now there's a good word, but no one quite seems to know its meaning." Odder still was the third door. Rikud suddenly thought there might exist an endless succession of them, especially when the third one opened on a bare tunnel which led to yet another door. Only this one was different. In it Rikud saw the viewport. But how? The viewport stood on the other end of the world. It did seem smaller, and, although it looked out on the garden, Rikud sensed that the topography was different. Then the garden extended even farther than he had thought. It was endless, extending all the way to a ridge of mounds way off in the distance. And this door one could walk through, into the garden. Rikud put his hand on the door, all the while watching the garden through the new viewport. He began to turn the handle. Then he trembled. What would he do out in the garden? He couldn't go alone. He'd die of the strangeness. It was a silly thought; no one ever died of anything until he was a hundred. Rikud couldn't fathom the rapid thumping of his heart. And Rikud's mouth felt dry; he wanted to swallow, but couldn't. Slowly, he took his hand off the door lever. He made his way back through the tunnel and then through the room of machinery and finally through the little room with the confusing voice to Crifer. By the time he reached the lame-footed man, Rikud was running. He did not dare once to look back. He stood shaking at Crifer's side, and sweat covered him in a clammy film. He never wanted to look at the garden again. Not when he knew there was a door through which he could walk and then might find himself in the garden. It was so big. Three or four days passed before Rikud calmed himself enough to talk about his experience. When he did, only Crifer seemed at all interested, yet the lame-footed man's mind was inadequate to cope with the situation. He suggested that the viewport might also be variable and Rikud found himself wishing that his friend had never read that book on astronomy. Chuls did not believe Rikud at all. "There are not that many doors in the world," he said. "The library has a door and there is a door to the women's quarters; in five years, the Calculator will send you through that. But there are no others." Chuls smiled an indulgent smile and Rikud came nearer to him. "Now, by the world, there are two other doors!" Rikud began to shout, and everyone looked at him queerly. "What are you doing that for?" demanded Wilm, who was shorter even than Crifer, but had no lame foot. "Doing what?" "Speaking so loudly when Chuls, who is close, obviously has no trouble hearing you." "Maybe yelling will make him understand." Crifer hobbled about on his good foot, doing a meaningless little jig. "Why don't we go see?" he suggested. Then, confused, he frowned. "Well, I won't go," Chuls replied. "There's no reason to go. If Rikud has been imagining things, why should I?" "I imagined nothing. I'll show you—" "You'll show me nothing because I won't go." Rikud grabbed Chuls' blouse with his big fist. Then, startled by what he did, his hands began to tremble. But he held on, and he tugged at the blouse. "Stop that," said the older man, mildly. Crifer hopped up and down. "Look what Rikud's doing! I don't know what he's doing, but look. He's holding Chuls' blouse." "Stop that," repeated Chuls, his face reddening. "Only if you'll go with me." Rikud was panting. Chuls tugged at his wrist. By this time a crowd had gathered. Some of them watched Crifer jump up and down, but most of them watched Rikud holding Chuls' blouse. "I think I can do that," declared Wilm, clutching a fistful of Crifer's shirt. Presently, the members of the crowd had pretty well paired off, each partner grabbing for his companion's blouse. They giggled and laughed and some began to hop up and down as Crifer had done. A buzzer sounded and automatically Rikud found himself releasing Chuls. Chuls said, forgetting the incident completely, "Time to retire." In a moment, the room was cleared. Rikud stood alone. He cleared his throat and listened to the sound, all by itself in the stillness. What would have happened if they hadn't retired? But they always did things punctually like that, whenever the buzzer sounded. They ate with the buzzer, bathed in the health-rays with it, slept with it. What would they do if the buzzer stopped buzzing? This frightened Rikud, although he didn't know why. He'd like it, though. Maybe then he could take them outside with him to the big garden of the two viewports. And then he wouldn't be afraid because he could huddle close to them and he wouldn't be alone. Rikud heard the throbbing again as he stood in the room of the machinery. For a long time he watched the wheels and cogs and gears spinning and humming. He watched for he knew not how long. And then he began to wonder. If he destroyed the wheels and the cogs and the gears, would the buzzer stop? It probably would, because, as Rikud saw it, he was clearly an "unauthorized person." He had heard the voice again upon entering the room. He found a metal rod, bright and shiny, three feet long and half as wide as his arm. He tugged at it and it came loose from the wires that held it in place. He hefted it carefully for a moment, and then he swung the bar into the mass of metal. Each time he heard a grinding, crashing sound. He looked as the gears and cogs and wheels crumbled under his blows, shattered by the strength of his arm. Almost casually he strode about the room, but his blows were not casual. Soon his easy strides had given way to frenzied running. Rikud smashed everything in sight. When the lights winked out, he stopped. Anyway, by that time the room was a shambles of twisted, broken metal. He laughed, softly at first, but presently he was roaring, and the sound doubled and redoubled in his ears because now the throbbing had stopped. He opened the door and ran through the little corridor to the smaller viewport. Outside he could see the stars, and, dimly, the terrain beneath them. But everything was so dark that only the stars shone clearly. All else was bathed in a shadow of unreality. Rikud never wanted to do anything more than he wanted to open that door. But his hands trembled too much when he touched it, and once, when he pressed his face close against the viewport, there in the darkness, something bright flashed briefly through the sky and was gone. Whimpering, he fled. All around Rikud were darkness and hunger and thirst. The buzzer did not sound because Rikud had silenced it forever. And no one went to eat or drink. Rikud himself had fumbled through the blackness and the whimpering to the dining room, his tongue dry and swollen, but the smooth belt that flowed with water and with savory dishes did not run any more. The machinery, Rikud realized, also was responsible for food. Chuls said, over and over, "I'm hungry." "We will eat and we will drink when the buzzer tells us," Wilm replied confidently. "It won't any more," Rikud said. "What won't?" "The buzzer will never sound again. I broke it." Crifer growled. "I know. You shouldn't have done it. That was a bad thing you did, Rikud." "It was not bad. The world has moved through the blackness and the stars and now we should go outside to live in the big garden there beyond the viewport." "That's ridiculous," Chuls said. Even Crifer now was angry at Rikud. "He broke the buzzer and no one can eat. I hate Rikud, I think." There was a lot of noise in the darkness, and someone else said, "I hate Rikud." Then everyone was saying it. Rikud was sad. Soon he would die, because no one would go outside with him and he could not go outside alone. In five more years he would have had a woman, too. He wondered if it was dark and hungry in the women's quarters. Did women eat? Perhaps they ate plants. Once, in the garden, Rikud had broken off a frond and tasted it. It had been bitter, but not unpleasant. Maybe the plants in the viewport would even be better. "We will not be hungry if we go outside," he said. "We can eat there." "We can eat if the buzzer sounds, but it is broken," Chuls said dully. Crifer shrilled, "Maybe it is only variable and will buzz again." "No," Rikud assured him. "It won't." "Then you broke it and I hate you," said Crifer. "We should break you, too, to show you how it is to be broken." "We must go outside—through the viewport." Rikud listened to the odd gurgling sound his stomach made. A hand reached out in the darkness and grabbed at his head. He heard Crifer's voice. "I have Rikud's head." The voice was nasty, hostile. Crifer, more than anyone, had been his friend. But now that he had broken the machinery, Crifer was his enemy, because Crifer came nearer to understanding the situation than anyone except Rikud. The hand reached out again, and it struck Rikud hard across the face. "I hit him! I hit him!" Other hands reached out, and Rikud stumbled. He fell and then someone was on top of him, and he struggled. He rolled and was up again, and he did not like the sound of the angry voices. Someone said, "Let us do to Rikud what he said he did to the machinery." Rikud ran. In the darkness, his feet prodded many bodies. There were those who were too weak to rise. Rikud, too, felt a strange light-headedness and a gnawing hurt in his stomach. But it didn't matter. He heard the angry voices and the feet pounding behind him, and he wanted only to get away. It was dark and he was hungry and everyone who was strong enough to run was chasing him, but every time he thought of the garden outside, and how big it was, the darkness and the hunger and the people chasing him were unimportant. It was so big that it would swallow him up completely and positively. He became sickly giddy thinking about it. But if he didn't open the door and go into the garden outside, he would die because he had no food and no water and his stomach gurgled and grumbled and hurt. And everyone was chasing him. He stumbled through the darkness and felt his way back to the library, through the inner door and into the room with the voice—but the voice didn't speak this time—through its door and into the place of machinery. Behind him, he could hear the voices at the first door, and he thought for a moment that no one would come after him. But he heard Crifer yell something, and then feet pounding in the passage. Rikud tripped over something and sprawled awkwardly across the floor. He felt a sharp hurt in his head, and when he reached up to touch it with his hands there in the darkness, his fingers came away wet. He got up slowly and opened the next door. The voices behind him were closer now. Light streamed in through the viewport. After the darkness, it frightened Rikud and it made his eyes smart, and he could hear those behind him retreating to a safe distance. But their voices were not far away, and he knew they would come after him because they wanted to break him. Rikud looked out upon the garden and he trembled. Out there was life. The garden stretched off in unthinkable immensity to the cluster of low mounds against the bright blue which roofed the many plants. If plants could live out there as they did within the world, then so could people. Rikud and his people should . This was why the world had moved across the darkness and the stars for all Rikud's lifetime and more. But he was afraid. He reached up and grasped the handle of the door and he saw that his fingers were red with the wetness which had come from his hurt head. Slowly he slipped to the cool floor—how his head was burning!—and for a long time he lay there, thinking he would never rise again. Inside he heard the voices again, and soon a foot and then another pounded on the metal of the passage. He heard Crifer's voice louder than the rest: "There is Rikud on the floor!" Tugging at the handle of the door, Rikud pulled himself upright. Something small and brown scurried across the other side of the viewport and Rikud imagined it turned to look at him with two hideous red eyes. Rikud screamed and hurtled back through the corridor, and his face was so terrible in the light streaming in through the viewport that everyone fled before him. He stumbled again in the place of the machinery, and down on his hands and knees he fondled the bits of metal which he could see in the dim light through the open door. "Where's the buzzer?" he sobbed. "I must find the buzzer." Crifer's voice, from the darkness inside, said, "You broke it. You broke it. And now we will break you—" Rikud got up and ran. He reached the door again and then he slipped down against it, exhausted. Behind him, the voices and the footsteps came, and soon he saw Crifer's head peer in through the passageway. Then there were others, and then they were walking toward him. His head whirled and the viewport seemed to swim in a haze. Could it be variable, as Crifer had suggested? He wondered if the scurrying brown thing waited somewhere, and nausea struck at the pit of his stomach. But if the plants could live out there and the scurrying thing could live and that was why the world had moved through the blackness, then so could he live out there, and Crifer and all the others.... So tightly did he grip the handle that his fingers began to hurt. And his heart pounded hard and he felt the pulses leaping on either side of his neck. He stared out into the garden, and off into the distance, where the blue-white globe which might have been a star stood just above the row of mounds. Crifer was tugging at him, trying to pull him away from the door, and someone was grabbing at his legs, trying to make him fall. He kicked out and the hands let go, and then he turned the handle and shoved the weight of his body with all his strength against the door. It opened and he stepped outside into the warmth. The air was fresh, fresher than any air Rikud had ever breathed. He walked around aimlessly, touching the plants and bending down to feel the floor, and sometimes he looked at the blue-white globe on the horizon. It was all very beautiful. Near the ship, water that did not come from a machine gurgled across the land, and Rikud lay down and drank. It was cool and good, and when he got up, Crifer and Wilm were outside the world, and some of the others followed. They stood around for a long time before going to the water to drink. Rikud sat down and tore off a piece of a plant, munching on it. It was good. Crifer picked his head up, from the water, his chin wet. "Even feelings are variable. I don't hate you now, Rikud." Rikud smiled, staring at the ship. "People are variable, too, Crifer. That is, if those creatures coming from the ship are people." "They're women," said Crifer. They were strangely shaped in some ways, and yet in others completely human, and their voices were high, like singing. Rikud found them oddly exciting. He liked them. He liked the garden, for all its hugeness. With so many people, and especially now with women, he was not afraid. It was much better than the small world of machinery, buzzer, frightening doors and women by appointment only. Rikud felt at home. | B. Hatred and anger. |
Who would want to fix the mistake made in the story?
A. MacBride wouldn't go into the house with Lanfierre
B. Humphrey would not create the wind maker in his house
C. Lanfierre would listen to MacBride in Humphrey's house
D. Agnes wouldn't give Humphrey a condition for marriage
| A FALL OF GLASS By STANLEY R. LEE Illustrated by DILLON [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.] The weatherman was always right: Temperature, 59; humidity, 47%; occasional light showers—but of what? The pockets of Mr. Humphrey Fownes were being picked outrageously. It was a splendid day. The temperature was a crisp 59 degrees, the humidity a mildly dessicated 47%. The sun was a flaming orange ball in a cloudless blue sky. His pockets were picked eleven times. It should have been difficult. Under the circumstances it was a masterpiece of pocket picking. What made it possible was Humphrey Fownes' abstraction; he was an uncommonly preoccupied individual. He was strolling along a quiet residential avenue: small private houses, one after another, a place of little traffic and minimum distractions. But he was thinking about weather, which was an unusual subject to begin with for a person living in a domed city. He was thinking so deeply about it that it never occurred to him that entirely too many people were bumping into him. He was thinking about Optimum Dome Conditions (a crisp 59 degrees, a mildly dessicated 47%) when a bogus postman, who pretended to be reading a postal card, jostled him. In the confusion of spilled letters and apologies from both sides, the postman rifled Fownes's handkerchief and inside jacket pockets. He was still thinking about temperature and humidity when a pretty girl happened along with something in her eye. They collided. She got his right and left jacket pockets. It was much too much for coincidence. The sidewalk was wide enough to allow four people to pass at one time. He should surely have become suspicious when two men engaged in a heated argument came along. In the ensuing contretemps they emptied his rear pants pockets, got his wristwatch and restored the contents of the handkerchief pocket. It all went off very smoothly, like a game of put and take—the sole difference being that Humphrey Fownes had no idea he was playing. There was an occasional tinkle of falling glass. It fell on the streets and houses, making small geysers of shiny mist, hitting with a gentle musical sound, like the ephemeral droppings of a celesta. It was precipitation peculiar to a dome: feather-light fragments showering harmlessly on the city from time to time. Dome weevils, their metal arms reaching out with molten glass, roamed the huge casserole, ceaselessly patching and repairing. Humphrey Fownes strode through the puffs of falling glass still intrigued by a temperature that was always 59 degrees, by a humidity that was always 47%, by weather that was always Optimum. It was this rather than skill that enabled the police to maintain such a tight surveillance on him, a surveillance that went to the extent of getting his fingerprints off the postman's bag, and which photographed, X-rayed and chemically analyzed the contents of his pockets before returning them. Two blocks away from his home a careless housewife spilled a five-pound bag of flour as he was passing. It was really plaster of Paris. He left his shoe prints, stride measurement, height, weight and handedness behind. By the time Fownes reached his front door an entire dossier complete with photographs had been prepared and was being read by two men in an orange patrol car parked down the street. Lanfierre had undoubtedly been affected by his job. Sitting behind the wheel of the orange car, he watched Humphrey Fownes approach with a distinct feeling of admiration, although it was an odd, objective kind of admiration, clinical in nature. It was similar to that of a pathologist observing for the first time a new and particularly virulent strain of pneumococcus under his microscope. Lanfierre's job was to ferret out aberration. It couldn't be tolerated within the confines of a dome. Conformity had become more than a social force; it was a physical necessity. And, after years of working at it, Lanfierre had become an admirer of eccentricity. He came to see that genuine quirks were rare and, as time went on, due partly to his own small efforts, rarer. Fownes was a masterpiece of queerness. He was utterly inexplicable. Lanfierre was almost proud of Humphrey Fownes. "Sometimes his house shakes ," Lanfierre said. "House shakes," Lieutenant MacBride wrote in his notebook. Then he stopped and frowned. He reread what he'd just written. "You heard right. The house shakes ," Lanfierre said, savoring it. MacBride looked at the Fownes house through the magnifying glass of the windshield. "Like from ... side to side ?" he asked in a somewhat patronizing tone of voice. "And up and down." MacBride returned the notebook to the breast pocket of his orange uniform. "Go on," he said, amused. "It sounds interesting." He tossed the dossier carelessly on the back seat. Lanfierre sat stiffly behind the wheel, affronted. The cynical MacBride couldn't really appreciate fine aberrations. In some ways MacBride was a barbarian. Lanfierre had held out on Fownes for months. He had even contrived to engage him in conversation once, a pleasantly absurd, irrational little chat that titillated him for weeks. It was only with the greatest reluctance that he finally mentioned Fownes to MacBride. After years of searching for differences Lanfierre had seen how extraordinarily repetitious people were, echoes really, dimly resounding echoes, each believing itself whole and separate. They spoke in an incessant chatter of cliches, and their actions were unbelievably trite. Then a fine robust freak came along and the others—the echoes—refused to believe it. The lieutenant was probably on the point of suggesting a vacation. "Why don't you take a vacation?" Lieutenant MacBride suggested. "It's like this, MacBride. Do you know what a wind is? A breeze? A zephyr?" "I've heard some." "They say there are mountain-tops where winds blow all the time. Strong winds, MacBride. Winds like you and I can't imagine. And if there was a house sitting on such a mountain and if winds did blow, it would shake exactly the way that one does. Sometimes I get the feeling the whole place is going to slide off its foundation and go sailing down the avenue." Lieutenant MacBride pursed his lips. "I'll tell you something else," Lanfierre went on. "The windows all close at the same time. You'll be watching and all of a sudden every single window in the place will drop to its sill." Lanfierre leaned back in the seat, his eyes still on the house. "Sometimes I think there's a whole crowd of people in there waiting for a signal—as if they all had something important to say but had to close the windows first so no one could hear. Why else close the windows in a domed city? And then as soon as the place is buttoned up they all explode into conversation—and that's why the house shakes." MacBride whistled. "No, I don't need a vacation." A falling piece of glass dissolved into a puff of gossamer against the windshield. Lanfierre started and bumped his knee on the steering wheel. "No, you don't need a rest," MacBride said. "You're starting to see flying houses, hear loud babbling voices. You've got winds in your brain, Lanfierre, breezes of fatigue, zephyrs of irrationality—" At that moment, all at once, every last window in the house slammed shut. The street was deserted and quiet, not a movement, not a sound. MacBride and Lanfierre both leaned forward, as if waiting for the ghostly babble of voices to commence. The house began to shake. It rocked from side to side, it pitched forward and back, it yawed and dipped and twisted, straining at the mooring of its foundation. The house could have been preparing to take off and sail down the.... MacBride looked at Lanfierre and Lanfierre looked at MacBride and then they both looked back at the dancing house. "And the water ," Lanfierre said. "The water he uses! He could be the thirstiest and cleanest man in the city. He could have a whole family of thirsty and clean kids, and he still wouldn't need all that water." The lieutenant had picked up the dossier. He thumbed through the pages now in amazement. "Where do you get a guy like this?" he asked. "Did you see what he carries in his pockets?" "And compasses won't work on this street." The lieutenant lit a cigarette and sighed. He usually sighed when making the decision to raid a dwelling. It expressed his weariness and distaste for people who went off and got neurotic when they could be enjoying a happy, normal existence. There was something implacable about his sighs. "He'll be coming out soon," Lanfierre said. "He eats supper next door with a widow. Then he goes to the library. Always the same. Supper at the widow's next door and then the library." MacBride's eyebrows went up a fraction of an inch. "The library?" he said. "Is he in with that bunch?" Lanfierre nodded. "Should be very interesting," MacBride said slowly. "I can't wait to see what he's got in there," Lanfierre murmured, watching the house with a consuming interest. They sat there smoking in silence and every now and then their eyes widened as the house danced a new step. Fownes stopped on the porch to brush the plaster of paris off his shoes. He hadn't seen the patrol car and this intense preoccupation of his was also responsible for the dancing house—he simply hadn't noticed. There was a certain amount of vibration, of course. He had a bootleg pipe connected into the dome blower system, and the high-pressure air caused some buffeting against the thin walls of the house. At least, he called it buffeting; he'd never thought to watch from outside. He went in and threw his jacket on the sofa, there being no room left in the closets. Crossing the living room he stopped to twist a draw-pull. Every window slammed shut. "Tight as a kite," he thought, satisfied. He continued on toward the closet at the foot of the stairs and then stopped again. Was that right? No, snug as a hug in a rug . He went on, thinking: The old devils. The downstairs closet was like a great watch case, a profusion of wheels surrounding the Master Mechanism, which was a miniature see-saw that went back and forth 365-1/4 times an hour. The wheels had a curious stateliness about them. They were all quite old, salvaged from grandfather's clocks and music boxes and they went around in graceful circles at the rate of 30 and 31 times an hour ... although there was one slightly eccentric cam that vacillated between 28 and 29. He watched as they spun and flashed in the darkness, and then set them for seven o'clock in the evening, April seventh, any year. Outside, the domed city vanished. It was replaced by an illusion. Or, as Fownes hoped it might appear, the illusion of the domed city vanished and was replaced by a more satisfactory, and, for his specific purpose, more functional, illusion. Looking through the window he saw only a garden. Instead of an orange sun at perpetual high noon, there was a red sun setting brilliantly, marred only by an occasional arcover which left the smell of ozone in the air. There was also a gigantic moon. It hid a huge area of sky, and it sang. The sun and moon both looked down upon a garden that was itself scintillant, composed largely of neon roses. Moonlight, he thought, and roses. Satisfactory. And cocktails for two. Blast, he'd never be able to figure that one out! He watched as the moon played, Oh, You Beautiful Doll and the neon roses flashed slowly from red to violet, then went back to the closet and turned on the scent. The house began to smell like an immensely concentrated rose as the moon shifted to People Will Say We're In Love . He rubbed his chin critically. It seemed all right. A dreamy sunset, an enchanted moon, flowers, scent. They were all purely speculative of course. He had no idea how a rose really smelled—or looked for that matter. Not to mention a moon. But then, neither did the widow. He'd have to be confident, assertive. Insist on it. I tell you, my dear, this is a genuine realistic romantic moon. Now, does it do anything to your pulse? Do you feel icy fingers marching up and down your spine? His own spine didn't seem to be affected. But then he hadn't read that book on ancient mores and courtship customs. How really odd the ancients were. Seduction seemed to be an incredibly long and drawn-out process, accompanied by a considerable amount of falsification. Communication seemed virtually impossible. "No" meant any number of things, depending on the tone of voice and the circumstances. It could mean yes, it could mean ask me again later on this evening. He went up the stairs to the bedroom closet and tried the rain-maker, thinking roguishly: Thou shalt not inundate. The risks he was taking! A shower fell gently on the garden and a male chorus began to chant Singing in the Rain . Undiminished, the yellow moon and the red sun continued to be brilliant, although the sun occasionally arced over and demolished several of the neon roses. The last wheel in the bedroom closet was a rather elegant steering wheel from an old 1995 Studebaker. This was on the bootleg pipe; he gingerly turned it. Far below in the cellar there was a rumble and then the soft whistle of winds came to him. He went downstairs to watch out the living room window. This was important; the window had a really fixed attitude about air currents. The neon roses bent and tinkled against each other as the wind rose and the moon shook a trifle as it whispered Cuddle Up a Little Closer . He watched with folded arms, considering how he would start. My dear Mrs. Deshazaway. Too formal. They'd be looking out at the romantic garden; time to be a bit forward. My very dear Mrs. Deshazaway. No. Contrived. How about a simple, Dear Mrs. Deshazaway . That might be it. I was wondering, seeing as how it's so late, if you wouldn't rather stay over instead of going home.... Preoccupied, he hadn't noticed the winds building up, didn't hear the shaking and rattling of the pipes. There were attic pipes connected to wall pipes and wall pipes connected to cellar pipes, and they made one gigantic skeleton that began to rattle its bones and dance as high-pressure air from the dome blower rushed in, slowly opening the Studebaker valve wider and wider.... The neon roses thrashed about, extinguishing each other. The red sun shot off a mass of sparks and then quickly sank out of sight. The moon fell on the garden and rolled ponderously along, crooning When the Blue of the Night Meets the Gold of the Day . The shaking house finally woke him up. He scrambled upstairs to the Studebaker wheel and shut it off. At the window again, he sighed. Repairs were in order. And it wasn't the first time the winds got out of line. Why didn't she marry him and save all this bother? He shut it all down and went out the front door, wondering about the rhyme of the months, about stately August and eccentric February and romantic April. April. Its days were thirty and it followed September. And all the rest have thirty-one. What a strange people, the ancients! He still didn't see the orange car parked down the street. "Men are too perishable," Mrs. Deshazaway said over dinner. "For all practical purposes I'm never going to marry again. All my husbands die." "Would you pass the beets, please?" Humphrey Fownes said. She handed him a platter of steaming red beets. "And don't look at me that way," she said. "I'm not going to marry you and if you want reasons I'll give you four of them. Andrew. Curt. Norman. And Alphonse." The widow was a passionate woman. She did everything passionately—talking, cooking, dressing. Her beets were passionately red. Her clothes rustled and her high heels clicked and her jewelry tinkled. She was possessed by an uncontrollable dynamism. Fownes had never known anyone like her. "You forgot to put salt on the potatoes," she said passionately, then went on as calmly as it was possible for her to be, to explain why she couldn't marry him. "Do you have any idea what people are saying? They're all saying I'm a cannibal! I rob my husbands of their life force and when they're empty I carry their bodies outside on my way to the justice of the peace." "As long as there are people," he said philosophically, "there'll be talk." "But it's the air! Why don't they talk about that? The air is stale, I'm positive. It's not nourishing. The air is stale and Andrew, Curt, Norman and Alphonse couldn't stand it. Poor Alphonse. He was never so healthy as on the day he was born. From then on things got steadily worse for him." "I don't seem to mind the air." She threw up her hands. "You'd be the worst of the lot!" She left the table, rustling and tinkling about the room. "I can just hear them. Try some of the asparagus. Five. That's what they'd say. That woman did it again. And the plain fact is I don't want you on my record." "Really," Fownes protested. "I feel splendid. Never better." He could hear her moving about and then felt her hands on his shoulders. "And what about those very elaborate plans you've been making to seduce me?" Fownes froze with three asparagus hanging from his fork. "Don't you think they'll find out? I found out and you can bet they will. It's my fault, I guess. I talk too much. And I don't always tell the truth. To be completely honest with you, Mr. Fownes, it wasn't the old customs at all standing between us, it was air. I can't have another man die on me, it's bad for my self-esteem. And now you've gone and done something good and criminal, something peculiar." Fownes put his fork down. "Dear Mrs. Deshazaway," he started to say. "And of course when they do find out and they ask you why, Mr. Fownes, you'll tell them. No, no heroics, please! When they ask a man a question he always answers and you will too. You'll tell them I wanted to be courted and when they hear that they'll be around to ask me a few questions. You see, we're both a bit queer." "I hadn't thought of that," Fownes said quietly. "Oh, it doesn't really matter. I'll join Andrew, Curt, Norman—" "That won't be necessary," Fownes said with unusual force. "With all due respect to Andrew, Curt, Norman and Alphonse, I might as well state here and now I have other plans for you, Mrs. Deshazaway." "But my dear Mr. Fownes," she said, leaning across the table. "We're lost, you and I." "Not if we could leave the dome," Fownes said quietly. "That's impossible! How?" In no hurry, now that he had the widow's complete attention, Fownes leaned across the table and whispered: "Fresh air, Mrs. Deshazaway? Space? Miles and miles of space where the real-estate monopoly has no control whatever? Where the wind blows across prairies ; or is it the other way around? No matter. How would you like that , Mrs. Deshazaway?" Breathing somewhat faster than usual, the widow rested her chin on her two hands. "Pray continue," she said. "Endless vistas of moonlight and roses? April showers, Mrs. Deshazaway. And June, which as you may know follows directly upon April and is supposed to be the month of brides, of marrying. June also lies beyond the dome." "I see." " And ," Mr. Fownes added, his voice a honeyed whisper, "they say that somewhere out in the space and the roses and the moonlight, the sleeping equinox yawns and rises because on a certain day it's vernal and that's when it roams the Open Country where geigers no longer scintillate." " My. " Mrs. Deshazaway rose, paced slowly to the window and then came back to the table, standing directly over Fownes. "If you can get us outside the dome," she said, "out where a man stays warm long enough for his wife to get to know him ... if you can do that, Mr. Fownes ... you may call me Agnes." When Humphrey Fownes stepped out of the widow's house, there was a look of such intense abstraction on his features that Lanfierre felt a wistful desire to get out of the car and walk along with the man. It would be such a deliciously insane experience. ("April has thirty days," Fownes mumbled, passing them, "because thirty is the largest number such that all smaller numbers not having a common divisor with it are primes ." MacBride frowned and added it to the dossier. Lanfierre sighed.) Pinning his hopes on the Movement, Fownes went straight to the library several blocks away, a shattered depressing place given over to government publications and censored old books with holes in them. It was used so infrequently that the Movement was able to meet there undisturbed. The librarian was a yellowed, dog-eared woman of eighty. She spent her days reading ancient library cards and, like the books around her, had been rendered by time's own censor into near unintelligibility. "Here's one," she said to him as he entered. " Gulliver's Travels. Loaned to John Wesley Davidson on March 14, 1979 for five days. What do you make of it?" In the litter of books and cards and dried out ink pads that surrounded the librarian, Fownes noticed a torn dust jacket with a curious illustration. "What's that?" he said. "A twister," she replied quickly. "Now listen to this . Seven years later on March 21, 1986, Ella Marshall Davidson took out the same book. What do you make of that ?" "I'd say," Humphrey Fownes said, "that he ... that he recommended it to her, that one day they met in the street and he told her about this book and then they ... they went to the library together and she borrowed it and eventually, why eventually they got married." "Hah! They were brother and sister!" the librarian shouted in her parched voice, her old buckram eyes laughing with cunning. Fownes smiled weakly and looked again at the dust jacket. The twister was unquestionably a meteorological phenomenon. It spun ominously, like a malevolent top, and coursed the countryside destructively, carrying a Dorothy to an Oz. He couldn't help wondering if twisters did anything to feminine pulses, if they could possibly be a part of a moonlit night, with cocktails and roses. He absently stuffed the dust jacket in his pocket and went on into the other rooms, the librarian mumbling after him: "Edna Murdoch Featherstone, April 21, 1991," as though reading inscriptions on a tombstone. The Movement met in what had been the children's room, where unpaid ladies of the afternoon had once upon a time read stories to other people's offspring. The members sat around at the miniature tables looking oddly like giants fled from their fairy tales, protesting. "Where did the old society fail?" the leader was demanding of them. He stood in the center of the room, leaning on a heavy knobbed cane. He glanced around at the group almost complacently, and waited as Humphrey Fownes squeezed into an empty chair. "We live in a dome," the leader said, "for lack of something. An invention! What is the one thing that the great technological societies before ours could not invent, notwithstanding their various giant brains, electronic and otherwise?" Fownes was the kind of man who never answered a rhetorical question. He waited, uncomfortable in the tight chair, while the others struggled with this problem in revolutionary dialectics. " A sound foreign policy ," the leader said, aware that no one else had obtained the insight. "If a sound foreign policy can't be created the only alternative is not to have any foreign policy at all. Thus the movement into domes began— by common consent of the governments . This is known as self-containment." Dialectically out in left field, Humphrey Fownes waited for a lull in the ensuing discussion and then politely inquired how it might be arranged for him to get out. "Out?" the leader said, frowning. "Out? Out where?" "Outside the dome." "Oh. All in good time, my friend. One day we shall all pick up and leave." "And that day I'll await impatiently," Fownes replied with marvelous tact, "because it will be lonely out there for the two of us. My future wife and I have to leave now ." "Nonsense. Ridiculous! You have to be prepared for the Open Country. You can't just up and leave, it would be suicide, Fownes. And dialectically very poor." "Then you have discussed preparations, the practical necessities of life in the Open Country. Food, clothing, a weapon perhaps? What else? Have I left anything out?" The leader sighed. "The gentleman wants to know if he's left anything out," he said to the group. Fownes looked around at them, at some dozen pained expressions. "Tell the man what he's forgotten," the leader said, walking to the far window and turning his back quite pointedly on them. Everyone spoke at the same moment. " A sound foreign policy ," they all said, it being almost too obvious for words. On his way out the librarian shouted at him: " A Tale of a Tub , thirty-five years overdue!" She was calculating the fine as he closed the door. Humphrey Fownes' preoccupation finally came to an end when he was one block away from his house. It was then that he realized something unusual must have occurred. An orange patrol car of the security police was parked at his front door. And something else was happening too. His house was dancing. It was disconcerting, and at the same time enchanting, to watch one's residence frisking about on its foundation. It was such a strange sight that for the moment he didn't give a thought to what might be causing it. But when he stepped gingerly onto the porch, which was doing its own independent gavotte, he reached for the doorknob with an immense curiosity. The door flung itself open and knocked him back off the porch. From a prone position on his miniscule front lawn, Fownes watched as his favorite easy chair sailed out of the living room on a blast of cold air and went pinwheeling down the avenue in the bright sunshine. A wild wind and a thick fog poured out of the house. It brought chairs, suits, small tables, lamps trailing their cords, ashtrays, sofa cushions. The house was emptying itself fiercely, as if disgorging an old, spoiled meal. From deep inside he could hear the rumble of his ancient upright piano as it rolled ponderously from room to room. He stood up; a wet wind swept over him, whipping at his face, toying with his hair. It was a whistling in his ears, and a tingle on his cheeks. He got hit by a shoe. As he forced his way back to the doorway needles of rain played over his face and he heard a voice cry out from somewhere in the living room. "Help!" Lieutenant MacBride called. Standing in the doorway with his wet hair plastered down on his dripping scalp, the wind roaring about him, the piano rumbling in the distance like thunder, Humphrey Fownes suddenly saw it all very clearly. " Winds ," he said in a whisper. "What's happening?" MacBride yelled, crouching behind the sofa. " March winds," he said. "What?!" "April showers!" The winds roared for a moment and then MacBride's lost voice emerged from the blackness of the living room. "These are not Optimum Dome Conditions!" the voice wailed. "The temperature is not 59 degrees. The humidity is not 47%!" Fownes held his face up to let the rain fall on it. "Moonlight!" he shouted. "Roses! My soul for a cocktail for two!" He grasped the doorway to keep from being blown out of the house. "Are you going to make it stop or aren't you!" MacBride yelled. "You'll have to tell me what you did first!" "I told him not to touch that wheel! Lanfierre. He's in the upstairs bedroom!" When he heard this Fownes plunged into the house and fought his way up the stairs. He found Lanfierre standing outside the bedroom with a wheel in his hand. "What have I done?" Lanfierre asked in the monotone of shock. Fownes took the wheel. It was off a 1995 Studebaker. "I'm not sure what's going to come of this," he said to Lanfierre with an astonishing amount of objectivity, "but the entire dome air supply is now coming through my bedroom." The wind screamed. "Is there something I can turn?" Lanfierre asked. "Not any more there isn't." They started down the stairs carefully, but the wind caught them and they quickly reached the bottom in a wet heap. Recruiting Lieutenant MacBride from behind his sofa, the men carefully edged out of the house and forced the front door shut. The wind died. The fog dispersed. They stood dripping in the Optimum Dome Conditions of the bright avenue. "I never figured on this ," Lanfierre said, shaking his head. With the front door closed the wind quickly built up inside the house. They could see the furnishing whirl past the windows. The house did a wild, elated jig. "What kind of a place is this?" MacBride said, his courage beginning to return. He took out his notebook but it was a soggy mess. He tossed it away. "Sure, he was different ," Lanfierre murmured. "I knew that much." When the roof blew off they weren't really surprised. With a certain amount of equanimity they watched it lift off almost gracefully, standing on end for a moment before toppling to the ground. It was strangely slow motion, as was the black twirling cloud that now rose out of the master bedroom, spewing shorts and socks and cases every which way. " Now what?" MacBride said, thoroughly exasperated, as this strange black cloud began to accelerate, whirling about like some malevolent top.... Humphrey Fownes took out the dust jacket he'd found in the library. He held it up and carefully compared the spinning cloud in his bedroom with the illustration. The cloud rose and spun, assuming the identical shape of the illustration. "It's a twister," he said softly. "A Kansas twister!" "What," MacBride asked, his bravado slipping away again, "what ... is a twister?" The twister roared and moved out of the bedroom, out over the rear of the house toward the side of the dome. "It says here," Fownes shouted over the roaring, "that Dorothy traveled from Kansas to Oz in a twister and that ... and that Oz is a wonderful and mysterious land beyond the confines of everyday living ." MacBride's eyes and mouth were great zeros. "Is there something I can turn?" Lanfierre asked. Huge chunks of glass began to fall around them. "Fownes!" MacBride shouted. "This is a direct order! Make it go back!" But Fownes had already begun to run on toward the next house, dodging mountainous puffs of glass as he went. "Mrs. Deshazaway!" he shouted. "Yoo-hoo, Mrs. Deshazaway!" The dome weevils were going berserk trying to keep up with the precipitation. They whirred back and forth at frightful speed, then, emptied of molten glass, rushed to the Trough which they quickly emptied and then rushed about empty-handed. "Yoo-hoo!" he yelled, running. The artificial sun vanished behind the mushrooming twister. Optimum temperature collapsed. "Mrs. Deshazaway! Agnes , will you marry me? Yoo-hoo!" Lanfierre and Lieutenant MacBride leaned against their car and waited, dazed. There was quite a large fall of glass. | C. Lanfierre would listen to MacBride in Humphrey's house |
What is the punishment for murder in the future?
A. Death
B. Erasure from the timeline
C. Life in prison
D. Psychiatric Care
| Illustrated by van Dongen A gun is an interesting weapon; it can be hired, of course, and naturally doesn't care who hires it. Something much the same can be said of the gunman, too.... GUN FOR HIRE By MACK REYNOLDS Joe Prantera called softly, "Al." The pleasurable, comfortable, warm feeling began spreading over him, the way it always did. The older man stopped and squinted, but not suspiciously, even now. The evening was dark, it was unlikely that the other even saw the circle of steel that was the mouth of the shotgun barrel, now resting on the car's window ledge. "Who's it?" he growled. Joe Prantera said softly, "Big Louis sent me, Al." And he pressed the trigger. And at that moment, the universe caved inward upon Joseph Marie Prantera. There was nausea and nausea upon nausea. There was a falling through all space and through all time. There was doubling and twisting and twitching of every muscle and nerve. There was pain, horror and tumultuous fear. And he came out of it as quickly and completely as he'd gone in. He was in, he thought, a hospital and his first reaction was to think, This here California. Everything different. Then his second thought was Something went wrong. Big Louis, he ain't going to like this. He brought his thinking to the present. So far as he could remember, he hadn't completely pulled the trigger. That at least meant that whatever the rap was it wouldn't be too tough. With luck, the syndicate would get him off with a couple of years at Quentin. A door slid open in the wall in a way that Joe had never seen a door operate before. This here California. The clothes on the newcomer were wrong, too. For the first time, Joe Prantera began to sense an alienness—a something that was awfully wrong. The other spoke precisely and slowly, the way a highly educated man speaks a language which he reads and writes fluently but has little occasion to practice vocally. "You have recovered?" Joe Prantera looked at the other expressionlessly. Maybe the old duck was one of these foreign doctors, like. The newcomer said, "You have undoubtedly been through a most harrowing experience. If you have any untoward symptoms, possibly I could be of assistance." Joe couldn't figure out how he stood. For one thing, there should have been some kind of police guard. The other said, "Perhaps a bit of stimulant?" Joe said flatly, "I wanta lawyer." The newcomer frowned at him. "A lawyer?" "I'm not sayin' nothin'. Not until I get a mouthpiece." The newcomer started off on another tack. "My name is Lawrence Reston-Farrell. If I am not mistaken, you are Joseph Salviati-Prantera." Salviati happened to be Joe's mother's maiden name. But it was unlikely this character could have known that. Joe had been born in Naples and his mother had died in childbirth. His father hadn't brought him to the States until the age of five and by that time he had a stepmother. "I wanta mouthpiece," Joe said flatly, "or let me outta here." Lawrence Reston-Farrell said, "You are not being constrained. There are clothes for you in the closet there." Joe gingerly tried swinging his feet to the floor and sitting up, while the other stood watching him, strangely. He came to his feet. With the exception of a faint nausea, which brought back memories of that extreme condition he'd suffered during ... during what? He hadn't the vaguest idea of what had happened. He was dressed in a hospital-type nightgown. He looked down at it and snorted and made his way over to the closet. It opened on his approach, the door sliding back into the wall in much the same manner as the room's door had opened for Reston-Farrell. Joe Prantera scowled and said, "These ain't my clothes." "No, I am afraid not." "You think I'd be seen dead wearing this stuff? What is this, some religious crackpot hospital?" Reston-Farrell said, "I am afraid, Mr. Salviati-Prantera, that these are the only garments available. I suggest you look out the window there." Joe gave him a long, chill look and then stepped to the window. He couldn't figure the other. Unless he was a fruitcake. Maybe he was in some kind of pressure cooker and this was one of the fruitcakes. He looked out, however, not on the lawns and walks of a sanitarium but upon a wide boulevard of what was obviously a populous city. And for a moment again, Joe Prantera felt the depths of nausea. This was not his world. He stared for a long, long moment. The cars didn't even have wheels, he noted dully. He turned slowly and faced the older man. Reston-Farrell said compassionately, "Try this, it's excellent cognac." Joe Prantera stared at him, said finally, flatly, "What's it all about?" The other put down the unaccepted glass. "We were afraid first realization would be a shock to you," he said. "My colleague is in the adjoining room. We will be glad to explain to you if you will join us there." "I wanta get out of here," Joe said. "Where would you go?" The fear of police, of Al Rossi's vengeance, of the measures that might be taken by Big Louis on his failure, were now far away. Reston-Farrell had approached the door by which he had entered and it reopened for him. He went through it without looking back. There was nothing else to do. Joe dressed, then followed him. In the adjoining room was a circular table that would have accommodated a dozen persons. Two were seated there now, papers, books and soiled coffee cups before them. There had evidently been a long wait. Reston-Farrell, the one Joe had already met, was tall and drawn of face and with a chainsmoker's nervousness. The other was heavier and more at ease. They were both, Joe estimated, somewhere in their middle fifties. They both looked like docs. He wondered, all over again, if this was some kind of pressure cooker. But that didn't explain the view from the window. Reston-Farrell said, "May I present my colleague, Citizen Warren Brett-James? Warren, this is our guest from ... from yesteryear, Mr. Joseph Salviati-Prantera." Brett-James nodded to him, friendly, so far as Joe could see. He said gently, "I think it would be Mr. Joseph Prantera, wouldn't it? The maternal linage was almost universally ignored." His voice too gave the impression he was speaking a language not usually on his tongue. Joe took an empty chair, hardly bothering to note its alien qualities. His body seemed to fit into the piece of furniture, as though it had been molded to his order. Joe said, "I think maybe I'll take that there drink, Doc." Reston-Farrell said, "Of course," and then something else Joe didn't get. Whatever the something else was, a slot opened in the middle of the table and a glass, so clear of texture as to be all but invisible, was elevated. It contained possibly three ounces of golden fluid. Joe didn't allow himself to think of its means of delivery. He took up the drink and bolted it. He put the glass down and said carefully, "What's it all about, huh?" Warren Brett-James said soothingly, "Prepare yourself for somewhat of a shock, Mr. Prantera. You are no longer in Los Angeles—" "Ya think I'm stupid? I can see that." "I was about to say, Los Angeles of 1960. Mr. Prantera, we welcome you to Nuevo Los Angeles." "Ta where?" "To Nuevo Los Angeles and to the year—" Brett-James looked at his companion. "What is the date, Old Calendar?" "2133," Reston-Farrell said. "2133 A.D. they would say." Joe Prantera looked from one of them to the other, scowling. "What are you guys talking about?" Warren Brett-James said softly, "Mr. Prantera, you are no longer in the year 1960, you are now in the year 2133." He said, uncomprehendingly, "You mean I been, like, unconscious for—" He let the sentence fall away as he realized the impossibility. Brett-James said gently, "Hardly for one hundred and seventy years, Mr. Prantera." Reston-Farrell said, "I am afraid we are confusing you. Briefly, we have transported you, I suppose one might say, from your own era to ours." Joe Prantera had never been exposed to the concept of time travel. He had simply never associated with anyone who had ever even remotely considered such an idea. Now he said, "You mean, like, I been asleep all that time?" "Not exactly," Brett-James said, frowning. Reston-Farrell said, "Suffice to say, you are now one hundred and seventy-three years after the last memory you have." Joe Prantera's mind suddenly reverted to those last memories and his eyes narrowed dangerously. He felt suddenly at bay. He said, "Maybe you guys better let me in on what's this all about." Reston-Farrell said, "Mr. Prantera, we have brought you from your era to perform a task for us." Joe stared at him, and then at the other. He couldn't believe he was getting through to them. Or, at least, that they were to him. Finally he said, "If I get this, you want me to do a job for you." "That is correct." Joe said, "You guys know the kind of jobs I do?" "That is correct." "Like hell you do. You think I'm stupid? I never even seen you before." Joe Prantera came abruptly to his feet. "I'm gettin' outta here." For the second time, Reston-Farrell said, "Where would you go, Mr. Prantera?" Joe glared at him. Then sat down again, as abruptly as he'd arisen. "Let's start all over again. I got this straight, you brought me, some screwy way, all the way ... here. O.K., I'll buy that. I seen what it looks like out that window—" The real comprehension was seeping through to him even as he talked. "Everybody I know, Jessie, Tony, the Kid, Big Louis, everybody, they're dead. Even Big Louis." "Yes," Brett-James said, his voice soft. "They are all dead, Mr. Prantera. Their children are all dead, and their grandchildren." The two men of the future said nothing more for long minutes while Joe Prantera's mind whirled its confusion. Finally he said, "What's this bit about you wanting me to give it to some guy." "That is why we brought you here, Mr. Prantera. You were ... you are, a professional assassin." "Hey, wait a minute, now." Reston-Farrell went on, ignoring the interruption. "There is small point in denying your calling. Pray remember that at the point when we ... transported you, you were about to dispose of a contemporary named Alphonso Annunziata-Rossi. A citizen, I might say, whose demise would probably have caused small dismay to society." They had him pegged all right. Joe said, "But why me? Why don't you get some heavy from now? Somebody knows the ropes these days." Brett-James said, "Mr. Prantera, there are no professional assassins in this age, nor have there been for over a century and a half." "Well, then do it yourself." Joe Prantera's irritation over this whole complicated mess was growing. And already he was beginning to long for the things he knew—for Jessie and Tony and the others, for his favorite bar, for the lasagne down at Papa Giovanni's. Right now he could have welcomed a calling down at the hands of Big Louis. Reston-Farrell had come to his feet and walked to one of the large room's windows. He looked out, as though unseeing. Then, his back turned, he said, "We have tried, but it is simply not in us, Mr. Prantera." "You mean you're yella?" "No, if by that you mean afraid. It is simply not within us to take the life of a fellow creature—not to speak of a fellow man." Joe snapped: "Everything you guys say sounds crazy. Let's start all over again." Brett-James said, "Let me do it, Lawrence." He turned his eyes to Joe. "Mr. Prantera, in your own era, did you ever consider the future?" Joe looked at him blankly. "In your day you were confronted with national and international, problems. Just as we are today and just as nations were a century or a millennium ago." "Sure, O.K., so we had problems. I know whatcha mean—like wars, and depressions and dictators and like that." "Yes, like that," Brett-James nodded. The heavy-set man paused a moment. "Yes, like that," he repeated. "That we confront you now indicates that the problems of your day were solved. Hadn't they been, the world most surely would have destroyed itself. Wars? Our pedagogues are hard put to convince their students that such ever existed. More than a century and a half ago our society eliminated the reasons for international conflict. For that matter," he added musingly, "we eliminated most international boundaries. Depressions? Shortly after your own period, man awoke to the fact that he had achieved to the point where it was possible to produce an abundance for all with a minimum of toil. Overnight, for all practical purposes, the whole world was industrialized, automated. The second industrial revolution was accompanied by revolutionary changes in almost every field, certainly in every science. Dictators? Your ancestors found, Mr. Prantera, that it is difficult for a man to be free so long as others are still enslaved. Today the democratic ethic has reached a pinnacle never dreamed of in your own era." "O.K., O.K.," Joe Prantera growled. "So everybody's got it made. What I wanta know is what's all this about me giving it ta somebody? If everything's so great, how come you want me to knock this guy off?" Reston-Farrell bent forward and thumped his right index finger twice on the table. "The bacterium of hate—a new strain—has found the human race unprotected from its disease. We had thought our vaccines immunized us." "What's that suppose to mean?" Brett-James took up the ball again. "Mr. Prantera, have you ever heard of Ghengis Khan, of Tamerlane, Alexander, Caesar?" Joe Prantera scowled at him emptily. "Or, more likely, of Napoleon, Hitler, Stalin?" "Sure I heard of Hitler and Stalin," Joe growled. "I ain't stupid." The other nodded. "Such men are unique. They have a drive ... a drive to power which exceeds by far the ambitions of the average man. They are genii in their way, Mr. Prantera, genii of evil. Such a genius of evil has appeared on the current scene." "Now we're getting somewheres," Joe snorted. "So you got a guy what's a little ambitious, like, eh? And you guys ain't got the guts to give it to him. O.K. What's in it for me?" The two of them frowned, exchanged glances. Reston-Farrell said, "You know, that is one aspect we had not considered." Brett-James said to Joe Prantera, "Had we not, ah, taken you at the time we did, do you realize what would have happened?" "Sure," Joe grunted. "I woulda let old Al Rossi have it right in the guts, five times. Then I woulda took the plane back to Chi." Brett-James was shaking his head. "No. You see, by coincidence, a police squad car was coming down the street just at that moment to arrest Mr. Rossi. You would have been apprehended. As I understand Californian law of the period, your life would have been forfeit, Mr. Prantera." Joe winced. It didn't occur to him to doubt their word. Reston-Farrell said, "As to reward, Mr. Prantera, we have already told you there is ultra-abundance in this age. Once this task has been performed, we will sponsor your entry into present day society. Competent psychiatric therapy will soon remove your present—" "Waita minute, now. You figure on gettin' me candled by some head shrinker, eh? No thanks, Buster. I'm going back to my own—" Brett-James was shaking his head again. "I am afraid there is no return, Mr. Prantera. Time travel works but in one direction, with the flow of the time stream. There can be no return to your own era." Joe Prantera had been rocking with the mental blows he had been assimilating, but this was the final haymaker. He was stuck in this squaresville of a world. Joe Prantera on a job was thorough. Careful, painstaking, competent. He spent the first three days of his life in the year 2133 getting the feel of things. Brett-James and Reston-Farrell had been appointed to work with him. Joe didn't meet any of the others who belonged to the group which had taken the measures to bring him from the past. He didn't want to meet them. The fewer persons involved, the better. He stayed in the apartment of Reston-Farrell. Joe had been right, Reston-Farrell was a medical doctor. Brett-James evidently had something to do with the process that had enabled them to bring Joe from the past. Joe didn't know how they'd done it, and he didn't care. Joe was a realist. He was here. The thing was to adapt. There didn't seem to be any hurry. Once the deal was made, they left it up to him to make the decisions. They drove him around the town, when he wished to check the traffic arteries. They flew him about the whole vicinity. From the air, Southern California looked much the same as it had in his own time. Oceans, mountains, and to a lesser extent, deserts, are fairly permanent even against man's corroding efforts. It was while he was flying with Brett-James on the second day that Joe said, "How about Mexico? Could I make the get to Mexico?" The physicist looked at him questioningly. "Get?" he said. Joe Prantera said impatiently, "The getaway. After I give it to this Howard Temple-Tracy guy, I gotta go on the run, don't I?" "I see." Brett-James cleared his throat. "Mexico is no longer a separate nation, Mr. Prantera. All North America has been united into one unit. Today, there are only eight nations in the world." "Where's the nearest?" "South America." "That's a helluva long way to go on a get." "We hadn't thought of the matter being handled in that manner." Joe eyed him in scorn. "Oh, you didn't, huh? What happens after I give it to this guy? I just sit around and wait for the cops to put the arm on me?" Brett-James grimaced in amusement. "Mr. Prantera, this will probably be difficult for you to comprehend, but there are no police in this era." Joe gaped at him. "No police! What happens if you gotta throw some guy in stir?" "If I understand your idiom correctly, you mean prison. There are no prisons in this era, Mr. Prantera." Joe stared. "No cops, no jails. What stops anybody? What stops anybody from just going into some bank, like, and collecting up all the bread?" Brett-James cleared his throat. "Mr. Prantera, there are no banks." "No banks! You gotta have banks!" "And no money to put in them. We found it a rather antiquated method of distribution well over a century ago." Joe had given up. Now he merely stared. Brett-James said reasonably, "We found we were devoting as much time to financial matters in all their endless ramifications—including bank robberies—as we were to productive efforts. So we turned to more efficient methods of distribution." On the fourth day, Joe said, "O.K., let's get down to facts. Summa the things you guys say don't stick together so good. Now, first place, where's this guy Temple-Tracy you want knocked off?" Reston-Farrell and Brett-James were both present. The three of them sat in the living room of the latter's apartment, sipping a sparkling wine which seemed to be the prevailing beverage of the day. For Joe's taste it was insipid stuff. Happily, rye was available to those who wanted it. Reston-Farrell said, "You mean, where does he reside? Why, here in this city." "Well, that's handy, eh?" Joe scratched himself thoughtfully. "You got somebody can finger him for me?" "Finger him?" "Look, before I can give it to this guy I gotta know some place where he'll be at some time. Get it? Like Al Rossi. My finger, he works in Rossi's house, see? He lets me know every Wednesday night, eight o'clock, Al leaves the house all by hisself. O.K., so I can make plans, like, to give it to him." Joe Prantera wound it up reasonably. "You gotta have a finger." Brett-James said, "Why not just go to Temple-Tracy's apartment and, ah, dispose of him?" "Jest walk in, eh? You think I'm stupid? How do I know how many witnesses hangin' around? How do I know if the guy's carryin' heat?" "Heat?" "A gun, a gun. Ya think I'm stupid? I come to give it to him and he gives it to me instead." Dr. Reston-Farrell said, "Howard Temple-Tracy lives alone. He customarily receives visitors every afternoon, largely potential followers. He is attempting to recruit members to an organization he is forming. It would be quite simple for you to enter his establishment and dispose of him. I assure you, he does not possess weapons." Joe was indignant. "Just like that, eh?" he said sarcastically. "Then what happens? How do I get out of the building? Where's my get car parked? Where do I hide out? Where do I dump the heat?" "Dump the heat?" "Get rid of the gun. You want I should get caught with the gun on me? I'd wind up in the gas chamber so quick—" "See here, Mr. Prantera," Brett-James said softly. "We no longer have capital punishment, you must realize." "O.K. I still don't wanta get caught. What is the rap these days, huh?" Joe scowled. "You said they didn't have no jails any more." "This is difficult for you to understand, I imagine," Reston-Farrell told him, "but, you see, we no longer punish people in this era." That took a long, unbelieving moment to sink in. "You mean, like, no matter what they do? That's crazy. Everybody'd be running around giving it to everybody else." "The motivation for crime has been removed, Mr. Prantera," Reston-Farrell attempted to explain. "A person who commits a violence against another is obviously in need of medical care. And, consequently, receives it." "You mean, like, if I steal a car or something, they just take me to a doctor?" Joe Prantera was unbelieving. "Why would anybody wish to steal a car?" Reston-Farrell said easily. "But if I give it to somebody?" "You will be turned over to a medical institution. Citizen Howard Temple-Tracy is the last man you will ever kill, Mr. Prantera." A chillness was in the belly of Joe Prantera. He said very slowly, very dangerously, "You guys figure on me getting caught, don't you?" "Yes," Brett-James said evenly. "Well then, figure something else. You think I'm stupid?" "Mr. Prantera," Dr. Reston-Farrell said, "there has been as much progress in the field of psychiatry in the past two centuries as there has in any other. Your treatment would be brief and painless, believe me." Joe said coldly, "And what happens to you guys? How do you know I won't rat on you?" Brett-James said gently, "The moment after you have accomplished your mission, we plan to turn ourselves over to the nearest institution to have determined whether or not we also need therapy." "Now I'm beginning to wonder about you guys," Joe said. "Look, all over again, what'd'ya wanta give it to this guy for?" The doctor said, "We explained the other day, Mr. Prantera. Citizen Howard Temple-Tracy is a dangerous, atavistic, evil genius. We are afraid for our institutions if his plans are allowed to mature." "Well if you got things so good, everybody's got it made, like, who'd listen to him?" The doctor nodded at the validity of the question. "Mr. Prantera, Homo sapiens is a unique animal. Physically he matures at approximately the age of thirteen. However, mental maturity and adjustment is often not fully realized until thirty or even more. Indeed, it is sometimes never achieved. Before such maturity is reached, our youth are susceptible to romantic appeal. Nationalism, chauvinism, racism, the supposed glory of the military, all seem romantic to the immature. They rebel at the orderliness of present society. They seek entertainment in excitement. Citizen Temple-Tracy is aware of this and finds his recruits among the young." "O.K., so this guy is dangerous. You want him knocked off before he screws everything up. But the way things are, there's no way of making a get. So you'll have to get some other patsy. Not me." "I am afraid you have no alternative," Brett-James said gently. "Without us, what will you do? Mr. Prantera, you do not even speak the language." "What'd'ya mean? I don't understand summa the big words you eggheads use, but I get by O.K." Brett-James said, "Amer-English is no longer the language spoken by the man in the street, Mr. Prantera. Only students of such subjects any longer speak such tongues as Amer-English, French, Russian or the many others that once confused the race with their limitations as a means of communication." "You mean there's no place in the whole world where they talk American?" Joe demanded, aghast. Dr. Reston-Farrell controlled the car. Joe Prantera sat in the seat next to him and Warren Brett-James sat in the back. Joe had, tucked in his belt, a .45 caliber automatic, once displayed in a museum. It had been more easily procured than the ammunition to fit it, but that problem too had been solved. The others were nervous, obviously repelled by the very conception of what they had planned. Inwardly, Joe was amused. Now that they had got in the clutch, the others were on the verge of chickening out. He knew it wouldn't have taken much for them to cancel the project. It wasn't any answer though. If they allowed him to call it off today, they'd talk themselves into it again before the week was through. Besides, already Joe was beginning to feel the comfortable, pleasurable, warm feeling that came to him on occasions like this. He said, "You're sure this guy talks American, eh?" Warren Brett-James said, "Quite sure. He is a student of history." "And he won't think it's funny I talk American to him, eh?" "He'll undoubtedly be intrigued." They pulled up before a large apartment building that overlooked the area once known as Wilmington. Joe was coolly efficient now. He pulled out the automatic, held it down below his knees and threw a shell into the barrel. He eased the hammer down, thumbed on the safety, stuck the weapon back in his belt and beneath the jacketlike garment he wore. He said, "O.K. See you guys later." He left them and entered the building. An elevator—he still wasn't used to their speed in this era—whooshed him to the penthouse duplex occupied by Citizen Howard Temple-Tracy. There were two persons in the reception room but they left on Joe's arrival, without bothering to look at him more than glancingly. He spotted the screen immediately and went over and stood before it. The screen lit and revealed a heavy-set, dour of countenance man seated at a desk. He looked into Joe Prantera's face, scowled and said something. Joe said, "Joseph Salviati-Prantera to interview Citizen Howard Temple-Tracy." The other's shaggy eyebrows rose. "Indeed," he said. "In Amer-English?" Joe nodded. "Enter," the other said. A door had slid open on the other side of the room. Joe walked through it and into what was obviously an office. Citizen Temple-Tracy sat at a desk. There was only one other chair in the room. Joe Prantera ignored it and remained standing. Citizen Temple-Tracy said, "What can I do for you?" Joe looked at him for a long, long moment. Then he reached down to his belt and brought forth the .45 automatic. He moistened his lips. Joe said softly, "You know what this here is?" Temple-Tracy stared at the weapon. "It's a handgun, circa, I would say, about 1925 Old Calendar. What in the world are you doing with it?" Joe said, very slowly, "Chief, in the line you're in these days you needa heavy around with wunna these. Otherwise, Chief, you're gunna wind up in some gutter with a lotta holes in you. What I'm doin', I'm askin' for a job. You need a good man knows how to handle wunna these, Chief." Citizen Howard Temple-Tracy eyed him appraisingly. "Perhaps," he said, "you are right at that. In the near future, I may well need an assistant knowledgeable in the field of violence. Tell me more about yourself. You surprise me considerably." "Sure, Chief. It's kinda a long story, though. First off, I better tell you you got some bad enemies, Chief. Two guys special, named Brett-James and Doc Reston-Farrell. I think one of the first jobs I'm gunna hafta do for you, Chief, is to give it to those two." THE END Transcriber's Note: This etext was produced from Analog December 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | D. Psychiatric Care |
What are the uncanny semantic structures of the embedding space? | ### Introduction
In recent years, digital libraries have moved towards open science and open access with several large scholarly datasets being constructed. Most popular datasets include millions of papers, authors, venues, and other information. Their large size and heterogeneous contents make it very challenging to effectively manage, explore, and utilize these datasets. The knowledge graph has emerged as a universal data format for representing knowledge about entities and their relationships in such complicated data. The main part of a knowledge graph is a collection of triples, with each triple $ (h, t, r) $ denoting the fact that relation $ r $ exists between head entity $ h $ and tail entity $ t $. This can also be formalized as a labeled directed multigraph where each triple $ (h, t, r) $ represents a directed edge from node $ h $ to node $ t $ with label $ r $. Therefore, it is straightforward to build knowledge graphs for scholarly data by representing natural connections between scholarly entities with triples such as (AuthorA, Paper1, write) and (Paper1, Paper2, cite). Notably, instead of using knowledge graphs directly in some tasks, we can model them by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between them to solve the knowledge graph completion task. There are many approaches BIBREF0 to modeling the interactions between embedding vectors resulting in many knowledge graph embedding methods such as ComplEx BIBREF1 and CP$ _h $ BIBREF2. In the case of word embedding methods such as word2vec, embedding vectors are known to contain rich semantic information that enables them to be used in many semantic applications BIBREF3. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embeddings are only used for knowledge graph completion but remain absent in the toolbox for data analysis of heterogeneous data in general and scholarly data in particular, although they have the potential to be highly effective and efficient. In this paper, we address these issues by providing a theoretical understanding of their semantic structures and designing a general semantic query framework to support data exploration. For theoretical analysis, we first analyze the state-of-the-art knowledge graph embedding model CP$ _h $ BIBREF2 in comparison to the popular word embedding model word2vec skipgram BIBREF3 to explain its components and provide understandings to its semantic structures. We then define the semantic queries on the knowledge graph embedding spaces, which are algebraic operations between the embedding vectors in the knowledge graph embedding space to solve queries such as similarity and analogy between the entities on the original datasets. Based on our theoretical results, we design a general framework for data exploration on scholarly data by semantic queries on knowledge graph embedding space. The main component in this framework is the conversion between the data exploration tasks and the semantic queries. We first outline the semantic query solutions to some traditional data exploration tasks, such as similar paper prediction and similar author prediction. We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries. ### Related Work ::: Knowledge graph for scholarly data
Knowledge graph has gradually become the standard data format for heterogeneous and complicated datasets BIBREF4. There have been several attempts to build knowledge graph for scholarly data, either adopting the scholarly network directly BIBREF5, or deriving the knowledge graph from some similarity measures BIBREF6 BIBREF7, or constructing the knowledge graph from survey papers BIBREF8. However, they mostly focus on the data format or graph inference aspects of knowledge graph. In this paper, we instead focus on the knowledge graph embedding methods and especially the application of embedding vectors in data exploration. ### Related Work ::: Knowledge graph embedding
For a more in depth survey of knowledge graph embedding methods, please refer to BIBREF0, which defines their architecture, categorization, and interaction mechanisms. In this paper, we only focus on the semantic structures of the state-of-the-art model CP$ _h $ BIBREF2, which is an extension of CP BIBREF9. In CP, each entity $ e $ has two embedding vectors $ $ and $ ^{(2)} $ depending on its role in a triple as head or as tail, respectively. CP$ _h $ augments the data by making an inverse triple $ (t, h, r^{(a)}) $ for each existing triple $ (h, t, r) $, where $ r^{(a)} $ is the augmented relation corresponding to $ r $. When maximizing the likelihood by stochastic gradient descent, its score function is the sum: where $ , ^{(2)}, , ^{(2)}, , ^{(a)} \in ^{D} $ are the embedding vectors of $ h $, $ t $, and $ r $, respectively, and the trilinear-product $ \langle \cdot , \cdot , \cdot \rangle $ is defined as: where $ D $ is the embedding size and $ d $ is the dimension for which $ h_d $, $ t_d $, and $ r_d $ are the scalar entries. The validity of each triple is modeled as a Bernoulli distribution and its validity probability is computed by the standard logistic function $ \sigma (\cdot ) $ as: ### Related Work ::: Word embedding
The most popular word embedding models in recent years are word2vec variants such as word2vec skipgram BIBREF3, which predicts the context-words $ c_i $ independently given the target-word $ w $, that is: In practice, the expensive softmax functions in these multinoulli distributions are avoided by approximating them with negative sampling and solve for the Bernoulli distributions by using the standard logistic function $ \sigma (\cdot ) $: where $ _{c_i} $ is the context-embedding vector of context-word $ c_i $ and $ _w $ is the word-embedding vector of target-word $ w $. ### Theoretical analysis
Word2vec skipgram and its semantic structures are well-studied both theoretically and empirically BIBREF3. CP$ _h $ is a new state of the art among many knowledge graph embedding models. We first ground the theoretical basis of CP$ _h $ on word2vec skipgram to explain its components and understand its semantic structures. We then define semantic queries on knowledge graph embedding space. ### Theoretical analysis ::: The semantic structures of CP@!START@$ _h $@!END@
We first look at Eq. DISPLAY_FORM8 of word2vec skipgram and consider only one context-word $ c $ for simplicity. We can write the probability in proportional format as: Note that the context-word $ c $ and target-word $ w $ are ordered and in word2vec skipgram, the target-word is the central word in a sliding window, e.g., $ w_i $ is the target-word and $ w_{i-k}, \dots , w_{i-1}, w_{i+1}, \dots , w_{i+k} $ are context-words. Therefore, the roles in each word pair are symmetric over the whole dataset. When maximizing the likelihood by stochastic gradient descent, we can write the approximate probability of unordered word pair and expand the dot products as: where $ _c $ and $ _c $ are the context-embedding and word-embedding vectors of $ c $, respectively, $ _w $ and $ _w $ are the context-embedding and word-embedding vectors of $ w $, respectively, and $ {u_c}_d, {v_c}_d, {u_w}_d $, and $ {v_w}_d $ are their scalar entries, respectively. We now return to Eq. DISPLAY_FORM3 of CP$ _h $ to also write the probability in Eq. DISPLAY_FORM5 in proportional format and expand the trilinear products according to Eq. DISPLAY_FORM4 as: where $ , ^{(2)} $, $ , ^{(2)} $, $ , ^{(a)} $ are knowledge graph embedding vectors and $ h_d, h^{(2)}_d $, $ t_d, t^{(2)}_d $, $ r_d, r^{(a)}_d $ are the scalar entries. Comparing Eq. of word2vec skipgram and Eq. of CP$ _h $, we can see they have essentially the same form and mechanism. Note that the embedding vectors in word2vec skipgram are learned by aligning each target-word to different context-words and vice versa, which is essentially the same for CP$ _h $ by aligning each head entity to different tail entities in different triples and vice versa, with regards to the dimensions weighted by each relation. This result suggests that the semantic structures of CP$ _h $ are similar to those in word2vec skipgram and we can use the head-role-based entity embedding vectors, such as $ $, for semantic applications similarly to word embedding vectors. The tail-role-based entity embedding vectors, such as $ ^{(2)} $, contain almost the same information due to their symmetric roles, thus can be discarded in semantic tasks, which justifies this common practices in word embedding applications BIBREF3. ### Theoretical analysis ::: Semantic query
We mainly concern with the two following structures of the embedding space. Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa. This structure can be identified by a vector similarity measure, such as the dot product between two embedding vectors. The similarity between two embedding vectors is computed as: Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors. The semantic direction between two embedding vectors is computed as: The algebraic operations, which include the above dot product and vector subtraction, or their combinations, can be used to approximate some important tasks on the original data. To do this, we first need to convert the data exploration task to the appropriate operations. We then conduct the operations on the embedding vectors and obtain the results. This process is defined as following. Definition 1 Semantic queries on knowledge graph embedding space are defined as the algebraic operations between the knowledge graph embedding vectors to approximate a given data exploration task on the original dataset. ### Semantic query framework
Given the theoretical results, here we design a general framework for scholarly data exploration by using semantic queries on knowledge graph embedding space. Figure FIGREF19 shows the architecture of the proposed framework. There are three main components, namely data processing, task processing, and query processing. Data processing: with two steps, (1) constructing the knowledge graph from scholarly data by using the scholarly graph directly with entities such as authors, papers, venues, and relations such as author-write-paper, paper-cite-paper, paper-in-venue, and (2) learning the knowledge graph embeddings as in BIBREF0. Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5. Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient. Note that the proposed semantic query framework makes no assumption on the specific knowledge graph embedding models and the induced embedding spaces. Any embedding space that contains rich semantic information such as the listed semantic structures can be applied in this framework. ### Exploration tasks and semantic queries conversion
Here we present and discuss the semantic queries for some traditional and newly proposed data exploration tasks on scholarly data. ### Exploration tasks and semantic queries conversion ::: Similar entities
Tasks Given an entity $ e \in $, find entities that are similar to $ e $. For example, given AuthorA, find authors, papers, and venues that are similar to AuthorA. Note that we can restrict to find specific entity types. This is a traditional tasks in scholarly data exploration, whereas other below tasks are new. Semantic query We can solve this task by looking for the entities with highest similarity to $ e $. For example, the first result is: ### Exploration tasks and semantic queries conversion ::: Similar entities with bias
Tasks Given an entity $ e \in $ and some positive bias entities $ A = \lbrace a_1, \dots , a_k\rbrace $ known as expected results, find entities that are similar to $ e $ following the bias in $ A $. For example, given AuthorA and some successfully collaborating authors, find other similar authors that may also result in good collaborations with AuthorA. Semantic query We can solve this task by looking for the entities with highest similarity to both $ e $ and $ A $. For example, denoting the arithmetic mean of embedding vectors in $ A $ as $ \bar{A} $, the first result is: ### Exploration tasks and semantic queries conversion ::: Analogy query
Tasks Given an entity $ e \in $, positive bias $ A = \lbrace a_1, \dots , a_k\rbrace $, and negative bias $ B = \lbrace b_1, \dots , b_k\rbrace $, find entities that are similar to $ e $ following the biases in $ A $ and $ B $. The essence of this task is tracing along a semantic direction defined by the positive and negative biases. For example, start with AuthorA, we can trace along the expertise direction to find authors that are similar to AuthorA but with higher or lower expertise. Semantic query We can solve this task by looking for the entities with highest similarity to $ e $ and $ A $ but not $ B $. For example, denoting the arithmetic mean of embedding vectors in $ A $ and $ B $ as $ \bar{A} $ and $ \bar{B} $, respectively, note that $ \bar{A} - \bar{B} $ defines the semantic direction along the positive and negative biases, the first result is: ### Exploration tasks and semantic queries conversion ::: Analogy browsing
Tasks This task is an extension of the above analogy query task, by tracing along multiple semantic directions defined by multiple pairs of positive and negative biases. This task can be implemented as an interactive data analysis tool. For example, start with AuthorA, we can trace to authors with higher expertise, then continue tracing to new domains to find all authors similar to AuthorA with high expertise in the new domain. For another example, start with Paper1, we can trace to papers with higher quality, then continue tracing to new domain to look for papers similar to Paper1 with high quality in the new domain. Semantic query We can solve this task by simply repeating the semantic query for analogy query with each pair of positive and negative bias. Note that we can also combine different operations in different order to support flexible browsing. ### Conclusion
In this paper, we studied the application of knowledge graph embedding in exploratory data analysis. We analyzed the CP$ _h $ model and provided understandings to its semantic structures. We then defined the semantic queries on knowledge graph embedding space to efficiently approximate some operations on heterogeneous data such as scholarly data. We designed a general framework to systematically apply semantic queries to solve scholarly data exploration tasks. Finally, we outlined and discussed the solutions to some traditional and pioneering exploration tasks emerged from the semantic structures of the knowledge graph embedding space. This paper is dedicated to the theoretical foundation of a new approach and discussions of emerging tasks, whereas experiments and evaluations are left for the future work. There are several other promising directions for future research. One direction is to explore new tasks or new solutions of traditional tasks using the proposed method. Another direction is to implement the proposed exploration tasks on real-life digital libraries for online evaluation. ### Acknowledgments
This work was supported by “Cross-ministerial Strategic Innovation Promotion Program (SIP) Second Phase, Big-data and AI-enabled Cyberspace Technologies” by New Energy and Industrial Technology Development Organization (NEDO). 1.0 Fig. 1. Architecture of the semantic query framework. Eclipse denotes operation, parallelogram denotes resulting data. | Semantic similarity structure, Semantic direction structure |
What is English mixed with in the TRAC dataset? | ### Introduction
The exponential increase of interactions on the various social media platforms has generated the huge amount of data on social media platforms like Facebook and Twitter, etc. These interactions resulted not only positive effect but also negative effect over billions of people owing to the fact that there are lots of aggressive comments (like hate, anger, and bullying). These cause not only mental and psychological stress but also account deactivation and even suicideBIBREF1. In this paper we concentrate on problems related to aggressiveness. The fine-grained definition of the aggressiveness/aggression identification is provided by the organizers of TRAC-2018 BIBREF0, BIBREF2. They have classified the aggressiveness into three labels (Overtly aggressive(OAG), Covertly aggressive(CAG), Non-aggressive(NAG)). The detailed description for each of the three labels is described as follows: Overtly Aggressive(OAG) - This type of aggression shows direct verbal attack pointing to the particular individual or group. For example, "Well said sonu..you have courage to stand against dadagiri of Muslims". Covertly Aggressive(CAG) - This type of aggression the attack is not direct but hidden, subtle and more indirect while being stated politely most of the times. For example, "Dear India, stop playing with the emotions of your people for votes." Non-Aggressive(NAG) - Generally these type of text lack any kind of aggression it is basically used to state facts, wishing on occasions and polite and supportive. The additional discussion on aggressiveness task can be found in Kaggle task , which just divided the task into two classes - i.e., presence or absence of aggression in tweets. The informal setting/environment of social media often encourage multilingual speakers to switch back and forth between languages when speaking or writing. These all resulted in code-mixing and code-switching. Code-mixing refers to the use of linguistic units from different languages in a single utterance or sentence, whereas code-switching refers to the co-occurrence of speech extracts belonging to two different grammatical systemsBIBREF3. This language interchange makes the grammar more complex and thus it becomes tough to handle it by traditional algorithms. Thus the presence of high percentage of code-mixed content in social media text has increased the complexity of the aggression detection task. For example, the dataset provided by the organizers of TRAC-2018 BIBREF0, BIBREF2 is actually a code-mixed dataset. The massive increase of the social media data rendered the manual methods of content moderation difficult and costly. Machine Learning and Deep Learning methods to identify such phenomena have attracted more attention to the research community in recent yearsBIBREF4. Based on the current context, we can divide the problem into three sub-problems: (a) detection of aggression levels, (b) handling code-mixed data and (c) handling styles (due to differences in social media platforms and text entry rules/restrictions). A lot of the previous approachesBIBREF5 have used an ensemble model for the task. For example, some of them uses ensemble of statistical modelsBIBREF6, BIBREF7, BIBREF8, BIBREF9 some used ensemble of statistical and deep learning modelsBIBREF10, BIBREF11, BIBREF12 some used ensemble of deep learning models BIBREF13. There are approaches which proposed unified architecture based on deep learningBIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19 while some proposed unified statistical modelBIBREF7. Additionally, there are some approaches uses data augmentation either through translation or labeling external data to make the model generalize across domainsBIBREF14, BIBREF10, BIBREF7. Most of the above-discussed systems either shows high performance on (a) Twitter dataset or (b) Facebook dataset (given in the TRAC-2018), but not on both English code-mixed datasets. This may be due to the text style or level of complexities of both datasets. So, we concentrated to develop a robust system for English code-mixed texts, and uni-lingual texts, which can also handle different writing styles. Our approach is based on three main ideas: Deep-Text Learning. The goal is to learn long range associations, dependencies between regions of text, N-grams, key-patterns, topical information, and sequential dependencies. Exploiting psycho-linguistic features with basic linguistic features as meta-data. The main aim is to minimize the direct dependencies on in-depth grammatical structure of the language (i.e., to support code-mixed data). We have also included emoticons, and punctuation features with it. We use the term "NLP Features" to represent it in the entire paper. Dual embedding based on FastText and Glove. This dual embedding helps in high vocabulary coverage and to capture the rare and partially incorrect words in the text (specially by FastText BIBREF20). Our "Deep-text architecture" uses model averaging strategy with three different deep learning architectures. Model averaging belongs to the family of ensemble learning techniques that uses multiple models for the same problem and combines their predictions to produce a more reliable and consistent prediction accuracy BIBREF21. This is the simplest form of weighted average ensemble based predictionBIBREF22 where, each ensemble member contribute equally to predictions. Specifically in our case, three different models have been used. The following contains the intuition behind the selection of these three models: Deep Pyramid CNN BIBREF23 being deeper helps to learn long range associations between temporal regions of text using two-view embeddings. Disconnected RNN BIBREF24 is very helpful in encoding the sequential information with temporal key patterns in the text. Pooled BiLSTM In this architecture the last hidden state of BiLSTM is concatenated with mean and max-pooled representation of the hidden states obtained over all the time steps of Bi-LSTM. The idea of using mean and max pooling layers together is taken from BIBREF25 to avoid the loss of information in longer sequences of texts and max-pooling is taken to capture the topical informationBIBREF26. NLP Features In each of the individual models, the NLP features are concatenated with last hidden state before the softmax classification layer as meta-data. The main aim is to provide additional information to the deep learning network. The intuition behind the NLP features are the following: Emotion Sensor Dataset We have introduced to use of emotion sensor features, as a meta-data information. We have obtained the word sensor dataset from Kaggle. In this dataset each word is statistically classified into 7 distinct classes (Disgust, Surprise, Neutral, Anger, Sad, Happy and Fear) using Naive Bayes, based on sentences collected from twitter and blogs. Controlled Topical Signals from Empath. Empath can analyse the text across 200 gold standard topics and emotions. Additionally, it uses neural embedding to draw connotation among words across more than 1.8 billion words. We have used only selected categories like violence, hate, anger, aggression, social media and dispute from 200 Empath categories useful for us unlikeBIBREF12 which takes 194 categories. Emoticons frequently used on social media indicates the sense of sentenceBIBREF17, BIBREF19, BIBREF9. Normalized frequency of POS tags According to BIBREF12, BIBREF11, BIBREF7, BIBREF15 POS Tags provide the degree of target aggressiveness. LikeBIBREF12, we have used only four tags (a) adjective (JJ, JJR, JJS), (b) adverb (RB, RBR, RBS), (c) verb (VB, VBD, VBG, VBN, VBP, VBZ) and (d) noun (NN, NNS, NNP, NNPS) (See Penn-Treebank POS Tags for abbreviations and the full list). The main reason behind the selection of these four tags is to just identify words related to persons, activities, quality, etc, in the text. Sentiment polarity obtained from VADER Sentiment Analysis BIBREF27 (positive, negative and neutral) like used in BIBREF15, BIBREF10, BIBREF11, BIBREF7. It helps to demarcate aggressiveness with non-aggressiveness in the text. The block diagram of the proposed system is shown in Figure FIGREF22. The proposed system does not use any data augmentation techniques like BIBREF14, which is the top performer in TRAC (in English code-mixed Facebook data). This means the performance achieved by our system totally depends on the training dataset provided by TRAC. This also proves the effectiveness of our approach. Our system outperforms all the previous state of the art approaches used for aggression identification on English code-mixed TRAC data, while being trained only from Facebook comments the system outperforms other approaches on the additional Twitter test set. The remaining part of this paper is organized as follows: Section SECREF2 is an overview of related work. Section SECREF3 presents the methodology and algorithmic details. Section SECREF4 discusses the experimental evaluation of the system, and Section SECREF5 concludes this paper. ### Related work
There are several works for aggression identification submitted at TRAC 2018 among them some approaches use the ensemble of multiple statistical modelsBIBREF6, BIBREF7, BIBREF8, BIBREF9. Similarly, some of the models likeBIBREF10, BIBREF11, BIBREF12 have used ensemble of statistical and deep learning models. In these models the statistical part of the model uses additional features from text analysis like parts-of-speech tags, punctuation, emotion, emoticon etc. Model like: BIBREF13 has used the ensemble of deep learning models based on majority voting. Some other models like: BIBREF28, BIBREF12, BIBREF9 have used different models for Facebook and twitter. While approaches like:BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19 have proposed unified architecture based on deep learning. Systems likeBIBREF14, BIBREF10, BIBREF7 have used data augmentation either through translation or labelling external data to make the model generalize across domains. While BIBREF7 has proposed a unified statistical model. Among approaches likeBIBREF6 extracted features from TF-IDF of character n-grams whileBIBREF28 uses LSTM with pre-trained embeddings from FastText. BIBREF15 have used the BiLSTM based model and the SVM metaclassifier model for the Facebook and Twitter test sets, respectively. While BIBREF13 tried ensembling of CNN, LSTM, and BILSTM. Some approaches like:BIBREF12 has used emotions frequency as one of the features, while some others use sentiment emotion as featureBIBREF11. Also,BIBREF17, BIBREF19 have converted emoticons to their description. BIBREF9 have used TF-IDF of emoticons per-class as one of the features. Compared to all these approaches, we have concentrated to capture multiple linguistic/pattern based relations, key-terms and key-patters (with their association in text) through a combination of deep learning architectures with model averaging. We have also used NLP features as additional features with our deep learning architecture, obtained from psycho-linguistic and basic linguistic features. ### Methodology
In this section, we describe our system architecture for aggressiveness classifier. In section SECREF23 we describe data preprocessing applied on the input text before feeding it to each of the classification models. Section SECREF26 describes the computation of NLP features. In Sections SECREF30, SECREF34 and SECREF45 we have described the architecture of different deep learning models like Deep Pyramid CNN, Disconnected RNN and Pooled BiLSTM respectively. Finally, in Section SECREF49, we describe model averaging based classification model which combines the prediction probabilities from three deep learninig architectures discussed above. (see Figure FIGREF22. for block diagram of system architecture). ### Methodology ::: Data Preprocessing
We consider the text to be well formatted before applying the text to the embedding layer. First, we detect non-English text(which are few) and translate all of them to English using Google Translate. Still, there is some code mixed words like "mc", "bc" and other English abbreviations and spelling errors like "nd" in place of "and", "u" in place of "you" causes deep learning model to confuse with sentences of the same meaning. We follow the strategy of preprocessor as inBIBREF17 to normalize the abbreviations and remove spelling errors, URLs and punctuation marks, converting emojis to their description. https://spacy.io/usage/linguistic-features#pos-tagging ### Methodology ::: NLP Features
We have identified a novel combination of features which are highly effective in aggression classification when applied in addition to the features obtained from the deep learning classifier at the classification layer. We have introduced two new features in addition to the previously available features. The first one is the Emotion Sensor Feature which use a statistical model to classify the words into 7 different classes based on the sentences obtained from twitter and blogs which contain total 1,185,540 words. The second one is the collection of selected topical signal from text collected using Empath (see Table 1.). Different from previous approachesBIBREF8, BIBREF12 where BIBREF12 have used Emotion features in the form of frequency while BIBREF8 have used emotion feature vector obtained from LIWC 2007BIBREF30. UnlikeBIBREF12 we have used only 6 topical signals from EmapthBIBREF29. We have borrowed the idea of using other features like punctuation features and parts-of-speech tags from BIBREF12. The Table 1. lists and describes features, tools used to obtain them and the number of features resulted from each type. ### Methodology ::: Deep Pyramid CNN(DPCNN)
Since it has been proved that CNNs are great feature extractors for text classificationBIBREF31, BIBREF32, BIBREF33, BIBREF34, BIBREF35, BIBREF23 while deeper networks(whether RNNs or CNN's) has been proven for learning long-range association like deeper character level CNN'sBIBREF36, BIBREF37, and complex combination of RNN and CNNBIBREF38, BIBREF39, BIBREF40, BIBREF41, BIBREF42. Deep Pyramid CNN (DPCNN)BIBREF23 has 15 layers of word-level CNN's and contains similar pre-activation as proposed in improved ResnetBIBREF43. DPCNN outperforms the 32-layer character CNNBIBREF37 and Hierarchical attention networksBIBREF42 it has added advantage that due to its pyramid structure it does not require dimension matching in shortcut connections defined as z + h(z) as inBIBREF43 where h(z) represents the skipped layers essentially contains two convolutional layers with pre-activation. It uses enhanced region embedding which consumes pre-trained embeddings (in our case it is FastText+Glove based dual embedding). Enhanced Region Embedding. The current DPCNNBIBREF23, uses two view type enhanced region embedding. For the text categorization, it defines a region of text as view-1 and its adjacent regions as view-2. Then using unlabeled data, it trains a neural network of one hidden layer with an artificial task of predicting view-2 from view-1. The obtained hidden layer, which is an embedding function that takes view-1 as input, serves as an unsupervised embedding function in the model for text categorization. The detailed architecture has been shown in Figure FIGREF29. Let each word input $x_j \in R^d$ be the d-dimensional vector for the $j^{th}$ word $w_{j}$ and the sentence $s_i$ contains sequence of $n$ words $\lbrace w_{1},w_{2},w_{3},......,w_{n}\rbrace $ as shown in Figure FIGREF29. In comparision to conventional convolution layer, DPCNN proposes to use pre-activation, thus essentially the convolutional layer of DPCNN is $\textbf {W}\sigma (\textbf {x})+\textbf {b}$, where $\textbf {W}$ and $\textbf {b}$(unique to each layer) are the weights matrix and bias respectively, we use $\sigma $ as PReLUBIBREF44. During implementation we use kernel size of 3(represented by $\textbf {x}$ to denote the small overlapping regions of text.), The number of filters(number of feature maps denoted by the number of rows of $\textbf {W}$) is 128 as depicted in Figure FIGREF29. With the number of filters same in each convolution layer and max-pooling with stride 2 makes the computation time halved, and doubles the net coverage of convolution kernel. Thus the deeper layers cause to learn long-range associations between regions of text. Let's say $h_{dpcnn} \in R^{p_1}$ be the hidden state obtained from DPCNN just before the classification layer and $f_{nlp} \in R^{24}$ be the NLP features computed from the text. Lets $z_1 \in R^{p_1 + 24}$ be another hidden state obtained as where, $\oplus $ denotes concatenation. The vector $z_1$ obtained, then fed to the fully connected layer with softmax activation. Let $y_{i1}^*$ be the softmax probabilities, specifically for class label $k$ is given as: where $K$ is the number of classes, $W_{dpcnn}$ and $b_{dpcnn}$ are the weight matrix and bias respectively. ### Methodology ::: Disconnected RNN(DRNN)
Given a sequence $s_i = [x_{1}, x_{2}, x_{3},....x_{n}]$ where $x_{j} \in R^d$ represents the d-dimensional word vector for word $w_{j}$ and $n$ is the length of input text applied to a variant of RNN called Long Short-Term Memory (LSTM)BIBREF45 as shown in Figure FIGREF33. It is widely used for sequential modelling with long-term dependencies. For sequence modelling it keeps on updating the memory cell with current input using an adaptive gating mechanism. At time step $t$ the memory $c_t$ and the hidden state $h_t$ are updated as follows: where $\hat{c}_t$ is the current cell state obtained from current input $x_t$ and previous hidden state $h_{t-1}$, $i_t$, $f_t$ and $o_t$ are the activation corresponding to input gate, forget gate and output gate respectively, $\sigma $ denotes the logistic sigmoid function and $\odot $ denotes the element-wise multiplication. Hence the hidden state representation at time step $t$ depends on all the previous input vectors given as Specifically we have used Bi-directional LSTM BIBREF45 to capture both past and future context. It provides $h_t$ from both directions(forward & backward). The forward LSTM takes the natural order of words from $x_{1}$ to $x_{n}$ to obtain $\overrightarrow{h_t}$, while backward-LSTM $x_{n}$ to $x_{1}$ to obtain $\overleftarrow{h_t}$. then $h_t$ is calculated as where $\oplus $ is the concatenation and $L$ is the size for one-directional LSTM. Therefore we denote the hidden state in equation DISPLAY_FORM37 with BiLSTM as To avoid handling of long sequence and to capture local information for each word we define the window size $k$ for each word such that the BiLSTM only sees the the previous $k-1$ words with the current word, where $k$ is a hyperparameterBIBREF24. We use padding <PAD> to make the slices of fixed size k(as shown in Figure FIGREF33). It provides each hidden state $h_t$ with sequence of $k$ previous words. Since the phrase of $k$ words can lie anywhere in the text it helps to model the position invariant phrase representation due to which the it identifies key phrases important for identifying particular category. In this case, the equation of $h_t$ is given as The output hidden vectors, $H = [h_1, h_2, h_3, ...... h_n] \in R^{n \times 2L}$ are converted to fixed-length vector $h_{drnn} \in R^{2L}$ with max pooling over time: Let's say $f_{nlp} \in R^{24}$ be the NLP features computed from the text. Let's $z_2 \in R^{2L + 24}$ be another hidden state obtained as where $\oplus $ denotes concatenation. The vector $z_2$ obtained, then fed to the fully connected layer with softmax activation. Let $y_{i2}^*$ be the softmax probabilities, specifically for class label $k$ is given as: where $K$ is the number of classes, $W_{drnn}$ is the weight matrix, and $b_{drnn}$ is the bias. ### Methodology ::: Pooled BiLSTM
The architecture has been shown in Figure FIGREF44. Given a sequence $s_i = [x_{1}, x_{2}, x_{3}, ..... x_{j}]$, where $x_j \in R^d$ is the d-dimensional word vector for word $w_j$, the hidden state obtained after BiLSTM is given as To avoid the loss of information because of modelling the entire sequence, we have concatenated the max-pooled($c_{max}$) and mean-pooled($c_{mean}$) representation of hidden states calculated over all time steps BIBREF25. We have also concatenated the nlp features, $f_{nlp} \in R^{24}$ the final feature vector $z_{3}$ is given as where $\oplus $ denotes concatenation. The final feature $z_3$ vector is fed to the fully connected layer with softmax activation. Let $y_{i3}^*$ be the softmax probablities, specifically for class label $k$ given as: where $K$ is the number of classes and $W_{bilstm}$ and $b_{bilstm}$ are the weight matrix and bias respectively. ### Methodology ::: Classification Model
According to deep learning literature BIBREF46, BIBREF47, BIBREF48, unweighted averaging might be a reasonable ensemble for similar base learners of comparable performance. Now, similar to the information discussed in BIBREF21, we can compute the model averaging (unweighted) by combining the softmax probabilities of three different classification models obtained from equations DISPLAY_FORM32, DISPLAY_FORM43, DISPLAY_FORM48. The averaged class probabilities are computed as: where K is the number of classes, and $\hat{y_i}$ is the predicted label for sentence $s_i$. ### Experiment and Evaluation ::: Dataset Description
We have used two datasets in our experimental evaluations: (1) TRAC 2018 Dataset and (2) Kaggle Dataset. TRAC 2018 Dataset: We have used the English code-mixed dataset provided by TRAC 2018. This dataset contains three labels, (a) Non-Aggressive(NAG), (b) Overtly-Aggressive (OAG) and (c) Covertly-Aggressive(CAG). The distribution of training, validation and test sets are described in Table TABREF56. Kaggle Dataset: This dataset contains 20001 tweets which are manually labeled. The labels are divided into two categories (indicating presence or absence of aggression in tweets) AGG(Aggressive) or NAG(Non-Aggressive). We have used the same test split available in the baseline code. The distribution for each of the training and test is given in Table TABREF56. ### Experiment and Evaluation ::: Experimental Setup
We have used Glove EmbeddingsBIBREF49 concatenated with FastText EmbeddingsBIBREF20 in all the three classification models presented in this paper. Specifically, we used Glove pre-trained vectors obtained from Twitter corpus containing 27 billion tokens and 1.2 million vocabulary entries where each word is represented using 100-dimensional vector. In the case of FastText the word is represented using 300-dimensional vector. Also, we have applied spatial dropoutBIBREF50 of 0.3 at embedding layer for DPCNN(in section SECREF30) and Pooled BiLSTM(in section SECREF45). For DPCNN model(in SECREF30) we have learnt 128-dimensional vector representation for unsupervised embeddings implicitly for task specific representation as in BIBREF23. Additionally, for DPCNN all the convolutional layers used 128 filters, kernel size of 3 and max-pooling stride 2. Additionally, in the case of DPCNN we have used kernel and bias regularizer of value 0.00001 for all convolutional kernels. The pre-activation function used in DPCNN is Parametric ReLU (PReLU) proposed in BIBREF44 while the activation at each of the convolutional kernel is linear. For, DRNN(in section SECREF34) we have used the window size of 8 and rest of the parameters related to LSTM units are same as given inBIBREF24. For, Pooled BiLSTM(in section SECREF45) we have used LSTM hidden units size as 256. The maximum sequence length is 200 in all three models. In each of the classification model the classification layer contains the fully connected layer with softmax activation with output size of 3 equal to number of classes in case of TRAC 2018 dataset and its 2 in case of Kaggle dataset. Training has been done using ADAM optimizerBIBREF51 for DPCNN and RMSPROPBIBREF52 for DRNN and Pooled Bi-LSTM models. All the models are trained end-to-end using softmax cross entropy lossBIBREF53 for TRAC 2018 dataset and binary cross entropy lossBIBREF53 for Kaggle dataset. To train our model for TRAC 2018 dataset, we merged the training and validation dataset and then used 10% split from shuffled dataset to save the best model, for all classifiers. We have used only 20 NLP features (except TF-IDF Emoticon feature and Punctuation feature as given in Table TABREF25) for Kaggle dataset (as these are not present in the Kaggle dataset). ### Experiment and Evaluation ::: Evaluation Strategy
To compare our experimental results we have used top-5 systems from the published results of TRAC-2018BIBREF5. To compare our results on Kaggle dataset, we have used the last & the best published result on Kaggle website as a baseline. We have conducted the separate experiments, to properly investigate the performance of (a) each of the classifiers (used in our model averaging based system), (b) impact of the NLP features on each of these classifiers and finally, (c) the performance of our proposed system. In Table TABREF57, TABREF57 and TABREF57, models, named as DPCNN(ref SECREF30), DRNN (ref SECREF34) and Pooled BiLSTM(ref SECREF45) are corresponding models without NLP features. Similarly, DPCNN+NLP Features, DRNN + NLP Features and Pooled BiLSTM + NLP Features are corresponding models with NLP features. The Model Averaging (A+B+C) is the ensemble of three models (i.e., model averaging of DPCNN, DRNN and Pooled BiLSTM) without NLP features. Finally, Our Proposed Method, which represents the model averaging of three models with NLP features. ### Experiment and Evaluation ::: Results and Discussion
In this paper, we have evaluated our model using weighted macro-averaged F-score. The measure is defined as in (See BIBREF5, BIBREF2). It weights the F-score computed per class based on the class composition in the test set and then takes the average of these per-class F-score gives the final F-score. Table TABREF57, TABREF57 and TABREF57. presents the comparative experimental results for the proposed method in this paper with respect to the state-of-the-art. The top 5 modelsBIBREF5 given in Table TABREF57 and TABREF57. are the best performing models for Facebook and Twitter test dataset respectively on TRAC 2018. We have followed all the experimental guidelines as discussed in TRAC contest guideline paperBIBREF2, BIBREF5. From the results given in Table TABREF57, TABREF57 and TABREF57 it is clear that our proposed model shows the best performance among all of the approaches. These results also state that all the deep learning architectures with NLP features, perform better than individual corresponding deep learning architectures. This means NLP features, adds some value to the architectures, even if it is not very high. ### Conclusion and Future Work
In this paper, we have briefly described the approach we have taken to solve the aggressive identification on online social media texts which is very challenging since the dataset is noisy and code-mixed. We presented an ensemble of deep learning models which outperform previous approaches by sufficient margin while having the ability to generalize across domains. In future, we will explore other methods to increase the understanding of deep learning models on group targeted text, although the categories are well defined we will look after if we further fine-tune the categories with more data. In the future, we are planning to pay attention on a generalized language model for code-mixed texts which can also handle Hindi-code-mixed and other multi-lingual code-mixed datasets (i.e., trying to reduce the dependencies on language-specific code-mixed resources). Figure 1: Block diagram of the proposed system Table 1: Details of NLP features Figure 2: DPCNN Figure 3: DRNN Figure 4: Pooled BiLSTM Table 2: TRAC 2018, Details of English Code-Mixed Dataset Table 6: Results on Kaggle Test Dataset Figure 5: Confusion Matrix for Facebook, Twitter and Kaggle Datasets. | Hindi |
What non-annotated datasets are considered? | ### Introduction
Natural Language Generation (NLG) is an NLP task that consists in generating a sequence of natural language sentences from non-linguistic data. Traditional approaches of NLG consist in creating specific algorithms in the consensual NLG pipeline BIBREF0, but there has been recently a strong interest in End-to-End (E2E) NLG systems which are able to jointly learn sentence planning and surface realization BIBREF1, BIBREF2, BIBREF3, BIBREF4. Probably the most well known effort of this trend is the E2E NLG challenge BIBREF5 whose task was to perform sentence planing and realization from dialogue act-based Meaning Representation (MR) on unaligned data. For instance, Figure FIGREF1 presents, on the upper part, a meaning representation and on the lower part, one possible textual realization to convey this meaning. Although the challenge was a great success, the data used in the challenge contained a lot of redundancy of structure and a limited amount of concepts and several reference texts per MR input (8.1 in average). This is an ideal case for machine learning but is it the one that is encountered in all E2E NLG real-world applications? In this work, we are interested in learning E2E models for real world applications in which there is a low amount of annotated data. Indeed, it is well known that neural approaches need a large amount of carefully annotated data to be able to induce NLP models. For the NLG task, that means that MR and (possibly many) reference texts must be paired together so that supervised learning is made possible. In NLG, such paired datasets are rare and remains tedious to acquire BIBREF5, BIBREF6, BIBREF7. On the contrary, large amount of unpaired meaning representations and texts can be available but cannot be exploited for supervised learning. In order to tackle this problem, we propose a semi-supervised learning approach which is able to benefit from unpaired (non-annotated) dataset which are much easier to acquire in real life applications. In an unpaired dataset, only the input data is assumed to be representative of the task. In such case, autoencoders can be used to learn an (often more compact) internal representation of the data. Monolingual word embeddings learning also benefit from unpaired data. However, none of these techniques are fit for the task of generating from a constrained MR representation. Hence, we extend the idea of autoencoder which is to regenerate the input sequence by using an NLG and an NLU models. To learn the NLG model, the input text is fed to the NLU model which in turn feeds the NLG model. The output of the NLG model is compared to the input and a loss can be computed. A similar strategy is applied for NLU. This approach brings several advantages: 1) the learning is performed from a large unpaired (non-annotated) dataset and a small amount of paired data to constrain the inner representation of the models to respect the format of the task (here MR and abstract text); 2) the architecture is completely differentiable which enables a fully joint learning; and 3) the two NLG and NLU models remain independent and can thus be applied to different tasks separately. The remaining of this paper gives some background about seq2seq models (Sec SECREF2) before introducing the joint learning approach (Sec SECREF3). Two benchmarks, described in Sec SECREF4, have been used to evaluate the method and whose results are presented in Sec SECREF5. The method is then positioned with respect to the state-of-the-art in Sec SECREF6 before providing some concluding remarks in Sec SECREF7. ### Background: E2E systems
E2E Natural Language Generation systems are typically based on the Recurrent Neural Network (RNN) architecture consisting of an encoder and a decoder also known as seq2seq BIBREF8. The encoder takes a sequence of source words $\mathbf {x}~=~\lbrace {x_1},{x_2}, ..., {x_{T_x}}\rbrace $ and encodes it to a fixed length vector. The decoder then decodes this vector into a sequence of target words $\mathbf {y}~=~\lbrace {y_1},{y_2}, ..., {y_{T_y}}\rbrace $. Seq2seq models are able to treat variable sized source and target sequences making them a great choice for NLG and NLU tasks. More formally, in a seq2seq model, the recurrent unit of the encoder, at each time step $t$ receives an input word $x_t$ (in practice the embedding vector of the word) and a previous hidden state ${h_t-1}$ then generates a new hidden state $h_t$ using: where the function $f$ is an RNN unit such as Long Short-Term Memory (LSTM) BIBREF9 or Gated Recurrent Unit (GRU) BIBREF10. Once the encoder has treated the entire source sequence, the last hidden state ${h_{T_x}}$ is passed to the decoder. To generate the sequence of target words, the decoder also uses an RNN and computes, at each time step, a new hidden state $s_t$ from its previous hidden state $s_{t-1}$ and the previously generated word $y_{t-1}$. At training time, $y_{t-1}$ is the previous word in the target sequence (teacher-forcing). Lastly, the conditional probability of each target word $y_t$ is computed as follows: where $W$ and $b$ are a trainable parameters used to map the output to the same size as the target vocabulary and $c_t$ is the context vector obtained using the sum of hidden states in the encoder, weighted by its attention BIBREF11, BIBREF12. The context is computed as follow: Attention weights $\alpha _{i}^{t}$ are computed by applying a softmax function over a score calculated using the encoder and decoder hidden states: The choice of the score adopted in this papers is based on the dot attention mechanism introduced in BIBREF12. The attention mechanism helps the decoder to find relevant information on the encoder side based on the current decoder hidden state. ### Joint NLG/NLU learning scheme
The joint NLG/NLU learning scheme is shown in Figure FIGREF7. It consists of two seq2seq models for NLG and NLU tasks. Both models can be trained separately on paired data. In that case, the NLG task is to predict the text $\hat{y}$ from the input MR $x$ while the NLU task is to predict the MR $\hat{x}$ from the input text $y$. On unpaired data, the two models are connected through two different loops. In the first case, when the unpaired input source is text, $y$ is provided to the NLU models which feeds the NLG model to produce $\hat{y}$. A loss is computed between $y$ and $\hat{y}$ (but not between $\hat{x}$ and $x$ since $x$ is unknown). In the second case, when the input is only MR, $x$ is provided to the NLG model which then feeds the NLU model and finally predicts $\hat{x}$. Similarly, a loss is computed between $x$ and $\hat{x}$ (but not between $\hat{y}$ and $y$ since $y$ is unknown). This section details these four steps and how the loss is backpropagated through the loops. Learning with Paired Data: The NLG model is a seq2seq model with attention as described in section SECREF2. It takes as input a MR and generates a natural language text. The objective is to find the model parameters $\theta ^{nlg}$ such that they minimize the loss which is defined as follows: The NLU model is based on the same architecture but takes a natural language text and outputs a MR and its loss can be formulated as: Learning with Unpaired Data: When data are unpaired, there is also a loop connection between the two seq2seq models. This is achieved by feeding MR to the NLG model in order to generate a sequence of natural language text $\hat{y}$ by applying an argmax over the probability distribution at each time step ($\hat{y}_t = \mbox{argmax}P(y_t|\mathbf {x};\theta ^{nlg})$). This text is then fed back into the NLU model which in turn generates an MR. Finally, we compute the loss between the original MR and the reconstructed MR: The same can be applied in the opposite direction where we feed text to the NLU model and then the NLG model reconstructs back the text. This loss is given by: To perform joint learning, all four losses are summed together to provide the uniq loss $\mathcal {L}$ as follows: The weights $\alpha , \beta , \delta $ and $\gamma \in [0,1]$ are defined to fine tune the contribution of each task and data to the learning or to bias the learning towards one specific task. We show in the experiment section the impact of different settings. Since the loss functions in Equation DISPLAY_FORM8 and DISPLAY_FORM9 force the model to generate a sequence of words based on the target and the losses in Equation DISPLAY_FORM11 and DISPLAY_FORM10 force the model to reconstruct back the input sequence, this way the model is encouraged to generate text that is supported by the facts found in the input sequence. It is important to note that the gradients based on $\mathcal {L}_{p}^{nlg}$ and $\mathcal {L}_{p}^{nlu}$ can only backpropagate through their respective model (i.e., NLG and NLU), while $\mathcal {L}_{u}^{nlg}$ and $\mathcal {L}_{u}^{nlu}$ gradients should backpropagate through both models. Straight-Through Gumbel-Softmax: A major problem with the proposed joint learning architecture in the unpaired case is that the model is not fully differentiable. Indeed, given the input $x$ and the intermediate output $\hat{y}$, the $\mathcal {L}_{u}^{nlu}$ and the NLG parameter $\theta _{nlg}$, the gradient is computed as: At each time step $t$, the output probability $p_{y_t}$ is computed trough the softmax layer and $\hat{y}_t$ is obtained using $\hat{y}_t = onehot(argmax_w p_{y_t}[w])$ that is the word index $w$ with maximum probability at time step $t$. To address this problem, one solution is to replace this operation by the identity matrix $\frac{\partial \hat{y}_t}{\partial p_{y_t}} \approx \mathbb {1}$. This approach is called the Straight-Through (ST) estimator, which simply consists of backpropagating through the argmax function as if it had been the identity function BIBREF13, BIBREF14. A more principled way of dealing with the non-differential nature of argmax, is to use the Gumbel-Softmax which proposes a continuous approximation to sampling from a categorical distribution BIBREF15. Hence, the discontinuous argmax is replaced by a differentiable and smooth function. More formally, consider a $k$-dimensional categorical distribution $u$ with probabilities $\pi _1, \pi _2, ..., \pi _k$. Samples from $u$ can be approximated using: where $g_i$ is the Gumbel noise drawn from a uniform distribution and $\tau $ is a temperature parameter. The sample distribution from the Gumbel-Softmax resembles the argmax operation as $\tau \rightarrow 0$, and it becomes uniform when $\tau \rightarrow \infty $. Although Gumbel-Softmax is differentiable, the samples drawn from it are not adequate input to the subsequent models which expect a discrete values in order to retrieve the embedding matrix of the input words. So, instead, we use the Straight-Through (ST) Gumbel-Softmax which is basically the discrete version of the Gumbel-Softmax. During the forward phase, ST Gumbel-Softmax discretizes $y$ in Equation DISPLAY_FORM14 but it uses the continuous approximation in the backward pass. Although the Gumbel-Softmax estimator is biased due to the sample mismatch between the backward and forward phases, many studies have shown that ST Gumbel-Softmax can lead to significant improvements in several tasks BIBREF16, BIBREF17, BIBREF18. ### Dataset
The models developed were evaluated on two datasets. The first one is the E2E NLG challenge dataset BIBREF5 which contains 51k of annotated samples. The second one is the Wikipedia Company Dataset BIBREF7 which consists of around 51K of noisy MR-abstract pairs of company descriptions. ### Dataset ::: E2E NLG challenge Dataset
The E2E NLG challenge Dataset has become one of the benchmarks of reference for end-to-end sentence-planning NLG systems. It is still one of the largest dataset available for this task. The dataset was collected via crowd-sourcing using pictorial representations in the domain of restaurant recommendation. Although the E2E challenge dataset contains more than 50k samples, each MR is associated on average with 8.1 different reference utterances leading to around 6K unique MRs. Each MR consists of 3 to 8 slots, such as name, food or area, and their values and slot types are fairly equally distributed. The majority of MRs consist of 5 or 6 slots while human utterances consist mainly of one or two sentences only. The vocabulary size of the dataset is of 2780 distinct tokens. ### Dataset ::: The Wikipedia Company Dataset
The wikipedia company dataset BIBREF7, is composed of a set of company data from English Wikipedia. The dataset contains 51k samples where each sample is composed of up to 3 components: the Wikipedia article abstract, the Wikipedia article body, and the infobox which is a set of attribute–value pairs containing primary information about the company (founder, creation date etc.). The infobox part was taken as MR where each attribute–value pair was represented as a sequence of string attribute [value]. The MR representation is composed of 41 attributes with 4.5 attributes per article and 2 words per value in average. The abstract length is between 1 to 5 sentences. The vocabulary size is of 158464 words. The Wikipedia company dataset contains much more lexical variation and semantic information than the E2E challenge dataset. Furthermore, company texts have been written by humans within the Wikipedia ecosystem and not during a controlled experiment whose human engagement was unknown. Hence, the Wikipedia dataset seems an ecological target for research in NLG. However, as pointed out by the authors, the Wikipedia dataset is not ideal for machine learning. First, the data is not controlled and each article contains only one reference (vs. 8.1 for the E2E challenge dataset). Second the abstract, the body and the infobox are only loosely correlated. Indeed, the meaning representation coverage is poor since, for some MR, none of the information is found in the text and vice-versa. To give a rough estimate of this coverage, we performed an analysis of 100 articles randomly selected in the test set. Over 868 total slot instances, 28% of the slots in the infobox cannot be found in their respective abstract text, while 13% are missing in the infobox. Despite these problems, we believe the E2E and the Wikipedia company datasets can provide contrasted evaluation, the first being well controlled and lexically focused, the latter representing the kind of data that can be found in real situations and that E2E systems must deal with in order to percolate in the society. ### Experiments
The performance of the joint learning architecture was evaluated on the two datasets described in the previous section. The joint learning model requires a paired and an unpaired dataset, so each of the two datasets was split into several parts. E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The unpaired database was composed of two sets, one containing MRs only and the other containing natural texts only. This process resulted in 3 training sets: paired set, unpaired text set and unpaired MR set. The original development set (4.7K) and test set (4.7K) of the E2E dataset have been kept. The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. As a result of this process, 43K companies were retained. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). Of course, there was no intersection between these sets. The training set was also partitioned in order to obtain the paired and unpaired datasets. Because of the loose correlation between the MRs and their corresponding text, the paired dataset was selected such that it contained the infobox values with the highest similarity with its reference text. The similarity was computed using “difflib” library, which is an extension of the Ratcliff and Obershelp algorithm BIBREF19. The paired set was selected in this way (rather than randomly) to get samples as close as possible to a carefully annotated set. At the end of partitioning, the following training sets were obtained: paired set (10.5K), unpaired text set (24.5K) and unpaired MR set (24.5K). The way the datasets are split into paired and unpaired sets is artificial and might be biased particularly for the E2E dataset as it is a rather easy dataset. This is why we included the Wikipedia dataset in our study since the possibility of having such bias is low because 1) each company summary/infobox was written by different authors at different time within the wikipedia eco-system making this data far more natural than in the E2E challenge case, 2) there is a large amount of variation in the dataset, and 3) the dataset was split in such a way that the paired set contains perfect matches between the MR and the text, while reserving the least matching samples for the the unpaired set (i.e., the more representative of real-life Wikipedia articles). As a result, the paired and unpaired sets of the Wikipedia dataset are different from each other and the text and MR unpaired samples are only loosely correlated. ### Experiments ::: Evaluation with Automatic Metrics
For the experiments, each seq2seq model was composed of 2 layers of Bi-LSTM in the encoder and two layers of LSTM in the decoder with 256 hidden units and dot attention trained using Adam optimization with learning rate of 0.001. The embeddings had 500 dimensions and the vocabulary was limited to 50K words. The Gumbel-Softmax temperature $\tau $ was set to 1. Hyper-parameters tuning was performed on the development set and models were trained until the loss on the development set stops decreasing for several consecutive iterations. All models were implemented with PyTorch library. Results of the experiment on the E2E challenge data are summarized Table TABREF21 for both the NLG and the NLU tasks. BLEU, Rouge-L and Meteor were computed using the E2E challenge metrics script with default settings. NLU performances were computed at the slot level. The model learned using paired+unpaired methods shows significant superior performances than the paired version. Among the paired+unpaired methods, the one of last row exhibits the highest balanced score between NLG and NLU. This is achieved when the weights $\alpha $ and $\gamma $ favor the NLG task against NLU ($\beta =\delta =0.1$). This setting has been chosen since the NLU task converged much quicker than the NLG task. Hence lower weight for NLU during the learning avoided over-fitting. This best system exhibits similar performances than the E2E challenge winner for ROUGE-L and METEOR whereas it did not use any pre-processing (delexicalisation, slot alignment, data augmentation) or re-scoring and was trained on far less annotated data. Results of the experiment on Wikipedia company dataset are summarized Table TABREF22 for both the NLG and the NLU tasks. Due to noise in the dataset and the fact that only one reference is available for each sample, the automatic metrics show very low scores. This is in line with BIBREF7 for which the best system obtained BLEU$=0.0413$, ROUGE-L$=0.266$ and METEOR$=0.1076$. Contrary to the previous results, the paired method brings one of the best performance. However, the best performing system is the one of the last row which again put more emphasis on the NLG task than on the NLU one. Once again, this system obtained performances comparable to the best system of BIBREF7 but without using any pointer generator or coverage mechanisms. In order to further analyze the results, in Table TABREF24 we show samples of the generated text by different models alongside the reference texts. The first two examples are from the model trained on the E2E NLG dataset and the last two are from the Wikipedia dataset. Although on the E2E dataset the outputs of paired and paired+unpaired models seem very similar, the latter resembles the reference slightly more and because of this it achieves a higher score in the automatic metrics. This resemblance to the reference could be attributed to the fact that we use a reconstruction loss which forces the model to generate text that is only supported by facts found in the input. As for the Wikipedia dataset examples, we can see that the model with paired+unpaired data is less noisy and the outputs are generally shorter. The model with only paired data generates unnecessarily longer text with lots of unsupported facts and repetitions. Needless to say that both models are doing lots of mistakes and this is because of all the noise contained in the training data. ### Experiments ::: Human Evaluation
It is well know that automatic metrics in NLG are poorly predictive of human ratings although they are useful for system analysis and development BIBREF20, BIBREF0. Hence, to gain more insight about the generation properties of each model, a human evaluation with 16 human subjects was performed on the Wikipedia dataset models. We set up a web-based experiment and used the same 4 questions as in BIBREF7 which were asked on a 5-point Lickert scale: How do you judge the Information Coverage of the company summary? How do you judge the Non-Redundancy of Information in the company summary? How do you judge the Semantic Adequacy of the company summary? How do you judge the Grammatical Correctness of the company summary? For this experiment, 40 company summaries were selected randomly from the test set. Each participant had to treat 10 summaries by first reading the summary and the infobox, then answering the aforementioned four questions. Results of the human experiment are reported in Table TABREF26. The first line reports the results of the reference (i.e., the Wikipedia abstract) for comparison, while the second line is the model with paired data, and the last line is the model trained on paired+unpaired data with parameters reported in the last row of Table TABREF22, i.e., $\alpha =\gamma =1$ and $\beta =\delta =0.1$ . It is clear from the coverage metric that no system nor the reference was seen as doing a good job at conveying the information present in the infobox. This is in line with the corpus analysis of section SECREF4. However, between the automatic methods, the unpaired models exhibit a clear superiority in coverage and in semantic adequacy, two measures that are linked. On the other side, the model learned with paired data is slightly more performing in term of non-redundancy and grammaticality. The results of the unpaired model with coverage and grammaticality are equivalent to best models of BIBREF7 but for non-redundancy and semantic adequacy the result are slightly below. This is probably because the authors have used a pointer generator mechanism BIBREF21, a trick we avoided and which is subject of further work. These results express the difference between the learning methods: on the one hand, the unpaired learning relaxes the intermediate labels which are noisy so that the model learns to express what is really in the input (this explain the higher result for coverage) while, on the other hand, the paired learning is only constrained by the output text (not also with the NLU loss as in the unpaired case) which results in slightly more grammatical sentence to the expense of semantic coverage. ### Experiments ::: Ablation Study
In this section, we further discuss different aspects of the proposed joint learning approach. In particular we are interested in studying the impact of: 1) having different amounts of paired data and 2) the weight of each loss function on the overall performance. Since only the E2E dataset is non-noisy and hence provide meaningful automatic metrics, the ablation study was performed only on this dataset. To evaluate the dependence on the amount of paired data, the best model was re-trained by changing the size of the paired data ranging from 3% of the training data (i.e., 1K) up to 24% (i.e., 10K). The results are shown in Figure FIGREF27. The figure reveals that regardless of the amount of paired data, the joint learning approach: 1) always improves over the model with only paired data and 2) is always able to benefit from supplementary paired data. This is particularly true when the amount of paired data is very small and the difference seems to get smaller as the percentage of the paired data increases. Next, to evaluate which of the four losses contribute most to the overall performance, the best model was re-trained in different settings. In short, in each setting, one of the weights was set to zero while the others three weights were kept similar as in the best case. The results are presented in Table TABREF29 and Table TABREF30 for NLG and NLU tasks respectively. In these table the first line if the best model as reported in Table TABREF21. It can be seen that all the four losses are important since setting any of the weights to zero leads to a decrease in performance. However, the results of both tables show that the most important loss is the NLG unpaired loss $\mathcal {L}_{u}^{nlg}$ since setting $\gamma $ to zeros leads to a significant reduction in the performance for both NLU and NLG. ### Related Work
The approach of joint learning has been tested in the literature in other domains than NLG/NLU for tasks such machine translation BIBREF22, BIBREF23, BIBREF24 and speech processing BIBREF25, BIBREF18, BIBREF26. In BIBREF24 an encoder-decoder-reconstructor for MT is proposed. The reconstructor, integrated to the NMT model, rebuilds the source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. In BIBREF18, a joint learning architecture of Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) is proposed which leverages unannotated data. In the unannotated case, during the learning, ASR output is fed to the TTS and the TTS output is compared with the original ASR signal input to compute a loss which is back-propagated through both modules. Regarding NLU, joint learning of NLU with other tasks remain scarce. In BIBREF27, an NLU model is jointly learned with a system action prediction (SAP) model on supervised dialogue data. The NLU model is integrated into the sequence-to-sequence SAP model so that three losses (intent prediction, slot prediction and action prediction) are used to backpropagate through both models. The paper shows that this approach is competitive against the baselines. To the best of our knowledge, the idea of joint NLG/NLU learning has not been tested previously in NLG. In NLG E2E models BIBREF1, BIBREF3, some approaches have learned a concept extractor (which is close to but simpler than an NLU model), but this was not integrated in the NLG learning scheme and only used for output re-scoring. Probably the closest work to our is BIBREF28 in which a seq2seq auto-encoder was used to generate biographies from MR. In this work, the generated text of the `forward' seq2seq model was constrained by a `backward' seq2seq model, which shared parameters. However, this works differs from ours since their model was not completely differentiable. Furthermore, their NLU backward model was only used as a support for the forward NLG. Finally, the shared parameters, although in line with the definition of an auto-encoder, make each model impossible to specialize. ### Conclusion and Further Work
In this paper, we describe a learning scheme which provides the ability to jointly learn two models for NLG and for NLU using large amount of unannotated data and small amount of annotated data. The results obtained with this method on the E2E challenge benchmark, show that the method can achieve a similar score of the winner of the challenge BIBREF3 but with far less annotated data and without using any pre-processing (delexicalisation, data augmentation) or re-scoring tricks. Results on the challenging Wikipedia company dataset shows that highest score can be achieve by mixing paired and unpaired datasets. These results are at the state-of-the-art level BIBREF7 but without using any pointer generator or coverage mechanisms. These findings open the way to the exploitation of unannotated data since the lack of large annotated data source is the current bottleneck of E2E NLG systems development for new applications. Next steps of the research include, replacing the ST Gumbel-Softmax with reinforcement learning techniques such as policy gradient. This is particularly interesting as with policy gradient we will be able do design reward functions that better suit the problem we are trying to solve. Furthermore, it would be interesting to evaluate how pointer generator mechanism BIBREF21 and coverage mechanism BIBREF29 can be integrated in the learning scheme to increase the non-redundancy and coverage performance of the generation. ### Acknowledgments
This project was partly funded by the IDEX Université Grenoble Alpes innovation grant (AI4I-2018-2019) and the Région Auvergne-Rhône-Alpes (AISUA-2018-2019). Figure 1: Example of Meaning Representation (MR) and one of its paired possible text realizations. This is a excerpt of the E2E NLG challenge dataset. Figure 2: The joint NLG/NLU learning scheme. Dashed arrows between NLG and NLU models show data flow in the case of learning with unpaired data. Table 1: Results on the test set of E2E dataset. † indicates t-test p < 0.001 against the paired NLG results. Table 2: Results on the test set of Wikipedia company dataset. † indicates t-test p < 0.001 against the Paired NLG results. Table 3: Sample of generated text from the E2E and Wikipedia test sets using our systems along with the reference text. Table 4: Results of the human evaluation per system on the Wikipedia corpus using the best unpaired system. † indicates wilcoxon p < 0.05 against the paired results. Figure 3: BLEU score as a function of percentage of paired data in the training set on the E2E dataset. Table 5: Effect of loss weights on the performance of the NLG model on the E2E dataset. Table 6: Effect of loss weights on the performance of the NLU model on the E2E dataset. | E2E NLG challenge Dataset, The Wikipedia Company Dataset |
The author promises all of the following returns from investing in Sharism EXCEPT for:
A. access to cultural capital
B. amplified networks
C. social validation
D. exclusive copyright privileges
| Sharism: A Mind Revolution With the People of the World Wide Web communicating more fully and freely in Social Media while rallying a Web 2.0 content boom, the inner dynamics of such a creative explosion must be studied more closely. What motivates those who join this movement and what future will they create? A key fact is that a superabundance of community respect and social capital are being accumulated by those who share. The key motivator of Social Media and the core spirit of Web 2.0 is a mind switch called Sharism. Sharism suggests a re-orientation of personal values. We see it in User Generated Content. It is the pledge of Creative Commons. It is in the plans of future-oriented cultural initiatives. Sharism is also a mental practice that anyone can try, a social-psychological attitude to transform a wide and isolated world into a super-smart Social Brain. The Neuron Doctrine Sharism is encoded in the Human Genome. Although eclipsed by the many pragmatisms of daily life, the theory of Sharism finds basis in neuroscience and its study of the working model of the human brain. Although we can’t entirely say how the brain works as a whole, we do have a model of the functional mechanism of the nervous system and its neurons. A neuron is not a simple organic cell, but a very powerful, electrically excitable biological processor. Groups of neurons form vastly interconnected networks, which, by changing the strength of the synapses between cells, can process information, and learn. A neuron, by sharing chemical signals with its neighbors, can be integrated into more meaningful patterns that keep the neuron active and alive. Moreover, such a simple logic can be iterated and amplified, since all neurons work on a similar principle of connecting and sharing. Originally, the brain is quite open. A neural network exists to share activity and information, and I believe this model of the brain should inspire ideas and decisions about human networks. Thus, our brain supports sharing in its very system-nature. This has profound implications for the creative process. Whenever you have an intention to create, you will find it easier to generate more creative ideas if you keep the sharing process firmly in mind. The idea-forming-process is not linear, but more like an avalanche of amplifications along the thinking path. It moves with the momentum of a creative snowball. If your internal cognitive system encourages sharing, you can engineer a feedback loop of happiness, which will help you generate even more ideas in return. It’s a kind of butterfly- effect, as the small creative energy you spend will eventually return to make you, and the world, more creative. However, daily decisions for most adults are quite low in creative productivity, if only because they’ve switched off their sharing paths. People generally like to share what they create, but in a culture that tells them to be protective of their ideas, people start to believe in the danger of sharing. Then Sharism will be degraded in their mind and not encouraged in their society. But if we can encourage someone to share, her sharing paths will stay open. Sharism will be kept in her mind as a memory and an instinct. If in the future she faces a creative choice, her choice will be, “Share.” These mind-switches are too subtle to be felt. But since the brain, and society, is a connected system, the accumulation of these micro-attitudes, from neuron to neuron and person to person, can result in observable behavior. It is easy to tell if a person, a group, a company, a nation is oriented toward Sharism or not. For those who are not, what they defend as “cultural goods” and “intellectual property” are just excuses for the status quo of keeping a community closed. Much of their “culture” will be protected, but the net result is the direct loss of many other precious ideas, and the subsequent loss of all the potential gains of sharing. This lost knowledge is a black hole in our life, which may start to swallow other values as well. Non-sharing culture misleads us with its absolute separation of Private and Public space. It makes creative action a binary choice between public and private, open and closed. This creates a gap in the spectrum of knowledge. Although this gap has the potential to become a valuable creative space, concerns about privacy make this gap hard to fill. We shouldn’t be surprised that, to be safe, most people keep their sharing private and stay “closed.” They may fear the Internet creates a potential for abuse that they can’t fight alone. However, the paradox is: The less you share, the less power you have. New Technologies and the Rise of Sharism Let’s track back to 1999, when there were only a few hundred pioneer bloggers around the world, and no more than ten times that many readers following each blog. Human history is always so: something important was happening, but the rest of the world hadn’t yet realized it. The shift toward easy-to-use online publishing triggered a soft revolution in just five years. People made a quick and easy transition from reading blogs, to leaving comments and taking part in online conversations, and then to the sudden realization that they should become bloggers themselves. More bloggers created more readers, and more readers made more blogs. The revolution was viral. Bloggers generate lively and timely information on the Internet, and connect to each other with RSS, hyperlinks, comments, trackbacks and quotes. The small-scale granularity of the content can fill discrete gaps in experience and thus record a new human history. Once you become a blogger, once you have accumulated so much social capital in such a small site, it’s hard to stop. We can’t explain this fact with a theory of addiction. It’s an impulse to share. It’s the energy of the memes that want to be passed from mouth to mouth and mind to mind. It’s more than just E-mail. It’s Sharism. Bloggers are always keen to keep the social context of their posts in mind, by asking themselves, “Who is going to see this?” Bloggers are agile in adjusting their tone−and privacy settings−to advance ideas and stay out of trouble. It’s not self-censorship, but a sense of smart expression. But once blogs reached the tipping point, they expanded into the blogosphere. This required a more delicate social networking system and content- sharing architecture. But people now understand that they can have better control over a wide spectrum of relationships. Like how Flickr allows people to share their photos widely, but safely. The checkbox-based privacy of Flickr may seem unfamiliar to a new user, but you can use it to toy with the mind-switches of Sharism. By checking a box we can choose to share or not to share. From my observations, I have seen photographers on Flickr become more open to sharing, while retaining flexible choices. The rapid emergence of Social Applications that can communicate and cooperate, by allowing people to output content from one service to another, is letting users pump their memes into a pipeline-like ecosystem. This interconnectedness allows memes to travel along multiple online social networks, and potentially reach a huge audience. As a result, such a Micro-pipeline system is making Social Media a true alternative to broadcast media. These new technologies are reviving Sharism in our closed culture. Local Practice, Global Gain If you happened to lose your Sharism in a bad educational or cultural setting, it’s hard to get it back. But it’s not impossible. A persistence of practice can lead to a full recovery. You can think of Sharism as a spiritual practice. But you must practice everyday. Otherwise, you might lose the power of sharing. Permanently. You might need something to spur you on, to keep you from quitting and returning to a closed mindset. Here’s an idea: put a sticky note on your desk that says, “What do you want to share today?” I’m not kidding. Then, if anything interesting comes your way: Share It! The easiest way to both start and keep sharing is by using different kinds of social software applications. Your first meme you want to share may be small, but you can amplify it with new technologies. Enlist some people from your network and invite them into a new social application. At first it might be hard to feel the gains of Sharism. The true test then is to see if you can keep track of the feedback that you get from sharing. You will realize that almost all sharing activities will generate positive results. The happiness that this will obtain is only the most immediate reward. But there are others. The first type of reward that you will get comes in the form of comments. Then you know you’ve provoked interest, appreciation, excitement. The second reward is access to all the other stuff being shared by friends in your network. Since you know and trust them, you will be that much more interested in what they have to share. Already, the return is a multiple of the small meme you first shared. But the third type of return is more dramatic still. Anything you share can be forwarded, circulated and republished via other people’s networks. This cascade effect can spread your work to the networked masses. Improvements in social software are making the speed of dissemination as fast as a mouse-click. You should get to know the Sharism-You. You’re about to become popular, and fast This brings us to the fourth and final type of return. It has a meaning not only for you, but for the whole of society. If you so choose, you may allow others to create derivative works from what you share. This one choice could easily snowball into more creations along the sharing path, from people at key nodes in the network who are all as passionate about creating and sharing as you are. After many iterative rounds of development, a large creative work may spring from your choice to share. Of course, you will get the credit that you asked for, and deserve. And it’s okay to seek financial rewards. But you will in every case get something just as substantial: Happiness. The more people who create in the spirit of Sharism, the easier it will be to attain well- balanced and equitable Social Media that is woven by people themselves. Media won’t be controlled by any single person but will rely on the even distribution of social networking. These “Shaeros” (Sharing Heroes) will naturally become the opinion leaders in the first wave of Social Media. However, these media rights will belong to everyone. You yourself can be both producer and consumer in such a system. Sharism Safeguards Your Rights Still, many questions will be raised about Sharism as an initiative in new age. The main one is copyright. One concern is that any loss of control over copyrighted content will lead to noticeable deficits in personal wealth, or just loss of control. 5 years ago, I would have said that this was a possibility. But things are changing today. The sharing environment is more protected than you might think. Many new social applications make it easy to set terms-of-use along your sharing path. Any infringement of those terms will be challenged not just by the law, but by your community. Your audience, who benefit form your sharing, can also be the gatekeepers of your rights. Even if you are a traditional copyright holder, this sounds ideal. Furthermore, by realizing all the immediate and emergent rewards that can be had by sharing, you may eventually find that copyright and “All Rights Reserved” are far from your mind. You will enjoy sharing too much to worry about who is keeping a copy. The new economic formula is, the more people remix your works, the higher the return. I want to point out that Sharism is not Communism, nor Socialism. As for those die- hard Communists we know, they have often abused people’s sharing nature and forced them to give up their rights, and their property. Socialism, that tender Communism, in our experience also lacked respect for these rights. Under these systems, the state owns all property. Under Sharism, you can keep ownership, if you want. But I like to share. And this is how I choose to spread ideas, and prosperity Sharism is totally based on your own consensus. It’s not a very hard concept to understand, especially since copyleft movements like the Free Software Foundation and Creative Commons have been around for years. These movements are redefining a more flexible spectrum of licenses for both developers and end-users to tag their works. Because the new licenses can be recognized by either humans or machines, it’s becoming easier to re-share those works in new online ecosystems. The Spirit of the Web, a Social Brain Sharism is the Spirit of the Age of Web 2.0. It has the consistency of a naturalized Epistemology and modernized Axiology, but also promises the power of a new Internet philosophy. Sharism will transform the world into an emergent Social Brain: a networked hybrid of people and software. We are Networked Neurons connected by the synapses of Social Software. This is an evolutionary leap, a small step for us and a giant one for human society. With new “hairy” emergent technologies sprouting all around us, we can generate higher connectivities and increase the throughput of our social links. The more open and strongly connected we social neurons are, the better the sharing environment will be for all people. The more collective our intelligence, the wiser our actions will be. People have always found better solutions through conversations. Now we can put it all online. Sharism will be the politics of the next global superpower. It will not be a country, but a new human network joined by Social Software. This may remain a distant dream, and even a well-defined public sharing policy might not be close at hand. But the ideas that I’m discussing can improve governments today. We can integrate our current and emerging democratic systems with new folksonomies (based on the collaborative, social indexing of information) to enable people to make queries, share data and remix information for public use. The collective intelligence of a vast and equitable sharing environment can be the gatekeeper of our rights, and a government watchdog. In the future, policymaking can be made more nuanced with the micro-involvement of the sharing community. This “Emergent Democracy” is more real-time than periodical parliamentary sessions. It will also increase the spectrum of our choices, beyond the binary options of “Yes” or “No” referenda. Representative democracy will become more timely and diligent, because we will represent ourselves within the system. Sharism will result in better social justice. In a healthy sharing environment, any evidence of injustice can get amplified to get the public’s attention. Anyone who has been abused can get real and instant support from her peers and her peers’ peers. Appeals to justice will take the form of petitions through multiple, interconnected channels. Using these tools, anyone can create a large social impact. With multiple devices and many social applications, each of us can become more sociable, and society more individual. We no longer have to act alone. Emergent democracy will only happen when Sharism becomes the literacy of the majority. Since Sharism can improve communication, collaboration and mutual understanding, I believe it has a place within the educational system. Sharism can be applied to any cultural discourse, CoP (Community of Practice) or problem-solving context. It is also an antidote to social depression, since sharelessness is just dragging our society down. In present or formerly totalitarian countries, this downward cycle is even more apparent. The future world will be a hybrid of human and machine that will generate better and faster decisions anytime, anywhere. The flow of information between minds will become more flexible and more productive. These vast networks of sharing will create a new social order−A Mind Revolution! | D. exclusive copyright privileges |
Based on the reviewer's description of Carolyn, a viewer might assume that she values all of the following EXCEPT:
A. social awareness
B. career success
C. whiteness
D. heterosexuality
| A Good Year for the Roses? Early in American Beauty , Lester Burnham (Kevin Spacey), a weary reporter for a media magazine, masturbates in the shower while informing us in voice-over that we're witnessing the highlight of his day. He peers through tired eyes out the window at his manicured suburban tract-house lawn, where his wife, Carolyn (Annette Bening)--whose gardening clogs, he points out, are color-coordinated with the handles of her shears--snips roses (American beauties) and twitters about Miracle-Gro to a gay yuppie (Scott Bakula) on the other side of a white picket fence. "I have lost something," says Lester. "I'm not exactly sure what it is but I know I didn't always feel this ... sedated." Apparently, Lester doesn't realize that snipped roses are garden-variety symbols of castration, or he'd know what he has lost. But the makers of American Beauty are about to give Lester his roses back. At a high-school basketball game, Lester is transfixed by a blonde cheerleader named Angela (Mena Suvari), who is twirling alongside his daughter, Jane (Thora Burch). Ambient noise falls away, the crowd disappears, and there she is, Lester's angel, writhing in slow motion--just for him. She opens her jacket (she's naked underneath) and red rose petals drift out. Later, Lester envisions her on a bed of red petals, then immersed in a bath of red petals. Back in the roses for the first time in years, he's soon pumping iron, smoking pot, and telling off his frigid wife and faceless bosses, convinced that whatever he has lost he's getting back, baby. The movie is convinced, too--which is odd, since the fantasy of an underage cheerleader making a middle-aged man's wilted roses bloom is a tad ... primitive. But American Beauty doesn't feel primitive. It feels lustrously hip and aware, and a lot of critics are making big claims for it. The script, by Alan Ball, a playwright and former sitcom writer, carries an invigorating blast of counterculture righteousness, along with the kind of pithily vicious marital bickering that makes some viewers (especially male) say, "Yeah! Tell that bitch off!" More important, it has a vein of metaphysical yearning, which the director, Sam Mendes, mines brilliantly. A hotshot English theater director (his Cabaret revival is still on the boards in New York), Mendes gives the film a patina of New Age lyricism and layer upon layer of visual irony. The movie's surface is velvety and immaculate--until the action is abruptly viewed through the video camera of the teen-age voyeur next door (Wes Bentley), and the graininess of the video image (along with the plangent music) suggests how unstable the molecules that constitute our "reality" really are. Mendes can distend the real into the surreal with imperceptible puffs. Aided by his cinematographer, Conrad Hall, and editors, Tariq Anwar and Chris Greenbury, he creates an entrancing vision of the American nuclear family on the verge of a meltdown. A merican Beauty is so wittily written and gorgeously directed that you might think you're seeing something archetypal--maybe even the Great American Movie. But when you stop and smell the roses ... Well, that scent isn't Miracle-Gro. The hairpin turns from farce to melodrama, from satire to bathos, are fresh and deftly navigated, but almost every one of the underlying attitudes is smug and easy: from the corporate flunky named "Brad" to the interchangeable gay neighbors (they're both called "Jim") to the brutally homophobic patriarch next door, an ex-Marine colonel (Chris Cooper) who has reduced his wife (the normally exuberant Allison Janney) to a catatonic mummy and his son, Ricky (Bentley), to a life of subterranean deception. (The colonel's idea of bliss is watching an old Ronald Reagan military picture on television: How's that for subtle?) Lester's wife, Carolyn, is even more stridently caricatured. A real-estate broker who fails to sell a big house (her only potential customers are blank-faced African-Americans, Indian-Americans, and surly lesbians), she wears a mask of perky efficiency and insists on listening to Muzak while she and her husband and daughter eat her "nutritious yet savory" dinners. It's amazing that Mendes and Ball get away with recycling so many stale and reactionary ideas under the all-purpose rubric of "black comedy." But it's also possible that those ideas have rarely been presented so seductively. Several months ago, Daniel Menaker in Slate in contemporary film in which the protagonist attempts to break through our cultural and technological anesthetization into "the real." That's the theme here, too, and it's extraordinarily potent, at times even heartbreaking. The symbols, however, have been cunningly reversed. In movies like sex, lies, and videotape (1989), the protagonist has to put away the video camera to "get real"; in American Beauty , it's Ricky Fitts, the damaged stoner videomaker next door, who sees beauty where nonartists see only horror or nothingness. In the film's most self-consciously poetic set piece, Ricky shows Lester's dour daughter Jane--in whom he recognizes a kindred spirit--a video of a plastic bag fluttering up, down, and around on invisible currents of wind. Ricky speaks of glimpsing in the bag's trajectory an "entire life behind things"--a "benevolent force" that holds the universe together. The teen-ager, who likes to train his lenses on dead bodies of animals and people, sells wildly expensive marijuana to Lester and somehow passes on this notion of "beauty." By the end, Lester is mouthing the same sentiments and has acquired the same deadpan radiance. That must be some really good shit they're smoking. It's not the druggy philosophizing, however, that makes American Beauty an emotional workout. It's that the caricatures are grounded in sympathy instead of derision. Everyone on screen is in serious pain. The manipulative sexpot Angela, who taunts her friend Jane with the idea of seducing her dad, acts chiefly out of a terror of appearing ordinary. As the military martinet, Cooper goes against the grain, turning Col. Fitts into a sour bulldog whose capaciously baggy eyes are moist with sadness over his inability to reach out. (When he stands helplessly in the rain at the end, the deluge completes him.) The character of Carolyn is so shrill as to constitute a libel on the female sex, but there isn't a second when Bening sends the woman up. She doesn't transcend the part, she fills it to the brim, anatomizes it. You can't hate Carolyn because the woman is trying so hard--to appear confident, composed, in control. When she fails to sell that house, she closes the shades and lets go with a naked wail--it's the sound of a vacuum crying to be filled--then furiously slaps herself while sputtering, "Shut up--you're weak--shut up. " Then she breathes, regains her go-get-'em poise, replaces her mask. Carolyn isn't a complicated dramatic construction, but Bening gives her a primal force. An actress who packs more psychological detail into a single gesture than others get into whole scenes, Bening was barreling down the road to greatness before she hit a speed bump called Warren. It's a joy to observe her--both here and in Neil Jordan's In Dreams (1999)--back at full throttle. American Beauty is Spacey's movie, though. He gives it--how weird to write this about Spacey, who made his name playing flamboyantly self-involved psychopaths--a heart. Early on, he lets his face and posture go slack and his eyes blurry. He mugs like crazy, telegraphing Lester's "loserness." But Spacey's genius is for mugging in character. He makes us believe that it's Lester who's caricaturing himself , and that bitter edge paves the way for the character's later, more comfortably Spacey-like scenes of insult and mockery. He even makes us take Lester's final, improbably rhapsodic moments straight. But do the filmmakers take them straight? If I read it correctly, the movie is saying that American society is unjust and absurd and loveless--full of people so afraid of seeming ordinary that they lose their capacity to see. It's saying that our only hope is to cultivate a kind of stoned aesthetic detachment whereby even a man with his brains blown out becomes an object of beauty and a signpost to a Higher Power. But to scrutinize a freshly dead body and not ask how it got that way--or if there's anyone nearby with a gun who might want to add to the body count--strikes me as either moronic or insane or both. The kind of detachment the movie is peddling isn't artistic, it isn't life--it's nihilism at its most fatuous. In the end, American Beauty is New Age Nihilism. Kevin Costner is 11 years older than he was as Crash Davis, the over-the-hill minor-league catcher in Bull Durham (1988), but he can still get away with playing a professional ballplayer. He moves and acts like a celebrity jock, and he can make his narcissistic self-containment look as if he's keeping something in reserve--to protect his "instrument," as it were. In For Love of the Game , he's a 40ish Detroit Tigers pitcher having his last hurrah: The team has been sold and the new owners don't necessarily want him back. For about half an hour, it's a great sports movie. Costner stands on the mound shaking off the signals of his longtime catcher (John C. Reilly); he forces himself to tune out the huge Yankee Stadium crowd (the background blurs before our eyes and the sound drops out); and he mutters darkly at a succession of batters, some old nemeses, some old buddies. He also thinks about his Manhattan-based ex-girlfriend (Kelly Preston), who tearfully told him that morning that things were absolutely over and she was moving to London. There's an appealing flashback to how they met (he stopped to fix her car while on the way to Yankee Stadium), then it's back to the game for more nail-biting at bats. But pretty soon the relationship flashbacks start coming thick and fast, and the balance of the movie shifts to whether Kevin can commit to Kelly and Kelly can commit to Kevin or whether his only commitment could ever be to the ball and the diamond and the game. Maybe it's because I'm a baseball nut that I hated to leave the mound. But maybe it's also because the relationships scenes are soft-focus, generic, and woozily drawn-out, whereas the stuff in the stadium is sharply edited and full of texture. The rhythms of the game feel right; the rhythms of the romance feel embarrassingly Harlequin, and the picture drags on for over two hours. I can't believe that the director, Sam Raimi ( The Evil Dead , 1983; last year's A Simple Plan ) thought that all those scenes of Costner and Preston staring into space while the piano plinks would end up in the final cut, but Raimi apparently gave up control of the final cut for the sake of making his first, real mainstream picture. He might as well have stuck his head over the plate and said, "Bean me." | A. social awareness |
What would Dr. Niemand think was the real benefit of visiting Henry Middletown?
A. Access to specialized graph paper to make sense of their data
B. Access to calendar records to find a pattern with
C. To establish the randomness of the solar flares
D. To provide a perspective from another field
| 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. | D. To provide a perspective from another field |
Did JnJ's net earnings as a percent of sales increase in Q2 of FY2023 compared to Q2 of FY2022? | Evidence 0:
Johnson & Johnson and Subsidiaries
Condensed Consolidated Statement of Earnings
(Unaudited; in Millions Except Per Share Figures)
Percent
Percent
Percent
Increase
Amount
to Sales
Amount
to Sales
(Decrease)
Sales to customers
25,530
$
100.0
24,020
$
100.0
6.3
Cost of products sold
8,212
32.2
7,919
33.0
3.7
Gross Profit
17,318
67.8
16,101
67.0
7.6
Selling, marketing and administrative expenses
6,665
26.1
6,226
25.9
7.1
Research and development expense
3,829
15.0
3,703
15.4
3.4
Interest (income) expense, net
(23)
(0.1)
(26)
(0.1)
Other (income) expense, net*
(60)
(0.2)
273
1.1
Restructuring
145
0.5
85
0.4
Earnings before provision for taxes on income
6,762
26.5
5,840
24.3
15.8
Provision for taxes on income
1,618
6.4
1,026
4.3
57.7
Net earnings
5,144
$
20.1
4,814
$
20.0
6.9 | Yes, net earnings as a percent of sales increased from 20% in Q2 of FY2022 to 20.1% in Q2 of FY2023. |
What metris are used for evaluation? | ### Introduction
Slot Filling (SF) is the task of identifying the semantic concept expressed in natural language utterance. For instance, consider a request to edit an image expressed in natural language: “Remove the blue ball on the table and change the color of the wall to brown”. Here, the user asks for an "Action" (i.e., removing) on one “Object” (blue ball on the table) in the image and changing an “Attribute” (i.e., color) of the image to new “Value” (i.e., brown). Our goal in SF is to provide a sequence of labels for the given sentence to identify the semantic concept expressed in the given sentence. Prior work have shown that contextual information could be useful for SF. They utilize contextual information either in word level representation (i.e., via contextualize embedding e.g., BERT BIBREF0) or in the model computation graph (e.g., concatenating the context feature to the word feature BIBREF1). However, such methods fail to capture the explicit dependence between the context of the word and its label. Moreover, such limited use of contextual information (i.e., concatenation of the feature vector and context vector) in the model cannot model the interaction between the word representation and its context. In order to alleviate these issues, in this work, we propose a novel model to explicitly increase the predictability of the word label using its context and increasing the interactivity between word representations and its context. More specifically, in our model we use the context of the word to predict its label and by doing so our model learns label-aware context for each word in the sentence. In order to improve the interactivity between the word representation and its context, we increase the mutual information between the word representations and its context. In addition to these contributions, we also propose an auxiliary task to predict which labels are expressed in a given sentence. Our model is trained in a mutli-tasking framework. Our experiments on a SF dataset for identifying semantic concepts from natural language request to edit an image show the superiority of our model compared to previous baselines. Our model achieves the state-of-the-art results on the benchmark dataset by improving the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction. ### Related Work
The task of Slot Filling is formulated as a sequence labeling problem. Deep learning has been extensively employed for this task (BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11). The prior work has mainly utilized the recurrent neural network as the encoder to extract features per word and Conditional Random Field (CRF) BIBREF12 as the decoder to generate the labels per word. Recently the work BIBREF1 shows that the global context of the sentence could be useful to enhance the performance of neural sequence labeling. In their approach, they use a separate sequential model to extract word features. Afterwards, using max pooling over the representations of the words, they obtain the sentence representations and concatenate it to the word embedding as the input to the main task encoder (i.e. the RNN model to perform sequence labeling). The benefit of using the global context along the word representation is 2-fold: 1) it enhance the representations of the word by the semantics of the entire sentence thus the word representation are more contextualized 2) The global view of the sentence would increase the model performance as it contains information about the entire sentence and this information might not be encoded in word representations due to long decencies. However, the simple concatenation of the global context and the word embeddings would not separately ensure these two benefits of the global context. In order to address this problem, we introduce a multi-task setting to separately ensure the aforementioned benefits of utilizing contextual information. In particular, to ensure the better contextualized representations of the words, the model is encourage to learn representations for the word which are consistent with its context. This is achieved via increasing the mutual information between the word representation and its context. To ensure the usefulness of the contextual information for the final task, we introduce two novel sub-tasks. The first one aims to employ the context of the word instead of the word representation to predict the label of the word. In the second sub-task, we use the global representation of the sentence to predict which labels exist in the given sentence in a multi-label classification setting. These two sub-tasks would encourage the contextual representations to be informative for both word level classification and sentence level classification. ### Model
Our model is trained in a multi-task setting in which the main task is slot filling to identify the best possible sequence of labels for the given sentence. In the first auxiliary task we aim to increase consistency between the word representation and its context. The second auxiliary task is to enhance task specific information in contextual information. In this section, we explain each of these tasks in more details. ### Model ::: Slot Filling
The input to the model is a sequence of words $x_1,x_2,...,x_N$. The goal is to assign each word one of the labels action, object, attribute, value or other. Following other methods for sequence labelling, we use the BIO encoding schema. In addition to the sequence of words, the part-of-speech (POS) tags and the dependency parse tree of the input are given to the model. The input word $x_i$ is represented by the concatenation of its pre-trained word embedding and its POS tag embedding, denoted by $e_i$. These representations are further abstracted using a 2-layer Bi-Directional Long Short-Term Memory (LSTM) to obtain feature vector $h_i$. We use the dependency tree of the sentence to utilize the syntactical information about the input text. This information could be useful to identify the important words and their dependents in the sentence. In order to model the syntactic tree, we utilize Graph Convolutional Network (GCN) BIBREF13 over the dependency tree. This model learns the contextualized representations of the words such that the representation of each word is contextualized by its neighbors. We employ 2-layer GCN with $h_i$ as the initial representation for the node (i.e., word) $i$th. The representations of the $i$th node is an aggregation of the representations of its neighbors. Formally the hidden representations of the $i$th word in $l$th layer of GCN is obtained by: where $N(i)$ is the neighbors of the $i$th word in the dependency tree, $W_l$ is the weight matrix in $l$th layer and $deg(i)$ is the degree of the $i$th word in the dependency tree. The biases are omitted for brevity. The final representations of the GCN for $i$th word, $\hat{h}_i$, represent the structural features for that word. Afterwards, we concatenate the structural features $\hat{h}_i$ and sequential features $h_i$ to represent $i$th word by feature vector $h^{\prime }_i$: Finally in order to label each word in the sentence we employ a task specific 2-layer feed forward neural net followed by a logistic regression model to generate class scores $S_i$ for each word: where $W_{LR}, W_1$ and $W_2$ are trainable parameters and $S_i$ is a vector of size number of classes in which each dimension of it is the score for the corresponding class. Since the main task is sequence labeling we exploit Conditional Random Field (CRF) as the final layer to predict the sequence of labels for the given sentence. More specifically, class scores $S_i$ are fed into the CRF layer as emission scores to obtain the final labeling score: where $T$ is the trainable transition matrix and $\theta $ is the parameters of the model to generate emission scores $S_i$. Viterbi loss $L_{VB}$ is used as the final loss function to be optimized during training. In the inference time, the Viterbi decoder is employed to find the sequence of labels with highest score. ### Model ::: Consistency with Contextual Representation
In this sub-task we aim to increase the consistency of the word representation and its context. To obtain the context of each word we perform max pooling over the all words of the sentence excluding the word itself: where $h_i$ is the representation of the $i$th word from the Bi-LSTM. We aim to increase the consistency between vectors $h_i$ and $h^c_i$. One way to achieve this is by decreasing the distance between these two vectors. However, directly enforcing the word representation and its context to be close to each other would not be efficient as in long sentences the context might substantially differs from the word. So in order to make enough room for the model to represent the context of each word while it is consistent with the word representation, we employ an indirect method. We propose to maximize the mutual information (MI) between the word representation and its context in the loss function. In information theory, MI evaluates how much information we know about one random variable if the value of another variable is revealed. Formally, the mutual information between two random variable $X_1$ and $X_2$ is obtained by: Using this definition of MI, we can reformulate the MI equation as KL Divergence between the joint distribution $P_{X_1X_2}=P(X_1,X_2)$ and the product of marginal distributions $P_{X_1\bigotimes X_2}=P(X_1)P(X_2)$: Based on this understanding of MI, we can see that if the two random variables are dependent then the mutual information between them (i.e. the KL-Divergence in equation DISPLAY_FORM9) would be the highest. Consequently, if the representations $h_i$ and $h^c_i$ are encouraged to have large mutual information, we expect them to share more information. The mutual information would be introduced directly into the loss function for optimization. One issue with this approach is that the computation of the MI for such high dimensional continuous vectors as $h_i$ and $h^c_i$ is prohibitively expensive. In this work, we propose to address this issue by employing the mutual information neural estimation (MINE) in BIBREF14 that seeks to estimate the lower bound of the mutual information between the high dimensional vectors via adversarial training. To this goal, MINE attempts to compute the lower bound of the KL divergence between the joint and marginal distributions of the given high dimensional vectors/variables. In particular, MINE computes the lower bound of the Donsker-Varadhan representation of KL-Divergence: However, recently, it has been shown that other divergence metrics (i.e., the Jensen-Shannon divergence) could also be used for this purpose BIBREF15, BIBREF16, offering simpler methods to compute the lower bound for the MI. Consequently, following such methods, we apply the adversarial approach to obtain the MI lower bound via the binary cross entropy of a variable discriminator. This discriminator differentiates the variables that are sampled from the joint distribution from those that are sampled from product of the marginal distributions. In our case, the two variables are the word representation $h_i$ and context representation $h^c_i$. In order to sample from joint distributions, we simply concatenate $h_i$ and $h^c_i$ (i.e., the positive example). To sample from the product of the marginal distributions, we concatenate the representation $h_i$ with $h^c_j$ where $i\ne j$ (i.e., the negative example). These samples are fed into a 2-layer feed forward neural network $D$ (i.e., the discriminator) to perform a binary classification (i.e., coming from the joint distribution or the product of the marginal distributions). Finally, we use the following binary cross entropy loss to estimate the mutual information between $h_i$ and $h^c_i$ to add into the overall loss function: where $N$ is the length of the sentence and $[h,h^c_i]$ is the concatenation of the two vectors $h$ and $h^c_i$. This loss is added to the final loss function of the model. ### Model ::: Prediction by Contextual Information
In addition to increasing consistency between the word representation and its context representation, we aim to increase the task specific information in contextual representations. This is desirable as the main task is utilizing the word representation to predict its label. Since our model enforce the consistency between the word representation and its context, increasing the task specific information in contextual representations would help the model's final performance. In order to increase task-specific information in contextual representation, we train the model on two auxiliary tasks. The first one aims to use the context of each word to predict the label of that word and the goal of the second auxiliary task is to use the global context information to predict sentence level labels. We describe each of these tasks in more details in the following sections. ### Model ::: Prediction by Contextual Information ::: Predicting Word Label
In this sub-task we use the context representations of each word to predict its label. It will increase the information encoded in the context of the word about the label of the word. We use the same context vector $h^c_i$ for the $i$th word as described in the previous section. This vector is fed into a 2-layer feed forward neural network with a softmax layer at the end to output the probabilities for each class: Where $W_2$ and $W_1$ are trainable parameters. Biases are omitted for brevity. Finally we use the following cross-entropy loss function to be optimized during training: where $N$ is the length of the sentence and $l_i$ is the label of the $i$th word. ### Model ::: Prediction by Contextual Information ::: Predicting Sentence Labels
The word label prediction enforces the context of each word to contain information about its label but it would not ensure the contextual information to capture the sentence level patterns for expressing intent. In other words, the word level prediction lacks a general view about the entire sentence. In order to increase the general information about the sentence in the representation of the words, we aim to predict the labels existing in a sentence from the representations of its words. More specifically, we introduce a new sub-task to predict which labels exit in the given sentence (Note that sentences might have only a subset of the labels; e.g. only action and object). We formulate this task as a multi-class classification problem. Formally, given the sentence $X=x_1,x_2,...,x_N$ and label set $S=\lbrace action, attribute, object, value\rbrace $ our goal is to predict the vector $L^s=l^s_1,l^s_2,...,l^s_{|S|}$ where $l^s_i$ is one if the sentence $X$ contains $i$th label from the label set $S$ otherwise it is zero. First, we find representation of the sentence from the word representations. To this end, we use max pooling over all words of the sentence to obtain vector $H$: Afterwards, the vector $H$ is further abstracted by a 2-layer feed forward neural net with a sigmoid function at the end: where $W_2$ and $W_1$ are trainable parameters. Note that since this tasks is a multi-class classification the number of neurons at the final layer is equal to $|S|$. We optimize the following binary cross entropy loss function: where $l_k$ is one if the sentence contains the $k$th label otherwise it is zero. Finally, to train the model we optimize the following loss function: where $\alpha $, $\beta $ and $\gamma $ are hyper parameters to be tuned using development set performance. ### Experiments
In our experiments, we use Onsei Intent Slot dataset. Table TABREF21 shows the statics of this dataset. We use the following hyper parameters in our model: We set the word embedding and POS embedding to 768 and 30 respectively; The pre-trained BERT BIBREF17 embedding are used to initialize word embeddings; The hidden dimension of the Bi-LSTM, GCN and feed forward networks are 200; the hyper parameters $\alpha $, $\beta $ and $\gamma $ are all set to 0.1; We use Adam optimizer with learning rate 0.003 to train the model. We use micro-averaged F1 score on all labels as the evaluation metric. We compare our method with the models trained using Adobe internal NLU tool, Pytext BIBREF18 and Rasa BIBREF19 NLU tools. Table TABREF22 shows the results on Test set. Our model improves the F1 score by almost 2%, which corresponds to a 12.3% error rate reduction. This improvements proves the effectiveness of using contextual information for the task of slot filling. In order to analyze the contribution of the proposed sub-tasks we also evaluate the model when we remove one of the sub-task and retrain the model. The results are reported in Table TABREF23. This table shows that all sub-tasks are required for the model to have its best performance. Among all sub-tasks the word level prediction using the contextual information has the major contribution to the model performance. This fact shows that contextual information trained to be informative about the final sub-task is necessary to obtain the representations which could boost the final model performance. ### Conclusion & Future Work
In this work we introduce a new deep model for the task of Slot Filling. In a multi-task setting, our model increase the mutual information between word representations and its context, improve the label information in the context and predict which concepts are expressed in the given sentence. Our experiments on an image edit request corpus shows that our model achieves state-of-the-art results on this dataset. Table 1: Label Statistics Table 3: Performance of the model when the loss function of each sub-task has been removed from the final loss function. MI, WP and SP stand for Mutual Information, Word Prediction and Sentence Prediction respectively. | micro-averaged F1 score |
How do they combine text representations with the knowledge graph embeddings? | ### Introduction
With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired level of detail have yet to be developed, this task is still at least supervised, and often (partially) resolved by humans; we refer to these humans as knowledge workers. Knowledge workers are professionals that have to go through large amounts of data and consolidate, prepare and process it on a daily basis. This data can originate from highly diverse portals and resources and depending on type or category, the data needs to be channelled through specific down-stream processing pipelines. We aim to create a platform for curation technologies that can deal with such data from diverse sources and that provides natural language processing (NLP) pipelines tailored to particular content types and genres, rendering this initial classification an important sub-task. In this paper, we work with the dataset of the 2019 GermEval shared task on hierarchical text classification BIBREF0 and use the predefined set of labels to evaluate our approach to this classification task. Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT; BIBREF1) outperformed previous state-of-the-art methods by a large margin on various NLP tasks. We adopt BERT for text-based classification and extend the model with additional metadata provided in the context of the shared task, such as author, publisher, publishing date, etc. A key contribution of this paper is the inclusion of additional (meta) data using a state-of-the-art approach for text processing. Being a transfer learning approach, it facilitates the task solution with external knowledge for a setup in which relatively little training data is available. More precisely, we enrich BERT, as our pre-trained text representation model, with knowledge graph embeddings that are based on Wikidata BIBREF2, add metadata provided by the shared task organisers (title, author(s), publishing date, etc.) and collect additional information on authors for this particular document classification task. As we do not rely on text-based features alone but also utilize document metadata, we consider this as a document classification problem. The proposed approach is an attempt to solve this problem exemplary for single dataset provided by the organisers of the shared task. ### Related Work
A central challenge in work on genre classification is the definition of a both rigid (for theoretical purposes) and flexible (for practical purposes) mode of representation that is able to model various dimensions and characteristics of arbitrary text genres. The size of the challenge can be illustrated by the observation that there is no clear agreement among researchers regarding actual genre labels or their scope and consistency. There is a substantial amount of previous work on the definition of genre taxonomies, genre ontologies, or sets of labels BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. Since we work with the dataset provided by the organisers of the 2019 GermEval shared task, we adopt their hierarchy of labels as our genre palette. In the following, we focus on related work more relevant to our contribution. With regard to text and document classification, BERT (Bidirectional Encoder Representations from Transformers) BIBREF1 is a pre-trained embedding model that yields state of the art results in a wide span of NLP tasks, such as question answering, textual entailment and natural language inference learning BIBREF8. BIBREF9 are among the first to apply BERT to document classification. Acknowledging challenges like incorporating syntactic information, or predicting multiple labels, they describe how they adapt BERT for the document classification task. In general, they introduce a fully-connected layer over the final hidden state that contains one neuron each representing an input token, and further optimize the model choosing soft-max classifier parameters to weight the hidden state layer. They report state of the art results in experiments based on four popular datasets. An approach exploiting Hierarchical Attention Networks is presented by BIBREF10. Their model introduces a hierarchical structure to represent the hierarchical nature of a document. BIBREF10 derive attention on the word and sentence level, which makes the attention mechanisms react flexibly to long and short distant context information during the building of the document representations. They test their approach on six large scale text classification problems and outperform previous methods substantially by increasing accuracy by about 3 to 4 percentage points. BIBREF11 (the organisers of the GermEval 2019 shared task on hierarchical text classification) use shallow capsule networks, reporting that these work well on structured data for example in the field of visual inference, and outperform CNNs, LSTMs and SVMs in this area. They use the Web of Science (WOS) dataset and introduce a new real-world scenario dataset called Blurb Genre Collection (BGC). With regard to external resources to enrich the classification task, BIBREF12 experiment with external knowledge graphs to enrich embedding information in order to ultimately improve language understanding. They use structural knowledge represented by Wikidata entities and their relation to each other. A mix of large-scale textual corpora and knowledge graphs is used to further train language representation exploiting ERNIE BIBREF13, considering lexical, syntactic, and structural information. BIBREF14 propose and evaluate an approach to improve text classification with knowledge from Wikipedia. Based on a bag of words approach, they derive a thesaurus of concepts from Wikipedia and use it for document expansion. The resulting document representation improves the performance of an SVM classifier for predicting text categories. ### Dataset and Task
Our experiments are modelled on the GermEval 2019 shared task and deal with the classification of books. The dataset contains 20,784 German books. Each record has: A title. A list of authors. The average number of authors per book is 1.13, with most books (14,970) having a single author and one outlier with 28 authors. A short descriptive text (blurb) with an average length of 95 words. A URL pointing to a page on the publisher's website. An ISBN number. The date of publication. The books are labeled according to the hierarchy used by the German publisher Random House. This taxonomy includes a mix of genre and topical categories. It has eight top-level genre categories, 93 on the second level and 242 on the most detailed third level. The eight top-level labels are `Ganzheitliches Bewusstsein' (holistic awareness/consciousness), `Künste' (arts), `Sachbuch' (non-fiction), `Kinderbuch & Jugendbuch' (children and young adults), `Ratgeber' (counselor/advisor), `Literatur & Unterhaltung' (literature and entertainment), `Glaube & Ethik' (faith and ethics), `Architektur & Garten' (architecture and garden). We refer to the shared task description for details on the lower levels of the ontology. Note that we do not have access to any of the full texts. Hence, we use the blurbs as input for BERT. Given the relatively short average length of the blurbs, this considerably decreases the amount of data points available for a single book. The shared task is divided into two sub-task. Sub-task A is to classify a book, using the information provided as explained above, according to the top-level of the taxonomy, selecting one or more of the eight labels. Sub-task B is to classify a book according to the detailed taxonomy, specifying labels on the second and third level of the taxonomy as well (in total 343 labels). This renders both sub-tasks a multi-label classification task. ### Experiments
As indicated in Section SECREF1, we base our experiments on BERT in order to explore if it can be successfully adopted to the task of book or document classification. We use the pre-trained models and enrich them with additional metadata and tune the models for both classification sub-tasks. ### Experiments ::: Metadata Features
In addition to the metadata provided by the organisers of the shared task (see Section SECREF3), we add the following features. Number of authors. Academic title (Dr. or Prof.), if found in author names (0 or 1). Number of words in title. Number of words in blurb. Length of longest word in blurb. Mean word length in blurb. Median word length in blurb. Age in years after publication date. Probability of first author being male or female based on the Gender-by-Name dataset. Available for 87% of books in training set (see Table TABREF21). The statistics (length, average, etc.) regarding blurbs and titles are added in an attempt to make certain characteristics explicit to the classifier. For example, books labeled `Kinderbuch & Jugendbuch' (children and young adults) have a title that is on average 5.47 words long, whereas books labeled `Künste' (arts) on average have shorter titles of 3.46 words. The binary feature for academic title is based on the assumption that academics are more likely to write non-fiction. The gender feature is included to explore (and potentially exploit) whether or not there is a gender-bias for particular genres. ### Experiments ::: Author Embeddings
Whereas one should not judge a book by its cover, we argue that additional information on the author can support the classification task. Authors often adhere to their specific style of writing and are likely to specialize in a specific genre. To be precise, we want to include author identity information, which can be retrieved by selecting particular properties from, for example, the Wikidata knowledge graph (such as date of birth, nationality, or other biographical features). A drawback of this approach, however, is that one has to manually select and filter those properties that improve classification performance. This is why, instead, we follow a more generic approach and utilize automatically generated graph embeddings as author representations. Graph embedding methods create dense vector representations for each node such that distances between these vectors predict the occurrence of edges in the graph. The node distance can be interpreted as topical similarity between the corresponding authors. We rely on pre-trained embeddings based on PyTorch BigGraph BIBREF15. The graph model is trained on the full Wikidata graph, using a translation operator to represent relations. Figure FIGREF23 visualizes the locality of the author embeddings. To derive the author embeddings, we look up Wikipedia articles that match with the author names and map the articles to the corresponding Wikidata items. If a book has multiple authors, the embedding of the first author for which an embedding is available is used. Following this method, we are able to retrieve embeddings for 72% of the books in the training and test set (see Table TABREF21). ### Experiments ::: Pre-trained German Language Model
Although the pre-trained BERT language models are multilingual and, therefore, support German, we rely on a BERT model that was exclusively pre-trained on German text, as published by the German company Deepset AI. This model was trained from scratch on the German Wikipedia, news articles and court decisions. Deepset AI reports better performance for the German BERT models compared to the multilingual models on previous German shared tasks (GermEval2018-Fine and GermEval 2014). ### Experiments ::: Model Architecture
Our neural network architecture, shown in Figure FIGREF31, resembles the original BERT model BIBREF1 and combines text- and non-text features with a multilayer perceptron (MLP). The BERT architecture uses 12 hidden layers, each layer consists of 768 units. To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT. To minimize the GPU memory consumption, we limit the input length to 300 tokens (which is shorter than BERT's hard-coded limit of 512 tokens). Only 0.25% of blurbs in the training set consist of more than 300 words, so this cut-off can be expected to have minor impact. The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function. During training, the MLP is supposed to learn a non-linear combination of its input representations. Finally, the output layer does the actual classification. In the SoftMax output layer each unit corresponds to a class label. For sub-task A the output dimension is eight. We treat sub-task B as a standard multi-label classification problem, i. e., we neglect any hierarchical information. Accordingly, the output layer for sub-task B has 343 units. When the value of an output unit is above a given threshold the corresponding label is predicted, whereby thresholds are defined separately for each class. The optimum was found by varying the threshold in steps of $0.1$ in the interval from 0 to 1. ### Experiments ::: Implementation
Training is performed with batch size $b=16$, dropout probability $d=0.1$, learning rate $\eta =2^{-5}$ (Adam optimizer) and 5 training epochs. These hyperparameters are the ones proposed by BIBREF1 for BERT fine-tuning. We did not experiment with hyperparameter tuning ourselves except for optimizing the classification threshold for each class separately. All experiments are run on a GeForce GTX 1080 Ti (11 GB), whereby a single training epoch takes up to 10min. If there is no single label for which prediction probability is above the classification threshold, the most popular label (Literatur & Unterhaltung) is used as prediction. ### Experiments ::: Baseline
To compare against a relatively simple baseline, we implemented a Logistic Regression classifier chain from scikit-learn BIBREF16. This baseline uses the text only and converts it to TF-IDF vectors. As with the BERT model, it performs 8-class multi-label classification for sub-task A and 343-class multi-label classification for sub-task B, ignoring the hierarchical aspect in the labels. ### Results
Table TABREF34 shows the results of our experiments. As prescribed by the shared task, the essential evaluation metric is the micro-averaged F1-score. All scores reported in this paper are obtained using models that are trained on the training set and evaluated on the validation set. For the final submission to the shared task competition, the best-scoring setup is used and trained on the training and validation sets combined. We are able to demonstrate that incorporating metadata features and author embeddings leads to better results for both sub-tasks. With an F1-score of 87.20 for task A and 64.70 for task B, the setup using BERT-German with metadata features and author embeddings (1) outperforms all other setups. Looking at the precision score only, BERT-German with metadata features (2) but without author embeddings performs best. In comparison to the baseline (7), our evaluation shows that deep transformer models like BERT considerably outperform the classical TF-IDF approach, also when the input is the same (using the title and blurb only). BERT-German (4) and BERT-Multilingual (5) are only using text-based features (title and blurb), whereby the text representations of the BERT-layers are directly fed into the classification layer. To establish the information gain of author embeddings, we train a linear classifier on author embeddings, using this as the only feature. The author-only model (6) is exclusively evaluated on books for which author embeddings are available, so the numbers are based on a slightly smaller validation set. With an F1-score of 61.99 and 32.13 for sub-tasks A and B, respectively, the author model yields the worst result. However, the information contained in the author embeddings help improve performance, as the results of the best-performing setup show. When evaluating the best model (1) only on books for that author embeddings are available, we find a further improvement with respect to F1 score (task A: from 87.20 to 87.81; task B: 64.70 to 65.74). ### Discussion
The best performing setup uses BERT-German with metadata features and author embeddings. In this setup the most data is made available to the model, indicating that, perhaps not surprisingly, more data leads to better classification performance. We expect that having access to the actual text of the book will further increase performance. The average number of words per blurb is 95 and only 0.25% of books exceed our cut-off point of 300 words per blurb. In addition, the distribution of labeled books is imbalanced, i.e. for many classes only a single digit number of training instances exist (Fig. FIGREF38). Thus, this task can be considered a low resource scenario, where including related data (such as author embeddings and author identity features such as gender and academic title) or making certain characteristics more explicit (title and blurb length statistics) helps. Furthermore, it should be noted that the blurbs do not provide summary-like abstracts of the book, but instead act as teasers, intended to persuade the reader to buy the book. As reflected by the recent popularity of deep transformer models, they considerably outperform the Logistic Regression baseline using TF-IDF representation of the blurbs. However, for the simpler sub-task A, the performance difference between the baseline model and the multilingual BERT model is only six points, while consuming only a fraction of BERT's computing resources. The BERT model trained for German (from scratch) outperforms the multilingual BERT model by under three points for sub-task A and over six points for sub-task B, confirming the findings reported by the creators of the BERT-German models for earlier GermEval shared tasks. While generally on par for sub-task A, for sub-task B there is a relatively large discrepancy between precision and recall scores. In all setups, precision is considerably higher than recall. We expect this to be down to the fact that for some of the 343 labels in sub-task B, there are very few instances. This means that if the classifier predicts a certain label, it is likely to be correct (i. e., high precision), but for many instances having low-frequency labels, this low-frequency label is never predicted (i. e., low recall). As mentioned in Section SECREF30, we neglect the hierarchical nature of the labels and flatten the hierarchy (with a depth of three levels) to a single set of 343 labels for sub-task B. We expect this to have negative impact on performance, because it allows a scenario in which, for a particular book, we predict a label from the first level and also a non-matching label from the second level of the hierarchy. The example Coenzym Q10 (Table TABREF36) demonstrates this issue. While the model correctly predicts the second level label Gesundheit & Ernährung (health & diet), it misses the corresponding first level label Ratgeber (advisor). Given the model's tendency to higher precision rather than recall in sub-task B, as a post-processing step we may want to take the most detailed label (on the third level of the hierarchy) to be correct and manually fix the higher level labels accordingly. We leave this for future work and note that we expect this to improve performance, but it is hard to say by how much. We hypothesize that an MLP with more and bigger layers could improve the classification performance. However, this would increase the number of parameters to be trained, and thus requires more training data (such as the book's text itself, or a summary of it). ### Conclusions and Future Work
In this paper we presented a way of enriching BERT with knowledge graph embeddings and additional metadata. Exploiting the linked knowledge that underlies Wikidata improves performance for our task of document classification. With this approach we improve the standard BERT models by up to four percentage points in accuracy. Furthermore, our results reveal that with task-specific information such as author names and publication metadata improves the classification task essentially compared a text-only approach. Especially, when metadata feature engineering is less trivial, adding additional task-specific information from an external knowledge source such as Wikidata can help significantly. The source code of our experiments and the trained models are publicly available. Future work comprises the use of hierarchical information in a post-processing step to refine the classification. Another promising approach to tackle the low resource problem for task B would be to use label embeddings. Many labels are similar and semantically related. The relationships between labels can be utilized to model in a joint embedding space BIBREF17. However, a severe challenge with regard to setting up label embeddings is the quite heterogeneous category system that can often be found in use online. The Random House taxonomy (see above) includes category names, i. e., labels, that relate to several different dimensions including, among others, genre, topic and function. This work is done in the context of a larger project that develops a platform for curation technologies. Under the umbrella of this project, the classification of pieces of incoming text content according to an ontology is an important step that allows the routing of this content to particular, specialized processing workflows, including parameterising the included pipelines. Depending on content type and genre, it may make sense to apply OCR post-processing (for digitized books from centuries ago), machine translation (for content in languages unknown to the user), information extraction, or other particular and specialized procedures. Constructing such a generic ontology for digital content is a challenging task, and classification performance is heavily dependent on input data (both in shape and amount) and on the nature of the ontology to be used (in the case of this paper, the one predefined by the shared task organisers). In the context of our project, we continue to work towards a maximally generic content ontology, and at the same time towards applied classification architectures such as the one presented in this paper. ### Acknowledgments
This research is funded by the German Federal Ministry of Education and Research (BMBF) through the “Unternehmen Region”, instrument “Wachstumskern” QURATOR (grant no. 03WKDA1A). We would like to thank the anonymous reviewers for comments on an earlier version of this manuscript. Table 1: Availability of additional data with respect to the dataset (relative numbers in parenthesis). Figure 1: Visualization of Wikidata embeddings for Franz Kafka (3D-projection with PCA)5. Nearest neighbours in original 200D space: Arthur Schnitzler, E.T.A Hoffmann and Hans Christian Andersen. Figure 2: Model architecture used in our experiments. Text-features are fed through BERT, concatenated with metadata and author embeddings and combined in a multilayer perceptron (MLP). Table 2: Evaluation scores (micro avg.) on validation set with respect to the features used for classification. The model with BERT-German, metadata and author embeddings yields the highest F1-scores on both tasks. Table 3: Book examples and their correct and predicted labels. Hierarchical label level is in parenthesis. Figure 3: In sub-task B for many low-hierarchical labels only a small number of training samples exist, making it more difficult to predict the correct label. | all three representations are concatenated and passed into a MLP |
How does Teena find out about radioactivity?
A. Eddie teaches her about radioactivity during their hike to Cedar Point.
B. Eddie teaches her about radioactivity while he helps her finish doing the dishes.
C. Eddie teaches her about radioactivity when he is explaining the dream he had about Cedar Point.
D. Eddie teaches Teena and her mother about about radioactivity after the news gets out about Mr. Taylor’s isotope being stolen.
| YOUNG READERS Atom Mystery 11 CHAPTER ONE It was only a dream. Eddie Taylor would like to have finished it, but the bar of morning sunlight poking in under the window shade pried his eyes open. The dream fled. Eddie kicked off the sheet, swung his feet to the floor, and groped under the bed for his tennis shoes. He heard his father’s heavy footsteps in the hallway. They stopped outside of his bedroom door. “You awake, Eddie?” “I’m awake, Dad,” Eddie answered. “Breakfast’s ready. Get washed and dressed.” 12 “Be right there,” Eddie said. Then, remembering the dream, he added, “Oh, Dad, is it all right if I use the Geiger counter today?” Mr. Taylor opened the door. He was a big man, broad-shouldered and still thin-waisted. Eddie found it easy to believe the stories he had heard about his father being an outstanding football player in his time. Even his glasses and the gray hair at his temples didn’t add much age, although Eddie knew it had been eighteen years since his father had played his last game of college football. “You may use the Geiger counter any time you want, Eddie,” Mr. Taylor said, “as long as you take good care of it. You figured out where you can find some uranium ore?” Eddie smiled sheepishly. “I—I had a dream,” he said. “Plain as day. It was out on Cedar Point. I was walking along over some rocks. Suddenly the Geiger counter began clicking like everything.” 13 “Cedar Point?” his father asked. “I’ve never been out there. But, from what I hear, there are plenty of rock formations. Might be worth a try, at that. You never can tell where you might strike some radioactivity.” “Do you believe in dreams, Dad?” “Well, now, that’s a tough question, son. I can’t say that I really do. Still, one clue is as good as another when it comes to hunting uranium ore, I guess. But right now we’d better get out to breakfast before your mother scalps us. Hurry it up.” His father turned and went back down the hallway toward the kitchen. Eddie pulled on his trousers and T shirt and went into the bathroom. He washed hurriedly, knowing that even if he missed a spot or two, he was fairly safe. During the summer months his freckles got so thick and dark that it would take a magnifying glass to detect any small smudges of dirt hiding among them. He plastered some water on his dark-red hair, pushed a comb through it, and shrugged as it snapped back almost to its original position. Oh, well, he had tried. 14 He grinned into the mirror, reached a finger into his mouth, and unhooked the small rubber bands from his tooth braces. He dropped them into the waste basket. He’d put fresh ones in after breakfast. He brushed his teeth carefully, taking particular pains around the metal braces. The tooth-straightening orthodontist had warned him about letting food gather around the metal clamps. It could start cavities. Finished, Eddie went out to breakfast. “Good morning, dear,” his mother greeted him, handing him a plate of eggs. “Hi, Mom,” Eddie said. “Gotta hurry. Big day today.” “So your father says. But I’m afraid your big day will have to start with sorting out and tying up those newspapers and magazines that have been collecting in the garage.” “Aw, Mom—” “Eddie, I asked you to do it three days ago. Remember? And the Goodwill truck comes around today.” “But, Mom—” 15 “No arguments, son,” his father put in calmly but firmly. “School vacation doesn’t mean that your chores around here are on vacation, too. Get at it right away, and you’ll still have time to hunt your uranium. “Well,” Mr. Taylor added, excusing himself from the table, “I’d better be getting over to school. I’m expecting to receive shipment of a new radioisotope today.” The very word excited Eddie. In fact, anything having to do with atomic science excited him. He knew something about isotopes—pronounced eye-suh-tope . You couldn’t have a father who was head of the atomic-science department at Oceanview College without picking up a little knowledge along the way. Eddie knew that a radioisotope was a material which had been “cooked” in an atomic reactor until it was “hot” with radioactivity. When carefully controlled, the radiation stored up in such isotopes was used in many beneficial ways. 16 “Why don’t college professors get summer vacations, too?” Eddie asked. One reason for asking that particular question was to keep from prying deeper into the subject of the radioisotope. Much of his father’s work at Oceanview College was of a secret nature. Eddie had learned not to ask questions about it. His father usually volunteered any information he wanted known, so Eddie stuck to questions which could and would be answered. “We get vacations,” his father said. “But—well, my work is a little different, you know. At the speed atomic science is moving today, we simply can’t afford to waste time. But don’t worry. We’ll take a week or so off before school starts in the fall. Maybe head for the mountains with our tent and sleeping bags.” “And Geiger counter?” Eddie asked eagerly. “Wouldn’t think of leaving it home,” his father said, smiling. “By the way, I put new batteries in it the other day. Take it easy on them. Remember to switch it off when you’re not actually using it.” “I will,” Eddie promised. He had forgotten several times before, weakening the batteries. 17 It took Eddie over an hour to sort out the newspapers and magazines in the garage, tie them in neat bundles, and place them out on the front curb for the Goodwill pickup. By that time the sun was high overhead. It had driven off the coolness which the ocean air had provided during the earlier hours. “Anything else, Mom?” he asked, returning to the house and getting the Geiger counter out of the closet. He edged toward the back door before his mother had much time to think of something more for him to do. “I guess not, dear,” Mrs. Taylor said, smiling over his hasty retreat. “What are you going to do?” “Think I’ll do a little prospecting,” Eddie said. “Where?” “Probably in the hills beyond the college,” Eddie said. The more he thought about it, the more he realized it was a little late in the day to go to Cedar Point. The best way to get there was by rowboat across Moon Bay, and that was too long a row to be starting now. Besides, there were plenty of other places around the outskirts of Oceanview where likely looking rock formations invited search with a Geiger counter. 18 “Are you going alone?” his mother asked. “Oh, guess I’ll stop by and see if Teena wants to go,” Eddie answered casually. He tried to make it sound as though he would be doing Teena Ross a big favor. After all, she was only a girl. Eddie didn’t figure a girl would make a very good uranium prospecting partner, but most of the fellows he knew were away at camp, or vacationing with their folks, or something like that. “She’ll enjoy it, I’m sure,” his mother said. “I’ll take Sandy, too,” Eddie said. “He needs the exercise.” “That’s a good idea, dear. Be back in time for an early dinner.” Eddie let Sandy off his chain. The taffy-colored cocker spaniel yipped wildly over his freedom, racing back and forth as Eddie started down the street. 19 Christina Ross—whom everybody called Teena—lived at the far end of the block. Eddie went around to the side door of the light-green stucco house and knocked. “Oh, hi, Eddie,” Teena greeted him, appearing at the screen door. “I was hoping you’d come over.” “Well, I—I just happened to be going by,” Eddie said. “Thought you might want to watch me do a little prospecting with the Geiger counter. But maybe you’re too busy.” That’s how to handle it, Eddie thought. Don’t act anxious. Let Teena be anxious. Then maybe she’ll even offer to bring along a couple of sandwiches or some fruit. “Oh, I’d love to go,” Teena said eagerly, “but I’m just finishing the dishes. Come on in.” “I’m in kind of a hurry.” “I’ll only be a minute.” She pushed the screen door open for him. “I’ll make us some sandwiches.” “Stay here, Sandy,” Eddie said. “Sit.” The dog minded, although he looked a bit rebellious. 20 Eddie went inside and followed Teena to the kitchen. He felt triumphant about the sandwiches. Teena tossed him a dish towel. “You dry them,” she said. “Who, me?” “Why not? You’re in a hurry, aren’t you? I can make the sandwiches while you dry the silverware.” She smiled, putting tiny crinkles in her small, slightly upturned nose. She wore her hair in a pony tail. Even though her hair was blond all year long, it seemed even lighter in the summer. Eddie couldn’t tell whether the sun had faded it, or whether her deep summer tan simply made her hair look lighter by contrast. Maybe both. “Hello, Eddie,” Mrs. Ross said, coming into the kitchen. “Looks like Teena put you to work.” “She always does, Mrs. Ross,” Eddie said, pretending great injury. “Don’t know why I keep coming over here.” “I know,” Teena spoke up quickly. “It’s because we’re friends, that’s why.” 21 Eddie knew she was right. They were friends—good friends. They had been ever since Eddie’s family had moved to Oceanview and his father had become head of the college’s atomic-science department. In fact, their parents were close friends, also. Teena’s father was chief engineer for the Acme Aviation Company, one of the coast town’s largest manufacturing concerns. “Well, I’ll be glad to finish them, Eddie,” Mrs. Ross offered. “I know how boys detest doing dishes.” “Oh, I don’t really mind, Mrs. Ross,” Eddie said. “Besides, Teena’s making sandwiches to take with us.” “Another prospecting trip?” Teena’s mother glanced at the Geiger counter which Eddie had set carefully on the dinette table. “I still think there must be some uranium around here,” Eddie insisted. “And we can find it if anyone can.” “I agree,” Mrs. Ross said. “But even if you don’t find it, you both seem to enjoy your hikes.” 22 “Oh, yes, it’s fun, Mother,” Teena replied, wrapping wax paper around a sandwich. “Guess I’m ready. I’ve got a bone for Sandy, too.” “Don’t go too far out from town,” Mrs. Ross cautioned, as Eddie picked up the Geiger counter. “And stick near the main roads. You know the rules.” “We sure do, Mrs. Ross,” Eddie assured her. “And we’ll be back early.” They walked past the college campus, and toward the rocky foothills beyond. At various rock mounds and outcroppings, Eddie switched on the Geiger counter. The needle of the dial on the black box wavered slightly. A slow clicking came through the earphones, but Eddie knew these indicated no more than a normal background count. There were slight traces of radioactivity in almost all earth or rocks. It was in the air itself, caused by mysterious and ever-present cosmic rays, so there was always a mild background count when the Geiger counter was turned on; but to mean anything, the needle had to jump far ahead on the gauge, and the clicking through the earphones had to speed up until it sounded almost like bacon frying in a hot skillet. 23 There was none of that today. After they had hiked and searched most of the forenoon, Eddie said, “We might as well call it a day, Teena. Doesn’t seem to be anything out here.” “It’s all right with me,” Teena agreed, plucking foxtails from Sandy’s ears. “Pretty hot, anyway. Let’s eat our sandwiches and go back home.” “All right,” Eddie said. “You know, one of these days I’d like to go out to Cedar Point and scout around. Maybe we’ll find something there.” Then he told Teena about his dream. Teena smiled. “A dream sure isn’t much to go on,” she said, “but they say it’s pretty out on Cedar Point. I’ll go any time you want to, Eddie.” She handed him one of the sandwiches. It was midafternoon by the time they arrived back at Teena’s house. They worked a while on a new jigsaw puzzle Teena had received on a recent birthday. Then Eddie said good-by and went on down the street toward his own home. 24 After putting Sandy on his long chain and filling his water dish, Eddie went in the back door. He put the Geiger counter in the closet and went into the kitchen. “What’s for dinner, Mom?” he asked. Mrs. Taylor turned from the sink. Eddie knew at once, just seeing the expression on his mother’s face, that something was wrong. “Dinner?” his mother said absently. “It’s not quite four o’clock yet, Eddie. Besides, dinner may be a little late today.” “But this morning you said it would be early,” Eddie reminded her, puzzled. “This morning I didn’t know what might happen.” 25 Then Eddie heard the sound of his father’s voice coming from the den. There was a strange urgent tone in it. The door to the den was open. Eddie went through the dining room and glanced into the den. His father sat stiffly behind his homemade desk, talking rapidly into the telephone. Eddie caught only the last few sketchy words. Then his father placed the telephone in its cradle, glanced up, and saw Eddie. If there had been even the slightest doubt in Eddie’s mind about something being wrong, it vanished now. Mr. Taylor looked years older than he had that very morning. Worry lay deep in his eyes. He fumbled thoughtfully with a pencil, turning it end over end on his desk. “Hello, son,” he said. He didn’t even ask whether Eddie had discovered any uranium ore that day. Always before, he had shown genuine interest in Eddie’s prospecting trips. “Dad,” Eddie said anxiously, “what—what’s the matter?” “It shows that much, does it, son?” his father said tiredly. “What’s wrong, Dad?” Eddie prompted. “Or can’t you tell me?” Mr. Taylor leaned back. “Quite a bit’s wrong, Eddie,” he said, “and I guess there’s no reason why I shouldn’t tell you. It’ll be in the evening papers, anyway.” 26 “Evening papers?” “Eddie, you remember me mentioning this morning about that radioisotope shipment I was expecting today?” “I remember,” Eddie said. “Did it come?” “It did—and it didn’t,” his father said. “What does that mean, Dad?” Eddie asked, puzzled. “The delivery truck arrived at the school with it,” his father explained, “but while the driver was inquiring where to put it, the container disappeared.” “Disappeared?” “The radioisotope was stolen, Eddie,” his father said slowly. “Stolen right out from under our noses!” 27 CHAPTER TWO At the moment, Eddie didn’t pry for further information on the theft of the valuable radioactive isotope. His father had plenty on his mind, as it was. The main information was in the evening Globe , which Eddie rushed out to get as soon as he heard it plop onto the front porch. He took the newspaper to his father to read first. After having finished, Mr. Taylor handed the paper to Eddie and leaned back thoughtfully in his chair. 28 “They’ve got it pretty straight, at that,” Mr. Taylor said, “but I’m afraid this is going to stir up quite a bit of trouble.” “It wasn’t your fault, was it, Dad?” Eddie defended. “It was as much mine as anybody’s, son,” his father said. “Probably more so. After all, I am head of the department. I knew about the shipment. That should make it my responsibility to see that it was properly received and placed in our atomic-materials storage vault. But there is little point in trying to place the blame on anyone. I’m willing to accept that part of it. The important thing is that we recover that radioisotope. Not only is it of a secret nature, but it is also dangerously radioactive if improperly handled.” “But—but wasn’t it in a safe container?” Eddie asked. 29 “Of course,” his father said. “There were only two ounces of it in a fifty-pound lead capsule. As long as it remains in that capsule it’s safe. As you know, the lead prevents any radiation from escaping. Out of that capsule, however, those two ounces of radioisotope can be very dangerous.” “Fifty pounds,” Eddie said thoughtfully. “That’s a pretty big thing to steal, isn’t it?” “Not when it’s lead, son,” his father replied. “Not much bigger than a two-quart milk bottle, in fact.” “Even at that, no kid could have taken it,” Eddie said. “Kid?” His father smiled thinly. “We don’t think it was any kid, Eddie. Not by a long shot. The whole thing was carefully planned and carefully carried out. It was not the work of amateurs.” Eddie read the newspaper account. The small truck from Drake Ridge, where one of the country’s newest atomic reactors was located, had arrived earlier than expected at Oceanview College. It had backed up to the receiving dock where all of the college supplies were delivered. Since deliveries during vacation months were few, there was no one on the dock when the truck arrived. A half hour later, when the delivery was expected, there would have been. The truck’s early arrival had caught them unprepared. 30 The driver had left the truck and had gone around the building to the front office. It had taken him less than five minutes to locate the receiving-dock foreman. Together, they had returned through the small warehouse and opened the rear door onto the dock. During that short time someone had pried open the heavy padlock on the delivery truck’s rear door and had stolen the fifty-pound lead capsule containing the radioisotope. Dusty footprints on the pavement around the rear of the truck indicated that two men had carried out the theft. A heavy iron pry bar had been dropped at the rear of the truck after the lock was sprung. It was a common type used by carpenters. There were no fingerprints or other identifying marks on it. The footprints were barely visible and of no help other than to indicate that two men were involved in the crime. 31 “Dad,” Eddie asked, looking up from the paper, “how could anyone carry away something weighing fifty pounds without being noticed?” “Chances are they had their car parked nearby,” his father said. “As you know, there are no fences or gates around Oceanview College. People come and go as they please. As a matter of fact, there are always quite a few automobiles parked around the shipping and receiving building, and parking space is scarce even during summer sessions. Anyone could park and wait there unnoticed. Or they could walk around without attracting any undue attention.” “But, Dad,” Eddie continued, “how would the men know that the delivery truck would arrive a half hour early?” “They wouldn’t,” his father said. “They may have had another plan. The way things worked out, they didn’t need to use it. The early delivery and the business of leaving the truck unguarded for a few minutes probably gave them a better opportunity than they had expected. At least, they took quick advantage of it.” 32 “I don’t see what anyone would want with a radioisotope,” Eddie said. “Maybe they figured there was something else inside of that lead capsule.” “That’s unlikely, son,” Mr. Taylor said. “Believe me, it was no common theft. Nor were the thieves ordinary thieves. That isotope was a new one. A very secret one. Our job at the college was to conduct various tests with it in order to find out exactly how it could best be put to use as a cure for disease, or for sterilizing food, or even as a source of power.” “Power?” Eddie said. “Boy, it must have been a strong isotope.” He knew that the strength of radioisotopes could be controlled largely by the length of time they were allowed to “cook” in an atomic reactor and soak up radioactivity. 33 “We weren’t planning to run a submarine with it,” his father said. “It wasn’t that strong. Still, it doesn’t take so very much radioactivity to make two ounces of an isotope quite powerful—and quite deadly. I only hope whoever stole it knows what he’s doing. However, I’m sure he does.” “You mean he must have been an atomic scientist himself?” Eddie asked. “Let’s just say he—or both of them—have enough training in the subject to know how to handle that isotope safely,” Mr. Taylor said. “But, Dad,” Eddie wondered, “what could they do with it?” “They could study it,” his father explained. “At least, they could send it somewhere to be broken down and studied. Being a new isotope, the formula is of great value.” “What do you mean, send it somewhere?” Eddie asked. “Perhaps to some other country.” “Then—then you mean whoever stole it were spies!” Eddie exclaimed breathlessly. “That’s entirely possible,” his father said. “In fact, it’s the only logical explanation I can think of. People simply don’t go around stealing radioactive isotopes without a mighty important reason.” 34 “Dinner’s ready,” Eddie’s mother called from the kitchen. During dinner Eddie wasn’t sure just what he was eating. The idea of spies stealing atomic materials kept building up in his mind. By the time dessert was finished, he was anxious to talk with someone, yet he knew he shouldn’t bother his father with any more questions. He asked if he could go over and visit with Teena for a while. “Well, you were together most of the day,” his mother said, “but I guess it’s all right. Be back in about an hour, though.” It was a balmy evening. On such evenings, he and Teena sometimes walked along the beach barefoot, collecting sea shells. Today Eddie had no desire to do that. He ran down the block. Teena answered his knock. “Come on in, Eddie,” she invited, seeming surprised to see him. “Mother and I are just finishing dinner.” “Oh, I figured you’d be through by now,” Eddie apologized, following her inside. 35 “Hello, Eddie,” Mrs. Ross said, but she didn’t seem as cheerful as usual. “Good evening, Mrs. Ross,” Eddie said. “I—I hope I’m not making a pest of myself.” He looked around for Mr. Ross, but Teena’s father apparently hadn’t arrived home from Acme Aircraft yet. There wasn’t a place set for him at the table, either. “You’re never a pest, Eddie,” Mrs. Ross assured him. “I was going to call your mother in a little while about that newspaper write-up.” “Oh, you read it?” Eddie said. “How could anyone miss it?” Teena said. “Right on the front page.” “I suppose your father is quite concerned over it,” Teena’s mother said. “Oh, yes,” Eddie affirmed. “He was the one who ordered the isotope.” “What’s an isotope?” Teena asked. “I’m not sure I know, either,” Mrs. Ross said. “Maybe we could understand more of what it’s all about if you could explain what a radioisotope is, Eddie.” 36 “Well,” Eddie said slowly, “it’s not easy to explain, but I’ll try. You know how rare uranium is. There’s not nearly enough of it to fill all the needs for radioactive materials. Besides, pure uranium is so powerful and expensive and dangerous to handle that it’s not a very good idea to try using it in its true form. So they build an atomic reactor like the one at Drake Ridge.” “We’ve driven by it,” Mrs. Ross said. “My, it’s a big place.” “I’ll say,” Eddie agreed. “Of course, only one building holds the reactor itself. It’s the biggest building near the center.” “I remember it,” Teena said. “Well, the reactor is about four stories high,” Eddie went on. “They call it a uranium ‘pile.’ It’s made up of hundreds and hundreds of graphite bricks. That’s where they get the name ‘pile’—from brick pile. Anyway, scattered around in between the bricks are small bits of uranium. Uranium atoms are radioactive. That is, they keep splitting up and sending out rays.” “Why do they do that?” Teena asked. 37 “It’s just the way nature made uranium, I guess,” Eddie said. “Most atoms stay in one piece, although they move around lickety-split all of the time. Uranium atoms not only move around, but they break apart. They shoot out little particles called neutrons. These neutrons hit other atoms and split them apart, sending out more neutrons. It’s a regular chain reaction.” “I’ve heard of chain reactions,” Mrs. Ross said. “Well, with all of the splitting up and moving around of the uranium atoms,” Eddie went on, “an awful lot of heat builds up. If they don’t control it—well, you’ve seen pictures of atomic-bomb explosions. That’s a chain reaction out of control.” “Out of control is right,” Teena said. 38 “But the atomic piles control the reaction,” Eddie said. “The graphite bricks keep the splitting-up atoms apart so one neutron won’t go smashing into other atoms unless they want it to. They have ways of controlling it so that only as much radiation builds up as they want. You can even hear the reactor hum as the radioactive rays go tearing through it. But by careful tending, the scientists keep the atomic collisions far enough apart so the thing doesn’t blow up.” “Boy, that sounds dangerous,” Teena said. “Well, they know just how to do it,” Eddie replied. “Aren’t the rays dangerous?” Mrs. Ross asked. “I’ll say they’re dangerous,” Eddie said. “But the whole pile is covered by a shield of concrete about eight feet thick. That keeps the rays from getting out and injuring the workmen.” “Goodness. Eight feet is a lot of cement.” “It takes a lot to stop radioactive atomic particles,” Eddie explained. “Especially the gamma rays. They’re the fastest and most dangerous, and the hardest to stop. Alpha and beta rays are fairly easy to stop. But the gamma rays are regular high-velocity invisible bullets. They’ll go right through a stone wall unless it’s plenty thick. Of course, you can’t see them. Not with even the most powerful microscope in the world.” 39 “I wouldn’t want to work around a place where I might get shot at by—by dangerous rays you can’t even see,” Teena said. “I would,” Eddie said. “Everyone is carefully protected. They see to that. Well, anyway, if all of those uranium atoms were shooting radioactive rays around inside of that pile and doing nothing, there would be an awful lot of energy going to waste. So the atomic scientists take certain elements which aren’t radioactive, but can be made radioactive, and shove small pieces of them into holes drilled in the pile.” “Isn’t that dangerous?” Teena asked. “They don’t shove them in with their bare hands,” Eddie said, trying not to show exasperation. “They use long holders to push the small chunks of material into the holes in the reactor. Then, as those uranium atoms keep splitting up and shooting particles around inside of the pile, some of them smack into the chunks of material, and stick there. Most elements will soak up radiation, just like a sponge soaks up water.” 40 “My, that’s interesting, Eddie,” Mrs. Ross said. “I’ve seen them do it,” Eddie said proudly, then added, “from behind a protective shield, of course. When the material has soaked up enough radiation, they pull it back out. They say it’s ‘cooked.’” “You mean it’s hot?” Teena asked. “It’s hot,” Eddie said, “but not like if it came out of a stove. By hot, they mean it’s radioactive. If you touched it, or even got near it, you would get burned, but you probably wouldn’t even know it for a while. It would be a radiation burn. That’s a kind of burn you don’t feel, but it destroys your blood cells and tissues, and—well, you’ve had it.” “So that’s what a radioisotope is,” Mrs. Ross said. “It’s like a sponge. Only instead of soaking up water, it soaks up radiation.” 41 “That’s about it,” Eddie said. “My dad says that as more is learned about the ways to use isotopes, the whole world is going to be improved. You’ve heard of radiocobalt for curing cancer. Well, that’s an isotope. They make it by cooking cobalt in an atomic reactor. Oh, there are hundreds of different isotopes. Like I said, isotopes can be made of most of the elements. And there are over a hundred elements. Some soak up a lot of radioactivity, and are strong and dangerous. Others absorb only a little and are pretty safe to use. Depends, too, on how long they let them cook in the reactor.” “What kind was the one stolen from the college today?” Teena asked. “Dad didn’t say exactly,” Eddie answered, “except he did say that if whoever took it didn’t know what he was doing and opened up the lead capsule, it could kill him. Of course, even the mild isotopes are deadly if they’re not handled right.” “My goodness, it is a serious matter, isn’t it?” Mrs. Ross said. 42 Eddie nodded. It was even more serious than its threat of danger to anyone who handled it carelessly. It was a new isotope—a secret isotope. His father hadn’t said whether it had been developed for curing things or for destroying things. But many radioisotopes could do either; it depended on how they were used. Eddie assumed that anyone who would stoop to stealing isotopes more than likely would be interested in their ability to destroy rather than their ability to benefit mankind. “Well, I certainly do hope everything works out all right,” Teena’s mother said. “So do I,” Teena agreed. Eddie glanced at the kitchen clock. “Oh, boy,” he said, “I’d better be heading back home. I didn’t mean to come over here and talk so long.” “Oh, we’re glad you did, Eddie,” Mrs. Ross said. “I’m afraid too few of us know anything about this atom business.” 43 “That’s right, Mrs. Ross,” Eddie agreed. “People should talk more and read more about it. After all, this is an atomic age. We might as well face it. My father says that in horse-and-buggy days everyone knew how to feed a horse and grease a wagon wheel. They knew what was needed to get the work done. But now that atoms are being harnessed to do the work, not many people even bother to find out what an atom is.” Mrs. Ross smiled. “I guess you’re right, Eddie,” she said, “but I wouldn’t quite know how to go about feeding an atom.” “Or greasing one,” Teena added. Eddie laughed. “I sure wouldn’t want the job of trying to feed a herd of them the size of a period,” he said. “Did you know that there are about three million billion atoms of carbon in a single period printed at the end of a sentence. That’s how small atoms are.” “Three million billion is a lot of something,” a man’s voice spoke behind him. “What are we talking about, Eddie?” “Oh, hello, Mr. Ross,” Eddie said, turning around and standing up. “I didn’t hear you come in.” 44 Teena’s father was a medium-sized man with light-brown hair which was getting somewhat thin on top. He was usually quite cheerful and full of fun, but tonight his face seemed unusually drawn and sober. He stepped to the table, leaned over, and gave both Teena and Mrs. Ross a kiss on the cheek. “Eddie was telling us about atoms,” Teena’s mother said. “Did you know there were three million billion of them in a period?” “How many in a comma?” Mr. Ross said to Eddie, then added quickly, “forget it, Eddie. It wasn’t very funny. I—I’m afraid I don’t feel very funny tonight.” “Sit down, dear,” Mrs. Ross said. “I’ll warm your dinner. You didn’t sound very cheerful when you called to say you would be late. How did everything go at the plant today?” “Not so good,” Teena’s father said tiredly. “In fact, not good at all.” Problems. It seemed that everyone had problems, Eddie thought, as he started to leave. | D. Eddie teaches Teena and her mother about about radioactivity after the news gets out about Mr. Taylor’s isotope being stolen. |
What is the significance of the players’ names?
A. The players’ names correspond with which countries won World War II
B. The players’ names represent how chess rivals reflect political rivals.
C. The players’ names signify the level of competence each chess master has, with American names being the most competent.
D. The players’ names correspond with what country has the most chess mastery, with Russian names hold the utmost interest.
| 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?" | D. The players’ names correspond with what country has the most chess mastery, with Russian names hold the utmost interest. |
Which is the best description of Linton?
A. He is a heartbroken man wanting to find new goals for his life
B. He is trying to recover from his past in the Mafia and wants to find legal ways to accomplish his goals
C. He is a gullible person determined to follow his instinct
D. He is a risk-taker who prefers to experience the more illegal things society has to offer
| 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. | C. He is a gullible person determined to follow his instinct |
What linguistic features were probed for? | ### Introduction
Neural networks for language processing have advanced rapidly in recent years. A key breakthrough was the introduction of transformer architectures BIBREF0 . One recent system based on this idea, BERT BIBREF1 , has proven to be extremely flexible: a single pretrained model can be fine-tuned to achieve state-of-the-art performance on a wide variety of NLP applications. This suggests the model is extracting a set of generally useful features from raw text. It is natural to ask, which features are extracted? And how is this information represented internally? Similar questions have arisen with other types of neural nets. Investigations of convolutional neural networks BIBREF2 , BIBREF3 have shown how representations change from layer to layer BIBREF4 ; how individual units in a network may have meaning BIBREF5 ; and that “meaningful” directions exist in the space of internal activation BIBREF6 . These explorations have led to a broader understanding of network behavior. Analyses on language-processing models (e.g., BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ) point to the existence of similarly rich internal representations of linguistic structure. Syntactic features seem to be extracted by RNNs (e.g., BIBREF7 , BIBREF9 ) as well as in BERT BIBREF11 , BIBREF12 , BIBREF13 , BIBREF10 . Inspirational work from Hewitt and Manning BIBREF8 found evidence of a geometric representation of entire parse trees in BERT's activation space. Our work extends these explorations of the geometry of internal representations. Investigating how BERT represents syntax, we describe evidence that attention matrices contain grammatical representations. We also provide mathematical arguments that may explain the particular form of the parse tree embeddings described in BIBREF8 . Turning to semantics, using visualizations of the activations created by different pieces of text, we show suggestive evidence that BERT distinguishes word senses at a very fine level. Moreover, much of this semantic information appears to be encoded in a relatively low-dimensional subspace. ### Context and related work
Our object of study is the BERT model introduced in BIBREF1 . To set context and terminology, we briefly describe the model's architecture. The input to BERT is based on a sequence of tokens (words or pieces of words). The output is a sequence of vectors, one for each input token. We will often refer to these vectors as context embeddings because they include information about a token's context. BERT's internals consist of two parts. First, an initial embedding for each token is created by combining a pre-trained wordpiece embedding with position and segment information. Next, this initial sequence of embeddings is run through multiple transformer layers, producing a new sequence of context embeddings at each step. (BERT comes in two versions, a 12-layer BERT-base model and a 24-layer BERT-large model.) Implicit in each transformer layer is a set of attention matrices, one for each attention head, each of which contains a scalar value for each ordered pair $(token_i, token_j)$ . ### Language representation by neural networks
Sentences are sequences of discrete symbols, yet neural networks operate on continuous data–vectors in high-dimensional space. Clearly a successful network translates discrete input into some kind of geometric representation–but in what form? And which linguistic features are represented? The influential Word2Vec system BIBREF14 , for example, has been shown to place related words near each other in space, with certain directions in space correspond to semantic distinctions. Grammatical information such as number and tense are also represented via directions in space. Analyses of the internal states of RNN-based models have shown that they represent information about soft hierarchical syntax in a form that can be extracted by a one-hidden-layer network BIBREF9 . One investigation of full-sentence embeddings found a wide variety of syntactic properties could be extracted not just by an MLP, but by logistic regression BIBREF15 . Several investigations have focused on transformer architectures. Experiments suggest context embeddings in BERT and related models contain enough information to perform many tasks in the traditional “NLP pipeline” BIBREF12 –tagging part-of-speech, co-reference resolution, dependency labeling, etc.–with simple classifiers (linear or small MLP models) BIBREF11 , BIBREF10 . Qualitative, visualization-based work BIBREF16 suggests attention matrices may encode important relations between words. A recent and fascinating discovery by Hewitt and Manning BIBREF8 , which motivates much of our work, is that BERT seems to create a direct representation of an entire dependency parse tree. The authors find that (after a single global linear transformation, which they term a “structural probe”) the square of the distance between context embeddings is roughly proportional to tree distance in the dependency parse. They ask why squaring distance is necessary; we address this question in the next section. The work cited above suggests that language-processing networks create a rich set of intermediate representations of both semantic and syntactic information. These results lead to two motivating questions for our research. Can we find other examples of intermediate representations? And, from a geometric perspective, how do all these different types of information coexist in a single vector? ### Geometry of syntax
We begin by exploring BERT's internal representation of syntactic information. This line of inquiry builds on the work by Hewitt and Manning in two ways. First, we look beyond context embeddings to investigate whether attention matrices encode syntactic features. Second, we provide a simple mathematical analysis of the tree embeddings that they found. ### Attention probes and dependency representations
As in BIBREF8 , we are interested in finding representations of dependency grammar relations BIBREF17 . While BIBREF8 analyzed context embeddings, another natural place to look for encodings is in the attention matrices. After all, attention matrices are explicitly built on the relations between pairs of words. To formalize what it means for attention matrices to encode linguistic features, we use an attention probe, an analog of edge probing BIBREF11 . An attention probe is a task for a pair of tokens, $(token_i, token_j)$ where the input is a model-wide attention vector formed by concatenating the entries $a_{ij}$ in every attention matrix from every attention head in every layer. The goal is to classify a given relation between the two tokens. If a linear model achieves reliable accuracy, it seems reasonable to say that the model-wide attention vector encodes that relation. We apply attention probes to the task of identifying the existence and type of dependency relation between two words. The data for our first experiment is a corpus of parsed sentences from the Penn Treebank BIBREF18 . This dataset has the constituency grammar for the sentences, which was translated to a dependency grammar using the PyStanfordDependencies library BIBREF19 . The entirety of the Penn Treebank consists of 3.1 million dependency relations; we filtered this by using only examples of the 30 dependency relations with more than 5,000 examples in the data set. We then ran each sentence through BERT-base, and obtained the model-wide attention vector (see Figure 1 ) between every pair of tokens in the sentence, excluding the $[SEP]$ and $[CLS]$ tokens. This and subsequent experiments were conducted using PyTorch on MacBook machines. With these labeled embeddings, we trained two L2 regularized linear classifiers via stochastic gradient descent, using BIBREF20 . The first of these probes was a simple linear binary classifier to predict whether or not an attention vector corresponds to the existence of a dependency relation between two tokens. This was trained with a balanced class split, and 30% train/test split. The second probe was a multiclass classifier to predict which type of dependency relation exists between two tokens, given the dependency relation’s existence. This probe was trained with distributions outlined in table 2 . The binary probe achieved an accuracy of 85.8%, and the multiclass probe achieved an accuracy of 71.9%. Our real aim, again, is not to create a state-of-the-art parser, but to gauge whether model-wide attention vectors contain a relatively simple representation of syntactic features. The success of this simple linear probe suggests that syntactic information is in fact encoded in the attention vectors. ### Geometry of parse tree embeddings
Hewitt and Manning's result that context embeddings represent dependency parse trees geometrically raises several questions. Is there a reason for the particular mathematical representation they found? Can we learn anything by visualizing these representations? Hewitt and Manning ask why parse tree distance seems to correspond specifically to the square of Euclidean distance, and whether some other metric might do better BIBREF8 . We describe mathematical reasons why squared Euclidean distance may be natural. First, one cannot generally embed a tree, with its tree metric $d$ , isometrically into Euclidean space (Appendix "Embedding trees in Euclidean space" ). Since an isometric embedding is impossible, motivated by the results of BIBREF8 we might ask about other possible representations. Definition 1 (power- $p$ embedding) Let $M$ be a metric space, with metric $d$ . We say $f: M \rightarrow \mathbb {R}^n$ is a power- $p$ embedding if for all $x, y \in M$ , we have $||f(x) - f(y)||^p = d(x, y)$ In these terms, we can say BIBREF8 found evidence of a power-2 embedding for parse trees. It turns out that power-2 embeddings are an especially elegant mapping. For one thing, it is easy to write down an explicit model–a mathematical idealization–for a power-2 embedding for any tree. Theorem 1 Any tree with $n$ nodes has a power-2 embedding into $\mathbb {R}^{n-1}$ . Let the nodes of the tree be $t_0, ..., t_{n-1}$ , with $t_0$ being the root node. Let $\lbrace e_1, ..., e_{n-1}\rbrace $ be orthogonal unit basis vectors for $\mathbb {R}^{n-1}$ . Inductively, define an embedding $f$ such that: $f(t_0) = 0$ $f(t_i) = e_i + f(parent(t_i))$ Given two distinct tree nodes $x$ and $y$ , where $m$ is the tree distance $d(x, y)$ , it follows that we can move from $f(x)$ to $f(y)$ using $m$ mutually perpendicular unit steps. Thus $||f(x) - f(y)||^2 = m = d(x, y)$ Remark 1 This embedding has a simple informal description: at each embedded vertex of the graph, all line segments to neighboring embedded vertices are unit-distance segments, orthogonal to each other and to every other edge segment. (It's even easy to write down a set of coordinates for each node.) By definition any two power-2 embeddings of the same tree are isometric; with that in mind, we refer to this as the canonical power-2 embedding. In the proof of Theorem 1, instead of choosing basis vectors in advance, one can choose random unit vectors. Because two random vectors will be nearly orthogonal in high-dimensional space, the power-2 embedding condition will approximately hold. This means that in space that is sufficiently high-dimensional (compared to the size of the tree) it is possible to construct an approximate power-2 embedding with essentially “local” information, where a tree node is connected to its children via random unit-length branches. We refer to this type of embedding as a random branch embedding. (See Appendix "Ideal vs. actual parse tree embeddings" for a visualization of these various embeddings.) In addition to these appealing aspects of power-2 embeddings, it is worth noting that power- $p$ embeddings will not necessarily even exist when $p < 2$ . (See Appendix "Embedding trees in Euclidean space" for the proof.) Theorem 2 For any $p < 2$ , there is a tree which has no power- $p$ embedding. Remark 2 On the other hand, the existence result for power-2 embeddings, coupled with results of BIBREF22 , implies that power- $p$ tree embeddings do exist for any $p > 2$ . The simplicity of power-2 tree embeddings, as well as the fact that they may be approximated by a simple random model, suggests they may be a generally useful alternative to approaches to tree embeddings that require hyperbolic geometry BIBREF23 . How do parse tree embeddings in BERT compare to exact power-2 embeddings? To explore this question, we created a simple visualization tool. The input to each visualization is a sentence from the Penn Treebank with associated dependency parse trees (see Section "Geometry of word senses" ). We then extracted the token embeddings produced by BERT-large in layer 16 (following BIBREF8 ), transformed by the Hewitt and Manning’s “structural probe” matrix $B$ , yielding a set of points in 1024-dimensional space. We used PCA to project to two dimensions. (Other dimensionality-reduction methods, such as t-SNE and UMAP BIBREF24 , were harder to interpret.) To visualize the tree structure, we connected pairs of points representing words with a dependency relation. The color of each edge indicates the deviation from true tree distance. We also connected, with dotted line, pairs of words without a dependency relation but whose positions (before PCA) were far closer than expected. The resulting image lets us see both the overall shape of the tree embedding, and fine-grained information on deviation from a true power-2 embedding. Two example visualizations are shown in Figure 2 , next to traditional diagrams of their underlying parse trees. These are typical cases, illustrating some common patterns; for instance, prepositions are embedded unexpectedly close to words they relate to. (Figure 7 shows additional examples.) A natural question is whether the difference between these projected trees and the canonical ones is merely noise, or a more interesting pattern. By looking at the average embedding distances of each dependency relation (see Figure 3 ) , we can see that they vary widely from around 1.2 ( $compound:prt$ , $advcl$ ) to 2.5 ( $mwe$ , $parataxis$ , $auxpass$ ). Such systematic differences suggest that BERT's syntactic representation has an additional quantitative aspect beyond traditional dependency grammar. ### Geometry of word senses
BERT seems to have several ways of representing syntactic information. What about semantic features? Since embeddings produced by transformer models depend on context, it is natural to speculate that they capture the particular shade of meaning of a word as used in a particular sentence. (E.g., is “bark” an animal noise or part of a tree?) We explored geometric representations of word sense both qualitatively and quantitatively. ### Visualization of word senses
Our first experiment is an exploratory visualization of how word sense affects context embeddings. For data on different word senses, we collected all sentences used in the introductions to English-language Wikipedia articles. (Text outside of introductions was frequently fragmentary.) We created an interactive application, which we plan to make public. A user enters a word, and the system retrieves 1,000 sentences containing that word. It sends these sentences to BERT-base as input, and for each one it retrieves the context embedding for the word from a layer of the user's choosing. The system visualizes these 1,000 context embeddings using UMAP BIBREF24 , generally showing clear clusters relating to word senses. Different senses of a word are typically spatially separated, and within the clusters there is often further structure related to fine shades of meaning. In Figure 4 , for example, we not only see crisp, well-separated clusters for three meanings of the word “die,” but within one of these clusters there is a kind of quantitative scale, related to the number of people dying. See Appendix "Additional word sense visualizations" for further examples. The apparent detail in the clusters we visualized raises two immediate questions. First, is it possible to find quantitative corroboration that word senses are well-represented? Second, how can we resolve a seeming contradiction: in the previous section, we saw how position represented syntax; yet here we see position representing semantics. ### Measurement of word sense disambiguation capability
The crisp clusters seen in visualizations such as Figure 4 suggest that BERT may create simple, effective internal representations of word senses, putting different meanings in different locations. To test this hypothesis quantitatively, we test whether a simple classifier on these internal representations can perform well at word-sense disambiguation (WSD). We follow the procedure described in BIBREF10 , which performed a similar experiment with the ELMo model. For a given word with $n$ senses, we make a nearest-neighbor classifier where each neighbor is the centroid of a given word sense's BERT-base embeddings in the training data. To classify a new word we find the closest of these centroids, defaulting to the most commonly used sense if the word was not present in the training data. We used the data and evaluation from BIBREF25 : the training data was SemCor BIBREF26 (33,362 senses), and the testing data was the suite described in BIBREF25 (3,669 senses). The simple nearest-neighbor classifier achieves an F1 score of 71.1, higher than the current state of the art (Table 1 ), with the accuracy monotonically increasing through the layers. This is a strong signal that context embeddings are representing word-sense information. Additionally, an even higher score of 71.5 was obtained using the technique described in the following section. We hypothesized that there might also exist a linear transformation under which distances between embeddings would better reflect their semantic relationships–that is, words of the same sense would be closer together and words of different senses would be further apart. To explore this hypothesis, we trained a probe following Hewitt and Manning's methodology. We initialized a random matrix $B\in {R}^{k\times m}$ , testing different values for $m$ . Loss is, roughly, defined as the difference between the average cosine similarity between embeddings of words with different senses, and that between embeddings of the same sense. However, we clamped the cosine similarity terms to within $\pm 0.1$ of the pre-training averages for same and different senses. (Without clamping, the trained matrix simply ended up taking well-separated clusters and separating them further. We tested values between $0.05$ and $0.2$ for the clamping range and $0.1$ had the best performance.) Our training corpus was the same dataset from 4.1.2., filtered to include only words with at least two senses, each with at least two occurrences (for 8,542 out of the original 33,362 senses). Embeddings came from BERT-base (12 layers, 768-dimensional embeddings). We evaluate our trained probes on the same dataset and WSD task used in 4.1.2 (Table 1 ). As a control, we compare each trained probe against a random probe of the same shape. As mentioned in 4.1.2, untransformed BERT embeddings achieve a state-of-the-art accuracy rate of 71.1%. We find that our trained probes are able to achieve slightly improved accuracy down to $m=128$ . Though our probe achieves only a modest improvement in accuracy for final-layer embeddings, we note that we were able to more dramatically improve the performance of embeddings at earlier layers (see Appendix for details: Figure 10 ). This suggests there is more semantic information in the geometry of earlier-layer embeddings than a first glance might reveal. Our results also support the idea that word sense information may be contained in a lower-dimensional space. This suggests a resolution to the seeming contradiction mentioned above: a vector encodes both syntax and semantics, but in separate complementary subspaces. ### Embedding distance and context: a concatenation experiment
If word sense is affected by context, and encoded by location in space, then we should be able to influence context embedding positions by systematically varying their context. To test this hypothesis, we performed an experiment based on a simple and controllable context change: concatenating sentences where the same word is used in different senses. We picked 25,096 sentence pairs from SemCor, using the same keyword in different senses. E.g.: A: "He thereupon went to London and spent the winter talking to men of wealth." went: to move from one place to another. B: "He went prone on his stomach, the better to pursue his examination." went: to enter into a specified state. We define a matching and an opposing sense centroid for each keyword. For sentence A, the matching sense centroid is the average embedding for all occurrences of “went” used with sense A. A's opposing sense centroid is the average embedding for all occurrences of “went” used with sense B. We gave each individual sentence in the pair to BERT-base and recorded the cosine similarity between the keyword embeddings and their matching sense centroids. We also recorded the similarity between the keyword embeddings and their opposing sense centroids. We call the ratio between the two similarities the individual similarity ratio. Generally this ratio is greater than one, meaning that the context embedding for the keyword is closer to the matching centroid than the opposing one. We joined each sentence pair with the word "and" to create a single new sentence. We gave these concatenations to BERT and recorded the similarities between the keyword embeddings and their matching/opposing sense centroids. Their ratio is the concatenated similarity ratio. Our hypothesis was that the keyword embeddings in the concatenated sentence would move towards their opposing sense centroids. Indeed, we found that the average individual similarity ratio was higher than the average concatenated similarity ratio at every layer (see Figure 5 ). Concatenating a random sentence did not change the individual similarity ratios. If the ratio is less than one for any sentence, that means BERT has misclassified its keyword sense. We found that the misclassification rate was significantly higher for final-layer embeddings in the concatenated sentences compared to the individual sentences: 8.23% versus 2.43% respectively. We also measured the effect of projecting the final-layer keyword embeddings into the semantic subspace discussed in 4.1.3. After multiplying each embedding by our trained semantic probe, we obtained an average concatenated similarity ratio of 1.578 and individual similarity ratio of 1.875, which suggests that the transformed embeddings are closer to their matching sense centroids than the original embeddings (the original concatenated similarity ratio is 1.284 and the individual similarity ratio is 1.430). We also measured lower average misclassification rates for the transformed embeddings: 7.31% for concatenated sentences and 2.27% for individual sentences. ### Conclusion and future work
We have presented a series of experiments that shed light on BERT's internal representations of linguistic information. We have found evidence of syntactic representation in attention matrices, with certain directions in space representing particular dependency relations. We have also provided a mathematical justification for the squared-distance tree embedding found by Hewitt and Manning. Meanwhile, we have shown that just as there are specific syntactic subspaces, there is evidence for subspaces that represent semantic information. We also have shown how mistakes in word sense disambiguation may correspond to changes in internal geometric representation of word meaning. Our experiments also suggest an answer to the question of how all these different representations fit together. We conjecture that the internal geometry of BERT may be broken into multiple linear subspaces, with separate spaces for different syntactic and semantic information. Investigating this kind of decomposition is a natural direction for future research. What other meaningful subspaces exist? After all, there are many types of linguistic information that we have not looked for. A second important avenue of exploration is what the internal geometry can tell us about the specifics of the transformer architecture. Can an understanding of the geometry of internal representations help us find areas for improvement, or refine BERT's architecture? Acknowledgments: We would like to thank David Belanger, Tolga Bolukbasi, Jasper Snoek, and Ian Tenney for helpful feedback and discussions. ### Embedding trees in Euclidean space
Here we provide additional detail on the existence of various forms of tree embeddings. Isometric embeddings of a tree (with its intrinsic tree metric) into Euclidean space are rare. Indeed, such an embedding is impossible even a four-point tree $T$ , consisting of a root node $R$ with three children $C_1, C_2, C_3$ . If $f:T \rightarrow \mathbb {R}^n$ is a tree isometry then $||f(R) - f(C_1)) || = ||f(R) - f(C_2)) || = 1$ , and $||f(C_1) - f(C_2)) || = 2$ . It follows that $f(R)$ , $f(C_1)$ , $f(C_2)$ are collinear. The same can be said of $f(R)$ , $R$0 , and $R$1 , meaning that $R$2 . Since this four-point tree cannot be embedded, it follows the only trees that can be embedded are simply chains. Not only are isometric embeddings generally impossible, but power- $p$ embeddings may also be unavailable when $p < 2$ , as the following argument shows. Proof of Theorem "Theorem 2" We covered the case of $p = 1$ above. When $p < 1$ , even a tree of three points is impossible to embed without violating the triangle inequality. To handle the case when $1 < p < 2$ , consider a “star-shaped” tree of one root node with $k$ children; without loss of generality, assume the root node is embedded at the origin. Then in any power- $p$ embedding the other vertices will be sent to unit vectors, and for each pair of these unit vectors we have $||v_i - v_j||^p = 2$ . On the other hand, a well-known folk theorem (e.g., see BIBREF27 ) says that given $k$ unit vectors $v_1, ..., v_k$ at least one pair of distinct vectors has $v_i \cdot v_j \ge -1/(k - 1)$ . By the law of cosines, it follows that $||v_i - v_j|| \le \sqrt{2 + \frac{2}{k-1}}$ . For any $p < 2$ , there is a sufficiently large $k$ such that $||v_i - v_j||^p \le (\sqrt{2 + \frac{2}{k-1}})^p = (2 + \frac{2}{k-1})^{p/2} < 2$ . Thus for any $p < 2$ a large enough star-shaped tree cannot have a power- $p$ embedding. ### Ideal vs. actual parse tree embeddings
Figure 2 shows (left) a visualization of a BERT parse tree embedding (as defined by the context embeddings for individual words in a sentence). We compare with PCA projections of the canonical power-2 embedding of the same tree structure, as well as a random branch embedding. Finally, we display a completely randomly embedded tree as a control. The visualizations show a clear visual similarity between the BERT embedding and the two mathematical idealizations. ### Additional BERT parse tree visualizations
Figure 7 shows four additional examples of PCA projections of BERT parse tree embeddings. ### Additional word sense visualizations
We provide two additional examples of word sense visualizations, hand-annotated to show key clusters. See Figure 8 and Figure 9 . Figure 1: A model-wide attention vector for an ordered pair of tokens contains the scalar attention values for that pair in all attention heads and layers. Shown: BERT-base. Figure 2: Visualizing embeddings of two sentences after applying the Hewitt-Manning probe. We compare the parse tree (left images) with a PCA projection of context embeddings (right images). Figure 3: The average squared edge length between two words with a given dependency. Figure 4: Embeddings for the word "die" in different contexts, visualized with UMAP. Sample points are annotated with corresponding sentences. Overall annotations (blue text) are added as a guide. Table 1: [Left] F1 scores for WSD task. [Right] Semantic probe % accuracy on final-layer BERT-base embeddings Figure 5: Average ratio of similarity to sense A vs. similarity to sense B. Figure 6: PCA projection of the context embeddings for the sentence “The field has reserves of 21 million barrels.” transformed by Hewitt and Manning’s “structural probe” matrix, compared to the canonical power-2 embedding, a random branch embedding, and a completely random embedding. Figure 7: Additional examples of BERT parse trees. In each pair, at left is a drawing of the abstract tree; at right is a PCA view of the embeddings. Colors are the same as in Figure 6. Figure 8: Context embeddings for “lie” as used in different sentences. Figure 9: Context embeddings for “lie” as used in different sentences. Table 2: Per-dependency results of multiclass linear classifier trained on attention vectors, with 300,000 training examples and 150,000 test examples. Figure 10: Change in classification accuracy by layer for different probe dimensionalities. | dependency relation between two words, word sense |
What is the multilingual baseline? | ### Multilingual Models for Sequence Labeling
We discuss two core models for addressing sequence labeling problems and describe, for each, training them in a single-model multilingual setting: (1) the Meta-LSTM BIBREF0 , an extremely strong baseline for our tasks, and (2) a multilingual BERT-based model BIBREF1 . ### Meta-LSTM
The Meta-LSTM is the best-performing model of the CoNLL 2018 Shared Task BIBREF2 for universal part-of-speech tagging and morphological features. The model is composed of 3 LSTMs: a character-BiLSTM, a word-BiLSTM and a single joint BiLSTM which takes the output of the character and word-BiLSTMs as input. The entire model structure is referred to as Meta-LSTM. To set up multilingual Meta-LSTM training, we take the union of all the word embeddings from the bojanowski2017enriching embeddings model on Wikipedia in all languages. For out-of-vocabulary words, a special unknown token is used in place of the word. The model is then trained as usual with cross-entropy loss. The char-BiLSTM and word-biLSTM are first trained independently. And finally we train the entire Meta-LSTM. ### Multilingual BERT
BERT is a transformer-based model BIBREF3 pretrained with a masked-LM task on millions of words of text. In this paper our BERT-based experiments make use of the cased multilingual BERT model available on GitHub and pretrained on 104 languages. Models fine-tuned on top of BERT models achieve state-of-the-art results on a variety of benchmark and real-world tasks. To train a multilingual BERT model for our sequence prediction tasks, we add a softmax layer on top of the the first wordpiece BIBREF4 of each token and finetune on data from all languages combined. During training, we concatenate examples from all treebanks and randomly shuffle the examples. ### Small and Practical Models
The results in Table TABREF1 make it clear that the BERT-based model for each task is a solid win over a Meta-LSTM model in both the per-language and multilingual settings. However, the number of parameters of the BERT model is very large (179M parameters), making deploying memory intensive and inference slow: 230ms on an Intel Xeon CPU. Our goal is to produce a model fast enough to run on a single CPU while maintaining the modeling capability of the large model on our tasks. ### Size and speed
We choose a three-layer BERT, we call MiniBERT, that has the same number of layers as the Meta-LSTM and has fewer embedding parameters and hidden units than both models. Table TABREF7 shows the parameters of each model. The Meta-LSTM has the largest number of parameters dominated by the large embeddings. BERT's parameters are mostly in the hidden units. The MiniBERT has the fewest total parameters. The inference-speed bottleneck for Meta-LSTM is the sequential character-LSTM-unrolling and for BERT is the large feedforward layers and attention computation that has time complexity quadratic to the sequence length. Table TABREF8 compares the model speeds. BERT is much slower than both MetaLSTM and MiniBERT on CPU. However, it is faster than Meta-LSTM on GPU due to the parallel computation of the transformer. The MiniBERT is significantly faster than the other models on both GPU and CPU. ### Distillation
For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0 where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 . To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on. ### Data
We use universal part-of-speech tagging and morphology data from the The CoNLL 2018 Shared Task BIBREF7 , BIBREF8 . For comparison simplicity, we remove the languages that the multilingual BERT public checkpoint is not pretrained on. For segmentation, we use a baseline segmenter (UDPipe v2.2) provided by the shared task organizer to segment raw text. We train and tune the models on gold-segmented data and apply the segmenter on the raw test of test data before applying our models. The part-of-speech tagging task has 17 labels for all languages. For morphology, we treat each morphological group as a class and union all classes as a output of 18334 labels. ### Tuning
For Meta-LSTM, we use the public repository's hyperparameters. Following devlin2019, we use a smaller learning rate of 3e-5 for fine-tuning and a larger learning rate of 1e-4 when training from scratch and during distillation. Training batch size is set to 16 for finetuning and 256 for distillation. For distillation, we try temperatures INLINEFORM0 and use the teacher-student accuracy for evaluation. We observe BERT is very confident on its predictions, and using a large temperature INLINEFORM1 to soften the distribution consistently yields the best result. ### Multilingual Models
We compare per-language models trained on single language treebanks with multilingual models in Table TABREF1 and Table TABREF14 . In the experimental results we use a prefix INLINEFORM0 to denote the model is a single multilingual model. We compare Meta-LSTM, BERT, and MiniBERT. mBERT performs the best among all multilingual models. The smallest and fastest model, mMiniBERT, performs comparably to mBERT, and outperforms mMeta-LSTM, a state-of-the-art model for this task. When comparing with per-language models, the multilingual models have lower F1. DBLP:journals/corr/abs-1904-02099 shows similar results. Meta-LSTM, when trained in a multilingual fashion, has bigger drops than BERT in general. Most of the Meta-LSTM drop is due to the character-LSTM, which drops by more than 4 points F1. ### Low Resource Languages
We pick languages with fewer than 500 training examples to investigate the performance of low-resource languages: Tamil (ta), Marathi (mr), Belarusian (be), Lithuanian (lt), Armenian (hy), Kazakh (kk). Table TABREF15 shows the performance of the models. While DBLP:journals/corr/abs-1904-09077 shows effective zero-shot crosslingual transfer from English to other high-resource languages, we show that cross-lingual transfer is even effective on low-resource languages when we train on all languages as mBERT is significantly better than BERT when we have fewer than 50 examples. In these cases, the mMiniBERT distilled from the multilingual mBERT yields results better than training individual BERT models. The gains becomes less significant when we have more training data. The multilingual baseline mMeta-LSTM does not do well on low-resource languages. On the contrary, mMiniBERT performs well and outperforms the state-of-the-art Meta-LSTM on the POS tagging task and on four out of size languages of the Morphology task. ### Codemixed Input
We use the Universal Dependencies' Hindi-English codemixed data set BIBREF9 to test the model's ability to label code-mixed data. This dataset is based on code-switching tweets of Hindi and English multilingual speakers. We use the Devanagari script provided by the data set as input tokens. In the Universal Dependency labeling guidelines, code-switched or foreign-word tokens are labeled as X along with other tokens that cannot be labeled. The trained model learns to partition the languages in a codemixed input by labeling tokens in one language with X, and tokens in the other language with any of the other POS tags. It turns out that the 2nd-most likely label is usually the correct label in this case; we evaluate on this label when the 1-best is X. Table TABREF25 shows that all multilingual models handle codemixed data reasonably well without supervised codemixed traininig data. ### Conclusion
We have described the benefits of multilingual models over models trained on a single language for a single task, and have shown that it is possible to resolve a major concern of deploying large BERT-based models by distilling our multilingual model into one that maintains the quality wins with performance fast enough to run on a single CPU. Our distilled model outperforms a multilingual version of a very strong baseline model, and for most languages yields comparable or better performance to a large BERT model. ### Training Hyperparameters
We use exactly the same hyperparameters as the public multilingual BERT for finetuning our models. We train the part-of-speech tagging task for 10 epochs and the morphology task for 50 epochs. For distillation, we use the following hyperparameters for all tasks. learning rate: 1e-4 temperature: 3 batch size: 256 num epochs: 24 We take the Wikipedia pretraining data as is and drop sentences with fewer than 10 characters. ### Small BERT structure
We use the vocab and wordpiece model included with the cased public multilingual model on GitHub. We use the BERT configuration of the public multilingual BERT with the following modifications for mMiniBERT. Hidden size = 256 Intermediate layer size = 1024 Num attention heads = 4 Layers = 3 ### The Importance of Distillation
To understand the importance of distillation in training mMiniBERT, we compare it to a model with the MiniBERT structure trained from scratch using only labeled multilingual data the teacher is trained on. Table TABREF37 shows that distillation plays an important role in closing the accuracy gap between teacher and student. ### Per-Language Results
We show per-language F1 results of each model in Table SECREF38 and Table SECREF38 . For per-language models, no models are trained for treebanks without tuning data, and metrics of those languages are not reported. All macro-averaged results reported exclude those languages. lccccc treebankBERTMeta-LSTMmBERT mMeta-LSTM mMiniBERT af_afribooms97.6297.6397.4993.1696.08 am_att3.285.63.16 ar_padt90.4690.5590.328990.06 ar_pud71.5968.9671.06 be_hse94.8191.0595.0287.5994.95 bg_btb99.0198.7798.7296.4398.19 ca_ancora98.8498.6298.7797.5798.45 cs_cac99.1799.4399.398.4698.48 cs_cltt87.4887.2587.6787.6287.53 cs_fictree98.6298.6398.2597.297.18 cs_pdt99.0699.0798.9998.2298.61 cs_pud97.1396.5397 da_ddt97.5997.4797.1892.3695.93 de_gsd94.8194.1794.5391.9493.82 de_pud88.7687.4288.7 el_gdt97.9797.497.9194.8797.16 en_ewt95.8295.4595.292.2494.19 en_gum96.2295.0294.7992.3394.24 en_lines97.2296.8195.7993.9695.25 en_partut96.1195.995.0293.2994.61 es_ancora98.8798.7898.1796.2797.8 es_gsd93.793.989.6590.6189.58 es_pud85.8786.185.71 et_edt97.2797.1797.0294.3295.64 eu_bdt96.296.195.5191.5394.15 fa_seraji97.5797.1797.1795.2996.92 fi_ftb96.2696.1293.1587.2389.79 fi_pud95.5593.2395.01 fi_tdt96.8197.0293.991.5892.6 fr_gsd96.6296.4596.2395.3796.05 fr_partut96.189695.4394.3594.93 fr_pud90.7790.190.64 fr_sequoia96.7797.5997.0795.9196.75 fr_spoken97.5595.7896.190.0793.25 ga_idt91.9291.5590.8384.1685.72 gl_ctg96.9997.2196.592.8795.84 gl_treegal93.491.2891.9 he_htb82.7682.4982.6980.9381.93 hi_hdtb97.3197.3997.196.296.43 hi_pud86.4885.3385.68 hr_set97.7997.9497.4796.2497.2 hu_szeged96.5194.7195.9985.595.47 hy_armtdp84.4286.6263.8286.98 id_gsd93.0693.3793.390.8193.35 id_pud63.5263.563.33 it_isdt98.3398.0698.2796.797.8 it_partut98.1298.1798.0996.9998.06 it_postwita95.6695.8695.694.1793.2 it_pud93.8492.7293.67 ja_gsd88.6388.7388.5487.0388.43 ja_modern41.5551.2621.61 ja_pud89.1587.9689.3 kk_ktb75.9361.781.3652.9180.06 ko_gsd95.9295.6490.386.3988.62 ko_kaist95.5695.4293.8687.4693.43 ko_pud41.9346.1131.96 la_ittb98.3498.4298.397.1897.65 la_perseus89.9183.8585.23 la_proiel96.3496.3795.9792.0293.78 lt_hse88.8881.4390.0165.686.9 lv_lvtb94.7994.4793.7188.2591.3 mr_ufal77.4572.175.9265.4875.41 nl_alpino97.196.1697.3393.7896.19 nl_lassysmall95.5495.9295.7294.495.47 no_bokmaal989897.9595.2797.04 no_nynorsklia94.0888.2792.55 no_nynorsk97.9497.9297.6994.9196.59 pl_lfg98.798.598.3995.2197.48 pl_sz98.5697.9198.0594.7397.29 pt_bosque96.7496.7396.1695.5395.85 pt_gsd95.8395.4493.8493.0794.44 pt_pud89.4889.6689.29 ro_nonstandard94.6794.489492.0591.9 ro_rrt97.6397.5297.4795.7896.71 ru_gsd92.2391.3990.8488.1390.14 ru_pud89.788.9289.52 ru_syntagrus98.398.6598.3297.1398.03 ru_taiga93.6292.7593.18 sa_ufal32.4729.5827.11 sk_snk97.0896.3296.9893.6196.35 sl_ssj97.0796.6896.8994.2495.58 sl_sst94.5190.3491.79 sr_set98.6398.3398.3194.7997.36 sv_lines97.2196.5996.9993.6495.57 sv_pud94.5292.0694.32 sv_talbanken98.0397.3497.7794.9196.76 ta_ttb75.7172.774.2861.5174.6 te_mtg94.2592.7293.4287.3293.42 th_pud2.372.731.54 tl_trg70.6928.6268.28 tr_imst93.9694.0393.184.6491.8 tr_pud73.168.3672.47 uk_iu97.2996.697.289396.88 ur_udtb93.8393.8793.699393.05 vi_vtb77.6776.4277.4472.0177.06 yo_ytb43.4830.8534.59 zh_cfl49.8339.7749.42 zh_gsd87.685.785.9682.7686.08 zh_hk66.2957.8865.86 zh_pud83.373.382.95 POS tagging F1 of all models. lccccc treebankBERT F1Meta-LSTM F1mBERT F1mMeta-LSTM F1mMiniBERT F1 af_afribooms97.1197.3696.5388.9893.75 am_att32.3632.36 ar_padt88.2688.2487.7683.1485.34 ar_pud36.3334.2836.08 be_hse82.8374.0387.5259.1681.82 bg_btb97.5497.5897.4791.4195.4 ca_ancora98.3798.2198.2896.0497.67 cs_cac96.3396.4996.5488.1193.47 cs_cltt81.6179.8983.8678.8280.61 cs_fictree96.3996.494.0983.3787.59 cs_pdt97.1896.9197.1589.7794.63 cs_pud93.8887.4491.81 da_ddt97.2297.0895.6289.8294.08 de_gsd90.8490.5890.480.6988.99 de_pud30.4130.5530.4 el_gdt94.5793.9594.8387.692.07 en_gum96.879693.7990.1193.71 en_lines97.3296.6893.1187.4992.07 en_partut94.8895.3890.7679.9990.18 en_pud93.2591.2393.1 es_ancora98.4598.4297.695.1797 es_gsd93.5293.7288.7289.2688.78 es_pud52.752.852.73 et_edt96.1496.1195.7890.5192.14 eu_bdt93.2792.5692.6776.7284.53 fa_seraji97.3597.2596.9193.8296.28 fi_ftb96.3496.4892.3277.8986.47 fi_pud93.5891.1291.65 fi_tdt95.0395.5890.9688.4487.48 fr_gsd96.0596.1194.6786.9794.51 fr_partut93.3292.9388.987.4887.05 fr_pud59.1557.558.94 fr_sequoia97.0997.1391.5485.2390.74 fr_spoken10010098.6280.6796.67 ga_idt82.281.7881.263.4466.82 gl_ctg98.9898.9595.2789.9895.1 gl_treegal80.0568.7375.97 he_htb81.2780.8580.7976.8978.74 hi_hdtb93.3293.8592.9189.0990.65 hi_pud22.122.3722.03 hr_set91.9991.8591.2481.6287.81 hu_szeged93.6591.2892.9371.2587.36 hy_armtdp41.1354.4551.0836.5946.43 id_gsd94.849694.8591.6294.39 id_pud39.8342.7939.79 it_isdt97.797.8297.8795.4797.37 it_partut97.3597.7398.0196.3397.9 it_postwita95.6296.0595.0391.5293.17 it_pud57.8257.4157.6 ja_gsd90.2990.4590.2990.3990.41 ja_modern63.961.1763.99 ja_pud57.457.2657.27 kk_ktb64.625.5559.49 ko_gsd99.6299.5599.498.9999.37 ko_kaist10010099.9499.2499.93 ko_pud38.3338.6638.27 la_ittb96.796.9497.1590.7893.91 la_perseus82.0964.7372.24 la_proiel90.8291.0191.5179.0883.99 lt_hse75.2169.6573.6142.5165.22 lv_lvtb88.6191.3488.179.1181.91 mr_ufal63.9559.1164.233.6354.01 nl_alpino96.2296.1396.5391.995.67 nl_lassysmall96.4696.0295.5592.1695.28 no_bokmaal96.8597.1396.4891.1795.31 no_nynorsklia94.2289.5691.08 no_nynorsk96.797.0496.4992.1294.79 pl_lfg95.8594.6884.9647.9984.56 pl_sz93.991.9371.473.0265.36 pt_bosque96.2796.1687.0483.1385.72 pt_gsd97.295.3367.7276.0171.88 pt_pud52.0649.7950.95 ro_nonstandard88.5288.9186.8982.182.14 ro_rrt97.0297.2396.5893.294.85 ru_gsd88.8386.7381.4464.278.93 ru_pud37.9735.2637.49 ru_syntagrus97.0296.995.9991.9694.33 ru_taiga88.5684.0286.01 sa_ufal15.916.1416.33 sk_snk92.0689.6391.5868.2585.29 sl_ssj94.3993.7894.4182.6989.23 sl_sst88.4691.8978.2285.59 sr_set94.8394.7192.7973.5190.48 sv_lines89.5489.5588.6683.2786.4 sv_pud77.3973.9476.79 sv_talbanken96.9296.5696.1390.2394.49 ta_ttb72.9171.0173.7546.970.22 te_mtg98.9698.9698.5498.6898.54 th_pud8.2708.43 tl_trg29.3128.6225.17 tr_imst89.59188.6373.2381.99 tr_pud23.7223.8423.46 uk_iu92.490.9892.6479.4988.79 ur_udtb82.2483.7282.6481.8982.48 vi_vtb83.748483.9383.5883.94 yo_ytb58.7886.8261.88 zh_cfl46.5543.5545.73 zh_gsd87.6488.3888.3187.0588.5 zh_hk66.3364.9766.23 zh_pud86.3583.686.14 Morphology F1 of all models. Table 1: Macro-averaged F1 comparison of per-language models and multilingual models over 48 languages. For non-multilingual models, F1 is the average over each per-language model trained. Table 2: The number of parameters of each model. Tokens refers to the number of tokens of the embedding rows. For the Meta-LSTM, a word-based model, this is the number of words in training. For BERT, this means the size of the Wordpiece vocabulary. And Hidden Units refers to all units that are not among the embedding layer or and output layer. Table 3: Relative inference speedup over BERT. We see MiniBERT is the fastest on both CPU and GPU. CPU is an Intel Xeon CPU E5-1650 v3 @3.50GHz. GPU is an Nvidia Titan V. Table 4: Macro-averaged F1 comparison of multilingual models. Multilingual models are prefixed with ‘m’. Table 5: POS tagging and Morphology F1 for all models on low-resource languages. Multilingual models are prefixed with ‘m’. Table 6: F1 score on Hindi-English codemixed POS tagging task. Each multilingual model is within 10 points of the supervised BERT model without having explicitly seen code-mixed data. Table 7: Ablation study to show the effect of distillation. The model without distillation has 3.5 points lower macro-averaged F1 on the part-of-speech task and 3.1 lower F1 on the morphology task. Table 8: POS tagging F1 of all models. | the Meta-LSTM BIBREF0 |
How is presence of three target styles detected? | ### Introduction
Every good article needs a good title, which should not only be able to condense the core meaning of the text, but also sound appealing to the readers for more exposure and memorableness. However, currently even the best Headline Generation (HG) system can only fulfill the above requirement yet performs poorly on the latter. For example, in Figure FIGREF2, the plain headline by an HG model “Summ: Leopard Frog Found in New York City” is less eye-catching than the style-carrying ones such as “What's That Chuckle You Hear? It May Be the New Frog From NYC.” To bridge the gap between the practical needs for attractive headlines and the plain HG by the current summarization systems, we propose a new task of Stylistic Headline Generation (SHG). Given an article, it aims to generate a headline with a target style such as humorous, romantic, and click-baity. It has broad applications in reader-adapted title generation, slogan suggestion, auto-fill for online post headlines, and many others. SHG is a highly skilled creative process, and usually only possessed by expert writers. One of the most famous headlines in American publications, “Sticks Nix Hick Pix,” could be such an example. In contrast, the current best summarization systems are at most comparable to novice writers who provide a plain descriptive representation of the text body as the title BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4. These systems usually use a language generation model that mixes styles with other linguistic patterns and inherently lacks a mechanism to control the style explicitly. More fundamentally, the training data comprise of a mixture of styles (e.g., the Gigaword dataset BIBREF5), obstructing the models from learning a distinct style. In this paper, we propose the new task SHG, to emphasize the explicit control of style in headline generation. We present a novel headline generation model, TitleStylist, to produce enticing titles with target styles including humorous, romantic, and click-baity. Our model leverages a multitasking framework to train both a summarization model on headline-article pairs, and a Denoising Autoencoder (DAE) on a style corpus. In particular, based on the transformer architecture BIBREF6, we use the style-dependent layer normalization and the style-guided encoder-attention to disentangle the language style factors from the text. This design enables us to use the shared content to generate headlines that are more relevant to the articles, as well as to control the style by plugging in a set of style-specific parameters. We validate the model on three tasks: humorous, romantic, and click-baity headline generation. Both automatic and human evaluations show that TitleStylist can generate headlines with the desired styles that appeal more to human readers, as in Figure FIGREF2. The main contributions of our paper are listed below: To the best of our knowledge, it is the first research on the generation of attractive news headlines with styles without any supervised style-specific article-headline paired data. Through both automatic and human evaluation, we demonstrated that our proposed TitleStylist can generate relevant, fluent headlines with three styles (humor, romance, and clickbait), and they are even more attractive than human-written ones. Our model can flexibly incorporate multiple styles, thus efficiently and automatically providing humans with various creative headline options for references and inspiring them to think out of the box. ### Related Work
Our work is related to summarization and text style transfer. ### Related Work ::: Headline Generation as Summarization
Headline generation is a very popular area of research. Traditional headline generation methods mostly focus on the extractive strategies using linguistic features and handcrafted rules BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13. To enrich the diversity of the extractive summarization, abstractive models were then proposed. With the help of neural networks, BIBREF14 proposed attention-based summarization (ABS) to make BIBREF15's framework of summarization more powerful. Many recent works extended ABS by utilizing additional features BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22. Other variants of the standard headline generation setting include headlines for community question answering BIBREF23, multiple headline generation BIBREF24, user-specific generation using user embeddings in recommendation systems BIBREF25, bilingual headline generation BIBREF26 and question-style headline generation BIBREF27. Only a few works have recently started to focus on increasing the attractiveness of generated headlines BIBREF28, BIBREF29. BIBREF28 focuses on controlling several features of the summary text such as text length, and the style of two different news outlets, CNN and DailyMail. These controls serve as a way to boost the model performance, and the CNN- and DailyMail-style control shows a negligible improvement. BIBREF29 utilized reinforcement learning to encourage the headline generation system to generate more sensational headlines via using the readers' comment rate as the reward, which however cannot explicitly control or manipulate the styles of headlines. BIBREF30 proposed a style transfer approach to transfer a non-clickbait headline into a clickbait one. This method requires paired news articles-headlines data for the target style; however, for many styles such as humor and romance, there are no available headlines. Our model does not have this limitation, thus enabling transferring to many more styles. ### Related Work ::: Text Style Transfer
Our work is also related to text style transfer, which aims to change the style attribute of the text while preserving its content. First proposed by BIBREF31, it has achieved great progress in recent years BIBREF32, BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF37, BIBREF38. However, all these methods demand a text corpus for the target style; however, in our case, it is expensive and technically challenging to collect news headlines with humor and romance styles, which makes this category of methods not applicable to our problem. ### Methods ::: Problem Formulation
The model is trained on a source dataset $S$ and target dataset $T$. The source dataset $S=\lbrace (\mathbf {a^{(i)}},\mathbf {h^{(i)}})\rbrace _{i=1}^N$ consists of pairs of a news article $\mathbf {a}$ and its plain headline $\mathbf {h}$. We assume that the source corpus has a distribution $P(A, H)$, where $A=\lbrace \mathbf {a^{(i)}}\rbrace _{i=1}^N$, and $H=\lbrace \mathbf {h^{(i)}}\rbrace _{i=1}^N$. The target corpus $T=\lbrace \mathbf {t^{(i)}}\rbrace _{i=1}^{M}$ comprises of sentences $\mathbf {t}$ written in a specific style (e.g., humor). We assume that it conforms to the distribution $P(T)$. Note that the target corpus $T$ only contains style-carrying sentences, not necessarily headlines — it can be just book text. Also no sentence $\mathbf {t}$ is paired with a news article. Overall, our task is to learn the conditional distribution $P(T|A)$ using only $S$ and $T$. This task is fully unsupervised because there is no sample from the joint distribution $P(A, T)$. ### Methods ::: Seq2Seq Model Architecture
For summarization, we adopt a sequence-to-sequence (Seq2Seq) model based on the Transformer architecture BIBREF6. As in Figure FIGREF8, it consists of a 6-layer encoder $E(\mathbf {\cdot }; \mathbf {\theta _E})$ and a 6-layer decoder $G(\mathbf {\cdot }; \mathbf {\theta _G})$ with a hidden size of 1024 and a feed-forward filter size of 4096. For better generation quality, we initialize with the MASS model BIBREF3. MASS is pretrained by masking a sentence fragment in the encoder, and then predicting it in the decoder on large-scale English monolingual data. This pretraining is adopted in the current state-of-the-art systems across various summarization benchmark tasks including HG. ### Methods ::: Multitask Training Scheme
To disentangle the latent style from the text, we adopt a multitask learning framework BIBREF39, training on summarization and DAE simultaneously (as shown in Figure FIGREF10). ### Methods ::: Multitask Training Scheme ::: Supervised Seq2Seq Training for @!START@$E_S$@!END@ and @!START@$G_S$@!END@
With the source domain dataset $S$, based on the encoder-decoder architecture, we can learn the conditional distribution $P(H|A)$ by training $\mathbf {z}_S=E_S(A)$ and $H_S=G_S(\mathbf {z_S})$ to solve the supervised Seq2Seq learning task, where $\mathbf {z_S}$ is the learned latent representation in the source domain. The loss function of this task is where $\mathbf {\theta _{E_S}}$ and $\mathbf {\theta _{G_S}}$ are the set of model parameters of the encoder and decoder in the source domain and $p(\mathbf {h}|\mathbf {a})$ denotes the overall probability of generating an output sequence $\mathbf {h}$ given the input article $\mathbf {a}$, which can be further expanded as follows: where $L$ is the sequence length. ### Methods ::: Multitask Training Scheme ::: DAE Training for @!START@$\mathbf {\theta _{E_T}}$@!END@ and @!START@$\mathbf {\theta _{G_T}}$@!END@
For the target style corpus $T$, since we only have the sentence $\mathbf {t}$ without paired news articles, we train $\mathbf {z_T}=E_T(\mathbf {\tilde{t}})$ and $\mathbf {t}=G_T(\mathbf {z_T})$ by solving an unsupervised reconstruction learning task, where $\mathbf {z_T}$ is the learned latent representation in the target domain, and $\mathbf {\tilde{t}}$ is the corrupted version of $\mathbf {t}$ by randomly deleting or blanking some words and shuffling the word orders. To train the model, we minimize the reconstruction error $\mathcal {L}_T$: where $\mathbf {\theta _{E_T}}$ and $\mathbf {\theta _{G_T}}$ are the set of model parameters for the encoder and generator in the target domain. We train the whole model by jointly minimizing the supervised Seq2Seq training loss $\mathcal {L}_S$ and the unsupervised denoised auto-encoding loss $\mathcal {L}_T$ via multitask learning, so the total loss becomes where $\lambda $ is a hyper-parameter. ### Methods ::: Parameter-Sharing Scheme
More constraints are necessary in the multitask training process. We aim to infer the conditional distribution as $ P(T|A)=G_T(E_S(A))$. However, without samples from $P(A, T)$, this is a challenging or even impossible task if $E_S$ and $E_T$, or $G_S$ and $G_T$ are completely independent of each other. Hence, we need to add some constraints to the network by relating $E_S$ and $E_T$, and $G_S$ and $G_T$. The simplest design is to share all parameters between $E_S$ and $E_T$, and apply the same strategy to $G_S$ and $G_T$. The intuition behind this design is that by exposing the model to both summarization task and style-carrying text reconstruction task, the model would acquire some sense of the target style while summarizing the article. However, to encourage the model to better disentangle the content and style of text and more explicitly learn the style contained in the target corpus $T$, we share all parameters of the encoder between two domains, i.e., between $E_S$ and $E_T$, whereas we divide the parameters of the decoder into two types: style-independent parameters $\mathbf {\theta _{\mathrm {ind}}}$ and style-dependent parameters $\mathbf {\theta _{\mathrm {dep}}}$. This means that only the style-independent parameters are shared between $G_S$ and $G_T$ while the style-dependent parameters are not. More specifically, the parameters of the layer normalization and encoder attention modules are made style-dependent as detailed below. ### Methods ::: Parameter-Sharing Scheme ::: Type 1. Style Layer Normalization
Inspired by previous work on image style transfer BIBREF40, we make the scaling and shifting parameters for layer normalization in the transformer architecture un-shared for each style. This style layer normalization approach aims to transform a layer’s activation $\mathbf {x}$ into a normalized activation $\mathbf {z}$ specific to the style $s$: where $\mu $ and $\sigma $ are the mean and standard deviation of the batch of $\mathbf {x}$, and $\gamma _s$ and $\beta _s$ are style-specific parameters learned from data. Specifically, for the transformer decoder architecture, we use a style-specific self-attention layer normalization and final layer normalization for the source and target domains on all six decoder layers. ### Methods ::: Parameter-Sharing Scheme ::: Type 2. Style-Guided Encoder Attention
Our model architecture contains the attention mechanism, where the decoder infers the probability of the next word not only conditioned on the previous words but also on the encoded input hidden states. The attention patterns should be different for the summarization and the reconstruction tasks due to their different inherent nature. We insert this thinking into the model by introducing the style-guided encoder attention into the multi-head attention module, which is defined as follows: where $\mathbf {\mathrm {query}}$, $\mathbf {\mathrm {key}}$, and $\mathbf {\mathrm {value}}$ denote the triple of inputs into the multi-head attention module; $\mathbf {W_q^s}$, $\mathbf {W_k}$, and $\mathbf {W_v}$ denote the scaled dot-product matrix for affine transformation; $d_{\mathrm {model}}$ is the dimension of the hidden states. We specialize the dot-product matrix $\mathbf {W_q^s}$ of the query for different styles, so that $\mathbf {Q}$ can be different to induce diverse attention patterns. ### Experiments ::: Datasets
We compile a rich source dataset by combining the New York Times (NYT) and CNN, as well as three target style corpora on humorous, romantic, and click-baity text. The average sentence length in the NYT, CNN, Humor, Romance, and Clickbait datasets are 8.8, 9.2, 12.6, 11.6 and 8.7 words, respectively. ### Experiments ::: Datasets ::: Source Dataset
The source dataset contains news articles paired with corresponding headlines. To enrich the training corpus, we combine two datasets: the New York Times (56K) and CNN (90K). After combining these two datasets, we randomly selected 3,000 pairs as the validation set and another 3,000 pairs as the test set. We first extracted the archival abstracts and headlines from the New York Times (NYT) corpus BIBREF41 and treat the abstracts as the news articles. Following the standard pre-processing procedures BIBREF42, we filtered out advertisement-related articles (as they are very different from news reports), resulting in 56,899 news abstracts-headlines pairs. We then add into our source set the CNN summarization dataset, which is widely used for training abstractive summarization models BIBREF43. We use the short summaries in the original dataset as the news abstracts and automatically parsed the headlines for each news from the dumped news web pages, and in total collected 90,236 news abstract-headline pairs. ### Experiments ::: Datasets ::: Three Target Style Corpora ::: Humor and Romance
For the target style datasets, we follow BIBREF44 to use humor and romance novel collections in BookCorpus BIBREF45 as the Humor and Romance datasets. We split the documents into sentences, tokenized the text, and collected 500K sentences as our datasets. ### Experiments ::: Datasets ::: Three Target Style Corpora ::: Clickbait
We also tried to learn the writing style from the click-baity headlines since they have shown superior attraction to readers. Thus we used The Examiner - SpamClickBait News dataset, denoted as the Clickbait dataset. We collected 500K headlines for our use. Some examples from each style corpus are listed in Table TABREF32. ### Experiments ::: Baselines
We compared the proposed TitleStylist against the following five strong baseline approaches. ### Experiments ::: Baselines ::: Neural Headline Generation (NHG)
We train the state-of-the-art summarization model, MASS BIBREF3, on our collected news abstracts-headlines paired data. ### Experiments ::: Baselines ::: Gigaword-MASS
We test an off-the-shelf headline generation model, MASS from BIBREF3, which is already trained on Gigaword, a large-scale headline generation dataset with around 4 million articles. ### Experiments ::: Baselines ::: Neural Story Teller (NST)
It breaks down the task into two steps, which first generates headlines from the aforementioned NHG model, then applies style shift techniques to generate style-specific headlines BIBREF46. In brief, this method uses the Skip-Thought model to encode a sentence into a representation vector and then manipulates its style by a linear transformation. Afterward, this transformed representation vector is used to initialize a language model pretrained on a style-specific corpus so that a stylistic headline can be generated. More details of this method can refer to the official website. ### Experiments ::: Baselines ::: Fine-Tuned
We first train the NHG model as mentioned above, then further fine-tuned it on the target style corpus via DAE training. ### Experiments ::: Baselines ::: Multitask
We share all parameters between $E_S$ and $E_T$, and between $G_S$ and $G_T$, and trained the model on both the summarization and DAE tasks. The model architecture is the same as NHG. ### Experiments ::: Evaluation Metrics
To evaluate the performance of the proposed TitleStylist in generating attractive headlines with styles, we propose a comprehensive twofold strategy of both automatic evaluation and human evaluation. ### Experiments ::: Evaluation Metrics ::: Setup of Human Evaluation
We randomly sampled 50 news abstracts from the test set and asked three native-speaker annotators for evaluation to score the generated headlines. Specifically, we conduct two tasks to evaluate on four criteria: (1) relevance, (2) attractiveness, (3) language fluency, and (4) style strength. For the first task, the human raters are asked to evaluate these outputs on the first three aspects, relevance, attractiveness, and language fluency on a Likert scale from 1 to 10 (integer values). For relevance, human annotators are asked to evaluate how semantically relevant the headline is to the news body. For attractiveness, annotators are asked how attractive the headlines are. For fluency, we ask the annotators to evaluate how fluent and readable the text is. After the collection of human evaluation results, we averaged the scores as the final score. In addition, we have another independent human evaluation task about the style strength – we present the generated headlines from TitleStylist and baselines to the human judges and let them choose the one that most conforms to the target style such as humor. Then we define the style strength score as the proportion of choices. ### Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation
Apart from the comprehensive human evaluation, we use automatic evaluation to measure the generation quality through two conventional aspects: summarization quality and language fluency. Note that the purpose of this two-way automatic evaluation is to confirm that the performance of our model is in an acceptable range. Good automatic evaluation performances are necessary proofs to compliment human evaluations on the model effectiveness. ### Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Summarization Quality
We use the standard automatic evaluation metrics for summarization with the original headlines as the reference: BLEU BIBREF47, METEOR BIBREF48, ROUGE BIBREF49 and CIDEr BIBREF50. For ROUGE, we used the Files2ROUGE toolkit, and for other metrics, we used the pycocoeval toolkit. ### Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Language Fluency
We fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs. ### Experiments ::: Experimental Details
We used the fairseq code base BIBREF52. During training, we use Adam optimizer with an initial learning rate of $5\times 10^{-4}$, and the batch size is set as 3072 tokens for each GPU with the parameters update frequency set as 4. For the random corruption for DAE training, we follow the standard practice to randomly delete or blank the word with a uniform probability of $0.2$, and randomly shuffled the word order within 5 tokens. All datasets are lower-cased. $\lambda $ is set as 0.5 in experiments. For each iteration of training, we randomly draw a batch of data either from the source dataset or from the target style corpus, and the sampling strategy follows the uniform distribution with the probability being equal to $\lambda $. ### Results and Discussion ::: Human Evaluation Results
The human evaluation is to have a comprehensive measurement of the performances. We conduct experiments on four criteria, relevance, attraction, fluency, and style strength. We summarize the human evaluation results on the first three criteria in Table TABREF51, and the last criteria in Table TABREF57. Note that through automatic evaluation, the baselines NST, Fine-tuned, and Gigaword-MASS perform poorer than other methods (in Section SECREF58), thereby we removed them in human evaluation to save unnecessary work for human raters. ### Results and Discussion ::: Human Evaluation Results ::: Relevance
We first look at the relevance scores in Table TABREF51. It is interesting but not surprising that the pure summarization model NHG achieves the highest relevance score. The outputs from NHG are usually like an organic reorganization of several keywords in the source context (as shown in Table TABREF52), thus appearing most relevant. It is noteworthy that the generated headlines of our TitleStylist for all three styles are close to the original human-written headlines in terms of relevance, validating that our generation results are qualified in this aspect. Another finding is that more attractive or more stylistic headlines would lose some relevance since they need to use more words outside the news body for improved creativity. ### Results and Discussion ::: Human Evaluation Results ::: Attraction
In terms of attraction scores in Table TABREF51, we have three findings: (1) The human-written headlines are more attractive than those from NHG, which agrees with our observation in Section SECREF1. (2) Our TitleStylist can generate more attractive headlines over the NHG and Multitask baselines for all three styles, demonstrating that adapting the model to these styles could improve the attraction and specialization of some parameters in the model for different styles can further enhance the attraction. (3) Adapting the model to the “Clickbait” style could create the most attractive headlines, even out-weighting the original ones, which agrees with the fact that click-baity headlines are better at drawing readers' attention. To be noted, although we learned the “Clickbait” style into our summarization system, we still made sure that we are generating relevant headlines instead of too exaggerated ones, which can be verified by our relevance scores. ### Results and Discussion ::: Human Evaluation Results ::: Fluency
The human-annotated fluency scores in Table TABREF51 verified that our TitleStylist generated headlines are comparable or superior to the human-written headlines in terms of readability. ### Results and Discussion ::: Human Evaluation Results ::: Style Strength
We also validated that our TitleStylist can carry more styles compared with the Multitask and NHG baselines by summarizing the percentage of choices by humans for the most humorous or romantic headlines in Table TABREF57. ### Results and Discussion ::: Automatic Evaluation Results
Apart from the human evaluation of the overall generation quality on four criteria, we also conducted a conventional automatic assessment to gauge only the summarization quality. This evaluation does not take other measures such as the style strength into consideration, but it serves as important complimentary proof to ensure that the model has an acceptable level of summarization ability. Table TABREF59 summarizes the automatic evaluation results of our proposed TitleStylist model and all baselines. We use the summarization-related evaluation metrics, i.e., BLEU, ROUGE, CIDEr, and METEOR, to measure how relevant the generated headlines are to the news articles, to some extent, by comparing them to the original human-written headlines. In Table TABREF59, the first row “NHG” shows the performance of the current state-of-the-art summarization model on our data, and Table TABREF52 provides two examples of its generation output. Our ultimate goal is to generate more attractive headlines than these while maintaining relevance to the news body. From Table TABREF59, the baseline Gigaword-MASS scored worse than NHG, revealing that directly applying an off-the-shelf headline generation model to new in-domain data is not feasible, although this model has been trained on more than 20 times larger dataset. Both NST and Fine-tuned baselines present very poor summarization performance, and the reason could be that both of them cast the problem into two steps: summarization and style transfer, and the latter step is absent of the summarization task, which prevents the model from maintaining its summarization capability. In contrast, the Multitask baseline involves the summarization and style transfer (via reconstruction training) processes at the same time and shows superior summarization performance even compared with NHG. This reveals that the unsupervised reconstruction task can indeed help improve the supervised summarization task. More importantly, we use two different types of corpora for the reconstruction task: one consists of headlines that are similar to the news data for the summarization task, and the other consists of text from novels that are entirely different from the news data. However, unsupervised reconstruction training on both types of data can contribute to the summarization task, which throws light on the potential future work in summarization by incorporating unsupervised learning as augmentation. We find that in Table TABREF59 TitleStylist-F achieves the best summarization performance. This implicates that, compared with the Multitask baseline where the two tasks share all parameters, specialization of layer normalization and encoder-attention parameters can make $G_S$ focus more on summarization. It is noteworthy that the summarization scores for TitleStylist are lower than TitleStylist-F but still comparable to NHG. This agrees with the fact that the $G_T$ branch more focuses on bringing in stylistic linguistic patterns into the generated summaries, thus the outputs would deviate from the pure summarization to some degree. However, the relevance degree of them remains close to the baseline NHG, which is the starting point we want to improve on. Later in the next section, we will further validate that these headlines are faithful to the new article through human evaluation. We also reported the perplexity (PPL) of the generated headlines to evaluate the language fluency, as shown in Table TABREF59. All outputs from baselines NHG and Multitask and our proposed TitleStylist show similar PPL compared with the test set (used in the fine-tuning stage) PPL 42.5, indicating that they are all fluent expressions for news headlines. ### Results and Discussion ::: Extension to Multi-Style
We progressively expand TitleStylist to include all three target styles (humor, romance, and clickbait) to demonstrate the flexibility of our model. That is, we simultaneously trained the summarization task on the headlines data and the DAE task on the three target style corpora. And we made the layer normalization and encoder-attention parameters specialized for these four styles (fact, humor, romance, and clickbait) and shared the other parameters. We compared this multi-style version, TitleStylist-Versatile, with the previously presented single-style counterpart, as shown in Table TABREF61. From this table, we see that the BLEU and ROUGE-L scores of TitleStylist-Versatile are comparable to TitleStylist for all three styles. Besides, we conducted another human study to determine the better headline between the two models in terms of attraction, and we allow human annotators to choose both options if they deem them as equivalent. The result is presented in the last column of Table TABREF61, which shows that the attraction of TitleStylist-Versatile outputs is competitive to TitleStylist. TitleStylist-Versatile thus generates multiple headlines in different styles altogether, which is a novel and efficient feature. ### Conclusion
We have proposed a new task of Stylistic Headline Generation (SHG) to emphasize explicit control of styles in headline generation for improved attraction. To this end, we presented a multitask framework to induce styles into summarization, and proposed the parameters sharing scheme to enhance both summarization and stylization capabilities. Through experiments, we validated our proposed TitleStylist can generate more attractive headlines than state-of-the-art HG models. ### Acknowledgement
We appreciate all the volunteer native speakers (Shreya Karpoor, Lisa Orii, Abhishek Mohan, Paloma Quiroga, etc.) for the human evaluation of our study, and thank the reviewers for their inspiring comments. Joey Tianyi Zhou is partially supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project No. A18A1b0045). Figure 1: Given a news article, current HG models can only generate plain, factual headlines, failing to learn from the original human reference. It is also much less attractive than the headlines with humorous, romantic and click-baity styles. Figure 2: The Transformer-based architecture of our model. Figure 3: Training scheme. Multitask training is adopted to combine the summarization and DAE tasks. Table 1: Examples of three target style corpora: humor, romance, and clickbait. Table 2: Human evaluation on three aspects: relevance, attraction, and fluency. “None” represents the original headlines in the dataset. Table 3: Examples of style-carrying headlines generated by TitleStylist. Table 4: Percentage of choices (%) for the most humorous or romantic headlines among TitleStylist and two baselines NHG and Multitask. Table 5: Automatic evaluation results of our TitleStylist and baselines. The test set of each style is the same, but the training set is different depending on the target style as shown in the “Style Corpus” column. “None” means no style-specific dataset, and “Humor”, “Romance” and “Clickbait” corresponds to the datasets we introduced in Section 4.1.2. During the inference phase, our TitleStylist can generate two outputs: one from GT and the other from GS . Outputs from GT are style-carrying, so we denote it as “TitleStylist”; outputs from GS are plain and factual, thus denoted as “TitleStylist-F.” The last column “Len. Ratio” denotes the average ratio of abstract length to the generated headline length by the number of words. Table 6: Comparison between TitleStylist-Versatile and TitleStylist. “RG-L” denotes ROUGE-L, and “Pref.” denotes preference. | human evaluation task about the style strength |
What was thought to be used as an indication to settle the confusion between the crew and the two humans in moon-town?
A. An inquisition about knowledge
B. A game of checkers
C. A contest of preternatural intellect
D. A physical test
| 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." | B. A game of checkers |
What is a reason that Dole attacked the Times?
A. he wanted to away with all newspapers
B. to glean positive support from anti-Times voters
C. his advisers recommended doing so
D. he was angry at the reporters from the Times
| Dole vs. the Times For several weeks now, pundits have debated how Bob Dole would exit the stage. Would he depart on a negative note about his opponent or a positive one about himself? Would he leave with anger or with humor? In the past several days, the issue has been settled. Dole, it appears, will end his political career raging against the New York Times . Dole's spat with the gray lady went public on Thursday, Oct. 24. In New Orleans, Dole charged the paper with ignoring a story about a Miami drug dealer who got invited to the White House. "This is a disgrace," Dole insisted. "I doubt if you even read it in the New York Times . They probably put it in the want ads. They don't put any anti-Clinton stories in the New York Times . Only anti-Dole stories in the New York Times ." Dole repeated his attack for the next five days. "We are not going to let the media steal this election," he told a crowd in Dallas on Friday. "This country belongs to the people, not the New York Times ." On Saturday, in Visalia, Calif., he added, "I know that with a crowd this size, the New York Times will write not many people showed up, but the other papers will get it right." On Sunday (the day the Times endorsed Clinton), Dole called the paper "the apologist for President Clinton for the last four years and an arm of the Democratic National Committee." In a CNN interview broadcast Monday, Dole said the Times "might as well be part of the Democratic Party. ... They hammer us on a daily basis. We make a major speech, they bury it back on section D. They put a front-page story that, well, Bob Dole and Jack Kemp didn't get along together 12 years ago." On Tuesday, Dole was still at it, referring to the 28 words of the 10th Amendment, and quipping, "That's about what I got in the New York Times today." The Times has reacted to this assault by highhandedly quoting everything and explaining none of it, leaving its readers baffled as to why the Republican nominee is so upset at the paper. In fact, Dole's fury at the Times is hardly news to those who work at the paper. According to Katharine Seelye, who has covered Dole since the beginning of his campaign, the complaints date from December 1995, when Dole staff members first protested that she had misunderstood the candidate's position on abortion. The real bitterness, however, began in May, when the paper played what Dole aides billed as a major address about welfare on Page 19 of the business section. Since then, campaign honchos have peppered the paper's reporters and editors with constant phone calls and letters complaining about unfair treatment. Reporters traveling with Dole caught a glimpse of the enmity Oct. 9, when Nelson Warfield, Dole's press secretary, staged a public confrontation with Seelye. The candidate, Warfield told reporters waiting to board the campaign plane, had just come from an appearance on G. Gordon Liddy's radio show. Why, Seelye asked, weren't reporters told about the appearance in advance? According to reporters present, Warfield snapped that it wouldn't make any difference because the Times would get the story wrong anyway. Then, on the plane, Warfield walked back to the press section and grandly served Seelye with a copy of a letter from Communications Director John Buckley to her boss, Times Washington Editor Andrew Rosenthal. That letter, which has fallen into the hands of Slate, protests Seelye's coverage of a speech the previous day. Dole, in New Jersey, had talked about Clinton being AWOL in the drug war. "Where has he been for four years? How many hundreds of thousands of young people started drugs?" Dole said. "Three million have started smoking while he was playing around with smoking and all this stuff finally in an election year." Seelye's front-page story reported that "Mr. Dole accused the President of 'playing around' while the drug war raged out of control." Buckley complains that the story "could lead the reader to believe that Dole was talking about a very different kind of 'playing around'--something he did not say, and something he would not say." The letter continues: "Since May, I have been pointing out to you a problem we see with the accuracy and understanding of context revealed in Kit's reporting," going on to assert that "Seelye has misquoted Dole on numerous occasions and done so in a manner that distorted the accuracy of her assertions and your coverage." No Dole staff would be quoted by name for this story, but speaking on background, a senior campaign official elaborated upon the complaint. "They've just done a miserable job throughout this campaign," the official said. "The coverage of Dole has been excessively bitchy from day one, in addition to having a number of extraordinary factual problems." With Seelye, the official says, the problem is "not being able to transcribe a tape accurately." With Adam Nagourney, the Times ' other reporter covering Dole full time since the summer, "the problem is an incredible focus on the little picture as opposed to the big picture." As an example, the official cites a September story in which Nagourney lumped together Dole's fall from a platform in Chico, Calif., and his mistaken reference to the "Brooklyn" Dodgers as "a rough stretch of politicking." Other than those two episodes, the official says, Dole actually had a great week. The campaign's complaint extends to unequal treatment--a nine-part series on Clinton's record, which the official describes as "the softest portrait since they invented black velvet"--and the Times perpetually underestimating the size of Dole crowds. "Clinton even gets better photographs," the official contends. Rosenthal, who has direct responsibility for campaign coverage at the Times , professes bewilderment at these complaints. "We don't make editorial judgments based on disposition to be tough on Bob Dole or nice to Bob Dole," he says. On the specifics, Rosenthal says that the Times ran an editor's note acknowledging that it shouldn't have truncated the "playing around" quote. He points out that the Times ran its story on the Miami drug dealer who visited the White House the same day Dole accused the paper of not covering it. As for the nine-part series on Clinton, Rosenthal says it is the long-standing practice of the paper to do a lengthy series on the incumbent's record. "If Dole wins and runs again in 2000, he will get nine-part series too," he says. "Ithink we have been tough on him," Seelye says. This stems, however, not from any bias, she says, but from the campaign's own internal problems. Dole's campaign has been especially "porous," with aides emulating the proverbial seafaring rats. This is true enough--in recent days ex-strategist Don Sipple has trashed the campaign on the record. But there's another point, too. Contrary to Buckley's charge that she misquotes Dole, Seelye routinely makes Dole look ridiculous by quoting him all too accurately, depicting him in what one colleague calls a "cinema verité " style. Famous for going over and over her tape recordings on the campaign plane, Seelye manages to get every Dole mumble, repetition, and verbal miscue down. For instance, in her Oct. 26 story reporting Dole's attack on the Times , Seelye writes: "In Phoenix on Friday night, he had a delightful time drawing out his vowels as he described financial contributions to the Clinton campaign. "From Indoneeesia," he said. "Yeah. From INdiaaaaah. Some fellow named Gandhi out there. He owes $10,000 in back taxes, but he found $300,000 to give to the Clinton campaign. And now Gandhi is gaaaawn. Gaaaaandhi, gone gone gone. They can't find him." Two days later, she quoted Dole in another story: "They've turned the White House into something else, I don't know what it is. It's the animal house! It's the animal house!" Most reporters would write, Bob Dole yesterday compared the White House to an "animal house," sparing the exclamation points, and making him sound at least compos mentis. But though unflattering, Seelye's Mametizing of Bob Dole can hardly be called unfair. It is not as if the Times cleans up Clinton's quotes; the president simply observes the rules of syntax most of the time. Something similar may be happening with the pictures. After four years, Clinton has learned how to avoid looking unpresidential. He no longer allows himself to be photographed wearing too-short running shorts, and he avoids pulling faces in public. Dole, who is simply less photogenic, is an easier victim for picture editors--who, like their editorial counterparts, have a strong bias against dullness. Take, for instance, the two pictures shown above. The front-page picture the Times ran the day after the second presidential debate does make Dole look like a decomposing monster. But unlike the picture in the Washington Post the same day, it captures the spirit of the event, with Dole grimly taking the offensive and Clinton watching warily but standing aside from the attacks. Dole sounds absurd when he alleges that the paper that broke Whitewater and the story of the first lady's commodities trades has not been aggressive in pursuing Clinton scandals. All sorts of potential Dole scandals have been soft-pedaled by the media, including the Times , because he is so far behind. It's true that coverage of Clinton on the campaign trail has been somewhat softer than the coverage of Dole, as even other Times reporters acknowledge. But the explanation is institutional, not ideological. The press, as many have complained, overemphasizes the "horse race" aspect of politics. As a side effect of that disease, reporters have excessive respect for a well-run campaign. (In 1988, Republican George Bush benefited from this phenomenon.) A cruder reality is that reporters need to have a relationship with Clinton after Tuesday. None of these factors, though, is unique to the Times . So why is Dole singling it out? Dole's attacks on the Times have the appearance of being an exercise in populist demagogy. In one of his great cue-card reading remarks, Dole tried to explain his recent attacks on CNN the other night by saying, "I like the media. They don't like them in the South." But this pat explanation doesn't entirely make sense. Red meat for right-wing crowds doesn't help Dole with the centrist voters he would need to turn around in order to make the miraculous happen. And in fact, according to a senior Dole aide, the attacks are heartfelt on the candidate's part. Dole has been going after the Times over the objections of advisers who have been telling him there's no percentage in picking fights with the press. But if Dole is attacking the Times because he is truly furious and not because he thinks it will help him get elected, what is he so angry about? The answer, I think, is that there has always been a Nixonian streak in Bob Dole, by which I mean a part of him which feels shut out of the closed circle of the Eastern establishment. At the Republican convention, Dole blasted the Clinton administration as a "corps of the elite who never grew up, never did anything real, never sacrificed, never suffered, and never learned." That phrase recalled an attack he made on the press long ago, in the days of Watergate, when he accused the Washington Post of being in bed with George McGovern. "There is a cultural and social affinity between the McGovernites and the Post executives and editors," Dole said then. "They belong to the same elite: They can be found living cheek-by-jowl in the same exclusive chic neighborhoods, and hob-nobbing at the same Georgetown parties." The deeper story here isn't whether Dole was wrongly shunted onto D19 when he ought to have been on A1. It's his feelings, as he says goodbye to politics, about the people who get to decide. | D. he was angry at the reporters from the Times |
Why did the aliens decide to land during wintertime?
A. They did not have enough gas to circle back in the summer time.
B. They preferred the cold in the northern hemisphere to the heat of the southern hemisphere.
C. They had to land now, and went where they could identify the best people to talk to.
D. Their clothing fit in better in colder climates.
| 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. | C. They had to land now, and went where they could identify the best people to talk to. |
Which three neural machine translation systems are analyzed? | ### Introduction
In the present paper, we analyse coreference in the output of three neural machine translation systems (NMT) that were trained under different settings. We use a transformer architecture BIBREF0 and train it on corpora of different sizes with and without the specific coreference information. Transformers are the current state-of-the-art in NMT BIBREF1 and are solely based on attention, therefore, the kind of errors they produce might be different from other architectures such as CNN or RNN-based ones. Here we focus on one architecture to study the different errors produced only under different data configurations. Coreference is an important component of discourse coherence which is achieved in how discourse entities (and events) are introduced and discussed. Coreference chains contain mentions of one and the same discourse element throughout a text. These mentions are realised by a variety of linguistic devices such as pronouns, nominal phrases (NPs) and other linguistic means. As languages differ in the range of such linguistic means BIBREF2, BIBREF3, BIBREF4, BIBREF5 and in their contextual restrictions BIBREF6, these differences give rise to problems that may result in incoherent (automatic) translations. We focus on coreference chains in English-German translations belonging to two different genres. In German, pronouns, articles and adjectives (and some nouns) are subject to grammatical gender agreement, whereas in English, only person pronouns carry gender marking. An incorrect translation of a pronoun or a nominal phrase may lead to an incorrect relation in a discourse and will destroy a coreference chain. Recent studies in automatic coreference translation have shown that dedicated systems can lead to improvements in pronoun translation BIBREF7, BIBREF8. However, standard NMT systems work at sentence level, so improvements in NMT translate into improvements on pronouns with intra-sentential antecedents, but the phenomenon of coreference is not limited to anaphoric pronouns, and even less to a subset of them. Document-level machine translation (MT) systems are needed to deal with coreference as a whole. Although some attempts to include extra-sentential information exist BIBREF9, BIBREF10, BIBREF11, BIBREF12, the problem is far from being solved. Besides that, some further problems of NMT that do not seem to be related to coreference at first glance (such as translation of unknown words and proper names or the hallucination of additional words) cause coreference-related errors. In our work, we focus on the analysis of complete coreference chains, manually annotating them in the three translation variants. We also evaluate them from the point of view of coreference chain translation. The goal of this paper is two-fold. On the one hand, we are interested in various properties of coreference chains in these translations. They include total number of chains, average chain length, the size of the longest chain and the total number of annotated mentions. These features are compared to those of the underlying source texts and also the corresponding human translation reference. On the other hand, we are also interested in the quality of coreference translations. Therefore, we define a typology of errors, and and chain members in MT output are annotated as to whether or not they are correct. The main focus is on such errors as gender, number and case of the mentions, but we also consider wrong word selection or missing words in a chain. Unlike previous work, we do not restrict ourselves to pronouns. Our analyses show that there are further errors that are not directly related to coreference but consequently have an influence on the correctness of coreference chains. The remainder of the paper is organised as follows. Section SECREF2 introduces the main concepts and presents an overview of related MT studies. Section SECREF3 provides details on the data, systems used and annotation procedures. Section SECREF4 analyses the performance of our transformer systems on coreferent mentions. Finally we summarise and draw conclusions in Section SECREF5. ### Background and Related Work ::: Coreference
Coreference is related to cohesion and coherence. The latter is the logical flow of inter-related ideas in a text, whereas cohesion refers to the text-internal relationship of linguistic elements that are overtly connected via lexico-grammatical devices across sentences BIBREF13. As stated by BIBREF14, this connectedness of texts implies dependencies between sentences. And if these dependencies are neglected in translation, the output text no longer has the property of connectedness which makes a sequence of sentences a text. Coreference expresses identity to a referent mentioned in another textual part (not necessarily in neighbouring sentences) contributing to text connectedness. An addressee is following the mentioned referents and identifies them when they are repeated. Identification of certain referents depends not only on a lexical form, but also on other linguistic means, e.g. articles or modifying pronouns BIBREF15. The use of these is influenced by various factors which can be language-dependent (range of linguistic means available in grammar) and also context-independent (pragmatic situation, genre). Thus, the means of expressing reference differ across languages and genres. This has been shown by some studies in the area of contrastive linguistics BIBREF6, BIBREF3, BIBREF5. Analyses in cross-lingual coreference resolution BIBREF16, BIBREF17, BIBREF18, BIBREF19 show that there are still unsolved problems that should be addressed. ### Background and Related Work ::: Translation studies
Differences between languages and genres in the linguistic means expressing reference are important for translation, as the choice of an appropriate referring expression in the target language poses challenges for both human and machine translation. In translation studies, there is a number of corpus-based works analysing these differences in translation. However, most of them are restricted to individual phenomena within coreference. For instance, BIBREF20 analyse abstract anaphors in English-German translations. To our knowledge, they do not consider chains. BIBREF21 in their contrastive analysis of potential coreference chain members in English-German translations, describe transformation patterns that contain different types of referring expressions. However, the authors rely on automatic tagging and parsing procedures and do not include chains into their analysis. The data used by BIBREF4 and BIBREF22 contain manual chain annotations. The authors focus on different categories of anaphoric pronouns in English-Czech translations, though not paying attention to chain features (e.g. their number or size). Chain features are considered in a contrastive analysis by BIBREF6. Their study concerns different phenomena in a variety of genres in English and German comparable texts. Using contrastive interpretations, they suggest preferred translation strategies from English into German, i.e. translators should use demonstrative pronouns instead of personal pronouns (e.g. dies/das instead of es/it) when translating from English into German and vice versa. However, corpus-based studies show that translators do not necessarily apply such strategies. Instead, they often preserve the source language anaphor's categories BIBREF20 which results in the shining through effects BIBREF23. Moreover, due to the tendency of translators to explicitly realise meanings in translations that were implicit in the source texts BIBREF24, translations are believed to contain more (explicit) referring expressions, and subsequently, more (and longer) coreference chains. Therefore, in our analysis, we focus on the chain features related to the phenomena of shining through and explicitation. These features include number of mentions, number of chains, average chain length and the longest chain size. Machine-translated texts are compared to their sources and the corresponding human translations in terms of these features. We expect to find shining through and explicitation effects in automatic translations. ### Background and Related Work ::: Coreference in MT
As explained in the introduction, several recent works tackle the automatic translation of pronouns and also coreference BIBREF25, BIBREF26 and this has, in part, motivated the creation of devoted shared tasks and test sets to evaluate the quality of pronoun translation BIBREF7, BIBREF27, BIBREF28, BIBREF29. But coreference is a wider phenomenon that affects more linguistic elements. Noun phrases also appear in coreference chains but they are usually studied under coherence and consistency in MT. BIBREF30 use topic modelling to extract coherence chains in the source, predict them in the target and then promote them as translations. BIBREF31 use word embeddings to enforce consistency within documents. Before these works, several methods to post-process the translations and even including a second decoding pass were used BIBREF32, BIBREF33, BIBREF34, BIBREF35. Recent NMT systems that include context deal with both phenomena, coreference and coherence, but usually context is limited to the previous sentence, so chains as a whole are never considered. BIBREF10 encode both a source and a context sentence and then combine them to obtain a context-aware input. The same idea was implemented before by BIBREF36 where they concatenate a source sentence with the previous one to include context. Caches BIBREF37, memory networks BIBREF38 and hierarchical attention methods BIBREF39 allow to use a wider context. Finally, our work is also related to BIBREF40 and BIBREF41 where their oracle translations are similar to the data-based approach we introduce in Section SECREF4. ### Systems, Methods and Resources ::: State-of-the-art NMT
Our NMT systems are based on a transformer architecture BIBREF0 as implemented in the Marian toolkit BIBREF42 using the transformer big configuration. We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1. A variant of the S3 system participated in the news machine translation of the shared task held at WMT 2019 BIBREF43. ### Systems, Methods and Resources ::: State-of-the-art NMT ::: S1
is trained with the concatenation of Common Crawl, Europarl, a cleaned version of Rapid and the News Commentary corpus. We oversample the latter in order to have a significant representation of data close to the news genre in the final corpus. ### Systems, Methods and Resources ::: State-of-the-art NMT ::: S2
uses the same data as S1 with the addition of a filtered portion of Paracrawl. This corpus is known to be noisy, so we use it to create a larger training corpus but it is diluted by a factor 4 to give more importance to high quality translations. ### Systems, Methods and Resources ::: State-of-the-art NMT ::: S3
S3 uses the same data as S1, but this time enriched with the cross- and intra-sentential coreference chain markup as described below. The information is included as follows. Source documents are annotated with coreference chains using the neural annotator of Stanford CoreNLP BIBREF44. The tool detects pronouns, nominal phrases and proper names as mentions in a chain. For every mention, CoreNLP extracts its gender (male, female, neutral, unknown), number (singular, plural, unknown), and animacy (animate, inanimate, unknown). This information is not added directly but used to enrich the single sentence-based MT training data by applying a set of heuristics implemented in DocTrans: We enrich pronominal mentions with the exception of "I" with the head (main noun phrase) of the chain. The head is cleaned by removing articles and Saxon genitives and we only consider heads with less than 4 tokens in order to avoid enriching a word with a full sentence We enrich nominal mentions including proper names with the gender of the head The head itself is enriched with she/he/it/they depending on its gender and animacy The enrichment is done with the addition of tags as shown in the examples: I never cook with $<$b_crf$>$ salt $<$e_crf$>$ it. $<$b_crf$>$ she $<$e_crf$>$ Biles arrived late. In the first case heuristic 1 is used, salt is the head of the chain and it is prepended to the pronoun. The second example shows a sentence where heuristic 2 has been used and the proper name Biles has now information about the gender of the person it is referring to. Afterwards, the NMT system is trained at sentence level in the usual way. The data used for the three systems is cleaned, tokenised, truecased with Moses scripts and BPEd with subword-nmt using separated vocabularies with 50 k subword units each. The validation set ($news2014$) and the test sets described in the following section are pre-processed in the same way. ### Systems, Methods and Resources ::: Test data under analysis
As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46. The corpus contains ca. 160.7 thousand tokens manually annotated with about 14.9 thousand mentions and 4.7 thousand coreference chains. For our analysis, we select a portion of English news texts and TED talks from ParCorFull and translate them with the three NMT systems described in SECREF4 above. As texts considerably differ in their length, we select 17 news texts (494 sentences) and four TED talks (518 sentences). The size (in tokens) of the total data set under analysis – source (src) and human translations (ref) from ParCorFull and the automatic translations produced within this study (S1, S2 and S3) are presented in Table TABREF20. Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set. ### Systems, Methods and Resources ::: Manual annotation process
The English sources and their corresponding human translations into German were already manually annotated for coreference chains. We follow the same scheme as BIBREF47 to annotate the MT outputs with coreference chains. This scheme allows the annotator to define each markable as a certain mention type (pronoun, NP, VP or clause). The mentions can be defined further in terms of their cohesive function (antecedent, anaphoric, cataphoric, comparative, substitution, ellipsis, apposition). Antecedents can either be marked as simple or split or as entity or event. The annotation scheme also includes pronoun type (personal, possessive, demonstrative, reflexive, relative) and modifier types of NPs (possessive, demonstrative, definite article, or none for proper names), see BIBREF46 for details. The mentions referring to the same discourse item are linked between each other. We use the annotation tool MMAX2 BIBREF48 which was also used for the annotation of ParCorFull. In the next step, chain members are annotated for their correctness. For the incorrect translations of mentions, we include the following error categories: gender, number, case, ambiguous and other. The latter category is open, which means that the annotators can add their own error types during the annotation process. With this, the final typology of errors also considered wrong named entity, wrong word, missing word, wrong syntactic structure, spelling error and addressee reference. The annotation of machine-translated texts was integrated into a university course on discourse phenomena. Our annotators, well-trained students of linguistics, worked in small groups on the assigned annotation tasks (4-5 texts, i.e. 12-15 translations per group). At the beginning of the annotation process, the categories under analysis were discussed within the small groups and also in the class. The final versions of the annotation were then corrected by the instructor. ### Results and Analyses ::: Chain features
First, we compare the distribution of several chain features in the three MT outputs, their source texts and the corresponding human translations. Table TABREF20 shows that, overall, all machine translations contain a greater number of annotated mentions in both news texts and TED talks than in the annotated source (src and src$_{\rm CoreNLP}$) and reference (ref) texts. Notice that src$_{\rm CoreNLP}$ —where coreferences are not manually but automatically annotated with CoreNLP— counts also the tokens that the mentions add to the sentences, but not the tags. The larger number of mentions may indicate a strong explicitation effect observed in machine-translated texts. Interestingly, CoreNLP detects a similar number of mentions in both genres, while human annotators clearly marked more chains for TED than for news. Both genres are in fact quite different in nature; whereas only $37\%$ of the mentions are pronominal in news texts (343 out of 915), the number grows to $58\%$ for TED (577 out of 989), and this could be an indicator of the difficulty of the genres for NMT systems. There is also a variation in terms of chain number between translations of TED talks and news. While automatic translations of news texts contain more chains than the corresponding human annotated sources and references, machine-translated TED talks contain less chains than the sources and human translations. However, there is not much variation between the chain features of the three MT outputs. The chains are also longer in machine-translated output than in reference translations as can be seen by the number of mentions per chain and the length of the longest chain. ### Results and Analyses ::: MT quality at system level
We evaluate the quality of the three transformer engines with two automatic metrics, BLEU BIBREF49 and METEOR BIBREF50. Table TABREF25 shows the scores in two cases: all, when the complete texts are evaluated and coref, when only the subset of sentences that have been augmented in S3 are considered – 265 out of 494 for news and 239 out of 518 for TED. For news, the best system is that trained on more data, S2; but for TED talks S3 with less data has the best performance. The difference between the behaviour of the systems can be related to the different genres. We have seen that news are dominated by nominal mentions while TED is dominated by pronominal ones. Pronouns mostly need coreference information to be properly translated, while noun phrases can be improved simply because more instances of the nouns appear in the training data. With this, S3 improves the baseline S1 in +1.1 BLEU points for TED$_{coref}$ but -0.2 BLEU points for news$_{coref}$. However, even if the systems differ in the overall performance, the change is not related to the number of errors in coreference chains. Table TABREF25 also reports the number of mistakes in the translation of coreferent mentions. Whereas the number of errors correlates with translation quality (as measured by BLEU) for news$_{coref}$ this is not the case of TED$_{coref}$. ### Results and Analyses ::: Error analysis
The total distribution for the 10 categories of errors defined in Section SECREF23 can be seen in Figure FIGREF29. Globally, the proportion of errors due to our closed categories (gender, number, case and ambiguous) is larger for TED talks than for news (see analysis in Section SECREF28). Gender is an issue with all systems and genres which does not get solved by the addition of more data. Additionally, news struggle with wrong words and named entities; for this genre the additional error types (see analysis in Section SECREF30) represent around 60% of the errors of S1/S3 to be compared to the 40% of TED talks. ### Results and Analyses ::: Error analysis ::: Predefined error categories
0.4em 0.4Within our predefined closed categories (gender, number, case and ambiguous), the gender errors belong to the most frequent errors. They include wrong gender translation of both pronouns, as sie (“her”) instead of ihn (“him”) in example SECREF28 referring to the masculine noun Mindestlohn, and nominal phrases, as der Stasi instead of die Stasi, where a masculine form of the definite article is used instead of a feminine one, in example SECREF28. .src: [The current minimum wage] of 7.25 US dollars is a pittance... She wants to raise [it] to 15 dollars an hour. S3: [Der aktuelle Mindestlohn] von 7,25 US-Dollar sei Almosen... Sie möchte [sie] auf 15 Dollar pro Stunde erhöhen. . src: ...let's have a short look at the history of [the Stasi], because it is really important for understanding [its] self-conception. S2: Lassen sie uns... einen kurzen Blick auf die Geschichte [des Stasi] werfen denn es wirklich wichtig, [seine] Selbstauffassung zu verstehen. The gender-related errors are common to all the automatic translations. Interestingly, systems S1 and S3 have more problems with gender in translations of TED talks, whereas they do better in translating news, which leads us to assume that this is a data-dependent issue: while the antecedent for news is in the same sentence it is not for TED talks. A closer look at the texts with a high number of gender problems confirms this assumption —they contain references to females who were translated with male forms of nouns and pronouns (e.g. Mannschaftskapitän instead of Mannschaftskapitänin). We also observe errors related to gender for the cases of explicitation in translation. Some impersonal English constructions not having direct equivalents in German are translated with personal constructions, which requires an addition of a pronoun. Such cases of explicitation were automatically detected in parallel data in BIBREF21, BIBREF2. They belong to the category of obligatory explicitation, i.e. explicitation dictated by differences in the syntactic and semantic structure of languages, as defined by BIBREF51. An MT system tends to insert a male form instead of a female one even if it's marked as feminine (S3 adds the feminine form she as markup), as illustrated in example SECREF28 where the automatic translation contains the masculine pronoun er (“he”) instead of sie (“she”). . src: [Biles] earned the first one on Tuesday while serving as the exclamation point to retiring national team coordinator Martha Karolyi's going away party. ref: [Biles] holte die erste Medaille am Dienstag, während [sie] auf der Abschiedsfeier der sich in Ruhestand begehenden Mannschaftskoordinatorin Martha Karolyi als Ausrufezeichen diente. S2: [Biles] verdiente den ersten am Dienstag, während [er] als Ausrufezeichen für den pensionierten Koordinator der Nationalmannschaft, Martha Karolyi, diente. Another interesting case of a problem related to gender is the dependence of the referring expressions on grammatical restrictions in German. In example SECREF28, the source chain contains the pronoun him referring to both a 6-year-old boy and The child. In German, these two nominal phrases have different gender (masculine vs. neutral). The pronoun has grammatical agreement with the second noun of the chain (des Kindes) and not its head (ein 6 Jahre alter Junge). . src: Police say [a 6-year-old boy] has been shot in Philadelphia... [The child]'s grandparents identified [him] to CBS Philadelphia as [Mahaj Brown]. S1: Die Polizei behauptet, [ein 6 Jahre alter Junge] sei in Philadelphia erschossen worden... Die Großeltern [des Kindes] identifizierten [ihn] mit CBS Philadelphia als [Mahaj Brown]. Case- and number-related errors are less frequent in our data. However, translations of TED talks with S2 contain much more number-related errors than other outputs. Example SECREF28 illustrates this error type which occurs within a sentence. The English source contains the nominal chain in singular the cost – it, whereas the German correspondence Kosten has a plural form and requires a plural pronoun (sie). However, the automatic translation contains the singular pronoun es. . src: ...to the point where [the cost] is now below 1,000 dollars, and it's confidently predicted that by the year 2015 [it] will be below 100 dollars... S2: bis zu dem Punkt, wo [die Kosten] jetzt unter 1.000 Dollar liegen, und es ist zuversichtlich, dass [es] bis zum Jahr 2015 unter 100 Dollar liegen wird... Ambiguous cases often contain a combination of errors or they are difficult to categorise due to the ambiguity of the source pronouns, as the pronoun it in example SECREF28 which may refer either to the noun trouble or even the clause Democracy is in trouble is translated with the pronoun sie (feminine). In case of the first meaning, the pronoun would be correct, but the form of the following verb should be in plural. In case of a singular form, we would need to use a demonstrative pronoun dies (or possibly the personal pronoun es). . src: Democracy is in trouble... and [it] comes in part from a deep dilemma... S2: Die Demokratie steckt in Schwierigkeiten ... und [sie] rührt teilweise aus einem tiefen Dilemma her... ### Results and Analyses ::: Error analysis ::: Additional error types
At first glance, the error types discussed in this section do not seem to be related to coreference —a wrong translation of a noun can be traced back to the training data available and the way NMT deals with unknown words. However, a wrong translation of a noun may result in its invalidity to be a referring expression for a certain discourse item. As a consequence, a coreference chain is damaged. We illustrate a chain with a wrong named entity translation in example SECREF30. The source chain contains five nominal mentions referring to an American gymnast Aly Raisman: silver medalist – “Final Five” teammate – Aly Raisman – Aly Raisman – Raisman. All the three systems used different names. Example SECREF30 illustrates the translation with S2, where Aly Donovan and Aly Encence were used instead of Aly Raisman, and the mention Raisman disappears completely from the chain. . src: Her total of 62.198 was well clear of [silver medalist] and [“Final Five” teammate] [Aly Raisman]...United States' Simone Biles, left, and [Aly Raisman] embrace after winning gold and silver respectively... [Raisman]'s performance was a bit of revenge from four years ago, when [she] tied... S2: Ihre Gesamtmenge von 62.198 war deutlich von [Silbermedaillengewinner] und [“Final Five” Teamkollegen] [Aly Donovan]... Die Vereinigten Staaten Simone Biles, links und [Aly Encence] Umarmung nach dem Gewinn von Gold und Silber... Vor vier Jahren, als [sie]... Example SECREF30 illustrates translation of the chain The scaling in the opposite direction – that scale. The noun phrases Die Verlagerung in die entgegengesetzte Richtung (“the shift in the opposite direction”) and dieses Ausmaß (“extent/scale”) used in the S1 output do not corefer (cf. Wachstum in die entgegengesetzte Richtung and Wachstum in the reference translation). Notice that these cases with long noun phrases are not tackled by S3 either. . src: [The scaling in the opposite direction]...drive the structure of business towards the creation of new kinds of institutions that can achieve [that scale]. ref: [Wachstum in die entgegengesetzte Richtung]... steuert die Struktur der Geschäfte in Richtung Erschaffung von neuen Institutionen, die [dieses Wachstum] erreichen können. S1: [Die Verlagerung in die entgegengesetzte Richtung]... treibt die Struktur der Unternehmen in Richtung der Schaffung neuer Arten von Institutionen, die [dieses Ausmaß] erreichen können. ### Results and Analyses ::: Error analysis ::: Types of erroneous mentions
Finally, we also analyse the types of the mentions marked as errors. They include either nominal phrases or pronouns. Table TABREF32 shows that there is a variation between the news texts and TED talks in terms of these features. News contain more erroneous nominal phrases, whereas TED talks contain more pronoun-related errors. Whereas both the news and the TED talks have more errors in translating anaphors, there is a higher proportion of erroneous antecedents in the news than in the TED talks. It is also interesting to see that S3 reduces the percentage of errors in anaphors for TED, but has a similar performance to S2 on news. ### Summary and Conclusions
We analysed coreferences in the translation outputs of three transformer systems that differ in the training data and in whether they have access to explicit intra- and cross-sentential anaphoric information (S3) or not (S1, S2). We see that the translation errors are more dependent on the genre than on the nature of the specific NMT system: whereas news (with mainly NP mentions) contain a majority of errors related to wrong word selection, TED talks (with mainly pronominal mentions) are prone to accumulate errors on gender and number. System S3 was specifically designed to solve this issue, but we cannot trace the improvement from S1 to S3 by just counting the errors and error types, as some errors disappear and others emerge: coreference quality and automatic translation quality do not correlate in our analysis on TED talks. As a further improvement to address the issue, we could add more parallel data to our training corpus with a higher density of coreference chains such as movie subtitles or parallel TED talks. We also characterised the originals and translations according to coreference features such as total number of chains and mentions, average chain length and size of the longest chain. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do. As future work, we consider a more detailed comparison of the human and machine translations, and analyse the purpose of the additional mentions added by the NMT systems. It would be also interesting to evaluate of the quality of the automatically computed coreferences chains used for S3. ### Acknowledgments
The annotation work was performed at Saarland University. We thank Anna Felsing, Francesco Fernicola, Viktoria Henn, Johanna Irsch, Kira Janine Jebing, Alicia Lauer, Friederike Lessau and Christina Pollkläsener for performing the manual annotation of the NMT outputs. The project on which this paper is based was partially funded by the German Federal Ministry of Education and Research under the funding code 01IW17001 (Deeplee) and by the German Research Foundation (DFG) as part of SFB 1102 Information Density and Linguistic Encoding. Responsibility for the content of this publication is with the authors. Table 1: Number of lines of the corpora used for training the NMT systems under study. The 2nd and 3rd columns show the amount of oversampling used. Table 2: Statistics on coreference features for news and TED texts considered. Table 3: BLEU and METEOR (MTR) scores for the 3 systems on our full test set (all) and the subset of sentences where coreference occurrs (coref ). The number of erroneous mentions is shown for comparison. Figure 1: Number of errors per system (S1, S2, S3) and genre (news, TED). Notice that the total number of errors differs for each plot, total numbers are reported in Table 3. Labels in Figure (b)–S3 apply to all the chart pies that use the same order and color scale for the different error types defined in Section 4.3. Table 4: Percentage of erroneous mentions: antencedent vs. anaphor, and noun phrase vs. pronominal. | first two systems are transformer models trained on different amounts of data, The third system includes a modification to consider the information of full coreference chains |
What is the significance of Rogers Snead?
A. His sighting gives LInton an idea of how to see his wife
B. He serves as proof that Linton is seeing things, and needs professional help
C. Snead is a reminder of a previous stage of Linton's life
D. Linton knows that Snead could take him where he needs to go
| 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. | A. His sighting gives LInton an idea of how to see his wife |
What classification approaches were experimented for this task? | ### Introduction
The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, usually a fact-based answer or a list. Given that the answer is based on an input that consists of a biomedical question and several relevant PubMed abstracts, the task can be seen as an instance of query-based multi-document summarisation. As in past participation BIBREF1, BIBREF2, we wanted to test the use of deep learning and reinforcement learning approaches for extractive summarisation. In contrast with past years where the training procedure was based on a regression set up, this year we experiment with various classification set ups. The main contributions of this paper are: We compare classification and regression approaches and show that classification produces better results than regression but the quality of the results depends on the approach followed to annotate the data labels. We conduct correlation analysis between various ROUGE evaluation metrics and the human evaluations conducted at BioASQ and show that Precision and F1 correlate better than Recall. Section SECREF2 briefly introduces some related work for context. Section SECREF3 describes our classification and regression experiments. Section SECREF4 details our experiments using deep learning architectures. Section SECREF5 explains the reinforcement learning approaches. Section SECREF6 shows the results of our correlation analysis between ROUGE scores and human annotations. Section SECREF7 lists the specific runs submitted at BioASQ 7b. Finally, Section SECREF8 concludes the paper. ### Related Work
The BioASQ challenge has organised annual challenges on biomedical semantic indexing and question answering since 2013 BIBREF0. Every year there has been a task about semantic indexing (task a) and another about question answering (task b), and occasionally there have been additional tasks. The tasks defined for 2019 are: Large Scale Online Biomedical Semantic Indexing. Biomedical Semantic QA involving Information Retrieval (IR), Question Answering (QA), and Summarisation. Medical Semantic Indexing in Spanish. BioASQ Task 7b consists of two phases. Phase A provides a biomedical question as an input, and participants are expected to find relevant concepts from designated terminologies and ontologies, relevant articles from PubMed, relevant snippets from the relevant articles, and relevant RDF triples from designated ontologies. Phase B provides a biomedical question and a list of relevant articles and snippets, and participant systems are expected to return the exact answers and the ideal answers. The training data is composed of the test data from all previous years, and amounts to 2,747 samples. There has been considerable research on the use of machine learning approaches for tasks related to text summarisation, especially on single-document summarisation. Abstractive approaches normally use an encoder-decoder architecture and variants of this architecture incorporate attention BIBREF3 and pointer-generator BIBREF4. Recent approaches leveraged the use of pre-trained models BIBREF5. Recent extractive approaches to summarisation incorporate recurrent neural networks that model sequences of sentence extractions BIBREF6 and may incorporate an abstractive component and reinforcement learning during the training stage BIBREF7. But relatively few approaches have been proposed for query-based multi-document summarisation. Table TABREF8 summarises the approaches presented in the proceedings of the 2018 BioASQ challenge. ### Classification vs. Regression Experiments
Our past participation in BioASQ BIBREF1, BIBREF2 and this paper focus on extractive approaches to summarisation. Our decision to focus on extractive approaches is based on the observation that a relatively large number of sentences from the input snippets has very high ROUGE scores, thus suggesting that human annotators had a general tendency to copy text from the input to generate the target summaries BIBREF1. Our past participating systems used regression approaches using the following framework: Train the regressor to predict the ROUGE-SU4 F1 score of the input sentence. Produce a summary by selecting the top $n$ input sentences. A novelty in the current participation is the introduction of classification approaches using the following framework. Train the classifier to predict the target label (“summary” or “not summary”) of the input sentence. Produce a summary by selecting all sentences predicted as “summary”. If the total number of sentences selected is less than $n$, select $n$ sentences with higher probability of label “summary”. Introducing a classifier makes labelling the training data not trivial, since the target summaries are human-generated and they do not have a perfect mapping to the input sentences. In addition, some samples have multiple reference summaries. BIBREF11 showed that different data labelling approaches influence the quality of the final summary, and some labelling approaches may lead to better results than using regression. In this paper we experiment with the following labelling approaches: : Label as “summary” all sentences from the input text that have a ROUGE score above a threshold $t$. : Label as “summary” the $m$ input text sentences with highest ROUGE score. As in BIBREF11, The ROUGE score of an input sentence was the ROUGE-SU4 F1 score of the sentence against the set of reference summaries. We conducted cross-validation experiments using various values of $t$ and $m$. Table TABREF26 shows the results for the best values of $t$ and $m$ obtained. The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. To enable a fair comparison we used the same input features in all systems. These input features combine information from the question and the input sentence and are shown in Fig. FIGREF16. The features are based on BIBREF12, and are the same as in BIBREF1, plus the addition of the position of the input snippet. The best SVC and SVR parameters were determined by grid search. Preliminary experiments showed a relatively high number of cases where the classifier did not classify any of the input sentences as “summary”. To solve this problem, and as mentioned above, the summariser used in Table TABREF26 introduces a backoff step that extracts the $n$ sentences with highest predicted values when the summary has less than $n$ sentences. The value of $n$ is as reported in our prior work and shown in Table TABREF25. The results confirm BIBREF11's finding that classification outperforms regression. However, the actual choice of optimal labelling scheme was different: whereas in BIBREF11 the optimal labelling was based on a labelling threshold of 0.1, our experiments show a better result when using the top 5 sentences as the target summary. The reason for this difference might be the fact that BIBREF11 used all sentences from the abstracts of the relevant PubMed articles, whereas we use only the snippets as the input to our summariser. Consequently, the number of input sentences is now much smaller. We therefore report the results of using the labelling schema of top 5 snippets in all subsequent classifier-based experiments of this paper. barchart=[fill=black!20,draw=black] errorbar=[very thin,draw=black!75] sscale=[very thin,draw=black!75] ### Deep Learning Models
Based on the findings of Section SECREF3, we apply minimal changes to the deep learning regression models of BIBREF2 to convert them to classification models. In particular, we add a sigmoid activation to the final layer, and use cross-entropy as the loss function. The complete architecture is shown in Fig. FIGREF28. The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. Of interest is the fact that “NNC SU4 F1” outperforms the neural regressor. We have not explored this further and we presume that the relatively good results are due to the fact that ROUGE values range between 0 and 1, which matches the full range of probability values that can be returned by the sigmoid activation of the classifier final layer. Table TABREF26 also shows the standard deviation across the cross-validation folds. Whereas this standard deviation is fairly large compared with the differences in results, in general the results are compatible with the top part of the table and prior work suggesting that classification-based approaches improve over regression-based approaches. ### Reinforcement Learning
We also experiment with the use of reinforcement learning techniques. Again these experiments are based on BIBREF2, who uses REINFORCE to train a global policy. The policy predictor uses a simple feedforward network with a hidden layer. The results reported by BIBREF2 used ROUGE Recall and indicated no improvement with respect to deep learning architectures. Human evaluation results are preferable over ROUGE but these were made available after the publication of the paper. When comparing the ROUGE and human evaluation results (Table TABREF29), we observe an inversion of the results. In particular, the reinforcement learning approaches (RL) of BIBREF2 receive good human evaluation results, and as a matter of fact they are the best of our runs in two of the batches. In contrast, the regression systems (NNR) fare relatively poorly. Section SECREF6 expands on the comparison between the ROUGE and human evaluation scores. Encouraged by the results of Table TABREF29, we decided to continue with our experiments with reinforcement learning. We use the same features as in BIBREF2, namely the length (in number of sentences) of the summary generated so far, plus the $tf.idf$ vectors of the following: Candidate sentence; Entire input to summarise; Summary generated so far; Candidate sentences that are yet to be processed; and Question. The reward used by REINFORCE is the ROUGE value of the summary generated by the system. Since BIBREF2 observed a difference between the ROUGE values of the Python implementation of ROUGE and the original Perl version (partly because the Python implementation does not include ROUGE-SU4), we compare the performance of our system when trained with each of them. Table TABREF35 summarises some of our experiments. We ran the version trained on Python ROUGE once, and the version trained on Perl twice. The two Perl runs have different results, and one of them clearly outperforms the Python run. However, given the differences of results between the two Perl runs we advice to re-run the experiments multiple times and obtain the mean and standard deviation of the runs before concluding whether there is any statistical difference between the results. But it seems that there may be an improvement of the final evaluation results when training on the Perl ROUGE values, presumably because the final evaluation results are measured using the Perl implementation of ROUGE. We have also tested the use of word embeddings instead of $tf.idf$ as input features to the policy model, while keeping the same neural architecture for the policy (one hidden layer using the same number of hidden nodes). In particular, we use the mean of word embeddings using 100 and 200 dimensions. These word embeddings were pre-trained using word2vec on PubMed documents provided by the organisers of BioASQ, as we did for the architectures described in previous sections. The results, not shown in the paper, indicated no major improvement, and re-runs of the experiments showed different results on different runs. Consequently, our submission to BioASQ included the original system using $tf.idf$ as input features in all batches but batch 2, as described in Section SECREF7. ### Evaluation Correlation Analysis
As mentioned in Section SECREF5, there appears to be a large discrepancy between ROUGE Recall and the human evaluations. This section describes a correlation analysis between human and ROUGE evaluations using the runs of all participants to all previous BioASQ challenges that included human evaluations (Phase B, ideal answers). The human evaluation results were scraped from the BioASQ Results page, and the ROUGE results were kindly provided by the organisers. We compute the correlation of each of the ROUGE metrics (recall, precision, F1 for ROUGE-2 and ROUGE-SU4) against the average of the human scores. The correlation metrics are Pearson, Kendall, and a revised Kendall correlation explained below. The Pearson correlation between two variables is computed as the covariance of the two variables divided by the product of their standard deviations. This correlation is a good indication of a linear relation between the two variables, but may not be very effective when there is non-linear correlation. The Spearman rank correlation and the Kendall rank correlation are two of the most popular among metrics that aim to detect non-linear correlations. The Spearman rank correlation between two variables can be computed as the Pearson correlation between the rank values of the two variables, whereas the Kendall rank correlation measures the ordinal association between the two variables using Equation DISPLAY_FORM36. It is useful to account for the fact that the results are from 28 independent sets (3 batches in BioASQ 1 and 5 batches each year between BioASQ 2 and BioASQ 6). We therefore also compute a revised Kendall rank correlation measure that only considers pairs of variable values within the same set. The revised metric is computed using Equation DISPLAY_FORM37, where $S$ is the list of different sets. Table TABREF38 shows the results of all correlation metrics. Overall, ROUGE-2 and ROUGE-SU4 give similar correlation values but ROUGE-SU4 is marginally better. Among precision, recall and F1, both precision and F1 are similar, but precision gives a better correlation. Recall shows poor correlation, and virtually no correlation when using the revised Kendall measure. For reporting the evaluation of results, it will be therefore more useful to use precision or F1. However, given the small difference between precision and F1, and given that precision may favour short summaries when used as a function to optimise in a machine learning setting (e.g. using reinforcement learning), it may be best to use F1 as the metric to optimise. Fig. FIGREF40 shows the scatterplots of ROUGE-SU4 recall, precision and F1 with respect to the average human evaluation. We observe that the relation between ROUGE and the human evaluations is not linear, and that Precision and F1 have a clear correlation. ### Submitted Runs
Table TABREF41 shows the results and details of the runs submitted to BioASQ. The table uses ROUGE-SU4 Recall since this is the metric available at the time of writing this paper. However, note that, as explained in Section SECREF6, these results might differ from the final human evaluation results. Therefore we do not comment on the results, other than observing that the “first $n$” baseline produces the same results as the neural regressor. As mentioned in Section SECREF3, the labels used for the classification experiments are the 5 sentences with highest ROUGE-SU4 F1 score. ### Conclusions
Macquarie University's participation in BioASQ 7 focused on the task of generating the ideal answers. The runs use query-based extractive techniques and we experiment with classification, regression, and reinforcement learning approaches. At the time of writing there were no human evaluation results, and based on ROUGE-F1 scores under cross-validation on the training data we observed that classification approaches outperform regression approaches. We experimented with several approaches to label the individual sentences for the classifier and observed that the optimal labelling policy for this task differed from prior work. We also observed poor correlation between ROUGE-Recall and human evaluation metrics and suggest to use alternative automatic evaluation metrics with better correlation, such as ROUGE-Precision or ROUGE-F1. Given the nature of precision-based metrics which could bias the system towards returning short summaries, ROUGE-F1 is probably more appropriate when using at development time, for example for the reward function used by a reinforcement learning system. Reinforcement learning gives promising results, especially in human evaluations made on the runs submitted to BioASQ 6b. This year we introduced very small changes to the runs using reinforcement learning, and will aim to explore more complex reinforcement learning strategies and more complex neural models in the policy and value estimators. Table 1. Summarisation techniques used in BioASQ 6b for the generation of ideal answers. The evaluation result is the human evaluation of the best run. Fig. 2. Architecture of the neural classification and regression systems. A matrix of pre-trained word embeddings (same pre-trained vectors as in Fig. 1) is used to find the embeddings of the words of the input sentence and the question. Then, LSTM chains are used to generate sentence embeddings — the weights of the LSTM chains of input sentence and question are not shared. Then, the sentence position is concatenated to the sentence embedding and the similarity of sentence and question embeddings, implemented as a product. A final layer predicts the label of the sentence. Table 5. Experiments using Perl and Python versions of ROUGE. The Python version used the average of ROUGE-2 and ROUGE-L, whereas the Perl version used ROUGESU4. Table 6. Correlation analysis of evaluation results Table 7. Runs submitted to BioASQ 7b Fig. 3. Scatterplots of ROUGE SU4 evaluation metrics against the average human evaluations. | NNC SU4 F1, NNC top 5, Support Vector Classification (SVC) |
Among operations, investing, and financing activities, which brought in the most (or lost the least) cash flow for Best Buy in FY2023? | Evidence 0:
Consolidated Statements of Cash Flows
$ in millions
Fiscal Years Ended
January 28, 2023
January 29, 2022
January 30, 2021
Operating activities
Net earnings
$
1,419
$
2,454
$
1,798
Adjustments to reconcile net earnings to total cash provided by operating activities:
Depreciation and amortization
918
869
839
Restructuring charges
147
(34)
254
Stock-based compensation
138
141
135
Deferred income taxes
51
14
(36)
Other, net
12
11
3
Changes in operating assets and liabilities, net of acquired assets and liabilities:
Receivables
(103)
17
73
Merchandise inventories
809
(328)
(435)
Other assets
(21)
(14)
(51)
Accounts payable
(1,099)
(201)
1,676
Income taxes
36
(156)
173
Other liabilities
(483)
479
498
Total cash provided by operating activities
1,824
3,252
4,927
Investing activities
Additions to property and equipment, net of $35, $46 and $32, respectively, of non-cash
capital expenditures
(930)
(737)
(713)
Purchases of investments
(46)
(233)
(620)
Sales of investments
7
66
546
Acquisitions, net of cash acquired
-
(468)
-
Other, net
7
-
(1)
Total cash used in investing activities
(962)
(1,372)
(788)
Financing activities
Repurchase of common stock
(1,014)
(3,502)
(312)
Issuance of common stock
16
29
28
Dividends paid
(789)
(688)
(568)
Borrowings of debt
-
-
1,892
Repayments of debt
(19)
(133)
(1,916)
Other, net
-
(3)
-
Total cash used in financing activities
(1,806)
(4,297)
(876)
Effect of exchange rate changes on cash
(8)
(3)
7
Increase (decrease) in cash, cash equivalents and restricted cash
(952)
(2,420)
3,270
Cash, cash equivalents and restricted cash at beginning of period
3,205
5,625
2,355
Cash, cash equivalents and restricted cash at end of period
$
2,253
$
3,205
$
5,625 | Best Buy generated the most cash flow from operating activities in FY 2023 ($1.8 bn) |
What is the FY2019 fixed asset turnover ratio for Activision Blizzard? Fixed asset turnover ratio is defined as: FY2019 revenue / (average PP&E between FY2018 and FY2019). Round your answer to two decimal places. Base your judgments on the information provided primarily in the statement of income and the statement of financial position. | Evidence 0:
Table of Contents
ACTIVISION BLIZZARD, INC. AND SUBSIDIARIES
CONSOLIDATED BALANCE SHEETS
(Amounts in millions, except share data)
At December 31, 2019
At December 31, 2018
Assets
Current assets:
Cash and cash equivalents
$
5,794
$
4,225
Accounts receivable, net of allowances of $132 and $190, at December 31, 2019 and December 31, 2018, respectively
848
1,035
Inventories, net
32
43
Software development
322
264
Other current assets
296
539
Total current assets
7,292
6,106
Software development
54
65
Property and equipment, net
253
282
Deferred income taxes, net
1,293
458
Other assets
658
482
Intangible assets, net
531
735
Goodwill
9,764
9,762
Total assets
$
19,845
$
17,890
Liabilities and Shareholders Equity
Current liabilities:
Accounts payable
$
292
$
253
Deferred revenues
1,375
1,493
Accrued expenses and other liabilities
1,248
896
Total current liabilities
2,915
2,642
Long-term debt, net
2,675
2,671
Deferred income taxes, net
505
18
Other liabilities
945
1,167
Total liabilities
7,040
6,498
Commitments and contingencies (Note 23)
Shareholders equity:
Common stock, $0.000001 par value, 2,400,000,000 shares authorized, 1,197,436,644 and 1,192,093,991 shares issued at
December 31, 2019 and December 31, 2018, respectively
Additional paid-in capital
11,174
10,963
Less: Treasury stock, at cost, 428,676,471 shares at December 31, 2019 and December 31, 2018
(5,563)
(5,563)
Retained earnings
7,813
6,593
Accumulated other comprehensive loss
(619)
(601)
Total shareholders equity
12,805
11,392
Total liabilities and shareholders equity
$
19,845
$
17,890
The accompanying notes are an integral part of these Consolidated Financial Statements.
F-4
Evidence 1:
Table of Contents
ACTIVISION BLIZZARD, INC. AND SUBSIDIARIES
CONSOLIDATED STATEMENTS OF OPERATIONS
(Amounts in millions, except per share data)
For the Years Ended December 31,
2019
2018
2017
Net revenues
Product sales
$
1,975
$
2,255 $
2,110
Subscription, licensing, and other revenues
4,514
5,245
4,907
Total net revenues
6,489
7,500
7,017
Costs and expenses
Cost of revenuesproduct sales:
Product costs
656
719
733
Software royalties, amortization, and intellectual property licenses
240
371
300
Cost of revenuessubscription, licensing, and other revenues:
Game operations and distribution costs
965
1,028
984
Software royalties, amortization, and intellectual property licenses
233
399
484
Product development
998
1,101
1,069
Sales and marketing
926
1,062
1,378
General and administrative
732
822
745
Restructuring and related costs
132
10
15
Total costs and expenses
4,882
5,512
5,708
Operating income
1,607
1,988
1,309
Interest and other expense (income), net (Note 18)
(26)
71
146
Loss on extinguishment of debt
40
12
Income before income tax expense
1,633
1,877
1,151
Income tax expense
130
29
878
Net income
$
1,503
$
1,848 $
273
Earnings per common share
Basic
$
1.96
$
2.43 $
0.36
Diluted
$
1.95
$
2.40 $
0.36
Weighted-average number of shares outstanding
Basic
767
762
754
Diluted
771
771
766
The accompanying notes are an integral part of these Consolidated Financial Statements.
F-5 | 24.26 |
Why did the crew mind that the cave-cat had kittens?
A. They didn't perform well while they were small.
B. They were too dangerous to keep onboard
C. They had no food for more mouths to feed
D. One had only four legs
| 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. | D. One had only four legs |
Who was Sally in relation to Milly in the story?
A. Her great-grandmother
B. Her grandmother
C. Her mother
D. Herself in a past life.
| RATTLE OK By HARRY WARNER, JR. Illustrated by FINLAY [Transcriber's Note: This etext was produced from Galaxy Science Fiction December 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] What better way to use a time machine than to handle department store complaints? But pleasing a customer should have its limits! The Christmas party at the Boston branch of Hartshorne-Logan was threatening to become more legendary than usual this Christmas. The farm machinery manager had already collapsed. When he slid under the table containing the drinks, Miss Pringle, who sold millinery, had screamed: "He'll drown!" One out of every three dirty stories started by party attendees had remained unfinished, because each had reminded someone else of another story. The recently developed liquors which affected the bloodstream three times faster had driven away twinges of conscience about untrimmed trees and midnight church services. The star salesman for mankies and the gentleman who was in charge of the janitors were putting on a display of Burmese foot-wrestling in one corner of the general office. The janitor foreman weighed fifty pounds less than the Burma gentleman, who was the salesman's customary opponent. So the climax of one tactic did not simply overturn the foreman. He glided through the air, crashing with a very loud thump against the wall. He wasn't hurt. But the impact knocked the hallowed portrait of H. H. Hartshorne, co-founder, from its nail. It tinkled imposingly as its glass splintered against the floor. The noise caused a temporary lull in the gaiety. Several employes even felt a passing suspicion that things might be getting out of hand. "It's all in the spirit of good, clean fun!" cried Mr. Hawkins, the assistant general manager. Since he was the highest executive present, worries vanished. Everyone felt fine. There was a scurry to shove the broken glass out of sight and to turn more attention to another type of glasses. Mr. Hawkins himself, acting by reflex, attempted to return the portrait to its place until new glass could be obtained. But the fall had sprung the frame at one corner and it wouldn't hang straight. "We'd better put old H. H. away for safekeeping until after the holiday," he told a small, blonde salesclerk who was beneath his attention on any working day. With the proper mixture of respect and bonhommie, he lifted the heavy picture out of its frame. A yellowed envelope slipped to the floor as the picture came free. Hawkins rolled the picture like a scroll and put it into a desk drawer, for later attention. Then he looked around for a drink that would make him feel even better. A sorting clerk in the mail order department wasn't used to liquor. She picked up the envelope and looked around vaguely for the mail-opening machine. "Hell, Milly, you aren't working!" someone shouted at her. "Have another!" Milly snapped out of it. She giggled, suppressed a ladylike belch and returned to reality. Looking at the envelope, she said: "Oh, I see. They must have stuck it in to tighten the frame. Gee, it's old." Mr. Hawkins had refreshed himself. He decided that he liked Milly's voice. To hear more of it, he said to her: "I'll bet that's been in there ever since the picture was framed. There's a company legend that that picture was put up the day this branch opened, eighty years ago." "I didn't know the company ever used buff envelopes like this." Milly turned it over in her hands. The ancient glue crackled as she did so. The flap popped open and an old-fashioned order blank fell out. Mr. Hawkins' eyes widened. He bent, reached painfully over his potbelly and picked up the order form. "This thing has never been processed!" Raising his voice, he shouted jovially, "Hey, people! You're all fired! Here's an order that Hartshorne-Logan never filled! We can't have such carelessness. This poor woman has waited eighty years for her merchandise!" Milly was reading aloud the scrawled words on the order form: "Best electric doorbell. Junior detective kit. Disposable sacks for vacuum cleaner. Dress for three-year-old girl." She turned to the assistant general manager, struck with an idea for the first time in her young life. "Let's fill this order right now!" "The poor woman must be dead by now," he objected, secretly angry that he hadn't thought of such a fine party stunt himself. Then he brightened. "Unless—" he said it loud enough for the employes to scent a great proposal and the room grew quiet—"unless we broke the rules just once and used the time warp on a big mission!" There was a silence. Finally, from an anonymous voice in one corner: "Would the warp work over eighty years? We were always told that it must be used only for complaints within three days." "Then let's find out!" Mr. Hawkins downed the rest of his drink and pulled a batch of keys from his pocket. "Someone scoot down to the warehouse. Tell the watchman that it's on my authority. Hunt up the stuff that's on the order. Get the best of everything. Ignore the catalogue numbers—they've changed a hundred times in all these years." Milly was still deciphering the form. Now she let out a little squeal of excitement. "Look, Mr. Hawkins! The name on this order—it's my great-grandmother! Isn't that wonderful? I was just a little girl when she died. I can barely remember her as a real old woman. But I remember that my grandmother never bought anything from Hartshorne-Logan because of some trouble her mother had once with the firm. My mother didn't want me to come to work here because of that." Mr. Hawkins put his arm around Milly in a way that he intended to look fatherly. It didn't. "Well, now. Since it's your relative, let's thrill the old girl. We wouldn't have vacuum sacks any more. So we'll substitute a manky!" Ann Hartley was returning from mailing the letter when she found the large parcel on her doorstep. She put her hands on her hips and stared pugnaciously at the bundle. "The minute I write a letter to complain about you, you turn up!" she told the parcel. She nudged her toe peevishly against the brown paper wrappings that were tied with a half-transparent twine she had never seen before. The label was addressed in a wandering scrawl, a sharp contrast to the impersonal typing on the customary Hartshorne-Logan bundles. But the familiar RATTLE OK sticker was pasted onto the box, indicating to the delivery man that the contents would make a rattling sound and therefore hadn't been broken in shipment. Ann sighed and picked up her bundle. With a last look at the lovely spring afternoon and the quiet suburban landscape, she went into the house. Two-year-old Sally heard the box rattling. She waddled up on chubby legs and grabbed her mother's skirt. "Want!" she said decisively. "Your dress ought to be here," Ann said. She found scissors in her sewing box, tossed a cushion onto the floor, sat on it, and began to open the parcel. "Now I'll have to write another letter to explain that they should throw away my letter of complaint," she told her daughter. "And by the time they get my second letter, they'll have answered my first letter. Then they'll write again." Out of consideration for Sally, she omitted the expletives that she wanted to add. The translucent cord was too tough for the scissors. Ann was about to hunt for a razor blade when Sally clutched at an intersection of the cord and yanked. The twine sprang away from the carton as if it were alive. The paper wrappings flapped open. "There!" Sally said. Ann repressed an irrational urge to slap her daughter. Instead, she tossed the wrappings aside and removed the lid from the carton. A slightly crushed thin cardboard box lay on top. Ann pulled out the dress and shook it into a freely hanging position. Then she groaned. It was green and she had ordered blue. It didn't remotely resemble the dress she had admired from the Hartshorne-Logan catalogue illustration. Moreover, the shoulders were lumpier than any small girl's dress should be. But Sally was delighted. "Mine!" she shrilled, grabbing for the dress. "It's probably the wrong size, too," Ann said, pulling off Sally's dress to try it on. "Let's find as many things to complain about as we can." The dress fitted precisely, except for the absurd shoulder bumps. Sally was radiant for a moment. Then her small face sobered and she started to look vacantly at the distant wall. "We'll have to send it back," Ann said, "and get the one we ordered." She tried to take it off, but the child squawked violently. Ann grabbed her daughter's arms, held them above her head and pulled at the dress. It seemed to be stuck somewhere. When Ann released the child's arms to loosen the dress, Sally squirmed away. She took one step forward, then began to float three inches above the ground. She landed just before she collided with the far wall. Sally looked scared until she saw her mother's face. Then she squealed in delight. Ann's legs were rubber. She was shaking her head and wobbling uncertainly toward her daughter when the door opened behind her. "It's me," her husband said. "Slow day at the office, so I came home early." "Les! I'm going crazy or something. Sally just—" Sally crouched to jump at her father. Before she could leap, he grabbed her up bodily and hugged her. Then he saw the box. "Your order's here? Good. What's this thing?" He was looking at a small box he had pulled from the carton. Its lid contained a single word: MANKY. The box rattled when he shook it. Les pulled off the lid and found inside a circular, shiny metal object. A triangular trio of jacks stuck out from one end. "Is this the doorbell? I've never seen a plug like this. And there's no wire." "I don't know," Ann said. "Les, listen. A minute ago, Sally—" He peered into the box for an instruction sheet, uselessly. "They must have made a mistake. It looks like some kind of farm equipment." He tossed the manky onto the hassock and delved into the carton again. Sally was still in his arms. "That's the doorbell, I think," he said, looking at the next object. It had a lovely, tubular shape, a half-dozen connecting rods and a plug for a wall socket. "That's funny," Ann mused, her mind distracted from Sally for a moment. "It looks terribly expensive. Maybe they sent door chimes instead of the doorbell." The bottom of the carton contained the detective outfit that they had ordered for their son. Ann glanced at its glaringly lithographed cover and said: "Les, about Sally. Put her down a minute and watch what she does." Les stared at his wife and put the child onto the rug. Sally began to walk, then rose and again floated, this time toward the hassock on which the manky lay. His jaw dropped. "My God! Ann, what—" Ann was staring, too, but not at her daughter. "Les! The hassock! It used to be brown!" The hassock was a livid shade of green. A neon, demanding, screaming green that clashed horribly with the soft browns and reds in which Ann had furnished the room. "That round thing must be leaking," Les said. "But did you see Sally when she—" Ann's frazzled nerves carried a frantic order to her muscles. She jumped up, strode to the hassock and picked up the manky with two fingers. She tossed it to Les. Immediately, she regretted her action. "Drop it!" she yelled. "Maybe it'll turn you green, too!" Les kicked the hassock into the hall closet, tossed the manky in after it and shut the door firmly. As the door closed, he saw the entire interior of the dark closet brighten into a wet-lettuce green. When he turned back to Ann, she was staring at her left hand. The wedding band that Les had put there a dozen years ago was a brilliant green, shedding its soft glow over the finger up to the first knuckle. Ann felt the scream building up inside her. She opened her mouth to let it out, then put her hand in front of her mouth to keep it in, finally jerked the hand away to prevent the glowing ring from turning her front teeth green. She collapsed into Les's arms, babbling incomprehensibly. He said: "It's all right. There must be balloons or something in the shoulders of that dress. I'll tie a paperweight to Sally's dress and that'll hold her down until we undress her. Don't worry. And that green dye or whatever it is will wash off." Ann immediately felt better. She put her hands behind her back, pulled off her ring and slipped it into her apron pocket. Les was sentimental about her removing it. "I'll get dinner," she said, trying to keep her voice on an even keel. "Maybe you'd better start a letter to Hartshorne-Logan. Let's go into the kitchen, Sally." Ann strode resolutely toward the rear of the house. She kept her eyes determinedly off the tinge of green that was showing through the apron pocket and didn't dare look back at her daughter's unsettling means of propulsion. A half-hour later, when the meal was almost ready, two things happened: Bob came home from school through the back door and a strange voice said from the front of the house, "Don't answer the front door." Ann stared at her son. He stared back at her, the detective outfit under his arm. She went into the front room. Her husband was standing with fists on hips, looking at the front door, chuckling. "Neatest trick I've seen in a long time. That voice you heard was the new doorbell. I put it up while you were in the kitchen. Did you hear what happened when old lady Burnett out there pushed the button?" "Oh. Something like those name cards with something funny printed on them, like 'Another hour shot.' Well, if there's a little tape in there repeating that message, you'd better shut that part off. It might get boring after a while. And it might insult someone." Ann went to the door and turned the knob. The door didn't open. The figure of Mrs. Burnett, half-visible through the heavy curtain, shifted impatiently on the porch. Les yanked at the doorknob. It didn't yield for him, either. He looked up at the doorbell, which he had installed just above the upper part of the door frame. "Queer," he said. "That isn't in contact with the door itself. I don't see how it can keep the door from opening." Ann put her mouth close to the glass, shouting: "Won't you come to the back door, Mrs. Burnett? This one is stuck." "I just wanted to borrow some sugar," the woman cried from the porch. "I realize that I'm a terrible bother." But she walked down the front steps and disappeared around the side of the house. "Don't open the back door." The well-modulated voice from the small doorbell box threatened to penetrate every corner of the house. Ann looked doubtfully at her husband's lips. They weren't moving. "If this is ventriloquism—" she began icily. "I'll have to order another doorbell just like this one, for the office," Les said. "But you'd better let the old girl in. No use letting her get peeved." The back door was already open, because it was a warm day. The screen door had no latch, held closed by a simple spring. Ann pushed it open when Mrs. Burnett waddled up the three back steps, and smiled at her neighbor. "I'm so sorry you had to walk around the house. It's been a rather hectic day in an awful lot of ways." Something seemed to impede Mrs. Burnett as she came to the threshold. She frowned and shoved her portly frame against something invisible. It apparently yielded abruptly, because she staggered forward into the kitchen, nearly falling. She stared grimly at Ann and looked suspiciously behind her. "The children have some new toys," Ann improvised hastily. "Sally is so excited over a new dress that she's positively feverish. Let's see now—it was sugar that you want, wasn't it?" "I already have it," Bob said, handing a filled cup to his mother. The boy turned back to the detective set which he had spread over the kitchen table. "Excitement isn't good for me," Mrs. Burnett said testily. "I've had a lot of troubles in my life. I like peace and quiet." "Your husband is better?" "Worse. I'm sure I don't know why everything happens to me." Mrs. Burnett edged toward the hall, trying to peer into the front of the house. Ann stood squarely in front of the door leading to the hall. Defeated, Mrs. Burnett left. A muffled volley of handclapping, mixed with a few faint cheers, came from the doorbell-box when she crossed the threshold. Ann went into the hall to order Les to disconnect the doorbell. She nearly collided with him, coming in the other direction. "Where did this come from?" Les held a small object in the palm of his hand, keeping it away from his body. A few drops of something unpleasant were dripping from his fingers. The object looked remarkably like a human eyeball. It was human-size, complete with pupil, iris and rather bloodshot veins. "Hey, that's mine," Bob said. "You know, this is a funny detective kit. That was in it. But there aren't instructions on how it works." "Well, put it away," Ann told Bob sharply. "It's slimy." Les laid the eyeball on the table and walked away. The eyeball rolled from the smooth, level table, bounced twice when it hit the floor, then rolled along, six inches behind him. He turned and kicked at it. The eyeball rolled nimbly out of the path of the kick. "Les, I think we've made poor Mrs. Burnett angry," Ann said. "She's so upset over her poor husband's health and she thinks we're insulting her." Les didn't hear her. He strode to the detective set, followed at a safe distance by the eyeball, and picked up the box. "Hey, watch out!" Bob cried. A small flashlight fell from the box, landed on its side and its bulb flashed on, throwing a pencil of light across Les's hands. Bob retrieved the flashlight and turned it off while Les glanced through an instruction booklet, frowning. "This toy is too complicated for a ten-year-old boy," Les told his wife. "I don't know why you ordered such a thing." He tossed the booklet into the empty box. "I'm going to return it, if you don't smudge it up," she replied. "Look at the marks you made on the instructions." The black finger-marks stood out clearly against the shiny, coated paper. Les looked at his hands. "I didn't do it," he said, pressing his clean fingertips against the kitchen table. Black fingerprints, a full set of them, stood out against the sparkling polished table's surface. "I think the Detectolite did it," Bob said. "The instructions say you've got to be very careful with it, because its effects last for a long time." Les began scrubbing his hands vigorously at the sink. Ann watched him silently, until she saw his fingerprints appear on the faucet, the soap and the towel. She began to yell at him for making such a mess, when Sally floated into the kitchen. The girl was wearing a nightgown. "My God!" Ann forgot her tongue before the children. "She got out of that dress herself. Where did she get that nightgown?" Ann fingered the garment. She didn't recognize it as a nightgown. But in cut and fold, it was suspiciously like the dress that had arrived in the parcel. Her heart sank. She picked up the child, felt the hot forehead, and said: "Les, I think it's the same dress. It must change color or something when it's time for a nap. It seems impossible, but—" She shrugged mutely. "And I think Sally's running a temperature. I'm going to put her to bed." She looked worriedly into the reddened eyes of the small girl, who whimpered on the way to the bedroom. Ann carried her up the stairs, keeping her balance with difficulty, as Sally threatened to pop upward out of her arms. The whole family decided that bed might be a good idea, soon after dinner. When the lights went out, the house seemed to be nearly normal. Les put on a pair of gloves and threw a pillowcase over the eyeball. Bob rigged up trestles to warn visitors from the front porch. Ann put small wads of cotton into her ears, because she didn't like the rhythmic rattle, soft but persistent, that emerged from the hall closet where the manky sat. Sally was whining occasionally in her sleep. When daylight entered her room, Sally's nightgown had turned back into the new dress. But the little girl was too sick to get out of bed. She wasn't hungry, her nose was running, and she had a dry cough. Les called the doctor before going to work. The only good thing about the morning for Ann was the fact that the manky had quieted down some time in the night. After she got Bob to school, she gingerly opened the closet door. The manky was now glowing a bright pink and seemed slightly larger. Deep violet lettering stood out on its side: " Today is Wednesday. For obvious reasons, the manky will not operate today. " The mailman brought a letter from Hartshorne-Logan. Ann stared stupidly at the envelope, until she realized that this wasn't an impossibly quick answer to the letter she had written yesterday. It must have crossed in the mail her complaint about the non-arrival of the order. She tore open the envelope and read: "We regret to inform you that your order cannot be filled until the balance you owe us has been reduced. From the attached form, you will readily ascertain that the payment of $87.56 will enable you to resume the purchasing of merchandise on credit. We shall fill your recent order as soon...." Ann crumpled the letter and threw it into the imitation fireplace, knowing perfectly well that it would need to be retrieved for Les after work tonight. She had just decided to call Hartshorne-Logan's complaint department when the phone rang. "I'm afraid I must ask you to come down to the school, Mrs. Morris," a voice said. "Your son is in trouble. He claims that it's connected with something that his parents gave him." "My son?" Ann asked incredulously. "Bob?" "Yes. It's a little gadget that looks like a water pistol. Your son insists that he didn't know it would make clothing transparent. He claims it was just accident that he tried it out when he was walking by the gym during calisthenics. We've had to call upon every family in the neighborhood for blankets. Bob has always been a good boy and we believe that we can expel him quietly without newspaper publicity involving his name, if you'll—" "I'll be right down," Ann said. "I mean I won't be right down. I've got a sick baby here. Don't do anything till I telephone my husband. And I'm sorry for Bob. I mean I'm sorry for the girls, and for the boys, too. I'm sorry for—for everything. Good-by." Just as she hung up the telephone, the doorbell rang. It rang with a normal buzz, then began to play soft music. Ann opened the door without difficulty, to admit Dr. Schwartz. "You aren't going to believe me, Doctor," Ann said while he took the child's temperature, "but we can't get that dress off Sally." "Kids are stubborn sometimes." Dr. Schwartz whistled softly when he looked at the thermometer. "She's pretty sick. I want a blood count before I try to move her. Let me undress her." Sally had been mumbling half-deliriously. She made no effort to resist as the doctor picked her up. But when he raised a fold of the dress and began to pull it back, she screamed. The doctor dropped the dress and looked in perplexity at the point where it touched Sally's skin. "It's apparently an allergy to some new kind of material. But I don't understand why the dress won't come off. It's not stuck tight." "Don't bother trying," Ann said miserably. "Just cut it off." Dr. Schwartz pulled scissors from his bag and clipped at a sleeve. When he had cut it to the shoulder, he gently began to peel back the edges of the cloth. Sally writhed and kicked, then collapsed in a faint. The physician smoothed the folds hastily back into place. He looked helpless as he said to Ann: "I don't know quite what to do. The flesh starts to hemorrhage when I pull at the cloth. She'd bleed to death if I yanked it off. But it's such an extreme allergy that it may kill her, if we leave it in contact with the skin." The manky's rattle suddenly began rhythmically from the lower part of the house. Ann clutched the side of the chair, trying to keep herself under control. A siren wailed somewhere down the street, grew louder rapidly, suddenly going silent at the peak of its crescendo. Dr. Schwartz glanced outside the window. "An ambulance. Looks as if they're stopping here." "Oh, no," Ann breathed. "Something's happened to Les." "It sure will," Les said grimly, walking into the bedroom. "I won't have a job if I can't get this stuff off my fingers. Big black fingerprints on everything I touch. I can't handle correspondence or shake hands with customers. How's the kid? What's the ambulance doing out front?" "They're going to the next house down the street," the physician said. "Has there been sickness there?" Les held up his hands, palms toward the doctor. "What's wrong with me? My fingers look all right. But they leave black marks on everything I touch." The doctor looked closely at the fingertips. "Every human has natural oil on the skin. That's how detectives get results with their fingerprint powder. But I've never heard of nigrification, in this sense. Better not try to commit any crimes until you've seen a skin specialist." Ann was peering through the window, curious about the ambulance despite her own troubles. She saw two attendants carry Mr. Burnett, motionless and white, on a stretcher from the house next door into the ambulance. A third member of the crew was struggling with a disheveled Mrs. Burnett at the door. Shrieks that sounded like "Murder!" came sharply through the window. "I know those bearers," Dr. Schwartz said. He yanked the window open. "Hey, Pete! What's wrong?" The front man with the stretcher looked up. "I don't know. This guy's awful sick. I think his wife is nuts." Mrs. Burnett had broken free. She dashed halfway down the sidewalk, gesticulating wildly to nobody in particular. "It's murder!" she screamed. "Murder again! He's been poisoned! He's going to die! It means the electric chair!" The orderly grabbed her again. This time he stuffed a handkerchief into her mouth to quiet her. "Come back to this house as soon as you deliver him," Dr. Schwartz shouted to the men. "We've got a very sick child up here." "I was afraid this would happen," Les said. "The poor woman already has lost three husbands. If this one is sick, it's no wonder she thinks that somebody is poisoning him." Bob stuck his head around the bedroom door. His mother stared unbelievingly for a moment, then advanced on him threateningly. Something in his face restrained her, just as she was about to start shaking him. "I got something important to tell you," Bob said rapidly, ready to duck. "I snuck out of the principal's office and came home. I got to tell you what I did." "I heard all about what you did," Ann said, advancing again. "And you're not going to slip away from me." "Give me a chance to explain something. Downstairs. So he won't hear," Bob ended in a whisper, nodding toward the doctor. Ann looked doubtfully at Les, then followed Bob down the stairs. The doorbell was monotonously saying in a monotone: "Don't answer me, don't answer me, don't go to the door." "Why did you do it?" Ann asked Bob, her anger suddenly slumping into weary sadness. "People will suspect you of being a sex maniac for the rest of your life. You can't possibly explain—" "Don't bother about the girls' clothing," Bob said, "because it was only an accident. The really important thing is something else I did before I left the house." Les, cursing softly, hurried past them on the way to answer the knocking. He ignored the doorbell's pleas. "I forgot about it," Bob continued, "when that ray gun accidentally went off. Then when they put me in the principal's office, I had time to think, and I remembered. I put some white stuff from the detective kit into that sugar we lent Mrs. Burnett last night. I just wanted to see what would happen. I don't know exactly what effect—" "He put stuff in the sugar?" A deep, booming voice came from the front of the house. Mother and son looked through the hall. A policeman stood on the threshold of the front door. "I heard that! The woman next door claims that her husband is poisoned. Young man, I'm going to put you under arrest." The policeman stepped over the threshold. A blue flash darted from the doorbell box, striking him squarely on the chest. The policeman staggered back, sitting down abruptly on the porch. A scent of ozone drifted through the house. "Close the door, close the door," the doorbell was chanting urgently. "Where's that ambulance?" Dr. Schwartz yelled from the top of the steps. "The child's getting worse." | B. Her grandmother |
What new emotion was Kalvin experiencing after quitting Piltdon Opener Company?
A. Cowardice
B. Anger
C. Misery
D. Submission
| THE SUPER OPENER BY MICHAEL ZUROY Here's why you should ask for a "Feetch M-D" next time you get a can opener! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, August 1958. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] "Feetch!" grated Ogden Piltdon, president of the Piltdon Opener Company, slamming the drafting board with his hairy fist, "I want results!" Heads lifted over boards. Kalvin Feetch shrunk visibly. "As chief engineer you're not carrying the ball," Piltdon went on savagely. "The Piltdon Can-Opener is trailing the competition. Advertising and Sales are breaking their necks. It's Engineering that's missing the boat!" "But Mr. Piltdon," remonstrated Feetch unsteadily under his employer's glare, "don't you remember? I tried to...." "For two years there hasn't been one lousy improvement in the Piltdon Can-Opener!" roared Mr. Piltdon. "Look at our competitors. The International rips apart cans in three and three-tenths seconds. Universal does it in four." "But Mr. Piltdon—" "The Minerva Mighty Midget does it in four point two two and plays Home Sweet Home in chimes. Our own Piltdon opener barely manages to open a can in eight point nine without chimes. Is this what I'm paying you for?" Feetch adjusted his spectacles with shaking hands. "But Mr. Piltdon, our opener still has stability, solidity. It is built to last. It has dignity...." "Dignity," pronounced Piltdon, "is for museums. Four months, Feetch! In four months I want a new can-opener that will be faster, lighter, stronger, flashier and more musical than any other on the market. I want it completely developed, engineered and tooled-up, ready for production. Otherwise, Feetch—" Feetch's body twitched. "But Mr. Piltdon, four months is hardly time enough for development, even with an adequate staff. I've been trying to tell you for years that we're bound to fall behind because we don't have enough personnel to conduct research. Our men can barely keep up with production and maintenance. If you would let me put on a few draftsmen and...." "Excuses," sneered Mr. Piltdon. "Your staff is more than adequate. I will not allow you to throw out my money. Four months, Feetch, no more!" Piltdon trudged out of the room, leaving behind him an oppressive silence. How could you set a time limit on research and development? A designer had to dream at his board, investigate, search, build, test, compare, discard. He had always wanted to devote all his time to research, but Piltdon Opener had not given him that opportunity. Twenty-five years! thought Feetch. Twenty-five years of close supervision, dead-lines, production headaches, inadequate facilities and assistance. What had happened, to the proud dream he once had, the dream of exploring uncharted engineering regions, of unlimited time to investigate and develop? Ah, well, thought Feetch straightening his thin shoulders, he had managed somehow to design a few good things during his twenty-five years with Piltdon. That was some satisfaction. What now? He had to hang on to his job. Technical work was scarce. Since the early 1980's the schools had been turning out more technicians than industry could absorb. He was too old to compete in the employment market. He couldn't afford to lose any money. Jenny wasn't well. How to meet this four month dead-line? He would get right on it himself, of course; Hanson—good man—could work with him. He shook his head despairingly. Something would be sure to blow up. Well, he had to start— "Chief," said Hanson a few weeks later as they entered the lab, "I'm beginning to wonder if the answer is in the hand mechanical type at all." "Got to be," answered Feetch tiredly. "We must work along classical can-opener lines. Departures, such as the thermal or motor-driven types, would be too expensive for mass production." Three new models and a group of cans were waiting for them on the bench. They began testing, Hanson operating the openers and Feetch clocking. "Four point four," announced Feetch after the last test. "Good, but not good enough. Too bulky. Appearance unsatisfactory. Chimes tinny. We've made progress, but we've a long way to go." The problem was tricky. It might seem that use of the proper gear ratios would give the required velocity, but there were too many other factors that negated this direct approach. The mechanism had to be compact and streamlined. Gear sizes had to be kept down. Can-top resistance, internal resistance, cutting tooth performance, handle size and moment, the minimum strength of a woman's hand were some of the variables that had to be balanced within rigid limits. Sector type cutters, traversing several arcs at the same time, had seemed to offer the answer for a while, but the adjusting mechanism necessary to compensate for variable can sizes had been too complex to be practical. There was the ever-present limit to production cost. Hanson's eyes were upon him. "Chief," he said, "it's a rotten shame. Twenty-five years of your life you put in with Piltdon, and he'd fire you just like that if you don't do the impossible. The Piltdon Company is built upon your designs and you get handed this deal!" "Well, well," said Feetch. "I drew my pay every week so I suppose I have no complaints. Although," a wistful note crept into his voice "I would have liked a little recognition. Piltdon is a household word, but who has heard of Feetch? Well,"—Feetch blew his nose—"how do we stand, Hanson?" Hanson's bull-dog features drew into a scowl. "Piltdon ought to be rayed," he growled. "O.K., Chief. Eleven experimental models designed to date. Two more on the boards. Nine completed and tested, two in work. Best performance, four point four, but model otherwise unsatisfactory." "Hello," said Feetch as an aproned machinist entered carrying a glistening mechanism. "Here's another model. Let's try it." The machinist departed and Hanson locked the opener on a can. "I hope——" he turned the handle, and stopped abruptly, staring down open-mouthed. A cylinder of close-packed beans rested on the bench under the opener. The can itself had disappeared. "Chief," said Hanson. "Chief." "Yes," said Feetch. "I see it too. Try another can." "Vegetable soup or spinach?" inquired Hanson dreamily. "Spinach, I think," said Feetch. "Where did the can go, do you suppose?" The spinach can disappeared. Likewise several corn cans, sweet potato cans and corned-beef hash cans, leaving their contents intact. It was rather disconcerting. "Dear, dear," said Feetch, regarding the piles of food on the bench. "There must be some explanation. I designed this opener with sixteen degree, twenty-two minute pressure angle modified involute gear teeth, seven degree, nineteen minute front clearance cutter angle and thirty-six degree, twelve minute back rake angle. I expected that such departures from the norm might achieve unconventional performance, but this—Dear, dear. Where do the cans go, I wonder?" "What's the difference? Don't you see what you've got here? It's the answer! It's more than the answer! We can put this right into work and beat the dead-line." Feetch shook his head. "No, Hanson. We're producing something we don't understand. What forces have we uncovered here? Where do the cans go? What makes them disappear? Are we dealing with a kinetic or a kinematic effect? What motions can we plot in the area of disappearance and what are their analytical mathematical formulae? What masses may be critical here? What transformations of energy are involved? No, Hanson, we must learn a lot more." "But Chief, your job." "I'll risk that. Not a word to Piltdon." Several days later, however, Piltdon himself charged into the drawing room and slapped Feetch heartily on the back, causing him to break a pencil point. "Feetch!" roared Piltdon. "Is this talk that's going around the plant true? Why didn't you tell me? Let's see it." After Piltdon had seen it his eyes took on a feverish glint. "This," he exulted, "will make can-opener history. Instantaneous opening! Automatic disposal! Wait until Advertising and Sales get hold of this! We'll throttle our competitors! The Piltdon Super-Opener we'll call it." "Mr. Piltdon—" said Feetch shakily. Piltdon stared at his chief engineer sharply. "What's the matter, Feetch? The thing can be duplicated, can't it?" "Yes, sir. I've just finished checking that. But I'm in the midst of further investigation of the effect. There's more here than just a new type can-opener, sir. A whole new field of physics. New principles. This is big, Mr. Piltdon. I recommend that we delay production until further research can be completed. Hire a few top scientists and engineers. Find out where the cans go. Put out a scientific paper on the effect." "Feetch," bit out Piltdon, his face growing hard. "Stow this hooey. I don't give a damn where the cans go. May I remind you that under our standard patent agreement, all rights to your invention belong to the company? As well as anything you may produce in the field within a year after leaving our employ? We have a good thing here, and I don't want you holding it back. We're going into production immediately." Close, thought Feetch, wearily. It had been a man-killing job, and it had been close, but he'd made it. Beat the time limit by a half-day. The first tentative shipments of Piltdon Super-Openers had gone to distributors along the Eastern seaboard. The first advertisements blazed in selected media. The first reorders came back, and then: "It's a sell-out!" crowed Piltdon, waving a sheaf of telegrams. "Step up production! Let 'er rip!" The Super-Openers rolled over the country. In a remarkably short time they appeared in millions of kitchens from coast-to-coast. Sales climbed to hundreds of thousands per day. Piltdon Opener went into peak production in three shifts, but was still unable to keep up with the demand. Construction was begun on a new plant, and additional plants were planned. Long lines waited in front of houseware stores. Department stores, lucky enough to have Super-Openers on hand, limited sales to one to a customer. Piltdon cancelled his advertising program. Newspapers, magazines, radio, television and word-of-mouth spread the fame of the opener so that advertising was unnecessary. Meanwhile, of course, government scientists, research foundations, universities and independent investigators began to look into this new phenomonen. Receiving no satisfactory explanation from Piltdon, they set up their own research. Far into the night burned the lights of countless laboratories. Noted physicists probed, measured, weighed, traced, X-rayed, dissolved, spun, peered at, photographed, magnetized, exploded, shattered and analyzed Super-Openers without achieving the glimmer of a satisfactory explanation. Competitors found the patent impossible to circumvent, for any departure from its exact specifications nullified the effect. Piltdon, genial these days with success and acclaim, roared at Feetch: "I'm putting you in for a raise. Yes sir! To reward you for assisting me with my invention I'm raising your pay two hundred dollars a year. That's almost four dollars a week, man." "Thank you, Mr. Piltdon." And still, thought Feetch wryly, he received no recognition. His name did not even appear on the patent. Well, well, that was the way it went. He must find his satisfaction in his work. And it had been interesting lately, the work he had been doing nights at home investigating what had been named the Piltdon Effect. It had been difficult, working alone and buying his own equipment. The oscillator and ultra microwave tracking unit had been particularly expensive. He was a fool, he supposed, to try independent research when so many huge scientific organizations were working on it. But he could no more keep away from it than he could stop eating. He still didn't know where the cans went, but somehow he felt that he was close to the answer. When he finally found the answer, it was too late. The Borenchuck incident was only hours away. As soon as he could get hold of Piltdon, Feetch said trembling, "Sir, I think I know where those cans are going. I recommend—" "Are you still worrying about that?" Piltdon roared jovially. "Leave that to the long-hairs. We're making money, that's all that counts, eh Feetch?" That night, at six-ten p.m., the Borenchuck family of Selby, South Dakota, sat down to their evening meal. Just as they started in on the soup, a rain of empty tin cans clattered down, splashed into the soup, raised a welt on the forehead of Borenchuck senior, settled down to a gentle, steady klunk! klunk! klunk! and inexorably began to pile up on the dining-room floor. They seemed to materialize from a plane just below the ceiling. The police called the fire department and the fire department stared helplessly and recommended the sanitation department. The incident made headlines in the local papers. The next day other local papers in widely scattered locations reported similar incidents. The following day, cans began falling on Chicago. St. Louis was next, and then over the entire nation the cans began to rain down. They fell outdoors and indoors, usually materializing at heights that were not dangerous. The deluge followed no pattern. Sometimes it would slacken, sometimes it would stop, sometimes begin heavily again. It fell in homes, on the streets, in theatres, trains, ships, universities and dog-food factories. No place was immune. People took to wearing hats indoors and out, and the sale of helmets boomed. All activity was seriously curtailed. A state of national emergency was declared. Government investigators went to work and soon confirmed what was generally suspected: these were the same cans that had been opened by the Piltdon Super-Opener. Statisticians and mathematicians calculated the mean rate of can precipitation and estimated that if all the cans opened by Piltdon openers were to come back, the deluge should be over in fifteen point twenty-nine days. Super-Opener sales of course immediately plummeted to zero and stayed there. Anti-Piltdon editorials appeared in the papers. Commentators accused Piltdon of deliberately hoaxing the public for his own gain. A Congressional investigation was demanded. Piltdon received threats of bodily injury. Lawsuits were filed against him. He barricaded himself in the plant, surrounded by bodyguards. Livid with fury and apprehension, he screamed at Feetch, "This is your doing, you vandal! I'm a ruined man!" A falling can caught him neatly on the tip of his nose. "But sir," trembled Feetch, dodging three spaghetti cans, "I tried to warn you." "You're through, Feetch!" raved Piltdon. "Fired! Get out! But before you go, I want you to know that I've directed the blame where it belongs. I've just released to the press the truth about who created the Super-Opener. Now, get out!" "Yes, sir," said Feetch paling. "Then you don't want to hear about my discovery of a way to prevent the cans from coming back?" Klunk! A barrage of cans hit the floor, and both men took refuge under Piltdon's huge desk. "No!" yelled Piltdon at Feetch's face which was inches away. "No, I——What did you say?" "A small design improvement sir, and the cans would disappear forever." Klunk! "Forever, Feetch?" "Yes sir." Klunk! Klunk! "You're positive, Feetch?" Piltdon's eyes glared into Feetch's. "Sir, I never make careless claims." "That's true," said Piltdon. His eyes grew dreamy. "It can be done," he mused. "The New Type Super-Opener. Free exchanges for the old. Cash guarantee that empty cans will never bother you. Take a licking at first, but then monopolize the market. All right, Feetch, I'll give you another chance. You'll turn over all the details to me. The patent on the improvement will naturally be mine. I'll get the credit for rectifying your blunder. Fine, fine. We'll work it out. Hop on production, at once, Feetch." Feetch felt himself sag inwardly. "Mr. Piltdon," he said. "I'm asking only one favor. Let me work full time on research and development, especially on the Piltdon effect. Hire a couple of extra men to help with production. I assure you the company will benefit in the end." "Damn it, no!" roared Piltdon. "How many times must I tell you? You got your job back, didn't you?" The prospect of long years of heavy production schedules, restricted engineering and tight supervision suddenly made Kalvin Feetch feel very tired. Research, he thought. Development. What he had always wanted. Over the years he had waited, thinking that there would be opportunities later. But now he was growing older, and he felt that there might not be a later. Somehow he would manage to get along. Perhaps someone would give him a job working in the new field he had pioneered. With a sense of relief he realized that he had made his decision. "Mr. Piltdon," Feetch said. "I—" klunk!—"resign." Piltdon started, extreme astonishment crossing his face. "No use," said Feetch. "Nothing you can say—" klunk! klunk! klunk!—"will make any difference now." "But see here, the New Type Super-Opener...!" "Will remain my secret. Good day." "Feetch!" howled Piltdon. "I order you to remain!" Feetch almost submitted from force of habit. He hesitated for a moment, then turned abruptly. "Good-day," said Feetch firmly, sprinting through the falling cans to the door. Money, Feetch decided after a while, was a good thing to have. His supply was running pretty low. He was not having any luck finding another job. Although the cans had stopped falling on the fifteenth day, as predicted by the statisticians, industry would not soon forget the inconvenience and losses caused by the deluge. It was not anxious to hire the man it regarded as responsible for the whole thing. "Feetch," the personnel man would read. "Kalvin Feetch." Then, looking up, "Not the Kalvin Feetch who—" "Yes," Feetch would admit miserably. "I am sorry, but—" He did no better with research organizations. Typical was a letter from the Van Terrel Foundation: "—cannot accept your application inasmuch as we feel your premature application of your discovery to profit-making denotes a lack of scientific responsibility and ethics not desirable in a member of our organization—former employer states the decision was yours entirely. Unfavorable reference—" Piltdon, Feetch thought, feeling a strange sensation deep within his chest that he had not the experience to recognize as the beginning of a slow anger, Piltdon was hitting low and getting away with it. Of course, if he were to agree to reveal his latest discoveries to a research organization, he would undoubtedly get an appointment. But how could he? Everything patentable in his work would automatically revert to Piltdon under the one year clause in the company patent agreement. No, Feetch told himself, he was revealing nothing that Piltdon might grab. The anger began to mount. But he was beginning to need money desperately. Jenny wasn't getting any better and medical bills were running high. The phone rang. Feetch seized it and said to the image: "Absolutely not." "I'll go up another ten dollars," grated the little Piltdon image. "Do you realize, man, this is the fourteenth raise I've offered you? A total increase of one hundred and twenty-six dollars? Be sensible, Feetch. I know you can't find work anywhere else." "Thanks to you. Mr. Piltdon, I wouldn't work for you if—" A barrage of rocks crashed against the heavy steel screening of the window. "What's going on!" yelled Piltdon. "Oh, I see. People throwing rocks at your house again? Oh, I know all about that, Feetch. I know that you're probably the most unpopular man alive to-day. I know about the rocks, the tomatoes, the rotten eggs, the sneaking out at night, the disguises you've had to use. Why don't you come back to us and change all that, Feetch? We'll put out the New Type Super-Opener and the world will soon forget about the old one." "No," said Feetch. "People will forget anyway—I hope." "If you won't think of yourself, at least think of your fellow workmen," begged Piltdon, his voice going blurry. "Do you realize that Piltdon Opener will soon be forced to close down, throwing all your former associates out of work? Think of Hanson, Sanchez, Forbes. They have families too. Think of the men in the shop, the girls in the office, the salesmen on the road. All, all unemployed because of you. Think of that, Feetch." Feetch blinked. This had not occurred to him. Piltdon eyed him sharply, then smiled with a hint of triumph. "Think it over, Feetch." Feetch sat, thinking it over. Was it right to let all these people lose their jobs? Frowning, he dialed Hanson's number. "Chief," said Hanson, "Forget it. The boys are behind you one hundred per cent. We'll make out." "But that's the trouble. I thought you'd feel like this, and I can't let you." "You're beginning to weaken. Don't. Think, chief, think. The brain that figured the Super-Opener can solve this." Feetch hung up. A glow of anger that had been building up in his chest grew warmer. He began pacing the floor. How he hated to do it. Think, Hanson had said. But he had. He's considered every angle, and there was no solution. Feetch walked into the kitchen and carefully poured himself a drink of water. He drank the water slowly and placed the glass on the washstand with a tiny click. It was the tiny click that did it. Something about it touched off the growing rage. If Piltdon were there he would have punched him in the nose. The twenty-five years. The tricks. The threats. Think? He'd figured the solution long ago, only he hadn't allowed himself to see it. Not lack of brains, lack of guts. Well, he thought grimly, dialing Piltdon's number, he was going through with it now. "Piltdon!" he barked. "Three p.m. tomorrow. My place. Be here. That's all." He hung up. In the same grim mood the following morning, he placed a few more calls. In the same mood that afternoon he stood in the middle of his living-room and looked at his visitors: Piltdon, Williams, the Government man; Billings from the Van Terrel Foundation; Steiner of Westchester University; the members of the press. "Gentlemen," he said. "I'll make it brief." He waved the papers in his hand. "Here is everything I know about what I call the Feetch Effect, including plans and specifications for the New Type Super-Opener. All of you have special reasons for being keenly interested in this information. I am now going to give a copy to each of you, providing one condition is met by Mr. Piltdon." He stared at Piltdon. "In short, I want fifty-one per cent of the stock of Piltdon Opener." Piltdon leaped from his chair. "Outrageous!" He roared. "Ridiculous!" "Fifty-one percent," said Feetch firmly. "Don't bother with any counterproposals or the interview is at an end." "Gentlemen!" squawked Piltdon, "I appeal to you—" "Stop bluffing," said Feetch coldly. "There's no other way out for you. Otherwise you're ruined. Here, sign this agreement." Piltdon threw the paper to the floor and screamed: "Gentlemen, will you be a party to this?" "Well," murmured the Government man, "I never did think Feetch got a fair shake." "This information is important to science," said the Van Terrel man. After Piltdon had signed, the papers were distributed. Published in the newspapers the following day, Feetch's statement read, in part: "The motion in space and time of the singular curvilinear proportions of the original Super-Opener combined with the capacitor effect built up as it increased its frictional electro-static charge in inverse proportion to the cube root of the tolerance between the involute teeth caused an instantaneous disruption of what I call the Alpha multi-dimensional screen. The can, being metallic, dropped through, leaving its non-metallic contents behind. The disruption was instantly repaired by the stable nature of the screen. "Beyond the screen is what I call Alpha space, a space apparently quite as extensive as our own universe. Unfortunately, as my investigations indicated, Alpha space seems to be thickly inhabited. These inhabitants, the nature of whom I have not yet ascertained, obviously resented the intrusion of the cans, developed a method of disrupting the screen from their side, and hurled the cans back at us. "However, I have established the existence of other spaces up to Mu space, and suspect that others exist beyond that. Beta space, which is also adjacent to our own space, is devoid of any form of life. The New Type Super-Opener is designed to pass cans through the Beta screen. Beta space will safely absorb an infinite number of cans. "I sincerely and humbly venture the opinion that we are on the threshold of tremendous and mighty discoveries. It is my belief that possibly an infinite number of universes exist in a type of laminated block separated by screens. "Therefore, might it not be that an infinite number of laminated blocks exist—?" "Mr Feetch—" said Piltdon. Feetch looked up from his desk in the newly constructed Feetch Multi-Dimensional Development Division of the Piltdon Opener Company. "Piltdon, don't bother me about production. Production is your problem." "But Mr. Feetch—" "Get out," said Feetch. Piltdon blanched and left. "As I was saying, Hanson—" continued Feetch. | B. Anger |
How does the animal kingdom feel about Ashitaka?
A. They too want to live together in harmony.
B. They look upon him with feral hatred.
C. They don't like him, some tolerate him.
D. They are enemies.
| Machines in the Garden In the animated ecological epic Princess Mononoke , the camera travels over landscapes with a clear, steady gaze, like a Zen hang glider. The images have none of the comin'-at-ya pop-surrealism of American cartoons, many of which have characters that spring out of the frame like jack-in-the-boxes. The Japanese director, Hayao Miyazaki, who spent three years on Princess Mononoke and is reported to have done 70 percent of its paintings himself, seems to work from the outside in: to begin with the curve of the earth, then the mossy hills, the watercolor foliage, the nubby stones, the whorls on the wood, the meticulous carvings on a teacup. He captures the texture of light and the currents of air. You could almost settle down in this landscape. A view of nature that some would call "tree-hugging" doesn't feel softheaded when the trees are rendered in such brilliant and robust detail. But then, "soft" is not a word you can apply to Princess Mononoke , however pantheistic its worldview. The film, which is rated PG-13, is full of splattery carnage. If Miyazaki in long shot is contemplative, in close-up he's ferocious. He's both inside and outside the action: He knows when to rock your world and when to induce a state of sorrowful detachment. According to the New York Times , Toy Story animators screened reels of his work when their imaginations flagged, and writers for Star Trek named an alien species after one of his features. Watching Princess Mononoke --which has been dubbed to Disney/Miramax specifications by American and English stars but retains its two-hour-plus length, its gory beheadings, and its grim, near-apocalyptic finale--you can understand their worship. It isn't that Miyazaki's work is technically so dazzling in this age of digitized miracles; it's that everything is sublimely in proportion. The movie has a scope that makes Hollywood's homiletic, follow-your-dream fables look even more solipsistic. Miyazaki is after nothing less than the moment in our history (the film is set in the 14 th and 15 th centuries) when the power shifted from a "natural" world to one shaped by human technology. It's the beginning of what Bill McKibben called "the end of nature"--that is, when nature became no longer an autonomous, self-regulating force but one touched (and, in Miyazaki's view, poisoned) by human industry. The hero, Ashitaka, a warrior from the isolationist Emishi clan, is forced in the first scene to kill a marauding boar--a god turned into a demon (covered in roiling, corrosive worms) by an iron ball lodged in its body. Infected, destined to be consumed by--and to die of--rage, Ashitaka leaves his village in search of the iron ball's source. He discovers a fortress-cum-arms-manufacturing plant called Irontown, presided over by one of the most complex villains in modern film: the regal Lady Eboshi. On one hand, she's a benevolent industrialist who presides over a warmly matriarchal society; on the other, she wants to destroy the forest, harness its resources, and exterminate its animal deities--chiefly the Spirit of the Forest, a magnificent deer god whose touch brings instant life or death, and who transforms at dusk into the towering Night Walker. P rincess Mononoke builds to a full-scale war between humans and the animal kingdom--which does not, by the way, consist of your father's cartoon critters. In fact, the boars and apes have little patience with Ashitaka's call for nature and mankind to live together in harmony; they'd like to eat him. The wolf god, Moro, is slightly more sympathetic, but that's because her adopted "daughter," San (a k a Princess Mononoke), is human. San is first seen sucking a wound of her huge wolf mother, then, as the gore drips from her mouth, training her dark eyes on Ashitaka with feral hatred. Her second appearance--a lone attack on Irontown to assassinate Lady Eboshi--is one of the movie's high points. It's Miyazaki's use of sound--and silence--that takes your breath away: the determined tap of the wolf princess's shoes as she scuttles over the fortress's rooftops; the silence of Eboshi and her army as they stare at this tiny yet formidable tomboy against the black sky. Their battle is so furious that the blades streak and lose definition--it's almost subliminal. It's a shame that the wolf princess warms up to Ashitaka and spends the rest of the film either saving him or being saved by him. She loses that punk-bitch allure. The voice of Claire Danes doesn't help. When Danes says, "I'd do anything to get you humans out of my forest," she sounds like a Valley Girl peeved over lack of parking spaces at the mall. (San needs a more ragged voice--I'd be interested to hear the original Japanese actress.) Billy Crudup is just as Disneyfied (Miramaxed?), but that doesn't hurt as much because Ashitaka is conceived from the start as a rather bland ingénu. Gillian Anderson's growling Moro sounds silly (she doesn't have the breath control), and the fey-hick tones of Billy Bob Thornton are too recognizable as the Akim Tamiroff-like mercenary, Jigo. But Minnie Driver--coming off a triumphantly dizzy Jane in Tarzan --once again provides a voice that the animators deserve. "Bring the strange-ah to me late-ah," she commands in sexy Martian Queen cadences that will stir the loins of Flash Gordon fans everywhere. "I would like to thank him puh-sonally." The overfamiliar voices nudge Princess Mononoke closer to its American counterparts--but not by a lot. There's always something wondrously strange. The "kodamas" are little tree spirits on doughboy bodies. They cock their trapezoidal dice heads and emit a series of clicks; then their heads pop back with a conclusive rattle. Something about them seems just right; I could watch them for hours. (Miyazaki limits their appearances to seconds--he doesn't wear out their mystery the way that, say, George Lucas would.) And no Hollywood animated feature would end with such a powerful vision of apocalypse, as the land is bestridden by a colossus dropping a thick, caustic, tarlike gel that recalls the post-Hiroshima "black rain." Can you take the kids? I think so. As Miyazaki said at a New York Film Festival press conference, "Children understand intuitively that the world they have been born into is not a blessed world." Princess Mononoke , at least, can tell them why. "A special smile ... a certain touch ..." So begins the elevator-music theme song of Music of the Heart ... "I never had a lot that I loved so much." The credits had just started and I was already looking for a barf bag. Did Miramax and director Wes Craven have to work so hard to schlockify the story of Roberta Guaspari (played here by Meryl Streep), whose violin courses in East Harlem elementary schools have become a beacon for such programs nationwide? A fabled taskmaster (her story was told in the 1996 documentary Small Wonders ), Guaspari used music as a way to teach self-discipline--along with the healthy self-respect that follows in its wake. When the New York school board cut the funding for her program, she proved a marvel of self-promotion, attracting features in all the major dailies and ending up along with her best students at Carnegie Hall for a benefit "Fiddlefest"--along with Itzhak Perlman, Isaac Stern, and other legendary "fiddlers." Streep has said that she spent so much of the time on the set learning the violin (she doesn't play any instruments) that she didn't bring the full force of her acting technique to bear on Roberta. Maybe that's why the performance seems so natural. Let her always learn an instrument on the set! Still, she doesn't make much sense of Guaspari. The script, by Pamela Gray ( A Walk on the Moon ), has her students complain of her nastiness and perfectionism, but Streep--who has made herself look dumpy, thick-waisted, and bedraggled--is so busy telegraphing her vulnerability that all we get is dippy niceness. Instead of a monument to an individual's iron will, Music of the Heart becomes the story of a woman so helpless that she arouses the kindness of strangers. Directors of violent genre pieces like Craven (who got this mainstream gig in return for doing the Scream sequels) or Carl Franklin or Sam Raimi sometimes want so badly to belong to Establishment Hollywood--to go to the Academy Awards--that they neuter themselves. Bending over backward to show how sensitive they can be, they forget that violence--even if it's just emotional violence--belongs in "ordinary" dramas, too. Craven does good work with the young actors in the classroom scenes, but the film has a reticence common to most biopics and a mushy, TV-movie humanism that blands out its texture. OK, I was a puddle after some scenes, like the one where Guaspari pushes a student to get her to improve her posture and discovers that the girl is wearing a leg brace. But how much more emotional the Carnegie Hall climax would have been if instead of suddenly seeing these East Harlem kids on stage with Perlman, Stern, Joshua Bell, etc., we'd seen them rehearsing first and struggling to keep up. There's too much music of the heart and not enough music of the callused fingers. In outline, The Limey is a lean little B-movie revenge melodrama about a felonious Brit (Terence Stamp) who's newly sprung from prison and flies to Southern California to get to the bottom of his beautiful daughter's death: "My name's Wilson ... Who dunnit?" The film, directed by Steven Soderbergh, would be worth seeing just for Stamp's performance, at once rock-hard and goofily blinkered, and for Peter Fonda's wittily self-parodic turn as the suspected killer, a music producer who coasts on '60s counterculture easiness while his lackeys do the dirty work. ("Oh, man," he says, the fear finally seeping through the ether. "This is getting all too close to me.") But the picture's glory is its layered and intricate syntax. The dialogue moves ahead--there are great gobs of exposition--but the images continually double back: to Stamp and Lesley Ann Warren, as his daughter's acting teacher, simply gazing at each other; or to Stamp sitting on a plane, remembering his daughter as a girl on the beach, the lens of his home movie camera creating an eerily bright--almost supernatural--spot that dances over her face. The film's most violent act happens well off screen. (You hear the distant "pop-pop-pop-pop-pop" of the hero's gun.) The rest is only half-glimpsed, fantasized, or saturated by memory--or is the present the memory? Is all of The Limey a temporal hiccup? Some, including the critic at Time , have questioned Soderbergh's sanity. (But of course--Soderbergh flouts time!) I see a method to his madness. Less grandiosely than Harmony Korine in Julien Donkey-Boy , Soderbergh pores over every scene in search of its essential dramatic gesture. He's saying: This --not all that other stuff--is what's important. He telegraphs the ending--you know the Limey will somehow be at the root of his daughter's death--but it's still an emotional wow. The climax justifies the technique. It says the point of this odyssey isn't revenge but regret--for irredeemably blown chances and a tragic waste of love. Soderbergh is one of those rare filmmakers who learn on the job. Working within a tight genre structure, he's discovering hundreds of ways of editing a given scene that can give it the richness of a novel. Is he totally successful? No; he misses now and then, which is why the technique sticks out. But what a fantastic effort. See it and weep for what's missing in most other movies. | C. They don't like him, some tolerate him. |
Orison’s introduction to Auga Vingt could best be described as...
A. Friendly
B. Cordial
C. Passive-aggressive
D. Heated
| 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. | C. Passive-aggressive |
Who is the CFO of the Bristol Pound
A. Stephen Clarke
B. Molly Scott Cato
C. Duncan McCann
D. Ciaran Mundy
| New money: Do local currencies actually work? It's lunchtime at Glasgow Chambers in late November, and Councillor George Redmond is getting worked up at the prospect a Glasgow Pound. "We would be Glasgow-centric about it," he says conspiratorially, as though there is any other way to be. "Can you imagine having the face of Billy Connolly on our local currency? Or Alex Ferguson, or Kenny Dalglish?" Inventing an alternative to sterling might sound far-fetched, even illegal. But it's not that strange. In the UK we think of the pound like fish think about water, which is to say not at all. It might never have occurred to many of us that there are other types of exchange that can stand in for ragged bank notes tucked away in pockets, or other objects that can stand in for those notes. Not every country is so lucky. In crisis-hit Greece, where the euro can be hard to come by, businesses and citizens have turned to bartering using a points system where goods like pianos, pot and pans can be exchanged for security services or loaned farming equipment. In India last year, desperate people burned sacks of illegal cash after the government withdrew two high-denomination notes as part of a crackdown on corruption. Hoarders woke up to discover the banknotes under their mattresses were suddenly worthless. The pound has been trading at its lowest level since 1985 since the UK voted to leave the European Union and there are fears that it could dip further as Brexit ensues. Timebanks, local exchange trading systems (LETS) and digital inventions like bitcoin can provide alternative ways for people to pay for goods and services when mainstream currencies hit crises. But they will only work if Britons are ready to accept that they have the power to invent their own currency. "At the moment, if the pound stops working for us, the whole economy grinds to a halt because there aren't alternatives," Duncan McCann, a researcher at the New Economics Foundation, tells those gathered in a gilded room at Glasgow Chambers to discuss the Glasgow Pound. McCann is a long-time advocate of alternative means of exchange. He is behind the ScotPound, a proposal for a new national currency for Scotland that emerged after the referendum on Scottish independence. It's an idea he no longer thinks will work, because the debate, since Brexit, has shifted from the currency issue back to ideas about Scottish independence. Today, he's preaching to the converted. Alex Walker, the chairman of the 250-person Ekopia community in Northern Scotland, listens at the back. The Eko has been the main means of buying everything from beer to bananas in Ekopia since Walker founded it 20 years ago. On an adjacent table, Tracy Duff, a community learning and development worker from Clackmannanshire Council, digs out some papers. She runs the Clacks Youth Timebank, a scheme where 12- to 15-year-olds can earn credit for volunteering. Taking notes up front is Ailie Rutherford, one of the people who organised the meeting. Rutherford runs the People's Bank of Govanhill, a currency that changes value depending on the income of the user. "I don't see any reason why we shouldn't invent our own currency and play with it," she says. Everyone has gathered to decide what a Glasgow Pound might look like at a time when many are asking if local currencies can work at all. Councillor Redmond says Glasgow has been closely watching existing alternative currencies like the Brixton Pound in London, which was introduced in 2011. The founders of the Brixton Pound wanted to do something to stop 80p of every £1 spent locally from leaking out of the area into the pockets of corporations, at the expense of small local traders. So they printed a currency that would have the same value as the pound, but could only be traded in independent Brixton shops, where the shopkeeper would also have to spend it locally. This year the Brixton Pound got its own cashpoint, from where people can withdraw local banknotes bearing colourful images of local heroes, like David Bowie and secret Agent Violette Szabo, to spend in over 150 local shops. It can also be used by residents to pay council tax and by employers to pay wages. No two local currencies are exactly the same. But the Brixton Pound and other recent schemes follow the example ten years ago of the Totnes Pound, a 'complementary currency': that is, one supplementing the national currency. As fears for financial stability took hold during the recession, complementary currencies grew in popularity. The Bank of England does not consider these forms of currency legal tender, but the notes hold value in the same way as a gift-card from a department store, with the same kind of restrictions about where they can be spent. Proponents say complementary currencies boost spending in smaller geographical areas, which can have environmental benefits as businesses cut transport distances to deal with local suppliers. Detractors say they have no real economic impact and work only as a game for the middle classes, who can afford to buy from independent shops rather than chains. In Britain, there are now schemes in Totnes, Lewes, Brixton, Bristol and Exeter. Hull has its own local digital currency that can be earned from volunteering and used to pay council tax. Kingston, Birmingham and Liverpool have schemes underway. Glasgow could be next. But the working group has some serious questions to answer first, not least: do complementary currencies actually work? "People don't understand money," Molly Scott Cato, Green MEP for the South West of England and Gibraltar, says over the phone. Scott Cato says the fish-in-water problem – the idea that sterling is so ubiquitous, it is never questioned – is the biggest challenge for complementary currencies. She knows all about it as a founder of the Stroud Pound in 2010, a currency that has since gone out of circulation. "[People] think they put money into a bank and someone else takes it out. What they don't understand is that banks have the power to create money. We've given the power to create money to private corporations and people don't understand that we can have it back," she says. In Stroud, suspicion of the local currency among local businesses became a barrier to success. Scott-Cato said traders refused to join the scheme because they were "running a business", as though putting the community first and placing the needs of others as equivalent to their own was in itself bad business practice, or as though they were somehow being disloyal to sterling. The Bristol Pound (£B) entered into circulation in September 2012. By June 2015, 1m £B had been issued, with £B700,000 of that still in circulation. In a population of some 450,000 people, that's the equivalent of each Bristolian carrying less than £B2 in change in their pocket. "The small scale is a problem and a strength," says Stephen Clarke, chief financial officer of the Bristol Pound. "The benefit comes from the fact that local currencies are trusted organisations: we're a Community Interest Company limited by guarantee." That means assets owned by the the Bristol Pound have to be used for the good of the community, rather than purely for profit. Without enough currency in circulation, it ceases to work. Scott-Cato says Stroud's size meant meant the Stroud Pound was never viable: "We couldn't get the velocity of circulation right, which contrasts with the Bristol Pound." Clarke also says the small scale of local currencies means they are "always scrabbling around looking for money". One way founders of the Bristol Pound have addressed his is by setting up an umbrella organisation, the Guild of Independent Currencies, to share information between local currencies in the UK and help new organisations. "At the moment we're all reinventing the wheel every time," Clarke says. Technology might also have a solution. Peter Ferry, a commercial director, travels to Glasgow to tell those working on the Glasgow Pound that that his company Wallet has come up with a way to use the blockchain, the technology behind bitcoin, to make it easier for people to use multiple types of currency. "There might be many currencies around the country that people want to use. We need to make it simple for them to do that and also to make it simple to earn these currencies in many ways," he says. Size doesn't always matter. Sometimes, the smallest places – like Totnes and the Ekopia community – are best able to support complementary currencies because the people who live there are engaged with their local economy in a meaningful way. "Bristol is seen as a quirky, individualistic kind of place," Clarke says. "When we first produced the Bristol Pound note, people were really proud of it. It got through to people not just sat around coffee shops. I'm not sure a London Pound would work, because people identify with their local area in London rather than the city as a whole." Bristol Pound users don't have high incomes necessarily, but surveys show they are engaged with their local community and they have a higher educational attainment than average. In the years since the financial crisis, as local authority budgets have shrunk, some areas have relied heavily on engaged communities to fill in gaps in public services. By contrast, deprived areas where people cannot afford time and money to put into their community have become more deprived, making them even harder for local currencies to reach. "It is difficult to get into more disadvantaged areas," Stephen Clarke says. "We have a ten-year life expectancy gap between different parts of the city. When you go to disadvantaged areas with the Bristol Pound hat on you realise there aren't independent shops there, there's an Aldi and Lidl and that's it." More than a third of children grow up in poverty in Glasgow. A Glasgow Pound might struggle to get poorer families to buy into a local currency that ties them to shopping at more expensive, independent shops, rather than getting deals at big supermarket chains. When Scott-Cato and her colleagues wrote about the experience of setting up the Stroud Pound, they said it was telling that complementary currencies have been accused of being a game for middle-class people, rather than a genuine economic solution. Perhaps for that reason, experts like Duncan McCann have stopped thinking of complementary currencies as a one-size-fits-all solution. He said they can function as a kind of 'gateway drug' to introduce people to a new way of thinking about money. "That is especially for those who use it, but also for those who just become aware of it," he says. Ciaran Mundy, CEO of the Bristol Pound, says it is important to think of the systemic impact rather than looking for targeted treatment of symptoms of economic deprivation. "Poverty has many causes," he says. "One of these is how the economy is structured in terms of how money flows out of poor areas due to high dependence on larger national and international companies paying lower wages and using offshore accounts to hide the money from the tax man." Nothing is tying Glasgow to existing models for complementary currencies. But during the first meeting about setting up the Glasgow Pound, the workshop shows just how hard it would be to invent a new system that works for everyone. Each table is handed a wad of Post-it notes and a piece of white paper. A table leader asks everyone to write on the Post-its what they want the Glasgow Pound to achieve. Elbowing teacups out the way, people get to work. They scrawl a dizzying number of proposals, from keeping more wealth in the local area to empowering people who feel cut out of the national economy, or to moving towards land reform and saving the environment. Team leaders try to assemble these ideas in themes to report back to the room. On one table, Duncan McCann encourages people to urge businesses to do things they have never done before. "One of the goals should be to move businesses from where they are today into the future," he says. After years of researc,h McCann believes the only way complementary currencies can create real value for local economies is if they make transactions happen that wouldn't otherwise have taken place. "They need to create additional spending power. This is this what the local currencies, despite all their good points, fail to do," McCann says. Every time a Brixton Pound transaction is made, 1.5 per cent goes into a Brixton Fund. This is used to give micro-grants of between a few hundred and £2000 to local projects and community groups. "We aim to target projects that aren't large enough to apply for more formal grant funding," says Lucy Çava, project manager at the Brixton Pound. "We see this as part of community building – linking the Brixton Pound user with community groups, so both groups become more visible to each other through the currency and fund. This is particularly important in Brixton because of the gentrification debates which are very salient round there," Çava says. Meanwhile, the people behind the Bristol Pound are readying a mutual credit network called Bristol Prospects. Through this network, businesses in Bristol can exchange credit in the form of loans that are neutralised within the network, helping one another to grow without relying on the high rates of commercial lenders. Once operational, loans offered through the Prospects network will have negative interest, so that businesses are encouraged to pass credit on as quickly as possible. "That's the plan," says Clarke, "because it's rather like a hot potato: people will want to pass it on." "We know from research that a number of small businesses in Bristol are struggling to get money on reasonable terms," says Clarke, "and that banks are not interested in smaller loans to businesses. So we think there is a strength in the Bristol Pound network to start something like this that is linked, but separate." Duncan McCann, with all his experience, knows that challenge is worthwhile. "As people we have a right to make credit and loan money. We mustn't forget that. We mustn't leave that to corporations and the state," he says. This article is part of a series on local economies Hazel is documenting at farnearer.org, with funding from the Friends Provident Foundation Illustration by PureSolution/Shutterstock This article was originally published on TheLong+Short. Read the original article. | A. Stephen Clarke |
Regarding Mr. Wells, hat was the sequence of imaging tests conducted on 11/20/2020?
Choose the correct answer from the following options:
A. MRI Liver followed by CT Chest/Abdomen/Pelvis
B. Gastroscopy followed by MRI Liver
C. Colonoscopy followed by MRI Liver
D. MRI Liver followed by Gastroscopy
E. CT Chest/Abdomen/Pelvis followed by MRI Liver
| ### Patient Report 0
**Dear colleague, **
We report to you on Mr. Paul Wells, born on 04/02/1953, who was in our
inpatient treatment from 07/26/2019 to 07/28/2019.
**Diagnoses:** Suspected multifocal HCC segment IV, VII/VIII, first
diagnosed: 07/19.
- COPD, current severity level Gold III.
- Pulmonary emphysema, respiratory partial insufficiency with home
oxygen.
- Postnasal drip syndrome
**Current Presentation:** The elective presentation of Mr. Wells was
made in accordance with the decision of the interdisciplinary liver
board of 07/20/2019 for further diagnostics in the case of multiple
malignoma-specific hepatic space demands.
**Medical History: **In brief, Mr. Wells presented to the Medical Center
St. Luke's with persistent right-sided pain in the upper abdomen.
Computer tomography showed multiple intrahepatic masses of the right
liver lobe (SIV, SVII/VIII). For diagnostic clarification of the
malignoma-specific findings, the patient was presented to our liver
outpatient clinic. The tumor marker diagnostics have not been
conclusive. Analogous to the recommendation of the liver board, a liver
puncture, staging, and endoscopic exclusion of a primary in the
gastrointestinal tract should be initiated.
**Physical Examination:** Physical examination reveals an alert patient.
- Oral mucosa: Moist and rosy, no plaques typical of thrush, no
plaques typical of herpes.
- Hear: Heart sounds pure, rhythmic, normofrequency.
- Lungs: Laterally attenuated breath sound with wheezing.
- Abdomen: Abdomen soft, regular bowel sounds over all 4 quadrants, no
defensive tension, no resistances, diffuse pressure pain over the
upper abdomen. No renal tap pain, no spinal tap pain. Spleen
palpable under the costal arch.
- Extremities: No edema, freely movable
- Neurology: GCS 15, pupils directly and indirectly reactive to light,
no flapping tremor. No meningism.
**Therapy and Progression:** Mr. Wells presented an age-appropriate
general status and cardiopulmonary stability. Anamnestically, there was
no evidence of an acute infection. Skin or scleral icterus and pruritus
were denied. No B symptoms. No stool changes, no dysuria. There would be
regular alcohol consumption of about 3-4 beers a day, as well as
nicotine abuse (120 PY). The general performance in COPD Gold grade III
was strongly limited, with a walking distance reduced to 100m due to
dyspnea. He had a home oxygen demand with 4L/min O2 during the day, up
to 6L/min under load. At night, 2L/min O2. The last colonoscopy was
performed 4 years ago, with no anamnestic abnormalities. No known
allergies. Family history is positive for colorectal cancer (mother).
Clinical examination revealed the typical auscultation findings of
advanced COPD with attenuated breath sounds bilateral, with
hyperinflation and clear wheezing. Otherwise, there were no significant
findings. Laboratory chemistry did not reveal any higher-grade
abnormalities. On the day of admission, after detailed clarification,
the patient was able to undergo the complication-free sonographically
guided puncture of the liver cavity in SIV. Thereby, two punch cylinders
were preserved for histopathological processing. Histologically, the
findings presented as infiltrates of a macrotrabecular and
pseudoglandular growing, well-differentiated hepatocellular carcinoma
(G1). The postinterventional course was unremarkable. In particular, no
clinical or laboratory signs were found for bleeding.
CT staging revealed a size constant known in the short term.
Hypervascularized hepatic space demands in both lobes of the liver
without further malignancy suspect thoracoabdominal tumor detection and
without metastasis aspects. MR also revealed the large, partly exophytic
growing, partly centrally hemorrhaged HCC lesions in S3/4 and S7/8 to
the illustration. In addition, complete enforcement of the left lobe of
the liver was evident with smaller satellites and macroinvasion of the
left portal vein branch. There was a low cholestasis of the left biliary
system. Gastroscopy and colonoscopy were also performed. Here, a reflux
esophagitis, sigmoid diverticulosis, multiple colonic diverticula, and a
4mm polyp were removed from the sigmoid colon to prevent bleeding; a
hemoclip was applied. Histologically, no adenoma was found. An
appointment to discuss the findings in our HCC outpatient clinic has
been arranged. We recommend further therapy preparation and the
performance of an echocardiography.
We were able to discharge Mr. Wells on 7/28/19.
**Addition:**
**Ultrasound on 07/26/2019 10:15 AM:**
- Indication: Targeted liver puncture for suspected metastatic liver
malignancy
- Organ puncture: Quick: 114%, PTT: 28 s, and platelets: 475 G/L. A
valid declaration of consent is available. According to the patient,
he does not receive antiplatelet drugs.
- In segment IV, an approximately 8.3 x 6 cm echo-depleted mass with
central cystic fusion is accessible in the dorsal position of a
sonographically guided puncture at 6.5 cm puncture depth. After
extensive skin disinfection, local anesthesia with 10 mL Mecaine 1%
and puncture incision with a scalpel. Repeated puncture with 18 G
Magnum needles is performed. Two approximately 1 cm fragile whitish
cylinders obtained for histologic examination. Band-aid dressing.
- **Assessment:** Hepatic space demand
**MRI of the liver plain + contrast agent from 07/26/2019 1:15 PM:**
**Technique**: Coronary and axial T2 weighted sequences, axial
diffusion-weighted EPI sequence with ADC map (b: 0, 50, 300 and 600
s/mmÇ), axial dynamic T1 weighted sequences with Dixon fat suppression
and (liver-specific) contrast agent (Dotagraf/Primovist); slice
thickness: 4 mm. Premedication with 2 mL Buscopan.
**Liver**: Centrally hemorrhagic masses observed in liver segments 4, 7,
and 8 demonstrate T2 hyperintensity, marked diffusion restriction,
arterial phase enhancement, and venous phase washout. These
characteristics are congruent with histopathological diagnosis of
hepatocellular carcinoma. The largest lesion in segment 4 exhibits
pronounced exophytic growth but no evidence of organ invasion. Notably,
branches of the mammary arteries penetrate directly into the tumor.
Diffusion-weighted imaging further reveals disseminated foci throughout
the entire left hepatic lobe. Disruption of the peripheral left portal
vein branch indicative of macrovascular invasion, accompanied by
peripheral cholestasis in the left biliary system.
**Biliary Tract:** Bile ducts are emphasized on both left and right
sides, with no evidence of mechanical obstruction in drainage. The
common hepatic duct remains non-dilated.
**Pancreas and Spleen:** Both organs exhibit no abnormalities.
**Kidneys:** Normal signal characteristics observed.
**Bone Marrow:** Signal behavior is within normal limits.
Assessment: Radiological features highly suggestive of hepatocellular
carcinoma in liver segments 4, 7, and 8, with evidence of macrovascular
invasion and peripheral cholestasis in the left biliary system. No signs
of organ invasion or biliary obstruction. Pancreas, spleen, kidneys, and
bone marrow appear unremarkable.
**Assessment:**
Large liver lesions, some exophytic and some centrally hemorrhagic, are
observed in segments 3/4 and 7/8.
In addition, the left lobe of the liver is completely involved with
smaller satellite lesions and macroinvasion of the left portal branch.
Mild cholestasis of the left biliary system is noted.
Dilated bile ducts are also found on the right side with no apparent
mechanical obstruction to outflow.
**CT Chest/Abdomen/Pelvis with contrast agent from 07/27/2019 2:00 PM:**
**Clinical Indication:** Evaluation of an unclear liver lesion
(approximately 9 cm) in a patient with severe COPD. No prior
liver-related medical history.
**Question:** Are there any suspicious lesions in the liver?
**Pre-recordings:** Previous external CT abdomen dated 09/13/2021.
**Findings:**
**Technique:** CT imaging involved a multi-line spiral CT through the
chest, abdomen, and pelvis in the venous contrast phase. Oral contrast
agent with Gastrolux 1:33 in water was administered. Thin-layer
reconstructions and coronary and sagittal secondary reconstructions were
performed.
**Chest:** No axillary or mediastinal lymphadenopathy is observed. There
is marked coronary sclerosis, as well as calcification of the aortic and
mitral valves. Nonspecific nodules smaller than 2 mm are noted in the
posterolateral lower lobe on the right side and lateral middle lobe. No
pneumonic infiltrates are observed. There is reduced aeration with
presumed additional scarring changes at the base of the lung
bilaterally, along with centrilobular emphysema.
**Abdomen:** Known exophytic liver lesions are confirmed, with
involvement in segment III extending to the subhepatic region (0.1 cm
extension) and a 6 cm lesion in segment VIII. Further spotty
hypervascularized lesions are observed throughout the left lobe of the
liver. No pathological dilatation of intra- or extrahepatic bile ducts
is seen, and there is no evidence of portal vein thrombosis. There are
no pathologically enlarged lymph nodes at the hepatic portal,
retroperitoneal, or inguinal regions. No ascites or pneumoperitoneum is
noted. There is no pancreatic duct congestion, and the spleen is not
enlarged. Additionally, there is a Bosniak 1 left renal cyst measuring
3.6 cm. Pronounced sigmoid diverticulosis is observed, with no evidence
of other masses in the gastrointestinal tract. Skeletal imaging reveals
no malignancy-specific osteodestructions but shows ventral pontifying
spondylophytes of the thoracic spine with no fractures.
**Assessment:**
Short-term size-constant known hypervascularized hepatic space lesions
are present in both lobes of the liver.
No other malignancy-susceptible thoracoabdominal tumor evidence is
found, and there are no metastasis-specific lymph nodes.
**Gastroscopy from 07/28/2019**
**Findings:**
**Esophagus:** Unobstructed intubation of the esophageal orifice under
visualization. Mucosa appears inconspicuous, with the Z-line at 37 cm
and measuring less than 5 mm. Small mucosal lesions are observed but do
not straddle mucosal folds.
**Stomach:** The gastric lumen is completely distended under air
insufflation. There are streaky changes in the antrum, while the fundus
and cardia appear regular on inversion. The pylorus is inconspicuous and
passable.
**Duodenum:** Good development of the bulbus duodeni is noted, with good
insight into the pars descendens duodeni. The mucosa appears overall
inconspicuous.
**Assessment:** Findings suggest reflux esophagitis (Los Angeles
Classification Grade A) and antrum gastritis.
**Colonoscopy from 07/28/2019**
**Findings:**
**Colon:** Some residual fluid contamination is noted in the sigmoid
(Boston Bowel Preparation Scale \[BBPS\] 8). There is pronounced sigmoid
diverticulosis, along with multiple colonic diverticula. A 4mm polyp in
the lower sigma (Paris IIa, NICE 1) is observed and ablated with a cold
snare, with hemoclip application for bleeding prophylaxis. Other mucosal
findings appear inconspicuous, with normal vascular markings. There is
no indication of inflammatory or malignant processes.
**Maximum Insight:** Terminal ileum.
**Anus:** Inspection of the anal region reveals no pathological
findings. Palpation is inconspicuous, and the mucosa is smooth and
displaceable, with no resistance and no blood on the glove.
**Assessment:** Polypectomy was performed for sigmoid diverticulosis and
a colonic diverticulum, with histology revealing minimally hyperplastic
colorectal mucosa and no evidence of malignancy.
**Pathology from 08/27/2019**
**Clinical Information/Question:**
**Macroscopy:** Unclear liver tumor: numerous tissue samples up to a
maximum of 0.7 cm in size. Complete embedding.
Processing: One tissue block processed and stained with Hematoxylin and
Eosin (H&E), Gomori\'s trichrome, Iron stain, Diastase Periodic
Acid-Schiff (D-PAS), and Van Gieson stain.
**Microscopic Findings:**
- Liver architecture is presented in fragmented liver core biopsies
with observable lobular structures and two included portal fields.
- Hepatic trabeculae are notably wider than the typical 2-3 cell
width, featuring the formation of druse-like luminal structures.
- Sinusoidal dilatation is markedly observed.
- Hepatocytes show mildly enlarged nuclei with minimal cytologic
atypia and isolated mitotic figures.
- Gomori staining reveals a notable, partial loss of the fine
reticulin fiber network.
- Adjacent areas show fibrosed liver parenchyma containing hemosiderin
pigmentation.
- No significant evidence of parenchymal fatty degeneration is
observed.
**Assessment**: Histologic features indicative of marked sinusoidal
dilatation, trabecular widening, and partial loss of reticulin network,
alongside minimally atypical hepatocytes and fibrosed parenchyma with
hemosiderin pigment. No significant hepatic fat degeneration noted.
### Patient Report 1
**Dear colleague, **
We would like to report on Paul Wells, born on 04/02/1953, who was under
our outpatient treatment on 08/24/2019.
**Diagnoses:**
- Multifocal HCC (Hepatocellular Carcinoma) involving segments IV,
VII/VIII, with portal vein invasion, classified as BCLC C, diagnosed
in July 2019.
- Extensive HCC lesions, some exophytic and others centrally
hemorrhagic, in segments S3/4 and S7/8, complete involvement of the
left liver lobe with smaller satellite lesions, and macrovascular
invasion of the left portal vein.
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
**Other Diagnoses:**
- COPD with a current severity level of Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency requiring home oxygen therapy.
- Postnasal Drip Syndrome.
- History of nicotine use (120 pack-years).
- Hypertension (high blood pressure).
**Medical History:** Mr. Wells presented with persistent right upper
abdominal pain and was initially treated at St. Luke\'s Medical Center.
CT scans revealed multiple intrahepatic lesions in the right liver lobe
(SIV, SVII/VIII). Short-term follow-up CT staging revealed a known,
size-stable, hypervascularized hepatic lesion in both lobes of the
liver, with no evidence of other thoracoabdominal malignancies or
suspicious lymph nodes. MRI also confirmed the presence of large HCC
lesions, some exophytic and others centrally hemorrhagic, in segments
S3/4 and S7/8, along with complete infiltration of the left liver lobe
with smaller satellite lesions and macroinvasion of the left portal
vein. There was mild cholestasis in the left biliary system.
**Current Recommendations: **
- Liver function remains good based on laboratory tests.
- Mr. Wells has been extensively informed about systemic therapy
options with Atezolizumab/Bevacizumab and the possibility of
alternative therapy with a tyrosine kinase inhibitor.
- The decision has been made to initiate standard first-line therapy
with Atezolizumab/Bevacizumab. Detailed information regarding
potential side effects has been provided, with particular emphasis
on the need for immediate medical evaluation in case of signs of
gastrointestinal bleeding (blood in stool, black tarry stool, or
vomiting blood) or worsening pulmonary symptoms.
- The patient has been strongly advised to abstain from alcohol
completely.
- A follow-up evaluation through liver MRI and CT has been scheduled
for January 4, 2020, at our HCC (Hepatocellular Carcinoma) clinic.
The exact appointment time will be communicated to the patient
separately.
- We are available for any questions or concerns.
- In case of persistent or worsening symptoms, we recommend an
immediate follow-up appointment.
### Patient Report 2
**Dear colleague, **
We would like to provide an update regarding Mr. Paul Wells, born on
04/02/1953, who was under our inpatient care from 08/13/2020 to
08/14/2020.
**Medical History:**
We assume familiarity with Mr. Wells\'s comprehensive medical history as
described in the previous referral letter. At the time of admission, he
reported significantly reduced physical performance due to his known
severe COPD. Following the consensus of the Liver Board, we admitted Mr.
Wells for a SIRT simulation.
**Current Presentation:** Mr. Wells is a 66-year-old patient with normal
consciousness and reduced general condition. He is largely compensated
on 3 liters of oxygen per minute. His abdomen is soft with regular
peristalsis. A palpable tumor mass in the right upper abdomen is noted.
**DSA Coeliac-Mesenteric on 08/13/2020:**
- Uncomplicated SIRT simulation.
- Catheter position 1: Right hepatic artery.
- Catheter position 2: Left hepatic artery.
- Catheter position 3: Liver segment arteries 4a/4b.
- Uncomplicated and technically successful embolization of parasitic
tumor supply from the inferior and superior epigastric arteries.
**Perfusion Scintigraphy of the Liver and Lungs, including SPECT/CT on
08/13/2020:**
- The liver/lung shunt volume is 9.4%.
- There is intense radioactivity accumulation in multiple lesions in
both the right and left liver lobes.
**Therapy and Progression:** On 08/13/2020, we performed a DSA
coeliac-mesenteric angiography on Mr. Wells, administering a total of
approximately 159 MBq Tc99m-MAA into the liver\'s arterial circulation
(simulation). This procedure revealed that a significant portion of
radioactivity would reach the lung parenchyma during therapy, posing a
risk of worsening his already compromised lung function. In view of
these comorbidities, SIRT was not considered a viable treatment option.
Therefore, an interdisciplinary decision was made during the conference
to recommend systemic therapy. With an uneventful course, we discharged
Mr. Wells in stable general condition on 08/14/2020.
### Patient Report 3
**Dear colleague, **
We are reporting on Paul Wells, born on 04/02/1953, who presented to our
interdisciplinary clinic for Hepato- and Cholangiocellular Tumors on
10/24/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019.
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
**Other Diagnoses:**
- Suspected Polyneuropathy or Restless Legs Syndrome
- COPD, current severity Gold III.
- Pulmonary emphysema
- Respiratory partial insufficiency with home oxygen
- Postnasal-Drip Syndrome
- History of nicotine abuse (120 py)
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- History of severe pneumonia (Medical Center St. Luke's) in 10/2019
- Pneumogenic sepsis with detection of Streptococcus pneumoniae
- Arterial hypertension
- Atrial fibrillation
- Treatment with Apixaban
- Reflux esophagitis Grade A (Esophagogastroduodenoscopy in 08/2019).
**Current Presentation**: Mr. Wells presented to discuss follow-up after
systemic therapy with Atezolizumab/Bevacizumab due to his impaired
general condition.
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
The therapy had to be paused after a single administration due to a
substantial increase in transaminases (GPT 164 U/L, GOT 151 U/L),
suspected to be associated with immunotherapy-induced hepatitis. With
only minimal improvement in transaminases, Prednisolone therapy was
initiated on and tapered successfully after significant transaminase
regression. However, before the next planned administration, the patient
experienced severe pneumonic sepsis, requiring hospitalization on
10/2019. Following discharge, there was a recurrent infection requiring
inpatient antibiotic therapy.
Staging examinations in 01/2020 showed a very good tumor response.
Subsequently, Atezolizumab/Bevacizumab was re-administered on 01/23/2020
and 02/14/2020. However, in the following days, the patient experienced
significant side effects, including oral burning, appetite and weight
loss, low blood pressure, and worsening pulmonary status. Steroid
treatment improved the pulmonary situation, but due to poor tolerance,
therapy was paused after 02/14/2020.
Currently, Mr. Wells reports a satisfactory general condition, although
his pulmonary function remains limited but stable.
**Summary:** Laboratory results from external testing on 01/02/2020
indicate excellent liver function, with transaminases within normal
range. The latest CT examination shows continued tumor regression.
However, MRI quality is limited due to the patient\'s inability to hold
their breath adequately. Given the excellent tumor response and previous
significant side effects, it was decided to continue the treatment pause
until the next tumor staging.
**Current Recommendations:** A follow-up imaging appointment has been
scheduled for four months from now. We kindly request you send the
latest CT images (Chest/Abdomen/Pelvis, including dynamic liver CT) and
current blood values to our HCC clinic. Due to limited assessability,
another MRI is not advisable.
We remain at your disposal for any further inquiries. In case of
persistent or worsened symptoms, we recommend prompt reevaluation.
**Medication upon discharge:**
**Medication** **Dosage** **Frequency**
------------------------------------- ------------ -------------------------
Ipratropium/Fenoterol (Combivent) As needed As needed
Beclomethasone/Formoterol (Fostair) 6+200 mcg 2-0-2
Tiotropium (Spiriva) 2.5 mcg 2-0-0
Prednisolone (Prelone) 5 mg 2-0-0 (or as necessary)
Pantoprazole (Protonix) 40 mg 1-0-0
Fenoterol 0.1 mg As needed
Apixaban (Eliquis) 5 mg On hold
Olmesartan (Benicar) 20 mg 1-0-0
Lab results upon Discharge:
**Parameter** **Results** **Reference Range**
----------------------------- ------------- ---------------------
Sodium (Na) 144 mEq/L 134-145 mEq/L
Potassium (K) 3.7 mEq/L 3.4-5.2 mEq/L
Calcium (Ca) 2.37 mEq/L 2.15-2.65 mEq/L
Chloride (Cl) 106 mEq/L 95-112 mEq/L
Inorganic Phosphate (PO4) 0.93 mEq/L 0.8-1.5 mEq/L
Transferrin Saturation 20 % 16-45 %
Magnesium 0.78 mEq/L 0.75-1.06 mEq/L
Creatinine 1.88 mg/dL \<1.2 mg/dL
GFR 36 mL/min \<90 mL/min
BUN 60 mg/dL 14-46 mg/dL
Uric Acid 4.6 mg/dL 3.0-6.9 mg/dL
Total Bilirubin 0.5 mg/dL \<1 mg/dL
Albumin 4.0 g/dL 3.6-5.0 g/dL
Total Protein 6.8 g/dL 6.5-8.7 g/dL
CRP 0.19 mg/dL \<0.5 mg/dL
Transferrin 269 mg/dL 200-360 mg/dL
Ferritin 110 mcg/L 30-300 mcg/L
ALT 339 U/L \<45 U/L
AST 424 U/L \<50 U/L
GGT 904 U/L \<55 U/L
Lipase 61 U/L \<70 U/L
Thyroid-Stimulating Hormone 0.54 mIU/L 0.27-4.20 mIU/L
Hemoglobin 14.5 g/dL 14.0-17.5 g/dL
Hematocrit 43 % 40-52 %
Red Blood Cells 4.60 M/µL 4.6-6.2 M/µL
White Blood Cells 8.78 K/µL 4.5-11.0 K/µL
Platelets 205 K/µL 150-400 K/µL
MCV 94 fL 81-100 fL
MCH 31.5 pg 27-34 pg
MCHC 33.5 g/dL 32.4-35.0 g/dL
MPV 11 fL 7-12 fL
RDW 14.8 % 11.9-14.5 %
Neutrophils 3.72 K/µL 1.8-7.7 K/µL
Lymphocytes 2.37 K/µL 1.4-3.7 K/µL
Monocytes 0.93 K/µL 0.2-1.0 K/µL
Eosinophils 1.67 K/µL \<0.7 K/µL
Basophils 0.09 K/µL 0.01-0.10 K/µL
Erythroblasts Negative \<0.01 K/µL
Antithrombin Activity 85 % 80-120 %
### Patient Report 4
**Dear colleague, **
We are reporting an update of the medical condition of Mr. Paul Wells
born on 04/02/1953, who presented for a follow up in our outpatient
clinic on 11/20/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation.
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased alcohol consumption (3-4 beers/day).
**Other diagnoses:**
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with detection of Streptococcus pneumonia
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** Mr. Wells initially presented with right upper
abdominal pain, which led to the discovery of multiple intrahepatic
masses in liver segments IV, VII/VIII. Subsequent investigations
confirmed the diagnosis of HCC. He also suffers from chronic obstructive
pulmonary disease (COPD), emphysema, and respiratory insufficiency
requiring home oxygen therapy. Previous investigations and treatments
were documented in detail in our previous medical records.
**Physical Examination:**
- General Appearance: Alert, cooperative, and oriented.
- Vital Signs: Stable blood pressure, heart rate, respiratory rate,
and temperature. Oxygen Saturation (SpO2): Within the normal range.
- Respiratory System: Normal chest symmetry, no accessory muscle use.
Clear breath sounds, no wheezing or crackles. Regular respiratory
rate.
- Cardiovascular System: Regular heart rate and rhythm, no murmurs.
Strong radial and pedal pulses bilaterally. No lower extremity
edema.
- Gastrointestinal System: Soft, nontender abdomen. Bowel sounds
present in all quadrants. Spleen palpable under the costal arch.
- Neurological Examination: Alert and oriented. Cranial nerves, motor,
sensory, reflexes, coordination and gait normal. No focal
neurological deficits.
- Skin and Mucous Membranes: Intact skin, no rashes or lesions. Moist
oral mucosa without lesions.
- Extremities: No edema. Full range of motion in all joints. Normal
capillary refill.
- Lymphatic System:
- No palpable lymphadenopathy.
**MRI Liver (plain + contrast agent) on 11/20/2020 09:01 AM.**
- Imaging revealed stable findings in the liver. The previously
identified HCC lesions in segments IV, VII/VIII, including their
size and characteristics, remained largely unchanged. There was no
evidence of new lesions or metastases. Detailed MRI imaging provided
valuable insight into the nature of the lesions, their vascularity,
and possible effects on adjacent structures.
**CT Chest/Abdomen/Pelvis with contrast agent on 11/20/2020 12:45 PM.**
- Thoracoabdominal CT scan showed the same results as the previous
examination. Known space-occupying lesions in the liver remained
stable, and there was no evidence of malignancy or metastasis
elsewhere in the body. The examination also included a thorough
evaluation of the thoracic and pelvic regions to rule out possible
metastasis.
**Gastroscopy on 11/20/2020 13:45 PM.**
- Gastroscopy follow-up confirmed the previous diagnosis of reflux
esophagitis (Los Angeles classification grade A) and antral
gastritis. These findings were consistent with previous
investigations. It is important to note that while these findings
are unrelated to HCC, they contribute to Mr. Wells\' overall medical
profile and require ongoing treatment.
**Colonoscopy on 11/20/2020 15:15 PM.**
- Colonoscopy showed that the sigmoid colon polyp, which had been
removed during the previous examination, had not recurred. No new
abnormalities or malignancies were detected in the gastrointestinal
tract. This examination provides assurance that there is no
concurrent colorectal malignancy complicating Mr. Wells\' medical
condition.
**Pulmonary Function Testing:**
Mr. Wells\' COPD, emphysema, and respiratory insufficiency were
evaluated in detail. Pulmonary function tests confirmed his current
severity score of Gold III, indicating advanced COPD. Despite the
chronic nature of his disease, there has been no significant
deterioration since the last assessment.
**Oxygen Therapy:**
As previously documented, Mr. Wells requires home oxygen therapy. His
oxygen requirements have been constant, with no significant increase in
oxygen requirements during daily activities or at rest. This stability
in his oxygen demand is encouraging and indicates effective management
of his respiratory disease.
**Overall Assessment:** Based on the results of recent follow-up, Mr.
Paul Wells\' hepatocellular carcinoma (HCC) has not progressed
significantly. The previously noted HCC lesions have remained stable in
terms of size and characteristics. In addition, there is no evidence of
malignancy elsewhere in his thoracoabdominal region.
Mr. Wells\' COPD, emphysema, and respiratory insufficiency, which is
being treated with home oxygen therapy, have also not changed
significantly during this follow-up period. His cardiopulmonary
condition remains well controlled, with no acute deterioration.
Psychosocially, Mr. Wells continues to demonstrate resilience and
actively participates in his care. His strong support system continues
to contribute to his overall well-being.
Additional monitoring and follow-up appointments have been scheduled to
ensure continued management of Mr. Wells\' health. In addition,
discussions continue regarding potential treatment options and
interventions to provide him with the best possible care.
**Current Recommendations:** In light of the stability observed in Mr.
Wells\' HCC and overall medical condition, we recommend the following
steps for his continued care:
1. Regular Follow-up: Maintain a schedule of regular follow-up
appointments to monitor the status of the HCC, cardiopulmonary
function, and other associated conditions.
2. Lifestyle-Modification
### Patient Report 5
**Dear colleague, **
We report to you about Mr. Paul Wells born on 04/02/1953 who received
inpatient treatment from 02/04/2021 to 02/12/2021.
**Diagnosis**: Community-Acquired Pneumonia (CAP)
**Previous Diagnoses and Treatment:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation attempt on 08/13/2019: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation (up to 4x ULN).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
- Suspected PNP DD RLS (Restless Legs Syndrome).
<!-- -->
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with Streptococcus pneumoniae detection.
- History of unclear infection vs. pneumonia in 10/2019-01/2020.
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
Currently, Mr. Wells complains about progressively worsening respiratory
symptoms, which included shortness of breath, productive cough with
yellow-green sputum, pleuritic chest pain, fever, and chills, spanning a
period of five days.
**Physical Examination:**
Temperature: 38.6°C, Blood Pressure: 140/80 mm Hg, Heart Rate: 110 beats
per minute Respiratory Rate: 30 breaths per minute, Oxygen Saturation
(SpO2): 88% on room air
Breath Sounds: Auscultation revealed diminished breath sounds and coarse
crackles, notably in the right lower lobe.
The patient further reported pleuritic chest pain localized to the right
lower chest.
**Therapy and Progression:**
During his hospitalization, Mr. Wells was in stable cardiopulmonary
condition. We initiated an empiric antibiotic therapy with intravenous
Ceftriaxone and Azithromycin to treat community-acquired pneumonia
(CAP). Oxygen supplementation was provided to maintain adequate oxygen
saturation levels, and pain management strategies were implemented to
alleviate pleuritic chest pain. Additionally, pulmonary hygiene measures
and chest physiotherapy were applied to facilitate sputum clearance.
Frequent respiratory treatments with bronchodilators were administered
to mitigate airway obstruction, and continuous monitoring of vital
signs, oxygen saturation, and respiratory status was carried out.
Throughout his hospital stay, Mr. Wells exhibited gradual clinical
improvement, marked by several positive developments. These included the
resolution of fever, improved oxygen saturation levels, and a follow-up
chest X-ray demonstrating the resolution of the right lower lobe
consolidation. Furthermore, antibiotic therapy was adjusted based on
sputum culture results, which identified Streptococcus pneumoniae as the
causative pathogen. Mr. Wells continued to receive supportive care and
respiratory interventions.
We were thus able to discharge Mr. Wells in a good general condition. | MRI Liver followed by CT Chest/Abdomen/Pelvis |
What is the significance of the restaurant's stained table cloth?
A. Only the cured people are allowed to dine in fine restaurants, but 'fine' is a loose term
B. Table cloths, like cures, can easily be switched out and cleaned (repaired) in order to appear flawless
C. They represent the stain that cure development has made on social progress
D. Like the cure, it obscures up a symptom but fails to address the root problem
| 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. | D. Like the cure, it obscures up a symptom but fails to address the root problem |
What does Lt. Britton think of her husband Jim?
A. She's frustrated with him for getting her pregnant when she'd be on the Station
B. She's disappointed that he can't be there for the delivery
C. She loves his dedication to his piloting
D. She is excited that he'll be on board as soon as he can
| 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. | D. She is excited that he'll be on board as soon as he can |
Which of the following sequences correctly represents the chronological order of the first set of diagnostic procedures following Mr. Wells' admission on 07/26/2019?
Choose the correct answer from the following options:
A. Ultrasound-guided liver puncture, MRI Liver, CT Thorax/Abdomen/Pelvis
B. MRI Liver, Ultrasound-guided liver puncture, Gastroscopy
C. Ultrasound-guided liver puncture, Gastroscopy, MRI Liver
D. MRI Liver, CT Thorax/Abdomen/Pelvis, Ultrasound-guided liver puncture
E. Gastroscopy, Ultrasound-guided liver puncture, MRI Liver
| ### Patient Report 0
**Dear colleague, **
We report to you on Mr. Paul Wells, born on 04/02/1953, who was in our
inpatient treatment from 07/26/2019 to 07/28/2019.
**Diagnoses:** Suspected multifocal HCC segment IV, VII/VIII, first
diagnosed: 07/19.
- COPD, current severity level Gold III.
- Pulmonary emphysema, respiratory partial insufficiency with home
oxygen.
- Postnasal drip syndrome
**Current Presentation:** The elective presentation of Mr. Wells was
made in accordance with the decision of the interdisciplinary liver
board of 07/20/2019 for further diagnostics in the case of multiple
malignoma-specific hepatic space demands.
**Medical History: **In brief, Mr. Wells presented to the Medical Center
St. Luke's with persistent right-sided pain in the upper abdomen.
Computer tomography showed multiple intrahepatic masses of the right
liver lobe (SIV, SVII/VIII). For diagnostic clarification of the
malignoma-specific findings, the patient was presented to our liver
outpatient clinic. The tumor marker diagnostics have not been
conclusive. Analogous to the recommendation of the liver board, a liver
puncture, staging, and endoscopic exclusion of a primary in the
gastrointestinal tract should be initiated.
**Physical Examination:** Physical examination reveals an alert patient.
- Oral mucosa: Moist and rosy, no plaques typical of thrush, no
plaques typical of herpes.
- Hear: Heart sounds pure, rhythmic, normofrequency.
- Lungs: Laterally attenuated breath sound with wheezing.
- Abdomen: Abdomen soft, regular bowel sounds over all 4 quadrants, no
defensive tension, no resistances, diffuse pressure pain over the
upper abdomen. No renal tap pain, no spinal tap pain. Spleen
palpable under the costal arch.
- Extremities: No edema, freely movable
- Neurology: GCS 15, pupils directly and indirectly reactive to light,
no flapping tremor. No meningism.
**Therapy and Progression:** Mr. Wells presented an age-appropriate
general status and cardiopulmonary stability. Anamnestically, there was
no evidence of an acute infection. Skin or scleral icterus and pruritus
were denied. No B symptoms. No stool changes, no dysuria. There would be
regular alcohol consumption of about 3-4 beers a day, as well as
nicotine abuse (120 PY). The general performance in COPD Gold grade III
was strongly limited, with a walking distance reduced to 100m due to
dyspnea. He had a home oxygen demand with 4L/min O2 during the day, up
to 6L/min under load. At night, 2L/min O2. The last colonoscopy was
performed 4 years ago, with no anamnestic abnormalities. No known
allergies. Family history is positive for colorectal cancer (mother).
Clinical examination revealed the typical auscultation findings of
advanced COPD with attenuated breath sounds bilateral, with
hyperinflation and clear wheezing. Otherwise, there were no significant
findings. Laboratory chemistry did not reveal any higher-grade
abnormalities. On the day of admission, after detailed clarification,
the patient was able to undergo the complication-free sonographically
guided puncture of the liver cavity in SIV. Thereby, two punch cylinders
were preserved for histopathological processing. Histologically, the
findings presented as infiltrates of a macrotrabecular and
pseudoglandular growing, well-differentiated hepatocellular carcinoma
(G1). The postinterventional course was unremarkable. In particular, no
clinical or laboratory signs were found for bleeding.
CT staging revealed a size constant known in the short term.
Hypervascularized hepatic space demands in both lobes of the liver
without further malignancy suspect thoracoabdominal tumor detection and
without metastasis aspects. MR also revealed the large, partly exophytic
growing, partly centrally hemorrhaged HCC lesions in S3/4 and S7/8 to
the illustration. In addition, complete enforcement of the left lobe of
the liver was evident with smaller satellites and macroinvasion of the
left portal vein branch. There was a low cholestasis of the left biliary
system. Gastroscopy and colonoscopy were also performed. Here, a reflux
esophagitis, sigmoid diverticulosis, multiple colonic diverticula, and a
4mm polyp were removed from the sigmoid colon to prevent bleeding; a
hemoclip was applied. Histologically, no adenoma was found. An
appointment to discuss the findings in our HCC outpatient clinic has
been arranged. We recommend further therapy preparation and the
performance of an echocardiography.
We were able to discharge Mr. Wells on 7/28/19.
**Addition:**
**Ultrasound on 07/26/2019 10:15 AM:**
- Indication: Targeted liver puncture for suspected metastatic liver
malignancy
- Organ puncture: Quick: 114%, PTT: 28 s, and platelets: 475 G/L. A
valid declaration of consent is available. According to the patient,
he does not receive antiplatelet drugs.
- In segment IV, an approximately 8.3 x 6 cm echo-depleted mass with
central cystic fusion is accessible in the dorsal position of a
sonographically guided puncture at 6.5 cm puncture depth. After
extensive skin disinfection, local anesthesia with 10 mL Mecaine 1%
and puncture incision with a scalpel. Repeated puncture with 18 G
Magnum needles is performed. Two approximately 1 cm fragile whitish
cylinders obtained for histologic examination. Band-aid dressing.
- **Assessment:** Hepatic space demand
**MRI of the liver plain + contrast agent from 07/26/2019 1:15 PM:**
**Technique**: Coronary and axial T2 weighted sequences, axial
diffusion-weighted EPI sequence with ADC map (b: 0, 50, 300 and 600
s/mmÇ), axial dynamic T1 weighted sequences with Dixon fat suppression
and (liver-specific) contrast agent (Dotagraf/Primovist); slice
thickness: 4 mm. Premedication with 2 mL Buscopan.
**Liver**: Centrally hemorrhagic masses observed in liver segments 4, 7,
and 8 demonstrate T2 hyperintensity, marked diffusion restriction,
arterial phase enhancement, and venous phase washout. These
characteristics are congruent with histopathological diagnosis of
hepatocellular carcinoma. The largest lesion in segment 4 exhibits
pronounced exophytic growth but no evidence of organ invasion. Notably,
branches of the mammary arteries penetrate directly into the tumor.
Diffusion-weighted imaging further reveals disseminated foci throughout
the entire left hepatic lobe. Disruption of the peripheral left portal
vein branch indicative of macrovascular invasion, accompanied by
peripheral cholestasis in the left biliary system.
**Biliary Tract:** Bile ducts are emphasized on both left and right
sides, with no evidence of mechanical obstruction in drainage. The
common hepatic duct remains non-dilated.
**Pancreas and Spleen:** Both organs exhibit no abnormalities.
**Kidneys:** Normal signal characteristics observed.
**Bone Marrow:** Signal behavior is within normal limits.
Assessment: Radiological features highly suggestive of hepatocellular
carcinoma in liver segments 4, 7, and 8, with evidence of macrovascular
invasion and peripheral cholestasis in the left biliary system. No signs
of organ invasion or biliary obstruction. Pancreas, spleen, kidneys, and
bone marrow appear unremarkable.
**Assessment:**
Large liver lesions, some exophytic and some centrally hemorrhagic, are
observed in segments 3/4 and 7/8.
In addition, the left lobe of the liver is completely involved with
smaller satellite lesions and macroinvasion of the left portal branch.
Mild cholestasis of the left biliary system is noted.
Dilated bile ducts are also found on the right side with no apparent
mechanical obstruction to outflow.
**CT Chest/Abdomen/Pelvis with contrast agent from 07/27/2019 2:00 PM:**
**Clinical Indication:** Evaluation of an unclear liver lesion
(approximately 9 cm) in a patient with severe COPD. No prior
liver-related medical history.
**Question:** Are there any suspicious lesions in the liver?
**Pre-recordings:** Previous external CT abdomen dated 09/13/2021.
**Findings:**
**Technique:** CT imaging involved a multi-line spiral CT through the
chest, abdomen, and pelvis in the venous contrast phase. Oral contrast
agent with Gastrolux 1:33 in water was administered. Thin-layer
reconstructions and coronary and sagittal secondary reconstructions were
performed.
**Chest:** No axillary or mediastinal lymphadenopathy is observed. There
is marked coronary sclerosis, as well as calcification of the aortic and
mitral valves. Nonspecific nodules smaller than 2 mm are noted in the
posterolateral lower lobe on the right side and lateral middle lobe. No
pneumonic infiltrates are observed. There is reduced aeration with
presumed additional scarring changes at the base of the lung
bilaterally, along with centrilobular emphysema.
**Abdomen:** Known exophytic liver lesions are confirmed, with
involvement in segment III extending to the subhepatic region (0.1 cm
extension) and a 6 cm lesion in segment VIII. Further spotty
hypervascularized lesions are observed throughout the left lobe of the
liver. No pathological dilatation of intra- or extrahepatic bile ducts
is seen, and there is no evidence of portal vein thrombosis. There are
no pathologically enlarged lymph nodes at the hepatic portal,
retroperitoneal, or inguinal regions. No ascites or pneumoperitoneum is
noted. There is no pancreatic duct congestion, and the spleen is not
enlarged. Additionally, there is a Bosniak 1 left renal cyst measuring
3.6 cm. Pronounced sigmoid diverticulosis is observed, with no evidence
of other masses in the gastrointestinal tract. Skeletal imaging reveals
no malignancy-specific osteodestructions but shows ventral pontifying
spondylophytes of the thoracic spine with no fractures.
**Assessment:**
Short-term size-constant known hypervascularized hepatic space lesions
are present in both lobes of the liver.
No other malignancy-susceptible thoracoabdominal tumor evidence is
found, and there are no metastasis-specific lymph nodes.
**Gastroscopy from 07/28/2019**
**Findings:**
**Esophagus:** Unobstructed intubation of the esophageal orifice under
visualization. Mucosa appears inconspicuous, with the Z-line at 37 cm
and measuring less than 5 mm. Small mucosal lesions are observed but do
not straddle mucosal folds.
**Stomach:** The gastric lumen is completely distended under air
insufflation. There are streaky changes in the antrum, while the fundus
and cardia appear regular on inversion. The pylorus is inconspicuous and
passable.
**Duodenum:** Good development of the bulbus duodeni is noted, with good
insight into the pars descendens duodeni. The mucosa appears overall
inconspicuous.
**Assessment:** Findings suggest reflux esophagitis (Los Angeles
Classification Grade A) and antrum gastritis.
**Colonoscopy from 07/28/2019**
**Findings:**
**Colon:** Some residual fluid contamination is noted in the sigmoid
(Boston Bowel Preparation Scale \[BBPS\] 8). There is pronounced sigmoid
diverticulosis, along with multiple colonic diverticula. A 4mm polyp in
the lower sigma (Paris IIa, NICE 1) is observed and ablated with a cold
snare, with hemoclip application for bleeding prophylaxis. Other mucosal
findings appear inconspicuous, with normal vascular markings. There is
no indication of inflammatory or malignant processes.
**Maximum Insight:** Terminal ileum.
**Anus:** Inspection of the anal region reveals no pathological
findings. Palpation is inconspicuous, and the mucosa is smooth and
displaceable, with no resistance and no blood on the glove.
**Assessment:** Polypectomy was performed for sigmoid diverticulosis and
a colonic diverticulum, with histology revealing minimally hyperplastic
colorectal mucosa and no evidence of malignancy.
**Pathology from 08/27/2019**
**Clinical Information/Question:**
**Macroscopy:** Unclear liver tumor: numerous tissue samples up to a
maximum of 0.7 cm in size. Complete embedding.
Processing: One tissue block processed and stained with Hematoxylin and
Eosin (H&E), Gomori\'s trichrome, Iron stain, Diastase Periodic
Acid-Schiff (D-PAS), and Van Gieson stain.
**Microscopic Findings:**
- Liver architecture is presented in fragmented liver core biopsies
with observable lobular structures and two included portal fields.
- Hepatic trabeculae are notably wider than the typical 2-3 cell
width, featuring the formation of druse-like luminal structures.
- Sinusoidal dilatation is markedly observed.
- Hepatocytes show mildly enlarged nuclei with minimal cytologic
atypia and isolated mitotic figures.
- Gomori staining reveals a notable, partial loss of the fine
reticulin fiber network.
- Adjacent areas show fibrosed liver parenchyma containing hemosiderin
pigmentation.
- No significant evidence of parenchymal fatty degeneration is
observed.
**Assessment**: Histologic features indicative of marked sinusoidal
dilatation, trabecular widening, and partial loss of reticulin network,
alongside minimally atypical hepatocytes and fibrosed parenchyma with
hemosiderin pigment. No significant hepatic fat degeneration noted.
### Patient Report 1
**Dear colleague, **
We would like to report on Paul Wells, born on 04/02/1953, who was under
our outpatient treatment on 08/24/2019.
**Diagnoses:**
- Multifocal HCC (Hepatocellular Carcinoma) involving segments IV,
VII/VIII, with portal vein invasion, classified as BCLC C, diagnosed
in July 2019.
- Extensive HCC lesions, some exophytic and others centrally
hemorrhagic, in segments S3/4 and S7/8, complete involvement of the
left liver lobe with smaller satellite lesions, and macrovascular
invasion of the left portal vein.
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
**Other Diagnoses:**
- COPD with a current severity level of Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency requiring home oxygen therapy.
- Postnasal Drip Syndrome.
- History of nicotine use (120 pack-years).
- Hypertension (high blood pressure).
**Medical History:** Mr. Wells presented with persistent right upper
abdominal pain and was initially treated at St. Luke\'s Medical Center.
CT scans revealed multiple intrahepatic lesions in the right liver lobe
(SIV, SVII/VIII). Short-term follow-up CT staging revealed a known,
size-stable, hypervascularized hepatic lesion in both lobes of the
liver, with no evidence of other thoracoabdominal malignancies or
suspicious lymph nodes. MRI also confirmed the presence of large HCC
lesions, some exophytic and others centrally hemorrhagic, in segments
S3/4 and S7/8, along with complete infiltration of the left liver lobe
with smaller satellite lesions and macroinvasion of the left portal
vein. There was mild cholestasis in the left biliary system.
**Current Recommendations: **
- Liver function remains good based on laboratory tests.
- Mr. Wells has been extensively informed about systemic therapy
options with Atezolizumab/Bevacizumab and the possibility of
alternative therapy with a tyrosine kinase inhibitor.
- The decision has been made to initiate standard first-line therapy
with Atezolizumab/Bevacizumab. Detailed information regarding
potential side effects has been provided, with particular emphasis
on the need for immediate medical evaluation in case of signs of
gastrointestinal bleeding (blood in stool, black tarry stool, or
vomiting blood) or worsening pulmonary symptoms.
- The patient has been strongly advised to abstain from alcohol
completely.
- A follow-up evaluation through liver MRI and CT has been scheduled
for January 4, 2020, at our HCC (Hepatocellular Carcinoma) clinic.
The exact appointment time will be communicated to the patient
separately.
- We are available for any questions or concerns.
- In case of persistent or worsening symptoms, we recommend an
immediate follow-up appointment.
### Patient Report 2
**Dear colleague, **
We would like to provide an update regarding Mr. Paul Wells, born on
04/02/1953, who was under our inpatient care from 08/13/2020 to
08/14/2020.
**Medical History:**
We assume familiarity with Mr. Wells\'s comprehensive medical history as
described in the previous referral letter. At the time of admission, he
reported significantly reduced physical performance due to his known
severe COPD. Following the consensus of the Liver Board, we admitted Mr.
Wells for a SIRT simulation.
**Current Presentation:** Mr. Wells is a 66-year-old patient with normal
consciousness and reduced general condition. He is largely compensated
on 3 liters of oxygen per minute. His abdomen is soft with regular
peristalsis. A palpable tumor mass in the right upper abdomen is noted.
**DSA Coeliac-Mesenteric on 08/13/2020:**
- Uncomplicated SIRT simulation.
- Catheter position 1: Right hepatic artery.
- Catheter position 2: Left hepatic artery.
- Catheter position 3: Liver segment arteries 4a/4b.
- Uncomplicated and technically successful embolization of parasitic
tumor supply from the inferior and superior epigastric arteries.
**Perfusion Scintigraphy of the Liver and Lungs, including SPECT/CT on
08/13/2020:**
- The liver/lung shunt volume is 9.4%.
- There is intense radioactivity accumulation in multiple lesions in
both the right and left liver lobes.
**Therapy and Progression:** On 08/13/2020, we performed a DSA
coeliac-mesenteric angiography on Mr. Wells, administering a total of
approximately 159 MBq Tc99m-MAA into the liver\'s arterial circulation
(simulation). This procedure revealed that a significant portion of
radioactivity would reach the lung parenchyma during therapy, posing a
risk of worsening his already compromised lung function. In view of
these comorbidities, SIRT was not considered a viable treatment option.
Therefore, an interdisciplinary decision was made during the conference
to recommend systemic therapy. With an uneventful course, we discharged
Mr. Wells in stable general condition on 08/14/2020.
### Patient Report 3
**Dear colleague, **
We are reporting on Paul Wells, born on 04/02/1953, who presented to our
interdisciplinary clinic for Hepato- and Cholangiocellular Tumors on
10/24/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- Histology from 07/27/2019: A well-differentiated hepatocellular
carcinoma (G1) with a macrotrabecular and pseudoglandular growth
pattern.
- Decision from the Liver Tumor Board on 08/18/2019: Recommending
systemic therapy.
- Initiation of Atezolizumab/Bevacizumab on 08/24/2019.
- Liver fibrosis: Elevated alcohol consumption (3-4 beers/day).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
**Other Diagnoses:**
- Suspected Polyneuropathy or Restless Legs Syndrome
- COPD, current severity Gold III.
- Pulmonary emphysema
- Respiratory partial insufficiency with home oxygen
- Postnasal-Drip Syndrome
- History of nicotine abuse (120 py)
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- History of severe pneumonia (Medical Center St. Luke's) in 10/2019
- Pneumogenic sepsis with detection of Streptococcus pneumoniae
- Arterial hypertension
- Atrial fibrillation
- Treatment with Apixaban
- Reflux esophagitis Grade A (Esophagogastroduodenoscopy in 08/2019).
**Current Presentation**: Mr. Wells presented to discuss follow-up after
systemic therapy with Atezolizumab/Bevacizumab due to his impaired
general condition.
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
The therapy had to be paused after a single administration due to a
substantial increase in transaminases (GPT 164 U/L, GOT 151 U/L),
suspected to be associated with immunotherapy-induced hepatitis. With
only minimal improvement in transaminases, Prednisolone therapy was
initiated on and tapered successfully after significant transaminase
regression. However, before the next planned administration, the patient
experienced severe pneumonic sepsis, requiring hospitalization on
10/2019. Following discharge, there was a recurrent infection requiring
inpatient antibiotic therapy.
Staging examinations in 01/2020 showed a very good tumor response.
Subsequently, Atezolizumab/Bevacizumab was re-administered on 01/23/2020
and 02/14/2020. However, in the following days, the patient experienced
significant side effects, including oral burning, appetite and weight
loss, low blood pressure, and worsening pulmonary status. Steroid
treatment improved the pulmonary situation, but due to poor tolerance,
therapy was paused after 02/14/2020.
Currently, Mr. Wells reports a satisfactory general condition, although
his pulmonary function remains limited but stable.
**Summary:** Laboratory results from external testing on 01/02/2020
indicate excellent liver function, with transaminases within normal
range. The latest CT examination shows continued tumor regression.
However, MRI quality is limited due to the patient\'s inability to hold
their breath adequately. Given the excellent tumor response and previous
significant side effects, it was decided to continue the treatment pause
until the next tumor staging.
**Current Recommendations:** A follow-up imaging appointment has been
scheduled for four months from now. We kindly request you send the
latest CT images (Chest/Abdomen/Pelvis, including dynamic liver CT) and
current blood values to our HCC clinic. Due to limited assessability,
another MRI is not advisable.
We remain at your disposal for any further inquiries. In case of
persistent or worsened symptoms, we recommend prompt reevaluation.
**Medication upon discharge:**
**Medication** **Dosage** **Frequency**
------------------------------------- ------------ -------------------------
Ipratropium/Fenoterol (Combivent) As needed As needed
Beclomethasone/Formoterol (Fostair) 6+200 mcg 2-0-2
Tiotropium (Spiriva) 2.5 mcg 2-0-0
Prednisolone (Prelone) 5 mg 2-0-0 (or as necessary)
Pantoprazole (Protonix) 40 mg 1-0-0
Fenoterol 0.1 mg As needed
Apixaban (Eliquis) 5 mg On hold
Olmesartan (Benicar) 20 mg 1-0-0
Lab results upon Discharge:
**Parameter** **Results** **Reference Range**
----------------------------- ------------- ---------------------
Sodium (Na) 144 mEq/L 134-145 mEq/L
Potassium (K) 3.7 mEq/L 3.4-5.2 mEq/L
Calcium (Ca) 2.37 mEq/L 2.15-2.65 mEq/L
Chloride (Cl) 106 mEq/L 95-112 mEq/L
Inorganic Phosphate (PO4) 0.93 mEq/L 0.8-1.5 mEq/L
Transferrin Saturation 20 % 16-45 %
Magnesium 0.78 mEq/L 0.75-1.06 mEq/L
Creatinine 1.88 mg/dL \<1.2 mg/dL
GFR 36 mL/min \<90 mL/min
BUN 60 mg/dL 14-46 mg/dL
Uric Acid 4.6 mg/dL 3.0-6.9 mg/dL
Total Bilirubin 0.5 mg/dL \<1 mg/dL
Albumin 4.0 g/dL 3.6-5.0 g/dL
Total Protein 6.8 g/dL 6.5-8.7 g/dL
CRP 0.19 mg/dL \<0.5 mg/dL
Transferrin 269 mg/dL 200-360 mg/dL
Ferritin 110 mcg/L 30-300 mcg/L
ALT 339 U/L \<45 U/L
AST 424 U/L \<50 U/L
GGT 904 U/L \<55 U/L
Lipase 61 U/L \<70 U/L
Thyroid-Stimulating Hormone 0.54 mIU/L 0.27-4.20 mIU/L
Hemoglobin 14.5 g/dL 14.0-17.5 g/dL
Hematocrit 43 % 40-52 %
Red Blood Cells 4.60 M/µL 4.6-6.2 M/µL
White Blood Cells 8.78 K/µL 4.5-11.0 K/µL
Platelets 205 K/µL 150-400 K/µL
MCV 94 fL 81-100 fL
MCH 31.5 pg 27-34 pg
MCHC 33.5 g/dL 32.4-35.0 g/dL
MPV 11 fL 7-12 fL
RDW 14.8 % 11.9-14.5 %
Neutrophils 3.72 K/µL 1.8-7.7 K/µL
Lymphocytes 2.37 K/µL 1.4-3.7 K/µL
Monocytes 0.93 K/µL 0.2-1.0 K/µL
Eosinophils 1.67 K/µL \<0.7 K/µL
Basophils 0.09 K/µL 0.01-0.10 K/µL
Erythroblasts Negative \<0.01 K/µL
Antithrombin Activity 85 % 80-120 %
### Patient Report 4
**Dear colleague, **
We are reporting an update of the medical condition of Mr. Paul Wells
born on 04/02/1953, who presented for a follow up in our outpatient
clinic on 11/20/2020.
**Diagnoses:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation.
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased alcohol consumption (3-4 beers/day).
**Other diagnoses:**
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with detection of Streptococcus pneumonia
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** Mr. Wells initially presented with right upper
abdominal pain, which led to the discovery of multiple intrahepatic
masses in liver segments IV, VII/VIII. Subsequent investigations
confirmed the diagnosis of HCC. He also suffers from chronic obstructive
pulmonary disease (COPD), emphysema, and respiratory insufficiency
requiring home oxygen therapy. Previous investigations and treatments
were documented in detail in our previous medical records.
**Physical Examination:**
- General Appearance: Alert, cooperative, and oriented.
- Vital Signs: Stable blood pressure, heart rate, respiratory rate,
and temperature. Oxygen Saturation (SpO2): Within the normal range.
- Respiratory System: Normal chest symmetry, no accessory muscle use.
Clear breath sounds, no wheezing or crackles. Regular respiratory
rate.
- Cardiovascular System: Regular heart rate and rhythm, no murmurs.
Strong radial and pedal pulses bilaterally. No lower extremity
edema.
- Gastrointestinal System: Soft, nontender abdomen. Bowel sounds
present in all quadrants. Spleen palpable under the costal arch.
- Neurological Examination: Alert and oriented. Cranial nerves, motor,
sensory, reflexes, coordination and gait normal. No focal
neurological deficits.
- Skin and Mucous Membranes: Intact skin, no rashes or lesions. Moist
oral mucosa without lesions.
- Extremities: No edema. Full range of motion in all joints. Normal
capillary refill.
- Lymphatic System:
- No palpable lymphadenopathy.
**MRI Liver (plain + contrast agent) on 11/20/2020 09:01 AM.**
- Imaging revealed stable findings in the liver. The previously
identified HCC lesions in segments IV, VII/VIII, including their
size and characteristics, remained largely unchanged. There was no
evidence of new lesions or metastases. Detailed MRI imaging provided
valuable insight into the nature of the lesions, their vascularity,
and possible effects on adjacent structures.
**CT Chest/Abdomen/Pelvis with contrast agent on 11/20/2020 12:45 PM.**
- Thoracoabdominal CT scan showed the same results as the previous
examination. Known space-occupying lesions in the liver remained
stable, and there was no evidence of malignancy or metastasis
elsewhere in the body. The examination also included a thorough
evaluation of the thoracic and pelvic regions to rule out possible
metastasis.
**Gastroscopy on 11/20/2020 13:45 PM.**
- Gastroscopy follow-up confirmed the previous diagnosis of reflux
esophagitis (Los Angeles classification grade A) and antral
gastritis. These findings were consistent with previous
investigations. It is important to note that while these findings
are unrelated to HCC, they contribute to Mr. Wells\' overall medical
profile and require ongoing treatment.
**Colonoscopy on 11/20/2020 15:15 PM.**
- Colonoscopy showed that the sigmoid colon polyp, which had been
removed during the previous examination, had not recurred. No new
abnormalities or malignancies were detected in the gastrointestinal
tract. This examination provides assurance that there is no
concurrent colorectal malignancy complicating Mr. Wells\' medical
condition.
**Pulmonary Function Testing:**
Mr. Wells\' COPD, emphysema, and respiratory insufficiency were
evaluated in detail. Pulmonary function tests confirmed his current
severity score of Gold III, indicating advanced COPD. Despite the
chronic nature of his disease, there has been no significant
deterioration since the last assessment.
**Oxygen Therapy:**
As previously documented, Mr. Wells requires home oxygen therapy. His
oxygen requirements have been constant, with no significant increase in
oxygen requirements during daily activities or at rest. This stability
in his oxygen demand is encouraging and indicates effective management
of his respiratory disease.
**Overall Assessment:** Based on the results of recent follow-up, Mr.
Paul Wells\' hepatocellular carcinoma (HCC) has not progressed
significantly. The previously noted HCC lesions have remained stable in
terms of size and characteristics. In addition, there is no evidence of
malignancy elsewhere in his thoracoabdominal region.
Mr. Wells\' COPD, emphysema, and respiratory insufficiency, which is
being treated with home oxygen therapy, have also not changed
significantly during this follow-up period. His cardiopulmonary
condition remains well controlled, with no acute deterioration.
Psychosocially, Mr. Wells continues to demonstrate resilience and
actively participates in his care. His strong support system continues
to contribute to his overall well-being.
Additional monitoring and follow-up appointments have been scheduled to
ensure continued management of Mr. Wells\' health. In addition,
discussions continue regarding potential treatment options and
interventions to provide him with the best possible care.
**Current Recommendations:** In light of the stability observed in Mr.
Wells\' HCC and overall medical condition, we recommend the following
steps for his continued care:
1. Regular Follow-up: Maintain a schedule of regular follow-up
appointments to monitor the status of the HCC, cardiopulmonary
function, and other associated conditions.
2. Lifestyle-Modification
### Patient Report 5
**Dear colleague, **
We report to you about Mr. Paul Wells born on 04/02/1953 who received
inpatient treatment from 02/04/2021 to 02/12/2021.
**Diagnosis**: Community-Acquired Pneumonia (CAP)
**Previous Diagnoses and Treatment:**
- Multifocal HCC Segment with portal vein invasion, BCLC C, first
diagnosed 07/19
- Large, partly exophytic, partly centrally hemorrhagic HCC lesions in
S3/4 and S7/8, complete infiltration of the left lateral lobe with
smaller satellites, macrovascular invasion of the left portal vein.
- Histology on 07/27/2019: Macrotrabecular and pseudoglandular growth
of well-differentiated hepatocellular carcinoma (G1).
- SIRT simulation attempt on 08/13/2019: No feasible SIRT.
- Liver Tumor Board decision on 08/18/2019: Systemic therapy.
- Atezolizumab/Bevacizumab since 10/26/2021, with a pause starting on
09/17/2019, due to transaminase elevation (up to 4x ULN).
- CT in 01/2020: Very good tumor response.
- Re-administration of Atezolizumab/Bevacizumab on 01/25/2022 and
02/16/2022, followed by a treatment pause due to limited tolerance.
- CT from 02/2020 to 08/2020: Continuously regressing tumor findings.
- Liver fibrosis: Increased C2 consumption (3-4 beers/day).
- Suspected PNP DD RLS (Restless Legs Syndrome).
<!-- -->
- COPD, current severity Gold III.
- Pulmonary emphysema.
- Respiratory partial insufficiency with home oxygen.
- Postnasal-Drip Syndrome.
- History of nicotine abuse (120 py).
- Transient worsening of lung function with steroid requirement after
Atezolizumab/Bevacizumab administrations
- Pneumogenic sepsis with Streptococcus pneumoniae detection.
- History of unclear infection vs. pneumonia in 10/2019-01/2020.
- Arterial hypertension.
- Atrial fibrillation
- Treatment with Apixaban.
- Reflux esophagitis LA Grade A (Esophagogastroduodenoscopy in
08/2019).
**Medical History:** For detailed medical history, please refer to the
previous medical reports. In summary, Mr. Wells presented in 07/2019
with persistent right upper abdominal pain. A CT scan showed multiple
intrahepatic lesions in the right liver lobe (SIV, SVII/VIII). MR
imaging also revealed large, partly exophytic, partly centrally
hemorrhagic HCC lesions in S3/4 and S7/8. There was complete
infiltration of the left liver lobe with smaller satellites and
macroinvasion of the left portal vein branch. Histology confirmed a
well-differentiated hepatocellular carcinoma (G1). There is no known
underlying liver disease, but peritumoral liver fibrosis was observed
histologically. Mr. Wells reported increased alcohol consumption of 3-4
beers per day.
Due to comorbidities and a large tumor with a relatively high liver-lung
shunt, SIRT simulation was initially attempted but found to be an
unsuitable treatment option. Therefore, our interdisciplinary liver
tumor board recommended systemic therapy. After comprehensive
counseling, treatment with Atezolizumab/Bevacizumab commenced on
08/24/2019.
Currently, Mr. Wells complains about progressively worsening respiratory
symptoms, which included shortness of breath, productive cough with
yellow-green sputum, pleuritic chest pain, fever, and chills, spanning a
period of five days.
**Physical Examination:**
Temperature: 38.6°C, Blood Pressure: 140/80 mm Hg, Heart Rate: 110 beats
per minute Respiratory Rate: 30 breaths per minute, Oxygen Saturation
(SpO2): 88% on room air
Breath Sounds: Auscultation revealed diminished breath sounds and coarse
crackles, notably in the right lower lobe.
The patient further reported pleuritic chest pain localized to the right
lower chest.
**Therapy and Progression:**
During his hospitalization, Mr. Wells was in stable cardiopulmonary
condition. We initiated an empiric antibiotic therapy with intravenous
Ceftriaxone and Azithromycin to treat community-acquired pneumonia
(CAP). Oxygen supplementation was provided to maintain adequate oxygen
saturation levels, and pain management strategies were implemented to
alleviate pleuritic chest pain. Additionally, pulmonary hygiene measures
and chest physiotherapy were applied to facilitate sputum clearance.
Frequent respiratory treatments with bronchodilators were administered
to mitigate airway obstruction, and continuous monitoring of vital
signs, oxygen saturation, and respiratory status was carried out.
Throughout his hospital stay, Mr. Wells exhibited gradual clinical
improvement, marked by several positive developments. These included the
resolution of fever, improved oxygen saturation levels, and a follow-up
chest X-ray demonstrating the resolution of the right lower lobe
consolidation. Furthermore, antibiotic therapy was adjusted based on
sputum culture results, which identified Streptococcus pneumoniae as the
causative pathogen. Mr. Wells continued to receive supportive care and
respiratory interventions.
We were thus able to discharge Mr. Wells in a good general condition. | Ultrasound-guided liver puncture, MRI Liver, CT Thorax/Abdomen/Pelvis |
To what commonality are Sara and her father oblivious?
A. Their realities both stem from limited, biased media spheres.
B. They both take Sara's mother for granted.
C. They both claim to support job generation, but invest in companies and entities that eliminate jobs.
D. The advertisements they watch are driving them apart, versus bringing them together.
| 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. | A. Their realities both stem from limited, biased media spheres. |
What features are proposed? | ### Introduction
Explanations are essential for understanding and learning BIBREF0. They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisticated formalization in the philosophy of science BIBREF1, to simply highlighting features in recent work on interpretable machine learning BIBREF2. Although everyday explanations are mostly encoded in natural language, natural language explanations remain understudied in NLP, partly due to a lack of appropriate datasets and problem formulations. To address these challenges, we leverage /r/ChangeMyView, a community dedicated to sharing counterarguments to controversial views on Reddit, to build a sizable dataset of naturally-occurring explanations. Specifically, in /r/ChangeMyView, an original poster (OP) first delineates the rationales for a (controversial) opinion (e.g., in Table TABREF1, “most hit music artists today are bad musicians”). Members of /r/ChangeMyView are invited to provide counterarguments. If a counterargument changes the OP's view, the OP awards a $\Delta $ to indicate the change and is required to explain why the counterargument is persuasive. In this work, we refer to what is being explained, including both the original post and the persuasive comment, as the explanandum. An important advantage of explanations in /r/ChangeMyView is that the explanandum contains most of the required information to provide its explanation. These explanations often select key counterarguments in the persuasive comment and connect them with the original post. As shown in Table TABREF1, the explanation naturally points to, or echoes, part of the explanandum (including both the persuasive comment and the original post) and in this case highlights the argument of “music serving different purposes.” These naturally-occurring explanations thus enable us to computationally investigate the selective nature of explanations: “people rarely, if ever, expect an explanation that consists of an actual and complete cause of an event. Humans are adept at selecting one or two causes from a sometimes infinite number of causes to be the explanation” BIBREF3. To understand the selective process of providing explanations, we formulate a word-level task to predict whether a word in an explanandum will be echoed in its explanation. Inspired by the observation that words that are likely to be echoed are either frequent or rare, we propose a variety of features to capture how a word is used in the explanandum as well as its non-contextual properties in Section SECREF4. We find that a word's usage in the original post and in the persuasive argument are similarly related to being echoed, except in part-of-speech tags and grammatical relations. For instance, verbs in the original post are less likely to be echoed, while the relationship is reversed in the persuasive argument. We further demonstrate that these features can significantly outperform a random baseline and even a neural model with significantly more knowledge of a word's context. The difficulty of predicting whether content words (i.e., non-stopwords) are echoed is much greater than that of stopwords, among which adjectives are the most difficult and nouns are relatively the easiest. This observation highlights the important role of nouns in explanations. We also find that the relationship between a word's usage in the original post and in the persuasive comment is crucial for predicting the echoing of content words. Our proposed features can also improve the performance of pointer generator networks with coverage in generating explanations BIBREF4. To summarize, our main contributions are: [itemsep=0pt,leftmargin=*,topsep=0pt] We highlight the importance of computationally characterizing human explanations and formulate a concrete problem of predicting how information is selected from explananda to form explanations, including building a novel dataset of naturally-occurring explanations. We provide a computational characterization of natural language explanations and demonstrate the U-shape in which words get echoed. We identify interesting patterns in what gets echoed through a novel word-level classification task, including the importance of nouns in shaping explanations and the importance of contextual properties of both the original post and persuasive comment in predicting the echoing of content words. We show that vanilla LSTMs fail to learn some of the features we develop and that the proposed features can even improve performance in generating explanations with pointer networks. Our code and dataset is available at https://chenhaot.com/papers/explanation-pointers.html. ### Related Work
To provide background for our study, we first present a brief overview of explanations for the NLP community, and then discuss the connection of our study with pointer networks, linguistic accommodation, and argumentation mining. The most developed discussion of explanations is in the philosophy of science. Extensive studies aim to develop formal models of explanations (e.g., the deductive-nomological model in BIBREF5, see BIBREF1 and BIBREF6 for a review). In this view, explanations are like proofs in logic. On the other hand, psychology and cognitive sciences examine “everyday explanations” BIBREF0, BIBREF7. These explanations tend to be selective, are typically encoded in natural language, and shape our understanding and learning in life despite the absence of “axioms.” Please refer to BIBREF8 for a detailed comparison of these two modes of explanation. Although explanations have attracted significant interest from the AI community thanks to the growing interest on interpretable machine learning BIBREF9, BIBREF10, BIBREF11, such studies seldom refer to prior work in social sciences BIBREF3. Recent studies also show that explanations such as highlighting important features induce limited improvement on human performance in detecting deceptive reviews and media biases BIBREF12, BIBREF13. Therefore, we believe that developing a computational understanding of everyday explanations is crucial for explainable AI. Here we provide a data-driven study of everyday explanations in the context of persuasion. In particular, we investigate the “pointers” in explanations, inspired by recent work on pointer networks BIBREF14. Copying mechanisms allow a decoder to generate a token by copying from the source, and have been shown to be effective in generation tasks ranging from summarization to program synthesis BIBREF4, BIBREF15, BIBREF16. To the best of our knowledge, our work is the first to investigate the phenomenon of pointers in explanations. Linguistic accommodation and studies on quotations also examine the phenomenon of reusing words BIBREF17, BIBREF18, BIBREF19, BIBREF20. For instance, BIBREF21 show that power differences are reflected in the echoing of function words; BIBREF22 find that news media prefer to quote locally distinct sentences in political debates. In comparison, our word-level formulation presents a fine-grained view of echoing words, and puts a stronger emphasis on content words than work on linguistic accommodation. Finally, our work is concerned with an especially challenging problem in social interaction: persuasion. A battery of studies have done work to enhance our understanding of persuasive arguments BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, and the area of argumentation mining specifically investigates the structure of arguments BIBREF28, BIBREF29, BIBREF30. We build on previous work by BIBREF31 and leverage the dynamics of /r/ChangeMyView. Although our findings are certainly related to the persuasion process, we focus on understanding the self-described reasons for persuasion, instead of the structure of arguments or the factors that drive effective persuasion. ### Dataset
Our dataset is derived from the /r/ChangeMyView subreddit, which has more than 720K subscribers BIBREF31. /r/ChangeMyView hosts conversations where someone expresses a view and others then try to change that person's mind. Despite being fundamentally based on argument, /r/ChangeMyView has a reputation for being remarkably civil and productive BIBREF32, e.g., a journalist wrote “In a culture of brittle talking points that we guard with our lives, Change My View is a source of motion and surprise” BIBREF33. The delta mechanism in /r/ChangeMyView allows members to acknowledge opinion changes and enables us to identify explanations for opinion changes BIBREF34. Specifically, it requires “Any user, whether they're the OP or not, should reply to a comment that changed their view with a delta symbol and an explanation of the change.” As a result, we have access to tens of thousands of naturally-occurring explanations and associated explananda. In this work, we focus on the opinion changes of the original posters. Throughout this paper, we use the following terminology: [itemsep=-5pt,leftmargin=*,topsep=0pt] An original post (OP) is an initial post where the original poster justifies his or her opinion. We also use OP to refer to the original poster. A persuasive comment (PC) is a comment that directly leads to an opinion change on the part of the OP (i.e., winning a $\Delta $). A top-level comment is a comment that directly replies to an OP, and /r/ChangeMyView requires the top-level comment to “challenge at least one aspect of OP’s stated view (however minor), unless they are asking a clarifying question.” An explanation is a comment where an OP acknowledges a change in his or her view and provides an explanation of the change. As shown in Table TABREF1, the explanation not only provides a rationale, it can also include other discourse acts, such as expressing gratitude. Using https://pushshift.io, we collect the posts and comments in /r/ChangeMyView from January 17th, 2013 to January 31st, 2019, and extract tuples of (OP, PC, explanation). We use the tuples from the final six months of our dataset as the test set, those from the six months before that as the validation set, and the remaining tuples as the training set. The sets contain 5,270, 5,831, and 26,617 tuples respectively. Note that there is no overlap in time between the three sets and the test set can therefore be used to assess generalization including potential changes in community norms and world events. Preprocessing. We perform a number of preprocessing steps, such as converting blockquotes in Markdown to quotes, filtering explicit edits made by authors, mapping all URLs to a special @url@ token, and replacing hyperlinks with the link text. We ignore all triples that contain any deleted comments or posts. We use spaCy for tokenization and tagging BIBREF35. We also use the NLTK implementation of the Porter stemming algorithm to store the stemmed version of each word, for later use in our prediction task BIBREF36, BIBREF37. Refer to the supplementary material for more information on preprocessing. Data statistics. Table TABREF16 provides basic statistics of the training tuples and how they compare to other comments. We highlight the fact that PCs are on average longer than top-level comments, suggesting that PCs contain substantial counterarguments that directly contribute to opinion change. Therefore, we simplify the problem by focusing on the (OP, PC, explanation) tuples and ignore any other exchanges between an OP and a commenter. Below, we highlight some notable features of explanations as they appear in our dataset. The length of explanations shows stronger correlation with that of OPs and PCs than between OPs and PCs (Figure FIGREF8). This observation indicates that explanations are somehow better related with OPs and PCs than PCs are with OPs in terms of language use. A possible reason is that the explainer combines their natural tendency towards length with accommodating the PC. Explanations have a greater fraction of “pointers” than do persuasive comments (Figure FIGREF8). We measure the likelihood of a word in an explanation being copied from either its OP or PC and provide a similar probability for a PC for copying from its OP. As we discussed in Section SECREF1, the words in an explanation are much more likely to come from the existing discussion than are the words in a PC (59.8% vs 39.0%). This phenomenon holds even if we restrict ourselves to considering words outside quotations, which removes the effect of quoting other parts of the discussion, and if we focus only on content words, which removes the effect of “reusing” stopwords. Relation between a word being echoed and its document frequency (Figure FIGREF8). Finally, as a preview of our main results, the document frequency of a word from the explanandum is related to the probability of being echoed in the explanation. Although the average likelihood declines as the document frequency gets lower, we observe an intriguing U-shape in the scatter plot. In other words, the words that are most likely to be echoed are either unusually frequent or unusually rare, while most words in the middle show a moderate likelihood of being echoed. ### Understanding the Pointers in Explanations
To further investigate how explanations select words from the explanandum, we formulate a word-level prediction task to predict whether words in an OP or PC are echoed in its explanation. Formally, given a tuple of (OP, PC, explanation), we extract the unique stemmed words as $\mathcal {V}_{\text{OP}}, \mathcal {V}_{\text{PC}}, \mathcal {V}_{\text{EXP}}$. We then define the label for each word in the OP or PC, $w \in \mathcal {V}_{\text{OP}} \cup \mathcal {V}_{\text{PC}}$, based on the explanation as follows: Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list): [itemsep=0pt,leftmargin=*,topsep=0pt] Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations. Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively. How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task. General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing. Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations: [itemsep=0pt,leftmargin=*,topsep=0pt] Although we expect more complicated words (#characters) to be echoed more often, this is not the case on average. We also observe an interesting example of Simpson's paradox in the results for Wordnet depth BIBREF38: shallower words are more likely to be echoed across all words, but deeper words are more likely to be echoed in content words and stopwords. OPs and PCs generally exhibit similar behavior for most features, except for part-of-speech and grammatical relation (subject, object, and other.) For instance, verbs in an OP are less likely to be echoed, while verbs in a PC are more likely to be echoed. Although nouns from both OPs and PCs are less likely to be echoed, within content words, subjects and objects from an OP are more likely to be echoed. Surprisingly, subjects and objects in a PC are less likely to be echoed, which suggests that the original poster tends to refer back to their own subjects and objects, or introduce new ones, when providing explanations. Later words in OPs and PCs are more likely to be echoed, especially in OPs. This could relate to OPs summarizing their rationales at the end of their post and PCs putting their strongest points last. Although the number of surface forms in an OP or PC is positively correlated with being echoed, the differences in surface forms show reverse trends: the more surface forms of a word that show up only in the PC (i.e., not in the OP), the more likely a word is to be echoed. However, the reverse is true for the number of surface forms in only the OP. Such contrast echoes BIBREF31, in which dissimilarity in word usage between the OP and PC was a predictive feature of successful persuasion. ### Predicting Pointers
We further examine the effectiveness of our proposed features in a predictive setting. These features achieve strong performance in the word-level classification task, and can enhance neural models in both the word-level task and generating explanations. However, the word-level task remains challenging, especially for content words. ### Predicting Pointers ::: Experiment setup
We consider two classifiers for our word-level classification task: logistic regression and gradient boosting tree (XGBoost) BIBREF39. We hypothesized that XGBoost would outperform logistic regression because our problem is non-linear, as shown in Figure FIGREF8. To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features. We scale all features to $[0, 1]$ before fitting the models. As introduced in Section SECREF3, we split our tuples of (OP, PC, explanation) into training, validation, and test sets, and use the validation set for hyperparameter tuning. Refer to the supplementary material for additional details in the experiment. Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). ### Predicting Pointers ::: Prediction Performance
Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem. Although the vanilla LSTM model incorporates additional knowledge (in the form of word embeddings), the feature-based XGBoost and logistic regression models both outperform the vanilla LSTM model. Concatenating our proposed features with word embeddings leads to improved performance from the LSTM model, which becomes comparable to XGBoost. This suggests that our proposed features can be difficult to learn with an LSTM alone. Despite the non-linearity observed in Figure FIGREF8, XGBoost only outperforms logistic regression by a small margin. In the rest of this section, we use XGBoost to further examine the effectiveness of different groups of features, and model performance in different conditions. Ablation performance (Table TABREF34). First, if we only consider a single group of features, as we hypothesized, the relation between OP and PC is crucial and leads to almost as strong performance in content words as using all features. To further understand the strong performance of OP-PC relation, Figure FIGREF28 shows the feature importance in the ablated model, measured by the normalized total gain (see the supplementary material for feature importance in the full model). A word's occurrence in both the OP and PC is clearly the most important feature, with distance between its POS tag distributions as the second most important. Recall that in Table TABREF18 we show that words that have similar POS behavior between the OP and PC are more likely to be echoed in the explanation. Overall, it seems that word-level properties contribute the most valuable signals for predicting stopwords. If we restrict ourselves to only information in either an OP or PC, how a word is used in a PC is much more predictive of content word echoing (0.233 vs 0.191). This observation suggests that, for content words, the PC captures more valuable information than the OP. This finding is somewhat surprising given that the OP sets the topic of discussion and writes the explanation. As for the effects of removing a group of features, we can see that there is little change in the performance on content words. This can be explained by the strong performance of the OP-PC relation on its own, and the possibility of the OP-PC relation being approximated by OP and PC usage. Again, word-level properties are valuable for strong performance in stopwords. Performance vs. word source (Figure FIGREF28). We further break down the performance by where a word is from. We can group a word based on whether it shows up only in an OP, a PC, or both OP and PC, as shown in Table TABREF1. There is a striking difference between the performance in the three categories (e.g., for all words, 0.63 in OP & PC vs. 0.271 in PC only). The strong performance on words in both the OP and PC applies to stopwords and content words, even accounting for the shift in the random baseline, and recalls the importance of occurring both in OP and PC as a feature. Furthermore, the echoing of words from the PC is harder to predict (0.271) than from the OP (0.347) despite the fact that words only in PCs are more likely to be echoed than words only in OPs (13.5% vs. 8.6%). The performance difference is driven by stopwords, suggesting that our overall model is better at capturing signals for stopwords used in OPs. This might relate to the fact that the OP and the explanation are written by the same author; prior studies have demonstrated the important role of stopwords for authorship attribution BIBREF43. Nouns are the most reliably predicted part-of-speech tag within content words (Table TABREF35). Next, we break down the performance by part-of-speech tags. We focus on the part-of-speech tags that are semantically important, namely, nouns, proper nouns, verbs, adverbs, and adjectives. Prediction performance can be seen as a proxy for how reliably a part-of-speech tag is reused when providing explanations. Consistent with our expectations for the importance of nouns and verbs, our models achieve the best performance on nouns within content words. Verbs are more challenging, but become the least difficult tag to predict when we consider all words, likely due to stopwords such as “have.” Adjectives turn out to be the most challenging category, suggesting that adjectival choice is perhaps more arbitrary than other parts of speech, and therefore less central to the process of constructing an explanation. The important role of nouns in shaping explanations resonates with the high recall rate of nouns in memory tasks BIBREF44. ### Predicting Pointers ::: The Effect on Generating Explanations
One way to measure the ultimate success of understanding pointers in explanations is to be able to generate explanations. We use the pointer generator network with coverage as our starting point BIBREF4, BIBREF46 (see the supplementary material for details). We investigate whether concatenating our proposed features with word embeddings can improve generation performance, as measured by ROUGE scores. Consistent with results in sequence tagging for word-level echoing prediction, our proposed features can enhance a neural model with copying mechanisms (see Table TABREF37). Specifically, their use leads to statistically significant improvement in ROUGE-1 and ROUGE-L, while slightly hurting the performance in ROUGE-2 (the difference is not statistically significant). We also find that our features can increase the likelihood of copying: an average of 17.59 unique words get copied to the generated explanation with our features, compared to 14.17 unique words without our features. For comparison, target explanations have an average of 34.81 unique words. We emphasize that generating explanations is a very challenging task (evidenced by the low ROUGE scores and examples in the supplementary material), and that fully solving the generation task requires more work. ### Concluding Discussions
In this work, we conduct the first large-scale empirical study of everyday explanations in the context of persuasion. We assemble a novel dataset and formulate a word-level prediction task to understand the selective nature of explanations. Our results suggest that the relation between an OP and PC plays an important role in predicting the echoing of content words, while a word's non-contextual properties matter for stopwords. We show that vanilla LSTMs fail to learn some of the features we develop and that our proposed features can improve the performance in generating explanations using pointer networks. We also demonstrate the important role of nouns in shaping explanations. Although our approach strongly outperforms random baselines, the relatively low F1 scores indicate that predicting which word is echoed in explanations is a very challenging task. It follows that we are only able to derive a limited understanding of how people choose to echo words in explanations. The extent to which explanation construction is fundamentally random BIBREF47, or whether there exist other unidentified patterns, is of course an open question. We hope that our study and the resources that we release encourage further work in understanding the pragmatics of explanations. There are many promising research directions for future work in advancing the computational understanding of explanations. First, although /r/ChangeMyView has the useful property that its explanations are closely connected to its explananda, it is important to further investigate the extent to which our findings generalize beyond /r/ChangeMyView and Reddit and establish universal properties of explanations. Second, it is important to connect the words in explanations that we investigate here to the structure of explanations in pyschology BIBREF7. Third, in addition to understanding what goes into an explanation, we need to understand what makes an explanation effective. A better understanding of explanations not only helps develop explainable AI, but also informs the process of collecting explanations that machine learning systems learn from BIBREF48, BIBREF49, BIBREF50. ### Acknowledgments
We thank Kimberley Buchan, anonymous reviewers, and members of the NLP+CSS research group at CU Boulder for their insightful comments and discussions; Jason Baumgartner for sharing the dataset that enabled this research. ### Supplemental Material ::: Preprocessing.
Before tokenizing, we pass each OP, PC, and explanation through a preprocessing pipeline, with the following steps: Occasionally, /r/ChangeMyView's moderators will edit comments, prefixing their edits with “Hello, users of CMV” or “This is a footnote” (see Table TABREF46). We remove this, and any text that follows on the same line. We replace URLs with a “@url@” token, defining a URL to be any string which matches the following regular expression: (https?://[^\s)]*). We replace “$\Delta $” symbols and their analogues—such as “$\delta $”, “&;#8710;”, and “!delta”—with the word “delta”. We also remove the word “delta” from explanations, if the explanation starts with delta. Reddit–specific prefixes, such as “u/” (denoting a user) and “r/” (denoting a subreddit) are removed, as we observed that they often interfered with spaCy's ability to correctly parse its inputs. We remove any text matching the regular expression EDIT(.*?):.* from the beginning of the match to the end of that line, as well as variations, such as Edit(.*?):.*. Reddit allows users to insert blockquoted text. We extract any blockquotes and surround them with standard quotation marks. We replace all contiguous whitespace with a single space. We also do this with tab characters and carriage returns, and with two or more hyphens, asterisks, or underscores. Tokenizing the data. After passing text through our preprocessing pipeline, we use the default spaCy pipeline to extract part-of-speech tags, dependency tags, and entity details for each token BIBREF35. In addition, we use NLTK to stem words BIBREF36. This is used to compute all word level features discussed in Section 4 of the main paper. ### Supplemental Material ::: PC Echoing OP
Figure FIGREF49 shows a similar U-shape in the probability of a word being echoed in PC. However, visually, we can see that rare words seem more likely to have high echoing probability in explanations, while that probability is higher for words with moderate frequency in PCs. As PCs tend to be longer than explanations, we also used the echoing probability of the most frequent words to normalize the probability of other words so that they are comparable. We indeed observed a higher likelihood of echoing the rare words, but lower likelihood of echoing words with moderate frequency in explanations than in PCs. ### Supplemental Material ::: Feature Calculation
Given an OP, PC, and explanation, we calculate a 66–dimensional vector for each unique stem in the concatenated OP and PC. Here, we describe the process of calculating each feature. Inverse document frequency: for a stem $s$, the inverse document frequency is given by $\log \frac{N}{\mathrm {df}_s}$, where $N$ is the total number of documents (here, OPs and PCs) in the training set, and $\mathrm {df}_s$ is the number of documents in the training data whose set of stemmed words contains $s$. Stem length: the number of characters in the stem. Wordnet depth (min): starting with the stem, this is the length of the minimum hypernym path to the synset root. Wordnet depth (max): similarly, this is the length of the maximum hypernym path. Stem transfer probability: the percentage of times in which a stem seen in the explanandum is also seen in the explanation. If, during validation or testing, a stem is encountered for the first time, we set this to be the mean probability of transfer over all stems seen in the training data. OP part–of–speech tags: a stem can represent multiple parts of speech. For example, both “traditions” and “traditional” will be stemmed to “tradit.” We count the percentage of times the given stem appears as each part–of–speech tag, following the Universal Dependencies scheme BIBREF53. If the stem does not appear in the OP, each part–of–speech feature will be $\frac{1}{16}$. OP subject, object, and other: Given a stem $s$, we calculate the percentage of times that $s$'s surface forms in the OP are classified as subjects, objects, or something else by SpaCy. We follow the CLEAR guidelines, BIBREF51 and use the following tags to indicate a subject: nsubj, nsubjpass, csubj, csubjpass, agent, and expl. Objects are identified using these tags: dobj, dative, attr, oprd. If $s$ does not appear at all in the OP, we let subject, object, and other each equal $\frac{1}{3}$. OP term frequency: the number of times any surface form of a stem appears in the list of tokens that make up the OP. OP normalized term frequency: the percentage of the OP's tokens which are a surface form of the given stem. OP # of surface forms: the number of different surface forms for the given stem. OP location: the average location of each surface form of the given stem which appears in the OP, where the location of a surface form is defined as the percentage of tokens which appear after that surface form. If the stem does not appear at all in the OP, this value is $\frac{1}{2}$. OP is in quotes: the number of times the stem appears in the OP surrounded by quotation marks. OP is entity: the percentage of tokens in the OP that are both a surface form for the given stem, and are tagged by SpaCy as one of the following entities: PERSON, NORP, FAC, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, and LANGUAGE. PC equivalents of features 6-30. In both OP and PC: 1, if one of the stem's surface forms appears in both the OP and PC. 0 otherwise. # of unique surface forms in OP: for the given stem, the number of surface forms that appear in the OP, but not in the PC. # of unique surface forms in PC: for the given stem, the number of surface forms that appear in the PC, but not in the OP. Stem part–of–speech distribution difference: we consider the concatenation of features 6-21, along with the concatenation of features 31-46, as two distributions, and calculate the Jensen–Shannon divergence between them. Stem dependency distribution difference: similarly, we consider the concatenation of features 22-24 (OP dependency labels), and the concatenation of features 47-49 (PC dependency labels), as two distributions, and calculate the Jensen–Shannon divergence between them. OP length: the number of tokens in the OP. PC length: the number of tokens in the PC. Length difference: the absolute value of the difference between OP length and PC length. Avg. word length difference: the difference between the average number of characters per token in the OP and the average number of characters per token in the PC. OP/PC part–of–speech tag distribution difference: the Jensen–Shannon divergence between the part–of–speech tag distributions of the OP on the one hand, and the PC on the other. Depth of the PC in the thread: since there can be many back–and–forth replies before a user awards a delta, we number each comment in a thread, starting at 0 for the OP, and incrementing for each new comment before the PC appears. ### Supplemental Material ::: Word–level Prediction Task
For each non–LSTM classifier, we train 11 models: one full model, and forward and backward models for each of the five feature groups. To train, we fit on the training set and use the validation set for hyperparameter tuning. For the random model, since the echo rate of the training set is 15%, we simply predict 1 with 15% probability, and 0 otherwise. For logistic regression, we use the lbfgs solver. To tune hyperparameters, we perform an exhaustive grid search, with $C$ taking values from $\lbrace 10^{x}:x\in \lbrace -1, 0, 1, 2, 3, 4\rbrace \rbrace $, and the respective weights of the negative and positive classes taking values from $\lbrace (x, 1-x): x\in \lbrace 0.25, 0.20, 0.15\rbrace \rbrace $. We also train XGBoost models. Here, we use a learning rate of $0.1$, 1000 estimator trees, and no subsampling. We perform an exhaustive grid search to tune hyperparameters, with the max tree depth equaling 5, 7, or 9, the minimum weight of a child equaling 3, 5, or 7, and the weight of a positive class instance equaling 3, 4, or 5. Finally, we train two LSTM models, each with a single 300–dimensional hidden layer. Due to efficiency considerations, we eschewed a full search of the parameter space, but experimented with different values of dropout, learning rate, positive class weight, and batch size. We ultimately trained each model for five epochs with a batch size of 32 and a learning rate of 0.001, using the Adam optimizer BIBREF52. We also weight positive instances four times more highly than negative instances. ### Supplemental Material ::: Generating Explanations
We formulate an abstractive summarization task using an OP concatenated with the PC as a source, and the explanation as target. We train two models, one with the features described above, and one without. A shared vocabulary of 50k words is constructed from the training set by setting the maximum encoding length to 500 words. We set the maximum decoding length to 100. We use a pointer generator network with coverage for generating explanations, using a bidirectional LSTM as an encoder and a unidirectional LSTM as a decoder. Both use a 256-dimensional hidden state. The parameters of this network are tuned using a validation set of five thousand instances. We constrain the batch size to 16 and train the network for 20k steps, using the parameters described in Table TABREF82. Table 1: Sample data that were affected by preprocessing. Figure 1: The U-shape exists both in Figure 1a and Figure 1b, but not in Figure 1c. Table 2: Full testing results after Bonferroni correction. Table 3: Feature importance for the full XGBoost model, as measured by total gain. Table 4: Parameters tuned on validation dataset containing 5k instances. | Non-contextual properties of a word, Word usage in an OP or PC (two groups), How a word connects an OP and PC, General OP/PC properties |
What dataset is used? | ### Introduction
While social media sites provide users with the revolutionized communication medium by bringing the communication efficiency to a new level, they can be easily misused for widely spreading misinformation and fake news. Fake news and misinformation have been a long-standing issue for various purposes such as political propaganda BIBREF0 and financial propaganda BIBREF1. To fight against fake news, traditional publishers employed human editors to manually and carefully check the content of news articles to maintain their reputation. However, social media provided a new way to spread news, which lead to broader information sources and expanded audience (i.e., anyone can be a media and create news). In particular, users share news articles with their own opinion or read articles shared by their friends from whatever the source of news is with mostly blind trust BIBREF2 or with their own ideologies BIBREF3, BIBREF4. Although social media posts usually have a very short life cycle, the unprecedented amount of fake news may lead to a catastrophic impact on both individuals and society. Besides from misleading users with false information BIBREF4, widely propagated fake news could even cause trust crisis of entire news ecosystem BIBREF5, even further affecting both the cyberspace and physical space. In literature, researchers focused on four topics regarding fake news: characterization (i.e., types of fake news), motivation, circulation, and countermeasures BIBREF6, BIBREF7. A large body of work has been done on fake news identification BIBREF5, BIBREF8, BIBREF9, BIBREF10 by exploiting multiple content-related and social-related components. However, we notice that the fake news still has been widely spread even after early detection BIBREF11. Therefore, we propose to study a complementary approach to mitigate the spread and impact of fake news. Recently, community and journalists started building and maintaining fact-checking websites (e.g., Snopes.com). Social media users called fact-checkers also started using these fact-checking pages as factual evidences to debunk fake news by replying to fake news posters. Figure FIGREF1 demonstrates a real-world example of a fact-checker's fact-checking behavior on Twitter by debunking another user's false claim with a Snopes page URL as an evidence to support the factual correction. In BIBREF12, researchers found that these fact-checkers actively debunked fake news mostly within one day, and their replies were exposed to hundreds of millions users. To motivate these fact-checkers further quickly engage with fake news posters and intelligently consume increased volume of fact-checking articles, in this paper we propose a novel personalized fact-checking URL recommender system. According to BIBREF13, co-occurrence matrix within the given context provides information of semantic similarity between two objects. Therefore, in our proposed deep-learning based recommender system, we employ two extended matrices: user-user co-occurrence matrix, and URL-URL co-occurrence matrix to facilitate our recommendation. In addition, users tend to form relationships with like-minded people BIBREF14. Therefore, we incorporate each user's social context to capture the semantic relation to enhance the recommendation performance. Our main contributions are summarized as follows: We propose a new framework for personalized fact-checking URL recommendation, which relies on multi-relational context neighbors. We propose two attention mechanisms which allow for learning deep semantic representation of both a target user and a target URL at different granularity. Experimental results show that our proposed model outperforms eight state-of-the-art baselines, covering various types of recommendation approaches. Ablation study confirm the effectiveness of each component in our proposed framework. ### Related Works
In this section, we briefly review related works and position our work within the following areas: (1) fake news and misinformation; (2) advancements in recommender systems; and (3) graph convolutional networks. ### Related Works ::: Fake News and Misinformation
Fake news has attracted considerable attention since it is related to our daily life and has become a serious problem related to multiple areas such as politics BIBREF0 and finance BIBREF1. Social media sites have become one of popular mediums to propagate fake news and misinformation. The dominant line of work in this topic is fake news detection BIBREF15 which was mostly formulated as a binary classification problem. Researchers began to incorporate social context and other features for identifying fake news at an early stage and preventing it from diffusion on the social network BIBREF5, BIBREF7. Some other researchers focus on investigating the propagation patterns of fake news in social network BIBREF16, BIBREF17. BIBREF18 also studied fake news intervention. Unlike most previous works, we follow the direction of BIBREF12 and propose to build a personalized recommender system for promoting the fact-checking article circulation to debunk fake news. ### Related Works ::: Advancements in Recommender System
Traditionally, recommendation algorithms can be divided into two categories: collaborative filtering BIBREF19 and content-based filtering. However, in the past few years, the recommendation has become a more integrated task due to the success of the deep neural network. Neural Networks (NNs) proves to be effective to capture underlying nonlinear relations BIBREF20. Another advantage is that the NNs enhanced the model's capability of extracting knowledge from multimodal data BIBREF21, BIBREF22, BIBREF23, which serves as auxiliary information and provide solutions to address the data sparsity problem. More recently, researchers introduced attention mechanism into recommender systems, which has achieved great success in various fields BIBREF24, BIBREF25. Researchers developed multiple variants of attention mechanism to improve both the recommendation precision and model interpretability BIBREF26, BIBREF27, BIBREF28, BIBREF29. In this paper, we also propose two novel designs of attention mechanism. Following BIBREF30, BIBREF31, we further explore multi-relational context of given user-URL pair, aiming at discriminating the most important elements towards URL-dependent user preference. ### Related Works ::: Graph Convolutional Networks
With the surge of Graph-based Neural Network, GCN-based approaches have shown strong effectiveness on various tasksBIBREF32, BIBREF33, BIBREF34, including recommender system. The core idea is to iteratively aggregate attributed node vectors around each node, and messages propagates by stacking multiple layers. However, the original design of GCN is not suitable for our scenario because of the following reasons: First, existing GCN works BIBREF33, BIBREF34 do not distinguish different types of nodes, whereas in our case, it does not make sense to aggregate user and URL nodes together. And the aggregation function proposed in most GCN works treats all its adjacency nodes with the same importance. It is inappropriate in real-world applications and probably tends to neglect necessary information. BIBREF35 breaks this schema by using a multi-head attention mechanism to replace the convolution-like operator, yet it requires significant extra computation and memory. Compared to the previous works, in this paper, we focus on a novel application and investigate both co-occurrence context and social context related influences for fact-checking URL recommendation. We also incorporate sets of auxiliary attributes, which enable more comprehensive learning of the compatibility between given pairs of user and URL. Moreover, we take advantage of advancements in graph neural networks and attention mechanisms, and solve the aforementioned research problems. ### Problem Formulation
We formally introduce definitions before describing our proposed framework. We define fact-checking behavior as a user (i.e., fact-checker) embeds a fact-checking URL in his reply in order to debunk fake news. We regard each fact-checking behavior as an implicit interaction between target user $i$ and target URL $j$. ### Problem Formulation ::: Definition 1 (Fact-checking URL Recommendation Task)
Let $\mathcal {U} = \lbrace u_1,u_2,...,u_n\rbrace $ denotes a set of fact-checkers on social media, and use $\mathcal {C} = \lbrace c_1,c_2,...,c_m\rbrace $ to index fact-checking URLs. We construct user-URL interaction matrix $Y = \lbrace y_{ij} | u\in \mathcal {U}, v \in \mathcal {C} \rbrace $ according to users' fact-checking behavior, where each value of 1 for $y_{ij}$ indicates the existence of implicit interaction between target user $i$ and target URL $j$. Each user $u_i$ and each URL $c_j$ associate with a set of attributes. The goal of the recommendation task is to recommend top-N URLs from the URL set $\mathcal {C}$ to each user. We also construct the entire dataset as a heterogeneous graph, which is a special kind of information network that consists of either multiple types of objects or different types of links, or both. ### Problem Formulation ::: Definition 2 (Heterogeneous Network) @!START@BIBREF36@!END@
Formally, consider a heterogeneous graph $\mathcal {G}=(\mathcal {V},\mathcal {E})$, where $\mathcal {V} (|V|= m + n)$ and $E$ denote the node set and edge set, respectively. The heterogeneity represents by the node type mapping function: $\phi : \mathcal {V} \rightarrow \mathcal {A}$ and edge type projection function: $\psi : \mathcal {E} \rightarrow \mathcal {R}$, where $\mathcal {A}$ and $\mathcal {R}$ denote the sets of predefined node types and edge types, and $|\mathcal {A}| + |\mathcal {R}| > 2$. Note that we does not consider self-loop in our graph construction. ### Problem Formulation ::: Definition 3 (Multi-relational Context)
Given target user $i$, we define his following fact-checkers and co-occurrenced fact-checkers as his social context user neighbors and co-occurrenced context user neighbors, respectively. Similarly, we name the other URLs posted by target user $i$ and co-occurrenced URLs of target URL $j$ as historical context URL neighbors and co-occurrenced context URL neighbors, respectively. In general, we call all the context neighbors as multi-relational context of given target user-URL pair. ### Problem Formulation ::: Example
Figure FIGREF12 illustrates the multi-relational context. In Figure FIGREF12, $c_1$, $c_2$, $c_3$ represents fact-checking URLs and $u_1$, $u_2$, $u_3$ are users who involve sharing these URLs. For example, $(u_1 \rightarrow u_2)$ indicates the social relationship between $u_1$ and $u_2$. Intuitively, we care more about the influence of $u_2$ on $u_1$. $(u_1 \rightarrow c_1 \leftarrow u_2)$ means $u_1$ and $u_2$ are co-occurrenced user neighbors. Similarly, we name $c_1$ and $c_2$ as co-occurrenced URL neighbors of $u_3$, and $c_2$ is historical context URL neighbor given target $u_3$-$c_3$ pair. ### Proposed Framework
We propose a novel framework called Attributed Multi-Relational Attention Network (AMRAN), to understand the influence of the multi-relational context to target user's fact-checking behavior. In this section, we elaborate our proposed AMRAN with using notations described in Table TABREF15. At the high level, AMRAN is composed of two modules as shown in Figure FIGREF16: (i) a convolutional spatial attention network (CSAN) and (ii) a heterogeneous graph attention network (HGAN). CSAN jointly models the influence of multi-relational context on target user-URL pair (Section 4.1). It enriches the neighborhood diversity, and expands the scope of information reception. HGAN leverages both global node connectivity and local node attributes, in order to incorporate the effect of information propagation and encode user's dynamic preference in depth (Section 4.2). At the final step, the model produces recommendations by combining wide context-aware target user embedding and URL embedding, multi-relational context user embedding and context URL embedding, and deep context-aware user embedding and URL embedding (Section 4.3). ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN)
The left bounding box in Figure FIGREF16 illustrates the structure of CSAN module. To provide a broad scope of knowledge for generating wide context-aware target user embedding and URL embedding, we adopt a multi-branch setting in CSAN. The two parallel branch models multi-relational context for target user and target URL respectively. Each branch contains two identical streams. We select $b_h$ context neighbors for each stream (e.g., historical context URL neighbors and co-occurrenced context URL neighbors of target URL, social context user neighbors and co-occurenced user neighbors of target user). These streams are employed to learn the most discriminative features from multi-relational neighbors of target user and target URL. Then we employ a gated fusion layer to capture the optimal global level representation of target user-URL pair. Note that we enable the embedding sharing within each branch as users/URLs share the same feature set. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Raw Attribute Input
User and URL associate with different feature sets. Therefore, CSAN starts from embedding the input attribute set of each context neighbor. We use $s$ and $t$ to denote the number of features related to user and URL, respectively. Note that the dimension of initial embedding for each attribute could be different since they may carry with different information volume. We use one-hot encoding for categorical feature inputs, and apply direct lookup on these features. However, the same solution performs poorly when it comes continuous attributes such as the post frequency of an URL. Empirically, we found that an available solution is to bucketize these features into small intervals. Specifically, we map these continuous attributes in range $[0,1), [1,2),..., [2^k, 2^{k+1})$ into $0,1,..., k$ in this work. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Attribute Embedding Layer
We then project them into the same latent space via a set of attribute-specific transformation matrices $W_1, W_2, ..., W_{s+t}$ to project all the attributes into a $w$-dimensional space. The attributes of each neighbor then are stacked as a matrix in shape of $s \times w$ for users and $t \times w$ for URLs. However, we treat the target user-URL pair differently. After projecting attributes by the same attribute-specific transformation matrix as their relational neighbors, instead of stacking them as a matrix, we concatenate the attribute embedding vectors together and feed it through a linear projection to generate $u^{\prime }_i \in \mathbb {R}^d$ and $c^{\prime }_j \in \mathbb {R}^d$ for future reference. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Spatial Attention Block
To prevent some unknown misalignment and conduct better comparison among the neighborhood features, we proposed a schema for jointly learning the layer-wise and channel-wise attention. In particular, for each stream, we pile the neighbors' representation matrices together to obtain a 3-dimensional tensor $M$. Intuitively, the design helps improve the alignment quality of neighbor's features. Then, inspired by BIBREF37, BIBREF38, we employ a spatial attention block in each stream for jointly learning channel-level and layer-level soft attention. See figure FIGREF21 for a high-level illustration of our spatial attention block. All the streams adopt identical spatial attention blocks, and each block attends the input attribute representations independently. In the figure, we use the historical context URL stream for illustration. The output of spatial attention block is an attention weight map $S \in \mathbb {R}^{t \times w \times b}$ which is in the same shape with the input tensor $M$. Intuitively, the layer-wise attention and channel-wise attention are dedicated to selecting the most discriminative features and the most important neighbors, respectively. Thus, they are highly complementary to each other in functionality; and we adopt a factorized manner for optimization and computational efficiency as: where $L \in \mathbb {R}^{t \times w \times 1}$ and $C \in \mathbb {R}^{1 \times 1 \times b}$ denote the layer-wise feature map and channel-wise feature map, respectively. $S$ is the result of tensor multiplication. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Spatial Attention Block ::: Layer-wise Attention
Conceptually, the layer-wise attention learns globally important elements in the feature. We apply a cross-channel average pooling operation onto the input tensor, following by 2 convolution layers of $3 \times 3$ and $1 \times 1$ filter, respectively. Specifically, cross-channel average pooling operation is defined as: where $b$ is the number of selected neighbors. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Spatial Attention Block ::: Channel-wise Attention
The design of channel-wise attention is very similar to layer-wise attention, which aims to acquire a global view of discriminative users. Formally, the global average pooling is defined as: where $t$ and $w$ are shared height and width of all channels. Similarly, we employ two convolution layers after the pooling operation. Note that each convolution layer was followed by batch normalization operation. Furthermore, as other work of modern CNN structure BIBREF39, we append a ReLU activation function to assure $L>0, C>0$. We further introduce one more convolution layer of $1 \times 1 \times b$ filter for enhancing the fusion of the layer-wise attention and channel-wise attention. The output tensor then is fed through a sigmoid function for normalization and generate the final attention weight tensor of spatial attention block. Formally, the output of the spatial attention module is the element-wise product of initial feature tensor $M$ and generated attention weights $S$: Intuitively, the attended feature map learned fine-grained important elements via high alignment and compatible attentions. ### Proposed Framework ::: Convolutional Spatial Attention Network (CSAN) ::: Gated Branch Fusion Layer
We apply another CNN layer of $3 \times 3$ filter after the attended user representation of each stream for feature extraction and dimension : which produces the multi-relational context representation vectors: $o_{i_h}, o_{i_c}, o_{u_f}$ and $o_{u_c}$ for each stream, respectively. We employ a gated mechanism to assigns different weights to relation-specific neighborhood representation as: where scalars $g_u$ and $g_v$ are learned automatically to control the importance of the two streams within each branch. ### Proposed Framework ::: Heterogeneous Graph Attention Network (HGAN)
Following recent success in Graph Convolutional Network (GCN) BIBREF32, BIBREF33, BIBREF40, BIBREF34, BIBREF35. We propose a heterogeneous graph attention network (HGAN) which is tailored for recommendation task. In particular, our proposed module adopts a parallel attention structure for the user neighbor and the URL neighbor of the central node, respectively. Considering a heterogeneous graph $\mathcal {G}=(\mathcal {V},\mathcal {E})$, the nodes represent objects in this network which can be either user or URL. The edges denote the relation between connected nodes. The node attributes pass along the edges during the propagation. We try to leverage between the local node attributes and global network structure. Our novelty lies in two aspects: (i) we differentiate the contribution of URL node and user node, respectively; and (ii) we consider both similarities of node and the influence of different relation types. While the CSAN obtains information from multi-relational immediate neighbors, which expand the scope of knowledge for target user and target URL representations, HGAN aims at learning deeper semantic representations of target user and target URL. ### Proposed Framework ::: Heterogeneous Graph Attention Network (HGAN) ::: Heterogeneous Graph Network
We try to capture different semantic relation behind various types of nodes and edges. For every single layer, if the central node is user node, its neighborhood contains its co-occurrenced users and posted URLs. If the central node type is URL, its neighborhood nodes consist of users who posted it and its co-occurrenced URLs. We adopt similar embedding approach as we did in CSAN for the initial representation of each node, but we concatenate all the features into a long vector $x_i$ for each node instead of stacking them as a matrix. Considering the different types of the node associated with the varied feature set, we use a set of node type-specific transformation matrices to project different types of node representation into the same feature space before aggregation as follows: Let $H^{(0)} \in \mathbb {R}^{(m+n) \times d}$ be the embedding matrix of all the attributed nodes, where $m+n$ is the total number of nodes and d is the dimension of latent embedding space; each row $h_i^{(0)}$ stands for the initial embedding vector of node $i$. We define edges based on users' reference of URL (user-URL edges), user co-occurrence relation (user-user edges), and URL co-occurrence (URL-URL edges). We then introduce an adjacency matrix $A$ of $\mathcal {G}$ based on the importance of each edge. In particular, to compute the weight of user-user edges and URL-URL edges, we adopt a matrix named Shifted Positive Point-wise Mutual Information (SPPMI) BIBREF41, a popular measure for word associations, to utilize the co-concurrence context information. In word embedding scenario, each cell within the matrix measures the relation of corresponding word-context pair. The factorization of such matrix is proved to be equivalent to skip-gram model with negative sampling (SGNS). The Point-wise Mutual Information (PMI) between node $i$ and node $j$ is computed as $PMI(i,j) = log \frac{P(i,j)}{P(i)P(j)}$ where $P(i,j) = \frac{\# (i,j)}{|D|}$ and $P(i) = \frac{\# (i)}{|D|}$. $|D|$ denotes the total number of observed word-context pairs within a predefined sliding window. $P(i,j)$ is the joint probability that word $i$ and word $j$ appear together within the window size. Furthermore, we introduce the SPPMI matrix as an extension based on PMI value: where $k$ is a hyperparameter, which represents the number of negative samples. Conceptually, a positive PMI value implies a semantically correlated word-context pair, Therefore, SPPMI, which only takes the positive value of PMI shifted by a global constant, reflects a closer semantic relation between word-context pairs. Inspired by this concept/idea, we use $|D|$ to denote the number of times of user (URL) co-occurrence and generate the user co-occurrence matrix in shape of $n \times n$ and URL co-occurrence matrix of $m \times m$. Note that we do not discriminate between the target node and context node. Similarly, we learn from the TF-IDF concept and redefine it on recommendation task with implicit feedback BIBREF42 as: where $\# (i,j)$ represents the number of times URL $j$ be posted by user $i$. $TF_{ij}$ further normalizes it by the maximum number of post times of any URL by user $i$. The $IDF_i$ is associated with the user's previous behavior as $m$ denotes the total number of URLs and $m_i$ is the number of URLs posted by user $i$. Formally, the weight of the edge between node $i$ and node $j$ is defined as: ### Proposed Framework ::: Heterogeneous Graph Attention Network (HGAN) ::: Heterogeneous Attention Layer (HGAL)
Given the node's initial representation defined as above, we then pass messages to aggregate the neighborhood nodes' information and combine it with the target user's interests. A popular propagation strategy in existing GCN works is the normalized Laplacian matrix BIBREF32. Even though it proves to be effective, it is not trainable and it assigns every adjacent node with the same weight. Following previous work BIBREF35, we propose to incorporate a hierarchical attention mechanism to learn the weight of each adjacent node adaptively. Since the distribution of the number of neighbors of each node disperses greatly, sub-sampling becomes an essential procedure in our task to avoid an explosion of computation cost after multiple hops stacked. We adopt Weighted Random Selection (WRS) BIBREF43 to select a fixed number of nodes for both node types in each graph attention layer. Figure FIGREF40 shows a graphical illustration of one HGAL. Assume that the central node is a user node. We separately calculate the attention weights between the user node and its user node neighbors, or between the user node and its URL node neighbors. The similarity between the target user's node representation $h^{(l)}_u$ and all of its selected neighbors are defined as: where $h^{(l)}_i$ is the representation of user $i$ at layer $l$, and $\mathcal {N}^{\phi _t}_i$ denotes the node type-based neighbor. We adopt $f(h^{(l)}_i,h^{(l)}_j)=cosine(h^{(l)}_i,h^{(l)}_j)$ as similarity function. Intuitively, $\alpha ^{\phi }_{ij}$ measures the importance of neighbor $j$ towards central node $i$. Meanwhile, we obtain the edge weight $A_{ij}$ as well. After this, we aggregate the type-based neighborhood node representation and generate the embedding of neighborhood as the average of different types of nodes: To model the information propagation and capture higher-order relations, we stack the HGAL multiple times. In addition, we introduce the residual connection BIBREF44 to help train a HGAN with many layers. where $\sigma $ denotes the sigmoid function. $W_g^{(l)}$ and $b_g^{(l-1)}$ are the shared weight matrix and bias term at layer $l$, respectively. The node representation at $l$-th layer provides knowledge of $l$ degrees away. ### Proposed Framework ::: Interaction Layer
The interaction layer is tailored for recommendation tasks. Recall that we obtained wide context-based user embedding $u^{\prime }_i$ and URL embedding $c^{\prime }_j$, context representations $p_i$, $p_j$ and deep context-based user embedding $h^{(l)}_i$ and URL embedding $h^{(l)}_j$ in the previous sections. Then we formulate the final URL-dependent user representation by using a fully connected layer as: where $W_o$ and $b_o$ are a linear transformation weight matrix and bias term, respectively. $\oplus $ denotes vector concatenation. Note that the fully-connected layer can be replaced by other techniques (e.g. CNN). Finally, we feed it through a softmax function to calculate the probability that user interested in the given URL. ### Proposed Framework ::: Training
We adopt the cross-entropy loss function during the training process. We follow a uniform sampling strategy to obtain negative samples $(i,j) \in Y^{-}$ from unobserved interactions. Since the entire architecture is differentiable, we use back propagation to achieve end-to-end training. ### Evaluation
In this section, we describe a dataset, baselines, experimental setting, and experimental results. In the experiments, we seek to answer the following research questions: RQ1: What is the performance of our model and baselines? RQ2: How beneficial is each submodule of our model? RQ3: How effective is our attention mechanisms? RQ4: What is sensitivity of our model with regard to hyperparameters? ### Evaluation ::: Dataset
We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. The interaction behavior collected in the dataset is consistent with our definition in SECREF3. As they did for their study, we only kept users who have at least three interactions (i.e., posting at least three fact-checking messages containing fact-checking URLs). We conducted additional preprocessing step by removing users whose posts are non-English, or their tweets were inaccessible, because some of our baselines require a fact-checker's tweets. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset. And we further take available feature values of both user and URL into consideration. For instance, a category of referred fact-checking article and the name of corresponding fact-checking website reveals linguistic characteristics such as writing style and topical interest of each URL; while the number of followers and number of followees of each user indicates the credibility and influence of the fact-checker. Statistics of the final dataset is presented in Table TABREF65. ### Evaluation ::: Baselines
To measure relative effectiveness of our model, we compare our model against eight state-of-the-art baselines including the traditional collaborative filtering method, neural network-based models, and context-aware approaches. MF BIBREF45 is a standard collaborative filtering technique. It factorizes an interaction matrix $X \in \mathbb {R}^{M \times N}$ into two matrices $U \in \mathbb {R}^{M \times d}$ and $X \in \mathbb {R}^{d \times N}$. $U$ contains each user's latent representation, and $X$ contains each URL's latent representation. GAU BIBREF12 is a framework specifically designed for fact-checking URL recommendation utilizing rich side information such as a user' social network, tweets, and referred fact-checking pages. It is the most relevant and domain-specific baseline. NeuMF BIBREF20 is a neural network based item recommendation algorithm. We adopted a composite version of MF jointly coupled with a MLP. CMN BIBREF30 combines a global latent factor model with an augmented memory network to capture personalized neighbor-based structure in a non-linear fashion. NAIS BIBREF31 is an item-based collaborative filtering architecture that integrates attention mechanism to distinguish the contribution of previously consumed items. The authors proposed two versions of NAIS: (1) $NAIS_{concat}$ which concatenates two vectors to learn the attention weight; and (2) $NAIS_{prod}$ which feeds the element-wise product of the two vectors to the attention network. Therefore, we also build two versions of NAIS, and compare them with our model. DeepCoNN BIBREF46 was originally proposed for an item rating prediction task which jointly model user and item based on their textual reviews. The prior work shows that it significantly outperforms other topic modeling based methods.We re-implemented the baseline and adapted it for our recommendation task with implicit feedback. NARRE BIBREF47 is a deep neural network based framework for a item rating prediction task. It employs the attention mechanism to distinguish the importance of each review. We re-implemented the framework for our implicit feedback situation. NGCF BIBREF48 is a new recommendation framework based on graph neural network, explicitly encoding the collaborative signal in the form of high-order connectivity in user-item bipartite graph by performing embedding propagation. Table TABREF66 presents characteristics of baselines and our model, showing what information each model utilizes. Note that even though CMN and NAIS both utilize co-occurrence context, CMN only utilizes user co-occurrence context whereas NAIS looks into URL co-occurrence context. ### Evaluation ::: Evaluation Protocol
We adopt the leave-one-out evaluation protocol to evaluate the performance of our model and baselines. The leave-one-out evaluation protocol has been widely used in top-K recommendation tasks. In particular, we held the latest interaction of each user as the test set and used the remaining interactions for training. Each testing instance was paired with 99 randomly sampled negative instances. Each recommendation model ranks the 100 instances according to its predicted results. The ranked list is judged by Hit Ratio (HR) BIBREF49 and Normalized Discount Cumulative Gain (NDCG) BIBREF50 at the position 10. HR@10 is a recall-based metric, measuring the percentage of the testing item being correctly recommended in the top-10 position. NDCG@10 is a ranked evaluation metric which considers the position of the correct hit in the ranked result. Since both modules in our framework introduce randomness, we repeat each experiment 5 times with different weight initialization and randomly selecting neighbors. We report the average score of the best performance in each training process for both metrics to ensure the robustness of our framework. ### Evaluation ::: Hyper-parameter Settings
We implement our framework by using Pytorch framework, initialize weight parameters by Xavier initialization BIBREF51, and optimize the model with Adam optimizer BIBREF52. The mini-batch size is set to 128. Empirically, in CSAN, we select 10 neighbors for each stream. In HGAN, we choose 8 user neighbors and 8 URL neighbors for each central node at a single layer, and the default number of graph attention layers is set to 2. If the object (i.e.g, user neighbor or URL neighbor) is not sufficient enough, we pad the sequence with zeros vectors. In the proposed AMRAN model, all hyperparameters are tuned by using the grid-search on the validation set, which is formed by holding out one interaction of each user from the training data like the prior work BIBREF20. We conduct the grid search over a latent dimension size from {8,16,32,64}, a regularization term from {0.1, 0.01, 0.001, 0.0001, 0.00001}, a learning rate from {0.0001, 0.0003, 0.001, 0.01, 0.05, 0.1}, and SPPMI shifted constant value $s$ from {1, 2, 5, 10}. The number of negative samples w.r.t each positive interaction is set to 4. We adopt the same latent dimension size for all sub-modules. For a fair comparison, we also thoroughly optimize the baselines' hyperparameters by using the validation set. ### Evaluation ::: RQ1: Performance of Our Model and Baselines
Table TABREF70 presents performance of our model and baselines. According to the results and information described in Table TABREF66, we had the following observations. First, deep learning-based approaches usually obtained better performance than traditional models (e.g., MF and GAU). This observation makes sense because (1) traditional models failed to capture the important non-linear relationship between users and fact-checking URLs; (2) Most deep-learning based baseline models employ attention mechanism which helps better understand the semantic relation between user and URL; and (3) training tricks such as drop out and batch normalization also contribute to a better quality of training. In particular, $NAIS_{concat}$ achieves better performance than $NAIS_{prod}$ which supports the reason (1). The second observation is that models with text review achieve better results compared with collaborative filtering-based methods. It is not surprising since that textual content contains rich information which could be auxiliary information to implicit feedback data and thus improve the recommendation accuracy. However, we observed that text-based recommendation approaches usually have a high complexity. Third, social context and co-occurrence context play important roles in improving recommendation results. NAIS significantly outperforms CMN and becomes the strongest baseline model. It indicates that URL-URL co-occurrence relationship is more important than user-user co-occurrence relationship since semantic representation of each user is much complex than semantic representation of a fact-checking URL. Overall, our AMRAN outperforms all baselines, achieving 0.657 HR@10 and 0.410 NDCG@10. It improves HR@10 by 5.3% and NDCG@10 by 3% over the best baseline (i.e., $NAIS_{concat}$). ### Evaluation ::: RQ2: Effectiveness of our submodules
In this experiment, we are interested in measuring effectiveness of our submodules of AMRAN: CSAN and HGAN. Table TABREF71 the experimental result. CSAN achieves 0.642 HR@10 and 0.387 HR@10, whereas HGAN achieves 0.653 HR@10 and 0.403 NDCG@10. Both of the submodules outperform all the baselines in HR@10. HGAN outperforms all the baselines, and CSAN is competitive over the baselines. This experimental result confirms that both CSAN and HGAN positively contributed to the performance of our AMRAN. ### Evaluation ::: RQ3: Effectiveness of our Attention Mechanisms
We proposed two attention mechanisms: (1) spatial attention block in CSAN; and (2) graph attention mechanism in HGAN described in Section SECREF4. In this experiment, we are interested in studying the impact of the attention mechanisms. In particular, we run each submodule of AMRAN (i.e., CSAN or HGAN) with/without a corresponding attention mechanism. Table TABREF74 shows performance of these models. In both submodules, our proposed attention mechanisms positively improved the performance of these submodules, confirming the positive impact toward correctly recommending fact-checking URLs. ### Evaluation ::: RQ4: Hyperparameter Sensitivity
Now, we turn to analyze how our model is sensitive to hyperparameter values, and which hyperparameter value produces the best recommendation result. Recall that we utilize the context information to generate comprehensive embedding of given user and URL. In CSAN, we employ four streams to capture fine-grained context characteristics and share the embedding weight matrix with the target user and target URL representations. In the first experiment, we vary the number of neighbors associated with each steam in CSAN to show how CSAN's performance is changed. Figure FIGREF76 shows that both $HR@10$ and $NDCG@10$ have similar trends, and selecting 10 neighbors at each stream produced the best result. Next, we measure how performance of HGAN is changed when varying the number of HGALs and a size of selected neighbor nodes at each layer. Figure FIGREF77 demonstrates the necessity of employing 2 HGALs, which consistently outperforms the one HGAL. The best performance was achieved when a size of selected neighbor nodes was set to 8. In addition, we vary the number of negative samples, and a size of latent semantic space for the target user and target URL (i.e., an embedding vector size of the target user and target URL). Figure FIGREF78 shows high dimensional latent semantic space produces high performance of AMRAN. 64 dimensional embeddings produced the best results. We also observe that one negative sample would not be enough to produce good results in especially when an embedding vector size is small. The top performance is achieved when one positive instance paired with 3 or 4 negative instances. ### Evaluation ::: Case Study: Visualization of Relevance Propagation
Attention mechanism not only improve recommendation performance of our model, but also provide explainability of our model. As a case study, we specifically chose an example to demonstrate relevance propagation. In particular, we randomly sampled a user 7849 as the example as shown in Figure FIGREF80. The user 7849 has 3 co-occurrenced users, 3 following users, and posted 4 URLs. Note that we omit less important 2nd-degree neighbors for simplicity. The most relevant neighbors and the propagation paths are highlighted automatically via the attention mechanism. In general, based on the user's historical context URLs, we observe that the topic that user 7849 would like to participate in debunking is fauxtography. However, in this very particular case, the most influential context neighbors of the user are user 25 (co-occurrence user) and user 4759 (social context) given URL 1623. Both of the context neighbors share the similar taste with user 7849 on the favorite website (Politifact.com). Moreover, we found that URL 2525 appeared in 2nd-degree neighborhood of the user 7849, and was originated from the same website (Snopes.com) with URL 1623. ### Conclusion
In this paper, we proposed a novel framework, which effectively recommends relevant fact-checking URLs to fact-checkers. The proposed framework inspired by recent advancements in graph neural network and attention mechanism leveraged user-URL specific context information to capture deep semantic and complex structure between target user and target URL. We compared the performance of our model, AMRAN, with eight state-of-the-art baselines. Experimental results showed that our model achieved up to 5.3% improvement against the best baseline. Both submodules of AMRAN positively contributed to the recommendation results. This work was supported in part by NSF grant CNS-1755536, AWS Cloud Credits for Research, and Google Cloud. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors. Figure 2: A toy example of multi-relational context w.r.t. given target user-URL pair. Table 1: Notations. Figure 3: A schematic overview of our proposed Attributed Multi-Relational Attention Network (AMRAN), consisting of two modules: (1) a convolutional spatial attention network (CSAN); and (2) a heterogeneous graph attention network (HGAN). Figure 4: The illustration of Spatial Attention Mechanism (show an attention block in the historical context URL stream for illustration). Figure 5: Graphical illustration of a single heterogeneous graph attention layer. In this example, we assume the central node as a user node. Circles denote users, and triangles denote URLs. Colored objects with a solid line are selected neighbors at each layer, and the nodes with a dotted line are randomly dropped. (Best viewed in color). Table 2: Statistics of our evaluation dataset. Table 3: Characteristics of baselines and our model. Table 5: Performance of two submodules (CSAN andHGAN), and AMRAN. Table 4: Performance of our AMRAN and baseline models. AMRAN outperforms all baselines in both evaluation metrics. Figure 9: Visualization of relevance propagation of a user 7849. Objects in yellow denote target user and target URL (best viewed in color). Table 6: Performance of submodules with/without our proposed attention mechanisms. Figure 6: Performance of CSAN when varying the number of neighbors in each stream. Figure 7: Performance of HGAN when varying a size of neighbor nodes at each layer (HGAL). | Twitter dataset obtained from the authors of BIBREF12 |
What is dialogue act recognition? | ### Introduction
Dialogue act (DA) characterizes the type of a speaker's intention in the course of producing an utterance and is approximately equivalent to the illocutionary act of BIBREF0 or the speech act of BIBREF1. The recognition of DA is essential for modeling and automatically detecting discourse structure, especially in developing a human-machine dialogue system. It is natural to predict the Answer acts following an utterance of type Question, and then match the Question utterance to each QA-pair in the knowledge base. The predicted DA can also guide the response generation process BIBREF2. For instance, system generates a Greeting type response to former Greeting type utterance. Moreover, DA is beneficial to other online dialogue strategies, such as conflict avoidance BIBREF3. In the offline system, DA also plays a significant role in summarizing and analyzing the collected utterances. For instance, recognizing DAs of a wholly online service record between customer and agent is beneficial to mine QA-pairs, which are selected and clustered then to expand the knowledge base. DA recognition is challenging due to the same utterance may have a different meaning in a different context. Table TABREF1 shows an example of some utterances together with their DAs from Switchboard dataset. In this example, utterance “Okay.” corresponds to two different DA labels within different semantic context. Many approaches have been proposed for DA recognition. Previous work relies heavily on handcrafted features which are domain-specific and difficult to scale up BIBREF4, BIBREF5, BIBREF6. Recently, with great ability to do feature extraction, deep learning has yielded state-of-the-art results for many NLP tasks, and also makes impressive advances in DA recognition. BIBREF7, BIBREF8 built hierarchical CNN/RNN models to encode sentence and incorporate context information for DA recognition. BIBREF9 achieved promising performance by adding the CRF to enhance the dependency between labels. BIBREF10 applied the self-attention mechanism coupled with a hierarchical recurrent neural network. However, previous approaches cannot make full use of the relative position relationship between utterances. It is natural that utterances in the local context always have strong dependencies in our daily dialog. In this paper, we propose a hierarchical model based on self-attention BIBREF11 and revise the attention distribution to focus on a local and contextual semantic information by a learnable Gaussian bias which represents the relative position information between utterances, inspired by BIBREF12. Further, to analyze the effect of dialog length quantitatively, we introduce a new dialog segmentation mechanism for the DA task and evaluate the performance of different dialogue length and context padding length under online and offline settings. Experiment and visualization show that our method can learn the local contextual dependency between utterances explicitly and achieve promising performance in two well-known datasets. The contributions of this paper are: We design a hierarchical model based on self-attention and revise the attention distribution to focus on a local and contextual semantic information by the relative position information between utterances. We introduce a new dialog segmentation mechaism for the DA task and analyze the effect of dialog length and context padding length. In addition to traditional offline prediction, we also analyze the accuracy and time complexity under the online setting. ### Background ::: Related Work
DA recognition is aimed to assign a label to each utterance in a conversation. It can be formulated as a supervised classification problem. There are two trends to solve this problem: 1) as a sequence labeling problem, it will predict the labels for all utterances in the whole dialogue history BIBREF13, BIBREF14, BIBREF9; 2) as a sentence classification problem, it will treat utterance independently without any context history BIBREF5, BIBREF15. Early studies rely heavily on handcrafted features such as lexical, syntactic, contextual, prosodic and speaker information and achieve good results BIBREF13, BIBREF4, BIBREF16. Recent studies have applied deep learning based model for DA recognition. BIBREF14 proposed a model based on RNNs and CNNs that incorporates preceding short texts to classify current DAs. BIBREF7, BIBREF8 used hierarchical CNN and RNN to model the utterance sequence in the conversation, which can extract high-level sentence information to predict its label. They found that there is a small performance difference among different hierarchical CNN and RNN approaches. BIBREF9 added a CRF layer on the top of the hierarchical network to model the label transition dependency. BIBREF10 applied the context-aware self-attention mechanism coupled with a hierarchical recurrent neural network and got a significant improvement over state-of-the-art results on SwDA datasets. On another aspect, BIBREF17 combined a recurrent neural network language model with a latent variable model over DAs. BIBREF18 proposed a Discrete Information Variational Autoencoders (DI-VAE) model to learn discrete latent actions to incorporate sentence-level distributional semantics for dialogue generation. ### Background ::: Self-Attention
Self-attention BIBREF11 achieves great success for its efficiently parallel computation and long-range dependency modeling. Given the input sequence $ s = \left( s_1,...,s_n \right) $ of n elements where $ s_i \in \mathbb {R}^{d_s} $. Each attention head holds three parameter matrices, $W_h^Q, W_h^K, W_h^V \in {\mathbb {R}}^{d_s \times d_z} $ where $ h $ present the index of head. For the head $h$, linear projection is applied to the sequence $s$ to obtain key (K), query (Q), and value (V) representations. the attention module gets the weight by computing dot-products between key/query pair and then $softmax$ normalizes the result. it is defined as: where $\sqrt{d_z}$ is the scaling factor to counteract this effect that the dot products may grow large in magnitude. For all the heads, where $W^O \in \mathbb {R}^{(d_z*h)\times d_s}$ is the output projection. One weakness of self-attention model it that they cannot encode the position information efficiently. Some methods have been proposed to encode the relative or absolute position of tokens in the sequence as the additional input to the model. BIBREF11 used sine and cosine functions of different frequencies and added positional encodings to the input embeddings together. It used absolute position embedding to capture relative positional relation by the characteristic of sine and cosine functions. Moreover, several studies show that explicitly modeling relative position can further improve performance. For example, BIBREF19 proposed relative position encoding to explicitly model relative position by independent semantic parameter. It demonstrated significant improvements even when entirely replacing conventional absolute position encodings. BIBREF12 proposed to model localness for the self-attention network by a learnable Gaussian bias which enhanced the ability to model local relationship and demonstrated the effectiveness on the translation task. In our study, we design a local contextual attention model, which incorporates relative position information by a learnable Gaussian bias into original attention distribution. Different from BIBREF12, in our method, the distribution center is regulated around the corresponding utterance with a window, which indicates the context dependency preference, for capturing more local contextual dependency. ### Methodology
Before we describe the proposed model in detail, we first define the mathematical notation for the DA recognition task in this paper. Given the dataset, $X = (D_1,D_2,... D_L)$ with corresponding DA labels $(Y_1,Y_2,...Y_L)$. Each dialogue is a sequence of $ N_l $ utterances $ D_l = (u_1,u_2,...u_{N_l})$ with $ Y_l = (y_1,y_2,...y_{N_l}) $. Each utterance is padded or truncated to the length of $ M $ words, $u_j = (w_1,w_2,...w_{M})$. Figure FIGREF6 shows our overall model structure. For the first layer, we encode each utterance $u_j$ into a vector representation. Each word $w_m$ of the utterance $u_j$ is converted into dense vector representations $e_m$ from one-hot token representation. And then, we apply LSTM BIBREF20, a powerful and effective structure for sequence modeling, to encode the word sequence. Formally, for the utterance $u_j$: where $embed$ represents the embedding layer which can be initialized by pre-trained embeddings. To make a fair comparison with previous work, we do not use the fine-grained embedding presented in BIBREF21. LSTM helps us get the context-aware sentence representation for the input sequence. There are several approaches to represent the sentence from the words. Following BIBREF22, we add a max-pooling layer after LSTM, which selects the maximum value in each dimension from the hidden units. In our experiment, LSTM with max-pooling does perform a little better than LSTM with last-pooling, which is used in BIBREF9. Afterwards, we get the utterances vector representations $ u = (u_1,...,u_{N_l}) $ of $N_l$ elements for the dialogue $D_l$ where $ u_j \in \mathbb {R}^{d_s}, d_s$ is the dimension of hidden units. As we discussed in section SECREF7, given the sequence $ s \in \mathbb {R}^{N_l*d_s}$, self-attention mechanism calculates the attention weights between each pair of utterances in the sequence and get the weighted sum as output. The attention module explicitly models the context dependency between utterances. We employ a residual connection BIBREF23 around the attention module, which represents the dependency encoder between utterances, and the current utterance encoder $s$: Finally, we apply a two-layer fully connected network with a Rectified Linear Unit (ReLU) to get the final classification output for each utterance. ### Methodology ::: Modeling Local Contextual Attention
The attention explicitly models the interaction between the utterances. However, for context modeling, original attention mechanism always considers all of the utterances in a dialogue which inhibits the relation among the local context and is prone to overfitting during training. It is natural that utterances in the local context always have strong dependencies in our daily dialog. Therefore, we add a learnable Gaussian bias with the local constraint to the weight normalized by $softmax$ to enhance the interaction between concerned utterances and its neighbors. The attention module formula is revised as: The first term is the original dot product self-attention model. $POS \in \mathbb {R}^{N\times N}$ is the bias matrix, where N is the length of dialogue. The element $POS_{i,j}$ is defined following by gaussian distribution: $POS_{i,j}$ measures the dependency between the utterance $u_j$ and the utterance $u_i$ in terms of the relative position prior. $w_{i}$ represents for the standard deviation, which controls the weight decaying. Because of local constraint, $|c_{i} - i| <= C$, for each utterance $u_i$, the predicted center position $c_{i}$ and window size $ w_{i}$ is defined as followed: where $W_i^c,W_i^d \in \mathbb {R}^{1*N}$ are both learnable parameters. We initialized the parameter $W_i^c$ to 0, which leads to center position $ c_i = i $ by default. Furthermore, $c_{i}$ and $w_{i}$ are both related to the semantic context of the utterances, so we assign the mean of key $\overline{K}$ in attention mechanism to represent the context information. Moreover, the central position also indicates the dependency preference of the preceding utterances or subsequent utterances. It is worth noting that there is a little difference with BIBREF12, although we both revise the attention module by the Gaussian distribution. In our method, for the given utterance $u_{i}$, the distribution center $c_{i}$ is regulated for capturing the not only local but also contextual dependency, which can be formally expressed as: $c_{i} \in (i-C,i+C)$. However, in their work, the distribution center can be anywhere in the sequence, and it is designed for capturing the phrasal patterns, which are essential for Neural Machine Translation task. ### Methodology ::: Online and Offline Predictions
Previous work mainly focuses on the offline setting where we can access the whole utterances in the dialogue and predict all the DA labels simultaneously. However, the online setting is the natural demand in our real-time applications. For the online setting, we only care about the recognition result of the last utterance in the given context, as seen in the area with the red dashed line in Figure FIGREF6, our model is well compatible with online setting, we can calculate the attention between the last utterance and the other utterances directly where $K \in \mathbb {R}^{1\times d}, Q \in \mathbb {R}^{n\times d}, V \in \mathbb {R}^{n\times d}$. For LSTM, we still have to model the entire sequence, which is slower than attention based models. Table TABREF17 shows the time complexity comparison excluding the time cost of first layer encoding, and the dialogue length $n$ is smaller than the representation dimension $d$. Our model is easy to expand into the online setting, however, to have a fair comparison with previous work, in our experiments, we applied the models under the offline setting by default. ### Methodology ::: Separate into Sub-dialogues
The length of different dialogues in the dataset varies a lot. It is worth noting that the length of dialog affects the model prediction. On the one hand, under the offline setting, we can access the whole utterances in the dialogue and predict all the DA labels simultaneously, so the more utterances, the more efficient. However, on the other hand, if we put too many utterances in once prediction, it will model too much unrelated dependency in the long utterances sequence for both LSTM and attention mechanism based model. The sub-dialogues with the same length also enable efficiently batch training. To study how the dialogue length and context padding length will affect the performance, so we defined a sliding window $W$ which is the sub-dialogue length. Then, we separate each long dialogue into several small sub-dialogues. For example, the dialog $D$ is a sequence of utterances with length $n$, and we will get $\lceil x/w \rceil $ sub-dialogues, for the k-th sub-dialogues, the utterances sequence is $(u_{(k-1)*W+1},u_{(k-1)*W+2},...,u_{k*W})$. In order to avoid losing some context information caused by being separated, which will affect the context modeling for the utterances in the begin and end of the sub-dialog, we add the corresponding context with $P$ (stands for context padding) utterances at the begin and the end of each sliding window, so for the k-th sub-dialogues, the revised utterances sequence is $(u_{(k-1)*W-P+1},u_{(k-1)*W-P+2},...,u_{k*W+P})$. Moreover, we mask the loss for the context padding utterances, which can be formally expressed as: $M(i)=0$ if utterance $i$ is in the context padding otherwise 1, $L$ is the cross entropy. The $W$ and $P$ are both hyperparameters; in the experiment SECREF21, we will talk about the effect of the window size and the context padding length. ### Experiments ::: Datasets
We evaluate the performance of our model on two high-quality datasets: Switchboard Dialogue Act Corpus (SwDA) BIBREF4 and DailyDialog BIBREF24. SwDA has been widely used in previous work for the DA recognition task. It is annotated on 1155 human to human telephonic conversations about the given topic. Each utterance in the conversation is manually labeled as one of 42 dialogue acts according to SWBD-DAMSL taxonomy BIBREF25. In BIBREF10, they used 43 categories of dialogue acts, which is different from us and previous work. The difference in the number of labels is mainly due to the special label “+”, which represents that the utterance is interrupted by the other speaker (and thus split into two or more parts). We used the same processing with BIBREF26, which concatenated the parts of an interrupted utterance together, giving the result the tag of the first part and putting it in its place in the conversation sequence. It is critical for fair comparison because there are nearly 8% data has the label “+”. Lacking standard splits, we followed the training/validation/test splits by BIBREF14. DailyDialog dataset contains 13118 multi-turn dialogues, which mainly reflect our daily communication style. It covers various topics about our daily life. Each utterance in the conversation is manually labeled as one out of 4 dialogue act classes. Table TABREF18 presents the statistics for both datasets. In our preprocessing, the text was lowercased before tokenized, and then sentences were tokenized by WordPiece tokenizer BIBREF27 with a 30,000 token vocabulary to alleviate the Out-of-Vocabulary problem. [1]The author claimed that they achieved 78.7%(81.3%) accuracy with pre-trained word embedding (fine-grained embedding). For a fair comparison, both previous and our work is simply based on pre-trained word embedding. [2]The author randomly selected two test sets which are different from previous and our work and achieved 77.15% and 79.74%, and we reimplemented in standard test sets. ### Experiments ::: Results on SwDA
In this section, we evaluate the proposed approaches on SwDA dataset. Table TABREF20 shows our experimental results and the previous ones on SwDA dataset. It is worth noting that BIBREF10 combined GloVeBIBREF28 and pre-trained ELMo representationsBIBREF29 as word embeddings. However, in our work, we only applied the pre-trained word embedding. To illustrate the importance of context information, we also evaluate several sentence classification methods (CNN, LSTM, BERT) as baselines. For baseline models, both CNN and LSTM, got similar accuracy (75.27% and 75.59% respectively). We also fine-tuned BERT BIBREF30 to do recognition based on single utterance. As seen, with the powerful unsupervised pre-trained language model, BERT (76.88% accuracy) outperformed LSTM and CNN models for single sentence classification. However, it was still much lower than the models based on context information. It indicates that context information is crucial in the DA recognition task. BERT can boost performance in a large margin. However, it costs too much time and resources. In this reason, we chose LSTM as our utterance encoder in further experiment. By modeling context information, the performance of the hierarchical model is improved by at least 3%, even compared to BERT. In order to better analyze the semantic dependency learned by attention, in our experiments, we removed the CRF module. In terms of different hierarchical models, our LSTM+BLSTM achieved good result. The accuracy was 80.00% which is even a little better than Hierarchical BLSTM-CRF BIBREF9. Relying on attention mechanism and local contextual modeling, our model, LSTM+Attention and LSTM+Local Contextual Attention, achieved 80.12% and 80.34% accuracy respectively. Compared with the previous best approach Hierarchical BLSTM-CRF, we can obtain a relative accuracy gain with 1.1% by our best model. It indicated that self-attention model can capture context dependency better than the BLSTM model. With adding the local constraint, we can get an even better result. To further illustrate the effect of the context length, we also performed experiments with different sliding window $W$ and context padding $P$. Table TABREF22 shows the result. It is worth noting that it is actually the same as single sentence classification when $P = 0$ (without any context provided). First, we set $W$ to 1 to discuss how the length of context padding will affect. As seen in the result, the accuracy increased when more context padding was used for both LSTM+BLSTM and LSTM+Attention approaches, so we did not evaluate the performance of LSTM+LC Attention when context padding is small. There was no further accuracy improvement when the length of context padding was beyond 5. Therefore, we fixed the context padding length $P$ to 5 and increased the size of the sliding window to see how it works. With sliding window size increasing, the more context was involved together with more unnecessary information. From the experiments, we can see that both LSTM+BLSTM and LSTM+Attention achieved the best performance when window size was 1 and context padding length was 5. When window size increased, the performances of these two models dropped. However, our model (LSTM+LC Attention) can leverage the context information more efficiently, which achieved the best performance when window size was 10, and the model was more stable and robust to the different setting of window size. For online prediction, we only care about the recognition result of the last utterance in the given context. We added 5 preceding utterances as context padding for every predicted utterance because we cannot access subsequent utterances in the online setting. As seen in Table TABREF22, without subsequent utterances, the performances of these three models dropped. However, LSTM+LC Attention still outperformed the other two models. ### Experiments ::: Result on DailyDialog
The classification accuracy of DailyDialog dataset is summarized in Table TABREF23. As for sentence classification without context information, the fine-tuned BERT still outperformed LSTM and CNN based models. From table TABREF18 we can see that, the average dialogue length $|U|$ in DailyDialog is much shorter than the average length of SwDA. So, in our experiment, we set the maximum of the $W$ to 10, which almost covers the whole utterances in the dialogue. Using the same way as SwDA dataset, we, first, set W to 1 and increased the length of context padding. As seen, modeling local context information, hierarchical models yielded significant improvement than sentence classification. There was no further accuracy improvement when the length of context padding was beyond 2, so we fixed the context padding length P to 2 and increased the size of sliding window size W. From the experiments, we can see that LSTM+Attention always got a little better accuracy than LSTM+BLSTM. With window size increasing, the performances of these two models dropped. Relying on modeling local contextual information, LSTM+LC Attention achieved the best accuracy (85.81%) when the window size was 5. For the longer sliding window, the performance of LSTM+LC Attention was still better and more robust than the other two models. For online prediction, we added 2 preceding utterances as context padding, and the experiment shows that LSTM+LC Attention outperformed the other two models under the online setting, although the performances of these three models dropped without subsequent utterances. ### Experiments ::: Visualization
In this section, we visualize the attention weights for analyzing how local contextual attention works in detail. Figure FIGREF24 shows the visualization of original attention and local contextual attention for the example dialogue shown in Table TABREF1. The attention matrix $M$ explicitly measures the dependency among utterances. Each row of grids is normalized by $softmax$, $M_{ij}$ represents for the dependency score between the utterance i and utterance j. As demonstrated in Figure FIGREF24, there are some wrong and uninterpretable attention weights annotated with red color, which is learned by the original attention. The original attention model gives the utterance “B: Hi” (position 0) and “A: Okay.” (position 7) a high dependency score. However, local contextual attention weakens its attention weights due to the long distance apart. Overall, the additional Gaussian bias trend to centralize the attention distribution to the diagonal of the matrix, which is in line with our linguistic intuition that utterances that are far apart usually don't have too strong dependencies. As demonstrated in Figure FIGREF24, benefiting of the additional Gaussian bias, the revised attention mechanism weakens the attention weights between utterances which cross the long relative distance. For the grids near diagonal, it strengthens their dependency score and doesn't bring other useless dependencies for its learnable magnitude. ### Conclusions and Future Work
In the paper, we propose our hierarchical model with local contextual attention to the Dialogue Act Recognition task. Our model can explicitly capture the semantic dependencies between utterances inside the dialogue. To enhance our model with local contextual information, we revise the attention distribution by a learnable Gaussian bias to make it focus on the local neighbors. Based on our dialog segmentation mechanism, we find that local contextual attention reduces the noises through relative position information, which is essential for dialogue act recognition. And this segmentation mechanism can be applied under online and offline settings. Our model achieves promising performance in two well-known datasets, which shows that modeling local contextual information is crucial for dialogue act recognition. There is a close relation between dialogue act recognition and discourse parsing BIBREF31. The most discourse parsing process is composed of two stages: structure construction and dependency labeling BIBREF32, BIBREF33. For future work, a promising direction is to apply our method to multi-task training with two stages jointly. Incorporating supervised information from dependency between utterances may enhance the self-attention and further improve the accuracy of dialogue act recognition. Table 1: A snippet of a conversation with the DA labels from Switchboard dataset. Figure 1: The model structure for DA recognition, where the LSTM with max pooling is simplified as utterance encoder in our experiment. The area in the red dashed line represents the structure for online prediction. Table 2: Time complexity between LSTM and selfattention for both online and offline predictions excluding the time cost of first layer encoding. The parameter n represents for the dialogue length in the sliding window and d represent for the dimension of representation unit. Table 3: |C| indicates the number of classes. |U | indicates the average length of dialogues. The train/validation/test columns indicate the number of dialogues (the number of sentences) in the respective splits. Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset. Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing. Table 6: Experiment results on DailyDialog dataset. Figure 2: Visualization of original attention and local contextual attention. Each colored grid represents the dependency score between two sentences. The deeper the color is, the higher the dependency score is. | DA recognition is aimed to assign a label to each utterance in a conversation. It can be formulated as a supervised classification problem. |
Why is Jakdane going to Earth?
A. Jakdane is a corporate spy from Moon 5 on a mission to infiltrate Dom Blessing's organization.
B. Jakdane is following Trella to Earth because he is stalking her.
C. Jakdane is transferring from his company's office on Ganymede to the corporate headquarters on Earth.
D. Jakdane is the captain of the ship that Trella and Quest are taking to earth.
| Transcriber's Note: Every effort has been made to replicate this text as faithfully as possible; changes (corrections of spelling and punctuation) made to the original text are marked like this . The original text appears when hovering the cursor over the marked text. This e-text was produced from Amazing Science Fiction Stories March 1959. Extensive research did not uncover any evidence that the U. S. copyright on this publication was renewed. 50 THE JUPITER WEAPON By CHARLES L. FONTENAY He was a living weapon of destruction— immeasurably powerful, utterly invulnerable. There was only one question: Was he human? Trella feared she was in for trouble even before Motwick's head dropped forward on his arms in a drunken stupor. The two evil-looking men at the table nearby had been watching her surreptitiously, and now they shifted restlessly in their chairs. Trella had not wanted to come to the Golden Satellite. It was a squalid saloon in the rougher section of Jupiter's View, the terrestrial dome-colony on Ganymede. Motwick, already drunk, had insisted. A woman could not possibly make her way through these streets alone to the better section of town, especially one clad in a silvery evening dress. Her only hope was that this place had a telephone. Perhaps she could call one of Motwick's friends; she had no one on Ganymede she could call a real friend herself. Tentatively, she pushed her chair back from the table and arose. She had to brush close by the other table to get to the bar. As she did, the dark, slick-haired man reached out and grabbed her around the waist with a steely arm. Trella swung with her whole body, and slapped him so hard he nearly fell from his chair. As she walked swiftly toward the bar, he leaped up to follow her. There were only two other people in the Golden Satellite: the fat, mustached bartender and a short, square-built man at the bar. The latter swung around at the pistol-like report of her slap, and she saw that, though no more than four and a half feet tall, he was as heavily muscled as a lion. 51 His face was clean and open, with close-cropped blond hair and honest blue eyes. She ran to him. “Help me!” she cried. “Please help me!” He began to back away from her. “I can't,” he muttered in a deep voice. “I can't help you. I can't do anything.” The dark man was at her heels. In desperation, she dodged around the short man and took refuge behind him. Her protector was obviously unwilling, but the dark man, faced with his massiveness, took no chances. He stopped and shouted: “Kregg!” The other man at the table arose, ponderously, and lumbered toward them. He was immense, at least six and a half feet tall, with a brutal, vacant face. Evading her attempts to stay behind him, the squat man began to move down the bar away from the approaching Kregg. The dark man moved in on Trella again as Kregg overtook his quarry and swung a huge fist like a sledgehammer. Exactly what happened, Trella wasn't sure. She had the impression that Kregg's fist connected squarely with the short man's chin before he dodged to one side in a movement so fast it was a blur. But that couldn't have been, because the short man wasn't moved by that blow that would have felled a steer, and Kregg roared in pain, grabbing his injured fist. “The bar!” yelled Kregg. “I hit the damn bar!” At this juncture, the bartender took a hand. Leaning far over the bar, he swung a full bottle in a complete arc. It smashed on Kregg's head, splashing the floor with liquor, and Kregg sank stunned to his knees. The dark man, who had grabbed Trella's arm, released her and ran for the door. Moving agilely around the end of the bar, the bartender stood over Kregg, holding the jagged-edged bottleneck in his hand menacingly. “Get out!” rumbled the bartender. “I'll have no coppers raiding my place for the likes of you!” Kregg stumbled to his feet and staggered out. Trella ran to the unconscious Motwick's side. “That means you, too, lady,” said the bartender beside her. “You and your boy friend get out of here. You oughtn't to have come here in the first place.” “May I help you, Miss?” asked a deep, resonant voice behind her. She straightened from her anxious examination of Motwick. The squat man was standing there, an apologetic look on his face. She looked contemptuously at the massive muscles whose help had been denied her. Her arm ached where the dark man had grasped it. The broad face before 52 her was not unhandsome, and the blue eyes were disconcertingly direct, but she despised him for a coward. “I'm sorry I couldn't fight those men for you, Miss, but I just couldn't,” he said miserably, as though reading her thoughts. “But no one will bother you on the street if I'm with you.” “A lot of protection you'd be if they did!” she snapped. “But I'm desperate. You can carry him to the Stellar Hotel for me.” The gravity of Ganymede was hardly more than that of Earth's moon, but the way the man picked up the limp Motwick with one hand and tossed him over a shoulder was startling: as though he lifted a feather pillow. He followed Trella out the door of the Golden Satellite and fell in step beside her. Immediately she was grateful for his presence. The dimly lighted street was not crowded, but she didn't like the looks of the men she saw. The transparent dome of Jupiter's View was faintly visible in the reflected night lights of the colonial city, but the lights were overwhelmed by the giant, vari-colored disc of Jupiter itself, riding high in the sky. “I'm Quest Mansard, Miss,” said her companion. “I'm just in from Jupiter.” “I'm Trella Nuspar,” she said, favoring him with a green-eyed glance. “You mean Io, don't you—or Moon Five?” “No,” he said, grinning at her. He had an engaging grin, with even white teeth. “I meant Jupiter.” “You're lying,” she said flatly. “No one has ever landed on Jupiter. It would be impossible to blast off again.” “My parents landed on Jupiter, and I blasted off from it,” he said soberly. “I was born there. Have you ever heard of Dr. Eriklund Mansard?” “I certainly have,” she said, her interest taking a sudden upward turn. “He developed the surgiscope, didn't he? But his ship was drawn into Jupiter and lost.” “It was drawn into Jupiter, but he landed it successfully,” said Quest. “He and my mother lived on Jupiter until the oxygen equipment wore out at last. I was born and brought up there, and I was finally able to build a small rocket with a powerful enough drive to clear the planet.” She looked at him. He was short, half a head shorter than she, but broad and powerful as a man might be who had grown up in heavy gravity. He trod the street with a light, controlled step, seeming to deliberately hold himself down. “If Dr. Mansard succeeded in landing on Jupiter, why didn't anyone ever hear from him again?” she demanded. “Because,” said Quest, “his radio was sabotaged, just as his ship's drive was.” “Jupiter strength,” she murmured, looking him over coolly. 53 “You wear Motwick on your shoulder like a scarf. But you couldn't bring yourself to help a woman against two thugs.” He flushed. “I'm sorry,” he said. “That's something I couldn't help.” “Why not?” “I don't know. It's not that I'm afraid, but there's something in me that makes me back away from the prospect of fighting anyone.” Trella sighed. Cowardice was a state of mind. It was peculiarly inappropriate, but not unbelievable, that the strongest and most agile man on Ganymede should be a coward. Well, she thought with a rush of sympathy, he couldn't help being what he was. They had reached the more brightly lighted section of the city now. Trella could get a cab from here, but the Stellar Hotel wasn't far. They walked on. Trella had the desk clerk call a cab to deliver the unconscious Motwick to his home. She and Quest had a late sandwich in the coffee shop. “I landed here only a week ago,” he told her, his eyes frankly admiring her honey-colored hair and comely face. “I'm heading for Earth on the next spaceship.” “We'll be traveling companions, then,” she said. “I'm going back on that ship, too.” For some reason she decided against telling him that the assignment on which she had come to the Jupiter system was to gather his own father's notebooks and take them back to Earth. Motwick was an irresponsible playboy whom Trella had known briefly on Earth, and Trella was glad to dispense with his company for the remaining three weeks before the spaceship blasted off. She found herself enjoying the steadier companionship of Quest. As a matter of fact, she found herself enjoying his companionship more than she intended to. She found herself falling in love with him. Now this did not suit her at all. Trella had always liked her men tall and dark. She had determined that when she married it would be to a curly-haired six-footer. She was not at all happy about being so strongly attracted to a man several inches shorter than she. She was particularly unhappy about feeling drawn to a man who was a coward. The ship that they boarded on Moon Nine was one of the newer ships that could attain a hundred-mile-per-second velocity and take a hyperbolic path to Earth, but it would still require fifty-four days to make the trip. So Trella was delighted to find that the ship was the Cometfire and its skipper was her old friend, dark-eyed, curly-haired Jakdane Gille. “Jakdane,” she said, flirting with him with her eyes as in 54 days gone by, “I need a chaperon this trip, and you're ideal for the job.” “I never thought of myself in quite that light, but maybe I'm getting old,” he answered, laughing. “What's your trouble, Trella?” “I'm in love with that huge chunk of man who came aboard with me, and I'm not sure I ought to be,” she confessed. “I may need protection against myself till we get to Earth.” “If it's to keep you out of another fellow's clutches, I'm your man,” agreed Jakdane heartily. “I always had a mind to save you for myself. I'll guarantee you won't have a moment alone with him the whole trip.” “You don't have to be that thorough about it,” she protested hastily. “I want to get a little enjoyment out of being in love. But if I feel myself weakening too much, I'll holler for help.” The Cometfire swung around great Jupiter in an opening arc and plummeted ever more swiftly toward the tight circles of the inner planets. There were four crew members and three passengers aboard the ship's tiny personnel sphere, and Trella was thrown with Quest almost constantly. She enjoyed every minute of it. She told him only that she was a messenger, sent out to Ganymede to pick up some important papers and take them back to Earth. She was tempted to tell him what the papers were. Her employer had impressed upon her that her mission was confidential, but surely Dom Blessing could not object to Dr. Mansard's son knowing about it. All these things had happened before she was born, and she did not know what Dom Blessing's relation to Dr. Mansard had been, but it must have been very close. She knew that Dr. Mansard had invented the surgiscope. This was an instrument with a three-dimensional screen as its heart. The screen was a cubical frame in which an apparently solid image was built up of an object under an electron microscope. The actual cutting instrument of the surgiscope was an ion stream. By operating a tool in the three-dimensional screen, corresponding movements were made by the ion stream on the object under the microscope. The principle was the same as that used in operation of remote control “hands” in atomic laboratories to handle hot material, and with the surgiscope very delicate operations could be performed at the cellular level. Dr. Mansard and his wife had disappeared into the turbulent atmosphere of Jupiter just after his invention of the surgiscope, and it had been developed by Dom Blessing. Its success had built Spaceway Instruments, Incorporated, which Blessing headed. Through all these years since Dr. Mansard's disappearance, 55 Blessing had been searching the Jovian moons for a second, hidden laboratory of Dr. Mansard. When it was found at last, he sent Trella, his most trusted secretary, to Ganymede to bring back to him the notebooks found there. Blessing would, of course, be happy to learn that a son of Dr. Mansard lived, and would see that he received his rightful share of the inheritance. Because of this, Trella was tempted to tell Quest the good news herself; but she decided against it. It was Blessing's privilege to do this his own way, and he might not appreciate her meddling. At midtrip, Trella made a rueful confession to Jakdane. “It seems I was taking unnecessary precautions when I asked you to be a chaperon,” she said. “I kept waiting for Quest to do something, and when he didn't I told him I loved him.” “What did he say?” “It's very peculiar,” she said unhappily. “He said he can't love me. He said he wants to love me and he feels that he should, but there's something in him that refuses to permit it.” She expected Jakdane to salve her wounded feelings with a sympathetic pleasantry, but he did not. Instead, he just looked at her very thoughtfully and said no more about the matter. He explained his attitude after Asrange ran amuck. Asrange was the third passenger. He was a lean, saturnine individual who said little and kept to himself as much as possible. He was distantly polite in his relations with both crew and other passengers, and never showed the slightest spark of emotion … until the day Quest squirted coffee on him. It was one of those accidents that can occur easily in space. The passengers and the two crewmen on that particular waking shift (including Jakdane) were eating lunch on the center-deck. Quest picked up his bulb of coffee, but inadvertently pressed it before he got it to his lips. The coffee squirted all over the front of Asrange's clean white tunic. “I'm sorry!” exclaimed Quest in distress. The man's eyes went wide and he snarled. So quickly it seemed impossible, he had unbuckled himself from his seat and hurled himself backward from the table with an incoherent cry. He seized the first object his hand touched—it happened to be a heavy wooden cane leaning against Jakdane's bunk—propelled himself like a projectile at Quest. Quest rose from the table in a sudden uncoiling of movement. He did not unbuckle his safety belt—he rose and it snapped like a string. For a moment Trella thought he was going to meet Asrange's assault. But he fled in a long leap toward the companionway leading to the astrogation deck 56 above. Landing feet-first in the middle of the table and rebounding, Asrange pursued with the stick upraised. In his haste, Quest missed the companionway in his leap and was cornered against one of the bunks. Asrange descended on him like an avenging angel and, holding onto the bunk with one hand, rained savage blows on his head and shoulders with the heavy stick. Quest made no effort to retaliate. He cowered under the attack, holding his hands in front of him as if to ward it off. In a moment, Jakdane and the other crewman had reached Asrange and pulled him off. When they had Asrange in irons, Jakdane turned to Quest, who was now sitting unhappily at the table. “Take it easy,” he advised. “I'll wake the psychosurgeon and have him look you over. Just stay there.” Quest shook his head. “Don't bother him,” he said. “It's nothing but a few bruises.” “Bruises? Man, that club could have broken your skull! Or a couple of ribs, at the very least.” “I'm all right,” insisted Quest; and when the skeptical Jakdane insisted on examining him carefully, he had to admit it. There was hardly a mark on him from the blows. “If it didn't hurt you any more than that, why didn't you take that stick away from him?” demanded Jakdane. “You could have, easily.” “I couldn't,” said Quest miserably, and turned his face away. Later, alone with Trella on the control deck, Jakdane gave her some sober advice. “If you think you're in love with Quest, forget it,” he said. “Why? Because he's a coward? I know that ought to make me despise him, but it doesn't any more.” “Not because he's a coward. Because he's an android!” “What? Jakdane, you can't be serious!” “I am. I say he's an android, an artificial imitation of a man. It all figures. “Look, Trella, he said he was born on Jupiter. A human could stand the gravity of Jupiter, inside a dome or a ship, but what human could stand the rocket acceleration necessary to break free of Jupiter? Here's a man strong enough to break a spaceship safety belt just by getting up out of his chair against it, tough enough to take a beating with a heavy stick without being injured. How can you believe he's really human?” Trella remembered the thug Kregg striking Quest in the face and then crying that he had injured his hand on the bar. “But he said Dr. Mansard was his father,” protested Trella. “Robots and androids frequently look on their makers as their parents,” said Jakdane. “Quest may not even know he's 57 artificial. Do you know how Mansard died?” “The oxygen equipment failed, Quest said.” “Yes. Do you know when?” “No. Quest never did tell me, that I remember.” “He told me: a year before Quest made his rocket flight to Ganymede! If the oxygen equipment failed, how do you think Quest lived in the poisonous atmosphere of Jupiter, if he's human?” Trella was silent. “For the protection of humans, there are two psychological traits built into every robot and android,” said Jakdane gently. “The first is that they can never, under any circumstances, attack a human being, even in self defense. The second is that, while they may understand sexual desire objectively, they can never experience it themselves. “Those characteristics fit your man Quest to a T, Trella. There is no other explanation for him: he must be an android.” Trella did not want to believe Jakdane was right, but his reasoning was unassailable. Looking upon Quest as an android, many things were explained: his great strength, his short, broad build, his immunity to injury, his refusal to defend himself against a human, his inability to return Trella's love for him. It was not inconceivable that she should have unknowingly fallen in love with an android. Humans could love androids, with real affection, even knowing that they were artificial. There were instances of android nursemaids who were virtually members of the families owning them. She was glad now that she had not told Quest of her mission to Ganymede. He thought he was Dr. Mansard's son, but an android had no legal right of inheritance from his owner. She would leave it to Dom Blessing to decide what to do about Quest. Thus she did not, as she had intended originally, speak to Quest about seeing him again after she had completed her assignment. Even if Jakdane was wrong and Quest was human—as now seemed unlikely—Quest had told her he could not love her. Her best course was to try to forget him. Nor did Quest try to arrange with her for a later meeting. “It has been pleasant knowing you, Trella,” he said when they left the G-boat at White Sands. A faraway look came into his blue eyes, and he added: “I'm sorry things couldn't have been different, somehow.” “Let's don't be sorry for what we can't help,” she said gently, taking his hand in farewell. Trella took a fast plane from White Sands, and twenty-four hours later walked up the front steps of the familiar brownstone house on the outskirts of Washington. Dom Blessing himself met her at the door, a stooped, graying 58 man who peered at her over his spectacles. “You have the papers, eh?” he said, spying the brief case. “Good, good. Come in and we'll see what we have, eh?” She accompanied him through the bare, windowless anteroom which had always seemed to her such a strange feature of this luxurious house, and they entered the big living room. They sat before a fire in the old-fashioned fireplace and Blessing opened the brief case with trembling hands. “There are things here,” he said, his eyes sparkling as he glanced through the notebooks. “Yes, there are things here. We shall make something of these, Miss Trella, eh?” “I'm glad they're something you can use, Mr. Blessing,” she said. “There's something else I found on my trip, that I think I should tell you about.” She told him about Quest. “He thinks he's the son of Dr. Mansard,” she finished, “but apparently he is, without knowing it, an android Dr. Mansard built on Jupiter.” “He came back to Earth with you, eh?” asked Blessing intently. “Yes. I'm afraid it's your decision whether to let him go on living as a man or to tell him he's an android and claim ownership as Dr. Mansard's heir.” Trella planned to spend a few days resting in her employer's spacious home, and then to take a short vacation before resuming her duties as his confidential secretary. The next morning when she came down from her room, a change had been made. Two armed men were with Dom Blessing at breakfast and accompanied him wherever he went. She discovered that two more men with guns were stationed in the bare anteroom and a guard was stationed at every entrance to the house. “Why all the protection?” she asked Blessing. “A wealthy man must be careful,” said Blessing cheerfully. “When we don't understand all the implications of new circumstances, we must be prepared for anything, eh?” There was only one new circumstance Trella could think of. Without actually intending to, she exclaimed: “You aren't afraid of Quest? Why, an android can't hurt a human!” Blessing peered at her over his spectacles. “And what if he isn't an android, eh? And if he is—what if old Mansard didn't build in the prohibition against harming humans that's required by law? What about that, eh?” Trella was silent, shocked. There was something here she hadn't known about, hadn't even suspected. For some reason, Dom Blessing feared Dr. Eriklund Mansard … or his heir … or his mechanical servant. She was sure that Blessing was wrong, that Quest, whether man or android, intended no 59 harm to him. Surely, Quest would have said something of such bitterness during their long time together on Ganymede and aspace, since he did not know of Trella's connection with Blessing. But, since this was to be the atmosphere of Blessing's house, she was glad that he decided to assign her to take the Mansard papers to the New York laboratory. Quest came the day before she was scheduled to leave. Trella was in the living room with Blessing, discussing the instructions she was to give to the laboratory officials in New York. The two bodyguards were with them. The other guards were at their posts. Trella heard the doorbell ring. The heavy oaken front door was kept locked now, and the guards in the anteroom examined callers through a tiny window. Suddenly alarm bells rang all over the house. There was a terrific crash outside the room as the front door splintered. There were shouts and the sound of a shot. “The steel doors!” cried Blessing, turning white. “Let's get out of here.” He and his bodyguards ran through the back of the house out of the garage. Blessing, ahead of the rest, leaped into one of the cars and started the engine. The door from the house shattered and Quest burst through. The two guards turned and fired together. He could be hurt by bullets. He was staggered momentarily. Then, in a blur of motion, he sprang forward and swept the guards aside with one hand with such force that they skidded across the floor and lay in an unconscious heap against the rear of the garage. Trella had opened the door of the car, but it was wrenched from her hand as Blessing stepped on the accelerator and it leaped into the driveway with spinning wheels. Quest was after it, like a chunky deer, running faster than Trella had ever seen a man run before. Blessing slowed for the turn at the end of the driveway and glanced back over his shoulder. Seeing Quest almost upon him, he slammed down the accelerator and twisted the wheel hard. The car whipped into the street, careened, and rolled over and over, bringing up against a tree on the other side in a twisted tangle of wreckage. With a horrified gasp, Trella ran down the driveway toward the smoking heap of metal. Quest was already beside it, probing it. As she reached his side, he lifted the torn body of Dom Blessing. Blessing was dead. “I'm lucky,” said Quest soberly. “I would have murdered him.” “But why, Quest? I knew he was afraid of you, but he didn't tell me why.” “It was conditioned into me,” answered Quest “I didn't know 60 it until just now, when it ended, but my father conditioned me psychologically from my birth to the task of hunting down Dom Blessing and killing him. It was an unconscious drive in me that wouldn't release me until the task was finished. “You see, Blessing was my father's assistant on Ganymede. Right after my father completed development of the surgiscope, he and my mother blasted off for Io. Blessing wanted the valuable rights to the surgiscope, and he sabotaged the ship's drive so it would fall into Jupiter. “But my father was able to control it in the heavy atmosphere of Jupiter, and landed it successfully. I was born there, and he conditioned me to come to Earth and track down Blessing. I know now that it was part of the conditioning that I was unable to fight any other man until my task was finished: it might have gotten me in trouble and diverted me from that purpose.” More gently than Trella would have believed possible for his Jupiter-strong muscles, Quest took her in his arms. “Now I can say I love you,” he said. “That was part of the conditioning too: I couldn't love any woman until my job was done.” Trella disengaged herself. “I'm sorry,” she said. “Don't you know this, too, now: that you're not a man, but an android?” He looked at her in astonishment, stunned by her words. “What in space makes you think that?” he demanded. “Why, Quest, it's obvious,” she cried, tears in her eyes. “Everything about you … your build, suited for Jupiter's gravity … your strength … the fact that you were able to live in Jupiter's atmosphere after the oxygen equipment failed. I know you think Dr. Mansard was your father, but androids often believe that.” He grinned at her. “I'm no android,” he said confidently. “Do you forget my father was inventor of the surgiscope? He knew I'd have to grow up on Jupiter, and he operated on the genes before I was born. He altered my inherited characteristics to adapt me to the climate of Jupiter … even to being able to breathe a chlorine atmosphere as well as an oxygen atmosphere.” Trella looked at him. He was not badly hurt, any more than an elephant would have been, but his tunic was stained with red blood where the bullets had struck him. Normal android blood was green. “How can you be sure?” she asked doubtfully. “Androids are made,” he answered with a laugh. “They don't grow up. And I remember my boyhood on Jupiter very well.” He took her in his arms again, and this time she did not resist. His lips were very human. THE END | D. Jakdane is the captain of the ship that Trella and Quest are taking to earth. |
Which metrics are they evaluating with? | ### Introduction
Narrative is a fundamental form of representation in human language and culture. Stories connect individuals and deliver experience, emotions and knowledge. Narrative comprehension has attracted long-standing interests in natural language processing (NLP) BIBREF1 , and is widely applicable to areas such as content creation. Enabling machines to understand narrative is also an important first step towards real intelligence. Previous studies on narrative comprehension include character roles identification BIBREF2 , narratives schema construction BIBREF3 , and plot pattern identification BIBREF4 . However, their main focus is on analyzing the stories themselves. In contrast, we concentrate on training machines to predict the end of the stories. Story completion tasks rely not only on the logic of the story itself, but also requires implicit commonsense knowledge outside the story. To understand stories, human can use the information from both the story itself and other implicit sources such as commonsense knowledge and normative social behaviors BIBREF5 . In this paper, we propose to imitate such behaviors to incorporate structured commonsense knowledge to aid the story ending prediction. Recently, BIBREF0 introduced a ROCStories dataset as a benchmark for evaluating models' ability to understand the narrative structures of a story, where the model is asked to select the correct ending from two candidates for a given story. To solve this task, both traditional machine learning approaches BIBREF6 and neural network models BIBREF7 have been used. Some works also exploit information such as sentiment and topic words BIBREF8 and event frames BIBREF9 . Recently, there has been work BIBREF10 that leverages large unlabeled corpus, like the BooksCorpus BIBREF11 dataset, to improve the performance. However, none of them explicitly uses structured commonsense knowledge, which humans would naturally incorporate to improve model performance. Figure 1 (a) shows a typical example in ROCStories dataset: a story about Dan and his parents. The blue words are key-words in the body of the story, and the red word is the key-word in the correct story ending. Figure 1 (b) shows the (implicit) relations among these key-words, which are obtained as a subgraph from ConceptNet BIBREF12 , a commonsense knowledge base. By incorporating such structured external commonsense knowledge, we are able to discover strong associations between these keywords and correctly predict the story ending. Note that these associations are not available from the story itself. To solve the story completion task, we propose a neural network model that integrates three types of information: (i) narrative sequence, (ii) sentiment evolution, and (iii) commonsense knowledge. The clues in narrative chain are captured by a transformer decoder, constructed from a pretrained language model. The sentiment prediction is obtained by using a LSTM model. Additionally, the commonsense knowledge is extracted from an existing structured knowledge base, ConceptNet. In particular, we use a combination gate to integrate all the information and train the model in an end-to-end manner. Experiments demonstrate the improved performance of our model on the task. ### Related Work
Our work on story completion is closely related to several research areas such as reading comprehension, sentiment analysis and commonsense knowledge integration, which will be briefly reviewed as below. Reading Comprehension is the ability to process text, understand its meaning, and to integrate it with what the readers already know. It has been an important field in NLP for a long time. The SQuAD dataset BIBREF13 presents a task to locate the correct answer to a question in a context document and recognizes unanswerable questions. The RACE dataset BIBREF14 , which is constructed from Chinese Students English Examination, introduces another task that requires not only retrieval but also reasoning. Usually they are solved by match-based model like QANET BIBREF15 , hierarchical attention model like HAF BIBREF16 , and dynamic fusion based model like DFN BIBREF17 . Also there exists more relevant research on story comprehension such as event understanding of narrative plots BIBREF3 , character personas BIBREF2 and inter-character relationships BIBREF18 . Sentiment Analysis aims to determine the attitude of a speaker (or a writer) with respect to some topic, the overall contextual polarity, or emotional reaction to a document, interaction or event. There have been rich studies on this field, such as learning word vectors for sentiment analysis BIBREF19 and recognizing contextual polarity in a phrase-level BIBREF20 . Recently, researchers studied large-scale sentiment analysis across news and blogs BIBREF21 , and also studied opinion mining on twitter BIBREF22 . Additionally, there have been studies focused on joint learning for better performance, such as detecting sentiment and topic simultaneously from text BIBREF23 . Commonsense Knowledge Integration If machines receive information from a commonsense knowledge base, they become more powerful for many tasks like reasoning BIBREF24 , dialogue generation BIBREF25 and cloze style reading comprehension BIBREF26 . Related works include BIBREF24 , which builds a knowledge graph and uses it to deduce the size of objects BIBREF24 , in addiiton to BIBREF27 , in which a music knowledge graph is built for a single round dialogue system. There are several ways to incorporate external knowledge base (e.g., ConceptNet). For example, BIBREF28 uses a knowledge based word embedding, BIBREF29 employs tri-LSTMs to encode the knowledge triple, and BIBREF30 and BIBREF26 apply graph attention embedding to encode sub-graphs from a knowledge base. However, their work does not involve narrative completion. Story Completion Traditional machine learning methods have been used to solve ROCStory Cloze Task such as BIBREF6 . To improve the performance, features like topic words and sentiment score are also extracted and incorporated BIBREF8 . Neural network models have also been applied to this task (e.g., BIBREF31 and BIBREF7 ), which use LSTM to encode different parts of the story and calculate their similarities. In addition, BIBREF9 introduces event frame to their model and leverages five different embeddings. Finally, BIBREF10 develops a transformer model and achieves state-of-the-art performance on ROCStories, where the transformer was pretrained on BooksCorpus (a large unlabeled corpus) and finetuned on ROCStories. ### Proposed Model
For a given story $S = \lbrace s_1, s_2, ..., s_L\rbrace $ consisting of a sequence of $L$ sentences, our task is to select the correct ending out of two candidates, $e_1$ and $e_2$ , so that the completed story is reasonable and consistent. On the face of it, the problem can be understood as a standard binary classification problem. However, learning binary classifier with standard NLP techniques on the explicit information in the story is not sufficient. This is because correctly predicting the story ending usually requires reasoning with implicit commonsense knowledge. Therefore, we develop a neural network model to predict the story ending by integrating three sources of information: narrative sequence, sentiment evolution and structured commonsense knowledge (see Figure 2 ). Note that the first two types of information are explicit in the story while the third type is implicit and has to be imported from external source such as a knowledge base. In this section, we will explain how we exploit these three information sources and integrate them to make the final prediction. ### Narrative Sequence
To describe a consistent story, plots should be planned in a logically reasonable sequence; that is there should be a narrative chain between different characters in the story. This is illustrated in the example in Figure 3 , where words in red are events and words in blue are characters. The story chain, “Agatha wanted pet birds $\rightarrow $ Agatha purchased pet finches $\rightarrow $ Agatha couldn't stand noise $\rightarrow $ mess was worse $\rightarrow $ Agatha return pet birds", describes a more coherent and reasonable story than “ Agatha wanted pet birds $\rightarrow $ Agatha purchased pet finches $\rightarrow $ Agatha couldn't stand noise $\rightarrow $ mess was worse $\rightarrow $ Agatha buy two more". When Agatha could not stand the noise, it is more likely for her to give these birds away rather than buy more. Therefore, developing a better semantic representation for narrative chains is important for us to predict the right endings. Inspired by the recent research from OpenAI BIBREF10 on forming semantic representations of narrative sequences, we first pre-train a high-capacity language model on a large unlabeled corpus of text to learn the general information hidden in the context, and then fine-tune the model on this story completion task. Given a large corpus of tokens $C = \lbrace c_1, c_2, ... , c_n\rbrace $ , we can pre-train a language model to maximize the likelihood : $$L_{lm}(C) = \sum _{i} \log P_l(c_i|c_{i-k}, ..., c_{i-1}; \theta )$$ (Eq. 6) where $k$ is the window size, and the conditional probability $P_l$ is modeled using a neural network with parameters $\theta $ . Similar to BIBREF10 , we use a multi-layer transformer decoder with multi-headed self-attention for the language model: $$h_0 &= C W_e+W_p \\
h_l &= transformer(h_{l-1}), l \in [1,M] \\
P(c) &= softmax(h_M W_e^T)$$ (Eq. 7) where $C = \lbrace c_1, c_2, ... , c_n\rbrace $ are tokens in corpus, $W_e$ is the token embedding matrix, $W_p$ is the position embedding matrix and $M$ is the number of transformer blocks. We use the pre-trained parameters released by OpenAI as the initialization for the transformer decoder. We adapt these parameters to our classification task. For each candidate story $(s_1, s_2, s_3, s_4, e_i)$ (i.e., the story body followed by one candidate ending), we serialize it into a sequence of tokens $X = \lbrace x_1, ... , x_k\rbrace $ , where $k$ is the number of tokens. Then the fine-tuned transformer takes $X$ as its input and outputs the probability of $e_i$ being the correct ending: $$P_N(y|s_1, ..., s_4, e_i) = softmax(W_M h_M^k + b_M)$$ (Eq. 9) where $y \in \lbrace 0,1\rbrace $ is the label indicating whether $e_i$ is the correct ending, $h_M^k$ denotes the hidden representation at the $M$ -th layer of the transformer associated with the $k$ -th token, and $W_M$ and $b_M$ are parameters in the linear output layer. ### Sentiment Evolution
Besides narrative sequence, getting a good sentiment prediction model is also important for choosing the correct endings. Note that stories are different from other objective texts (e.g., news), as they have emotions within the context. Usually there is a sentiment evolution when a storyline is being revealed BIBREF32 . First, we pre-train a sentiment prediction model using the training set of the ROCStories, which does not have alternative endings (i.e., no negative samples). Given a five-sentence story $S = \lbrace s_1, s_2, s_3, s_4, s_5\rbrace $ , we take the first four sentences as the body $B$ and the last sentence as the ending $e$ . We extract the sentiment polarity of each sentence by utilizing a lexicon and rule-based sentiment analysis tool (VADER) BIBREF33 : $$E_i = \text{VADER}(s_i), i \in [1,5]$$ (Eq. 12) where $E_i$ is a vector of three elements including probabilities of the $i$ -th sentence being positive, negative and neutral. Then, we use a Long Short-Term Memory (LSTM) neural network to encode the sentence sentiments $E_i$ with its context into the hidden state $h_i$ , which summarizes the contextual sentiment information around the sentence $s_i$ . And we use the last hidden state $h_4$ to predict the sentiment vector $E_p$ in the ending $e$ : $$h_i &= \text{LSTM}(E_i,h_{i-1}) , i\in [1,4] \\
E_p &= softmax(W_e h_4 + b_e)$$ (Eq. 13) We train the sentiment model by maximizing the cosine similarity between the predicted sentiment vector $E_p$ and the sentiment vector $E_5$ of the correct ending: $$sim(S) = \dfrac{E_p \cdot E_5}{\Vert E_p \Vert _2 \cdot \Vert E_5 \Vert _2}$$ (Eq. 14) Afterwards, we adapt the parameters to the story ending selection task and calculate the following conditional probability $P_S$ : $$P_S(y|s_1, ..., s_4, e_i) = softmax(E_pW_sE_e)$$ (Eq. 15) where $S = \lbrace s_1, s_2, s_3, s_4\rbrace $ is the body, $e_i$ is the candidate ending, $E_p$ is the predicted sentiment vector, $E_e$ is the sentiment vector extracted from ending $e_i$ , and $W_s$ is the similarity matrix to be learned. ### Commonsense Knowledge
Narrative sequence and sentiment evolution, though useful, are not sufficient to make correct predictions. In a typical story, newly introduced key-words may not be explained in the story because story-writers are not given enough narrative space and time to develop and describe them BIBREF34 . In fact, there are many hidden relationships among key-words in natural stories. In Figure 1 (a), although the key-word “diet" in the ending is not mentioned in the body, there are hidden relationships among “diet", “overweight" and “unhealthy" as shown in Figure 1 (b). When this kind of implicit information is uncovered in the model, it is easier to predict the correct story ending. We leverage the implicit knowledge by using a numberbatch word embedding BIBREF12 , which is trained on data from ConceptNet, word2vec, GloVe, and OpenSubtitles. The numberbatch achieves good performance on tasks related to commonsense knowledge BIBREF28 . For instance, the cosine similarity between “diet" and “overweight" in numberbatch is 0.453, but it is 0.326 in GloVe. This is because numberbatch makes use of the relationship between them as shown in Figure 1 (b) while GloVe does not. [h] Knowledge distance computation [1] sentence $s_j$ such that $s_j\in S$ $distance_j = 0$ $num = 0$ word $w$ such that $w\in e_i$ $max_d$ = 0 $num += 1$ word $u$ such that $u\in s_j$ $s_j\in S$0 $s_j\in S$1 = cosine similarity(w, u) $s_j\in S$2 $s_j\in S$3 $distance_j += max_d$ $distance_j /= num$ return $(distance_1, ..., distance_4)$ Given the body $S = \lbrace s_1, s_2, s_3, s_4\rbrace $ , a candidate ending $e_i$ and the label $y$ , we tokenize each sentence using NLTK and Standford's CoreNLP tools BIBREF35 . After deleting the stop words, we calculate the knowledge distance vector $D$ between the candidate ending and the body by Algorithm 1. We compute the similarity between two key-words using the cosine similarity of their vector space representations in numberbatch. For each sentence $s_i$ in the body, we then quantify the distance with the ending using averaged alignment score of every key-word in the ending. Then we use a linear layer to model the conditional probability $P_C$ : $$P_C(y|s_1, ..., s_4, e_i) = softmax(W_dD + b_d)$$ (Eq. 17) where $W_d$ and $b_d$ are parameters in the linear output layer, and $D$ is the four-dimensional distance vector. ### Combination Gate
Finally, we predict the story ending by combining the above three sources of information. We utilize the feature vectors $h_M^k$ in the narrative sequence, $E_e$ in the sentiment evolution, and $D$ in the commonsense knowledge and calculate their cosine similarities. Then we concatenate them into a vector $g$ . We use a linear layer to model the combination gate and use that gate to combine three conditional probabilities. $$G &= softmax(W_gg + b_g) \\
\tilde{P}(y|s_1, ..., s_4, e_i) &= softmax(sum(G \odot [P_N; P_S; P_C]))$$ (Eq. 19) where $W_g$ and $b_g$ are parameters in the linear layer, $(P_N, P_S, P_C)$ are the three probabilities modeled in ( 9 ), ( 15 ) and ( 17 ), $G$ is the hidden variable that weighs three different conditional probabilities and $\odot $ is element-wise multiplication. Finally, since each of the three components ( $P_N$ , $P_S$ and $P_C$ ) are either pre-trained on a separate corpus or individually tuned on the task, we fine-tune the entire model in an end-to-end manner by minimizing the following cost: $$\tilde{L} = L_{cm}(S) - \lambda * L_{lm}(C)$$ (Eq. 20) where $L_{cm}(s) = \sum -ylog(\tilde{P})$ is the cross-entropy between the final predicted probability and the true label, $L_{lm}$ is a regularization term of language model cost, and $\lambda $ is the regularization parameter. ### Dataset
We evaluated our model on ROCStories BIBREF0 , a publicly available collection of commonsense short stories. This corpus consists of 100,000 five-sentence stories. Each story logically follows everyday topics created by Amazon Mechanical Turk (MTurk) workers. These stories contain a variety of commonsense causal and temporal relations between everyday events. Writers also develop an additional 3,742 stories which contain a four-sentence-long body and two candidate endings. The endings were collected by asking MTurk workers to write both a right ending and a wrong ending after eliminating original endings of given short stories. Both endings were required to include at least one character from the main story line and to make logical sense. and were tested on AMT to ensure the quality. The published ROCStories dataset is constructed with ROCStories as a training set that includes 98,162 stories that exclude candidate wrong endings, an evaluation set, and a test set, which have the same structure (1 body + 2 candidate endings) and a size of 1,871. We find that the dataset contains 43,095 unique words, and 28,012 key-words in ConceptNet. The average number of words and key-words in ConceptNet for each sentence are shown in Table 1 . $s_1$ , $s_2$ , $s_3$ and $s_4$ are four sentences in the body of stories. $e_1$ and $e_2$ are the two candidate endings. A large portion (65%) of words mentioned in stories are key-words in ConceptNet. Thus we believe ConceptNet can provide additional information to the model. In our experiments, we use a training set which does not have candidate endings to pre-train the sentiment prediction model. For learning to select the right ending, we randomly split 80% of stories with two candidates endings in ROCStories evaluation set as our training set (1,479 cases), 20% of stories in ROCStories evaluation set as our validation set (374 cases). And we utilize the ROCStories test set as our testing set (1,871 cases). ### Baselines
We use the following models as our baselines: Msap BIBREF6 : Msap uses a linear classifier based on language modeling probabilities of the entire story, and utilizes linguistic features of the ending sentences. These ending “style” features include sentence length, word and character n-gram in each candidate ending (independent of story). HCM BIBREF8 : HCM uses FC-SemLM BIBREF36 in order to represent events in the story, learns sentiment trajectories in a form of N-gram language model, and uses topic-words' GloVe to extract topical consistency feature. It uses Expectation-Maximization for training. DSSM BIBREF31 : DSSM first uses two deep neural networks to project the context and the candidate endings into the same vector space, and ending choices based on the cosine similarity of the context. Cai BIBREF7 : Cai uses BiLSTM RNN with attention mechanisms to encode the body and ending of the story separately and uses a cosine similarity between their representations to calculate the score for each ending during selection process. SeqMANN BIBREF9 : SeqMANN uses a multi-attention neural network and introduces semantic sequence information extracted from FC-SemLM as external knowledge. The embedding layer concatenates five representations including word embedding, character feature, part-of-speech (POS) tagging, sentiment polarity and negation. The model uses DenseNet to match body with an ending. FTLM BIBREF10 : FTLM solves the stories cloze test by pre-training a language model using a multi-layer transformer on a diverse corpus of unlabeled text, followed by discriminative fine-tuning. ### Experimental Settings
We tune the hyper parameters of models on the validation set. Specifically, we set the dimension of LSTM for sentiment prediction to 64. We use a mini-batch size of 8, and Adam to train all parameters. The learning rate is set to 0.001 initially with a decay rate of 0.5 per epoch. ### Results
We evaluated baselines and our model using accuracy as the metric on the ROCStories dataset, and summarized these results in Table 2 . The linear classifier with language model, Msap, achieved an accuracy of 75.2%. When adding additional features, such as sentiment trajectories and topic words to traditional machine learning methods, HCM achieved an accuracy of 77.6%. Recently, more neural network-based models are used. DSSM simply used a deep structured semantic model to learn representations for both bodies and endings only achieved an accuracy of 58.5%. Utilizing Cai improved neural model performance to 74.7% by applying attention mechanisms on a BiLSTM RNN structure. SeqMANN further improved the performance to 84.7%, when combining more information from embedding layers, like character features, part-of-speech (POS) tagging features, sentiment polarity, negation information and some external knowledge of semantic sequence. Researchers also improved model performance by pre-training word embeddings on external large corpus. FTLM pre-trained a language model on a large unlabeled corpus and fine-tuned on the ROCStories dataset, and achieved an accuracy of 86.5%. We tried two different ways to construct narrative sequence features: Plot&End and FullStory. Plot&End encodes the body and ending of a story separately and then computes their cosine similarity. We use a hierarchy structure to encode the four body sentences. However using such encoding method, our model only achieved an accuracy of 78.4%. One possible reason is that the relation between sentences learned through pre-trained language models are not fully explored if we encode each sentence separately. FullStory encodes all five sentences together. Our model achieved the best performance when using FullStory mode to encode narrative sequence information. We achieved an accuracy of 87.6%, outperforming all baseline models. Such improvement may come from the full use of the pre-trained transformer block, as well as the incorporation of the structured commonsense knowledge and sentiment information in the model. ### Ablation Study
We conducted another two groups of experiments to investigate the contribution of the three different types of information: narrative sequence, sentiment evolution and commonsense knowledge. First, we measure the accuracy of only using one type of information at a time and describe the result in Table 3 . When we use just one type of information, the performances are worse than when using all of the information, suggesting a single type of information is insufficient for story ending selection. We also measure the performance of our model by stripping one type of information at a time and display the results in Table 4 . We observe that by removing the narrative sequence information, the model performance decreases most significantly. We suspect this is because the narrative chain is the key element that differentiates a story from other types of writing. Therefore, removing narrative sequence information makes it difficult to predict the story ending. If we only use the narrative sequence information, the performance is 85.3%. When commonsense knowledge is added to the model on top of the narrative sequence information, the performance improves to 87.2% which is statistically significant. When sentiment evolution information is added, the model only improves to 87.6%. We speculate this is because the pre-trained language model from narrative sequence information may already capture some sentiment information, as it is trained on an ensemble of several large corpus. This suggests that commonsense knowledge has a large impact on narrative prediction task. ### Case Study
We present several examples to describe the decision made at the combination gate. All the examples are shown in Table 5 . The first story shows how narrative sequence can be the key in detecting the coherent story ending. This one tells a story of Agatha and birds. As we have analyzed in the narrative sequence, the narrative chain is apparently the most effective clue in deciding the right ending. In the combination gate, the narrative part's weight is 0.5135, which is larger than the sentiment component's weight, 0.2214 as well as the commonsense component's weight of 0.2633. The conditional probability of the correct ending given the narrative information is 0.8634, which is much larger than the wrong ending. As both sentences' sentiments are neutral, the sentiment information is not useful . And as the word “buy” has closer relation to “want" and “purchase" mentioned in the sentence body than the word,“return", the commonsense knowledge actually makes the wrong decision which gives slightly higher probabilities to the wrong ending(0.5642). The second story shows why and how sentiment evolution is influencing the final performance. It is a story about Jackson's beard: Jackson wanted to grow a beard regardless of what his friends said, and he was satisfied with his bushy, thick beard. Clearly the emotions between the two candidate endings are different. Based on the rule of consistent sentiment evolution, an appropriate ending should have a positive emotion rather than a negative emotion. The output of our model shows that in the combination gate, the sentiment evolution component received the largest weight, 0.4880, while the narrative sequence and the commonsense knowledge component have a weight of 0.2287 and 0.2833. Finally, the probability of the correct ending is 0.5360, larger than that of the wrong ending which is 0.4640 in sentiment part. Whereas in the narrative sequence component, the probability of the correct option is 0.4640, smaller than the wrong ending which is 0.5360. Other models like FTLM BIBREF10 that only rely on narrative sequence will make the wrong decision in this case. The probabilities of the commonsense knowledge component is 0.5257 versus 0.4725. Through combination gate, our model mainly relies on the sentiment to make a selection. As a result, it will identify the right ending despite other components influence toward a wrong decision. The third example presents the roles commonsense knowledge plays in our model. It tells a story about a person finding a dog. The sentiments of the two candidates are both neutral again. But based on the knowledge graph in ConceptNet, shown in Figure 4 , there exists many relations between the correct ending and the story body. The key-words in the ending are in red, and the key-words in the story body are in blue. The key-words such as “stray" and “collar" are highly associated with “dog" and “find" in the correct ending. The result shows that the gate gives the commonsense knowledge component a weight of 0.5156, which is the largest among the three components. The conditional probability of the correct ending considering commonsense information (0.5540) is larger than the wrong ending as we expected. In this case, the narrative sequence component makes the wrong decision, which gives higher probabilities to the wrong ending (0.5283). Thus models like FTLM BIBREF10 which only consider narrative chain will identify the wrong ending. However, as the combination gate learns to trust the commonsense knowledge component in this example more, our model still predicts the correct ending. We can see that our model is able to learn to rely on different information types based on the content of different stories. We obtain such model effectiveness by using a combination gate to fuse all three types of information, and in doing so, understand how all three are imperative in covering all possible variations in the dataset. However, it is still challenging for our model to handle the stories that have negations. Figure 5 shows an example. It tells a story between Johnny and Anita. But the only difference between two candidate endings is the negation word. Even when fusing three types of information, our model still cannot get the answer right. Because both event chains are about “asking Anita out", they are both neutral in sentiment, and the key-words in these two endings are the same as well. In the future, we plan to incorporate natural language inference information to the model to handle such cases. ### Conclusion
Narrative completion is a complex task that requires both explicit and implicit knowledge. We proposed a neural network model that utilized a combination gate to fuse three types of information including: narrative sequence, sentiment evolution and structured commonsense knowledge to predict story endings. The model outperformed state-of-the-art methods. We found that introducing external knowledge such as structured commonsense knowledge helps narrative completion. Figure 1: (a) shows an example story from ROCStories dataset, words in colors are key-words. (b) shows the keywords and their relations in ConceptNet Knowledge Graph Figure 2: Our proposed model architecture. The inputs: S1 through S4 denote the story body, and ei (i = 1, 2) denotes two candidate endings. The bottom-left component encodes sentiment evolution information (green), the top-left component models the narrative sequence (yellow), and the top-right component integrates commonsense knowledge (blue). The combination gate in the bottom-right integrates all three types of information and outputs the probability on which ending is correct. Figure 3: An example story in the ROCStories dataset Table 1: The average number of words and key-words exist in ConceptNet in each sentence of the story Table 2: Performance comparison with baselines, *indicates that the model is significantly better than best baseline model Table 3: Performance on only using one type of information Table 4: Performance on stripping one type of information, e.g. “- Sentiment” means removing sentiment information. Table 5: Three examples from ROCStories. The first column is the body of the story, the second column is the correct ending, and the third column is the wrong ending. Figure 4: Sub-graph in ConceptNet | accuracy |
Why was Retief still upset after seeing the ship?
A. He found something at the ship he wasn't expecting.
B. The Groacians wouldn't show him inside of the ship.
C. There was a much larger ship still unaccounted for.
D. He's upset about the deceased Terrestrials he found.
| 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. | C. There was a much larger ship still unaccounted for. |
What is ironic about Russell's decision to kill Dunbar?
A. After killing Dunbar, Russell became just as delusional as Dunbar
B. If Russell had not killed Dunbar, the three men would have never reached their ultimate paradise
C. The four men were all going to die anyway, but they could have died together.
D. If the four men had followed Dunbar, they all would have survived.
| Transcriber's Note: This etext was produced from Space Science Fiction May 1952. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. TO EACH HIS STAR by BRYCE WALTON "Nothing around those other suns but ashes and dried blood," old Dunbar told the space-wrecked, desperate men. "Only one way to go, where we can float down through the clouds to Paradise. That's straight ahead to the sun with the red rim around it." But Dunbar's eyes were old and uncertain. How could they believe in his choice when every star in this forsaken section of space was surrounded by a beckoning red rim? There was just blackness, frosty glimmering terrible blackness, going out and out forever in all directions. Russell didn't think they could remain sane in all this blackness much longer. Bitterly he thought of how they would die—not knowing within maybe thousands of light years where they were, or where they were going. After the wreck, the four of them had floated a while, floated and drifted together, four men in bulbous pressure suits like small individual rockets, held together by an awful pressing need for each other and by the "gravity-rope" beam. Dunbar, the oldest of the four, an old space-buster with a face wrinkled like a dried prune, burned by cosmic rays and the suns of worlds so far away they were scarcely credible, had taken command. Suddenly, Old Dunbar had known where they were. Suddenly, Dunbar knew where they were going. They could talk to one another through the etheric transmitters inside their helmets. They could live ... if this was living ... a long time, if only a man's brain would hold up, Russell thought. The suits were complete units. 700 pounds each, all enclosing shelters, with atmosphere pressure, temperature control, mobility in space, and electric power. Each suit had its own power-plant, reprocessing continuously the precious air breathed by the occupants, putting it back into circulation again after enriching it. Packed with food concentrates. Each suit a rocket, each human being part of a rocket, and the special "life-gun" that went with each suit each blast of which sent a man a few hundred thousand miles further on toward wherever he was going. Four men, thought Russell, held together by an invisible string of gravity, plunging through a lost pocket of hell's dark where there had never been any sound or life, with old Dunbar the first in line, taking the lead because he was older and knew where he was and where he was going. Maybe Johnson, second in line, and Alvar who was third, knew too, but were afraid to admit it. But Russell knew it and he'd admitted it from the first—that old Dunbar was as crazy as a Jovian juke-bird. A lot of time had rushed past into darkness. Russell had no idea now how long the four of them had been plunging toward the red-rimmed sun that never seemed to get any nearer. When the ultra-drive had gone crazy the four of them had blanked out and nobody could say now how long an interim that had been. Nobody knew what happened to a man who suffered a space-time warping like that. When they had regained consciousness, the ship was pretty banged up, and the meteor-repeller shields cracked. A meteor ripped the ship down the center like an old breakfast cannister. How long ago that had been, Russell didn't know. All Russell knew was that they were millions of light years from any place he had ever heard about, where the galactic space lanterns had absolutely no recognizable pattern. But Dunbar knew. And Russell was looking at Dunbar's suit up ahead, watching it more and more intently, thinking about how Dunbar looked inside that suit—and hating Dunbar more and more for claiming he knew when he didn't, for his drooling optimism—because he was taking them on into deeper darkness and calling their destination Paradise. Russell wanted to laugh, but the last time he'd given way to this impulse, the results inside his helmet had been too unpleasant to repeat. Sometimes Russell thought of other things besides his growing hatred of the old man. Sometimes he thought about the ship, lost back there in the void, and he wondered if wrecked space ships were ever found. Compared with the universe in which one of them drifted, a wrecked ship was a lot smaller than a grain of sand on a nice warm beach back on Earth, or one of those specks of silver dust that floated like strange seeds down the night winds of Venus. And a human was smaller still, thought Russell when he was not hating Dunbar. Out here, a human being is the smallest thing of all. He thought then of what Dunbar would say to such a thought, how Dunbar would laugh that high piping squawking laugh of his and say that the human being was bigger than the Universe itself. Dunbar had a big answer for every little thing. When the four of them had escaped from that prison colony on a sizzling hot asteroid rock in the Ronlwhyn system, that wasn't enough for Dunbar. Hell no—Dunbar had to start talking about a place they could go where they'd never be apprehended, in a system no one else had ever heard of, where they could live like gods on a green soft world like the Earth had been a long time back. And Dunbar had spouted endlessly about a world of treasure they would find, if they would just follow old Dunbar. That's what all four of them had been trying to find all their lives in the big cold grabbag of eternity—a rich star, a rich far fertile star where no one else had ever been, loaded with treasure that had no name, that no one had ever heard of before. And was, because of that, the richest treasure of all. We all look alike out here in these big rocket pressure suits, Russell thought. No one for God only knew how many of millions of light years away could see or care. Still—we might have a chance to live, even now, Russell thought—if it weren't for old crazy Dunbar. They might have a chance if Alvar and Johnson weren't so damn lacking in self-confidence as to put all their trust in that crazed old rum-dum. Russell had known now for some time that they were going in the wrong direction. No reason for knowing. Just a hunch. And Russell was sure his hunch was right. Russell said. "Look—look to your left and to your right and behind us. Four suns. You guys see those other three suns all around you, don't you?" "Sure," someone said. "Well, if you'll notice," Russell said, "the one on the left also now has a red rim around it. Can't you guys see that?" "Yeah, I see it," Alvar said. "So now," Johnson said, "there's two suns with red rims around them." "We're about in the middle of those four suns aren't we, Dunbar?" Russell said. "That's right, boys!" yelled old Dunbar in that sickeningly optimistic voice. Like a hysterical old woman's. "Just about in the sweet dark old middle." "You're still sure it's the sun up ahead ... that's the only one with life on it, Dunbar ... the only one we can live on?" Russell asked. "That's right! That's right," Dunbar yelled. "That's the only one—and it's a paradise. Not just a place to live, boys—but a place you'll have trouble believing in because it's like a dream!" "And none of these other three suns have worlds we could live on, Dunbar?" Russell asked. Keep the old duck talking like this and maybe Alvar and Johnson would see that he was cracked. "Yeah," said Alvar. "You still say that, Dunbar?" "No life, boys, nothing," Dunbar laughed. "Nothing on these other worlds but ashes ... just ashes and iron and dried blood, dried a million years or more." "When in hell were you ever here?" Johnson said. "You say you were here before. You never said when, or why or anything!" "It was a long time back boys. Don't remember too well, but it was when we had an old ship called the DOG STAR that I was here. A pirate ship and I was second in command, and we came through this sector. That was—hell, it musta' been fifty years ago. I been too many places nobody's ever bothered to name or chart, to remember where it is, but I been here. I remember those four suns all spotted to form a perfect circle from this point, with us squarely in the middle. We explored all these suns and the worlds that go round 'em. Trust me, boys, and we'll reach the right one. And that one's just like Paradise." "Paradise is it," Russell whispered hoarsely. "Paradise and there we'll be like gods, like Mercuries with wings flying on nights of sweet song. These other suns, don't let them bother you. They're Jezebels of stars. All painted up in the darkness and pretty and waiting and calling and lying! They make you think of nice green worlds all running waters and dews and forests thick as fleas on a wet dog. But it ain't there, boys. I know this place. I been here, long time back." Russell said tightly. "It'll take us a long time won't it? If it's got air we can breath, and water we can drink and shade we can rest in—that'll be paradise enough for us. But it'll take a long time won't it? And what if it isn't there—what if after all the time we spend hoping and getting there—there won't be nothing but ashes and cracked clay?" "I know we're going right," Dunbar said cheerfully. "I can tell. Like I said—you can tell it because of the red rim around it." "But the sun on our left, you can see—it's got a red rim too now," Russell said. "Yeah, that's right," said Alvar. "Sometimes I see a red rim around the one we're going for, sometimes a red rim around that one on the left. Now, sometimes I'm not sure either of them's got a red rim. You said that one had a red rim, Dunbar, and I wanted to believe it. So now maybe we're all seeing a red rim that was never there." Old Dunbar laughed. The sound brought blood hotly to Russell's face. "We're heading to the right one, boys. Don't doubt me ... I been here. We explored all these sun systems. And I remember it all. The second planet from that red-rimmed sun. You come down through a soft atmosphere, floating like in a dream. You see the green lakes coming up through the clouds and the women dancing and the music playing. I remember seeing a ship there that brought those women there, a long long time before ever I got there. A land like heaven and women like angels singing and dancing and laughing with red lips and arms white as milk, and soft silky hair floating in the winds." Russell was very sick of the old man's voice. He was at least glad he didn't have to look at the old man now. His bald head, his skinny bobbing neck, his simpering watery blue eyes. But he still had to suffer that immutable babbling, that idiotic cheerfulness ... and knowing all the time the old man was crazy, that he was leading them wrong. I'd break away, go it alone to the right sun, Russell thought—but I'd never make it alone. A little while out here alone and I'd be nuttier than old Dunbar will ever be, even if he keeps on getting nuttier all the time. Somewhere, sometime then ... Russell got the idea that the only way was to get rid of Dunbar. You mean to tell us there are people living by that red-rimmed sun," Russell said. "Lost people ... lost ... who knows how long," Dunbar said, as the four of them hurtled along. "You never know where you'll find people on a world somewhere nobody's ever named or knows about. Places where a lost ship's landed and never got up again, or wrecked itself so far off the lanes they'll never be found except by accident for millions of years. That's what this world is, boys. Must have been a ship load of beautiful people, maybe actresses and people like that being hauled to some outpost to entertain. They're like angels now, living in a land all free from care. Every place you see green forests and fields and blue lakes, and at nights there's three moons that come around the sky in a thousand different colors. And it never gets cold ... it's always spring, always spring, boys, and the music plays all night, every night of a long long year...." Russell suddenly shouted. "Keep quiet, Dunbar. Shut up will you?" Johnson said. "Dunbar—how long'll it take us?" "Six months to a year, I'd say," Dunbar yelled happily. "That is—of our hereditary time." "What?" croaked Alvar. Johnson didn't say anything at all. Russell screamed at Dunbar, then quieted down. He whispered. "Six months to a year—out here—cooped up in these damn suits. You're crazy as hell, Dunbar. Crazy ... crazy! Nobody could stand it. We'll all be crazier than you are—" "We'll make it, boys. Trust ole' Dunbar. What's a year when we know we're getting to Paradise at the end of it? What's a year out here ... it's paradise ain't it, compared with that prison hole we were rotting in? We can make it. We have the food concentrates, and all the rest. All we need's the will, boys, and we got that. The whole damn Universe isn't big enough to kill the will of a human being, boys. I been over a whole lot of it, and I know. In the old days—" "The hell with the old days," screamed Russell. "Now quiet down, Russ," Dunbar said in a kind of dreadful crooning whisper. "You calm down now. You younger fellows—you don't look at things the way we used to. Thing is, we got to go straight. People trapped like this liable to start meandering. Liable to start losing the old will-power." He chuckled. "Yeah," said Alvar. "Someone says maybe we ought to go left, and someone says to go right, and someone else says to go in another direction. And then someone says maybe they'd better go back the old way. An' pretty soon something breaks, or the food runs out, and you're a million million miles from someplace you don't care about any more because you're dead. All frozen up in space ... preserved like a piece of meat in a cold storage locker. And then maybe in a million years or so some lousy insect man from Jupiter comes along and finds you and takes you away to a museum...." "Shut up!" Johnson yelled. Dunbar laughed. "Boys, boys, don't get panicky. Keep your heads. Just stick to old Dunbar and he'll see you through. I'm always lucky. Only one way to go ... an' that's straight ahead to the sun with the red-rim around it ... and then we tune in the gravity repellers, and coast down, floating and singing down through the clouds to paradise." After that they traveled on for what seemed months to Russell, but it couldn't have been over a day or two of the kind of time-sense he had inherited from Earth. Then he saw how the other two stars also were beginning to develop red rims. He yelled this fact out to the others. And Alvar said. "Russ's right. That sun to the right, and the one behind us ... now they ALL have red rims around them. Dunbar—" A pause and no awareness of motion. Dunbar laughed. "Sure, they all maybe have a touch of red, but it isn't the same, boys. I can tell the difference. Trust me—" Russell half choked on his words. "You old goat! With those old eyes of yours, you couldn't see your way into a fire!" "Don't get panicky now. Keep your heads. In another year, we'll be there—" "God, you gotta' be sure," Alvar said. "I don't mind dyin' out here. But after a year of this, and then to get to a world that was only ashes, and not able to go any further—" "I always come through, boys. I'm lucky. Angel women will take us to their houses on the edges of cool lakes, little houses that sit there in the sun like fancy jewels. And we'll walk under colored fountains, pretty colored fountains just splashing and splashing like pretty rain on our hungry hides. That's worth waiting for." Russell did it before he hardly realized he was killing the old man. It was something he had had to do for a long time and that made it easy. There was a flash of burning oxygen from inside the suit of Dunbar. If he'd aimed right, Russell knew the fire-bullet should have pierced Dunbar's back. Now the fire was gone, extinguished automatically by units inside the suit. The suit was still inflated, self-sealing. Nothing appeared to have changed. The four of them hurtling on together, but inside that first suit up there on the front of the gravity rope, Dunbar was dead. He was dead and his mouth was shut for good. Dunbar's last faint cry from inside his suit still rang in Russell's ears, and he knew Alvar and Johnson had heard it too. Alvar and Johnson both called Dunbar's name a few times. There was no answer. "Russ—you shouldn't have done that," Johnson whispered. "You shouldn't have done that to the old man!" "No," Alvar said, so low he could barely be heard. "You shouldn't have done it." "I did it for the three of us," Russell said. "It was either him or us. Lies ... lies that was all he had left in his crazy head. Paradise ... don't tell me you guys don't see the red rims around all four suns, all four suns all around us. Don't tell me you guys didn't know he was batty, that you really believed all that stuff he was spouting all the time!" "Maybe he was lying, maybe not," Johnson said. "Now he's dead anyway." "Maybe he was wrong, crazy, full of lies," Alvar said. "But now he's dead." "How could he see any difference in those four stars?" Russell said, louder. "He thought he was right," Alvar said. "He wanted to take us to paradise. He was happy, nothing could stop the old man—but he's dead now." He sighed. "He was taking us wrong ... wrong!" Russell screamed. "Angels—music all night—houses like jewels—and women like angels—" " Shhhh ," said Alvar. It was quiet. How could it be so quiet, Russell thought? And up ahead the old man's pressure suit with a corpse inside went on ahead, leading the other three at the front of the gravity-rope. "Maybe he was wrong," Alvar said. "But now do we know which way is right?" Sometime later, Johnson said, "We got to decide now. Let's forget the old man. Let's forget him and all that's gone and let's start now and decide what to do." And Alvar said, "Guess he was crazy all right, and I guess we trusted him because we didn't have the strength to make up our own minds. Why does a crazy man's laugh sound so good when you're desperate and don't know what to do?" "I always had a feeling we were going wrong," Johnson said. "Anyway, it's forgotten, Russ. It's swallowed up in the darkness all around. It's never been." Russell said, "I've had a hunch all along that maybe the old man was here before, and that he was right about there being a star here with a world we can live on. But I've known we was heading wrong. I've had a hunch all along that the right star was the one to the left." "I don't know," Johnson sighed. "I been feeling partial toward that one on the right. What about you, Alvar?" "I always thought we were going straight in the opposite direction from what we should, I guess. I always wanted to turn around and go back. It won't make over maybe a month's difference. And what does a month matter anyway out here—hell there never was any time out here until we came along. We make our own time here, and a month don't matter to me." Sweat ran down Russell's face. His voice trembled. "No—that's wrong. You're both wrong." He could see himself going it alone. Going crazy because he was alone. He'd have broken away, gone his own direction, long ago but for that fear. "How can we tell which of us is right?" Alvar said. "It's like everything was changing all the time out here. Sometimes I'd swear none of those suns had red rims, and at other times—like the old man said, they're all pretty and lying and saying nothing, just changing all the time. Jezebel stars, the old man said." "I know I'm right," Russell pleaded. "My hunches always been right. My hunch got us out of that prison didn't it? Listen—I tell you it's that star to the left—" "The one to the right," said Johnson. "We been going away from the right one all the time," said Alvar. "We got to stay together," said Russell. "Nobody could spend a year out here ... alone...." "Ah ... in another month or so we'd be lousy company anyway," Alvar said. "Maybe a guy could get to the point where he'd sleep most of the time ... just wake up enough times to give himself another boost with the old life-gun." "We got to face it," Johnson said finally. "We three don't go on together any more." "That's it," said Alvar. "There's three suns that look like they might be right seeing as how we all agree the old man was wrong. But we believe there is one we can live by, because we all seem to agree that the old man might have been right about that. If we stick together, the chance is three to one against us. But if each of us makes for one star, one of us has a chance to live. Maybe not in paradise like the old man said, but a place where we can live. And maybe there'll be intelligent life, maybe even a ship, and whoever gets the right star can come and help the other two...." "No ... God no...." Russell whispered over and over. "None of us can ever make it alone...." Alvar said, "We each take the star he likes best. I'll go back the other way. Russ, you take the left. And you, Johnson, go to the right." Johnson started to laugh. Russell was yelling wildly at them, and above his own yelling he could hear Johnson's rising laughter. "Every guy's got a star of his own," Johnson said when he stopped laughing. "And we got ours. A nice red-rimmed sun for each of us to call his very own." "Okay," Alvar said. "We cut off the gravity rope, and each to his own sun." Now Russell wasn't saying anything. "And the old man," Alvar said, "can keep right on going toward what he thought was right. And he'll keep on going. Course he won't be able to give himself another boost with the life-gun, but he'll keep going. Someday he'll get to that red-rimmed star of his. Out here in space, once you're going, you never stop ... and I guess there isn't any other body to pull him off his course. And what will time matter to old Dunbar? Even less than to us, I guess. He's dead and he won't care." "Ready," Johnson said. "I'll cut off the gravity rope." "I'm ready," Alvar said. "To go back toward whatever it was I started from." "Ready, Russ?" Russell couldn't say anything. He stared at the endless void which now he would share with no one. Not even crazy old Dunbar. "All right," Johnson said. "Good-bye." Russell felt the release, felt the sudden inexplicable isolation and aloneness even before Alvar and Johnson used their life-guns and shot out of sight, Johnson toward the left and Alvar back toward that other red-rimmed sun behind them. And old Dunbar shooting right on ahead. And all three of them dwindling and dwindling and blinking out like little lights. Fading, he could hear their voices. "Each to his own star," Johnson said. "On a bee line." "On a bee line," Alvar said. Russell used his own life-gun and in a little while he didn't hear Alvar or Johnson's voices, nor could he see them. They were thousands of miles away, and going further all the time. Russell's head fell forward against the front of his helmet, and he closed his eyes. "Maybe," he thought, "I shouldn't have killed the old man. Maybe one sun's as good as another...." Then he raised his body and looked out into the year of blackness that waited for him, stretching away to the red-rimmed sun. Even if he were right—he was sure now he'd never make it alone. The body inside the pressure suit drifted into a low-level orbit around the second planet from the sun of its choice, and drifted there a long time. A strato-cruiser detected it by chance because of the strong concentration of radio-activity that came from it. They took the body down to one of the small, quiet towns on the edge of one of the many blue lakes where the domed houses were like bright joyful jewels. They got the leathery, well-preserved body from the pressure suit. "An old man," one of them mused. "A very old man. From one of the lost sectors. I wonder how and why he came so very far from his home?" "Wrecked a ship out there, probably," one of the others said. "But he managed to get this far. It looks as though a small meteor fragment pierced his body. Here. You see?" "Yes," another of them said. "But what amazes me is that this old man picked this planet out of all the others. The only one in this entire sector that would sustain life." "Maybe he was just a very lucky old man. Yes ... a man who attains such an age was usually lucky. Or at least that is what they say about the lost sectors." "Maybe he knew the way here. Maybe he was here before—sometime." The other shook his head. "I don't think so. They say some humans from that far sector did land here—but that's probably only a myth. And if they did, it was well over a thousand years ago." Another said. "He has a fine face, this old man. A noble face. Whoever he is ... wherever he came from, he died bravely and he knew the way, though he never reached this haven of the lost alive." "Nor is it irony that he reached here dead," said the Lake Chieftain. He had been listening and he stepped forward and raised his arm. "He was old. It is obvious that he fought bravely, that he had great courage, and that he knew the way. He will be given a burial suitable to his stature, and he will rest here among the brave. "Let the women dance and the music play for this old man. Let the trumpets speak, and the rockets fly up. And let flowers be strewn over the path above which the women will carry him to rest." | D. If the four men had followed Dunbar, they all would have survived. |
When was Mr. Alan Fisher first diagnosed with a heart disease?
Choose the correct answer from the following options:
A. 1989
B. 1995
C. 2005
D. 2006
E. 2008
| ### Patient Report 0
**Dear colleague, **
We are reporting to you regarding our patient, Mr. Alan Fisher, born on
12/09/1953. He was under our inpatient care from 04/19/2009 to
04/28/2009.
**Diagnoses:**
- Progressive deterioration of renal transplant function (creeping
creatinine) without evidence of biopsy-proven rejection
- Isovolumetric tubular epithelial vacuolization
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** Mr. Fisher was admitted for a renal transplant
biopsy due to progressive deterioration of transplant function (creeping
creatinine). His recent creatinine values had increased to around 1.4 --
1.6 mg/dL, while they had previously been around 1.1 mg/dL.
**Therapy and Progression:** Following appropriate preparation and
informed consent, a complication-free transplant puncture was performed.
The biopsy showed isometric tubular epithelial vacuolization without
significant findings. This was followed by adjustment of Cyclosporin-A
levels and the addition of a lymphocyte proliferation inhibitor to the
existing immunosuppressive dual therapy. There was a significant
increase in Cyclosporin-A levels at one point due to accidental double
dosing by the patient, but levels returned to the target range. This
might explain the current rise in creatinine. Another explanation could
be recurrent hypotensive blood pressure dysregulations, leading to the
discontinuation of Minoxidile medication. For chronic atrial
fibrillation, anticoagulation therapy with Marcumar was restarted during
hospitalization and should be continued as an outpatient according to
the target INR. Low molecular weight heparin administration could be
discontinued.
**Physical Examination:** Patient in good general condition. Oriented in
all aspects. No dyspnea. No cyanosis. No edema. Warm and dry skin.
Normal nasal and pharyngeal findings. Pupils round, equal, and react
promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa
unremarkable. No jugular vein distension. No carotid bruits heard.
Palpation of lymph nodes unremarkable. Palpation of the thyroid gland
unremarkable, freely movable.
Lungs: Normal chest shape, moderately mobile, resonant percussion sound,
vesicular breath sounds bilaterally, no wheezing or crackles heard.
Heart: Irregular heart action, normal rate; heart sounds clear, no
pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all
quadrants; soft abdomen, markedly obese, no tenderness, no palpable
masses, liver and spleen not palpable due to limited access, non-tender
kidneys. Large reducible incisional hernia on the right side following
nephrectomy.
Extremities: Occluded fistula on the right forearm. Normal peripheral
pulses; joints freely movable. Strength, motor function, and sensation
are unremarkable.
**Kidney Biopsy on 04/19/2009:** Complication-free biopsy of the
transplant kidney.
Findings: Erythematous macules.
Recommendation: Follow-up in 3 months.
**Ultrasound of Transplant Kidney on 04/20/2009:** Transplant kidney
well visualized, located in the left iliac fossa, measurable,
oval-shaped. Parenchymal echogenicity normal, normal corticomedullary
differentiation. No evidence of arteriovenous fistula or hematoma after
kidney biopsy.
**Pathological-anatomical assessment on 04/19/2009:**
**Macroscopic Findings:** Singular Nodule Identified: Dimensions
measuring 8 mm.
**Microscopic Examination:**
Sampled Tissue: Renal cortex
Identified Components:
- Glomeruli: Nine observed
- Interlobular Artery: One segment present
- Absence of medullary tissue
**Diagnostic Observations:** There were no signs of inflammation or
scarring in the renal cortex. The glomeruli appeared normocellular, and
no signs of inflammation or pathological changes were observed in them.
The peritubular capillaries were free of inflammation, and the specific
test for C4d staining yielded negative results. The arterioles within
the tissue had thin walls, and there was no evidence of inflammation in
this vascular component.
The interlobular artery was also thin-walled and showed no evidence of
inflammation.
A notable finding was extensive damage to the tubular epithelium. The
damage was characterized by isometric microvesicular cytoplasmic
transformation, which exceeded 80%. Importantly, there was no evidence
of cell necrosis and only minimal flattening of cells was observed. In
addition, no pathological imprints, microcalcifications, or nuclear
inclusion bodies were observed in the tubular epithelium.
**Summary:** The predominant pathological finding in this case is
substantial tubular damage. Consequently, it is highly advisable to
closely monitor immunosuppression levels in the patient\'s management.
Further comprehensive evaluation is strongly recommended to determine
the underlying cause of the observed tubular damage and to address the
clinical question concerning the presence of Chronic Allograft
Nephropathy or the potential involvement of an infection in the clinical
presentation.
**Chest X-ray (2 views) on 04/22/2009:**
[Findings]{.underline}: No pneumothorax, no effusion. No evidence of
pneumonia. No focal findings. Left-biased heart without decompensation.
Mediastinum centrally positioned, not widened. Unremarkable depiction of
central hilar structures. Thoracic hyperkyphosis.
**Current Recommenations:** We request regular outpatient monitoring of
retention parameters (initially every 2-3 weeks) and are available for
further questions at the provided telephone number.
**Lab results upon Discharge**
**Parameter** **Results** **Reference Range**
---------------------------------------------- ------------- ---------------------
Sodium 144 mEq/L 134-145 mEq/L
Potassium 3.7 mEq/L 3.4-5.2 mEq/L
Calcium 9.48 mg/dL 8.6-10.6 mg/dL
Chloride 106 mEq/L 95-112 mEq/L
Phosphorus 2.88 mg/dL 2.5-4.5 mg/dL
Transferrin Saturation 20 % 16-45 %
Magnesium 1.9 mg/dL 1.8-2.6 mg/dL
Creatinine 1.88 mg/dL \<1.2 mg/dL
Glomerular Filtration Rate 36 mL/min \>90 mL/min
BUN (Blood Urea Nitrogen) 60 mg/dL 14-46 mg/dL
Uric Acid 4.6 mg/dL 3.0-6.9 mg/dL
Total Bilirubin 0.5 mg/dL \<1 mg/dL
Albumin 4.0 g/dL 3.6-5.0 g/dL
Total Protein 6.8 g/dL 6.5-8.7 g/dL
C-Reactive Protein 0.19 mg/dL \<0.5 mg/dL
Transferrin 269 mg/dL 200-360 mg/dL
Ferritin 110 ng/mL 30-300 ng/mL
ALT (Alanine Aminotransferase) 17 U/L \<45 U/L
AST (Aspartate Aminotransferase) 20 U/L \<50 U/L
Alkaline Phosphatase 119 U/L 40-129 U/L
GGT (Gamma-Glutamyltransferase) 94 U/L \<55 U/L
Lipase 61 U/L \<70 U/L
TSH (Thyroid-Stimulating Hormone) 0.54 mIU/L 0.27-4.20 mIU/L
Hemoglobin 14.5 g/dL 14.0-17.5 g/dL
Hematocrit 43% 40-52%
Red Blood Cells 4.60 M/uL 4.6-6.2 M/uL
White Blood Cells 8.78 K/uL 4.5-11.0 K/uL
Platelets 205 K/uL 150-400 K/uL
MCV 94 fL 81-100 fL
MCH 31.5 pg 27-34 pg
MCHC 33.5 g/dL 32.4-35.0 g/dL
MPV 11 fL 7-12 fL
RDW 14.8 % 11.9-14.5 %
Neutrophils 3.72 K/uL 1.8-7.7 K/uL
Lymphocytes 2.37 K/uL 1.4-3.7 K/uL
Monocytes 0.93 K/uL 0.2-1.0 K/uL
Eosinophils 1.67 K/uL \<0.7 K/uL
Basophils 0.09 K/uL 0.01-0.10 K/uL
Nucleated Red Blood Cells Negative \<0.01 K/uL
APTT (Activated Partial Thromboplastin Time) 45.1 sec 26-40 sec
Antithrombin Activity 85 % 80-120 %
**Medication upon discharge**
**Medication ** **Dosage** **Frequency**
-------------------------------- ------------ ---------------
Cyclosporine (Neoral) 1 mg 1-0-1
Mycophenolic Acid (Myfortic) 180 mg 1-0-1
Prednisone (Deltasone) 5 mg 1-0-0
Aspirin 81 mg 1-0-0
Candesartan (Atacand) 16 mg 0-0-1
Metoprolol (Lopressor) 50 mg 1-1-1-1
Isosorbide Dinitrate (Isordil) 60 mg 1-0-0
Torsemide (Demadex) 10 mg As directed
Ranitidine (Zantac) 300 mg 0-0-1
Fluvastatin (Lescol) 20 mg 0-0-1
Allopurinol (Zyloprim) 100 mg 0-1-0
Tamsulosin (Flomax) 0.4 mg 1-0-0
### Patient Report 1
**Dear colleague, **
We are writing to provide an update on our patient, Mr. Alan Fisher,
born on 12/09/1953.
He was under our inpatient care from 10/02/2018 to 10/03/2018.
**Diagnoses:**
- Urosepsis
- Acute postrenal kidney failure
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Previous Surgeries:** Previous prostate vesiculectomy with regional
lymphadenectomy
**Planned procedure:** Urethro-cystoscopy with catheter placement for
urethral stricture
**Medical History:** The patient was admitted through our emergency
department upon referral by the outpatient urologist due to suspicion of
a urethral stricture. Mr. Fisher reports a worsening urinary retention
for approximately 6 months. Despite multiple unsuccessful attempts at
catheter placement, ureterocystoscopy with catheter insertion was
performed. Intraoperatively, purulent cystitis and a bladder outlet
obstruction were observed.
Mr. Fisher regularly attends follow-up examinations for his history of
kidney transplantation in 1995 and previous prostate vesiculectomy with
regional lymphadenectomy in 01/2018.
**Physical Examination:** Neurology: RASS 0, alert, CAM-ICU negative, no
new focal neurology
Lungs: Bilateral air entry, no rales or wheezing, sufficient gas
exchange on 2L/O2 Cardiovascular: Normal sinus rhythm, normotensive on
0.01 µg/kg/min NA
Abdomen: Soft, no guarding, sparse peristalsis, advanced oral diet,
regular bowel movements
Diuresis: Normal urine output, retention values within normal range,
goal: balanced fluid status
Skin/Wounds: Non-irritated, no peripheral edema
**Therapy and Progression**: We received Mr. Fisher, who was awake and
spontaneously breathing under a 2L O2 mask via nasal cannula, to our
intensive care unit due to urosepsis. To maintain an adequate
circulation, low-dose catecholamine therapy was required but could be
discontinued on the first postoperative day. Pulmonary function remained
stable with intensive non-invasive ventilation and breathing training.
Given his immunosuppression, we escalated the intraoperatively initiated
anti-infective therapy from Ceftriaxone to Piperacillin/Tazobactam.
Pneumococcal and Legionella rapid tests were negative. Following
appropriate volume resuscitation and diuretic therapy with Furosemide,
diuresis became sufficient. Oral diet progression occurred without
complications. Anticoagulation was initially in prophylactic dosing with
Heparin and later switched to therapeutic dosing with Enoxaparin.
**Current Recommendations:**
- Switch unfractionated Heparin to Fragmin
- baseline Crea 2mg/dL, target CyA level: 50-60ng/mL, Myfortic
continued.
- Urological care of the stricture in progress, leave catheter until
then.
- Mobilization
**Medication upon Discharge:**
**Medication (Brand)** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Torsemide (Demadex) 10 mg 1-1-0-0
Prednisone (Deltasone) 5 mg 1-1-0-0
Pantoprazole (Protonix) 20 mg 1-1-0-0
Mycophenolate Mofetil (CellCept) 360 mg 1-0-1-0
Metoprolol Succinate (Toprol-XL) 100 mg 1-0-1-0
Magnesium Oxide 400 mg 1-0-0-0
Ciclosporin (Neoral) 100 mg 60-0-70-0
Candesartan (Atacand) 16 mg 0-0.5-0-0
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Allopurinol (Zyloprim) 100 mg 1-0-0-0
Aspirin 81 mg 1-0-0-0
Paracetamol (Tylenol) 500 mg As needed
### Patient Report 2
**Dear colleague, **
We are reporting to you about our patient, Mr. Alan Fisher, born on
12/09/1953.
He was under our inpatient care from 11/04/2018 to 11/12/2018.
**Current Symptoms:** Decreased diuresis, rising creatinine, frustrating
catheterization.
**Diagnoses:**
- Acute on chronic graft failure
<!-- -->
- Creatinine increased from 1.56 mg/dL to a maximum of 2.35 mg/dL.
- Likely postrenal origin due to urethral stricture; sonographically,
Grade II urinary stasis with urinary retention and residual urine
formation.
- Frustrating catheterization due to urethral stricture
- Urethro-cystoscopy with bougie and catheter placement
- Parainfectious component in purulent cystitis with urosepsis
- Discharged with indwelling catheter
- Inpatient readmission to the colleagues in Urology for internal
urethrotomy
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** The patient was admitted through our emergency
department upon referral by an outpatient urologist due to suspected
urethral stricture. Mr. Fisher reports increasing difficulty urinating
for approximately 6 months. He has to \"squeeze out\" his bladder
completely. Frustrating catheterization was performed due to urinary
retention. Intraoperatively, purulent cystitis and bladder outlet
stenosis were observed. Mr. Fisher regularly undergoes follow-up
examinations for a history of kidney transplantation in 1995 and a
prostate vesiculectomy with regional lymphadenectomy in 01/2018.
**Vegetative Findings:** The patient had a bowel movement 4 days ago,
indwelling catheter irritation (3L of diuresis the previous day), no
nausea/vomiting, no fever or night sweats, weight loss of 30kg from
February to April 2020.
**Physical Capacity:** Limited, can still climb 2 stairs but needs to
take a break due to shortness of breath.
**Physical Examination:** Temperature 37.4°C, Blood pressure 128/72
mmHg; Pulse 72/min; Respiratory rate 15/min, O2 saturation under 2L O2:
96%
Awake, alert, cooperative, oriented to time, place, person, and
situation.
[Head/Neck:]{.underline} Non-tender nerve exit points; Clear paranasal
sinuses; moist and pink mucous membranes; unremarkable dentition; moist
and glossy tongue; non-palpable thyroid enlargement.
[Chest]{.underline}: Normal configuration; Non-tender spine; free renal
beds bilaterally.
[Heart]{.underline}: Rhythmic, clear heart sounds, normal rate, no
splitting; non-distended jugular veins. [Lungs]{.underline}: Vesicular
breath sounds; Resonant percussion note; no adventitious sounds; no
stridor; normal chest expansion.
[Abdomen]{.underline}: Protuberant, known incisional hernia, normal
peristalsis in all quadrants; soft; no pathological resistance; no
tenderness; liver palpable below the costal margin; spleen not palpable.
[Lymph nodes:]{.underline} No pathologically enlarged cervical nodes
palpable; axillary and inguinal nodes not palpable.
Skin: No pathological skin findings.
[Extremities:]{.underline} Warm; mild bilateral ankle edema.
[Pulse status (right/left):]{.underline} A. carotis +/+, A. radialis
+/+, A. femoralis +/+, A. tibialis post. +/+, A. dorsalis ped. +/+
[Neurological]{.underline}: Oriented and unremarkable.
**Therapy and Progression:** The patient was admitted through our
emergency department upon referral by an outpatient urologist due to
suspected urethral stricture, which had been causing increasingly
difficult urination for approximately 6 months. Sonography showed Grade
II urinary stasis with urinary retention and residual urine. Frustrating
catheterization was performed, followed by ureterocystoscopy with bougie
and catheter placement. Intraoperatively, purulent cystitis and bladder
outlet stenosis were observed. Laboratory tests revealed acute kidney
transplant failure, with creatinine increasing from 1.56 mg/dL to 2.35
mg/dL, along with significantly elevated infection parameters: CRP up to
186 mg/dL, PCT 12.82 µg/L, and leukocytosis of 21.6/nL. After obtaining
blood cultures, empirical antibiotic therapy with Ceftriaxone was
initiated. Upon detecting Pseudomonas aeruginosa, therapy was switched
to Piperacillin/Tazobactam on 12/06/20 and continued until 12/13/20.
Under this treatment, infection parameters significantly improved, and
Mr. Fisher remained afebrile. Kidney retention parameters also decreased
to a discharge creatinine of 2.05 mg/dL. Regarding the urethral
stricture, he was initially discharged with an indwelling catheter. A
follow-up appointment for internal urethrotomy and potentially Allium
stent placement was scheduled for 4 weeks later. During the hospital
stay, ciclosporin levels remained within the target range. Following
prostate vesiculectomy earlier in the year, anticoagulation was switched
from Enoxaparin to Apixaban 2.5 mg twice daily, and Aspirin therapy was
discontinued.
**Recommendations**: We recommend regular monitoring of kidney retention
parameters and infection parameters. Regarding the urethral stricture,
the patient will be discharged with an indwelling catheter. We scheduled
a follow-up with colleagues in Urology for internal urethrotomy and
potentially Allium stent placement. Pause oral anticoagulation with
Apixaban one day before inpatient admission.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Ciclosporin (Neoral) 100 mg 60-0-70-0
Mycophenolic Acid (Myfortic) 360 mg 1-0-1-0
Prednisone (Deltasone) 5 mg 1-0-0-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Candesartan (Atacand) 8 mg 0-1-0-0
Torsemide (Demadex) 10 mg 1-1-0-0
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Pantoprazole (Protonix) 20 mg 1-0-0-0
Vitamin D3 (Cholecalciferol) 20,000 IU Pause
Magnesium Oxide 400 mg 1-0-0-0
### Patient Report 3
**Dear colleague, **
We are reporting to you regarding our patient, Mr. Alan Fisher, born on
12/09/1953, who was under outpatient care on 07/01/2019.
**Current Symptoms:** Pain on the left side at rib level**,** Dyspnea
**Diagnoses:**
- Infection of unclear origin
- CT Thorax and Abdomen showed no focus
- Urine dipstick and cultures were bland
- Antibiotics: Meropenem from 06/11/2019 to 06/19/2019
- Acute Transplant Dysfunction
- Serum Creatinine: 2.4 -\> 4.5 -\> 2.6 mg/dl
- Renal ultrasound: 123 x 54 x 24 mm, not dilated, some areas of
increased echogenicity, no twinkling, no acoustic shadowing, no
signs of urolithiasis.
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** Initial presentation was at the local emergency
department on referral from the primary care physician for suspected
acute coronary syndrome. Mr. Fisher described left-sided rib pain, which
was related to breathing and pressure, as well as dyspnea for a few
days. Laboratory tests showed acute-on-chronic kidney failure and
elevated infection parameters. A urine dipstick test was negative for
nitrites and leukocytes. Chest CT ruled out pulmonary pathology, and
acute coronary syndrome was also excluded. Mr. Fisher reported a urinary
tract infection about 4 weeks ago, which was treated with antibiotics as
an outpatient.
**Physical Examination:** Alert, oriented, cooperative, and responsive
to time, place, person, and situation
[Head/Neck:]{.underline} Non-tender nerve exit points; clear nasal
sinuses; moist pink mucous membranes; unremarkable dental status; moist
tongue
[Chest]{.underline}: Normal configuration; no tenderness in the spine;
both renal beds free
[Heart]{.underline}: Arrhythmic heart sounds, pure, tachycardic, not
split
[Lungs]{.underline}: Vesicular breath sounds; somewhat decreased breath
sounds bilaterally; no adventitious sounds; no stridor
[Abdomen]{.underline}: Regular peristalsis in all quadrants; soft; right
lower abdomen notably distended with increased vascular markings, liver
and spleen not palpable, transplant kidney non-tender
[Lymph Nodes:]{.underline} No pathologically enlarged cervical lymph
nodes palpable
[Skin]{.underline}: No pathological skin findings
[Extremities:]{.underline} Warm; no edema; cyanosis of toes bilaterally
after prolonged leg dependency
- Pulse status (right/left): Carotid artery +/+, Radial artery +/+,
Posterior tibial artery +/+
- Neurology: Normal cranial nerves; round, moderately dilated pupils;
prompt bilateral pupillary light reflex; no sensory or motor
deficits; ubiquitous muscle strength 5/5
**Therapy and Progression:** We admitted the patient for further
diagnosis and treatment. Initially suspected acute coronary syndrome was
ruled out. Laboratory results showed elevated retention and infection
parameters. With volume substitution, we achieved baseline creatinine
levels again. The transplant kidney appeared non-dilated and
well-perfused. For the infection, the patient received the mentioned
imaging studies, which did not reveal any definitive findings. Our urine
analyses and cultures also showed bland results. It should be noted that
prior outpatient treatment for suspected urinary tract infection was
likely with cotrimoxazole. Ultimately, considering the recent
antibiograms, we decided on a calculated antibiotic therapy with
Meropenem. This led to a significant improvement in infection
parameters. The last measured Ciclosporin level was slightly
subtherapeutic, so we adjusted the dosage accordingly. We recommend
follow-up with the primary care physician.
**Chest CT on 06/10/2019:**
[Clinical Information, Question, Justification]{.underline}: Patient
with a history of kidney transplantation. Bursting pain on both sides at
the ribcage. Cough. Elevated inflammatory markers. Question about
infiltrates, pleural effusion, congestion.
[Technique]{.underline}: Digital overview radiographs. Plain 80-line CT
of the chest. MPR (Multiplanar reconstruction). DLP (Dose-Length
Product) 120.6 mGy\*cm.
[Findings]{.underline}: No previous images available for comparison.
Symmetric thyroid. Minimal pericardial effusion, accentuated at the
base, measuring up to 8 mm in width (Series 5, Image 293). Coronary
atherosclerosis. No pathologically enlarged lymph nodes in the
mediastinum, axilla, or hilum on plain images. Multisegmental calcified
(micro)nodules. No suspicious pulmonary nodules indicative of
malignancy. No pneumonic infiltrates. No pleural effusions. No
pneumothorax. No pulmonary venous congestion. Delicate scar tissue at
the bases bilaterally. Small axial hiatal hernia. Rounded soft tissue
structure in the right adrenal space (Series 5, measuring 411 x 10 mm).
Incidentally captured at the image margins is a shrunken left kidney.
Spondylosis deformans of the thoracic spine. Interpretation: No
pneumonic infiltrates. No pleural effusions. No pulmonary venous
congestion. Minimal pericardial effusion. Multisegmental calcified
(micro)nodules, likely post-inflammatory.
**Abdomen/Pelvis CT on 06/14/2019:**
[Clinical Information, Question, Justification:]{.underline} Acute
kidney failure. Question regarding kidney or ureteral stones.
[Technique]{.underline}: Plain 80-line CT of the abdomen. MPR. DLP 947
mGy\*cm. Findings and [Interpretation:]{.underline}
The left transplant kidney shows pelvic dilation with an expanded renal
pelvis and ureter (hydronephrosis grade II) but no evidence of stones.
Status post-right nephrectomy. Shrunken left kidney. Known large,
broad-based right-sided abdominal wall weakness with prolapsed
intestinal loops and mesenteric fat tissue without evidence of
incarceration. No ileus. Diverticulosis of the sigmoid colon. Small
axial hiatal hernia. No free or encapsulated fluid or free air in the
abdomen with the right diaphragmatic dome not fully visualized.
Cholecystolithiasis. No cholestasis. Vascular sclerosis. No
lymphadenopathy. Bilaterally aerated lung bases captured without change.
Unchanged irregularly thickened and coarsely structured right iliac
bone, consistent with Paget\'s disease.
**Recommendations:**
Ciclosporin level monitoring
**Medication upon discharge:**
**Medication ** **Dosage** **Frequency**
---------------------------------- ------------ ---------------
Atorvastatin (Lipitor) 40 mg 0-0-0-1
Candesartan Cilexetil (Atacand) 8 mg 0-1-0-0
Prednisone (Deltasone) 5 mg 1-0-0-0
Vitamin D3 (Cholecalciferol) 20,000 IU 1 x/week
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Magnesium Oxide 400 mg 1-0-0-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Mycophenolic Acid (Myfortic) 360 mg 1-0-1-0
Pantoprazole (Protonix) 20 mg 1-0-0-0
Ciclosporin (Neoral) 100 mg 70-0-70-0
### Patient Report 4
**Dear colleague, **
We would like to inform you about our patient, Mr. Alan Fisher, born on
12/09/1953, who was under our inpatient care from 02/19/2020 to
03/01/2020.
**Current Symptoms:** Decreased general condition, weakness,
decompensation
**Diagnosis**: Acute episode of recurrent urinary tract infection with
detection of E. faecalis, E. faecium, and Enterobacter cloacae in urine
(blood cultures sterile).
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Medical History:** The patient was admitted through our internal
medicine emergency department. He presented with worsening general
condition and increasing weakness, following the recommendation of our
local nephrological telemedicine. He particularly noticed the increasing
weakness when getting up, describing his legs as feeling like rubber. He
also experienced shortness of breath. His walking distance was greater
than 100 meters. There was no fever, chills, nausea, vomiting, dysuria,
or changes in bowel movements. Before the outpatient visit, the patient
had collected urine for 24 hours, totaling 1700 ml, with a fluid intake
of approximately 2 liters. His blood pressure at home was approximately
120/60 mmHg. In the emergency department, he had negative urinary
dipstick results and a non-specific chest X-ray. Blood and urine
cultures were obtained, and he was subsequently transferred to our
general ward. No angina pectoris symptoms. The patient had normal bowel
movements, specifically no melena, and no blood-tinged stools. Urine was
described as clear and light.
**Physical Examination:** Alert, oriented, cooperative, oriented to
time, place, person, and situation. Height 179 cm; Weight 114 kg
[Head/neck:]{.underline} No tender nerve exit points; Clear nasal
sinuses; No tenderness over the skull; Mucous membranes pink and moist;
Dental status is rehabilitated; Tongue moist and glossy
[Thorax]{.underline}: Normally shaped; Spine without tenderness; Renal
regions free of tenderness
[Heart]{.underline}: Heart sounds are faint, arrhythmic, clear, regular
rate, no splitting of heart sounds; Jugular veins are not distended
[Lungs]{.underline}: Faint vesicular breath sounds; Resonant percussion
note; Dullness on the left, no added sounds; No stridor; Normal breath
excursion
[Abdomen]{.underline}: Large right abdominal wall hernia, normal
peristalsis in all quadrants; Soft; No pathological resistances; No
tenderness (especially not over the left lower abdomen)
[Skin status:]{.underline} No pathological skin findings
Extremities: Warm; Mild edema.
[Neurology:]{.underline} Alert. No focal deficits
**Treatment and Progression:** The patient was admitted through our
emergency department due to a decrease in general condition and
weakness, accompanied by significantly elevated laboratory infection
parameters and slightly worsened retention parameters (Creatinine max
3.4 mg/dL compared to the current baseline of 3 mg/dL). Upon admission,
apart from a known and persistent leukocyturia since 2020, there were no
indications of any other infectious focus. We were able to detect
Enterobacter cloacae and Enterococcus faecalis in the urine, and we
initially treated the patient with intravenous Tazobactam. Blood
cultures remained sterile. The patient\'s general condition improved
within a few days, along with a regression of infection parameters.
For further investigation of recurrent urinary tract infections (UTIs)
and in the context of a history of urethral stricture treatment in
February 2021 with bougienage of the urethra one year ago, a urological
consultation was arranged. During this consultation, there was suspicion
of a recurrence of the urethral stricture due to a significant residual
urine volume of 175 ml. A scheduled readmission for repeat surgical
management was set for May 16, 2022. Due to the lack of normalization of
elevated infection parameters and significant residual urine, a urinary
catheter was inserted. Subsequently, Enterococcus faecium was detected,
and we continued treatment with oral Linezolid after the completion of
intravenous antibiotic therapy.
The antibiotic treatment was planned to continue on an outpatient basis
for a total of 10 days. We kindly request an outpatient follow-up to
monitor infection parameters next week. The urinary catheter will be
maintained until the urological follow-up appointment, and the patient
has been provided with a prescription for medication.
Furthermore, the patient exhibited atrial tachyarrhythmia. We reduced
the heart rate using Digoxin, as the patient was already on maximum
beta-blocker therapy. The atrial tachyarrhythmia significantly improved
under this treatment.
Additionally, there was a non-puncture-worthy pleural effusion and a
chronic pericardial effusion, which was not hemodynamically relevant.
There were no clinical indications of pericarditis.
**Current Recommendations:**
1. Inpatient admission to Urology Department.
2. Outpatient laboratory monitoring and referral issuance by the
primary care physician.
### Patient Report 5
**Dear colleague, **
We are reporting on mutual patient, Mr. Alan Fisher, born on 12/09/1953,
who was under our inpatient care from 03/14/2020 to 03/15/2020.
**Diagnoses**: Anastomotic stricture following history of prostatectomy
and history of urethrotomy interna.
**Other Diagnoses:**
- History of renal transplantation on 10/25/1995
- Dual immunosuppression with Cyclosporin A/Steroid since 10/1995
- Terminal renal insufficiency due to chronic pyelonephritis and
nephrolithiasis
- Chronic hemodialysis from 09-10/1995
- Right laparoscopic nephrectomy on 03/2007 due to suspected renal
cell carcinoma, histologically not confirmed
- Incisional hernia after nephrectomy, diagnosed on 07/2007
- Secondary hyperparathyroidism
- Coronary artery disease, CAD-3:
- Previous anterior wall infarction in 1989, treated with
thrombolysis and PTCA
- PTCA + stent in the right coronary artery (RIVA) in 05/1995
- PTCA + drug-eluting stent (DES) in RIVA on 05/15/2005
- PTCA + Genous (anti CD34+ Antibody-coated) in RIVA on 08/14/2006
- Remaining: 75% stenosis in D1 and occlusion of the small RCA
(last cardiac catheterization on 03/24/2008)
- Stress echocardiography planned for 01/09 if ischemia is
detected, followed by bypass surgery if necessary
- Right superficial femoral artery profundaplasty on 03/11/2007
- Permanent atrial fibrillation, diagnosed in 03/07, unsuccessful
cardioversion on 03/2008, anticoagulated with Marcumar
- Arterial hypertension
- Hyperlipoproteinemia, possible dose-dependent Fluvastatin
intolerance
- COPD GOLD Stage II
- Mild sleep apnea syndrome in 01/2005
- Massive diverticulosis (last colonoscopy on 07/2008)
- History of Hepatitis B infection
- Cholecystolithiasis
**Procedure**:
- Urethrotomy interna according to Sachse
- Calculated intravenous antibiotic therapy with Meropenem starting on
03/14/2020
- Extension of therapy to include antifungal treatment with
Fluconazole on 03/15/2020
**Medical History:** The patient presents with a recurrence of
symptomatic urethral stricture at the anastomosis site following
prostatectomy. The main symptoms are frequent urination, dysuria, and
residual urine formation up to 175 ml. In January 2019, urethrotomy
interna was already performed. Since the last hospitalization due to a
urinary tract infection, the patient has had a continuous
catheterization.
**Physical Examination:** Patient in a reduced general condition and
obese nutritional status. The abdomen is soft, without signs of
resistance or pain. Kidney beds on both sides are indolent.
**Urine Diagnostics**: Urine dipstick: Leukocytes 500, Nitrite negative,
Erythrocytes 50
**Microbiology**: Candida in urine, collected by the general
practitioner on 03/11/2020.
**Chest X-ray in two planes on 02/19/2020:**
[Clinical Information, Question, Justification for the
Examination]{.underline}: Deterioration of general condition. History of
recurrent sepsis. History of lung transplantation. Infiltrates?
**Findings**: The heart is shifted to the left and has a mitral
configuration. No signs of acute congestion. The mediastinum shows no
signs of emphysema, is centrally located, and of normal width. No active
pneumonia in the ventilated lung regions. Progressive costophrenic angle
effusion on the left. No pleural effusion on the right, as far as can be
assessed. No pneumothorax. Degenerative changes in the spine.
Hyperkyphosis of the thoracic spine.
**Therapy and Progression:** The above-mentioned procedure was performed
without complication. Scar tissue at the level of the bladder sphincter
was incised. The postoperative course was uneventful. The transurethral
indwelling catheter was removed on the 19th postoperative day. At the
time of discharge, the patient could urinate without residual urine with
a good urinary stream. We discharged the patient on 03/19/2020 for
further outpatient care.
**Medication upon Discharge:**
**Medication ** **Dosage** **Frequency**
---------------------------------------- ------------------- ------------------
Magnesium Oxide 400 mg 1-0-0-0
Atorvastatin (Lipitor) 43.3 mg 0-0-1-0
Candesartan Cilexetil (Atacand) 16 mg 1-1-0-1
Prednisone (Deltasone) 5 mg 1-0-0-0
Vitamin D3 (Cholecalciferol, oily) 20,000 IU 1x every 2 weeks
Apixaban (Eliquis) 2.5 mg 1-0-1-0
Metoprolol Succinate (Toprol-XL) 95 mg 1-0-1-0
Mycophenolic Acid (Myfortic) 385 mg 1-0-1-0
Pantoprazole (Protonix) 22.6 mg 1-0-0-0
Piperacillin/Tazobactam (Zosyn) 4.17 g and 0.54 g 1-1-1-0
Cyclosporine, microemulsified (Neoral) 10 mg 1-0-1-0
Cyclosporine, microemulsified (Neoral) 50 mg 1-0-1-0
Torsemide (Demadex) 10 mg 2-1-0-0
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
------------------------------------------- ------------------ ---------------------
Sodium 141 mEq/L 136-145 mEq/L
Potassium 3.9 mEq/L 3.5-4.5 mEq/L
Creatinine 3.02 mg/dL 0.70-1.20 mg/dL
Estimated GFR (eGFR) 19 mL/min/1.73m² \-
Total Bilirubin 0.73 mg/dL \< 1.20 mg/dL
Direct Bilirubin 0.41 mg/dL \< 0.30 mg/dL
C-reactive Protein 78.3 mg/dL \< 5.0 mg/dL
Alanine Aminotransferase 35 U/L \< 41 U/L
Aspartate Aminotransferase 33 U/L \< 50 U/L
Alkaline Phosphatase 273 U/L 40-130 U/L
Gamma-Glutamyl Transferase 184 U/L 8-61 U/L
Lipase 102 U/L 13-60 U/L
Hemoglobin 12.3 g/dL 12.5-17.2 g/dL
Hematocrit 39.0% 37.0-49.0%
Red Blood Cells 4.2 M/uL 4.0-5.6 M/uL
White Blood Cells 10.41 K/uL 3.90-10.50 K/uL
Platelets 488 K/uL 150-370 K/uL
Mean Corpuscular Volume 92.4 fL 80.0-101.0 fL
Mean Corpuscular Hemoglobin 29.1 pg 27.0-34.0 pg
Mean Corpuscular Hemoglobin Concentration 31.5 g/dL 31.5-36.0 g/dL
Mean Platelet Volume 10.3 fL 7.0-12.0 fL
Red Cell Distribution Width 13.5% 11.5-15.0% | 1989 |
What happened to Mr. Palugger?
A. He was pushed out of the airlock.
B. He was beaten to death.
C. The man in the red mask shot him.
D. He died of his illness.
| 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. | D. He died of his illness. |
What recommendation was made by the Gastrointestinal Tumor Board on September 30th, 2021 regarding Mrs. Anderson's treatment?
Choose the correct answer from the following options:
A. Immediate surgery to remove the tumor.
B. Start radiation therapy immediately.
C. Begin neoadjuvant chemotherapy with FOLFIRINOX and review after 4 cycles.
D. Wait for final histology and then decide the treatment plan.
E. Perform another ERCP.
| ### 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. | Begin neoadjuvant chemotherapy with FOLFIRINOX and review after 4 cycles. |
What are all machine learning approaches compared in this work? | ### Introduction
In an organization, the Information Technology (IT) support help desk operation is an important unit which handles the IT services of a business. Many large scale organizations would have a comprehensive IT support team to handle engagement and requests with employees on a 24$\times $7 basis. As any routinized tasks, most processes of the support help desk unit are considered repetitive in nature BIBREF0. Some may occur on a daily basis and others may occur more frequently. Many support engineers and agent would spend time on these repetitive task such as entering information to an application, resetting passwords, unlocking applications, creating credentials, activating services, preparing documentation, etc. The industry has now come realize that many repetitive business processes and tasks can be automated by using Robotic Process Automation (RPA) bots or robotic processes automotive software bots BIBREF1. The idea is to take the repetitive workload and hand it over to the RPA bots so that the employees could focus on more value adding tasks and decision making to the organization. The RPA bot would also help to reduce the human errors and make processes more efficient, which would finally intent results in cost saving and productivity increase. Our proposed automated approach is not only focused on automating repetitive tasks but also looking at historical data, enabling IT support desk process to identify unforeseen insights and patterns. Analyzing the data from various sources such as email communications, service request information generated from support ticketing applications and even conversational data from chats has helped us to identify the type of Service Requests (SR) raised and their respective solutions, as well as fixes done by the support agents. This approach has helped us create a classification model to identify the issue types and provide quick fixes and resolutions from the collected data. ### Related Work
WrÃblewska has conducted a project on the topic of RPA of unstructured data which was focused on building an Artificial Intelligence (AI) system dedicated to tasks regarding the processing of formal documents used in different kinds of business procedures BIBREF2. His approach was introduced to automate the debt collecting process. Possible applications of Machine Learning (ML) methods to improve the efficacy of these processes were described. In the case study done by Aguirre, it was concluded that companies should consider RPA to be more suitable for high volume standardized tasks that are rule-driven, with no requirement for subjective judgement, creativity or interpretation skills BIBREF3. Back office business processes such as accounts payable, accounts receivable, billing, travel and expenses, fixed assets and human resource administration are good candidates for RPA. Extreme multi-class and multi-label text classification problems are solved by the methodology named Hierarchical Label Set Expansion (HLSE) BIBREF4. This paper presents the deep Learning architecture devoted to text classification, in which the data labels are regularized, the hierarchical label set is defined and different word embeddings are used BIBREF3, BIBREF5, BIBREF6. The traditional model performed better than the the deep learning models for 8,841 emails collected over 3 years, because this particular classification task carried out by Haoran may not require the ordered sequence representation of tokens that deep learning models provide BIBREF7. This paper claims that a bagged voting model surpasses the performance of any individual models. In their survey, Kamran and other researchers analyzed text feature extractions BIBREF8, BIBREF9, dimentionality reduction methods, existing algorithms and techniques, evaluation methods and limitations BIBREF6 and advantages based on applications. Paramesh et al and Seongwook et al compare the different classification algorithms such as multinomial naive bayes logistic regression, K-Nearest neighbour and Support Vector Machines (SVM) on real-world IT infrastructure ticket classifier system data, using different evaluation metrics in their research BIBREF10, BIBREF11. They claimed that SVM to have performed well on all the data samples. Random forest (RF) or naive bayes (NB) performed best in terms of correctly uncovering human intuitions. Hartmann et al and his team present in their study that RF exhibits high performance in sentiment classification research done on 41 social media data sets covering major social media platforms, where the SVM never outperforms the RF BIBREF12. Cognitive RPA is efficiently undertaken as a low cost solution with Microsoft Azure Language Understanding Intelligent Service (LUIS) BIBREF8 and Azure machine learning studio. Section III of this paper elaborates the process of automation. The section IV explains about the email classification approach, and the section V illustrates the results and their respective analysis. Finally, section VI contains the conclusion of the results. ### Method
We are proposing a hybrid-process automation, in which we are introducing the automation architecture while adopting the manual process methodology. Incoming emails, that cannot be classified or understood by the knowledge base of the automation system will be sent for manual classification solution. ### Method ::: Manual Process
Providing technical support for large firms around the world has many challenges such as coordinating a vast amounts of mails and matching experts with employees who are in need of that expertise. When a technical issue is raised from a base level employee who works with applications, it is sent to the middle level and then to the higher level management of the respective regional branches throughout the hierarchical business architecture. Once it is approved by the branch manager, the issue email is forwarded to the technical coordinator to categorize the issue based on the priority level and technical requirements. Technical coordinator is responsible for the issues raised from the regional branches all over the world. Each regional branch is given a unique name such as New York, Sydney, London, Beijing and Toronto mentioned as Category1 (cat1). Category1 is identified by looking at the email address of the sender. Each regional branch has different plant applications that need different experts' consultation. Plant applications such as SAP, Darwin and infrastructure are mentioned as Category2 (cat2). The possible plot of the issue emails such as computer, manufacturing, userID, userunlock, financial, planning, purchasing issue generated by employees working in various plant applications across various regions are mentioned as Category3. Mapping table is created with the plants placed in the regional offices and the issues created by the plants. Category1, Category2, Category3 contains 84, 8 and 77 unique categories to be classified. Table I shows some examples for each categories. Once all three categories are finalized by the technical coordinator, email tickets will be created and assigned to the admin-groups. Respective technical people in the admin-groups will provide consultancy and solve the issues. Not only one technician can handle issues assigned to many different admin groups allocated to him, but also particular admin category can be handled by many technicians as a group as well. ### Method ::: Proposed Automation System
In addition to replacing the technical coordinator role with AI bot to classify the raised email-issue tickets for respective admin groups, we propose instant quick fixes for some emails in an automated manner. High level workflow is described in Fig. 1. The AI bot has three main stages Quick fixes Static rules Email classifier All the incoming mails are preprocessed for better quality of inputs. Signatures, greetings, Uniform Resource Locators (URL) are removed. Key body is extracted from the forwarded mails by digging deep into the mail contents. If an email contains attachments, Optical Character Recognition (OCR) is used to extract the text contents from the attachments. ### Method ::: Proposed Automation System ::: Quickfixes
Microsoft LUIS is used for instant quick fixes to provide solution based on prioritized emails. Fig. 2 shows the bot framework LUIS architecture that handles the quick fixes. Quick fixes are trained with most occurring samples that need quick solutions. LUIS is a model that artificial intelligence applications use to predict the intention of phrases spoke. There are 3 main key phases categorized as defining phase, training phase and publishing phase. Natural language is extremely flexible with LUIS. Intents are the type of defined words that are supported by utterances. An action the user wants to perform can be defined by an intent. Fig. 3 elaborates the intent matching breakdown mechanism. Entities are identified form the sentences. Suitable entity will be selected for generating tickets. If an incoming email is identified with the matched intent, cat1, cat2, cat3 will be allocated. Tickets will be created for admin-groups. The issue will be solved using automated messages through a chat bot solution. If the issue is solved, then the ticket will be closed by the quick fixes. If it is too complicated for the knowledge of the BOT then it creates ticket for adminGroup for the assistance of consultants. The emails identified by static rules and keywords are classified with the highest accuracy. The knowledge base of static rules and keywords are gathered using feature engineering and the insights from the technical coordinator. Remaining emails are sent to a complex ensemble machine learning model to be classified. Different types of emails are treated in a different way for efficient execution and to reduce the error. ### Method ::: Proposed Automation System ::: First mail
Fig. 4 shows the flow of email categorization response for new incoming emails. If an incoming mail is a fresh new mail, it is initially subjected to cleaning. OCR will extract the texts from the attachment depending on the attachments' availability. Cat1 is assigned according to the knowledge of the database and sender details. According to the priority, emails are passed through LUIS. Thereafter if LUIS fails to solve the issue ML model will assign the cat2, cat3, Admin group for ticket creation. ### Method ::: Proposed Automation System ::: Forwarded mail
If incoming mail is a continuation of previous email, it is directly handled by LUIS question and answer self automated support. Then it follows the normal procedure of categorization. Fig. 5 clearly illustrates the flow. Fig. 6 explains the overall architecture. Static rules are mentioned as T-codes. Every categorized mails has to be assigned to respective consultant denoted as assignTo. ### Email classifier using machine learning ::: Preprocessing
Preprocessing is necessary to increase the accuracy of a text classification model, because it avoids the classification model focusing attention on unwanted sentences and intents. Emails are fed into Microsoft-Bot services. It handles the headers and outputs the processed channel-data in JavaScript Object Notation (JSON) format. The channel data summarizes the information such like sender, receiver, body, subject and important metadata. Regular expression (regex) can be used for searching strings by defining a search pattern. Regex findings are created to remove unwanted words from the channel data queries for further processing of the emails. OCR has to be accurate in detecting text in an image. Microsoft-OCR is used for text recognition of this automation process. It extracts the recognized characters into a machine-usable character stream. Accuracy of the text recognition depends on the image quality such as blurry images, small text size, complex background, shadows and handwritten text. Since most of the image attachments are computer generated images and screen shots of error messages, Microsoft-OCR capabilities fits for the use case. 260000 emails are taken from past history. Extracted preprocessed data from Microsoft-Bot and OCR services are saved as Comma-separated Values (CSV) files. It is further processed before feeding to machine learning model. Unwanted words are removed from the context using nltk library stopwords and manually collected stopwords. URLs, punctuation marks are removed. Every word is tokenized, lemmatized and normalized, i.e. title, body, OCR, from, to, CC, Cat1, Cat2, and Cat3. ### Email classifier using machine learning ::: Feature selection
Since the sender and receiver varies with time because of new employees' arrivals and old employees' resignations. In order to handle this fluctuating situation, To, CC, From columns are dropped from the input data. Cat1 is known from the email address. Cat2, Cat3 for specific cat1 is described in the table1. Cat2 and Cat3 are merged and defined as target category for classification. Nearly 180 custom features are created based on the plant's availability and region mapping. It is embedded to understand the availability of plant and the issue for the given region denoted as Unique-Category. Based on mapping table (extension of table1), custom features ensures that whether the plant application (cat2) and the technical issue (cat3) belongs to the regional plant (cat1). By the analysis made from the existing samples and from the human semantic knowledge of the technical coordinator, it is sensed that not only the title of the email is enough to predict the category, but also the attachment and body play a major role. ### Email classifier using machine learning ::: Machine learning approach
Even though labelled data set was provided, initially unsupervised learning algorithm K-Nearest Neighbor (KNN) clustering was applied to the data set to observe the possibility of clusters BIBREF13. Since number of unique categories of the target field (Unique-Cat) is 77, there are many common words between categories. It is too confusing and not showing promising categories and accuracies. Multi class multi label classification supervised algorithms such as random forest, XGBoost are used as benchmarks. ### Email classifier using machine learning ::: Machine learning approach ::: Feature selection
Ngrams are a continuous sequence of n items from a given sample of text. From title, body and OCR text words are selected. Ngrams of 3 nearby words are extracted with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizing, then features are filtered using chi squared the feature scoring method. Feature hashing is a method to extract the features from the text. It allows to use variable size of feature vectors with standard learning algorithms. 12000 features are hashed from the text, OCR and title. Then using chi-squared statistical analysis 200 best features that fits with target unique-category are selected. ### Email classifier using machine learning ::: Machine learning approach ::: Random forest
Random Forest is a bagging Algorithm, an ensemble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that has highest mean majority vote of the classesBIBREF14. ### Email classifier using machine learning ::: Machine learning approach ::: XGBoost
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. It is used commonly in the classification problems involving unstructured dataBIBREF5. ### Email classifier using machine learning ::: Machine learning approach ::: Hierarchical Model
Since the number of target labels are high, achieving the higher accuracy is difficult, while keeping all the categories under same feature selection method. Some categories performs well with lower TF-IDF vectorizing range and higher n grams features even though they showed lower accuracy in the overall single model. Therefore, hierarchical machine learning models are built to classify 31 categories in the first classification model and remaining categories are named as low-accu and predicted as one category. In the next model, predicted low-accu categories are again classified into 47 categories. Comparatively this hierarchical model works well since various feature selection methods are used for various categoriesBIBREF5. ### Email classifier using machine learning ::: Deep learning approach ::: LSTM
Long short term memory is an artificial neural network architecture which outperforms most of the machine learning algorithms. In the deep learning approach, feature selection is done in neurons weight matrix by itself. Bidirectional long short term memory (LSTM) is used with glove word embedding to predict the categoriesBIBREF15. ### Email classifier using machine learning ::: Deep learning approach ::: BERT
Even though Bert is the state of the art model, for the considered data set it hasn't shown-up with the maximum breach of accuracy for the expected automationBIBREF16. When we consider the commercial model for the inference, having a dedicated Kubernetes cluster with high performance computer is costly. So complex models with high computation power are not considered as abetter solution. ### Email classifier using machine learning ::: Threshold Selection
In order to classify only higher confident emails, the thresholds for each and every 73 categories are defined. For an incoming email, the probability of assigning each category will be calculated. Best category will be selected based on the maximum probability out of those 73 probabilities. By looking at overall F-score, thresholding decisions are made. For the low accuracy categories (accuracy less than 75 percentage) higher threshold level is set. For middle accuracy categories (accuracy less than 90 percentage) min probability of correctly classified samples are taken. Higher accuracy categories (accuracy greater than 90 percentage) are left free with 0 threshold to classify all the incoming emails. The threshold techniques as a bottle neck decreases the number of samples classified by the autonomous process, but it increases the accuracy of the classified samples. The proposed thresholds satisfy the expected manual workload reduction as well as the accuracy percentage. In this paper Randomforest, XGBoost, LSTM, Bidirectional LSTM with embeddings are analyzed with different input features. Complex deep-learning models such like transformers are not used in order to go for low cost inference solution. Train set and test set are divided as 80:20 percentage. Precision, recall, F-score are taken as evaluation metrics. ### Results and Analysis
Automation of quick email replies for technical queries increase the overall efficiency of day to day processes by 3 percentage. Even though replacing the manual Human email-assigner entirely with AI bot is not possible, yet the automation ML model handles 61 percentage of incoming emails correctly. It is reducing massive human effort per day. For generalization purpose email's title, body, attachments are considered in increasing accuracy, while ignoring sender, receiver, carbon copy information. Table II shows the accuracy percentages for different models with different feature selection methods. An accuracy of 77.3 percentage was obtained without any thresholding techniques for 73 classes multiclasss multi label classification problem. With threshold adjustments for each categories, it was increased to 85.6 percentage. Increasing threshold values results in reducing the number of mails classified by ML-model. It is necessary to handle limited number of high confident emails by the ML-model due to ensure the promising accuracy levels. Feature Engineering for custom feature selection and, Hierarchical cascade modelling increases the accuracy of the XGBoost machine learning model to reach accuracy of the LSTM models. By cascading model1 (mod1) with 83.2 accuracy for 31 classes and model2 (mod2) with 71.1 accuracy for 47 low-accuracy classes, overall hierarchical model exhibited 76.5 accuracy. All the accuracy terms refers F-score. Selected keywords were used as static rules accurate classification. Since accuracy is considerably satisfactory for the automation process, the system was deployed. The incorrectly classified mails are handled manually after the proper notification by the technical consultant. Fig. 7 Shows emails classified by the ML, static rules and manual process represented in daily basis. Incoming emails per day varies between 30 to 120. It clearly illustrates the effect of retraining. After 10-April, the percentages of emails classified per day was increased as well as accuracy. Fig. 8 shows average monthly analysis of incoming mails after each retraining. Average Monthly incoming mails are calculated as 1467 per month by considering a 4 months period. Initial training was done on august 2018 with 170,000 samples, model was able to classify nearly 50 percentage of incoming emails. After the second retraining on january 2019 with 200,000 sample, model classified 58 percentage of incoming mails per month. Third retraining was done on April 2019 with 260000 samples. Results stated that nearly 61 percentage of incoming mails were handled by ML model. Nearly 20 percentage of incoming emails were handled by static rules. Automation bot was proved to handle 81 percentage of the total incoming mails per month including ML and static rules, leading to efficient human-machine interaction, Instant problem solving and fast process. ### Conclusion
Quick fixes from Microsoft LUIS Bot framework provides instant solutions for the raised email queries. Input text features of emails such as title, body, attachment OCR text and the feature engineered custom features all together outperform for the considered real word email data set. Sure-shot Static rules and hierarchical machine learning model with statistically calculated threshold enhances the accuracy of the overall system to an acceptable percentage. Bidirectional LSTM with word embedding techniques are implemented finally with thresholding techniques. Less complex Machine learning models lead to low cost virtual machine solutions for serving. Robotic Process Automation Architecture reduces human effort of email support desk by 81 percentage while having a reasonable accuracy of 85.6 percentage. TABLE I LIST OF CATEGORIES Fig. 1. High-Level System Workflow Fig. 3. LUIS intent matching Fig. 2. Quick fixes Bot architecture Fig. 4. First mail flow Fig. 5. Forwarded mail flow with LUIS support Fig. 6. Overall architecture TABLE II MODEL ACCURACY Fig. 7. Daily solved emails Fig. 8. Monthly efficiency. | Feature selection, Random forest, XGBoost, Hierarchical Model |
Regarding Mr. Carter, what condition was indicated by the echo-free cystic structure identified in the right lower lobe on the ultrasound abdomen performed on 11/01/2021?
Choose the correct answer from the following options:
A. Echinococcus
B. Atypical pneumonia
C. Congenital cyst
D. Pulmonary embolism
E. Hepatic lesion
| ### Patient Report 0
**Dear colleague, **
We are reporting on our mutual patient, Mr. Brian Carter, born on
04/24/1956, who was under our inpatient care from 09/28/2021 to
09/30/2021.
**Diagnosis**: ARDS in the context of a COVID-19 infection
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Current Presentation:** Mr. Carter presented to our facility on foot
on 09/28/2021 with a five-day history of slowly progressive dyspnea, dry
cough, and non-apoplectic, holocentral headache. His initial room air
saturation was 78%, which improved to 86% with 10 liters of oxygen.
Arterial blood gas analysis revealed an oxygenation disorder with a paO2
of 50 mmHg, prompting the initiation of NIV therapy, under which Mr.
Carter remained hemodynamically stable. CT imaging showed bilateral
interstitial pneumonia with COVID-typical infiltrates. Both a rapid test
in the initial care unit and one from his primary care physician were
negative for COVID-19. Therefore, we admitted Mr. Carter to our
intensive care unit for further evaluation.
**Medication upon Admission:**
**Medication** **Dosage** **Frequency**
-------------------------- ------------ ---------------
Prednisone (Deltasone) 5 mg 1-0-0
Methotrexate (Trexall) 25 mg 1-0-0
Candesartan (Atacand) 4 mg 1-0-0
Quetiapine (Seroquel) 300 mg 0-0-1
Amitriptyline (Elavil) 25 mg 0-0-1
Citalopram (Celexa) 40 mg 1-0-0
Montelukast (Singulair) 10 mg 1-0-0
Desloratadine (Clarinex) 5 mg 1-0-0
**Physical Examination:**
[Neurology]{.underline}: Alert and cooperative
[Cardiovascular/Abdominal Examination]{.underline}: Severely impaired
oxygenation improved with NIV; Sinus rhythm at 80 beats per minute
[Abdomen]{.underline}: Surgical abdomen
[Renal System:]{.underline} Urination initially scant, then polyuria
Others.
**Therapy and Progression:** Upon admission, Mr. Carter was alert,
cooperative, and hemodynamically stable despite significant oxygenation
impairment. Temporary improvement was achieved with differentiated NIV
mask ventilation. On 09/30, there was a further deterioration in
oxygenation with an increase in respiratory rate and escalation of
ventilator settings, leading to the decision to intubate. A tolerable
ventilation situation was achieved with an oxygenation index of 125. Due
to radiological suspicion of atypical pneumonia, we initiated empirical
anti-infective therapy with Piperacillin/Tazobactam, Clarithromycin, and
Cotrimoxazole. Microbiological test results were pending at the time of
transfer. We also initiated mucolytic therapy with Ambroxol. The
pre-existing immunosuppressive therapy with Prednisolone was
discontinued and switched to Dexamethasone 10 mg. At the time of
transfer, Mr. Carter was hemodynamically stable with low catecholamine
doses (0.07 µg/kg/min). A central venous catheter was placed, and
enteral or parenteral nutrition had not yet been initiated. Diuresis was
sufficient after a single dose of 20 mg furosemide, with retention
parameters within the normal range. Prophylactic anticoagulation with
heparin 500 U/h was initiated.
**Status at Transfer**:
[Neurology]{.underline}: RASS -5 under Propofol and Sufentanil sedation
[Cardiovascular]{.underline}: Normal sinus rhythm, noradrenaline (NA)
0.07; Hemoglobin 12.8 g/dL
[Lungs]{.underline}: Adequate decarboxylation with borderline
oxygenation: paO2 87.6 under FiO2 0.7; PEEP 16; PEAK 27
[Abdomen]{.underline}: Soft abdomen, no nutrition initiated
[Renal System]{.underline}: Normal urine output without stimulation.
Retention values within normal range. Clear urine.
[Access]{.underline}: CVC placed on 09/30, left radial artery catheter
placed on 09/30.
### Patient Report 1
**Dear colleague, **
We are reporting on our patient, Mr. Brian Carter, born on 04/24/1956,
who was under our inpatient care from 09/30/2021 to 10/13/2021.
**Diagnosis:** ARDS due to COVID-19 pneumonia with superinfection by
Aspergillus fumigatus
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** The patient was admitted from the emergency
department, presenting with dyspnea and confirmed SARS-CoV-2 infection.
After initial management in the intensive care unit, a non-invasive
ventilation (NIV) trial was attempted, followed by successful
intubation. The patient was then transferred to the Intensive Care Unit.
**Therapy and Progression:** Upon admission, the patient was sedated,
intubated, and controlled on mechanical ventilation with mild
catecholamine support. Due to oxygenation impairment despite
lung-protective ventilation and inhaled supportive NO therapy,
conservative ARDS therapy was initiated, including positioning therapy
(a total of 9 prone positions). After stabilization of gas exchange with
positioning therapy, sedation and ventilation weaning were performed.
Gas exchange and oxygenation are currently stable under BIPAP
ventilation (PiP 25 mbar, PEEP 13 mbar, breathing rate 18/min). The
patient, under reduced analgosedation with Sufentanil and Clonidine,
exhibits a sufficient awakening response, is adequately responsive, and
follows commands with reduced muscle strength.
The home medication of Methotrexate and Prednisolone for uveitis was
discontinued upon admission. The patient received Dexamethasone for 10
days initially and, starting from 11/10, prednisolone with prophylactic
Cotrimoxazole therapy.
Upon detection of Aspergillus in tracheobronchial secretions, antifungal
therapy with Voriconazole and Caspofungin (until target Voriconazole
levels were achieved) was initiated. The initially started antimicrobial
therapy with Piperacillin + Tazobactam was escalated to Meropenem on
10/05/2021 due to worsening infection parameters and progression of
infiltrates on X-ray. Infection parameters have been fluctuating, and
fever is not currently observed. Diuresis is qualitatively and
quantitatively within normal limits, and retention parameters are within
the normal range.
Anticoagulation was administered in therapeutic doses using
low-molecular-weight heparin.
Enteral nutrition is provided through a nasogastric tube, and the
patient has regular bowel movements.
**Physical Examination:**
[Neurology]{.underline}: Analgosedated, GCS 10, pupils equal and
reactive, limb movement prompt, follows commands with reduced strength
[Lungs]{.underline}: Intubated with BIPAP 25/13, FiO2 0.4
[Cardiovascular]{.underline}: Normal sinus rhythm, noradrenaline 0.05
[Abdomen]{.underline}: Obese, no tenderness, abdomen soft, oral intake
via a nasogastric tube, regular bowel movements
[Diuresis]{.underline}: Normal urine output, retention parameters within
normal limits
Skin/Wounds: Some pressure sores from positioning (see nursing handover
sheet)
[Mobilization]{.underline}: Not conducted
**Imaging:**
**Bedside Chest X-ray from 10/11/2021:**
[Clinical information, question, justifying indication:]{.underline}
COVID pneumonia, insertion of a central venous catheter (CVC)
**Assessment**: Comparison with 10/05/21: Endotracheal tube identical,
gastric tube seen extending well into the abdomen, left CVC currently
positioned in the brachiocephalic vein region, right CVC via internal
jugular vein with tip in superior vena cava. No pneumothorax, no
effusions, increasing consolidation of infiltrates in the right lower
lobe and retrocardially on the left without significant cavitation as
far as can be assessed. Left heart without significant central
congestion.
### Patient Report 2
**Dear colleague, **
We are reporting on our patient, Mr. Brian Carter, born on 04/24/1956,
who was under intensive care treatment from 09/28/2021 to 10/12/2021 and
in our intensive care unit from 10/13/2021 to 10/21/2021.
**Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8
- Intubation on 09/30, initial extubation on 10/13
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** The initial hospital admission of the patient was
through our emergency department due to severe respiratory insufficiency
in the context of COVID-19 pneumonia.
**Current Medication:**
**Medication** **Dosage** **Frequency**
-------------------------- ------------ ---------------
Prednisone (Deltasone) 5 mg 1-0-0
Methotrexate (Trexall) 25 mg 1-0-0
Candesartan (Atacand) 4 mg 1-0-0
Quetiapine (Seroquel) 300 mg 0-0-1
Amitriptyline (Elavil) 25 mg 0-0-1
Citalopram (Celexa) 40 mg 1-0-0
Montelukast (Singulair) 10 mg 1-0-0
Desloratadine (Clarinex) 5 mg 1-0-0
**Physical Examination:**
[Skin/Mucous Membranes]{.underline}: Warm, Skin Perfusion: Good
perfusion, Edema: Lower legs
[Head]{.underline}: Mobility: Active and passive free movement, Tongue:
Moist
[Thorax]{.underline}: Auscultation: Clear bilaterally
[Abdomen]{.underline}: Soft, no guarding, Bowel Sounds: Sparse
peristalsis, Tenderness: None
[Neurology]{.underline}: Pupil Shape: Round, Pupil Size: Moderate, Light
Reaction: Both sides +++
Alertness: Awake
**ECG on admission:** Tachycardic sinus rhythm with 107/min, Left type,
P-wave normally configured, normal PQ interval, no pathological Q as in
Pardee-Q, narrow QRS, regular R progression, R/S transition in V3/4, no
S persistence, no ST segment changes, no discordant T-negatives.
**Therapy and Progression:** Despite intensified oxygen therapy with
nasal high-flow and mask CPAP, adequate oxygenation could not be
achieved, and the patient was intubated on 09/29/21. Leading oxygenation
impairment led to lung-protective ventilation with inhaled supportive NO
therapy and conservative ARDS therapy, including positioning therapy (a
total of 9 prone positions, 16 hours each, from 09/29/21 to 10/8/21).
Due to elevated procalcitonin, the patient received empirical antibiotic
treatment with Piperacillin/Tazobactam starting from 10/2/21, which was
escalated to Meropenem on 10/5/21 and continued until 10/14/21.
After the detection of Aspergillus in tracheobronchial secretions and
BAL, the patient received Voriconazole since 10/7/2021 (treatment
duration formally 4-6 weeks). Most recently, the level was
subtherapeutic, so the dose was adjusted to 2 x 400 mg daily.
The immunosuppressive therapy with Methotrexate and Prednisolone for
rheumatoid arthritis was switched to Dexamethasone (09/29 to 10/8) and,
since 10/09, Prednisolone monotherapy. After controlling the fungal
infection, a rheumatology re-consultation was planned. Furthermore,
subtherapeutic anticoagulation with Fraxiparine was initiated for the
prevention of thrombotic complications in the context of COVID-19.
Under this treatment regimen, gas exchange continuously improved, and on
10/12/21, the patient was transferred with low catecholamine
requirements for ventilation and sedation weaning. Mr. Carter was
extubated on 12/13/21 and now maintains good oxygenation with less than
3L oxygen via nasal cannula. Delirium symptoms after extubation
completely regressed within a few days.
Severe dysphagia was observed after invasive ventilation, leading to a
speech therapy consultation. Oral feeding is currently not possible, so
Mr. Carter is receiving parenteral nutrition. As a result, there was a
paravasate in the upper right extremity with painful erythema. Adequate
pain control was achieved with local cooling and Piritramide as needed.
Due to continued dietary restrictions, a central venous catheter was
placed on 10/20/2021 for parenteral nutrition.
We request continued speech therapy treatment.
On 10/21/21, Staphylococcus aureus was detected in blood culture, so we
initiated the administration of Flucloxacillin. The MRSA rapid test was
negative.
We are transferring Mr. Carter on 10/21/21 in stable condition, awake,
and appropriately responsive for further treatment. We appreciate the
transfer of our patient and are available for any further questions.
**Current Recommendations:**
- Continuation of antifungal therapy for a total of at least 4-6 weeks
- Voriconazole level measurement
- Speech therapy consultation
- Rheumatology re-consultation
- Follow-up blood cultures upon detection of Staph. aureus
### Patient Report 3
**Dear colleague, **
We are reporting on Mr. Brian Carter, born on 04/24/1956, who was under
our inpatient care from 10/21/2021 to 11/08/2021.
**Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8 and NO therapy
- Intubation on 09/30, initial extubation on 10/13
- Reporting to the health department by the referring physician
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
**Other Diagnoses:**
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Current Presentation:** Transfer for continuation of antimicrobial
therapy for MSSA bacteremia. Transesophageal echocardiogram planned for
tomorrow. Cleared for full diet by speech therapy today. Patient
mobilized to standing position for the first time today. Overall,
mobility is significantly limited, but the patient can mobilize to the
edge of the bed independently. No pain, no fever, mild cough without
sputum. No shortness of breath. Mood is significantly depressed, but
this is a known issue. Before COVID-19, he was heavily affected by
rheumatoid arthritis.
**Medical History:** The patient was transferred to our COVID ward after
a positive SARS-CoV-2 RNA PCR test in the naso-oropharyngeal swab and
respiratory failure. On physical examination, he had a reduced general
condition. Respiratory rate was 24/min, and oxygen saturation was 97% on
4 L/min of O2 via nasal cannula. Oxygen supply of 4 L via nasal cannula
could not be reduced during the course. A chest X-ray performed on 11/21
showed increasingly loosened infiltrates in the left basal region and a
minimal effusion at the base.
A SARS-CoV-2 RNA PCR test from 11/10/2020 was negative, so Mr. Carter
was no longer in isolation.
Due to the detection of Aspergillus fumigatus in bronchoalveolar lavage,
intravenous Voriconazole therapy initiated on 10/07/2021 was continued
and was planned to be adjusted according to drug level monitoring.
Additionally, Staph. aureus was identified in a blood culture, and
Staph. epidermidis. Antibiotic therapy with Cefazolin was started on
10/22 and was to be continued for a total of 14 days after the first
negative blood culture. The central venous catheter, likely the source
of infection, was removed on 10/22, and microbiological examination of
the catheter tip indicated suspicion of Staphylococci. To rule out
endocarditis, a transesophageal echocardiogram was scheduled for 10/24.
Mr. Carter has already been informed about this intervention, and
Fraxiparine was to be paused on the evening of 10/24 and the morning of
10/27, with the patient kept fasting.
There is also a known history of rheumatoid arthritis, which was treated
on an outpatient basis with Methotrexate and Prednisolone. Due to the
current infection, Methotrexate was paused, and after consultation with
the rheumatologists, it was decided to continue with prednisolone 5mg.
After complete pulmonary recovery, a rheumatology re-consultation was
planned, and the resumption of methotrexate was considered.
Upon admission, the patient had significant dysphagia, which improved
during the course. A flexible endoscopic swallowing examination
performed on 10/24/2021 by speech therapists and phoniatrics revealed a
normal swallow reflex. Mr. Carter can now resume a regular diet.
**Physical Examination:** Weight: 83 kg, Height: 182 cm. Temperature:
36.5°C, Heart rate: 80/min, Respiratory rate: 25/min, Blood pressure:
130/80 mmHg, Oxygen saturation: 98% with 2 L/min O2
[Skin/mucous membranes:]{.underline} No edema, no skin abnormalities.
Central venous catheter exit site on the neck is unremarkable.
[Head/neck:]{.underline} Own teeth, intact mucous membranes
[Heart]{.underline}: Rhythmic, tachycardic up to 100/min, clear heart
sounds, no murmurs
[Lungs]{.underline}: Bilateral vesicular breath sounds, no adventitious
sounds
[Abdomen]{.underline}: Soft, active bowel sounds, no tenderness, no
resistance
[Lymph nodes:]{.underline} Cervical, axillary nodes not palpable
[Vessels]{.underline}: Foot pulses palpable
[Musculoskeletal:]{.underline} Muscle strength reduced due to CIP/CIM.
Can mobilize to the bedside independently
[Basic neurological examination:]{.underline} Alert, oriented, friendly
[Psychological state]{.underline}: Depressed mood
**Therapy and Progression:** The emergency presentation of Mr. Carter
was on 09/28/2021 due to severe dyspnea and respiratory insufficiency.
After direct transfer to Intensive Care Unit, despite intensified oxygen
therapy with nasal high flow and mask CPAP, adequate oxygenation could
not be achieved, leading to intubation on 10/29/21.
Lung-protective ventilation was initiated due to leading oxygenation
impairment, with inhalational supportive NO therapy and conservative
ARDS therapy, including positional changes (a total of 9 sessions of 16
hours each from 09/29/21 to 10/08/21). Due to elevated PCT levels, the
patient received empiric antibiotic therapy with
Piperacillin/Tazobactam, escalated to Meropenem. Voriconazole was
initiated on 10/07/2021 after the detection of Aspergillus in
tracheobronchial secretions and BAL (intended treatment duration 4-6
weeks).
Subtherapeutic anticoagulation with Fraxiparine was administered for the
prevention of thrombotic complications in the context of COVID-19. Under
this treatment regimen, gas exchange steadily improved, and on 10/12/21,
the patient was transferred with low catecholamine requirements for
weaning from mechanical ventilation and sedation. There, he was
extubated on 10/13/21.
After extubation, severe dysphagia was observed, and speech therapy was
consulted. Oral diet is currently not possible, so Mr. Carter is on
parenteral nutrition. This led to a paravasate in the right upper
extremity with painful erythema. Adequate pain control was achieved with
local cooling and subcutaneous Piritramide, as needed.
**Lab results:**
**Parameter** **Result** **Reference Range**
--------------------------------------- ------------ ---------------------
Absolute Reticulocytes 0.01/nL \< 0.01/nL
Sodium 138 mEq/L 136-145 mEq/L
Potassium 4.3 mEq/L 3.5-4.5 mEq/L
Creatinine 0.61 mg/dL 0.70-1.20 mg/dL
Estimated GFR \>90 \>90
BUN 23 mg/dL 17-48 mg/dL
Total Bilirubin 0.18 mg/dL \< 1.20 mg/dL
C-Reactive Protein 4.1 mg/L \< 5.0 mg/L
Troponin-T 6.1 ng/L \< 14 ng/L
ALT 50 U/L \< 41 U/L
AST 40 U/L \< 50 U/L
Alkaline Phosphatase 111 U/L 40-130 U/L
Gamma-GT 200 U/L 8-61 U/L
Free Triiodothyronine (T3) 2.3 ng/L 2.00-4.40 ng/L
Free Thyroxine (T4) 14.2 ng/L 9.30-17.00 ng/L
Thyroid Stimulating Hormone (TSH) 4.1 mIU/L 0.27-4.20 mIU/L
Hemoglobin 11.6 g/dL 13.5-17.0 g/dL
Hematocrit 34.5% 39.5-50.5%
Red Blood Cells 3.7 M/µL 4.3-5.8 M/µL
White Blood Cells 9.56 K/µL 3.90-10.50 K/µL
Platelets 280 K/µL 150-370 K/µL
MCV 92.5 fL 80.0-99.0 fL
MCH 31.1 pg 27.0-33.5 pg
MCHC 33.6 g/dL 31.5-36.0 g/dL
MPV 8.9 fL 7.0-12.0 fL
RDW-CV 14.0% 11.5-15.0%
Prothrombin Time 89% 78-123%
INR 1.09 0.90-1.25
Activated Partial Thromboplastin Time 25.3 sec. 22.0-29.0 sec.
**Imaging:**
**Chest X-ray bedside on 09/29/2021:** CT scan of the chest from
9/28/2021 is available for comparison. Tracheal tube tip supracarinal.
Central venous catheter (CVC) via right internal jugular vein, tip in
the confluence of veins. Gastric tube tip infradiaphragmatic. Patchy
confluent bilateral lung infiltrates, mainly perihilar, left and right
upper and lower fields. No significant changes compared to the previous
day. Small bilateral pleural effusions. No pneumothorax in the lying
position. Left-sided heart prominence with mild stasis/capillary leak.
**Chest X-ray bedside on 10/3/2021:**
[Findings]{.underline}: Compared to 09/29/2021. Tracheal tube with tip
approximately 4 cm above the carina. Gastric tube slightly retracted,
tip located just below the diaphragm. Central venous catheter via the
right internal jugular vein, currently with the tip in the superior vena
cava. Regression and loosening of infiltrates (mainly in the lower
fields on both sides). No significant effusion or pneumothorax. No
substantial volume overload.
**Chest X-ray bedside on 10/6/2021:**
[Findings]{.underline}: Compared to the previous examination on
11/4/2020. New central venous catheter (CVC) from the left internal
jugular vein with tip in the confluence. No pneumothorax in the lying
position, no large pleural effusions. Progressive infiltrates in the
right lower field, perihilar regions on both sides. No significant
central stasis. Heart not enlarged, mediastinum slim.
**Chest X-ray bedside on 10/11/2021:**
[Findings]{.underline}: Compared to 10/5/2021. Tracheal tube and gastric
tube as before. Left CVC with the tip currently in the region of the
brachiocephalic vein, right CVC via the internal jugular vein with the
tip in the superior vena cava. No pneumothorax, no effusions, increasing
consolidation of infiltrates in the right lower field and retrocardial
left with no significant cavitation. Left-biased heart without
significant central congestion.
**Chest X-ray bedside on 10/16/2021:**
[Findings]{.underline}: Compared to previous examinations on 10/11/2021.
Heart borderline enlarged. Mediastinum, as far as can be assessed from
slightly rotated images, appears central and slim. Increasing
consolidation in the right lower lobe and left lower lobe, which is well
compatible with pneumonic infiltrates. At most, a small pleural effusion
on the left. No pneumothorax in the lying position. No signs of
significant congestion. Right jugular catheter projecting into the
superior vena cava. Tracheal tube and left jugular catheter have been
removed since the last examination.
**Chest X-ray bedside on 10/20/2021:**
[Findings]{.underline}: Compared to the examination on 10/16/2021. In
the course of known COVID pneumonia, there is an increasingly loosened
appearance of infiltrates in the left basal region. A small effusion
continues to drain basally. Otherwise, no significant changes in the
short-term follow-up. Right jugular catheter projecting into the
superior vena cava, as before.
**EKG on 10/27/2021:** Normal sinus rhythm, 86/min. Indeterminate axis.
PQ interval: 108ms QRS duration: 108ms. QTc interval: 484ms. Peripheral
low voltage. Delayed R progression up to and including V3. RS transition
in V4. No significant ST-T wave changes.
**Ultrasound Abdomen on 11/01/2021:**
[Reason for referral:]{.underline} History of COVID, Aspergillosis
[Liver]{.underline}: Vertical diameter in the midclavicular line on the
right is 120 mm.
[Biliary tract]{.underline}: Well visualized. No abnormalities in the
intrahepatic and extrahepatic bile ducts. Maximum width of the common
bile duct is 3 mm.
[Gallbladder]{.underline}: Well visualized. Normal findings.
[Pancreas]{.underline}: Maximum diameters - Head: 17 mm, Body: 12 mm,
Tail: 15 mm. Well visualized. Normal findings.
[Spleen]{.underline}: Normal size, normal homogeneous internal echo
pattern, no focal changes, hilum is free. Organ size: 120 mm x 38 mm.
[Right kidney:]{.underline} Partially assessable, as far as
recognizable, parenchymal edge is age-appropriate, smooth organ contour,
no urinary obstruction, no stones. Size: 120 mm x 45 mm, parenchymal
thickness 21 mm.
[Left kidney:]{.underline} Partially assessable, as far as recognizable,
parenchymal edge is age-appropriate, smooth organ contour, no urinary
obstruction, no stones. Size: 115 mm x 61 mm, parenchymal thickness 19
mm.
[Bladder:]{.underline} Well visualized, orthotopically located, normal
wall proportions, no pathological echo structures in the lumen, normal
organ size.
[Abdominal vessels:]{.underline} Normal findings.
[Abdominal lymph nodes:]{.underline} No evidence of enlarged lymph nodes
in the subphrenic region.
[Peritoneum]{.underline}: No free fluid.
[Color duplex sonography of the portal vein:]{.underline} Orthograde
flow, no evidence of thrombosis.
[Assessment]{.underline}: In the right lower lobe cranial-lateral
(segment VII), there is an entirely echo-free cystic structure with a
slightly lobulated contour. There is no \"double wall,\" and there are
no features suggestive of Echinococcus. This is most likely a congenital
cyst. The overall structure, architecture, and texture of the liver are
normal, with no other focal abnormalities. In the rest of the abdomen,
there are no other pathological findings.
**Cardiology Consultation on 10/29/2021:**
**Medical History:** The patient reports thoracic complaints following
the intensive care unit stay post-COVID. These pains have been noticed
with mild exertion and are described as retrosternal with radiation to
the left chest. This last occurred on Sunday afternoon, lasting for
approximately 1 hour and then spontaneously resolving at rest. This pain
cannot be induced by a change in position, coughing, or deep
inspiration. Dyspnea is continuously present, and the patient still
requires oxygen. Dyspnea worsens when lying down.
**Cardiovascular risk factors**: Mildly elevated blood pressure
(hypertension) since May of this year, managed with half a tablet
according to self-measurements (averaging 120/80 mmHg, rarely in the
130s). Lipid profile checked by the general practitioner earlier this
year, presumably with good results. No known diabetes. Former smoker
until 2007, but it is difficult to estimate the pack-years, as smoking
occurred on occasions and during stressful times, less than 15
pack-years. No family history of cardiovascular diseases.
Uveitis/scleritis/episcleritis managed with 10mg MTX per week and 5 mg p
Prednisolone orally daily, well-controlled without recurrence.
**Physical Examination**: Lungs with moist rales bilaterally. Cardiac
examination with faint heart sounds. Regular heart rate of 80/min. No
pericardial rub. Pale-gray skin color. Respiratory rate of 15/min while
sitting. Radial pulses palpable bilaterally. Groin pulses not examined.
Allen\'s test operable on the right, borderline on the left.
**ECG**: tachycardic sinus rhythm with a heart rate of 109/min, left
axis deviation, regular R-wave progression in chest leads, mild
S-persistence in V6, no significant ST-T wave changes.
**Transthoracic and transesophageal echocardiography on 11/27/2020**:
[Kinetics]{.underline}: Hypokinesia of the lateral and anterior walls,
otherwise normokinetic and synergistic. Systolic function (right
ventricle): TAPSE 18 mm (\> 16 mm), RV-S\' 17.6 cm/s (\> 10 cm/s).
[Valves]{.underline}: Mitral valve - Delicate leaflets, good opening
motion, no significant insufficiency. Lambl\'s excrescences on the
atrial side. Small fluttering structure at the subvalvular apparatus,
compatible with chordae tendineae. Aortic valve - Tricuspid, delicate
valve. Functionally intact (AV Vmax 1.0 m/s). Tricuspid valve -
Morphologically normal. Mild insufficiency. TR Vmax 1.9 m/s, sPAP 15
mmHg + CVP. Pulmonic valve - Morphologically and functionally normal.
[Other Findings:]{.underline} No pericardial effusion. Small Persistent
Foramen Ovale. Left atrial appendage free of intracavitary thrombi at
60°/90°/150°. Thoracic aorta with smooth-walled plaques, no dissections
or thrombi.
[Assessment]{.underline}: No structures suggestive of endocarditis. No
relevant valvular abnormalities. Incidentally, there is a moderately
reduced LVEF with wall motion abnormalities in the RIVA (right
ventricular anterior) region. We request a cardiology consultation and
further diagnostics.
**Phoniatric Consultation on 10/24/2021:**
[Medical History:]{.underline} Patient with a history of COVID
pneumonia, twice tested negative. Currently, the patient has Aspergillus
and pneumonia. Previously, the patient was in the ICU and intubated for
two weeks due to COVID. Following speech therapy for dysphagia, a
flexible endoscopic evaluation of swallowing (FEES) is requested.
[Findings]{.underline}: FEES reveals a normal configuration of the
larynx with good mobility of the tongue and lips. Normal gross mobility
of the vocal cords during phonation and respiration transitions. Full
glottic closure appears complete. Flexible transnasal swallow evaluation
(FEES) with blue dye: Sufficient oral bolus control for liquids, purees,
and solids. No drooling or leakage. Swallow reflex present. Voluntary
initiation of the swallow act is possible. Side-by-side swallowing of
test substances over the valleculae without evidence of
pre-/intra-/post-deglutitive penetration or aspiration for all test
consistencies. Rosenbeck\'s Penetration-Aspiration Scale score: 1
(Minimal retention in the valleculae with puree, which can be completely
cleared by swallowing). Normal sensitivity, strong cough reflex. No
nasal regurgitation.
[Assessment]{.underline}: Normal swallowing function.
[Current Recommendations:]{.underline} Able to consume regular diet and
thin liquids, as well as medications with water.
**Therapy and Progression:** The patient was admitted for further
treatment. Upon admission, the patient was in a reduced general
condition with significant mobility limitations.
Staphylococcus aureus was detected in a blood culture, leading to a
transesophageal echocardiogram (TEE) on 11/26/2020. No vegetations were
found, but a moderate hypokinesia of the left ventricle in the RIVA area
was observed. Cardiac enzymes were within normal limits. This was
interpreted as post-COVID myocarditis, differential diagnosis myocardial
injury in severe ARDS, coronary artery disease, or mixed picture.
In consultation with the cardiology colleagues, a cautious heart failure
medication regimen with beta-blockers and ACE inhibitors was initiated.
We recommend an elective coronary angiography in the future. Currently,
the patient was symptom-free with low cardiac markers and a normal ECG,
so acute diagnostic procedures were not indicated.
The antibiotic therapy with Cefazolin was continued until 11/05/2021
(last sterile blood cultures from 10/24/2021). Staphylococcus
epidermidis detected in the blood culture on 10/20/21 and at the tip of
the central venous catheter on 10/22/21 were considered
catheter-associated. The catheter was immediately removed. The patient
did not develop a fever during the hospital stay. Inflammatory markers
improved over time.
An abdominal ultrasound was performed due to an unclear liver lesion,
which was found to be a congenital cyst. Echinococcus serology was
negative.
In consultation with the psychiatric colleagues, Quetiapine medication
was cautiously resumed for known depression, but it had to be
discontinued later due to significant QTc prolongation. Long-term oxygen
therapy of 2 liters was indicated.
Our ophthalmology colleagues recommended the resumption of MTX therapy
given the patient\'s stable vision. We request this therapy be initiated
and an outpatient follow-up appointment in ophthalmology arranged after
the patient completes rehabilitation.
With physiotherapy, the patient achieved mobilization up to walking.
Swallowing and articulation difficulties also significantly improved.
**Medication upon Discharge:**
**Medication** **Dosage** **Frequency** **Route**
---------------------------------- --------------- ------------------------------ ------------
Metoprolol Succinate (Toprol XL) 23.8 mg 1-0-1-0 Oral
Dicloxacillin Sodium (Dynapen) 2176 mg 1-1-1-0 Oral
Voriconazole (Vfend) 200 mg 2-0-2-0 Oral
Acetaminophen (Tylenol) 500 mg As needed Oral
Ipratropium Bromide (Atrovent) 0.26 mg/2 ml 6-0-0-0 Inhalation
Albuterol Sulfate (ProAir) 1.5 mg/2.5 ml 6-0-0-0 Inhalation
Amitriptyline (Elavil) 28.3 mg 0-0-1-0 Oral
Citalopram (Celexa) 50 mg 1-0-0-0 Oral
Melatonin 2 mg 0-0-2-0 Oral
Montelukast (Singulair) 10 mg 1-0-0-0 Oral
Pantoprazole (Protonix) 45 mg 0-0-1-0 Oral
Eplerenone (Inspra) 25 mg 1-0-0-0 Oral
Ramipril (Altace) 2.5 mg 0-0-1-0 Oral
Folic Acid 5 mg 0-0-1-0 48h after MTX intake Oral
Methotrexate (Trexall) 15 mg 1-0-0-0 Once a Week Oral
### Patient Report 4
**Dear colleague, **
We thank you for referring your patient, Mr. Brian Carter, born on
04/24/1956 to our outpatient care on 02/03/2022.
**Diagnoses**: Suspected Post-Intensive-Care Syndrome with:
- Dysphagia
- ICU-acquired weakness
- Depressive mood, anxiety
**Other Diagnoses:**
- COVID-19 with severe ARDS
- Symptoms began on 09/24/2021 with progressive dyspnea, cough, and
headache
- Initial detection of SARS-CoV-2 on 09/28/2021 in nasopharyngeal swab
- Dexamethasone from 09/29 to 10/8/2021
- Prone positioning from 09/18 to 10/8 and NO therapy
- Intubation on 09/30, initial extubation on 10/13
- Reporting to the health department by the referring physician
- Pulmonary superinfection with detection of Aspergillus fumigatus
- Voriconazole therapy since 10/7/2021
- Bacteremia with detection of Staphylococcus aureus in blood culture
on 10/19/21
- Flucloxacillin since 10/21/2021
- Thrombophlebitis of the right forearm
- Left eye pigment epithelium clumping, suggestive of a history of
retinal central serous chorioretinopathy
<!-- -->
- Rheumatoid arthritis
- Uveitis
- Bronchial asthma
- Arterial hypertension
- Depression
**Medical History:** Mr. Carter was treated in the intensive care unit
for a total of 24 days in September and October 2021 due to COVID-19.
Following intensive care treatment, he underwent neurological
rehabilitation from 11/08/2021 to 01/18/2022, with the following
rehabilitation results: \"Mr. I. benefited well from the therapies.
Particularly, physiotherapy helped regain confidence in walking. During
treatment, breathing difficulties improved, and oxygen supplementation
was no longer necessary.\" An antidepressant therapy with Mirtazapine
was initiated for sleep disorders and mood swings, resulting in a
reduction in sleep disturbances.
**Assessment**: Since the illness, Mr. Carter reports general fatigue,
quick fatigue, and weakness, especially in the lower extremities. He is
currently not undergoing physiotherapy or any other treatments.
Regarding psychopharmacological therapy, the patient has been seeing a
psychiatrist once a month based on anamnesis. After a short exertion, he
experiences dyspnea and regularly needs to take breaks. Room air
saturation was at 94%. Physical examination revealed significant
expiratory wheezing and prolonged expiration bilaterally. Furthermore,
the patient reports cognitive impairments with marked forgetfulness and
difficulty concentrating. This is evident in the reduced results of the
MiniCog (2/3 words, normal clock, 4 points) and animal naming tests
(correct single naming of 10 animals, 3 points). Additionally, the
patient reports an exacerbation of symptoms of depression known since
2011, including sadness, fatigue, sleep disturbances, and anxiety. These
worsened during the ICU stay. The current medication includes Citalopram
40 mg and Mirtazapine 7.5 mg, which have somewhat improved previously
worsened sleep disturbances. Psychotherapy is not currently taking place
but is strongly recommended.
Dysphagia diagnosed during the intensive care unit stay has slightly
improved, allowing Mr. Carter to consume regular food again. However, he
still experiences dysphagia and coughing during each meal. An
appointment at the swallowing clinic has been scheduled by us (see
below).
**Current Recommendations:**
As swallowing difficulties persist, an appointment has been scheduled at
our local swallowing clinic. We also recommend a pulmonary evaluation.
Contact has already been made, and the colleagues from Pulmonology will
get in touch with Mr. Carter. Furthermore, due to a previously existing
depressive mood with currently exacerbated symptoms, we recommend
connecting the patient with an outpatient psychotherapist. Some
therapists have already been suggested by the patient\'s general
practitioner, and we strongly recommend further contact. A prescription
for physiotherapy has been issued for pronounced muscle weakness and
suspected ICU-acquired weakness. Further physiotherapeutic engagement
with the general practitioner\'s assistance is urgently required.
**Lab results upon Discharge:**
**Parameter** **Results** **Reference Range**
----------------------- ------------- ---------------------
Neutrophils 49.0 % 42.0-77.0 %
Lymphocytes 31.9 % 20.0-44.0 %
Monocytes 7.2 % 2.0-9.5 %
Basophils 0.7 % 0.0-1.8 %
Eosinophils 10.8 % 0.5-5.5 %
Immature Granulocytes 0.4 % 0.0-1.0 %
Sodium 139 mEq/L 136-145 mEq/L
Potassium 3.5 mEq/L 3.5-4.5 mEq/L
Creatinine 0.99 mg/dL 0.70-1.20 mg/dL
BUN 25 mg/dL 17-48 mg/dL
Total Bilirubin 0.45 mg/dL \< 1.20 mg/dL
CRP 3.9 mg/dL \< 5.0 mg/dL
ALT 21 U/L \< 41 U/L
AST 20 U/L \< 50 U/L
Alkaline Phosphatase 65 U/L 40-130 U/L
Gamma-GT 38 U/L 8-61 U/L
LDH 160 U/L 135-250 U/L
Lipase 20 U/L 13-60 U/L
Procalcitonin 0.03 µg/L \< 0.50 µg/L
Hemoglobin 13.2 g/dL 13.5-17.0 g/dL
Hematocrit 44.2 % 39.5-50.5 %
Red Blood Cells 5.2 M/µL 4.3-5.8 M/µL
White Blood Cells 7.62 K/µL 3.90-10.50 K/µL
Platelets 281 K/µL 150-370 K/µL | Congenital cyst |
What is Terry's mother's core tension of being a star mother?
A. People see her star mother status as an opportunity, while she wishes someone else could have it
B. People are generally critical of the star mother law, but she is grateful for it
C. People want to know more about Terry's journey, and she has no way of accurately representing it
D. People are skeptical of the exploration, while she is a firm supporter
| STAR MOTHER By ROBERT F. YOUNG A touching story of the most enduring love in all eternity. That night her son was the first star. She stood motionless in the garden, one hand pressed against her heart, watching him rise above the fields where he had played as a boy, where he had worked as a young man; and she wondered whether he was thinking of those fields now, whether he was thinking of her standing alone in the April night with her memories; whether he was thinking of the verandahed house behind her, with its empty rooms and silent halls, that once upon a time had been his birthplace. Higher still and higher he rose in the southern sky, and then, when he had reached his zenith, he dropped swiftly down past the dark edge of the Earth and disappeared from sight. A boy grown up too soon, riding round and round the world on a celestial carousel, encased in an airtight metal capsule in an airtight metal chariot ... Why don't they leave the stars alone? she thought. Why don't they leave the stars to God? The general's second telegram came early the next morning: Explorer XII doing splendidly. Expect to bring your son down sometime tomorrow . She went about her work as usual, collecting the eggs and allocating them in their cardboard boxes, then setting off in the station wagon on her Tuesday morning run. She had expected a deluge of questions from her customers. She was not disappointed. "Is Terry really way up there all alone, Martha?" "Aren't you scared , Martha?" "I do hope they can get him back down all right, Martha." She supposed it must have given them quite a turn to have their egg woman change into a star mother overnight. She hadn't expected the TV interview, though, and she would have avoided it if it had been politely possible. But what could she do when the line of cars and trucks pulled into the drive and the technicians got out and started setting up their equipment in the backyard? What could she say when the suave young man came up to her and said, "We want you to know that we're all very proud of your boy up there, ma'am, and we hope you'll do us the honor of answering a few questions." Most of the questions concerned Terry, as was fitting. From the way the suave young man asked them, though, she got the impression that he was trying to prove that her son was just like any other average American boy, and such just didn't happen to be the case. But whenever she opened her mouth to mention, say, how he used to study till all hours of the night, or how difficult it had been for him to make friends because of his shyness, or the fact that he had never gone out for football—whenever she started to mention any of these things, the suave young man was in great haste to interrupt her and to twist her words, by requestioning, into a different meaning altogether, till Terry's behavior pattern seemed to coincide with the behavior pattern which the suave young man apparently considered the norm, but which, if followed, Martha was sure, would produce not young men bent on exploring space but young men bent on exploring trivia. A few of the questions concerned herself: Was Terry her only child? ("Yes.") What had happened to her husband? ("He was killed in the Korean War.") What did she think of the new law granting star mothers top priority on any and all information relating to their sons? ("I think it's a fine law ... It's too bad they couldn't have shown similar humanity toward the war mothers of World War II.") It was late in the afternoon by the time the TV crew got everything repacked into their cars and trucks and made their departure. Martha fixed herself a light supper, then donned an old suede jacket of Terry's and went out into the garden to wait for the sun to go down. According to the time table the general had outlined in his first telegram, Terry's first Tuesday night passage wasn't due to occur till 9:05. But it seemed only right that she should be outside when the stars started to come out. Presently they did, and she watched them wink on, one by one, in the deepening darkness of the sky. She'd never been much of a one for the stars; most of her life she'd been much too busy on Earth to bother with things celestial. She could remember, when she was much younger and Bill was courting her, looking up at the moon sometimes; and once in a while, when a star fell, making a wish. But this was different. It was different because now she had a personal interest in the sky, a new affinity with its myriad inhabitants. And how bright they became when you kept looking at them! They seemed to come alive, almost, pulsing brilliantly down out of the blackness of the night ... And they were different colors, too, she noticed with a start. Some of them were blue and some were red, others were yellow ... green ... orange ... It grew cold in the April garden and she could see her breath. There was a strange crispness, a strange clarity about the night, that she had never known before ... She glanced at her watch, was astonished to see that the hands indicated two minutes after nine. Where had the time gone? Tremulously she faced the southern horizon ... and saw her Terry appear in his shining chariot, riding up the star-pebbled path of his orbit, a star in his own right, dropping swiftly now, down, down, and out of sight beyond the dark wheeling mass of the Earth ... She took a deep, proud breath, realized that she was wildly waving her hand and let it fall slowly to her side. Make a wish! she thought, like a little girl, and she wished him pleasant dreams and a safe return and wrapped the wish in all her love and cast it starward. Sometime tomorrow, the general's telegram had said— That meant sometime today! She rose with the sun and fed the chickens, fixed and ate her breakfast, collected the eggs and put them in their cardboard boxes, then started out on her Wednesday morning run. "My land, Martha, I don't see how you stand it with him way up there! Doesn't it get on your nerves ?" ("Yes ... Yes, it does.") "Martha, when are they bringing him back down?" ("Today ... Today !") "It must be wonderful being a star mother, Martha." ("Yes, it is—in a way.") Wonderful ... and terrible. If only he can last it out for a few more hours, she thought. If only they can bring him down safe and sound. Then the vigil will be over, and some other mother can take over the awesome responsibility of having a son become a star— If only ... The general's third telegram arrived that afternoon: Regret to inform you that meteorite impact on satellite hull severely damaged capsule-detachment mechanism, making ejection impossible. Will make every effort to find another means of accomplishing your son's return. Terry!— See the little boy playing beneath the maple tree, moving his tiny cars up and down the tiny streets of his make-believe village; the little boy, his fuzz of hair gold in the sunlight, his cherub-cheeks pink in the summer wind— Terry!— Up the lane the blue-denimed young man walks, swinging his thin tanned arms, his long legs making near-grownup strides over the sun-seared grass; the sky blue and bright behind him, the song of cicada rising and falling in the hazy September air— Terry ... —probably won't get a chance to write you again before take-off, but don't worry, Ma. The Explorer XII is the greatest bird they ever built. Nothing short of a direct meteorite hit can hurt it, and the odds are a million to one ... Why don't they leave the stars alone? Why don't they leave the stars to God? The afternoon shadows lengthened on the lawn and the sun grew red and swollen over the western hills. Martha fixed supper, tried to eat, and couldn't. After a while, when the light began to fade, she slipped into Terry's jacket and went outside. Slowly the sky darkened and the stars began to appear. At length her star appeared, but its swift passage blurred before her eyes. Tires crunched on the gravel then, and headlights washed the darkness from the drive. A car door slammed. Martha did not move. Please God , she thought, let it be Terry , even though she knew that it couldn't possibly be Terry. Footsteps sounded behind her, paused. Someone coughed softly. She turned then— "Good evening, ma'am." She saw the circlet of stars on the gray epaulet; she saw the stern handsome face; she saw the dark tired eyes. And she knew. Even before he spoke again, she knew— "The same meteorite that damaged the ejection mechanism, ma'am. It penetrated the capsule, too. We didn't find out till just a while ago—but there was nothing we could have done anyway ... Are you all right, ma'am?" "Yes. I'm all right." "I wanted to express my regrets personally. I know how you must feel." "It's all right." "We will, of course, make every effort to bring back his ... remains ... so that he can have a fitting burial on Earth." "No," she said. "I beg your pardon, ma'am?" She raised her eyes to the patch of sky where her son had passed in his shining metal sarcophagus. Sirius blossomed there, blue-white and beautiful. She raised her eyes still higher—and beheld the vast parterre of Orion with its central motif of vivid forget-me-nots, its far-flung blooms of Betelguese and Rigel, of Bellatrix and Saiph ... And higher yet—and there flamed the exquisite flower beds of Taurus and Gemini, there burgeoned the riotous wreath of the Crab; there lay the pulsing petals of the Pleiades ... And down the ecliptic garden path, wafted by a stellar breeze, drifted the ocher rose of Mars ... "No," she said again. The general had raised his eyes, too; now, slowly, he lowered them. "I think I understand, ma'am. And I'm glad that's the way you want it ... The stars are beautiful tonight, aren't they." "More beautiful than they've ever been," she said. After the general had gone, she looked up once more at the vast and variegated garden of the sky where her son lay buried, then she turned and walked slowly back to the memoried house. THE END Transcriber's Note: This etext was produced from Amazing Stories January 1959. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note. | A. People see her star mother status as an opportunity, while she wishes someone else could have it |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.