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minions-stanford
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15K_token_sft_QA

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train · 4.21k rows

question stringlengths 12 1.77k	context stringlengths 79 71.7k	answer stringlengths 1 1.63k
What neural architectures are used?	### Motivations Figurative language makes use of figures of speech to convey non-literal meaning BIBREF0, BIBREF1. It encompasses a variety of phenomena, including metaphor, humor, and irony. We focus here on irony and uses it as an umbrella term that covers satire, parody and sarcasm. Irony detection (ID) has gained relevance recently, due to its importance to extract information from texts. For example, to go beyond the literal matches of user queries, Veale enriched information retrieval with new operators to enable the non-literal retrieval of creative expressions BIBREF2. Also, the performances of sentiment analysis systems drastically decrease when applied to ironic texts BIBREF3, BIBREF4. Most related work concern English BIBREF5, BIBREF6 with some efforts in French BIBREF7, Portuguese BIBREF8, Italian BIBREF9, Dutch BIBREF10, Hindi BIBREF11, Spanish variants BIBREF12 and Arabic BIBREF13, BIBREF14. Bilingual ID with one model per language has also been explored, like English-Czech BIBREF15 and English-Chinese BIBREF16, but not within a cross-lingual perspective. In social media, such as Twitter, specific hashtags (#irony, #sarcasm) are often used as gold labels to detect irony in a supervised learning setting. Although recent studies pointed out the issue of false-alarm hashtags in self-labeled data BIBREF17, ID via hashtag filtering provides researchers positive examples with high precision. On the other hand, systems are not able to detect irony in languages where such filtering is not always possible. Multilingual prediction (either relying on machine translation or multilingual embedding methods) is a common solution to tackle under-resourced languages BIBREF18, BIBREF19. While multilinguality has been widely investigated in information retrieval BIBREF20, BIBREF21 and several NLP tasks (e.g., sentiment analysis BIBREF22, BIBREF23 and named entity recognition BIBREF24), no one explored it for irony. We aim here to bridge the gap by tackling ID in tweets from both multilingual (French, English and Arabic) and multicultural perspectives (Indo-European languages whose speakers share quite the same cultural background vs. less culturally close languages). Our approach does not rely either on machine translation or parallel corpora (which are not always available), but rather builds on previous corpus-based studies that show that irony is a universal phenomenon and many languages share similar irony devices. For example, Karoui et. al BIBREF25 concluded that their multi-layer annotated schema, initially used to annotate French tweets, is portable to English and Italian, observing relatively the same tendencies in terms of irony categories and markers. Similarly, Chakhachiro BIBREF26 studies irony in English and Arabic, and shows that both languages share several similarities in the rhetorical (e.g., overstatement), grammatical (e.g., redundancy) and lexical (e.g., synonymy) usage of irony devices. The next step now is to show to what extent these observations are still valid from a computational point of view. Our contributions are: A new freely available corpus of Arabic tweets manually annotated for irony detection. Monolingual ID: We propose both feature-based models (relying on language-dependent and language-independent features) and neural models to measure to what extent ID is language dependent. Cross-lingual ID: We experiment using cross-lingual word representation by training on one language and testing on another one to measure how the proposed models are culture-dependent. Our results are encouraging and open the door to ID in languages that lack of annotated data for irony. ### Data Arabic dataset (Ar=$11,225$ tweets). Our starting point was the corpus built by BIBREF13 that we extended to different political issues and events related to the Middle East and Maghreb that hold during the years 2011 to 2018. Tweets were collected using a set of predefined keywords (which targeted specific political figures or events) and containing or not Arabic ironic hashtags (سخرية>#, مسخرة>#, تهكم>#, استهزاء>#) . The collection process resulted in a set of $6,809$ ironic tweets ($I$) vs. $15,509$ non ironic ($NI$) written using standard (formal) and different Arabic language varieties: Egypt, Gulf, Levantine, and Maghrebi dialects. To investigate the validity of using the original tweets labels, a sample of $3,000$ $I$ and $3,000$ $NI$ was manually annotated by two Arabic native speakers which resulted in $2,636$ $I$ vs. $2,876$ $NI$. The inter-annotator agreement using Cohen's Kappa was $0.76$, while the agreement score between the annotators' labels and the original labels was $0.6$. Agreements being relatively good knowing the difficulty of the task, we sampled $5,713$ instances from the original unlabeled dataset to our manually labeled part. The added tweets have been manually checked to remove duplicates, very short tweets and tweets that depend on external links, images or videos to understand their meaning. French dataset (Fr=$7,307$ tweets). We rely on the corpus used for the DEFT 2017 French shared task on irony BIBREF3 which consists of tweets relative to a set of topics discussed in the media between 2014 and 2016 and contains topic keywords and/or French irony hashtags (#ironie, #sarcasme). Tweets have been annotated by three annotators (after removing the original labels) with a reported Cohen's Kappa of $0.69$. English dataset (En=$11,225$ tweets). We use the corpus built by BIBREF15 which consists of $100,000$ tweets collected using the hashtag #sarcasm. It was used as benchmark in several works BIBREF27, BIBREF28. We sliced a subset of approximately $11,200$ tweets to match the sizes of the other languages' datasets. Table TABREF6 shows the tweet distribution in all corpora. Across the three languages, we keep a similar number of instances for train and test sets to have fair cross-lingual experiments as well (see Section SECREF4). Also, for French, we use the original dataset without any modification, keeping the same number of records for train and test to better compare with state-of-the-art results. For the classes distribution (ironic vs. non ironic), we do not choose a specific ratio but we use the resulted distribution from the random shuffling process. ### Monolingual Irony Detection It is important to note that our aim is not to outperform state-of-the-art models in monolingual ID but to investigate which of the monolingual architectures (neural or feature-based) can achieve comparable results with existing systems. The result can show which kind of features works better in the monolingual settings and can be employed to detect irony in a multilingual setting. In addition, it can show us to what extend ID is language dependent by comparing their results to multilingual results. Two models have been built, as explained below. Prior to learning, basic preprocessing steps were performed for each language (e.g., removing foreign characters, ironic hashtags, mentions, and URLs). Feature-based models. We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). Several classical machine learning classifiers were tested with several feature combinations, among them Random Forest (RF) achieved the best result with all features. Neural model with monolingual embeddings. We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English . For the three languages, the size of the embeddings is 300 and the embeddings were fine-tuned during the training process. The CNN network was tuned with 20% of the training corpus using the $Hyperopt$ library. Results. Table TABREF9 shows the results obtained when using train-test configurations for each language. For English, our results, in terms of macro F-score ($F$), were not comparable to those of BIBREF15, BIBREF33, as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved $F=78.3$ BIBREF3). They outperform those obtained for Arabic ($A=71.7$) BIBREF13 and are comparable to those recently reported in the irony detection shared task in Arabic tweets BIBREF14, BIBREF34 ($F=84.4$). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features. ### Cross-lingual Irony Detection We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) to verify which pair of the three languages: (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages BIBREF35, we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries BIBREF35 or unsupervised methods relying on monolingual corpora BIBREF36, BIBREF37, BIBREF38. For our experiments, we use Conneau et al 's approach as it showed superior results with respect to the literature BIBREF36. We perform several experiments by training on one language ($lang_1$) and testing on another one ($lang_2$) (henceforth $lang_1\rightarrow lang_2$). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process. Table TABREF11 presents the results. From a semantic perspective, despite the language and cultural differences between Arabic and French languages, CNN results show a high performance comparing to the other languages pairs when we train on each of these two languages and test on the other one. Similarly, for the French and English pair, but when we train on French they are quite lower. We have a similar case when we train on Arabic and test on English. We can justify that by, the language presentation of the Arabic and French tweets are quite informal and have many dialect words that may not exist in the pretrained embeddings we used comparing to the English ones (lower embeddings coverage ratio), which become harder for the CNN to learn a clear semantic pattern. Another point is the presence of Arabic dialects, where some dialect words may not exist in the multilingual pretrained embedding model that we used. On the other hand, from the text-based perspective, the results show that the text-based features can help in the case when the semantic aspect shows weak detection; this is the case for the $Ar\longrightarrow En$ configuration. It is worthy to mention that the highest result we get in this experiment is from the En$\rightarrow $Fr pair, as both languages use Latin characters. Finally, when investigating the relatedness between European vs. non European languages (cf. (En/Fr)$\rightarrow $Ar), we obtain similar results than those obtained in the monolingual experiment (macro F-score 62.4 vs. 68.0) and best results are achieved by Ar $\rightarrow $(En/Fr). This shows that there are pragmatic devices in common between both sides and, in a similar way, similar text-based patterns in the narrative way of the ironic tweets. ### Discussions and Conclusion This paper proposes the first multilingual ID in tweets. We show that simple monolingual architectures (either neural or feature-based) trained separately on each language can be successfully used in a multilingual setting providing a cross-lingual word representation or basic surface features. Our monolingual results are comparable to state of the art for the three languages. The CNN architecture trained on cross-lingual word representation shows that irony has a certain similarity between the languages we targeted despite the cultural differences which confirm that irony is a universal phenomena, as already shown in previous linguistic studies BIBREF39, BIBREF25, BIBREF40. The manual analysis of the common misclassified tweets across the languages in the multilingual setup, shows that classification errors are due to three main factors. (1) First, the absence of context where writers did not provide sufficient information to capture the ironic sense even in the monolingual setting, as in نبدا تاني يسقط يسقط حسني مبارك !! > (Let's start again, get off get off Mubarak!!) where the writer mocks the Egyptian revolution, as the actual president "Sisi" is viewed as Mubarak's fellows. (2) Second, the presence of out of vocabulary (OOV) terms because of the weak coverage of the mutlilingual embeddings which make the system fails to generalize when the OOV set of unseen words is large during the training process. We found tweets in all the three languages written in a very informal way, where some characters of the words were deleted, duplicated or written phonetically (e.g phat instead of fat). (3) Another important issue is the difficulty to deal with the Arabic language. Arabic tweets are often characterized by non-diacritised texts, a large variations of unstandardized dialectal Arabic (recall that our dataset has 4 main varieties, namely Egypt, Gulf, Levantine, and Maghrebi), presence of transliterated words (e.g. the word table becomes طابلة> (tabla)), and finally linguistic code switching between Modern Standard Arabic and several dialects, and between Arabic and other languages like English and French. We found some tweets contain only words from one of the varieties and most of these words do not exist in the Arabic embeddings model. For example in مبارك بقاله كام يوم مامتش .. هو عيان ولاه ايه #مصر > (Since many days Mubarak didn't die .. is he sick or what? #Egypt), only the words يوم> (day), مبارك> (Mubarak), and هو> (he) exist in the embeddings. Clearly, considering only these three available words, we are not able to understand the context or the ironic meaning of the tweet. To conclude, our multilingual experiments confirmed that the door is open towards multilingual approaches for ID. Furthermore, our results showed that ID can be applied to languages that lack of annotated data. Our next step is to experiment with other languages such as Hindi and Italian. ### Acknowledgment The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE (PGC2018-096212-B-C31). Table 1. Tweet distribution in all corpora. Table 2. Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F). Table 3. Results of the cross-lingual experiments.	Convolutional Neural Network (CNN)
Which of these best describes the doctor that Linton meets at the end? A. Generous in that he is willing to help Linton with this problem that involves illegal work on his part B. Greedy in that he manipulates vulnerable people to take money from them C. Love-stricken, wanting to help people in similar situations D. Cunning in his cutting-edge technology he is developing	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.	B. Greedy in that he manipulates vulnerable people to take money from them
What had given it away to Don that the attractive redhead he saw on the train was not actually a natural redhead? A. Her skin tone was too off to match the hair color. B. The red tone of her hair was far too bright to be considered natural. C. She was carrying box hair dye. D. Her dark roots were showing.	And Then the Town Took Off by RICHARD WILSON ACE BOOKS, INC. 23 West 47th Street, New York 36, N.Y. AND THEN THE TOWN TOOK OFF Copyright ©, 1960, by Ace Books, Inc. All Rights Reserved For Felicitas K. Wilson THE SIOUX SPACEMAN Copyright ©, 1960, by Ace Books, Inc. Printed in U.S.A. THE CITY THAT RAN OFF THE MAP The town of Superior, Ohio, certainly was living up to its name! In what was undoubtedly the most spectacular feat of the century, it simply picked itself up one night and rose two full miles above Earth! Radio messages stated simply that Superior had seceded from Earth. But Don Cort, stranded on that rising town, was beginning to suspect that nothing was simple about Superior except its citizens. Calmly they accepted their rise in the world as being due to one of their local townspeople, a crackpot professor. But after a couple of weeks of floating around, it began to be obvious that the professor had no idea how to get them down. So then it was up to Cort: either find a way to anchor Superior, or spend the rest of his days on the smallest—and the nuttiest—planet in the galaxy! I The town of Superior, Ohio, disappeared on the night of October 31. A truck driver named Pierce Knaubloch was the first to report it. He had been highballing west along Route 202, making up for the time he'd spent over a second cup of coffee in a diner, when he screeched to a stop. If he'd gone another twenty-five feet he'd have gone into the pit where Superior had been. Knaubloch couldn't see the extent of the pit because it was too dark, but it looked big. Bigger than if a nitro truck had blown up, which was his first thought. He backed up two hundred feet, set out flares, then sped off to a telephone. The state police converged on the former site of Superior from several directions. Communicating by radiophone across the vast pit, they confirmed that the town undoubtedly was missing. They put in a call to the National Guard. The guard surrounded the area with troops—more than a thousand were needed—to keep people from falling into the pit. A pilot who flew over it reported that it looked as if a great ice-cream scoop had bitten into the Ohio countryside. The Pennsylvania Railroad complained that one of its passenger trains was missing. The train's schedule called for it to pass through but not stop at Superior at 11:58. That seemed to fix the time of the disappearance at midnight. The truck driver had made his discovery shortly after midnight. Someone pointed out that October 31 was Halloween and that midnight was the witching hour. Somebody else said nonsense, they'd better check for radiation. A civil defense official brought up a Geiger counter, but no matter how he shook it and rapped on it, it refused to click. A National Guard officer volunteered to take a jeep down into the pit, having found a spot that seemed navigable. He was gone a long time but when he came out the other side he reported that the pit was concave, relatively smooth, and did not smell of high explosives. He'd found no people, no houses—no sign of anything except the pit itself. The Governor of Ohio asked Washington whether any unidentified planes had been over the state. Washington said no. The Pentagon and the Atomic Energy Commission denied that they had been conducting secret experiments. Nor had there been any defense plants in Superior that might have blown up. The town's biggest factory made kitchen sinks and the next biggest made bubble gum. A United Airlines pilot found Superior early on the morning of November 1. The pilot, Captain Eric Studley, who had never seen a flying saucer and hoped never to see one, was afraid now that he had. The object loomed out of a cloudbank at twelve thousand feet and Studley changed course to avoid it. He noted with only minimum satisfaction that his co-pilot also saw the thing and wondered why it wasn't moving at the terrific speed flying saucers were allegedly capable of. Then he saw the church steeple on it. A few minutes later he had relayed a message from Superior, formerly of Ohio, addressed to whom it might concern: It said that Superior had seceded from Earth. One other radio message came from Superior, now airborne, on that first day. A ham radio operator reported an unidentified voice as saying plaintively: " Cold up here!" Don Cort had been dozing in what passed for the club car on the Buckeye Cannonball when the train braked to a stop. He looked out the window, hoping this was Columbus, where he planned to catch a plane east. But it wasn't Columbus. All he could see were some lanterns jogging as trainmen hurried along the tracks. The conductor looked into the car. The redhead across the aisle in whom Don had taken a passing interest earlier in the evening asked, "Why did we stop?" "Somebody flagged us down," the conductor said. "We don't make a station stop at Superior on this run." The girl's hair was a subtle red, but false. When Don had entered the club car he'd seen her hatless head from above and noticed that the hair along the part was dark. Her eyes had been on a book and Don had the opportunity for a brief study of her face. The cheeks were full and untouched by make-up. There were lines at the corners of her mouth which indicated a tendency to arrange her expression into one of disapproval. The lips were full, like the cheeks, but it was obvious that the scarlet lipstick had contrived a mouth a trifle bigger than the one nature had given her. Her glance upward at that moment interrupted his examination, which had been about to go on to her figure. Later, though, he was able to observe that it was more than adequate. If the girl had given Don Cort more than that one glance, or if it had been a trained, all-encompassing glance, she would have seen a man in his mid-twenties—about her age—lean, tall and straight-shouldered, with once-blond hair now verging on dark brown, a face neither handsome nor ugly, and a habit of drawing the inside of his left cheek between his teeth and nibbling at it thoughtfully. But it was likely that all she noticed then was the brief case he carried, attached by a chain to a handcuff on his left wrist. "Will we be here long?" Don asked the conductor. He didn't want to miss his plane at Columbus. The sooner he got to Washington, the sooner he'd get rid of the brief case. The handcuff it was attached to was one reason why his interest in the redhead had been only passing. "Can't say," the conductor told him. He let the door close again and went down to the tracks. Don hesitated, shrugged at the redhead, said, "Excuse me," and followed the conductor. About a dozen people were milling around the train as it sat in the dark, hissing steam. Don made his way up to the locomotive and found a bigger knot of people gathered in front of the cowcatcher. Some sort of barricade had been put up across the tracks and it was covered with every imaginable kind of warning device. There were red lanterns, both battery and electric; flashlights; road flares; and even an old red shirt. Don saw two men who must have been the engineer and the fireman talking to an old bearded gentleman wearing a civil defense helmet, a topcoat and riding boots. "You'd go over the edge, I tell you," the old gentleman was saying. "If you don't get this junk off the line," the engineer said, "I'll plow right through it. Off the edge! you crazy or something?" "Look for yourself," the old man in the white helmet said. "Go ahead. Look." The engineer was exasperated. He turned to the fireman. "You look. Humor the old man. Then let's go." The bearded man—he called himself Professor Garet—went off with the fireman. Don followed them. They had tramped a quarter of a mile along the gravel when the fireman stopped. "Okay," he said "where's the edge? I don't see nothing." The tracks seemed to stretch forever into the darkness. "It's another half mile or so," the professor said. "Well, let's hurry up. We haven't got all night." The old man chuckled. "I'm afraid you have." They came to it at last, stopping well back from it. Professor Garet swelled with pride, it seemed, as he made a theatrical gesture. "Behold," he said. "Something even Columbus couldn't find. The edge of the world." True, everything seemed to stop, and they could see stars shining low on the horizon where stars could not properly be expected to be seen. Don Cort and the fireman walked cautiously toward the edge while the professor ambled ahead with the familiarity of one who had been there before. But there was a wind and they did not venture too close. Nevertheless, Don could see that it apparently was a neat, sharp edge, not one of your old ragged, random edges such as might have been caused by an explosion. This one had the feeling of design behind it. Standing on tiptoe and repressing a touch of giddiness, Don looked over the edge. He didn't have to stand on tiptoe any more than he had to sit on the edge of his seat during the exciting part of a movie, but the situation seemed to call for it. Over the edge could be seen a big section of Ohio. At least he supposed it was Ohio. Don looked at the fireman, who had an unbelieving expression on his face, then at the bearded old man, who was smiling and nodding. "You see what I mean," he said. "You would have gone right over. I believe you would have had a two-mile fall." "Of course you could have stayed aboard the train," the man driving the old Pontiac said, "but I really think you'll be more comfortable at Cavalier." Don Cort, sitting in the back seat of the car with the redhead from the club car, asked, "Cavalier?" "The college. The institute, really; it's not accredited. What did you say your name was, miss?" "Jen Jervis," she said. "Geneva Jervis, formally." "Miss Jervis. I'm Civek. You know Mr. Cort, I suppose." The girl smiled sideways. "We have a nodding acquaintance." Don nodded and grinned. "There's plenty of room in the dormitories," Civek said. "People don't exactly pound on the gates and scream to be admitted to Cavalier." "Are you connected with the college?" Don asked. "Me? No. I'm the mayor of Superior. The old town's really come up in the world, hasn't it?" "Overnight," Geneva Jervis said. "If what Mr. Cort and the fireman say is true. I haven't seen the edge myself." "You'll have a better chance to look at it in the morning," the mayor said, "if we don't settle back in the meantime." "Was there any sort of explosion?" Don asked. "No. There wasn't any sensation at all, as far as I noticed. I was watching the late show—or trying to. My house is down in a hollow and reception isn't very good, especially with old English movies. Well, all of a sudden the picture sharpened up and I could see just as plain. Then the phone rang and it was Professor Garet." "The old fellow with the whiskers and the riding boots?" Jen Jervis asked. "Yes. Osbert Garet, Professor of Magnology at the Cavalier Institute of Applied Sciences." "Professor of what?" "Magnology. As I say, the school isn't accredited. Well, Professor Garet telephoned and said, 'Hector'—that's my name, Hector Civek—'everything's up in the air.' He was having his little joke, of course. I said, 'What?' and then he told me." "Told you what?" Jen Jervis asked. "I mean, does he have any theory about it?" "He has a theory about everything. I think what he was trying to convey was that this—this levitation confirmed his magnology principle." "What's that?" Don asked. "I haven't the faintest idea. I'm a politician, not a scientist. Professor Garet went on about it for a while, on the telephone, about magnetism and gravity, but I think he was only calling as a courtesy, so the mayor wouldn't look foolish the next morning, not knowing his town had flown the coop." "What's the population of Superior?" "Three thousand, including the students at the institute. Three thousand and forty, counting you people from the train. I guess you'll be with us for a while." "What do you mean by that?" Jen Jervis asked. "Well, I don't see how you can get down. Do you?" "Does Superior have an airport?" Don asked. "I've got to get back to—to Earth." It sounded odd to put it that way. "Nope," Civek said. "No airport. No place for a plane to land, either." "Maybe not a plane," Don said, "but a helicopter could land just about anywhere." "No helicopters here, either." "Maybe not. But I'll bet they're swarming all over you by morning." "Hm," said Hector Civek. Don couldn't quite catch his expression in the rearview mirror. "I suppose they could, at that. Well, here's Cavalier. You go right in that door, where the others are going. There's Professor Garet. I've got to see him—excuse me." The mayor was off across the campus. Don looked at Geneva Jervis, who was frowning. "Are you thinking," he asked, "that Mayor Civek was perhaps just a little less than completely honest with us?" "I'm thinking," she said, "that I should have stayed with Aunt Hattie another night, then taken a plane to Washington." "Washington?" Don said. "That's where I'm going. I mean where I was going before Superior became airborne. What do you do in Washington, Miss Jervis?" "I work for the Government. Doesn't everybody?" "Not everybody. Me, for instance." "No?" she said. "Judging by that satchel you're handcuffed to, I'd have thought you were a courier for the Pentagon. Or maybe State." He laughed quickly and loudly because she was getting uncomfortably close. "Oh, no. Nothing so glamorous. I'm a messenger for the Riggs National Bank, that's all. Where do you work?" "I'm with Senator Bobby Thebold, S.O.B." Don laughed again. "He sure is." " Mister Cort!" she said, annoyed. "You know as well as I do that S.O.B. stands for Senate Office Building. I'm his secretary." "I'm sorry. We'd better get out and find a place to sleep. It's getting late." " Places to sleep," she corrected. She looked angry. "Of course," Don said, puzzled by her emphasis. "Come on. Where they put you, you'll probably be surrounded by co-eds, even if I could get out of this cuff." He took her bag in his free hand and they were met by a gray-haired woman who introduced herself as Mrs. Garet. "We'll try to make you comfortable," she said. "What a night, eh? The professor is simply beside himself. We haven't had so much excitement since the cosmolineator blew up." They had a glimpse of the professor, still in his CD helmet, going around a corner, gesticulating wildly to someone wearing a white laboratory smock. II Don Cort had slept, but not well. He had tried to fold the brief case to pull it through his sleeve so he could take his coat off, but whatever was inside the brief case was too big. Cavalier had given him a room to himself at one end of a dormitory and he'd taken his pants off but had had to sleep with his coat and shirt on. He got up, feeling gritty, and did what little dressing was necessary. It was eight o'clock, according to the watch on the unhandcuffed wrist, and things were going on. He had a view of the campus from his window. A bright sun shone on young people moving generally toward a squat building, and other people going in random directions. The first were students going to breakfast, he supposed, and the others were faculty members. The air was very clear and the long morning shadows distinct. Only then did he remember completely that he and the whole town of Superior were up in the air. He went through the dormitory. A few students were still sleeping. The others had gone from their unmade beds. He shivered as he stepped outdoors. It was crisp, if not freezing, and his breath came out visibly. First he'd eat, he decided, so he'd be strong enough to go take a good look over the edge, in broad daylight, to the Earth below. The mess hall, or whatever they called it, was cafeteria style and he got in line with a tray for juice, eggs and coffee. He saw no one he knew, but as he was looking for a table a willowy blonde girl smiled and gestured to the empty place opposite her. "You're Mr. Cort," she said. "Won't you join me?" "Thanks," he said, unloading his tray. "How did you know?" "The mystery man with the handcuff. You'd be hard to miss. I'm Alis—that's A-l-i-s, not A-l-i-c-e—Garet. Are you with the FBI? Or did you escape from jail?" "How do you do. No, just a bank messenger. What an unusual name. Professor Garet's daughter?" "The same," she said. "Also the only. A pity, because if there'd been two of us I'd have had a fifty-fifty chance of going to OSU. As it is, I'm duty-bound to represent the second generation at the nut factory." "Nut factory? You mean Cavalier?" Don struggled to manipulate knife and fork without knocking things off the table with his clinging brief case. "Here, let me cut your eggs for you," Alis said. "You'd better order them scrambled tomorrow. Yes, Cavalier. Home of the crackpot theory and the latter-day alchemist." "I'm sure it's not that bad. Thanks. As for tomorrow, I hope to be out of here by then." "How do you get down from an elephant? Old riddle. You don't; you get down from ducks. How do you plan to get down from Superior?" "I'll find a way. I'm more interested at the moment in how I got up here." "You were levitated, like everybody else." "You make it sound deliberate, Miss Garet, as if somebody hoisted a whole patch of real estate for some fell purpose." "Scarcely fell , Mr. Cort. As for it being deliberate, that seems to be a matter of opinion. Apparently you haven't seen the papers." "I didn't know there were any." "Actually there's only one, the Superior Sentry , a weekly. This is an extra. Ed Clark must have been up all night getting it out." She opened her purse and unfolded a four-page tabloid. Don blinked at the headline: Town Gets High "Ed Clark's something of an eccentric, like everybody else in Superior," Alis said. Don read the story, which seemed to him a capricious treatment of an apparently grave situation. Residents having business beyond the outskirts of town today are advised not to. It's a long way down. Where Superior was surrounded by Ohio, as usual, today Superior ends literally at the town line. A Citizens' Emergency Fence-Building Committee is being formed, but in the meantime all are warned to stay well away from the edge. The law of gravity seems to have been repealed for the town but it is doubtful if the same exemption would apply to a dubious individual bent on investigating.... Don skimmed the rest. "I don't see anything about it being deliberate." Alis had been creaming and sugaring Don's coffee. She pushed it across to him and said, "It's not on page one. Ed Clark and Mayor Civek don't get along, so you'll find the mayor's statement in a box on page three, bottom." Don creased the paper the other way, took a sip of coffee, nodded his thanks, and read: Mayor Claims Secession From Earth Mayor Hector Civek, in a proclamation issued locally by hand and dropped to the rest of the world in a plastic shatter-proof bottle, said today that Superior has seceded from Earth. His reasons were as vague as his explanation. The "reasons" include these: (1) Superior has been discriminated against by county, state and federal agencies; (2) Cavalier Institute has been held up to global derision by orthodox (presumably meaning accredited) colleges and universities; and (3) chicle exporters have conspired against the Superior Bubble Gum Company by unreasonably raising prices. The "explanation" consists of a 63-page treatise on applied magnology by Professor Osbert Garet of Cavalier which the editor (a) does not understand; (b) lacks space to publish; and which (it being atrociously handwritten) he (c) has not the temerity to ask his linotype operator to set. Don said, "I'm beginning to like this Ed Clark." "He's a doll," Alis said. "He's about the only one in town who stands up to Father." "Does your father claim that he levitated Superior off the face of the Earth?" "Not to me he doesn't. I'm one of those banes of his existence, a skeptic. He gave up trying to magnolize me when I was sixteen. I had a science teacher in high school—not in Superior, incidentally—who gave me all kinds of embarrassing questions to ask Father. I asked them, being a natural-born needler, and Father has disowned me intellectually ever since." "How old are you, Miss Garet, if I may ask?" She sat up straight and tucked her sweater tightly into her skirt, emphasizing her good figure. To a male friend Don would have described the figure as outstanding. She had mocking eyes, a pert nose and a mouth of such moist red softness that it seemed perpetually waiting to be kissed. All in all she could have been the queen of a campus much more densely populated with co-eds than Cavalier was. "You may call me Alis," she said. "And I'm nineteen." Don grinned. "Going on?" "Three months past. How old are you , Mr. Cort?" "Don's the name I've had for twenty-six years. Please use it." "Gladly. And now, Don, unless you want another cup of coffee, I'll go with you to the end of the world." "On such short notice?" Don was intrigued. Last night the redhead from the club car had repelled an advance that hadn't been made, and this morning a blonde was apparently making an advance that hadn't been solicited. He wondered where Geneva Jervis was, but only vaguely. "I'll admit to the double entendre ," Alis said. "What I meant—for now—was that we can stroll out to where Superior used to be attached to the rest of Ohio and see how the Earth is getting along without us." "Delighted. But don't you have any classes?" "Sure I do. Non-Einsteinian Relativity 1, at nine o'clock. But I'm a demon class-cutter, which is why I'm still a Senior at my advanced age. On to the brink!" They walked south from the campus and came to the railroad track. The train was standing there with nowhere to go. It had been abandoned except for the conductor, who had dutifully spent the night aboard. "What's happening?" he asked when he saw them. "Any word from down there?" "Not that I know of," Don said. He introduced him to Alis Garet. "What are you going to do?" "What can I do?" the conductor asked. "You can go over to Cavalier and have breakfast," Alis said. "Nobody's going to steal your old train." The conductor reckoned as how he might just do that, and did. "You know," Don said, "I was half-asleep last night but before the train stopped I thought it was running alongside a creek for a while." "South Creek," Alis said. "That's right. It's just over there." "Is it still? I mean hasn't it all poured off the edge by now? Was that Superior's water supply?" Alis shrugged. "All I know is you turn on the faucet and there's water. Let's go look at the creek." They found it coursing along between the banks. "Looks just about the same," she said. "That's funny. Come on; let's follow it to the edge." The brink, as Alis called it, looked even more awesome by daylight. Everything stopped short. There were the remnants of a cornfield, with the withered stalks cut down, then there was nothing. There was South Creek surging along, then nothing. In the distance a clump of trees, with a few autumn leaves still clinging to their branches, simply ended. "Where is the water going?" Don asked. "I can't make it out." "Down, I'd say. Rain for the Earth-people." "I should think it'd be all dried up by now. I'm going to have a look." "Don't! You'll fall off!" "I'll be careful." He walked cautiously toward the edge. Alis followed him, a few feet behind. He stopped a yard from the brink and waited for a spell of dizziness to pass. The Earth was spread out like a topographer's map, far below. Don took another wary step, then sat down. "Chicken," said Alis. She laughed uncertainly, then she sat down, too. "I still can't see where the water goes," Don said. He stretched out on his stomach and began to inch forward. "You stay there." Finally he had inched to a point where, by stretching out a hand, he could almost reach the edge. He gave another wriggle and the fingers of his right hand closed over the brink. For a moment he lay there, panting, head pressed to the ground. "How do you feel?" Alis asked. "Scared. When I get my courage back I'll pick up my head and look." Alis put a hand out tentatively, then purposefully took hold of his ankle and held it tight. "Just in case a high wind comes along," she said. "Thanks. It helps. Okay, here we go." He lifted his head. "Damn." "What?" "It still isn't clear. Do you have a pocket mirror?" "I have a compact." She took it out of her bag with her free hand and tossed it to him. It rolled and Don had to grab to keep it from going over the edge. Alis gave a little shriek. Don was momentarily unnerved and had to put his head back on the ground. "Sorry," she said. Don opened the compact and carefully transferred it to his right hand. He held it out beyond the edge and peered into it, focusing it on the end of the creek. "Now I've got it. The water isn't going off the edge!" "It isn't? Then where is it going?" "Down, of course, but it's as if it's going into a well, or a vertical tunnel, just short of the edge." "Why? How?" "I can't see too well, but that's my impression. Hold on now. I'm coming back." He inched away from the edge, then got up and brushed himself off. He returned her compact. "I guess you know where we go next." "The other end of the creek?" "Exactly." South Creek did not bisect Superior, as Don thought it might, but flowed in an arc through a southern segment of it. They had about two miles to go, past South Creek Bridge—which used to lead to Ladenburg, Alis said—past Raleigh Country Club (a long drive would really put the ball out of play, Don thought) and on to the edge again. But as they approached what they were forced to consider the source of the creek, they found a wire fence at the spot. "This is new," Alis said. The fence, which had a sign on it, warning—electrified , was semicircular, with each end at the edge and tarpaulins strung behind it so they could see the mouth of the creek. The water flowed from under the tarp and fence. "Look how it comes in spurts," Alis said. "As if it's being pumped." Smaller print on the sign said: Protecting mouth of South Creek, one of two sources of water for Superior. Electrical charge in fence is sufficient to kill. It was signed: Vincent Grande, Chief of Police, Hector Civek, Mayor . "What's the other source, besides the faucet in your bathroom?" Don asked. "North Lake, maybe," Alis said. "People fish there but nobody's allowed to swim." "Is the lake entirely within the town limits?" "I don't know." "If it were on the edge, and if I took a rowboat out on it, I wonder what would happen?" "I know one thing—I wouldn't be there holding your ankle while you found out." She took his arm as they gazed past the electrified fence at the Earth below and to the west. "It's impressive, isn't it?" she said. "I wonder if that's Indiana way over there?" He patted her hand absent-mindedly. "I wonder if it's west at all. I mean, how do we know Superior is maintaining the same position up here as it used to down there?" "We could tell by the sun, silly." "Of course," he said, grinning at his stupidity. "And I guess we're not high enough to see very far. If we were we'd be able to see the Great Lakes—or Lake Erie, anyway." They were musing about the geography when a plane came out of a cloudbank and, a second later, veered sharply. They could make out UAL on the underside of a wing. As it turned they imagined they could see faces peering out of the windows. They waved and thought they saw one or two people wave back. Then the plane climbed toward the east and was gone. "Well," Don said as they turned to go back to Cavalier, "now we know that they know. Maybe we'll begin to get some answers. Or, if not answers, then transportation." "Transportation?" Alis squeezed the arm she was holding. "Why? Don't you like it here?" "If you mean don't I like you, the answer is yes, of course I do. But if I don't get out of this handcuff soon so I can take a bath and get into clean clothes, you're not going to like me." "You're still quite acceptable, if a bit whiskery." She stopped, still holding his arm, and he turned so they were face to face. "So kiss me," she said, "before you deteriorate." They were in the midst of an extremely pleasant kiss when the brief case at the end of Don's handcuff began to talk to him.	D. Her dark roots were showing.
How has being a star mother changed Martha? A. Martha has become more extroverted B. She has a new appreciation for the stars. C. She has become conceited thanks to her newfound fame. D. Martha's new celebrity status has doubled her egg business.	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.	B. She has a new appreciation for the stars.
What datasets are used?	### Introduction In recent years, Transformer has been remarkably adept at sequence learning tasks like machine translation BIBREF0, BIBREF1, text classification BIBREF2, BIBREF3, language modeling BIBREF4, BIBREF5, etc. It is solely based on an attention mechanism that captures global dependencies between input tokens, dispensing with recurrence and convolutions entirely. The key idea of the self-attention mechanism is updating token representations based on a weighted sum of all input representations. However, recent research BIBREF6 has shown that the Transformer has surprising shortcomings in long sequence learning, exactly because of its use of self-attention. As shown in Figure 1 (a), in the task of machine translation, the performance of Transformer drops with the increase of the source sentence length, especially for long sequences. The reason is that the attention can be over-concentrated and disperse, as shown in Figure 1 (b), and only a small number of tokens are represented by attention. It may work fine for shorter sequences, but for longer sequences, it causes insufficient representation of information and brings difficulty for the model to comprehend the source information intactly. In recent work, local attention that constrains the attention to focus on only part of the sequences BIBREF7, BIBREF8 is used to address this problem. However, it costs self-attention the ability to capture long-range dependencies and also does not demonstrate effectiveness in sequence to sequence learning tasks. To build a module with both inductive bias of local and global context modelling in sequence to sequence learning, we hybrid self-attention with convolution and present Parallel multi-scale attention called MUSE. It encodes inputs into hidden representations and then applies self-attention and depth-separable convolution transformations in parallel. The convolution compensates for the insufficient use of local information while the self-attention focuses on capturing the dependencies. Moreover, this parallel structure is highly extensible, and new transformations can be easily introduced as new parallel branches, and is also favourable to parallel computation. The main contributions are summarized as follows: We find that the attention mechanism alone suffers from dispersed weights and is not suitable for long sequence representation learning. The proposed method tries to address this problem and achieves much better performance on generating long sequence. We propose a parallel multi-scale attention and explore a simple but efficient method to successfully combine convolution with self-attention all in one module. MUSE outperforms all previous models with same training data and the comparable model size, with state-of-the-art BLEU scores on three main machine translation tasks. MUSE-simple introduce parallel representation learning and brings expansibility and parallelism. Experiments show that the inference speed can be increased by 31% on GPUs. ### MUSE: Parallel Multi-Scale Attention Like other sequence-to-sequence models, MUSE also adopts an encoder-decoder framework. The encoder takes a sequence of word embeddings $(x_1, \cdots , x_n)$ as input where $n$ is the length of input. It transfers word embeddings to a sequence of hidden representation ${z} = (z_1, \cdots , z_n)$. Given ${z}$, the decoder is responsible for generating a sequence of text $(y_1, \cdots , y_m)$ token by token. The encoder is a stack of $N$ MUSE modules. Residual mechanism and layer normalization are used to connect two adjacent layers. The decoder is similar to encoder, except that each MUSE module in the decoder not only captures features from the generated text representations but also performs attention over the output of the encoder stack through additional context attention. Residual mechanism and layer normalization are also used to connect two modules and two adjacent layers. The key part in the proposed model is the MUSE module, which contains three main parts: self-attention for capturing global features, depth-wise separable convolution for capturing local features, and a position-wise feed-forward network for capturing token features. The module takes the output of $(i-1)$ layer as input and generates the output representation in a fusion way: where “Attention” refers to self-attention, “Conv” refers to dynamic convolution, “Pointwise” refers to a position-wise feed-forward network. The followings list the details of each part. We also propose MUSE-simple, a simple version of MUSE, which generates the output representation similar to the MUSE model except for that it dose not the include convolution operation: ### MUSE: Parallel Multi-Scale Attention ::: Attention Mechanism for Global Context Representation Self-attention is responsible for learning representations of global context. For a given input sequence $X$, it first projects $X$ into three representations, key $K$, query $Q$, and value $V$. Then, it uses a self-attention mechanism to get the output representation: Where $W^O$, $W^Q$, $W^K$, and $W^V$ are projection parameters. The self-attention operation $\sigma $ is the dot-production between key, query, and value pairs: Note that we conduct a projecting operation over the value in our self-attention mechanism $V_1=VW^V$ here. ### MUSE: Parallel Multi-Scale Attention ::: Convolution for Local Context Modeling We introduce convolution operations into MUSE to capture local context. To learn contextual sequence representations in the same hidden space, we choose depth-wise convolution BIBREF9 (we denote it as DepthConv in the experiments) as the convolution operation because it includes two separate transformations, namely, point-wise projecting transformation and contextual transformation. It is because that original convolution operator is not separable, but DepthConv can share the same point-wise projecting transformation with self-attention mechanism. We choose dynamic convolution BIBREF10, the best variant of DepthConv, as our implementation. Each convolution sub-module contains multiple cells with different kernel sizes. They are used for capturing different-range features. The output of the convolution cell with kernel size $k$ is: where $W^{V}$ and $W^{out}$ are parameters, $W^{V}$ is a point-wise projecting transformation matrix. The $Depth\_conv$ refers to depth convolution in the work of BIBREF10. For an input sequence $X$, the output $O$ is computed as: where $d$ is the hidden size. Note that we conduct the same projecting operation over the input in our convolution mechanism $V_2=XW^V$ here with that in self-attention mechanism. Shared projection To learn contextual sequence representations in the same hidden space, the projection in the self-attention mechanism $V_1=VW_V$ and that in the convolution mechanism $V_2=XW^V$ is shared. Because the shared projection can project the input feature into the same hidden space. If we conduct two independent projection here: $V_1=VW_1^V$ and $V_2=XW^V_2$, where $W_1^V$ and $W_2^V$ are two parameter matrices, we call it as separate projection. We will analyze the necessity of applying shared projection here instead of separate projection. Dynamically Selected Convolution Kernels We introduce a gating mechanism to automatically select the weight of different convolution cells. ### MUSE: Parallel Multi-Scale Attention ::: Point-wise Feed-forward Network for Capturing Token Representations To learn token level representations, MUSE concatenates an self-attention network with a position-wise feed-forward network at each layer. Since the linear transformations are the same across different positions, the position-wise feed-forward network can be seen as a token feature extractor. where $W_1$, $b_1$, $W_2$, and $b_2$ are projection parameters. ### Experiment We evaluate MUSE on four machine translation tasks. This section describes the datasets, experimental settings, detailed results, and analysis. ### Experiment ::: Datasets WMT14 En-Fr and En-De datasets The WMT 2014 English-French translation dataset, consisting of $36M$ sentence pairs, is adopted as a big dataset to test our model. We use the standard split of development set and test set. We use newstest2014 as the test set and use newstest2012 +newstest2013 as the development set. Following BIBREF11, we also adopt a joint source and target BPE factorization with the vocabulary size of $40K$. For medium dataset, we borrow the setup of BIBREF0 and adopt the WMT 2014 English-German translation dataset which consists of $4.5M$ sentence pairs, the BPE vocabulary size is set to $32K$. The test and validation datasets we used are the same as BIBREF0. IWSLT De-En and En-Vi datasets Besides, we perform experiments on two small IWSLT datasets to test the small version of MUSE with other comparable models. The IWSLT 2014 German-English translation dataset consists of $160k$ sentence pairs. We also adopt a joint source and target BPE factorization with the vocabulary size of $32K$. The IWSLT 2015 English-Vietnamese translation dataset consists of $133K$ training sentence pairs. For the En-Vi task, we build a dictionary including all source and target tokens. The vocabulary size for English is $17.2K$, and the vocabulary size for the Vietnamese is $6.8K$. ### Experiment ::: Experimental Settings ::: Model For fair comparisons, we only compare models reported with the comparable model size and the same training data. We do not compare BIBREF12 because it is an ensemble method. We build MUSE-base and MUSE-large with the parameter size comparable to Transformer-base and Transformer-large. We adopt multi-head attention BIBREF0 as implementation of self-attention in MUSE module. The number of attention head is set to 4 for MUSE-base and 16 for MUSE-large. We also add the network architecture built by MUSE-simple in the similar way into the comparison. MUSE consists of 12 residual blocks for encoder and 12 residual blocks for decoder, the dimension is set to 384 for MUSE-base and 768 for MUSE-large. The hidden dimension of non linear transformation is set to 768 for MUSE-base and 3072 for MUSE-large. The MUSE-large is trained on 4 Titan RTX GPUs while the MUSE-base is trained on a single NVIDIA RTX 2080Ti GPU. The batch size is calculated at the token level, which is called dynamic batching BIBREF0. We adopt dynamic convolution as the variant of depth-wise separable convolution. We tune the kernel size on the validation set. For convolution with a single kernel, we use the kernel size of 7 for all layers. In case of dynamic selected kernels, the kernel size is 3 for small kernels and 15 for large kernels for all layers. ### Experiment ::: Experimental Settings ::: Training The training hyper-parameters are tuned on the validation set. MUSE-large For training MUSE-large, following BIBREF13, parameters are updated every 32 steps. We train the model for $80K$ updates with a batch size of 5120 for En-Fr, and train the model for ${30K}$ updates with a batch size of 3584 for En-De. The dropout rate is set to $0.1$ for En-Fr and ${0.3}$ for En-De. We borrow the setup of optimizer from BIBREF10 and use the cosine learning rate schedule with 10000 warmup steps. The max learning rate is set to $0.001$ on En-De translation and ${0.0007}$ on En-Fr translation. For checkpoint averaging, following BIBREF10, we tune the average checkpoints for En-De translation tasks. For En-Fr translation, we do not average checkpoint but use the final single checkpoint. MUSE-base We train and test MUSE-base on two small datasets, IWSLT 2014 De-En translation and IWSLT2015 En-Vi translation. Following BIBREF0, we use Adam optimizer with a learning rate of $0.001$. We use the warmup mechanism and invert the learning rate decay with warmup updates of $4K$. For the De-En dataset, we train the model for $20K$ steps with a batch size of $4K$. The parameters are updated every 4 steps. The dropout rate is set to $0.4$. For the En-Vi dataset, we train the model for $10K$ steps with a batch size of $4K$. The parameters are also updated every 4 steps. The dropout rate is set to $0.3$. We save checkpoints every epoch and average the last 10 checkpoints for inference. ### Experiment ::: Experimental Settings ::: Evaluation During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks. The length penalty is set to 0.8 for En-Fr according to the validation results, 1 for the two small datasets following the default setting of BIBREF14. We do not tune beam width and length penalty but use the setting reported in BIBREF0. The BLEU metric is adopted to evaluate the model performance during evaluation. ### Experiment ::: Results As shown in Table TABREF24, MUSE outperforms all previously models on En-De and En-Fr translation, including both state-of-the-art models of stand alone self-attention BIBREF0, BIBREF13, and convolutional models BIBREF11, BIBREF15, BIBREF10. This result shows that either self-attention or convolution alone is not enough for sequence to sequence learning. The proposed parallel multi-scale attention improves over them both on En-De and En-Fr. Compared to Evolved Transformer BIBREF19 which is constructed by NAS and also mixes convolutions of different kernel size, MUSE achieves 2.2 BLEU gains in En-Fr translation. Relative position or local attention constraints bring improvements over origin self-attention model, but parallel multi-scale outperforms them. MUSE can also scale to small model and small datasets, as depicted in Table TABREF25, MUSE-base pushes the state-of-the-art from 35.7 to 36.3 on IWSLT De-En translation dataset. It is shown in Table TABREF24 and Table TABREF25 that MUSE-simple which contains the basic idea of parallel multi-scale attention achieves state-of-the-art performance on three major machine translation datasets. ### Experiment ::: How do we propose effective parallel multi-scale attention? In this subsection we compare MUSE and its variants on IWSLT 2015 De-En translation to answer the question. Does concatenating self-attention with convolution certainly improve the model? To bridge the gap between point-wise transformation which learns token level representations and self-attention which learns representations of global context, we introduce convolution to enhance our multi-scale attention. As we can see from the first experiment group of Table TABREF27, convolution is important in the parallel multi-scale attention. However, it is not easy to combine convolution and self-attention in one module to build better representations on sequence to sequence tasks. As shown in the first line of both second and third group of Table TABREF27, simply learning local representations by using convolution or depth-wise separable convolution in parallel with self-attention harms the performance. Furthermore, combining depth-wise separable convolution (in this work we choose its best variant dynamic convolution as implementation) is even worse than combining convolution. Why do we choose DepthConv and what is the importance of sharing Projection of DepthConv and self-attention? We conjecture that convolution and self-attention both learn contextual sequence representations and they should share the point transformation and perform the contextual transformation in the same hidden space. We first project the input to a hidden representation and perform a variant of depth-wise convolution and self-attention transformations in parallel. The fist two experiments in third group of Table TABREF27 show that validating the utility of sharing Projection in parallel multi-scale attention, shared projection gain 1.4 BLEU scores over separate projection, and bring improvement of 0.5 BLEU scores over MUSE-simple (without DepthConv). How much is the kernel size? Comparative experiments show that the too large kernel harms performance both for DepthConv and convolution. Since there exists self-attention and point-wise transformations, simply applying the growing kernel size schedule proposed in SliceNet BIBREF15 doesn't work. Thus, we propose to use dynamically selected kernel size to let the learned network decide the kernel size for each layer. ### Experiment ::: Further Analysis ::: Parallel multi-scale attention brings time efficiency on GPUs The underlying parallel structure (compared to the sequential structure in each block of Transformer) allows MUSE to be efficiently computed on GPUs. For example, we can combine small matrices into large matrices, and while it does not reduce the number of actual operations, it can be better paralleled by GPUs to speed up computation. Concretely, for each MUSE module, we first concentrate $W^Q,W^K,W^V$ of self-attention and $W_1$ of point feed-forward transformation into a single encoder matrix $W^{Enc}$, and then perform transformation such as self-attention, depth-separable convolution, and nonlinear transformation, in parallel, to learn multi-scale representations in the hidden layer. $W^O,W_2,W^{out}$ can also be combined a single decoder matrix $W^{Dec}$. The decoder of sequence to sequence architecture can be implemented similarly. In Table TABREF31, we conduct comparisons to show the speed gains with the aforementioned implementation, and the batch size is set to one sample per batch to simulate online inference environment. Under the settings, where the numbers of parameters are similar for MUSE and Transformer, about 31% increase in inference speed can be obtained. The experiments use MUSE with 6 MUSE-simple modules and Transformer with 6 base blocks. The hidden size is set to 512. Parallel multi-scale attention generates much better long sequence As demonstrated in Figure FIGREF32, MUSE generates better sequences of various length than self-attention, but it is remarkably adept at generate long sequence, e.g. for sequence longer than 100, MUSE is two times better. Lower layers prefer local context and higher layers prefer more contextual representations MUSE contains multiple dynamic convolution cells, whose streams are fused by a gated mechanism. The weight for each dynamic cell is a scalar. Here we analyze the weight of different dynamic convolution cells in different layers. Figure FIGREF32 shows that as the layer depth increases, the weight of dynamic convolution cells with small kernel sizes gradually decreases. It demonstrates that lower layers prefer local features while higher layers prefer global features. It is corresponding to the finding in BIBREF26. MUSE not only gains BLEU scores, but also generates more reasonable sentences and increases the translation quality. We conduct the case study on the De-En dataset and the cases are shown in Table TABREF34 in Appendix. In case 1, although the baseline transformer translates many correct words according to the source sentence, the translated sentence is not fluent at all. It indicates that Transformer does not capture the relationship between some words and their neighbors, such as “right” and “clap”. By contrast, MUSE captures them well by combining local convolution with global self-attention. In case 2, the cause adverbial clause is correctly translated by MUSE while transformer misses the word “why” and fails to translate it. ### Related Work Sequence to sequence learning is an important task in machine learning. It evolves understanding and generating sequence. Machine translation is the touchstone of sequence to sequence learning. Traditional approaches usually adopt long-short term memory networks BIBREF27, BIBREF28 to learn the representation of sequences. However, these models either are built upon auto-regressive structures requiring longer encoding time or perform worse on real-world natural language processing tasks. Recent studies explore convolutional neural networks (CNN) BIBREF11 or self-attention BIBREF0 to support high-parallel sequence modeling and does not require auto-regressive structure during encoding, thus bringing large efficiency improvements. They are strong at capturing local or global dependencies. There are several studies on combining self-attention and convolution. However, they do not surpass both convectional and self-attention mechanisms. BIBREF4 propose to augment convolution with self attention by directly concentrating them in computer vision tasks. However, as demonstrated in Table TABREF27 there method does not work for sequence to sequence learning task. Since state-of-the-art models on question answering tasks still consist on self-attention and do no adopt ideas in QAnet BIBREF29. Both self-attention BIBREF13 and convolution BIBREF10 outperforms Evolved transformer by near 2 BLEU scores on En-Fr translation. It seems that learning global and local context through stacking self-attention and convolution layers does not beat either self-attention or convolution models. In contrast, the proposed parallel multi-scale attention outperforms previous convolution or self-attention based models on main translation tasks, showing its effectiveness for sequence to sequence learning. ### Conclusion and Future work Although the self-attention mechanism has been prevalent in sequence modeling, we find that attention suffers from dispersed weights especially for long sequences, resulting from the insufficient local information. To address this problem, we present Parallel Multi-scale Attention (MUSE) and MUSE-simple. MUSE-simple introduces the idea of parallel multi-scale attention into sequence to sequence learning. And MUSE fuses self-attention, convolution, and point-wise transformation together to explicitly learn global, local and token level sequence representations. Especially, we find from empirical results that the shared projection plays important part in its success, and is essential for our multi-scale learning. Beyond the inspiring new state-of-the-art results on three major machine translation datasets, detailed analysis and model variants also verify the effectiveness of MUSE. For future work, the parallel structure is highly extensible and provide many opportunities to improve these models. In addition, given the success of shared projection, we would like to explore its detailed effects on contextual representation learning. Finally, we are exited about future of parallel multi-scale attention and plan to apply this simple but effective idea to other tasks including image and speech. ### Conclusion and Future work ::: Acknowledgments This work was supported in part by National Natural Science Foundation of China (No. 61673028). Figure 1: The left figure shows that the performance drops largely with the increase of sentence length on the De-En dataset. The right figure shows the attention map from the 3-th encoder layer. As we can see, the attention map is too dispersed to capture sufficient information. For example, “[EOS]”, contributing little to word alignment, is surprisingly over attended. Figure 2: Multi-scale attention hybrids point-wise transformation, convolution, and self-attention to learn multi-scale sequence representations in parallel. We project convolution and self-attention into the same space to learn contextual representations. Table 1: MUSE-large outperforms all previous models under the standard training and evaluation setting on WMT14 En-De and WMT14 En-Fr datasets. Table 2: MUSE-base outperforms previous state-of-the-art models on IWSLT De-En translation datasets and outperforms previous models without BPE processing on IWSLT En-Vi. Table 3: Comparisons between MUSE and its variants on the IWSLT 2015 De-En translation task. Table 4: The comparison between the inference speed of MUSE and Transformer. Figure 3: BLEU scores of models on different groups with different source sentence lengths. The experiments are conducted on the De-En dataset. MUSE performs better than Transformer, especially on long sentences. Figure 4: Dynamically selected kernels at each layer: The blue bars represent the ratio between the percentage of the convolution with smaller kernel sizes and the percentage of the convolution with large kernel sizes. Table 5: Case study on the De-En dataset. The red bolded words denote the wrong translation and blue bolded words denote the correct translation. In case 1, transformer fails to capture the relationship between some words and their neighbors, such as “right” and “clap”. In case 2, the cause adverbial clause is correctly translated by MUSE while transformer misses the word “why” and fails to translate it.	WMT14 En-Fr and En-De datasets, IWSLT De-En and En-Vi datasets
As per Mr. Havers' discharge lab results in September 2022, which parameter is outside its reference range? Choose the correct answer from the following options: A. Sodium level B. Potassium level C. Creatinine level D. Hemoglobin level E. White Blood Cell count	### Patient Report 0 Dear colleague, We are reporting on our patient, John Havers, born on 05/29/1953, who received an MRI of the right proximal thigh for further clarification of a potential tumor. MRI of the right thigh, plain and with contrast agent, on 02/19/2017: [Technique]{.underline}: Surface coil, localization scan, coronal T1 SE, transverse, coronal, sagittal T2 TSE with fat suppression. After intravenous contrast administration, T1-TSE transverse and T1-TSE FS (coronal, T2 TSE FS coronal as an additional fat-saturated sequence in the same section level for exploring relevant edema). [Findings]{.underline}: Normal bone marrow signal consistent with age. No signs of fractures. Coexistence of moderate degenerative changes in the hip joints, more pronounced on the right than on the left. Mild activation of the muscles in the left proximal adductor region. Ventral to the gracilis muscle and dorsal to the sartorius muscle at the level of the middle third of the right thigh is a subfascial intermuscular oval mass lesion with a high-signal appearance on T2-weighted images and a low-signal appearance on T1-weighted images. It is partially septated, well-demarcated, and shows strong contrast enhancement. No evidence of blood degradation products. Dimensions are 35 x 45 x 40 mm. No evidence of suspiciously enlarged lymph nodes. Other assessed soft tissues are unremarkable for the patient\'s age. [Assessment]{.underline}: Overall, a high suspicion of a mucinous mass lesion in the region of the right adductor compartment. Differential Diagnosis: Mucinous liposarcoma. Further histological evaluation is strongly recommended. Current Recommendations: Presentation at the clinic for surgery for further differential diagnostic clarification. ### Patient Report 1 Dear colleague, We are reporting on our patient, John Havers, born on 05/29/1953. He was under our inpatient care from 03/10/2017 to 03/12/2017. Diagnosis: Soft tissue tumor of the right proximal thigh Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus - Coronary artery disease with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Allergies: Hay fever Treatment: Incisional biopsy on 03/10/2017 Histology: [Microscopy]{.underline}: (Hematoxylin and Eosin staining): Histologically, an infiltrate of a mesenchymal neoplasm is evident in a section prepared by us and stained with HE. There are areas with an estimated tumor percentage of approximately 90% that were selected and labeled for molecular pathology analysis. [Molecular Pathology]{.underline}: After macrodissection of labeled tumor areas from unstained consecutive sections, RNA was extracted and analyzed using focused next-generation sequencing technology. The analysis was performed using FusioPlex Sarcoma v2 assays, allowing detection of fusions in 63 genes. Medical History: We may kindly assume that you are familiar with Mr. Havers's medical history. The patient presented to our surgery clinic due to a mass in the right proximal thigh. The swelling was first noticed approximately 3 months ago and has shown significant enlargement since. The patient subsequently consulted a general surgeon, who referred him to our center after performing an MRI, suspecting an intramuscular liposarcoma. After presenting the case to our interdisciplinary tumor board, the decision was made to perform an incisional biopsy. The patient was admitted for the above procedure on 03/10/2017. Physical Examination: On clinical examination, a patient in slightly reduced general and nutritional status was observed. Approximately 6 x 7 x 4 cm-sized tumor in the right proximal thigh, well mobile, intramuscular. Numbness in both legs at L5/S1. No change in skin color. No fluctuation or redness. The rest of the clinical examination was unremarkable. Treatment and Progression: Following routine preoperative preparations and informed consent, the above-mentioned procedure was performed under general anesthesia on 03/10/2017. The intraoperative and postoperative courses were uncomplicated. Initial mild swelling regressed over time. The inserted drainage was removed on the second postoperative day. The patient mobilized independently on the ward. Pain management was provided as needed. With the patient\'s subjective well-being and inconspicuous wound conditions, we were able to discharge Mr. Havers on 03/12/2017 for outpatient follow-up. Current Recommendations: - Suture material to be shortened on the 14th postoperative day. - Follow-up appointments in our outpatient clinic Medication upon Discharge: Medication Dosage Frequency -------------------------------------- ------------ --------------- Empagliflozin (Jardiance) 10 mg 1-0-0-0 Metformin Hydrochloride (Glucophage) 1000 mg 1-0-1-0 Atorvastatin Calcium (Lipitor) 21.7 mg 0-0-1-0 Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0 Aspirin 100 mg 1-0-0-0 Pantoprazole Sodium (Protonix) 22.6 mg 1-0-0-0 Lab results upon Discharge: Parameter Results Reference Range ---------------------- ------------- --------------------- Sodium 138 mEq/L 136-145 mEq/L Potassium 4.9 mEq/L 3.5-4.5 mEq/L Creatinine 0.81 mg/dL 0.70-1.20 mg/dL Estimated GFR \- \- Urea 38 mg/dL 17-48 mg/dL C-Reactive Protein 2.6 mg/dL \< 5.0 mg/dL Complete Blood Count \- \- Hemoglobin 16.7 g/dL 13.5-17.0 g/dL Hematocrit 49.5% 39.5-50.5% Erythrocytes 5.2 M/µL 4.3-5.8 M/µL Leukocytes 10.07 K/µL 3.90-10.50 K/µL Platelets 167 K/µL 150-370 K/µL MCV 95.4 fL 80.0-99.0 fL MCH 32.2 pg 27.0-33.5 pg MCHC 33.7 g/dL 31.5-36.0 g/dL MPV 11.7 fL 7.0-12.0 fL RDW-CV 12.6% 11.5-15.0% Prothrombin Time 120% 78-123% INR 0.94 0.90-1.25 aPTT 30.1 sec 25.0-38.0 sec Addition: Histology Report: [Microscopy:]{.underline} (Hematoxylin and Eosin staining): Histologically, infiltrates of a mesenchymal neoplasm can be seen in a section we prepared. Below this are areas estimated to contain 90% tumor, which have been selected and labeled for molecular pathological analysis. [Molecular Pathology:]{.underline} After macrodissection of the marked tumor areas from unstained consecutive sections, RNA was extracted and analyzed using focused Next-Generation Sequencing technology. The examination was performed using the FusioPlex Sarcoma v2 Assays, that allows for the detection of fusions involving 63 genes. [Diagnosis:]{.underline} 1. Incisional biopsy from a myxoid liposarcoma, Grade 1 according to FNCLCC (Sum score 2 + 0 + 1 = 3), with the detection of a FUS: DDIT3 fusion transcript (right adductor compartment). 2. Predominantly mature fatty tissue as well as fascial tissue. - In addition to previous reports, myxoid neoplasm is characterized by minimal cell density/round cell areas, here less than 25%, according to FNCLCC (=2 points for tumor differentiation). - No evidence of necrosis (=0 points). - 2 mitotic figures in 10 high-power fields (=1 point). - Total score is 2 + 0 + 1 = 3, corresponding to Grade 1 according to FNCLCC. [Diagnosis]{.underline} 1. Incisional biopsy from a myxoid liposarcoma (right adductor compartment). 2. Predominantly mature fatty tissue as well as fascial tissue (subcutaneous). [Comment]{.underline}: The present biopsy material corresponds to Grade 1 according to FNCLCC. A supplementary report follows. Supplementary Report from: 03/29/2017: [Clinical Information:]{.underline} Suspected liposarcoma of the right proximal thigh. Encapsulated subfascial tumor, palpably indurated. Adipose tissue adjacent to the tumor, macroscopically lighter and finer than the subcutaneous adipose tissue towards the skin. [Material]{.underline}: Microscopy and Molecular Pathology Interphase FISH analysis using a two-color break-apart probe to examine a chromosomal break in the FUS gene (chromosome 16p11.2) and in the DDIT3 gene (chromosome 12q13.3-q14.1). Interphase FISH analysis reveals a specific break event in the FUS gene (FUS-FISH positive). This indicates the presence of a FUS translocation. Similarly, in interphase FISH analysis, a specific break event is detectable in the DDIT3 gene (DDIT3-FISH positive), indicating the presence of a DDIT3 translocation. [Diagnosis:]{.underline} Incisional biopsy from a myxoid liposarcoma of the right adductor compartment. Predominantly mature fatty tissue as well as fascial tissue. [Comment]{.underline}: The cytogenetic findings are indicative of a myxoid liposarcoma. Technical validation by RNA sequencing will be provided in a supplemental report. This does not affect the above diagnosis. Supplementary Report from: 03/18/2017: [Microscopy: MDM2, S100:]{.underline} Partial weak expression of S100 protein by the lesional cells, occasionally including pre-existing adipocytes. No abnormal expression of MDM2. No abnormal expression of MDM2 in mature adipose tissue. [Diagnosis:]{.underline} Incisional biopsy from a myxoid liposarcoma of the right adductor compartment. Predominantly mature fatty tissue as well as fascial tissue. Main Report from: 03/18/2017 [Clinical Information:]{.underline} Suspected liposarcoma of the right proximal thigh, as per MRI 02/19/2017. Encapsulated subfascial tumor, palpably indurated located in the right adductor compartment. Adipose tissue adjacent to the tumor, macroscopically lighter and finer than the subcutaneous adipose tissue towards the skin. [Macroscopy:]{.underline} Tumor: Brown, nodular piece of tissue, 20 x 14 x 10 mm, with smooth and rough surface. Cut surface shiny and mottled, sometimes gray, sometimes brown. Subcutaneous adipose tissue: A piece of adipose tissue, 25 x 20 x 5 mm. [Microscopy:]{.underline} Moderately cell dense mesenchymal proliferation with a myxoid matrix. Predominantly round nuclei, moderately dense nuclear chromatin, slight pleomorphism. Occasional adipocytic cells with univacuolar cytoplasm. Partially dense, ribbon-like connective tissue as well as mature univacuolar adipose tissue. [Diagnosis:]{.underline} Incisional biopsy suspected of a myxoid liposarcoma. Predominantly mature fatty tissue as well as fascial tissue. ### Patient Report 2 Dear colleague, We would like to inform you about our patient Mr. John Havers, born on 05/29/1953, who was admitted to our hospital from 03/29/2017 to 04/05/2017. Diagnoses: - Myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB - Incisional biopsy on 03/10/2017 Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus - Coronary artery disease with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Allergies: Hay fever Current Presentation: Neoplasm of uncertain or unknown behavior. Treatment: On 04/01/2017, en bloc tumor excision with removal of the old biopsy scar, partial resection of the M. gracilis, fibers of the M. sartorius and M. adductor longus, and ligation of the V. saphena magna was performed. Histology from 04/11/2017 Clinical Information: Myxoid liposarcoma, localized in the right thigh. [Macroscopy Tumor, right thigh]{.underline}: A triple surgical resection was performed, removing skin and subcutaneous tissue and the underlying soft tissue and muscle. The size of the excised skin spindle was 130 x 45 mm with a resection depth of up to 48 mm. A wound 25 mm long and 6 mm wide was noted on the skin surface. The muscle attached laterally/dorsally measured 75 x 25 x 6 mm. Two nodules were noted on the cut surface. The larger nodule, located in the subcutaneous tissue, measured 33 mm (proximal/distal) x 36 mm (anterior/dorsal) x 30 mm. Its distance from the proximal preparation cap was 26 mm, from the distal preparation cap more than 60 mm, from the ventral soft tissue 20 mm, and from the dorsal soft tissue 3 mm, with less than 1 mm basal extension. Superficially, it was surrounded by a delicate capsule. A separate nodule measuring 20 mm (proximal/distal) x 24 mm (ventral/dorsal) x 20 mm was found immediately ventro-distal to the first nodule. This nodule was located more than 40 mm from the proximal preparation cap, more than 50 mm from the distal preparation cap, 12 mm ventrally, 11 mm dorsally, and 5 mm basally. Consequently, the maximum size of the tumor from proximal to distal was 53 mm. No macroscopic necrotic areas were evident on the cut surface of the nodule. However, partial necrosis of the subcutaneous fatty tissue in the vicinity of the described wound was observed. [Microscopy HE, PAS:]{.underline} Histomorphologically, there is a moderately cell-dense proliferation with a significant myxoid matrix in the area of the two confluent nodules, with a maximum diameter of 53 mm. There are also areas with relative cell poverty. Within the myxoid matrix, there are blood vessels with a distinct growth pattern referred to as the \"chicken wire pattern.\" No clear tumor necroses are evident. The tumor cell nuclei have a round configuration with moderately dense chromatin. Apoptotic figures are increased. The number of mitoses is low.The lesion was completely removed with a minimal margin of 0.5 mm from the posterior resection edge. In the superficial subcutaneous tissue, there is a band-like necrosis directly related to superficial granulation tissue. The included skin spindle shows regular epidermal covering and a largely unremarkable dermis. [Diagnosis]{.underline}: Skin/subcutaneous excision with a maximum 53 mm myxoid liposarcoma that was completely removed (minimum distance to posterior cutoff plane 0.5 mm). [Comment]{.underline}: In view of the present morphology and knowledge of the molecular pathological examination results with proven break events in the FUS gene and DDIT3 gene as part of interphase FISH analysis, the diagnosed condition is myxoid liposarcoma. According to the FNCLCC grading scheme, this corresponds to grade 1: Histological type: 2 points + mitotic index 1 point + necrosis index 0 points = 3 points. ICD-O-3 tumor classification: Myxoid liposarcoma TNM (8th edition): pT2 pNX L0 V0 Pn0 G1 R0 Medical History: We assume that you are familiar with Mr. Havers's medical history, and we refer to our previous correspondence. Physical Examination: Patient in good general condition. Oriented in all aspects. No cyanosis. No edema. Warm and dry skin. Normal nasal and pharyngeal findings. Pupils round, equal, and react promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa unremarkable. No jugular vein distension. No carotid bruits heard. Palpation of lymph nodes unremarkable. Palpation of the thyroid gland unremarkable, freely movable. Lungs: Normal chest shape, moderately mobile, vesicular breath sounds. Heart: Regular heart action, normal rate; heart sounds clear, no pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all quadrants; soft abdomen, no tenderness, no palpable masses, liver and spleen not palpable due to limited access, non-tender kidneys. Normal peripheral pulses; joints freely movable. Strength, motor function, and sensation are unremarkable. Therapy and Progression: The patient presented to our surgical clinic because of a mass in the right proximal thigh. The swelling was first noticed about 3 months ago and has increased significantly in size since then. MRI findings raised suspicion of a liposarcoma. After consultation in the interdisciplinary tumor board, the indication for incisional biopsy was performed on 03/10/2017. The histopathological examination confirmed the presence of a myxoid liposarcoma, leading to the decision for en bloc excision. The patient was extensively informed about the procedure and the risks and gave his consent. The patient was admitted for the procedure on 03/29/2017. Upon clinical examination, a patient in good general and nutritional condition was noted. Other general clinical findings were unremarkable. A wound healing disorder of 2 cm was observed in the area of the wound after incisional biopsy. Sarcoma Tumor Board Recommendation dated 03/11/2017: R0 G1 finding, standard sarcoma follow-up. Procedure: Following standard preoperative preparations and informed consent, the aforementioned procedure was performed on 03/01/2017 under general anesthesia. The intraoperative and postoperative course was uneventful. On the first postoperative day, there was slight swelling in the affected area, which gradually subsided. Analgesia was sufficient with Acetaminophen as needed. Thrombosis prophylaxis was administered with subcutaneous Enoxaparin 0.4 mL. The patient mobilized independently on the ward. The inserted drainage could not be removed so far due to excessive drainage output. During the hospital stay, a staging CT of the chest and abdomen was performed. No thoracoabdominal metastases were detected. Summary: With a good subjective well-being and unremarkable wound conditions, Mr. Havers was discharged on 04/05/2017 for further outpatient care. Clinical examination reveals slight swelling of the wound area. The wound is not dehiscent and shows no signs of irritation. The patient is mobilizing independently. CT Chest/Abdomen/Pelvis from 04/01/2017: [Clinical Information, Question, Justification]{.underline}: Liposarcoma of the thigh. Staging. [Technique]{.underline}: Digital overview radiographs. Following intravenous contrast agent administration (100 ml Xenetix), CT of the chest and entire abdomen in the venous contrast phase. Reconstruction of the primary dataset with a slice thickness of 0.625 mm. Multiplanar reconstruction. Total DLP: 885 mGy\cm. [Findings]{.underline}: There are no prior images available for comparison. [Chest]{.underline}: Lungs are evenly ventilated and normally developed bilaterally. No pneumothorax on either side. Minimal right-sided pleural effusion. Mild basilar hypoventilation, particularly in the right lower lobe. Calcified granuloma in the apical right lower lobe. No suspicious pulmonary nodules. Heart shows enlargement of the left ventricle and left atrium. Coronary artery sclerosis. Atherosclerosis of the aortic arch. No pericardial effusion. Aorta and pulmonary trunk have normal diameters. No central pulmonary artery embolism. No pathologically enlarged mediastinal or hilar lymph nodes. Symmetric appearance of the neck soft tissues. Thyroid gland without focal lesions. Axillary lymph nodes are of normal size. [Abdomen]{.underline}: Liver is of normal size and has a smooth contour. No signs of cholestasis. No portal vein thrombosis. No suspicious intrahepatic lesions. Gallbladder appears normal. Common bile duct is not dilated. Spleen is not enlarged. Pancreas shows regular lobulation, and there is no dilatation of the pancreatic duct. Both kidneys are free from urinary tract obstruction. No solid intrarenal masses. Few renal cysts. Adrenal glands appear unremarkable. Urinary bladder shows no focal wall thickening. Prostate is not enlarged. Advanced atherosclerosis of the abdominal aorta and pelvic vessels. History of stenting of the left external iliac artery with no reocclusion. Mesenteric, para-aortic, and parailiac lymph nodes are not pathologically enlarged. No free intraperitoneal fluid or air is detected. Osseous Structures: Degenerative changes in the spine. No evidence of suspicious osseous destruction suggestive of tumors. Soft tissue mantle appears unremarkable. Assessment: No thoracoabdominal metastases. Current Recommendations: - Regular wound inspections and dressing changes. - Documentation of drainage output and removal if the output is \<20 ml/24 hours, expected removal on 04/23/2017 at our outpatient clinic. - Removal of sutures is not required for absorbable sutures. - According to the tumor board decision dated 04/11/2017, we recommend regular follow-up according to the schedule. Sarcoma Follow-up Schedule Stage I* - Local Follow-up: 1. MRI right thigh: Years 1-5: every 6 months 2. Years 6-10: every 12 months - Pulmonary Follow-up: 3. Chest X-ray, CT chest with contrast agent Years 1-5: every 6 months in alternation 4. Years 6-10: every 12 months in alternation Medication upon Discharge: Medication Dosage Frequency -------------------------------------- ------------ ------------------- Aspirin 100 mg 1-0-0-0 Atorvastatin (Lipitor) 20 mg 0-0-1-0 Enoxaparin (Lovenox) Variable 0-0-1-0 Empagliflozin (Jardiance) 10 mg 1-0-0-0 Metformin Hydrochloride (Glucophage) 1000 mg 1-0-1-0 Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0 Acetaminophen (Tylenol) 500 mg 2-2-2-2 if needed Pantoprazole (Protonix) 20 mg 1-0-0-0 Lab results upon Discharge: Parameter Results Reference Range ------------------------------------------- ------------- --------------------- Sodium 137 mEq/L 136-145 mEq/L Potassium 4.4 mEq/L 3.5-4.5 mEq/L Creatinine 0.74 mg/dL 0.70-1.20 mg/dL Blood Urea Nitrogen 33 mg/dL 17-48 mg/dL C-Reactive Protein 1.7 mg/dL \< 5.0 mg/dL Thyroid-Stimulating Hormone 3.58 mIU/L 0.27-4.20 mIU/L Hemoglobin 16.5 g/dL 13.5-17.0 g/dL Hematocrit 49.3% 39.5-50.5% Red Blood Cells 5.2 M/µL 4.3-5.8 M/µL White Blood Cells 9.63 K/µL 3.90-10.50 K/µL Platelets 301 K/µL 150-370 K/µL Mean Corpuscular Volume 95.7 fL 80.0-99.0 fL Mean Corpuscular Hemoglobin 32.0 pg 27.0-33.5 pg Mean Corpuscular Hemoglobin Concentration 33.5 g/dL 31.5-36.0 g/dL Mean Platelet Volume 10.4 fL 7.0-12.0 fL Red Cell Distribution Width 12.1% 11.6-14.4% Activated Partial Thromboplastin Time 32.4 sec 25.0-38.0 sec ### Patient Report 3 Dear colleague, We are writing to provide an update on our patient Mr. John Havers, born on 05/29/1953, who presented to our outpatient surgery clinic on 04/23/2017. Diagnosis: Myxoid liposarcoma, right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB - Following incisional biopsy - After en bloc tumor excision with removal of the previous biopsy scar, partial resection of the gracilis, sartorius and adductor longus muscles and ligation of the great saphenous vein. Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus - Coronary artery disease with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Allergies: Hay fever Medical History: We kindly assume that you are familiar with the patient\'s detailed medical history and refer to our previous discharge letter. Current Presentation: The patient presented today for a follow-up visit in our clinic. He reported no complaints. The Redon drain has not produced any secretions in the last 2 days. Clinical examination revealed uneventful wound conditions with applied Steri-strips. There is no evidence of infection. The Redon drain contains serous wound secretions. Procedure: The Redon drain is being removed today. With nearly fully healed wound conditions, we recommend initiating scar massage with fatty topical products in the near future. MRI of the Right Thigh on 04/23/2017: [Clinical Background, Question, Justification:]{.underline} Sarcoma follow-up for myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB. Recurrence? Regional behavior? Lymph nodes? [Technique]{.underline}: 3 Tesla MRI of the right thigh, both plain and after the administration of 8 ml of Gadovist intravenously. Supine position, surface coil. Sequences: TIRM coronal and axial, T2-TSE coronal and axial, T1 VIBE Dixon axial, EPI-DWI with ADC map axial, T1-Starvibe vascular images plain and post-contrast axial with subtraction images, T1-TSE FS post-contrast coronal. [Findings]{.underline}: Minor FLAIR hyperintense streaky signal alteration in the surgical area, most likely scar-related, with slight diffusion restriction and streaky contrast enhancement. No evidence of a recurrent suspicious substrate. No nodular contrast enhancement. Slightly accentuated inguinal lymph nodes on the right, most likely reactive. Unremarkable visualization of the remaining soft tissue. Normal bone marrow signal. Bladder filled. Unremarkable representation of the imaged pelvic organs. [Assessment]{.underline}: Following the resection of a myxoid liposarcoma on the right medial thigh, there is a regular postoperative finding. No indication of local recurrence. Chest X-ray in Two Planes on 04/23/2017: [Clinical Background, Question, Justification]{.underline}: Myxoid liposarcoma of the right thigh, initial diagnosis in 2022. Follow-up. Metastases? [Findings]{.underline}: No corresponding prior images for comparison. The upper mediastinum is centrally located and not widened. Hila are free. No acute congestion. No confluent pneumonic infiltrate. No evidence of larger intrapulmonary lesions. A 7 mm spot shadow is noted right suprahilar, primarily representing a vascular structure. No effusions. No pneumothorax. Current Recommendations: The patient would like to continue follow-up care with us, so we scheduled an MRI control appointment to assess the possibility of local recurrence. On this day, a two-view chest X-ray is also required. We recommend the following follow-up schedule: - Local Follow-up: 5. MRI right thigh: Years 1-5: every 6 months 6. Years 6-10: every 12 months - Pulmonary Follow-up: 7. Chest X-ray, CT chest with contrast agent Years 1-5: every 6 months in alternation 8. Years 6-10: every 12 months in alternation ### Patient Report 4 Dear colleague, We are writing to provide an update on our patient Mr. John Havers, born on 05/29/1953, who presented for tumor follow-up on 02/10/2018, in our outpatient surgery clinic for a discussion of findings. Diagnosis: Myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB - Following incisional biopsy - After en bloc tumor excision with removal of the previous biopsy scar, partial resection of the gracilis, sartorius and adductor longus muscles and ligation of the great saphenous vein. Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus - Coronary artery disease with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Summary: Clinically, there is a regular postoperative finding on the right thigh. The control MRI with contrast of the right thigh on 04/23/2017 revealed morphologically: - No evidence of a local-regional recurrence. - In pulmonary follow-up using conventional chest X-ray on 04/23/2017, no signs of pulmonary metastasis were detected. Current Recommendations: Sarcoma Follow-up Schedule Stage I - Local Follow-up: 9. MRI right thigh: Years 1-5: every 6 months 10. Years 6-10: every 12 months - Pulmonary Follow-up: 11. Chest X-ray, CT chest with contrast agent Years 1-5: every 6 months in alternation 12. Years 6-10: every 12 months in alternation ### Patient Report 5 Dear colleague, We are reporting to you on our patient Mr. John Havers, born on 05/29/1953, who presented himself on 08/01/2018 at our outpatient surgery clinic for a discussion of findings as part of tumor follow-up. Diagnosis: Myxoid liposarcoma, right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB - Post-incision biopsy - After en bloc tumor excision with removal of the previous biopsy scar, partial resection of the gracilis, sartorius and adductor longus muscles and ligation of the great saphenous vein. Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus (NIDDM) - Coronary artery disease (CAD) with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement (THR) - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Summary: Clinically, there is a normal postoperative condition in the right thigh. MRI of the Right Thigh on 08/01/2018: [Clinical Background, Question, Justification:]{.underline} Sarcoma follow-up for myxoid liposarcoma on the right medial thigh. Progress assessment. [Method]{.underline}: 1.5 Tesla. Localization sequences. TIRM and T2 TSE coronal. TIRM, T2 TSE, VIBE DIXON, and RESOLVE-DWI axial. StarVIBE FS before and after contrast + subtraction. T1 TSE FS coronal after contrast. [Findings]{.underline}: Comparison with MRI from 04/23/2017. Post-resection of a myxoid liposarcoma in the proximal medial right thigh soft tissue. In the surgical area, there is no evidence of a suspicious nodular, contrast-affine lesion, and no evidence of malignancy-suspected diffusion restriction. Slight scar-related changes in the access path. Otherwise, unremarkable presentation of soft tissues and included bony structures. No inguinal lymphadenopathy. Assessment: For myxoid liposarcoma, there has been consistent evidence since 02/2018: Chest CT on 08/01/2018: [Clinical Background, Question, Justification: Liposarcoma on the thigh. Staging.]{.underline} After risk history assessment, oral and written explanation of contrast agent application and examination procedure, as well as potential risks of the examination (see also informed consent form). Written patient consent. [Method]{.underline}: Digital overview radiographs. After intravenous contrast agent administration (80 ml of Imeron), CT of the chest in venous contrast phase, reconstruction of the primary dataset with a slice thickness of 0.625 mm. Total DLP 185 mGy\cm. [Findings]{.underline}: For comparison, there is a CT of the chest/abdomen/pelvis from 04/01/2018. No evidence of suspicious pulmonary nodules. Several partly calcified micronodules bipulmonary, especially in the right lower lobe (ex. S303/IMA179). Partial underventilation bipulmonary. No pleural effusion. No evidence of pathologically enlarged lymph nodes. Constant calcified right hilar lymph nodes. Calcifying aortic sclerosis along with coronary sclerosis. Hepatic steatosis. Individual renal cysts. Slightly shrunken left adrenal gland. Degenerative changes of the axial skeleton without evidence of a malignancy-suspected osseous lesion. [Assessment]{.underline}: No evidence of a new thoracic tumor manifestation. Recommendations:* Sarcoma Follow-up - Local Follow-up: 13. MRI right thigh: Years 1-5: every 6 months 14. Years 6-10: every 12 months - Pulmonary Follow-up: 15. Chest X-ray, CT chest with contrast agent Years 1-5: every 6 months in alternation 16. Years 6-10: every 12 months in alternation Lab results upon Discharge: Parameter Results Reference Range ---------------------------------------------- ------------- --------------------- Sodium 138 mEq/L 136-145 mEq/L Potassium 4.9 mEq/L 3.5-4.5 mEq/L Creatinine 0.81 mg/dL 0.70-1.20 mg/dL Estimated GFR \- \- Blood Urea Nitrogen 38 mg/dL 17-48 mg/dL C-Reactive Protein 2.6 mg/dL \< 5.0 mg/dL Hemoglobin 16.7 g/dL 13.5-17.0 g/dL Hematocrit 49.5% 39.5-50.5% RBC 5.2 M/µL 4.3-5.8 M/µL WBC 10.07 K/µL 3.90-10.50 K/µL Platelets 167 K/µL 150-370 K/µL MCV 95.4 fL 80.0-99.0 fL MCH 32.2 pg 27.0-33.5 pg MCHC 33.7 g/dL 31.5-36.0 g/dL MPV 11.7 fL 7.0-12.0 fL RDW-CV 12.6% 11.5-15.0% Prothrombin Time 120% 78-123% International Normalized Ratio (INR) 0.94 0.90-1.25 Activated Partial Thromboplastin Time (aPTT) 30.1 sec 25.0-38.0 sec ### Patient Report 6 Dear colleague, We are writing to provide an update on our patient Mr. John Havers, born on 05/29/1953, who was admitted to our clinic from 08/14/2023 to 09/02/2023. Diagnosis: Pulmonary Metastasis from Myxoid Liposarcoma - Myxoid liposarcoma on the right medial thigh, pT2 pNX L0 V0 Pn0 G1 R0, Stage IB <!-- --> - Post-incision biopsy - After en bloc tumor excision with removal of the previous biopsy scar, partial resection of the gracilis, sartorius and adductor longus muscles and ligation of the great saphenous vein. Other Diagnoses: - Arterial hypertension - Non-insulin-dependent diabetes mellitus (NIDDM) - Coronary artery disease (CAD) with stent placement - Nicotine abuse (80-100 pack-years) - Arterial hypertension - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement (THR) - Status post Polypectomy for polyposis coli (minimal dysplasia) - Status post appendectomy - M. Meniere Medical History: Mr. Havers has been under our care for myxoid liposarcoma, which was previously excised from his right medial thigh. He had a stable postoperative course and was scheduled for regular follow-up to monitor for any potential recurrence or metastasis. Current Presentation: During a follow-up appointment on 08/14/2023, Mr. Havers complained of mild shortness of breath, occasional coughing, and intermittent chest discomfort. He reported no significant weight loss but noted a decrease in his overall energy levels. Physical examination revealed decreased breath sounds in the right lung base. Physical Examination: Patient in adequate general condition. Oriented in all aspects. No cyanosis. No edema. Warm and dry skin. Normal nasal and pharyngeal findings. Pupils round, equal, and react promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa unremarkable. No jugular vein distension. No carotid bruits heard. Palpation of lymph nodes unremarkable. Palpation of the thyroid gland unremarkable, freely movable. Lungs: Normal chest shape, moderately mobile, decreased breath sounds in the right lung base. Heart: Regular heart action, normal rate; heart sounds clear, no pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all quadrants; soft abdomen, markedly obese, no tenderness, no palpable masses, liver and spleen not palpable due to limited access, non-tender kidneys. Normal peripheral pulses; joints freely movable. Strength, motor function, and sensation is unremarkable. Chest X-ray (08/14/2023): A chest X-ray was performed, which revealed a suspicious opacity in the right lower lung field. CT Chest (08/16/2023): In light of the chest X-ray findings, a contrast-enhanced CT scan of the chest was conducted to obtain more detailed information. The CT imaging demonstrated a well-defined, irregularly shaped lesion in the right lower lobe of the lung, measuring approximately 2.5 cm in diameter. The lesion exhibited characteristics highly suggestive of a metastatic deposit. There were no other significant abnormalities noted in the chest. Histology (08/21/2023): Based on the CT findings, a CT-guided core needle biopsy of the pulmonary lesion was performed to confirm the nature of the lesion. Histopathological examination of the biopsy specimen confirmed the presence of myxoid liposarcoma cells in the pulmonary lesion. Immunohistochemical staining for MDM2 and CDK4 supported the diagnosis of metastatic myxoid liposarcoma. Treatment Discussion: Given the diagnosis of a pulmonary metastasis from myxoid liposarcoma, the case was reviewed in the interdisciplinary tumor board. The consensus decision was to pursue surgical resection of the pulmonary metastasis, as it remained localized and resectable. The patient and his family were informed of the treatment options and associated risks, and they provided informed consent for the procedure. Surgery Report (08/29/2023): Mr. Havers underwent a right lower lobectomy with lymph node dissection to remove the pulmonary metastasis. The procedure was performed by our thoracic surgery team and was completed without any immediate complications. Intraoperative frozen section analysis confirmed the presence of metastatic myxoid liposarcoma in the resected lung tissue. Postoperative Course: Mr. Havers postoperative course was uneventful, and he demonstrated good respiratory recovery. He was managed with adequate pain control and underwent chest physiotherapy to prevent postoperative complications. Pathological examination of the resected lung tissue confirmed the presence of metastatic myxoid liposarcoma, with clear surgical margins. Current Recommendations: 1. Follow-up: A strict follow-up plan should be established for Mr. Havers to monitor for any potential recurrence or new metastatic lesions. This should include regular clinical assessments, chest imaging, and other relevant investigations. Medication upon Discharge: Medication Dosage Frequency --------------------------------- ------------ --------------- Empagliflozin (Jardiance) 10 mg 1-0-0-0 Metformin (Glucophage) 1000 mg 1-0-1-0 Atorvastatin (Lipitor) 20 mg 0-0-1-0 Metoprolol Tartrate (Lopressor) 50 mg 0.5-0-0.5-0 Aspirin 100 mg 1-0-0-0 Pantoprazole (Protonix) 20 mg 1-0-0-0 Lab results upon Discharge: Parameter Results Reference Range --------------------- ------------- --------------------- Sodium 135 mEq/L 136-145 mEq/L Potassium 4.4 mEq/L 3.5-4.5 mEq/L Creatinine 0.82 mg/dL 0.70-1.20 mg/dL Estimated GFR \- \- Blood Urea Nitrogen 39 mg/dL 17-48 mg/dL C-Reactive Protein 2.5 mg/dL \< 5.0 mg/dL Hemoglobin 16.6 g/dL 13.5-17.0 g/dL Hematocrit 49.4 % 39.5-50.5 % RBC 5.1 M/µL 4.3-5.8 M/µL WBC 10.04 K/µL 3.90-10.50 K/µL Platelets 166 K/µL 150-370 K/µL MCV 95.2 fL 80.0-99.0 fL MCH 32.6 pg 27.0-33.5 pg MCHC 33.2 g/dL 31.5-36.0 g/dL MPV 11.4 fL 7.0-12.0 fL RDW-CV 12.5 % 11.5-15.0 % Prothrombin Time 122 % 78-123 % INR 0.99 0.90-1.25 aPTT 30.1 sec 25.0-38.0 sec	Sodium level
How does Glmpauszn change over the story? A. He finds that he doesn't want to invate the not-world. B. He understands humans less as he encounters them and tries to mirror their behavior. C. He eventually finds defeat in his conquest. D. He seems more and more interested in human mannerisms and in adopting them.	A Gleeb for Earth By CHARLES SHAFHAUSER Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1953. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Not to be or not to not be ... that was the not-question for the invader of the not-world. Dear Editor: My 14 year old boy, Ronnie, is typing this letter for me because he can do it neater and use better grammar. I had to get in touch with somebody about this because if there is something to it, then somebody, everybody, is going to point finger at me, Ivan Smernda, and say, "Why didn't you warn us?" I could not go to the police because they are not too friendly to me because of some of my guests who frankly are stew bums. Also they might think I was on booze, too, or maybe the hops, and get my license revoked. I run a strictly legit hotel even though some of my guests might be down on their luck now and then. What really got me mixed up in this was the mysterious disappearance of two of my guests. They both took a powder last Wednesday morning. Now get this. In one room, that of Joe Binkle, which maybe is an alias, I find nothing but a suit of clothes, some butts and the letters I include here in same package. Binkle had only one suit. That I know. And this was it laying right in the middle of the room. Inside the coat was the vest, inside the vest the shirt, inside the shirt the underwear. The pants were up in the coat and inside of them was also the underwear. All this was buttoned up like Binkle had melted out of it and dripped through a crack in the floor. In a bureau drawer were the letters I told you about. Now. In the room right under Binkle's lived another stew bum that checked in Thursday ... name Ed Smith, alias maybe, too. This guy was a real case. He brought with him a big mirror with a heavy bronze frame. Airloom, he says. He pays a week in advance, staggers up the stairs to his room with the mirror and that's the last I see of him. In Smith's room on Wednesday I find only a suit of clothes, the same suit he wore when he came in. In the coat the vest, in the vest the shirt, in the shirt the underwear. Also in the pants. Also all in the middle of the floor. Against the far wall stands the frame of the mirror. Only the frame! What a spot to be in! Now it might have been a gag. Sometimes these guys get funny ideas when they are on the stuff. But then I read the letters. This knocks me for a loop. They are all in different handwritings. All from different places. Stamps all legit, my kid says. India, China, England, everywhere. My kid, he reads. He says it's no joke. He wants to call the cops or maybe some doctor. But I say no. He reads your magazine so he says write to you, send you the letters. You know what to do. Now you have them. Maybe you print. Whatever you do, Mr. Editor, remember my place, the Plaza Ritz Arms, is straight establishment. I don't drink. I never touch junk, not even aspirin. Yours very truly, Ivan Smernda Bombay, India June 8 Mr. Joe Binkle Plaza Ritz Arms New York City Dear Joe: Greetings, greetings, greetings. Hold firm in your wretched projection, for tomorrow you will not be alone in the not-world. In two days I, Glmpauszn, will be born. Today I hang in our newly developed not-pod just within the mirror gateway, torn with the agony that we calculated must go with such tremendous wavelength fluctuations. I have attuned myself to a fetus within the body of a not-woman in the not-world. Already I am static and for hours have looked into this weird extension of the Universe with fear and trepidation. As soon as my stasis was achieved, I tried to contact you, but got no response. What could have diminished your powers of articulate wave interaction to make you incapable of receiving my messages and returning them? My wave went out to yours and found it, barely pulsing and surrounded with an impregnable chimera. Quickly, from the not-world vibrations about you, I learned the not-knowledge of your location. So I must communicate with you by what the not-world calls "mail" till we meet. For this purpose I must utilize the feeble vibrations of various not-people through whose inadequate articulation I will attempt to make my moves known to you. Each time I will pick a city other than the one I am in at the time. I, Glmpauszn, come equipped with powers evolved from your fragmentary reports before you ceased to vibrate to us and with a vast treasury of facts from indirect sources. Soon our tortured people will be free of the fearsome not-folk and I will be their liberator. You failed in your task, but I will try to get you off with light punishment when we return again. The hand that writes this letter is that of a boy in the not-city of Bombay in the not-country of India. He does not know he writes it. Tomorrow it will be someone else. You must never know of my exact location, for the not-people might have access to the information. I must leave off now because the not-child is about to be born. When it is alone in the room, it will be spirited away and I will spring from the pod on the gateway into its crib and will be its exact vibrational likeness. I have tremendous powers. But the not-people must never know I am among them. This is the only way I could arrive in the room where the gateway lies without arousing suspicion. I will grow up as the not-child in order that I might destroy the not-people completely. All is well, only they shot this information file into my matrix too fast. I'm having a hard time sorting facts and make the right decision. Gezsltrysk, what a task! Farewell till later. Glmpauszn Wichita, Kansas June 13 Dear Joe: Mnghjkl, fhfjgfhjklop phelnoprausynks. No. When I communicate with you, I see I must avoid those complexities of procedure for which there are no terms in this language. There is no way of describing to you in not-language what I had to go through during the first moments of my birth. Now I know what difficulties you must have had with your limited equipment. These not-people are unpredictable and strange. Their doctor came in and weighed me again the day after my birth. Consternation reigned when it was discovered I was ten pounds heavier. What difference could it possibly make? Many doctors then came in to see me. As they arrived hourly, they found me heavier and heavier. Naturally, since I am growing. This is part of my instructions. My not-mother (Gezsltrysk!) then burst into tears. The doctors conferred, threw up their hands and left. I learned the following day that the opposite component of my not-mother, my not-father, had been away riding on some conveyance during my birth. He was out on ... what did they call it? Oh, yes, a bender. He did not arrive till three days after I was born. When I heard them say that he was straightening up to come see me, I made a special effort and grew marvelously in one afternoon. I was 36 not-world inches tall by evening. My not-father entered while I was standing by the crib examining a syringe the doctor had left behind. He stopped in his tracks on entering the room and seemed incapable of speech. Dredging into the treasury of knowledge I had come equipped with, I produced the proper phrase for occasions of this kind in the not-world. "Poppa," I said. This was the first use I had made of the so-called vocal cords that are now part of my extended matrix. The sound I emitted sounded low-pitched, guttural and penetrating even to myself. It must have jarred on my not-father's ears, for he turned and ran shouting from the room. They apprehended him on the stairs and I heard him babble something about my being a monster and no child of his. My not-mother appeared at the doorway and instead of being pleased at the progress of my growth, she fell down heavily. She made a distinct thump on the floor. This brought the rest of them on the run, so I climbed out the window and retreated across a nearby field. A prolonged search was launched, but I eluded them. What unpredictable beings! I reported my tremendous progress back to our world, including the cleverness by which I managed to escape my pursuers. I received a reply from Blgftury which, on careful analysis, seems to be small praise indeed. In fact, some of his phrases apparently contain veiled threats. But you know old Blgftury. He wanted to go on this expedition himself and it's his nature never to flatter anyone. From now on I will refer to not-people simply as people, dropping the qualifying preface except where comparisons must be made between this alleged world and our own. It is merely an offshoot of our primitive mythology when this was considered a spirit world, just as these people refer to our world as never-never land and other anomalies. But we learned otherwise, while they never have. New sensations crowd into my consciousness and I am having a hard time classifying them. Anyway, I shall carry on swiftly now to the inevitable climax in which I singlehanded will obliterate the terror of the not-world and return to our world a hero. I cannot understand your not replying to my letters. I have given you a box number. What could have happened to your vibrations? Glmpauszn Albuquerque, New Mexico June 15 Dear Joe: I had tremendous difficulty getting a letter off to you this time. My process—original with myself, by the way—is to send out feeler vibrations for what these people call the psychic individual. Then I establish contact with him while he sleeps and compel him without his knowledge to translate my ideas into written language. He writes my letter and mails it to you. Of course, he has no awareness of what he has done. My first five tries were unfortunate. Each time I took control of an individual who could not read or write! Finally I found my man, but I fear his words are limited. Ah, well. I had great things to tell you about my progress, but I cannot convey even a hint of how I have accomplished these miracles through the thick skull of this incompetent. In simple terms then: I crept into a cave and slipped into a kind of sleep, directing my squhjkl ulytz & uhrytzg ... no, it won't come out. Anyway, I grew overnight to the size of an average person here. As I said before, floods of impressions are driving into my xzbyl ... my brain ... from various nerve and sense areas and I am having a hard time classifying them. My one idea was to get to a chemist and acquire the stuff needed for the destruction of these people. Sunrise came as I expected. According to my catalog of information, the impressions aroused by it are of beauty. It took little conditioning for me finally to react in this manner. This is truly an efficient mechanism I inhabit. I gazed about me at the mixture of lights, forms and impressions. It was strange and ... now I know ... beautiful. However, I hurried immediately toward the nearest chemist. At the same time I looked up and all about me at the beauty. Soon an individual approached. I knew what to do from my information. I simply acted natural. You know, one of your earliest instructions was to realize that these people see nothing unusual in you if you do not let yourself believe they do. This individual I classified as a female of a singular variety here. Her hair was short, her upper torso clad in a woolen garment. She wore ... what are they? ... oh, yes, sneakers. My attention was diverted by a scream as I passed her. I stopped. The woman gesticulated and continued to scream. People hurried from nearby houses. I linked my hands behind me and watched the scene with an attitude of mild interest. They weren't interested in me, I told myself. But they were. I became alarmed, dived into a bush and used a mechanism that you unfortunately do not have—invisibility. I lay there and listened. "He was stark naked," the girl with the sneakers said. A figure I recognized as a police officer spoke to her. "Lizzy, you'll just have to keep these crackpot friends of yours out of this area." "But—" "No more buck-bathing, Lizzy," the officer ordered. "No more speeches in the Square. Not when it results in riots at five in the morning. Now where is your naked friend? I'm going to make an example of him." That was it—I had forgotten clothes. There is only one answer to this oversight on my part. My mind is confused by the barrage of impressions that assault it. I must retire now and get them all classified. Beauty, pain, fear, hate, love, laughter. I don't know one from the other. I must feel each, become accustomed to it. The more I think about it, the more I realize that the information I have been given is very unrealistic. You have been inefficient, Joe. What will Blgftury and the others say of this? My great mission is impaired. Farewell, till I find a more intelligent mind so I can write you with more enlightenment. Glmpauszn Moscow, Idaho June 17 Dear Joe: I received your first communication today. It baffles me. Do you greet me in the proper fringe-zone manner? No. Do you express joy, hope, pride, helpfulness at my arrival? No. You ask me for a loan of five bucks! It took me some time, culling my information catalog to come up with the correct variant of the slang term "buck." Is it possible that you are powerless even to provide yourself with the wherewithal to live in this inferior world? A reminder, please. You and I—I in particular—are now engaged in a struggle to free our world from the terrible, maiming intrusions of this not-world. Through many long gleebs, our people have lived a semi-terrorized existence while errant vibrations from this world ripped across the closely joined vibration flux, whose individual fluctuations make up our sentient population. Even our eminent, all-high Frequency himself has often been jeopardized by these people. The not-world and our world are like two baskets as you and I see them in our present forms. Baskets woven with the greatest intricacy, design and color; but baskets whose convex sides are joined by a thin fringe of filaments. Our world, on the vibrational plane, extends just a bit into this, the not-world. But being a world of higher vibration, it is ultimately tenuous to these gross peoples. While we vibrate only within a restricted plane because of our purer, more stable existence, these people radiate widely into our world. They even send what they call psychic reproductions of their own selves into ours. And most infamous of all, they sometimes are able to force some of our individuals over the fringe into their world temporarily, causing them much agony and fright. The latter atrocity is perpetrated through what these people call mediums, spiritualists and other fatuous names. I intend to visit one of them at the first opportunity to see for myself. Meanwhile, as to you, I would offer a few words of advice. I picked them up while examining the "slang" portion of my information catalog which you unfortunately caused me to use. So, for the ultimate cause—in this, the penultimate adventure, and for the glory and peace of our world—shake a leg, bub. Straighten up and fly right. In short, get hep. As far as the five bucks is concerned, no dice. Glmpauszn Des Moines, Iowa June 19 Dear Joe: Your letter was imponderable till I had thrashed through long passages in my information catalog that I had never imagined I would need. Biological functions and bodily processes which are labeled here "revolting" are used freely in your missive. You can be sure they are all being forwarded to Blgftury. If I were not involved in the most important part of my journey—completion of the weapon against the not-worlders—I would come to New York immediately. You would rue that day, I assure you. Glmpauszn Boise, Idaho July 15 Dear Joe: A great deal has happened to me since I wrote to you last. Systematically, I have tested each emotion and sensation listed in our catalog. I have been, as has been said in this world, like a reed bending before the winds of passion. In fact, I'm rather badly bent indeed. Ah! You'll pardon me, but I just took time for what is known quaintly in this tongue as a "hooker of red-eye." Ha! I've mastered even the vagaries of slang in the not-language.... Ahhh! Pardon me again. I feel much better now. You see, Joe, as I attuned myself to the various impressions that constantly assaulted my mind through this body, I conditioned myself to react exactly as our information catalog instructed me to. Now it is all automatic, pure reflex. A sensation comes to me when I am burned; then I experience a burning pain. If the sensation is a tickle, I experience a tickle. This morning I have what is known medically as a syndrome ... a group of symptoms popularly referred to as a hangover ... Ahhh! Pardon me again. Strangely ... now what was I saying? Oh, yes. Ha, ha. Strangely enough, the reactions that come easiest to the people in this world came most difficult to me. Money-love, for example. It is a great thing here, both among those who haven't got it and those who have. I went out and got plenty of money. I walked invisible into a bank and carried away piles of it. Then I sat and looked at it. I took the money to a remote room of the twenty room suite I have rented in the best hotel here in—no, sorry—and stared at it for hours. Nothing happened. I didn't love the stuff or feel one way or the other about it. Yet all around me people are actually killing one another for the love of it. Anyway.... Ahhh. Pardon me. I got myself enough money to fill ten or fifteen rooms. By the end of the week I should have all eighteen spare rooms filled with money. If I don't love it then, I'll feel I have failed. This alcohol is taking effect now. Blgftury has been goading me for reports. To hell with his reports! I've got a lot more emotions to try, such as romantic love. I've been studying this phenomenon, along with other racial characteristics of these people, in the movies. This is the best place to see these people as they really are. They all go into the movie houses and there do homage to their own images. Very quaint type of idolatry. Love. Ha! What an adventure this is becoming. By the way, Joe, I'm forwarding that five dollars. You see, it won't cost me anything. It'll come out of the pocket of the idiot who's writing this letter. Pretty shrewd of me, eh? I'm going out and look at that money again. I think I'm at last learning to love it, though not as much as I admire liquor. Well, one simply must persevere, I always say. Glmpauszn Penobscot, Maine July 20 Dear Joe: Now you tell me not to drink alcohol. Why not? You never mentioned it in any of your vibrations to us, gleebs ago, when you first came across to this world. It will stint my powers? Nonsense! Already I have had a quart of the liquid today. I feel wonderful. Get that? I actually feel wonderful, in spite of this miserable imitation of a body. There are long hours during which I am so well-integrated into this body and this world that I almost consider myself a member of it. Now I can function efficiently. I sent Blgftury some long reports today outlining my experiments in the realm of chemistry where we must finally defeat these people. Of course, I haven't made the experiments yet, but I will. This is not deceit, merely realistic anticipation of the inevitable. Anyway, what the old xbyzrt doesn't know won't muss his vibrations. I went to what they call a nightclub here and picked out a blonde-haired woman, the kind that the books say men prefer. She was attracted to me instantly. After all, the body I have devised is perfect in every detail ... actually a not-world ideal. I didn't lose any time overwhelming her susceptibilities. I remember distinctly that just as I stooped to pick up a large roll of money I had dropped, her eyes met mine and in them I could see her admiration. We went to my suite and I showed her one of the money rooms. Would you believe it? She actually took off her shoes and ran around through the money in her bare feet! Then we kissed. Concealed in the dermis of the lips are tiny, highly sensitized nerve ends which send sensations to the brain. The brain interprets these impulses in a certain manner. As a result, the fate of secretion in the adrenals on the ends of the kidneys increases and an enlivening of the entire endocrine system follows. Thus I felt the beginnings of love. I sat her down on a pile of money and kissed her again. Again the tingling, again the secretion and activation. I integrated myself quickly. Now in all the motion pictures—true representations of life and love in this world—the man with a lot of money or virtue kisses the girl and tries to induce her to do something biological. She then refuses. This pleases both of them, for he wanted her to refuse. She, in turn, wanted him to want her, but also wanted to prevent him so that he would have a high opinion of her. Do I make myself clear? I kissed the blonde girl and gave her to understand what I then wanted. Well, you can imagine my surprise when she said yes! So I had failed. I had not found love. I became so abstracted by this problem that the blonde girl fell asleep. I thoughtfully drank quantities of excellent alcohol called gin and didn't even notice when the blonde girl left. I am now beginning to feel the effects of this alcohol again. Ha. Don't I wish old Blgftury were here in the vibrational pattern of an olive? I'd get the blonde in and have her eat him out of a Martini. That is a gin mixture. I think I'll get a hot report off to the old so-and-so right now. It'll take him a gleeb to figure this one out. I'll tell him I'm setting up an atomic reactor in the sewage systems here and that all we have to do is activate it and all the not-people will die of chain asphyxiation. Boy, what an easy job this turned out to be. It's just a vacation. Joe, you old gold-bricker, imagine you here all these gleebs living off the fat of the land. Yak, yak. Affectionately. Glmpauszn Sacramento, Calif. July 25 Dear Joe: All is lost unless we work swiftly. I received your revealing letter the morning after having a terrible experience of my own. I drank a lot of gin for two days and then decided to go to one of these seance things. Somewhere along the way I picked up a red-headed girl. When we got to the darkened seance room, I took the redhead into a corner and continued my investigations into the realm of love. I failed again because she said yes immediately. The nerves of my dermis were working overtime when suddenly I had the most frightening experience of my life. Now I know what a horror these people really are to our world. The medium had turned out all the lights. He said there was a strong psychic influence in the room somewhere. That was me, of course, but I was too busy with the redhead to notice. Anyway, Mrs. Somebody wanted to make contact with her paternal grandmother, Lucy, from the beyond. The medium went into his act. He concentrated and sweated and suddenly something began to take form in the room. The best way to describe it in not-world language is a white, shapeless cascade of light. Mrs. Somebody reared to her feet and screeched, "Grandma Lucy!" Then I really took notice. Grandma Lucy, nothing! This medium had actually brought Blgftury partially across the vibration barrier. He must have been vibrating in the fringe area and got caught in the works. Did he look mad! His zyhku was open and his btgrimms were down. Worst of all, he saw me. Looked right at me with an unbelievable pattern of pain, anger, fear and amazement in his matrix. Me and the redhead. Then comes your letter today telling of the fate that befell you as a result of drinking alcohol. Our wrenchingly attuned faculties in these not-world bodies need the loathsome drug to escape from the reality of not-reality. It's true. I cannot do without it now. The day is only half over and I have consumed a quart and a half. And it is dulling all my powers as it has practically obliterated yours. I can't even become invisible any more. I must find the formula that will wipe out the not-world men quickly. Quickly! Glmpauszn Florence, Italy September 10 Dear Joe: This telepathic control becomes more difficult every time. I must pick closer points of communication soon. I have nothing to report but failure. I bought a ton of equipment and went to work on the formula that is half complete in my instructions. Six of my hotel rooms were filled with tubes, pipes and apparatus of all kinds. I had got my mechanism as close to perfect as possible when I realized that, in my befuddled condition, I had set off a reaction that inevitably would result in an explosion. I had to leave there immediately, but I could not create suspicion. The management was not aware of the nature of my activities. I moved swiftly. I could not afford time to bring my baggage. I stuffed as much money into my pockets as I could and then sauntered into the hotel lobby. Assuming my most casual air, I told the manager I was checking out. Naturally he was stunned since I was his best customer. "But why, sir?" he asked plaintively. I was baffled. What could I tell him? "Don't you like the rooms?" he persisted. "Isn't the service good?" "It's the rooms," I told him. "They're—they're—" "They're what?" he wanted to know. "They're not safe." "Not safe? But that is ridiculous. This hotel is...." At this point the blast came. My nerves were a wreck from the alcohol. "See?" I screamed. "Not safe. I knew they were going to blow up!" He stood paralyzed as I ran from the lobby. Oh, well, never say die. Another day, another hotel. I swear I'm even beginning to think like the not-men, curse them. Glmpauszn Rochester, New York September 25 Dear Joe: I have it! It is done! In spite of the alcohol, in spite of Blgftury's niggling criticism, I have succeeded. I now have developed a form of mold, somewhat similar to the antibiotics of this world, that, transmitted to the human organism, will cause a disease whose end will be swift and fatal. First the brain will dissolve and then the body will fall apart. Nothing in this world can stop the spread of it once it is loose. Absolutely nothing. We must use care. Stock in as much gin as you are able. I will bring with me all that I can. Meanwhile I must return to my original place of birth into this world of horrors. There I will secure the gateway, a large mirror, the vibrational point at which we shall meet and slowly climb the frequency scale to emerge into our own beautiful, now secure world. You and I together, Joe, conquerors, liberators. You say you eat little and drink as much as you can. The same with me. Even in this revolting world I am a sad sight. My not-world senses falter. This is the last letter. Tomorrow I come with the gateway. When the gin is gone, we will plant the mold in the hotel where you live. In only a single gleeb it will begin to work. The men of this queer world will be no more. But we can't say we didn't have some fun, can we, Joe? And just let Blgftury make one crack. Just one xyzprlt. I'll have hgutry before the ghjdksla! Glmpauszn Dear Editor: These guys might be queer drunk hopheads. But if not? If soon brain dissolve, body fall apart, how long have we got? Please, anybody who knows answer, write to me—Ivan Smernda, Plaza Ritz Arms—how long is a gleeb?	D. He seems more and more interested in human mannerisms and in adopting them.
Which antibiotic therapy was initiated in Mr. Chapman for suspected ventriculitis? Choose the correct answer from the following options: A. Vancomycin B. Linezolid C. Amoxicillin D. Ciprofloxacin E. Doxycycline	### Patient Report 0 Dear colleague, We are reporting on our shared patient, Mr. John Chapman, born on 11/16/1994, who received emergency treatment at our clinic on 04/03/2017. Diagnoses: - Severe open traumatic brain injury with fractures of the cranial vault, mastoid, and skull base - Dissection of the distal internal carotid artery on both sides - Subarachnoid hemorrhage involving both hemispheres and extending into the basal cisterns - Aspiration pneumonia Other Diagnoses: - Status post rib fracture 2005 - Status post appendectomy 2006 - Status post distal radius fracture 2008 - Status post elbow fracture 20010 Procedure: External ventricular drain (EVD) placement. Medical History: Admission through the emergency department as a polytrauma alert. The patient was involved in a motocross accident, where he jumped, fell, and landed face-first. He was intubated at the scene, and either during or before intubation, aspiration occurred. No issues with airway, breathing, or circulation (A, B, or C problems) were noted. A CT scan performed in the emergency department revealed an open traumatic brain injury with fractures of the cranial vault, mastoid, and skull base, as well as dissection of both carotid arteries. Upon admission, we encountered an intubated and sedated patient with a Richmond Agitation-Sedation Scale (RASS) score of -4. He was hemodynamically stable at all times. Current Recommendations: - Regular checks of vigilance, laboratory values and microbiological findings. - Careful balancing ### Patient Report 1 Dear colleague, We report on Mr. John Chapman, born on 11/16/1994, who was admitted to our Intensive Care Unit from 04/03/2017 to 05/01/2017. Diagnoses: - Open severe traumatic brain injury with fractures of the skull vault, mastoid, and skull base - Dissection of the distal ACI on both sides - Subarachnoid hemorrhage involving both hemispheres and extending into basal cisterns - Infarct areas in the border zone between MCA-ACA on the right frontal and left parietal sides - Malresorptive hydrocephalus <!-- --> - Rhabdomyolysis - Aspiration pneumonia Other Diagnoses: - Status post rib fracture in 2005 - Status post appendectomy in 2006 - Status post distal radius fracture in 2008 - Status post elbow fracture in 20010 Surgical Procedures: - 04/03/2017: Placement of external ventricular drain - 04/08/2017: Placement of an intracranial pressure monitoring catheter - 04/13/2017: Surgical tracheostomy - 05/01/2017: Left ventriculoperitoneal shunt placement Medical History: The patient was admitted through the emergency department as a polytrauma alert. The patient had fallen while riding a motocross bike, landing face-first after jumping. He was intubated at the scene. Aspiration occurred either during or before intubation. No problems with breathing or circulation were noted. The CT performed in the emergency department showed an open traumatic brain injury with fractures of the skull vault, mastoid, and skull base, as well as dissection of the carotid arteries on both sides and bilateral subarachnoid hemorrhage. Upon admission, the patient was sedated and intubated, with a Richmond Agitation-Sedation Scale (RASS) score of -4, and was hemodynamically stable under controlled ventilation. Therapy and Progression: [Neurology]{.underline}: Following the patient\'s admission, an external ventricular drain was placed. Reduction of sedation had to be discontinued due to increased intracranial pressure. A right pupil size greater than the left showed no intracranial correlate. With persistently elevated intracranial pressure, intensive intracranial pressure therapy was initiated using deeper sedation, administration of hyperosmolar sodium, and cerebrospinal fluid drainage, which normalized intracranial pressure. Intermittently, there were recurrent intracranial pressure peaks, which could be treated conservatively. Transcranial Doppler examinations showed normal flow velocities. Microbiological samples from cerebrospinal fluid were obtained when the patient had elevated temperatures, but no bacterial growth was observed. Due to the inability to adequately monitor intracranial pressure via the external ventricular drain, an intracranial pressure monitoring catheter was placed to facilitate adequate intracranial pressure monitoring. In the perfusion computed tomography, progressive edema with increasingly obstructed external ventricular spaces and previously known infarcts in the border zone area were observed. To ensure appropriate intracranial pressure monitoring, a Tuohy drain was inserted due to cerebrospinal fluid buildup on 04/21/2017. After the initiation of antibiotic therapy for suspected ventriculitis, the intracranial pressure monitoring catheter was removed on 04/20/2017. Subsequently, a liquorrhea developed, leading to the placement of a Tuohy drain. After successful antibiotic treatment of ventriculitis, a ventriculoperitoneal shunt was placed on 05/01/2017 without complications, and the Tuohy drain was removed. Radiological control confirmed the correct positioning. The patient gradually became more alert. Both pupils were isochoric and reacted to light. All extremities showed movement, although the patient only intermittently responded to commands. On 05/01/2017, a VP shunt was placed on the left side without complications. Currently, the patient is sedated with continuous clonidine at 60µg/h. Hemodynamics: To maintain cerebral perfusion pressure in the presence of increased intracranial pressure, circulatory support with vasopressors was necessary. Echocardiography revealed preserved cardiac function without wall motion abnormalities or right heart strain, despite the increasing need for noradrenaline support. As the patient had bilateral carotid dissection, a therapy with Aspirin 100mg was initiated. On 04/16/2017, clinical examination revealed right\>left leg circumference difference and redness of the right leg. Utrasound revealed a long-segment deep vein thrombosis in the right leg, extending from the pelvis (proximal end of the thrombus not clearly delineated) to the lower leg. Therefore, Heparin was increased to a therapeutic dose. Heparin therapy was paused on postoperative day 1, and prophylactic anticoagulation started, followed by therapeutic anticoagulation on postoperative day 2. The patient was switched to subcutaneous Lovenox. Pulmonary: Due to the history of aspiration in the prehospital setting, a bronchoscopy was performed, revealing a moderately obstructed bronchial system with several clots. As prolonged sedation was necessary, a surgical tracheostomy was performed without complications on 04/13/2017. Subsequently, we initiated weaning from mechanical ventilation. The current weaning strategy includes 12 hours of synchronized intermittent mandatory ventilation (SIMV) during the night, with nighttime pressure support ventilation (DuoPAP: Ti high 1.3s, respiratory rate 11/min, Phigh 11 mbar, PEEP 5 mbar, Psupport 5 mbar, trigger 4l, ramp 50 ms, expiratory trigger sensitivity 25%). Abdomen: FAST examinations did not reveal any signs of intra-abdominal trauma. Enteral feeding was initiated via a gastric tube, along with supportive parenteral nutrition. With forced bowel movement measures, the patient had regular bowel movements. On 04/17/2017, a complication-free PEG (percutaneous endoscopic gastrostomy) placement was performed due to the potential long-term need for enteral nutrition. The PEG tube is currently being fed with tube feed nutrition, with no bowel movement for the past four days. Additionally, supportive parenteral nutrition is being provided. Kidney: Initially, the patient had polyuria without confirming diabetes insipidus, and subsequently, adequate diuresis developed. Retention parameters were within the normal range. As crush parameters increased, a therapy involving forced diuresis was initiated, resulting in a significant reduction of crush parameters. Infection Course: Upon admission, with elevated infection parameters and intermittently febrile temperatures, empirical antibiotic therapy was initiated for suspected pneumonia using Piperacillin/Tazobactam. Staphylococcus capitis was identified in blood cultures, and Staphylococcus aureus was found in bronchial lavage. Both microbes were sensitive to the current antibiotic therapy, so treatment with Piperacillin/Tazobactam continued. Additionally, Enterobacter cloacae was identified in tracheobronchial secretions during the course, also sensitive to the ongoing antibiotic therapy. On 05/17, the patient experienced another fever episode with elevated infection parameters and right lower lobe infiltrates in the chest X-ray. After obtaining microbiological samples, antibiotic therapy was switched to Meropenem for suspected pneumonia. Microbiological findings from cerebrospinal fluid indicated gram-negative rods. Therefore, antibiotic therapy was adjusted to Ciprofloxacin in accordance with susceptibility testing due to suspected ventriculitis, and the Meropenem dose was increased. This led to a reduction in infection parameters. Finally, microbiological examination of cerebrospinal fluid, blood cultures, and urine revealed no pathological findings. Infection parameters decreased. We recommend continuing antibiotic therapy until 05/02/2017. Anti-Infective Course: - Piperacillin/Tazobactam 04/03/2017-04/16/2017: Staph. Capitis in Blood Culture Staph. Aureus in Bronchial Lavage - Meropenem 04/16/2017-present (increased dose since 04/18) CSF: gram-negative rods in Blood Culture: Pseudomonas aeruginosa Acinetobacter radioresistens - Ciprofloxacin 04/18/2017-present CSF: gram-negative rods in Blood Culture: Pseudomonas aeruginosa, Acinetobacter radioresistens Weaning Settings: Weaning Stage 6: 12-hour synchronized intermittent mandatory ventilation (SIMV) with DuoPAP during the night (Thigh 1.3s, respiratory rate 11/min, Phigh 11 mbar, PEEP 5 mbar, Psupport 5 mbar, trigger 4l, ramp 50 ms, expiratory trigger sensitivity 25%). Status at transfer: Currently, Mr. Chapman is monosedated with Clonidine. He spontaneously opens both eyes and spontaneously moves all four extremities. Pupils are bilaterally moderately dilated, round and sensitive to light. There is bulbar divergence. Circulation is stable without catecholamine therapy. He is in the process of weaning, currently spontaneous breathing with intermittent CPAP. Renal function is sufficient, enteral nutrition via PEG with supportive parenteral nutrition is successful. Current Medication: Medication Dosage Frequency ------------------------------------ ---------------- --------------- Bisoprolol (Zebeta) 2.5 mg 1-0-0 Ciprofloxacin (Cipro) 400 mg 1-1-1 Meropenem (Merrem) 4 g Every 4 hours Morphine Hydrochloride (MS Contin) 10 mg 1-1-1-1-1-1 Polyethylene Glycol 3350 (MiraLAX) 13.1 g 1-1-1 Acetaminophen (Tylenol) 1000 mg 1-1-1-1 Aspirin 100 mg 1-0-0 Enoxaparin (Lovenox) 30 mg (0.3 mL) 0-0-1 Enoxaparin (Lovenox) 70 mg (0.7 mL) 1-0-1 Lab results: Parameter Results Reference Range -------------------- ------------- --------------------- Creatinine (Jaffé) 0.42 mg/dL 0.70-1.20 mg/dL Urea 31 mg/dL 17-48 mg/dL Total Bilirubin 0.35 mg/dL \< 1.20 mg/dL Hemoglobin 7.6 g/dL 13.5-17.0 g/dL Hematocrit 28% 39.5-50.5% Red Blood Cells 3.5 M/uL 4.3-5.8 M/uL White Blood Cells 10.35 K/uL 3.90-10.50 K/uL Platelets 379 K/uL 150-370 K/uL MCV 77.2 fL 80.0-99.0 fL MCH 24.1 pg 27.0-33.5 pg MCHC 32.5 g/dL 31.5-36.0 g/dL MPV 11.3 fL 7.0-12.0 fL RDW-CV 17.7% 11.5-15.0% Quick 54% 78-123% INR 1.36 0.90-1.25 aPTT 32.8 sec 25.0-38.0 sec Addition: Radiological Findings [Clinical Information and Justification:]{.underline} Suspected deep vein thrombosis (DVT) on the right leg. [Special Notes:]{.underline} Examination at the bedside in the intensive care unit, no digital image archiving available. [Findings]{.underline}: Confirmation of a long-segment deep venous thrombosis in the right leg, starting in the pelvis (proximal end not clearly delineated) and extending to the lower leg. Visible Inferior Vena Cava without evidence of thrombosis. The findings were communicated to the treating physician. Full-Body Trauma CT on 04/03/2017: [Clinical Information and Justification:]{.underline} Motocross accident. Polytrauma alert. Consequences of trauma? Informed consent: Emergency indication. Recommended monitoring of kidney and thyroid laboratory parameters. Findings: CCT: Dissection of the distal internal carotid artery on both sides (left 2-fold). Signs of generalized elevated intracranial pressure. Open skull-brain trauma with intracranial air inclusions and skull base fracture at the level of the roof of the ethmoidal/sphenoidal sinuses and clivus (in a close relationship to the bilateral internal carotid arteries) and the temporal CT Head on 04/16/2017: [Clinical Information and Justification:]{.underline} History of skull fracture, removal of EVD (External Ventricular Drain). Inquiry about the course. [Findings]{.underline}: Regression of ventricular system width (distance of SVVH currently 41 mm, previously 46 mm) with residual liquor caps, indicative of regressed hydrocephalus. Interhemispheric fissure in the midline. No herniation. Complete regression of subdural hematoma on the left, tentorial region. Known defect areas on the right frontal lobe where previous catheters were inserted. Progression of a newly hypodense demarcated cortical infarct on the left, postcentral. Known bilateral skull base fractures involving the petrous bone, with secretion retention in the mastoid air cells bilaterally. Minimal secretion also in the sphenoid sinuses. Postoperative bone fragments dislocated intracranially after right frontal trepanation. Chest X-ray on 04/24/2017. [Clinical Information and Justification:]{.underline} Mechanically ventilated patient. Suspected pneumonia. Question about infiltrates. [Findings]{.underline}: Several previous images for comparison, last one from 08/20/2021. Persistence of infiltrates in the right lower lobe. No evidence of new infiltrates. Removal of the tracheal tube and central venous catheter with a newly inserted tracheal cannula. No evidence of pleural effusion or pneumothorax. CT Head on 04/25/2017: [Clinical Information and Justification:]{.underline} Severe traumatic brain injury with brain edema, one External Ventricular Drain removed, one parenchymal catheter removed; Follow-up. [Findings]{.underline}: Previous images available, CT last performed on 04/09/17, and MRI on 04/16/17. Massive cerebrospinal fluid (CSF) stasis supra- and infratentorially with CSF pressure caps at the ventricular and cisternal levels with completely depleted external CSF spaces, differential diagnosis: malresorptive hydrocephalus. The EVD and parenchymal catheter have been completely removed. No evidence of fresh intracranial hemorrhage. Residual subdural hematoma on the left, tentorial. Slight regression of the cerebellar tonsils. Increasing hypodensity of the known defect zone on the right frontal region, differential diagnosis: CSF diapedesis. Otherwise, the status is the same as for the other defects. Secretion in the sphenoid sinus and mastoid cells bilaterally, known bilateral skull base fractures. Bedside Chest X-ray on 04/262017: [Clinical Information and Justification]{.underline}: Respiratory insufficiency. Inquiry about cardiorespiratory status. [Findings]{.underline}: Previous image from 08/17/2021. Left Central Venous Catheter and gastric tube in unchanged position. Persistent consolidation in the right para-hilar region, differential diagnosis: contusion or partial atelectasis. No evidence of new pulmonary infiltrates. No pleural effusion. No pneumothorax. No pulmonary congestion. Brain MRI on 04/26/2017: [Clinical Information and Justification:]{.underline} Severe skull-brain trauma with skull calvarium, mastoid, and skull base fractures. Assessment of infarct areas/edema for rehabilitation planning. [Findings:]{.underline} Several previous examinations available. Persistent small sulcal hemorrhages in both hemispheres (left \> right) and parenchymal hemorrhage on the left frontal with minimal perifocal edema. Narrow subdural hematoma on the left occipital extending tentorially (up to 2 mm). No current signs of hypoxic brain damage. No evidence of fresh ischemia. Slightly regressed ventricular size. No herniation. Unchanged placement of catheters on the right frontal side. Mastoid air cells blocked bilaterally due to known bilateral skull base fractures, mucosal swelling in the sphenoid and ethmoid sinuses. Polypous mucosal swelling in the left maxillary sinus. Other involved paranasal sinuses and mastoids are clear. Bedside Chest X-ray on 04/27/2017: [Clinical Information and Justification:]{.underline} Tracheal cannula placement. Inquiry about the position. [Findings]{.underline}: Images from 04/03/2017 for comparison. Tracheal cannula with tip projecting onto the trachea. No pneumothorax. Regressing infiltrate in the right lower lung field. No leaking pleural effusions. Left ubclavian central venous catheter with tip projecting onto the superior vena cava. Gastric tube in situ. CT Head on 04/28/2017: [Clinical Information and Justification:]{.underline} Open head injury, bilateral subarachnoid hemorrhage (SAH), EVD placement. Inquiry about herniation. [Findings]{.underline}: Comparison with the last prior examination from the previous day. Generalized signs of cerebral edema remain constant, slightly progressing with a somewhat increasing blurred cortical border, particularly high frontal. Essentially constant transtentorial herniation of the midbrain and low position of the cerebellar tonsils. Marked reduction of inner CSF spaces and depleted external CSF spaces, unchanged position of the ventricular drainage catheter with the tip in the left lateral ventricle. Constant small parenchymal hemorrhage on the left frontal and constant SDH at the tentorial edge on both sides. No evidence of new intracranial space-occupying hemorrhage. Slightly less distinct demarcation of the demarcated infarcts/defect zones, e.g., on the right frontal region, differential diagnosis: fogging. CT Head Angiography with Perfusion on 04/28/2017: [Clinical Information and Justification]{.underline}: Post-traumatic head injury, rising intracranial pressure, bilateral internal carotid artery dissection. Inquiry about intracranial bleeding, edema course, herniation, brain perfusion. [Emergency indication:]{.underline} Vital indication. Recommended monitoring of kidney and thyroid laboratory parameters. Consultation with the attending physician from and the neuroradiology service was conducted. [Technique]{.underline}: Native moderately of the neurocranium. CT angiography of brain-supplying cervical intracranial vessels during arterial contrast agent phase and perfusion imaging of the neurocranium after intravenous injection of a total of 140 ml of Xenetix-350. DLP Head 502.4 mGycm. DLP Body 597.4 mGycm. [Findings]{.underline}: Previous images from 08/11/2021 and the last CTA of the head/neck from 04/03/2017 for comparison. [Brain]{.underline}: Constant bihemispheric and cerebellar brain edema with a slit-like appearance of the internal and completely compressed external ventricular spaces. Constant compression of the midbrain with transtentorial herniation and a constant tonsillar descent. Increasing demarcation of infarct areas in the border zone of MCA-ACA on the right frontal, possibly also on the left frontal. Predominantly preserved cortex-gray matter contrast, sometimes discontinuous on both frontal sides, differential diagnosis: artifact-related, differential diagnosis: disseminated infarct demarcations/contusions. Unchanged placement of the ventricular drainage from the right frontal with the catheter tip in the left lateral ventricle anterior horn. Constant subdural hematoma tentorial and posterior falx. Increasingly vague delineation of the small frontal parenchymal hemorrhage. No new space-occupying intracranial bleeding. No evidence of secondary dislocation of the skull base fracture with constant fluid collections in the paranasal sinuses and mastoid air cells. Hematoma possible, cerebrospinal fluid leakage possible. [CT Angiography Head/Neck]{.underline}: Constant presentation of bilateral internal carotid artery dissection. No evidence of higher-grade vessel stenosis or occlusion of the brain-supplying intracranial arteries. Moderately dilated venous collateral circuits in the cranial soft tissues on both sides, right \> left. Moderately dilated ophthalmic veins on both sides, right \> left. No evidence of sinus or cerebral venous thrombosis. Slight perfusion deficits in the area of the described infarct areas and contusions. No evidence of perfusion mismatches in the perfusion imaging. Unchanged presentation of the other documented skeletal segments. Additional Note: Discussion of findings with the responsible medical colleagues on-site and by telephone, as well as with the neuroradiology service by telephone, was conducted. CT Head on 04/30/2017: [Clinical Information and Justification]{.underline}: Open head injury following a motorcycle accident.. Inquiry about rebleeding, edema, EVD displacement. [Findings and Assessment:]{.underline} CT last performed on 04/05/2017 for comparison. Constant narrow subdural hematoma on both sides, tentorial and posterior parasagittal. Constant small parenchymal hemorrhage on the left frontal. No new intracranial bleeding. Progressively demarcated infarcts on the right frontal and left parietal. Slightly progressive compression of the narrow ventricles as an indication of progressive edema. Completely depleted external CSF spaces with the ventricular drain catheter in the left lateral ventricle. Increasing compression of the midbrain due to transtentorial herniation, progressive tonsillar descent of 6 mm. Fracture of the skull base and the petrous part of the temporal bone on both sides without significant displacement. Hematoma in the mastoid and sphenoid sinuses and the maxillary sinus. CT Head on 05/01/2017: [Clinical Information and Justification:]{.underline} Open skull-brain trauma. Inquiry about CSF stasis, bleeding, edema. [Findings]{.underline}: CT last performed on 04/05/17 for comparison. Completely regressed subarachnoid hemorrhages on both sides. Minimal SDH components on the tentorial edges bilaterally (left more than right, with a 3 mm margin width). No new intracranial bleeding. Continuously narrow inner ventricular system and narrow basal cisterns. The fourth ventricle is unfolded. Narrow external CSF spaces and consistently swollen gyration with global cerebral edema. Better demarcated circumscribed hypodensity in the centrum semiovale on the right (Series 3, Image 176) and left (Series 3, Image 203); Differential diagnosis: fresh infarcts due to distal ACI dissections. Consider repeat vascular imaging. No midline shift. No herniation. Regressing intracranial air inclusions. Fracture of the skull base and the petrous part of the temporal bone on both sides without significant displacement. Hematoma in the maxillary, sphenoidal, and ethmoidal sinuses. Consultation Reports: 1) Consultation with Ophthalmology on 04/03/2017 [Patient Information:]{.underline} - Motorbike accident, heavily contaminated eyes. - Request for assessment. Diagnosis: Motorbike accident Findings: Patient intubated, unresponsive. In cranial CT, the eyeball appears intact, no retrobulbar hematoma. Intraocular pressure: Right/left within the normal range. Eyelid margins of both eyes crusty with sand, inferiorly in the lower lid sac, and on the upper lid with sand. Lower lid somewhat chemotic. Slight temporal hyperemia in the left eyelid angle. Both eyes have erosions, small, multiple, superficial. Lower conjunctival sac clean. Round pupils, anisocoria right larger than left. Left iris hyperemia, no iris defects in the direct light. Lens unremarkable. Reduced view of the optic nerve head due to miosis, somewhat pale, rather sharp-edged, central neuroretinal rim present, central vessels normal. Left eye, due to narrow pupil, limited view, optic nerve head not visible, central vessels normal, no retinal hemorrhages. Assessment: Eyelid and conjunctival foreign bodies removed. Mild erosions in the lower conjunctival sac. Right optic nerve head somewhat pale, rather sharp-edged. Current Recommendations: - Antibiotic eye drops three times a day for both eyes. - Ensure complete eyelid closure. 2) Consultation with Craniomaxillofacial (CMF) Surgery on 04/05/2017 Patient Information: - Motorbike accident with severe open traumatic brain injury with fractures of the cranial vault, mastoid, and skull base <!-- --> - Request for assessment. - Patient with maxillary fracture. Findings: According to the responsible attending physician, \"minimal handling in case of decompensating intracranial pressure\" is indicated. Therefore, currently, a cautious approach is suggested regarding surgical intervention for the radiologically hardly displaced maxillary fracture. Re-consultation is possible if there are changes in the clinical outcome. Assessment: Awaiting developments. 3) Consultation with Neurology on 04/06/2017 Patient Information: - Brain edema following a severe open traumatic brain injury with fractures of the cranial vault, mastoid, and skull base <!-- --> - Request for assessment. - Traumatic subarachnoid hemorrhage, intracranial artery dissection, and various other injuries. Findings: Patient comatose, intubated, sedated. Isocoric pupils. No light reaction in either eye. No reaction to pain stimuli for vestibulo-ocular reflex and oculomotor responses. Babinski reflex negative. Assessment: Long-term ventilation due to a history of intracerebral bleeding and skull base fracture. No response to pain stimuli or light reactions in the eyes. Procedure/Therapy Suggestion: Monitoring of patient condition. 4) Consultation with ENT on 04/16/2017 Patient Information: Tracheostomy tube change. Findings: Tracheostomy tube change performed. Stoma unremarkable. Trachea clear up to the bifurcation. Sutures in place. Assessment: Re-consultation on 08/27/2021 for suture removal. 5) Consultation with Neurology on 04/22/2017 Patient Information: Adduction deficit., Request for assessment. Findings: Long-term ventilation due to a history of intracerebral bleeding and skull base fracture. Adduction deficit in the right eye and horizontal nystagmus. Assessment: Suspected mesencephalic lesion due to horizontal nystagmus, but no diagnostic or therapeutic action required. 6) Consultation with ENT on 04/23/2017 Patient Information: Suture removal. Request for assessment. Findings: Tracheostomy site unremarkable. Sutures trimmed, and skin sutures removed. Assessment: Procedure completed successfully. Please note that some information is clinical and may not include specific dates or recommendations for further treatment. Antibiogram: Antibiotic Organism 1 (Pseudomonas aeruginosa) Organism 2 (Acinetobacter radioresistens) ------------------------- ----------------------------------------- ----------------------------------------------- Aztreonam I (4.0) \- Cefepime I (2.0) \- Cefotaxime \- \- Amikacin S (\<=2.0) S (4.0) Ampicillin \- \- Piperacillin I (\<=4.0) \- Piperacillin/Tazobactam I (8.0) \- Imipenem I (2.0) S (\<=0.25) Meropenem S (\<=0.25) S (\<=0.25) Ceftriaxone \- \- Ceftazidime I (4.0) \- Gentamicin . (\<=1.0) S (\<=1.0) Tobramycin S (\<=1.0) S (\<=1.0) Cotrimoxazole \- S (\<=20.0) Ciprofloxacin I (\<=0.25) I (0.5) Moxifloxacin \- \- Fosfomycin \- \- Tigecyclin \- \- \"S\" means Susceptible \"I\" means Intermediate \".\" indicates not specified \"-\" means Resistant ### Patient Report 2 Dear colleague, We are reporting on our mutual patient, Mr. John Chapman, born on 11/16/1994, who presented himself to our Outpatient Clinic from 08/08/2018. Diagnoses: - Right abducens Nerve Palsy and Facial Nerve Palsy - Lagophthalmos with corneal opacities due to eyelid closure deficit - Left Abducens Nerve Palsy with slight compensatory head leftward rotation and preferred leftward gaze - Bilateral disc swelling - Suspected left cavernous internal carotid artery aneurysm following traumatic ICA dissection - History of shunt explantation due to dysfunction and right-sided re-implantation (Codman, current pressure setting 12 cm H2O) - History of left VP shunt placement (programmable ventriculoperitoneal shunt, initial pressure setting 5/25 cm H2O, adjusted to 3 cm H2O before discharge) - Malresorptive hydrocephalus - History of severe open head injury in a motocross accident with multiple skull fractures and distal dissection Procedure: We conducted the following preoperative assessment: - Visual acuity: Distant vision: Right eye: 0.5, Left eye: 0.8p - Eye position: Fusion/Normal with significant esotropia in the right eye; no fusion reflex observed - Ocular deviation: After CT, at distance, esodeviation simulating alternating 100 prism diopters (overcorrection); at near, esodeviation simulating alternating 90 prism diopters - Head posture: Fusion/Normal with leftward head turn of 5-10 degrees - Correspondence: Bagolini test shows suppression at both distance and near fixation - Motility: Right eye abduction limited to 25 degrees from the midline, abduction in up and down gaze limited to 30 degrees from midline; left eye abduction limited to 30 degrees - Binocular functions: Bagolini test shows suppression in the right eye at both distance and near fixation; Lang I negative Current Presentation: Mr. Chapman presented himself today in our neurovascular clinic, providing an MRI of the head. Medical History: The patient is known to have a pseudoaneurysm of the cavernous left internal carotid artery following traumatic carotid dissection in 04/2017, along with ipsilateral abducens nerve palsy. Physical Examination: Patient in good general condition. Oriented in all aspects. No cyanosis. No edema. Warm and dry skin. Normal nasal and pharyngeal findings. Pupils round, equal, and react promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa unremarkable. No jugular vein distension. No carotid bruits heard. Palpation of lymph nodes unremarkable. Palpation of the thyroid gland unremarkable, freely movable. Lungs: Normal chest shape, moderately mobile, vesicular breath sounds. Heart: Regular heart action, normal rate; heart sounds clear, no pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all quadrants; soft abdomen, no tenderness, no palpable masses, liver and spleen not palpable due to limited access, non-tender kidneys. Normal peripheral pulses; joints freely movable. Strength, motor function, and sensation are unremarkable. Therapy and Progression: The pseudoaneurysm has shown slight enlargement in the recent follow-up imaging and remains partially thrombosed. The findings were discussed on during a neurovascular board meeting, where a recommendation for endovascular treatment was made, which the patient has not yet pursued. Since Mr. Chapman has not been able to decide on treatment thus far, it is advisable to further evaluate this still asymptomatic condition through a diagnostic angiography. This examination would also help in better planning any potential intervention. Mr. Chapman agreed to this course of action, and we will provide him with a timely appointment for the angiography. Lab results upon Discharge: Parameter Results Reference Range -------------------- ------------- --------------------- Creatinine (Jaffé) 0.44 mg/dL 0.70-1.20 mg/dL Urea 31 mg/dL 17-48 mg/dL Total Bilirubin 0.35 mg/dL \< 1.20 mg/dL Hemoglobin 7.8 g/dL 13.5-17.0 g/dL Hematocrit 28% 39.5-50.5% Red Blood Cells 3.5 M/uL 4.3-5.8 M/uL White Blood Cells 10.35 K/uL 3.90-10.50 K/uL Platelets 379 K/uL 150-370 K/uL MCV 77.2 fL 80.0-99.0 fL MCH 24.1 pg 27.0-33.5 pg MCHC 32.5 g/dL 31.5-36.0 g/dL MPV 11.3 fL 7.0-12.0 fL RDW-CV 17.7% 11.5-15.0% Quick 54% 78-123% INR 1.36 0.90-1.25 aPTT 32.8 sec 25.0-38.0 sec ### Patient Report 3 Dear colleague, We are reporting on our patient, Mr. John Chapman, born on 11/16/1994, who was under our inpatient care from 05/25/2019 to 05/26/2019. Diagnoses: - Pseudoaneurysm of the cavernous left internal carotid artery following traumatic carotid dissection - Abducens nerve palsy. - History of severe open head trauma with fractures of the cranial vault, mastoid, and skull base. Distal ICA dissection bilaterally. Bilateral hemispheric subarachnoid hemorrhage extending into the basal cisterns.mInfarct areas in the MCA-ACA border zones, right frontal, and left parietal. Malresorptive hydrocephalus. <!-- --> - Rhabdomyolysis. - History of aspiration pneumonia. - Suspected Propofol infusion syndrome. Current Presentation: For cerebral digital subtraction angiography of the intracranial vessels. The patient presented with stable cardiopulmonary conditions. Medical History: The patient was admitted for the evaluation of a pseudoaneurysm of the supra-aortic vessels. Further medical history can be assumed to be known. Physical Examination: Patient in good general condition. Oriented in all aspects. No cyanosis. No edema. Warm and dry skin. Normal nasal and pharyngeal findings. Pupils round, equal, and react promptly to light bilaterally. Moist tongue. Pharynx and buccal mucosa unremarkable. No jugular vein distension. No carotid bruits heard. Palpation of lymph nodes unremarkable. Palpation of the thyroid gland unremarkable, freely movable. Lungs: Normal chest shape, moderately mobile, vesicular breath sounds. Heart: Regular heart action, normal rate; heart sounds clear, no pathological sounds. Abdomen: Peristalsis and bowel sounds normal in all quadrants; soft abdomen, no tenderness, no palpable masses, liver and spleen not palpable due to limited access, non-tender kidneys. Normal peripheral pulses; joints freely movable. Strength, motor function, and sensation are unremarkable. Supra-aortic angiography on 05/25/2019: [Clinical context, question, justifying indication:]{.underline} Pseudoaneurysm of the left ICA. Written consent was obtained for the procedure. Anesthesia, Medications: Procedure performed under local anesthesia. Medications: 500 IU Heparin in 500 mL NaCl for flushing. [Methodology]{.underline}: Puncture of the right common femoral artery under local anesthesia. 4F sheath, 4F vertebral catheter. Serial angiographies after selective catheterization of the internal carotid arteries. Uncomplicated manual intra-arterial contrast medium injection with a total of 50 mL of Iomeron 300. Post-interventional closure of the puncture site by manual compression. Subsequent application of a circular pressure bandage. [Technique]{.underline}: Biplanar imaging technique, area dose product 1330 cGy x cm², fluoroscopy time 3:43 minutes. [Findings]{.underline}: The perfused portion of the partially thrombosed cavernous aneurysm of the left internal carotid artery measures 4 x 2 mm. No evidence of other vascular pathologies in the anterior circulation. [Recommendation]{.underline}: In case of post-procedural bleeding, immediate manual compression of the puncture site and notification of the on-call neuroradiologist are advised. - Pressure bandage to be kept until 2:30 PM. Bed rest until 6:30 PM. - Follow-up in our Neurovascular Clinic Addition: Doppler ultrasound of the right groin on 05/26/2019: [Clinical context, question, justifying indication:]{.underline} Free fluid? Hematoma? [Findings]{.underline}: A CT scan from 04/05/2017 is available for comparison. No evidence of a significant hematoma or an aneurysm in the right groin puncture site. No evidence of an arteriovenous fistula. Normal flow profiles of the femoral artery and vein. No evidence of thrombosis. Treatment and Progression: Pre-admission occurred on 05/24/2019 due to a medically justified increase in risk for DSA of intracranial vessels. After appropriate preparation, the angiography was performed on 05/25/2019. The puncture site was managed with a pressure bandage. In the color Doppler sonographic control the following day, neither a puncture aneurysm nor an arteriovenous fistula was detected. On 05/25/2019, we discharged the patient in good subjective condition for your outpatient follow-up care. Current Recommendations: Outpatient follow-up Lab results: Parameter Reference Range Result ----------------------- --------------------- ------------- Sodium 136-145 mEq/L 141 mEq/L Potassium 3.5-4.5 mEq/L 4.9 mEq/L Chloride 98-107 mEq/L 100 mEq/L Osmolality 280-300 mOsm/kg 290 mOsm/kg Glucose in Fluoride 60-110 mg/dL 76 mg/dL Creatinine (Jaffé) 0.70-1.20 mg/dL 0.98 mg/dL CRP \< 5.0 mg/L 4.5 mg/L Triglycerides \< 150 mg/dL 119 mg/dL Creatine Kinase \< 190 U/L 142 U/L Free Triiodothyronine 2.00-4.40 ng/L 3.25 ng/L Free Thyroxine 9.30-17.00 ng/L 14.12 ng/L TSH Basal 0.27-4.20 mU/L 1.65 mU/L Hemoglobin 13.5-17.0 g/dL 14.3 g/dL Hematocrit 39.5-50.5% 43.4% Erythrocytes 4.3-5.8 M/uL 5.6 M/uL Leukocytes 3.90-10.50 K/uL 10.25 K/uL Platelets 150-370 K/uL 198 K/uL MCV 80.0-99.0 fL 83.2 fL MCH 27.0-33.5 pg 28.1 pg MCHC 31.5-36.0 g/dL 33.4 g/dL MPV 7.0-12.0 fL 11.6 fL RDW-CV 11.5-15.0% 13.5% Quick \> 78% 90% INR \< 1.25 1.07 aPTT 25.0-38.0 sec 36.1 sec	Ciprofloxacin
Sensatia most likely refers to ________. A. illicit drugs B. microphone shorters C. pornography D. virtual reality equipment	SEA LEGS By FRANK QUATTROCCHI Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction November 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Rootless and footloose, a man in space can't help but dream of coming home. But something nobody should do is bet on the validity of a homesick dream! Flight Officer Robert Craig surrendered the tube containing his service record tapes and stood waiting while the bored process clerk examined the seal. "Your clearance," said the clerk. Craig handed him a battered punch card and watched the man insert it in the reproducer. He felt anxiety as the much-handled card refused for a time to match the instrument's metal contact points. The line of men behind Craig fidgeted. "You got to get this punched by Territorial," said the clerk. "Take it back to your unit's clearance office." "Look again, Sergeant," Craig said, repressing his irritation. "It ain't notched." "The hell it isn't." The man examined the card with squinting care and nodded finally. "It's so damn notched," he complained. "You ought to take care of that card; can't get on without one." Craig hesitated before moving. "Next," said the clerk, "What you waiting for?" "Don't I take my 201 file?" "We send it on ahead. Go to Grav 1 desk." A murmur greeted the order. Craig experienced the thrill of knowing the envy of the others. Grav 1—that meant Terra. He crossed the long, dreary room, knowing the eyes of the other men were upon him. "Your service tapes," the next noncom said. "Where you going?" "Grav 1—Terra," fumbled Craig. "Los Angeles." "Los Angeles, eh? Where in Los Angeles?" "I—I—" Craig muttered, fumbling in his pockets. "No specific destination," supplied the man as he punched a key on a small instrument, "Air-lock ahead and to your right. Strip and follow the robot's orders. Any metal?" "Metal?" asked Craig. "You know, metal ." "Well, my identification key." "Here," commanded the clerk, extending a plastic envelope. Craig moved in the direction indicated. He fought the irrational fear that he had missed an important step in the complicated clerical process. He cursed the grudging attitude of the headquarters satellite personnel and felt the impotence of a spaceman who had long forgotten the bureaucracy of a rear area base. The knowledge that much of it was motivated by envy soothed him as he clumsily let himself into the lock. "Place your clothing in the receptacle provided and assume a stationary position on the raised podium in the center of the lock." Craig obeyed the robot voice and began reluctantly to remove his flight jacket. Its incredibly fine-grained leather would carry none of the strange, foreign associations for the base station clerk who would appropriate it. He would never know the beautiful, gentle beast that supplied this skin. "You are retarding the progress of others. Please respond more quickly to your orders." Craig quickly removed the last of his clothing. It was impossible to hate a robot, but one could certainly hate those who set it into operation. "You will find a red button at your feet. Lower your head and depress that button." Stepping on the button with his bare foot produced an instant of brilliant blue illumination. A small scratch on his arm stung briefly and he was somewhat blinded by the flash even through his eyelids, but that was all there was to the sterilizing process. "Your clothing and effects will be in the dressing room immediately beyond the locked door." He found his clothing cleanly and neatly hung on plastic hangers just inside the door to the dressing room. The few personal items he carried in his pockets were still there. The Schtann flight jacket was actually there, looking like new, its space-blue unfaded and as wonderfully pliant as before. "Insert your right arm into the instrument on the central table," commanded the same voice he had heard before. "Turn your arm until the scratch is in contact with the metal plate. There will be a slight pain, but it is necessary to treat the small injury you have been disregarding." Craig obeyed and clenched his teeth against a sharp stinging. His respect for the robot-controlled equipment of bases had risen. When he withdrew his arm, the scratch was neatly coated with a layer of flesh-colored plastic material. He dressed quickly and was on the verge of asking the robot for instructions, when a man appeared in the open doorway. "I am Captain Wyandotte," said the man in a pleasant voice. "Well, what's next?" asked Craig somewhat more belligerently than he had intended. The man smiled. "Your reaction is quite natural. You are somewhat aggressive after Clerical, eh?" "I'm a little anxious to get home, I suppose," said Craig defensively. "By 'home' you mean Terra. But you've never been there, have you?" "No, but my father—" "Your parents left Terra during the Second Colonization of Cassiopeia II, didn't they?" "Yes," Craig said. He was uncomfortable; Wyandotte seemed to know all about him. "We might say you've been away quite a while, eh?" "I was entered as a spaceman when I was 16," Craig said. "I've never been down for any period as yet." "You mean you haven't been in a gravity system?" "Oh, I've landed a few times, even walked around for a while...." "With the help of paraoxylnebutal," supplied the captain. "Well, sure." "Mr. Craig, I suppose you've guessed that the next step in our little torture system here is psych." "So I gathered." The captain laughed reassuringly. "No, don't put up your guard again. The worst is over. Short of Gravitational conditioning, there is nothing to stop you from going to Terra." "Sorry, I guess I'm a little touchy. This is my first time...." "Quite natural. But it being your first time—in quite a number of ways, I might add—it will be necessary for you to undergo some conditioning." "Conditioning?" asked Craig. "Yes. You have spent eleven years in space. Your body is conditioned to a normal state of free fall, or at best to a state of acceleration." "Yeah, I know. Once on Gerymeade...." "You were ill, couldn't keep your balance, felt dizzy. That is why all spacemen carry PON, paraoxylnebutal, with them. It helps suppress certain physiological reactions to an entirely new set of conditions. Channels of the ear, for example. They play an important part in our awareness of balance. They operate on a simple gravity principle. Without gravity they act up for a time, then gradually lose function. Returning to gravity is rather frightening at first." "I know all about this, Captain." "You've undoubtedly read popularizations in tapezines. But you have experienced it briefly." "I expect to have some trouble at first." Craig was disturbed by the wordy psychologist. What was the man actually saying? "Do you know what sailors of ancient times meant by 'sea legs?'" asked Wyandotte. "Men on a rolling ocean acclimated themselves to a rolling horizontal. They had trouble when they went ashore and the horizontal didn't roll any more. "It meant more than that. There were excellent psychological reasons for the old stereotype, the 'drunken sailor.' A port city was a frightening thing to an old sailor—but let's begin our little job at the beginning. I'll turn you over to psychometry for the usual tests and pick you up tomorrow morning at, say, 0900." During the days that followed, the psychologist seemed to Craig to become progressively more didactic. He would deliver long speeches about the "freedom of open space." He spoke repetitiously of the "growing complexity of Terran society." And yet the man could not be pinned down to any specific condition the spaceman would find intolerable. Craig began to hate the delay that kept him from Terra. Through the ports of the headquarters base satellite, he scanned the constellations for the scores of worlds he had visited during his eleven years in space. They were incredibly varied, even those that supported life. He had weathered difficult landings on worlds with rip-tide gravities, had felt the pull of the incredible star-tides imparted by twin and even triple star systems. He had been on Einstein IV, the planet of eight moons, and had felt the pulse of all eight of the satellites at once that no PON could completely nullify. But even if he could accept the psychologist's authority for the cumulative effect of a gravity system, he could not understand the unspoken warning he felt underlying all that the man said. "Of course it has changed," Craig was protesting. "Anyway, I never really knew very much about Terra. So what? I know it won't be as it was in tapezines either." "Yet you are so completely sure you will want to live out your life there, that you are willing to give up space service for it." "We've gone through this time and time again," Craig said wearily. "I gave you my reasons for quitting space. We analyzed them. You agreed that you could not decide that for me and that my decision is logical. You tell me spacemen don't settle down on Terra. Yet you won't—or can't—tell me why. I've got a damned good job there—" "You may find that 'damned good jobs' become boring." "So I'll transfer. I don't know what you're trying to get at, Captain, but you're not talking me out of going back. If the service needs men so badly, let them get somebody else. I've put in my time." "Do you really think that's my reason?" "Sure. What else can it be?" "Mr. Craig," the psychologist said slowly, "you have my authorization for you to return to Terra as a private citizen of that planet. You will be given a very liberal supply of PON—which you will definitely need. Good luck. You'll need that too." On the eighth day, two attendants, who showed the effects of massive doses of PON to protect themselves from the centrifugal force, had to carry a man out of the tank. Many others asked to be removed, begged to be allowed to withdraw their resignations. "The twelfth day is the worst," a grizzled spaceman told Craig. "That's when the best of 'em want out." Craig clenched the iron rung of his bed and struggled to bring the old man's face into focus. "How ... how do they know when you ought ... to come out?" he asked between waves of nausea. "Blood pressure. They get you just before you go into shock." "How can they tell?" Craig fought down his growing panic. "I can't." "That strap around your belly. You mean you ain't noticed it?" "Haven't noticed much of anything." "Well, it's keyed to give them some kind of signal." The old man lapsed into silence. Craig wished him to continue. He desperately wanted something to distract his mind from the ghastly conditioning process. Slowly at first, the lines formed by seams in the metal ceiling began to bend. Here it came again! "Old man!" shouted Craig. "Yeah, son. They've dropped it down a notch." "Dropped ... it ... down?" "Maybe that ain't scientific, but it's the way I always think of it." "Can't they ... drop it down continuously?" "They tried that a few times—once when I was aboard. You wouldn't like it, kid. You wouldn't like it at all." "How ... many times ... do they drop it?" "Four times during the day, three at night. Twenty days." A nightmare of visual sensations ebbed into Craig's mind. He was vaguely aware of the moans of other men in the vaultlike room. Wave upon wave of nausea swept him as he watched the seam lines bend and warp fantastically. He snapped his eyelids shut, only to begin feeling the nightmarish bodily sensations once more. He felt the cot slowly rise longitudinally, felt himself upside down, then the snap of turning right side up once more—and he knew that neither he nor the cot had moved so much as an inch. Craig heard the voices around him, muffled, as though talking through wadding. "... got it bad." "We better take him out." "... pretty bad." "He'll go into shock." "... never make it the twelfth." "We better yank him." "I'm ... all right," Craig mumbled at the voices. He struggled with the bonds of his cot. With terrible effort he forced his eyes open. Two white-clad figures, ridiculously out of proportion, hovered wraithlike over him. Four elongated eyes peered at him. Attendants coming for to take me home.... "Touch me and I'll kick your teeth in!" he yelled. "I'm going to Terra. Wish you were going to Terra?" Then it was better. Oddly, he passed the twelfth day easily. By the fourteenth day, Craig knew he could stand Grav 1. The whine of the centrifuge's motors had diminished to a low hum. Either that or they had begun to produce ultra-sonic waves. Craig was not sure. Most of the men had passed through the torments of gravitational conditioning. The huge headquarters base centrifuge aboard the man-made satellite had gradually caused their bodies to respond once more to a single source of pull. They were now ready to become inhabitants of planets again, instead of free-falling ships. On the eighteenth day, automatic machinery freed them from their imprisoning cots. Clumsily and awkwardly at first, the men began to walk, to hold their heads and arms in proper attitudes. They laughed and joked about it and kidded those who were slow at adjusting. Then they again began taking paraoxylnebutal in preparation for the free-fall flight to Terra. Only one of the score of men in the centrifuge tank remained voluntarily in his cot. "Space article violator," the old man informed Craig. "Psycho, I think. Went amuck with some extraterritorials. Killed a dozen." "What will they do, exile him?" "Not to Chociante, if that's what you mean. They just jerked his space card and gave him a one-way ticket to Terra." "For twelve murders?" asked Craig incredulously. "That's enough, son." The old man eyed Craig for an instant before looking away. "Pick something to talk about. What do you figure on doing when you get to Terra, for instance?" "I'm going into Import. My father was in it for twenty years." "Sure," said the old spaceman, watching a group of young crewmen engaged in an animated conversation. "It's a good job. There's a future to it." "Yeah." Why did he have to explain anything at all to the old space tramp? "Once I get set up, I'll probably try to open my own business." "And spend your weekends on Luna." Craig half rose from his cot, jarred into anger. But the old spaceman turned, smiling wryly. "Don't get hot, kid. I guess I spent too long in Zone V." He paused to examine his wrinkled hands. They were indelibly marked with lever callouses. "You get to thinking anyone who stays closer'n eighty light years from Terra is a land-lubber." Craig relaxed, realizing he had acted childishly. "Used to think the same. Then I took the exam and got this job." "Whereabouts?" "Los Angeles." The old man looked up at Craig. "You don't know much about Terra, do you, son?" "Not much." "Yeah. Well, I hope you ain't disappointed." "My father was born there, but I never saw it. Never hit the Solar System, matter of fact. Never saw much of anything close up. I stood it a long time, old man, this hitting atmospheres all over the Universe." But the spaceman seemed to have lost interest. He was unpacking some personal belongings from a kit. "What are you doing in Grav 1?" Craig asked. The old man's face clouded for an instant. "In the old days, they used to say us old-timers acted like clocks. They used to say we just ran down. Now they got some fancy psychology name for it." Craig regretted his question. He would have muttered some word of apology, but the old man continued. "Maybe you've read some of the old sea stories, or more'n likely had 'em read to you. Sailors could go to sea until they just sort of dried up. The sea tanned their skins and stiffened their bones, but it never stiffened their hearts. When they got old, it just pulled them in. "But space is different. Space is raw and new. It tugs at your guts. It sends the blood rushing through your veins. It's like loving. You don't become a part of space the way you do the old sea, though. It leaves you strictly alone. Except that it sucks you dry, takes all the soup out of you, leaves you brittle and old—old as a dehydrated piece of split leather. "Then one day it shoots a spurt of blood around in one of your old veins. Something gives. Space is through with you then. And if you can stand this whirligig conditioning, you're through with space." " You can't figure it. Some of 'em urp all over and turn six shades of green. " " You got to watch the ones that don't. " " Yeah, you got to watch the ones that don't. Especially the old ones. " " He's old. You think it was his heart? " " Who knows? " " They'll dump him, won't they? " " After a tracer is sent through. But it won't do any good. " " He probably outlived everybody that ever knew him. " " Wouldn't be surprised. Here, grab his leg. " Robert Craig folded the flight jacket tightly and stuffed it into the cylindrical carton. A sleeve unwound just as he did so, making it difficult to fit into the place he had made for it. Exasperated, he refolded it and jammed it in place. Smaller rolls of underclothing were then fitted in. When he was satisfied with the layer, he tossed in a small handful of crystals and began to fill the next layer. After the carton was completely filled, he ignited the sealing strip and watched as the plastic melted into a single, seamless whole. It was ready for irradiation. Probably in another ten years his son-to-be would put it on and play spaceman. But Craig swore he'd make sure that the kid knew what a stinking life it was. At 1300 hours, the ferry bumped heavily alongside the starboard lock. It was the signal for relief in the passengers' quarters; many were beginning to feel a reaction to the short free-fall flight from the headquarters satellite. The audio called out: "Flight Officer Robert Craig. Flight Officer Robert Craig. Report to Orderly 12. Report to Orderly 12 through the aft door." With pangs of anxiety he could not completely suppress, Craig obeyed. Orderly 12 handed him a message container. "Who's it from? Somebody on Terra?" "From a private spaceman named Morgan Brockman." " Brockman? " "He was with you in the grav tank." "The old man!" The message container produced a battered punch card. Craig straightened it and was about to reach into his pocket for a hand transcriber. But then he noticed the card bore only a few irregular punches and was covered with rough hand printing. Son, when the flunkies get around to giving you this, they'll have shot me out the tube. How do I know? Same way you know when your turbos are going to throw a blade. It's good this way. There's something you can do for me if you want to. Way back, some fifty years ago, there was a woman. She was my wife. It's a long story I won't bother you with. Anyway, I left her. Wanted to take her along with me, but she wouldn't go. Earth was a lot different then than it is now. They don't have to tell me; I know. I saw it coming and so did Ethel. We talked about it and I knew I had to go. She wouldn't or couldn't go. Wanted me to stay, but I couldn't. I tried to send her some units once in a while. Don't know if she ever got them. Sometimes I forgot to send them at all. You know, you're way out across the Galaxy, while she's home. Go see her if you can, son. Will you? Make sure she gets the unit transfer I made out. It isn't much out of seventy years of living, but she may need it. And maybe you can tell her a little bit about what it means to be out there. Tell her it's open and free and when you got hold of those levers and you're trying for an orbit on something big and new and green.... Hell, you remember. You know how to tell her. Her name is Ethel Brockman. I know she'll still use my name. Her address is or was East 71, North 101, Number 4. You can trace her easy if she moved. Women don't generally shove off and not leave a forwarding address. Not Ethel, at least. Craig put the battered card in his pocket and walked back through the door to the passenger room. How did you explain to an old woman why her husband deserted her fifty years before? Some kind of story about one's duty to the Universe? No, the old man had not been in Intergalactic. He had been a tramp spaceman. Well, why had he left? Fifty years in space. Fifty years! Zone V had been beyond anybody's imagination that long ago. He must have been in on the first Cetusian flights and shot the early landings in Cetus II. God only knew how many times he had battled Zone 111b pirates.... Damn the old man! How did one explain? Craig descended the ramp from the huge jet and concentrated on his impressions. One day he would recall this moment, his first on the planet Terra. He tried to recall his first thrill at seeing Los Angeles, 1500 square miles of it, from the ship as it entered the atmosphere. He was about to step off the last step when a man appeared hurriedly. A rather plump man, he displayed a toothy smile on his puffy red face. "A moment, sir. Just a little greeting from the Terra. You understand, of course. Purely routine." Craig remained on the final step of the ramp, puzzled. The man turned to a companion at his right. "We can see that this gentleman has come from a long, long way off, can't we?" The other man did not look up. He was peering into what seemed to Craig to be a kind of camera. "We can allow the gentlemen to continue now, can't we? It wasn't that we believed for a minute, you understand ... purely routine." Both men were gone in an instant, leaving Craig completely bewildered. "You goin' to move on, buddy, or you want to go back?" Craig turned to face a line of his fellow passengers up the ramp behind him. "Who was that?" Craig asked. "Customs. Bet you never got such a smooth screening before, eh?" "You mean he screened me? What for?" "Hard to say," the other passenger said. "You'll get used to this. They get it over with quick." Craig made his way toward the spaceport administration building. His first physical contact with Terra had passed unnoticed. "Sir! Sir!" cried a voice behind him. He wheeled to see a man walking briskly toward him. "You dropped this, sir. Quite by accident, of course." Craig examined the small object the man had given him before rushing off toward an exit. It was an empty PON tube he had just discarded. He couldn't understand why the man had bothered until he realized that the plastaloid floor of the lobby displayed not the faintest scrap of paper nor trace of dirt. The Import personnel man was toying with a small chip of gleaming metal. He did not look directly at Craig for more than an instant at a time, and commented on Craig's description of his trip through the city only very briefly between questions. "It's a good deal bigger than I imagined," Craig was saying. "Haven't seen much of it, of course. Thought I'd check in here with you first." "Yes, naturally." "Thought you could give me some idea of conditions...." "Conditions?" "For instance, what part of the city I should live in. That is, what part is closest to where I'll work." "I see," said the man noncommittally. It seemed to Craig that he was about to add something. He did not, however, but instead rose from his chair and walked to the large window overlooking an enormous section of the city far below. He stared out the window for a time, leaving Craig seated uncomfortably in the silent room. There was a distracted quality about him, Craig thought. "You are the first man we have had from the Intergalactic Service," the personnel man said finally. "That so?" "Yes." He turned to face Craig briefly before continuing. "You must find it very strange here." "Well, I've never seen a city so big." "Yes, so big. And also...." He seemed to consider many words before completing the sentence. "And also different." "I haven't been here very long," said Craig. "Matter of fact, I haven't been anywhere very long. This is my first real experience with life on a planet. As an adult, anyway." The personnel man seated himself once more and pressed a button on a small instrument. A secretary entered the office from a door to Craig's left. "Miss Wendel, this is Mr. Craig. Mr. Craig, my secretary. Mr. Craig will enter Minerals and Metals, Zone V." They exchanged formal greetings. She was a moderately pretty girl of medium height and, to Craig, a pleasantly rounded figure. He would have attempted to catch her eye had she not immediately occupied herself with unfolding the legs of a small instrument she was carrying. "This is Mr. Craig's first landing on Terra, Miss Wendel," the personnel man continued. "Actually, we shall have to consider him in much the same way we would an extraterrestrial." The girl glanced at Craig, casting him a cool, impersonal smile. "He was formerly a flight officer in the Intergalactic Space Service." The statement was delivered in an almost exaggeratedly casual tone. The girl glanced at him once more, this time with a definite quizzical look in her brown eyes. "Three complete tours of duty, I believe." "Four," corrected Craig. "Four tours of three years each, minus a year's terminal leave." "I take it you have no identification card?" the man asked. "The one I held in the service. It's pretty comprehensive." The other turned to the secretary. "You'll see that he is assisted in filing his application, won't you? A provisional Code II. That will enable you to enter all Import offices freely, Mr. Craig." "Will he need a food and—clothing ration also?" asked the girl, without looking at Craig. "Yes." The man laughed. "You'll excuse us, Mr. Craig. We realize that you couldn't be expected to be familiar with Terra's fashions. In your present outfit you would certainly be typed as a ... well, you'd be made uncomfortable." Craig reddened in spite of himself. He had bought the suit on Ghandii. "A hick," he supplied. "I wouldn't go that far, but some people might." Craig noted the pleasant way the girl filled her trim, rather severe business suit. He amused himself by calculating stress patterns in its plain woven material as she assembled the forms for him. "Here, Mr. Craig. I believe these are complete." "They look pretty complicated." "Not at all. The questions are quite explicit." Craig looked them over quickly. "I guess so. Say, Miss Wendel, I was wondering—I don't know the city at all. Maybe you could go with me to have dinner. It must be almost dinnertime now. You could sort of check me out on some...." "I'm afraid that would be quite impossible. You couldn't gain admittance to any office you need to visit tonight. Therefore, it is impossible for me to be of any assistance to you." "Oh, come now, Miss Wendel. There are women aboard spaceships. I'm not a starved wolf." "Certainly you are not, Mr. Craig. But it is not possible for me...." "You said that already, but you can have dinner with me. Just company." "I'm afraid I don't understand." The Galactic hotel strove to preserve an archaic tone of hospitality. It advertised "a night's lodgings" and it possessed a bellboy. The bellboy actually carried Craig's plasticarton and large file of punch cards and forms to his room. Tired from the long, confusing day, Craig was not impressed. He vaguely wondered if the little drama of the hotel carried so far as a small fee to be paid the bellboy, and he hoped he would have the right size of Terran units in his wallet. Outside the door to the room, the bellboy stopped and turned to Craig. "For five I'll tell you where it is," he said in a subdued tone. "Tell me where what is?" "You know, the mike." "Mike?" "All right, mister, three units, then. I wasn't trying to hold you up." "You mean a microphone?" asked Craig, mechanically fishing for his wallet. "Sure, they don't put in screens here. Wanted to, but the boss convinced 'em there aren't any Freedomites ever stay here." "Where is the microphone?" Craig asked as he found a ten unit note. He was too puzzled to wonder what he was expected to do with the information. "It's in the bed illuminator. You can short it out with a razor blade. Or I'll do it for another two." "Never mind," Craig said wearily. He waited while the bellboy inserted a key into the door and opened it for him. "I can get you a sensatia-tape," whispered the boy when they had entered. He nudged Craig wickedly. "You know what they're like?" "Yeah," Craig said disgustedly. Traffic in the illicit mental-image tapes was known as far into space as lonely men had penetrated. Intergalactic considered them as great a menace to mental and moral stability as the hectopiates. Craig wearily got the man out of the room, took a PON pill, and eased himself into the bed. It had been a weird day and he had not liked it. There was no telling how long it would take him to shake his—sea legs, the psychologist had called it. One thing was sure: Terra aggressively went after its strangers.	C. pornography
What do FHIR and RDF stand for?	### Introduction ::: Healthcare Information Technology and the Interoperability Problem Since the early 1970s, healthcare information technology has moved toward comprehensive electronic medical records (EMR) in which almost every aspect of the patient's healthcare has been digitized and retained indefinitelyBIBREF0, which has vastly improved the efficiency with which patient information can be retained, communicated, and analyzed. At the same time, the healthcare industry has moved from a fee-for-service model to a value-based model, facilitated in part by the existence of such a record and in part by public policy, such as the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 BIBREF1, which provided financial incentives for the "meaningful use" of electronic medical records. The realization of a holistic medical record has been slowed by various obstacles, chief among them is the problem of interoperability between systems. The problem of interoperability arises almost as soon as a healthcare organization begins to choose a vendor for their electronic medical record, when they are faced with a choice between an architecture based on a single monolithic system or a so-called best-of-breed approach involving multiple discrete systems, each chosen for its superior performance in a narrow domain. The monolith claims to handle all aspects of healthcare information management; the best-of-breed approach entails a multiplicity of systems, each of which may be superior in its domain but which are not smoothly integrated. A major difference between the two architectures is how they solve the problem of interoperability. In the case of the monolith, the problem is solved by the system vendor, at least in principle, but at the cost to the customer of a loss of choice. In the best-of-breed approach, the problem of interoperability is shifted onto the customer, who incurs an often hefty cost in the form of a more complex systems architecture and the resulting need for specialized hardware, software, and staff to maintain it. In a best-of-breed approach, the need for instantaneous intersystems communication is typically handled via an Enterprise Service Bus (ESB)BIBREF2, which ensures real-time message delivery to subscribing systems. Additionally, if the data is to be analyzed in combination, rather than in isolation within the silo of a single system, it must be recombined and stored outside of these systems. This is typically done in an Enterprise Data Warehouse (EDW)BIBREF3 and requires further specialized hardware, software, and staff. However, most EDWs are based on a batch-loading system that runs during off-peak hours for the previous calendar day's businessBIBREF3; thus, while an EDW can be a powerful tool for retrospective analysis, it is unsuitable to real-time applications. Interoperability is a serious challenge that modern healthcare systems must address in order to adequately serve their patients. In this paper we demonstrate a hitherto underused approach that combines the attractive aspects of both an enterprise service bus and an enterprise data warehouse to arrive at real-time analytics. ### Background ::: Health Level Seven Version 2 (HL7v2) HL7v2 is a healthcare messaging standard developed by the standards organization Health Level Seven International. It first emerged in 1988 and today is the most widely used such standard, having been adopted by over ninety-five percent of health systems in the United States and thirty-five countries worldwide BIBREF4. As such, it is something of a universal medium in the field of healthcare interoperability, yet it is terse and, without specialized training and access to the standard reference, cryptic. Each HL7 message describes an event in a healthcare workflow and breaks down hierarchically into segments, fields, components, subcomponents, repeated components, and so on. There are well over one hundred types of messages and several times as many types of segments in HL7v2. The current version of the specification, for HL7 v2.8, is well over 2,500 pages long and contains nearly one million words. BIBREF0 Partly as a consequence of this complexity, health interoperability has become a specialized field, replete with certifications and training and entire careers built on knowledge of HL7v2. An example HL7 message describing the following information is shown in Figure FIGREF4 The PID (Patient Identification) segment contains the demographic information of the patient. Eve E. Everywoman was born on 1962-03-20 and lives in Statesville OH. Her patient ID number (presumably assigned to her by the Good Health Hospital) is 555-44-4444. The OBR (Observation Request) segment identifies the observation as it was originally ordered: 15545 GLUCOSE. The observation was ordered by Particia Primary MD and performed by Howard Hippocrates MD. The OBX (Observation) segment contains the results of the observation: 182 mg/dl. ### Background ::: Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) FHIR BIBREF5 is a new open standard for healthcare data developed by the same company that developed HL7v2. However, whereas HL7v2 uses an idiosyncratic data exchange format, FHIR uses data exchange formats based on those already in wide use on the World-Wide Web such as Extensible Markup Language (XML) and JavaScript Object Notation (JSON) BIBREF6, as well as the web's familiar transfer control protocols such as HyperText Transfer Protocol Secure (HTTPS) and Representational State Transfer (REST) BIBREF6 and system of contextual hyperlinks implemented with Uniform Resource Locators / Identifiers (URL/URI) BIBREF7. This design choice simplifies interoperability and discoverability and enables applications to be built rapidly on top of FHIR by the large number of engineers already familiar with web application design without a steep learning curve. In contrast to HL7v2, which is based on events in a healthcare workflow such as admit, discharge, and transfer, FHIR is built on the notion of conceptual entities from the healthcare domain, such as Patient, Encounter, and Observation, i.e. resources. Currently, FHIR encompasses 143 resources, each of which is described abstractly in the FHIR standard with the attributes Name, Flags, Cardinality, Type, and Description & Constraints. BIBREF7. In a concrete implementation of FHIR, resources are serialized to one of the data exchange formats listed above. An example of an FIHR XML message is shown in Figure FIGREF5. ### Background ::: Semantic Web The term Semantic Web BIBREF8 denotes an interconnected machine-readable network of information. In some ways it is analogous to the World-Wide Web, but with some crucial differences. The most important similarity is in the vision for the two technologies: Like the World-Wide Web, the Semantic Web was envisioned as a way for users from different institutions, countries, disciplines, etc. to exchange information openly and in doing so to add to the sum of human knowledge. The difference, however, is in the different emphases put on human readability versus machine readability: Whereas the World-Wide Web was intended to be visually rendered by one of any number of web browsers before being read by humans and therefore prioritizes fault tolerance and general compatibility over precision, the semantic web prioritizes precision and logical rigor in order for the information contained in it to be machine readable and used for logical inference. The similarities continue in the technologies used to implement the two webs. Information in both the Semantic Web and the World-Wide Web is intended to be accessed using the familiar data exchange protocol Hypertext Transfer Protocol (HTTP) and addressed using Uniform Resource Identifiers (URI) for the Semantic Web and Uniform Resource Locations (URL) for the World-Wide Web that tell the agent/browser how to find linked information. Even the data exchange formats are remarkably similar: The World-Wide Web uses Hypertext Markup Language (HTML)BIBREF9, a tree-structured subset of Standard Generalized Markup Language (SGML)BIBREF10, whereas the Semantic Web uses a variety of tree-structured formats such as XML, JSON, Terse RDF Triple Language (i.e. Turtle/TTL)BIBREF11, etc. The most significant difference between the World-Wide Web and the Semantic Web is in the type of information that they encode. The Semantic Web delivers a payload of simple logical statements known as triples, each consisting of a subject, predicate, and object, whereas the World-Wide Web delivers a series of directives to the web browser that govern the layout of the rendered page as well as the content of the page, in the form of text, images, videos, scripts, and so on. This difference in payloads corresponds to their different purposes – the payload is delivered in the first case to an intelligent agent and in the second case to a web browser. In more technical terms, the semantic web can be thought of as a distributed directed graph whose vertices are resources and whose edges are statements describing those resources. In its openness and decentralized nature, it bears some resemblance to the World Wide Web; however, whereas the World Wide Web consists of ad hoc, unsynchronized data presented in a variety of formats, the semantic web is a machine-readable body of information that can be synchronized while still coming from a variety of sources. ### Background ::: Resource Description Framework (RDF) RDF is the backbone of the semantic webBIBREF8. It is described as a framework, rather than a protocol or a standard, because it is an abstact model of information whose stated goal is "to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines (a priori) the semantics of any application domain." BIBREF12 Its concrete realization is typically a serialization into one of several formats including XML, JSON, TTL, etc. The basic unit of information in RDF is a statement expressed as a logical triple; that is, a statement of the form <subject> <predicate> <object>, in which the predicate expresses a relationship between the subject and the object: for instance, bloodPressure :value 120. The subject must be a resource, that is, an object consisting of one or more statements, and the object may be either a literal, that is, a simple numeric or textual value, or another resource. The predicate describes some aspect or property of the subject. Because both the subject and the object can be resources, the object may also be described by statements in which it is the subject, leading to a complex graph structure. A group of statements can be used to perform inference on their resources, thus creating new statements and enriching the semantic universe of the data set. For instance, the canonical syllogism "Socrates is a man; all men are mortal; therefore, Socrates is mortal" can be reproduced in the two statements Socrates :isA man and man :is mortal, resulting in a synthesized third statement: Socrates :is mortal. RDF supports "inference, shared semantics across multiple standards and data formats, data integration, semantic data validation, compliance enforcement, SPARQL [SPARQL Protocol and RDF Query Language (SPARQL)] queries and other uses." BIBREF13. ### Background ::: FHIR/RDF One of the several formats into which FHIR can be serialized is RDF. However, because RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting, there is the potential for a slight mismatch between the models. This comes up in two ways: One, RDF makes statements of fact, whereas FHIR makes records of events. The example given in the FHIR documentation is the difference between "patient x has viral pneumonia" (statement of fact) and "Dr. Jones diagnosed patient x with viral pneumonia" (record of event). Two, RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts. The example given for this mismatch is "a modifier extension indicates that the surrounding element's meaning will likely be misunderstood if the modifier extension is not understood." The potential for serious error resulting from this mismatch is small, but it is worth bearing in mind when designing information systems. ### Background ::: SPARQL Protocol and RDF Query Language (SPARQL) RDF has an associated query language that can be used to search for matching statements, known as SPARQL. Although syntactically and semantically based on Structured Query Language (SQL), the information model over which it searches is RDF's directed graph of resources and statements, not the familiar relations stored in a relational database. The syntax is beyond the scope of this paper, but in general SPARQL queries outline the shape of the graph they wish to find. For an example SPARQL query that searches for blood pressure readings over 120 b.p.m., see Figure FIGREF6. ### Method At a high level, the semantic enrichment engine is designed to take healthcare data in a variety of formats as input and store it in a triplestore database that users can query. In this way, the engine acts as both a collector, receiving messages from numerous sources, and a bus for delivering messages to multiple sources, as well as a real-time analytics platform. For example, a message from a vital signs monitor and from a registration system can be coalesced into a new stream containing blood pressure, temperature, and laboratory values for use in a machine learning model to predict sepsis. To support future large-scale operations, a multi-protocol message passing system was used for inter-module communication. This modular design also allows different components to be swapped out seamlessly, provided they continue to communicate via the expected interface. Routines were developed to simulate input data based on the authors experience with real healthcare data. The reasons for this choice were twofold: One, healthcare data can be high in incidental complexity, requiring one-off code to handle unusual inputs, but not necessarily in such a way as to significantly alter the fundamental engineering choices in a semantic enrichment engine such as this one. Two, healthcare data is strictly regulated, and the process for obtaining access to healthcare data for research can be cumbersome and time-consuming. A simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting, was used for simulation. In a production setting, the Java module that generates simulation data would be replaced by either a data source that directly writes to the input message queue or a Java module that intercepts or extracts production data, transforms it as needed, and writes it to the input message queue. A component-level view of the systems architecture is shown in Figure FIGREF7 ### Method ::: Class Hierarchy The project was written in Java, with each major component in its own package. There is a top-level class named ActiveMQEnabled that handles common tasks, such as connecting to the message broker, logging, event handling, and other such functionality. Each type of component in the pipeline - input, encoder, store, query, output, and application - is a subclass of ActiveMQEnabled as well as a superclass to specific types of those components. Most components are able both to send and receive messages, with certain exceptions: for example, inputs can only send and outputs can only receive. Stores can both receive and send, but in the concrete implementation in this project, the TDB store only receives (queries are better handled as timed polls, rather than being event-driven). ### Method ::: Inputs In the first stage of the module, simulated inputs represent a variety of healthcare entities and arrive in a variety of formats: patients in a pipe-delimited list, encounters as FHIR messages, and observations as HL7v2 messages. As discussed in the Background section, all of these are widely used input formats in modern health systems and realistically represent the heterogeneous message exchanges that are likely to occur in a real healthcare setting. Each input is configurable with regard to message timing and frequency, and the vitals signs can be made to simulate various conditions such as hypertension or hypothermia. An example of a generate vital sign is shown in Figure FIGREF8 ### Method ::: Encoder The encoder stage itself has two stages. In the first, input messages arriving at queues named according to the convention "INPUT.ENTITY.FORMAT" are retrieved, parsed, and transformed into internal representations of common domain objects, in this case Patient, Encounter, and Observation. In the second stage, these internal representations are transformed into internal representations of RDF graphs of FHIR resources and written out to the next message queue. By decoupling the parsing phase from the RDF-generating phase, the number of parsing and generating routines required for N sources and M resource types is reduced from N x M to N + M. This also allows parsing and generating jobs to be written in parallel and by different developers using the common internal representations as an intermediate layer. For instance, one developer could be writing the code to parse an HL7 ADT (admit/discharge/transfer) message while another developer was writing the code to turn this message into Patient, Encounter, and Observation resources. (Note that a single HL7 message can be used to create multiple FHIR resources BIBREF14). An example of a POJO to FIHR/RDF message encoder class is shown in Figure FIGREF9 ### Method ::: Store The store stage writes RDF-encoded statements to a triplestore database (TDB). For this implementation, the database was Apache Jena Triplestore Database (TDB) BIBREF15, which operates as a local on-disk database, although it could equally be a distributed in-memory cache or other implementation in production. It is at this point that the incoming messages are truly conformed to a universal model, as TDB does not record any information relating to encoding. An example of a RDF to TDB (RDB Database) class is shown in Figure FIGREF10 ### Method ::: Query The query stage polls the triplestore database for RDF graphs matching specified criteria, for instance, low blood pressure combined with low body temperature and high pulse rate, indicating hypothermia, or patients with blood pressure readings over a certain threshold, indicating hypertension. It passes matching patients on to the output stage for data capture or immediate use in applications. SPARQL queries against FHIR/RDF (see Figure FIGREF6), can often be complex and verbose, simply because a high level of detail was required to represent healthcare data unambiguously in FHIR, and an equally high level of detail was required to extract it unambigously. As a means of simplifying the work required to query the data, We considered a two-phase design in which the first layer would extract the relevant data from the TDB database in great detail before using RDF's CONSTRUCT syntax to build a simplified representation of the data for use by the second layer. This idea has potential, but after a few tries at writing the code to implement it, there was too much loss of detail for it to be worth pursuing in this iteration. In the end, the default option of writing a detailed, if verbose, RDF query once was deemed a better option than the added complexity and potential loss of fidelity of the two-layer approach. ### Method ::: Output In the output stage, the results of the queries in the previous stage are written out to an output destination such as a text file or a screen. This differs from the Application stage in that the input was intended to be written immediately to an output sink such as a file or screen on the local computer. Its use in this project was limited to debugging. ### Method ::: Application In the application stage, a variety of applications (complex event processors, common data models, machine learning models, etc.) receive the outputs of the queries from the prior stages and use them as inputs to particular applications. A high-level view of how the semantic encoder might be used in clinical workflow is shown in Figure FIGREF11 Several applications presented themselves as potentially benefiting from a semantic enrichment engine such as this one. One such application was complex event processing (CEP), in which streams of data are analyzed in search of events in real timeBIBREF16. From simple events more complex events can be derived, so that a number of individually innocuous events may add up to either an opportunity or a threat event. In a healthcare setting, this could mean monitoring patient vital signs and flagging them as high, low, or normal, then analyzing the combination of vital signs for a condition or set of conditions. Additionally, a patient's individual health conditions, such as comorbidities, recent procedures, and so on could be used to inform the meaning of the instantaneous vital signs as they are received. Using data from the TDB store, I was able to write several queries in Esper, a well-known complex event processing engineBIBREF17, to detect conditions that were initially simulated by the vital signs input, such as hypothermia or hypertension. To some extent, the RDF queries used to feed Esper overlapped with the capabilities of Esper itself, although Esper's query language EPL is much more versatile than SPARQL for event processing. Another such project was the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)BIBREF18. This is an analytical database intended to collate data from multiple partner data sources and conform it to a common representation, using standardized vocabularies such as LOINCBIBREF19 and SNOMED-CTBIBREF20 in order to facilitate collaborative research. Using data queried from the TDB store, I was able to build several data-loading jobs to populate an OMOP-CDM database. This application takes advantage of the semantic enrichment engine's ability to conform data from disparate sources, since by the application stage all the data has been conformed to FHIR/RDF and is ready to be loaded to the OMOP database with only one transformation (from FHIR/RDF to OMOP schemas). ### Method ::: Validation Health Level Seven International (HL7) provides a FHIR validator, which was useful for ensuring that the FHIR generated by the encoder was correctly formed. ShEx (Shape Expressions) BIBREF21 language is a language used for describing the expected shape of RDF and testing it for conformity to that shape. Its syntax is similar to Turtle and SPARQL, while its semantics resemble those of regular expression languages such as RelaxNG BIBREF22. I were limited in our ability to validate FHIR conformance due to limitations of the FHIR validation tool (vague error messages, program crashes, etc.) ### Method ::: Challenges Our needs are twofold and, at first, apparently contradictory. The first was to store data from disparate sources so that the sources could be joined up and benefit from synergies among the different semantic components embedded in the data. The second was to answer queries about the data over a finite time range. The challenge is that the mechanism that was to trigger the execution of a query, the receipt of a message from the store, happened with such frequency that the query engine quickly became overloaded and unable to respond in a timely fashion to new requests. This necessitated a redesign of parts of the encoder module and the query engine, such that each resource was timestamped when it was encoded and each query specified a time range within which to return results. Prior to this redesign, the query engine was querying the triple store each time a message arrived without specifying a time bound, resulting in a constantly increasing number of results that eventually would overmatch the system's capabilities. Another challenge was that RDF does not easily support streamsBIBREF23. With each query, all matching results are returned, not only the new results since the last query. This means the result size of the query increases monotonically until the system is overwhelmed. To design around this, we timestamped each entity as it arrived and used this timestamp as a filter in the subsequent queries. This worked well and is not unlike what CEP systems do natively. ### Conclusion The semantic enrichment engine designed described in this paper has broad applicability in healthcare operations and research. The data exchange standards, protocols, databases, query languages, and so forth used to implement this system are freely available. This system has characteristics of both an enterprise service bus and an enterprise data warehouse, but augments the analytical capability of the former and addresses the high latency of the former. We expect the system can be used to inform artificial intelligence for inference, populate structured databases with enriched data streams, and derive new data for use in machine learning training. Figure 2: Example FHIR Bundle and Header Message Figure 3: Example SPARQL Query Figure 4: Semantic Enrichment Engine Architecture Figure 5: Java Simulated HL7 Message Figure 6: POJO to FIHR/RDF Encoder Figure 7: FIHR/RDF to TDB Storage Class Figure 8: Semantic Engine Use Clinical Workflow	Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), Resource Description Framework (RDF)
Why does proposed term help to avoid posterior collapse?	### Introduction Despite the recent success of deep generative models such as Variational Autoencoders (VAEs) BIBREF0 and Generative Adversarial Networks (GANs) BIBREF1 in different areas of Machine Learning, they have failed to produce similar generative quality in NLP. In this paper we focus on VAEs and their mathematical underpinning to explain their behaviors in the context of text generation. The vanilla VAE applied to text BIBREF2 consists of an encoder (inference) and decoder (generative) networks: Given an input $x$, the encoder network parameterizes $q_\phi (z\|x)$ and infers about latent continuous representations of $x$, while the decoder network parameterizes $p_\theta (x\|z)$ and generates $x$ from the continuous code $z$. The two models are jointly trained by maximizing the Evidence Lower Bound (ELBO), $\mathcal {L}(\theta , \phi ; x,z)$: where the first term is the reconstruction term, and the second term is the Kullback-Leibler (KL) divergence between the posterior distribution of latent variable $z$ and its prior $p({z})$ (i.e., $\mathcal {N}(0,I)$). The KL term can be interpreted as a regularizer which prevents the inference network from copying ${x}$ into ${z}$, and for the case of a Gaussian prior and posterior has a closed-form solution. With powerful autoregressive decoders, such as LSTMs, the internal decoder's cells are likely to suffice for representing the sentence, leading to a sub-optimal solution where the decoder ignores the inferred latent code ${z}$. This allows the encoder to become independent of $x$, an issue known as posterior collapse ($q_\phi ({z}\|{x})\approx p({z})$) where the inference network produces uninformative latent variables. Several solutions have been proposed to address the posterior collapse issue: (i) Modifying the architecture of the model by weakening decoders BIBREF2, BIBREF3, BIBREF4, BIBREF5, or introducing additional connections between the encoder and decoder to enforce the dependence between $x$ and $z$ BIBREF6, BIBREF7, BIBREF8; (ii) Using more flexible or multimodal priors BIBREF9, BIBREF10; (iii) Alternating the training by focusing on the inference network in the earlier stages BIBREF11, or augmenting amortized optimization of VAEs with instance-based optimization of stochastic variational inference BIBREF12, BIBREF13. All of the aforementioned approaches impose one or more of the following limitations: restraining the choice of decoder, modifying the training algorithm, or requiring a substantial alternation of the objective function. As exceptions to these, $\delta $-VAE BIBREF14 and $\beta $-VAE BIBREF15 aim to avoid the posterior collapse by explicitly controlling the regularizer term in eqn. DISPLAY_FORM2. While $\delta $-VAE aims to impose a lower bound on the divergence term, $\beta $-VAE (betavae) controls the impact of regularization via an additional hyperparameter (i.e., $\beta D_{KL}\big (q_\phi ({z}\|{x}) \|\| p({z})\big )$). A special case of $\beta $-VAE is annealing BIBREF2, where $\beta $ increases from 0 to 1 during training. In this study, we propose to use an extension of $\beta $-VAE BIBREF16 which permits us to explicitly control the magnitude of the KL term while avoiding the posterior collapse issue even in the existence of a powerful decoder. We use this framework to examine different properties of the estimated posterior and the generative behaviour of VAEs and discuss them in the context of text generation via various qualitative and quantitative experiments. ### Kullback-Leibler Divergence in VAE We take the encoder-decoder of VAEs as the sender-receiver in a communication network. Given an input message $x$, a sender generates a compressed encoding of $x$ denoted by $z$, while the receiver aims to fully decode $z$ back into $x$. The quality of this communication can be explained in terms of rate (R) which measures the compression level of $z$ as compared to the original message $x$, and distortion (D) which quantities the overall performance of the communication in encoding a message at sender and successfully decoding it at the receiver. Additionally, the capacity of the encoder channel can be measured in terms of the amount of mutual information between $x$ and $z$, denoted by $\text{I}({x};{z})$ BIBREF17. ### Kullback-Leibler Divergence in VAE ::: Reconstruction vs. KL The reconstruction loss can naturally measure distortion ($D := - \big \langle \log p_\theta ({x}\|{z}) \big \rangle $), while the KL term quantifies the amount of compression (rate; $R := D_{KL}[q_\phi ({z}\|{x})\|\| p({z})]$) by measuring the divergence between a channel that transmits zero bit of information about $x$, denoted by $p(z)$, and the encoder channel of VAEs, $q_\phi (z\|x)$. BIBREF18 introduced the $H-D \le \text{I}({x};{z}) \le R$ bounds, where $H$ is the empirical data entropy (a constant). These bounds on mutual information allow us to analyze the trade-off between the reconstruction and KL terms in eqn. (DISPLAY_FORM2). For instance, since $\text{I}({x};{z})$ is non-negative (using Jensen's inequality), the posterior collapse can be explained as the situation where $\text{I}({x};{z})=0$, where encoder transmits no information about $x$, causing $R=0, D=H$. Increasing $\text{I}({x};{z})$ can be encouraged by increasing both bounds: increasing the upper-bound (KL term) can be seen as the mean to control the maximum capacity of the encoder channel, while reducing the distortion (reconstruction loss) will tighten the bound by pushing the lower bound to its limits ($H-D\rightarrow H$). A similar effect on the lower-bound can be encouraged by using stronger decoders which could potentially decrease the reconstruction loss. Hence, having a framework that permits the use of strong decoders while avoiding the posterior collapse is desirable. Similarly, channel capacity can be decreased. ### Kullback-Leibler Divergence in VAE ::: Explicit KL Control via @!START@$\beta $@!END@-VAE Given the above interpretation, we now turn to a slightly different formulation of ELBO based on $\beta $-VAE BIBREF15. This allows control of the trade-off between the reconstruction and KL terms, as well as to set explicit KL value. While $\beta $-VAE offers regularizing the ELBO via an additional coefficient $\beta \in {\rm I\!R}^+$, a simple extension BIBREF16 of its objective function incorporates an additional hyperparameter $C$ to explicitly control the magnitude of the KL term, where $C\!\! \in \!\! {\rm I\!R}^+$ and $\| . \|$ denotes the absolute value. While we could apply constraint optimization to impose the explicit constraint of $\text{KL}\!\!=\!\!C$, we found that the above objective function satisfies the constraint (experiment). Alternatively, it has been shown BIBREF21 the similar effect could be reached by replacing the second term in eqn. DISPLAY_FORM6 with $\max \big (C,D_{KL}\big (q_\phi ({z}\|{x}) \|\| p({z})\big )\big )$ at the risk of breaking the ELBO when $\text{KL}\!\!<\!\!C$ BIBREF22. ### Experiments We conduct various experiments to illustrate the properties that are encouraged via different KL magnitudes. In particular, we revisit the interdependence between rate and distortion, and shed light on the impact of KL on the sharpness of the approximated posteriors. Then, through a set of qualitative and quantitative experiments for text generation, we demonstrate how certain generative behaviours could be imposed on VAEs via a range of maximum channel capacities. Finally, we run some experiments to find if any form of syntactic information is encoded in the latent space. For all experiments, we use the objective function of eqn. DISPLAY_FORM6 with $\beta =1$. We do not use larger $\beta $s because the constraint $\text{KL}=C$ is always satisfied. ### Experiments ::: Corpora We use 5 different corpora covering different domains and size through this section: Yelp and Yahoo BIBREF4 both have ($100k$,$10k$,$10k$) sentences in (train, dev, test) sets and $20k$ words in vocabulary, Children's Book Test (CBT; BIBREF23) has ($192k$,$10k$,$12k$) sentences and $12k$ vocab, Wikipedia (WIKI; BIBREF24) has ($2m$,$270k$,$270k$) sentences and $20k$ vocab, and WebText BIBREF25 has ($1m$,$23k$,$24k$) sentences and $22k$ vocab. ### Experiments ::: Models We examine three VAE architectures, covering a range of decoding strengths to examine if the objective function in eqn. DISPLAY_FORM6 is immune to posterior collapse regardless of the choice of encoder-decoder architectures: $\beta _C$-VAELSTM with (LSTM encoder, LSTM decoder), $\beta _C$-VAEGRU with (GRU encoder, GRU decoder) BIBREF26, and $\beta _C$-VAECNN with (LSTM encoder, CNN decoder) BIBREF27. The dimension of word embeddings is 256 and the dimension of the latent variable is 64. The encoder and the decoder, for both VAELSTM and VAEGRU, have hidden size of 512 dimensions. VAECNN has exactly the same encoder as VAELSTM, while the decoder follows similar architecture to GLU with a bottleneck structure (with two blocks) BIBREF27 and has 512 channels externally and 128 internally for the convolutions with the filter size of 20. All models were trained for 10 epochs and optimised the objective function (eqn. DISPLAY_FORM6) with Adam BIBREF28 with following learning rates: $10^{-5}\times 85$ for VAEGRU and VAELSTM, and $10^{-4}$ for VAECNN. To couple the encoder with the decoder we concatenate the latent variable to word embeddings at each time step without initialisation of hidden state. ### Experiments ::: Rate and Distortion To analyse the dependence between the values of explicit rate ($C$) and distortion, we trained our models with different values of $C$, ranging from 10 to 100. Figure FIGREF8 reports the results for $\beta _C$-VAEGRU, $\beta _C$-VAELSTM, and $\beta _C$-VAECNN models on Yahoo and Yelp corpora. In all our experiments we found that $C\!-\!1\!\le KL\!\le \! C\!+\!1$, demonstrating that the objective function effectively imposed the desired constraint on KL term. Hence, setting any $C>0$ can in practice avoid the collapse issue. The general trend is that by increasing the value of $C$ one can get a better reconstruction (lower distortion) while the amount of gain varies depending on the VAE's architecture and corpus. Additionally, we measured rate and distortion on CBT, WIKI, and WebText corpora using $\beta _C$-VAELSTM and observed the same trend with the increase of $C$, see Table TABREF12. This observation is consistent with the bound on $\text{I}({x};{z})$ we discussed earlier (expl) such that with an increase of KL we increase an upper bound on $\text{I}({x};{z})$ which in turn allows to have smaller values of reconstruction loss. Additionally, as reported in Table TABREF12, encouraging higher rates (via larger $C$) encourages more active units (AU; BIBREF29) in the latent code $z$. As an additional verification, we also group the test sentences into buckets based on their length and report BLEU-2/4 and ROUGE-2/4 metrics to measure the quality of reconstruction step in Table TABREF12. As expected, we observe that increasing rate has a consistently positive impact on improving BLEU and ROUGE scores. ### Experiments ::: Aggregated Posterior To understand how the approximated posteriors are being affected by the magnitude of the KL, we adopted an approach from BIBREF6 and looked at the divergence between the aggregated posterior, $q_\phi (z)=\sum _{x\sim q(x)} q_\phi (z\|x)$, and prior $p(z$). Since during generation we generate samples from the prior, ideally we would like the aggregated posterior to be as close as possible to the prior. We obtained unbiased samples of ${z}$ first by sampling an ${x}$ from data and then ${z} \sim q_\phi ({z}\|{x})$, and measured the log determinant of covariance of the samples ($\log \det (\mathrm {Cov}[q_\phi ({z})])$). As reported in Figure FIGREF8, we observed that $\log \det (\mathrm {Cov}[q_\phi ({z})])$ degrades as $C$ grows, indicating sharper approximate posteriors. We then consider the difference of $p(z)$ and $q(z)$ in their means and variances, by computing the KL divergence from the moment-matching Gaussian fit of $q(z)$ to $p(z)$: This returns smaller values for $\beta _{C=5}$-VAEGRU (Yelp: 0, Yahoo: 0), and larger values for $\beta _{C=100}$-VAEGRU (Yelp: 8, Yahoo: 5), which illustrates that the overlap between $q_\phi ({z})$ and $p(z)$ shrinks further as $C$ grows. The above observation is better pronounced in Table TABREF12, where we also report the mean ($\|\|\mu \|\|^2_2$) of unbiased samples of $z$, highlighting the divergence from the mean of the prior distribution as rate increases. Therefore, for the case of lower $C$, the latent variables observed during training are closer to the generated sample from the prior which makes the decoder more suitable for generation purpose. We will examine this hypothesis in the following section. ### Experiments ::: Text Generation To empirically examine how channel capacity translates into generative capacity of the model, we experimented with the $\beta _C$-VAELSTM models from Table TABREF12. To generate a novel sentence, after a model was trained, a latent variable $z$ is sampled from the prior distribution and then transformed into a sequence of words by the decoder $p(x\|z)$. During decoding for generation we try three decoding schemes: (i) Greedy: which selects the most probable word at each step, (ii) Top-k BIBREF30: which at each step samples from the K most probable words, and (iii) Nucleus Sampling (NS) BIBREF31: which at each step samples from a flexible subset of most probable words chosen based on their cumulative mass (set by a threshold $p$, where $p = 1$ means sampling from the full distribution). While similar to Top-k, the benefit of NS scheme is that the vocabulary size at each time step of decoding varies, a property that encourages diversity and avoids degenerate text patterns of greedy or beam search decoding BIBREF31. We experiment with NS $(p=\lbrace 0.5, 0.9\rbrace )$ and Top-k $(k=\lbrace 5, 15\rbrace )$. ### Experiments ::: Text Generation ::: Qualitative Analysis We follow the settings of homotopy experiment BIBREF2 where first a set of latent variables was obtained by performing a linear interpolation between $z_1 \sim p(z)$ and $z_2 \sim p(z)$. Then each $z$ in the set was converted into a sequence of words by the decoder $p(x\|z)$. Besides the initial motivation of BIBREF2 to examine how neighbouring latent codes look like, our additional incentive is to analyse how sensitive the decoder is to small variations in the latent variable when trained with different channel capacities, $C=\lbrace 3,15,100\rbrace $. Table TABREF17 shows the generated sentences via different decoding schemes for each channel capacity. For space reason, we only report the generated sentences for greedy, Top-$k=15$, and NS $p=0.9$. To make the generated sequences comparable across different decoding schemes or C values, we use the same samples of $z$ for decoding. ### Experiments ::: Text Generation ::: Qualitative Analysis ::: Sensitivity of Decoder To examine the sensitivity of the decoder to variations of the latent variable, we consider the sentences generate with the greedy decoding scheme (the first column in Table TABREF17). The other two schemes are not suitable for this analysis as they include sampling procedure. This means that if we decode the same latent variable twice we will get two different sentences. We observed that with lower channel capacity ($C=3$) the decoder tends to generate identical sentences for the interpolated latent variables (we highlight these sentences in gray), exhibiting decoder's lower sensitivity to $z$'s variations. However, with the increase of channel capacity ($C=15,100$) the decoder becomes more sensitive. This observation is further supported by the increasing pattern of active units in Table TABREF12: Given that AU increases with increase of $C$ one would expect that activation pattern of a latent variable becomes more complex as it comprises more information. Therefore small change in the pattern would have a greater effect on the decoder. ### Experiments ::: Text Generation ::: Qualitative Analysis ::: Coherence of Sequences We observe that the model trained with large values of $C$ compromises sequences' coherence during the sampling. This is especially evident when we compare $C=3$ with $C=100$. Analysis of Top-15 and NS (p=0.9) generated samples reveals that the lack of coherence is not due to the greedy decoding scheme per se, and can be attributed to the model in general. To understand this behavior further, we need two additional results from Table TABREF12: LogDetCov and $\|\|\mu \|\|^2_2$. One can notice that as $C$ increases LogDetCov decreases and $\|\|\mu \|\|^2_2$ increases. This indicates that the aggregated posterior becomes further apart from the prior, hence the latent codes seen during the training diverge more from the codes sampled from the prior during generation. We speculate this contributes to the coherence of the generated samples, as the decoder is not equipped to decode prior samples properly at higher $C$s. ### Experiments ::: Text Generation ::: Quantitative Analysis Quantitative analysis of generated text without gold reference sequences (e.g. in Machine Translation or Summarization) has been a long-standing challenge. Recently, there have been efforts towards this direction, with proposal such as self-BLEU BIBREF32, forward cross entropy BIBREF33 and Fréchet InferSent Distance BIBREF33. We opted for FCE as a complementary metric to our qualitative analysis. To calculate FCE, first a collection of synthetic sentences are generated by sampling $z\sim p(z)$ and decoding the samples into sentences. The synthetic sequences are then used to train a language model (an LSTM with the parametrisation of our decoder). The FCE score is estimated by reporting the negative log likelihood (NLL) of the trained LM on the set of human generated sentences. We generated synthetic corpora using trained models from Table TABREF12 with different C and decoding schemes and using the same exact $z$ samples for all corpora. Since the generated corpora using different C values would have different coverage of words in the test set (i.e., Out-of-Vocabulary ratios), we used a fixed vocabulary to minimize the effect of different vocabularies in our analysis. Our dictionary contains words that are common in all of the three corpora, while the rest of the words that don't exist in this dictionary are replaced with 〈unk〉 symbol. Similarly, we used this fixed dictionary to preprocess the test sets. Also, to reduce bias to a particular set of sampled $z$'s we measure the FCE score three times, each time we sampled a new training corpus from a $\beta _C$-VAELSTM decoder and trained an LM from scratch. In Table TABREF20 we report the average FCE (NLL) for the generated corpora. In the qualitative analysis we observed that the text generated by the $\beta _C$-VAELSTM trained with large values of $C=100$ exhibits lower quality (i.e., in terms of coherence). This observation is supported by the FCE score of NS(p=0.9) decoding scheme (TABREF20), since the performance drops when the LM is trained on the corpus generated with $C=100$. The generated corpora with $C=3$ and $C=15$ achieve similar FCE score. However, these patterns are reversed for Greedy decoding scheme, where the general tendency of FCE scores suggests that for larger values of $C$ the $\beta _C$-VAELSTM seems to generate text which better approximates the natural sentences in the test set. To understand this further, we report additional statistics in Table TABREF20: percentage of 〈unk〉 symbols, self-BLEU and average sentence length in the corpus. The average sentence length, in the generated corpora is very similar for both decoding schemes, removing the possibility that the pathological pattern on FCE scores was caused by difference in sentence length. However, we observe that for Greedy decoding more than $30\%$ of the test set consists of 〈unk〉. Intuitively, seeing more evidence of this symbol during training would improve our estimate for the 〈unk〉. As reported in the table, the $\%$unk increases on almost all corpora as $C$ grows, which is then translated into getting a better FCE score at test. Therefore, we believe that FCE at high $\%$unk is not a reliable quantitative metric to assess the quality of the generated syntactic corpora. Furthermore, for Greedy decoding, self-BLEU decreases when $C$ increases. This suggests that generated sentences for higher value of $C$ are more diverse. Hence, the LM trained on more diverse corpora can generalise better, which in turn affects the FCE. In contrast, the effect the 〈unk〉 symbol has on the corpora generated with the NS(p=0.9) decoding scheme is minimal for two reasons: First, the vocabulary size for the generated corpora, for all values of $C$ is close to the original corpus (the corpus we used to train the $\beta _C$-VAELSTM). Second, the vocabularies of the corpora generated with three values of $C$ is very close to each other. As a result, minimum replacement of the words with the 〈unk〉 symbol is required, making the experiment to be more reflective of the quality of the generated text. Similarly, self-BLEU for the NS(p=0.9) is the same for all values of $C$. This suggests that the diversity of sentences has minimal, if any, effect on the FCE. ### Experiments ::: Syntactic Test In this section, we explore if any form of syntactic information is captured by the encoder and represented in the latent codes despite the lack of any explicit syntactic signal during the training of the $\beta _C$-VAELSTM. To train the models we used the same WIKI data set as in BIBREF24, but we filtered out all the sentences that are longer than 50 space-separated tokens. We use the data set of BIBREF24 which consists of pairs of grammatical and ungrammatical sentences to test various syntactic phenomenon. For example, a pair in subject-verb agreement category would be: (The author laughs, The author laugh). We encode both the grammatical and ungrammatical sentences into the latent codes $z^+$ and $z^-$, respectively. Then we condition the decoder on the $z^+$ and try to determine whether the decoder assigns higher probability to the grammatical sentence (denoted by $x^+$): $p(x^-\|z^+) < p(x^+\|z^+)$ (denoted by p1 in Table TABREF28). We repeat the same experiment but this time try to determine whether the decoder, when conditioned on the ungrammatical code ($z^-$), still prefers to assign higher probability to the grammatical sentence: $p(x^-\|z^-) < p(x^+\|z^-)$ (denoted by p2 in Table TABREF28). Table TABREF28 shows the p1 and p2 for the $\beta _C$-VAELSTM model trained with $C=\lbrace 3,100\rbrace $. Both the p1 and p2 are similar to the accuracy and correspond to how many times a grammatical sentence was assigned a higher probability. As reported for C=3, p1 and p2 match in almost all cases. This is to some degree expected since lower channel capacity encourages a more dominating decoder which in our case was trained on grammatical sentences from the WIKI. On the other hand, this illustrates that despite avoiding the KL-collapse issue, the dependence of the decoder on the latent code is so negligible that the decoder hardly distinguishes the grammatical and ungrammatical inputs. This changes for $C=100$, as in almost all the cases the decoder becomes strongly dependent on the latent code and can differentiate between what it has seen as input and the closely similar sentence it hasn't received as the input: The decoder assigns larger probability to the ungrammatical sentence when conditioned on the $z^-$ and, similarly, larger probability to the grammatical sentence when conditioned on the $z^+$. However, the above observations neither confirm nor reject existence of grammar signal in the latent codes. We run a second set of experiments where we aim to discard sentence specific information from the latent codes by averaging the codes inside each syntactic category. The averaged codes are denoted by $\bar{z}^+$ and $\bar{z}^-$, and the corresponding accuracies are reported by p̄1 and p̄2 in Table TABREF28. Our hypothesis is that the only invariant factor during averaging the codes inside a category is the grammatical property of its corresponding sentences. As expected, due to the weak dependence of decoder on latent code, the performance of the model under $C=3$ is almost identical (not included for space limits) when comparing p1 vs. p̄1, and p2 vs. p̄2. However, for $C=100$ the performance of the model deteriorates. While we leave further exploration of this behavior to our future work, we speculate this could be an indication of two things: the increase of complexity in the latent code which encourages a higher variance around the mean, or the absence of syntactic signal in the latent codes. ### Discussion and Conclusion In this paper we analysed the interdependence of the KL term in Evidence Lower Bound (ELBO) and the properties of the approximated posterior for text generation. To perform the analysis we used an information theoretic framework based on a variant of $\beta $-VAE objective, which permits explicit control of the KL term, and treats KL as a mechanism to control the amount of information transmitted between the encoder and decoder. The immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($\|D_{KL}\big (q_\phi ({z}\|{x}) \|\| p({z})\big )-C\|$). We experimented with a range of constraints ($C$) on the KL term and various powerful and weak decoder architectures (LSTM, GRU, and CNN), and empirically confirmed that in all cases the constraint was satisfied. We showed that the higher value of KL encourages not only divergence from the prior distribution, but also a sharper and more concentrated approximated posteriors. It encourages the decoder to be more sensitive to the variations on the latent code, and makes the model with higher KL less suitable for generation as the latent variables observed during training are farther away from the prior samples used during generation. To analyse its impact on generation we conducted a set of qualitative and quantitative experiments. In the qualitative analysis we showed that small and large values of KL term impose different properties on the generated text: the decoder trained under smaller KL term tends to generate repetitive but mainly plausible sentences, while for larger KL the generated sentences were diverse but incoherent. This behaviour was observed across three different decoding schemes and complemented by a quantitative analysis where we measured the performance of an LSTM LM trained on different VAE-generated synthetic corpora via different KL magnitudes, and tested on human generated sentences. Finally, in an attempt to understand the ability of the latent code in VAEs to represent some form of syntactic information, we tested the ability of the model to distinguish between grammatical and ungrammatical sentences. We verified that at lower (and still non-zero) KL the decoder tends to pay less attention to the latent code, but our findings regarding the presence of a syntactic signal in the latent code were inconclusive. We leave it as a possible avenue to explore in our future work. Also, we plan to develop practical algorithms for the automatic selection of the $C$'s value, and verify our findings under multi-modal priors and complex posteriors. ### Acknowledgments The authors would like to thank the anonymous reviewers for their helpful suggestions. This research was supported by an EPSRC Experienced Researcher Fellowship (N. Collier: EP/M005089/1), an MRC grant (M.T. Pilehvar: MR/M025160/1) and E. Shareghi is supported by the ERC Consolidator Grant LEXICAL (648909). We gratefully acknowledge the donation of a GPU from the NVIDIA. Figure 1: Rate-Distortion and LogDetCov for C = {10, 20, ..., 100} on Yahoo and Yelp corpora. Table 1: βC-VAELSTM performance with C = {3, 15, 100} on the test sets of CBT, WIKI, and WebText. Each bucket groups sentences of certain length. Bucket 1: length ≤ 10; Bucket 2: 10 < length ≤ 20; Bucket 3: 20 < length ≤ 30, and All contains all sentences. BL2/RG2 denotes BLEU-2/ROUGE-2, BL4/RG4 denotes BLEU2/ROUGE-2 BLEU-4/ROUGE-4, AU denotes active units, D denotes distortion, and R denotes rate. Table 2: Homotopy (CBT corpus) - The three blocks correspond to C = {3, 15, 100} values used for training βC-VAELSTM. The columns correspond to the three decoding schemes: greedy, top-k (with k=15), and the nucleus sampling (NS; with p=0.9). Initial two latent variables z were sampled from a the prior distribution i.e. z ∼ p(z) and the other five latent variables were obtained by interpolation. The sequences that highlighted in gray are the one that decoded into the same sentences condition on different latent variable. Note: Even though the learned latent representation should be quite different for different models (trained with different C) in order to be consistent all the generated sequences presented in the table were decoded from the same seven latent variables. Table 3: Forward Cross Entropy (FCE). Columns represent stats for Greedy and NS decoding schemes for βCVAELSTM models trained with C = {3, 15, 100} on CBT, WIKI or WebText. Each entry in the table is a mean of negative log likelihood of an LM. The values in the brackets are the standard deviations. \|V\| is the vocabulary size; Test stands for test set; %unk is the percentage of 〈unk〉 symbols in a corpora; len. is the average length of a sentence in the generated corpus; SB is the self-BLEU:4 score calculated on the 10K sentences in the generated corpus. Table 4: p1: p(x−\|z+) < p(x+\|z+) and p2: p(x−\|z−) < p(x+\|z−); p̄1: p(x−\|z̄+) < p(x+\|z̄+) and p̄2: p(x−\|z̄−) < p(x+\|z̄−); βC=3-VAELSTM (D:103, R:3); βC=100-VAELSTM (D:39, R:101).	by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($\|D_{KL}\big (q_\phi ({z}\|{x}) \|\| p({z})\big )-C\|$)
What statistics on the VIST dataset are reported?	### Introduction Visual storytelling and album summarization tasks have recently been of focus in the domain of computer vision and natural language processing. With the advent of new architectures, solutions for problems like image captioning and language modeling are getting better. Therefore it is only natural to work towards storytelling; deeper visual context yielding a more expressive style language, as it could potentially improve various applications involving tasks using visual descriptions and visual question answering. BIBREF0. Since the release of the VIST visual storytelling dataset BIBREF1, there have been numerous approaches modeling the behavior of stories, leveraging and extending successful sequence-to-sequence based image captioning architectures. Some of them primarily addressed means of incorporating image-sequence feature information into a narrative generating network BIBREF2, BIBREF3, while others focused on model learning patterns and behavioral orientations with changes in back-propagation methods BIBREF4, BIBREF5. Motivated by these works we now want to understand the importance of characters and their relationships in visual storytelling. Specifically, we extract characters from the VIST dataset, analyze their influence across the dataset and exploit them for paying attention to relevant visual segments during story-generation. We report our findings, discuss the directions of our ongoing work and suggest recommendations for using characters as semantics in visual storytelling. ### Related work BIBREF1 published the VIST dataset along with a baseline sequence-to-sequence learning model that generates stories for image sequences in the dataset. Gradually, as a result of the 2018 storytelling challenge, there have been other works on VIST. Most of them extended the encoder-decoder architecture introduced in the baseline publication by adding attention mechanisms BIBREF3, learning positionally dependent parameters BIBREF2 and using reinforcement learning based methods BIBREF4, BIBREF5. To our best knowledge, there are no prior works making use of characters for visual storytelling. The only work that uses any additional semantics for story generation is BIBREF5. They propose a hierarchical model structure which first generates a “semantic topic" for each image in the sequence and then uses that information during the generation phase. The core module of their hierarchical model is a Semantic Compositional Network (SCN) BIBREF6, a recurrent neural network variant generating text conditioned on the provided semantic concepts. Unlike traditional attention mechanisms, the SCN assembles the information on semantics directly into the neural network cell. It achieves this by extending the gate and state weight matrices to adhere to additional semantic information provided for the language generation phase. Inspired by the results SCN achieved for image and video captioning, we use it for storytelling. The semantic concepts we use are based on character frequencies and their co-occurrence information extracted from the stories of the VIST dataset. Our expectation is that the parameters of the language decoder network generating the story are dependent on the character semantics and would learn to capture linguistic patterns while simultaneously learning mappings to respective visual features of the image sequence. ### Data We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories. ### Data ::: Character extraction We extracted characters out of the VIST dataset. To this end, we considered that a character is either “a person" or “an animal". We decided that the best way to do this would be by making use of the human-annotated text instead of images for the sake of being diverse (e.g.: detection on images would yield “person", as opposed to father). The extraction takes place as a two-step process: Identification of nouns: We first used a pretrained part-of-speech tagger BIBREF7 to identify all kinds of nouns in the annotations. Specifically, these noun categories are NN – common, singular or mass, NNS – noun, common, plural, NNP – noun, proper, singular, and NNPS – noun, proper, plural. Filtering for hypernyms: WordNet BIBREF8 is a lexical database over the English language containing various semantic relations and synonym sets. Hypernym is one such semantic relation constituting a category into which words with more specific meanings fall. From among the extracted nouns, we thereby filtered those words that have their lowest common hypernym as either “person" or “animal". ### Data ::: Character analysis We analyzed the VIST dataset from the perspective of the extracted characters and observed that 20,405 training, 2,349 validation and 2,768 testing data samples have at least one character present among their stories. This is approximately 50% of the data samples in the entire dataset. To pursue the prominence of relationships between these characters, we analyzed these extractions for both individual and co-occurrence frequencies. We found a total of 1,470 distinct characters with 1,333 in training, 387 in validation and 466 in the testing splits. This can be considered as an indication to the limited size of the dataset because the number of distinct characters within each split is strongly dependent on the respective size of that split. Figure FIGREF3 plots the top 30 most frequent characters in the training split of the dataset. Apart from the character “friends" there is a gradual decrease in the occurrence frequencies of the other characters from “mom" to “grandmother". Similarly, in Figure FIGREF4, which plots the top 30 most co-occurring character pairs, (“dad", “mom"), (“friend", “friends") pairs occur drastically more number of times than other pairs in the stories. This can lead to an inclination bias of the story generator towards these characters owing to the data size limitations we discussed. In the process of detecting characters, we observed also that $\sim $5000 distinct words failed on WordNet due to their misspellings (“webxites"), for being proper nouns (“cathrine"), for being an abbreviation (“geez"), and simply because they were compound words (“sing-a-long"). Though most of the models ignore these words based on a vocabulary threshold value (typically 3), we would like to comment that language model creation without accounting for these words could adversely affect the behavior of narrative generation. ### Model Our model in Figure FIGREF6 follows the encoder-decoder structure. The encoder module incorporates the image sequence features, obtained using a pretrained convolutional network, into a subject vector. The decoder module, a semantically compositional recurrent network (SCN) BIBREF6, uses the subject vector along with character probabilities and generates a relevant story. ### Model ::: Character semantics The relevant characters with respect to each data-sample are obtained as a preprocessing step. We denote characters extracted from the human-annotated stories of respective image-sequences as active characters. We then use these active characters to obtain other characters which could potentially influence the narrative to be generated. We denote these as passive characters and they can be obtained using various methods. We describe some methods we tried in Section SECREF5. The individual frequencies of these relevant characters, active and passive are then normalized by the vocabulary size and constitute the character probabilities. ### Model ::: Encoder Images of a sequence are initially passed through a pretrained ResNet network BIBREF9, for obtaining their features. The features extracted are then provided to the encoder module, which is a simple recurrent neural network employed to learn parameters for incorporating the subjects in the individual feature sets into a subject vector. ### Model ::: Decoder We use the SCN-LSTM variant of the recurrent neural network for the decoder module as shown in Figure FIGREF10. The network extends each weight matrix of the conventional LSTM to be an ensemble of a set of tag-dependent weight matrices, subjective to the character probabilities. Subject vector from the encoder is fed into the LSTM to initialize the first step. The LSTM parameters utilized when decoding are weighted by the character probabilities, for generating a respective story. Gradients $\nabla $, propagated back to the network, nudge the parameters $W$ to learn while adhering to respective character probabilities $\vec{cp}$: Consequently, the encoder parameters move towards incorporating the image-sequence features better. ### Experiments We report the current status of our work and the intended directions of progress we wish to make using the designed model. All experiments were performed on the VIST dataset. As mentioned in Section SECREF5, passive characters can be selected by conditioning their relationships on several factors. We explain two such methods: ### Experiments ::: Method 1 In the first method we naïvely select all the characters co-occurring with respective active characters. Subsequently, probabilities for these passive characters are co-occurrence counts normalized by the corpus vocabulary size. This method enables the model to learn parameters on the distribution of character relationships. ### Experiments ::: Method 2 In the second approach, we conditionally select a limited number of characters that collectively co-occur most with the respective active characters. This is visualized in Figure FIGREF13. The selected passive characters “girlfriend", “father" and “son" collectively co-occur in the most co-occurring characters of the active characters. $K$ in this case is a tunable hyperparameter. ### Discussion Both methods we are experimenting with exhibit different initial traits. We are currently working towards analyzing the character relationships learned by the models and understanding the abstract concepts that get generated as a result of such learning. We do not report any generated stories and evaluations yet as we consider that to be premature without proper examination. However, we feel the training process metrics are encouraging and provide us with enough intuition for pursuing the proposed approach to its fullest scope. ### Conclusion We have extracted, analyzed and exploited characters in the realm of storytelling using the VIST dataset. We have provided a model that can make use of the extracted characters to learn their relationships and thereby generate grounded and subjective narratives for respective image sequences. For future work we would like to make the encoder semantically compositional by extracting visual tags and also explore ways to improve learning of character relationships while avoiding overfitting. Figure 1: Character frequencies (training split) Figure 2: Characters co-occurrence frequencies (training split) Figure 3: The model follows the encoder-decoder structure. Additional character semantics passed to the decoder module regulate its state parameters. Figure 4: (Gan et al., 2016), v and s denote the visual and semantic features respectively. Each triangle symbol represents an ensemble of tag dependent weight matrices Figure 5: Conditional on collective co-occurrences	In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
How many people are employed for the subjective evaluation?	### Introduction Automatic dubbing can be regarded as an extension of the speech-to-speech translation (STST) task BIBREF0, which is generally seen as the combination of three sub-tasks: (i) transcribing speech to text in a source language (ASR), (ii) translating text from a source to a target language (MT) and (iii) generating speech from text in a target language (TTS). Independently from the implementation approach BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, the main goal of STST is producing an output that reflects the linguistic content of the original sentence. On the other hand, automatic dubbing aims to replace all speech contained in a video document with speech in a different language, so that the result sounds and looks as natural as the original. Hence, in addition to conveying the same content of the original utterance, dubbing should also match the original timbre, emotion, duration, prosody, background noise, and reverberation. While STST has been addressed for long time and by several research labs BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF0, relatively less and more sparse efforts have been devoted to automatic dubbing BIBREF7, BIBREF8, BIBREF9, BIBREF10, although the potential demand of such technology could be huge. In fact, multimedia content created and put online has been growing at exponential rate, in the last decade, while availability and cost of human skills for subtitling and dubbing still remains a barrier for its diffusion worldwide. Professional dubbing of a video file is a very labor intensive process that involves many steps: (i) extracting speech segments from the audio track and annotating these with speaker information; (ii) transcribing the speech segments, (iii) translating the transcript in the target language, (iv) adapting the translation for timing, (v) choosing the voice actors, (vi) performing the dubbing sessions, (vii) fine-aligning the dubbed speech segments, (viii) mixing the new voice tracks within the original soundtrack. Automatic dubbing has been addressed both in monolingual cross-lingual settings. In BIBREF14, synchronization of two speech signals with the same content was tackled with time-alignment via dynamic time warping. In BIBREF15 automatic monolingual dubbing for TV users with special needs was generated from subtitles. However, due to the poor correlation between length and timing of the subtitles, TTS output frequently broke the timing boundaries. To avoid unnatural time compression of TTS's voice when fitting timing constraints, BIBREF7 proposed phone-dependent time compression and text simplification to shorten the subtitles, while BIBREF8 leveraged scene-change detection to relax the subtitle time boundaries. Regarding cross-lingual dubbing, lip movements synchronization was tackled in BIBREF9 by directly modifying the actor's mouth motion via shuffling of the actor's video frames. While the method does not use any prior linguistic or phonetic knowledge, it has been only demonstrated on very simple and controlled conditions. Finally, mostly related to our contribution is BIBREF10, which discusses speech synchronization at the phrase level (prosodic alignment) for English-to-Spanish automatic dubbing. In this paper we present research work to enhance a STST pipeline in order to comply with the timing and rendering requirements posed by cross-lingual automatic dubbing of TED Talk videos. Similarly to BIBREF7, we also shorten the TTS script by directly modifying the MT engine rather than via text simplification. As in BIBREF10, we synchronize phrases across languages, but follow a fluency-based rather than content-based criterion and replace generation and rescoring of hypotheses in BIBREF10 with a more efficient dynamic programming solution. Moreover, we extend BIBREF10 by enhancing neural MT and neural TTS to improve speech synchronization, and by performing audio rendering on the dubbed speech to make it sound more real inside the video. In the following sections, we introduce the overall architecture (Section 2) and the proposed enhancements (Sections 3-6). Then, we present results (Section 7) of experiments evaluating the naturalness of automatic dubbing of TED Talk clips from English into Italian. To our knowledge, this is the first work on automatic dubbing that integrates enhanced deep learning models for MT, TTS and audio rendering, and evaluates them on real-world videos. ### Automatic Dubbing With some approximation, we consider here automatic dubbing of the audio track of a video as the task of STST, i.e. ASR + MT + TTS, with the additional requirement that the output must be temporally, prosodically and acoustically close to the original audio. We investigate an architecture (see Figure 1) that enhances the STST pipeline with (i) enhanced MT able to generate translations of variable lengths, (ii) a prosodic alignment module that temporally aligns the MT output with the speech segments in the original audio, (iii) enhanced TTS to accurately control the duration of each produce utterance, and, finally, (iv) audio rendering that adds to the TTS output background noise and reverberation extracted from the original audio. In the following, we describe each component in detail, with the exception of ASR, for which we use BIBREF16 an of-the-shelf online service. ˜ ### Machine Translation Our approach to control the length of MT output is inspired by target forcing in multilingual neural MT BIBREF17, BIBREF18. We partition the training sentence pairs into three groups (short, normal, long) according to the target/source string-length ratio. In practice, we select two thresholds $t_1$ and $t_2$, and partition training data according to the length-ratio intervals $[0,t_1)$, $[t_1,t_2)$ and $[t_2,\infty ]$. At training time a length token is prepended to each source sentence according to its group, in order to let the neural MT model discriminate between the groups. At inference time, the length token is instead prepended to bias the model to generate a translation of the desired length type. We trained a Transformer model BIBREF19 with output length control on web crawled and proprietary data amounting to 150 million English-Italian sentence pairs (with no overlap with the test data). The model has encoder and decoder with 6 layers, layer size of 1024, hidden size of 4096 on feed forward layers, and 16 heads in the multi-head attention. For the reported experiments, we trained the models with thresholds $t_1=0.95$ and $t_2=1.05$ and generated at inference time translations of the shortest type, resulting in an average length ratio of $0.97$ on our test set. A detailed account of the approach, the followed training procedure and experimental results on the same task of this paper can be found in BIBREF20. Finally, as baseline MT system we used an online service. ### Prosodic Alignment Prosodic alignmentBIBREF10 is the problem of segmenting the target sentence to optimally match the distribution of words and pauses of the source sentence. Let ${\bf e}=e_1,e_2,\ldots ,e_n$ be a source sentence of $n$ words which is segmented according to $k$ breakpoints $1 \le i_1 < i_2 < \ldots i_k=n$, shortly denoted with ${\bf i}$. Given a target sentence ${\bf f}=f_1,f_2,\ldots ,f_m$ of $m$ words, the goal is to find within it $k$ corresponding breakpoints $1 \le j_1 < j_2 < \ldots j_k=m$ (shortly denoted with ${\bf j}$) that maximize the probability: By assuming a Markovian dependency on ${\bf j}$, i.e.: and omitting from the notation the constant terms ${\bf i},{\bf e},{\bf f}$, we can derive the following recurrent quantity: where $Q(j,t)$ denotes the log-probability of the optimal segmentation of ${\bf f}$ up to position $j$ with $t$ break points. It is easy to show that the solution of (DISPLAY_FORM5) corresponds to $Q(m,k)$ and that optimal segmentation can be efficiently computed via dynamic-programming. Let ${\tilde{f}}_t = f_{j_{t-1}+1},\ldots ,f_{j_t}$ and ${\tilde{e}}_t =e_{i_{t-1}+1},\ldots ,e_{i_t}$ indicate the $t$-th segments of ${\bf f}$ and ${\bf e}$, respectively, we define the conditional probability of the $t$-th break point in ${\bf f}$ by: The first term computes the relative match in duration between the corresponding $t$-th segments, while the second term measure the linguistic plausibility of a placing a break after the ${j_t}$ in ${\bf f}$. For this, we simply compute the following ratio of language model perplexities computed over a text window centered on the break point, by assuming or not the presence of a pause (comma, semicolon or dash) in the middle: In our implementation, we use a larger text window (last and first two words), we replace words with parts-of speech, and estimate the language model on a large English corpus. ### Text To Speech Our neural TTS system consists of two modules: a Context Generation module, which generates a context sequence from the input text, and a Neural Vocoder module, which converts the context sequence into a speech waveform. The first one is an attention-based sequence-to-sequence network BIBREF21, BIBREF22 that predicts a Mel-spectrogram given an input text. A grapheme-to-phoneme module converts the sequence of words into a sequence of phonemes plus augmented features like punctuation marks and prosody related features derived from the text (e.g. lexical stress). For the Context Generation module, we trained speaker-dependent models on two Italian voices, male and female, with 10 and 37 hours of high quality recordings, respectively. We use the Universal Neural Vocoder introduced in BIBREF23, pre-trained with 2000 utterances per each of the 74 voices from a proprietary database. To ensure close matching of the duration of Italian TTS output with timing information extracted from the original English audio, for each utterance we resize the generated Mel spectrogram using spline interpolation prior to running the Neural Vocoder. We empirically observed that this method produces speech of better quality than traditional time-stretching. ### Audio Rendering ::: Foreground-Background Separation The input audio can be seen as a mixture of foreground (speech) and background (everything else) and our goal is to extract the background and add it to the dubbed speech to make it sound more real and similar to the original. For the foreground-background separation task, we adapted the popular U-Net BIBREF24 architecture, which is described in detail in BIBREF25 for a music-vocal separation task. It consists of a series of down-sampling blocks, followed by one ’bottom’ convolutional layer, followed by a series of up-sampling blocks with skip connections from the down-sampling to the up-sampling blocks. Because of the down-sampling blocks, the model can compute a number of high-level features on coarser time scales, which are concatenated with the local, high-resolution features computed from the same-level up-sampling block. This concatenation results into multi-scale features for prediction. The model operates on a time-frequency representation (spectrograms) of the audio mixture and it outputs two soft ratio masks corresponding to foreground and background, respectively, which are multiplied element-wise with the mixed spectrogram, to obtain the final estimates of the two sources. Finally, the estimated spectrograms go through an inverse short-term Fourier transform block to produce raw time domain signals. The loss function used to train the model is the sum of the $L_1$ losses between the target and the masked input spectrograms, for the foreground and the background BIBREF25, respectively. The model is trained with the Adam optimizer on mixed audio provided with foreground and background ground truths. Training data was created from 360 hours of clean speech from Librispeech (foreground) and 120 hours of recording taken from audioset BIBREF26 (background), from which speech was filtered out using a Voice Activity Detector (VAD). Foreground and background are mixed for different signal-to-noise ratio (SNR), to generate the audio mixtures. ### Audio Rendering ::: Re-reverberation In this step, we estimate the environment reverberation from the original audio and apply it to the dubbed audio. Unfortunately, estimating the room impulse response (RIR) from a reverberated signal requires solving an ill-posed blind deconvolution problem. Hence, instead of estimating the RIR, we do a blind estimation of the reverberation time (RT), which is commonly used to assess the amount of room reverberation or its effects. The RT is defined as the time interval in which the energy of a steady-state sound field decays 60 dB below its initial level after switching off the excitation source. In this work we use a Maximum Likelihood Estimation (MLE) based RT estimate (see details of the method in BIBREF27). Estimated RT is then used to generate a synthetic RIR using a publicly available RIR generator BIBREF28. This synthetic RIR is finally applied to the dubbed audio. ### Experimental Evaluation We evaluated our automatic dubbing architecture (Figure 1), by running perceptual evaluations in which users are asked to grade the naturalness of video clips dubbed with three configurations (see Table TABREF12): (A) speech-to-speech translation baseline, (B) the baseline with enhanced MT and prosodic alignment, (C) the former system enhanced with audio rendering. Our evaluation focuses on two questions: What is the overall naturalness of automatic dubbing? How does each introduced enhancement contribute to the naturalness of automatic dubbing? We adopt the MUSHRA (MUlti Stimulus test with Hidden Reference and Anchor) methodology BIBREF29, originally designed to evaluate audio codecs and later also TTS. We asked listeners to evaluate the naturalness of each versions of a video clip on a 0-100 scale. Figure FIGREF15 shows the user interface. In absence of a human dubbed version of each clip, we decided to use, for calibration purposes, the clip in the original language as hidden reference. The clip versions to evaluate are not labeled and randomly ordered. The observer has to play each version at least once before moving forward and can leave a comment about the worse version. In order to limit randomness introduced by ASR and TTS across the clips and by MT across versions of the same clip, we decided to run the experiments using manual speech transcripts, one TTS voice per gender, and MT output by the baseline (A) and enhanced MT system (B-C) of quality judged at least acceptable by an expert. With these criteria in mind, we selected 24 video clips from 6 TED Talks (3 female and 3 male speakers, 5 clips per talk) from the official test set of the MUST-C corpus BIBREF30 with the following criteria: duration of around 10-15 seconds, only one speaker talking, at least two sentences, speaker face mostly visible. We involved in the experiment both Italian and non Italian listeners. We recommended all participants to disregard the content and only focus on the naturalness of the output. Our goal is to measure both language independent and language dependent naturalness, i.e. to verify how speech in the video resembles human speech with respect to acoustics and synchronization, and how intelligible it is to native listeners. ### Experimental Evaluation ::: Results We collected a total of 657 ratings by 14 volunteers, 5 Italian and 9 non-Italian listeners, spread over the 24 clips and three testing conditions. We conducted a statistical analysis of the data with linear mixed-effects models using the lme4 package for R BIBREF31. We analyzed the naturalness score (response variable) against the following two-level fixed effects: dubbing system A vs. B, system A vs. C, and system B vs. C. We run separate analysis for Italian and non-Italian listeners. In our mixed models, listeners and video clips are random effects, as they represent a tiny sample of the respective true populationsBIBREF31. We keep models maximal, i.e. with intercepts and slopes for each random effect, end remove terms required to avoid singularities BIBREF32. Each model is fitted by maximum likelihood and significance of intercepts and slopes are computed via t-test. Table TABREF18 summarized our results. In the first comparison, baseline (A) versus the system with enhanced MT and prosody alignment (B), we see that both non-Italian and Italian listeners perceive a similar naturalness of system A (46.81 vs. 47.22). When movid to system B, non-Italian listeners perceive a small improvement (+1.14), although not statistically significant, while Italian speaker perceive a statistically significant degradation (-10.93). In the comparison between B and C (i.e. B enhanced with audio rendering), we see that non-Italian listeners observe a significant increase in naturalness (+10.34), statistically significant, while Italian listeners perceive a smaller and not statistical significant improvement (+1.05). The final comparison between A and C gives almost consistent results with the previous two evaluations: non-Italian listeners perceive better quality in condition C (+11.01) while Italian listeners perceive lower quality (-9.60). Both measured variations are however not statistically significant due to the higher standard errors of the slope estimates $\Delta $C. Notice in fact that each mixed-effects model is trained on distinct data sets and with different random effect variables. A closer look at the random effects parameters indeed shows that for the B vs. C comparison, the standard deviation estimate of the listener intercept is 3.70, while for the A vs. C one it is 11.02. In other words, much higher variability across user scores is observed in the A vs. C case rather than in the B vs. C case. A much smaller increase is instead observed across the video-clip random intercepts, i.e. from 11.80 to 12.66. The comments left by the Italian listeners tell that the main problem of system B is the unnaturalness of the speaking rate, i.e. is is either too slow, too fast, or too uneven. The distributions of the MUSHRA scores presented at the top of Figure FIGREF19 confirm our analysis. What is more relevant, the distribution of the rank order (bottom) strengths our previous analysis. Italian listeners tend to rank system A the best system (median $1.0$) and vary their preference between systems B and C (both with median $2.0$). In contrast, non-Italian rank system A as the worse system (median $2.5$), system B as the second (median $2.0$), and statistically significantly prefer system C as the best system (median $1.0$). Hence, while our preliminary evaluation found that shorter MT output can potentially enable better synchronization, the combination of MT and prosodic alignment appears to be still problematic and prone to generate unnatural speech. The incorporation of audio rendering (system $C$) significantly improves the experience of the non-Italian listeners (66 in median) respect to systems $B$ and $C$. This points out the relevance of including para-linguist aspects (i.e. applause's, audience laughs in jokes,etc.) and acoustic conditions (i.e. reverberation, ambient noise, etc.). For the target (Italian) listeners this improvement appears instead masked by the disfluencies introduced by the prosodic alignment step. If we try to directly measure the relative gains given by audio rendering, we see that Italian listeners score system B better than system A 27% of the times and system C better than A 31% of the times, which is a 15% relative gain. On the contrary non-Italian speakers score B better than A 52% of the times, and C better than A 66% of the times, which is a 27% relative gain. ### Conclusions We have perceptually evaluated the impact on the naturalness of automatic speech dubbing when we enhance a baseline speech-to-speech translation system with the possibility to control the length of the translation output, align target words with the speech-pause segmentation of the source, and enrich speech output with ambient noise and reverberation extracted from the original audio. We tested our system with both Italian and non-Italian listeners in order to evaluate both language independent and language dependent naturalness of dubbed videos. Results show that while we succeeded at achieving synchronization at the phrasal level, our prosodic alignment step negatively impacts on the fluency and prosody of the generated language. The impact of these disfluencies on native listeners seems to partially mask the effect of the audio rendering with background noise and reverberation, which instead results in a major increase of naturalness for non-Italian listeners. Future work will definitely devoted to improving the prosodic alignment component, by computing better segmentation and introducing more flexible lip-synchronization. ### Acknowledgements The authors would like to thank the Amazon Polly, Translate and Transcribe research teams; Adam Michalski, Alessandra Brusadin, Mattia Di Gangi and Surafel Melaku for contributions to the project, and all colleagues at Amazon AWS who helped with the evaluation. Fig. 1. Speech-to-speech translation pipeline (dotted box) with enhancements to perform automatic dubbing (in bold). Table 1. Evaluated dubbing conditions. Table 2. Summary of the analysis of the evaluation with mixedeffects models. From top down: A vs. B, B vs. C, A vs. C. For each fixed effect, we report the estimate and standard error. Symbols ●, ∗, + indicate significance levels of 0.001, 0.01, and 0.05, respectively. Fig. 3. Boxplots with the MUSHRA scores (top) and Rank Order (bottom) per system and mother language (Italian vs Non-Italian).	14 volunteers
Of the following, which personality traits best describe David? A. Attractive, witty, and charismatic B. Smart, calculating, and cautious C. Bold, quiet, and dumb D. Focused, funny, and attractive	CAPTAIN CHAOS By D. ALLEN MORRISSEY Science equipped David Corbin with borrowed time; sent him winging out in a state of suspension to future centuries ... to a dark blue world whose only defense was to seal tight the prying minds of foolish interlopers. [Transcriber's Note: This etext was produced from Planet Stories November 1952. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I heard the voice as I opened my eyes. I was lying down, still not aware of where I was, waiting for the voice. "Your name is David Corbin. Do you understand?" I looked in the direction of the sound. Above my feet a bulkhead loomed. There were round dials set in a row above a speaker. Over the mesh-covered speaker, two knobs glowed red. I ran the words over in my sluggish mind, thinking about an answer. The muscles in my throat tightened up in reflex as I tried to bring some unity into the jumble of thoughts and ideas that kept forming. One word formed out of the rush of anxiety. "No." I shouted a protest against the strangeness of the room. I looked to the right, my eyes following the curving ceiling that started at the cot. The curve met another straight bulkhead on the left. I was in a small room, gray in color, like dull metal. Overhead a bright light burned into my vision. I wondered where in the universe I was. "Your name is David Corbin. If you understand, press button A on your right." I stared at the speaker in the wall. The mesh-covered hole and the two lights looked like a caricature of a face, set in a panel of dials. I twisted my head to look for the button. I pushed away from the close wall but I couldn't move. I reached down to the tightness that held my body, found the wide strap that held me and fumbled with the buckle. I threw it off and pushed myself up from the hard cot. I heard myself yell in surprise as I floated up towards the light overhead. I was weightless. How do you describe being weightless when you are born into a world bound by gravity. I twisted and shut my eyes in terror. There was no sensation of place, no feeling of up or down, no direction. My back bumped against the ceiling and I opened my eyes to stare at the cot and floor. I was concentrating too hard on remembering to be frightened for long. I pushed away from the warm metal and the floor moved up to meet me. "If you understand, press button A on your right." What should I understand? That I was floating in a room that had a curved wall ... that nothing was right in this hostile room? When I reached the cot I held it and drew myself down. I glanced at the planes of the room, trying to place it with other rooms I could see in my mind. Gray walls with a crazy curved ceiling ... a door to my left that appeared to be air tight. I stared at my familiar hands. I rubbed them across my face, feeling the solidity of flesh and bone, afraid to think too hard about myself. "My name ... my name is...." "Your name is David Corbin." I stared at the speaker. How long did this go on? The name meant nothing to me, but I thought about it, watching the relentless lights that shone below the dials. I stood up slowly and looked at myself. I was naked except for heavy shorts, and there was no clue to my name in the pockets. The room was warm and the air I had been breathing was good but it seemed wrong to be dressed like this. I didn't know why. I thought about insanity, and the room seemed to fit my thoughts. When the voice repeated the message again I had to act. Walking was like treading water that couldn't be seen or felt. I floated against the door, twisting the handle in fear that it wouldn't turn. The handle clanged as I pushed it down and I stared at the opposite wall of a narrow gray passageway. I pushed out into it and grasped the metal rail that ran along the wall. I reasoned it was there to propel yourself through the passageway in this weightless atmosphere. It was effortless to move. I turned on my side like a swimmer and went hand over hand, shooting down the corridor. I braced against forward motion and stopped against a door at the end. Behind me I could see the opened door I had left, and the thought of that questioning voice made me want to move. I swung the door open, catching a glimpse of a room crowded with equipment and.... I will always remember the scream of terror, the paralyzing fright of what I saw through the portholes in the wall of the room. I saw the blackest night, pierced by brilliance that blinded me. There was no depth to the searing brightness of countless stars. They seemed to press against the glass, blobs of fire against a black curtain burning into my eyes and brain. It was space. I looked out at deep space, star systems in clusters. I shut my eyes. When I looked again I knew where I was. Why the little room had been shaped like quarter round. Why I drifted weightlessly. Why I was.... David Corbin. I knew more of the puzzle. Something was wrong. After the first shock of looking out, I accepted the fact that I was in a space ship, yet I couldn't read the maps that were fastened to a table, nor understand the function or design of the compact machinery. WHY, Why, Why? The thought kept pounding at me. I was afraid to touch anything in the room. I pressed against the clear window, wondering if the stars were familiar. I had a brief vivid picture of a night sky on Earth. This was not the same sky. Back in the room where I had awakened, I touched the panel with the glowing eyes. It had asked me if I understood. Now it must tell me why I didn't. It had to help me, that flat metallic voice that repeated the same words. It must tell me.... "Your name is David Corbin. If you understand, press button A on your right." I pressed the button by the cot. The red lights blinked out as I stood in patient attention, trying to outguess the voice. I recalled a phrase ... some words about precaution. Precaution against forgetting. It was crazy, but I trusted the panel. It was the only thing I saw that could help me, guard me against another shock like seeing outside of the clear portholes. "It is assumed the experiment is a success," the voice said. What experiment? "You have been removed from suspension. Assume manual control of this ship." Control of a ship? Going where? "Do not begin operations until the others are removed from suspension." What others? Tell me what to do. "Rely on instructions for factoring when you check the coordinates. Your maximum deviation from schedule cannot exceed two degrees. Adopt emergency procedures as you see fit. Good luck." The voice snapped off and I laughed hysterically. None of it had made sense, and I cursed whatever madness had put me here. "Tell me what to do," I shouted wildly. I hammered the hard metal until the pain in my hands made me stop. "I can't remember what to do." I held my bruised hands to my mouth, and I knew that was all the message there was. In blind panic I pushed away from the panel. Something tripped me and I fell back in a graceless arc. I pushed away from the floor, barely feeling the pain in my leg, and went into the hall. Pain burned along my leg but I couldn't stop. In the first panic of waking up in strangeness I had missed the other doors in the passage. The first swung back to reveal a deep closet holding five bulky suits. The second room was like my own. A dark haired, deep chested man lay on the cot. His muscular body was secured by a wide belt. He was as still as death, motionless without warmth or breath as I hovered over him. I couldn't remember his face. The next room held another man. He was young and wiry, like an athlete cast in marble, dark haired and big jawed. A glassy eye stared up when I rolled back his eyelid. The eyelid remained open until I closed it and went on. Another room ... another man ... another stranger. This man was tall and raw boned, light of skin and hair, as dead as the others. A flat, illogical voice had instructed me to revive these men. I shivered in spite of the warmth of the room, studying the black box that squatted on a shelf by his head. My hand shook when I touched the metal. I dared not try to operate anything. Revive the others ... instructions without knowledge were useless to me. I stopped looking into the doors in the passageway and went back to the room with the portholes. Everything lay in readiness, fastened down star charts, instruments, glittering equipment. There was no feeling of disorder or use in the room. It waited for human hands to make it operate. Not mine. Not now. I went past the room into another, where the curves were more sharp. I could visualize the tapering hull leading to the nose of the ship. This room was filled with equipment that formed a room out of the bordered area I stood in. I sat in the deep chair facing the panel of dials and instruments, in easy reach. I ran my hands over the dials, the rows of smooth colored buttons, wondering. The ports on the side were shielded and I stared out at static energy, hung motionless in a world of searing light. There was no distortion, no movement outside and I glanced back at the dials. What speeds were they recording? What speeds and perhaps, what distance? It was useless to translate the markings. They stood for anything I might guess, and something kept pricking my mind, telling me I had no time to guess. I thought of time again. I was supposed to act according to ... plan. Did that mean ... in time ... in time. I went back down the passageway. The fourth small room was the same. Except for the woman. She lay on a cot, young and beautiful, even in the death-like immobility I had come to accept. Her beauty was graceful lines of face and her figure—smooth tapering legs, soft curves that were carved out of flesh colored stone. Yet not stone. I held her small hand, then put it back on the cot. Her attire was brief like the rest of us, shorts and a man's shirt. Golden hair curled up around her lovely face. I wondered if she would ever smile or move that graceful head. I rolled back her eyelid and looked at a deep blue eye that stared back in glassy surprise. Four people in all, depending on a blind helpless fool who didn't know their names or the reason for that dependence. I sat beside her on the cot until I could stand it no longer. Searching the ship made me forget my fear. I hoped I would find some answers. I went from the nose to the last bulkhead in a frenzy of floating motion, looking behind each door until I went as far as I could. There were two levels to the ship. They both ended in the lead shield that was set where the swell of the curve was biggest. It meant the engine or engines took up half the ship, cut off from the forward half by the instrument studded shield. I retraced my steps and took a rough estimate of size. The ship, as I called it, was at least four hundred feet long, fifty feet in diameter on the inside. The silence was a force in itself, pressing down from the metal walls, driving me back to the comforting smallness of the room where I had been reborn. I laughed bitterly, thinking about the aptness of that. I had literally been reborn in this room, equipped with half ideas, and no point to start from, no premise to seek. I sensed the place to start from was back in the room. I searched it carefully. Minutes later I realized the apparatus by the cot was different. It was the same type of black box, but out from it was a metal arm, bent in a funny angle. At the tip of the arm, a needle gleamed dully and I rubbed the deep gash on my leg. I bent the arm back until the angle looked right. It was then I realized the needle came to a spot where it could have hit my neck when I lay down. My shout of excitement rang out in the room, as I pictured the action of the extended arm. I lost my sudden elation in the cabin where the girl lay. The box behind her head was completely closed, and it didn't yield to the pressure I applied. It had a cover, but no other opening where an arm could extend. I ran my fingers over the unbroken surface, prying over the thin crack at the base helplessly. If some sort of antidote was to be administered manually I was lost. I had no knowledge of what to inject or where to look for it. The chamber of the needle that had awakened me was empty. That meant a measured amount. In the laboratory on the lower level I went over the rows of cans and tubes fastened to the shelves. There were earths and minerals, seeds and chemicals, testing equipment in compact drawers, but nothing marked for me. I wondered if I was an engineer or a pilot, or perhaps a doctor sent along to safeguard the others. Complete amnesia would have been terrible enough but this half knowledge, part awareness and association with the ship was a frightening force that seemed ready to break out of me. I went back to the cabin where the powerful man lay. I had to risk failure with one of them. I didn't want it to be the girl. I fought down the thought that he might be the key man, remembering the voice that had given the message. It was up to me, and soon. The metal in the box would have withstood a bullet. It couldn't be pried apart, and I searched again and again for a release mechanism. I found it. I swung the massive cover off and set it down. The equipment waited for the touch of a button and it went into operation. I stepped back as the tubes glowed to life and the arm swung down with the gleaming needle. The needle went into the corded neck of the man. The fluid chamber drained under pressure and the arm moved back. I stood by the man for long minutes. Finally it came. He stirred restlessly, closing his hands into fists. The deep chest rose and fell unevenly as he breathed. Finally the eyes opened and he looked at me. I watched him adjust to the room. It was in his eyes, wide at first, moving about the confines of the room back to me. "It looks like we made it," he said. "Yes." He unfastened the belt and sat up. I pushed him back as he floated up finding little humor in the comic expression on his face. "No gravity," he grunted and sat back. "You get used to it fast," I answered. I thought of what to say as he watched me. "How do you feel?" He shrugged at the question. "Fine, I guess. Funny, I can't remember." He saw it in my face, making him stop. "I can't remember dropping off to sleep," he finished. I held his hard arm. "What else? How much do you remember?" "I'm all right," he answered. "There aren't supposed to be any effects from this." "Who is in charge of this ship?" I asked. He tensed suddenly. "You are, sir. Why?" I moved away from the cot. "Listen, I can't remember. I don't know your name or anything about this ship." "What do you mean? What can't you remember?" he asked. He stood up slowly, edging around towards the door. I didn't want to fight him. I wanted him to understand. "Look, I'm in trouble. Nothing fits, except my name." "You don't know me?" "No." "Are you serious?" "Yes, yes. I don't know why but it's happened." He let his breath out in a whistle. "For God's sake. Any bump on your head?" "I feel all right physically. I just can't place enough." "The others. What about the others?" he blurted. "I don't know. You're the first besides myself. I don't know how I stumbled on the way to revive you." He shook his head, watching me like I was a freak. "Let's check the rest right away." "Yes. I've got to know if they are like me. I'm afraid to think they might be." "Maybe it's temporary. We can figure something out." II The second man, the dark haired one, opened his eyes and recognized us. He asked questions in rapid fire excitement. The third man, the tall Viking, was all right until he moved. The weightless sensation made him violently sick. We put him back on the cot, securing him again with the belt, but the sight of us floating made him shake. He was retching without results when we drifted out. I followed him to the girl's quarters. "What about her. Why is she here?" I asked my companion. He lifted the cover from the apparatus. "She's the chemist in the crew." "A girl?" "Dr. Thiesen is an expert, trained for this," he said. I looked at her. She looked anything but like a chemist. "There must be men who could have been sent. I've been wondering why a girl." "I don't know why, Captain. You tried to stop her before. Age and experience were all that mattered to the brass." "It's a bad thing to do." "I suppose. The mission stated one chemist." "What is the mission of this ship?" I asked. He held up his hand. "We'd better wait, sir. Everything was supposed to be all right on this end. First you, then Carl, sick to his stomach." "Okay. I'll hold the questions until we see about her." We were out of luck with the girl. She woke up and she was frightened. We questioned her and she was coherent but she couldn't remember. I tried to smile as I sat on the cot, wondering what she was thinking. "How do you feel?" I asked. Her face was a mask of wide-eyed fear as she shook her head. "Can you remember?" "I don't know." Blue eyes stared at me in fear. Her voice was low. "Do you know my name?" The question frightened her. "Should I? I feel so strange. Give me a minute to think." I let her sit up slowly. "Do you know your name?" She tightened up in my arms. "Yes. It's...." She looked at us for help, frightened by the lack of clothing we wore, by the bleak room. Her eyes circled the room. "I'm afraid," she cried. I held her and she shook uncontrollably. "What's happened to me?" she asked. The dark haired man came into the room, silent and watchful. My companion motioned to him. "Get Carl and meet us in Control." The man looked at me and I nodded. "We'll be there in a moment. I'm afraid we've got trouble." He nodded and pushed away from us. The girl screamed and covered her face with her hands. I turned to the other man. "What's your name?" "Croft. John Croft." "John, what are your duties if any?" "Automatic control. I helped to install it." "Can you run this ship? How about the other two?" He hit his hands together. "You fly it, sir. Can't you think?" "I'm trying. I know the ship is familiar, but I've looked it over. Maybe I'm trying too hard." "You flew her from earth until we went into suspension," he said. "I can't remember when," I said. I held the trembling girl against me, shaking my head. He glanced at the girl. "If the calculations are right it was more than a hundred years ago." We assembled in the control room for a council. We were all a little better for being together. John Croft named the others for me. I searched each face without recognition. The blond man was Carl Herrick, a metallurgist. His lean face was white from his spell but he was better. Paul Sample was a biologist, John said. He was lithe and restless, with dark eyes that studied the rest of us. I looked at the girl. She was staring out of the ports, her hands pressed against the transparent break in the smooth wall. Karen Thiesen was a chemist, now frightened and trying to remember. I wasn't in much better condition. "Look, if it comes too fast for me, for any of us, we'll stop. John, you can lead off." "You ask the questions," he said. I indicated the ship. "Where in creation are we going?" "We set out from Earth for a single star in the direction of the center of our Galaxy." "From Earth? How could we?" "Let's move slowly, sir," he said. "We're moving fast. I don't know if you can picture it, but we're going about one hundred thousand miles an hour." "Through space?" "Yes." "What direction?" Paul cut in. "It's a G type star, like our own sun in mass and luminosity. We hope to find a planetary system capable of supporting life." "I can't grasp it. How can we go very far in a lifetime?" "It can be done in two lifetimes," John said quietly. "You said I had flown this ship. You meant before this suspension." "Yes. That's why we can cross space to a near star." "How long ago was it?" "It was set at about a hundred years, sir. Doesn't that fit at all?" "I can't believe it's possible." Carl caught my eye. "Captain, we save this time without aging at all. It puts us near a calculated destination." "We've lost our lifetime." It was Karen. She had been crying silently while we talked. "Don't think about it," Paul said. "We can still pull this out all right if you don't lose your nerve." "What are we to do?" she asked. John answered for me. "First we've got to find out where we are. I know this ship but I can't fly it." "Can I?" I asked. We set up a temporary plan of action. Paul took Karen to the laboratory in an effort to help her remember her job. Carl went back to divide the rations. I was to study the charts and manuals. It was better than doing nothing, and I went into the navigation room and sat down. Earth was an infinitesimal point somewhere behind us on the galactic plane, and no one else was trained to navigate. The ship thundered to life as I sat there. The blast roared once ... twice, then settled into a muted crescendo of sound that hummed through the walls. I went into the control room and watched John at the panel. "I wish I knew what you were doing," I said savagely. "Give it time." "We can't spare any, can we?" I asked. "I wish we knew. What about her—Dr. Thiesen?" "She's in the lab. I don't think that will do much good. She's got to be shocked out of a mental state like that." "I guess you're right," he said slowly. "She's trained to administer the suspension on the return trip." I let my breath out slowly. "I didn't think about that." "We couldn't even get part way back in a lifetime," he said. "How old are you, John?" "Twenty-eight." "What about me?" "Thirty." He stared at the panel in thought for a minutes. "What about shock treatment? It sounds risky." "I know. It's the only thing I could think of. Why didn't everyone react the same?" "That had me wondering for a while. I don't know. Anyway how could you go about making her remember?" "Throw a crisis, some situation at her, I guess." He shrugged, letting his sure hands rest on the panel of dials. I headed back towards the lab. If I could help her I might help myself. I was past the rooms when the horn blasted through the corridor. I turned automatically with the sound, pushing against the rail, towards the control room. Deep in my mind I could see danger, and without questioning why I knew I had to be at Control when the sound knifed through the stillness. John was shouting as I thrust my way into the room. "Turn the ship. There's something dead ahead." I had a glimpse of his contorted face as I dove at the control board. My hands hit buttons, thumbed a switch and then a sudden force threw me to the right. I slammed into the panel on the right, as the pressure of the change dimmed my vision. Reflex made me look up at the radar control screen. It wasn't operating. John let go of the padded chair, grinning weakly. I was busy for a few seconds, feeding compensation into the gyros. Relief flooded through me like warm liquid. I hung on the intercom for support, drawing air into my heaving lungs. "What—made you—think of that," I asked weakly. "Shock treatment." "I must have acted on instinct." "You did. Even for a sick man that was pretty fast," he laughed. "I can think again, John. I know who I am," I shouted. I threw my arms around his massive shoulders. "You did it." "You gave me the idea, Mister, talking about Dr. Thiesen." "It worked. I'm okay," I said in giddy relief. "I don't have to tell you I was scared as hell. I wish you could have seen your face, the look in your eyes when I woke up." "I wouldn't want to wake up like that again." "You're all right now?" he asked. I grinned and nodded an answer. I saw John as he was at the base, big and competent, sweating in the blazing sun. I thought about the rest of the crew too. "We're heading right for a star...." "It's been dead ahead for hours," he grunted. I leaned over and threw the intercom to open. "This is control. Listen ... everyone. I'm over it. Disregard the warning siren ... we were testing the ship." The lab light blinked on as Paul cut in. "What was it ... hey, you said you're all right." "John did it. He hit the alarm figuring I would react. Listen, Paul. Is any one hurt?" "No. Carl is here too. His stomach flopped again but he's okay. What about food. We're supposed to be checked before we eat." "We'll have to go ahead without it. Any change?" "No, I put her to bed. Shall I bring food?" I glanced at John. He rubbed his stomach. "Yes," I answered. "Bring it when you can. I've got to find out where we are." We had to get off course before we ran into the yellow-white star that had been picked for us. Food was set down by me, grew cold and was carried away and I was still rechecking the figures. We were on a line ten degrees above the galactic plane. The parallactic baseline from Earth to the single star could be in error several degrees, or we could be right on the calculated position of the star. The radar confirmed my findings ... and my worst fears. When we set it for direction and distance, the screen glowed to life and recorded the star dead ahead. In all the distant star clusters, only this G type star was thought to have a planetary system like our own. We were out on a gamble to find a planet capable of supporting life. The idea had intrigued scientists before I had first looked up at the night sky. When I was sure the electronically recorded course was accurate for time, I checked direction and speed from the readings and plotted our position. If I was right we were much closer than we wanted to be. The bright pips on the screen gave us the distance and size of the star while we fed the figures into the calculator for our rate of approach. Spectroscopic tests were run on the sun and checked against the figures that had been calculated on Earth. We analyzed temperature, magnetic fields, radial motion, density and luminosity, checking against the standards the scientists had constructed. It was a G type star like our own. It had more density and temperature and suitable planets or not, we had to change course in a hurry. Carl analyzed the findings while we came to a decision. Somewhere along an orbit that might be two hundred miles across, our hypothetical planet circled this star. That distance was selected when the planets in Earth's solar system had proved to be barren. If the observations on this star were correct, we could expect to find a planet in a state of fertility ... if it existed ... if it were suitable for colonization ... if we could find it.	B. Smart, calculating, and cautious
What does the dead man represent for Ben? A. His conscious. He is manifesting as Ben's rage, and the anger that he felt during the incident. B. The end of his freedom. He represents his new life as an outlaw. C. His conscious. He is manifesting as Ben's unaddressed guilt, and what he can never run away from. D. The end of his career. He sees the dead man as the loss of his livelihood.	A Coffin for Jacob By EDWARD W. LUDWIG Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] With never a moment to rest, the pursuit through space felt like a game of hounds and hares ... or was it follow the leader? Ben Curtis eased his pale, gaunt body through the open doorway of the Blast Inn, the dead man following silently behind him. His fear-borne gaze traveled into the dimly illumined Venusian gin mill. The place was like an evil caldron steaming with a brew whose ingredients had been culled from the back corners of three planets. Most of the big room lay obscured behind a shimmering veil of tobacco smoke and the sweet, heavy fumes of Martian Devil's Egg. Here and there, Ben saw moving figures. He could not tell if they were Earthmen, Martians or Venusians. Someone tugged at his greasy coat. He jumped, thinking absurdly that it was the dead man's hand. " Coma esta, senor? " a small voice piped. " Speken die Deutsch? Desirez-vous d'amour? Da? Nyet? " Ben looked down. The speaker was an eager-eyed Martian boy of about ten. He was like a red-skinned marionette with pipestem arms and legs, clad in a torn skivvy shirt and faded blue dungarees. "I'm American," Ben muttered. "Ah, buena ! I speak English tres fine, senor . I have Martian friend, she tres pretty and tres fat. She weigh almost eighty pounds, monsieur . I take you to her, si ?" Ben shook his head. He thought, I don't want your Martian wench. I don't want your opium or your Devil's Egg or your Venusian kali. But if you had a drug that'd bring a dead man to life, I'd buy and pay with my soul. "It is deal, monsieur ? Five dollars or twenty keelis for visit Martian friend. Maybe you like House of Dreams. For House of Dreams—" "I'm not buying." The dirty-faced kid shrugged. "Then I show you to good table,— tres bien . I do not charge you, senor ." The boy grabbed his hand. Because Ben could think of no reason for resisting, he followed. They plunged into shifting layers of smoke and through the drone of alcohol-cracked voices. They passed the bar with its line of lean-featured, slit-eyed Earthmen—merchant spacemen. They wormed down a narrow aisle flanked by booths carved from Venusian marble that jutted up into the semi-darkness like fog-blanketed tombstones. Several times, Ben glimpsed the bulky figures of CO 2 -breathing Venusians, the first he'd ever seen. They were smoky gray, scaly, naked giants, toads in human shape. They stood solitary and motionless, aloof, their green-lidded eyes unblinking. They certainly didn't look like telepaths, as Ben had heard they were, but the thought sent a fresh rivulet of fear down his spine. Once he spied a white-uniformed officer of Hoover City's Security Police. The man was striding down an aisle, idly tapping his neuro-club against the stone booths. Keep walking , Ben told himself. You look the same as anyone else here. Keep walking. Look straight ahead. The officer passed. Ben breathed easier. "Here we are, monsieur ," piped the Martian boy. "A tres fine table. Close in the shadows." Ben winced. How did this kid know he wanted to sit in the shadows? Frowning, he sat down—he and the dead man. He listened to the lonely rhythms of the four-piece Martian orchestra. The Martians were fragile, doll-like creatures with heads too large for their spindly bodies. Their long fingers played upon the strings of their cirillas or crawled over the holes of their flutes like spider legs. Their tune was sad. Even when they played an Earth tune, it still seemed a song of old Mars, charged with echoes of lost voices and forgotten grandeur. For an instant, Ben's mind rose above the haunting vision of the dead man. He thought, What are they doing here, these Martians? Here, in a smoke-filled room under a metalite dome on a dust-covered world? Couldn't they have played their music on Mars? Or had they, like me, felt the challenge of new worlds? He sobered. It didn't matter. He ordered a whiskey from a Chinese waiter. He wet his lips but did not drink. His gaze wandered over the faces of the Inn's other occupants. You've got to find him , he thought. You've got to find the man with the red beard. It's the only way you can escape the dead man. The dead man was real. His name was Cobb. He was stout and flabby and about forty and he hated spacemen. His body was buried now—probably in the silent gray wastes outside Luna City. But he'd become a kind of invisible Siamese twin, as much a part of Ben as sight in his eyes. Sometimes the image would be shuffling drunkenly beside him, its lips spitting whiskey-slurred curses. Again, its face would be a pop-eyed mask of surprise as Ben's fist thudded into its jaw. More often, the face would be frozen in the whiteness of death. The large eyes would stare. Blood would trickle from a corner of the gaping mouth. You can forget a living man. You can defeat him or submit to him or ignore him, and the matter is over and done. You can't escape from a memory that has burned into your mind. It had begun a week ago in Luna City. The flight from White Sands had been successful. Ben, quietly and moderately, wanted to celebrate. He stopped alone in a rocketfront bar for a beer. The man named Cobb plopped his portly and unsteady posterior on the stool next to him. "Spacemen," he muttered, "are getting like flies. Everywhere, all you see's spacemen." He was a neatly dressed civilian. Ben smiled. "If it weren't for spacemen, you wouldn't be here." "The name's Cobb." The man hiccoughed. "Spacemen in their white monkey suits. They think they're little tin gods. Betcha you think you're a little tin god." He downed a shot of whiskey. Ben stiffened. He was twenty-four and dressed in the white, crimson-braided uniform of the Odyssey's junior astrogation officer. He was three months out of the Academy at White Sands and the shining uniform was like a key to all the mysteries of the Universe. He'd sought long for that key. At the age of five—perhaps in order to dull the memory of his parents' death in a recent strato-jet crash—he'd spent hours watching the night sky for streaking flame-tails of Moon rockets. At ten, he'd ground his first telescope. At fourteen, he'd converted an abandoned shed on the government boarding-school grounds to a retreat which housed his collection of astronomy and rocketry books. At sixteen, he'd spent every weekend holiday hitchhiking from Boys Town No. 5 in the Catskills to Long Island Spaceport. There, among the grizzled veterans of the old Moon Patrol, he'd found friends who understood his dream and who later recommended his appointment to the U. S. Academy for the Conquest of Space. And a month ago, he'd signed aboard the Odyssey —the first ship, it was rumored, equipped to venture as far as the asteroids and perhaps beyond. Cobb was persistent: "Damn fools shoulda known enough to stay on Earth. What the hell good is it, jumpin' from planet to planet?" The guy's drunk , Ben thought. He took his drink and moved three stools down the bar. Cobb followed. "You don't like the truth, eh, kid? You don't like people to call you a sucker." Ben rose and started to leave the bar, but Cobb grabbed his arm and held him there. "Thas what you are—a sucker. You're young now. Wait ten years. You'll be dyin' of radiation rot or a meteor'll get you. Wait and see, sucker!" Until this instant, Ben had suppressed his anger. Now, suddenly and without warning, it welled up into savage fury. His fist struck the man on the chin. Cobb's eyes gaped in shocked horror. He spun backward. His head cracked sickeningly on the edge of the bar. The sound was like a punctuation mark signaling the end of life. He sank to the floor, eyes glassy, blood tricking down his jaw. Ben knew that he was dead. Then, for a single absurd second, Ben was seized with terror—just as, a moment before, he'd been overwhelmed with anger. He ran. For some twenty minutes, he raced through a dizzying, nightmare world of dark rocketfront alleys and shouting voices and pursuing feet. At last, abruptly, he realized that he was alone and in silence. He saw that he was still on the rocketfront, but in the Tycho-ward side of the city. He huddled in a dark corner of a loading platform and lit a cigarette. A thousand stars—a thousand motionless balls of silver fire—shone above him through Luna City's transparent dome. He was sorry he'd hit Cobb, of course. He was not sorry he'd run. Escaping at least gave him a power of choice, of decision. You can do two things , he thought. You can give yourself up, and that's what a good officer would do. That would eliminate the escape charge. You'd get off with voluntary manslaughter. Under interplanetary law, that would mean ten years in prison and a dishonorable discharge. And then you'd be free. But you'd be through with rockets and space. They don't want new men over thirty-four for officers on rockets or even for third-class jet-men on beat-up freighters—they don't want convicted killers. You'd get the rest of the thrill of conquering space through video and by peeking through electric fences of spaceports. Or— There were old wives' tales of a group of renegade spacemen who operated from the Solar System's frontiers. The spacemen weren't outlaws. They were misfits, rejectees from the clearing houses on Earth. And whereas no legally recognized ship had ventured past Mars, the souped-up renegade rigs had supposedly hit the asteroids. Their headquarters was Venus. Their leader—a subject of popular and fantastic conjecture in the men's audiozines—was rumored to be a red-bearded giant. So , Ben reflected, you can take a beer-and-pretzels tale seriously. You can hide for a couple of days, get rid of your uniform, change your name. You can wait for a chance to get to Venus. To hell with your duty. You can try to stay in space, even if you exile yourself from Earth. After all, was it right for a single second, a single insignificant second, to destroy a man's life and his dream? He was lucky. He found a tramp freighter whose skipper was on his last flight before retirement. Discipline was lax, investigation of new personnel even more so. Ben Curtis made it to Venus. There was just one flaw in his decision. He hadn't realized that the memory of the dead man's face would haunt him, torment him, follow him as constantly as breath flowed into his lungs. But might not the rumble of atomic engines drown the murmuring dead voice? Might not the vision of alien worlds and infinite spaceways obscure the dead face? So now he sat searching for a perhaps nonexistent red-bearded giant, and hoping and doubting and fearing, all at once. "You look for someone, senor ?" He jumped. "Oh. You still here?" " Oui. " The Martian kid grinned, his mouth full of purple teeth. "I keep you company on your first night in Hoover City, n'est-ce-pas ?" "This isn't my first night here," Ben lied. "I've been around a while." "You are spacemen?" Ben threw a fifty-cent credit piece on the table. "Here. Take off, will you?" Spiderlike fingers swept down upon the coin. " Ich danke, senor. You know why city is called Hoover City?" Ben didn't answer. "They say it is because after women come, they want first thing a thousand vacuum cleaners for dust. What is vacuum cleaner, monsieur ?" Ben raised his hand as if to strike the boy. " Ai-yee , I go. You keep listen to good Martian music." The toothpick of a body melted into the semi-darkness. Minutes passed. There were two more whiskeys. A ceaseless parade of faces broke through the smoky veil that enclosed him—reddish balloon faces, scaly reptilian faces, white-skinned, slit-eyed faces, and occasionally a white, rouged, powdered face. But nowhere was there a face with a red beard. A sense of hopelessness gripped Ben Curtis. Hoover City was but one of a dozen cities of Venus. Each had twenty dives such as this. He needed help. But his picture must have been 'scoped to Venusian visiscreens. A reward must have been offered for his capture. Whom could he trust? The Martian kid, perhaps? Far down the darkened aisle nearest him, his eyes caught a flash of white. He tensed. Like the uniform of a Security Policeman, he thought. His gaze shifted to another aisle and another hint of whiteness. And then he saw another and another and another. Each whiteness became brighter and closer, like shrinking spokes of a wheel with Ben as their focal point. You idiot! The damned Martian kid! You should have known! Light showered the room in a dazzling explosion. Ben, half blinded, realized that a broad circle of unshaded globes in the ceiling had been turned on. The light washed away the room's strangeness and its air of brooding wickedness, revealing drab concrete walls and a debris-strewn floor. Eyes blinked and squinted. There were swift, frightened movements and a chorus of angry murmurs. The patrons of the Blast Inn were like tatter-clad occupants of a house whose walls have been ripped away. Ben Curtis twisted his lean body erect. His chair tumbled backward, falling. The white-clad men charged, neuro-clubs upraised. A woman screamed. The music ceased. The Martian orchestra slunk with feline stealth to a rear exit. Only the giant Venusians remained undisturbed. They stood unmoving, their staring eyes shifting lazily in Ben's direction. "Curtis!" one of the policemen yelled. "You're covered! Hold it!" Ben whirled away from the advancing police, made for the exit into which the musicians had disappeared. A hissing sound traveled past his left ear, a sound like compressed air escaping from a container. A dime-sized section of the concrete wall ahead of him crumbled. He stumbled forward. They were using deadly neuro-pistols now, not the mildly stunning neuro-clubs. Another hiss passed his cheek. He was about twelve feet from the exit. Another second , his brain screamed. Just another second— Or would the exits be guarded? He heard the hiss. It hit directly in the small of his back. There was no pain, just a slight pricking sensation, like the shallow jab of a needle. He froze as if yanked to a stop by a noose. His body seemed to be growing, swelling into balloon proportions. He knew that the tiny needle had imbedded itself deep in his flesh, knew that the paralyzing mortocain was spreading like icy fire into every fiber and muscle of his body. He staggered like a man of stone moving in slow motion. He'd have fifteen—maybe twenty—seconds before complete lethargy of mind and body overpowered him. In the dark world beyond his fading consciousness, he heard a voice yell, "Turn on the damn lights!" Then a pressure and a coldness were on his left hand. He realized that someone had seized it. A soft feminine voice spoke to him. "You're wounded? They hit you?" "Yes." His thick lips wouldn't let go of the word. "You want to escape—even now?" "Yes." "You may die if you don't give yourself up." "No, no." He tried to stumble toward the exit. "All right then. Not that way. Here, this way." Heavy footsteps thudded toward them. A few yards away, a flashlight flicked on. Hands were guiding him. He was aware of being pushed and pulled. A door closed behind him. The glare of the flashlight faded from his vision—if he still had vision. "You're sure?" the voice persisted. "I'm sure," Ben managed to say. "I have no antidote. You may die." His mind fought to comprehend. With the anti-paralysis injection, massage and rest, a man could recover from the effects of mortocain within half a day. Without treatment, the paralysis could spread to heart and lungs. It could become a paralysis of death. An effective weapon: the slightest wound compelled the average criminal to surrender at once. "Anti ... anti ..." The words were as heavy as blobs of mercury forced from his throat. "No ... I'm sure ... sure." He didn't hear the answer or anything else. Ben Curtis had no precise sensation of awakening. Return to consciousness was an intangible evolution from a world of black nothingness to a dream-like state of awareness. He felt the pressure of hands on his naked arms and shoulders, hands that massaged, manipulated, fought to restore circulation and sensitivity. He knew they were strong hands. Their strength seemed to transfer itself to his own body. For a long time, he tried to open his eyes. His lids felt welded shut. But after a while, they opened. His world of darkness gave way to a translucent cloak of mist. A round, featureless shape hovered constantly above him—a face, he supposed. He tried to talk. Although his lips moved slightly, the only sound was a deep, staccato grunting. But he heard someone say, "Don't try to talk." It was the same gentle voice he'd heard in the Blast Inn. "Don't talk. Just lie still and rest. Everything'll be all right." Everything all right , he thought dimly. There were long periods of lethargy when he was aware of nothing. There were periods of light and of darkness. Gradually he grew aware of things. He realized that the soft rubber mouth of a spaceman's oxygen mask was clamped over his nose. He felt the heat of electric blankets swathed about his body. Occasionally a tube would be in his mouth and he would taste liquid food and feel a pleasant warmth in his stomach. Always, it seemed, the face was above him, floating in the obscuring mist. Always, it seemed, the soft voice was echoing in his ears: "Swallow this now. That's it. You must have food." Or, "Close your eyes. Don't strain. It won't be long. You're getting better." Better , he'd think. Getting better.... At last, after one of the periods of lethargy, his eyes opened. The mist brightened, then dissolved. He beheld the cracked, unpainted ceiling of a small room, its colorless walls broken with a single, round window. He saw the footboard of his aluminite bed and the outlines of his feet beneath a faded blanket. Finally he saw the face and figure that stood at his side. "You are better?" the kind voice asked. The face was that of a girl probably somewhere between twenty-five and thirty. Her features, devoid of makeup, had an unhealthy-looking pallor, as if she hadn't used a sunlamp for many weeks. Yet, at the same time, her firm slim body suggested a solidity and a strength. Her straight brown hair was combed backward, tight upon her scalp, and drawn together in a knot at the nape of her neck. "I—I am better," he murmured. His words were still slow and thick. "I am going to live?" "You will live." He thought for a moment. "How long have I been here?" "Nine days." "You took care of me?" He noted the deep, dark circles beneath her sleep-robbed eyes. She nodded. "You're the one who carried me when I was shot?" "Yes." "Why?" Suddenly he began to cough. Breath came hard. She held the oxygen mask in readiness. He shook his head, not wanting it. "Why?" he asked again. "It would be a long story. Perhaps I'll tell you tomorrow." A new thought, cloaked in sudden fear, entered his murky consciousness. "Tell me, will—will I be well again? Will I be able to walk?" He lay back then, panting, exhausted. "You have nothing to worry about," the girl said softly. Her cool hand touched his hot forehead. "Rest. We'll talk later." His eyes closed and breath came easier. He slept. When he next awoke, his gaze turned first to the window. There was light outside, but he had no way of knowing if this was morning, noon or afternoon—or on what planet. He saw no white-domed buildings of Hoover City, no formal lines of green-treed parks, no streams of buzzing gyro-cars. There was only a translucent and infinite whiteness. It was as if the window were set on the edge of the Universe overlooking a solemn, silent and matterless void. The girl entered the room. "Hi," she said, smiling. The dark half-moons under her eyes were less prominent. Her face was relaxed. She increased the pressure in his rubberex pillows and helped him rise to a sitting position. "Where are we?" he asked. "Venus." "We're not in Hoover City?" "No." He looked at her, wondering. "You won't tell me?" "Not yet. Later, perhaps." "Then how did you get me here? How did we escape from the Inn?" She shrugged. "We have friends who can be bribed. A hiding place in the city, the use of a small desert-taxi, a pass to leave the city—these can be had for a price." "You'll tell me your name?" "Maggie." "Why did you save me?" Her eyes twinkled mischievously. "Because you're a good astrogator." His own eyes widened. "How did you know that?" She sat on a plain chair beside his bed. "I know everything about you, Lieutenant Curtis." "How did you learn my name? I destroyed all my papers—" "I know that you're twenty-four. Born July 10, 1971. Orphaned at four, you attended Boys Town in the Catskills till you were 19. You graduated from the Academy at White Sands last June with a major in Astrogation. Your rating for the five-year period was 3.8—the second highest in a class of fifty-seven. Your only low mark in the five years was a 3.2 in History of Martian Civilization. Want me to go on?" Fascinated, Ben nodded. "You were accepted as junior astrogation officer aboard the Odyssey . You did well on your flight from Roswell to Luna City. In a barroom fight in Luna City, you struck and killed a man named Arthur Cobb, a pre-fab salesman. You've been charged with second degree murder and escape. A reward of 5,000 credits has been offered for your capture. You came to Hoover City in the hope of finding a renegade group of spacemen who operate beyond Mars. You were looking for them in the Blast Inn." He gaped incredulously, struggling to rise from his pillows. "I—don't get it." "There are ways of finding out what we want to know. As I told you, we have many friends." He fell back into his pillows, breathing hard. She rose quickly. "I'm sorry," she said. "I shouldn't have told you yet. I felt so happy because you're alive. Rest now. We'll talk again soon." "Maggie, you—you said I'd live. You didn't say I'd be able to walk again." She lowered her gaze. "I hope you'll be able to." "But you don't think I will, do you?" "I don't know. We'll try walking tomorrow. Don't think about it now. Rest." He tried to relax, but his mind was a vortex of conjecture. "Just one more question," he almost whispered. "Yes?" "The man I killed—did he have a wife?" She hesitated. He thought, Damn it, of all the questions, why did I ask that? Finally she said, "He had a wife." "Children?" "Two. I don't know their ages." She left the room. He sank into the softness of his bed. As he turned over on his side, his gaze fell upon an object on a bureau in a far corner of the room. He sat straight up, his chest heaving. The object was a tri-dimensional photo of a rock-faced man in a merchant spaceman's uniform. He was a giant of a man with a neatly trimmed red beard ! Ben stared at the photo for a long time. At length, he slipped into restless sleep. Images of faces and echoes of words spun through his brain. The dead man returned to him. Bloodied lips cursed at him. Glassy eyes accused him. Somewhere were two lost children crying in the night. And towering above him was a red-bearded man whose great hands reached down and beckoned to him. Ben crawled through the night on hands and knees, his legs numb and useless. The crying of the children was a chilling wail in his ears. His head rose and turned to the red-bearded man. His pleading voice screamed out to him in a thick, harsh cackle. Yet even as he screamed, the giant disappeared, to be replaced by white-booted feet stomping relentlessly toward him. He awoke still screaming.... A night without darkness passed. Ben lay waiting for Maggie's return, a question already formed in his mind. She came and at once he asked, "Who is the man with the red beard?" She smiled. "I was right then when I gave you that thumbnail biog. You were looking for him, weren't you?" "Who is he?" She sat on the chair beside him. "My husband," she said softly. He began to understand. "And your husband needs an astrogator? That's why you saved me?" "We need all the good men we can get." "Where is he?" She cocked her head in mock suspicion. "Somewhere between Mercury and Pluto. He's building a new base for us—and a home for me. When his ship returns, I'll be going to him." "Why aren't you with him now?" "He said unexplored space is no place for a woman. So I've been studying criminal reports and photos from the Interplanetary Bureau of Investigation and trying to find recruits like yourself. You know how we operate?" He told her the tales he'd heard. She nodded. "There are quite a few of us now—about a thousand—and a dozen ships. Our base used to be here on Venus, down toward the Pole. The dome we're in now was designed and built by us a few years ago after we got pushed off Mars. We lost a few men in the construction, but with almost every advance in space, someone dies." "Venus is getting too civilized. We're moving out and this dome is only a temporary base when we have cases like yours. The new base—I might as well tell you it's going to be an asteroid. I won't say which one." "Don't get the idea that we're outlaws. Sure, about half our group is wanted by the Bureau, but we make honest livings. We're just people like yourself and Jacob." "Jacob? Your husband?" She laughed. "Makes you think of a Biblical character, doesn't it? Jacob's anything but that. And just plain 'Jake' reminds one of a grizzled old uranium prospector and he isn't like that, either." She lit a cigarette. "Anyway, the wanted ones stay out beyond the frontiers. Jacob and those like him can never return to Earth—not even to Hoover City—except dead. The others are physical or psycho rejects who couldn't get clearance if they went back to Earth. They know nothing but rocketing and won't give up. They bring in our ships to frontier ports like Hoover City to unload cargo and take on supplies." "Don't the authorities object?" "Not very strongly. The I. B. I. has too many problems right here to search the whole System for a few two-bit crooks. Besides, we carry cargoes of almost pure uranium and tungsten and all the stuff that's scarce on Earth and Mars and Venus. Nobody really cares whether it comes from the asteroids or Hades. If we want to risk our lives mining it, that's our business." She pursed her lips. "But if they guessed how strong we are or that we have friends planted in the I. B. I.—well, things might be different. There probably would be a crackdown." Ben scowled. "What happens if there is a crackdown? And what will you do when Space Corps ships officially reach the asteroids? They can't ignore you then." "Then we move on. We dream up new gimmicks for our crates and take them to Jupiter, Saturn, Uranus, Neptune, Pluto. In time, maybe, we'll be pushed out of the System itself. Maybe it won't be the white-suited boys who'll make that first hop to the stars. It could be us, you know—if we live long enough. But that Asteroid Belt is murder. You can't follow the text-book rules of astrogation out there. You make up your own." Ben stiffened. "And that's why you want me for an astrogator." Maggie rose, her eyes wistful. "If you want to come—and if you get well." She looked at him strangely. "Suppose—" He fought to find the right words. "Suppose I got well and decided not to join Jacob. What would happen to me? Would you let me go?" Her thin face was criss-crossed by emotion—alarm, then bewilderment, then fear. "I don't know. That would be up to Jacob." He lay biting his lip, staring at the photo of Jacob. She touched his hand and it seemed that sadness now dominated the flurry of emotion that had coursed through her. "The only thing that matters, really," she murmured, "is your walking again. We'll try this afternoon. Okay?" "Okay," he said. When she left, his eyes were still turned toward Jacob's photo. He was like two people, he thought. Half of him was an officer of the Space Corps. Perhaps one single starry-eyed boy out of ten thousand was lucky enough to reach that goal. He remembered a little picture book his mother had given him when she was alive. Under the bright pictures of spacemen were the captions: "A Space Officer Is Honest" "A Space Officer Is Loyal." "A Space Officer Is Dutiful." Honesty, loyalty, duty. Trite words, but without those concepts, mankind would never have broken away from the planet that held it prisoner for half a million years. Without them, Everson, after three failures and a hundred men dead, would never have landed on the Moon twenty-seven years ago.	C. His conscious. He is manifesting as Ben's unaddressed guilt, and what he can never run away from.
Does Earth want peace with Karn? A. The Earth is ready for peace, as interstellar war is costly. B. Earth needs to eliminate the Karna to protect the galaxy. C. The Earth does not want peace with the planet Karn. The Karna are an evil race. D. The Earth wants peace but doesn't trust the Karna to hold up their end of the bargain.	IN CASE OF FIRE By RANDALL GARRETT There are times when a broken tool is better than a sound one, or a twisted personality more useful than a whole one. For instance, a whole beer bottle isn't half the weapon that half a beer bottle is ... Illustrated by Martinez In his office apartment, on the top floor of the Terran Embassy Building in Occeq City, Bertrand Malloy leafed casually through the dossiers of the four new men who had been assigned to him. They were typical of the kind of men who were sent to him, he thought. Which meant, as usual, that they were atypical. Every man in the Diplomatic Corps who developed a twitch or a quirk was shipped to Saarkkad IV to work under Bertrand Malloy, Permanent Terran Ambassador to His Utter Munificence, the Occeq of Saarkkad. Take this first one, for instance. Malloy ran his finger down the columns of complex symbolism that showed the complete psychological analysis of the man. Psychopathic paranoia. The man wasn't technically insane; he could be as lucid as the next man most of the time. But he was morbidly suspicious that every man's hand was turned against him. He trusted no one, and was perpetually on his guard against imaginary plots and persecutions. Number two suffered from some sort of emotional block that left him continually on the horns of one dilemma or another. He was psychologically incapable of making a decision if he were faced with two or more possible alternatives of any major importance. Number three ... Malloy sighed and pushed the dossiers away from him. No two men were alike, and yet there sometimes seemed to be an eternal sameness about all men. He considered himself an individual, for instance, but wasn't the basic similarity there, after all? He was—how old? He glanced at the Earth calendar dial that was automatically correlated with the Saarkkadic calendar just above it. Fifty-nine next week. Fifty-nine years old. And what did he have to show for it besides flabby muscles, sagging skin, a wrinkled face, and gray hair? Well, he had an excellent record in the Corps, if nothing else. One of the top men in his field. And he had his memories of Diane, dead these ten years, but still beautiful and alive in his recollections. And—he grinned softly to himself—he had Saarkkad. He glanced up at the ceiling, and mentally allowed his gaze to penetrate it to the blue sky beyond it. Out there was the terrible emptiness of interstellar space—a great, yawning, infinite chasm capable of swallowing men, ships, planets, suns, and whole galaxies without filling its insatiable void. Malloy closed his eyes. Somewhere out there, a war was raging. He didn't even like to think of that, but it was necessary to keep it in mind. Somewhere out there, the ships of Earth were ranged against the ships of the alien Karna in the most important war that Mankind had yet fought. And, Malloy knew, his own position was not unimportant in that war. He was not in the battle line, nor even in the major production line, but it was necessary to keep the drug supply lines flowing from Saarkkad, and that meant keeping on good terms with the Saarkkadic government. The Saarkkada themselves were humanoid in physical form—if one allowed the term to cover a wide range of differences—but their minds just didn't function along the same lines. For nine years, Bertrand Malloy had been Ambassador to Saarkkad, and for nine years, no Saarkkada had ever seen him. To have shown himself to one of them would have meant instant loss of prestige. To their way of thinking, an important official was aloof. The greater his importance, the greater must be his isolation. The Occeq of Saarkkad himself was never seen except by a handful of picked nobles, who, themselves, were never seen except by their underlings. It was a long, roundabout way of doing business, but it was the only way Saarkkad would do any business at all. To violate the rigid social setup of Saarkkad would mean the instant closing off of the supply of biochemical products that the Saarkkadic laboratories produced from native plants and animals—products that were vitally necessary to Earth's war, and which could be duplicated nowhere else in the known universe. It was Bertrand Malloy's job to keep the production output high and to keep the materiel flowing towards Earth and her allies and outposts. The job would have been a snap cinch in the right circumstances; the Saarkkada weren't difficult to get along with. A staff of top-grade men could have handled them without half trying. But Malloy didn't have top-grade men. They couldn't be spared from work that required their total capacity. It's inefficient to waste a man on a job that he can do without half trying where there are more important jobs that will tax his full output. So Malloy was stuck with the culls. Not the worst ones, of course; there were places in the galaxy that were less important than Saarkkad to the war effort. Malloy knew that, no matter what was wrong with a man, as long as he had the mental ability to dress himself and get himself to work, useful work could be found for him. Physical handicaps weren't at all difficult to deal with. A blind man can work very well in the total darkness of an infrared-film darkroom. Partial or total losses of limbs can be compensated for in one way or another. The mental disabilities were harder to deal with, but not totally impossible. On a world without liquor, a dipsomaniac could be channeled easily enough; and he'd better not try fermenting his own on Saarkkad unless he brought his own yeast—which was impossible, in view of the sterilization regulations. But Malloy didn't like to stop at merely thwarting mental quirks; he liked to find places where they were useful . The phone chimed. Malloy flipped it on with a practiced hand. "Malloy here." "Mr. Malloy?" said a careful voice. "A special communication for you has been teletyped in from Earth. Shall I bring it in?" "Bring it in, Miss Drayson." Miss Drayson was a case in point. She was uncommunicative. She liked to gather in information, but she found it difficult to give it up once it was in her possession. Malloy had made her his private secretary. Nothing—but nothing —got out of Malloy's office without his direct order. It had taken Malloy a long time to get it into Miss Drayson's head that it was perfectly all right—even desirable—for her to keep secrets from everyone except Malloy. She came in through the door, a rather handsome woman in her middle thirties, clutching a sheaf of papers in her right hand as though someone might at any instant snatch it from her before she could turn it over to Malloy. She laid them carefully on the desk. "If anything else comes in, I'll let you know immediately, sir," she said. "Will there be anything else?" Malloy let her stand there while he picked up the communique. She wanted to know what his reaction was going to be; it didn't matter because no one would ever find out from her what he had done unless she was ordered to tell someone. He read the first paragraph, and his eyes widened involuntarily. "Armistice," he said in a low whisper. "There's a chance that the war may be over." "Yes, sir," said Miss Drayson in a hushed voice. Malloy read the whole thing through, fighting to keep his emotions in check. Miss Drayson stood there calmly, her face a mask; her emotions were a secret. Finally, Malloy looked up. "I'll let you know as soon as I reach a decision, Miss Drayson. I think I hardly need say that no news of this is to leave this office." "Of course not, sir." Malloy watched her go out the door without actually seeing her. The war was over—at least for a while. He looked down at the papers again. The Karna, slowly being beaten back on every front, were suing for peace. They wanted an armistice conference—immediately. Earth was willing. Interstellar war is too costly to allow it to continue any longer than necessary, and this one had been going on for more than thirteen years now. Peace was necessary. But not peace at any price. The trouble was that the Karna had a reputation for losing wars and winning at the peace table. They were clever, persuasive talkers. They could twist a disadvantage to an advantage, and make their own strengths look like weaknesses. If they won the armistice, they'd be able to retrench and rearm, and the war would break out again within a few years. Now—at this point in time—they could be beaten. They could be forced to allow supervision of the production potential, forced to disarm, rendered impotent. But if the armistice went to their own advantage ... Already, they had taken the offensive in the matter of the peace talks. They had sent a full delegation to Saarkkad V, the next planet out from the Saarkkad sun, a chilly world inhabited only by low-intelligence animals. The Karna considered this to be fully neutral territory, and Earth couldn't argue the point very well. In addition, they demanded that the conference begin in three days, Terrestrial time. The trouble was that interstellar communication beams travel a devil of a lot faster than ships. It would take more than a week for the Earth government to get a vessel to Saarkkad V. Earth had been caught unprepared for an armistice. They objected. The Karna pointed out that the Saarkkad sun was just as far from Karn as it was from Earth, that it was only a few million miles from a planet which was allied with Earth, and that it was unfair for Earth to take so much time in preparing for an armistice. Why hadn't Earth been prepared? Did they intend to fight to the utter destruction of Karn? It wouldn't have been a problem at all if Earth and Karn had fostered the only two intelligent races in the galaxy. The sort of grandstanding the Karna were putting on had to be played to an audience. But there were other intelligent races throughout the galaxy, most of whom had remained as neutral as possible during the Earth-Karn war. They had no intention of sticking their figurative noses into a battle between the two most powerful races in the galaxy. But whoever won the armistice would find that some of the now-neutral races would come in on their side if war broke out again. If the Karna played their cards right, their side would be strong enough next time to win. So Earth had to get a delegation to meet with the Karna representatives within the three-day limit or lose what might be a vital point in the negotiations. And that was where Bertrand Malloy came in. He had been appointed Minister and Plenipotentiary Extraordinary to the Earth-Karn peace conference. He looked up at the ceiling again. "What can I do?" he said softly. On the second day after the arrival of the communique, Malloy made his decision. He flipped on his intercom and said: "Miss Drayson, get hold of James Nordon and Kylen Braynek. I want to see them both immediately. Send Nordon in first, and tell Braynek to wait." "Yes, sir." "And keep the recorder on. You can file the tape later." "Yes, sir." Malloy knew the woman would listen in on the intercom anyway, and it was better to give her permission to do so. James Nordon was tall, broad-shouldered, and thirty-eight. His hair was graying at the temples, and his handsome face looked cool and efficient. Malloy waved him to a seat. "Nordon, I have a job for you. It's probably one of the most important jobs you'll ever have in your life. It can mean big things for you—promotion and prestige if you do it well." Nordon nodded slowly. "Yes, sir." Malloy explained the problem of the Karna peace talks. "We need a man who can outthink them," Malloy finished, "and judging from your record, I think you're that man. It involves risk, of course. If you make the wrong decisions, your name will be mud back on Earth. But I don't think there's much chance of that, really. Do you want to handle small-time operations all your life? Of course not. "You'll be leaving within an hour for Saarkkad V." Nordon nodded again. "Yes, sir; certainly. Am I to go alone?" "No," said Malloy, "I'm sending an assistant with you—a man named Kylen Braynek. Ever heard of him?" Nordon shook his head. "Not that I recall, Mr. Malloy. Should I have?" "Not necessarily. He's a pretty shrewd operator, though. He knows a lot about interstellar law, and he's capable of spotting a trap a mile away. You'll be in charge, of course, but I want you to pay special attention to his advice." "I will, sir," Nordon said gratefully. "A man like that can be useful." "Right. Now, you go into the anteroom over there. I've prepared a summary of the situation, and you'll have to study it and get it into your head before the ship leaves. That isn't much time, but it's the Karna who are doing the pushing, not us." As soon as Nordon had left, Malloy said softly: "Send in Braynek, Miss Drayson." Kylen Braynek was a smallish man with mouse-brown hair that lay flat against his skull, and hard, penetrating, dark eyes that were shadowed by heavy, protruding brows. Malloy asked him to sit down. Again Malloy went through the explanation of the peace conference. "Naturally, they'll be trying to trick you every step of the way," Malloy went on. "They're shrewd and underhanded; we'll simply have to be more shrewd and more underhanded. Nordon's job is to sit quietly and evaluate the data; yours will be to find the loopholes they're laying out for themselves and plug them. Don't antagonize them, but don't baby them, either. If you see anything underhanded going on, let Nordon know immediately." "They won't get anything by me, Mr. Malloy." By the time the ship from Earth got there, the peace conference had been going on for four days. Bertrand Malloy had full reports on the whole parley, as relayed to him through the ship that had taken Nordon and Braynek to Saarkkad V. Secretary of State Blendwell stopped off at Saarkkad IV before going on to V to take charge of the conference. He was a tallish, lean man with a few strands of gray hair on the top of his otherwise bald scalp, and he wore a hearty, professional smile that didn't quite make it to his calculating eyes. He took Malloy's hand and shook it warmly. "How are you, Mr. Ambassador?" "Fine, Mr. Secretary. How's everything on Earth?" "Tense. They're waiting to see what is going to happen on Five. So am I, for that matter." His eyes were curious. "You decided not to go yourself, eh?" "I thought it better not to. I sent a good team, instead. Would you like to see the reports?" "I certainly would." Malloy handed them to the secretary, and as he read, Malloy watched him. Blendwell was a political appointee—a good man, Malloy had to admit, but he didn't know all the ins and outs of the Diplomatic Corps. When Blendwell looked up from the reports at last, he said: "Amazing! They've held off the Karna at every point! They've beaten them back! They've managed to cope with and outdo the finest team of negotiators the Karna could send." "I thought they would," said Malloy, trying to appear modest. The secretary's eyes narrowed. "I've heard of the work you've been doing here with ... ah ... sick men. Is this one of your ... ah ... successes?" Malloy nodded. "I think so. The Karna put us in a dilemma, so I threw a dilemma right back at them." "How do you mean?" "Nordon had a mental block against making decisions. If he took a girl out on a date, he'd have trouble making up his mind whether to kiss her or not until she made up his mind for him, one way or the other. He's that kind of guy. Until he's presented with one, single, clear decision which admits of no alternatives, he can't move at all. "As you can see, the Karna tried to give us several choices on each point, and they were all rigged. Until they backed down to a single point and proved that it wasn't rigged, Nordon couldn't possibly make up his mind. I drummed into him how important this was, and the more importance there is attached to his decisions, the more incapable he becomes of making them." The Secretary nodded slowly. "What about Braynek?" "Paranoid," said Malloy. "He thinks everyone is plotting against him. In this case, that's all to the good because the Karna are plotting against him. No matter what they put forth, Braynek is convinced that there's a trap in it somewhere, and he digs to find out what the trap is. Even if there isn't a trap, the Karna can't satisfy Braynek, because he's convinced that there has to be—somewhere. As a result, all his advice to Nordon, and all his questioning on the wildest possibilities, just serves to keep Nordon from getting unconfused. "These two men are honestly doing their best to win at the peace conference, and they've got the Karna reeling. The Karna can see that we're not trying to stall; our men are actually working at trying to reach a decision. But what the Karna don't see is that those men, as a team, are unbeatable because, in this situation, they're psychologically incapable of losing." Again the Secretary of State nodded his approval, but there was still a question in his mind. "Since you know all that, couldn't you have handled it yourself?" "Maybe, but I doubt it. They might have gotten around me someway by sneaking up on a blind spot. Nordon and Braynek have blind spots, but they're covered with armor. No, I'm glad I couldn't go; it's better this way." The Secretary of State raised an eyebrow. " Couldn't go, Mr. Ambassador?" Malloy looked at him. "Didn't you know? I wondered why you appointed me, in the first place. No, I couldn't go. The reason why I'm here, cooped up in this office, hiding from the Saarkkada the way a good Saarkkadic bigshot should, is because I like it that way. I suffer from agoraphobia and xenophobia. "I have to be drugged to be put on a spaceship because I can't take all that empty space, even if I'm protected from it by a steel shell." A look of revulsion came over his face. "And I can't stand aliens!" THE END Transcriber's Note: This etext was produced from Astounding Science Fiction March 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	A. The Earth is ready for peace, as interstellar war is costly.
How did Lane eventually find the Mayor? A. Gerri helped him plan his route B. He had some clues from Gerri and the rest was instinct C. The cybrain knew exactly where to go after he jumped D. The Mayor had a flag indicating his room	MUTINEER By ROBERT J. SHEA For every weapon there was a defense, but not against the deadliest weapon—man himself! Raging , Trooper Lane hovered three thousand feet above Tammany Square. The cool cybrain surgically implanted in him was working on the problem. But Lane had no more patience. They'd sweat, he thought, hating the chill air-currents that threw his hovering body this way and that. He glared down at the three towers bordering on the Square. He spat, and watched the little white speck fall, fall. Lock me up in barracks. All I wanted was a little time off. Did I fight in Chi for them? Damn right I did. Just a little time off, so I shouldn't blow my top. Now the lid's gone. He was going over all their heads. He'd bowled those city cops over like paper dolls, back at the Armory. The black dog was on Lane's back. Old Mayor himself was going to hear about it. Why not? Ain't old Mayor the CinC of the Newyork Troopers? The humming paragrav-paks embedded beneath his shoulder blades held him motionless above Newyork's three administrative towers. Tammany Hall. Mayor's Palace. Court House. Lane cursed his stupidity. He hadn't found out which one was which ahead of time. They keep Troopers in the Armory and teach them how to fight. They don't teach them about their own city, that they'll be fighting for. There's no time. From seven years old up, Troopers have too much to learn about fighting. The Mayor was behind one of those thousands of windows. Old cybrain, a gift from the Trooper surgeons, compliments of the city, would have to figure out which one. Blood churned in his veins, nerves shrieked with impatience. Lane waited for the electronic brain to come up with the answer. Then his head jerked up, to a distant buzz. There were cops coming. Two black paragrav-boats whirred along the translucent underside of Newyork's anti-missile force-shield, the Shell. Old cybrain better be fast. Damn fast! The cybrain jolted an impulse through his spine. Lane somersaulted. Cybrain had taken charge of his motor nerves. Lane's own mind was just along for the ride. His body snapped into a stiff dive position. He began to plummet down, picking up speed. His mailed hands glittered like arrowheads out in front. They pointed to a particular window in one of the towers. A predatory excitement rippled through him as he sailed down through the air. It was like going into battle again. A little red-white-and-green flag fluttered on a staff below the window. Whose flag? The city flag was orange and blue. He shrugged away the problem. Cybrain knew what it was doing. The little finger of his right hand vibrated in its metal sheath. A pale vibray leaped from the lensed fingertip. Breakthrough! The glasstic pane dissolved. Lane streamed through the window. The paragrav-paks cut off. Lane dropped lightly to the floor, inside the room, in battle-crouch. A 3V set was yammering. A girl screamed. Lane's hand shot out automatically. A finger vibrated. Out of the corner of his eye, Lane saw the girl fold to the floor. There was no one else in the room. Lane, still in a crouch, chewed his lip. The Mayor? His head swung around and he peered at the 3V set. He saw his own face. "Lashing police with his vibray," said the announcer, "Lane broke through the cordon surrounding Manhattan Armory. Two policemen were killed, four others seriously injured. Tammany Hall has warned that this man is extremely dangerous. Citizens are cautioned to keep clear of him. Lane is an insane killer. He is armed with the latest military weapons. A built-in electronic brain controls his reflexes—" "At ease with that jazz," said Lane, and a sheathed finger snapped out. There was a loud bang. The 3V screen dissolved into a puddle of glasstic. The Mayor. Lane strode to the window. The two police boats were hovering above the towers. Lane's mailed hand snapped open a pouch at his belt. He flipped a fist-sized cube to the floor. The force-bomb "exploded"—swelled or inflated, really, but with the speed of a blast. Lane glanced out the window. A section of the energy globe bellied out from above. It shaded the view from his window and re-entered the tower wall just below. Now the girl. He turned back to the room. "Wake up, outa-towner." He gave the blonde girl a light dose of the vibray to slap her awake. "Who are you?" she said, shakily. Lane grinned. "Trooper Lane, of the Newyork Special Troops, is all." He threw her a mock salute. "You from outa-town, girlie. I ain't seen a Newyork girl with yellow hair in years. Orange or green is the action. Whatcha doing in the Mayor's room?" The girl pushed herself to her feet. Built, Lane saw. She was pretty and clean-looking, very out-of-town. She held herself straight and her blue-violet eyes snapped at him. "What the devil do you think you're doing, soldier? I am a diplomat of the Grassroots Republic of Mars. This is an embassy, if you know what that means." "I don't," said Lane, unconcerned. "Well, you should have had brains enough to honor the flag outside this window. That's the Martian flag, soldier. If you've never heard of diplomatic immunity, you'll suffer for your ignorance." Her large, dark eyes narrowed. "Who sent you?" "My cybrain sent me." She went openmouthed. "You're Lane ." "I'm the guy they told you about on the 3V. Where's the Mayor? Ain't this his place?" "No. No, you're in the wrong room. The wrong building. That's the Mayor's suite over there." She pointed. "See where the balcony is? This is the Embassy suite. If you want the Mayor you'll have to go over there." "Whaddaya know," said Lane. "Cybrain didn't know, no more than me." The girl noticed the dark swell of the force-globe. "What's that out there?" "Force-screen. Nothing gets past, except maybe a full-size blaster-beam. Keeps cops out. Keeps you in. You anybody important?" "I told you, I'm an ambassador. From Mars. I'm on a diplomatic mission." "Yeah? Mars a big city?" She stared at him, violet eyes wide. "The planet Mars." "Planet? Oh, that Mars. Sure, I've heard of it—you gotta go by spaceship. What's your name?" "Gerri Kin. Look, Lane, holding me is no good. It'll just get you in worse trouble. What are you trying to do?" "I wanna see the Mayor. Me and my buddies, we just come back from fighting in Chi, Gerri. We won. They got a new Mayor out there in Chi. He takes orders from Newyork." Gerri Kin said, "That's what the force-domes did. The perfect defense. But also the road to the return to city-states. Anarchy." Lane said, "Yeah? Well, we done what they wanted us to do. We did the fighting for them. So we come back home to Newyork and they lock us up in the Armory. Won't pay us. Won't let us go nowhere. They had cops guarding us. City cops." Lane sneered. "I busted out. I wanna see the Mayor and find out why we can't have time off. I don't play games, Gerri. I go right to the top." Lane broke off. There was a hum outside the window. He whirled and stared out. The rounded black hulls of the two police paragrav-boats were nosing toward the force-screen. Lane could read the white numbers painted on their bows. A loudspeaker shouted into the room: "Come out of there, Lane, or we'll blast you out." "You can't," Lane called. "This girl from Mars is here." "I repeat, Lane—come out or we'll blast you out." Lane turned to the girl. "I thought you were important." She stood there with her hands together, calmly looking at him. "I am. But you are too, to them. Mars is millions of miles away, and you're right across the Square from the Mayor's suite." "Yeah, but—" Lane shook his head and turned back to the window. "All right, look! Move them boats away and I'll let this girl out!" "No deal, Lane. We're coming in." The police boats backed away slowly, then shot straight up, out of the line of vision. Lane looked down at the Square. Far below, the long, gleaming barrel of a blaster cannon caught the dim light filtering down through Newyork's Shell. The cannon trundled into the Square on its olive-drab, box-shaped caterpillar mounting and took up a position equidistant from the bases of the three towers. Now a rumble of many voices rose from below. Lane stared down to see a large crowd gathering in Tammany Square. Sound trucks were rolling to a stop around the edges of the crowd. The people were all looking up. Lane looked across the Square. The windows of the tower opposite, the ones he could see clearly, were crowded with faces. There were white dot faces on the balcony that Gerri Kin had pointed out as the Mayor's suite. The voice of a 3V newscaster rolled up from the Square, reechoing against the tower walls. "Lane is holding the Martian Ambassador, Gerri Kin, hostage. You can see the Martian tricolor behind his force-globe. Police are bringing up blaster cannon. Lane's defense is a globe of energy similar to the one which protects Newyork from aerial attack." Lane grinned back at Gerri Kin. "Whole town's down there." Then his grin faded. Nice-looking, nice-talking girl like this probably cared a lot more about dying than he did. Why the hell didn't they give him a chance to let her out? Maybe he could do it now. Cybrain said no. It said the second he dropped his force-screen, they'd blast this room to hell. Poor girl from Mars, she didn't have a chance. Gerri Kin put her hand to her forehead. "Why did you have to pick my room? Why did they send me to this crazy city? Private soldiers. Twenty million people living under a Shell like worms in a corpse. Earth is sick and it's going to kill me. What's going to happen?" Lane looked sadly at her. Only two kinds of girls ever went near a Trooper—the crazy ones and the ones the city paid. Why did he have to be so near getting killed when he met one he liked? Now that she was showing a little less fear and anger, she was talking straight to him. She was good, but she wasn't acting as if she was too good for him. "They'll start shooting pretty quick," said Lane. "I'm sorry about you." "I wish I could write a letter to my parents," she said. "What?" "Didn't you understand what I said?" "What's a letter?" "You don't know where Mars is. You don't know what a letter is. You probably can't even read and write!" Lane shrugged. He carried on the conversation disinterestedly, professionally relaxed before battle. "What's these things I can't do? They important?" "Yes. The more I see of this city and its people, the more important I realize they are. You know how to fight, don't you? I'll bet you're perfect with those weapons." "Listen. They been training me to fight since I was a little kid. Why shouldn't I be a great little fighter?" "Specialization," said the girl from Mars. "What?" "Specialization. Everyone I've met in this city is a specialist. SocioSpecs run the government. TechnoSpecs run the machinery. Troopers fight the wars. And ninety per cent of the people don't work at all because they're not trained to do anything." "The Fans," said Lane. "They got it soft. That's them down there, come to watch the fight." "You know why you were kept in the Armory, Lane? I heard them talking about it, at the dinner I went to last night." "Why?" "Because they're afraid of the Troopers. You men did too good a job out in Chi. You are the deadliest weapon that has ever been made. You. Single airborne infantrymen!" Lane said, "They told us in Trooper Academy that it's the men that win the wars." "Yes, but people had forgotten it until the SocioSpecs of Newyork came up with the Troopers. Before the Troopers, governments concentrated on the big weapons, the missiles, the bombs. And the cities, with the Shells, were safe from bombs. They learned to be self-sufficient under the Shells. They were so safe, so isolated, that national governments collapsed. But you Troopers wiped out that feeling of security, when you infiltrated Chi and conquered it." "We scared them, huh?" Gerri said, "You scared them so much that they were afraid to let you have a furlough in the city when you came back. Afraid you Troopers would realize that you could easily take over the city if you wanted to. You scared them so much that they'll let me be killed. They'll actually risk trouble with Mars just to kill you." "I'm sorry about you. I mean it, I like—" At that moment a titanic, ear-splitting explosion hurled him to the carpet, deafened and blinded him. He recovered and saw Gerri a few feet away, dazed, groping on hands and knees. Lane jumped to the window, looked quickly, sprang back. Cybrain pumped orders to his nervous system. "Blaster cannon," he said. "But just one. Gotcha, cybrain. I can beat that." He picked up the black box that generated his protective screen. Snapping it open with thumb-pressure, he turned a small dial. Then he waited. Again an enormous, brain-shattering concussion. Again Lane and Gerri were thrown to the floor. But this time there was a second explosion and a blinding flash from below. Lane laughed boyishly and ran to the window. "Look!" he called to Gerri. There was a huge gap in the crowd below. The pavement was blackened and shattered to rubble. In and around the open space sprawled dozens of tiny black figures, not moving. "Backfire," said Lane. "I set the screen to throw their blaster beam right back at them." "And they knew you might—and yet they let a crowd congregate!" Gerri reeled away from the window, sick. Lane said, "I can do that a couple times more, but it burns out the force-globe. Then I'm dead." He heard the 3V newscaster's amplified voice: "—approximately fifty killed. But Lane is through now. He has been able to outthink police with the help of his cybrain. Now police are feeding the problem to their giant analogue computer in the sub-basement of the Court House. The police analogue computer will be able to outthink Lane's cybrain, will predict Lane's moves in advance. Four more blaster cannon are coming down Broadway—" "Why don't they clear those people out of the Square?" Gerri cried. "What? Oh, the Fans—nobody clears them out." He paused. "I got one more chance to try." He raised a mailed glove to his mouth and pressed a small stud in the wrist. He said, "Trooper HQ, this is Lane." A voice spoke in his helmet. "Lane, this is Trooper HQ. We figured you'd call." "Get me Colonel Klett." Thirty seconds passed. Lane could hear the clank of caterpillar treads as the mobile blaster cannon rolled into Tammany Square. The voice of the commanding officer of the Troopers rasped into Lane's ear: "Meat-head! You broke out against my orders! Now look at you!" "I knew you didn't mean them orders, sir." "If you get out of there alive, I'll hang you for disobeying them!" "Yes, sir. Sir, there's a girl here—somebody important—from Mars. You know, the planet. Sir, she told me we could take over the city if we got loose. That right, sir?" There was a pause. "Your girl from Mars is right, Lane. But it's too late now. If we had moved first, captured the city government, we might have done it. But they're ready for us. They'd chop us down with blaster cannon." "Sir, I'm asking for help. I know you're on my side." "I am, Lane." The voice of Colonel Klett was lower. "I'd never admit it if you had a chance of getting out of there alive. You've had it, son. I'd only lose more men trying to rescue you. When they feed the data into that analogue computer, you're finished." "Yes, sir." "I'm sorry, Lane." "Yes, sir. Over and out." Lane pressed the stud on his gauntlet again. He turned to Gerri. "You're okay. I wish I could let you out. Old cybrain says I can't. Says if I drop the force-globe for a second, they'll fire into the room, and then we'll both be dead." Gerri stood with folded arms and looked at him. "Do what you have to do. As far as I can see, you're the only person in this city that has even a little bit of right on his side." Lane laughed. "Any of them purple-haired broads I know would be crazy scared. You're different." "When my grandparents landed on Mars, they found out that selfishness was a luxury. Martians can't afford it." Lane frowned with the effort of thinking. "You said I had a little right on my side. That's a good feeling. Nobody ever told me to feel that way about myself before. It'll be better to die knowing that." "I know," she said. The amplified voice from below said, "The police analogue computer is now hooked directly to the controls of the blaster cannon battery. It will outguess Lane's cybrain and check his moves ahead of time." Lane looked at Gerri. "How about giving me a kiss before they get us? Be nice if I kissed a girl like you just once in my life." She smiled and walked forward. "You deserve it, Lane." He kissed her and it filled him with longings for things he couldn't name. Then he stepped back and shook his head. "It ain't right you should get killed. If I take a dive out that window, they shoot at me, not in here." "And kill you all the sooner." "Better than getting burned up in this lousy little room. You also got right on your side. There's too many damn Troopers and not enough good persons like you. Old cybrain says stay here, but I don't guess I will. I'm gonna pay you back for that kiss." "But you're safe in here!" "Worry about yourself, not about me." Lane picked up the force-bomb and handed it to her. "When I say now, press this. Then take your hand off, real fast. It'll shut off the screen for a second." He stepped up on to the window ledge. Automatically, the cybrain cut in his paragrav-paks. "So long, outa-towner. Now! " He jumped. He was hurtling across the Square when the blaster cannons opened up. They weren't aimed at the window where the little red-white-and-green tricolor was flying. But they weren't aimed at Lane, either. They were shooting wild. Which way now? Looks like I got a chance. Old cybrain says fly right for the cannons. He saw the Mayor's balcony ahead. Go to hell, old cybrain. I'm doing all right by myself. I come to see the Mayor, and I'm gonna see him. Lane plunged forward. He heard the shouts of frightened men. He swooped over the balcony railing. A man was pointing a blaster pistol at him. There were five men on the balcony—emergency! Years of training and cybrain took over. Lane's hand shot out, fingers vibrating. As he dropped to the balcony floor in battle-crouch, the men slumped around him. He had seen the man with the blaster pistol before. It was the Mayor of Newyork. Lane stood for a moment in the midst of the sprawled men, the shrieks of the crowd floating up to him. Then he raised his glove to his lips. He made contact with Manhattan Armory. "Colonel Klett, sir. You said if we captured the city government we might have a chance. Well, I captured the city government. What do we do with it now?" Lane was uncomfortable in his dress uniform. First there had been a ceremony in Tammany Square inaugurating Newyork's new Military Protectorate, and honoring Trooper Lane. Now there was a formal dinner. Colonel Klett and Gerri Kin sat on either side of Lane. Klett said, "Call me an opportunist if you like, Miss Kin, my government will be stable, and Mars can negotiate with it." He was a lean, sharp-featured man with deep grooves in his face, and gray hair. Gerri shook her head. "Recognition for a new government takes time. I'm going back to Mars, and I think they'll send another ambassador next time. Nothing personal—I just don't like it here." Lane said, "I'm going to Mars, too." "Did she ask you to?" demanded Klett. Lane shook his head. "She's got too much class for me. But I like what she told me about Mars. It's healthy, like." Klett frowned. "If I thought there was a gram of talent involved in your capture of the Mayor, Lane, I'd never release you from duty. But I know better. You beat that analogue computer by sheer stupidity—by disregarding your cybrain." Lane said, "It wasn't so stupid if it worked." "That's what bothers me. It calls for a revision in our tactics. We've got a way of beating those big computers now, should anyone use them against us." "I just didn't want her to be hurt." "Exactly. The computer could outguess a machine, like your cybrain. But you introduced a totally unpredictable factor—human emotion. Which proves what I, as a military man, have always maintained—that the deadliest weapon in man's arsenal is still, and will always be, the individual soldier." "What you just said there, sir," said Lane. "That's why I'm leaving Newyork." "What do you mean?" asked Colonel Klett. "I'm tired of being a weapon, sir. I want to be a human being." END Work is the elimination of the traces of work. —Michelangelo Transcriber's Note: This etext was produced from If July 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.	B. He had some clues from Gerri and the rest was instinct
What is the name of the pilot who flies Mia’s scoutship and how does she characterize his piloting style? A. Venie Morlock. His style twists the stomach B. George Fuhonin. His style drops the stomach out of everybody. C. Jimmy D. His style is smart on the slap D. Horst. His style is beneath the notice of a Losel	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.	B. George Fuhonin. His style drops the stomach out of everybody.
Which languages do they work with?	### Introduction Named entity recognition (NER) is a sequence tagging task that extracts the continuous tokens into specified classes, such as person names, organizations and locations. Current state-of-the-art approaches for NER usually base themselves on long short-term memory recurrent neural networks (LSTM RNNs) and a subsequent conditional random field (CRF) to predict the sequence labels BIBREF0 . Performances of neural NER methods are compromised if the training data are not enough BIBREF1 . This problem is severe for many languages due to a lack of labeled datasets, e.g., German and Spanish. In comparison, NER on English is well developed and there exist abundant labeled data for training purpose. Therefore, in this work, we regard English as a high-resource language, while other languages, even Chinese, as low-resource languages. There is an intractable problem when leveraging English NER system for other languages. The sentences with the same meaning in different languages may have different lengths and the positions of words in these sentences usually do not correspond. Previous work such as BIBREF2 used each single word translation information to enrich the monolingual word embedding. To our knowledge, there is no approach that employs the whole translation information to improve the performance of the monolingual NER system. To address above problem, we introduce an extension to the BiLSTM-CRF model, which could obtain transferred knowledge from a pre-trained English NER system. First, we translate other languages into English. Since the proposed models of BIBREF3 and BIBREF4 , the performance of attention-based machine translation systems is close to the human level. The attention mechanism can make the translation results more accurate. Furthermore, this mechanism has another useful property: the attention weights can represent the alignment information. After translating the low-resource language into English, we utilize the pre-trained English NER model to predict the sentences and record the output states of BiLSTM in this model. The states contain the semantic and task-specific information of the sentences. By using soft alignment attention weights as a transformation matrix, we manage to transfer the knowledge of high resource language — English to other languages. Finally, using both word vectors and the transfer knowledge, we obtain new state-of-the-art results on four datasets. ### Model In this section, we will introduce the BAN in three parts. Our model is based on the mainstream NER model BIBREF5 , using BiLSTM-CRF as the basic network structure. Given a sentence INLINEFORM0 and corresponding labels INLINEFORM1 , where INLINEFORM2 denotes the INLINEFORM3 th token and INLINEFORM4 denotes the INLINEFORM5 th label. The NER task is to estimate the probability INLINEFORM6 . Figure FIGREF1 shows the main architecture of our model. ### Pre-trained Translation and NER Model Attention-base translation model We use the system of BIBREF6 , a convolutional sequence to sequence model. It divides translation process into two steps. First, in the encoder step, given an input sentence INLINEFORM0 of length INLINEFORM1 , INLINEFORM2 represents each word as word embedding INLINEFORM3 . After that, we obtain the absolute position of input elements INLINEFORM4 . Both vectors are concatenated to get input sentence representations INLINEFORM5 . Similarly, output elements INLINEFORM6 generated from decoder network have the same structure. A convolutional neural network (CNN) is used to get the hidden state of the sentence representation from left to right. Second, in the decoder step, attention mechanism is used in each CNN layer. In order to acquire the attention value, we combine the current decoder state INLINEFORM7 with the embedding of previous decoder output value INLINEFORM8 : DISPLAYFORM0 For INLINEFORM0 th layer, the attention INLINEFORM1 of the INLINEFORM2 th source element and INLINEFORM3 th state is computed as a dot-product between the decoder state summary INLINEFORM4 and each output INLINEFORM5 of the last encoder layer: DISPLAYFORM0 Then we follow the normal decoder implementation and get target sentence INLINEFORM0 by beam search algorithm. Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation. The hidden states of the character language model (CharLM) are used to create contextualized word embeddings. The final embedding INLINEFORM0 is concatenated by the CharLM embedding INLINEFORM1 and GLOVE embedding INLINEFORM2 BIBREF8 . A standard BiLSTM-CRF named entity recognition model BIBREF0 takes INLINEFORM3 to address the NER task. ### Back Attention Knowledge Transfer The sentences in low-resource languages are used as input to the model. Given a input sentence INLINEFORM0 in low-resource language, we use pre-trained translation model to translate INLINEFORM1 into English and the output is INLINEFORM2 . Simultaneously, we record the average of values for all INLINEFORM3 attention layers: DISPLAYFORM0 After that, we use the pre-trained English NER model to predict the translated sentence INLINEFORM0 . Then, we have the BiLSTM output states: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 denote the INLINEFORM2 th forward and backward outputs, respectively. INLINEFORM3 contains the semantic and task-specific information of the translated sentence. And the INLINEFORM4 th row of attention weights matrix INLINEFORM5 represents the correlation between source word INLINEFORM6 with all words in target sentence INLINEFORM7 . Thereafter, to obtain the transfer information INLINEFORM8 of source word, we reversely use the attention weights: DISPLAYFORM0 where INLINEFORM0 represent the whole outputs of BiLSTM, and INLINEFORM1 , INLINEFORM2 . INLINEFORM3 denotes the transfer information of INLINEFORM4 th word in low-resource language and has the same dimensions with INLINEFORM5 . ### Named Entity Recognition Architecture The low-resource language named entity recognition architecture is based on BIBREF5 . The word embeddings of low-resource language are passed into a BiLSTM-CRF sequence labeling network. The embeddings INLINEFORM0 are used as inputs to the BiLSTM. Then we have: DISPLAYFORM0 Before passing the forward and backward output states INLINEFORM0 into CRF, we concatenate INLINEFORM1 and INLINEFORM2 as a new representation: DISPLAYFORM0 CRF model uses INLINEFORM0 to give the final sequence probability on the possible sequence label INLINEFORM1 : DISPLAYFORM0 At last, the named entity labels are predicted by: DISPLAYFORM0 ### Experiments We use experiments to evaluate the effectiveness of our proposed method on NER task. On three different low-resource languages, we conducted an experimental evaluation to prove the effectiveness of our back attention mechanism on the NER task. Four datasets are used in our work, including CoNLL 2003 German BIBREF9 , CoNLL 2002 Spanish BIBREF10 , OntoNotes 4 BIBREF11 and Weibo NER BIBREF12 . All the annotations are mapped to the BIOES format. Table TABREF14 shows the detailed statistics of the datasets. ### Experimental Setup We implement the basic BiLSTM-CRF model using PyTorch framework. FASTTEXT embeddings are used for generating word embeddings. Translation models are trained on United Nation Parallel Corpus. For pre-trained English NER system, we use the default NER model of Flair. ### Settings We train our NER model using vanilla SGD with no momentum for 150 epochs, with an initial learning rate of 0.1 and a learning rate annealing method in which the train loss does not fall in 3 consecutive epochs. The hidden size of BiLSTM model is set to 256 and mini-batch size is set to 16. Dropout is applied to word embeddings with a rate of 0.1 and to BiLSTM with a rate of 0.5. We repeat each experiment 5 times under different random seeds and report the average of test set as final performance. ### German and Spanish NER Experimental results of German and Spanish are shown in table TABREF20 . Evaluation metric is F1-score. We can find that our method CharLM+BiLSTM-CRF+BAN yields the best performance on two languages. And after adding our network to each of the basic models, the performance of each model has been improved. This suggests that the transfer information, obtained from BAN, is helpful for low-resource NER. ### Chinese NER Chinese is distinct from Latin-based languages. Thence, there are some tricks when processing Chinese corpus. But we only suppose to verify the validity of our method, so we just use the character-level embeddings. Table TABREF22 shows the results on Chinese OntoNotes 4.0. Adding BAN to baseline model leads to an increase from 63.25% to 72.15% F1-score. In order to further improve the performance, we use the BERT model BIBREF20 to produce word embeddings. With no segmentation, we surpass the previous state-of-the-art approach by 6.33% F1-score. For Weibo dataset, the experiment results are shown in Table TABREF23 , where NE, NM and Overall denote named entities, nominal entities and both. The baseline model gives a 33.18% F1-score. Using the transfer knowledge by BAN, the baseline model achieves an immense improvement in F1-score, rising by 10.39%. We find that BAN still gets consistent improvement on a strong model. With BAN, the F1-score of BERT+BiLSTM+CRF increases to 70.76%. ### Task-Specific Information from Back Attention Network BIBREF21 indicates that the representations from higher-level layers of NLP models are more task-specific. Although we do the same task among different languages, the target domains of different datasets are slightly different. So, to prove that back attention knowledge generated by BAN could capture valuable task-specific information between different languages, we use the back attention knowledge alone as word embedding to predict Weibo dataset. We compare three different word embeddings on the baseline model. Experimental results are shown in Table TABREF25 and illustrate that back attention knowledge from BAN has inherent semantic information. ### Analysis Our proposed approach is the first to leverage hidden states of NER model from another language to improve monolingual NER performance. The training time with or without BAN is almost the same due to the translation module and the English NER module are pre-trained. On large datasets, our model makes a small improvement because some of transfer knowledge obtained from our method is duplicated with the information learned by the monolingual models. On small datasets, e.g., Weibo dataset, a great improvement has been achieved after adding transfer knowledge to the baseline model. The reason maybe is that these datasets are too small to be fully trained and the test datasets have many non-existent characters of the training dataset, even some unrecognized characters. Therefore, some tags labeled incorrectly by monolingual models could be labeled correctly with the additional transfer knowledge which contains task-specific information obtained from BAN. So, the transfer information plays an important role in this dataset. ### Conclusion In this paper, we seek to improve the performance of NER on low-resource languages by leveraging the well-trained English NER system. This is achieved by way of BAN, which is a simple but extensible approach. It can transfer information between different languages. Empirical experiments show that, on small datasets, our approach can lead to significant improvement on the performance. This property is of great practical importance for low-resource languages. In future work, we plan to extend our method on other NLP tasks, e.g., relation extraction, coreference resolution. Figure 1: The architecture of BAN. The source sentences are translated into English and recorded the attention weights. Then the sentences are put into English NER model. After acquiring the outputs of BiLSTM in the English model, we use back attention mechanism to obtain transfer knowledge to aid in generation of the result. Table 1: statistic of sentences Table 2: Evaluation on low-resource NER Table 3: Evaluation on OntoNotes 4.0 Table 4: Evaluation on Weibo NER Table 5: Comparison of different embeddings	German, Spanish, Chinese
Which is the best description of how Julia reacted to the narrator trying to take her bag? A. She was surprised enough by the request that she wasn't quite sure how to react B. She was unsettled because a strange man had approached her trying to take her things C. She was nervous because she thought the narrator had figured out her plan and the existence of the bomb D. She was frustrated with him for further delaying her already postponed trip	Nuts to wild talents! Mine was no satisfaction, never earned me a penny—and now it had me fighting for my life in ... THE LITTLE RED BAG By JERRY SOHL [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, January 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] About an hour out of San Francisco on the flight to Los Angeles, I made the discovery. I had finished reading the Chronicle , folded and put it beside me, turned and looked out the window, expecting to see the San Joaquin Valley but finding only a sea of clouds instead. So I returned my attention to the inside of the plane, to the overstuffed gray-haired woman asleep beside me, to the backs of heads in seats before me, across the aisle to other heads, and down to the blonde. I had seen her in the concourse and at the gate, a shapely thing. Now she had crossed her legs and I was privileged to view a trim ankle and calf, and her profile as she stared moodily across the aisle and out a window where there was nothing to see. I slid my eyes past her to others. A crossword-puzzle worker, a togetherness-type-magazine reader. Inventory completed, I went back to looking at the clouds, knowing I should be thinking about the printing order I was going to Los Angeles for, and not wanting to. So I started going through the purse of the woman next to me. Perhaps that sounds bad. It wasn't. I'd been doing it for years and nobody ever complained. It started when I was a kid, this business of being able to explore the insides of things like purses and sealed boxes and locked drawers and—well, human beings. But human beings aren't worth the trouble. It's like swimming through spaghetti. And I've got to stay away from electric wires. They hurt. Now don't ask me how they hurt. Maybe you think it's fun. For the most part, it really isn't. I always knew what was in Christmas presents before I unwrapped them, and therefore Christmas was always spoiled for me as a kid. I can't feel the color of anything, just its consistency. An apple senses about the same as a potato, except for the core and the stem. I can't even tell if there's writing on a piece of paper. So you see it isn't much. Just the feel of shapes, the hardnesses and softnesses. But I've learned to become pretty good at guessing. Like this woman next to me. She had a short, cylindrical metal object in her purse with waxlike stuff inside it—a lipstick. A round, hard object with dust inside—a compact. Handkerchief, chewing gum, a small book, probably an address book, money in a change purse—a few bills and coins. Not much else. I was a little disappointed. I've run across a gun or two in my time. But I never say anything. I learned the wisdom of keeping my mouth shut in the fourth grade when Miss Winters, a stern, white-haired disciplinarian, ordered me to eat my sack lunch in the classroom with her instead of outside with some of the other kids. This was the punishment for some minor infraction. Lunchtime was nearly over and we'd both finished eating; she said she'd be gone for a few moments and that I was to erase the blackboard during her absence, which I dutifully did. Class had hardly resumed when she started looking around the desk for her favorite mechanical pencil, asking if any of us had seen it, and looking straight at me. I didn't want her to think I had taken it while she was out of the room, so I probed the contents of her purse, which she always kept in the upper right drawer of her desk. "It's in your purse," I blurted out. I was sent home with a stinging note. Since then I've kept quiet. At one time I assumed everybody was able to sense. I've known better for years. Still, I wonder how many other people are as close-mouthed about their special gift as I am about mine. I used to think that some day I'd make a lot of money out of it, but how? I can't read thoughts. I can't even be sure what some of the things I sense in probing really are. But I've learned to move things. Ever so little. A piece of paper. A feather. Once I stopped one of those little glass-enclosed light or heat-powered devices with vanes you see now and then in a jeweler's window. And I can stop clocks. Take this morning, for example. I had set my alarm for five-thirty because I had to catch the seven o'clock plane at San Francisco International Airport. This being earlier than I usually get up, it seems all I did during the night was feel my way past the escapement and balance wheel to see where the notch for the alarm was. The last time I did it there was just the merest fraction of an inch between the pawl and the notch. So I sighed and moved to the balance wheel and its delicate ribbon of spiraling steel. I hung onto the wheel, exerting influence to decrease the restoring torque. The wheel slowed down until there was no more ticking. It took quite a bit of effort, as it always does, but I did it, as I usually do. I can't stand the alarm. When I first learned to do this, I thought I had it made. I even went to Las Vegas to try my hand, so to speak, with the ratchets and pawls and cams and springs on the slot machines. But there's nothing delicate about a slot machine, and the spring tensions are too strong. I dropped quite a lot of nickels before I finally gave up. So I'm stuck with a talent I've found little real use for. Except that it amuses me. Sometimes. Not like this time on the plane. The woman beside me stirred, sat up suddenly and looked across me out the window. "Where are we?" she asked in a surprised voice. I told her we were probably a little north of Bakersfield. She said, "Oh," glanced at her wristwatch and sank back again. Soon the stewardesses would bring coffee and doughnuts around, so I contented myself with looking at the clouds and trying to think about Amos Magaffey, who was purchasing agent for a Los Angeles amusement chain, and how I was going to convince him our printing prices were maybe a little higher but the quality and service were better. My mind wandered below where I was sitting, idly moving from one piece of luggage to another, looking for my beat-up suitcase. I went through slips and slippers, lingerie and laundry, a jig saw puzzle and a ukulele. I never did find my suitcase because I found the bomb first. The bomb was in a small bag—a woman's bag judging by the soft, flimsy things you'd never find in a man's—and I didn't know it was a bomb right away. I thought it was just a clock, one of those small, quiet alarms. I was going to pass it by and go on, but what held me was that something was taped to it. By the feel, I knew it must be electrician's tape. Interested and curious, I explored the clock more closely, found two wires. One went to a battery and the other to hard round cylinders taped together. The hairs stood up at the base of my neck when I suddenly realized what it was. The clock's balance wheel was rocking merrily. Quickly I went up past the train of gears to the alarm wheel. If this was anything like my own alarm clock, this one had something like ten minutes to go. It was forty minutes to Burbank and Lockheed Air Terminal. My mind was churning when I turned from the window to look around at the unconcerned passengers, the woman at my side asleep again. I thought: Which one of these.... No, none of them would know it was there. I glanced out the window again; clouds were still in the way. We'd be leaving the valley for the mountain range north of Los Angeles soon, if we hadn't left it already. No place to land the plane there. But of course that had been the plan! My heart was beating in jackhammer rhythm; my mouth was dry and my mind was numb. Tell somebody about the bomb before it's too late! No, they'd think I put it there. Besides, what good would it do? There would be panic and they'd never get the plane down in time—if they believed me. "Sir." My head jerked around. The stewardess stood in the aisle, smiling, extending a tray to me, a brown plastic tray bearing a small paper cup of tomato juice, a cup of coffee, a cellophane-wrapped doughnut, paper spoon, sugar and dehydrated cream envelopes, and a napkin. I goggled at her, managed to croak, "No, thanks." She gave me an odd look and moved along. My seatmate had accepted hers and was tearing at the cellophane. I couldn't bear to watch her. I closed my eyes, forced my mind back to the luggage compartment, spent a frantic moment before I found the bag again. I had to stop that balance wheel, just as I stopped my alarm clock every morning. I tried to close everything off—the throb of engines, the rush of air, the woman sipping coffee noisily beside me—and I went into the clock and surrounded the seesawing wheel. When it went forward, I pulled it back; when it went back, I pulled it forward. I struggled with it, and it was like trying to work with greasy hands, and I was afraid I wasn't going to be able to stop it. Then, little by little, it started to slow its beat. But I could not afford to relax. I pushed and pulled and didn't dare release my hold until it came to a dead stop. "Anything the matter?" My eyelids flew open and I looked into the eyes of the woman next to me. There was sugar from the doughnut around her mouth and she was still chewing. "No," I said, letting out my breath. "I'm all right." "You were moaning, it sounded like. And you kept moving your head back and forth." "Must have been dreaming," I said as I rang for the stewardess. When she came I told her I'd take some of that coffee now. No, nothing else, just coffee. I didn't tell her how much I needed it. I sat there clammy with sweat until she returned. Coffee never tasted so good. All right, so I had stopped the bomb's timer. My mind raced ahead to the landing. When they unloaded the luggage, the balance wheel would start again. I wouldn't be able to stay with it, keeping it still. I considered telling the authorities as soon as we landed, or maybe calling in ahead, but wouldn't that just bring suspicion, questions. Maybe I could convince them I could stop a clock—but not before the bomb exploded. And then what? My secret would be out and my life would be changed. I'd be a man not to be trusted, a prying man, a man literally with gimlet eyes. Mountain crags jutted through the clouds. We were in the range north of the city. Here and there were clear spots and I could see roads below, but there were also clouds far above us. It was very beautiful, but it was also very bumpy, and we started to slip and slide. To my horror I found that the balance wheel was rocking again. Closing my eyes and gritting my teeth, I forced my senses to the wheel, tugging and pulling and shoving and pushing until it finally stopped. A jab in the shoulder. I jumped, startled. "Your cup," my seat partner said, pointing. I looked down at the coffee cup I had crushed in my hands. Then I looked up into the eyes of the stewardess. I handed it to her. She took it without a word and went away. "Were you really asleep that time?" "Not really," I said. I was tempted to tell the woman I was subject to fits, but I didn't. It was only a few minutes to landing, but they became the longest minutes of my life as time after time I stopped the rocking wheel when the plane dipped and bumped to a landing. Leaving the apron with the other passengers, I tried to walk as unconcernedly as they through the exit gate. I would have liked walking through the terminal and out the entrance and away, but I could not. I had my suitcase to get, for one thing. The damned bomb was the other. So I strolled out into the concourse again to look at the plane and watch the baggagemen at work, transferring the luggage to two airfield carts. They weren't as careful as I would have been. It was impossible to tell from this distance just which bag contained the bomb; I could hardly identify my own scarred suitcase. The assortment of bags—a strange conglomeration of sizes and colors—was packed in some places six deep, and it rolled toward the gate where I was standing. I didn't know whether to stay or run, imagining the balance wheel now happily rocking again. The load went past me down a ramp to the front of the air terminal where the luggage was unloaded and placed in a long rack. I went with it. There was a flurry of ticket matching, hands grabbing for suitcases, and a general exodus on the part of my fellow passengers, too fast to determine who had got the one with the bomb. Now all that was left was the attendant and I had two bags—my own battered veteran of years, and a fine new red overnight case, small enough to be the one. I lit a cigarette, reached out. Inside were a woman's things and—a clock. The escapement was clicking vigorously. I didn't moan this time. I just closed my eyes, stretched toward and grabbed the balance wheel I was getting to know like my own. I entered into a union with it so strong that after I had reduced it to immobility, it was like waking when I opened my eyes. The baggage claim attendant was staring at me. For only a moment I stared back. Then I quickly reached for my baggage check and presented it to him. His hand hovered over the handle of the little red bag and I was ready to yell at him. But then, matching numbers on the tags with his eyes, his hand grasped the handle of my own suitcase and pushed it toward me. "Thanks," I said, taking it. I glanced ever so casually toward the remaining bag. "One left over, eh?" "Yeah." He was so bored I was tempted to tell him what was in it. But he was eying me with a "well-why-don't-you-get-along?" look. I said, "What happens if nobody claims it?" "Take it inside. Why?" He was getting too curious. "Oh, I just wondered, that's all." I stepped on my cigarette and walked toward the air terminal entrance and put my suitcase on the stone steps there. A redcap came hurrying over. "Cab?" I shook my head. "Just waiting." Just waiting for somebody to pick up a bomb. I lit another cigarette and glanced now and then toward the baggage claim area. The red bag was still there. All sorts of theories ran through my head as to why it should still be there, and none satisfied me. I should not have been there, that much I knew; I should be with a man named Amos Magaffey on Sixth Street at ten o'clock, discussing something very mundane, the matter of a printing order. But what could I do? If I left the airport, the attendant would eventually take the bag inside and there would be an explosion, and I wouldn't be able to live with myself. No. I had to stay to keep the balance wheel stationary until—until what? A man in tan gabardine, wearing a police cap and badge, walked out of the entrance to stand on the stone steps beside me while he put on a pair of dark glasses. A member of the airport police detail. I could tell him. I could take him down to the little red bag and explain the whole thing. Then it would be his baby and I would be off on my own business. But he moved on down the steps, nodded at the redcap, and started across the street to the parking area. I could have called to him, "Hey, officer, let me tell you about a bomb in a little red bag." But I didn't. I didn't because I caught a movement at the baggage claim counter out of the side of my eye. The attendant had picked up the bag and was walking with it up the ramp to the rear of the air terminal. Picking up my own suitcase, I went inside in time to see him enter through a side door and deposit the bag on the scales at the airline desk and say something to the clerk. The clerk nodded and moved the bag to the rear room. I could visualize the balance wheel once again rocking like crazy. How many minutes—or seconds—were left? I was sweating when I moved to the counter, and it wasn't because of the sunshine I'd been soaking in. I had to get as close to the bag as I could if I was going to stop the clock again. "Can I help you?" the clerk asked. "No. I'm waiting for someone." I turned my back to him, put down my suitcase, leaned against the counter and reached out for the wheel. I found I could reach the device, but it was far away. When I tried to dampen it, the wheel escaped my grasp. "Do you have my suitcase?" I blinked my eyes open and looked around. The blonde in the plane stood there looking very fresh and bright and unconcerned. In her right hand she had a green baggage claim check. The clerk took it, nodded, and in a moment brought out the overnight case and set it on the scales. The girl thanked him, picked it up, glanced at me indifferently, and then started for the entrance with it. "Just a moment," I found myself saying, grabbing my bag and hurrying after her. At her side and a little ahead of her, I said, "Listen to me." She looked annoyed and increased her stride toward the door. "It's a matter of life or death," I said. I wanted to wrest the bag from her and hurl it out through the doorway into the street, but I restrained myself. She stopped and stared. I noticed a short, fat man in a rumpled suitcoat and unpressed pants staring, too. Ignoring him, I said, "Please put the bag down. Over there." I indicated a spot beside a telephone booth where it would be out of the way. She didn't move. She just said, "Why?" "For God's sake!" I took the case. She offered no resistance. I put her bag and mine next to the booth. When I turned around she was standing there looking at me as if I had gone out of my mind. Her eyes were blue and brown-flecked, very pretty eyes, and my thought at the moment was, I'm glad the bomb didn't go off; these eyes wouldn't be looking at me or anything else right now if it had. "I've got to talk to you. It's very important." The girl said, "Why?" I was beginning to think it was the only word she knew. At the same time I was wondering why anyone would want to kill someone so lovely. "I'll explain in a moment. Please stand right here while I make a telephone call." I moved toward the phone booth, paused and said, "And don't ask me why." She gave me a speculative look. I must not have seemed a complete idiot because she said, "All right, but—" I didn't listen for the rest. I went into the booth, closed the door, pretended to drop a coin and dial a number. But all the time I was in there, I was reaching out through the glass for the clock. At this range it wasn't difficult to stop the balance wheel. Just the same, when I came out I was wringing wet. "Now will you please tell me what this is all about?" she said stiffly. "Gladly. Let me buy you a cup of coffee and I'll explain." She glanced at the bags. I told her they'd be all right. We followed the short, fat man into the coffee shop. Over coffee I explained it all to her, how I had this extrasensory ability, how she was the first person I had ever revealed it to, and how I had discovered what was in her overnight bag. During the telling, her untouched coffee grew a skin, her face grew pale, her eyes grew less curious and more troubled. There were tears there when I finished. I asked her who put the bomb in her bag. "Joe did," she said in a toneless voice, not looking at me any more but staring vacantly across the room. "Joe put it there." Behind her eyes she was reliving some recent scene. "Who is Joe?" "My husband." I thought she was going to really bawl, but she got control again. "This trip was his idea, my coming down here to visit my sister." Her smile was bleak. "I see now why he wanted to put in those books. I'd finished packing and was in the bathroom. He said he'd put in some books we'd both finished reading—for my sister. That's when he must have put the—put it in there." I said gently, "Why would he want to do a thing like that?" "I don't know." She shook her head. "I just don't know." And she was close to bawling again. Then she recovered and said, "I'm not sure I want to know." I admired her for saying it. Joe must have been crazy. "It's all right now?" she asked. I nodded. "As long as we don't move it." I told her I didn't know how much more time there was, that I'd been thinking it over and that the only way out seemed to be to tell the airport policeman. After I explained it to her, the girl—she said her name was Julia Claremont—agreed to tell him she thought there was a bomb in her bag, that she had noticed a ticking and had become worried because she knew she hadn't packed a clock. It wasn't good, but it would have to do. "We've got to get it deactivated," I said, watching the fat man pay for his coffee and leave. "The sooner the better." I finished my coffee in one gulp and went to pay the bill with her. I asked her why she didn't claim the bag at the same time the other people had. She said she had called her sister and the phone was busy for a long while. "She was supposed to meet me, and when she wasn't here, I got worried. She said she isn't feeling well and asked me to take a cab." She smiled a little. It was a bright, cheery thing. I had the feeling it was all for me. "That's where I was going when you caught up with me." It had become a very nice day. But the bottom dropped out of it again when we reached the lobby. The two bags weren't there. I ran to the entrance and nearly collided with the redcap. "See anybody go out of here with a little red bag and an old battered suitcase?" "Bag? Suitcase?" he mumbled. Then he became excited. "Why, a man just stepped out of here—" He turned to look down the street. "That's him." The dumpy man I'd seen was walking off; Julia's bag in his right hand, mine in his left. He seemed in no hurry. "Hey!" I shouted, starting toward him. The man turned, took one look at me, and started to run. He came abreast an old gray, mud-spattered coupe, ran around, opened the door and threw both bags into the rear seat as he got in. The car was a hundred feet away and gathering speed by the time I reached where it had been parked. I watched it for a moment, then walked back to the entranceway where Julia was standing with the redcap, who said, "That man steal them suitcases?" "That he did," I said. Just then the airport policeman started across the street from the parking lot. Redcap said, "Better tell him about it." The policeman was sympathetic and concerned. He said, "We'd better get over to the office." But we never left the spot because an explosion some blocks distant shattered the air. Julia's hand grasped my arm. Hard. "Jets," the redcap said, eying the sky. "I don't know," the policeman said. "Didn't sound much like a jet to me." We stood there. I could visualize the wreckage of an old gray coupe in the middle of a street, but I couldn't visualize the driver. That was all right. I didn't want to see him. I didn't know what Julia was thinking. She said, "About those bags," and looked at me. The officer said, "Yes, miss?" "I—I don't care about mine. I didn't have much of anything in it." "I feel the same way," I said. "Would it be all right if we didn't bother to report it?" "Well," the policeman said, "I can't make you report it." "I'd rather not then," Julia said. She turned to me. "I'd like some air. Can't we walk a little?" "Sure," I said. We started down the street, her arm in mine, as the air began to fill with the distant sounds of sirens.	A. She was surprised enough by the request that she wasn't quite sure how to react
What can be inferred about the personality of Grannie Annie from the passage? A. She's fearless and quick-witted B. She's fearful and hard to work with C. She lacks the experience that she claims she has for her novels D. She lacks the knowledge that is needed for her novels	DOUBLE TROUBLE by CARL JACOBI Grannie Annie, that waspish science-fiction writer, was in a jam again. What with red-spot fever, talking cockatoos and flagpole trees, I was running in circles—especially since Grannie became twins every now and then. [Transcriber's Note: This etext was produced from Planet Stories Spring 1945. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] We had left the offices of Interstellar Voice three days ago, Earth time, and now as the immense disc of Jupiter flamed across the sky, entered the outer limits of the Baldric. Grannie Annie strode in the lead, her absurd long-skirted black dress looking as out of place in this desert as the trees. Flagpole trees. They rose straight up like enormous cat-tails, with only a melon-shaped protuberance at the top to show they were a form of vegetation. Everything else was blanketed by the sand and the powerful wind that blew from all quarters. As we reached the first of those trees, Grannie came to a halt. "This is the Baldric all right. If my calculations are right, we've hit it at its narrowest spot." Ezra Karn took a greasy pipe from his lips and spat. "It looks like the rest of this God-forsaken moon," he said, "'ceptin for them sticks." Xartal, the Martian illustrator, said nothing. He was like that, taciturn, speaking only when spoken to. He could be excused this time, however, for this was only our third day on Jupiter's Eighth Moon, and the country was still strange to us. When Annabella C. Flowers, that renowned writer of science fiction, visiphoned me at Crater City, Mars, to meet her here, I had thought she was crazy. But Miss Flowers, known to her friends as Grannie Annie, had always been mildly crazy. If you haven't read her books, you've missed something. She's the author of Lady of the Green Flames , Lady of the Runaway Planet , Lady of the Crimson Space-Beast , and other works of science fiction. Blood-and-thunder as these books are, however, they have one redeeming feature—authenticity of background. Grannie Annie was the original research digger-upper, and when she laid the setting of a yarn on a star of the sixth magnitude, only a transportation-velocity of less than light could prevent her from visiting her "stage" in person. Therefore when she asked me to meet her at the landing field of Interstellar Voice on Jupiter's Eighth Moon, I knew she had another novel in the state of embryo. What I didn't expect was Ezra Karn. He was an old prospector Grannie had met, and he had become so attached to the authoress he now followed her wherever she went. As for Xartal, he was a Martian and was slated to do the illustrations for Grannie's new book. Five minutes after my ship had blasted down, the four of us met in the offices of Interstellar Voice . And then I was shaking hands with Antlers Park, the manager of I. V. himself. "Glad to meet you," he said cordially. "I've just been trying to persuade Miss Flowers not to attempt a trip into the Baldric." "What's the Baldric?" I had asked. Antlers Park flicked the ash from his cheroot and shrugged. "Will you believe me, sir," he said, "when I tell you I've been out here on this forsaken moon five years and don't rightly know myself?" I scowled at that; it didn't make sense. "However, as you perhaps know, the only reason for colonial activities here at all is because of the presence of an ore known as Acoustix. It's no use to the people of Earth but of untold value on Mars. I'm not up on the scientific reasons, but it seems that life on the red planet has developed with a supersonic method of vocal communication. The Martian speaks as the Earthman does, but he amplifies his thoughts' transmission by way of wave lengths as high as three million vibrations per second. The trouble is that by the time the average Martian reaches middle age, his ability to produce those vibrations steadily decreases. Then it was found that this ore, Acoustix, revitalized their sounding apparatus, and the rush was on." "What do you mean?" Park leaned back. "The rush to find more of the ore," he explained. "But up until now this moon is the only place where it can be found. "There are two companies here," he continued, " Interstellar Voice and Larynx Incorporated . Chap by the name of Jimmy Baker runs that. However, the point is, between the properties of these two companies stretches a band or belt which has become known as the Baldric. "There are two principal forms of life in the Baldric; flagpole trees and a species of ornithoid resembling cockatoos. So far no one has crossed the Baldric without trouble." "What sort of trouble?" Grannie Annie had demanded. And when Antlers Park stuttered evasively, the old lady snorted, "Fiddlesticks, I never saw trouble yet that couldn't be explained. We leave in an hour." So now here we were at the outer reaches of the Baldric, four travelers on foot with only the barest necessities in the way of equipment and supplies. I walked forward to get a closer view of one of the flagpole trees. And then abruptly I saw something else. A queer-looking bird squatted there in the sand, looking up at me. Silver in plumage, it resembled a parrot with a crest; and yet it didn't. In some strange way the thing was a hideous caricature. "Look what I found," I yelled. "What I found," said the cockatoo in a very human voice. "Thunder, it talks," I said amazed. "Talks," repeated the bird, blinking its eyes. The cockatoo repeated my last statement again, then rose on its short legs, flapped its wings once and soared off into the sky. Xartal, the Martian illustrator, already had a notebook in his hands and was sketching a likeness of the creature. Ten minutes later we were on the move again. We saw more silver cockatoos and more flagpole trees. Above us, the great disc of Jupiter began to descend toward the horizon. And then all at once Grannie stopped again, this time at the top of a high ridge. She shielded her eyes and stared off into the plain we had just crossed. "Billy-boy," she said to me in a strange voice, "look down there and tell me what you see." I followed the direction of her hand and a shock went through me from head to foot. Down there, slowly toiling across the sand, advanced a party of four persons. In the lead was a little old lady in a black dress. Behind her strode a grizzled Earth man in a flop-brimmed hat, another Earth man, and a Martian. Detail for detail they were a duplicate of ourselves! "A mirage!" said Ezra Karn. But it wasn't a mirage. As the party came closer, we could see that their lips were moving, and their voices became audible. I listened in awe. The duplicate of myself was talking to the duplicate of Grannie Annie, and she was replying in the most natural way. Steadily the four travelers approached. Then, when a dozen yards away, they suddenly faded like a negative exposed to light and disappeared. "What do you make of it?" I said in a hushed voice. Grannie shook her head. "Might be a form of mass hypnosis superinduced by some chemical radiations," she replied. "Whatever it is, we'd better watch our step. There's no telling what might lie ahead." We walked after that with taut nerves and watchful eyes, but we saw no repetition of the "mirage." The wind continued to blow ceaselessly, and the sand seemed to grow more and more powdery. For some time I had fixed my gaze on a dot in the sky which I supposed to be a high-flying cockatoo. As that dot continued to move across the heavens in a single direction, I called Grannie's attention to it. "It's a kite," she nodded. "There should be a car attached to it somewhere." She offered no further explanation, but a quarter of an hour later as we topped another rise a curious elliptical car with a long slanting windscreen came into view. Attached to its hood was a taut wire which slanted up into the sky to connect with the kite. A man was driving and when he saw us, he waved. Five minutes later Grannie was shaking his hand vigorously and mumbling introductions. "This is Jimmy Baker," she said. "He manages Larynx Incorporated , and he's the real reason we're here." I decided I liked Baker the moment I saw him. In his middle thirties, he was tall and lean, with pleasant blue eyes which even his sand goggles could not conceal. "I can't tell you how glad I am you're here, Grannie," he said. "If anybody can help me, you can." Grannie's eyes glittered. "Trouble with the mine laborers?" she questioned. Jimmy Baker nodded. He told his story over the roar of the wind as we headed back across the desert. Occasionally he touched a stud on an electric windlass to which the kite wire was attached. Apparently these adjustments moved planes or fins on the kite and accounted for the car's ability to move in any direction. "If I weren't a realist, I'd say that Larynx Incorporated has been bewitched," he began slowly. "We pay our men high wages and give them excellent living conditions with a vacation on Callisto every year. Up until a short time ago most of them were in excellent health and spirits. Then the Red Spot Fever got them." "Red Spot Fever?" Grannie looked at him curiously. Jimmy Baker nodded. "The first symptoms are a tendency to garrulousness on the part of the patient. Then they disappear." He paused to make an adjustment of the windlass. "They walk out into the Baldric," he continued, "and nothing can stop them. We tried following them, of course, but it was no go. As soon as they realize they're being followed, they stop. But the moment our eyes are turned, they give us the slip." "But surely you must have some idea of where they go," Grannie said. Baker lit a cigarette. "There's all kinds of rumors," he replied, "but none of them will hold water. By the way, there's a cockatoo eyrie ahead of us." I followed his gaze and saw a curious structure suspended between a rude circle of flagpole trees. A strange web-like formation of translucent gauzy material, it was. Fully two hundred cockatoos were perched upon it. They watched us with their mild eyes as we passed, but they didn't move. After that we were rolling up the driveway that led to the offices of Larynx Incorporated . As Jimmy Baker led the way up the inclined ramp, a door in the central building opened, and a man emerged. His face was drawn. "Mr. Baker," he said breathlessly, "seventy-five workers at Shaft Four have headed out into the Baldric." Baker dropped his cigarette and ground his heel on it savagely. "Shaft Four, eh?" he repeated. "That's our principal mine. If the fever spreads there, I'm licked." He motioned us into his office and strode across to a desk. Silent Xartal, the Martian illustrator, took a chair in a corner and got his notebook out, sketching the room's interior. Grannie Annie remained standing. Presently the old lady walked across to the desk and helped herself to the bottle of Martian whiskey there. "There must be ways of stopping this," she said. "Have you called in any physicians? Why don't you call an enforced vacation and send the men away until the plague has died down?" Baker shook his head. "Three doctors from Callisto were here last month. They were as much at loss as I am. As for sending the men away, I may have to do that, but when I do, it means quits. Our company is chartered with Spacolonial, and you know what that means. Failure to produce during a period of thirty days or more, and you lose all rights." A visiphone bell sounded, and Baker walked across to the instrument. A man's face formed in the vision plate. Baker listened, said "Okay" and threw off the switch. "The entire crew of Shaft Four have gone out into the Baldric," he said slowly. There was a large map hanging on the wall back of Baker's desk. Grannie Annie walked across to it and began to study its markings. "Shaft Four is at the outer edge of the Baldric at a point where that corridor is at its widest," she said. Baker looked up. "That's right. We only began operations there a comparatively short time ago. Struck a rich vein of Acoustix that runs deep in. If that vein holds out, we'll double the output of Interstellar Voice , our rival, in a year." Grannie nodded. "I think you and I and Xartal had better take a run up there," she said. "But first I want to see your laboratory." There was no refusing her. Jimmy Baker led the way down to a lower level where a huge laboratory and experimental shop ran the length of the building. Grannie seized a light weight carry-case and began dropping articles into it. A pontocated glass lens, three or four Wellington radite bulbs, each with a spectroscopic filament, a small dynamo that would operate on a kite windlass, and a quantity of wire and other items. The kite car was brought out again, and the old woman, Baker and the Martian took their places in it. Then Jimmy waved, and the car began to roll down the ramp. Not until they had vanished in the desert haze did I sense the loneliness of this outpost. With that loneliness came a sudden sense of foreboding. Had I been a fool to let Grannie go? I thought of her, an old woman who should be in a rocking chair, knitting socks. If anything happened to Annabella C. Flowers, I would never forgive myself and neither would her millions of readers. Ezra Karn and I went back into the office. The old prospector chuckled. "Dang human dynamo. Got more energy than a runaway comet." A connecting door on the far side of the office opened onto a long corridor which ended at a staircase. "Let's look around," I said. We passed down the corridor and climbed the staircase to the second floor. Here were the general offices of Larynx Incorporated , and through glass doors I could see clerks busy with counting machines and report tapes. In another chamber the extremely light Acoustix ore was being packed into big cases and marked for shipment. At the far end a door to a small room stood open. Inside a young man was tilted back in a swivel chair before a complicated instrument panel. "C'mon in," he said, seeing us. "If you want a look at your friends, here they are." He flicked a stud, and the entire wall above the panel underwent a slow change of colors. Those colors whirled kaleidescopically, then coalesced into a three-dimensional scene. It was a scene of a rapidly unfolding desert country as seen from the rear of a kite car. Directly behind the windscreen, backs turned to me, were Jimmy Baker, Grannie, and Xartal. It was as if I were standing directly behind them. "It's Mr. Baker's own invention," the operator said. "An improvement on the visiphone." "Do you mean to say you can follow the movements of that car and its passengers wherever it goes? Can you hear them talk too?" "Sure." The operator turned another dial, and Grannie's falsetto voice entered the room. It stopped abruptly. "The machine uses a lot of power," the operator said, "and as yet we haven't got much." The cloud of anxiety which had wrapped itself about me disappeared somewhat as I viewed this device. At least I could now keep myself posted of Grannie's movements. Karn and I went down to the commissary where we ate our supper. When we returned to Jimmy Baker's office, the visiphone bell was ringing. I went over to it and turned it on, and to my surprise the face of Antlers Park flashed on the screen. "Hello," he said in his friendly way. "I see you arrived all right. Is Miss Flowers there?" "Miss Flowers left with Mr. Baker for Shaft Four," I said. "There's trouble up there. Red spot fever." "Fever, eh?" repeated Park. "That's a shame. Is there anything I can do?" "Tell me," I said, "has your company had any trouble with this plague?" "A little. But up until yesterday the fever's been confined to the other side of the Baldric. We had one partial case, but my chemists gave the chap an antitoxin that seems to have worked. Come to think of it, I might drive over to Shaft Four and give Jimmy Baker the formula. I haven't been out in the Baldric for years, but if you didn't have any trouble, I shouldn't either." We exchanged a few more pleasantries, and then he rang off. In exactly an hour I went upstairs to the visiscreen room. Then once more I was directly behind my friends, listening in on their conversation. The view through the windscreen showed an irregular array of flagpole trees, with the sky dotted by high-flying cockatoos. "There's an eyrie over there," Jimmy Baker was saying. "We might as well camp beside it." Moments later a rude circle of flagpole trees loomed ahead. Across the top of them was stretched a translucent web. Jimmy and Grannie got out of the car and began making camp. Xartal remained in his seat. He was drawing pictures on large pieces of pasteboard, and as I stood there in the visiscreen room, I watched him. There was no doubt about it, the Martian was clever. He would make a few rapid lines on one of the pasteboards, rub it a little to get the proper shading and then go on to the next. In swift rotation likenesses of Ezra Karn, of myself, of Jimmy Baker, and of Antlers Park took form. Ezra spoke over my shoulder. "He's doing scenes for Grannie's new book," he said. "The old lady figures on using the events here for a plot. Look at that damned nosy bird! " A silver cockatoo had alighted on the kite car and was surveying curiously Xartal's work. As each drawing was completed, the bird scanned it with rapt attention. Abruptly it flew to the top of the eyrie, where it seemed to be having a consultation with its bird companions. And then abruptly it happened. The cockatoos took off in mass flight. A group of Earth people suddenly materialized on the eyrie, talking and moving about as if it were the most natural thing in the world. With a shock I saw the likeness of myself; I saw Ezra Karn; and I saw the image of Jimmy Baker. The real Jimmy Baker stood next to Grannie, staring up at this incredible mirage. Grannie let out a whoop. "I've got it!" she said. "Those things we see up there are nothing more than mental images. They're Xartal's drawings!" "Don't you see," the lady continued. "Everything that Xartal put on paper has been seen by one or more of these cockatoos. The cockatoos are like Earth parrots all right, but not only have they the power of copying speech, they also have the ability to recreate a mental image of what they have seen. In other words their brains form a powerful photographic impression of the object. That impression is then transmitted simultaneously in telepathic wavelengths to common foci. That eyrie might be likened to a cinema screen, receiving brain vibrations from a hundred different sources that blend into the light field to form what are apparently three-dimensional images." The Larynx manager nodded slowly. "I see," he said. "But why don't the birds reconstruct images from the actual person. Why use drawings?" "Probably because the drawings are exaggerated in certain details and made a greater impression on their brains," Grannie replied. Up on the eyrie a strange performance was taking place. The duplicate of Grannie Annie was bowing to the duplicate of Jimmy Baker, and the image of Ezra Karn was playing leap frog with the image of Antlers Park. Then abruptly the screen before me blurred and went blank. "Sorry," the operator said. "I've used too much power already. Have to give the generators a chance to build it up again." Nodding, I turned and motioned to Karn. We went back downstairs. "That explains something at any rate," the old prospector said. "But how about that Red spot fever?" On Jimmy Baker's desk was a large file marked: FEVER VICTIMS. I opened it and found it contained the case histories of those men who had been attacked by the strange malady. Reading them over, I was struck by one detail. Each patient had received the first symptoms, not while working in the mines, but while sleeping or lounging in the barracks. Five minutes later Karn and I were striding down a white ramp that led to the nearest barracks. The building came into sight, a low rectangular structure, dome-roofed to withstand the violent winds. Inside double tiers of bunks stretched along either wall. In those bunks some thirty men lay sleeping. The far wall was taken up by a huge window of denvo-quartz. As I stood there, something suddenly caught Ezra Karn's eye. He began to walk toward that window. "Look here," he said. Six feet up on that window a small almost imperceptible button of dull metal had been wedged into an aperture cut in the quartz. The central part of the button appeared to be a powerful lens of some kind, and as I seized it and pulled it loose, I felt the hum of tiny clock work. All at once I had it! Red spot fever. Heat fever from the infra-red rays of Jupiter's great spot. Someone had constructed this lens to concentrate and amplify the power of those rays. The internal clockwork served a double purpose. It opened a shutter, and it rotated the lens slowly so that it played for a time on each of the sleeping men. I slid the metal button in my pocket and left the barracks at a run. Back in the visiscreen room, I snapped to the operator: "Turn it on!" The kite car swam into view in the screen above the instrument panel. I stared with open eyes. Jimmy Baker no longer was in the car, nor was Xartal, the Martian. Grannie Annie was there, but seated at the controls was Antlers Park, the manager of Interstellar Voice. Ezra Karn jabbed my elbow. "Grannie's coming back. I thought she'd be getting sick of this blamed moon." It didn't make sense. In all the years I'd known Annabella C. Flowers, never yet had I seen her desert a case until she had woven the clues and facts to a logical conclusion. "Ezra," I said, "we're going to drive out and meet them. There's something screwy here." Ten minutes later in another kite car we were driving at a fast clip through the powdery sands of the Baldric. And before long we saw another car approaching. It was Grannie. As the car drew up alongside I saw her sitting in her prim way next to Antlers Park. Park said: "We left the others at the mine. Miss Flowers is going back with me to my offices to help me improve the formula for that new antitoxin." He waved his hand, and the car moved off. I watched it as it sped across the desert, and a growing suspicion began to form in my mind. Then, like a knife thrust, the truth struck me. "Ezra!" I yelled, swinging the car. "That wasn't Grannie! That was one of those damned cockatoo images. We've got to catch him." The other car was some distance ahead now. Park looked back and saw us following. He did something to the kite wire, and his car leaped ahead. I threw the speed indicator hard over. Our kite was a huge box affair with a steady powerful pull to the connecting wire. Park's vehicle was drawn by a flat triangular kite that dove and fluttered with each variance of the wind. Steadily we began to close in. The manager of Interstellar Voice turned again, and something glinted in his hand. There was a flash of purple flame, and a round hole appeared in our windscreen inches above Karn's head. "Heat gun!" Ezra yelled. Now we were rocketing over the sand dunes, winding in and out between the flagpole trees. I had to catch that car I told myself. Grannie Annie's very life might be at stake, not to mention the lives of hundreds of mine workers. Again Park took aim and again a hole shattered our windscreen. The wind shifted and blew from another quarter. The box kite soared, but the triangular kite faltered. Taking advantage of Park's loss of speed, I raced alongside. The I. V. manager lifted his weapon frantically. But before he could use it a third time, Ezra Karn had whipped a lariat from his belt and sent it coiling across the intervening space. The thong yanked tight about the manager's throat. Park did the only thing he could do. He shut off power, and the two cars coasted to a halt. Then I was across in the other seat, wrenching the weapon free from his grasp. "What have you done with Miss Flowers?" I demanded. The manager's eyes glittered with fear as he saw my finger tense on the trigger. Weakly he lifted an arm and pointed to the northwest. "Val-ley. Thir-ty miles. Entrance hidden by wall of ... flagpole trees." I leaped into the driver's seat and gave the kite its head. And now the country began to undergo a subtle change. The trees seemed to group themselves in a long flanking corridor in a northwesterly direction, as if to hide some secret that lay beyond. Twice I attempted to penetrate that wall, only to find my way blocked by those curious growths. Then a corridor opened before me; a mile forward and the desert began again. But it was a new desert this time: the sand packed hard as granite, the way ahead utterly devoid of vegetation. In the distance black bulging hills extended to right and left, with a narrow chasm or doorway between. I headed for that entrance, and when I reached it, I shut off power with an exclamation of astonishment. There was a huge chair-shaped rock there, and seated upon it was Grannie Annie. She had a tablet in her hands, and she was writing. "Grannie!" I yelled. "What're you doing here? Where's Mr. Baker?" She rose to her feet and clambered down the rock. "Getting back Jimmy's mine laborers," she said, a twinkle in her eyes. "I see you've got Antlers Park. I'm glad of that. It saves me a lot of trouble." She took off her spectacles and wiped them on her sleeve. "Don't look so fuddled, Billy-boy. Come along, and I'll show you." She led the way through the narrow passage into the valley. A deep gorge, it was, with the black sheer cliffs on either side pressing close. Ten feet forward, I stopped short, staring in amazement. Advancing toward me like a column of infantry came a long line of Larynx miners. They walked slowly, looking straight ahead, moving down the center of the gorge toward the entrance. But there was more! A kite car was drawn up to the side. The windscreen had been removed, and mounted on the hood was a large bullet-like contrivance that looked not unlike a search lamp. A blinding shaft of bluish radiance spewed from its open end. Playing it back and forth upon the marching men were Jimmy Baker and Xartal, the Martian. "Ultra violet," Grannie Annie explained. "The opposite end of the vibratory scale and the only thing that will combat the infra-red rays that cause red spot fever. Those men won't stop walking until they've reached Shaft Four." Grannie Annie told her story during the long ride back to Shaft Four. We drove slowly, keeping the line of marching Larynx miners always ahead of us. Jimmy Baker had struck a new big lode of Acoustix, a lode which if worked successfully would see Larynx Incorporated become a far more powerful exporting concern than Interstellar Voice . Antlers Park didn't want that. It was he or his agents who placed those lens buttons in the Larynx barracks. For he knew that just as Jupiter's great spot was responsible for a climate and atmosphere suitable for an Earthman on this Eighth Moon, so also was that spot a deadly power in itself, capable when its rays were concentrated of causing a fatal sickness. Then suddenly becoming fearful of Grannie's prying, Antlers Park strove to head her off before she reached Shaft Four. He did head her off and managed to lure her and Baker and Xartal into the Shaft barracks where they would be exposed to the rays from the lens button. But Grannie only pretended to contract the plague. Park then attempted to outwit Ezra Karn and me by returning in Jimmy Baker's kite car with a cockatoo image of Grannie.	A. She's fearless and quick-witted
What is not increasing as time goes on? A. the amount of people allowed to swear without punishment B. the amount of words considered taboo C. the amount of profanity heard D. societal tolerance	Maledict oratory The high costs of low language. Sunday, Jan. 14, 1996: A day that will live in--well, not infamy, exactly. Blasphemy would be closer to it. Early that afternoon, the Pittsburgh Steelers defeated the Indianapolis Colts to win the American Football Conference championship. Linebacker Greg Lloyd, accepting the trophy in front of a national television audience, responded with enthusiasm. "Let's see if we can bring this damn thing back here next year," he said, "along with the [expletive] Super Bowl." A few hours later, Michael Irvin of the Dallas Cowboys offered this spirited defense of his coach on TV after his team won the National Football Conference title: "Nobody deserves it more than Barry Switzer. He took all of this [expletive] ." Iwatched those episodes, and, incongruous as it may sound, I thought of Kenneth Tynan. Britain's great postwar drama critic was no fan of American football, but he was a fan of swearing. Thirty years earlier, almost to the week, Tynan was interviewed on BBC television in his capacity as literary director of Britain's National Theater and asked if he would allow the theater to present a play in which sex took place on stage. "Certainly," he replied. "I think there are very few rational people in this world to whom the word '[expletive]' is particularly diabolical or revolting or totally forbidden." It turned out there were a few more than Tynan thought. Within 24 hours, resolutions had been introduced in the House of Commons calling for his prosecution on charges of obscenity, for his removal as a theater official, and for censure of the network for allowing an obscene word to go out on the airwaves. Tynan escaped punishment, but he acquired a public reputation for tastelessness that he carried for the rest his life. To much of ordinary Britain, he became the man who had said "[expletive]" on the BBC. Neither Greg Lloyd nor Michael Irvin was so stigmatized. "It's live television," NBC Vice President Ed Markey said, rationalizing the outbursts. "It's an emotional moment. These things happen." Irvin wasn't about to let that stand. "I knew exactly what I was saying," he insisted later. "Those of you who can't believe I said it--believe it." Swearing isn't the only public act that Western civilization condones today but didn't 30 years ago. But it is one of the most interesting. It is everywhere, impossible to avoid or tune out. I am sitting in a meeting at the office, talking with a colleague about a business circumstance that may possibly go against us. "In that case, we're [expletive] ," he says. Five years ago, he would have said "screwed." Twenty years ago, he would have said, "We're in big trouble." Societal tolerance of profanity requires us to increase our dosage as time goes on. I am walking along a suburban street, trailing a class of pre-schoolers who are linked to each other by a rope. A pair of teen-agers passes us in the other direction. By the time they have reached the end of the line of children, they have tossed off a whole catalog of obscenities I did not even hear until I was well into adolescence, let alone use in casual conversation on a public street. I am talking to a distinguished professor of public policy about a foundation grant. I tell her something she wasn't aware of before. In 1965, the appropriate response was "no kidding." In 1996, you do not say "no kidding." It is limp and ineffectual. If you are surprised at all, you say what she says: "No shit." What word is taboo in middle-class America in 1996? There are a couple of credible candidates: The four-letter word for "vagina" remains off-limits in polite conversation (although that has more to do with feminism than with profanity), and the slang expression for those who engage in oral sex with males is not yet acceptable by the standards of office-meeting etiquette. But aside from a few exceptions, the supply of genuinely offensive language has dwindled almost to nothing as the 20th century comes to an end; the currency of swearing has been inflated to the brink of worthlessness. When almost anything can be said in public, profanity ceases to exist in any meaningful way at all. That most of the forbidden words of the 1950s are no longer forbidden will come as news to nobody: The steady debasement of the common language is only one of many social strictures that have loosened from the previous generation to the current. What is important is that profanity served a variety of purposes for a long time in Western culture. It does not serve those purposes any more. What purposes? There are a couple of plausible answers. One of them is emotional release. Robert Graves, who wrote a book in the 1920s called The Future of Swearing , thought that profanity was the adult replacement for childhood tears. There comes a point in life, he wrote, when "wailing is rightly discouraged, and groans are also considered a signal of extreme weakness. Silence under suffering is usually impossible." So one reaches back for a word one does not normally use, and utters it without undue embarrassment or guilt. And one feels better--even stimulated. The anthropologist Ashley Montagu, whose Anatomy of Swearing , published in 1967, is the definitive modern take on the subject, saw profanity as a safety valve rather than a stimulant, a verbal substitute for physical aggression. When someone swears, Montagu wrote, "potentially noxious energy is converted into a form that renders it comparatively innocuous." One could point out, in arguing against the safety-valve theory, that as America has grown more profane in the past 30 years, it has also grown more violent, not less. But this is too simple. It isn't just the supply of dirty words that matters, it's their emotive power. If they have lost that power through overuse, it's perfectly plausible to say that their capacity to deter aggressive behavior has weakened as well. But there is something else important to say about swearing--that it represents the invocation of those ideas a society considers powerful, awesome, and a little scary. I'm not sure there is an easy way to convey to anybody under 30, for example, the sheer emotive force that the word "[expletive]" possessed in the urban childhood culture of 40 years ago. It was the verbal link to a secret act none of us understood but that was known to carry enormous consequences in the adult world. It was the embodiment of both pleasure and danger. It was not a word or an idea to mess with. When it was used, it was used, as Ashley Montagu said, "sotto voce , like a smuggler cautiously making his way across a forbidden frontier." In that culture, the word "[expletive]" was not only obscene, it was profane, in the original sense: It took an important idea in vain. Profanity can be an act of religious defiance, but it doesn't have to be. The Greeks tempted fate by invoking the names of their superiors on Mount Olympus; they also swore upon everyday objects whose properties they respected but did not fully understand. "By the Cabbage!" Socrates is supposed to have said in moments of stress, and that was for good reason. He believed that cabbage cured hangovers, and as such, carried sufficient power and mystery to invest any moment with the requisite emotional charge. These days, none of us believes in cabbage in the way Socrates did, or in the gods in the way most Athenians did. Most Americans tell poll-takers that they believe in God, but few of them in a way that would make it impossible to take His name in vain: That requires an Old Testament piety that disappeared from American middle-class life a long time ago. Nor do we believe in sex any more the way most American children and millions of adults believed in it a generation ago: as an act of profound mystery and importance that one did not engage in, or discuss, or even invoke, without a certain amount of excitement and risk. We have trivialized and routinized sex to the point where it just doesn't carry the emotional freight it carried in the schoolyards and bedrooms of the 1950s. Many enlightened people consider this to be a great improvement over a society in which sex generated not only emotion and power, but fear. For the moment, I wish to insist only on this one point: When sexuality loses its power to awe, it loses its power to create genuine swearing. When we convert it into a casual form of recreation, we shouldn't be surprised to hear linebackers using the word "[expletive]" on national television. To profane something, in other words, one must believe in it. The cheapening of profanity in modern America represents, more than anything else, the crumbling of belief. There are very few ideas left at this point that are awesome or frightening enough for us to enforce a taboo against them. The instinctive response of most educated people to the disappearance of any taboo is to applaud it, but this is wrong. Healthy societies need a decent supply of verbal taboos and prohibitions, if only as yardsticks by which ordinary people can measure and define themselves. By violating these taboos over and over, some succeed in defining themselves as rebels. Others violate them on special occasions to derive an emotional release. Forbidden language is one of the ways we remind children that there are rules to everyday life, and consequences for breaking them. When we forget this principle, or cease to accept it, it is not just our language that begins to fray at the edges. What do we do about it? Well, we could pass a law against swearing. Mussolini actually did that. He decreed that trains and buses, in addition to running on time, had to carry signs that read "Non bestemmiare per l'onore d'Italia." ("Do not swear for the honor of Italy.") The commuters of Rome reacted to those signs exactly as you would expect: They cursed them. What Mussolini could not do, I am reasonably sure that American governments of the 1990s cannot do, nor would I wish it. I merely predict that sometime in the coming generation, profanity will return in a meaningful way. It served too many purposes for too many years of American life to disappear on a permanent basis. We need it. And so I am reasonably sure that when my children have children, there will once again be words so awesome that they cannot be uttered without important consequences. This will not only represent a new stage of linguistic evolution, it will be a token of moral revival. What the dirty words will be, God only knows.	B. the amount of words considered taboo
What kind of person is Ron? A. A curious and determined man who does his best B. An impulsive man who does not pay attention to others' needs C. A doting husband who follows his wife to Mars D. An adventuresome soul but still a timid one	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. A curious and determined man who does his best
Which medication was initiated for Mrs. Sanders in 2016 for her condition? Choose the correct answer from the following options: A. Hizentra 20% B. Intravenous immunoglobulin C. Corticosteroids D. Antibiotics E. Antihypertensives	### Patient Report 0 Dear colleague, We are writing to provide an update on the examination results of our patient Mrs. Hilary Sanders, born on 08/24/1976, who presented to our outpatient clinic on 10/09/2016. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy with human immunoglobulin Medical History: Mrs. Sanders presented with suspected previously undiagnosed immunodeficiency. There were no reports of frequent infections during childhood and adolescence. No increased herpes infections. No history of pneumonia, meningitis, or other serious infections. Current Presentation: Mrs. Sanders has experienced recurrent respiratory infections (bronchitis, pharyngitis) for about 3 years. Physical Examination: She reported joint pain in the left knee and numbness below the shoulder blade. A tendency to bruise easily. No mucosal lesions, recurrent axillary lymph node swelling. No recurrent fevers. No B-symptoms. No resting dyspnea, no subjective heart rhythm disturbances, no syncope, no peripheral edema, or other signs of cardiopulmonary decompensation. Immunological Diagnostics: - Immunoglobulins including subclasses: IgA, IgG, IgM, and all IgG subclasses were reduced. - Numerically unremarkable monocytes and granulocytes, lymphocytopenia with reduced B- and NK-cells, normal CD4/CD8 ratio. - B-lymphocyte subpopulation with numerically reduced B-cells. - Monocytic HLA-DR expression (immune competence marker) within the normal range. - No evidence of acute or chronic T-cell activation. - IL-6, LBP (Lipopolysaccharide-Binding Protein), and IL-8 post-erylysis were unremarkable, elevated s-IL-2. - Monocytic TNF-alpha secretion after 4h LPS stimulation was unremarkable. - T-cell function after 24h polyvalent ConA stimulation: TNF-alpha, IFN-gamma, IL-2, IL-4 unremarkable Assessment: In the immunological diagnostics, as in previous outpatient findings, a reduction in all major immunoglobulin classes and subclasses was observed. Cellular immune status revealed lymphocytopenia with reduced B- and natural killer-cells. Further cellular immune status, including the complement system and soluble mediators, showed no significant abnormalities except for an elevated soluble IL-2 receptor. Given the unremarkable monocytic TNF-alpha secretion after LPS stimulation, a significant Toll-like Receptor 4 defect is unlikely. An antibody response to Tetanus Toxoid was demonstrated in a vaccine titer test. Protective pneumococcal-specific antibodies could not be detected. There were no abnormalities in autoimmune diagnostics. Immunofixation showed no evidence of monoclonal gammopathy. Hypogammaglobulinemia due to enteral or renal protein loss is unlikely in the presence of normal albumin. Overall, the picture is consistent with Common Variable Immunodeficiency. Formally, CVID is defined by a reduction in the major immunoglobulin class IgG, with accompanying reduction in IgA and/or IgM, in the absence of normal or impaired vaccine response. Due to very low immunoglobulin levels and planned travel, determination of vaccine response was currently omitted in the absence of therapeutic consequence. After stable substitution, specific vaccine antibody levels can be determined before or after vaccination, with the assumption that stable antibody concentrations exist due to continuous immunoglobulin substitution. According to B-cell differentiation, it corresponds to Type Ib according to the Freiburg Classification and Type B+smB-CD21lo according to the Euro Classification. The classification is clinically relevant, as Type Ia is associated with increased immunocytopenias (especially ITP and AIH) and splenomegaly. In CVID with a high proportion (\>10%) of CD-21 low B-cells, increased granulomatous diseases and splenomegaly have also been observed. The indication for immunoglobulin substitution therapy exists because of recurrent infections. The form of substitution therapy (intravenous. vs. subcutaneous) is primarily based on patient preferences, but also on medical conditions (concomitant diseases such as thrombocytopenia, convenience, insurance, etc.). Current Recommendations: We propose to initiate immunoglobulin substitution therapy with Hizentra 20% (subcutaneous) at a dose of 200 ml once a week on Tuesdays. Further information and training on subcutaneous immunoglobulin substitution therapy will be provided by a home care nursing service. Mrs. Sanders will remain under regular medical supervision with close monitoring of clinical symptoms, laboratory parameters, and the effectiveness of immunoglobulin substitution therapy. Any unexpected side effects or changes in her condition should be reported immediately. Lab results: Parameter Results Reference Range --------------------------------------- --------------- --------------------- Sodium 141 mEq/L 132-146 mEq/L Potassium 4.2 mEq/L 3.4-4.5 mEq/L Calcium 2.41 mg/dL 2.15-2.50 mg/dL Inorganic Phosphate 1.00 mg/dL 0.87-1.45 mg/dL Selenium 0.79 µmol/L 0.60-1.50 µmol/L Zinc 10.1 µmol/L 9.0-22.0 µmol/L Creatinine 0.75 mg/dL 0.50-0.90 mg/dL Estimated GFR (eGFR CKD-EPI) \>90 mL/min \>90 mL/min Total Bilirubin 0.37 mg/dL \< 1.20 mg/dL Albumin 4.55 g/dL 3.50-5.20 g/dL Total Protein 6.3 g/dL 6.4-8.3 g/dL Albumin Fraction 71.8% 55.8-66.1% A1-Globulin 5.1% 2.9-4.9% A2-Globulin in Serum 10.7% 7.1-11.8% ß-Globulin in Serum 9.2% 8.4-13.1% Gamma-Globulin in Serum 3.2% 11.1-18.8% Immunoglobulin G 514 mg/dL 700-1600 mg/dL Immunoglobulin A 14 mg/dL 70-400 mg/dL Immunoglobulin M 19 mg/dL 40-230 mg/dL Immunoglobulin E 90 kU/L 0.0-100.0 kU/L IgG 1 299.5 mg/dL 280-800 mg/dL IgG 2 162.7 mg/dL 115-570 mg/dL IgG 3 49.1 mg/dL 24-125 mg/dL IgG 4 4.0 mg/dL 5.2-125 mg/dL Serum Immunofixation CRP 4.8 mg/L \< 5.0 mg/L C3 Complement 980 mg/L 900-1800 mg/L C4 Complement 120 mg/L 100-400 mg/L ß-2-Microglobulin 3.6 mg/L 0.8-2.2 mg/L HBs Antigen Negative HBc Antibody Negative HBs Antibody Negative Ferritin 56 µg/L 13-140 µg/L ALT (GPT) 33 U/L \< 31 U/L AST (GOT) 29 U/L \< 35 U/L Alkaline Phosphatase 84 U/L 35-105 U/L Creatine Kinase 90 U/L \< 167 U/L CK-MB 8.3 U/L \< 24.0 U/L Gamma-GT 40 U/L 5-36 U/L LDH 204 U/L 135-214 U/L Lipase 50 U/L 13-60 U/L Cortisol 306.6 nmol/L 64.0-327.0 nmol/L 25-OH-Vitamin D3 65.3 nmol/L 50.0-150.0 nmol/L 1.25-OH-Vitamin D3 134 pmol/L 18.0-155.0 pmol/L TSH 1.42 mU/L 0.27-4.20 mU/L Vitamin B12 770 pg/mL 191-663 pg/mL Folic Acid 14.6 ng/mL 4.6-18.7 ng/mL Hemoglobin 13.9 g/dL 12.0-15.6 g/dL Hematocrit 41.0% 35.5-45.5% Erythrocytes 5.2 M/uL 3.9-5.2 M/uL Leukocytes 4.13 K/uL 3.90-10.50 K/uL Platelets 174 K/uL 150-370 K/uL MCV 80.0 fL 80.0-99.0 fL MCH 26.7 pg 27.0-33.5 pg MCHC 33.6 g/dL 31.5-36.0 g/dL RDW-CV 13.7% 11.5-15.0% Absolute Neutrophils 2.87 K/uL 1.50-7.70 K/uL Absolute Immature Granulocytes 0.010 K/uL \< 0.050 K/uL Absolute Lymphocytes 0.71 K/uL 1.10-4.50 K/uL Absolute Monocytes 0.42 K/uL 0.10-0.90 K/uL Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL Absolute Basophils 0.03 K/uL 0.00-0.20 K/uL HbA1c 4.9% \< 6.0% HbA1c (IFCC) 30.1 mmol/mol \< 42.0 HBV Serology Result Negative HIV1/2 Antibodies, P24 Antigen Negative Hepatitis C Virus Antibodies in Serum Negative Dear colleague, We report the examination results of Mrs. Hilary Sanders, born on 08/24/1976 who presented at our outpatient clinic on 03/04/2017. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin Immunological Diagnostics: - Immunoglobulins including subclasses: IgA, IgG, IgM, and all IgG-Subclasses were reduced. - Numerically unremarkable monocytes and granulocytes, lymphocytopenia with reduced B- and natural killer-cells, normal CD4/CD8 ratio. - B-lymphocyte subpopulation with numerically reduced B cells. - Monocytic HLA-DR expression within the normal range. - No evidence of acute or chronic T-cell activation. - IL-6, LBP (Lipopolysaccharide-Binding Protein), and IL-8 post-erylysis were unremarkable, elevated s-IL-2. - Monocytic TNF-alpha secretion after 4h LPS stimulation was unremarkable. Assessment: In the immunological diagnostics, as in previous outpatient findings, a reduction in all major immunoglobulin classes and subclasses was observed. Cellular immune status revealed lymphocytopenia with reduced B- and natural killer-cells. The further cellular immune status, including the complement system and soluble mediators, showed no significant abnormalities except for an elevated soluble IL-2 receptor. Current Presentation: Mrs. Sanders was again provided with detailed information about her condition and the planned course of action. We scheduled an appointment to initiate regular subcutaneous immunoglobulin therapy. Medical History: Mrs. Sanders received her first dose of Hizentra 20% subcutaneously as immunoglobulin substitution therapy for CVID. The administration was well-tolerated initially, with no evidence of significant local or systemic side effects. Mrs. Sanders was once again informed about possible risks (especially hypersensitivity reactions) and advised to contact us immediately in case of questions, uncertainties, or any abnormalities. The dosing for the first four weeks was 3x20mL Hizentra 20% subcutaneously, and from the fifth week onward, it was changed to either 1x40mL or 2x20mL Hizentra 20% subcutaneously per week. In the past days, Mrs. Sanders has been experiencing a cold: runny nose, cough (green-yellow), difficulty clearing mucus, slight fever, sinus inflammation, sore throat, difficulty speaking, and swallowing problems. There was no improvement. Physical Examination: Reddened throat, no exudates, non-swollen cervical lymph nodes, lung examination showed bronchitis-like breathing sounds, no rales. Therapy and Progression: Today\'s CRP is not elevated. IgGs are still below normal. We recommended increasing immunoglobulin substitution during the infection. The patient had difficulty finding a suitable injection site on her abdomen. However, she reported that the secretions were gradually becoming lighter, so she decided to wait with the antibiotic and only use it if there was no improvement. The patient has been receiving 3x20mL Hizentra 20% per week since her last visit. She complained of developing skin hardening at the injection sites, so a slower infusion time was discussed. She has been experiencing a strong cough for several weeks without fever. No rales or signs of pleuritis were detected on auscultation. No abnormalities were observed on the chest X-ray. Laboratory results now show normal IgG levels, so the dose was reduced to 2x20mL per week. A CT scan of the thorax and abdominal ultrasound were requested. Chest X-ray in two planes from 03/04/2017: [Findings/Assessment:]{.underline} No previous images are available for comparison. Upper mediastinum and heart appear normal, with no central congestion. No pneumothorax, effusions, confluent infiltrates, or significant focal lesions. Abdominal ultrasound on 03/04/2017: Hepatosplenomegaly and retroperitoneal lymphadenopathy up to 26mm. CT Chest/Abdomen/ from 03/04/2017: [Methodology]{.underline}: Digital overview radiographs. After intravenous injection of contrast agent a 16-row CT scan of the thorax and entire abdomen was performed in the venous contrast phase, with primary data set reconstruction at a thickness of 1.25 mm. Multiplanar reconstructions were created. [Findings]{.underline}: A conventional radiographic pre-image from 11/18/2014 is available for comparison. [Thorax]{.underline}: Normal lung parenchyma with normal vascular markings. Small, sometimes hazy, sometimes nodular densities measuring up to 4mm in both lower lobes and the left upper lobe. Small pleura-adjacent density in the right lower lobe. No evidence of confluent infiltrates. No pleural effusion or pneumothorax. Normal heart size and configuration. Normal diameter of the thoracic aorta and pulmonary trunk. Increased number and enlarged retroclavicular lymph nodes on the right and left, axillary on both sides measuring up to 30mm in diameter. Trachea and esophagus displayed normally. No hiatus hernia. Thyroid and neck soft tissues were unremarkable, as far as depicted. Normal thoracic soft tissue mantle. No soft tissue emphysema. [Abdomen]{.underline}: Hepatomegaly with morphologically normal liver parenchyma. No portal vein thrombosis. Gallbladder is unremarkable with no calculi. Intrahepatic and extrahepatic bile ducts are not dilated. Pancreas is normally lobulated and structured, with no dilation of the pancreatic duct. Splenomegaly. Accessory spleen measuring approximately 20 mm in diameter. Splenic parenchyma is homogeneously contrasted in the venous phase. Kidneys are orthotopically positioned, normal size with no side differences, and contrasted equally on both sides. Two regularly configured hypodense lesions in the left kidney, suggestive of uncomplicated renal cysts. No dilation of the urinary tract, and no evidence of stones. Adrenal glands are not visualized. Increased and enlarged mesenteric, pararaortic, parailiacal, and inguinal lymph nodes up to 30 mm in size. Gastrointestinal tract is displayed normally, as far as assessable. Normal representation of major abdominal vessels. No free intraperitoneal fluid or air. [Osseous structures:]{.underline} No evidence of suspicious osseous destruction. Normal soft tissue mantle. [Assessment:]{.underline} Intrapulmonary multifocal, sometimes hazy, sometimes nodular densities, differential diagnosis includes atypical pneumonia. Thoracoabdominal lymphadenopathy. Hepatosplenomegaly without suspicious lesions. Current Recommendations: - Outpatient follow-up for discussion of findings - Continue regular subcutaneous immunoglobulin administration with current regimen of Hizentra 20% 2x20mL/week - Lung function test - Gastroscopy - In case of acute infection: increase immunoglobulin administration - Abdominal ultrasound: annually - H. pylori testing, e.g., breath test or H. pylori antigen in stool: annually Seasonal influenza vaccination: annually Lab results upon discharge: Parameter Results Reference Range ---------------------- ------------- --------------------- Total Protein 6.3 g/dL 6.4-8.3 g/dL Albumin Fraction 71.8% 55.8-66.1% A1-Globulin 5.1% 2.9-4.9% Gamma-Globulin 3.2% 11.1-18.8% Immunoglobulin G 188 mg/dL 700-1600 mg/dL Immunoglobulin A 11 mg/dL 70-400 mg/dL Immunoglobulin M 12 mg/dL 40-230 mg/dL IgG Subclass 1 113 mg/dL 280-800 mg/dL IgG Subclass 2 49.1 mg/dL 115-570 mg/dL IgG Subclass 4 \<0.0 mg/dL 5.2-125 mg/dL aPCP-IgG 7.32 mg/dL 10.00-191.20 mg/dL aPCP-IgG2 2.74 mg/dL 4.70-89.40 mg/dL ß-2-Microglobulin 3.6 mg/L 0.8-2.2 mg/L LDH 224 U/L 135-214 U/L Vitamin B12 708 pg/mL 191-663 pg/mL Erythrocytes 5.3 M/uL 3.9-5.2 M/uL Platelets 129 K/uL 150-370 K/uL MCV 78.0 fL 80.0-99.0 fL MCH 25.1 pg 27.0-33.5 pg Absolute Lymphocytes 0.91 K/uL 1.10-4.50 K/uL ### Patient Report 1 Dear colleague, We are reporting on Mrs. Hilary Sanders, born on 08/24/1976, who presented to our Immunodeficiency Clinic on 10/06/2017. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Leukopenia and lymphopenia - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Hepatosplenomegaly - Thoracoabdominal, inguinal, and axillary lymphadenopathy - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin Medical History: Mrs. Sanders first presented herself to our clinic, with suspected undiagnosed immunodeficiency. Regular subcutaneous immunoglobulin therapy with Hizentra 20% (2x20mL/week) has been well-tolerated. Initially, there were frequent upper respiratory tract infections with sore throat and cough. In the absence of fever, a one-time course of Cotrim was prescribed for 7 days due to sinusitis. We discussed Mrs. Sanders' medical history in detail, including the recent CT findings. She has been informed about the necessity of vigilance in case of unclear and especially persistent lymph node swellings. Regarding the inguinal and axillary lymph nodes measuring up to 30mm in diameter found on CT, we recommend an observational approach with regular sonographic monitoring. There have been no significant changes in laboratory parameters, with good IgG levels during ongoing substitution therapy and known moderate leukopenia and lymphopenia. During the next appointment, an additional lung function test, including diffusion measurement, will be conducted Current Recommendations: - Outpatient follow-up, including lung function test - Continue regular subcutaneous immunoglobulin administration with current regimen of Hizentra 20% (2x20mL/week). - Current gastroscopy. <!-- --> - In case of acute infection: increase immunoglobulin administration. - Administer targeted, sufficiently long, and high-dose antibiotic therapy if bacterial infections require treatment. - Ideally, obtain material for microbiological diagnostics. - In case of increasing diarrhea, consider outpatient stool examinations, including Giardia lamblia and Cryptosporidium. - Abdominal ultrasound: annually. - Lung function test, including diffusion measurement: annually. - H. pylori testing, e.g., breath test or H. pylori antigen in stool: annually. - Gastroscopy: approximately every 2-3 years, depending on previous findings or H. pylori testing - Chest X-ray or CT thorax: if clinical symptoms or lung function abnormalities are observed. - Seasonal influenza vaccination: annually. Lab results upon Discharge: Parameter Results Reference Range -------------------------------- ------------- --------------------- Sodium 141 mEq/L 132-146 mEq/L Potassium 4.1 mEq/L 3.4-4.5 mEq/L Creatinine (Jaffé) 0.82 mg/dL 0.50-0.90 mg/dL Estimated GFR (eGFR CKD-EPI) \>90 \- Total Bilirubin 0.21 mg/dL \< 1.20 mg/dL Albumin 4.09 g/dL 3.5-5.2 g/dL Immunoglobulin G 1025 mg/dL 700-1600 mg/dL Immunoglobulin A 16 mg/dL 70-400 mg/dL Immunoglobulin M 28 mg/dL 40-230 mg/dL Free Lambda Light Chains 5.86 5.70-26.30 Free Kappa Light Chains 6.05 3.30-19.40 Kappa/Lambda Ratio 1.03 0.26-1.65 IgG Subclass 1 580.9 mg/dL 280-800 mg/dL IgG Subclass 2 340.7 mg/dL 115-570 mg/dL IgG Subclass 3 50.9 mg/dL 24-125 mg/dL IgG Subclass 4 5.7 mg/dL 5.2-125 mg/dL CRP 7.3 mg/L \< 5.0 mg/L Haptoglobin 108 mg/dL 30-200 mg/dL Ferritin 24 µg/L 13-140 µg/L ALT 24 U/L \< 31 U/L AST 37 U/L \< 35 U/L Gamma-GT 27 U/L 5-36 U/L Lactate Dehydrogenase 244 U/L 135-214 U/L 25-OH-Vitamin D3 91.7 nmol/L 50.0-150.0 nmol/L Hemoglobin 13.1 g/dL 12.0-15.6 g/dL Hematocrit 40.0% 35.5-45.5% Red Blood Cells 5.5 M/uL 3.9-5.2 M/uL White Blood Cells 2.41 K/uL 3.90-10.50 K/uL Platelets 142 K/uL 150-370 K/uL MCV 73.0 fL 80.0-99.0 fL MCH 23.9 pg 27.0-33.5 pg MCHC 32.7 g/dL 31.5-36.0 g/dL MPV 10.7 fL 7.0-12.0 fL RDW-CV 14.8% 11.5-15.0% Absolute Neutrophils 1.27 K/uL 1.50-7.70 K/uL Absolute Immature Granulocytes 0.000 K/uL \< 0.050 K/uL Absolute Lymphocytes 0.67 K/uL 1.10-4.50 K/uL Absolute Monocytes 0.34 K/uL 0.10-0.90 K/uL Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL Absolute Basophils 0.04 K/uL 0.00-0.20 K/uL Free Hemoglobin 5.00 mg/dL \< 20.00 mg/dL Abdominal Ultrasound on 10/06/2017: [Liver]{.underline}: Measures 19 cm in the MCL, homogeneous parenchyma, no focal lesions. [Gallbladder/Biliary Tract:]{.underline} No evidence of calculi, no signs of inflammation, no congestion. [Spleen]{.underline}: Measures 14 cm in diameter, homogeneous. Accessory spleen measures 16 mm at the hilus. [Pancreas]{.underline}: Morphologically unremarkable, as far as visible due to intestinal gas overlay, no evidence of space-occupying processes. Retroperitoneum: No signs of aneurysms. Enlarged retroperitoneal and iliac lymph nodes, measuring up to approximately 2.5 cm in diameter. [Kidneys]{.underline}: Both kidneys are of normal size (right 4.3 x 11.8 cm, left 4.6 cm x 11.9 cm). No congestion, no evidence of calculi (stones), no evidence of space-occupying processes. [Bladder]{.underline}: Smoothly defined and normally configured. Minimally filled. [Uterus]{.underline}: Size within the normal range, homogeneous. No ascites. [Assessment:]{.underline} Evidence of enlarged lymph nodes up to 2.5 cm retroperitoneal and iliac. Compared to previous findings, a slight decrease in splenomegaly. ### Patient Report 2 Dear colleague, We are reporting on the examination results of our patient, Mrs. Hilary Sanders, born on 08/24/1976, who presented herself in our Immunodeficiency Clinic on 02/10/2018. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin Medical History: For a detailed medical history, please refer to our previous medical records. Therapy and Progression: Ongoing diarrhea in the morning, often recurring in the afternoon. No melena, no fresh blood. Resolving respiratory infection, positive influenza. Currently, IgG levels remain within the target range. An increased need for immunoglobulins is expected, especially in the third trimester of pregnancy. Therefore, we recommend close monitoring with us during pregnancy. Ferritin levels have further declined, indicating the need for iron substitution. Anamnestically, there is an intolerance to oral iron preparations. Recommendations: - Outpatient follow-up - Early follow-up in case of infections or persistent diarrhea - Continue regular subcutaneous immunoglobulin therapy, currently with Hizentra 20% 2x20mL/week - In case of increasing diarrhea, conduct outpatient stool examinations, including testing for Giardia lamblia and Cryptosporidium - Pulmonary function tests including diffusion measurement: annually - Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in stool: annually - Gastroscopy: approximately every 2-3 years, depending on previous findings and HP testing - Chest X-ray or chest CT: in case of abnormal clinical presentation or pulmonary function - Annual seasonal influenza vaccination ### Patient Report 3 Dear colleague, We are writing to provide an update on Mrs. Hilary Sanders, born on 08/24/1976, who presented to our outpatient Immunodeficiency Clinic on 04/12/2018. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin - Suspected CVID Enteropathy Medical History: For a detailed medical history, please refer to our previous medical records. Therapy and Progression: Respiratory infection with symptoms for 3-4 weeks. No antibiotics. No significant infections since then. Hizentra 3x20 mL with good tolerance. IgG levels remain within the target range; therefore, we recommend continuing the current treatment unchanged. Since the last visit, mild upper respiratory tract infections. No fever (except for one episode of sinusitis), no antibiotics. SCIG treatment unchanged with 3x20mL/week of Hizentra ®. Mrs. Sanders continues to experience watery diarrhea about 5-7 times daily. No blood in stools, no pain, no vomiting, no nausea. There has been no clear association with specific foods observed. Current weight: 69kg. We discussed further diagnostic steps. Initially, outpatient endoscopic diagnostics should be performed. Current Recommendations: - Outpatient follow-up in three months - Continue SCIG treatment as is - External upper gastrointestinal endoscopy and colonoscopy (please return with findings) - In case of increasing diarrhea, conduct outpatient stool examinations, including testing for Giardia lamblia and Cryptosporidium - Abdominal ultrasound: annually - Pulmonary function tests including diffusion measurement: annually - Helicobacter pylori testing: e.g., breath test or Helicobacter pylori antigen in stool: annually - Gastroscopy: approximately every 2-3 years, depending on previous findings and Helicobacter pylori testing - Chest X-ray or chest CT: in case of abnormal clinical presentation or pulmonary function - Annual seasonal influenza vaccination ### Patient Report 4 Dear colleague, We are writing to provide an update on Mrs. Hilary Sanders, born on 08/24/1976, who presented to our outpatient Immunodeficiency Clinic on 02/18/2019. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin - Suspected CVID Enteropathy Medical History: For a detailed medical history, please refer to our previous medical records. Therapy and Progression: Respiratory infections with symptoms for 7 weeks. No antibiotics. No significant infections since then. Hizentra 3x20 mL with good tolerance. Continued diarrhea, approximately 6 times a day, without weight loss. IgG levels remain within the target range; therefore, we recommend continuing the current treatment unchanged. We discussed further diagnostic steps. Initially, outpatient endoscopic diagnostics should be performed. Current Recommendations: - Outpatient follow-up in three months - Continue treatment as is - External upper gastrointestinal endoscopy (and colonoscopy (please return with findings) - In case of increasing diarrhea, conduct outpatient stool examinations, including testing for Giardia lamblia and Cryptosporidium - Abdominal ultrasound: annually - Pulmonary function tests including diffusion measurement: annually - Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in stool: annually - Gastroscopy: approximately every 2-3 years, depending on previous findings and HP testing - Chest X-ray or chest CT: in case of abnormal clinical presentation or pulmonary function - Annual seasonal influenza vaccination ### Patient Report 5 Dear colleague, We are writing to provide summary on the clinical course of Mrs. Hilary Sanders, born on 08/24/1976, who presented at our outpatient Immunodeficiency Clinic. Diagnoses: - Common Variable Immunodeficiency Syndrome (CVID) - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Recurrent respiratory infections - Idiopathic thombocytopenic purpura - Arterial hypertension - Initiation of subcutaneous immunoglobulin substitution therapy human immunoglobulin - Suspected CVID Enteropathy - Iron-deficiency anemia Medical History: For a detailed medical history, please refer to our previous medical records. Therapy and Progression: Overall stable condition. No longer experiencing cough. Persistent fatigue. Upcoming appointment with the Gastroenterology department next week. There is again an indication for iron substitution. Update on 11/15/2019: Laboratory results from 11/15/2019: Transaminase elevation, Protein 18, markedly elevated BNP. However, IgA is at 0.5 (otherwise not detectable), IgG subclasses within normal range. Findings do not align. Patient informed by phone, returning for further evaluation today; also screening for Hepatitis A, B, C, and E, EBV, CMV, TSH, coagulation. No shortness of breath, no edema, no abdominal enlargement, stable weight at 69 kg. In case of worsening symptoms, shortness of breath, or fever, immediate referral to the emergency department recommended. 02/12/2020: The patient is doing reasonably well. She has had a mild cold for about 2 weeks, no fever, but nasal congestion and yellowish-green sputum. No other infections. No antibiotics prescribed. She has adapted to her gastrointestinal issues. An appointment with the Gastroenterology department. She is currently working from home. Medication: no new medications, only Cuvitru 20mL 3x weekly. Weight remains stable at 67 kg. The last lung function test was in the summer of this year and was within normal limits. Imaging has not been performed recently. Gastroscopy and colonoscopy have not been conducted for some time. 04/14/2020: Referral to Gastroenterology at is recommended for persistent abdominal symptoms. 10/24/2020: The patient has mostly avoided social contacts due to the pandemic. She continues to experience digestive problems (food intolerances, diarrhea, flatulence). She has less stamina. Few infections in the past year, at most a minor cold. No significant infections. Hizentra injections remain unchanged at 20 mL 3 times a week. 03/22/2021: Constant colds since December 2020. One-time antibiotic treatment in October 2019. Subcutaneous Immunoglobulin therapy remains unchanged at 20 mL 3 times weekly. 09/19/2021: She feels disoriented and very tired, more so than usual. Difficulty maintaining a steady gaze. No steroid therapy was administered. CT showed enlarged lymph nodes. Diarrhea, especially in the morning, 3-4 times a day, additional bowel movements with meals, sometimes watery. No fever, no infections. Hizentra injections continued unchanged. Summary: IgG levels are currently within the target range, so we recommend continuing immunoglobulin substitution therapy without changes. The antibody response (SARS-CoV-2 (S-Ag) IgG ELISA) to the Covid-19 vaccination is, as expected, negative. However, there is a positive detection of SARS-CoV-2 (N-Ag) IgG ELISA, as expected in the case of viral contact (not vaccination). We consider this to be an unspecific reaction and recommend further monitoring at the next follow-up appointment. With a platelet count currently at 55 K/uL, we recommend a short-term blood count check with us or your primary care physician. Due to the immunodeficiency, a lack of antibody response to vaccination was expected. In the medium term, passive protection through immunoglobulin substitution therapy will play a role. This is contingent on a significant portion of plasma donors having antibodies against SARS-CoV2. There is a multi-month delay from the time of donation to the release of the preparations, so we anticipate that meaningful protection through immunoglobulin products will not be expected. An exact prognosis in this regard is not possible. Current Recommendations: - Outpatient follow-up in three months - Consultation with Gastroenterology - Continue SCIG treatment as is - External upper gastrointestinal endoscopy and colonoscopy (please return with findings) - In case of increasing diarrhea, conduct outpatient stool examinations, including testing for Giardia lamblia and Cryptosporidium - Abdominal ultrasound: annually - Pulmonary function tests including diffusion measurement: annually - Helicobacter pylori (HP) testing: e.g., breath test or HP antigen in stool: annually - Gastroscopy: approximately every 2-3 years, depending on previous findings and HP testing - Chest X-ray or chest CT: in case of abnormal clinical presentation or pulmonary function - Annual seasonal influenza vaccination ### Patient Report 6 Dear colleague, We are providing you with an update regarding our patient Mrs. Hilary Sanders, born on 08/24/1976. She was under our inpatient care from 03/29/2023 to 04/05/2023. Diagnoses: - Suspected CVID-Associated enteropathy - Known hepatosplenomegaly with a borderline enlarged portal vein, no significant portocaval shunts. Multiple liver lesions, possibly hemangiomas further evaluation if not already done. - Known retroperitoneal and iliac lymphadenopathy, likely related to the underlying condition. - Known changes in the lower lung bases, likely associated with the underlying condition, e.g., ILD. Refer to previous examinations. - Capsule endoscopy: Incomplete capsule enteroscopy with no evidence of inflammatory changes. Some hyperemia and blurry vascular pattern observed in the visible colon. - CVID-Associated Hepatopathy in the Form of Nodular Regenerative Hyperplasia Other Diagnoses: Common Variable Immunodeficiency Syndrome (CVID) with: - Complete IgG deficiency - Complete IgM deficiency - Complete IgA deficiency - Leukopenia and lymphopenia - Initiation of subcutaneous immunoglobulin substitution therapy with Hizentra 20% - Infectious manifestations: Frequent respiratory tract infections - Non-Infectious manifestations: - ITP (Immune Thrombocytopenia) - Hepatosplenomegaly - Lymphadenopathy in supraclavicular, infraclavicular, thoracoabdominal, inguinal, and axillary regions - Suspected Granulomatous-Lymphocytic Interstitial Lung Disease in CVID <!-- --> - Iron-deficiency anemia Pysical Examination: Patient in normal general condition and nutritional status (175 cm, 65.8 kg. No resting dyspnea. [Neuro (grossly orienting):]{.underline} awake, oriented to time/place/person/situation, No evidence of focal neurological deficit. No meningism. [Head/neck]{.underline}: pharynx non-irritable. Moist, rosy mucous membranes. Tongue occupied. [Skin]{.underline}: intact, turgor normal, no icterus, no cyanosis. [Thorax]{.underline}: normal configuration, no spinal palpitation, renal bed clear. [Lung]{.underline}: vesicular breath sound bds, no accessory sounds, sonorous tapping sound bds. [Cor]{.underline}: Cardiac action pure, rhythmic, no vitia typical murmurs. [Abdomen]{.underline}: regular bowel sounds, soft abdominal wall, no tenderness, no resistances, no hepatosplenomegaly. [Extremities]{.underline}: no edema. Feet warm. Dorsalis pedis +/+ and posterior tibial artery +/+. Current Presentation: The patient was admitted for further evaluation of suspected CVID-associated enteropathy, as she had been experiencing chronic diarrhea for the past three years. On admission, the patient reported an overall good general and nutritional condition. She described her current subjective well-being as good but mentioned having chronic diarrhea for the past three years, with up to 7 bowel movements per day. The stools were watery without any signs of blood. There were no indications of infection, such as fever, chills, dysuria, hematuria, cough, sputum, or dyspnea. She also experienced intermittent left-sided upper abdominal pain, primarily postprandially. She had a good appetite. On the day of admission, an esophagogastroduodenoscopy was performed, which revealed erythematous antral gastritis. Additionally, there was an approximately 1 cm irregular mucosal area at the corpus-antrum junction on the greater curvature side. A magnetic resonance imaging scan showed no evidence of inflamed bowel loops, ruling out chronic inflammatory bowel disease or celiac disease. To further investigate, a capsule endoscopy was performed, with results pending at the time of discharge. Hypovitaminosis B12 and folate deficiency were ruled out. However, iron-deficiency anemia was confirmed, and the patient had already scheduled an outpatient appointment for iron substitution. Serum levels of vitamin B6 and zinc were pending at discharge. Due to a moderate increase in transaminases and evidence of hepatosplenomegaly, we decided, after detailed explanation and with the patient\'s consent, to perform a sonographically guided liver biopsy in addition to the planned endoscopy. The differential diagnosis included CVID-associated hepatopathy. The biopsy was successfully conducted , without any post-interventional bleeding. Histology revealed mild acute hepatitis and nodular regenerative hyperplasia.This finding could be consistent with changes in CVID-associated hepatopathy. Granulomas were not observed. With only slightly elevated liver values, a trial therapy with budesonide was initiated, and clinical (diarrhea?) and laboratory (transaminases?) follow-up will be performed in the outpatient setting. We discharged Mrs. Sanders in a cardiopulmonarily stable condition. [Current Recommendations:]{.underline} - Follow-up in the gastroenterological outpatient clinic Esophagogastroduodenoscopy (EGD) on 04/01/2023: Introduction of the gastroscope in a left lateral position. Visualized up to the descending part of the duodenum. Unremarkable upper esophageal sphincter. Normal motility and mucosa in the upper, middle, and distal esophagus. The Z-line is sharply demarcated in the hiatus. The cardia closes sufficiently. The stomach expands normally in all parts under air insufflation. Multiple glandular cysts \< 8 mm in size in the fundus and corpus. Approximately 1 cm irregular mucosal area at the corpus-antrum junction on the greater curvature side. Streaky redness of the mucosa in the antrum. Unremarkable mucosa in the bulb. Unremarkable mucosa in the descending part of the duodenum. Step biopsies performed. [Summary]{.underline}: Erythematous antral gastritis. Approximately 1 cm irregular mucosal area at the corpus-antrum junction on the greater curvature side, suggestive of inflammation. Multiple glandular cysts observed in the fundus and corpus. [Abdominal MRI on 04/02/2023:]{.underline} [Clinical information, questions, and justification for the exam]{.underline}: Chronic diarrhea, suspected CVID-associated enteropathy, differential diagnosis of celiac disease, and inflammatory bowel disease (IBD). Assessment of malignancy. Technique: After oral administration of mannitol solution and injection of 40 mg Buscopan, a 3-Tesla abdominal MRI was performed. [Findings]{.underline}: Multiple nodular consolidations and opacities detected in the lower basal lung segments, measuring 7 x 4 mm, for example, in the right lateral lower lobe (Series 18, Image 3). Additionally, streaky-reticular changes observed. Left diaphragmatic elevation. Liver globally enlarged and smooth-bordered with several lesions showing mild to moderately hyperintense signals in T2-weighted images and hypointense signals in T1-weighted images. These lesions demonstrated increased enhancement in the early contrast phases, especially those at the periphery, and more diffuse enhancement in the late phases. For example, a lesion measuring 12 x 11 mm in Segment 2, a lesion measuring 8 mm in Segment 8 and a lesion measuring 21 x 13 mm in Segment 7. The portal vein measures borderline wide, up to 15 mm in diameter. Gallbladder is unremarkable without evidence of stones. Intra- and extrahepatic bile ducts are not dilated. Spleen significantly enlarged, measuring 14 cm in pole-to-pole distance and 7.2 cm in transverse diameter, homogeneous enhancement in native phases and late contrast phase. Large accessory spleen located hilarly. Bilateral adrenal glands appear slender. Pancreas displays typical appearance with no ductal dilatation. Both kidneys are in orthotopic position, with unremarkable cortical cysts on the right side. No signs of urinary obstruction. The urinary bladder is moderately filled. No free fluid. Adequate dilation of small bowel loops. No evidence of significant bowel obstruction. No thickened bowel walls or increased post-contrast signal in the bowel loops. Cystic lesion in the right ovary measuring 17 x 11 mm consistent with a corpus luteum cyst. Multiple enlarged retroperitoneal lymph nodes observed, for example, paracaval node with a short-axis diameter of 14 mm and right iliacoexternal node with a short-axis diameter of 14.5 mm No evidence of enlarged mesenteric or inguinal lymph nodes.	Hizentra 20%
Why did Dink punch Kraft? A. Self-defense B. He wasn’t listening C. He insulted Dink D. He was threatening Orison	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.	D. He was threatening Orison
What is the conflict between Glmpauszn and the not-world? A. Glmpauszn's world wants to conquer the not-world, because they deem the not-world valuable. B. The not-world unkowningly overlaps and disrupts his. C. The not-world is full of humans that terrorize his. D. Glmpauszn's world doesn't understand how people in the not-world operate..	A Gleeb for Earth By CHARLES SHAFHAUSER Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1953. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Not to be or not to not be ... that was the not-question for the invader of the not-world. Dear Editor: My 14 year old boy, Ronnie, is typing this letter for me because he can do it neater and use better grammar. I had to get in touch with somebody about this because if there is something to it, then somebody, everybody, is going to point finger at me, Ivan Smernda, and say, "Why didn't you warn us?" I could not go to the police because they are not too friendly to me because of some of my guests who frankly are stew bums. Also they might think I was on booze, too, or maybe the hops, and get my license revoked. I run a strictly legit hotel even though some of my guests might be down on their luck now and then. What really got me mixed up in this was the mysterious disappearance of two of my guests. They both took a powder last Wednesday morning. Now get this. In one room, that of Joe Binkle, which maybe is an alias, I find nothing but a suit of clothes, some butts and the letters I include here in same package. Binkle had only one suit. That I know. And this was it laying right in the middle of the room. Inside the coat was the vest, inside the vest the shirt, inside the shirt the underwear. The pants were up in the coat and inside of them was also the underwear. All this was buttoned up like Binkle had melted out of it and dripped through a crack in the floor. In a bureau drawer were the letters I told you about. Now. In the room right under Binkle's lived another stew bum that checked in Thursday ... name Ed Smith, alias maybe, too. This guy was a real case. He brought with him a big mirror with a heavy bronze frame. Airloom, he says. He pays a week in advance, staggers up the stairs to his room with the mirror and that's the last I see of him. In Smith's room on Wednesday I find only a suit of clothes, the same suit he wore when he came in. In the coat the vest, in the vest the shirt, in the shirt the underwear. Also in the pants. Also all in the middle of the floor. Against the far wall stands the frame of the mirror. Only the frame! What a spot to be in! Now it might have been a gag. Sometimes these guys get funny ideas when they are on the stuff. But then I read the letters. This knocks me for a loop. They are all in different handwritings. All from different places. Stamps all legit, my kid says. India, China, England, everywhere. My kid, he reads. He says it's no joke. He wants to call the cops or maybe some doctor. But I say no. He reads your magazine so he says write to you, send you the letters. You know what to do. Now you have them. Maybe you print. Whatever you do, Mr. Editor, remember my place, the Plaza Ritz Arms, is straight establishment. I don't drink. I never touch junk, not even aspirin. Yours very truly, Ivan Smernda Bombay, India June 8 Mr. Joe Binkle Plaza Ritz Arms New York City Dear Joe: Greetings, greetings, greetings. Hold firm in your wretched projection, for tomorrow you will not be alone in the not-world. In two days I, Glmpauszn, will be born. Today I hang in our newly developed not-pod just within the mirror gateway, torn with the agony that we calculated must go with such tremendous wavelength fluctuations. I have attuned myself to a fetus within the body of a not-woman in the not-world. Already I am static and for hours have looked into this weird extension of the Universe with fear and trepidation. As soon as my stasis was achieved, I tried to contact you, but got no response. What could have diminished your powers of articulate wave interaction to make you incapable of receiving my messages and returning them? My wave went out to yours and found it, barely pulsing and surrounded with an impregnable chimera. Quickly, from the not-world vibrations about you, I learned the not-knowledge of your location. So I must communicate with you by what the not-world calls "mail" till we meet. For this purpose I must utilize the feeble vibrations of various not-people through whose inadequate articulation I will attempt to make my moves known to you. Each time I will pick a city other than the one I am in at the time. I, Glmpauszn, come equipped with powers evolved from your fragmentary reports before you ceased to vibrate to us and with a vast treasury of facts from indirect sources. Soon our tortured people will be free of the fearsome not-folk and I will be their liberator. You failed in your task, but I will try to get you off with light punishment when we return again. The hand that writes this letter is that of a boy in the not-city of Bombay in the not-country of India. He does not know he writes it. Tomorrow it will be someone else. You must never know of my exact location, for the not-people might have access to the information. I must leave off now because the not-child is about to be born. When it is alone in the room, it will be spirited away and I will spring from the pod on the gateway into its crib and will be its exact vibrational likeness. I have tremendous powers. But the not-people must never know I am among them. This is the only way I could arrive in the room where the gateway lies without arousing suspicion. I will grow up as the not-child in order that I might destroy the not-people completely. All is well, only they shot this information file into my matrix too fast. I'm having a hard time sorting facts and make the right decision. Gezsltrysk, what a task! Farewell till later. Glmpauszn Wichita, Kansas June 13 Dear Joe: Mnghjkl, fhfjgfhjklop phelnoprausynks. No. When I communicate with you, I see I must avoid those complexities of procedure for which there are no terms in this language. There is no way of describing to you in not-language what I had to go through during the first moments of my birth. Now I know what difficulties you must have had with your limited equipment. These not-people are unpredictable and strange. Their doctor came in and weighed me again the day after my birth. Consternation reigned when it was discovered I was ten pounds heavier. What difference could it possibly make? Many doctors then came in to see me. As they arrived hourly, they found me heavier and heavier. Naturally, since I am growing. This is part of my instructions. My not-mother (Gezsltrysk!) then burst into tears. The doctors conferred, threw up their hands and left. I learned the following day that the opposite component of my not-mother, my not-father, had been away riding on some conveyance during my birth. He was out on ... what did they call it? Oh, yes, a bender. He did not arrive till three days after I was born. When I heard them say that he was straightening up to come see me, I made a special effort and grew marvelously in one afternoon. I was 36 not-world inches tall by evening. My not-father entered while I was standing by the crib examining a syringe the doctor had left behind. He stopped in his tracks on entering the room and seemed incapable of speech. Dredging into the treasury of knowledge I had come equipped with, I produced the proper phrase for occasions of this kind in the not-world. "Poppa," I said. This was the first use I had made of the so-called vocal cords that are now part of my extended matrix. The sound I emitted sounded low-pitched, guttural and penetrating even to myself. It must have jarred on my not-father's ears, for he turned and ran shouting from the room. They apprehended him on the stairs and I heard him babble something about my being a monster and no child of his. My not-mother appeared at the doorway and instead of being pleased at the progress of my growth, she fell down heavily. She made a distinct thump on the floor. This brought the rest of them on the run, so I climbed out the window and retreated across a nearby field. A prolonged search was launched, but I eluded them. What unpredictable beings! I reported my tremendous progress back to our world, including the cleverness by which I managed to escape my pursuers. I received a reply from Blgftury which, on careful analysis, seems to be small praise indeed. In fact, some of his phrases apparently contain veiled threats. But you know old Blgftury. He wanted to go on this expedition himself and it's his nature never to flatter anyone. From now on I will refer to not-people simply as people, dropping the qualifying preface except where comparisons must be made between this alleged world and our own. It is merely an offshoot of our primitive mythology when this was considered a spirit world, just as these people refer to our world as never-never land and other anomalies. But we learned otherwise, while they never have. New sensations crowd into my consciousness and I am having a hard time classifying them. Anyway, I shall carry on swiftly now to the inevitable climax in which I singlehanded will obliterate the terror of the not-world and return to our world a hero. I cannot understand your not replying to my letters. I have given you a box number. What could have happened to your vibrations? Glmpauszn Albuquerque, New Mexico June 15 Dear Joe: I had tremendous difficulty getting a letter off to you this time. My process—original with myself, by the way—is to send out feeler vibrations for what these people call the psychic individual. Then I establish contact with him while he sleeps and compel him without his knowledge to translate my ideas into written language. He writes my letter and mails it to you. Of course, he has no awareness of what he has done. My first five tries were unfortunate. Each time I took control of an individual who could not read or write! Finally I found my man, but I fear his words are limited. Ah, well. I had great things to tell you about my progress, but I cannot convey even a hint of how I have accomplished these miracles through the thick skull of this incompetent. In simple terms then: I crept into a cave and slipped into a kind of sleep, directing my squhjkl ulytz & uhrytzg ... no, it won't come out. Anyway, I grew overnight to the size of an average person here. As I said before, floods of impressions are driving into my xzbyl ... my brain ... from various nerve and sense areas and I am having a hard time classifying them. My one idea was to get to a chemist and acquire the stuff needed for the destruction of these people. Sunrise came as I expected. According to my catalog of information, the impressions aroused by it are of beauty. It took little conditioning for me finally to react in this manner. This is truly an efficient mechanism I inhabit. I gazed about me at the mixture of lights, forms and impressions. It was strange and ... now I know ... beautiful. However, I hurried immediately toward the nearest chemist. At the same time I looked up and all about me at the beauty. Soon an individual approached. I knew what to do from my information. I simply acted natural. You know, one of your earliest instructions was to realize that these people see nothing unusual in you if you do not let yourself believe they do. This individual I classified as a female of a singular variety here. Her hair was short, her upper torso clad in a woolen garment. She wore ... what are they? ... oh, yes, sneakers. My attention was diverted by a scream as I passed her. I stopped. The woman gesticulated and continued to scream. People hurried from nearby houses. I linked my hands behind me and watched the scene with an attitude of mild interest. They weren't interested in me, I told myself. But they were. I became alarmed, dived into a bush and used a mechanism that you unfortunately do not have—invisibility. I lay there and listened. "He was stark naked," the girl with the sneakers said. A figure I recognized as a police officer spoke to her. "Lizzy, you'll just have to keep these crackpot friends of yours out of this area." "But—" "No more buck-bathing, Lizzy," the officer ordered. "No more speeches in the Square. Not when it results in riots at five in the morning. Now where is your naked friend? I'm going to make an example of him." That was it—I had forgotten clothes. There is only one answer to this oversight on my part. My mind is confused by the barrage of impressions that assault it. I must retire now and get them all classified. Beauty, pain, fear, hate, love, laughter. I don't know one from the other. I must feel each, become accustomed to it. The more I think about it, the more I realize that the information I have been given is very unrealistic. You have been inefficient, Joe. What will Blgftury and the others say of this? My great mission is impaired. Farewell, till I find a more intelligent mind so I can write you with more enlightenment. Glmpauszn Moscow, Idaho June 17 Dear Joe: I received your first communication today. It baffles me. Do you greet me in the proper fringe-zone manner? No. Do you express joy, hope, pride, helpfulness at my arrival? No. You ask me for a loan of five bucks! It took me some time, culling my information catalog to come up with the correct variant of the slang term "buck." Is it possible that you are powerless even to provide yourself with the wherewithal to live in this inferior world? A reminder, please. You and I—I in particular—are now engaged in a struggle to free our world from the terrible, maiming intrusions of this not-world. Through many long gleebs, our people have lived a semi-terrorized existence while errant vibrations from this world ripped across the closely joined vibration flux, whose individual fluctuations make up our sentient population. Even our eminent, all-high Frequency himself has often been jeopardized by these people. The not-world and our world are like two baskets as you and I see them in our present forms. Baskets woven with the greatest intricacy, design and color; but baskets whose convex sides are joined by a thin fringe of filaments. Our world, on the vibrational plane, extends just a bit into this, the not-world. But being a world of higher vibration, it is ultimately tenuous to these gross peoples. While we vibrate only within a restricted plane because of our purer, more stable existence, these people radiate widely into our world. They even send what they call psychic reproductions of their own selves into ours. And most infamous of all, they sometimes are able to force some of our individuals over the fringe into their world temporarily, causing them much agony and fright. The latter atrocity is perpetrated through what these people call mediums, spiritualists and other fatuous names. I intend to visit one of them at the first opportunity to see for myself. Meanwhile, as to you, I would offer a few words of advice. I picked them up while examining the "slang" portion of my information catalog which you unfortunately caused me to use. So, for the ultimate cause—in this, the penultimate adventure, and for the glory and peace of our world—shake a leg, bub. Straighten up and fly right. In short, get hep. As far as the five bucks is concerned, no dice. Glmpauszn Des Moines, Iowa June 19 Dear Joe: Your letter was imponderable till I had thrashed through long passages in my information catalog that I had never imagined I would need. Biological functions and bodily processes which are labeled here "revolting" are used freely in your missive. You can be sure they are all being forwarded to Blgftury. If I were not involved in the most important part of my journey—completion of the weapon against the not-worlders—I would come to New York immediately. You would rue that day, I assure you. Glmpauszn Boise, Idaho July 15 Dear Joe: A great deal has happened to me since I wrote to you last. Systematically, I have tested each emotion and sensation listed in our catalog. I have been, as has been said in this world, like a reed bending before the winds of passion. In fact, I'm rather badly bent indeed. Ah! You'll pardon me, but I just took time for what is known quaintly in this tongue as a "hooker of red-eye." Ha! I've mastered even the vagaries of slang in the not-language.... Ahhh! Pardon me again. I feel much better now. You see, Joe, as I attuned myself to the various impressions that constantly assaulted my mind through this body, I conditioned myself to react exactly as our information catalog instructed me to. Now it is all automatic, pure reflex. A sensation comes to me when I am burned; then I experience a burning pain. If the sensation is a tickle, I experience a tickle. This morning I have what is known medically as a syndrome ... a group of symptoms popularly referred to as a hangover ... Ahhh! Pardon me again. Strangely ... now what was I saying? Oh, yes. Ha, ha. Strangely enough, the reactions that come easiest to the people in this world came most difficult to me. Money-love, for example. It is a great thing here, both among those who haven't got it and those who have. I went out and got plenty of money. I walked invisible into a bank and carried away piles of it. Then I sat and looked at it. I took the money to a remote room of the twenty room suite I have rented in the best hotel here in—no, sorry—and stared at it for hours. Nothing happened. I didn't love the stuff or feel one way or the other about it. Yet all around me people are actually killing one another for the love of it. Anyway.... Ahhh. Pardon me. I got myself enough money to fill ten or fifteen rooms. By the end of the week I should have all eighteen spare rooms filled with money. If I don't love it then, I'll feel I have failed. This alcohol is taking effect now. Blgftury has been goading me for reports. To hell with his reports! I've got a lot more emotions to try, such as romantic love. I've been studying this phenomenon, along with other racial characteristics of these people, in the movies. This is the best place to see these people as they really are. They all go into the movie houses and there do homage to their own images. Very quaint type of idolatry. Love. Ha! What an adventure this is becoming. By the way, Joe, I'm forwarding that five dollars. You see, it won't cost me anything. It'll come out of the pocket of the idiot who's writing this letter. Pretty shrewd of me, eh? I'm going out and look at that money again. I think I'm at last learning to love it, though not as much as I admire liquor. Well, one simply must persevere, I always say. Glmpauszn Penobscot, Maine July 20 Dear Joe: Now you tell me not to drink alcohol. Why not? You never mentioned it in any of your vibrations to us, gleebs ago, when you first came across to this world. It will stint my powers? Nonsense! Already I have had a quart of the liquid today. I feel wonderful. Get that? I actually feel wonderful, in spite of this miserable imitation of a body. There are long hours during which I am so well-integrated into this body and this world that I almost consider myself a member of it. Now I can function efficiently. I sent Blgftury some long reports today outlining my experiments in the realm of chemistry where we must finally defeat these people. Of course, I haven't made the experiments yet, but I will. This is not deceit, merely realistic anticipation of the inevitable. Anyway, what the old xbyzrt doesn't know won't muss his vibrations. I went to what they call a nightclub here and picked out a blonde-haired woman, the kind that the books say men prefer. She was attracted to me instantly. After all, the body I have devised is perfect in every detail ... actually a not-world ideal. I didn't lose any time overwhelming her susceptibilities. I remember distinctly that just as I stooped to pick up a large roll of money I had dropped, her eyes met mine and in them I could see her admiration. We went to my suite and I showed her one of the money rooms. Would you believe it? She actually took off her shoes and ran around through the money in her bare feet! Then we kissed. Concealed in the dermis of the lips are tiny, highly sensitized nerve ends which send sensations to the brain. The brain interprets these impulses in a certain manner. As a result, the fate of secretion in the adrenals on the ends of the kidneys increases and an enlivening of the entire endocrine system follows. Thus I felt the beginnings of love. I sat her down on a pile of money and kissed her again. Again the tingling, again the secretion and activation. I integrated myself quickly. Now in all the motion pictures—true representations of life and love in this world—the man with a lot of money or virtue kisses the girl and tries to induce her to do something biological. She then refuses. This pleases both of them, for he wanted her to refuse. She, in turn, wanted him to want her, but also wanted to prevent him so that he would have a high opinion of her. Do I make myself clear? I kissed the blonde girl and gave her to understand what I then wanted. Well, you can imagine my surprise when she said yes! So I had failed. I had not found love. I became so abstracted by this problem that the blonde girl fell asleep. I thoughtfully drank quantities of excellent alcohol called gin and didn't even notice when the blonde girl left. I am now beginning to feel the effects of this alcohol again. Ha. Don't I wish old Blgftury were here in the vibrational pattern of an olive? I'd get the blonde in and have her eat him out of a Martini. That is a gin mixture. I think I'll get a hot report off to the old so-and-so right now. It'll take him a gleeb to figure this one out. I'll tell him I'm setting up an atomic reactor in the sewage systems here and that all we have to do is activate it and all the not-people will die of chain asphyxiation. Boy, what an easy job this turned out to be. It's just a vacation. Joe, you old gold-bricker, imagine you here all these gleebs living off the fat of the land. Yak, yak. Affectionately. Glmpauszn Sacramento, Calif. July 25 Dear Joe: All is lost unless we work swiftly. I received your revealing letter the morning after having a terrible experience of my own. I drank a lot of gin for two days and then decided to go to one of these seance things. Somewhere along the way I picked up a red-headed girl. When we got to the darkened seance room, I took the redhead into a corner and continued my investigations into the realm of love. I failed again because she said yes immediately. The nerves of my dermis were working overtime when suddenly I had the most frightening experience of my life. Now I know what a horror these people really are to our world. The medium had turned out all the lights. He said there was a strong psychic influence in the room somewhere. That was me, of course, but I was too busy with the redhead to notice. Anyway, Mrs. Somebody wanted to make contact with her paternal grandmother, Lucy, from the beyond. The medium went into his act. He concentrated and sweated and suddenly something began to take form in the room. The best way to describe it in not-world language is a white, shapeless cascade of light. Mrs. Somebody reared to her feet and screeched, "Grandma Lucy!" Then I really took notice. Grandma Lucy, nothing! This medium had actually brought Blgftury partially across the vibration barrier. He must have been vibrating in the fringe area and got caught in the works. Did he look mad! His zyhku was open and his btgrimms were down. Worst of all, he saw me. Looked right at me with an unbelievable pattern of pain, anger, fear and amazement in his matrix. Me and the redhead. Then comes your letter today telling of the fate that befell you as a result of drinking alcohol. Our wrenchingly attuned faculties in these not-world bodies need the loathsome drug to escape from the reality of not-reality. It's true. I cannot do without it now. The day is only half over and I have consumed a quart and a half. And it is dulling all my powers as it has practically obliterated yours. I can't even become invisible any more. I must find the formula that will wipe out the not-world men quickly. Quickly! Glmpauszn Florence, Italy September 10 Dear Joe: This telepathic control becomes more difficult every time. I must pick closer points of communication soon. I have nothing to report but failure. I bought a ton of equipment and went to work on the formula that is half complete in my instructions. Six of my hotel rooms were filled with tubes, pipes and apparatus of all kinds. I had got my mechanism as close to perfect as possible when I realized that, in my befuddled condition, I had set off a reaction that inevitably would result in an explosion. I had to leave there immediately, but I could not create suspicion. The management was not aware of the nature of my activities. I moved swiftly. I could not afford time to bring my baggage. I stuffed as much money into my pockets as I could and then sauntered into the hotel lobby. Assuming my most casual air, I told the manager I was checking out. Naturally he was stunned since I was his best customer. "But why, sir?" he asked plaintively. I was baffled. What could I tell him? "Don't you like the rooms?" he persisted. "Isn't the service good?" "It's the rooms," I told him. "They're—they're—" "They're what?" he wanted to know. "They're not safe." "Not safe? But that is ridiculous. This hotel is...." At this point the blast came. My nerves were a wreck from the alcohol. "See?" I screamed. "Not safe. I knew they were going to blow up!" He stood paralyzed as I ran from the lobby. Oh, well, never say die. Another day, another hotel. I swear I'm even beginning to think like the not-men, curse them. Glmpauszn Rochester, New York September 25 Dear Joe: I have it! It is done! In spite of the alcohol, in spite of Blgftury's niggling criticism, I have succeeded. I now have developed a form of mold, somewhat similar to the antibiotics of this world, that, transmitted to the human organism, will cause a disease whose end will be swift and fatal. First the brain will dissolve and then the body will fall apart. Nothing in this world can stop the spread of it once it is loose. Absolutely nothing. We must use care. Stock in as much gin as you are able. I will bring with me all that I can. Meanwhile I must return to my original place of birth into this world of horrors. There I will secure the gateway, a large mirror, the vibrational point at which we shall meet and slowly climb the frequency scale to emerge into our own beautiful, now secure world. You and I together, Joe, conquerors, liberators. You say you eat little and drink as much as you can. The same with me. Even in this revolting world I am a sad sight. My not-world senses falter. This is the last letter. Tomorrow I come with the gateway. When the gin is gone, we will plant the mold in the hotel where you live. In only a single gleeb it will begin to work. The men of this queer world will be no more. But we can't say we didn't have some fun, can we, Joe? And just let Blgftury make one crack. Just one xyzprlt. I'll have hgutry before the ghjdksla! Glmpauszn Dear Editor: These guys might be queer drunk hopheads. But if not? If soon brain dissolve, body fall apart, how long have we got? Please, anybody who knows answer, write to me—Ivan Smernda, Plaza Ritz Arms—how long is a gleeb?	B. The not-world unkowningly overlaps and disrupts his.
Why can't Joe go back to 1960? A. Temple-Tracy destroyed the vortex manipulator. B. The time circuits were damaged when they brought Joe into the future. C. Temple -Tracy destroyed the time transmitter. D. Time only moves one way.	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. Time only moves one way.
What is Peggy's importance to the mission? A. She is Alan's motivation for making it out alive. B. She is the one who successfully shuts down the robots. C. She is a medical officer on board the larger ship. D. She is Pete's wife and helped him design the robots.	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.	A. She is Alan's motivation for making it out alive.
What is the primary purpose of the article? A. To share a historical account of 14th century commerce practices and why they were replaced B. To propose a model for international commerce in nation-states with divided populations C. To lament and decry Britain's misguided decision to abandon the European Union D. To entertain readers with an ironic predicament that has resulted from western globalization	What cities in the post-Brexit era could learn from a 14th-century trading bloc As you fly from the country now known as Germany to Britain, the coastal geography of northern European cities gently unfurls. You can see where the sea smacks into them, or where yawning estuaries unfold like funnels between green and brown city and choppy blue water. You can track the snaking rivers and canals that form unrepentant umbilical connections to the settlements set a little further inland. By their nature cities along coasts and rivers developed so they could be open to trade with each other. From the middle of the 13th century, and for some 300 years after, many settlements dotted along this route formed the prosperous Hanseatic League, a European trading confederation of market towns, before the rise of the nation state led to its dissolution. The Hanseatic League is not well known, and today it lives on most prominently in the name of the German national airline Lufthansa, literally the 'Hansa of the skies', whose planes you can look out of – and down towards the Hanseatic cities – on the short journeys between mainland Europe and Britain. The letters HH on the number plates of cars in Hamburg stand for Hansestadt Hamburg: another proud little memory of this hidden history. In the traumatised atmosphere of post-Brexit Britain, it is worth remembering the Hanseatic League. It could point us towards new relationships between progressive city dwellers in a world that otherwise seems to be putting the brakes on modernity. Despite some of Britain's Leave voters longing to inhabit a fantastical realm immune to foreign influence, the reality is patently very different to that. In the late 1300s, Chaucer wrote about characters travelling to Jerusalem, and others who came from Europe; and it was at exactly this point that the Hanseatic League slowly started to coalesce, eventually influencing our isles. The League is most easily understood as a loose federation of cities that acted together in self-interest to promote trade. The Hanseatic cities developed their own legal system, and their armies came to one another's aid. Merchants who wanted to buy and sell and travel were taking the lead at a time when nation states were not fit for purpose: in the case of England or Denmark, leadership was too centralised and authoritarian, while in German-speaking lands a nation had yet to be formed. We think of nations today as elemental almost, immovable. Yet look at any city of Mitteleuropa and you'll see the many different names it has had as borders and regimes have shifted with the sands of time. Nations come and go. Cities endure. "It is often said that great cities survived great empires," says Cristina Ampatzidou, editor-in-chief of the Rotterdam-based online publishing platform Amateur Cities. "So it is not unrealistic to think of cities as discrete entities that compete and collaborate with each other, independently from the states to which they belong." The cities involved in the Hanseatic League are found along the Baltic and North Sea coasts, and slightly inland too. The League stretched from Novgorod in the east – in what is now Russia – to London in the west. Tallinn, Riga, Gdańsk, Visby, Berlin, Cologne, Antwerp, Stockholm, Bergen, Kiel, Rostock, Dinant, Bruges, Turku, Groningen, Hanover, Wroclaw, Kaliningrad: all were involved at different stages in the Hanse's history, which ran on into the 1500s. The League covered lands that today find themselves a part of the modern nations of Finland, Sweden, Poland, the Netherlands, Belgium, France, Norway, Lithuania, Estonia and Latvia. It was a huge – and hugely ambitious – undertaking in the days when communications consisted of ink and paper and the only viable method of travel was by ship. Wood, fur, wool, silver, herring, cod and salt were the main items traded. But what was also exchanged was knowledge. In some ways it was an exercise in what we today call 'soft diplomacy'. There was no maniacal ruler overseeing things – merchants met and talked. They raised armies and waged war against kings who threatened their businesses and their freedoms and their peace. There was a kind of proto-democracy at work. Professor Rainer Postel, of the Bundeswehr Universität (Germany's equivalent of Sandhurst military academy), has described the Hanse as "a community of interests without power politics". As David Abulafia, Professor of Mediterranean History at Cambridge points out, "The lack of an elaborate superstructure was one of the things that made the Hanse work. Having said that, one should recognise that Lübeck in particular dominated the League for long periods." Lübeck was where the merchants most often met; and where renewed recent interest in the Hanse eventually led to Angela Merkel cutting the ribbon at the brand new European Hansemuseum in the city last year. Germany today – multicultural, economically and culturally motoring, free and fair – seems like the ideal model for a modern European nation state. And part of that success lies in the gravitas the country has given to its Hanseatic history. For Germany is not a top-down country with one city unhealthily dominating as with France and Britain (regional economic inequalities have plagued Britain since the painful de-industrialisation of the 1980s, especially in the north). Germany respects federalism and its cities exist on a much more even keel. The way that Cologne, Munich, Frankfurt, Dusseldorf and Stuttgart all bring varied economic and cultural character to the party is pure Hanse. The former Hanseatic cities of Hamburg, Berlin and Bremen have city state status within Germany, putting them on the same level as a whole region or 'land' like Bavaria or Brandenburg. So how about a new Hanseatic League? I ask Benjamin Barber, senior fellow at New York's Fordham University. "I believe you will find there is a new Hanse," he says, "that constituted itself about 10 or 11 years ago – including many of the original Hanseatic League cities." Barber is founder of the Global Parliament of Mayors, which he describes as a kind of Hanse of all cities, not just European ports, which will give cities a global urban voice and a common platform for action. The parliament convenes for its inaugural session in The Hague in September. "Cities both exist within nations and transcend nations. Their power lies not just in the extent of de jure autonomy ceded or granted by 'higher' levels of government," says Bruce Katz, centennial scholar at the Washington DC thinktank the Brookings Institution. "Rather, cities have de facto power, the result of larger market and demographic forces and environmental imperatives that value proximity, density, connectivity and quality. Smart nations will see themselves as partners to their cities, setting strong platforms for urban prosperity and devolving powers, where appropriate, to give cities the flexibility to perform… Dumb nations will continue to dictate from above, stifling market activity and urban potential." But could we go further? Could cities like London declare independence from the UK? London's economy is larger than that of Scotland and Wales combined. "States will not vanish or surrender their waning sovereignty," says Benjamin Barber. "But cities will meet across frontiers and work together to solve problems. The objective is not an independent London or New York, but interdependent cities collaborating globally. And that is happening." London's voters largely wanted to remain a part of the EU and to maintain the city's status as an entrepôt. There is clearly a widening chasm between urban and rural life at the heart of many nations. Visualisations of Austria's recent presidential election showed the issue clearly: the country's cities voted for the Green candidate Alexander Van der Bellen, while the the rural districts went for right-wing nationalist Norbert Hofer (whose legal challenge to the close result has resulted in a rerun being announced for October). And in the USA in November, it's likely that Trump voters will also come from rural areas and Clinton voters from the cities. City dwellers are finding ever more in common with the world's other city dwellers than with their countrymen 50 miles down the road. Back in Britain, one of history's little oddities pops up on the east coast. Boston in Lincolnshire and King's Lynn in Norfolk were both forward-looking Hanseatic League towns that traded with far-flung ports and hosted foreign merchants. King's Lynn contains the only extantHanse House left in Britain (London's was knocked down to build Cannon Street Station in the 1800s). Yet in the EU referendum these two areas polled among the highest Leave votes of anywhere in the country. "Things change," says LSE's Professor Tony Travers. "[King's Lynn] used to be very highly connected, but the economy moved on and left those trading ports like it in a different situation." Take, for example, the pivot towards the New World, with which trade made more sense from the west-coast ports like Bristol and Liverpool. While these boomed between the 1600s and 1800s, the Hanseatic ports declined and then died out. "One of the things that's interesting about the [referendum] decision is that it begs all sorts of questions about the future of the UK and its relationship with Europe; and of London and Scotland and their relationship with the rest of Europe. When the EU began as the EEC in the mid-20th century some saw it as a modern day Hanse. Now the EU seems to be waning, perhaps its successor will have to ape the Hanse even more." For all its complex beauty, life can ultimately be reduced to a series of binary options: yes or no, stick or twist, in or out, innovation or stagnation, modernity or mythology. The referendum result was disappointing for many progressive observers because it felt like a step backwards. Despite being primarily about trade monopolies and money making, the Hanse was, in its way, an early stab at stepping forwards: it encompassed internationalism, rational thought, free trade, loose democratic institutions and, most crucially of all, movement. The future, for many observers, can only be understood in terms of the free movement of people, capital, goods and ideas. It is this necessary movement, and its possible curtailment, that could be the spark that leads to cities like London to seek independence and parity with other world cities – rather than with the rural hinterlands of Britain. Of course, cities seceding from their nation states would provide huge headaches for countries whose biggest economic driver had been removed – as well as likely deepening ideological differences between city and rural dwellers. Moreover, cities need the food the countryside provides. Yet for all the potential pitfalls, city states can thrive. Look at Singapore, Hong Kong, or de facto city states like Dubai and Abu Dhabi. One of the most telling characteristics about these four – all of course former British imperial enclaves – is that they are utterly outward looking. To return to the sky analogy, it's the airlines of each of these (Singapore Airlines, Cathay Pacific, Emirates and Etihad) that open up each respective city to the world in the way that the machinery of the Hanse did on the Baltic Sea 600 years ago. And it's the unions each city makes with other places that also look thoroughly Hanseatic in character. A model for modern city states, then. But is it one that we want? "The Hanseatic League was not always accepted by local citizens," says Cristina Ampatzidou, "because the privileges granted to the Hanse merchants were forcing local traders out of competition and many cities took steps to eliminate them. The reasons the countryside is turning to the right [globally] are not independent from cities turning increasingly into speculation machines for the profit of a happy few. It is basically these systemic contradictions that must be addressed before we resort to more isolationist ideas that would intensify the urban-rural political divide. The bottom line is not whether a contemporary Hanse-esque federation is possible, it probably is; but whether it is actually desirable." This article was originally published on TheLong+Short. Read the original article.	B. To propose a model for international commerce in nation-states with divided populations
Which metrics do they use to evaluate simultaneous translation?	### Introduction Simultaneous translation is a translation task where the translation process starts before the end of an input. It helps real-time spoken language communications such as human conversations and public talks. A usual machine translation system works in the sentence level and starts its translation process after it reads the end of a sentence. It would not be appropriate for spoken languages due to roughly two issues: (1) sentence boundaries are not clear and (2) a large latency occurs for a long input. Previous studies tackled this problem by an incremental process, in order to reduce the translation latency for a given input. fujita13interspeech proposed a phrase-based approach to the simultaneous translation based on phrasal reordering probabilities. oda-etal-2015-syntax proposed a syntax-based method to determine when to start translation of observed inputs. Such an approach faces a trade-off between speed and accuracy; reducing the translation latency using very limited context information also causes the loss in the translation accuracy. This becomes more serious especially in a syntactically-distant language pair such as English and Japanese, where we sometimes have to wait a latter part of a source language to determine the corresponding former part in a target language. Recent neural machine translation (NMT) studies tried an incremental processing for the simultaneous translation. gu2017learning proposed a reinforcement learning approach to determine when to translate based on two different actions: READ to take one input token and WRITE to generate one output token. While they reported some latency reduction without the loss of translation accuracy, the NMT model itself is trained independently from this incremental manner and is not fully optimized for simultaneous translation. ma2018stacl proposed a very simple incremental method called Wait-k, where the decoder starts to generate output tokens after the encoder reads k tokens and then works token-by-token. Here, some required inputs may not be observed by the encoder; however, the decoder has to predict the next output token even in that case. This approach enables a simple end-to-end simultaneous NMT with implicit anticipation of unobserved inputs. It showed high translation accuracy with small latency on some common English-to-German and Chinese-to-English datasets. The latency hyperparameter k can be used to control the speed-accuracy trade-off, but it has to be large enough for a distant language pair like English-Japanese. We observed a problem in translating a phrase longer than k tokens in our pilot study on English-to-Japanese translation. In this work, we propose a novel incremental NMT method that uses a special token <wait> in the target language which is generated when the translation model chooses to read the next input token instead of generating an output token. The proposed method uses Connectionist Temporal Classification (CTC) BIBREF0 to handle ambiguities in possible positions inserting <wait> in the training time. CTC is applied to sequential model training such as automatic speech recognition, where we have a reference word sequence but do not have the corresponding segmentation or alignment in an acoustic signal. We conduct experiments in English-to-Japanese simultaneous translation with the proposed and baseline methods and show the proposed method achieves a good translation performance with relatively small latency. The proposed method can determine when to wait or translate in an adaptive manner and is useful in simultaneous translation tasks. ### Simultaneous machine translation by Wait-k model First, we review a general NMT model following the formulation by BIBREF1 and the “Wait-k" model BIBREF2 that is the baseline model for simultaneous NMT. Given a source sentence $X$ and a target sentence $Y$ as follows: where $\textbf {x}_i \in \mathbb {R}^{S \times 1}$ is a one-hot vector of the i-th input word, $I$ is the length of the input sentence $X$, $\textbf {y}_i \in \mathbb {R}^{T \times 1}$ is a one-hot vector of the i-th output word, and $J$ is the length of the output sentence $Y$. The problem of translation from the source to the target language can be solved by finding the best target language sentence $\hat{Y}$ that maximizes the conditional probability In general NMT manner, the conditional probability is decomposed by the product of conditional generation probabilities of $\textbf {y}_{j}$ given the source sentence $X$ and preceding target words $\textbf {y}_{<j}$: where $\textbf {y}_{<j}$ represents the target words up to position $j$, and $\theta $ indicates the model parameters. In contrast, the model for simultaneous translation has to output translated words given only prefix words of the source sentence. Therefore, the conditional probability is decomposed as follows: where $\textbf {x}_{<g(j)}$ are the target words up to position $g(j)$ and $g(j)$ represents the number of encoded source tokens when the model outputs $j$ words. In the “Wait-k" model, $g(j)$ is defined as follows: Here, $k$ is the hyperparameter which indicates the target sentence generation is $k$ tokens behind the source sentence input and it takes a constant value in the “Wait-k" model. The model is composed of an encoder (§SECREF5) and a decoder with the attention mechanism (§SECREF7) that are both implemented using recurrent neural networks (RNNs); the encoder converts source words into a sequence of vectors, and the decoder generates target language words one-by-one with the attention mechanism based on the conditional probability shown in the equation DISPLAY_FORM2 and DISPLAY_FORM3. The details are described below. ### Simultaneous machine translation by Wait-k model ::: Encoder The encoder takes a sequence of a source sentence $X$ as inputs and returns forward hidden vectors $\overrightarrow{\textbf {h}_i}(1 \le i \le I)$ of the forward RNNs: In the general NMT model, they also calculate backward hidden vectors of backward RNNs from a reversed source sentence. However, we only use forward hidden vectors because we cannot use the information of the whole sentence on the simultaneous translation task. ### Simultaneous machine translation by Wait-k model ::: Decoder with Attention The decoder takes source hidden vectors as inputs and returns target language words one-by-one with the attention mechanism. The decoder RNNs recurrently generates target words using its hidden state and an output context. The conditional generation probability of the target word $\textbf {y}_i$ defined as follows: Here, $\textbf {W}_c, \textbf {W}_p$ are trainable parameters and $\textbf {c}_j$ is a context vector to retrieve source language inputs in forms of a weighted sum of the source hidden vectors $\textbf {h}_j$, defined as follows. The score function above can be defined in some different ways as discussed by BIBREF1. In this paper, we use dot attention for this score function. ### Proposed Method In this work, we proposed the method to decide the output timing adaptively. The proposed method introduces a special token <wait> which is output instead of delaying translation to target-side vocabulary. In this section, we first review a standard objective function, softmax cross-entropy and show the problem that occurs when this function is applied to <wait> (§SECREF10). After that, we introduce an objective function, called Connectionist Temporal Classification, to handle this problem (§SECREF12). Finally, we propose a new objective function to adjust a trade-off between translation accuracy and latency (§SECREF14) and explain how to combine these objective functions (§SECREF16). ### Proposed Method ::: Softmax Cross-Entropy Softmax Cross-Entropy (SCE) is a commonly used token-level objective function for multi-class classification including word generation in NMT, defined as follows: where $\textbf {y}_{ij}$ is a j-th element of the one-hot vector corresponding to the i-th words of the reference sentence and $p(\textbf {y}_{jk}\|\cdot )$ is the generation probability of $\textbf {y}_{jk}$. A correct sequence that corresponds to an output sequence one-by-one is necessary to use SCE as an objective function for NMT. However, in the proposed method, we cannot simply use SCE because we don't know when we should cause delay. To avoid this problem, we set the loss for delay tokens to 0 during the time step $t\ (t \le g(I))$ which the model can output <wait> , or while a source sentence is inputted. ### Proposed Method ::: Connectionist Temporal Classification As we mentioned in the previous section, we set the loss value for <wait> to 0, but this causes the problem that it does not optimize about generating <wait> . Against this problem, we use an objective function called Connectionist Temporal Classification (CTC) BIBREF0 for sequence-level optimization. CTC extends output sequence, called Path $\mathbf {\pi } = \Omega (\textbf {y})$, to the length $T$ by allowing token repetitions and outputting <wait> . Conversely, we can obtain an original output sequence $\textbf {y} = \Omega ^{-1}(\mathbf {\pi })$ by removing <wait> and all token repetitions. The objective function is defined the sum of the probabilities of all possible paths $\mathbf {\pi } \in \Omega (\textbf {y})$ by using the forward-backward algorithm, as follows: where $\pi _t$ is a t-th element of $\mathbf {\pi }$. ### Proposed Method ::: Delay Penalty Furthermore, we introduce a new objective function, called Delay Penalty, to control latency. We use this function only when an output token causes the delay; that is, when the model outputs <wait> or the same token as a previous one. Delay Penalty is defined by a negative log-likelihood of the probabilities for non-delayed tokens, as follows: ### Proposed Method ::: Objective Function For optimization, we combine three objective functions introduced so far, as follows: Here, $\alpha $ is a hyperparameter to adjust the amount of latency directly. ### Experiments We conducted simultaneous translation experiments from English to Japanese and discussed accuracy, latency, and issues for translation results. ### Experiments ::: Settings All models were implemented as described in the previous sections using PyTorch. Both the encoders and the decoders were two-layered uni-direcitional LSTM BIBREF3, and the decoder used input feedingBIBREF1. The number of dimensions in word embeddings and hidden vectors was set to 512, and the minibatch size was 64. We use Adam BIBREF4 for optimization with the default parameters. The learning rate was set to $10^{-1}$, and gradient clipping was set to 5. The dropout probability was set to $0.3$. The learning rate was adjusted by a decay factor of $1/\sqrt{2}$ when the validation loss was larger than that in the previous epoch. Then, we chose the best parameter/model with the smallest validation loss for evaluation. We used two different corpora for the experiments: small_parallel_enja and Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF5. small_parallel_enja is a small-scale corpus that is consist of sentences filtered sentence length 4 to 16 words, and ASPEC is a mid-scale corpus of the scientific paper domain. Table TABREF21 shows their detailed statistics. All datasets were tokenized into subword unit BIBREF6, BIBREF7 by using Sentencepiece . The source and target language vocabularies were independent, and their size was set to 4000 tokens for small_parallel_enja and 8000 tokens for ASPEC, respectively. We filtered out the sentence whose number of tokens was more than 60 tokens, or the length ratio was more than 9 from the training set. We used “Wait-k” models and general NMT models as baseline models. General NMT models were attention-based encoder-decoder and it translated sentences from full-length source sentences (called Full Sentence). For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy. ### Experiments ::: Experiments with Small-scale Corpus We conducted small-scale experiments using small_parallel_enja. We compared different hyperparameters: $k = \lbrace 3, 5\rbrace $ and $\alpha = \lbrace 0, 0.01, 0.03, 0.05\rbrace $. Table TABREF24 shows the results in latency and automatic evaluation scores on small_parallel_enja. The full sentence scores are upper bounds of incremental methods. The proposed method reduced the average latency in more than 50% from the full sentence baseline with some loss in BLEU and RIBES. The BLEU and RIBES results by the proposed method were worse than those by Wait-k. Th would be due to some degradation in smaller latency parts that were determined adaptively by the proposed methods while Wait-k keeps the fixed latency. ### Experiments ::: Experiments with Mid-scale Corpus We investigated the performance on longer and more complex sentences by the experiments using ASPEC. We compared different hyperparameters: $k = \lbrace 5, 7\rbrace $ and $\alpha = \lbrace 0.03, 0.05, 0.1\rbrace $. Table TABREF26 shows the results in latency and automatic evaluation scores on ASPEC. We can see the proposed method showed much larger latency than Wait-k. This is probably due to many long and complex phrases used in scientific articles in ASPEC. Wait-k has to translate such a long phrase without sufficient input observations due to its strict fixed latency strategy. On the other hand, the proposed method can wait for more input tokens adaptively by generating <wait> at the cost of large latency. ### Experiments ::: Discussion In the experimental results above, the proposed method determined the translation latency adaptively, short delay for short and simple inputs as in small_parallel_enja and long delay for long and complex inputs as in ASPEC. Here we discuss our results in detail using some examples. Table TABREF28 shows translation examples on small_parallel_enja. In the first example, the proposed method gives a correct translation result by adaptive waits. Wait-k generated unrelated words UTF8min野球 (baseball) and UTF8min飲-み (drink) due to the poor input observations with its small fixed latency. The proposed method waited until a subword swim was observed and successfully generate a word UTF8min泳-ぐ (swim). However, the proposed method sometimes generated consecutive <wait> symbols until the end of input, as shown in the second example. This is probably due to our training strategy; the latency penalty would not be large enough to choose small latency translation at the cost of some increase in SCE- and CTC-based loss. The translation data in the experiments are not from simultaneous interpretation but standard translation, so the current task does not match with the proposed approach. The use of specialized data for simultaneous translation would be important in practice, such as monotonic translations like simultaneous translation. ### Conclusion In this paper, we proposed an adaptive latency control method for simultaneous neural machine translation in syntactically distant language pairs. We introduced a meta token <wait> to wait until the observation of the next input token. We proposed a CTC-based loss function to perform optimization using bilingual data without appropriate positions of <wait> , which is used along with the latency penalty and a standard word prediction loss. The experimental results suggest the proposed method determines when to translate or when to wait in an adaptive manner. Future work includes further analyses on translation accuracy in different latency conditions and time-based latency evaluation instead of the token-based one. ### Acknowledgments A part of this work is supported by JSPS Kakenhi JP17H06101. Table 1: Number of sentences for each corpus used in the experiments. Table 2: Latency and automatic evaluation scores with small parallel enja. Latencies are shown by averages and standard deviations (in parentheses) in the number of tokens. Table 3: Latency and automatic evaluation scores with ASPEC. Table 4: Translation examples in small parallel enja. w shows the generation of <wait> token.	BLEU BIBREF8, RIBES BIBREF9, token-level delay
Which three Twitter sentiment classification datasets are used for experiments?	### Introduction Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of SVMs and Naive Bayes (NB) BIBREF3 . In addition, hybrid approaches combining lexicon-based and machine learning methods also achieved high performance described in BIBREF4 . However, a problem of traditional machine learning is how to define a feature extractor for a specific domain in order to extract important features. Deep learning models are different from traditional machine learning methods in that a deep learning model does not depend on feature extractors because features are extracted during training progress. The use of deep learning methods becomes to achieve remarkable results for sentiment analysis BIBREF5 BIBREF6 BIBREF7 . Some researchers used Convolutional Neural Network (CNN) for sentiment classification. CNN models have been shown to be effective for NLP. For example, BIBREF6 proposed various kinds of CNN to learn sentiment-bearing sentence vectors, BIBREF5 adopted two CNNs in character-level to sentence-level representation for sentiment analysis. BIBREF7 constructs experiments on a character-level CNN for several large-scale datasets. In addition, Long Short-Term Memory (LSTM) is another state-of-the-art semantic composition model for sentiment classification with many variants described in BIBREF8 . The studies reveal that using a CNN is useful in extracting information and finding feature detectors from texts. In addition, a LSTM can be good in maintaining word order and the context of words. However, in some important aspects, the use of CNN or LSTM separately may not capture enough information. Inspired by the models above, the goal of this research is using a Deep Convolutional Neural Network (DeepCNN) to exploit the information of characters of words in order to support word-level embedding. A Bi-LSTM produces a sentence-wide feature representation based on these embeddings. The Bi-LSTM is a version of BIBREF9 with Full Gradient described in BIBREF10 . In addition, the rules-based approach also effects classification accuracy by focusing on important sub-sentences expressing the main sentiment of a tweet while removing unnecessary parts of a tweet. The paper makes the following contributions: The organization of the present paper is as follows: In section 2, we describe the model architecture which introduces the structure of the model. We explain the basic idea of model and the way of constructing the model. Section 3 show results and analysis and section 4 summarize this paper. ### Basic idea Our proposed model consists of a deep learning classifier and a tweet processor. The deep learning classifier is a combination of DeepCNN and Bi-LSTM. The tweet processor standardizes tweets and then applies semantic rules on datasets. We construct a framework that treats the deep learning classifier and the tweet processor as two distinct components. We believe that standardizing data is an important step to achieve high accuracy. To formulate our problem in increasing the accuracy of the classifier, we illustrate our model in Figure. FIGREF4 as follows: Tweets are firstly considered via a processor based on preprocessing steps BIBREF0 and the semantic rules-based method BIBREF11 in order to standardize tweets and capture only important information containing the main sentiment of a tweet. We use DeepCNN with Wide convolution for character-level embeddings. A wide convolution can learn to recognize specific n-grams at every position in a word that allows features to be extracted independently of these positions in the word. These features maintain the order and relative positions of characters. A DeepCNN is constructed by two wide convolution layers and the need of multiple wide convolution layers is widely accepted that a model constructing by multiple processing layers have the ability to learn representations of data with higher levels of abstraction BIBREF12 . Therefore, we use DeepCNN for character-level embeddings to support morphological and shape information for a word. The DeepCNN produces INLINEFORM0 global fixed-sized feature vectors for INLINEFORM1 words. A combination of the global fixed-size feature vectors and word-level embedding is fed into Bi-LSTM. The Bi-LSTM produces a sentence-level representation by maintaining the order of words. Our work is philosophically similar to BIBREF5 . However, our model is distinguished with their approaches in two aspects: Using DeepCNN with two wide convolution layers to increase representation with multiple levels of abstraction. Integrating global character fixed-sized feature vectors with word-level embedding to extract a sentence-wide feature set via Bi-LSTM. This deals with three main problems: (i) Sentences have any different size; (ii) The semantic and the syntactic of words in a sentence are captured in order to increase information for a word; (iii) Important information of characters that can appear at any position in a word are extracted. In sub-section B, we introduce various kinds of dataset. The modules of our model are constructed in other sub-sections. ### Data Preparation Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 . Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification. Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 . ### Preprocessing We firstly take unique properties of Twitter in order to reduce the feature space such as Username, Usage of links, None, URLs and Repeated Letters. We then process retweets, stop words, links, URLs, mentions, punctuation and accentuation. For emoticons, BIBREF0 revealed that the training process makes the use of emoticons as noisy labels and they stripped the emoticons out from their training dataset because BIBREF0 believed that if we consider the emoticons, there is a negative impact on the accuracies of classifiers. In addition, removing emoticons makes the classifiers learns from other features (e.g. unigrams and bi-grams) presented in tweets and the classifiers only use these non-emoticon features to predict the sentiment of tweets. However, there is a problem is that if the test set contains emoticons, they do not influence the classifiers because emoticon features do not contain in its training data. This is a limitation of BIBREF0 , because the emoticon features would be useful when classifying test data. Therefore, we keep emoticon features in the datasets because deep learning models can capture more information from emoticon features for increasing classification accuracy. ### Semantic Rules (SR) In Twitter social networking, people express their opinions containing sub-sentences. These sub-sentences using specific PoS particles (Conjunction and Conjunctive adverbs), like "but, while, however, despite, however" have different polarities. However, the overall sentiment of tweets often focus on certain sub-sentences. For example: @lonedog bwahahah...you are amazing! However, it was quite the letdown. @kirstiealley my dentist is great but she's expensive...=( In two tweets above, the overall sentiment is negative. However, the main sentiment is only in the sub-sentences following but and however. This inspires a processing step to remove unessential parts in a tweet. Rule-based approach can assists these problems in handling negation and dealing with specific PoS particles led to effectively affect the final output of classification BIBREF11 BIBREF16 . BIBREF11 summarized a full presentation of their semantic rules approach and devised ten semantic rules in their hybrid approach based on the presentation of BIBREF16 . We use five rules in the semantic rules set because other five rules are only used to compute polarity of words after POS tagging or Parsing steps. We follow the same naming convention for rules utilized by BIBREF11 to represent the rules utilized in our proposed method. The rules utilized in the proposed method are displayed in Table TABREF15 in which is included examples from STS Corpus and output after using the rules. Table TABREF16 illustrates the number of processed sentences on each dataset. ### Representation Levels To construct embedding inputs for our model, we use a fixed-sized word vocabulary INLINEFORM0 and a fixed-sized character vocabulary INLINEFORM1 . Given a word INLINEFORM2 is composed from characters INLINEFORM3 , the character-level embeddings are encoded by column vectors INLINEFORM4 in the embedding matrix INLINEFORM5 , where INLINEFORM6 is the size of the character vocabulary. For word-level embedding INLINEFORM7 , we use a pre-trained word-level embedding with dimension 200 or 300. A pre-trained word-level embedding can capture the syntactic and semantic information of words BIBREF17 . We build every word INLINEFORM8 into an embedding INLINEFORM9 which is constructed by two sub-vectors: the word-level embedding INLINEFORM10 and the character fixed-size feature vector INLINEFORM11 of INLINEFORM12 where INLINEFORM13 is the length of the filter of wide convolutions. We have INLINEFORM14 character fixed-size feature vectors corresponding to word-level embedding in a sentence. ### Deep Learning Module DeepCNN in the deep learning module is illustrated in Figure. FIGREF22 . The DeepCNN has two wide convolution layers. The first layer extract local features around each character windows of the given word and using a max pooling over character windows to produce a global fixed-sized feature vector for the word. The second layer retrieves important context characters and transforms the representation at previous level into a representation at higher abstract level. We have INLINEFORM0 global character fixed-sized feature vectors for INLINEFORM1 words. In the next step of Figure. FIGREF4 , we construct the vector INLINEFORM0 by concatenating the word-level embedding with the global character fixed-size feature vectors. The input of Bi-LSTM is a sequence of embeddings INLINEFORM1 . The use of the global character fixed-size feature vectors increases the relationship of words in the word-level embedding. The purpose of this Bi-LSTM is to capture the context of words in a sentence and maintain the order of words toward to extract sentence-level representation. The top of the model is a softmax function to predict sentiment label. We describe in detail the kinds of CNN and LSTM that we use in next sub-part 1 and 2. The one-dimensional convolution called time-delay neural net has a filter vector INLINEFORM0 and take the dot product of filter INLINEFORM1 with each m-grams in the sequence of characters INLINEFORM2 of a word in order to obtain a sequence INLINEFORM3 : DISPLAYFORM0 Based on Equation 1, we have two types of convolutions that depend on the range of the index INLINEFORM0 . The narrow type of convolution requires that INLINEFORM1 and produce a sequence INLINEFORM2 . The wide type of convolution does not require on INLINEFORM3 or INLINEFORM4 and produce a sequence INLINEFORM5 . Out-of-range input values INLINEFORM6 where INLINEFORM7 or INLINEFORM8 are taken to be zero. We use wide convolution for our model. Given a word INLINEFORM0 composed of INLINEFORM1 characters INLINEFORM2 , we take a character embedding INLINEFORM3 for each character INLINEFORM4 and construct a character matrix INLINEFORM5 as following Equation. 2: DISPLAYFORM0 The values of the embeddings INLINEFORM0 are parameters that are optimized during training. The trained weights in the filter INLINEFORM1 correspond to a feature detector which learns to recognize a specific class of n-grams. The n-grams have size INLINEFORM2 . The use of a wide convolution has some advantages more than a narrow convolution because a wide convolution ensures that all weights of filter reach the whole characters of a word at the margins. The resulting matrix has dimension INLINEFORM3 . Long Short-Term Memory networks usually called LSTMs are a improved version of RNN. The core idea behind LSTMs is the cell state which can maintain its state over time, and non-linear gating units which regulate the information flow into and out of the cell. The LSTM architecture that we used in our proposed model is described in BIBREF9 . A single LSTM memory cell is implemented by the following composite function: DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 is the logistic sigmoid function, INLINEFORM1 and INLINEFORM2 are the input gate, forget gate, output gate, cell and cell input activation vectors respectively. All of them have a same size as the hidden vector INLINEFORM3 . INLINEFORM4 is the hidden-input gate matrix, INLINEFORM5 is the input-output gate matrix. The bias terms which are added to INLINEFORM6 and INLINEFORM7 have been omitted for clarity. In addition, we also use the full gradient for calculating with full backpropagation through time (BPTT) described in BIBREF10 . A LSTM gradients using finite differences could be checked and making practical implementations more reliable. ### Regularization For regularization, we use a constraint on INLINEFORM0 of the weight vectors BIBREF18 . ### Experimental setups For the Stanford Twitter Sentiment Corpus, we use the number of samples as BIBREF5 . The training data is selected 80K tweets for a training data and 16K tweets for the development set randomly from the training data of BIBREF0 . We conduct a binary prediction for STS Corpus. For Sander dataset, we use standard 10-fold cross validation as BIBREF14 . We construct the development set by selecting 10% randomly from 9-fold training data. In Health Care Reform Corpus, we also select 10% randomly for the development set in a training set and construct as BIBREF14 for comparison. We describe the summary of datasets in Table III. for all datasets, the filter window size ( INLINEFORM0 ) is 7 with 6 feature maps each for the first wide convolution layer, the second wide convolution layer has a filter window size of 5 with 14 feature maps each. Dropout rate ( INLINEFORM1 ) is 0.5, INLINEFORM2 constraint, learning rate is 0.1 and momentum of 0.9. Mini-batch size for STS Corpus is 100 and others are 4. In addition, training is done through stochastic gradient descent over shuffled mini-batches with Adadelta update rule BIBREF19 . we use the publicly available Word2Vec trained from 100 billion words from Google and TwitterGlove of Stanford is performed on aggregated global word-word co-occurrence statistics from a corpus. Word2Vec has dimensionality of 300 and Twitter Glove have dimensionality of 200. Words that do not present in the set of pre-train words are initialized randomly. ### Experimental results Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus. BIBREF0 reported the results of Maximum Entropy (MaxEnt), NB, SVM on STS Corpus having good performance in previous time. The model of BIBREF5 is a state-of-the-art so far by using a CharSCNN. As can be seen, 86.63 is the best prediction accuracy of our model so far for the STS Corpus. For Sanders and HCR datasets, we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR). The ENS model is combined with bag-of-words (BoW), feature hashing (FH) and lexicons. The model of BIBREF14 is a state-of-the-art on Sanders and HCR datasets. Our models outperform the model of BIBREF14 for the Sanders dataset and HCR dataset. ### Analysis As can be seen, the models with SR outperforms the model with no SR. Semantic rules is effective in order to increase classification accuracy. We evaluate the efficiency of SR for the model in Table V of our full paper . We also conduct two experiments on two separate models: DeepCNN and Bi-LSTM in order to show the effectiveness of combination of DeepCNN and Bi-LSTM. In addition, the model using TwitterGlove outperform the model using GoogleW2V because TwitterGlove captures more information in Twitter than GoogleW2V. These results show that the character-level information and SR have a great impact on Twitter Data. The pre-train word vectors are good, universal feature extractors. The difference between our model and other approaches is the ability of our model to capture important features by using SR and combine these features at high benefit. The use of DeepCNN can learn a representation of words in higher abstract level. The combination of global character fixed-sized feature vectors and a word embedding helps the model to find important detectors for particles such as 'not' that negate sentiment and potentiate sentiment such as 'too', 'so' standing beside expected features. The model not only learns to recognize single n-grams, but also patterns in n-grams lead to form a structure significance of a sentence. ### Conclusions In the present work, we have pointed out that the use of character embeddings through a DeepCNN to enhance information for word embeddings built on top of Word2Vec or TwitterGlove improves classification accuracy in Tweet sentiment classification. Our results add to the well-establish evidence that character vectors are an important ingredient for word-level in deep learning for NLP. In addition, semantic rules contribute handling non-essential sub-tweets in order to improve classification accuracy. Figure 1. The overview of a deep learning system. Table II THE NUMBER OF TWEETS ARE PROCESSED BY USING SEMANTIC RULES Table I SEMANTIC RULES [12] Figure 2. Deep Convolutional Neural Network (DeepCNN) for the sequence of character embeddings of a word. For example with 1 region size is 2 and 4 feature maps in the first convolution and 1 region size is 3 with 3 feature maps in the second convolution. Table IV ACCURACY OF DIFFERENT MODELS FOR BINARY CLASSIFICATION Table III SUMMARY STATISTICS FOR THE DATASETS AFTER USING SEMANTIC RULES. c: THE NUMBER OF CLASSES. N : THE NUMBER OF TWEETS. lw : MAXIMUM SENTENCE LENGTH. lc : MAXIMUM CHARACTER LENGTH. \|Vw\|: WORD ALPHABET SIZE. \|Vc\|: CHARACTER ALPHABET SIZE.	Stanford - Twitter Sentiment Corpus (STS Corpus), Sanders - Twitter Sentiment Corpus, Health Care Reform (HCR)
What crowdsourcing platform did they use?	### Introduction Understanding most nontrivial claims requires insights from various perspectives. Today, we make use of search engines or recommendation systems to retrieve information relevant to a claim, but this process carries multiple forms of bias. In particular, they are optimized relative to the claim (query) presented, and the popularity of the relevant documents returned, rather than with respect to the diversity of the perspectives presented in them or whether they are supported by evidence. In this paper, we explore an approach to mitigating this selection bias BIBREF0 when studying (disputed) claims. Consider the claim shown in Figure FIGREF1 : “animals should have lawful rights.” One might compare the biological similarities/differences between humans and other animals to support/oppose the claim. Alternatively, one can base an argument on morality and rationality of animals, or lack thereof. Each of these arguments, which we refer to as perspectives throughout the paper, is an opinion, possibly conditional, in support of a given claim or against it. A perspective thus constitutes a particular attitude towards a given claim. Natural language understanding is at the heart of developing an ability to identify diverse perspectives for claims. In this work, we propose and study a setting that would facilitate discovering diverse perspectives and their supporting evidence with respect to a given claim. Our goal is to identify and formulate the key NLP challenges underlying this task, and develop a dataset that would allow a systematic study of these challenges. For example, for the claim in Figure FIGREF1 , multiple (non-redundant) perspectives should be retrieved from a pool of perspectives; one of them is “animals have no interest or rationality”, a perspective that should be identified as taking an opposing stance with respect to the claim. Each perspective should also be well-supported by evidence found in a pool of potential pieces of evidence. While it might be impractical to provide an exhaustive spectrum of ideas with respect to a claim, presenting a small but diverse set of perspectives could be an important step towards addressing the selection bias problem. Moreover, it would be impractical to develop an exhaustive pool of evidence for all perspectives, from a diverse set of credible sources. We are not attempting to do that. We aim at formulating the core NLP problems, and developing a dataset that will facilitate studying these problems from the NLP angle, realizing that using the outcomes of this research in practice requires addressing issues such as trustworthiness BIBREF1 , BIBREF2 and possibly others. Inherently, our objective requires understanding the relations between perspectives and claims, the nuances in the meaning of various perspectives in the context of claims, and relations between perspectives and evidence. This, we argue, can be done with a diverse enough, but not exhaustive, dataset. And it can be done without attending to the legitimacy and credibility of sources contributing evidence, an important problem but orthogonal to the one studied here. To facilitate the research towards developing solutions to such challenging issues, we propose [wave]390P[wave]415e[wave]440r[wave]465s[wave]485p[wave]525e[wave]535c[wave]595t[wave]610r[wave]635u[wave]660m, a dataset of claims, perspectives and evidence paragraphs. For a given claim and pools of perspectives and evidence paragraphs, a hypothetical system is expected to select the relevant perspectives and their supporting paragraphs. Our dataset contains 907 claims, 11,164 perspectives and 8,092 evidence paragraphs. In constructing it, we use online debate websites as our initial seed data, and augment it with search data and paraphrases to make it richer and more challenging. We make extensive use of crowdsourcing to increase the quality of the data and clean it from annotation noise. The contributions of this paper are as follows: ### Design Principles and Challenges In this section we provide a closer look into the challenge and propose a collection of tasks that move us closer to substantiated perspective discovery. To clarify our description we use to following notation. Let INLINEFORM0 indicate a target claim of interest (for example, the claims INLINEFORM1 and INLINEFORM2 in Figure FIGREF6 ). Each claim INLINEFORM3 is addressed by a collection of perspectives INLINEFORM4 that are grouped into clusters of equivalent perspectives. Additionally, each perspective INLINEFORM5 is supported, relative to INLINEFORM6 , by at least one evidence paragraph INLINEFORM7 , denoted INLINEFORM8 . Creating systems that would address our challenge in its full glory requires solving the following interdependent tasks: Determination of argue-worthy claims: not every claim requires an in-depth discussion of perspectives. For a system to be practical, it needs to be equipped with understanding argumentative structures BIBREF3 in order to discern disputed claims from those with straightforward responses. We set aside this problem in this work and assume that all the inputs to the systems are discussion-worthy claims. Discovery of pertinent perspectives: a system is expected to recognize argumentative sentences BIBREF4 that directly address the points raised in the disputed claim. For example, while the perspectives in Figure FIGREF6 are topically related to the claims, INLINEFORM0 do not directly address the focus of claim INLINEFORM1 (i.e., “use of animals” in “entertainment”). Perspective equivalence: a system is expected to extract a minimal and diverse set of perspectives. This requires the ability to discover equivalent perspectives INLINEFORM0 , with respect to a claim INLINEFORM1 : INLINEFORM2 . For instance, INLINEFORM3 and INLINEFORM4 are equivalent in the context of INLINEFORM5 ; however, they might not be equivalent with respect to any other claim. The conditional nature of perspective equivalence differentiates it from the paraphrasing task BIBREF5 . Stance classification of perspectives: a system is supposed to assess the stances of the perspectives with respect to the given claim (supporting, opposing, etc.) BIBREF6 . Substantiating the perspectives: a system is expected to find valid evidence paragraph(s) in support of each perspective. Conceptually, this is similar to the well-studied problem of textual entailment BIBREF7 except that here the entailment decisions depend on the choice of claims. ### Dataset construction In this section we describe a multi-step process, constructed with detailed analysis, substantial refinements and multiple pilots studies. We use crowdsourcing to annotate different aspects of the dataset. We used Amazon Mechanical Turk (AMT) for our annotations, restricting the task to workers in five English-speaking countries (USA, UK, Canada, New Zealand, and Australia), more than 1000 finished HITs and at least a 95% acceptance rate. To ensure the diversity of responses, we do not require additional qualifications or demographic information from our annotators. For any of the annotations steps described below, the users are guided to an external platform where they first read the instructions and try a verification step to make sure they have understood the instructions. Only after successful completion are they allowed to start the annotation tasks. Throughout our annotations, it is our aim to make sure that the workers are responding objectively to the tasks (as opposed to using their personal opinions or preferences). The screen-shots of the annotation interfaces for each step are included in the Appendix (Section SECREF56 ). In the steps outlined below, we filter out a subset of the data with low rater–rater agreement INLINEFORM0 (see Appendix SECREF47 ). In certain steps, we use an information retrieval (IR) system to generate the best candidates for the task at hand. We start by crawling the content of a few notable debating websites: idebate.com, debatewise.org, procon.org. This yields INLINEFORM0 claims, INLINEFORM1 perspectives and INLINEFORM2 evidence paragraphs (for complete statistics, see Table TABREF46 in the Appendix). This data is significantly noisy and lacks the structure we would like. In the following steps we explain how we denoise it and augment it with additional data. For each perspective we verify that it is a complete English sentence, with a clear stance with respect to the given claim. For a fixed pair of claim and perspective, we ask the crowd-workers to label the perspective with one of the five categories of support, oppose, mildly-support, mildly-oppose, or not a valid perspective. The reason that we ask for two levels of intensity is to distinguish mild or conditional arguments from those that express stronger positions. Every 10 claims (and their relevant perspectives) are bundled to form a HIT. Three independent annotators solve a HIT, and each gets paid $1.5-2 per HIT. To get rid of the ambiguous/noisy perspectives we measure rater-rater agreement on the resulting data and retain only the subset which has a significant agreement of INLINEFORM0 . To account for minor disagreements in the intensity of perspective stances, before measuring any notion of agreement, we collapse the five labels into three labels, by collapsing mildly-support and mildly-oppose into support and oppose, respectively. To assess the quality of these annotations, two of the authors independently annotate a random subset of instances in the previous step (328 perspectives for 10 claims). Afterwards, the differences were adjudicated. We measure the accuracy adjudicated results with AMT annotations to estimate the quality of our annotation. This results in an accuracy of 94%, which shows high-agreement with the crowdsourced annotations. To enrich the ways the perspectives are phrased, we crowdsource paraphrases of our perspectives. We ask annotators to generate two paraphrases for each of the 15 perspectives in each HIT, for a reward of $1.50. Subsequently, we perform another round of crowdsourcing to verify the generated paraphrases. We create HITs of 24 candidate paraphrases to be verified, with a reward of $1. Overall, this process gives us INLINEFORM0 paraphrased perspectives. The collected paraphrases form clusters of equivalent perspectives, which we refine further in the later steps. In order to ensure that our dataset contains more realistic sentences, we use web search to augment our pool of perspectives with additional sentences that are topically related to what we already have. Specifically, we use Bing search to extract sentences that are similar to our current pool of perspectives, by querying “claim+perspective”. We create a pool of relevant web sentences and use an IR system (introduced earlier) to retrieve the 10 most similar sentences. These candidate perspectives are annotated using (similar to step 2a) and only those that were agreed upon are retained. In a final round of annotation for perspectives, an expert annotator went over all the claims in order to verify that all the equivalent perspectives are clustered together. Subsequently, the expert annotator went over the most similar claim-pairs (and their perspectives), in order to annotate the missing perspectives shared between the two claims. To cut the space of claim pairs, the annotation was done on the top 350 most similar claim pairs retrieved by the IR system. The goal of this step is to decide whether a given evidence paragraph provides enough substantiations for a perspective or not. Performing these annotations exhaustively for any perspective-evidence pair is not possible. Instead, we make use of a retrieval system to annotate only the relevant pairs. In particular, we create an index of all the perspectives retained from step 2a. For a given evidence paragraph, we retrieve the top relevant perspectives. We ask the annotators to note whether a given evidence paragraph supports a given perspective or not. Each HIT contains a 20 evidence paragraphs and their top 8 relevant candidate perspectives. Each HIT is paid $1 and annotated by at least 4 independent annotators. In order to assess the quality of our annotations, a random subset of instances (4 evidence-perspective pairs) are annotated by two independent authors and the differences are adjudicated. We measure the accuracy of our adjudicated labels versus AMT labels, resulting in 87.7%. This indicates the high quality of the crowdsourced data. ### Statistics on the dataset We now provide a brief summary of [wave]390P[wave]415e[wave]440r[wave]465s[wave]485p[wave]525e[wave]535c[wave]595t[wave]610r[wave]635u[wave]660m. The dataset contains about INLINEFORM0 claims with a significant length diversity (Table TABREF19 ). Additionally, the dataset comes with INLINEFORM1 perspectives, most of which were generated through paraphrasing (step 2b). The perspectives which convey the same point with respect to a claim are grouped into clusters. On average, each cluster has a size of INLINEFORM2 which shows that, on average, many perspectives have equivalents. More granular details are available in Table TABREF19 . To better understand the topical breakdown of claims in the dataset, we crowdsource the set of “topics” associated with each claim (e.g., Law, Ethics, etc.) We observe that, as expected, the three topics of Politics, World, and Society have the biggest portions (Figure FIGREF21 ). Additionally, the included claims touch upon 10+ different topics. Figure FIGREF22 depicts a few popular categories and sampled questions from each. ### Required skills We perform a closer investigation of the abilities required to solve the stance classification task. One of the authors went through a random subset of claim-perspectives pairs and annotated each with the abilities required in determining their stances labels. We follow the common definitions used in prior work BIBREF37 , BIBREF38 . The result of this annotation is depicted in Figure FIGREF24 . As can be seen, the problem requires understanding of common-sense, i.e., an understanding that is commonly shared among humans and rarely gets explicitly mentioned in the text. Additionally, the task requires various types of coreference understanding, such as event coreference and entity coreference. ### Empirical Analysis In this section we provide empirical analysis to address the tasks. We create a split of 60%/15%/25% of the data train/dev/test. In order to make sure our baselines are not overfitting to the keywords of each topic (the “topic” annotation from Section SECREF20 ), we make sure to have claims with the same topic fall into the same split. For simplicity, we define a notation which we will extensively use for the rest of this paper. The clusters of equivalent perspectives are denoted as INLINEFORM0 , given a representative member INLINEFORM1 . Let INLINEFORM2 denote the collection of relevant perspectives to a claim INLINEFORM3 , which is the union of all the equivalent perspectives participating in the claim: INLINEFORM4 . Let INLINEFORM5 denote the set of evidence documents lending support to a perspective INLINEFORM6 . Additionally, denote the two pools of perspectives and evidence with INLINEFORM7 and INLINEFORM8 , respectively. ### Systems We make use of the following systems in our evaluation: (Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 . We use this system to retrieve a ranked list of best matching perspective/evidence from the corresponding index. (Contextual representations). A recent state-of-the-art contextualized representation BIBREF40 . This system has been shown to be effective on a broad range of natural language understanding tasks. Human performance provides us with an estimate of the best achievable results on datasets. We use human annotators to measure human performance for each task. We randomly sample 10 claims from the test set, and instruct two expert annotators to solve each of T1 to T4. ### Evaluation metrics. We perform evaluations on four different subtasks in our dataset. In all of the following evaluations, the systems are given the two pools of perspectives INLINEFORM0 and evidences INLINEFORM1 . A system is expected to return the collection of mutually disjoint perspectives with respect to a given claim. Let INLINEFORM0 be the set of output perspectives. Define the precision and recall as INLINEFORM1 and INLINEFORM2 respectively. To calculate dataset metrics, the aforementioned per-claim metrics are averaged across all the claims in the test set. Given a claim, a system is expected to label every perspective in INLINEFORM0 with one of two labels support or oppose. We use the well-established definitions of precision-recall for this binary classification task. A system is expected to decide whether two given perspectives are equivalent or not, with respect to a given claim. We evaluate this task in a way similar to a clustering problem. For a pair of perspectives INLINEFORM0 , a system predicts whether the two are in the same cluster or not. The ground-truth is whether there is a cluster which contains both of the perspectives or not: INLINEFORM1 . We use this pairwise definition for all the pairs in INLINEFORM2 , for any claim INLINEFORM3 in the test set. Given a perspective INLINEFORM0 , we expect a system to return all the evidence INLINEFORM1 from the pool of evidence INLINEFORM2 . Let INLINEFORM3 and INLINEFORM4 be the predicted and gold evidence for a perspective INLINEFORM5 . Define macro-precision and macro-recall as INLINEFORM6 and INLINEFORM7 , respectively. The metrics are averaged across all the perspectives INLINEFORM8 participating in the test set. The goal is to get estimates of the overall performance of the systems. Instead of creating a complex measure that would take all the aspects into account, we approximate the overall performance by multiplying the disjoint measures in INLINEFORM0 , INLINEFORM1 and INLINEFORM2 . While this gives an estimate on the overall quality, it ignores the pipeline structure of the task (e.g., the propagation of the errors throughout the pipeline). We note that the task of INLINEFORM3 (perspective equivalence) is indirectly being measured within INLINEFORM4 . Furthermore, since we do not report an IR performance on INLINEFORM5 , we use the “always supp” baseline instead to estimate an overall performance for IR. ### Results Table TABREF40 shows a summary of the experimental results. To measure the performance of the IR system, we use the index containing INLINEFORM0 . Given each claim, we query the top INLINEFORM1 perspectives, ranked according to their retrieval scores. We tune INLINEFORM2 on our development set and report the results on the test section according to the tuned parameter. We use IR results as candidates for other solvers (including humans). For this task, IR with top-15 candidates yields INLINEFORM3 90% recall (for the PR-curve, see Figure FIGREF53 in the Appendix). In order to train BERT on this task, we use the IR candidates as the training instances. We then tune a threshold on the dev data to select the top relevant perspectives. In order to measure human performance, we create an interface where two human annotators see IR top- INLINEFORM4 and select a minimal set of perspectives (i.e., no two equivalent perspectives). We measure the quality of perspective stance classification, where the input is a claim-perspective pair, mapped to {support, oppose}. The candidate inputs are generated on the collection of perspectives INLINEFORM0 relevant to a claim INLINEFORM1 . To have an understanding of a lower bound for the metric, we measure the quality of an always-support baseline. We measure the performance of BERT on this task as well, which is about 20% below human performance. This might be because this task requires a deep understanding of commonsense knowledge/reasoning (as indicated earlier in Section SECREF5 ). Since a retrieval system is unlikely to distinguish perspectives with different stances, we do not report the IR performance for this task. We create instances in the form of INLINEFORM0 where INLINEFORM1 . The expected label is whether the two perspectives belong to the same equivalence class or not. In the experiments, we observe that BERT has a significant performance gain of INLINEFORM2 over the IR baseline. Meanwhile, this system is behind human performance by a margin of INLINEFORM3 . We evaluate the systems on the extraction of items from the pool of evidences INLINEFORM0 , given a claim-perspective pair. To measure the performance of the IR system working with the index containing INLINEFORM1 we issue a query containing the concatenation of a perspective-claim pair. Given the sorted results (according to their retrieval confidence score), we select the top candidates using a threshold parameter tuned on the dev set. We also use the IR system's candidates (top-60) for other baselines. This set of candidates yields a INLINEFORM2 85% recall (for the PR-curve, see Figure FIGREF53 in the Appendix). We train BERT system to map each (gold) claim-perspective pair to its corresponding evidence paragraph(s). Since each evidence paragraph could be long (hence hard to feed into BERT), we split each evidence paragraph into sliding windows of 3 sentences. For each claim-perspective pair, we use all 3-sentences windows of gold evidence paragraphs as positive examples, and rest of the IR candidates as negative examples. In the run-time, if a certain percentage (tuned on the dev set) of the sentences from a given evidence paragraph are predicted as positive by BERT, we consider the whole evidence as positive (i.e. it supports a given perspective). Overall, the performances on this task are lower, which could probably be expected, considering the length of the evidence paragraphs. Similar to the previous scenarios, the BERT solver has a significant gain over a trivial baseline, while standing behind human with a significant margin. ### Discussion As one of the key consequences of the information revolution, information pollution and over-personalization have already had detrimental effects on our life. In this work, we attempt to facilitate the development of systems that aid in better organization and access to information, with the hope that the access to more diverse information can address over-personalization too BIBREF41 . The dataset presented here is not intended to be exhaustive, nor does it attempt to reflect a true distribution of the important claims and perspectives in the world, or to associate any of the perspective and identified evidence with levels of expertise and trustworthiness. Moreover, it is important to note that when we ask crowd-workers to evaluate the validity of perspectives and evidence, their judgement process can potentially be influenced by their prior beliefs BIBREF42 . To avoid additional biases introduced in the process of dataset construction, we try to take the least restrictive approach in filtering dataset content beyond the necessary quality assurances. For this reason, we choose not to explicitly ask annotators to filter contents based on the intention of their creators (e.g. offensive content). A few algorithmic components were not addressed in this work, although they are important to the complete perspective discovery and presentation pipeline. For instance, one has to first verify that the input to the system is a reasonably well-phrased and an argue-worthy claim. And, to construct the pool of perspectives, one has to extract relevant arguments BIBREF43 . In a similar vein, since our main focus is the study of the relations between claims, perspectives, and evidence, we leave out important issues such as their degree of factuality BIBREF8 or trustworthiness BIBREF44 , BIBREF1 as separate aspects of problem. We hope that some of these challenges and limitations will be addressed in future work. ### Conclusion The importance of this work is three-fold; we define the problem of substantiated perspective discovery and characterize language understanding tasks necessary to address this problem. We combine online resources, web data and crowdsourcing and create a high-quality dataset, in order to drive research on this problem. Finally, we build and evaluate strong baseline supervised systems for this problem. Our hope is that this dataset would bring more attention to this important problem and would speed up the progress in this direction. There are two aspects that we defer to future work. First, the systems designed here assumed that the input are valid claim sentences. To make use of such systems, one needs to develop mechanisms to recognize valid argumentative structures. In addition, we ignore trustworthiness and credibility issues, important research issues that are addressed in other works. ### Acknowledgments The authors would like to thank Jennifer Sheffield, Stephen Mayhew, Shyam Upadhyay, Nitish Gupta and the anonymous reviewers for insightful comments and suggestions. This work was supported in part by a gift from Google and by Contract HR0011-15-2-0025 with the US Defense Advanced Research Projects Agency (DARPA). The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. ### Statistics We provide brief statistics on the sources of different content in our dataset in Table TABREF46 . In particular, this table shows: the size of the data collected from online debate websites (step 1). the size of the data filtered out (step 2a). the size of the perspectives added by paraphrases (step 2b). the size of the perspective candidates added by web (step 2c). ### Measure of agreement We use the following definition formula in calculation of our measure of agreement. For a fixed subject (problem instance), let INLINEFORM0 represent the number of raters who assigned the given subject to the INLINEFORM1 -th category. The measure of agreement is defined as INLINEFORM2 where for INLINEFORM0 . Intuitively, this function measure concentration of values the vector INLINEFORM1 . Take the edge cases: Values concentrated: INLINEFORM0 (in other words INLINEFORM1 ) INLINEFORM2 . Least concentration (uniformly distribution): INLINEFORM0 . This definition is used in calculation of more extensive agreement measures (e.g, Fleiss' kappa BIBREF49 ). There multiple ways of interpreting this formula: It indicates how many rater–rater pairs are in agreement, relative to the number of all possible rater–rater pairs. One can interpret this measure by a simple combinatorial notions. Suppose we have sets INLINEFORM0 which are pairwise disjunct and for each INLINEFORM1 let INLINEFORM2 . We choose randomly two elements from INLINEFORM3 . Then the probability that they are from the same set is the expressed by INLINEFORM4 . We can write INLINEFORM0 in terms of INLINEFORM1 which is the conventional Chi-Square statistic for testing if the vector of INLINEFORM2 values comes from the all-categories-equally-likely flat multinomial model. Figure 1: Given a claim, a hypothetical system is expected to discover various perspectives that are substantiated with evidence and their stance with respect to the claim. Figure 2: Depiction of a few claims, their perspectives and evidences from PERSPECTRUM. The supporting Table 1: Comparison of PERSPECTRUM to a few notable datasets in the field. Table 2: A summary of PERSPECTRUM statistics Figure 3: Distribution of claim topics. Figure 4: Visualization of the major topics and sample claims in each category. Figure 5: The set of reasoning abilities required to solve the stance classification task. Table 3: Quality of different baselines on different subtasks (Section 5). All the numbers are in percentage. Top machine baselines are in bold. Figure 6: Candidates retrieved from IR baselines vs Precision, Recall, F1, for T1 and T4 respectively. Table 4: The dataset statistics (See section 4.1). Figure 7: Histogram of popular noun-phrases in our dataset. The y-axis shows count in logarithmic scale. Figure 8: Graph visualization of three related example claims (colored in red) in our dataset with their perspectives. Each edge indicates a supporting/opposing relation between a perspective and a claim. Figure 9: Interfaces shown to the human annotators. Top: the interface for verification of perspectives (step 2a). Middle: the interface for annotation of evidences (step 3a). Bottom: the interface for generation of perspective paraphrases (step 2b). Figure 10: Annotation interface used for topic of claims (Section 4.2)	Amazon Mechanical Turk (AMT)
What finding was noted in the CT Thorax/Abdomen/Pelvis performed on 06/18/2020? Choose the correct answer from the following options: A. Bilateral small nodular opacities B. Absence of pleural effusion C. No signs of liver cirrhosis D. Significant intestinal abnormalities E. Bilateral minor pleural effusions	### Patient Report 0 Dear colleague, We are writing to report on the outpatient treatment of Mrs. Laura Miller, born on 04/03/1967, on 05/22/2020. Diagnosis: Osteoporotic vertebral body sintering (lumbar vertebra 2 and thoracic vertebra 8). Medical History: Mrs. Miller presents with pain in her back, legs radiating, after fall on the back 6 weeks ago. The complaints were progressive with intermittent paresthesia in both legs. Other Diagnoses (not fully collectible): - Status post apoplexy Current Medication (not ascertainable): - Blood pressure medication - Osteoporosis medication - No anticoagulation. Physical Examination: Lumbar spine: Skin without pathological findings. No redness, no evidence of infection. Tapping pain over thoracic spine/lumbar spine, no compression pain, no torsion pain, no pressure pain over spinous process. Radiation of pain into the right and left leg, paravertebral muscle hardness. No paresthesias in the genital area, no breech anesthesia, no peripheral neurologic deficits, No bladder or rectal dysfunction. Peripheral Circulation/Motor function/Sensitivity intact. Strength grade on all sides: Hip Flex/Ex: 5/5, Knee Flex/Ex: 5/5, Foot extensor muscles of the lower leg/flexor muscles of the lower leg: 3/5. Big toe extensor muscles of the lower leg/big toe flexor muscles of the lower leg: 2/5. Thoracic Spine 2 levels from 05/22/2020, Lumbar spine in 2 planes from 05/22/2020: Clinical indication: Status post fall Question: new fracture? Preliminary images: none comparable Findings 1\) [Thoracic Spine]{.underline}: Multiple thoracic vertebral bodies exhibit decreased height, most notably at the central region where a measurement of approximately 17 mm suggests significant height loss and potential acute fracture. Additionally, there are endplate impressions in individual vertebrae of the lower thoracic spine. Aortic sclerosis is present, along with degenerative changes throughout the thoracic vertebrae. The osseous structure presents osteoporotic features. A suspected hemangioma is identified in a vertebral body of the lower thoracic spine. 2\) [Lumbar Spine]{.underline}: In a presumed five-segment lumbar spine, the L1 vertebra shows a subtle reduction in height with a questionable endplate impression. Osteoporotic features are evident in the bony structure. Assessment: Multiple fractures are evident in both thoracic vertebrae and the first lumbar vertebra, some of which may be acute. MRI is recommended for further evaluation. Osseous structure displays pronounced osteoporotic features. Grade III esophageal varices present without definitive high-risk stigmata. Varices also noted at the gastroesophageal junction, classified as GOV 1 according to Sarin\'s classification. Band ligation of the varices is not performed, as no unambiguous source of bleeding is identifiable and a significant portion of the stomach remains outside the field of view. Recommendation: Terlipressin, monitor surveillance, Erythromycin Computed Tomography Thoracic spine from 05/22/2020: Fracture at the base plate of lumbar vertebra 2 with involvement of the posterior margin. Left lateral, no significant reduction in height of the vertebral body. No tension of the spine. Suspicion of new small fracture also at the cover plate at thoracic vertebra 8. Multiple, older, osteoporotic compression fractures at the thoracic spine and upper lumbar spine. Additional Finding: Liver cirrhosis with multiple nodules, low ascites, and portal vein congestion. Splenomegaly. If not already known, further workup of liver lesions is recommended. Hydrops of the gallbladder. Current Recommendations: There is a general indication for admission of the patient and further diagnostics before surgical treatment of the fractures. Mrs. Miller is generally opposed to surgical care. She was thoroughly informed about the risks (progression, cross-section, death). Re-presentation with current bone densitometry and update of osteoporosis medication, as well as current holospinal MRI. In the meantime, analgesia as needed using Acetaminophen 500mg 1-1-1 under gastric protection. Esophagogastroduodenoscopy from 05/22/2020: [Esophagus]{.underline}: Unobstructed intubation of the esophageal opening was achieved under direct visualization. In the upper third of the esophagus, multiple prominent varices protrude into the lumen, unaltered by air insufflation. In the middle third, there are areas with whitish overlying material that do not resemble the typical white nipple sign. Despite meticulous inspection, no active bleeding sites were identified. The Z-line reveals isolated minor erosions. Cardiac sphincter closure is complete. [Stomach]{.underline}: Full distension of the gastric lumen was accomplished with air insufflation. The major curvature of the stomach contained food mixed with hematin. The mucosal surface was similarly coated with hematin, but no active bleeding was discernible in the visualized areas. Peristaltic movement was widespread. Upon retroflexion, pronounced varices were noted near the cardiac region on the lesser curvature. The pylorus was unremarkable and easily traversable. [Duodenum]{.underline}: Adequate distension of the duodenal bulb was achieved, providing a clear view up to the descending part of the duodenum. Overall, the mucosa appeared normal with minor remnants of hematin, and no source of bleeding was identified. ### Patient Report 1 Dear colleague, We report to you about Mrs. Laura Miller, born on 04/03/1967, who was in our inpatient treatment from 05/27/2020 to 06/22/2020. Diagnoses: Initial diagnosis of hepatocellular carcinoma. - MRI liver: disseminated HCC foci in all segments, the largest foci is localized in segments 5 / 7 / 8 - Hydropic, decompensated liver cirrhosis Child B, first diagnosis: 05/20, ethyltoxic genesis - Anemia requiring transfusion - EGD of 05/28/20: sophageal varices III° without risk signs, rubber band ligation; cardia varices I°, Histoacryl injection - EGD of 06/13/20: Residual varices in the esophagus, application of 3 rubber band ligations, injection of 0.5 ml. Histoacryl; portal hypertensive gastropathy - Transfusion of 2 ECs - Fresh osteoporotic thoracic vertebra 8 fracture - Kyphoplasty thoracic vertebra 8 under C-arm fluoroscopy - Portal hypertension with bypass circuits - Splenomegaly - Cholecystolithiasis - Arterial hypertension - Osteoporosis - Status post stroke - Allergies: None known Physical Examination: Patient in mildly reduced general and normal nutritional status (BMI 20.3). - Lungs: Vesicular breath sound, no pathological secondary sounds. SpO2 96%. - Heart: Pure, rhythmic. Systolic with maximum at Erb\'s point and continuation into the carotids. HR: 87/min. BP: 124/54mmHg - Abdomen: Lively bowel sounds, no tenderness, no guarding, no resistance, no peritonism. Soft abdomen. Liver not enlarged, palpable. Renal bed not palpable. - Extremities: Edema of the lower extremities on both sides, foot pulses bilaterally well palpable. - Spine: No tap pain. - Orienting neurological examination: Right leg weakness, known paresis of the extensor muscles of the lower leg/flexor muscles of the lower leg. Therapy and Progression: Mrs. Miller was taken over for suspected upper GI bleeding. Initially, the patient had presented with increasing back pain since approximately 6 weeks at status post fall in the Park Clinic on 04/26/2020. The patient was known there for her stroke in 2016. On the day of admission to the Park Clinic, normochromic normocytic anemia (Hemoglobin 3 g/dL) was noticed, which is why the transfer to our clinic was made. On inpatient admission, the patient presented in slightly reduced general condition. She reported having black stools once. In addition, Ms. Miller had coffee ground-like emesis once. Dyspnea, angina pectoris complaints, and B symptoms were denied. There were no problems with micturition. Recently, there were no abnormalities in bowel movements. So far, no colonoscopy has been performed. There were no known intestinal diseases in the family. [Noxae]{.underline}: Ex-nicotine use (since 1996, previously cumulative ca. 3 PY), occasional alcohol consumption (probably abstinent for about 5 years). Laboratory results showed that the patient had elevated infectious parameters. The urinalysis was unremarkable. X-ray chest showed no clear picture of pneumonia. In a first emergency esophagogastroduodenoscopy on 05/28/2020, esophageal varices °III were found without clear signs of risk. Furthermore, varices in the area of the cardia (GOV 1 after Sarin) were seen. When the source of bleeding was inconclusive, it was referred to a banding initially waived. In a renewed esophagogastroduodenoscopy in the morning of 05/30/2020, 2 ampoules of Histoacryl were applied to the cardia varices. In addition, the picture of an incipient portal-hypertensive gastropathy. Antibiotic intravenous therapy with ceftriaxone was initiated. At 06/13/2020, a re-esophagogastroduodenoscopy took place, during which a renewed twofold banding of the esophageal varices was performed with injection of 0.5 ml Histoacryl. Abdominal ultrasound showed a picture of liver cirrhosis with splenomegaly and perihepatic ascites. In addition, the liver showed multiple echo-poor nodes in the right lobe and a suspicious 2.4x3.6x4.4cm echo-poor area with a halo in SII. This was followed by an MRI of the liver, in which the HCC in segment II was confirmed by imaging. Multiple additional arterial hypervascularized areas/round foci in all liver lobes without definite washout. There is no evidence of suspicious nodular changes on CT chest/abdomen/pelvis. At an in-house liver conference, systemic therapy (Lenvatinib or Sorafenib in Child B7) was recommended. Due to the back pain, a holospinal MRI was performed, which showed a subacute cover plate compression fracture in the thoracic vertebra 8 as well as multiple older compression fractures of the thoracic spine and upper lumbar spine. The colleagues of neurosurgery were consulted, who gave the indication for surgery. On 06/14/2020, the planned surgery with kyphoplasty thoracic vertebra 8 under C-arm fluoroscopy could be performed without complications. Postoperatively, the patient remained circulatory stable. Due to auscultatory suspicion of aortic valve stenosis, further clarification was performed by cardiac echography, showing no higher-grade valvular vitiation. We are transferring Mrs. Miller in improved general condition to the Senior Citizens\' Residence Seaview. If you have any questions, please do not hesitate to contact us. Addition: Ultrasound of the upper abdomen on 05/27/2020: Limited assessable due to meteorism. Liver: Liver dimensions are within normal limits, measuring 15.9 cm in the craniocaudal axis. Echotexture demonstrates inhomogeneous granularity. There is hepatic margin convexity and nodular surface appearance. Rarification of hepatic veins. Segment III reveals two hypoechoic lesions measuring 3 cm and 1 cm in diameter. Portal vein demonstrates orthograde flow with a maximum velocity of 17 cm/s. Gallbladder: Gallbladder is partially contracted; its wall appears unremarkable without sonographic evidence of cholecystitis. No tenderness elicited upon sonographic examination. Biliary Tract: Intrahepatic bile ducts are patent. Common hepatic duct measures 6 mm in diameter. Common bile duct appears transiently dilated up to 9 mm and is otherwise unremarkable as far as can be visualized prepapillary. Pancreas: Limited visualization of the pancreas; however, the visible parenchyma appears homogeneously echoic. Spleen: Spleen is enlarged, measuring 13 x 4 cm, with homogeneous parenchyma. Kidneys: Kidneys are morphologically unremarkable, without evidence of pelvicalyceal system dilation. Abdominal Vessels: Aorta is partially visualized and appears within normal calibers. Intestinal/Peritoneal Cavity: No evidence of free intraperitoneal fluid. Urinary Bladder/Genitalia: Urinary bladder is adequately distended, appearing unremarkable upon limited assessment. Uterus is not visualized. Virology from 05/28/2020: - SARS CoV-2 PCR PCR negative Geq/ml - Findings: No detection of SARS-CoV-2 by PCR in the submitted material. Chest X-ray a.p. from 05/28/2020: Limited assessability in supine position, malrotation. The diaphragmatic crests are smooth. The marginal sinuses are free of effusions and calluses. Heart and mediastinum lie cryptically. The aorta is sclerosed. Cranialization of the vessels as well as slightly elevated vascular markings in the supine position, especially in the right upper field. Dystelectasis on the right. Sharply defined vertical shadowing in the left upper field. The upper mediastinum is narrow, the trachea is in the midline and is not constricted. Degenerative changes in the cervical spine. Overlying foreign material. Assessment: Sharply defined vertical shadowing in the left upper field. Dystelectasis on the right side. Conventional radiographic examination No evidence of a mass. No effusion. Esophagogastroduodenoscopy of 05/28/2020: Esophagus: Successful intubation of the esophageal orifice under direct visualization. Multiple intraluminal protrusions noted in the upper third of the esophagus. Non-collapsible variceal strands observed upon air insufflation, beginning from the middle third. Whitish proliferations seen at multiple sites, not consistent with typical white nipple signs. No evidence of active bleeding on close inspection. Z-line observed with isolated minor erosions. Cardiac sphincter fully competent. Stomach: Gastric lumen fully distended under air insufflation. Corpus predominantly contains hematin-laden food remnants. Mucosal surface also stained with hematin but without visible active bleeding. Peristalsis noted throughout. Distinct coronary vasculature observed on the lesser curvature. Pylorus unremarkable, offering no resistance to passage. Duodenum: Bulbus duodeni well-formed. Pars descendens duodeni visualized clearly. Overall mucosa appears unremarkable, with scattered hematin remnants observed without an identifiable bleeding source. Assessment: Esophageal varices graded as °III, with no definitive high-risk stigmata. Varices also noted in the cardia, classified as GOV 1 according to Sarin\'s classification. Ligation of varices was not performed due to the absence of an identifiable bleeding source and incomplete visualization of the gastric lumen. Ultrasound of the abdomen on 05/29/2020: Quality of Exam: Limited due to patient non-cooperation and meteorism. Liver: Liver size is paradoxically reported both as normal at 15.9 cm and enlarged at 18.7 cm. Margins are rounded. Echotexture is markedly inhomogeneous with nodular surface. Multiple hypoechoic nodules are present in the right lobe, along with a suspicious hypoechoic area measuring 2.4 x 3.6 x 4.4 cm with peripheral halo in segment II. Hepatic veins are rarified. Portal vein shows orthograde flow with a vmax of 28 cm/s. Gallbladder: Morphologically unremarkable with no wall thickening. Cholelithiasis noted with concretions measuring at least 2 to 1.6 cm. Biliary Tract: Intrahepatic bile ducts are not dilated; Common hepatic duct measures up to 8.5 mm and common bile duct measures 6.6 mm in diameter. Pancreas: Partially visualized; adequacy of assessment is compromised. Spleen: Enlarged with homogeneous internal echotexture. Kidneys: Morphologically unremarkable; no evidence of hydronephrosis. Abdominal Vessels: Aorta is not dilated. Gastrointestinal: Perihepatic ascites noted. Both small and large intestines appear unremarkable upon limited assessment. Urinary Bladder/Genitalia: Bladder is moderately filled and unremarkable in shape and size. Assessment: Limited study due to patient non-cooperation and meteorism. Findings are suggestive of liver cirrhosis and grade I ascites. Additional findings include suspected hepatic space-occupying lesions, splenomegaly, and cholelithiasis. Mild dilation of DHC and DC observed without signs of intrahepatic cholestasis. Virology from 06/01/2020: Parameter Result Interpretation --------------- ------------ -------------------- Anti HAV IgG 0.73 negative Anti HAV IgM \<0.1 negative Interpretation: Serologically no evidence of fresh or expired infection with Hepatitis A virus, no immunity. Parameter Result Interpretation --------------- ------------ -------------------- HBs antigen 0.21 negative Anti HBs \<0.1 negative Anti HBc 0.1 negative Interpretation: Serologically no evidence of acute, chronic or expired Hepatitis B virus infection. No immunity. Parameter Result Interpretation --------------- ------------ -------------------- Anti HCV 0.06 negative Interpretation: Serologically no evidence of hepatitis C virus infection. At possible fresh infection resubmission in 2-4 weeks and HCV PCR recommended. MRI total spine plain from 06/04/2020: Technique: T2 Dixon Sagittal and T2 Axial MRI Sequences. Coverage extends from the craniocervical junction to the sacrum. Findings: General Spine: Full extent from craniocervical junction to sacrum visualized. Conus medullaris appropriately located at T12-L1 level. Myelon demonstrates uniform width and homogeneous signal. Evaluation of thoracolumbar transition and lumbar spine is compromised by artifact superimposition from ascites. Cervical Spine: Irregular alignment of the posterior vertebral body margins noted, with evidence of disc protrusions and ligamentum flavum hypertrophy. Focal T2 hyperintensity observed at C5 level. No evidence of prevertebral soft tissue proliferation. Thoracic Spine: Maintained alignment of the posterior vertebral body margins. Multiple anterior endplate compression fractures noted at T5, T8, T9, T11, T12 levels. Focal T2 hyperintensity near the anterior endplate of T8 involving the posterior margin, indicative of a non-displaced fracture without spinal canal compromise. Hypertrophic facet joint arthrosis at T10-T11 levels resulting in relative spinal narrowing. Bilateral pleural effusions noted, more pronounced on the right, with a maximum width of approximately 2 cm. No evidence of significant neuroforaminal stenosis. Lumbar Spine: Maintained alignment of posterior vertebral body margins. Known anterior endplate compression fractures at L1 and baseplate compression fracture at L2. No evidence of pathological T2 edema within the vertebral bodies, although assessment is limited due to superimposed artifacts from ascites. Spinal canal dimensions appear adequate throughout. Moderate fatty degeneration of sacral bone noted. Assessment: Evaluation limited due to ascites-related artifacts. Subacute anterior endplate compression fracture at T8, along with several other likely older compression fractures in the thoracic and upper lumbar spine. Bilateral pleural effusions observed. Multiple neuroforaminal narrowings as detailed above. MR Liver plain + contast agent from 06/06/2020 Findings: 1) [Lesion 1]{.underline} - Size of the lesion 41 mm - Segment 2 - Behavior arterial strongly enriching: yes - Portal venous early washing out: yes - Pseudocapsule: yes - Behavior delayed leaching: yes - pseudocapsule macrovascular invasion: no 2) [Lesion 2]{.underline} - Size of the lesion 104 mm - Segment 5 / 7 / 8 - Behavior arterial strongly enriching: yes - Portal venous early washing out: no - Pseudocapsule: no - Behavior delayed washing out: no - Pseudocapsule: no - Macrovascular invasion: yes Comments: - MRI with Gadovist intravenous. - Multiple other satellite foci in all liver segments. - Signs of liver cirrhosis with nodular liver parenchyma and hypertrophy of the left lobe. - Cholecystolithiasis and gallbladder hydrops. No cholestasis. - Varices of the esophagus and fundus. Splenomegaly. Ascites. Pleural effusions on both sides. - Lymph node (approximately 8 mm) between the small curvature of the Stomach and S1 of the liver. - Axial hernia. Assessment: Milan fulfilled. Dissiminated HCC foci in all segments, the largest foci being in segments 5 / 7 / 8 localized. Portal hypertension with bypass circulation and splenomegaly. Ascites and pleural effusions. Microbiology from 06/09/2020: [Material]{.underline}: Ascites in blood culture bottles [Microscopic]{.underline}: No cells, no germs - Anaerobic culture negative after 48 hours - So far, no growth in the anaerobic cultures. The cultures are incubated for a total of 5 days. In case of growth of anaerobes we will send you a follow-up report. - No growth after 48 hours Esophagogastroduodenoscopy of 06/11/2020: Esophagus: In the distal esophagus, multiple band-like ulcerations as well as residual varices with risk signs that may not completely pass air insufflation. Z-line without erosions. Stomach: Mosaic-like occupancy of the gastric mucosa. With inversion the known small-curved lateral cardiavarii, which is hard on palpation with closed forceps after histoacryl injection. Directly next to it, another cardiavarice can now be seen, which has not yet been injected with histoacryl and is soft on palpation. Duodenum: Endoscopic therapy: Injection of 0.5 mL Histoacryl (+0.5 mL Lipoidol) in the new cardiavarice. Application of 3 rubber band ligatures to the residual varices in the esophagus. Assessment: Residual varices in the esophagus, application of 3 rubber band ligations; portal hypertensive gastropathy Spine-whole: 2 planes from 06/13/2020 The perpendicular of C7 protrudes about 15 mm laterally to the left of sacral vertebra 1 in the anterior-posterior image and about 9.3 cm in front of sacral vertebra 1 in the lateral ray path. Slight left convex scoliosis thoracolumbally with thoracic counter-swing (Cobb angle \< 10° in each case). The lungs are unremarkable as far as technically assessable. Assessment: decompensated positive sagittal spinal imbalance. There is no relevant lateral trunk overhang. Critical Findings Report: A conspicuous single cell population of cells with partial signet ring cell character is detected in the smears. A cell block is prepared from the remaining liquid material for further typing of these cells. A follow-up report will follow. Thoracic spine in 2 planes from 06/15/2020: Findings: Thoracic vertebra 8: Post-kyphoplasty status with notable improvement in vertebral height, now measuring 21 mm compared to a preoperative height of 13 mm. Mild straightening of the vertebral column observed at this level. Thoracic vertebra 9: Known older anterior endplate collapse. Thoracolumbar spine: Multisegmental height reduction in vertebral bodies consistent with osteoporotic changes. No signs of contrast extravasation. Additional Finding: Pre-existing calcified structure projecting onto the left upper abdomen; likely unrelated to current surgical site. Assessment: Unremarkable postoperative imaging following kyphoplasty of T8. No evidence of postoperative sintering or newly identifiable fractures. Overall, the surgical intervention appears successful in increasing vertebral height and stabilizing the fracture site. CT Chest/Abdomen/Pelvis with contrast agent from 06/18/2020: Technique: Multislice spiral CT of the chest, abdomen, and pelvis was performed post-bolus intravenous injection of 120 ml of Imeron 400. Imaging conducted in arterial, portal venous, and venous phases. Oral contrast agent administered with Micropaque 1:7 in water and Gastrolux 1:33 in water. Thin-slice reconstructions and secondary coronal and sagittal reformats were performed. Chest: Presence of struma nodosa. Bilateral minor pleural effusions with adjacent atelectasis, more pronounced on the right and extending into the interlobar region. No signs of infiltrative changes. Isolated small nodular opacities in the right lung. Few small bullae noted. Mediastinal lymph nodes mildly enlarged up to 0.5 cm; axillary and hilar nodes are not enlarged. No pericardial effusion observed. Abdomen/Pelvis: Known esophageal and fundal varices present. Liver demonstrates nodular changes in the context of known Child-Pugh B stage cirrhosis. A solid hepatic cellular carcinoma lesion in segment II and diffuse HCC nodules in segments V/VII/VIII visualized, corroborating prior MRI findings. They show pronounced arterial enhancement and central washout. Splenomegaly noted. Adrenal glands unremarkable. Renal and urinary systems are inconspicuous. No intestinal motility abnormalities detected. Marked ascites present; no pathologically enlarged abdominal lymph nodes noted upon limited assessment. Skeletal: Moderate coxarthrosis bilaterally. An old, minimally displaced fracture of the right 7th rib noted. Advanced degenerative changes in thoracic vertebrae 10, 12, and lumbar spine. Post-vertebroplasty status at thoracic vertebra 9. Hemangioma at thoracic vertebra 11. Assessment: - Marked ascites in the setting of liver cirrhosis with multifocal HCC lesions, as corroborated by prior MRI. No evidence of extrahepatic or lymphatic spread. - Bilateral minor pleural effusions with associated atelectasis. - Skeletal findings include moderate coxarthrosis and degenerative changes in the spine. Overall, the scan provides vital information that aligns with and elaborates upon existing clinical and imaging data. Histology: Pathology from 06/19/2020: [Clinical Data:]{.underline} Hepatocellular carcinoma, hydropic decompensated liver cirrhosis Child B, [Extraction date:]{.underline} 06/13/2020 [Material:]{.underline} 1 Liquid material 7 ml light yellow [Editing]{.underline}: Papanicolaou and MGG staining \+ Protein precipitation \+ Erythrocytes \+ Lymphocytes (+) Granulocytes Eosinophils \+ Histiocytic cell forms \+ Mesothelium \+ Active mesothelium Other: Single mononuclear cells with large, eccentric nuclei with nucleoli and a narrow cytoplasmic space, partly with signet ring cells. Supplementary findings from 06/19/2020 [Processing]{.underline}: Cell block, HE Microscopic: As announced, from the remaining liquid material a cell block was prepared. In the HE stain only isolated evidence of mononuclear Cells and some blood. No cell atypia. Critical Findings Report: After examination of the remaining liquid material in the cell block no Extension of the initial findings in the absence of further diagnostic cell material. The finding is thus based exclusively on the Smear material: - Detection of a single-cell population consisting of cells with partial signet ring cell character. Differentially it could be The mesothelium may be a reactive change of the approaching mesothelium. Cells of an epithelial neoplasia are not visible on the present material. to be ruled out with certainty. Diagnostic classification: Suspicious Current Recommendations: - An appointment at our outpatient clinic to start therapy was organized for 06/26/2020. - An appointment for a health department check-up with varicose vein status survey and, if necessary, repeat rubber band ligation has been scheduled for 07/22/2020. Please come to the endoscopy on this day at 08:30 am fasting with current lab results. incl. coagulation, signed consent form as well as SARS-CoV-2 PCR not older than 48h. ### Patient Report 2 Dear colleague, We report to you on Mrs. Laura Miller, born 04/03/1967, whom we examined on 06/08/2020 in the course of a consultation. Consilar Request: - Liver cirrhosis, Child-Pugh B, ethyltoxic genesis - HCC - Laboratory albumin: 2.6 - Nutritional advice requested Nutritional counseling in cirrhosis of the liver: - Albumin at 2.6 - 70kg at admission (stable weight in recent years). - Height: 1.72m - BMI falsified by ascites - Patient reports that she always a \"bad eater\" - She reports to eat less due to numerous medication intake - Patient is noticeably overwhelmed and seems very burdened by diagnosis Assessment: - Protein malnutrition with inadequate oral nutrition - Patient appears desperate and overwhelmed, questionable compliance Recommendations: - High-calorie food for more choices (already ordered) - High-calorie drinks (contains more protein) - Incorporate protein-rich snacks such as yogurt, sippy cups, crispbread - with cheese. - A high-energy, high-protein food choice was made with the patient\'s discussed in detail - Contact details were handed out ### Patient Report 3 Dear colleague, We are writing to inform you about Mrs. Laura Miller, born on 04/03/1967, who was under our inpatient care from 08/21/2020 to 08/23/2020. Diagnoses: - MRI of the liver: disseminated HCC foci in all segments, the largest foci is localized in segments 5 / 7 / 8 <!-- --> - Hydropic, decompensated liver cirrhosis Child B, first diagnosis: 05/20, ethyltoxic genesis - Anemia requiring transfusion - EGD of 05/28/20: esophageal varices III° without risk signs, rubber band ligation; cardia varices I°, Histoacryl injection - EGD of 06/13/20: residual varices in the esophagus, application of 3 rubber band ligations, injection of 0.5 ml. Histoacryl; portal hypertensive gastropathy - Transfusion of 2 ECs - Fresh osteoporotic thoracic vertebra 8 fracture - Kyphoplasty thoracic vertebra 8 under C-arm fluoroscopy - Portal hypertension with bypass circuits - Splenomegaly - Cholecystolithiasis - Arterial hypertension - Osteoporosis - Status post stroke - Allergies: None known Current Presentation: Mrs. Miller presented electively for gastroscopy for variceal screening with continuation of banding therapy due to esophageal variceal bleeding. Medical History: For a detailed medical history, we refer to previous reports from our department. In summary, we present a liver cirrhosis due to ethyl toxicity leading to the development of multifocal HCC. Similar to the liver board decision of 06/13/20, a recommendation for systemic therapy with Lenvatinib or Sorafenib was made in the setting of partially compensated Child B7 cirrhosis with multifocal HCC in both lobes of the liver. Therapy and Course: Upon admission, the patient was in age-appropriate general condition and largely symptom-free. There were no signs of acute infection, jaundice, encephalopathic symptoms, or GI bleeding. No irregularities in bowel movements or urination were reported. The patient denied abdominal pain and dyspnea. There were no known allergies. On the day of admission, an uncomplicated gastroscopy was performed, including the application of 4 rubber band ligations for residual esophageal varices. Post-interventional pain was adequately controlled with double-standard doses of Pantoprazole and intravenous analgesic therapy. The further inpatient course was uneventful, and the patient tolerated the post-interventional diet without signs of GI bleeding. Based on laboratory findings and clinical evaluation, particularly with regressed ascites, a compensated Child A6 cirrhosis was confirmed. Therefore, a re-presentation at our interdisciplinary liver board was initiated for discussion of potential treatment options in the context of compensated liver function. As per the consensus recommendation from the liver board, a follow-up gastroscopy is scheduled within the next two weeks. Depending on the variceal status, systemic therapy with Atezolizumab/Bevacizumab or Lenvatinib will follow. Throughout the monitoring period, the patient remained stable in terms of circulation and hemoglobin levels. Therefore, on 08/23/20, we discharged Mrs. Miller for outpatient follow-up care. The patient was thoroughly informed about reasons that necessitate immediate re-presentation. Please note the listed procedural appointments. Physical Examination: Awake, alert, oriented - Heart: Regular heart tones, no murmurs - Lungs: Clear vesicular breath sounds, no crackles or wheezes - Abdomen: Soft, non-tender, no masses, normal bowel sounds in all quadrants, palpable firm liver edge under the rib cage, no palpable spleen enlargement, non-painful renal angle - Extremities: Good peripheral pulses, no edema - Neurology: No focal neurological deficits. EGD on 08/21/2020: Findings: Esophagus: Unobstructed intubation of the esophagus under direct vision. Multiple variceal cords and scarring changes due to banding were observed in the lower half of the esophagus. Z-line at 35 cm diaphragmatic passage at 39 cm. Two variceal cords extend along the small curve into the stomach, two of the varices show alarm signs (red spots). 4 rubber band ligations were performed. Stomach: In the proximal corpus a picture of portal hypertensive gastropathy, otherwise unremarkable. No fundus varices. Duodenum: Good unfolding of the duodenal bulb, contact-sensitive mucosa. Good insight into the descending part of the duodenum. Overall, unremarkable mucosa. Assessment: Esophageal varices, Gastroesophageal varices Type I. Banding therapy. Current Recommendations: - Regular clinical and laboratory checks by the primary care physician. - In case of fever, acute deterioration of the general condition, or clinical signs of bleeding such as melena or hematemesis, we request immediate re-presentation, even at night and on weekends, through our interdisciplinary emergency department. - Decision of the liver board: Improvement of liver function with alcohol abstinence, but also progression of multifocal HCC over 2 months without tumor-specific therapy. Consensus: Repeat EGD in 7-14 days, depending on variceal status, Atezolizumab/Bevacizumab, or Lenvatinib. - Follow-up appointment on 09/11/20 in our HCC outpatient clinic for clinical control and explanation of the EGD. - Follow-up in our endoscopy for EGD to determine variceal status and possible banding -\> Please bring a COVID PCR test (maximum 48 hours old) for inpatient admission. If complaints persist or worsen, we recommend immediate re-presentation. ### Patient Report 4 Dear colleague, We are writing to inform you about Mrs. Laura Miller, born on 04/03/1967, who was under our inpatient care from 09/18/2020 to 09/20/2020. Diagnoses: - 4-time banding therapy with 4 rubber band ligations for residual esophageal varices without alarm signs - Hepatocellular Carcinoma (HCC) - MR Liver: Disseminated HCC lesions in all segments, with the largest lesion located in segments 5/7/8 - Decompensated cirrhosis of the liver (Child B) since 05/20 due to ethyltoxic origin. - Transfusion-dependent anemia due to a history of variceal bleeding. - Osteoporotic thoracic vertebral fracture of vertebra BWK8 (OF3) with kyphoplasty. - Portal hypertension with portosystemic collaterals - Splenomegaly - Cholelithiasis - Arterial hypertension - Osteoporosis - History of stroke (2016) - Allergies: Amalgam Presentation: Mrs. Miller\'s elective presentation was for a follow-up examination for known esophageal varices. Medical History: For a detailed medical history, please refer to previous reports from our department. In summary, in June 2020, the patient was diagnosed with decompensated liver cirrhosis attributed to ethyltoxicity. MR imaging showed multifocal HCC. According to the liver board decision on 06/15/20, initial therapy was recommended with Lenvatinib or Sorafenib for partially compensated Child B7 cirrhosis with multifocal HCC in both liver lobes. Despite improvement in liver function with alcohol cessation, there was a short-term progression of multifocal HCC without tumor-specific therapy, leading to a recommendation for a repeat variceal screening on 08/28/20. Depending on the findings, therapy with Atezolizumab/Bevacizumab or Lenvatinib was advised. The last EGD was performed on 08/21/20, revealing esophageal varices with alarm signs, and a 4-time banding was performed. Physical Examination upon Admission: Blood pressure: 80/150 mmHg, heart rate 88/min, temperature 36.4°C, SpO2 97% in room air. Patient in good general condition and normal mental status. Mrs. Miller is fully oriented. Pupils are equal and reactive. - Cardiovascular: Clear heart sounds, no murmurs. - Lungs: Equal breath sounds bilaterally, no crackles, resonant percussion. - Abdomen: Soft, non-tender, no masses, normal bowel sounds in all quadrants, liver and spleen not palpable. - Extremities: No edema, good peripheral pulses. No focal neurological deficits. Course and Therapy: On the day of admission, the EGD was performed without complications. Residual varices without warning signs were observed, and a 4-time rubber band ligation was performed. Portal hypertensive gastropathy was also diagnosed. After the procedure, the patient was transferred to our gastroenterological normal ward. The post-interventional course was uneventful. There were no clinical or laboratory signs of post-interventional bleeding. The diet was reintroduced without any issues. Therefore, on 09/20/2020, we discharged Mrs. Miller for outpatient care. We request a follow-up appointment at our in-house HCC outpatient clinic. Additionally, we request a follow-up EGD with variceal control.	Bilateral minor pleural effusions
How did they use the domain tags?	### Introduction One of the most attractive features of neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 is that it is possible to train an end to end system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine translation (SMT) systems. However, it is reported that NMT works better than SMT only when there is an abundance of parallel corpora. In the case of low resource domains, vanilla NMT is either worse than or comparable to SMT BIBREF3 . Domain adaptation has been shown to be effective for low resource NMT. The conventional domain adaptation method is fine tuning, in which an out-of-domain model is further trained on in-domain data BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . However, fine tuning tends to overfit quickly due to the small size of the in-domain data. On the other hand, multi domain NMT BIBREF8 involves training a single NMT model for multiple domains. This method adds tags “<2domain>" by modifying the parallel corpora to indicate domains without any modifications to the NMT system architecture. However, this method has not been studied for domain adaptation in particular. Motivated by these two lines of studies, we propose a new domain adaptation method called “mixed fine tuning," where we first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus that is a mix of the in-domain and out-of-domain corpora. Fine tuning on the mixed corpus instead of the in-domain corpus can address the overfitting problem. All corpora are augmented with artificial tags to indicate specific domains. We tried two different corpora settings: We observed that “mixed fine tuning" works significantly better than methods that use fine tuning and domain tag based approaches separately. Our contributions are twofold: ### Related Work Besides fine tuning and multi domian NMT using tags, another direction for domain adaptation is using in-domain monolingual data. Either training an in-domain recurrent neural language (RNN) language model for the NMT decoder BIBREF13 or generating synthetic data by back translating target in-domain monolingual data BIBREF5 have been studied. ### Methods for Comparison All the methods that we compare are simple and do not need any modifications to the NMT system. ### Fine Tuning Fine tuning is the conventional way for domain adaptation, and thus serves as a baseline in this study. In this method, we first train an NMT system on a resource rich out-of-domain corpus till convergence, and then fine tune its parameters on a resource poor in-domain corpus (Figure 1 ). ### Multi Domain The multi domain method is originally motivated by BIBREF14 , which uses tags to control the politeness of NMT translations. The overview of this method is shown in the dotted section in Figure 2 . In this method, we simply concatenate the corpora of multiple domains with two small modifications: a. Appending the domain tag “<2domain>" to the source sentences of the respective corpora. This primes the NMT decoder to generate sentences for the specific domain. b. Oversampling the smaller corpus so that the training procedure pays equal attention to each domain. We can further fine tune the multi domain model on the in-domain data, which is named as “multi domain + fine tuning.” ### Mixed Fine Tuning The proposed mixed fine tuning method is a combination of the above methods (shown in Figure 2 ). The training procedure is as follows: Train an NMT model on out-of-domain data till convergence. Resume training the NMT model from step 1 on a mix of in-domain and out-of-domain data (by oversampling the in-domain data) till convergence. By default, we utilize domain tags, but we also consider settings where we do not use them (i.e., “w/o tags”). We can further fine tune the model from step 2 on the in-domain data, which is named as “mixed fine tuning + fine tuning.” Note that in the “fine tuning” method, the vocabulary obtained from the out-of-domain data is used for the in-domain data; while for the “multi domain” and “mixed fine tuning” methods, we use a vocabulary obtained from the mixed in-domain and out-of-domain data for all the training stages. ### Experimental Settings We conducted NMT domain adaptation experiments in two different settings as follows: ### High Quality In-domain Corpus Setting Chinese-to-English translation was the focus of the high quality in-domain corpus setting. We utilized the resource rich patent out-of-domain data to augment the resource poor spoken language in-domain data. The patent domain MT was conducted on the Chinese-English subtask (NTCIR-CE) of the patent MT task at the NTCIR-10 workshop BIBREF9 . The NTCIR-CE task uses 1000000, 2000, and 2000 sentences for training, development, and testing, respectively. The spoken domain MT was conducted on the Chinese-English subtask (IWSLT-CE) of the TED talk MT task at the IWSLT 2015 workshop BIBREF10 . The IWSLT-CE task contains 209,491 sentences for training. We used the dev 2010 set for development, containing 887 sentences. We evaluated all methods on the 2010, 2011, 2012, and 2013 test sets, containing 1570, 1245, 1397, and 1261 sentences, respectively. ### Low Quality In-domain Corpus Setting Chinese-to-Japanese translation was the focus of the low quality in-domain corpus setting. We utilized the resource rich scientific out-of-domain data to augment the resource poor Wikipedia (essentially open) in-domain data. The scientific domain MT was conducted on the Chinese-Japanese paper excerpt corpus (ASPEC-CJ) BIBREF11 , which is one subtask of the workshop on Asian translation (WAT) BIBREF15 . The ASPEC-CJ task uses 672315, 2090, and 2107 sentences for training, development, and testing, respectively. The Wikipedia domain task was conducted on a Chinese-Japanese corpus automatically extracted from Wikipedia (WIKI-CJ) BIBREF12 using the ASPEC-CJ corpus as a seed. The WIKI-CJ task contains 136013, 198, and 198 sentences for training, development, and testing, respectively. ### MT Systems For NMT, we used the KyotoNMT system BIBREF16 . The NMT training settings are the same as those of the best systems that participated in WAT 2016. The sizes of the source and target vocabularies, the source and target side embeddings, the hidden states, the attention mechanism hidden states, and the deep softmax output with a 2-maxout layer were set to 32,000, 620, 1000, 1000, and 500, respectively. We used 2-layer LSTMs for both the source and target sides. ADAM was used as the learning algorithm, with a dropout rate of 20% for the inter-layer dropout, and L2 regularization with a weight decay coefficient of 1e-6. The mini batch size was 64, and sentences longer than 80 tokens were discarded. We early stopped the training process when we observed that the BLEU score of the development set converges. For testing, we self ensembled the three parameters of the best development loss, the best development BLEU, and the final parameters. Beam size was set to 100. For performance comparison, we also conducted experiments on phrase based SMT (PBSMT). We used the Moses PBSMT system BIBREF17 for all of our MT experiments. For the respective tasks, we trained 5-gram language models on the target side of the training data using the KenLM toolkit with interpolated Kneser-Ney discounting, respectively. In all of our experiments, we used the GIZA++ toolkit for word alignment; tuning was performed by minimum error rate training BIBREF18 , and it was re-run for every experiment. For both MT systems, we preprocessed the data as follows. For Chinese, we used KyotoMorph for segmentation, which was trained on the CTB version 5 (CTB5) and SCTB BIBREF19 . For English, we tokenized and lowercased the sentences using the tokenizer.perl script in Moses. Japanese was segmented using JUMAN BIBREF20 . For NMT, we further split the words into sub-words using byte pair encoding (BPE) BIBREF21 , which has been shown to be effective for the rare word problem in NMT. Another motivation of using sub-words is making the different domains share more vocabulary, which is important especially for the resource poor domain. For the Chinese-to-English tasks, we trained two BPE models on the Chinese and English vocabularies, respectively. For the Chinese-to-Japanese tasks, we trained a joint BPE model on both of the Chinese and Japanese vocabularies, because Chinese and Japanese could share some vocabularies of Chinese characters. The number of merge operations was set to 30,000 for all the tasks. ### Results Tables 1 and 2 show the translation results on the Chinese-to-English and Chinese-to-Japanese tasks, respectively. The entries with SMT and NMT are the PBSMT and NMT systems, respectively; others are the different methods described in Section "Methods for Comparison" . In both tables, the numbers in bold indicate the best system and all systems that were not significantly different from the best system. The significance tests were performed using the bootstrap resampling method BIBREF22 at $p < 0.05$ . We can see that without domain adaptation, the SMT systems perform significantly better than the NMT system on the resource poor domains, i.e., IWSLT-CE and WIKI-CJ; while on the resource rich domains, i.e., NTCIR-CE and ASPEC-CJ, NMT outperforms SMT. Directly using the SMT/NMT models trained on the out-of-domain data to translate the in-domain data shows bad performance. With our proposed “Mixed fine tuning" domain adaptation method, NMT significantly outperforms SMT on the in-domain tasks. Comparing different domain adaptation methods, “Mixed fine tuning” shows the best performance. We believe the reason for this is that “Mixed fine tuning” can address the over-fitting problem of “Fine tuning.” We observed that while “Fine tuning” overfits quickly after only 1 epoch of training, “Mixed fine tuning” only slightly overfits until covergence. In addition, “Mixed fine tuning” does not worsen the quality of out-of-domain translations, while “Fine tuning” and “Multi domain” do. One shortcoming of “Mixed fine tuning” is that compared to “fine tuning,” it took a longer time for the fine tuning process, as the time until convergence is essentially proportional to the size of the data used for fine tuning. “Multi domain” performs either as well as (IWSLT-CE) or worse than (WIKI-CJ) “Fine tuning,” but “Mixed fine tuning” performs either significantly better than (IWSLT-CE) or is comparable to (WIKI-CJ) “Fine tuning.” We believe the performance difference between the two tasks is due to their unique characteristics. As WIKI-CJ data is of relatively poorer quality, mixing it with out-of-domain data does not have the same level of positive effects as those obtained by the IWSLT-CE data. The domain tags are helpful for both “Multi domain” and “Mixed fine tuning.” Essentially, further fine tuning on in-domain data does not help for both “Multi domain” and “Mixed fine tuning.” We believe the reason for this is that the “Multi domain” and “Mixed fine tuning” methods already utilize the in-domain data used for fine tuning. ### Conclusion In this paper, we proposed a novel domain adaptation method named “mixed fine tuning” for NMT. We empirically compared our proposed method against fine tuning and multi domain methods, and have shown that it is effective but is sensitive to the quality of the in-domain data used. In the future, we plan to incorporate an RNN model into our current architecture to leverage abundant in-domain monolingual corpora. We also plan on exploring the effects of synthetic data by back translating large in-domain monolingual corpora. Figure 1: Fine tuning for domain adaptation Figure 2: Tag based multi domain NMT Table 1: Domain adaptation results (BLEU-4 scores) for IWSLT-CE using NTCIR-CE. Table 2: Domain adaptation results (BLEU-4 scores) for WIKI-CJ using ASPEC-CJ.	Appending the domain tag “<2domain>" to the source sentences of the respective corpora
What is the first mate trying to express when he says "You all want me ta die uv old age"? A. He's grumbling because he hates his job and knows he doesn't want to do it forever B. Only the most important members of the society die of old age and he does not want that responsibility C. If he dies of old age, that means he will not be rewarded when he passes D. If he dies of old age, that means he'll be around without a lot of his friends, and he doesn't want that	VOYAGE TO FAR N'JURD By KRIS NEVILLE Illustrated by MACK [Transcriber's Note: This etext was produced from Galaxy Magazine April 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] They would never live to see the trip's end. So they made a few changes in their way of life—and many in their way of death! I "I don't see why we have to be here," a crewman said. "He ain't liable to say anything." "He shore better," the man in front of him said loudly. "Be still," his wife said. "People's lookin' at ya." "I don't care a smidgen," he said, "if en they ayre." "Please," she said. "Joanne Marie," he said, "you know that when I aims ta do somethin', I'm jest natcher'lly bound to do hit. An' iffen I aims ta talk...." "Here comes the priest. Now, be still." The man looked up. "So he do; an' I'll tell ya, hit shore is time he's a-gittin' hyere. I ain't got no all night fer ta sit." The crewman to his left bent over and whispered, "I'll bet he's gonna tell us it's gonna be another postponement." "Iffen he does, I'm jest a-gonna stand up an' yell right out that I ain't gonna stand fer hit no longer." "Now, dear," said Joanne Marie, "the captain can hear ya, if you're gonna talk so loud." "I hope he does; I jest hope he does. He's th' one that's a-keepin' us all from our Reward, an' I jest hope he does heyar me, so he'll know I'm a-gittin' mighty tyird uv waitin'." "You tell 'im!" someone said from two rows behind him. The captain, in the officer's section, sat very straight and tall. He was studiously ignoring the crew. This confined his field of vision to the left half of the recreation area. While the priest stood before the speaker's rostrum waiting for silence, the captain reached back with great dignity and scratched his right shoulder blade. Nestir, the priest, was dressed out in the full ceremonial costume of office. His high, strapless boots glistened with polish. His fez perched jauntily on his shiny, shaven head. The baldness was symbolic of diligent mental application to abstruse points of doctrine. Cotian exentiati pablum re overum est : "Grass grows not in the middle of a busy thoroughfare." The baldness was the result of the diligent application of an effective depilatory. His blood-red cloak had been freshly cleaned for the occasion, and it rustled around him in silky sibilants. "Men," he said. And then, more loudly, "Men!" The hiss and sputter of conversation guttered away. "Men," he said. "The other evening," he said, "—Gelday it was, to be exact—one of the crew came to me with a complaint." "Well, I'll be damned," Joanne Marie's husband said loudly. Nestir cleared his throat. "It was about the Casting Off. That's why I called you all together today." He stared away, at a point over the head and to the rear of the audience. "It puts me in mind of the parable of the six Vergios." Joanne Marie's husband sighed deeply. "Three, you will recall, were wise. When Prophet was at Meizque, they came to him and said, 'Prophet, we are afflicted. We have great sores upon our bodies.' The Prophet looked at them and did see that it was true. Then he blessed them and took out His knife and lay open their sores. For which the three wise Vergios were passing grateful. And within the last week, they were dead of infection. But three were foolish and hid their sores; and these three did live." The captain rubbed his nose. " Calex i pundendem hoy , my children. 'Secrecy makes for a long life,' as it says in the Jarcon ." Nestir tugged behind him at his cloak. "I want you all to remember that little story. I want you all to take it away from here with you and think about it, tonight, in the privacy of your cabins. "And like the three wise Vergios who went to the Prophet, one of the crewmen came to me. He came to me, and he said: 'Father, I am weary of sailing.' "Yes, he said, 'I am weary of sailing.' "Now, don't you think I don't know that. Every one of you—every blessed one of you—is weary of sailing. I know that as well as I know my own name, yes. "But because he came to me and said, 'Father, I am weary of sailing,' I went to the captain, and I said, 'Captain, the men are weary of sailing.' "And then the captain said: 'All right, Father,' he said, 'I will set the day for the Festival of the Casting Off!'" The little fellow was pleased by the rustle of approval from the audience. "God damn, hit's about time!" Joanne Marie's husband said. Nestir cleared his throat again. "Hummm. Uh. And the day is not very far distant," said Nestir. "I knowed there was a catch to hit," Joanne Marie's husband said. "I know you will have many questions; yes, I know you will have—ah, ah—well, many questions. You are thinking: 'What kind of a Festival can we have here on this ship?' You are thinking: 'What a fine thing—ah, what a good thing, that is—ah, how nice it would be to have the Casting Off at home, among friends.'" Nestir waved his hands. "Well, I just want to tell you: I come from Koltah. And you know that Koltah never let any city state outdo her in a Festival, uh-huh. "The arena in Koltah is the greatest arena in the whole system. We have as many as sixty thousand accepted applicants. All of them together in the arena is a—uh, uh, well—a sight to behold. People come from all over to behold it. I never will forget the Festival at which my father was accepted. He.... "Well, the point I want to make is this: I just wanted to tell you that I know what a Festival should be, and the captain and I will do everything in our power to make our Casting Off as wonderful as any anywhere. "And I want to tell you that if you'll come to me with your suggestions, I'll do all I can to see that we do this thing just the way you want it done. I want you to be proud of this Casting Off Festival, so you can look back on it and say, uh, uh—this day was the real high point of your whole life!" Everyone but Joanne Marie's husband cheered. He sat glumly muttering to himself. Nestir bobbed his shiny head at them and beamed his cherubic smile. And noticed that there was a little blonde, one of the crewmen's wives, in the front row that had very cute ankles. While they were still cheering and stomping and otherwise expressing their enthusiasm and approval, Nestir walked off the speaker's platform and into the officer's corridor. He wiped his forehead indecorously on the hem of his cloak and felt quite relieved that the announcement was over with and the public speaking done. II Dinner that evening was a gala occasion aboard the ship. The steward ordered the holiday feast prepared in celebration of Nestir's announcement. And, for the officers, he broke out of the special cellar the last case allotment for Crew One of the delicate Colta Barauche ('94). He ordered the messman to put a bottle of it to the right of each plate. The captain came down from his stateroom after the meal had begun. He nodded curtly to the officers when he entered the mess hall, walked directly to his place at the head of the table, sat down and morosely began to work the cork out of his wine bottle with his teeth. "You'll spoil the flavor, shaking it that way," the third mate cautioned. He was particularly fond of that year. The captain twisted the bottle savagely, and the cork came free with a little pop. He removed the cork from between his teeth, placed it very carefully beside his fork, and poured himself a full glass of the wine. "Very probably," he said sadly. "I don't think hit'll do hit," the first mate said. "He hain't shook hard enough to matter." The captain picked up the glass, brought it toward his lips—then, suddenly having thought of something, he put it back down and turned to Nestir. "I say. Have you decided on this Carstar thing yet, Father?" The little priest looked up. He laid his knife across the rim of his plate. "It has ramifications," he said. When the third mate saw that his opinion on the wine was not immediately to be justified, he settled back in his chair with a little sigh of disapproval. "Well, what do you think your decision will be, Father?" the steward asked. Nestir picked up his knife and fork and cut off a piece of meat. "Hummmm," he said. "It's hard to say. The whole issue involves, as a core point, the principle of casta cum mae stotiti ." The first mate nodded sagely. "The intent, of course, could actually be—ah— sub mailloux ; and in that event, naturally, the decision would be even more difficult. I wish I could talk to higher authority about it; but of course I haven't the time. I'll have to decide something." "He had a very pretty wife," the third mate said. "Yes, very." Nestir agreed. "But as I was saying, if it could be proven that the culstem fell due to no negligence on his part, either consciously or subconsciously, then the obvious conclusion would be that no stigma would be attached." He speared his meat and chewed it thoughtfully. "But it wasn't at all bloody," the wife of the second mate said. "I scarcely think he felt it at all. It happened too fast." Nestir swallowed the mouthful of food and washed it down with a gulp of wine. "The problem, my dear Helen," he said, "is one of intent. To raise the issue of concomitant agonies is to confuse the whole matter. For instance. Take Wilson, in my home state of Koltah. Certainly he died as miserable a death as anyone could desire." "Yes," said the second mate's wife. "I remember that. I read about it in the newspapers." "But it was a case of obvious intent ," continued Nestir, "and therefore constituted a clear out attempt to avoid his duty by hastening to his Reward." Upon hearing the word duty, the captain brightened. "That," he said to Nestir, "my dear Father, is the cardinal point of the whole game, y'know." He scratched the back of his left hand. "Duty. And I must say, I think you're being quite short-sighted about the Casting Off date. After all, it's not only a question of how we go, but also a question of leaving only after having done our duty. And that's equally important." "The Synod of Cathau—" Nestir began. "Plague take it, Father! Really, now, I must say. The Synod of Cathau! Certainly you've misinterpreted that. Anticipation can be a joy, y'know: almost equal to the very Reward. Anticipation should spur man in duty. It's all noble and self sacrificing." He scratched the back of his right hand. The second mate had been trying to get a word in edgewise for several minutes; he finally succeeded by utilizing the temporary silence following the captain's outburst. "You don't need to worry about your Casting Off, Captain. You can leave that to me. I assure you, I have in mind a most ingenious method." The captain was not visibly cheered; he was still brooding about the sad absence of a sense of duty on the part of Nestir. "I will welcome it," he said, "at the proper time, sir. And I certainly hope—" His eyes swept the table. "I certainly hope to be Cast Off by an officer. It would be very humiliating, y'know, to have a crew member do it." "Oh, very," said the steward. "I don't know," the second mate's wife said, "whether you better count on my husband or not. I have my own plans for him." "This problem of Carstar interests me," the third mate said. "Did I ever tell you about my wife? She strangled our second baby." "He was a very annoying child," his wife said. "He probably wouldn't have lived, anyway," the third mate said. "Puny baby." "That," said Nestir, "is not at all like the Carstar case. Not at all. Yours is a question of saliex y cuminzund ." The first mate nodded. "It seems to me that the whole thing would depend on the intent of the strangler." "Captain," the steward said, "you really must let me give you some of that salve." "That's very kind of you, but I...." "No bother at all," the steward said. "As I see it," Nestir said, "if the intent was the natural maternal instinct of the mother to release her child from its duty, then...." "Oh, not at all," the third mate's wife said. "I did it to make him stop crying." "Well, in that case, I see no reason why he shouldn't get his Reward." "I certainly hope so," the third mate said. "Jane worries about it all the time." "I do not," Jane contradicted. "Now, honey, you know you do so." At that moment, he lost interest in his wife and leaned across the table toward the captain, "Well?" he asked. The captain rolled the wine over his tongue. "You were right, of course." The third mate turned triumphantly to the first mate. "There, I told you so." The first mate shrugged. "I never do say nothin' right," he said. "I hain't got no luck. I've spent more years un all ya, carpenterin' up a duty log that's better un even th' captain's. An' hit's Martha an' me that gotta wait an' help th' next crew. Lord above knows how long time hit'll be afore we uns'll got ta have a Festival." "Oh, really, now. Now. Duty, duty," the captain reprimanded him mildly. "Duty! Duty! Duty! You all ur in a conspiracy. You all want me ta die uv old age." "Nonsense," said the steward. "We don't want anything of the sort. After all, someone has to orient the new crew." "Quite right," said the captain. "You ought to be proud." The first mate slammed his napkin in the middle of his food and stalked out of the mess hall. "Quite touchy today," Nestir observed. "By the way," the third mate said. "Wanda gave me a petition to give to you, Father." "Wanda?" "Yes. She's sixteen, now." "Wanda who?" the steward asked. "Wanda Miller, the bosun's daughter." "I know her," Helen said. "She's the oldest child on the ship, and she wants you to sign her adult petition so she can be in the Festival, Father." "She's so young...." "Sixteen, Father." "After all, one must have done some duty," the captain said. "He wants you to sign it so he can take her in the Changing of the Wives," Jane said. Nestir fidgeted uncomfortably. "Well, I'll look at her record," he said. "It's an idea," the second mate said. "Otherwise, we'll be short one woman." "There wouldn't be one short if he had brought a wife," the first mate's wife said, looking squarely at the captain. "Now, Martha. I place duty above pleasure. You're just angry, y'know, because you have to stay with your husband." "All right, so I am. But it's true. And if Carstar hadn't been killed, there would have been two short." She shot a wicked glance at Nestir. "Why don't you and him share a woman—" "Martha!" "Although the Prophet knows what woman in her right mind would consent to...." "Well," said Nestir hesitantly. "Listen," the third mate said, "the second's right. If you don't sign it, someone will have to do without a woman." Nestir blushed. "I'll look it over very carefully, but you must realize that the priestcraft...." "Actually, in a way, it would be her duty to, you see. Think of it like that: as her way to do her duty." "She's too young for you, dear," Jane said to her husband. "Oh, I don't know," the steward said. "Sometimes they're the best, I hear." III The third mate, whose name was Harry, stood before the mirror combing his hair. He had been combing his hair for the last fifteen minutes. "I suppose the crew is celebrating?" his wife said. "I suppose." She stood up and walked over to the dresser. Absently she began to finger the articles on it. "You really shouldn't have told them about little Glenn tonight." "Pish-tush." "No, Harry. I mean it. Helen looked at me strangely all through dinner. She has three children, you know." "You're imagining things." "But she does have three children." "I mean about her looking at you." "Oh." Harry fiddled with his tie without speaking. "I mean, as much as to say: 'Well, I raised all of mine.'" "But honey, about little Glenn. That was an accident, almost. You didn't really mean to choke him that hard." "But still ... it ... I mean, there was Helen, looking at me like I wasn't doing my duty. You know." "No," he said. "That's nonsense, Jane. Sheer nonsense. You know what the priest said." He polished one of his brass buttons with the sleeve of his coat. "Harry?" "Yes?" "I don't think all that is necessary just to go on duty." "Probably not." She walked to the bed and sat down. "Harry?" "Yes, dear?" "Don't you really think she's awful young?" "Huh-uh." "I mean, why don't you pick someone else? Like Mary? She's awful sweet. I'll bet she'd be better." "Probably." "She's a lot of fun." He brushed at his hair again. "Who do you want, Jane?" "Oh, I don't know." She looked down at her legs, raised them up from the floor and held them out in front of her. "I think I'd kind of like Nestir. With his funny bald head. I hope he asks me." "I'll mention it to him." "Would you really, Harry? That would be sweet." "Sure, honey." He looked down at his watch. "Harry? Are you going to meet Wanda in the control room?" "Uh-huh." "I thought so. Well, remember this, dear: It isn't the day of the Changing of the Wives yet. Don't forget." "Honey! You don't think for a minute that...." "No, dear. I know you wouldn't. But just don't , I mean." He walked over and kissed her forehead and patted her cheek. "Course not," he said, comfortingly. He left her sitting on the bed and strolled down the officers' corridor, whistling. He made a mental note to have the bosun send some of the crew in tomorrow to wash down these bulkheads. They needed it. In one corner a spider spun its silver web. He jogged up the companionway, turned left and felt the air as fresh as spring when he stepped under the great ventilator. And beneath it lay one of the crew. He kicked the man several times in the ribs until he came to consciousness. "Can't sleep here, my man," Harry explained. "Awww. Go way an' le' me 'lone, huh?" "Here. Here." He pulled the fellow erect and slapped him in the face briskly. "This is the officers' corridor." "Oh? Ish it? Schorry. Shore schorry, shir. So schorry." Harry assisted him to the crew's corridor where he sank to the floor and relapsed once more into a profound slumber. Harry continued on to the control room. When he entered it, the second mate was yawning. "Hi, John. Sleepy?" "Uh-huh. You're early." "Don't mind, do you?" "No ... Quiet tonight. Had to cut the motors an hour ago. Control technician passed out." "Oh?" The second mate took out a cigarette and lit it. "Can't blow the ship up, you know. Look like hell on the record. Hope the captain don't find out about it, though. He'll figure the man was neglecting his duty." He blew a smoke ring. "Might even bar him from the Festival." "Yeah," said Harry, "the captain's funny that way." The second mate blew another smoke ring. "Well," Harry said. "Uh. Harry? Are you really going to take that Wanda girl?" "If Nestir lets me." "Say. Harry. Do you suppose your wife would...?" Harry crossed to the second mate and put a hand on his shoulder. "Sorry, old fellow. She's got it in her head to take Nestir." He shrugged. "I don't exactly approve, of course, but ... I'm sure if he doesn't want her, she'd be glad to hear your offer." "Aw, that's all right," John said. "Don't really matter. Say. By the way. Have I told you what I intend to do to the captain? I've got it all thought out. You know that saber I picked up on Queglat? Well...." "Look. How about telling me another time?" "Uh, Sure. If you say so. Uh?" "I'm kind of expecting Wanda." "Oh. Sure. I should have known you weren't here early for nothing. In that case, I better be shoving off. Luck." "Thanks. See you at breakfast." "Right-o." After the second mate left, Harry walked over to the control panel. The jet lights were dead. He picked up the intercom and switched over the engine call bell. "'Lo," he said into the microphone. "This is the bridge.... Oh, hi, Barney. Harry.... Have you got a sober control technician down there yet...? Fine. We'll start the jets again. If the captain comes in now—well, you know how he is.... Okay, thanks. Night." He replaced the microphone. He reached over and threw the forward firing lever. The jet lights came on and the ship began to brake acceleration again. Having done that, he switched on the space viewer. The steady buzz of the equipment warming sounded in his ears. Wanda would be sure to want to look at the stars. She was simple minded. "Hello." He swiveled around. "Oh, hello, Wanda, honey." "Hello, Haireee. Are you glad little ol' me could come, huh?" "Sure am." "Me, too. Can I look at the—oh. It's already on." "Uh-huh. Look. Wanda." "Hum?" "I talked to Nestir today." "Goody. What did he say, huh? I can be an adult and get to play in the Festival, can I?" "I don't know, yet. He's thinking about it. That's why I want to see you. He's going to check your record. And Wanda?" "Them stars shore are purty." "Wanda, listen to me." "I'm a-listenin', Haireee." "You're simply going to have to stop carrying that doll around with you if you want to be an adult." In Nestir's cabin the next morning, the captain and the priest held a conference. "No, Captain. I'm afraid I can't agree to that," Nestir said. The captain said, "Oh, don't be unreasonable, Father. After all, this is a ship, y'know. And I am, after all, the captain." Nestir shook his head. "The crew and the officers will participate together in the Festival. I will not put the officers' corridor off limits, and—Oh! Yes? Come in!" The door opened. "Father?" "Yes, my son? Come in." "Thank you, Father. Good morning, Captain, sir." "Sit down, my son. Now, Captain, as I was saying: no segregation. It's contrary to the spirit, if not the wording, of the Jarcon ." "But Father! A crewman! In the officers' corridor! Think!" "Before the Prophet, we are all equal. I'm sorry, Captain. Now on Koltah, we practiced it with very good results, and...." "I say, really—" "Father?" said the crewman who had just entered. "Yes, my son. In one moment. Now, Captain. As I have been explaining: The arena method has advantages. In Koltah we always used it. But here—due to the—ah—exigencies of deep space—I feel convinced that a departure from normal procedure is warranted. It is not without precedent. Such things were fairly common, in astoli tavoro , up until centralization, three hundred years before Allth. Indeed, in my home city—Koltah—in the year of the seventh plague, a most unusual expedient was adopted. It seems...." "You're perfectly correct, of course," the captain said. "That's just what I wanted to see you about, Father," the crewman said. "Now, in my city state of Ni, for the Festivals, we...." "Shut up," said the captain softly. "Yes, sir." "Now, as I was saying, Captain, when the methods used in...." "If you'll excuse me, Father, I really should return to duty," said the crewman. "Quite all right, my son. Close the door after you." "I must say, fellow, your sense of duty is commendable." "Well, uh, thank you, sir. And thank you, Father, for your time." "Quite all right, my son. That's what I'm here for. Come in as often as you like." The crewman closed the door after him. He had been gone only a moment, scarcely time for Nestir to get properly launched on his account, when Harry, the third mate, knocked on the door and was admitted. "Oh? Good morning, Captain. I didn't know you were here." Then, to the priest: "I'll come back later, Father." "Nonsense," said the captain. "Come in." "Well, I had hoped to see the Father for a minute on ... private business." "I have to be toddling along," said the captain. "But Captain! I haven't finished telling you about...." "I'll just go down and get a cup of coffee," the captain said. "I'll call you when I'm through," said Harry. The captain left the room. "It's about Wanda, Father," said the third mate. The priest studied the table top. He rearranged some papers. "Ah, yes. The young girl." "Well, I mean, it's not only about Wanda," said Harry. "You see, my wife, Jane, that is...." "Yes?" said the priest. He took his pen out of the holder. "I think, with the proper ... ah ... you know. What I mean is, I think she might look with favor on you in the Changing of the Wives, if I said a few well chosen words in your behalf." "That is very flattering, my son." He returned the pen to the holder. "Such bounty, as it says in the Jarcon , is cull tensio ." "And with your permission, Father...." "Ah...." "She's a very pretty woman." "Ah.... Quite so." "Well, about Wanda. I really shouldn't mention this. But Father, if we are short one woman...." "Hummmm." "I mean, the girls might think a man gets rusty." "I see what you mean." Nestir blinked his eyes. "It wouldn't be fair, all things considered." He stood up. "I may tell you, my son, that, in thinking this matter over last night, I decided that Wanda—ah—Miller, yes, has had sufficient duty to merit participation in the Festival." "Justice is a priestly virtue," Harry said. "And you really think your wife would...?" "Oh, yes, Father." "Well, ahem. But...." "Yes, Father?" " Ad dulce verboten. " "Uh?" "That is to say, in order for a woman to join in the ritual of the Changing of the Wives, she must, ahem, be married." "I never thought of that," said the third mate disconsolately. "I think that can be arranged, however," said Nestir. "If you go by the mess hall on your way out, please tell the captain we can continue our discussion at his pleasure." IV "Sit down, Captain," said Nestir, when the captain entered. "No. Over there, in the comfortable chair. There. Are you comfortable, Captain?" "Of course I am." "Good. I have a question to ask you, Captain." "I say?" Nestir rubbed his bald head. "Sir," he said by way of preamble, "I know you have the greatest sensibility in questions of duty." "That's quite so, y'know. I pride myself upon it, if I do say so." "Exactly. Argot y calpex. No sacrifice is too great." "True; true." "Well, then, say the first day of Wenslaus, that would be—ah, a Zentahday—I may depend upon you to wed Wanda Miller, the bosun's daughter, yes?" "No," said the captain. "Come now, sir. I realize she is the daughter of a crewman, but—" "Father," said the captain, "did I ever tell you about the time I led an expeditionary force against Zelthalta?" "I don't believe you have." "Then I will tell you. Came about this way. I was given command of fifty-three thousand Barains. Savage devils. Uncivilized, but fine fighters. I was to march them ninety-seven miles across the desert that...." "Captain! I fear I must be very severe with you. I will be forced to announce in the mess hall this evening that you have refused to do your duty when it was plainly and properly called to your attention." "Very well, Father," the captain said after several minutes. "I will do it." He was trembling slightly. That morning was to be the time of the captain's wedding. He had insisted that it be done in privacy. For the ceremony, he refused to make the slightest change in his everyday uniform; nor would he consent to Nestir's suggestion that he carry a nosegay of hydroponic flowers. He had intended, after the ceremony, to go about his duty as if nothing out of the ordinary had happened; but after it was done with, the vast indignity of it came home to him even more poignantly than he had imagined it would. Without a word, he left the priest's stateroom and walked slowly, ponderously, with great dignity, to his own. It was a very fine stateroom. The finest, but for Nestir's, in the whole ship. The velvet and gold drapes (his single esthetic joy) were scented with exotic perfume. The carpet was an inch and a half thick. He walked through his office without breaking his stride. The bed was large and fluffy. An unbroken expanse of white coverlette jutting out from the far bulkhead. It looked as soft as feather down. Without even a sigh, he threw himself upon the bed and lay very, very quiet. His left leg was suspended in the air, intersecting, at the thigh, the plane of the coverlet at forty-five degrees; the number of degrees remained stiffly, unrelaxingly forty-five. Only after a long, long time did he roll over on his back and then it was merely to stare fixedly at the ceiling. It is entirely possible that he would have lain there until Doomsday had not his introspection been, around noon, interrupted by an apologetic tap on the door. "Come in," he whispered, hoping she would not hear him and go away. But she heard him. "Husband," Wanda said simply. She closed the door behind her and stood staring at him. "Madam," he said, "I hope you will have the kindness not to refer to me by that indecent appelation a second time." "Gee. You say the cutest things. I'm awful glad you had to marry me, huh." The captain stood up, adjusted his coat and his shoulders, and walked across the room to the dressing table. He opened the left-hand drawer, removed a bottle, poured himself half a water-glass full and drank it off. "Ah," he said. He returned to the bed and sat down. "Can'tcha even say hello ta little ol' me, huh?" she asked. "Hello," he said. "Madam, sit down. I intend to give you an instructive lecture in the natural order of...." "Huh?" "Ah," he said. "Quite true, of course." She walked over to the chair and sat down. "I don't like them," she said. "Them cloth things over there." "Those, Madam," he said, "are priceless drapes I had imported from the province of San Xalthan. They have a long, strange history. "About three thousand years ago, a family by the name of Soong was forced to flee from the city of Xan because the eldest son of the family had become involved in a conspiracy against the illustrious King Fod. As the Soong family was traveling...." "I don't like 'em anyway," said Wanda. "Madam," said the captain, "kindly bring me that." "This?" "Yes. Thank you." He took the doll from her. He got up again, walked to the chest of drawers, searched around for a penknife. Finally he located it under a stack of socks.	C. If he dies of old age, that means he will not be rewarded when he passes
What wasn't strange about the dress purchased? A. it spoke to Sally B. it wouldn't come off C. it made Sally levitate D. it changed colors	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."	A. it spoke to Sally
How is Hilary's product going to kill the razor industry? A. Before-shave breaks off whiskers, just apply and wipe away. B. Before-shave dissolves whiskers permanently. C. Before-shave dissolves whiskers for four to six weeks at a time. D. Before-shave will never kill the razor industry. That's just wishful thinking.	Fallout is, of course, always disastrous— one way or another JUNIOR ACHIEVEMENT BY WILLIAM LEE ILLUSTRATED BY SCHOENHERR "What would you think," I asked Marjorie over supper, "if I should undertake to lead a junior achievement group this summer?" She pondered it while she went to the kitchen to bring in the dessert. It was dried apricot pie, and very tasty, I might add. "Why, Donald," she said, "it could be quite interesting, if I understand what a junior achievement group is. What gave you the idea?" "It wasn't my idea, really," I admitted. "Mr. McCormack called me to the office today, and told me that some of the children in the lower grades wanted to start one. They need adult guidance of course, and one of the group suggested my name." I should explain, perhaps, that I teach a course in general science in our Ridgeville Junior High School, and another in general physics in the Senior High School. It's a privilege which I'm sure many educators must envy, teaching in Ridgeville, for our new school is a fine one, and our academic standards are high. On the other hand, the fathers of most of my students work for the Commission and a constant awareness of the Commission and its work pervades the town. It is an uneasy privilege then, at least sometimes, to teach my old-fashioned brand of science to these children of a new age. "That's very nice," said Marjorie. "What does a junior achievement group do?" "It has the purpose," I told her, "of teaching the members something about commerce and industry. They manufacture simple compositions like polishing waxes and sell them from door-to-door. Some groups have built up tidy little bank accounts which are available for later educational expenses." "Gracious, you wouldn't have to sell from door-to-door, would you?" "Of course not. I'd just tell the kids how to do it." Marjorie put back her head and laughed, and I was forced to join her, for we both recognize that my understanding and "feel" for commercial matters—if I may use that expression—is almost nonexistent. "Oh, all right," I said, "laugh at my commercial aspirations. But don't worry about it, really. Mr. McCormack said we could get Mr. Wells from Commercial Department to help out if he was needed. There is one problem, though. Mr. McCormack is going to put up fifty dollars to buy any raw materials wanted and he rather suggested that I might advance another fifty. The question is, could we do it?" Marjorie did mental arithmetic. "Yes," she said, "yes, if it's something you'd like to do." We've had to watch such things rather closely for the last ten—no, eleven years. Back in the old Ridgeville, fifty-odd miles to the south, we had our home almost paid for, when the accident occurred. It was in the path of the heaviest fallout, and we couldn't have kept on living there even if the town had stayed. When Ridgeville moved to its present site, so, of course, did we, which meant starting mortgage payments all over again. Thus it was that on a Wednesday morning about three weeks later, I was sitting at one end of a plank picnic table with five boys and girls lined up along the sides. This was to be our headquarters and factory for the summer—a roomy unused barn belonging to the parents of one of the group members, Tommy Miller. "O.K.," I said, "let's relax. You don't need to treat me as a teacher, you know. I stopped being a school teacher when the final grades went in last Friday. I'm on vacation now. My job here is only to advise, and I'm going to do that as little as possible. You're going to decide what to do, and if it's safe and legal and possible to do with the starting capital we have, I'll go along with it and help in any way I can. This is your meeting." Mr. McCormack had told me, and in some detail, about the youngsters I'd be dealing with. The three who were sitting to my left were the ones who had proposed the group in the first place. Doris Enright was a grave young lady of ten years, who might, I thought, be quite a beauty in a few more years, but was at the moment rather angular—all shoulders and elbows. Peter Cope, Jr. and Hilary Matlack were skinny kids, too. The three were of an age and were all tall for ten-year-olds. I had the impression during that first meeting that they looked rather alike, but this wasn't so. Their features were quite different. Perhaps from association, for they were close friends, they had just come to have a certain similarity of restrained gesture and of modulated voice. And they were all tanned by sun and wind to a degree that made their eyes seem light and their teeth startlingly white. The two on my right were cast in a different mold. Mary McCready was a big husky redhead of twelve, with a face full of freckles and an infectious laugh, and Tommy Miller, a few months younger, was just an average, extroverted, well adjusted youngster, noisy and restless, tee-shirted and butch-barbered. The group exchanged looks to see who would lead off, and Peter Cope seemed to be elected. "Well, Mr. Henderson, a junior achievement group is a bunch of kids who get together to manufacture and sell things, and maybe make some money." "Is that what you want to do," I asked, "make money?" "Why not?" Tommy asked. "There's something wrong with making money?" "Well, sure, I suppose we want to," said Hilary. "We'll need some money to do the things we want to do later." "And what sort of things would you like to make and sell?" I asked. The usual products, of course, with these junior achievement efforts, are chemical specialties that can be made safely and that people will buy and use without misgivings—solvent to free up rusty bolts, cleaner to remove road tar, mechanic's hand soap—that sort of thing. Mr. McCormack had told me, though, that I might find these youngsters a bit more ambitious. "The Miller boy and Mary McCready," he had said, "have exceptionally high IQ's—around one forty or one fifty. The other three are hard to classify. They have some of the attributes of exceptional pupils, but much of the time they seem to have little interest in their studies. The junior achievement idea has sparked their imaginations. Maybe it'll be just what they need." Mary said, "Why don't we make a freckle remover? I'd be our first customer." "The thing to do," Tommy offered, "is to figure out what people in Ridgeville want to buy, then sell it to them." "I'd like to make something by powder metallurgy techniques," said Pete. He fixed me with a challenging eye. "You should be able to make ball bearings by molding, then densify them by electroplating." "And all we'd need is a hydraulic press," I told him, "which, on a guess, might cost ten thousand dollars. Let's think of something easier." Pete mulled it over and nodded reluctantly. "Then maybe something in the electronics field. A hi-fi sub-assembly of some kind." "How about a new detergent?" Hilary put in. "Like the liquid dishwashing detergents?" I asked. He was scornful. "No, they're formulations—you know, mixtures. That's cookbook chemistry. I mean a brand new synthetic detergent. I've got an idea for one that ought to be good even in the hard water we've got around here." "Well, now," I said, "organic synthesis sounds like another operation calling for capital investment. If we should keep the achievement group going for several summers, it might be possible later on to carry out a safe synthesis of some sort. You're Dr. Matlack's son, aren't you? Been dipping into your father's library?" "Some," said Hilary, "and I've got a home laboratory." "How about you, Doris?" I prompted. "Do you have a special field of interest?" "No." She shook her head in mock despondency. "I'm not very technical. Just sort of miscellaneous. But if the group wanted to raise some mice, I'd be willing to turn over a project I've had going at home." "You could sell mice?" Tommy demanded incredulously. "Mice," I echoed, then sat back and thought about it. "Are they a pure strain? One of the recognized laboratory strains? Healthy mice of the right strain," I explained to Tommy, "might be sold to laboratories. I have an idea the Commission buys a supply every month." "No," said Doris, "these aren't laboratory mice. They're fancy ones. I got the first four pairs from a pet shop in Denver, but they're red—sort of chipmunk color, you know. I've carried them through seventeen generations of careful selection." "Well, now," I admitted, "the market for red mice might be rather limited. Why don't you consider making an after-shave lotion? Denatured alcohol, glycerine, water, a little color and perfume. You could buy some bottles and have some labels printed. You'd be in business before you knew it." There was a pause, then Tommy inquired, "How do you sell it?" "Door-to-door." He made a face. "Never build up any volume. Unless it did something extra. You say we'd put color in it. How about enough color to leave your face looking tanned. Men won't use cosmetics and junk, but if they didn't have to admit it, they might like the shave lotion." Hilary had been deep in thought. He said suddenly, "Gosh, I think I know how to make a—what do you want to call it—a before-shave lotion." "What would that be?" I asked. "You'd use it before you shaved." "I suppose there might be people who'd prefer to use it beforehand," I conceded. "There will be people," he said darkly, and subsided. Mrs. Miller came out to the barn after a while, bringing a bucket of soft drinks and ice, a couple of loaves of bread and ingredients for a variety of sandwiches. The parents had agreed to underwrite lunches at the barn and Betty Miller philosophically assumed the role of commissary officer. She paused only to say hello and to ask how we were progressing with our organization meeting. I'd forgotten all about organization, and that, according to all the articles I had perused, is most important to such groups. It's standard practice for every member of the group to be a company officer. Of course a young boy who doesn't know any better, may wind up a sales manager. Over the sandwiches, then, I suggested nominating company officers, but they seemed not to be interested. Peter Cope waved it off by remarking that they'd each do what came naturally. On the other hand, they pondered at some length about a name for the organization, without reaching any conclusions, so we returned to the problem of what to make. It was Mary, finally, who advanced the thought of kites. At first there was little enthusiasm, then Peter said, "You know, we could work up something new. Has anybody ever seen a kite made like a wind sock?" Nobody had. Pete drew figures in the air with his hands. "How about the hole at the small end?" "I'll make one tonight," said Doris, "and think about the small end. It'll work out all right." I wished that the youngsters weren't starting out by inventing a new article to manufacture, and risking an almost certain disappointment, but to hold my guidance to the minimum, I said nothing, knowing that later I could help them redesign it along standard lines. At supper I reviewed the day's happenings with Marjorie and tried to recall all of the ideas which had been propounded. Most of them were impractical, of course, for a group of children to attempt, but several of them appeared quite attractive. Tommy, for example, wanted to put tooth powder into tablets that one would chew before brushing the teeth. He thought there should be two colors in the same bottle—orange for morning and blue for night, the blue ones designed to leave the mouth alkaline at bed time. Pete wanted to make a combination nail and wood screw. You'd drive it in with a hammer up to the threaded part, then send it home with a few turns of a screwdriver. Hilary, reluctantly forsaking his ideas on detergents, suggested we make black plastic discs, like poker chips but thinner and as cheap as possible, to scatter on a snowy sidewalk where they would pick up extra heat from the sun and melt the snow more rapidly. Afterward one would sweep up and collect the discs. Doris added to this that if you could make the discs light enough to float, they might be colored white and spread on the surface of a reservoir to reduce evaporation. These latter ideas had made unknowing use of some basic physics, and I'm afraid I relapsed for a few minutes into the role of teacher and told them a little bit about the laws of radiation and absorption of heat. "My," said Marjorie, "they're really smart boys and girls. Tommy Miller does sound like a born salesman. Somehow I don't think you're going to have to call in Mr. Wells." I do feel just a little embarrassed about the kite, even now. The fact that it flew surprised me. That it flew so confoundedly well was humiliating. Four of them were at the barn when I arrived next morning; or rather on the rise of ground just beyond it, and the kite hung motionless and almost out of sight in the pale sky. I stood and watched for a moment, then they saw me. "Hello, Mr. Henderson," Mary said, and proffered the cord which was wound on a fishing reel. I played the kite up and down for a few minutes, then reeled it in. It was, almost exactly, a wind sock, but the hole at the small end was shaped—by wire—into the general form of a kidney bean. It was beautifully made, and had a sort of professional look about it. "It flies too well," Mary told Doris. "A kite ought to get caught in a tree sometimes." "You're right," Doris agreed. "Let's see it." She gave the wire at the small end the slightest of twists. "There, it ought to swoop." Sure enough, in the moderate breeze of that morning, the kite swooped and yawed to Mary's entire satisfaction. As we trailed back to the barn I asked Doris, "How did you know that flattening the lower edge of the hole would create instability?" She looked doubtful. "Why it would have to, wouldn't it? It changed the pattern of air pressures." She glanced at me quickly. "Of course, I tried a lot of different shapes while I was making it." "Naturally," I said, and let it go at that. "Where's Tommy?" "He stopped off at the bank," Pete Cope told me, "to borrow some money. We'll want to buy materials to make some of these kites." "But I said yesterday that Mr. McCormack and I were going to advance some cash to get started." "Oh, sure, but don't you think it would be better to borrow from a bank? More businesslike?" "Doubtless," I said, "but banks generally want some security." I would have gone on and explained matters further, except that Tommy walked in and handed me a pocket check book. "I got two hundred and fifty," he volunteered—not without a hint of complacency in his voice. "It didn't take long, but they sure made it out a big deal. Half the guys in the bank had to be called in to listen to the proposition. The account's in your name, Mr. Henderson, and you'll have to make out the checks. And they want you to stop in at the bank and give them a specimen signature. Oh, yes, and cosign the note." My heart sank. I'd never had any dealings with banks except in the matter of mortgages, and bank people make me most uneasy. To say nothing of finding myself responsible for a two-hundred-and-fifty-dollar note—over two weeks salary. I made a mental vow to sign very few checks. "So then I stopped by at Apex Stationers," Tommy went on, "and ordered some paper and envelopes. We hadn't picked a name yesterday, but I figured what's to lose, and picked one. Ridge Industries, how's that?" Everybody nodded. "Just three lines on the letterhead," he explained. "Ridge Industries—Ridgeville—Montana." I got my voice back and said, "Engraved, I trust." "Well, sure," he replied. "You can't afford to look chintzy." My appetite was not at its best that evening, and Marjorie recognized that something was concerning me, but she asked no questions, and I only told her about the success of the kite, and the youngsters embarking on a shopping trip for paper, glue and wood splints. There was no use in both of us worrying. On Friday we all got down to work, and presently had a regular production line under way; stapling the wood splints, then wetting them with a resin solution and shaping them over a mandrel to stiffen, cutting the plastic film around a pattern, assembling and hanging the finished kites from an overhead beam until the cement had set. Pete Cope had located a big roll of red plastic film from somewhere, and it made a wonderful-looking kite. Happily, I didn't know what the film cost until the first kites were sold. By Wednesday of the following week we had almost three hundred kites finished and packed into flat cardboard boxes, and frankly I didn't care if I never saw another. Tommy, who by mutual consent, was our authority on sales, didn't want to sell any until we had, as he put it, enough to meet the demand, but this quantity seemed to satisfy him. He said he would sell them the next week and Mary McCready, with a fine burst of confidence, asked him in all seriousness to be sure to hold out a dozen. Three other things occurred that day, two of which I knew about immediately. Mary brought a portable typewriter from home and spent part of the afternoon banging away at what seemed to me, since I use two fingers only, a very creditable speed. And Hilary brought in a bottle of his new detergent. It was a syrupy yellow liquid with a nice collar of suds. He'd been busy in his home laboratory after all, it seemed. "What is it?" I asked. "You never told us." Hilary grinned. "Lauryl benzyl phosphonic acid, dipotassium salt, in 20% solution." "Goodness." I protested, "it's been twenty-five years since my last course in chemistry. Perhaps if I saw the formula—." He gave me a singularly adult smile and jotted down a scrawl of symbols and lines. It meant little to me. "Is it good?" For answer he seized the ice bucket, now empty of its soda bottles, trickled in a few drops from the bottle and swished the contents. Foam mounted to the rim and spilled over. "And that's our best grade of Ridgeville water," he pointed out. "Hardest in the country." The third event of Wednesday came to my ears on Thursday morning. I was a little late arriving at the barn, and was taken a bit aback to find the roadway leading to it rather full of parked automobiles, and the barn itself rather full of people, including two policemen. Our Ridgeville police are quite young men, but in uniform they still look ominous and I was relieved to see that they were laughing and evidently enjoying themselves. "Well, now," I demanded, in my best classroom voice. "What is all this?" "Are you Henderson?" the larger policeman asked. "I am indeed," I said, and a flash bulb went off. A young lady grasped my arm. "Oh, please, Mr. Henderson, come outside where it's quieter and tell me all about it." "Perhaps," I countered, "somebody should tell me." "You mean you don't know, honestly? Oh, it's fabulous. Best story I've had for ages. It'll make the city papers." She led me around the corner of the barn to a spot of comparative quiet. "You didn't know that one of your junior whatsisnames poured detergent in the Memorial Fountain basin last night?" I shook my head numbly. "It was priceless. Just before rush hour. Suds built up in the basin and overflowed, and down the library steps and covered the whole street. And the funniest part was they kept right on coming. You couldn't imagine so much suds coming from that little pool of water. There was a three-block traffic jam and Harry got us some marvelous pictures—men rolling up their trousers to wade across the street. And this morning," she chortled, "somebody phoned in an anonymous tip to the police—of course it was the same boy that did it—Tommy—Miller?—and so here we are. And we just saw a demonstration of that fabulous kite and saw all those simply captivating mice." "Mice?" "Yes, of course. Who would ever have thought you could breed mice with those cute furry tails?" Well, after a while things quieted down. They had to. The police left after sobering up long enough to give me a serious warning against letting such a thing happen again. Mr. Miller, who had come home to see what all the excitement was, went back to work and Mrs. Miller went back to the house and the reporter and photographer drifted off to file their story, or whatever it is they do. Tommy was jubilant. "Did you hear what she said? It'll make the city papers. I wish we had a thousand kites. Ten thousand. Oh boy, selling is fun. Hilary, when can you make some more of that stuff? And Doris, how many mice do you have?" Those mice! I have always kept my enthusiasm for rodents within bounds, but I must admit they were charming little beasts, with tails as bushy as miniature squirrels. "How many generations?" I asked Doris. "Seventeen. No, eighteen, now. Want to see the genetic charts?" I won't try to explain it as she did to me, but it was quite evident that the new mice were breeding true. Presently we asked Betty Miller to come back down to the barn for a conference. She listened and asked questions. At last she said, "Well, all right, if you promise me they can't get out of their cages. But heaven knows what you'll do when fall comes. They won't live in an unheated barn and you can't bring them into the house." "We'll be out of the mouse business by then," Doris predicted. "Every pet shop in the country will have them and they'll be down to nothing apiece." Doris was right, of course, in spite of our efforts to protect the market. Anyhow that ushered in our cage building phase, and for the next week—with a few interruptions—we built cages, hundreds of them, a good many for breeding, but mostly for shipping. It was rather regrettable that, after the Courier gave us most of the third page, including photographs, we rarely had a day without a few visitors. Many of them wanted to buy mice or kites, but Tommy refused to sell any mice at retail and we soon had to disappoint those who wanted kites. The Supermarket took all we had—except a dozen—and at a dollar fifty each. Tommy's ideas of pricing rather frightened me, but he set the value of the mice at ten dollars a pair and got it without any arguments. Our beautiful stationery arrived, and we had some invoice forms printed up in a hurry—not engraved, for a wonder. It was on Tuesday—following the Thursday—that a lanky young man disentangled himself from his car and strolled into the barn. I looked up from the floor where I was tacking squares of screening onto wooden frames. "Hi," he said. "You're Donald Henderson, right? My name is McCord—Jeff McCord—and I work in the Patent Section at the Commission's downtown office. My boss sent me over here, but if he hadn't, I think I'd have come anyway. What are you doing to get patent protection on Ridge Industries' new developments?" I got my back unkinked and dusted off my knees. "Well, now," I said, "I've been wondering whether something shouldn't be done, but I know very little about such matters—." "Exactly," he broke in, "we guessed that might be the case, and there are three patent men in our office who'd like to chip in and contribute some time. Partly for the kicks and partly because we think you may have some things worth protecting. How about it? You worry about the filing and final fees. That's sixty bucks per brainstorm. We'll worry about everything else." "What's to lose," Tommy interjected. And so we acquired a patent attorney, several of them, in fact. The day that our application on the kite design went to Washington, Mary wrote a dozen toy manufacturers scattered from New York to Los Angeles, sent a kite to each one and offered to license the design. Result, one licensee with a thousand dollar advance against next season's royalties. It was a rainy morning about three weeks later that I arrived at the barn. Jeff McCord was there, and the whole team except Tommy. Jeff lowered his feet from the picnic table and said, "Hi." "Hi yourself," I told him. "You look pleased." "I am," he replied, "in a cautious legal sense, of course. Hilary and I were just going over the situation on his phosphonate detergent. I've spent the last three nights studying the patent literature and a few standard texts touching on phosphonates. There are a zillion patents on synthetic detergents and a good round fifty on phosphonates, but it looks"—he held up a long admonitory hand—"it just looks as though we had a clear spot. If we do get protection, you've got a real salable property." "That's fine, Mr. McCord," Hilary said, "but it's not very important." "No?" Jeff tilted an inquiring eyebrow at me, and I handed him a small bottle. He opened and sniffed at it gingerly. "What gives?" "Before-shave lotion," Hilary told him. "You've shaved this morning, but try some anyway." Jeff looked momentarily dubious, then puddled some in his palm and moistened his jaw line. "Smells good," he noted, "and feels nice and cool. Now what?" "Wipe your face." Jeff located a handkerchief and wiped, looked at the cloth, wiped again, and stared. "What is it?" "A whisker stiffener. It makes each hair brittle enough to break off right at the surface of your skin." "So I perceive. What is it?" "Oh, just a mixture of stuff. Cookbook chemistry. Cysteine thiolactone and a fat-soluble magnesium compound." "I see. Just a mixture of stuff. And do your whiskers grow back the next day?" "Right on schedule," I said. McCord unfolded his length and stood staring out into the rain. Presently he said, "Henderson, Hilary and I are heading for my office. We can work there better than here, and if we're going to break the hearts of the razor industry, there's no better time to start than now." When they had driven off I turned and said, "Let's talk a while. We can always clean mouse cages later. Where's Tommy?" "Oh, he stopped at the bank to get a loan." "What on earth for? We have over six thousand in the account." "Well," Peter said, looking a little embarrassed, "we were planning to buy a hydraulic press. You see, Doris put some embroidery on that scheme of mine for making ball bearings." He grabbed a sheet of paper. "Look, we make a roller bearing, this shape only it's a permanent magnet. Then you see—." And he was off. "What did they do today, dear?" Marge asked as she refilled my coffee cup. "Thanks," I said. "Let's see, it was a big day. We picked out a hydraulic press, Doris read us the first chapter of the book she's starting, and we found a place over a garage on Fourth Street that we can rent for winter quarters. Oh, yes, and Jeff is starting action to get the company incorporated." "Winter quarters," Marge repeated. "You mean you're going to try to keep the group going after school starts?" "Why not? The kids can sail through their courses without thinking about them, and actually they won't put in more than a few hours a week during the school year." "Even so, it's child labor, isn't it?" "Child labor nothing. They're the employers. Jeff McCord and I will be the only employees—just at first, anyway." Marge choked on something. "Did you say you'd be an employee?" "Sure," I told her. "They've offered me a small share of the company, and I'd be crazy to turn it down. After all, what's to lose?" Transcriber's Note: This etext was produced from Analog Science Fact & Fiction July 1962. 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. Before-shave breaks off whiskers, just apply and wipe away.
Why have Sara and her father not spoken in over a year? A. Sara attended college in New York and stayed there after graduating. B. They have intense disagreements on most political issues. C. Sara and her father voted for different presidential candidates. D. Sara's father was an authoritative presence during her high school years.	Divided we stand Sara lets the Lyft park itself in the drive, lets out a sigh, and tweets wish me luck plus some emojis before slipping her phone into a hoody pocket. Curtains twitch, and before she can get her bag out of the back Mom is there, right there next to her, their hands touching on the handle as they compete for control. "It's OK Mom, I got it." "You should have let us come pick you up." "It's fine, there was no need. I didn't want to put any-" "But you shouldn't be wasting money, not with how much rent you pay and-" Jesus. Not this already. "Mom. I can afford a cab ride. I'm not that much of a failure." Mom sighs, shoulders falling, looks at Sara directly. "I'm sorry honey." She looks old, Sara thinks, watching a resigned tiredness flicker across her face in a way she'd not noticed before. Like she's exhausted by conflict, surrendered to it. "Now, don't I get a hug?" Sara smiles. They hold each other for a few long seconds, rubbing and squeezing each other as the Lyft silently backs itself out of the driveway. When they part it's Mom's hand that's on the bag's handle. Inside she unwraps herself from scarves and layers, the heat in the house almost a shock after the cold air. Michigan in February. Mom is already halfway up the stairs, bag in tow, headed for her room. "Mom, just leave that and I'll…" "Your father's in the front room," she says, just before she disappears from view. "Go say hi." For a few seconds Sara is alone in the hallway, the smell of cooking meat coming from one doorway, the sound of rolling news from another. She shakes her head, kicks off shoes, tucks hair behind her ears. Braces herself. He's sat in the living room, reclining in the Lazy Boy. He doesn't hear her enter - her socked feet silent on the pile carpet floor, his attention lost in the screen that fills most of the wall. Fox News. She braces herself again. "Hey Dad." His head jerks to look at her. "Hey! When did you get here?" He starts to push himself up. "Don't get up Dad, it's fine. Really." She takes a seat on the couch. "I just got here, like two minutes ago." "Good flight?" "Yeah. Fine. Y'know. Same as always." He smiles back at her, nods knowingly. Their first words in nearly a year. Fine. So far. She relaxes. Of course it is. How bad could it be? "I thought I was gonna come pick you up from the airport?" "Ah, no. I got a cab. I didn't want to bother you." "Bother me? You think I'm too old and infirm to pick my own daughter up from the airport?" "No Dad, of course not." The war spills out of Fox News, casualty figures scrolling across monochrome drone footage, attack helicopters circling over Caracas apartment blocks, pundits with bronzed skin and immaculate blond hair smiling from four-way split screens. "So you just got a cab?" "Yeah." "How much did that cost?" "Not much. Really. I can afford-" "Cabs are expensive. You shouldn't be wasting your money." "It wasn't expensive. It wasn't a cab, it was a Lyft." "One of those driverless things?" "Yeah." Ad break. An elderly couple ride a tandem bicycle through a park, laughing and smiling in Instagram-perfect sunshine, as a calm, relaxing voice lists the potentially lethal side effects of a diabetes drug. Dad shakes his head. "I don't know how you can use those things. I don't trust them." "Dad, they're perfectly safe." "That's not what I mean. They're stealing people's jobs." There's a brief second, a fleeting moment, where Sara can bite her lip, let it go. She misses it. "But I thought it was immigrants that are stealing people's jobs?" "You might think it's funny little lady, but let me tell you - you remember Kyle and Max, Bill Cooper's boys? Live up off Lafayette, past the Checkers?" "Nope." "Well let me tell you," He shifts in the recliner, with some obvious pain and effort, to face her. "Both of 'em lost their jobs just this last year. Both of 'em were truckers. Both of 'em been driving trucks since high school. Now the damn trucks are driving themselves and they're both out of work. And they got families to support. Kids." "Well I'm sure they'll be fine." She regrets the sarcasm as soon as she hears it in her own voice, but she still can't stop herself, like it's expected, like it's part of the routine. Part of their schtick. "They just got to get themselves out there, huh Dad? Pull themselves up by their bootstraps. That's the American way, right?" "I'm glad you think this is funny, I really do. But what you New York types need to realise is-" "Ed!" Mom had appeared in the doorway. "Please! Both of you. No fighting today, please." "Sheryl-" "No. I don't want to hear you two as much as disagreeing about anything today, unless it's about the game. And even then you'd better keep it civil. Otherwise you can both go hungry. Understand?" Awkward pause. "Fine." "Sorry Mom." Sara turns back to the TV, to watching the war, to trying to work out which one it is. It had always been this way, ever since she was about thirteen. Up until then it just seemed like constant warmth, as though she didn't have any childhood concept of Dad apart from him getting home from work, then her sitting on his knee, eating cookies and watching football highlights until Mom came in and scolded them both for ruining their appetites before dinner. And then everything changed. Suddenly there was rap music and nose rings, sneaking out of the house to see her friends and not wanting to go to church. Suddenly he was no longer this lovable bear-man that ruffled her hair and gave her candy and explained defensive plays to her, but this huge obelisk of injustice that just wanted to crush her high school life into dust. It was constant warfare; every opinion she had became a battle, every decision she made a conflict. Getting away to college gave her escape, but bred resentment too; he hated that she went to New York, even though NYU was a good school, and her decision to stay there after she finished made things even worse. And then politics got all crazy, weirder then ever, and it became impossible for them to talk without it erupting into fights almost instantly. It was bad enough when the smart, young guy she liked was president and Dad constantly spewed his hate for him at her, but somehow it got even worse when the old, racist, women hating war-starter he liked won. Twice. So they didn't talk much now, barely online, never on the phone. Since her second year of school he'd never been to NYC to visit her. She came back when she could face it; sometimes for birthdays, sometimes for Thanksgiving. Maybe for Christmas. But somehow always, like now, for the Super Bowl. Like football was the one thing they still had, that one thing they could still sit in the same room together for. Shouting at players, screaming at the ref, laughing at the ads. Dad is in the bathroom, and Sara has had enough of Fox and whichever war this is. She reaches over and grabs the remote from the arm of his chair, and tries to find something else to watch. The government had scrapped all the rules about how the internet worked, and for most people like her parents it had suddenly gotten a lot cheaper to get their TV through Facebook, so all she can find is Fox, Breitbart News, Family Values TV, Info Wars, The Rebel, Glenn Beck, The Voice of America, America First, The Bible Today and lots of hunting and sports channels she doesn't even recognise. It's signed in to her Dad's FB account, and the last thing she wants is to try and log in on hers before he gets back from the john. Yeah. There was no way that would end up with them keeping it civil. In her pocket her phone vibrates, purrs against her skin, reminding her it's there, making sure she's not forgotten where her real friends are, that there's a world outside, beyond Dad and his TV. She takes it out and cradles it in her hands, the dark screen fleetingly reflecting back her face before it jumps awake at her very touch, opening up to bathe her in blue light, in comfort and warmth and the familiar. For the first time since she got home she feels herself relax. Dinner is Mom's meatloaf, with gravy and mashed potatoes. Cornbread and broccoli. Every mouthful tastes like nostalgia, and Sara can feel herself being encompassed by a bubble, this barrier of warm air and long forgotten simplicity enveloping her body, protecting her from the confusion of the world outside. "How's work, honey?" Mom asks. "Yeah, going OK." Sara works for a non-profit in Brooklyn that helps big organisations to transition to renewable energy. The pay is lousy but it feels important. "We just got the last few schools in the city to agree to put solar panels on their roofs. Big deal for us. I've been working on them for the last two years." Mom says nothing, just looks down at her plate. Dad finishes chewing his mouthful, swallows, wipes his beard with a napkin. Sighs, barely controlled anger simmering behind his face. "Solar panels cause cancer." Sara laughs, covering her mouth as she nearly chokes on chewed food. "What? No they don't Dad." "They do. The material they use to coat them reacts to sunlight, and produces an airborne carcinogen. It's based on a particular kind of rare earth. It's a bit like teflon. The Chinese have known about this for decades but have kept it covered up, because they-" "Dad, no. Just no. Trust me." "-because they are the world's largest manufacturers of solar panels. But the research has been done. The scientific evidence is out there. Look it up." "Look it up?" Sara shakes her head, not knowing where to even start. "Dad, who is telling you this stuff?" "No one is telling me it, Sara. I read it. It's in the news. I mean, really, I'm surprised you've not seen it. It was all over Facebook." "Maybe on yours, but it's not all over my Facebook." She doesn't have the heart to tell him she muted him six months ago. "Well, I don't read the news and I don't know any science," says Mom, "But I do know this: after they opened that solar farm up near Mary, within just a few years her and two of her neighbours had cancer. I mean I don't know anything for sure honey, but given the risk are you sure it's safe to be putting these panels on top of schools?" "There's no risk, Mom. None at all. Dad, I wish you'd stop believing everything you see on Facebook." "Well, maybe you should read things yourself before passing judgement on them." He pushes himself up from his seat, steps away from the table. Sara sighs, thinking she's upset him that much that he's actually abandoning his dinner, but he stops to grab something off a nearby shelf. His iPad. He heads back and takes his seat again. Oh, here we fucking go she thinks to herself. He stabs at the screen, looks for a while, stabs again. Flips it over and hands it to her. "Here. Read." Reluctantly, she takes it. His Facebook feed. Somewhere in the middle of it is the article, a very to the point CHINESE SOLAR PANELS CAUSE CANCER headline. But she can't even focus on it, because the rest of the screen is filled with distractions, looping videos and animated gifs, all adverts, and all for guns. Or security systems. Panic rooms. Back up power generators. Emergency rations. More guns. "Jesus Christ Dad, these ads!" "No blasphemy at the dinner table, please honey" says Mom. "What about them?" "Just… just look at them. They're terrifying. They're like… like adverts for the end of the world! You know they show you this stuff just to make you scared, right? Just to keep you paranoid." "They show me this stuff because they've got products to sell. That's how the economy works. That's how we create jobs. Godammit Sara, are you telling me you hate advertising now? Do you just hate everything about America?" Sara looks over to Mom, who looks like she's on the brink of tears. Suddenly she finds she's also lost the will to fight. Gently she closes the iPad and puts it down on the table, next to her plate. "No, of course not Dad. Maybe I'll read this later, after the game." After dinner she helps Mom clean-up, the two of them loading the dishwasher in near silence. She's leaning against the counter, scrolling through Twitter on her phone, when Mom finally speaks. "You should go easy on your father, you know. He's worried about a lot of things." "What things? Solar panel cancer?" "Don't joke Sara, I'm serious. There's a lot that bothers him. The state of the world. The future. All these damn wars." "We're all worried about all that, Mom." "He's worried about his health. I'm worried about his health. Probably more than he is." Sara looks up from her phone, genuine concern. "Is he OK?" "I don't know. He won't go to the doctor. Hasn't been in months. He's worried about his insurance." "I had no idea-" "Yeah, well you know your father. Doesn't like to talk about it. Doesn't want to burden other people with his problems. Hates pity." She pauses, looks out the window into the yard. When she turns back to Sara her eyes are damp. "This is why I was so excited about you coming back. Why he was so excited! I thought it'd take his mind of all this. He was so excited to see you. You know he loves watching the game with you, Sara." "I know. I'm sorry I-" "And the ads! The Super Bowl ads! You know how much he loves watching the new ads with you. It's a stupid thing, sure, but he loves it. Talks about it all the time. It's like a tradition to him. That's why he got so upset over dinner when you got angry at his ads. It's something special he has with you, he doesn't want to lose it." Sara slips her phone into her pocket, genuine guilt. Feels like a spoiled kid. "I didn't realise. I'm sorry." Mom smiles, walks over and kisses her on the forehead. "It's OK honey. Don't feel bad. Just go. Just go sit in there with him and watch some TV. Please." It's the second down on the Falcon's 60 yard line with 30 yards to cover, and the Lions need one touchdown to equalise. Sara and her Dad are sat in the front room, working their way through a family sized pack of Oreos, when the ad break starts. Dawn. Red skies over the desert. A Chevrolet truck pulls up next to a large, trailer. Low shot next to the front tire, as a cowboy booted foot drops down from the door, disturbing dust. Cut to: internal shot of the trailer, darkness split by morning light through the opening door. The figure enters, flicks on lights. The room is full of equipment, computers. The figure takes a seat, puts on a headset, thumbs on screens. Rests their hands on two large joysticks on the desk. Cut to: airfield, the desert. The distinctive silhouette of a Predator drone taxis across the screen, rising heat shimmering the air around it. Cut to: interior of the trailer. The faceless figure works controls, the joysticks, touch screens. Voiceover: They say you need to get up pretty early to get past America's finest. But the truth is we never sleep. Cut to: a uniformed guard on top of the border wall. He looks up and gives a salute to the drone as it soars above him, out and across the desert. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. "Fuck this," says Sara, getting up from her seat. "Sara!" says Mom. "No I'm sorry, I can't. I can't sit here and watch this… this bullshit. This propaganda." She storms out of the room. "Sara!" Mom makes to get up. "No, just leave her," says Dad, gently, his eyes still fixed on the screen. "Just let her go." Out in the kitchen Sara sits at the table and wants to scream. She's angry, mainly with herself. She should never have fucking come here. She should have known better. There was never any fucking way anything good was going to come from this. As much as Mom wants to romanticise things, to make them sound cute and adorable, the truth is shit with Dad has never been right since she was a teenager. Too much resentment, too much bad blood, too much control and rebellion. They hadn't agreed on anything - they hadn't managed to have a simple conversation that didn't descend into fighting - in 15 goddamn years, and no amount of eating cookies and watching fucking Super Bowl ads on the TV was going to fix that. She sighs, wipes a tear from her cheek. On autopilot she takes her phone from her pocket, feels its reassuring warmth in her hand, and swipes open Twitter. Everybody seems to be talking about the same thing. omg im crying holy shit that chevrolet ad /fire emoji that was sooooo beautiful who knew chevrolet were so woke i can't believe they did that, so amazing Hang on, are they taking about the same ad? Hastily she opens her FB TV app, pulls up the game. The ad is just finishing. She hits the 10-second rewind icon a couple of times, then leans the phone on its side against a ketchup bottle. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. Cut to: on the ground, in the desert. The group of figures are revealed to be a Mexican family, maybe two. Men, women, children. They look tired, hungry. They stop to rest, sipping the little water they have left from tattered plastic bottles. A little way away from the main group sits a small child, a girl. Maybe 8 years old. She is drawing shapes in the dust with a stick. She's drawn quite a bit it looks like, but from our angle we can't see what. Cut to: drone footage. The pilot is watching the group. As he tracks away from the main party to where the girl is sat, the camera reveals what she has drawn. A large, child's rendition of the American flag. Underneath it, it childlike handwriting, some words. 'I have a dream' Text flashes across the screen. ALERT CANCELLED. ALL PATROLS: STAND DOWN Cut to: the drone, banking and turning, flying away. Cut to: exterior shot of the trailer. The still anonymous pilot exits, walks back towards his jeep. Voiceover: Keeping America safe means never sleeping, but keeping America great means never forgetting who we are, and how we got here. The jeep starts up, pulls away from the camera in a cloud of dust. Fade to black. Chevrolet logo. White text against black. 'We know what really makes America great' Sara finds herself in the front room, sobbing. "Honey?" Dad pauses the TV, looks up at her. It looks like he's been crying too. "Sara?" "Did you - did you watch it?" "The Chevrolet ad?" "Yeah." "Yeah, we did." Embarrassed, he wipes a tear from his cheek. "It was… it was very moving." She falls on him, wrapping her arms around his neck, burying her face in his chest. "I'm sorry Dad. I'm so sorry. I didn't mean to be so mean-" "It's OK, honey. It really is." "No, no it's not. We always fight. And I know that's mainly my fault-" 'Well, now, c'mon-" "No, it is. It's my fault. I got myself into thinking we can never agree on anything, that we can never see eye to eye. That we've got nothing in common anymore." She lifts her head to look up at him. "But I know that's wrong. That I shouldn't assume things about you. That there's still things that can bring us together." He grins back at her. "Like Super Bowl ads?" She laughs. "I guess. But you know what I mean, really." "I know honey. And I'm sorry too. I didn't mean what I said earlier. I know you don't really hate this country." He gestures to the couch next to him. "Why don't you sit down, huh? We can watch the rest of the game together." She straightens herself up, wipes her eyes. Suddenly feels a little self conscious. "Sure. Let me just go freshen up first." "Of course honey." Mom and Dad watch Sara leave the room, and then look at each other. "Well." "Well indeed." "What did I tell you? You two just needed to spend some time together. Some quality time." "I guess so. What did I ever do to deserve a woman as hot and as smart as you, huh Sheryl?" Mom stands up and makes to leave the room, leaning down to kiss him as she passes. "I ask myself that question every day." Alone, seen only by the TV, Dad smiles to himself. He picks up the remote, but instead of hitting play, he finds himself hitting rewind. Cut to: drone footage. Grainy, monochrome. A group of figures move slowly through the desert. The camera tracks them. Zooms in. The pilot punches buttons. The figures become highlighted by a computer overlay, text appears next to them. ILLEGAL ENTRY ATTEMPT SUSPECTED. GROUND PATROLS ALERTED. Cut to: on the ground, in the desert. The group of figures are all men. Dirty, scruffy, furtive. Like they mean business.They carry guns, pistols, and assault riffles. Bad hombres. One of them pulls open a bag, looks inside. Cut to: close up of the inside of the bag. Inside are packets of white powder. Suddenly, one of the party looks up, shouts something in Spanish. They all go to grab their guns. But it's too late. From three different directions, three different Chevrolet jeeps appear, screeching to a halt, kicking up dust. From them jump Border Patrol agents and Minutemen militia, guns drawn and ready. The gang of men don't even put up a fight. They know they're surrounded, they drop their weapons and pathetically raise their hands. All except one. The guy with the bag full of drugs. He's got nothing to lose. He reaches for his rifle. Cut to: Border Patrol agents, opening fire. Text flashes across the screen. ALERT CANCELLED. THREAT NEUTRALISED. Cut to: the drone, banking and turning, flying away. Cut to: exterior shot of the trailer. The still anonymous pilot exits, walks back towards his jeep. Voiceover: Keeping America safe means never sleeping, but keeping America great means never forgetting who we are, and what keeps us strong. The jeep starts up, pulls away from the camera in a cloud of dust. Fade to black. Chevrolet logo. White text against black. 'We know what really makes America great' Dad wipes another team from his eye. "I think we're going to be OK," he says to himself. "I think we're going to be just fine." This article was originally published on TheLong+Short. Read the original article.	B. They have intense disagreements on most political issues.
Where was David? A. Dead B. On Earth C. A weightless spaceship D. A small room	CAPTAIN CHAOS By D. ALLEN MORRISSEY Science equipped David Corbin with borrowed time; sent him winging out in a state of suspension to future centuries ... to a dark blue world whose only defense was to seal tight the prying minds of foolish interlopers. [Transcriber's Note: This etext was produced from Planet Stories November 1952. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] I heard the voice as I opened my eyes. I was lying down, still not aware of where I was, waiting for the voice. "Your name is David Corbin. Do you understand?" I looked in the direction of the sound. Above my feet a bulkhead loomed. There were round dials set in a row above a speaker. Over the mesh-covered speaker, two knobs glowed red. I ran the words over in my sluggish mind, thinking about an answer. The muscles in my throat tightened up in reflex as I tried to bring some unity into the jumble of thoughts and ideas that kept forming. One word formed out of the rush of anxiety. "No." I shouted a protest against the strangeness of the room. I looked to the right, my eyes following the curving ceiling that started at the cot. The curve met another straight bulkhead on the left. I was in a small room, gray in color, like dull metal. Overhead a bright light burned into my vision. I wondered where in the universe I was. "Your name is David Corbin. If you understand, press button A on your right." I stared at the speaker in the wall. The mesh-covered hole and the two lights looked like a caricature of a face, set in a panel of dials. I twisted my head to look for the button. I pushed away from the close wall but I couldn't move. I reached down to the tightness that held my body, found the wide strap that held me and fumbled with the buckle. I threw it off and pushed myself up from the hard cot. I heard myself yell in surprise as I floated up towards the light overhead. I was weightless. How do you describe being weightless when you are born into a world bound by gravity. I twisted and shut my eyes in terror. There was no sensation of place, no feeling of up or down, no direction. My back bumped against the ceiling and I opened my eyes to stare at the cot and floor. I was concentrating too hard on remembering to be frightened for long. I pushed away from the warm metal and the floor moved up to meet me. "If you understand, press button A on your right." What should I understand? That I was floating in a room that had a curved wall ... that nothing was right in this hostile room? When I reached the cot I held it and drew myself down. I glanced at the planes of the room, trying to place it with other rooms I could see in my mind. Gray walls with a crazy curved ceiling ... a door to my left that appeared to be air tight. I stared at my familiar hands. I rubbed them across my face, feeling the solidity of flesh and bone, afraid to think too hard about myself. "My name ... my name is...." "Your name is David Corbin." I stared at the speaker. How long did this go on? The name meant nothing to me, but I thought about it, watching the relentless lights that shone below the dials. I stood up slowly and looked at myself. I was naked except for heavy shorts, and there was no clue to my name in the pockets. The room was warm and the air I had been breathing was good but it seemed wrong to be dressed like this. I didn't know why. I thought about insanity, and the room seemed to fit my thoughts. When the voice repeated the message again I had to act. Walking was like treading water that couldn't be seen or felt. I floated against the door, twisting the handle in fear that it wouldn't turn. The handle clanged as I pushed it down and I stared at the opposite wall of a narrow gray passageway. I pushed out into it and grasped the metal rail that ran along the wall. I reasoned it was there to propel yourself through the passageway in this weightless atmosphere. It was effortless to move. I turned on my side like a swimmer and went hand over hand, shooting down the corridor. I braced against forward motion and stopped against a door at the end. Behind me I could see the opened door I had left, and the thought of that questioning voice made me want to move. I swung the door open, catching a glimpse of a room crowded with equipment and.... I will always remember the scream of terror, the paralyzing fright of what I saw through the portholes in the wall of the room. I saw the blackest night, pierced by brilliance that blinded me. There was no depth to the searing brightness of countless stars. They seemed to press against the glass, blobs of fire against a black curtain burning into my eyes and brain. It was space. I looked out at deep space, star systems in clusters. I shut my eyes. When I looked again I knew where I was. Why the little room had been shaped like quarter round. Why I drifted weightlessly. Why I was.... David Corbin. I knew more of the puzzle. Something was wrong. After the first shock of looking out, I accepted the fact that I was in a space ship, yet I couldn't read the maps that were fastened to a table, nor understand the function or design of the compact machinery. WHY, Why, Why? The thought kept pounding at me. I was afraid to touch anything in the room. I pressed against the clear window, wondering if the stars were familiar. I had a brief vivid picture of a night sky on Earth. This was not the same sky. Back in the room where I had awakened, I touched the panel with the glowing eyes. It had asked me if I understood. Now it must tell me why I didn't. It had to help me, that flat metallic voice that repeated the same words. It must tell me.... "Your name is David Corbin. If you understand, press button A on your right." I pressed the button by the cot. The red lights blinked out as I stood in patient attention, trying to outguess the voice. I recalled a phrase ... some words about precaution. Precaution against forgetting. It was crazy, but I trusted the panel. It was the only thing I saw that could help me, guard me against another shock like seeing outside of the clear portholes. "It is assumed the experiment is a success," the voice said. What experiment? "You have been removed from suspension. Assume manual control of this ship." Control of a ship? Going where? "Do not begin operations until the others are removed from suspension." What others? Tell me what to do. "Rely on instructions for factoring when you check the coordinates. Your maximum deviation from schedule cannot exceed two degrees. Adopt emergency procedures as you see fit. Good luck." The voice snapped off and I laughed hysterically. None of it had made sense, and I cursed whatever madness had put me here. "Tell me what to do," I shouted wildly. I hammered the hard metal until the pain in my hands made me stop. "I can't remember what to do." I held my bruised hands to my mouth, and I knew that was all the message there was. In blind panic I pushed away from the panel. Something tripped me and I fell back in a graceless arc. I pushed away from the floor, barely feeling the pain in my leg, and went into the hall. Pain burned along my leg but I couldn't stop. In the first panic of waking up in strangeness I had missed the other doors in the passage. The first swung back to reveal a deep closet holding five bulky suits. The second room was like my own. A dark haired, deep chested man lay on the cot. His muscular body was secured by a wide belt. He was as still as death, motionless without warmth or breath as I hovered over him. I couldn't remember his face. The next room held another man. He was young and wiry, like an athlete cast in marble, dark haired and big jawed. A glassy eye stared up when I rolled back his eyelid. The eyelid remained open until I closed it and went on. Another room ... another man ... another stranger. This man was tall and raw boned, light of skin and hair, as dead as the others. A flat, illogical voice had instructed me to revive these men. I shivered in spite of the warmth of the room, studying the black box that squatted on a shelf by his head. My hand shook when I touched the metal. I dared not try to operate anything. Revive the others ... instructions without knowledge were useless to me. I stopped looking into the doors in the passageway and went back to the room with the portholes. Everything lay in readiness, fastened down star charts, instruments, glittering equipment. There was no feeling of disorder or use in the room. It waited for human hands to make it operate. Not mine. Not now. I went past the room into another, where the curves were more sharp. I could visualize the tapering hull leading to the nose of the ship. This room was filled with equipment that formed a room out of the bordered area I stood in. I sat in the deep chair facing the panel of dials and instruments, in easy reach. I ran my hands over the dials, the rows of smooth colored buttons, wondering. The ports on the side were shielded and I stared out at static energy, hung motionless in a world of searing light. There was no distortion, no movement outside and I glanced back at the dials. What speeds were they recording? What speeds and perhaps, what distance? It was useless to translate the markings. They stood for anything I might guess, and something kept pricking my mind, telling me I had no time to guess. I thought of time again. I was supposed to act according to ... plan. Did that mean ... in time ... in time. I went back down the passageway. The fourth small room was the same. Except for the woman. She lay on a cot, young and beautiful, even in the death-like immobility I had come to accept. Her beauty was graceful lines of face and her figure—smooth tapering legs, soft curves that were carved out of flesh colored stone. Yet not stone. I held her small hand, then put it back on the cot. Her attire was brief like the rest of us, shorts and a man's shirt. Golden hair curled up around her lovely face. I wondered if she would ever smile or move that graceful head. I rolled back her eyelid and looked at a deep blue eye that stared back in glassy surprise. Four people in all, depending on a blind helpless fool who didn't know their names or the reason for that dependence. I sat beside her on the cot until I could stand it no longer. Searching the ship made me forget my fear. I hoped I would find some answers. I went from the nose to the last bulkhead in a frenzy of floating motion, looking behind each door until I went as far as I could. There were two levels to the ship. They both ended in the lead shield that was set where the swell of the curve was biggest. It meant the engine or engines took up half the ship, cut off from the forward half by the instrument studded shield. I retraced my steps and took a rough estimate of size. The ship, as I called it, was at least four hundred feet long, fifty feet in diameter on the inside. The silence was a force in itself, pressing down from the metal walls, driving me back to the comforting smallness of the room where I had been reborn. I laughed bitterly, thinking about the aptness of that. I had literally been reborn in this room, equipped with half ideas, and no point to start from, no premise to seek. I sensed the place to start from was back in the room. I searched it carefully. Minutes later I realized the apparatus by the cot was different. It was the same type of black box, but out from it was a metal arm, bent in a funny angle. At the tip of the arm, a needle gleamed dully and I rubbed the deep gash on my leg. I bent the arm back until the angle looked right. It was then I realized the needle came to a spot where it could have hit my neck when I lay down. My shout of excitement rang out in the room, as I pictured the action of the extended arm. I lost my sudden elation in the cabin where the girl lay. The box behind her head was completely closed, and it didn't yield to the pressure I applied. It had a cover, but no other opening where an arm could extend. I ran my fingers over the unbroken surface, prying over the thin crack at the base helplessly. If some sort of antidote was to be administered manually I was lost. I had no knowledge of what to inject or where to look for it. The chamber of the needle that had awakened me was empty. That meant a measured amount. In the laboratory on the lower level I went over the rows of cans and tubes fastened to the shelves. There were earths and minerals, seeds and chemicals, testing equipment in compact drawers, but nothing marked for me. I wondered if I was an engineer or a pilot, or perhaps a doctor sent along to safeguard the others. Complete amnesia would have been terrible enough but this half knowledge, part awareness and association with the ship was a frightening force that seemed ready to break out of me. I went back to the cabin where the powerful man lay. I had to risk failure with one of them. I didn't want it to be the girl. I fought down the thought that he might be the key man, remembering the voice that had given the message. It was up to me, and soon. The metal in the box would have withstood a bullet. It couldn't be pried apart, and I searched again and again for a release mechanism. I found it. I swung the massive cover off and set it down. The equipment waited for the touch of a button and it went into operation. I stepped back as the tubes glowed to life and the arm swung down with the gleaming needle. The needle went into the corded neck of the man. The fluid chamber drained under pressure and the arm moved back. I stood by the man for long minutes. Finally it came. He stirred restlessly, closing his hands into fists. The deep chest rose and fell unevenly as he breathed. Finally the eyes opened and he looked at me. I watched him adjust to the room. It was in his eyes, wide at first, moving about the confines of the room back to me. "It looks like we made it," he said. "Yes." He unfastened the belt and sat up. I pushed him back as he floated up finding little humor in the comic expression on his face. "No gravity," he grunted and sat back. "You get used to it fast," I answered. I thought of what to say as he watched me. "How do you feel?" He shrugged at the question. "Fine, I guess. Funny, I can't remember." He saw it in my face, making him stop. "I can't remember dropping off to sleep," he finished. I held his hard arm. "What else? How much do you remember?" "I'm all right," he answered. "There aren't supposed to be any effects from this." "Who is in charge of this ship?" I asked. He tensed suddenly. "You are, sir. Why?" I moved away from the cot. "Listen, I can't remember. I don't know your name or anything about this ship." "What do you mean? What can't you remember?" he asked. He stood up slowly, edging around towards the door. I didn't want to fight him. I wanted him to understand. "Look, I'm in trouble. Nothing fits, except my name." "You don't know me?" "No." "Are you serious?" "Yes, yes. I don't know why but it's happened." He let his breath out in a whistle. "For God's sake. Any bump on your head?" "I feel all right physically. I just can't place enough." "The others. What about the others?" he blurted. "I don't know. You're the first besides myself. I don't know how I stumbled on the way to revive you." He shook his head, watching me like I was a freak. "Let's check the rest right away." "Yes. I've got to know if they are like me. I'm afraid to think they might be." "Maybe it's temporary. We can figure something out." II The second man, the dark haired one, opened his eyes and recognized us. He asked questions in rapid fire excitement. The third man, the tall Viking, was all right until he moved. The weightless sensation made him violently sick. We put him back on the cot, securing him again with the belt, but the sight of us floating made him shake. He was retching without results when we drifted out. I followed him to the girl's quarters. "What about her. Why is she here?" I asked my companion. He lifted the cover from the apparatus. "She's the chemist in the crew." "A girl?" "Dr. Thiesen is an expert, trained for this," he said. I looked at her. She looked anything but like a chemist. "There must be men who could have been sent. I've been wondering why a girl." "I don't know why, Captain. You tried to stop her before. Age and experience were all that mattered to the brass." "It's a bad thing to do." "I suppose. The mission stated one chemist." "What is the mission of this ship?" I asked. He held up his hand. "We'd better wait, sir. Everything was supposed to be all right on this end. First you, then Carl, sick to his stomach." "Okay. I'll hold the questions until we see about her." We were out of luck with the girl. She woke up and she was frightened. We questioned her and she was coherent but she couldn't remember. I tried to smile as I sat on the cot, wondering what she was thinking. "How do you feel?" I asked. Her face was a mask of wide-eyed fear as she shook her head. "Can you remember?" "I don't know." Blue eyes stared at me in fear. Her voice was low. "Do you know my name?" The question frightened her. "Should I? I feel so strange. Give me a minute to think." I let her sit up slowly. "Do you know your name?" She tightened up in my arms. "Yes. It's...." She looked at us for help, frightened by the lack of clothing we wore, by the bleak room. Her eyes circled the room. "I'm afraid," she cried. I held her and she shook uncontrollably. "What's happened to me?" she asked. The dark haired man came into the room, silent and watchful. My companion motioned to him. "Get Carl and meet us in Control." The man looked at me and I nodded. "We'll be there in a moment. I'm afraid we've got trouble." He nodded and pushed away from us. The girl screamed and covered her face with her hands. I turned to the other man. "What's your name?" "Croft. John Croft." "John, what are your duties if any?" "Automatic control. I helped to install it." "Can you run this ship? How about the other two?" He hit his hands together. "You fly it, sir. Can't you think?" "I'm trying. I know the ship is familiar, but I've looked it over. Maybe I'm trying too hard." "You flew her from earth until we went into suspension," he said. "I can't remember when," I said. I held the trembling girl against me, shaking my head. He glanced at the girl. "If the calculations are right it was more than a hundred years ago." We assembled in the control room for a council. We were all a little better for being together. John Croft named the others for me. I searched each face without recognition. The blond man was Carl Herrick, a metallurgist. His lean face was white from his spell but he was better. Paul Sample was a biologist, John said. He was lithe and restless, with dark eyes that studied the rest of us. I looked at the girl. She was staring out of the ports, her hands pressed against the transparent break in the smooth wall. Karen Thiesen was a chemist, now frightened and trying to remember. I wasn't in much better condition. "Look, if it comes too fast for me, for any of us, we'll stop. John, you can lead off." "You ask the questions," he said. I indicated the ship. "Where in creation are we going?" "We set out from Earth for a single star in the direction of the center of our Galaxy." "From Earth? How could we?" "Let's move slowly, sir," he said. "We're moving fast. I don't know if you can picture it, but we're going about one hundred thousand miles an hour." "Through space?" "Yes." "What direction?" Paul cut in. "It's a G type star, like our own sun in mass and luminosity. We hope to find a planetary system capable of supporting life." "I can't grasp it. How can we go very far in a lifetime?" "It can be done in two lifetimes," John said quietly. "You said I had flown this ship. You meant before this suspension." "Yes. That's why we can cross space to a near star." "How long ago was it?" "It was set at about a hundred years, sir. Doesn't that fit at all?" "I can't believe it's possible." Carl caught my eye. "Captain, we save this time without aging at all. It puts us near a calculated destination." "We've lost our lifetime." It was Karen. She had been crying silently while we talked. "Don't think about it," Paul said. "We can still pull this out all right if you don't lose your nerve." "What are we to do?" she asked. John answered for me. "First we've got to find out where we are. I know this ship but I can't fly it." "Can I?" I asked. We set up a temporary plan of action. Paul took Karen to the laboratory in an effort to help her remember her job. Carl went back to divide the rations. I was to study the charts and manuals. It was better than doing nothing, and I went into the navigation room and sat down. Earth was an infinitesimal point somewhere behind us on the galactic plane, and no one else was trained to navigate. The ship thundered to life as I sat there. The blast roared once ... twice, then settled into a muted crescendo of sound that hummed through the walls. I went into the control room and watched John at the panel. "I wish I knew what you were doing," I said savagely. "Give it time." "We can't spare any, can we?" I asked. "I wish we knew. What about her—Dr. Thiesen?" "She's in the lab. I don't think that will do much good. She's got to be shocked out of a mental state like that." "I guess you're right," he said slowly. "She's trained to administer the suspension on the return trip." I let my breath out slowly. "I didn't think about that." "We couldn't even get part way back in a lifetime," he said. "How old are you, John?" "Twenty-eight." "What about me?" "Thirty." He stared at the panel in thought for a minutes. "What about shock treatment? It sounds risky." "I know. It's the only thing I could think of. Why didn't everyone react the same?" "That had me wondering for a while. I don't know. Anyway how could you go about making her remember?" "Throw a crisis, some situation at her, I guess." He shrugged, letting his sure hands rest on the panel of dials. I headed back towards the lab. If I could help her I might help myself. I was past the rooms when the horn blasted through the corridor. I turned automatically with the sound, pushing against the rail, towards the control room. Deep in my mind I could see danger, and without questioning why I knew I had to be at Control when the sound knifed through the stillness. John was shouting as I thrust my way into the room. "Turn the ship. There's something dead ahead." I had a glimpse of his contorted face as I dove at the control board. My hands hit buttons, thumbed a switch and then a sudden force threw me to the right. I slammed into the panel on the right, as the pressure of the change dimmed my vision. Reflex made me look up at the radar control screen. It wasn't operating. John let go of the padded chair, grinning weakly. I was busy for a few seconds, feeding compensation into the gyros. Relief flooded through me like warm liquid. I hung on the intercom for support, drawing air into my heaving lungs. "What—made you—think of that," I asked weakly. "Shock treatment." "I must have acted on instinct." "You did. Even for a sick man that was pretty fast," he laughed. "I can think again, John. I know who I am," I shouted. I threw my arms around his massive shoulders. "You did it." "You gave me the idea, Mister, talking about Dr. Thiesen." "It worked. I'm okay," I said in giddy relief. "I don't have to tell you I was scared as hell. I wish you could have seen your face, the look in your eyes when I woke up." "I wouldn't want to wake up like that again." "You're all right now?" he asked. I grinned and nodded an answer. I saw John as he was at the base, big and competent, sweating in the blazing sun. I thought about the rest of the crew too. "We're heading right for a star...." "It's been dead ahead for hours," he grunted. I leaned over and threw the intercom to open. "This is control. Listen ... everyone. I'm over it. Disregard the warning siren ... we were testing the ship." The lab light blinked on as Paul cut in. "What was it ... hey, you said you're all right." "John did it. He hit the alarm figuring I would react. Listen, Paul. Is any one hurt?" "No. Carl is here too. His stomach flopped again but he's okay. What about food. We're supposed to be checked before we eat." "We'll have to go ahead without it. Any change?" "No, I put her to bed. Shall I bring food?" I glanced at John. He rubbed his stomach. "Yes," I answered. "Bring it when you can. I've got to find out where we are." We had to get off course before we ran into the yellow-white star that had been picked for us. Food was set down by me, grew cold and was carried away and I was still rechecking the figures. We were on a line ten degrees above the galactic plane. The parallactic baseline from Earth to the single star could be in error several degrees, or we could be right on the calculated position of the star. The radar confirmed my findings ... and my worst fears. When we set it for direction and distance, the screen glowed to life and recorded the star dead ahead. In all the distant star clusters, only this G type star was thought to have a planetary system like our own. We were out on a gamble to find a planet capable of supporting life. The idea had intrigued scientists before I had first looked up at the night sky. When I was sure the electronically recorded course was accurate for time, I checked direction and speed from the readings and plotted our position. If I was right we were much closer than we wanted to be. The bright pips on the screen gave us the distance and size of the star while we fed the figures into the calculator for our rate of approach. Spectroscopic tests were run on the sun and checked against the figures that had been calculated on Earth. We analyzed temperature, magnetic fields, radial motion, density and luminosity, checking against the standards the scientists had constructed. It was a G type star like our own. It had more density and temperature and suitable planets or not, we had to change course in a hurry. Carl analyzed the findings while we came to a decision. Somewhere along an orbit that might be two hundred miles across, our hypothetical planet circled this star. That distance was selected when the planets in Earth's solar system had proved to be barren. If the observations on this star were correct, we could expect to find a planet in a state of fertility ... if it existed ... if it were suitable for colonization ... if we could find it.	C. A weightless spaceship
Regarding Mrs. Anderson, which is NOT a finding from the CT scan dated 12/02/2022? Choose the correct answer from the following options: A. Pancreatic tumor decreasing in size B. Suspicious pulmonary nodules C. Fatty liver D. Elongation of the right iliac artery E. No new liver lesions	### 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.	Suspicious pulmonary nodules
According to the author, why do people stop themselves from sharing as much as they could? A. They are distrustful and apprehensive of a negative social response B. They are unsure of the best venue for sharing their content C. They believe that people who share on a frequent basis are desperate for attention D. They generally feel that the cost of their content is not as high as the value	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!	A. They are distrustful and apprehensive of a negative social response
Which terms most likely describe how the author views Brexit? A. perplexing and disturbing B. ambitious and progressive C. ill-conceived and quixotic D. haphazard and inequitable	What cities in the post-Brexit era could learn from a 14th-century trading bloc As you fly from the country now known as Germany to Britain, the coastal geography of northern European cities gently unfurls. You can see where the sea smacks into them, or where yawning estuaries unfold like funnels between green and brown city and choppy blue water. You can track the snaking rivers and canals that form unrepentant umbilical connections to the settlements set a little further inland. By their nature cities along coasts and rivers developed so they could be open to trade with each other. From the middle of the 13th century, and for some 300 years after, many settlements dotted along this route formed the prosperous Hanseatic League, a European trading confederation of market towns, before the rise of the nation state led to its dissolution. The Hanseatic League is not well known, and today it lives on most prominently in the name of the German national airline Lufthansa, literally the 'Hansa of the skies', whose planes you can look out of – and down towards the Hanseatic cities – on the short journeys between mainland Europe and Britain. The letters HH on the number plates of cars in Hamburg stand for Hansestadt Hamburg: another proud little memory of this hidden history. In the traumatised atmosphere of post-Brexit Britain, it is worth remembering the Hanseatic League. It could point us towards new relationships between progressive city dwellers in a world that otherwise seems to be putting the brakes on modernity. Despite some of Britain's Leave voters longing to inhabit a fantastical realm immune to foreign influence, the reality is patently very different to that. In the late 1300s, Chaucer wrote about characters travelling to Jerusalem, and others who came from Europe; and it was at exactly this point that the Hanseatic League slowly started to coalesce, eventually influencing our isles. The League is most easily understood as a loose federation of cities that acted together in self-interest to promote trade. The Hanseatic cities developed their own legal system, and their armies came to one another's aid. Merchants who wanted to buy and sell and travel were taking the lead at a time when nation states were not fit for purpose: in the case of England or Denmark, leadership was too centralised and authoritarian, while in German-speaking lands a nation had yet to be formed. We think of nations today as elemental almost, immovable. Yet look at any city of Mitteleuropa and you'll see the many different names it has had as borders and regimes have shifted with the sands of time. Nations come and go. Cities endure. "It is often said that great cities survived great empires," says Cristina Ampatzidou, editor-in-chief of the Rotterdam-based online publishing platform Amateur Cities. "So it is not unrealistic to think of cities as discrete entities that compete and collaborate with each other, independently from the states to which they belong." The cities involved in the Hanseatic League are found along the Baltic and North Sea coasts, and slightly inland too. The League stretched from Novgorod in the east – in what is now Russia – to London in the west. Tallinn, Riga, Gdańsk, Visby, Berlin, Cologne, Antwerp, Stockholm, Bergen, Kiel, Rostock, Dinant, Bruges, Turku, Groningen, Hanover, Wroclaw, Kaliningrad: all were involved at different stages in the Hanse's history, which ran on into the 1500s. The League covered lands that today find themselves a part of the modern nations of Finland, Sweden, Poland, the Netherlands, Belgium, France, Norway, Lithuania, Estonia and Latvia. It was a huge – and hugely ambitious – undertaking in the days when communications consisted of ink and paper and the only viable method of travel was by ship. Wood, fur, wool, silver, herring, cod and salt were the main items traded. But what was also exchanged was knowledge. In some ways it was an exercise in what we today call 'soft diplomacy'. There was no maniacal ruler overseeing things – merchants met and talked. They raised armies and waged war against kings who threatened their businesses and their freedoms and their peace. There was a kind of proto-democracy at work. Professor Rainer Postel, of the Bundeswehr Universität (Germany's equivalent of Sandhurst military academy), has described the Hanse as "a community of interests without power politics". As David Abulafia, Professor of Mediterranean History at Cambridge points out, "The lack of an elaborate superstructure was one of the things that made the Hanse work. Having said that, one should recognise that Lübeck in particular dominated the League for long periods." Lübeck was where the merchants most often met; and where renewed recent interest in the Hanse eventually led to Angela Merkel cutting the ribbon at the brand new European Hansemuseum in the city last year. Germany today – multicultural, economically and culturally motoring, free and fair – seems like the ideal model for a modern European nation state. And part of that success lies in the gravitas the country has given to its Hanseatic history. For Germany is not a top-down country with one city unhealthily dominating as with France and Britain (regional economic inequalities have plagued Britain since the painful de-industrialisation of the 1980s, especially in the north). Germany respects federalism and its cities exist on a much more even keel. The way that Cologne, Munich, Frankfurt, Dusseldorf and Stuttgart all bring varied economic and cultural character to the party is pure Hanse. The former Hanseatic cities of Hamburg, Berlin and Bremen have city state status within Germany, putting them on the same level as a whole region or 'land' like Bavaria or Brandenburg. So how about a new Hanseatic League? I ask Benjamin Barber, senior fellow at New York's Fordham University. "I believe you will find there is a new Hanse," he says, "that constituted itself about 10 or 11 years ago – including many of the original Hanseatic League cities." Barber is founder of the Global Parliament of Mayors, which he describes as a kind of Hanse of all cities, not just European ports, which will give cities a global urban voice and a common platform for action. The parliament convenes for its inaugural session in The Hague in September. "Cities both exist within nations and transcend nations. Their power lies not just in the extent of de jure autonomy ceded or granted by 'higher' levels of government," says Bruce Katz, centennial scholar at the Washington DC thinktank the Brookings Institution. "Rather, cities have de facto power, the result of larger market and demographic forces and environmental imperatives that value proximity, density, connectivity and quality. Smart nations will see themselves as partners to their cities, setting strong platforms for urban prosperity and devolving powers, where appropriate, to give cities the flexibility to perform… Dumb nations will continue to dictate from above, stifling market activity and urban potential." But could we go further? Could cities like London declare independence from the UK? London's economy is larger than that of Scotland and Wales combined. "States will not vanish or surrender their waning sovereignty," says Benjamin Barber. "But cities will meet across frontiers and work together to solve problems. The objective is not an independent London or New York, but interdependent cities collaborating globally. And that is happening." London's voters largely wanted to remain a part of the EU and to maintain the city's status as an entrepôt. There is clearly a widening chasm between urban and rural life at the heart of many nations. Visualisations of Austria's recent presidential election showed the issue clearly: the country's cities voted for the Green candidate Alexander Van der Bellen, while the the rural districts went for right-wing nationalist Norbert Hofer (whose legal challenge to the close result has resulted in a rerun being announced for October). And in the USA in November, it's likely that Trump voters will also come from rural areas and Clinton voters from the cities. City dwellers are finding ever more in common with the world's other city dwellers than with their countrymen 50 miles down the road. Back in Britain, one of history's little oddities pops up on the east coast. Boston in Lincolnshire and King's Lynn in Norfolk were both forward-looking Hanseatic League towns that traded with far-flung ports and hosted foreign merchants. King's Lynn contains the only extantHanse House left in Britain (London's was knocked down to build Cannon Street Station in the 1800s). Yet in the EU referendum these two areas polled among the highest Leave votes of anywhere in the country. "Things change," says LSE's Professor Tony Travers. "[King's Lynn] used to be very highly connected, but the economy moved on and left those trading ports like it in a different situation." Take, for example, the pivot towards the New World, with which trade made more sense from the west-coast ports like Bristol and Liverpool. While these boomed between the 1600s and 1800s, the Hanseatic ports declined and then died out. "One of the things that's interesting about the [referendum] decision is that it begs all sorts of questions about the future of the UK and its relationship with Europe; and of London and Scotland and their relationship with the rest of Europe. When the EU began as the EEC in the mid-20th century some saw it as a modern day Hanse. Now the EU seems to be waning, perhaps its successor will have to ape the Hanse even more." For all its complex beauty, life can ultimately be reduced to a series of binary options: yes or no, stick or twist, in or out, innovation or stagnation, modernity or mythology. The referendum result was disappointing for many progressive observers because it felt like a step backwards. Despite being primarily about trade monopolies and money making, the Hanse was, in its way, an early stab at stepping forwards: it encompassed internationalism, rational thought, free trade, loose democratic institutions and, most crucially of all, movement. The future, for many observers, can only be understood in terms of the free movement of people, capital, goods and ideas. It is this necessary movement, and its possible curtailment, that could be the spark that leads to cities like London to seek independence and parity with other world cities – rather than with the rural hinterlands of Britain. Of course, cities seceding from their nation states would provide huge headaches for countries whose biggest economic driver had been removed – as well as likely deepening ideological differences between city and rural dwellers. Moreover, cities need the food the countryside provides. Yet for all the potential pitfalls, city states can thrive. Look at Singapore, Hong Kong, or de facto city states like Dubai and Abu Dhabi. One of the most telling characteristics about these four – all of course former British imperial enclaves – is that they are utterly outward looking. To return to the sky analogy, it's the airlines of each of these (Singapore Airlines, Cathay Pacific, Emirates and Etihad) that open up each respective city to the world in the way that the machinery of the Hanse did on the Baltic Sea 600 years ago. And it's the unions each city makes with other places that also look thoroughly Hanseatic in character. A model for modern city states, then. But is it one that we want? "The Hanseatic League was not always accepted by local citizens," says Cristina Ampatzidou, "because the privileges granted to the Hanse merchants were forcing local traders out of competition and many cities took steps to eliminate them. The reasons the countryside is turning to the right [globally] are not independent from cities turning increasingly into speculation machines for the profit of a happy few. It is basically these systemic contradictions that must be addressed before we resort to more isolationist ideas that would intensify the urban-rural political divide. The bottom line is not whether a contemporary Hanse-esque federation is possible, it probably is; but whether it is actually desirable." This article was originally published on TheLong+Short. Read the original article.	C. ill-conceived and quixotic
What kind of trouble could unauthorized neutroids mean for Norris? A. Unauthorized neutroid animals could be used as an alternate food source for the skyrocketing population. B. Unauthorized neutroids could cause food scarcity. C. Unauthorized neutroids would mean more taking "babies" away from their mothers and more killing. D. A black market for nuetroids could result in neutroid slavery.	Conditionally Human By WALTER M. MILLER, JR. Illustrated by DAVID STONE [Transcriber's Note: This etext was produced from Galaxy Science Fiction February 1952. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] They were such cute synthetic creatures, it was impossible not to love them. Of course, that was precisely why they were dangerous! There was no use hanging around after breakfast. His wife was in a hurt mood, and he could neither endure the hurt nor remove it. He put on his coat in the kitchen and stood for a moment with his hat in his hands. His wife was still at the table, absently fingering the handle of her cup and staring fixedly out the window at the kennels behind the house. He moved quietly up behind her and touched her silk-clad shoulder. The shoulder shivered away from him, and her dark hair swung shiningly as she shuddered. He drew his hand back and his bewildered face went slack and miserable. "Honeymoon's over, huh?" She said nothing, but shrugged faintly. "You knew I worked for the F.B.A.," he said. "You knew I'd have charge of a district pound. You knew it before we got married." "I didn't know you killed them," she said venomously. "I won't have to kill many. Besides, they're only animals." " Intelligent animals!" "Intelligent as a human imbecile, maybe." "A small child is an imbecile. Would you kill a small child?" "You're taking intelligence as the only criterion of humanity," he protested hopelessly, knowing that a logical defense was useless against sentimentality. "Baby—" "Don't call me baby! Call them baby!" Norris backed a few steps toward the door. Against his better judgment, he spoke again. "Anne honey, look! Think of the good things about the job. Sure, everything has its ugly angles. But think—we get this house rent-free; I've got my own district with no bosses around; I make my own hours; you'll meet lots of people that stop in at the pound. It's a fine job, honey!" She sipped her coffee and appeared to be listening, so he went on. "And what can I do? You know how the Federation handles employment. They looked over my aptitude tests and sent me to Bio-Administration. If I don't want to follow my aptitudes, the only choice is common labor. That's the law ." "I suppose you have an aptitude for killing babies?" she said sweetly. Norris withered. His voice went desperate. "They assigned me to it because I liked babies. And because I have a B.S. in biology and an aptitude for dealing with people. Can't you understand? Destroying unclaimed units is the smallest part of it. Honey, before the evolvotron, before Anthropos went into the mutant-animal business, people used to elect dogcatchers. Think of it that way—I'm just a dogcatcher." Her cool green eyes turned slowly to meet his gaze. Her face was delicately cut from cold marble. She was a small woman, slender and fragile, but her quiet contempt made her loom. He backed closer to the door. "Well, I've got to get on the job." He put on his hat and picked at a splinter on the door. He frowned studiously at the splinter. "I—I'll see you tonight." He ripped the splinter loose when it became obvious that she didn't want to be kissed. He grunted a nervous good-by and stumbled down the hall and out of the house. The honeymoon was over, all right. He climbed in the kennel-truck and drove east toward the highway. The suburban street wound among the pastel plasticoid cottages that were set approximately two to an acre on the lightly wooded land. With its population legally fixed at three hundred million, most of the country had become one big suburb, dotted with community centers and lined with narrow belts of industrial development. Norris wished there were someplace where he could be completely alone. As he approached an intersection, he saw a small animal sitting on the curb, wrapped in its own bushy tail. Its oversized head was bald on top, but the rest of its body was covered with blue-gray fur. Its tiny pink tongue was licking daintily at small forepaws with prehensile thumbs. It was a cat-Q-5. It glanced curiously at the truck as Norris pulled to a halt. He smiled at it from the window and called, "What's your name, kitten?" The cat-Q-5 stared at him impassively for a moment, let out a stuttering high-pitched wail, then: "Kiyi Rorry." "Whose child are you, Rorry?" he asked. "Where do you live?" The cat-Q-5 took its time about answering. There were no houses near the intersection, and Norris feared that the animal might be lost. It blinked at him, sleepily bored, and resumed its paw-washing. He repeated the questions. "Mama kiyi," said the cat-Q-5 disgustedly. "That's right, Mama's kitty. But where is Mama? Do you suppose she ran away?" The cat-Q-5 looked startled. It stuttered for a moment, and its fur crept slowly erect. It glanced around hurriedly, then shot off down the street at a fast scamper. He followed it in the truck until it darted onto a porch and began wailing through the screen, "Mama no run ray! Mama no run ray!" Norris grinned and drove on. A class-C couple, allowed no children of their own, could get quite attached to a cat-Q-5. The felines were emotionally safer than the quasi-human chimp-K series called "neutroids." When a pet neutroid died, a family was broken with grief; but most couples could endure the death of a cat-Q or a dog-F. Class-C couples were allowed two lesser units or one neutroid. His grin faded as he wondered which Anne would choose. The Norrises were class-C—defective heredity. He found himself in Sherman III Community Center—eight blocks of commercial buildings, serving the surrounding suburbs. He stopped at the message office to pick up his mail. There was a memo from Chief Franklin. He tore it open nervously and read it in the truck. It was something he had been expecting for several days. Attention All District Inspectors: Subject: Deviant Neutroid. You will immediately begin a systematic and thorough survey of all animals whose serial numbers fall in the Bermuda-K-99 series for birth dates during July 2234. This is in connection with the Delmont Negligency Case. Seize all animals in this category, impound, and run proper sections of normalcy tests. Watch for mental and glandular deviation. Delmont has confessed to passing only one non-standard unit, but there may be others. He disclaims memory of deviant's serial number. This could be a ruse to bring a stop to investigations when one animal is found. Be thorough. If allowed to reach age-set or adulthood, such a deviant could be dangerous to its owner or to others. Hold all seized K-99s who show the slightest abnormality in the normalcy tests. Forward to central lab. Return standard units to their owners. Accomplish entire survey project within seven days. C. Franklin Norris frowned at the last sentence. His district covered about two hundred square miles. Its replacement-quota of new neutroids was around three hundred animals a month. He tried to estimate how many of July's influx had been K-99s from Bermuda Factory. Forty, at least. Could he do it in a week? And there were only eleven empty neutroid cages in his kennel. The other forty-nine were occupied by the previous inspector's "unclaimed" inventory—awaiting destruction. He wadded the memo in his pocket, then nosed the truck onto the highway and headed toward Wylo City and the district wholesale offices of Anthropos, Inc. They should be able to give him a list of all July's Bermuda K-99 serial numbers that had entered his territory, together with the retailers to whom the animals had been sold. A week's deadline for finding and testing forty neutroids would put him in a tight squeeze. He was halfway to Wylo City when the radiophone buzzed on his dashboard. He pulled into the slow lane and answered quickly, hoping for Anne's voice. A polite professional purr came instead. "Inspector Norris? This is Doctor Georges. We haven't met, but I imagine we will. Are you extremely busy at the moment?" Norris hesitated. "Extremely," he said. "Well, this won't take long. One of my patients—a Mrs. Sarah Glubbes—called a while ago and said her baby was sick. I must be getting absent-minded, because I forgot she was class C until I got there." He hesitated. "The baby turned out to be a neutroid. It's dying. Eighteenth order virus." "So?" "Well, she's—uh—rather a peculiar woman, Inspector. Keeps telling me how much trouble she had in childbirth, and how she can't ever have another one. It's pathetic. She believes it's her own. Do you understand?" "I think so," Norris replied slowly. "But what do you want me to do? Can't you send the neutroid to a vet?" "She insists it's going to a hospital. Worst part is that she's heard of the disease. Knows it can be cured with the proper treatment—in humans. Of course, no hospital would play along with her fantasy and take a neutroid, especially since she couldn't pay for its treatment." "I still don't see—" "I thought perhaps you could help me fake a substitution. It's a K-48 series, five-year-old, three-year set. Do you have one in the pound that's not claimed?" Norris thought for a moment. "I think I have one . You're welcome to it, Doctor, but you can't fake a serial number. She'll know it. And even though they look exactly alike, the new one won't recognize her. It'll be spooky." There was a long pause, followed by a sigh. "I'll try it anyway. Can I come get the animal now?" "I'm on the highway—" "Please, Norris! This is urgent. That woman will lose her mind completely if—" "All right, I'll call my wife and tell her to open the pound for you. Pick out the K-48 and sign for it. And listen—" "Yes?" "Don't let me catch you falsifying a serial number." Doctor Georges laughed faintly. "I won't, Norris. Thanks a million." He hung up quickly. Norris immediately regretted his consent. It bordered on being illegal. But he saw it as a quick way to get rid of an animal that might later have to be killed. He called Anne. Her voice was dull. She seemed depressed, but not angry. When he finished talking, she said, "All right, Terry," and hung up. By noon, he had finished checking the shipping lists at the wholesale house in Wylo City. Only thirty-five of July's Bermuda-K-99s had entered his territory, and they were about equally divided among five pet shops, three of which were in Wylo City. After lunch, he called each of the retail dealers, read them the serial numbers, and asked them to check the sales records for names and addresses of individual buyers. By three o'clock, he had the entire list filled out, and the task began to look easier. All that remained was to pick up the thirty-five animals. And that , he thought, was like trying to take a year-old baby away from its doting mother. He sighed and drove to the Wylo suburbs to begin his rounds. Anne met him at the door when he came home at six. He stood on the porch for a moment, smiling at her weakly. The smile was not returned. "Doctor Georges came," she told him. "He signed for the—" She stopped to stare at him. "Darling, your face! What happened?" Gingerly he touch the livid welts down the side of his cheek. "Just scratched a little," he muttered. He pushed past her and went to the phone in the hall. He sat eying it distastefully for a moment, not liking what he had to do. Anne came to stand beside him and examine the scratches. Finally he lifted the phone and dialed the Wylo exchange. A grating mechanical voice answered, "Locator center. Your party, please." "Sheriff Yates," Norris grunted. The robot operator, which had on tape the working habits of each Wylo City citizen, began calling numbers. It found the off-duty sheriff on its third try, in a Wylo pool hall. "I'm getting so I hate that infernal gadget," Yates grumbled. "I think it's got me psyched. What do you want, Norris?" "Cooperation. I'm mailing you three letters charging three Wylo citizens with resisting a Federal official—namely me —and charging one of them with assault. I tried to pick up their neutroids for a pound inspection—" Yates bellowed lusty laughter into the phone. "It's not funny. I've got to get those neutroids. It's in connection with the Delmont case." Yates stopped laughing. "Oh. Well, I'll take care of it." "It's a rush-order, Sheriff. Can you get the warrants tonight and pick up the animals in the morning?" "Easy on those warrants, boy. Judge Charleman can't be disturbed just any time. I can get the newts to you by noon, I guess, provided we don't have to get a helicopter posse to chase down the mothers." "That'll be all right. And listen, Yates—fix it so the charges will be dropped if they cooperate. Don't shake those warrants around unless they just won't listen to reason. But get those neutroids." "Okay, boy. Gotcha." Norris gave him the names and addresses of the three unwilling mothers. As soon as he hung up, Anne touched his shoulders and said, "Sit still." She began smoothing a chilly ointment over his burning cheek. "Hard day?" she asked. "Not too hard. Those were just three out of fifteen. I got the other twelve. They're in the truck." "That's good," she said. "You've got only twelve empty cages." He neglected to tell her that he had stopped at twelve for just this reason. "Guess I better get them unloaded," he said, standing up. "Can I help you?" He stared at her for a moment, saying nothing. She smiled a little and looked aside. "Terry, I'm sorry—about this morning. I—I know you've got a job that has to be—" Her lip quivered slightly. Norris grinned, caught her shoulders, and pulled her close. "Honeymoon's on again, huh?" she whispered against his neck. "Come on," he grunted. "Let's unload some neutroids, before I forget all about work." They went out to the kennels together. The cages were inside a sprawling concrete barn, which was divided into three large rooms—one for the fragile neuter humanoid creatures, and another for the lesser mutants, such as cat-Qs, dog-Fs, dwarf bears, and foot-high lambs that never matured into sheep. The third room contained a small gas chamber with a conveyor belt leading from it to a crematory-incinerator. Norris kept the third locked lest his wife see its furnishings. The doll-like neutroids began their mindless chatter as soon as their keepers entered the building. Dozens of blazing blond heads began dancing about their cages. Their bodies thwacked against the wire mesh as they leaped about their compartments with monkey grace. Their human appearance was broken by only two distinct features: short beaverlike tails decorated with fluffy curls of fur, and an erect thatch of scalp-hair that grew up into a bright candleflame. Otherwise, they appeared completely human, with baby-pink skin, quick little smiles, and cherubic faces. They were sexually neuter and never grew beyond a predetermined age-set which varied for each series. Age-sets were available from one to ten years human equivalent. Once a neutroid reached its age-set, it remained at the set's child-development level until death. "They must be getting to know you pretty well," Anne said, glancing around at the cages. Norris was wearing a slight frown as he inspected the room. "They've never gotten this excited before." He walked along a row of cages, then stopped by a K-76 to stare. " Apple cores! " He turned to face his wife. "How did apples get in there?" She reddened. "I felt sorry for them, eating that goo from the mechanical feeder. I drove down to Sherman III and bought six dozen cooking apples." "That was a mistake." She frowned irritably. "We can afford it." "That's not the point. There's a reason for the mechanical feeders." He paused, wondering how he could tell her the truth. He blundered on: "They get to love whoever feeds them." "I can't see—" "How would you feel about disposing of something that loved you?" Anne folded her arms and stared at him. "Planning to dispose of any soon?" she asked acidly. "Honeymoon's off again, eh?" She turned away. "I'm sorry, Terry. I'll try not to mention it again." He began unloading the truck, pulling the frightened and squirming doll-things forth one at a time with a snare-pole. They were one-man pets, always frightened of strangers. "What's the Delmont case, Terry?" Anne asked while he worked. "Huh?" "I heard you mention it on the phone. Anything to do with why you got your face scratched?" He nodded sourly. "Indirectly, yes. It's a long story." "Tell me." "Well, Delmont was a green-horn evolvotron operator at the Bermuda plant. His job was taking the unfertilized chimpanzee ova out of the egg-multiplier, mounting them in his machine, and bombarding the gene structure with sub-atomic particles. It's tricky business. He flashes a huge enlargement of the ovum on the electron microscope screen—large enough so he can see the individual protein molecules. He has an artificial gene pattern to compare it with. It's like shooting sub-atomic billiards. He's got to fire alpha-particles into the gene structure and displace certain links by just the right amount. And he's got to be quick about it before the ovum dies from an overdose of radiation from the enlarger. A good operator can get one success out of seven tries. "Well, Delmont worked a week and spoiled over a hundred ova without a single success. They threatened to fire him. I guess he got hysterical. Anyway, he reported one success the next day. It was faked. The ovum had a couple of flaws—something wrong in the central nervous system's determinants, and in the glandular makeup. Not a standard neutroid ovum. He passed it on to the incubators to get a credit, knowing it wouldn't be caught until after birth." "It wasn't caught at all?" Anne asked. "Funny thing, he was afraid it wouldn't be. He got to worrying about it, thought maybe a mental-deviant would pass, and that it might be dangerous. So he went back to its incubator and cut off the hormone flow into its compartment." "Why that?" "So it would develop sexuality. A neutroid would be born a female if they didn't give it suppressive doses of male hormone prenatally. That keeps ovaries from developing and it comes out neuter. But Delmont figured a female would be caught and stopped before the final inspection. They'd dispose of her without even bothering to examine for the other defects. And he could blame the sexuality on an equipment malfunction. He thought it was pretty smart. Trouble was they didn't catch the female. She went on through; they all look female." "How did they find out about it now?" "He got caught last month, trying it again. And he confessed to doing it once before. No telling how many times he really did it." Norris held up the final kicking, squealing, tassel-haired doll from the back of the kennel-truck. He grinned at his wife. "This little fellow, for instance. It might be a potential she. It might also be a potential murderer. All these kiddos are from the machines in the section where Delmont worked." Anne snorted and caught the baby-creature in her arms. It struggled and tried to bite, but subsided a little when she disentangled it from the snare. "Kkr-r-reee," it cooed nervously. "Kkr-r-reee!" "You tell him you're no murderer," Anne purred to it. Norris watched disapprovingly while she fondled it. One thing he had learned: to steer clear of emotional attachments. It was eight months old and looked like a child of two years—a year short of its age-set. And it was designed to be as affectionate as a human child. "Put it in the cage, Anne," he said quietly. She looked up and shook her head. "It belongs to somebody else. If it fixes a libido attachment on you, you're actually robbing its owner. They can't love many people at once." She snorted, but installed the thing in its cage. "Anne—" Norris hesitated, hating to approach the subject. "Do you—want one—for yourself? I can sign an unclaimed one over to you to keep in the house. It won't cost us anything." Slowly she shook her head, and her pale eyes went moody and luminous. "I'm going to have one of my own," she said. He stood in the back of the truck, staring down at her. "Do you realize what—" "I know what I'm saying. We're class-C on account of heart-trouble in both our families. Well, I don't care, Terry. I'm not going to waste a heart over one of these pathetic little artificial animals. We're going to have a baby." "You know what they'd do to us?" "If they catch us, yes—compulsory divorce, sterilization. But they won't catch us. I'll have it at home, Terry. Not even a doctor. We'll hide it." "I won't let you do such a thing." She faced him angrily. "Oh, this whole rotten world !" she choked. Suddenly she turned and fled out of the building. She was sobbing. Norris climbed slowly down from the truck and wandered on into the house. She was not in the kitchen nor the living room. The bedroom door was locked. He shrugged and went to sit on the sofa. The television set was on, and a newscast was coming from a local station. "... we were unable to get shots of the body," the announcer was saying. "But here is a view of the Georges residence. I'll switch you to our mobile unit in Sherman II, James Duncan reporting." Norris frowned with bewilderment as the scene shifted to a two-story plasticoid house among the elm trees. It was after dark, but the mobile unit's powerful floodlights made daylight of the house and its yard and the police 'copters sitting in a side lot. An ambulance was parked in the street. A new voice came on the audio. "This is James Duncan, ladies and gentlemen, speaking to you from our mobile unit in front of the late Doctor Hiram Georges' residence just west of Sherman II. We are waiting for the stretcher to be brought out, and Police Chief Erskine Miler is standing here beside me to give us a word about the case. Doctor Georges' death has shocked the community deeply. Most of you local listeners have known him for many years—some of you have depended upon his services as a family physician. He was a man well known, well loved. But now let's listen to Chief Miler." Norris sat breathing quickly. There could scarcely be two Doctor Georges in the community, but only this morning.... A growling drawl came from the audio. "This's Chief Miler speaking, folks. I just want to say that if any of you know the whereabouts of a Mrs. Sarah Glubbes, call me immediately. She's wanted for questioning." "Thank you, Chief. This is James Duncan again. I'll review the facts for you briefly again, ladies and gentlemen. At seven o'clock, less than an hour ago, a woman—allegedly Mrs. Glubbes—burst into Doctor Georges' dining room while the family was at dinner. She was brandishing a pistol and screaming, 'You stole my baby! You gave me the wrong baby! Where's my baby?' "When the doctor assured her that there was no other baby, she fired, shattering his salad plate. Glancing off it, the bullet pierced his heart. The woman fled. A peculiar feature of the case is that Mrs. Glubbes, the alleged intruder, has no baby . Just a minute—just a minute—here comes the stretcher now." Norris turned the set off and went to call the police. He told them what he knew and promised to make himself available for questioning if it became necessary. When he turned from the phone, Anne was standing in the bedroom doorway. She might have been crying a little, but she concealed it well. "What was all that?" she asked. "Woman killed a man. I happened to know the motive." "What was it?" "Neutroid trouble." "You meet up with a lot of unpleasantness in this business, don't you?" "Lot of unpleasant emotions tangled up in it," he admitted. "I know. Well, supper's been keeping hot for two hours. Shall we eat?" They went to bed at midnight, but it was after one when he became certain that his wife was asleep. He lay in darkness for a time, listening to her even breathing. Then he cautiously eased himself out of bed and tiptoed quietly through the door, carrying his shoes and trousers. He put them on in the kitchen and stole silently out to the kennels. A half moon hung low in a misty sky, and the wind was chilly out of the north. He went into the neutroid room and flicked a switch. A few sleepy chatters greeted the light. One at a time, he awoke twenty-three of the older doll-things and carried them to a large glass-walled compartment. These were the long-time residents; they knew him well, and they came with him willingly—like children after the Piper of Hamlin. When he had gotten them in the glass chamber, he sealed the door and turned on the gas. The conveyor would automatically carry them on to the incinerator. Now he had enough cages for the Bermuda-K-99s. He hurriedly quit the kennels and went to sit on the back steps. His eyes were burning, but the thought of tears made him sicker. It was like an assassin crying while he stabbed his victim. It was more honest just to retch. When he tiptoed back inside, he got as far as the hall. Then he saw Anne's small figure framed in the bedroom window, silhouetted against the moonlit yard. She had slipped into her negligee and was sitting on the narrow windowstool, staring silently out at the dull red tongue of exhaust gases from the crematory's chimney. Norris backed away. He went to the parlor and lay down on the couch. After a while he heard her come into the room. She paused in the center of the rug, a fragile mist in the darkness. He turned his face away and waited for the rasping accusation. But soon she came to sit on the edge of the sofa. She said nothing. Her hand crept out and touched his cheek lightly. He felt her cool finger-tips trace a soft line up his temple. "It's all right, Terry," she whispered. He kept his face averted. Her fingers traced a last stroke. Then she padded quietly back to the bedroom. He lay awake until dawn, knowing that it would never be all right, neither the creating nor the killing, until he—and the whole world—completely lost sanity. And then everything would be all right, only it still wouldn't make sense. Anne was asleep when he left the house. The night mist had gathered into clouds that made a gloomy morning of it. He drove on out in the kennel-truck, meaning to get the rest of the Bermuda-K-99s so that he could begin his testing. Still he felt the night's guilt, like a sticky dew that refused to depart with morning. Why should he have to kill the things? The answer was obvious. Society manufactured them because killing them was permissible. Human babies could not be disposed of when the market became glutted. The neutroids offered solace to childless women, kept them satisfied with a restricted birth rate. And why a restricted birth rate? Because by keeping the population at five billions, the Federation could insure a decent living standard for everybody. Where there was giving, Norris thought glumly, there was also taking away. Man had always deluded himself by thinking that he "created," but he created nothing. He thought that he had created—with his medical science and his end to wars—a longer life for the individual. But he found that he had only taken the lives of the unborn and added them to the years of the aged. Man now had a life expectancy of eighty, except that he had damn little chance of being born to enjoy it. A neutroid filled the cradle in his stead. A neutroid that never ate as much, or grew up to be unemployed. A neutroid could be killed if things got tough, but could still satisfy a woman's craving to mother something small. Norris gave up thinking about it. Eventually he would have to adjust to it. He was already adjusted to a world that loved the artificial mutants as children. He had been brought up in it. Emotion came in conflict with the grim necessities of his job. Somehow he would have to love them in the parlor and kill them in the kennel. It was only a matter of adjustment. At noon, he brought back another dozen K-99s and installed them in his cages. There had been two highly reluctant mothers, but he skipped them and left the seizure to the local authorities. Yates had already brought in the three from yesterday. "No more scratches?" Anne asked him while they ate lunch. They did not speak of the night's mass-disposal. Norris smiled mechanically. "I learned my lesson yesterday. If they bare their fangs, I get out without another word. Funny thing though—I've got a feeling one mother pulled a fast one." "What happened?" "Well, I told her what I wanted and why. She didn't like it, but she let me in. I started out with her newt, but she wanted a receipt. So I gave her one; took the serial number off my checklist. She looked at it and said, 'Why, that's not Chichi's number!' I looked at the newt's foot, and sure enough it wasn't. I had to leave it. It was a K-99, but not even from Bermuda." "I thought they were all registered," Anne said. "They are. I told her she had the wrong neutroid, but she got mad. Went and got the sales receipt. It checked with her newt, and it was from O'Reilley's pet shop—right place, wrong number. I just don't get it." "Nothing to worry about, is it Terry?" He looked at her peculiarly. "Ever think what might happen if someone started a black market in neutroids?" They finished the meal in silence. After lunch he went out again to gather up the rest of the group. By four o'clock, he had gotten all that were to be had without the threat of a warrant. The screams and pleas and tears of the owners left him gloomily despising himself. If Delmont's falsification had been widespread, he might have to turn several of the thirty-five over to central lab for dissection and ultimate destruction. That would bring the murderous wrath of their owners down upon him. He began to understand why bio-inspectors were frequently shifted from one territory to another. On the way home, he stopped in Sherman II to check on the missing number. It was the largest of the Sherman communities, covering fifty blocks of commercial buildings. He parked in the outskirts and took a sidewalk escalator toward O'Reilley's address. It was on a dingy sidestreet, reminiscent of past centuries, a street of small bars and bowling alleys and cigar stores. There was even a shop with three gold balls above the entrance, but the place was now an antique store. A light mist was falling when he stepped off the escalator and stood in front of the pet shop. A sign hung out over the sidewalk, announcing: J. "DOGGY" O'REILLEY PETS FOR SALE DUMB BLONDES AND GOLDFISH MUTANTS FOR THE CHILDLESS BUY A BUNDLE OF JOY Norris frowned at the sign and wandered inside. The place was warm and gloomy. He wrinkled his nose at the strong musk of animal odors. O'Reilley's was not a shining example of cleanliness. Somewhere a puppy was yapping, and a parrot croaked the lyrics of A Chimp to Call My Own , which Norris recognized as the theme song of a popular soap-opera about a lady evolvotron operator. He paused briefly by a tank of silk-draped goldfish. The shop had a customer. An elderly lady was haggling with a wizened manager over the price of a half grown second-hand dog-F. She was shaking her last dog's death certificate under his nose and demanding a guarantee of the dog's alleged F-5 intelligence. The old man offered to swear on a Bible, but he demurred when it came to swearing on a ledger. The dog was saying, "Don' sell me, Dada. Don' sell me." Norris smiled sardonically to himself. The non-human pets were smarter than the neutroids. A K-108 could speak a dozen words, and a K-99 never got farther than "mamma," "pappa," and "cookie." Anthropos was afraid to make the quasi-humans too intelligent, lest sentimentalists proclaim them really human. He wandered on toward the back of the building, pausing briefly by the cash register to inspect O'Reilley's license, which hung in a dusty frame on the wall behind the counter. "James Fallon O'Reilley ... authorized dealer in mutant animals ... all non-predatory mammals including chimpanzee-K series ... license expires June 1, 2235." It seemed in order, although the expiration date was approaching. He started toward a bank of neutroid cages along the opposite wall, but O'Reilley was mincing across the floor to meet him. The customer had gone. The little manager wore an elfin professional smile, and his bald head bobbled in a welcoming nod. "Good day, sir, good day! May I show you a dwarf kangaroo, or a—" He stopped and adjusted his spectacles. He blinked and peered as Norris flashed his badge. His smile waned. "I'm Agent Norris, Mr. O'Reilley. Called you yesterday for that rundown on K-99 sales." O'Reilley looked suddenly nervous. "Oh, yes. Find 'em all?" Norris shook his head. "No. That's why I stopped by. There's some mistake on—" he glanced at his list—"on K-99-LJZ-351. Let's check it again." O'Reilley seemed to cringe. "No mistake. I gave you the buyer's name." "She has a different number." "Can I help it if she traded with somebody?" "She didn't. She bought it here. I saw the receipt." "Then she traded with one of my other customers!" snapped the old man. "Two of your customers have the same name—Adelia Schultz? Not likely. Let's see your duplicate receipt book." O'Reilley's wrinkled face set itself into a stubborn mask. "Doubt if it's still around." Norris frowned. "Look, pop, I've had a rough day. I could start naming some things around here that need fixing—sanitary violations and such. Not to mention that sign—'dumb blondes.' They outlawed that one when they executed that shyster doctor for shooting K-108s full of growth hormones, trying to raise himself a harem to sell. Besides, you're required to keep sales records until they've been micro-filmed. There hasn't been a microfilming since July." The wrinkled face twitched with frustrated anger. O'Reilley shuffled to the counter while Norris followed. He got a fat binder from under the register and started toward a wooden stairway. "Where you going?" Norris called. "Get my old glasses," the manager grumbled. "Can't see through these new things." "Leave the book here and I'll check it," Norris offered. But O'Reilley was already limping quickly up the stairs. He seemed not to hear. He shut the door behind him, and Norris heard the lock click. The bio-agent waited. Again the thought of a black market troubled him. Unauthorized neutroids could mean lots of trouble.	C. Unauthorized neutroids would mean more taking "babies" away from their mothers and more killing.
What was the concentration of the prednicarbate cream used in Mrs. Mayer's treatment starting on 01/08/2021? Choose the correct answer from the following options: A. 0,05% B. 0,15% C. 0,2% D. 0,25% E. 0,3%	### Patient Report 0 Dear colleague, We report to you about Mrs. Linda Mayer, born on 01/12/1948, who presented to our outpatient clinic on 07/13/19. Diagnoses: - BIRADS IV, recommended biopsy during breast diagnostics. - Left breast carcinoma: iT1b; iN0; MX; ER: 12/12; PR: 2/12; Her-2: neg; Ki67: 15%. Other Diagnoses: - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement (THR) - Pemphigus vulgaris under azathioprine therapy - Osteoporosis - Obesity with a BMI of 35 - Undergoing immunosuppressive therapy with prednisolone Family History: - Sister deceased at age 39 from breast cancer. - Mother and grandmother (maternal and paternal) were diagnosed with breast cancer. Medical History: The CT thorax report indicates the presence of inflammatory foci, warranting further follow-up. The relevant data was documented and presented during the tumor conference. Subsequently, a telephone conversation was conducted with the patient to discuss the next steps. Tumor board decision from 07/13/2019: Imaging: 1) MRI examination detected a unifocal lesion on the left external aspect, measuring approximately 2.4 cm in size. 2) CT scan (thorax/abdomen 07/12/2019) revealed a previously known liver lesion, likely a hemangioma. No evidence of metastases was identified. Nonspecific, small foci were observed in the lungs, likely indicative of post-inflammatory changes. Recommendations: 1. If no metastasis (M0): Fast-track BRCA testing is recommended. 2. If BRCA testing returns negative: Proceed with a selective excision of the left breast after ultrasound-guided fine needle marking and sentinel lymph node biopsy on the left side. Additionally, perform Endopredict analysis on the surgical specimen. Current Medication: Medication Dosage Route Frequency ------------------------------- ------------ ----------- --------------- Aspirin 100mg Oral 1-0-0 Simvastatin (Zocor) 40mg Oral 0-1-0 Haloperidol (Haldol) 100mg Oral ½-0-½ Zopiclone (Imovane) 7.5mg Oral 0-0-1 Trazodone (Desyrel) 100mg Oral 0-0-½- Calcium Supplement (Caltrate) 500mg Oral 1-0-1 Nystatin (Bio-Statin) As advised Oral 1-1-1-1 Pantoprazole (Protonix) 40mg Oral 1-0-0 Prednisolone (Prelone) 40mg Oral As advised Tramadol/Naloxone (Ultram) 50/4mg Oral 1-0-1 Acyclovir (Zovirax) 800mg Oral 1-1-1 Mammography and Tomosynthesis from 07/8/2019: [Findings]{.underline}: During the inspection and palpation, no significant findings were noted on either side. Some areas with higher mammographic density were observed, which slightly limited the assessment. However, during the initial examination, a small architectural irregularity was identified on the outer left side. This irregularity appeared as a small, roundish compression measuring approximately 6mm and was visible only in the medio-lateral oblique image, with a nipple distance of 8cm. Apart from this discovery, there were no other suspicious focal findings on either side. No clustered or irregular microcalcifications were detected. Additionally, a long-term, unchanged observation noted some asymmetry with denser breast tissue present on both sides, particularly on the outer aspects. Sonographic evaluation posed challenges due to the mixed echogenic glandular tissue. As a possible corresponding feature to the questionable architectural irregularity on the outer left side, a blurred, echo-poor area with a vertical alignment measuring about 7x5mm was identified. Importantly, no other suspicious focal findings were observed, and there was no evidence of enlarged lymph nodes in the axilla on both sides. [Assessment]{.underline}: The observed finding on the left side presents an uncertain nature, categorized as BIRADS IVb. In contrast, the finding on the right side appears benign, categorized as BIRADS II. To gain a more conclusive understanding of the left-sided finding, we recommend a histological assessment through a sonographically guided high-speed punch biopsy. An appointment has been scheduled with the patient to proceed with this biopsy and obtain a definitive diagnosis.Formularbeginn FormularendeCurrent Recommendations:\ A fast-track decision will be made regarding tumor genetics, and the patient will be notified of the appointment via telephone. The patient should bring the pathology blocks from Fairview Clinic on the day of blood collection for genetic testing, along with a referral for an Endopredict test. A multidisciplinary team meeting will be convened after the Endopredict test and genetic testing results are available. If there is persistence or worsening of symptoms, we strongly advise the patient to seek immediate re-evaluation. Additionally, outside of regular office hours, the patient can seek assistance at the emergency care unit in case of emergency. MRI from 07/11/2019: [Technique:]{.underline} Breast MRI (3T scanner) with dedicated mammary surface coil: [Findings:]{.underline} The overall contrast enhancement was observed bilaterally to evaluate the Grade II findings. There was low to moderate small-spotted contrast enhancement with slightly limited assessability. The contrast dynamics revealed a patchy, confluent, blurred, and elongated contrast enhancement, corresponding to the primary lesion, which measured approximately 2.4 cm on the lower left exterior. Single spicules were noted, and the lesion appeared hypointense in T1w imaging. No suspicious focal findings with contrast enhancement were detected on the right side. Small axillary lymph nodes were observed on the left side, but they did not appear suspicious based on MR morphology. Additionally, there were no suspicious lymph nodes on the right side. [Assessment:]{.underline} An unifocal primary lesion measuring approximately 2.4 cm in diameter was identified on the lower left exterior. It exhibited patchy confluent enhancement and architectural disturbance, with single spicules. No evidence of suspicious lymph nodes was found. The left side is categorized as BIRADS 6, indicating a high suspicion of malignancy, while the right side is categorized as BIRADS 2, indicating a benign finding. ### Patient Report 1 Dear colleague, We are writing to provide you with an update on the medical condition of Mrs. Linda Mayer, born on 01/12/1948, who attended our outpatient clinic on 08/02/2019. Diagnoses: - Vacuum-assisted biopsy-confirmed ductal carcinoma in situ (DCIS) of the right breast (17mm) - Histological grade G3, estrogen receptor (ER) and progesterone receptor (PR) negative. - Postmenopausal for the past eight years. - Previous surgical history includes an appendectomy. - Allergies: Hay fever Current Presentation: The patient sought consultation following a confirmed diagnosis of DCIS (Ductal Carcinoma In Situ) in the right breast, which was determined through a vacuum-assisted biopsy. Physical Examination: Upon physical examination, there is evidence of a post-intervention hematoma located in the upper right quadrant of the right breast. However, the clip from the biopsy is not clearly visible. A sonographic examination of the right axilla reveals no abnormalities. Current Recommendations: - Imaging studies have been conducted. - A case presentation is scheduled for our mammary conference tomorrow. - Subsequently, planning for surgery will commence, including the evaluation of sentinel lymph nodes following a right mastectomy and axillary lymph node dissection. ### Patient Report 2 Dear colleague, We are writing to provide an update regarding Mrs. Linda Mayer, born on 01/12/1948, who received outpatient care at our facility on 08/29/2019. Diagnoses: - Vacuum-assisted biopsy-confirmed ductal carcinoma in situ (DCIS) of the right breast, measuring 17mm in size, classified as Grade 3, and testing negative for estrogen receptors (ER) and progesterone receptors (PR). - Mrs. Mayer has been postmenopausal for eight years. - Notable allergy: Hay fever Tumor Board Decision: Mammography imaging revealed a clip associated with a focal finding in the right breast adjacent to calcifications. [Recommendation]{.underline}: Proceed with sentinel lymph node evaluation after right mastectomy, including clip localization on the right side. Current Presentation: During the patient\'s recent outpatient visit, an extensive pre-operative consultation was conducted. This discussion covered the indications for the surgery, details of the surgical process, potential alternative options, as well as general and specific risks associated with the procedure. These risks included the possibility of an aesthetically suboptimal outcome and the chance of encountering an R1 situation. The patient did not have any further questions and provided written consent for the procedure. Physical Examination: Both breasts appear normal upon inspection and palpation. The right axilla shows no abnormalities. Medical History: Mrs. Linda Mayer presented to our clinic with a vacuum biopsy-confirmed DCIS of the right breast for therapeutic intervention. The decision for surgery was reached following a comprehensive review by our interdisciplinary breast board. After an extensive discussion of the procedure\'s scope, associated risks, and alternative options, the patient provided informed consent for the proposed surgery. Preoperative Procedure: Sonographic and mammographic fine needle marking of the remaining findings and the clip in the right breast. Surgical Report: Team time-out conducted with colleagues of anesthesia. Patient positioned in the supine position. Surgical site disinfection and sterile draping. Marking of the incision site. A semicircular incision was made laterally on the right breast. Visualization and dissection along the marking wire towards the marked finding. Excision of the marked findings, with a safety margin of approximately 1-2 cm. The excised specimen measured approximately 4 x 5 x 3 cm. Markings using standard protocol (green thread cranially, blue thread ventrally). The excised specimen was sent for preparation radiography. Hemostasis was meticulously ensured. Insertion of a 10Ch Blake drain into the segmental cavity, followed by suturing. Verification of a blood-dry wound cavity. Preparation radiography included the marked area and the marking wires. The excised material was transferred to our pathology colleagues for histological examination. Subdermal and intracutaneous sutures with Monocryl 3/0 in a continuous manner. Application of Steristrips and dressing. Instruments, swabs, and cloths were accounted for per the nurse\'s checklist. The patient was correctly positioned throughout the operation. The anesthesiologic course was without significant problems. A thorax compression bandage was applied in the operating room as a preventive measure against bleeding. Postoperative Procedure: Pain management, thrombosis prophylaxis, application of a pressure dressing, drainage under suction. Examinations: Digital Mammography performed on 08/29/2019 [Clinical indication]{.underline}: DCIS right [Question]{.underline}: Please send specimen + Mx-FNM Findings: Sonographically guided wire marking of the maximum microcalcification group measuring about 12 mm. Local hematoma cavity and inset clip marking directly cranial to the finding. Stitch direction from lateral to medial. The wire is positioned with the tip caudal to the clip in close proximity to the microcalcification. Additional marking of the focal localization on the skin. Documentation of the wire course in two planes. - Telephone discussion of findings with the surgeon. - Preparation radiography and preparation sonography are recommended. - Marking wire and suspicious focal findings centrally included in the preparation. - Intraoperative report of findings has been conveyed to the surgeon. Current Recommendations: - Scheduled for inpatient admission on ward 22 tomorrow. - Right breast mastectomy with sentinel lymph node evaluation. ### Patient Report 3 Dear colleague, We are writing to update you on the clinical course of Mrs. Linda Mayer, born on 01/12/1948, who was under our inpatient care from 08/30/2019 to 09/12/2019. Diagnosis: Vacuum-assisted biopsy confirmed Ductal Carcinoma In Situ (DCIS) in the right breast, measuring 17mm, Grade 3, ER/PR negative. Tumor Board Decision (07/13/2019): [Imaging:]{.underline} Clip identified in focal lesion in the right breast, adjacent to calcifications. [Recommendation]{.underline}: Spin Echo following fine-needle localization with mammography-guided control of the clip in the right breast. [Subsequent Recommendation (08/27/2019):]{.underline} Radiation therapy to the right breast. Regular follow-up is advised. Medical History: Ms. Linda Mayer presented to our facility on 08/30/2019 for the aforementioned surgical procedure. After a comprehensive discussion regarding the surgical plan, potential risks, and possible complications, the patient consented to proceed. The surgery was executed without complications on 09/01/2019. The postoperative course was unremarkable, allowing for Ms. Mayer\'s discharge on 09/12/2019 in stable condition and with no signs of wound irritation. Histopathological Findings (09/01/2019): The resected segment from the right breast showed a maximum necrotic zone of 1.6 cm with foreign body reaction, chronic resorptive inflammation, fibrosis, and residual hemorrhage. These findings primarily correspond to the pre-biopsy site. Surrounding this were areas of DCIS with solid and cribriform growth patterns and comedonecrosis, WHO Grade 3, Nuclear Grade 3, with a reconstructed extent of 3.5 cm. Resection margins were as follows: ventral 0.15 cm, caudal 0.2 cm, dorsal 0.4 cm, with remaining margins exceeding 0.5 cm. TNM Classification (8th Edition, 2017): pTis (DCIS), R0, G3. Additional immunohistochemical studies are underway to determine hormone receptor status; a supplementary report will follow. Postoperative Plan: The patient was educated on standard postoperative care and the importance of immediate re-evaluation for any persistent or worsening symptoms. Radiation therapy to the right breast is planned, along with regular follow-up appointments. Should you have any questions or require further clarification, we are readily available. For urgent concerns outside of regular office hours, emergency care is available at the Emergency Department. Internal Histopathological Findings Report Clinical Data: DCIS in the right breast (17 mm), Grade 3, ER/PR negative. Macroscopic Examination: The resected mammary segment from the right breast, marked with dual threads and containing a fine-needle marker inserted ventro-laterally, measures 4.5 x 5.5 x 3 cm (HxWxD) and weighs 35 grams. The specimen was sectioned from medial to lateral into 14 lamellae. The cut surface predominantly shows yellowish, lobulated mammary parenchyma with sparse striated whitish glandular components. A DCIS-suspected area, up to 2.1 cm in size, is evident caudally and centro-ventrally (from lamellae 4-10), displaying both reddish-hemorrhagic and whitish-nodular indurations. Minimal distances from the suspicious area to the resection margins are as follows: cranial 2 cm, caudal 0.2 cm, dorsal 0.2 cm, ventral 0.1 cm, medial 1.6 cm, lateral 2.5 cm. The suspect area was completely embedded. Ink markings: green/cranial, yellow/caudal, blue/ventral, black/dorsal. Microscopic Examination: Histological sections of the mammary parenchyma reveal fibro-lipomatous stroma and glandular lobules with a two-layered epithelial lining. In lamellae 3-6 and 11, solid and cribriform epithelial proliferations are evident. Cells are cuboidal with variably enlarged, predominantly moderately pleomorphic, round to oval nuclei. Comedo-like necroses are occasionally observed in secondary lumina. Microscopic distances to the deposition margins are consistent with the macroscopic findings. The surrounding stroma in lamellae 6-9 shows extensive geographic adipose tissue necrosis, multinucleated foreign body-type giant cells, foamy cell macrophages, collagen fiber proliferation, and fresh hemorrhages. Supplemental Immunohistochemical Findings (09/04/2019): Microscopy: In the meantime, the material was further processed as announced. Here, the previously described intraductal epithelial growths, each with negative staining reaction for the estrogen and progesterone receptor (with regular external and internal control reaction). Critical Findings: Resected mammary segment with paracentral, max. 1.6 cm necrotic zone with foreign body reaction, chronic resorptive. Chronic resorptive inflammation, fibrosis, and hemorrhage remnants (primarily corresponding to the pre-biopsy site), and surrounding portions of ductal carcinoma in situ. Ductal carcinoma in situ, solid and rib-shaped growth type with comedonecrosis, WHO grade 3, nuclear grade 3. The resection was locally complete with the following Safety margins: ventral 0.15 cm, caudal 0.2 cm, dorsal 0.4 cm, and the remaining sedimentation margins more than 0.5 cm. TNM classification (8th edition 2017): pTis (DCIS), R0, G3. [Hormone receptor status:]{.underline} - Estrogen receptor: negative (0%). - Progesterone receptor: negative (0%). ### Patient Report 4 Dear colleague, We are writing to provide an update regarding Mrs. Linda Mayer, born on 01/12/1948, who received outpatient treatment on 27/09/2019. Diagnoses: Left breast carcinoma; iT1c; iN0; MX; ER:12/12; PR:2/12; Her-2: neg; Ki67:15%, BRCA 2 mutation. Other Diagnoses: - Hailey-Hailey disease - currently regressing under prednisolone. - History of apoplexy in 2016 with no residuals - Depressive episodes - Right hip total hip replacement - History of left adnexectomy in 1980 due to extrauterine pregnancy - Tubal sterilization in 1988. - Uterine curettage (Abrasio) in 2004 - Hysterectomy in 2005 Allergies: Hay fever Imaging: - CT revealed a cystic lesion in the liver, not suspicious for metastasis. Granulomatous, post-inflammatory changes in the lung. - An MRI of the left breast showed a unifocal lesion on the outer left side with a 2.4 cm extension. Histology: Gene score of 6.5, indicating a high-risk profile (pT2 or pN1) if BRCA negative. Recommendation: If BRCA negative, SE left mamma after ultrasound-FNM with correlation in Mx and SLNB on the left. Current Presentation: Mrs. Linda Mayer presented for pre-operative evaluation for left mastectomy. BRCA testing confirmed a BRCA2 mutation, warranting bilateral subcutaneous mastectomy and SLNB on the left. Reconstruction with implants and mesh is planned, along with a breast lift as requested by the patient. Macroscopy: Left Subcutaneous Mastectomy (Blue/Ventral, Green/Cranial): - Specimen Size: 17 x 15 x 6 cm (Height x Width x Depth), Weight: 410 g - Description: Dual filament-labeled subcutaneous mastectomy specimen - Specimen Workup: 27 lamellae from lateral to medial - Tumor-Suspect Area (Lamellae 17-21): Max. 1.6 cm, white dermal, partly blurred - Margins from Tumor Area: Ventral 0.1 cm, Caudal 1 cm, Dorsal 1.2 cm, Cranial \> 5 cm, Lateral \> 5 cm, Medial \> 2 cm - Remaining Mammary Parenchyma: Predominantly yellowish lipomatous with focal nodular appearance - Ink Markings: Cranial/Green, Caudal/Yellow, Ventral/Blue, Dorsal/Black - A: Lamella 17 - Covers dorsal and caudal - B: Lamella 18 - Covers ventral - C: Lamella 19 - Covers ventral - D: Blade 21 - Covers ventral - E: Lamella 20 - Reference cranial - F: Lamella 16 - Immediately laterally following mammary parenchyma - G: Blade 22 - Reference immediately medial following mammary tissue - H: Lamella 12 - Central section - I: Lamella 8 - Documented section top/outside - J: Lamella 3 - Vestigial section below/outside - K: Lamella 21 - White-nodular imposing area - L: Lamella 8 - Further section below/outside with nodular area - M: Lateral border lamella perpendicularly - N: Medial border lamella perpendicular (Exemplary) Second Sentinel Lymph Node on the Left: - Specimen: Maximum of 6 cm of fat tissue resectate with 1 to 2 cm of lymph nodes and smaller nodular indurations. - A, B: One lymph node each divided - C: Further nodular indurations Palpable Lymph Nodes Level I: - Specimen: One max. 4.5 cm large fat resectate with nodular indurations up to 1.5 cm in size - A: One nodular induration divided - B: Further nodular indurated portions Right Subcutaneous Mastectomy: - Specimen: Double thread-labeled 450 g subcutaneous mastectomy specimen - Assumed Suture Markings: Blue (Ventral) and Green (Cranial) - Dorsal Fascia Intact - [Specimen Preparation:]{.underline} 16 lamellae from medial to lateral - Predominantly yellowish lobulated with streaky, beige, impinging strands of tissue - Isolated hemorrhages in the parenchyma - Ink Markings: Green = Cranial, Yellow = Caudal, Blue = Ventral, Black = Dorsal <!-- --> - A: Medial border lamella perpendicular (Exemplary) - B: Lamella 5 with reference ventrally (below inside) - C: Lamella 8 with reference ventrally (below inside) - D: Lamella 6 with ventral and dorsal reference (upper inside) - E: Blade 8 with ventral and dorsal cover (top inside) - F: Blade 11 with cover dorsal and caudal (bottom outside) - G: Blade 13 with dorsal cover (bottom outside) - H: Blade 10 with ventral and dorsal cover (top outside) - I: Lamella 14 with reference cranial and dorsal and bleeding in (upper outer) - J: Lateral border lamella perpendicular (Exemplary) Microscopy: 1\) In the tumor-suspicious area, a blurred large fibrosis zone with star-shaped extensions is visible. Intercalated are single-cell and stranded epithelial cells with a high nuclear-cytoplasmic ratio. The nuclei are monomorphic with finely dispersed chromatin, at most, very isolated mitoses. Adjacent distended glandular ducts with a discohesive cell proliferate with the same cytomorphology. Sporadically, preexistent glandular ducts are sheared disc-like by the infiltrative tumor cells. Samples from the nodular area of lamella 21 show areas of cell-poor hyaline sclerosis with partly ectatically dilated glandular ducts. 2\) Second lymph node with partial infiltrates of the neoplasia described above. The cells here are relatively densely packed. Somewhat increased mitoses. In the lymph nodes, iron deposition is also in the sinus histiocytes. 3\) Lymph nodes with partly sparse iron deposition. No epithelial foreign infiltrates. 4\) Regular mammary gland parenchyma. No tumor infiltrates. Part of the glandular ducts are slightly cystically dilated. Preliminary Critical Findings Report: Left breast carcinoma measuring max 1.6 cm diagnosed as moderately differentiated invasive lobular carcinoma, B.R.E. score 6 (3+2+1, G2). Presence of tumor-associated and peritumoral lobular carcinoma in situ. Resection status indicates locally complete excision of both invasive and non-invasive carcinoma; minimal margins as follows: ventral \<0.1 cm, caudal 0.2 cm, dorsal 0.8 cm, remaining margins ≥0.5 cm. Nodal status reveals max 0.25 cm metastasis in 1/5 nodes, 0/2 additional nodes, without extracapsular spread. Right mammary gland from subcutaneous mastectomy shows tumor-free parenchyma. TNM classification (8th ed. 2017): pT1c, pTis (LCIS), pN1a, G2, L0, V0, Pn0, R0. Investigations to determine tumor biology were initiated. Addendum follows. Supplementary findings on 10/07/2019 Editing: immunohistochemistry: Estrogen receptor, Progesterone receptor, Her2neu, MIB-1 (block 1D). Critical Findings Report: Breast carcinoma on the left with a 1.6 cm invasive lobular carcinoma, moderately differentiated, with a B.R.E. score of 6 (3+2+1, G2). Additionally, tumor-associated and peritumoral lobular carcinoma in situ are noted. Resection status confirms locally complete excision of both invasive and non-invasive carcinomas; minimal resection margins are ventral \<0.1 cm, caudal (LCIS) 0.2 cm, dorsal 0.8 cm, and all other margins ≥0.5 cm. Nodal assessment reveals a single metastasis with a maximum dimension of 0.25 cm among 7 lymph nodes, specifically found in 1/5 nodes, with no additional metastasis in 0/2 nodes and no extracapsular extension. Contralateral right mammary gland from subcutaneous mastectomy is tumor-free. Tumor biology of the invasive carcinoma demonstrates strong positive estrogen receptor expression in 100% of tumor cells, strong positive progesterone receptor expression in 1% of tumor cells, negative HER2/neu status (Score 1+), and a Ki67 (MIB-1) proliferation index of 25%. TNM classification (8th Edition 2017): pT1c, pTis (LCIS), pN1a (1/7 ECE-, sn), G2, L0, V0, Pn0, R0. Surgery Report (Vac Change + Irrigation): Indication for VAC change. After a detailed explanation of the procedure, its risks, and alternatives, the patient agrees to the proposed procedure. The course of surgery: Proper positioning in a supine position. Removal of the VAC sponge. A foul odor appears from the wound cavity. Careful disinfection of the surgical area. Sterile draping. Detailed inspection of the wound conditions. Wound debridement with removal of fibrin coatings and freshening of the wound. Resection of necrotic material in places with sharp spoon. Followed by extensive Irrigation of the entire wound bed and wound edges using 1 l Polyhexanide solution. Renewed VAC sponge application according to standard. Postoperative procedure: Pain medication, thrombosis prophylaxis, continuation of antibiotic therapy. In the case of abundant Staphylococcus aureus and isolated Pseudomosas in the smear and still clinical suspected infection, extension of antibiotic treatment to Meropenem. Surgery Report: Implant Placement Type of Surgery: Implant placement and wound closure. Report: After infection and VAC therapy, clean smears and planning of reinsertion. Informed consent. Intraoperative consults: Anesthesia. Course of Surgery: Team time out. Removal VAC sponge. Disinfection and covering. Irrigation of the wound cavity with Serasept. Blust irrigation. Fixation cranially and laterally with 4 fixation sutures with Vircryl 2-0. Choice of trial implant. Temporary insertion. Control in sitting and lying positions. Choice of the implant. Repeated disinfection. Change of gloves. Insertion of the implant into the pocket. Careful hemostasis. Insertion of a Blake drain into the wound cavity. Suturing of the drainage. Subcutaneous sutures with Monocryl 3-0. Type of Surgery: Prophylactic open Laparoscopy, extensive adhesiolysis Type of Anesthesia: ITN Report: Patient presented for prophylactic right adnexectomy in the course of hysterectomy and left adnexectomy due to genetic burden. Intraoperatively, secondary wound closure was to be performed in the case of a right mammary wound weeping more than one year postoperatively. The patient agreed to the planned procedure in writing after receiving detailed information about the extent, the risks, and the alternatives. Course of the Operation: Team time out with anesthesia colleagues. Flat lithotomy positioning, disinfection, and sterile draping. Placement of permanent transurethral catheter. Subumbilical incision and dissection onto the fascia. Opening of the fascia and suturing of the same. Exposure of the peritoneum and opening of the same. Insertion of the 10-mm optic trocar. Insertion of three additional trocars into the lower abdomen (left and center right, each 5mm; right 10mm). The following situation is seen: when the camera is inserted from the umbilical region, an extensive adhesion is seen. Only by changing the camera to the right lower bay is extensive adhesiolysis possible. The omentum is fused with the peritoneum and the serosa of the uterus. Upper abdomen as far as visible inconspicuous. After hysterectomy and adnexectomy on the left side, adnexa on the right side atrophic and inconspicuous. The peritoneum is smooth as far as can be seen. Visualization of the right adnexa and the suspensory ligament of ovary. Coagulation of the suspensory ligament of ovary ligament after visualization of the ureter on the same side. Stepwise dissection of the adnexa from the pelvic wall. Recovery via endobag. Hemostasis. Inspection of the situs. Removal of instrumentation under vision and draining of pneumoperitoneum. Closure of the abdominal fascia at the umbilicus and right lower abdomen. Suturing of the skin with Monocryl 3/0. Compression bandage at each trocar insertion site. Inspection of the right mamma. In the area of the surgical scar laterally/externally, 2-3 small epithelium-lined pore-like openings are visible; here, on pressure, discharge of rather viscous/sebaceous, non-odorous, or purulent fluid. No dehiscence is visible, suspected. fistula ducts to the implant cavity. After consultation with the mamma surgeon, a two-stage procedure was planned for the treatment of the fistula tracts. Correct positioning and inconspicuous anesthesiological course. Instrumentation, swabs, and cloths complete according to the operating room nurse. Postoperative procedures include analgesia, mobilization, thrombosis prophylaxis, and waiting for histology. Internal Histopathological Report [Clinical information/question]{.underline}: Fistula formation mammary right. Dignity? [Macroscopy]{.underline}: Skin spindle from scar mammary right: fix. a 2.4 cm long, stranded skin-subcutaneous excidate. Lamellation and complete embedding. [Processing]{.underline}: 1 block, HE [Microscopy]{.underline}: Histologic skin/subcutaneous cross-sections with overlay by a multilayered keratinizing squamous epithelium. The dermis with few inset regular skin adnexal structures, sparse to moderately dense mononuclear-dominated inflammatory infiltrates, and proliferation of cell-poor, fiber-rich collagenous connective tissue. Critical Findings Report: Skin spindle on scar mamma right: skin/subcutaneous resectate with fibrosis and chronic inflammation. To ensure that all findings are recorded, the material will be further processed. A follow-up report will follow. [Microscopy]{.underline}: In the meantime, the material was further processed as announced. The van Gieson stain showed extensive proliferation of collagenous and, in some places elastic fibers. Also in the additional immunohistochemical staining against no evidence of atypical epithelial infiltrates. Lab results upon Discharge: Parameter Results Reference Range -------------------------------- ------------- --------------------- Sodium 141 mEq/L 132-146 mEq/L Potassium 4.2 mEq/L 3.4-4.5 mEq/L Creatinine 0.82 mg/dL 0.50-0.90 mg/dL Estimated GFR (eGFR CKD-EPI) \>90 \- Total Bilirubin 0.21 mg/dL \< 1.20 mg/dL Albumin 4.09 g/dL 3.5-5.2 g/dL CRP 7.8 mg/L \< 5.0 mg/L Haptoglobin 108 mg/dL 30-200 mg/dL Ferritin 24 µg/L 13-140 µg/L ALT 24 U/L \< 31 U/L AST 37 U/L \< 35 U/L Gamma-GT 27 U/L 5-36 U/L Lactate Dehydrogenase 244 U/L 135-214 U/L 25-OH-Vitamin D3 91.7 nmol/L 50.0-150.0 nmol/L Hemoglobin 11.1 g/dL 12.0-15.6 g/dL Hematocrit 40.0% 35.5-45.5% Red Blood Cells 3.5 M/uL 3.9-5.2 M/uL White Blood Cells 2.41 K/uL 3.90-10.50 K/uL Platelets 142 K/uL 150-370 K/uL MCV 73.0 fL 80.0-99.0 fL MCH 23.9 pg 27.0-33.5 pg MCHC 32.7 g/dL 31.5-36.0 g/dL MPV 10.7 fL 7.0-12.0 fL RDW-CV 14.8% 11.5-15.0% Absolute Neutrophils 1.27 K/uL 1.50-7.70 K/uL Absolute Immature Granulocytes 0.000 K/uL \< 0.050 K/uL Absolute Lymphocytes 0.67 K/uL 1.10-4.50 K/uL Absolute Monocytes 0.34 K/uL 0.10-0.90 K/uL Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL Absolute Basophils 0.04 K/uL 0.00-0.20 K/uL Free Hemoglobin 5.00 mg/dL \< 20.00 mg/dL ### Patient Report 5 Dear colleague, We would like to provide an update on Mrs. Linda Mayer, born on 01/12/1948, who received inpatient care at our facility from 01/01/2021 to 01/14/2021. Diagnosis: Hailey-Hailey disease. - Upon admission, the patient was under treatment with Acitretin 25mg. Other Diagnoses: - History of apoplexy in 2016 with no residuals - Depressive episodes - Right hip total hip replacement - History of left adnexectomy in 1980 die to extrauterine pregnancy - Tubal sterilization in 1988. - Uterine curettage in 2004 - Hysterectomy in 2005 Medical History: Mrs. Linda Mayer was referred to our hospital for the management of Hailey-Hailey disease after assessment in our outpatient clinic. She reported a worsening of painful skin erosions on her neck and inner thighs over a span of approximately 3 weeks. Itchiness was not reported. Prior attempts at treatment, including the topical use of Fucicort, Prednisolone with Octenidine, and Polidocanol gel, had provided limited relief. She denied any other physical complaints, dyspnea, B symptoms, infections, or irregularities in stool and micturition. Her history revealed the initial onset of Hailey-Hailey disease, initially presenting as itching followed by skin erosions, which subsequently healed with scarring. The diagnosis was established at the Fairview Clinic. Previous therapeutic interventions included systemic cortisone shock therapy, as-needed application of Fucicort ointment, and axillary laser therapy. Family History: - Father: Hailey-Hailey Disease (M. Hailey-Hailey) - Mother and Sister: Breast carcinoma Psychosocial History: Socially, Ms. Linda Mayer is described as a retiree, having previously worked as a nurse. Physical Examination on Admission: Height: 16 cm, Body Weight: 80.0 kg, BMI: 29.7 Physical Examination Findings: Generally stable condition with increased nutritional status. Her consciousness was unremarkable, and cranial mobility was free. Ocular mobility was regular, with prompt pupillary reflexes to accommodation and light. She exhibited a normal heart rate, and cardiac and pulmonary examinations were unremarkable. No heart murmurs were detected. Renal bed and spine were not palpable. Further internal and orienting neurological examinations revealed no pathological findings. Skin Findings on Admission: Sharp erosions, approximately 10x10 cm in size, with a livid-erythematous base, partly crusty, were observed on the neck and proximal inner thighs. In the axillary regions on both sides, there were marginal, livid-erythematous, well-demarcated plaques interspersed with scarring strands, more pronounced on the right side. Skin type II. Mucous membranes appeared normal. Dermographism was noted to be ruber. Medication Dosage Frequency ------------------------------ ------------ ------------------------------- Prednisolone (Deltasone) 5 mg 1.5-0-0-0-0-0 Aspirin (Bayer) 100 mg 0-1-0-0-0-0 Simvastatin (Zocor) 40 mg 0-0-0-0-1 Pantoprazole (Protonix) 45.1 mg 1-0-0-0-0 Acitretin (Soriatane) 25 mg 1-0-0-0-0 Tetrabenazine (Xenazine) 111 mg 0.25-0.25-0.25-0.25-0.25-0.25 Letrozole (Femara) 2.5 mg 0-0-1-0 Risedronate Sodium (Actonel) 35 mg 1-0-0-0-0 Acetaminophen (Tylenol) 500 mg 0-1-0-1 Naloxone (Narcan) 8.8 mg 1-0-1-0 Eszopiclone (Lunesta) 7.5 mg 0-0-1-0 Other Findings: MRSA Smears: - Nasal Smear: Normal flora, no MRSA. - Throat Swab: Normal flora, no MRSA. - Non-lesional Skin Smear: Normal flora. - Lesional Skin Swab: Abundant Pseudomonas aeruginosa, abundant Klebsiella oxytoca, and abundant Serratia sp., sensitive to piperacillin-tazobactam. Therapy and Progression: Mrs. Linda Mayer was admitted on 01/01/2021 as an inpatient for a refractory exacerbation of previously diagnosed Hailey-Hailey disease. On admission, both bacteriological and mycological smears were conducted, which indicated abundant levels of Pseudomonas aeruginosa, Klebsiella oxytoca, and Serratia sp. Lab tests showed a CRP level of 2.83 mg/dL and a leukocyte count of 8.8 G/L. Initial topical therapy consisted of Zinc oxide ointment, Clotrimazole paste, and Triamcinolone Acetonide shake lotion. Treatment was modified on 01/04/2021 to include Clotrimazole (Lotrimin) paste in the mornings and methylprednisolone emulsion in the evenings. Starting on 01/08, eosin aqueous solution was introduced for application on the thighs, serving antiseptic and drying purposes. A hydrophilic prednicarbate cream at 0.25% concentration, combined with octenidine at 0.1%, was applied to the neck and thighs twice daily, also starting on 01/08. For showering, octenidine-based wash lotion was utilized. Additionally, Mrs. Linda Mayer received an emulsifying ointment as part of her treatment. ### Patient Report 6 Dear colleague, We are providing an update on our patient Mrs. Linda Mayer, born on 01/12/1948, who presented to our outpatient clinic on 09/22/2021. Diagnoses: M. Hailey-Hailey Medical History: - Diagnosis of M. Hailey-Hailey at the Fairview Clinic <!-- --> - Treatment involved systemic steroid shock therapy, laser therapy, and the initiation of Acitretin in October 2021, with no observed improvement. <!-- --> - A dermabrasion procedure was scheduled on 03/18/2021, during a previous inpatient admission. - Acitretin 25mg has been administered daily, with favorable outcomes noted when using Triamcinolone/Triclosan or Prednisolone + Octenidine. - A history of mastectomy with Vacuum-Assisted Closure (VAC) has resulted in breast erosion. Skin Findings: - Erythematous and partially mottled lesions have been identified in the axillary and inguinal regions, with some scarring observed in the axillary area. - On 04/28/2021, somewhat erosive plaques were noted in the inguinal regions. - As of 05/05/2021 discrete erosions are currently present on both forearms. Current Recommendations: - Inpatient admission is scheduled for September 2021. - The prescribed treatment plan includes topical prednicarbate (Dermatop) 0.25% with Octenidine 0.1%, per NRF 11.145, in a 50g container, to be applied once daily for 1-2 weeks. - Hydrocortisone 5% in a suitable base, 200g, is to be applied daily. - The regimen also includes prednicarbate (Dermatop) combined with Octenidine. - Acitretin will be continued temporarily. - A follow-up appointment in the outpatient clinic is scheduled for three months from now. - Discontinuation of Acitretin. - It is recommended to avoid the use of compresses on the erosions to prevent constant trauma. - Topical therapy with petrolatum-based wound ointment and sterile compresses.	0,25%
What is the role of the Saarkkadic people in this story? A. The other races are vying for attention from them for support in the war B. They are overseeing the peace talks. C. They produce some materials important to the Terrans. D. They provide a place for Malloy to hide from his own people.	IN CASE OF FIRE By RANDALL GARRETT There are times when a broken tool is better than a sound one, or a twisted personality more useful than a whole one. For instance, a whole beer bottle isn't half the weapon that half a beer bottle is ... Illustrated by Martinez In his office apartment, on the top floor of the Terran Embassy Building in Occeq City, Bertrand Malloy leafed casually through the dossiers of the four new men who had been assigned to him. They were typical of the kind of men who were sent to him, he thought. Which meant, as usual, that they were atypical. Every man in the Diplomatic Corps who developed a twitch or a quirk was shipped to Saarkkad IV to work under Bertrand Malloy, Permanent Terran Ambassador to His Utter Munificence, the Occeq of Saarkkad. Take this first one, for instance. Malloy ran his finger down the columns of complex symbolism that showed the complete psychological analysis of the man. Psychopathic paranoia. The man wasn't technically insane; he could be as lucid as the next man most of the time. But he was morbidly suspicious that every man's hand was turned against him. He trusted no one, and was perpetually on his guard against imaginary plots and persecutions. Number two suffered from some sort of emotional block that left him continually on the horns of one dilemma or another. He was psychologically incapable of making a decision if he were faced with two or more possible alternatives of any major importance. Number three ... Malloy sighed and pushed the dossiers away from him. No two men were alike, and yet there sometimes seemed to be an eternal sameness about all men. He considered himself an individual, for instance, but wasn't the basic similarity there, after all? He was—how old? He glanced at the Earth calendar dial that was automatically correlated with the Saarkkadic calendar just above it. Fifty-nine next week. Fifty-nine years old. And what did he have to show for it besides flabby muscles, sagging skin, a wrinkled face, and gray hair? Well, he had an excellent record in the Corps, if nothing else. One of the top men in his field. And he had his memories of Diane, dead these ten years, but still beautiful and alive in his recollections. And—he grinned softly to himself—he had Saarkkad. He glanced up at the ceiling, and mentally allowed his gaze to penetrate it to the blue sky beyond it. Out there was the terrible emptiness of interstellar space—a great, yawning, infinite chasm capable of swallowing men, ships, planets, suns, and whole galaxies without filling its insatiable void. Malloy closed his eyes. Somewhere out there, a war was raging. He didn't even like to think of that, but it was necessary to keep it in mind. Somewhere out there, the ships of Earth were ranged against the ships of the alien Karna in the most important war that Mankind had yet fought. And, Malloy knew, his own position was not unimportant in that war. He was not in the battle line, nor even in the major production line, but it was necessary to keep the drug supply lines flowing from Saarkkad, and that meant keeping on good terms with the Saarkkadic government. The Saarkkada themselves were humanoid in physical form—if one allowed the term to cover a wide range of differences—but their minds just didn't function along the same lines. For nine years, Bertrand Malloy had been Ambassador to Saarkkad, and for nine years, no Saarkkada had ever seen him. To have shown himself to one of them would have meant instant loss of prestige. To their way of thinking, an important official was aloof. The greater his importance, the greater must be his isolation. The Occeq of Saarkkad himself was never seen except by a handful of picked nobles, who, themselves, were never seen except by their underlings. It was a long, roundabout way of doing business, but it was the only way Saarkkad would do any business at all. To violate the rigid social setup of Saarkkad would mean the instant closing off of the supply of biochemical products that the Saarkkadic laboratories produced from native plants and animals—products that were vitally necessary to Earth's war, and which could be duplicated nowhere else in the known universe. It was Bertrand Malloy's job to keep the production output high and to keep the materiel flowing towards Earth and her allies and outposts. The job would have been a snap cinch in the right circumstances; the Saarkkada weren't difficult to get along with. A staff of top-grade men could have handled them without half trying. But Malloy didn't have top-grade men. They couldn't be spared from work that required their total capacity. It's inefficient to waste a man on a job that he can do without half trying where there are more important jobs that will tax his full output. So Malloy was stuck with the culls. Not the worst ones, of course; there were places in the galaxy that were less important than Saarkkad to the war effort. Malloy knew that, no matter what was wrong with a man, as long as he had the mental ability to dress himself and get himself to work, useful work could be found for him. Physical handicaps weren't at all difficult to deal with. A blind man can work very well in the total darkness of an infrared-film darkroom. Partial or total losses of limbs can be compensated for in one way or another. The mental disabilities were harder to deal with, but not totally impossible. On a world without liquor, a dipsomaniac could be channeled easily enough; and he'd better not try fermenting his own on Saarkkad unless he brought his own yeast—which was impossible, in view of the sterilization regulations. But Malloy didn't like to stop at merely thwarting mental quirks; he liked to find places where they were useful . The phone chimed. Malloy flipped it on with a practiced hand. "Malloy here." "Mr. Malloy?" said a careful voice. "A special communication for you has been teletyped in from Earth. Shall I bring it in?" "Bring it in, Miss Drayson." Miss Drayson was a case in point. She was uncommunicative. She liked to gather in information, but she found it difficult to give it up once it was in her possession. Malloy had made her his private secretary. Nothing—but nothing —got out of Malloy's office without his direct order. It had taken Malloy a long time to get it into Miss Drayson's head that it was perfectly all right—even desirable—for her to keep secrets from everyone except Malloy. She came in through the door, a rather handsome woman in her middle thirties, clutching a sheaf of papers in her right hand as though someone might at any instant snatch it from her before she could turn it over to Malloy. She laid them carefully on the desk. "If anything else comes in, I'll let you know immediately, sir," she said. "Will there be anything else?" Malloy let her stand there while he picked up the communique. She wanted to know what his reaction was going to be; it didn't matter because no one would ever find out from her what he had done unless she was ordered to tell someone. He read the first paragraph, and his eyes widened involuntarily. "Armistice," he said in a low whisper. "There's a chance that the war may be over." "Yes, sir," said Miss Drayson in a hushed voice. Malloy read the whole thing through, fighting to keep his emotions in check. Miss Drayson stood there calmly, her face a mask; her emotions were a secret. Finally, Malloy looked up. "I'll let you know as soon as I reach a decision, Miss Drayson. I think I hardly need say that no news of this is to leave this office." "Of course not, sir." Malloy watched her go out the door without actually seeing her. The war was over—at least for a while. He looked down at the papers again. The Karna, slowly being beaten back on every front, were suing for peace. They wanted an armistice conference—immediately. Earth was willing. Interstellar war is too costly to allow it to continue any longer than necessary, and this one had been going on for more than thirteen years now. Peace was necessary. But not peace at any price. The trouble was that the Karna had a reputation for losing wars and winning at the peace table. They were clever, persuasive talkers. They could twist a disadvantage to an advantage, and make their own strengths look like weaknesses. If they won the armistice, they'd be able to retrench and rearm, and the war would break out again within a few years. Now—at this point in time—they could be beaten. They could be forced to allow supervision of the production potential, forced to disarm, rendered impotent. But if the armistice went to their own advantage ... Already, they had taken the offensive in the matter of the peace talks. They had sent a full delegation to Saarkkad V, the next planet out from the Saarkkad sun, a chilly world inhabited only by low-intelligence animals. The Karna considered this to be fully neutral territory, and Earth couldn't argue the point very well. In addition, they demanded that the conference begin in three days, Terrestrial time. The trouble was that interstellar communication beams travel a devil of a lot faster than ships. It would take more than a week for the Earth government to get a vessel to Saarkkad V. Earth had been caught unprepared for an armistice. They objected. The Karna pointed out that the Saarkkad sun was just as far from Karn as it was from Earth, that it was only a few million miles from a planet which was allied with Earth, and that it was unfair for Earth to take so much time in preparing for an armistice. Why hadn't Earth been prepared? Did they intend to fight to the utter destruction of Karn? It wouldn't have been a problem at all if Earth and Karn had fostered the only two intelligent races in the galaxy. The sort of grandstanding the Karna were putting on had to be played to an audience. But there were other intelligent races throughout the galaxy, most of whom had remained as neutral as possible during the Earth-Karn war. They had no intention of sticking their figurative noses into a battle between the two most powerful races in the galaxy. But whoever won the armistice would find that some of the now-neutral races would come in on their side if war broke out again. If the Karna played their cards right, their side would be strong enough next time to win. So Earth had to get a delegation to meet with the Karna representatives within the three-day limit or lose what might be a vital point in the negotiations. And that was where Bertrand Malloy came in. He had been appointed Minister and Plenipotentiary Extraordinary to the Earth-Karn peace conference. He looked up at the ceiling again. "What can I do?" he said softly. On the second day after the arrival of the communique, Malloy made his decision. He flipped on his intercom and said: "Miss Drayson, get hold of James Nordon and Kylen Braynek. I want to see them both immediately. Send Nordon in first, and tell Braynek to wait." "Yes, sir." "And keep the recorder on. You can file the tape later." "Yes, sir." Malloy knew the woman would listen in on the intercom anyway, and it was better to give her permission to do so. James Nordon was tall, broad-shouldered, and thirty-eight. His hair was graying at the temples, and his handsome face looked cool and efficient. Malloy waved him to a seat. "Nordon, I have a job for you. It's probably one of the most important jobs you'll ever have in your life. It can mean big things for you—promotion and prestige if you do it well." Nordon nodded slowly. "Yes, sir." Malloy explained the problem of the Karna peace talks. "We need a man who can outthink them," Malloy finished, "and judging from your record, I think you're that man. It involves risk, of course. If you make the wrong decisions, your name will be mud back on Earth. But I don't think there's much chance of that, really. Do you want to handle small-time operations all your life? Of course not. "You'll be leaving within an hour for Saarkkad V." Nordon nodded again. "Yes, sir; certainly. Am I to go alone?" "No," said Malloy, "I'm sending an assistant with you—a man named Kylen Braynek. Ever heard of him?" Nordon shook his head. "Not that I recall, Mr. Malloy. Should I have?" "Not necessarily. He's a pretty shrewd operator, though. He knows a lot about interstellar law, and he's capable of spotting a trap a mile away. You'll be in charge, of course, but I want you to pay special attention to his advice." "I will, sir," Nordon said gratefully. "A man like that can be useful." "Right. Now, you go into the anteroom over there. I've prepared a summary of the situation, and you'll have to study it and get it into your head before the ship leaves. That isn't much time, but it's the Karna who are doing the pushing, not us." As soon as Nordon had left, Malloy said softly: "Send in Braynek, Miss Drayson." Kylen Braynek was a smallish man with mouse-brown hair that lay flat against his skull, and hard, penetrating, dark eyes that were shadowed by heavy, protruding brows. Malloy asked him to sit down. Again Malloy went through the explanation of the peace conference. "Naturally, they'll be trying to trick you every step of the way," Malloy went on. "They're shrewd and underhanded; we'll simply have to be more shrewd and more underhanded. Nordon's job is to sit quietly and evaluate the data; yours will be to find the loopholes they're laying out for themselves and plug them. Don't antagonize them, but don't baby them, either. If you see anything underhanded going on, let Nordon know immediately." "They won't get anything by me, Mr. Malloy." By the time the ship from Earth got there, the peace conference had been going on for four days. Bertrand Malloy had full reports on the whole parley, as relayed to him through the ship that had taken Nordon and Braynek to Saarkkad V. Secretary of State Blendwell stopped off at Saarkkad IV before going on to V to take charge of the conference. He was a tallish, lean man with a few strands of gray hair on the top of his otherwise bald scalp, and he wore a hearty, professional smile that didn't quite make it to his calculating eyes. He took Malloy's hand and shook it warmly. "How are you, Mr. Ambassador?" "Fine, Mr. Secretary. How's everything on Earth?" "Tense. They're waiting to see what is going to happen on Five. So am I, for that matter." His eyes were curious. "You decided not to go yourself, eh?" "I thought it better not to. I sent a good team, instead. Would you like to see the reports?" "I certainly would." Malloy handed them to the secretary, and as he read, Malloy watched him. Blendwell was a political appointee—a good man, Malloy had to admit, but he didn't know all the ins and outs of the Diplomatic Corps. When Blendwell looked up from the reports at last, he said: "Amazing! They've held off the Karna at every point! They've beaten them back! They've managed to cope with and outdo the finest team of negotiators the Karna could send." "I thought they would," said Malloy, trying to appear modest. The secretary's eyes narrowed. "I've heard of the work you've been doing here with ... ah ... sick men. Is this one of your ... ah ... successes?" Malloy nodded. "I think so. The Karna put us in a dilemma, so I threw a dilemma right back at them." "How do you mean?" "Nordon had a mental block against making decisions. If he took a girl out on a date, he'd have trouble making up his mind whether to kiss her or not until she made up his mind for him, one way or the other. He's that kind of guy. Until he's presented with one, single, clear decision which admits of no alternatives, he can't move at all. "As you can see, the Karna tried to give us several choices on each point, and they were all rigged. Until they backed down to a single point and proved that it wasn't rigged, Nordon couldn't possibly make up his mind. I drummed into him how important this was, and the more importance there is attached to his decisions, the more incapable he becomes of making them." The Secretary nodded slowly. "What about Braynek?" "Paranoid," said Malloy. "He thinks everyone is plotting against him. In this case, that's all to the good because the Karna are plotting against him. No matter what they put forth, Braynek is convinced that there's a trap in it somewhere, and he digs to find out what the trap is. Even if there isn't a trap, the Karna can't satisfy Braynek, because he's convinced that there has to be—somewhere. As a result, all his advice to Nordon, and all his questioning on the wildest possibilities, just serves to keep Nordon from getting unconfused. "These two men are honestly doing their best to win at the peace conference, and they've got the Karna reeling. The Karna can see that we're not trying to stall; our men are actually working at trying to reach a decision. But what the Karna don't see is that those men, as a team, are unbeatable because, in this situation, they're psychologically incapable of losing." Again the Secretary of State nodded his approval, but there was still a question in his mind. "Since you know all that, couldn't you have handled it yourself?" "Maybe, but I doubt it. They might have gotten around me someway by sneaking up on a blind spot. Nordon and Braynek have blind spots, but they're covered with armor. No, I'm glad I couldn't go; it's better this way." The Secretary of State raised an eyebrow. " Couldn't go, Mr. Ambassador?" Malloy looked at him. "Didn't you know? I wondered why you appointed me, in the first place. No, I couldn't go. The reason why I'm here, cooped up in this office, hiding from the Saarkkada the way a good Saarkkadic bigshot should, is because I like it that way. I suffer from agoraphobia and xenophobia. "I have to be drugged to be put on a spaceship because I can't take all that empty space, even if I'm protected from it by a steel shell." A look of revulsion came over his face. "And I can't stand aliens!" THE END Transcriber's Note: This etext was produced from Astounding Science Fiction March 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	C. They produce some materials important to the Terrans.
What languages are explored in the work?	### Introduction Code-switching has received a lot of attention from speech and computational linguistic communities especially on how to automatically recognize text from speech and understand the structure within it. This phenomenon is very common in bilingual and multilingual communities. For decades, linguists studied this phenomenon and found that speakers switch at certain points, not randomly and obeys several constraints which point to the code-switched position in an utterance BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . These hypotheses have been empirically proven by observing that bilinguals tend to code-switch intra-sententially at certain (morpho)-syntactic boundaries BIBREF5 . BIBREF1 defined the well-known theory that constraints the code-switch between a functional head and its complement is given the strong relationship between the two constituents, which corresponds to a hierarchical structure in terms of Part-of-Speech (POS) tags. BIBREF3 introduced Matrix-Language Model Framework for an intra-sentential case where the primary language is called Matrix Language and the second one called Embedded Language BIBREF2 . A language island was then introduced which is a constituent composed entirely of the language morphemes. From the Matrix-Language Frame Model, both matrix language (ML) island and embedded language (EL) islands are well-formed in their grammars and the EL islands are constrained under ML grammar BIBREF6 . BIBREF7 studied determiner–noun switches in Spanish–English bilinguals . Code-switching can be classified into two categories: intra-sentential and inter-sentential switches BIBREF0 . Intra-sentential switch defines a shift from one language to another language within an utterance. Inter-sentential switch refers to the change between two languages in a single discourse, where the switching occurs after a sentence in the first language has been completed and the next sentence starts with a new language. The example of the intra-sentential switch is shown in (1), and the inter-sentential switch is shown in (2). Language modeling using only word lexicons is not adequate to learn the complexity of code-switching patterns, especially in a low resource setting. Learning at the same time syntactic features such as POS tag and language identifier allows to have a shared grammatical information that constraint the next word prediction. Due to this reason, we propose a multi-task learning framework for code-switching language modeling task which is able to leverage syntactic features such as language and POS tag. The main contribution of this paper is two-fold. First, multi-task learning model is proposed to jointly learn language modeling task and POS sequence tagging task on code-switched utterances. Second, we incorporate language information into POS tags to create bilingual tags - it distinguishes tags between Chinese and English. The POS tag features are shared towards the language model and enrich the features to better learn where to switch. From our experiments result, we found that our method improves the perplexity on SEAME Phase I and Phase II dataset BIBREF8 . ### Related Work The earliest language modeling research on code-switching data was applying linguistic theories on computational modelings such as Inversion Constraints and Functional Head Constraints on Chinese-English code-switching data BIBREF9 , BIBREF10 . BIBREF11 built a bilingual language model which is trained by interpolating two monolingual language models with statistical machine translation (SMT) based text generation to generate artificial code-switching text. BIBREF12 , BIBREF13 introduced a class-based method using RNNLM for computing the posterior probability and added POS tags in the input. BIBREF14 explored the combination of brown word clusters, open class words, and clusters of open class word embeddings as hand-crafted features for improving the factored language model. In addition, BIBREF15 proposed a generative language modeling with explicit phrase structure. A method of tying input and output embedding helped to reduce the number of parameters in language model and improved the perplexity BIBREF16 . Learning multiple NLP tasks using multi-task learning have been recently used in many domains BIBREF17 , BIBREF18 , BIBREF19 . They presented a joint many-task model to handle multiple NLP tasks and share parameters with growing depth in a single end-to-end model. A work by BIBREF20 showed the potential of combining POS tagging with Named-Entity Recognition task. ### Methodology This section shows how to build the features and how to train our multi-task learning language model. Multi-task learning consists of two NLP tasks: Language modeling and POS sequence tagging. ### Feature Representation In the model, word lexicons and syntactic features are used as input. Word Lexicons: Sentences are encoded as 1-hot vectors and our vocabulary is built from training data. Syntactic Features: For each language island, phrase within the same language, we extract POS Tags iteratively using Chinese and English Penn Tree Bank Parser BIBREF21 , BIBREF22 . There are 31 English POS Tags and 34 Chinese POS Tags. Chinese words are distinguishable from English words since they have different encoding. We add language information in the POS tag label to discriminate POS tag between two languages. ### Model Description faFigure FIGREF7 illustrates our multi-task learning extension to recurrent language model. In this multi-task learning setting, the tasks are language modeling and POS tagging. The POS tagging task shares the POS tag vector and the hidden states to LM task, but it does not receive any information from the other loss. Let INLINEFORM0 be the word lexicon in the document and INLINEFORM1 be the POS tag of the corresponding INLINEFORM2 at index INLINEFORM3 . They are mapped into embedding matrices to get their INLINEFORM4 -dimensional vector representations INLINEFORM5 and INLINEFORM6 . The input embedding weights are tied with the output weights. We concatenate INLINEFORM7 and INLINEFORM8 as the input of INLINEFORM9 . The information from the POS tag sequence is shared to the language model through this step. INLINEFORM10 INLINEFORM11 where INLINEFORM0 denotes the concatenation operator, INLINEFORM1 and INLINEFORM2 are the final hidden states of INLINEFORM3 and INLINEFORM4 respectively. INLINEFORM5 and INLINEFORM6 , the hidden states from both LSTMs are summed before predicting the next word. INLINEFORM7 INLINEFORM8 The word distribution of the next word INLINEFORM0 is normalized using softmax function. The model uses cross-entropy losses as error functions INLINEFORM1 and INLINEFORM2 for language modeling task and POS tagging task respectively. We optimize the multi-objective losses using the Back Propagation algorithm and we perform a weighted linear sum of the losses for each individual task. INLINEFORM3 where INLINEFORM0 is the weight of the loss in the training. ### Experimental Setup In this section, we present the experimental setting for this task Corpus: SEAME (South East Asia Mandarin-English), a conversational Mandarin-English code-switching speech corpus consists of spontaneously spoken interviews and conversations BIBREF8 . Our dataset (LDC2015S04) is the most updated version of the Linguistic Data Consortium (LDC) database. However, the statistics are not identical to BIBREF23 . The corpus consists of two phases. In Phase I, only selected audio segments were transcribed. In Phase II, most of the audio segments were transcribed. According to the authors, it was not possible to restore the original dataset. The authors only used Phase I corpus. Few speaker ids are not in the speaker list provided by the authors BIBREF23 . Therefore as a workaround, we added these ids to the train set. As our future reference, the recording lists are included in the supplementary material. Preprocessing: First, we tokenized English and Chinese word using Stanford NLP toolkit BIBREF24 . Second, all hesitations and punctuations were removed except apostrophe, for examples: “let's" and “it's". Table TABREF9 and Table TABREF10 show the statistics of SEAME Phase I and II corpora. Table TABREF11 shows the most common trigger POS tag for Phase II corpus. Training: The baseline model was trained using RNNLM BIBREF25 . Then, we trained our LSTM models with different hidden sizes [200, 500]. All LSTMs have 2 layers and unrolled for 35 steps. The embedding size is equal to the LSTM hidden size. A dropout regularization BIBREF26 was applied to the word embedding vector and POS tag embedding vector, and to the recurrent output BIBREF27 with values between [0.2, 0.4]. We used a batch size of 20 in the training. EOS tag was used to separate every sentence. We chose Stochastic Gradient Descent and started with a learning rate of 20 and if there was no improvement during the evaluation, we reduced the learning rate by a factor of 0.75. The gradient was clipped to a maximum of 0.25. For the multi-task learning, we used different loss weights hyper-parameters INLINEFORM0 in the range of [0.25, 0.5, 0.75]. We tuned our model with the development set and we evaluated our best model using the test set, taking perplexity as the final evaluation metric. Where the latter was calculated by taking the exponential of the error in the negative log-form. INLINEFORM1 ### Results Table TABREF14 and Table TABREF15 show the results of multi-task learning with different values of the hyper-parameter INLINEFORM0 . We observe that the multi-task model with INLINEFORM1 achieved the best performance. We compare our multi-task learning model against RNNLM and LSTM baselines. The baselines correspond to recurrent neural networks that are trained with word lexicons. Table TABREF16 and Table TABREF17 present the overall results from different models. The multi-task model performs better than LSTM baseline by 9.7% perplexity in Phase I and 7.4% perplexity in Phase II. The performance of our model in Phase II is also better than the RNNLM (8.9%) and far better than the one presented in BIBREF13 in Phase I. Moreover, the results show that adding shared POS tag representation to INLINEFORM0 does not hurt the performance of the language modeling task. This implies that the syntactic information helps the model to better predict the next word in the sequence. To further verify this hypothesis, we conduct two analysis by visualizing our prediction examples in Figure FIGREF13 : Results with different hyper-parameter settings ### Conclusion In this paper, we propose a multi-task learning approach for code-switched language modeling. The multi-task learning models achieve the best performance and outperform LSTM baseline with 9.7% and 7.4% improvement in perplexity for Phase I and Phase II SEAME corpus respectively. This implies that by training two different NLP tasks together the model can correctly learn the correlation between them. Indeed, the syntactic information helps the model to be aware of code-switching points and it improves the performance over the language model. Finally, we conclude that multi-task learning has good potential on code-switching language modeling research and there are still rooms for improvements, especially by adding more language pairs and corpora. ### Acknowledgments This work is partially funded by ITS/319/16FP of the Innovation Technology Commission, HKUST 16214415 & 16248016 of Hong Kong Research Grants Council, and RDC 1718050-0 of EMOS.AI. ### Recording Lists We split the recording ids into train, development, and test set as the following: Figure 1: Multi-Task Learning Framework Table 1: Data Statistics in SEAME Phase I Table 2: Data Statistics in SEAME Phase II Table 3: Code-Switching Trigger Words in SEAME Phase II Table 4: Multi-task results with different weighted loss hyper-parameter in Phase I Table 5: Multi-task results with different weighted loss hyper-parameter in Phase II Table 7: Results in Phase II Figure 2: Prediction examples in Phase II. Left: Each square shows the target word’s log probability improvement by multi-task model compared to LSTM model (Darker color is better). Right: Each square shows the probability of the next POS tag is Chinese (Darker color represents higher probability) Table 8: Results in Phase I Table 9: Results in Phase II	Mandarin, English
What is the size of the corpus?	### Introduction The output of the world's scientists doubles roughly every nine years BIBREF0, and their pace is quickening. As a result, scientists and other experts must devote significant time to the difficult task of literature review, or coming to understand the context in which they work. Might artificial intelligence help to reduce that time? Several lines of research seek to do so. Citation recommendations systems BIBREF1, BIBREF2, BIBREF3 suggest references to relevant published work for a given document such as a current draft. Summarization systems BIBREF4, BIBREF5 condense the information in one or more documents, allowing researchers to more quickly understand the basic ideas in a piece of research. We introduce a complementary—but so far unaddressed—problem, citation text generation, where the relationship between a document and one or several others is expressed in natural language text. This differs from traditional summarization in that the primary focus is explaining the relationship between the two documents rather than their content. Automatically describing inter-document relationships could dramatically decrease the time researchers devote to literature review. For instance, a new paper could be explained in terms of its relationships to relevant works that a particular reader is most familiar with, rather than just those which the authors elected to cite (personalization). Further, such technology could be incorporated into writing assistance systems to help less experienced or non-native writers better articulate the connection between their work and prior art. Additionally, users of citation recommendation systems can benefit from natural language explanations of recommendation system choices. Beyond the immediate utility of citation text generation systems, the task offers significant challenges for language understanding and generation research. A major challenge is how to represent the information in one or more scientific texts. These documents are longer than those in most other domains typically studied in NLP, and make use of a long-tailed, open-domain technical vocabulary. Often an important phrase in the citing sentence output occurs only in a specific cited document and not elsewhere in the corpus. This requires a model that can learn phrase meanings from very few exposures, an important but unsolved problem for text generation systems. Possibly more challenging is understanding and expressing the various and nuanced relationships between related scientific works. In this work, we introduce the task of citation text generation. Leveraging the full texts of English-language scientific articles, we construct a dataset of citation sentences in the computer science domain for training and evaluating citation text generation models. We investigate strong retrieval and neural baseline models against which future work can compare. For use cases where large models can be trained, we extend the successful GPT2 architecture BIBREF6 to the scientific domain with additional pre-training and subsequent fine-tuning on the citation generation task. We experiment with different kinds of document context in the fine-tuning and inference stages. We also explore retrieval-based techniques which may more easily generalize to lower-resource settings. These models retrieve citation sentences from training documents which are most similar to test inputs. Our evaluations show that these techniques often produce plausible citation sentences, but indicate clear directions for improvement. Code and artifacts are provided for future research. ### Task Given the important research challenges posed by the citation text generation task, along with the potential social benefits of its solutions, let us continue with a formalization of the problem. Citation text generation is the task of generating a natural language citing sentence which explains the relationship between two documents. Examples of such citing sentences can be found in scientific documents as in-text citations to a previous work. Thus, we will formally distinguish one document as the source document, from which we will draw citing sentences which reference the cited document. If we want to leverage powerful modern neural text generation systems, we are faced with the problem of how to represent the documents in a way that these models can consume. In particular, language models like GPT2 are trained to predict next token probabilities given long stretches of contiguous text from a single document. It is not clear how to mix information from more than one document when providing context to these models. An additional difficulty of the citation text generation task is the vocabulary. In this domain, low-frequency, highly meaningful terms regularly appear in output texts. These terms may be completely novel to a single or small collection of papers (consider the phrase “citation text generation”, for instance), yet they are necessary for explaining the paper. This framing suggests a supervised learning setup. Let $t$ denote a citing sentence drawn from $S$, and $S^{\prime }$ denote $S$ without $t$. Then let be the probability of $t$ given $S^{\prime }$, cited document $C$, and model parameters $\theta $. The goal of learning a citation text generation model would be to maximize this probability across a large number of $t,S,C$ triples, so long as the parameters also generalize to unseen instances. At inference time, the goal is to generate a sentence $t^\ast $ which accurately describes the relationship between $S$ and $C$. The most appropriate evaluation metric for most text generation tasks is human judgment by potential users of the system. Evaluating citation text requires human judges with scientific expertise. For exploratory purposes, we use the standard automatic metrics for text generation tasks described in Section SECREF4, and we an expert error analysis in Section SECREF14. For source and cited documents, we use English-language computer science articles and annotation from the S2-GORC dataset BIBREF7. S2-GORC is a large citation graph dataset which includes full texts of 8.1 million scientific documents. We select a subset of 154K computer science articles as our corpus. From these, we extract 622K citing sentences that link back to other documents in our corpus. We hold 2500 examples for each of the validation and test sets. Detailed statistics can be found in Table TABREF4. ### Models We explore two basic styles of model for citation text generation. Following current work in neural text generation, we fine-tune the predictions of a large pre-trained language model to the citation text generation task. Additionally, we investigate approximate nearest neighbor methods to retrieve plausible human-authored citation sentences from the training data. ### Models ::: Neural Text Generation Recent work has shown that adapting large pre-trained language models to text generation tasks yields strong results BIBREF8. Due to its widespread use in text generation, we investigate the GPT model of BIBREF6 for the citation text generation task. GPT2 is a transformer model trained on 40 gigabytes of internet text with a language modeling objective BIBREF9. The adaptation process, called fine-tuning, involves continued training of the model on the target objective, in our case citation text generation. To fine-tune GPT2 for text generation, it is typical to concatenate the conditioning context $X = x_1 \ldots x_n$ and citing sentence $Y = y_1 \ldots y_m$ with a special separator token $\mho $. The model learns to approximate next token probabilities for each index after $\mho $: for $0<i<m$ and model parameters $\theta $. Cross-entropy loss is calculated for each $y_i$ and backpropagation is used find parameters $\theta $ which maximize $p(y_{i+1} \mid X,\mho ,y_1,\ldots ,y_i)$. To adapt Equation DISPLAY_FORM6 to the citation text generation task, we construct the conditioning context $X$ from the source and cited documents. We take $j$ tokens from source document $s_1,\ldots ,s_j$ along with $k$ tokens from the cited document $c_1,\ldots ,c_k$. (Which tokens are drawn from the two documents is an independent variable that we explore experimentally.) We then condition the generation of citing sentence $Y$ on $X = s_1,\ldots ,s_j,\mho ,c_1,\ldots ,c_k$. This model is trained to predict each token of $Y$ as described above. ### Models ::: Neural Text Generation ::: Context The primary question we investigate with this model is what kind of input is best for generating accurate and informative citation sentences. Prior works in citation recommendation have made use of abstracts, which perhaps act as sufficient summaries of document content for this task. Additionally, we explore variants of extended context, such as the introduction or first section after the abstract. Since scientific texts are too long to fit into the context window of our generation model, we also investigate a “sampling” approach which samples sentences from throughout the document until the context window is full. In this work, we combine either the abstract or introduction of the source document with each of the abstract, introduction, or sampled sentences from the cited document. ### Models ::: Retrieval with Approximate Nearest Neighbors While neural text generation techniques have advanced significantly in recent years, they are still inferior to human authored texts. For some tasks, it is better to retrieve a relevant human-authored text rather than generating novel text automatically BIBREF10. Is this also the case for citation text generation? To answer this question, we adapt an approximate nearest neighbor search algorithm to find similar pairs of documents. The basic search procedure is as follows: Given a test instance input $(S,C)$ for source $S$ and cited document $C$, we find the set $\bf {N}_C$, the nearest neighbors to $C$ in the training data. For each document $N_C$ from $\bf {N}_C$, let $\bf {N}_S$ be the set of documents that cite $N_C$. This means that each $N_S \in {\bf N}_S$ contains at least one citing sentence $t^{\prime }$ which cites $N_C$. We return the $t^{\prime }$ associated with the $(N_S,N_C)$ pair from the training which is closest to $(S,C)$. We measure the closeness of two pairs of documents by measuring cosine distances between vector representations of their content. The abstract of each document is embedded into a single dense vector by averaging the contextualized embeddings provided by the SciBERT model of BIBREF11 and normalizing. The distance between $(S,C)$ and candidate $(N_S,N_C)$ is computed as: where $\alpha $ and $\beta $ control the relative contribution of the two document similarities. We explore setting both $\alpha $ and $\beta $ to 1, or tuning them to optimize either BLEU or BERTScore on the validation set. ### Models ::: Language Model Pretraining GPT2-based models have demonstrated an ability to capture long distance dependencies over hundreds of tokens, which we hypothesize will allow them to synthesize information in both the source and cited documents. But citation text generation models must also handle the challenging technical vocabulary of the scientific domain. Prior work has shown that pretraining on in-domain data improves the performance of large language models on domain-specific tasks BIBREF11. Inspired by this, we experiment with additional pretraining of GPT2 in the science domain. This model, SciGPT2, is trained for an additional 3 epochs over the full text of the documents in our corpus using a language modeling objective. We note that both SciGPT2 and the SciBERT language models used here have been exposed to citing sentences from the test and validation sets as in-line citations during their pre-training phases, which may improve their performance versus models without this exposure. Such exposure is typical when using pretrained language models, as text from test data cannot be guaranteed to be absent from the large task-independent corpora upon which these models are trained. ### Evaluation We compare the different baseline systems using BLEU BIBREF12, ROUGE (specifically ROUGE 1, 2, and L; BIBREF13), and the recently introduced BertScore BIBREF14, a similarity metric based on BERT embeddings which has been shown to correlate well with human judgements on other tasks. To adapt the BertScore metric to the scientific text domain, we use SciBERT embeddings. Table TABREF7 (above the double line) shows the performance of the SciGPT2 model on the test set when provided with the different input context combinations outlined in Section SECREF5. We find that context does make a difference for this category of model, and that models which have access to the intro of the documents outperform those which use abstracts or sampling. Automatic evaluation of the retrieval-based methods on the test data are shown below the double line in Table TABREF7. This table shows that the retrieval methods perform well on this task. However we will show the limitations of these automatic metrics in Section SECREF14. We also observe that tuning the $\alpha $ and $\beta $ parameters on the validation set results in overfitting for this method. Outputs are largely unchanged by this tuning; fewer than 400 test datapoints differ from the untuned outputs. A larger validation split may alleviate this problem. Statistical significance is assessed for select results using bootstrapping with 1000 samples in each of 100 iterations. This test shows that conditioning on the introduction of the source document improves performance compared to conditioning on the abstract when using the SciGPT2 model. However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used. ### Analysis In this section we take a closer look at the details of the SciGPT2 and IR system outputs on a collection of validation datapoints. We provide a quantitative error analysis as well as qualitative analysis and examples. ### Analysis ::: Errors In order to better understand the performance of the models, we undertake a quantitative analysis of its output. One author randomly selected 200 datapoints from the validation set and their associated model outputs. Source and cited papers in the topic of NLP were used so as to facilitate expert judgement. For tractability, we limited the context presented to the annotator to the document abstracts and analyze the outputs of the abs $\times $ abs and IR systems. In this analysis, we ask whether the models are producing believable citing sentences given their input. In particular, we are interested in the relative believability of the SciGPT2 and IR systems, as well as how believability of a citing sentence changes when a reader can see the abstract of one document or both. We use 100 datapoints with outputs from the SciGPT2 system and 100 with outputs from the IR system. For 50 datapoints from each system, the cited document's abstract is initially masked such that only the source context is visible (Source, One Visible). Based only on the source context, the annotator judged whether the model output (1) could have convincingly been a citation in the source document based solely on the abstract (believable), (2) could have been a citation in the source document, but unclear from the abstract alone and depends on the rest of the paper's content (content-dependent), or (3) is unlikely to appear in this document (not believable). After making this judgment, the annotator was then shown the abstract of the cited document and asked to make the 3-way believability judgment based on both source and cited abstracts (Source, Both Visible). This process is repeated with the remaining 50 datapoints, but with the cited context masked initially (Cited, One Visible and Cited, Both Visible). The results of our analysis presented in Table TABREF13. We find that believability in the Cited, One Visible condition correlates well with the Cited, Both Visible condition. In the Source conditions, we see a greater difference in believability between One Visible and Both Visible. These findings makes sense: in-line citations often summarize a prior study rather than highlight the paper's own contributions. Together, these results indicate that the believability of citing sentences is more related to the cited document than to the source. Another interesting feature of this analysis is the difference between SciGPT2 and IR in terms of context-dependent citing sentences. We observe fewer such judgements in the IR outputs. This is probably due to the fact that neural text generation systems such as SciGPT2 will sometimes produce generic, uninformative outputs while the IR system outputs are usually specific enough that a stronger believability judgement can be made. We also observe an overall higher instance of not believable judgements of the IR model outputs. This implies that automatic metrics such as BLEU, where the IR system scored higher than SciGPT2, do not correlate with factual accuracy in citation text generation. Example citations and annotations are shown in Table TABREF15. We find that in the cases where the model generated outputs are unconvincing they are still on topic. All 10 cases in the Source, One Visible and 4 of the cases in Cited, One Visible that were no longer believable in the Both Visible conditions exhibit this quality. A common example (4 cases) of this phenomenon occurs when the model output references a dataset. While the dataset would be potentially relevant to both papers, the cited papers focus on modeling contributions and do not introduce a novel corpus. ### Analysis ::: Examples Example system outputs for randomly selected validation instances are shown in Table TABREF18. We see that both the SciGPT2 and IR model outputs regularly hit on the correct broad topic of the cited text (such “literary analysis” or “image captioning evaluation metrics”). It is notable that the SciGPT2 model outputs syntactically correct and coherent citation sentences, even given the difficulty of the vocabulary in this domain. This is a testament to the power of the domain-specific language model training. We also observe that the outputs of the SciGPT2 model are often shorter than the desired citing sentence. Brevity is a known issue for neural text generation and may be alleviated by penalizing brevity in the inference procedure. More problematic are the factual errors in the generated text. In the last example, for instance, we see that SciGPT2 fails to cite the specific image captioning dataset described in the cited paper (Pascal1K) and instead focuses on the more general evaluation metric for the image captioning task (CIDEr). This is typical of neural text generation systems, which often assign high probability to generic or frequent phrases and revert to these in the face of uncertainty. ### Analysis ::: Future Work The fluency and topical relevance of the baseline models show the plausibility of the citation text generation task as well as the utility of including pretrained scientific language models in future models. But based on the kinds of errors we have seen, future work should focus on two complementary goals: ensuring the factual accuracy of the generated text and improved modeling of the cited document. Factual accuracy is difficult to enforce in statistical text generation systems, especially where inference includes sampling procedures. Grounding to knowledge bases could help. For this task, knowledge extracted from candidate generations could be compared with knowledge from the full source and cited documents to prune false or irrelevant statements. Further, modeling input documents as knowledge graphs of their contents may help these algorithms better understand the cited document, resulting in better outputs. However, such a model will have to address the open problem of combining pretrained language models with graph encoding techniques. ### Related Work The current work builds on recent research in scientific document understanding, including citation recommendation and categorization, as well as scientific document summarization. Citation recommendation, or the task of selecting works related to a source document which would be suitable for citing, is a longstanding goal of AI research BIBREF15, BIBREF2, BIBREF16. Recently, researchers have sought to categorize citations using various ontologies of citation intents. BIBREF1 sought to discern “highly influential” citations from others. BIBREF17 uses six categories including “motivation”, “uses”, and “future work” among others. BIBREF3 condense this ontology to just three: “background”,“method”, and “result comparison”. We view the citation text generation task as an extension of these classification approaches with distinct advantages. While classification requires an extant citation link to exist, our generation task can describe possible relationships between works which do not cite each other, such as contemporaneous works. Additionally, because gold citation texts are readily available in scientific documents, the citation text generation task requires no task-specific annotated training data. In practice, citation classification is used to assist in suggesting relevant works to researchers; citation text generation complements this goal by providing rationales for the recommendation and furthering progress toward explainable AI. Generating a citation is also connected to summarizing scientific documents. There is a long history research on summarizing scientific documents BIBREF18, BIBREF19. More recently, researchers have included citing sentences as part of the input for summarization, hoping to capture the contribution of a work along with its content BIBREF20, BIBREF21, BIBREF5. Ours is the first to focus on the specific relationship between two documents when generating such sentences. Because of the emphasis on relational document understanding in our task, citation generation models can be used to assist with drafting papers as well, reducing researcher workload and providing non-native writers with a helpful first draft. Our work builds on recent advances in transfer learning in NLP. In particular, large pretrained models such as BERT BIBREF22 and GPT2 BIBREF6 have made strong advances on a number of tasks BIBREF23. It has also been shown that pretraining these models on domain-specific data further improves results on domain-speicific tasks BIBREF11, BIBREF24. In this work, we apply that methodology by adding an additional pretraining phase on in-domain data before finetuning a GPT2 model on the citation text generation task. ### Conclusion We have introduced the challenging but useful task of citation text generation. This task requires reasoning about the relationships between documents and expressing these relationships in natural language text. We have established a dataset for this task and studied the performance of contemporary neural text generation and information retrieval models. Our analysis shows that while these models produce fluent and topical outputs, more research is needed to ensure factual accuracy and specificity in the generated text. ### Acknowledgements This research was supported by the Office of Naval Research under the MURI grant N00014-18-1-2670. Figure 1: Overview of Citation Text Generation Task. Given a source and cited document, the goal is to write the sentence describing the specific relationship between the two. For the same source document, the output will vary depending on the cited document. Table 1: Dataset statistics. Table 2: Automatic evaluation of generated texts. Differences between entries in bold are statistically significant. Table 3: Error analysis of SCIGPT2 and IR generated texts. Table 5: Randomly selected examples of system inputs and outputs from validation set.	8.1 million scientific documents, 154K computer science articles, 622K citing sentences
Why does Terry's mom not want them to bring back his remains? A. She knows that her son would not find it practical to return to Earth B. She knows that it will not be physically possible for them to return him to Earth C. She cannot bear to see the tainted carcass of her beloved son D. She wishes to continue the ritual of greeting him every night when she looks to the sky	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.	D. She wishes to continue the ritual of greeting him every night when she looks to the sky
How does Pat feel about the narrator? A. Pat thinks the narrator is an idiot. He cannot believe the space agency allowed the journalist to tag along. B. Pat is highly annoyed to have an untrained passenger like the narrator aborad for this long, scientific journey. C. Pat thinks the narrator is simple-minded and tells him as much. D. Pat hates the narrator. Pat tells him to go to hell.	THE DOPE on Mars By JACK SHARKEY Somebody had to get the human angle on this trip ... but what was humane about sending me? Illustrated by WOOD My agent was the one who got me the job of going along to write up the first trip to Mars. He was always getting me things like that—appearances on TV shows, or mentions in writers' magazines. If he didn't sell much of my stuff, at least he sold me . "It'll be the biggest break a writer ever got," he told me, two days before blastoff. "Oh, sure there'll be scientific reports on the trip, but the public doesn't want them; they want the human slant on things." "But, Louie," I said weakly, "I'll probably be locked up for the whole trip. If there are fights or accidents, they won't tell me about them." "Nonsense," said Louie, sipping carefully at a paper cup of scalding coffee. "It'll be just like the public going along vicariously. They'll identify with you." "But, Louie," I said, wiping the dampness from my palms on the knees of my trousers as I sat there, "how'll I go about it? A story? An article? A you-are-there type of report? What?" Louie shrugged. "So keep a diary. It'll be more intimate, like." "But what if nothing happens?" I insisted hopelessly. Louie smiled. "So you fake it." I got up from the chair in his office and stepped to the door. "That's dishonest," I pointed out. "Creative is the word," Louie said. So I went on the first trip to Mars. And I kept a diary. This is it. And it is honest. Honest it is. October 1, 1960 They picked the launching date from the March, 1959, New York Times , which stated that this was the most likely time for launching. Trip time is supposed to take 260 days (that's one way), so we're aimed toward where Mars will be (had better be, or else). There are five of us on board. A pilot, co-pilot, navigator and biochemist. And, of course, me. I've met all but the pilot (he's very busy today), and they seem friendly enough. Dwight Kroger, the biochemist, is rather old to take the "rigors of the journey," as he puts it, but the government had a choice between sending a green scientist who could stand the trip or an accomplished man who would probably not survive, so they picked Kroger. We've blasted off, though, and he's still with us. He looks a damn sight better than I feel. He's kind of balding, and very iron-gray-haired and skinny, but his skin is tan as an Indian's, and right now he's telling jokes in the washroom with the co-pilot. Jones (that's the co-pilot; I didn't quite catch his first name) is scarlet-faced, barrel-chested and gives the general appearance of belonging under the spreading chestnut tree, not in a metal bullet flinging itself out into airless space. Come to think of it, who does belong where we are? The navigator's name is Lloyd Streeter, but I haven't seen his face yet. He has a little cubicle behind the pilot's compartment, with all kinds of maps and rulers and things. He keeps bent low over a welded-to-the-wall (they call it the bulkhead, for some reason or other) table, scratching away with a ballpoint pen on the maps, and now and then calling numbers over a microphone to the pilot. His hair is red and curly, and he looks as though he'd be tall if he ever gets to stand up. There are freckles on the backs of his hands, so I think he's probably got them on his face, too. So far, all he's said is, "Scram, I'm busy." Kroger tells me that the pilot's name is Patrick Desmond, but that I can call him Pat when I get to know him better. So far, he's still Captain Desmond to me. I haven't the vaguest idea what he looks like. He was already on board when I got here, with my typewriter and ream of paper, so we didn't meet. My compartment is small but clean. I mean clean now. It wasn't during blastoff. The inertial gravities didn't bother me so much as the gyroscopic spin they put on the ship so we have a sort of artificial gravity to hold us against the curved floor. It's that constant whirly feeling that gets me. I get sick on merry-go-rounds, too. They're having pork for dinner today. Not me. October 2, 1960 Feeling much better today. Kroger gave me a box of Dramamine pills. He says they'll help my stomach. So far, so good. Lloyd came by, also. "You play chess?" he asked. "A little," I admitted. "How about a game sometime?" "Sure," I said. "Do you have a board?" He didn't. Lloyd went away then, but the interview wasn't wasted. I learned that he is tall and does have a freckled face. Maybe we can build a chessboard. With my paper and his ballpoint pen and ruler, it should be easy. Don't know what we'll use for pieces, though. Jones (I still haven't learned his first name) has been up with the pilot all day. He passed my room on the way to the galley (the kitchen) for a cup of dark brown coffee (they like it thick) and told me that we were almost past the Moon. I asked to look, but he said not yet; the instrument panel is Top Secret. They'd have to cover it so I could look out the viewing screen, and they still need it for steering or something. I still haven't met the pilot. October 3, 1960 Well, I've met the pilot. He is kind of squat, with a vulturish neck and close-set jet-black eyes that make him look rather mean, but he was pleasant enough, and said I could call him Pat. I still don't know Jones' first name, though Pat spoke to him, and it sounded like Flants. That can't be right. Also, I am one of the first five men in the history of the world to see the opposite side of the Moon, with a bluish blurred crescent beyond it that Pat said was the Earth. The back of the Moon isn't much different from the front. As to the space in front of the ship, well, it's all black with white dots in it, and none of the dots move, except in a circle that Pat says is a "torque" result from the gyroscopic spin we're in. Actually, he explained to me, the screen is supposed to keep the image of space locked into place no matter how much we spin. But there's some kind of a "drag." I told him I hoped it didn't mean we'd land on Mars upside down. He just stared at me. I can't say I was too impressed with that 16 x 19 view of outer space. It's been done much better in the movies. There's just no awesomeness to it, no sense of depth or immensity. It's as impressive as a piece of velvet with salt sprinkled on it. Lloyd and I made a chessboard out of a carton. Right now we're using buttons for men. He's one of these fast players who don't stop and think out their moves. And so far I haven't won a game. It looks like a long trip. October 4, 1960 I won a game. Lloyd mistook my queen-button for my bishop-button and left his king in jeopardy, and I checkmated him next move. He said chess was a waste of time and he had important work to do and he went away. I went to the galley for coffee and had a talk about moss with Kroger. He said there was a good chance of lichen on Mars, and I misunderstood and said, "A good chance of liking what on Mars?" and Kroger finished his coffee and went up front. When I got back to my compartment, Lloyd had taken away the chessboard and all his buttons. He told me later he needed it to back up a star map. Pat slept mostly all day in his compartment, and Jones sat and watched the screen revolve. There wasn't much to do, so I wrote a poem, sort of. Mary, Mary, quite contrary, How does your garden grow? With Martian rime, Venusian slime, And a radioactive hoe. I showed it to Kroger. He says it may prove to be environmentally accurate, but that I should stick to prose. October 5, 1960 Learned Jones' first name. He wrote something in the ship's log, and I saw his signature. His name is Fleance, like in "Macbeth." He prefers to be called Jones. Pat uses his first name as a gag. Some fun. And only 255 days to go. April 1, 1961 I've skipped over the last 177 days or so, because there's nothing much new. I brought some books with me on the trip, books that I'd always meant to read and never had the time. So now I know all about Vanity Fair , Pride and Prejudice , War and Peace , Gone with the Wind , and Babbitt . They didn't take as long as I thought they would, except for Vanity Fair . It must have been a riot when it first came out. I mean, all those sly digs at the aristocracy, with copious interpolations by Mr. Thackeray in case you didn't get it when he'd pulled a particularly good gag. Some fun. And only 78 days to go. June 1, 1961 Only 17 days to go. I saw Mars on the screen today. It seems to be descending from overhead, but Pat says that that's the "torque" doing it. Actually, it's we who are coming in sideways. We've all grown beards, too. Pat said it was against regulations, but what the hell. We have a contest. Longest whiskers on landing gets a prize. I asked Pat what the prize was and he told me to go to hell. June 18, 1961 Mars has the whole screen filled. Looks like Death Valley. No sign of canals, but Pat says that's because of the dust storm down below. It's nice to have a "down below" again. We're going to land, so I have to go to my bunk. It's all foam rubber, nylon braid supports and magnesium tubing. Might as well be cement for all the good it did me at takeoff. Earth seems awfully far away. June 19, 1961 Well, we're down. We have to wear gas masks with oxygen hook-ups. Kroger says the air is breathable, but thin, and it has too much dust in it to be any fun to inhale. He's all for going out and looking for lichen, but Pat says he's got to set up camp, then get instructions from Earth. So we just have to wait. The air is very cold, but the Sun is hot as hell when it hits you. The sky is a blinding pink, or maybe more of a pale fuchsia. Kroger says it's the dust. The sand underfoot is kind of rose-colored, and not really gritty. The particles are round and smooth. No lichen so far. Kroger says maybe in the canals, if there are any canals. Lloyd wants to play chess again. Jones won the beard contest. Pat gave him a cigar he'd smuggled on board (no smoking was allowed on the ship), and Jones threw it away. He doesn't smoke. June 20, 1961 Got lost today. Pat told me not to go too far from camp, so, when I took a stroll, I made sure every so often that I could still see the rocket behind me. Walked for maybe an hour; then the oxygen gauge got past the halfway mark, so I started back toward the rocket. After maybe ten steps, the rocket disappeared. One minute it was standing there, tall and silvery, the next instant it was gone. Turned on my radio pack and got hold of Pat. Told him what happened, and he told Kroger. Kroger said I had been following a mirage, to step back a bit. I did, and I could see the ship again. Kroger said to try and walk toward where the ship seemed to be, even when it wasn't in view, and meantime they'd come out after me in the jeep, following my footprints. Started walking back, and the ship vanished again. It reappeared, disappeared, but I kept going. Finally saw the real ship, and Lloyd and Jones waving their arms at me. They were shouting through their masks, but I couldn't hear them. The air is too thin to carry sound well. All at once, something gleamed in their hands, and they started shooting at me with their rifles. That's when I heard the noise behind me. I was too scared to turn around, but finally Jones and Lloyd came running over, and I got up enough nerve to look. There was nothing there, but on the sand, paralleling mine, were footprints. At least I think they were footprints. Twice as long as mine, and three times as wide, but kind of featureless because the sand's loose and dry. They doubled back on themselves, spaced considerably farther apart. "What was it?" I asked Lloyd when he got to me. "Damned if I know," he said. "It was red and scaly, and I think it had a tail. It was two heads taller than you." He shuddered. "Ran off when we fired." "Where," said Jones, "are Pat and Kroger?" I didn't know. I hadn't seen them, nor the jeep, on my trip back. So we followed the wheel tracks for a while, and they veered off from my trail and followed another, very much like the one that had been paralleling mine when Jones and Lloyd had taken a shot at the scaly thing. "We'd better get them on the radio," said Jones, turning back toward the ship. There wasn't anything on the radio but static. Pat and Kroger haven't come back yet, either. June 21, 1961 We're not alone here. More of the scaly things have come toward the camp, but a few rifle shots send them away. They hop like kangaroos when they're startled. Their attitudes aren't menacing, but their appearance is. And Jones says, "Who knows what's 'menacing' in an alien?" We're going to look for Kroger and Pat today. Jones says we'd better before another windstorm blows away the jeep tracks. Fortunately, the jeep has a leaky oil pan, so we always have the smears to follow, unless they get covered up, too. We're taking extra oxygen, shells, and rifles. Food, too, of course. And we're locking up the ship. It's later , now. We found the jeep, but no Kroger or Pat. Lots of those big tracks nearby. We're taking the jeep to follow the aliens' tracks. There's some moss around here, on reddish brown rocks that stick up through the sand, just on the shady side, though. Kroger must be happy to have found his lichen. The trail ended at the brink of a deep crevice in the ground. Seems to be an earthquake-type split in solid rock, with the sand sifting over this and the far edge like pink silk cataracts. The bottom is in the shade and can't be seen. The crack seems to extend to our left and right as far as we can look. There looks like a trail down the inside of the crevice, but the Sun's setting, so we're waiting till tomorrow to go down. Going down was Jones' idea, not mine. June 22, 1961 Well, we're at the bottom, and there's water here, a shallow stream about thirty feet wide that runs along the center of the canal (we've decided we're in a canal). No sign of Pat or Kroger yet, but the sand here is hard-packed and damp, and there are normal-size footprints mingled with the alien ones, sharp and clear. The aliens seem to have six or seven toes. It varies from print to print. And they're barefoot, too, or else they have the damnedest-looking shoes in creation. The constant shower of sand near the cliff walls is annoying, but it's sandless (shower-wise) near the stream, so we're following the footprints along the bank. Also, the air's better down here. Still thin, but not so bad as on the surface. We're going without masks to save oxygen for the return trip (Jones assures me there'll be a return trip), and the air's only a little bit sandy, but handkerchiefs over nose and mouth solve this. We look like desperadoes, what with the rifles and covered faces. I said as much to Lloyd and he told me to shut up. Moss all over the cliff walls. Swell luck for Kroger. We've found Kroger and Pat, with the help of the aliens. Or maybe I should call them the Martians. Either way, it's better than what Jones calls them. They took away our rifles and brought us right to Kroger and Pat, without our even asking. Jones is mad at the way they got the rifles so easily. When we came upon them (a group of maybe ten, huddling behind a boulder in ambush), he fired, but the shots either bounced off their scales or stuck in their thick hides. Anyway, they took the rifles away and threw them into the stream, and picked us all up and took us into a hole in the cliff wall. The hole went on practically forever, but it didn't get dark. Kroger tells me that there are phosphorescent bacteria living in the mold on the walls. The air has a fresh-dug-grave smell, but it's richer in oxygen than even at the stream. We're in a small cave that is just off a bigger cave where lots of tunnels come together. I can't remember which one we came in through, and neither can anyone else. Jones asked me what the hell I kept writing in the diary for, did I want to make it a gift to Martian archeologists? But I said where there's life there's hope, and now he won't talk to me. I congratulated Kroger on the lichen I'd seen, but he just said a short and unscientific word and went to sleep. There's a Martian guarding the entrance to our cave. I don't know what they intend to do with us. Feed us, I hope. So far, they've just left us here, and we're out of rations. Kroger tried talking to the guard once, but he (or it) made a whistling kind of sound and flashed a mouthful of teeth. Kroger says the teeth are in multiple rows, like a tiger shark's. I'd rather he hadn't told me. June 23, 1961, I think We're either in a docket or a zoo. I can't tell which. There's a rather square platform surrounded on all four sides by running water, maybe twenty feet across, and we're on it. Martians keep coming to the far edge of the water and looking at us and whistling at each other. A little Martian came near the edge of the water and a larger Martian whistled like crazy and dragged it away. "Water must be dangerous to them," said Kroger. "We shoulda brought water pistols," Jones muttered. Pat said maybe we can swim to safety. Kroger told Pat he was crazy, that the little island we're on here underground is bordered by a fast river that goes into the planet. We'd end up drowned in some grotto in the heart of the planet, says Kroger. "What the hell," says Pat, "it's better than starving." It is not. June 24, 1961, probably I'm hungry . So is everybody else. Right now I could eat a dinner raw, in a centrifuge, and keep it down. A Martian threw a stone at Jones today, and Jones threw one back at him and broke off a couple of scales. The Martian whistled furiously and went away. When the crowd thinned out, same as it did yesterday (must be some sort of sleeping cycle here), Kroger talked Lloyd into swimming across the river and getting the red scales. Lloyd started at the upstream part of the current, and was about a hundred yards below this underground island before he made the far side. Sure is a swift current. But he got the scales, walked very far upstream of us, and swam back with them. The stream sides are steep, like in a fjord, and we had to lift him out of the swirling cold water, with the scales gripped in his fist. Or what was left of the scales. They had melted down in the water and left his hand all sticky. Kroger took the gummy things, studied them in the uncertain light, then tasted them and grinned. The Martians are made of sugar. Later, same day . Kroger said that the Martian metabolism must be like Terran (Earth-type) metabolism, only with no pancreas to make insulin. They store their energy on the outside of their bodies, in the form of scales. He's watched them more closely and seen that they have long rubbery tubes for tongues, and that they now and then suck up water from the stream while they're watching us, being careful not to get their lips (all sugar, of course) wet. He guesses that their "blood" must be almost pure water, and that it washes away (from the inside, of course) the sugar they need for energy. I asked him where the sugar came from, and he said probably their bodies isolated carbon from something (he thought it might be the moss) and combined it with the hydrogen and oxygen in the water (even I knew the formula for water) to make sugar, a common carbohydrate. Like plants, on Earth, he said. Except, instead of using special cells on leaves to form carbohydrates with the help of sunpower, as Earth plants do in photosynthesis (Kroger spelled that word for me), they used the shape of the scales like prisms, to isolate the spectra (another Kroger word) necessary to form the sugar. "I don't get it," I said politely, when he'd finished his spiel. "Simple," he said, as though he were addressing me by name. "They have a twofold reason to fear water. One: by complete solvency in that medium, they lose all energy and die. Two: even partial sprinkling alters the shape of the scales, and they are unable to use sunpower to form more sugar, and still die, if a bit slower." "Oh," I said, taking it down verbatim. "So now what do we do?" "We remove our boots," said Kroger, sitting on the ground and doing so, "and then we cross this stream, fill the boots with water, and spray our way to freedom." "Which tunnel do we take?" asked Pat, his eyes aglow at the thought of escape. Kroger shrugged. "We'll have to chance taking any that seem to slope upward. In any event, we can always follow it back and start again." "I dunno," said Jones. "Remember those teeth of theirs. They must be for biting something more substantial than moss, Kroger." "We'll risk it," said Pat. "It's better to go down fighting than to die of starvation." The hell it is. June 24, 1961, for sure The Martians have coal mines. That's what they use those teeth for. We passed through one and surprised a lot of them chewing gritty hunks of anthracite out of the walls. They came running at us, whistling with those tubelike tongues, and drooling dry coal dust, but Pat swung one of his boots in an arc that splashed all over the ground in front of them, and they turned tail (literally) and clattered off down another tunnel, sounding like a locomotive whistle gone berserk. We made the surface in another hour, back in the canal, and were lucky enough to find our own trail to follow toward the place above which the jeep still waited. Jones got the rifles out of the stream (the Martians had probably thought they were beyond recovery there) and we found the jeep. It was nearly buried in sand, but we got it cleaned off and running, and got back to the ship quickly. First thing we did on arriving was to break out the stores and have a celebration feast just outside the door of the ship. It was pork again, and I got sick. June 25, 1961 We're going back . Pat says that a week is all we were allowed to stay and that it's urgent to return and tell what we've learned about Mars (we know there are Martians, and they're made of sugar). "Why," I said, "can't we just tell it on the radio?" "Because," said Pat, "if we tell them now, by the time we get back we'll be yesterday's news. This way we may be lucky and get a parade." "Maybe even money," said Kroger, whose mind wasn't always on science. "But they'll ask why we didn't radio the info, sir," said Jones uneasily. "The radio," said Pat, nodding to Lloyd, "was unfortunately broken shortly after landing." Lloyd blinked, then nodded back and walked around the rocket. I heard a crunching sound and the shattering of glass, not unlike the noise made when one drives a rifle butt through a radio. Well, it's time for takeoff. This time it wasn't so bad. I thought I was getting my space-legs, but Pat says there's less gravity on Mars, so escape velocity didn't have to be so fast, hence a smoother (relatively) trip on our shock-absorbing bunks. Lloyd wants to play chess again. I'll be careful not to win this time. However, if I don't win, maybe this time I'll be the one to quit. Kroger is busy in his cramped lab space trying to classify the little moss he was able to gather, and Jones and Pat are up front watching the white specks revolve on that black velvet again. Guess I'll take a nap. June 26, 1961 Hell's bells . Kroger says there are two baby Martians loose on board ship. Pat told him he was nuts, but there are certain signs he's right. Like the missing charcoal in the air-filtration-and-reclaiming (AFAR) system. And the water gauges are going down. But the clincher is those two sugar crystals Lloyd had grabbed up when we were in that zoo. They're gone. Pat has declared a state of emergency. Quick thinking, that's Pat. Lloyd, before he remembered and turned scarlet, suggested we radio Earth for instructions. We can't. Here we are, somewhere in a void headed for Earth, with enough air and water left for maybe three days—if the Martians don't take any more. Kroger is thrilled that he is learning something, maybe, about Martian reproductive processes. When he told Pat, Pat put it to a vote whether or not to jettison Kroger through the airlock. However, it was decided that responsibility was pretty well divided. Lloyd had gotten the crystals, Kroger had only studied them, and Jones had brought them aboard. So Kroger stays, but meanwhile the air is getting worse. Pat suggested Kroger put us all into a state of suspended animation till landing time, eight months away. Kroger said, "How?" June 27, 1961 Air is foul and I'm very thirsty. Kroger says that at least—when the Martians get bigger—they'll have to show themselves. Pat says what do we do then ? We can't afford the water we need to melt them down. Besides, the melted crystals might all turn into little Martians. Jones says he'll go down spitting. Pat says why not dismantle interior of rocket to find out where they're holing up? Fine idea. How do you dismantle riveted metal plates? June 28, 1961 The AFAR system is no more and the water gauges are still dropping. Kroger suggests baking bread, then slicing it, then toasting it till it turns to carbon, and we can use the carbon in the AFAR system. We'll have to try it, I guess. The Martians ate the bread. Jones came forward to tell us the loaves were cooling, and when he got back they were gone. However, he did find a few of the red crystals on the galley deck (floor). They're good-sized crystals, too. Which means so are the Martians. Kroger says the Martians must be intelligent, otherwise they couldn't have guessed at the carbohydrates present in the bread after a lifelong diet of anthracite. Pat says let's jettison Kroger. This time the vote went against Kroger, but he got a last-minute reprieve by suggesting the crystals be pulverized and mixed with sulphuric acid. He says this'll produce carbon. I certainly hope so. So does Kroger. Brief reprieve for us. The acid-sugar combination not only produces carbon but water vapor, and the gauge has gone up a notch. That means that we have a quart of water in the tanks for drinking. However, the air's a bit better, and we voted to let Kroger stay inside the rocket. Meantime, we have to catch those Martians. June 29, 1961 Worse and worse . Lloyd caught one of the Martians in the firing chamber. We had to flood the chamber with acid to subdue the creature, which carbonized nicely. So now we have plenty of air and water again, but besides having another Martian still on the loose, we now don't have enough acid left in the fuel tanks to make a landing. Pat says at least our vector will carry us to Earth and we can die on our home planet, which is better than perishing in space. The hell it is. March 3, 1962 Earth in sight . The other Martian is still with us. He's where we can't get at him without blow-torches, but he can't get at the carbon in the AFAR system, either, which is a help. However, his tail is prehensile, and now and then it snakes out through an air duct and yanks food right off the table from under our noses. Kroger says watch out. We are made of carbohydrates, too. I'd rather not have known. March 4, 1962 Earth fills the screen in the control room. Pat says if we're lucky, he might be able to use the bit of fuel we have left to set us in a descending spiral into one of the oceans. The rocket is tighter than a submarine, he insists, and it will float till we're rescued, if the plates don't crack under the impact. We all agreed to try it. Not that we thought it had a good chance of working, but none of us had a better idea. I guess you know the rest of the story, about how that destroyer spotted us and got us and my diary aboard, and towed the rocket to San Francisco. News of the "captured Martian" leaked out, and we all became nine-day wonders until the dismantling of the rocket. Kroger says he must have dissolved in the water, and wonders what that would do. There are about a thousand of those crystal-scales on a Martian. So last week we found out, when those red-scaled things began clambering out of the sea on every coastal region on Earth. Kroger tried to explain to me about salinity osmosis and hydrostatic pressure and crystalline life, but in no time at all he lost me. The point is, bullets won't stop these things, and wherever a crystal falls, a new Martian springs up in a few weeks. It looks like the five of us have abetted an invasion from Mars. Needless to say, we're no longer heroes. I haven't heard from Pat or Lloyd for a week. Jones was picked up attacking a candy factory yesterday, and Kroger and I were allowed to sign on for the flight to Venus scheduled within the next few days—because of our experience. Kroger says there's only enough fuel for a one-way trip. I don't care. I've always wanted to travel with the President. —JACK SHARKEY Transcriber's Note: This etext was produced from Galaxy Magazine June 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	B. Pat is highly annoyed to have an untrained passenger like the narrator aborad for this long, scientific journey.
How does the author feel about the film, Elizabeth? A. the story is well-told but inaccurate B. it has great acting, but confusing plot C. it is overall enjoyable to watch D. the focus of the film takes away from the plot	Warrior Queens Elizabeth is a lurid paraphrase of the old Groucho Marx line about Doris Day: "I knew the Virgin Queen before she was a virgin." As the movie tells it, she was a sylvan, redheaded princess (Cate Blanchett) given to gamboling with her fella (Joseph Fiennes) between periods of internment in the Tower of London on charges of conspiring to overthrow her half-sister, the heatedly Catholic Queen Mary (Kathy Burke). The daughter of the second wife of Henry VIII, Anne Boleyn, and therefore dubbed a bastard by the papists, the Protestant Elizabeth ascends the throne to find the air still thick with smoke from roasted heretics, a team of skulking Catholics plotting her assassination, and a council of advisers (lords, bishops, sundry old boys) who snigger openly at the prospect of taking orders from a woman. Only a strategic marriage to a Spaniard or a Frenchman will mollify all factions, her advisers insist, but the pickings prove dismal. (Her French suitor enjoys wearing dresses.) After skulls are smashed, throats slit, and bosoms skewered in the name of Christ, Elizabeth decides to: a) "unsex" herself and become a symbol--the Virgin Queen, married only to England; and b) entertain dissenting opinions exclusively from those whose heads are affixed to spikes. You can't be both a queenly queen and a womanly woman, says the script (by Michael Hirst)--at least not in 1554. (The director, Shekhar Kapur, made the same point in his grim 1994 Indian epic The Bandit Queen , against a backdrop of scrubby plains along the Ganges.) Is this feminist take historically accurate? Probably, although the evidence suggests that Elizabeth had developed a head for stratagems earlier in life (her position had been precarious since the beheading of her mother) and came to the throne with few girlish illusions about How Things Work in a barbarous state. That said, the movie's approach makes for juicy melodrama. The tone of Elizabeth comes nearer to the nihilistic relish of Jacobeans such as John Ford and John Webster than to the more sorrowful horror of the Elizabethan dramatists Ben Jonson and William Shakespeare. It's even closer to a Jacobean drama of our own age: The Godfather (1972), which it emulates by cutting back-and-forth between queen and courtiers in prayer and the roundup and slaughter of Catholics on their privies, in bed with their mistresses, and so on. Their severed heads look on, wide-eyed, as Elizabeth directs her hair to be shorn--images of her girlhood flashing by as her locks rain down--and then walks weightily to her throne, now a chalk-faced gorgon. With all due respect to Blanchett, Bette Davis, and Glenda Jackson, my favorite Elizabeth I remains Miranda Richardson's capricious, baby-talking psychopath on the BBC comedy Blackadder II . (Casting about for a new lord high executioner, she mews to Rowan Atkinson, "There are thousands of Catholics simply dying to have their heads sneaked off --and there's no one to organize it.") But Blanchett comes in a close second, pulling off the transition from hapless young woman to coolly ruthless monarch with uncommon subtlety. Gradually expunging all empathy from her moist, pink eyes and permitting her visage to ossify, she gives this carnival of carnage an awe-inspiring center. A more subversive sort of queen is on display in Velvet Goldmine , Todd Haynes' musical fantasia on the early '70s era of "glam" or "glitter" rock. Here the monarch is a David Bowie-esque singer called Brian Slade (Jonathan Rhys-Meyers) and his spidery, space-age alter ego, Maxwell Demon. The movie opens with a spaceship depositing an infant Oscar Wilde on the stoop of a Dublin townhouse. Then it skips ahead to track a jade pin (it signifies hedonistic liberation) from the custody of a young Wilde to a swishy fringe creature called Jack Fairy to the regal Slade, a bisexual superstar who carries the news to all the young dudes. After that, we're in an Orwellian 1984 that's presided over by a vaguely fascist president and by arena rockers who serve as propagandists for a repressively conformist state. Whatever happened to Brian Slade, the glitter kids, the visionary exhibitionists and gleeful poseurs? Borrowing its framework from Citizen Kane , the movie follows a reporter (Christian Bale) assigned to reconstruct Slade's life and solve the mystery of his whereabouts. Whatever you make of Velvet Goldmine (opinions have ranged from rapturous to casually dismissive), it's like no other musical ever made. It's determinedly swirling, discursive, elliptical. Now the story is told by an omniscient narrator, now a TV reporter, now a participant. Now it's flashing back, now forward. Every other line of dialogue is a cue for one of its dazzling numbers, largely covers of songs by Brian Eno, Bryan Ferry, and T. Rex. The narrative is a challenge to keep up with, but then, great artists often invent their own syntax. In the '80s, Haynes employed Barbie dolls to depict the rise and wasting away from anorexia of the singer Karen Carpenter. Lucky audiences who caught Superstar: The Karen Carpenter Story (it was shelved when Richard Carpenter served the producers with an order to cease and desist exhibition) began by laughing at this elaborately posed, soft-rock femme, only to discover by the climax that the cultural forces that were eating at her (and that kept her from eating) had grown heartbreakingly palpable. Poison (1991), Haynes' Genêt-inspired exploration of transgression, didn't overcome its own artiness. But Safe (1995), the story of a Reagan-era housewife (Julianne Moore) convinced that her environment is poisoning her, is an entrancing meditation on the power of culture to crush the individual. Despite its ironic detachment, the film draws you into its heroine's sickly state: Breathing oxygen from a canister inside a high-tech igloo, she dwindles to nearly nothing, the modern incarnation of the Incredible Shrinking Man. (It was partly my passion for Haynes' films that led me to accept a job offer from his indefatigable producer Christine Vachon last year to collaborate on a nuts-and-bolts book about producing, Shooting To Kill . So my review of Velvet Goldmine --like my review of Vachon's other recent release, Happiness --should be read as the work of a partisan. But not a blind partisan.) In Velvet Goldmine , Haynes sets out to demonstrate the power of popular music to change people's lives--to tell them it's OK to fashion themselves into anything they please. The core of the movie turns out not to be the Bowie figure but the journalist, Arthur Stuart, who was a witness to the events he's now reconstructing. Bale is such an expressive performer that Stuart's remembrance of things past attains a Proustian intensity. To him, Slade was a sexual messiah. I've never seen a more vivid distillation of rock's allure than the scene in which he reverently opens the new Brian Slade album--its centerfold image is a lithe, naked, green-tinged Maxwell Demon--slips the vinyl out of its paper jacket and, after gingerly setting the LP on the turntable, props a chair under the doorknob to keep the uncomprehending world at bay. But if Haynes wants Velvet Goldmine to be an anthem to the principles Bowie once embodied--the embrace of artifice and the smashing of conventional sexual roles--he also wants to portray the rocker as a hollow opportunist who abandoned glam and bisexuality for the life of a corporate superstar, throwing in his lot with the forces of repression. That's a lot to cover. An actor of stature might have bridged these two impulses, but the beautiful, brazenly slim-hipped Rhys-Meyers doesn't make his lines sound as if he's thinking them up on the spot, and Slade's self-destructive passion for Curt Wild (Ewan McGregor), the film's fuzzy, sweet Iggy Pop figure, seems less an emotional imperative than a thematic one. A case can be made that Velvet Goldmine isn't fully filled in, and that Haynes, who has never shaken off his background as a semiotics major, has made a movie that's all signifiers. I sometimes found myself wishing he would let the picture catch its breath, that the performers would stop coming at me in stroboscopic flashes. But then I'd be swept up in the sinuous motion of his filmmaking, in the elation of watching point of view passed like a baton from hand to hand, in the liberating force of his language and soundtrack. Velvet Goldmine might seem like a collection of baubles, but those baubles are strung. Is Brad Pitt the worst actor on earth? The case could be made, and Meet Joe Black could serve as Exhibit A. Pitt plays two roles in this seven course schlockfest. He's (briefly) a slick but wholesome yuppie and then (interminably) Death, who takes over the young man's body when he's thumped by a couple of cars in the movie's most promising moment. Bleached so blond that he looks like an irradiated android, Pitt expels all expression from his face and all tone from his voice. He speaks very, very slowly. The stunt half-works, at least until he's supposed to undergo an inner transformation and acquire human emotions--whereupon his face remains just as blank. Pitt's conception of the role is an idée fixe by someone who doesn't appear to have an idée in his head. Martin Brest, the director, is known for shooting a ton of footage and then "finding" his films in the editing room. What do you suppose he "found" when he scrutinized these miles of celluloid with Pitt doing nothing and taking his sweet time doing it? The first adaptation of this story (originally a play) was the 1934 Death Takes a Holiday , which came in at a perky 78 minutes. A conceit this fragile needs to whiz along to keep our disbelief in suspension, but Meet Joe Black grinds on for three hours (longer than either Beloved or Saving Private Ryan ), and Pitt acts as if he has leased the screen by the year. Anthony Hopkins plays the zillionaire communications baron whom Death enlists in the hope of understanding the human condition--an odd choice for a tour guide, since most people's condition doesn't involve personal helicopters, sprawling mansions on Long Island Sound, or Manhattan apartments that sport Olympic-size swimming pools. Four screenwriters, among them the great Bo Goldman ( Melvin and Howard , 1980; Shoot the Moon , 1982), labored on this moldy script, which features characters who ask questions that begin "Am I to understand that ...?" and a corporate villain who directs another character to "wake up and smell the thorns." It apparently never occurred to even one of these overpaid scribes to eliminate Hopkins' rueful realization that he'd "never write the great American novel"--no kidding, given his flagrantly Welsh accent. Actually, Hopkins gives this humanistic magnate considerable weight, so that whether or not Death takes him before he can stop to smell the roses and make amends to his neglected children becomes a matter of some suspense. The rest of the cast works with equal fortitude, especially Jeffrey Tambor (Hank "Hey now!" Kingsley on The Larry Sanders Show ) as Hopkins' milksop son-in-law and Marcia Gay Harden as his party planning, perpetually wilting elder daughter. As the younger daughter, the dark eyed, spaghetti thin Claire Forlani has to carry the picture's bathos on her exquisite shoulders. Her tremulous thoroughbred act wears thin, but it's hardly her fault: She has to emote like mad opposite a black pit of death--or is that the Black Death of Pitt?	C. it is overall enjoyable to watch
Which word least describes Charlie? A. proud B. sick C. experienced D. regretful	Spacemen Die at Home By EDWARD W. LUDWIG Illustrated by THORNE [Transcriber's Note: This etext was produced from Galaxy Science Fiction October 1951. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] One man's retreat is another's prison ... and it takes a heap of flying to make a hulk a home! Forty days of heaven and forty nights of hell. That's the way it's been, Laura. But how can I make you understand? How can I tell you what it's like to be young and a man and to dream of reaching the stars? And yet, at the same time, to be filled with a terrible, gnawing fear—a fear locked in my mind during the day and bursting out like an evil jack-in-the-box at night. I must tell you, Laura. Perhaps if I start at the beginning, the very beginning.... It was the Big Day. All the examinations, the physicals and psychos, were over. The Academy, with its great halls and classrooms and laboratories, lay hollow and silent, an exhausted thing at sleep after spawning its first-born. For it was June in this year of 1995, and we were the graduating class of the U. S. Academy of Interplanetary Flight. The first graduating class, Laura. That's why it was so important, because we were the first . We sat on a little platform, twenty-five of us. Below us was a beach of faces, most of them strange, shining like pebbles in the warm New Mexican sunlight. They were the faces of mothers and fathers and grandparents and kid brothers and sisters—the people who a short time ago had been only scrawled names on letters from home or words spoken wistfully at Christmas. They were the memory-people who, to me, had never really existed. But today they had become real, and they were here and looking at us with pride in their eyes. A voice was speaking, deep, sure, resonant. "... these boys have worked hard for six years, and now they're going to do a lot of big things. They're going to bring us the metals and minerals that we desperately need. They're going to find new land for our colonists, good rich land that will bear food and be a home for our children. And perhaps most important of all, they'll make other men think of the stars and look up at them and feel humility—for mankind needs humility." The speaker was Robert Chandler, who'd brought the first rocket down on Mars just five years ago, who'd established the first colony there, and who had just returned from his second hop to Venus. Instead of listening to his words, I was staring at his broad shoulders and his dark, crew-cut hair and his white uniform which was silk-smooth and skin-tight. I was worshiping him and hating him at the same time, for I was thinking: He's already reached Mars and Venus. Let him leave Jupiter and the others alone! Let us be the first to land somewhere! Let us be the first! Mickey Cameron, sitting next to me, dug an elbow into my ribs. "I don't see 'em, Ben," he whispered. "Where do you suppose they are?" I blinked. "Who?" "My folks." That was something I didn't have to worry about. My parents had died in a strato-jet crash when I was four, so I hadn't needed many of those "You are cordially invited" cards. Just one, which I'd sent to Charlie Taggart. Stardust Charlie, we called him, although I never knew why. He was a veteran of Everson's first trip to the Moon nearly twenty-five years ago, and he was still at it. He was Chief Jetman now on the Lunar Lady , a commercial ore ship on a shuttle between Luna City and White Sands. I remembered how, as a kid, I'd pestered him in the Long Island Spaceport, tagging after him like a puppy, and how he'd grown to like me until he became father, mother, and buddy all in one to me. And I remembered, too, how his recommendation had finally made me a cadet. My gaze wandered over the faces, but I couldn't find Charlie's. It wasn't surprising. The Lunar Lady was in White Sands now, but liberties, as Charlie said, were as scarce as water on Mars. It doesn't matter , I told myself. Then Mickey stiffened. "I see 'em, Ben! There in the fifth row!" Usually Mickey was the same whether in a furnace-hot engine room or a garden party, smiling, accepting whatever the world offered. But now a tenseness and an excitement had gripped even him. I was grateful that he was beside me; we'd been a good team during those final months at the Academy and I knew we'd be a good team in space. The Universe was mighty big, but with two of us to face it together, it would be only half as big. And then it seemed that all the proud faces were looking at us as if we were gods. A shiver went through my body. Though it was daytime, I saw the stars in my mind's vision, the great shining balls of silver, each like a voice crying out and pleading to be explored, to be touched by the sons of Earth. They expect a lot from us. They expect us to make a new kind of civilization and a better place out of Earth. They expect all this and a hell of a lot more. They think there's nothing we can't do. I felt very small and very humble. I was scared. Damned scared. At last it was over, and the proud faces descended upon us in a huge, babbling wave. Then I saw him. Good old Stardust Charlie. His wizened little body was shuffling down an aisle, his eyes shining like a child's. He'd been sandwiched, evidently, in one of the rear rows. But he wasn't the Charlie I'd seen a year ago. He'd become gaunt and old, and he walked with an unnatural stiffness. He looked so old that it was hard to believe he'd once been young. He scratched his mop of steel-gray hair and grinned. "You made it, boy," he chortled, "and by Jupiter, we'll celebrate tonight. Yes, siree, I got twenty-four hours, and we'll celebrate as good spacemen should!" Then Mickey strode up to us. He was his normal, boyish self again, walking lightly, his blond, curly-haired skull swaying as if in rhythm with some silent melody. And you, Laura, were with him. "Meet the Brat," he said. "My sister Laura." I stared almost rudely. You were like a doll lost in the immensity of your fluffy pink dress. Your hair was long and transformed into a golden froth where sunlight touched it. But your eyes were the eyes of a woman, glowing like dark stars and reflecting a softness, a gentleness that I'd never seen in eyes before. "I'm happy to meet you, Ben," you said. "I've heard of no one else for the past year." A tide of heat crept up from my collar. I stuttered through an introduction of Charlie. You and Mickey looked strangely at Charlie, and I realized that old Stardust was not a cadet's notion of the ideal spaceman. Charlie scorned the skin-tight uniforms of the government service and wore a shiny black suit that was a relic of Everson's early-day Moon Patrol. His tie was clumsily knotted, and a button on his coat was missing. And the left side of his face was streaked with dark scar tissue, the result of an atomic blowup on one of the old Moon ships. I was so accustomed to the scars, I was seldom aware of them; but others, I knew, would find them ugly. You were kind. You shook hands and said, softly: "It's a privilege to meet you, Charlie. Just think—one of Everson's men, one of the first to reach the Moon!" Charlie gulped helplessly, and Mickey said: "Still going to spend the weekend with us, aren't you, Ben?" I shook my head. "Charlie has only twenty-four hours liberty. We're planning to see the town tonight." "Why don't you both come with us?" you asked. "Our folks have their own plane, so it would be no problem. And we've got a big guest room. Charlie, wouldn't you like a home-cooked meal before going back to the Moon?" Charlie's answer was obscured by a sudden burst of coughing. I knew that he'd infinitely prefer to spend his liberty sampling Martian fizzes and Plutonian zombies. But this night seemed too sacred for Charlie's kind of celebration. "We'd really like to come," I said. On our way to the 'copter parking field, Dean Dawson passed us. He was a tall, willowy man, spectacled, looking the way an academy professor should look. "Ben," he called, "don't forget that offer. Remember you've got two months to decide." "No, thanks," I answered. "Better not count on me." A moment later Mickey said, frowning, "What was he talking about, Ben? Did he make you an offer?" I laughed. "He offered me a job here at the Academy teaching astrogation. What a life that would be! Imagine standing in a classroom for forty years when I've got the chance to—" I hesitated, and you supplied the right words: "When you've got the chance to be the first to reach a new planet. That's what most of you want, isn't it? That's what Mickey used to want." I looked at you as if you were Everson himself, because you seemed to understand the hunger that could lie in a man's heart. Then your last words came back and jabbed me: "That's what Mickey used to want." " Used to want?" I asked. "What do you mean?" You bit your lip, not answering. "What did she mean, Mickey?" Mickey looked down at his feet. "I didn't want to tell you yet, Ben. We've been together a long time, planning to be on a rocket. But—" "Yes?" "Well, what does it add up to? You become a spaceman and wear a pretty uniform. You wade through the sands of Mars and the dust of Venus. If you're lucky, you're good for five, maybe ten years. Then one thing or another gets you. They don't insure rocketmen, you know." My stomach was full of churning, biting ice. "What are you trying to say, Mickey?" "I've thought about it a long time. They want me for Cargo Supervisor of White Sands Port." He raised his hand to stop me. "I know. It's not so exciting. I'll just live a lot longer. I'm sorry, Ben." I couldn't answer. It was as if someone had whacked the back of my knees with the blast of a jet. "It doesn't change anything, Ben—right now, I mean. We can still have a good weekend." Charlie was muttering under his breath, smoldering like a bomb about to reach critical mass. I shook my head dazedly at him as we got to the 'copter. "Sure," I said to Mickey, "we can still have a good weekend." I liked your folks, Laura. There was no star-hunger in them, of course. They were simple and solid and settled, like green growing things, deep-rooted, belonging to Earth. They were content with a home that was cool on this warm summer night, with a 'copter and a tri-dimensional video, and a handsome automatic home that needed no servants or housework. Stardust Charlie was as comfortable as a Martian sand-monkey in a shower, but he tried courageously to be himself. At the dinner table he stared glassily at nothing and grated, "Only hit Mars once, but I'll never forget the kid who called himself a medic. Skipper started coughing, kept it up for three days. Whoopin' cough, the medic says, not knowin' the air had chemicals that turned to acid in your lungs. I'd never been to Mars before, but I knew better'n that. Hell, I says, that ain't whoopin' cough, that's lung-rot." That was when your father said he wasn't so hungry after all. Afterward, you and I walked onto the terrace, into the moonlit night, to watch for crimson-tailed continental rockets that occasionally streaked up from White Sands. We gazed for a few seconds up into the dark sky, and then you said: "Charlie is funny, isn't he? He's nice and I'm glad he's here, but he's sort of funny." "He's an old-time spaceman. You didn't need much education in those days, just a lot of brawn and a quick mind. It took guts to be a spaceman then." "But he wasn't always a spaceman. Didn't he ever have a family?" I smiled and shook my head. "If he had, he never mentioned it. Charlie doesn't like to be sentimental, at least not on the outside. As far as I know, his life began when he took off for the Moon with Everson." You stared at me strangely, almost in a sacred kind of way. I knew suddenly that you liked me, and my heart began to beat faster. There was silence. You were lovely, your soft hair like strands of gold, and there were flecks of silver in your dark eyes. Somehow I was afraid. I had the feeling that I shouldn't have come here. You kept looking at me until I had to ask: "What are you thinking, Laura?" You laughed, but it was a sad, fearful laugh. "No, I shouldn't be thinking it. You'd hate me if I told you, and I wouldn't want that." "I could never hate you." "It—it's about the stars," you said very softly. "I understand why you want to go to them. Mickey and I used to dream about them when we were kids. Of course I was a girl, so it was just a game to me. But once I dreamed of going to England. Oh, it was going to be so wonderful. I lived for months, just thinking about it. "One summer we went. I had fun. I saw the old buildings and castles, and the spaceports and the Channel Tube. But after it was over, I realized England wasn't so different from America. Places seem exciting before you get to them, and afterward they're not really." I frowned. "And you mean it might be the same with the stars? You think maybe I haven't grown up yet?" Anxiety darkened your features. "No, it'd be good to be a spaceman, to see the strange places and make history. But is it worth it? Is it worth the things you'd have to give up?" I didn't understand at first, and I wanted to ask, "Give up what ?" Then I looked at you and the promise in your eyes, and I knew. All through the years I'd been walking down a single, narrow path. Government boarding school, the Academy, my eyes always upward and on the stars. Now I'd stumbled into a cross-roads, beholding a strange new path that I'd never noticed before. You can go into space , I thought, and try to do as much living in ten years as normal men do in fifty. You can be like Everson, who died in a Moon crash at the age of 36, or like a thousand others who lie buried in Martian sand and Venusian dust. Or, if you're lucky, like Charlie—a kind of human meteor streaking through space, eternally alone, never finding a home. Or there's the other path. To stay on this little prison of an Earth in cool, comfortable houses. To be one of the solid, rooted people with a wife and kids. To be one of the people who live long enough to grow old, who awake to the song of birds instead of rocket grumblings, who fill their lungs with the clean rich air of Earth instead of poisonous dust. "I'm sorry," you said. "I didn't mean to make you sad, Ben." "It's all right," I said, clenching my fists. "You made sense—a lot of sense." The next morning Charlie said good-bye in our room. He rubbed his scarred face nervously as he cleared his throat with a series of thin, tight coughs. Then he pointed to a brown, faded tin box lying on the bed. "I'm leavin' that for you. It's full of old stuff, souvenirs mostly. Thought maybe you'd like to have 'em." I scowled, not understanding. "Why, Charlie? What for?" He shrugged as if afraid he might be accused of sentimentality. "Oh, it's just that I've been dodgin' meteors now for twenty-five years. That's a long time, boy. Ain't one spaceman in a thousand that lucky. Some of these days, I won't be so lucky." I tried to laugh. "You're good for another twenty-five years, Charlie." He shook his head stiffly, staring at nothing. "Maybe. Anyway, I'm gonna get off the Shuttle this time, make one more trip to Mars. Tell you what. There's a little stone cafe on Mars, the Space Rat , just off Chandler Field on the Grand Canal. When you get to Mars, take a look inside. I'll probably be there." He coughed again, a deep, rasping cough that filled his eyes with tears. "Not used to this Earth air," he muttered. "What I need's some Martian climate." Suddenly that cough frightened me. It didn't seem normal. I wondered, too, about his stiff movements and glassy stare. It was as if he were drugged. I shook the thought away. If Charlie was sick, he wouldn't talk about going to Mars. The medics wouldn't let him go even as far as Luna. We watched him leave, you and Mickey and I. "When will you be back?" you asked. Charlie's hard face contorted itself into a gargoylish grin. "Maybe a couple of months, maybe a couple of years. You know spacemen." Then he waved and strode away, a strange, gray, withered gnome of a man. I wanted him to say something, to tell me the secret that would kill the doubt worming through my brain. But he rounded a corner, still grinning and waving, and then he was gone. That afternoon Mickey showed me his room. It was more like a boy's room than a spaceman's. In it were all the little things that kids treasure—pennants, models of Everson's two ships, a tennis trophy, books, a home-made video. I began to realize how important a room like this could be to a boy. I could imagine, too, the happiness that parents felt as they watched their children grow to adulthood. I'd missed something. My folks were shadow-people, my impressions of them drawn half from ancient photos, half from imagination. For me, it had been a cold, automatic kind of life, the life of dormitories and routines and rules. I'd been so blinded by the brilliancy of my dreams, I hadn't realized I was different. My folks were killed in a rocket crash. If it weren't for rockets, I'd have lived the kind of life a kid should live. Mickey noticed my frown. "What's the matter, Ben? Still sore? I feel like a heel, but I'm just not like you and Charlie, I guess. I—" "No, I understand, Mickey. I'm not sore, really." "Listen, then. You haven't accepted any offer yet, have you?" "No. I got a couple of possibilities. Could get a berth on the Odyssey , the new ship being finished at Los Angeles. They want me, too, for the Moon Patrol, but that's old stuff, not much better than teaching. I want to be in deep space." "Well, how about staying with us till you decide? Might as well enjoy Earth life while you can. Okay?" I felt like running from the house, to forget that it existed. I wanted someone to tell me one of the old stories about space, a tale of courage that would put fuel on dying dreams. But I wanted, also, to be with you, Laura, to see your smile and the flecks of silver in your eyes and the way your nose turned upward ever so slightly when you laughed. You see, I loved you already, almost as much as I loved the stars. And I said, slowly, my voice sounding unfamiliar and far away, "Sure, I'll stay, Mickey. Sure." Forty days of joy, forty nights of fear and indecision. We did all the little things, like watching the rockets land at White Sands and flying down to the Gulf to swim in cool waters. You tried, unsuccessfully, to teach me to dance, and we talked about Everson and Charlie and the Moon and the stars. You felt you had to give the stars all the beauty and promise of a child's dream, because you knew that was what I wanted. One morning I thought, Why must I make a choice? Why can't I have both you and the stars? Would that be asking too much? All day the thought lay in my mind like fire. That evening I asked you to marry me. I said it very simply: "Laura, I want you to be my wife." You looked up at Venus, and you were silent for a long while, your face flushed. Then you murmured, "I—I want to marry you, Ben, but are you asking me to marry a spaceman or a teacher?" "Can't a spaceman marry, too?" "Yes, a spaceman can marry, but what would it be like? Don't you see, Ben? You'd be like Charlie. Gone for maybe two months, maybe two years. Then you'd have a twenty-four hour liberty—and I'd have what?" Somehow I'd expected words like these, but still they hurt. "I wouldn't have to be a spaceman forever. I could try it for a couple of years, then teach." "Would you, Ben? Would you be satisfied with just seeing Mars? Wouldn't you want to go on to Jupiter and Saturn and Uranus and on and on?" Your voice was choked, and even in the semi-darkness I saw tears glittering in your eyes. "Do you think I'd dare have children, Ben? Mickey told me what happened on the Cyclops . There was a leak in the atomic engines. The ship was flooded with radiation—just for a second. It didn't seem serious. The men had no burns. But a year later the captain had a child. And it was—" "I know, Laura. Don't say it." You had to finish. "It was a monster." That night I lay awake, the fears and doubts too frantic to let me sleep. You've got to decide now , I told myself. You can't stay here. You've got to make a choice. The teaching job was still open. The spot on the Odyssey was still open—and the big ship, it was rumored, was equipped to make it all the way to Pluto. You can take Dean Dawson's job and stay with Laura and have kids and a home and live to see what happens in this world sixty years from now. Or you can see what's on the other side of the mountain. You can be a line in a history book. I cursed. I knew what Charlie would say. He'd say, "Get the hell out of there, boy. Don't let a fool woman make a sucker out of you. Get out there on the Odyssey where you belong. We got a date on Mars, remember? At the Space Rat , just off Chandler Field on the Grand Canal." That's what he'd say. And yet I wanted you, Laura. I wanted to be with you, always. "Oh God," I moaned, "what shall I do?" Next morning the door chimes pealed, and you went to the door and brought back the audiogram. It was addressed to me; I wondered who could be sending me a message. I pressed the stud on the little gray cylinder, and a rasping, automatic voice droned: "Luna City, Luna, July 27, 1995. Regret to inform you of death of Charles Taggart, Chief Jetman...." Then there was a Latin name which was more polite than the word "lung-rot" and the metallic phrase, "This message brought to you by courtesy of United Nations Earth-Luna Communication Corps." I stood staring at the cylinder. Charles Taggart was dead. Charles Taggart was Charlie. Stardust Charlie. My heart thudded crazily against my chest. It couldn't be! Not Charlie! The audiogram had lied! I pressed the stud again. "... regret to inform you of death of Charles ..." I hurled the cylinder at the wall. It thudded, fell, rolled. The broken voice droned on. You ran to it, shut it off. "I'm sorry, Ben, so terribly—" Without answering, I walked into my room. I knew it was true now. I remembered Charlie's coughing, his gaunt features, his drugged gaze. The metallic words had told the truth. I sat for a long time on my bed, crying inside, but staring dry-eyed at Charlie's faded tin box. Then, finally, I fingered his meager possessions—a few wrinkled photos, some letters, a small black statue of a forgotten Martian god, a gold service medal from the Moon Patrol. This was what remained of Charlie after twenty-five years in space. It was a bitter bargain. A statue instead of a wife, yellowed letters instead of children, a medal instead of a home. It'd be a great future , I thought. You'd dream of sitting in a dingy stone dive on the Grand Canal with sand-wasps buzzing around smoky, stinking candles. A bottle of luchu juice and a couple of Martian girls with dirty feet for company. And a sudden cough that would be the first sign of lung-rot. To hell with it! I walked into your living room and called Dean Dawson on the visiphone. I accepted that job teaching. And now, Laura, it's nearly midnight. You're in your room, sleeping, and the house is silent. It's hard to tell you, to make you understand, and that is why I am writing this. I looked through Charlie's box again, more carefully this time, reading the old letters and studying the photographs. I believe now that Charlie sensed my indecision, that he left these things so that they could tell me what he could not express in words. And among the things, Laura, I found a ring. A wedding ring. In that past he never talked about, there was a woman—his wife. Charlie was young once, his eyes full of dreams, and he faced the same decision that I am facing. Two paths were before him, but he tried to travel both. He later learned what we already know—that there can be no compromise. And you know, too, which path he finally chose. Do you know why he had to drug himself to watch me graduate? So he could look at me, knowing that I would see the worlds he could never live to see. Charlie didn't leave just a few trinkets behind him. He left himself, Laura, for he showed me that a boy's dream can also be a man's dream. He made his last trip to Luna when he knew he was going to die. Heaven knows how he escaped a checkup. Maybe the captain understood and was kind—but that doesn't matter now. Do you know why he wanted to reach Mars? Do you know why he didn't want to die in the clean, cool air of Earth? It was because he wanted to die nearer home. His home, Laura, was the Universe, where the ship was his house, the crew his father, mother, brothers, the planets his children. You say that the beauty of the other side of the mountain vanishes after you reach it. But how can one ever be sure until the journey is made? Could I or Charlie or the thousand before us bear to look upon a star and think, I might have gone there; I could have been the first ? We said, too, that the life of a spaceman is lonely. Yet how could one be lonely when men like Charlie roam the spaceways? Charlie wanted me to himself that night after graduation. He wanted us to celebrate as spacemen should, for he knew that this would be his last night on Earth. It might have seemed an ugly kind of celebration to you, but he wanted it with all his heart, and we robbed him of it. Because of these things, Laura, I will be gone in the morning. Explain the best you can to Mickey and to your parents and Dean Dawson. Right now I've got a date that I'm going to keep—at a dingy stone cafe on Mars, the Space Rat , just off Chandler Field on the Grand Canal. Stardust Charlie will be there; he'll go with me in memory to whatever part of the Galaxy I may live to reach. And so will you, Laura. I have two wedding rings with me—his wife's ring and yours.	D. regretful
What SMT models did they look at?	### Introduction The availability of cross-language parallel corpora is one of the basis of current Statistical and Neural Machine Translation systems (e.g. SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, specially NMT ones, is not a trivial task, since it usually demands human curating and correct alignment. In light of that, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Processing (NLP), enabling the development of accurate MT solutions. Many parallel corpora are already available, some with bilingual alignment, while others are multilingually aligned, with 3 or more languages, such as Europarl BIBREF0 , from the European Parliament, JRC-Acquis BIBREF1 , from the European Commission, OpenSubtitles BIBREF2 , from movies subtitles. The extraction of parallel sentences from scientific writing can be a valuable language resource for MT and other NLP tasks. The development of parallel corpora from scientific texts has been researched by several authors, aiming at translation of biomedical articles BIBREF3 , BIBREF4 , or named entity recognition of biomedical concepts BIBREF5 . Regarding Portuguese/English and English/Spanish language pairs, the FAPESP corpus BIBREF6 , from the Brazilian magazine revista pesquisa FAPESP, contains more than 150,000 aligned sentences per language pair, constituting an important language resource. In Brazil, the governmental body responsible for overseeing post-graduate programs across the country, called CAPES, tracks every enrolled student and scientific production. In addition, CAPES maintains a freely accessible database of theses and dissertations produced by the graduate students (i.e. Theses and Dissertations Catalog - TDC) since 1987, with abstracts available since 2013. Under recent governmental efforts in data sharing, CAPES made TDC available in CSV format, making it easily accessible for data mining tasks. Recent data files, from 2013 to 2016, contain valuable information for NLP purposes, such as abstracts in Portuguese and English, scientific categories, and keywords. Thus, TDC can be an important source of parallel Portuguese/English scientific abstracts. In this work, we developed a sentence aligned parallel corpus gathered from CAPES TDC comprised of abstracts in English and Portuguese spanning the years from 2013 to 2016. In addition, we included metadata regarding the respective theses and dissertations. ### Material and Methods In this section, we detail the information retrieved from CAPES website, the filtering process, the sentence alignment, and the evaluation experiments. An overview of the steps employed in this article is shown in Figure FIGREF1 . ### Document retrieval and parsing The TDC datasets are available in the CAPES open data website divided by years, from 2013 to 2016 in CSV and XLSX formats. We downloaded all CSV files from the respective website and loaded them into an SQL database for better manipulation. The database was then filtered to remove documents without both Portuguese and English abstracts, and additional metadata selected. After the initial filtering, the resulting documents were processed for language checking to make sure that there was no misplacing of English abstracts in the Portuguese field, or the other way around, removing the documents that presented such inconsistency. We also performed a case folding to lower case letters, since the TDC datasets present all fields with uppercase letters. In addition, we also removed newline/carriage return characters (i.e \n and \r), as they would interfere with the sentence alignment tool. ### Sentence alignment For sentence alignment, we used the LF aligner tool, a wrapper around the Hunalign tool BIBREF7 , which provides an easy to use and complete solution for sentence alignment, including pre-loaded dictionaries for several languages. Hunalign uses Gale-Church sentence-length information to first automatically build a dictionary based on this alignment. Once the dictionary is built, the algorithm realigns the input text in a second iteration, this time combining sentence-length information with the dictionary. When a dictionary is supplied to the algorithm, the first step is skipped. A drawback of Hunalign is that it is not designed to handle large corpora (above 10 thousand sentences), causing large memory consumption. In these cases, the algorithm cuts the large corpus in smaller manageable chunks, which may affect dictionary building. The parallel abstracts were supplied to the aligner, which performed sentence segmentation followed by sentence alignment. A small modification in the sentence segmentation algorithm was performed to handle the fact that all words are in lowercase letters, which originally prevented segmentation. After sentence alignment, the following post-processing steps were performed: (i) removal of all non-aligned sentences; (ii) removal of all sentences with fewer than three characters, since they are likely to be noise. ### Machine translation evaluation To evaluate the usefulness of our corpus for SMT purposes, we used it to train an automatic translator with Moses BIBREF8 . We also trained an NMT model using the OpenNMT system BIBREF9 , and used the Google Translate Toolkit to produce state-of-the-art comparison results. The produced translations were evaluated according to the BLEU score BIBREF10 . ### Manual evaluation Although the Hunalign tool usually presents a good alignment between sentences, we also conducted a manual validation to evaluate the quality of the aligned sentences. We randomly selected 400 pairs of sentences. If the pair was fully aligned, we marked it as "correct"; if the pair was incompletely aligned, due to segmentation errors, for instance, we marked it as "partial"; otherwise, when the pair was incorrectly aligned, we marked it as "no alignment". ### Results and Discussion In this section, we present the corpus' statistics and quality evaluation regarding SMT and NMT systems, as well as the manual evaluation of sentence alignment. ### Corpus statistics Table TABREF12 shows the statistics (i.e. number of documents and sentences) for the aligned corpus according to the 9 main knowledge areas defined by CAPES. The dataset is available in TMX format BIBREF11 , since it is the standard format for translation memories. We also made available the aligned corpus in an SQLite database in order to facilitate future stratification according to knowledge area, for instance. In this database, we included the following metadata information: year, university, title in Portuguese, type of document (i.e. theses or dissertation), keywords in both languages, knowledge areas and subareas according to CAPES, and URL for the full-text PDF in Portuguese. An excerpt of the corpus is shown in Table TABREF13 ### Translation experiments Prior to the MT experiments, sentences were randomly split in three disjoint datasets: training, development, and test. Approximately 13,000 sentences were allocated in the development and test sets, while the remaining was used for training. For the SMT experiment, we followed the instructions of Moses baseline system. For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs. During translation, the option to replace UNK words by the word in the input language was used, since this is also the default in Moses. Table TABREF17 presents the BLEU scores for both translation directions with English and Portuguese on the development and test partitions for Moses and OpenNMT models. We also included the scores for Google Translate (GT) as a benchmark of a state-of-the-art system which is widely used. NMT model achieved better performance than the SMT one for EN INLINEFORM0 PT direction, with approximately 2.17 percentage points (pp) higher, while presenting almost the same score for PT INLINEFORM1 EN. When comparing our models to GT, both of them presented better BLEU scores, specially for the EN INLINEFORM2 PT direction, with values ranging from 1.27 pp to 4.30 pp higher than GT. We highlight that these results may be due to two main factors: corpus size, and domain. Our corpus is fairly large for both SMT and NMT approaches, comprised of almost 1.3M sentences, which enables the development of robust models. Regarding domain, GT is a generic tool not trained for a specific domain, thus it may produce lower results than a domain specific model such as ours. Scientific writing usually has a strict writing style, with less variation than novels or speeches, for instance, favoring the development of tailored MT systems. Below, we demonstrate some sentences translated by Moses and OpenNMT compared to the suggested human translation. One can notice that in fact NMT model tend to produce more fluent results, specially regarding verbal regency. Human translation: this paper presents a study of efficiency and power management in a packaging industry and plastic films. OpenNMT: this work presents a study of efficiency and electricity management in a packaging industry and plastic films. Moses: in this work presents a study of efficiency and power management in a packaging industry and plastic films. GT: this paper presents a study of the efficiency and management of electric power in a packaging and plastic film industry. Human translation: this fact corroborates the difficulty in modeling human behavior. OpenNMT: this fact corroborates the difficulty in modeling human behavior. Moses: this fact corroborated the difficulty in model the human behavior. GT: this fact corroborates the difficulty in modeling human behavior. ### Sentence alignment quality We manually validated the alignment quality for 400 sentences randomly selected from the parsed corpus and assigned quality labels according Section SECREF9 . From all the evaluated sentences, 82.30% were correctly aligned, while 13.33% were partially aligned, and 4.35% presented no alignment. The small percentage of no alignment is probably due to the use of Hunalign tool with the provided EN/PT dictionary. Regarding the partial alignment, most of the problems are result of segmentation issues previous to the alignment, which wrongly split the sentences. Since all words were case folded to lowercase letters, the segmenter lost an important source of information for the correct segmentation, generating malformed sentences. Some examples of partial alignment errors are shown in Table TABREF19 , where most senteces were truncated in the wrong part. ### Conclusion and future work We developed a parallel corpus of theses and dissertations abstracts in Portuguese and English. Our corpus is based on the CAPES TDC dataset, which contains information regarding all theses and dissertations presented in Brazil from 2013 to 2016, including abstracts and other metadata. Our corpus was evaluated through SMT and NMT experiments with Moses and OpenNMT systems, presenting superior performance regarding BLEU score than Google Translate. The NMT model also presented superior results than the SMT one for the EN INLINEFORM0 PT translation direction. We also manually evaluated sentences regarding alignment quality, with average 82.30% of sentences correctly aligned. For future work, we foresee the use of the presented corpus in mono and cross-language text mining tasks, such as text classification and clustering. As we included several metadata, these tasks can be facilitated. Other machine translation approaches can also be tested, including the concatenation of this corpus with other multi-domain ones. Fig. 1. Steps employed in the development of the parallel corpora. Table 1. Corpus statistics according to knowledge area. Table 2. Excerpt of the corpus with document ID. Table 3. BLEU scores for the translations using Moses, OpenNMT, and Google Translate. Bold numbers indicate the best results in the test set. Table 4. Examples of partial alignment errors.	automatic translator with Moses
According to the reviewer, Carolyn's preference for "Muzak" and "nutritious yet savory" food most likely symbolize: A. The characters' desperate desire to be perceived as ordinary B. The deterioration of the American nuclear family C. The tendency for people to be consumed by what their values D. The dangers of standing out in a society that demands conformity	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. The characters' desperate desire to be perceived as ordinary
How many papers are used in experiment?	### Introduction The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP) / Computational Linguistics (CL). It includes papers published in the family of ACL conferences as well as in other NLP conferences such as LREC and RANLP. AA is the largest single source of scientific literature on NLP. This project, which we call NLP Scholar, examines the literature as a whole to identify broad trends in productivity, focus, and impact. We will present the analyses in a sequence of questions and answers. The questions range from fairly mundane to oh-that-will-be-good-to-know. Our broader goal here is simply to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations. We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender). Target Audience: The analyses presented here are likely to be of interest to any NLP researcher. This might be particularly the case for those that are new to the field and wish to get a broad overview of the NLP publishing landscape. On the other hand, even seasoned NLP'ers have likely wondered about the questions raised here and might be interested in the empirical evidence. Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). Thus, all subsequent papers and citations are not included in the analysis. A fresh data collection is planned for January 2020. Interactive Visualizations: The visualizations we are developing for this work (using Tableau) are interactive—so one can hover, click to select and filter, move sliders, etc. Since this work is high in the number of visualizations, the main visualizations are presented as figures in the paper and some sets of visualizations are pointed to online. The interactive visualizations and data will be made available through the first author's website after peer review. Related Work: This work builds on past research, including that on Google Scholar BIBREF0, BIBREF1, BIBREF2, BIBREF3, on the analysis of NLP papers BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, on citation intent BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, and on measuring scholarly impact BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21. Caveats and Ethical Considerations: We list several caveats and limitations throughout the paper. A compilation of these is also available online in the About NLP Scholar page. The analyses presented here are also available as a series of blog posts. ### Size Q. How big is the ACL Anthology (AA)? How is it changing with time? A. As of June 2019, AA had $\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. Figure FIGREF6 shows a graph of the number of papers published in each of the years from 1965 to 2018. Discussion: Observe that there was a spurt in the 1990s, but things really took off since the year 2000, and the growth continues. Also, note that the number of publications is considerably higher in alternate years. This is due to biennial conferences. Since 1998 the largest of such conferences has been LREC (In 2018 alone LREC had over 700 main conferences papers and additional papers from its 29 workshops). COLING, another biennial conference (also occurring in the even years) has about 45% of the number of main conference papers as LREC. Q. How many people publish in the ACL Anthology (NLP conferences)? A. Figure FIGREF7 shows a graph of the number of authors (of AA papers) over the years: Discussion: It is a good sign for the field to have a growing number of people join its ranks as researchers. A further interesting question would be: Q. How many people are actively publishing in NLP? A. It is hard to know the exact number, but we can determine the number of people who have published in AA in the last N years. #people who published at least one paper in 2017 and 2018 (2 years): $\sim $12k (11,957 to be precise) #people who published at least one paper 2015 through 2018 (4 years):$\sim $17.5k (17,457 to be precise) Of course, some number of researchers published NLP papers in non-AA venues, and some number are active NLP researchers who may not have published papers in the last few years. Q. How many journal papers exist in the AA? How many main conference papers? How many workshop papers? A. See Figure FIGREF8. Discussion: The number of journal papers is dwarfed by the number of conference and workshop papers. (This is common in computer science. Even though NLP is a broad interdisciplinary field, the influence of computer science practices on NLP is particularly strong.) Shared task and system demo papers are relatively new (introduced in the 2000s), but their numbers are already significant and growing. Creating a separate class for “Top-tier Conference” is somewhat arbitrary, but it helps make certain comparisons more meaningful (for example, when comparing the average number of citations, etc.). For this work, we consider ACL, EMNLP, NAACL, COLING, and EACL as top-tier conferences, but certainly other groupings are also reasonable. Q. How many papers have been published at ACL (main conference papers)? What are the other NLP venues and what is the distribution of the number of papers across various CL/NLP venues? A. # ACL (main conference papers) as of June 2018: 4,839 The same workshop can co-occur with different conferences in different years, so we grouped all workshop papers in their own class. We did the same for tutorials, system demonstration papers (demos), and student research papers. Figure FIGREF9 shows the number of main conference papers for various venues and paper types (workshop papers, demos, etc.). Discussion: Even though LREC is a relatively new conference that occurs only once in two years, it tends to have a high acceptance rate ($\sim $60%), and enjoys substantial participation. Thus, LREC is already the largest single source of NLP conference papers. SemEval, which started as SenseEval in 1998 and occurred once in two or three years, has now morphed into an annual two-day workshop—SemEval. It is the largest single source of NLP shared task papers. ### Demographics (focus of analysis: gender, age, and geographic diversity) NLP, like most other areas of research, suffers from poor demographic diversity. There is very little to low representation from certain nationalities, race, gender, language, income, age, physical abilities, etc. This impacts the breadth of technologies we create, how useful they are, and whether they reach those that need it most. In this section, we analyze three specific attributes among many that deserve attention: gender (specifically, the number of women researchers in NLP), age (more precisely, the number of years of NLP paper publishing experience), and the amount of research in various languages (which loosely correlates with geographic diversity). ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Gender The ACL Anthology does not record demographic information about the paper authors. (Until recently, ACL and other NLP conferences did not record demographic information of the authors.) However, many first names have strong associations with a male or female gender. We will use these names to estimate the percentage of female first authors in NLP. The US Social Security Administration publishes a database of names and genders of newborns. We use the dataset to identify 55,133 first names that are strongly associated with females (probability $\ge $99%) and 29,873 first names that are strongly associated with males (probability $\ge $99%). (As a side, it is interesting to note that there is markedly greater diversity in female names than in male names.) We identified 26,637 of the 44,896 AA papers ($\sim $60%) where the first authors have one of these names and determine the percentage of female first author papers across the years. We will refer to this subset of AA papers as AA. Note the following caveats associated with this analysis: The names dataset used has a lower representation of names from nationalities other than the US. However, there is a large expatriate population living in the US. Chinese names (especially in the romanized form) are not good indicators of gender. Thus the method presented here disregards most Chinese names, and the results of the analysis apply to the group of researchers excluding those with Chinese names. The dataset only records names associated with two genders. The approach presented here is meant to be an approximation in the absence of true gender information. Q. What percent of the AA papers have female first authors (FFA)? How has this percentage changed with time? A. Overall FFA%: 30.3%. Figure FIGREF16 shows how FFA% has changed with time. Common paper title words and FFA% of papers that have those words are shown in the bottom half of the image. Note that the slider at the bottom has been set to 400, i.e., only those title words that occur in 400 or more papers are shown. The legend on the bottom right shows that low FFA scores are shown in shades of blue, whereas relatively higher FFA scores are shown in shades of green. Discussion: Observe that as a community, we are far from obtaining male-female parity in terms of first authors. A further striking (and concerning) observation is that the female first author percentage has not improved since the years 1999 and 2000 when the FFA percentages were highest (32.9% and 32.8%, respectively). In fact there seems to even be a slight downward trend in recent years. The calculations shown above are for the percentage of papers that have female first authors. The percentage of female first authors is about the same ($\sim $31%). On average male authors had a slightly higher average number of publications than female authors. To put these numbers in context, the percentage of female scientists world wide (considering all areas of research) has been estimated to be around 30%. The reported percentages for many computer science sub-fields are much lower. (See Women in Science (2015).) The percentages are much higher for certain other fields such as psychology and linguistics. (See this study for psychology and this study for linguistics.) If we can identify ways to move the needle on the FFA percentage and get it closer to 50% (or more), NLP can be a beacon to many other fields, especially in the sciences. FFA percentages are particularly low for papers that have parsing, neural, and unsupervised in the title. There are some areas within NLP that enjoy a healthier female-male parity in terms of first authors of papers. Figure FIGREF20 shows FFA percentages for papers that have the word discourse in the title. There is burgeoning research on neural NLP in the last few years. Figure FIGREF21 shows FFA percentages for papers that have the word neural in the title. Figure FIGREF22 shows lists of terms with the highest and lowest FFA percentages, respectively, when considering terms that occur in at least 50 paper titles (instead of 400 in the analysis above). Observe that FFA percentages are relatively higher in non-English European language research such as papers on Russian, Portuguese, French, and Italian. FFA percentages are also relatively higher for certain areas of NLP such as work on prosody, readability, discourse, dialogue, paraphrasing, and individual parts of speech such as adjectives and verbs. FFA percentages are particularly low for papers on theoretical aspects of statistical modelling, and areas such as machine translation, parsing, and logic. The full lists of terms and FFA percentages will be made available with the rest of the data. ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Academic Age While the actual age of NLP researchers might be an interesting aspect to explore, we do not have that information. Thus, instead, we can explore a slightly different (and perhaps more useful) attribute: NLP academic age. We can define NLP academic age as the number of years one has been publishing in AA. So if this is the first year one has published in AA, then their NLP academic age is 1. If one published their first AA paper in 2001 and their latest AA paper in 2018, then their academic age is 18. Q. How old are we? That is, what is the average NLP academic age of those who published papers in 2018? How has the average changed over the years? That is, have we been getting older or younger? What percentage of authors that published in 2018 were publishing their first AA paper? A. Average NLP Academic Age of people that published in 2018: 5.41 years Median NLP Academic Age of people that published in 2018: 2 years Percentage of 2018 authors that published their first AA paper in 2018: 44.9% Figure FIGREF24 shows how these numbers have changed over the years. Discussion: Observe that the Average academic age has been steadily increasing over the years until 2016 and 2017, when the trend has shifted and the average academic age has started to decrease. The median age was 1 year for most of the 1965 to 1990 period, 2 years for most of the 1991 to 2006 period, 3 years for most of the 2007 to 2015 period, and back to 2 years since then. The first-time AA author percentage decreased until about 1988, after which it sort of stayed steady at around 48% until 2004 with occasional bursts to $\sim $56%. Since 2005, the first-time author percentage has gone up and down every other year. It seems that the even years (which are also LREC years) have a higher first-time author percentage. Perhaps, this oscillation in first-time authors percentage is related to LREC’s high acceptance rate. Q. What is the distribution of authors in various academic age bins? For example, what percentage of authors that published in 2018 had an academic age of 2, 3, or 4? What percentage had an age between 5 and 9? And so on? A. See Figure FIGREF25. Discussion: Observe that about 65% of the authors that published in 2018 had an academic age of less than 5. This number has steadily reduced since 1965, was in the 60 to 70% range in 1990s, rose to the 70 to 72% range in early 2000s, then declined again until it reached the lowest value ($\sim $60%) in 2010, and has again steadily risen until 2018 (65%). Thus, even though it may sometimes seem at recent conferences that there is a large influx of new people into NLP (and that is true), proportionally speaking, the average NLP academic age is higher (more experienced) than what it has been in much of its history. ### Demographics (focus of analysis: gender, age, and geographic diversity) ::: Location (Languages) Automatic systems with natural language abilities are growing to be increasingly pervasive in our lives. Not only are they sources of mere convenience, but are crucial in making sure large sections of society and the world are not left behind by the information divide. Thus, the limits of what automatic systems can do in a language, limit the world for the speakers of that language. We know that much of the research in NLP is on English or uses English datasets. Many reasons have been proffered, and we will not go into that here. Instead, we will focus on estimating how much research pertains to non-English languages. We will make use of the idea that often when work is done focusing on a non-English language, then the language is mentioned in the title. We collected a list of 122 languages indexed by Wiktionary and looked for the presence of these words in the titles of AA papers. (Of course there are hundreds of other lesser known languages as well, but here we wanted to see the representation of these more prominent languages in NLP literature.) Figure FIGREF27 is a treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green. Discussion: Even though the amount of work done on English is much larger than that on any other language, often the word English does not appear in the title, and this explains why English is not the first (but the second-most) common language name to appear in the titles. This is likely due to the fact that many papers fail to mention the language of study or the language of the datasets used if it is English. There is growing realization in the community that this is not quite right. However, the language of study can be named in other less prominent places than the title, for example the abstract, introduction, or when the datasets are introduced, depending on how central it is to the paper. We can see from the treemap that the most widely spoken Asian and Western European languages enjoy good representation in AA. These include: Chinese, Arabic, Korean, Japanese, and Hindi (Asian) as well as French, German, Swedish, Spanish, Portuguese, and Italian (European). This is followed by the relatively less widely spoken European languages (such as Russian, Polish, Norwegian, Romanian, Dutch, and Czech) and Asian languages (such as Turkish, Thai, and Urdu). Most of the well-represented languages are from the Indo-European language family. Yet, even in the limited landscape of the most common 122 languages, vast swathes are barren with inattention. Notable among these is the extremely low representation of languages from Africa, languages from non-Indo-European language families, and Indigenous languages from around the world. ### Areas of Research Natural Language Processing addresses a wide range of research questions and tasks pertaining to language and computing. It encompasses many areas of research that have seen an ebb and flow of interest over the years. In this section, we examine the terms that have been used in the titles of ACL Anthology (AA) papers. The terms in a title are particularly informative because they are used to clearly and precisely convey what the paper is about. Some journals ask authors to separately include keywords in the paper or in the meta-information, but AA papers are largely devoid of this information. Thus titles are an especially useful source of keywords for papers—keywords that are often indicative of the area of research. Keywords could also be extracted from abstracts and papers; we leave that for future work. Further work is also planned on inferring areas of research using word embeddings, techniques from topic modelling, and clustering. There are clear benefits to performing analyses using that information. However, those approaches can be sensitive to the parameters used. Here, we keep things simple and explore counts of terms in paper titles. Thus the results are easily reproducible and verifiable. Caveat: Even though there is an association between title terms and areas of research, the association can be less strong for some terms and areas. We use the association as one (imperfect) source of information about areas of research. This information may be combined with other sources of information to draw more robust conclusions. Title Terms: The title has a privileged position in a paper. It serves many functions, and here are three key ones (from an article by Sneha Kulkarni): "A good research paper title: 1. Condenses the paper's content in a few words 2. Captures the readers' attention 3. Differentiates the paper from other papers of the same subject area". If we examine the titles of papers in the ACL Anthology, we would expect that because of Function 1 many of the most common terms will be associated with the dominant areas of research. Function 2 (or attempting to have a catchy title) on the other hand, arguably leads to more unique and less frequent title terms. Function 3 seems crucial to the effectiveness of a title; and while at first glance it may seem like this will lead to unique title terms, often one needs to establish a connection with something familiar in order to convey how the work being presented is new or different. It is also worth noting that a catchy term today, will likely not be catchy tomorrow. Similarly, a distinctive term today, may not be distinctive tomorrow. For example, early papers used neural in the title to distinguish themselves from non-nerual approaches, but these days neural is not particularly discriminative as far as NLP papers go. Thus, competing and complex interactions are involved in the making of titles. Nonetheless, an arguable hypothesis is that: broad trends in interest towards an area of research will be reflected, to some degree, in the frequencies of title terms associated with that area over time. However, even if one does not believe in that hypothesis, it is worth examining the terms in the titles of tens of thousands of papers in the ACL Anthology—spread across many decades. Q. What terms are used most commonly in the titles of the AA papers? How has that changed with time? A. Figure FIGREF28 shows the most common unigrams (single word) and bigrams (two-word sequences) in the titles of papers published from 1980 to 2019. (Ignoring function words.) The timeline graph at the bottom shows the percentage of occurrences of the unigrams over the years (the colors of the unigrams in the Timeline match those in the Title Unigram list). Note: For a given year, the timeline graph includes a point for a unigram if the sum of the frequency of the unigram in that year and the two years before it is at least ten. The period before 1980 is not included because of the small number of papers. Discussion: Appropriately enough, the most common term in the titles of NLP papers is language. Presence of high-ranking terms pertaining to machine translation suggest that it is the area of research that has received considerable attention. Other areas associated with the high-frequency title terms include lexical semantics, named entity recognition, question answering, word sense disambiguation, and sentiment analysis. In fact, the common bigrams in the titles often correspond to names of NLP research areas. Some of the bigrams like shared task and large scale are not areas of research, but rather mechanisms or trends of research that apply broadly to many areas of research. The unigrams, also provide additional insights, such as the interest of the community in Chinese language, and in areas such as speech and parsing. The Timeline graph is crowded in this view, but clicking on a term from the unigram list will filter out all other lines from the timeline. This is especially useful for determining whether the popularity of a term is growing or declining. (One can already see from above that neural has broken away from the pack in recent years.) Since there are many lines in the Timeline graph, Tableau labels only some (you can see neural and machine). However, hovering over a line, in the eventual interactive visualization, will display the corresponding term—as shown in the figure. Despite being busy, the graph sheds light on the relative dominance of the most frequent terms and how that has changed with time. The vocabulary of title words is smaller when considering papers from the 1980's than in recent years. (As would be expected since the number of papers then was also relatively fewer.) Further, dominant terms such as language and translation accounted for a higher percentage than in recent years where there is a much larger diversity of topics and the dominant research areas are not as dominant as they once were. Q. What are the most frequent unigrams and bigrams in the titles of recent papers? A. Figure FIGREF29 shows the most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection). Discussion: Some of the terms that have made notable gains in the top 20 unigrams and bigrams lists in recent years include: neural machine (presumably largely due to the phrase neural machine translation), neural network(s), word embeddings, recurrent neural, deep learning and the corresponding unigrams (neural, networks, etc.). We also see gains for terms related to shared tasks such as SemEval and task. The sets of most frequent unigrams and bigrams in the titles of AA papers from various time spans are available online. Apart from clicking on terms, one can also enter the query (say parsing) in the search box at the bottom. Apart from filtering the timeline graph (bottom), this action also filters the unigram list (top left) to provide information only about the search term. This is useful because the query term may not be one of the visible top unigrams. FigureFIGREF31 shows the timeline graph for parsing. Discussion: Parsing seems to have enjoyed considerable attention in the 1980s, began a period of steep decline in the early 1990s, and a period of gradual decline ever since. One can enter multiple terms in the search box or shift/command click multiple terms to show graphs for more than one term. FigureFIGREF32 shows the timelines for three bigrams statistical machine, neural machine, and machine translation: Discussion: The graph indicates that there was a spike in machine translation papers in 1996, but the number of papers dropped substantially after that. Yet, its numbers have been comparatively much higher than other terms. One can also see the rise of statistical machine translation in the early 2000s followed by its decline with the rise of neural machine translation. ### Impact Research articles can have impact in a number of ways—pushing the state of the art, answering crucial questions, finding practical solutions that directly help people, making a new generation of potential-scientists excited about a field of study, and more. As scientists, it seems attractive to quantitatively measure scientific impact, and this is particularly appealing to governments and funding agencies; however, it should be noted that individual measures of research impact are limited in scope—they measure only some kinds of contributions. Citations The most commonly used metrics of research impact are derived from citations. A citation of a scholarly article is the explicit reference to that article. Citations serve many functions. However, a simplifying assumption is that regardless of the reason for citation, every citation counts as credit to the influence or impact of the cited work. Thus several citation-based metrics have emerged over the years including: number of citations, average citations, h-index, relative citation ratio, and impact factor. It is not always clear why some papers get lots of citations and others do not. One can argue that highly cited papers have captured the imagination of the field: perhaps because they were particularly creative, opened up a new area of research, pushed the state of the art by a substantial degree, tested compelling hypotheses, or produced useful datasets, among other things. Note however, that the number of citations is not always a reflection of the quality or importance of a piece of work. Note also that there are systematic biases that prevent certain kinds of papers from accruing citations, especially when the contributions of a piece of work are atypical, not easily quantified, or in an area where the number of scientific publications is low. Further, the citations process can be abused, for example, by egregious self-citations. Nonetheless, given the immense volume of scientific literature, the relative ease with which one can track citations using services such as Google Scholar and Semantic Scholar, and given the lack of other easily applicable and effective metrics, citation analysis is an imperfect but useful window into research impact. In this section, we examine citations of AA papers. We focus on two aspects: Most cited papers: We begin by looking at the most cited papers overall and in various time spans. We will then look at most cited papers by paper-type (long, short, demo, etc) and venue (ACL, LREC, etc.). Perhaps these make interesting reading lists. Perhaps they also lead to a qualitative understanding of the kinds of AA papers that have received lots of citations. Aggregate citation metrics by time span, paper type, and venue: Access to citation information allows us to calculate aggregate citation metrics such as average and median citations of papers published in different time periods, published in different venues, etc. These can help answer questions such as: on average, how well cited are papers published in the 1990s? on average, how many citations does a short paper get? how many citations does a long paper get? how many citations for a workshop paper? etc. Data: The analyses presented below are based on information about the papers taken directly from AA (as of June 2019) and citation information extracted from Google Scholar (as of June 2019). We extracted citation information from Google Scholar profiles of authors who had a Google Scholar Profile page and had published at least three papers in the ACL Anthology. This yielded citation information for about 75% of the papers (33,051 out of the 44,896 papers). We will refer to this subset of the ACL Anthology papers as AA’. All citation analysis below is on AA’. ### Impact ::: #Citations and Most Cited Papers Q. How many citations have the AA’ papers received? How is that distributed among the papers published in various decades? A. $\sim $1.2 million citations (as of June 2019). Figure FIGREF36 shows a timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. Further, the bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Thus it is easy to spot the papers that received a large number of citations, and the years when the published papers received a large number of citations. Hovering over individual papers reveals an information box showing the paper title, authors, year of publication, publication venue, and #citations. Discussion: With time, not only have the number of papers grown, but also the number of high-citation papers. We see a marked jump in the 1990s over the previous decades, but the 2000s are the most notable in terms of the high number of citations. The 2010s papers will likely surpass the 2000s papers in the years to come. Q. What are the most cited papers in AA'? A. Figure FIGREF37 shoes the most cited papers in the AA'. Discussion: We see that the top-tier conference papers (green) are some of the most cited papers in AA’. There are a notable number of journal papers (dark green) in the most cited list as well, but very few demo (purple) and workshop (orange) papers. In the interactive visualizations (to be released later), one can click on the url to be to taken directly to the paper’s landing page in the ACL Anthology website. That page includes links to meta information, the pdf, and associated files such as videos and appendices. There will also be functionality to download the lists. Alas, copying the lists from the screenshots shown here is not easy. Q. What are the most cited AA' journal papers ? What are the most cited AA' workshop papers? What are the most cited AA' shared task papers? What are the most cited AA' demo papers? What are the most cited tutorials? A. The most cited AA’ journal papers, conference papers, workshop papers, system demo papers, shared task papers, and tutorials can be viewed online. The most cited papers from individual venues (ACL, CL journal, TACL, EMNLP, LREC, etc.) can also be viewed there. Discussion: Machine translation papers are well-represented in many of these lists, but especially in the system demo papers list. Toolkits such as MT evaluation ones, NLTK, Stanford Core NLP, WordNet Similarity, and OpenNMT have highly cited demo or workshop papers. The shared task papers list is dominated by task description papers (papers by task organizers describing the data and task), especially for sentiment analysis tasks. However, the list also includes papers by top-performing systems in these shared tasks, such as the NRC-Canada, HidelTime, and UKP papers. Q. What are the most cited AA' papers in the last decade? A. Figure FIGREF39 shows the most cited AA' papers in the 2010s. The most cited AA' papers from the earlier periods are available online. Discussion: The early period (1965–1989) list includes papers focused on grammar and linguistic structure. The 1990s list has papers addressing many different NLP problems with statistical approaches. Papers on MT and sentiment analysis are frequent in the 2000s list. The 2010s are dominated by papers on word embeddings and neural representations. ### Impact ::: Average Citations by Time Span Q. How many citations did the papers published between 1990 and 1994 receive? What is the average number of citations that a paper published between 1990 and 1994 has received? What are the numbers for other time spans? A. Total citations for papers published between 1990 and 1994: $\sim $92k Average citations for papers published between 1990 and 1994: 94.3 Figure FIGREF41 shows the numbers for various time spans. Discussion: The early 1990s were an interesting period for NLP with the use of data from the World Wide Web and technologies from speech processing. This was the period with the highest average citations per paper, closely followed by the 1965–1969 and 1995–1999 periods. The 2000–2004 period is notable for: (1) a markedly larger number of citations than the previous decades; (2) third highest average number of citations. The drop off in the average citations for recent 5-year spans is largely because they have not had as much time to collect citations. ### Impact ::: Aggregate Citation Statistics, by Paper Type and Venue Q. What are the average number of citations received by different types of papers: main conference papers, workshop papers, student research papers, shared task papers, and system demonstration papers? A. In this analysis, we include only those AA’ papers that were published in 2016 or earlier (to allow for at least 2.5 years to collect citations). There are 26,949 such papers. Figures FIGREF42 and FIGREF43 show the average citations by paper type when considering papers published 1965–2016 and 2010–2016, respectively. Figures FIGREF45 and FIGREF46 show the medians. Discussion: Journal papers have much higher average and median citations than other papers, but the gap between them and top-tier conferences is markedly reduced when considering papers published since 2010. System demo papers have the third highest average citations; however, shared task papers have the third highest median citations. The popularity of shared tasks and the general importance given to beating the state of the art (SOTA) seems to have grown in recent years—something that has come under criticism. It is interesting to note that in terms of citations, workshop papers are doing somewhat better than the conferences that are not top tier. Finally, the citation numbers for tutorials show that even though a small number of tutorials are well cited, a majority receive 1 or no citations. This is in contrast to system demo papers that have average and median citations that are higher or comparable to workshop papers. Throughout the analyses in this article, we see that median citation numbers are markedly lower than average citation numbers. This is particularly telling. It shows that while there are some very highly cited papers, a majority of the papers obtain much lower number of citations—and when considering papers other than journals and top-tier conferences, the number of citations is frequently lower than ten. Q. What are the average number of citations received by the long and short ACL main conference papers, respectively? A. Short papers were introduced at ACL in 2003. Since then ACL is by far the venue with the most number of short papers (compared to other venues). So we compare long and short papers published at ACL since 2003 to determine their average citations. Once again, we limit the papers to those published until 2016 to allow for the papers to have time to collect citations. Figure FIGREF47 shows the average and median citations for long and short papers. Discussion: On average, long papers get almost three times as many citations as short papers. However, the median for long papers is two-and-half times that of short papers. This difference might be because some very heavily cited long papers push the average up for long papers. Q. Which venue has publications with the highest average number of citations? What is the average number of citations for ACL and EMNLP papers? What is this average for other venues? What are the average citations for workshop papers, system demonstration papers, and shared task papers? A. CL journal has the highest average citations per paper. Figure FIGREF49 shows the average citations for AA’ papers published 1965–2016 and 2010–2016, respectively, grouped by venue and paper type. (Figure with median citations is available online.) Discussion: In terms of citations, TACL papers have not been as successful as EMNLP and ACL; however, CL journal (the more traditional journal paper venue) has the highest average and median paper citations (by a large margin). This gap has reduced in papers published since 2010. When considering papers published between 2010 and 2016, the system demonstration papers, the SemEval shared task papers, and non-SemEval shared task papers have notably high average (surpassing those of EACL and COLING); however their median citations are lower. This is likely because some heavily cited papers have pushed the average up. Nonetheless, it is interesting to note how, in terms of citations, demo and shared task papers have surpassed many conferences and even become competitive with some top-tier conferences such as EACL and COLING. Q. What percent of the AA’ papers that were published in 2016 or earlier are cited more than 1000 times? How many more than 10 times? How many papers are cited 0 times? A. Google Scholar invented the i-10 index as another measure of author research impact. It stands for the number of papers by an author that received ten or more citations. (Ten here is somewhat arbitrary, but reasonable.) Similar to that, one can look at the impact of AA’ as a whole and the impact of various subsets of AA’ through the number of papers in various citation bins. Figure FIGREF50 shows the percentage of AA’ papers in various citation bins. (The percentages of papers when considering papers from specific time spans are available online.) Discussion: About 56% of the papers are cited ten or more times. 6.4% of the papers are never cited. Note also that some portion of the 1–9 bin likely includes papers that only received self-citations. It is interesting that the percentage of papers with 0 citations is rather steady (between 7.4% and 8.7%) for the 1965–1989, 1990–1999, and 2010–2016 periods. The majority of the papers lie in the 10 to 99 citations bin, for all except the recent periods (2010–2016 and 2016Jan–2016Dec). With time, the recent period should also have the majority of the papers in the 10 to 99 citations bin. The numbers for the 2016Jan–2016Dec papers show that after 2.5 years, about 89% of the papers have at least one citation and about 33% of the papers have ten or more citations. Q. What are the citation bin percentages for individual venues and paper types? A. See Figure FIGREF51. Discussion: Observe that 70 to 80% of the papers in journals and top-tier conferences have ten or more citations. The percentages are markedly lower (between 30 and 70%) for the other conferences shown above, and even lower for some other conferences (not shown above). CL Journal is particularly notable for the largest percentage of papers with 100 or more citations. The somewhat high percentage of papers that are never cited (4.3%) are likely because some of the book reviews from earlier years are not explicitly marked in CL journal, and thus they were not removed from analysis. Also, letters to editors, which are more common in CL journal, tend to often obtain 0 citations. CL, EMNLP, and ACL have the best track record for accepting papers that have gone on to receive 1000 or more citations. *Sem, the semantics conference, seems to have notably lower percentage of high-citation papers, even though it has fairly competitive acceptance rates. Instead of percentage, if one considers raw numbers of papers that have at least ten citations (i-10 index), then LREC is particularly notable in terms of the large number of papers it accepts that have gone on to obtain ten or more citations ($\sim $1600). Thus, by producing a large number of moderate-to-high citation papers, and introducing many first-time authors, LREC is one of the notable (yet perhaps undervalued) engines of impact on NLP. About 50% of the SemEval shared task papers received 10 or more citations, and about 46% of the non-SemEval Shared Task Papers received 10 or more citations. About 47% of the workshop papers received ten or more citations. About 43% of the demo papers received 10 or more citations. ### Impact ::: Citations to Papers by Areas of Research Q. What is the average number of citations of AA' papers that have machine translation in the title? What about papers that have the term sentiment analysis or word representations? A. Different areas of research within NLP enjoy varying amounts of attention. In Part II, we looked at the relative popularity of various areas over time—estimated through the number of paper titles that had corresponding terms. (You may also want to see the discussion on the use of paper title terms to sample papers from various, possibly overlapping, areas.) Figure FIGREF53 shows the top 50 title bigrams ordered by decreasing number of total citations. Only those bigrams that occur in at least 30 AA' papers (published between 1965 and 2016) are considered. (The papers from 2017 and later are not included, to allow for at least 2.5 years for the papers to accumulate citations.) Discussion: The graph shows that the bigram machine translation occurred in 1,659 papers that together accrued more than 93k citations. These papers have on average 68.8 citations and the median citations is 14. Not all machine translation (MT) papers have machine translation in the title. However, arguably, this set of 1,659 papers is a representative enough sample of machine translation papers; and thus, the average and median are estimates of MT in general. Second in the list are papers with statistical machine in the title—most commonly from the phrase statistical machine translation. One expects considerable overlap in the papers across the sets of papers with machine translation and statistical machine, but machine translation likely covers a broader range of research including work before statistical MT was introduced, neural MT, and MT evaluation. There are fewer papers with sentiment analysis in the title (356), but these have acquired citations at a higher average (104) than both machine translation and statistical machine. The bigram automatic evaluation jumps out because of its high average citations (337). Some of the neural-related bigrams have high median citations, for example, neural machine (49) and convolutional neural (40.5). Figure FIGREF54 shows the lists of top 25 bigrams ordered by average citations. Discussion: Observe the wide variety of topics covered by this list. In some ways that is reassuring for the health of the field as a whole; however, this list does not show which areas are not receiving sufficient attention. It is less clear to me how to highlight those, as simply showing the bottom 50 bigrams by average citations is not meaningful. Also note that this is not in any way an endorsement to write papers with these high-citation bigrams in the title. Doing so is of course no guarantee of receiving a large number of citations. ### Correlation of Age and Gender with Citations In this section, we examine citations across two demographic dimensions: Academic age (number of years one has been publishing) and Gender. There are good reasons to study citations across each of these dimensions including, but not limited to, the following: Areas of research: To better understand research contributions in the context of the area where the contribution is made. Academic age: To better understand how the challenges faced by researchers at various stages of their career may impact the citations of their papers. For example, how well-cited are first-time NLP authors? On average, at what academic age do citations peak? etc. Gender: To better understand the extent to which systematic biases (explicit and implicit) pervasive in society and scientific publishing impact author citations. Some of these aspects of study may seem controversial. So it is worth addressing that first. The goal here is not to perpetuate stereotypes about age, gender, or even areas of research. The history of scientific discovery is awash with plenty of examples of bad science that has tried to erroneously show that one group of people is “better” than another, with devastating consequences. People are far more alike than different. However, different demographic groups have faced (and continue to face) various socio-cultural inequities and biases. Gender and race studies look at how demographic differences shape our experiences. They examine the roles of social institutions in maintaining the inequities and biases. This work is in support of those studies. Unless we measure differences in outcomes such as scientific productivity and impact across demographic groups, we will not fully know the extent to which these inequities and biases impact our scientific community; and we cannot track the effectiveness of measures to make our universities, research labs, and conferences more inclusive, equitable, and fair. ### Correlation of Age and Gender with Citations ::: Correlation of Academic Age with Citations We introduced NLP academic age earlier in the paper, where we defined NLP academic age as the number of years one has been publishing in AA. Here we examine whether NLP academic age impacts citations. The analyses are done in terms of the academic age of the first author; however, similar analyses can be done for the last author and all authors. (There are limitations to each of these analyses though as discussed further below.) First author is a privileged position in the author list as it is usually reserved for the researcher that has done the most work and writing. The first author is also usually the main driver of the project; although, their mentor or advisor may also be a significant driver of the project. Sometimes multiple authors may be marked as first authors in the paper, but the current analysis simply takes the first author from the author list. In many academic communities, the last author position is reserved for the most senior or mentoring researcher. However, in non-university research labs and in large collaboration projects, the meaning of the last author position is less clear. (Personally, I prefer author names ordered by the amount of work done.) Examining all authors is slightly more tricky as one has to decide how to credit the citations to the possibly multiple authors. It might also not be a clear indicator of differences across gender as a large number of the papers in AA have both male and female authors. Q. How does the NLP academic age of the first author correlate with the amount of citations? Are first-year authors less cited than those with more experience? A. Figure FIGREF59 shows various aggregate citation statistics corresponding to academic age. To produce the graph we put each paper in a bin corresponding to the academic age of the first author when the paper was published. For example, if the first author of a paper had an academic age of 3 when that paper was published, then the paper goes in bin 3. We then calculate #papers, #citations, median citations, and average citations for each bin. For the figure below, We further group the bins 10 to 14, 15 to 19, 20 to 34, and 35 to 50. These groupings are done to avoid clutter, and also because many of the higher age bins have a low number of papers. Discussion: Observe that the number of papers where the first author has academic age 1 is much larger than the number of papers in any other bin. This is largely because a large number of authors in AA have written exactly one paper as first author. Also, about 60% of the authors in AA (17,874 out of the 29,941 authors) have written exactly one paper (regardless of author position). The curves for the average and median citations have a slight upside down U shape. The relatively lower average and median citations in year 1 (37.26 and 10, respectively) indicate that being new to the field has some negative impact on citations. The average increases steadily from year 1 to year 4, but the median is already at the highest point by year 2. One might say, that year 2 to year 14 are the period of steady and high citations. Year 15 onwards, there is a steady decline in the citations. It is probably wise to not draw too many conclusions from the averages of the 35 to 50 bin, because of the small number of papers. There seems to be a peak in average citations at age 7. However, there is not a corresponding peak in the median. Thus the peak in average might be due to an increase in the number of very highly cited papers. Citations to Papers by First Author Gender As noted in Part I, neither ACL nor the ACL Anthology have recorded demographic information for the vast majority of the authors. Thus we use the same setup discussed earlier in the section on demographics, to determine gender using the United States Social Security Administration database of names and genders of newborns to identify 55,133 first names that are strongly associated with females (probability $\ge $99%) and 29,873 first names that are strongly associated with males (probability $\ge $99%). Q. On average, are women cited less than men? A. Yes, on average, female first author papers have received markedly fewer citations than male first author papers (36.4 compared to 52.4). The difference in median is smaller (11 compared to 13). See Figure FIGREF60. Discussion: The large difference in averages and smaller difference in medians suggests that there are markedly more very heavily cited male first-author papers than female first-author papers. The gender-unknown category, which here largely consist of authors with Chinese origin names and names that are less strongly associated with one gender have a slightly higher average, but the same median citations, as authors with female-associated first names. The differences in citations, or citation gap, across genders may: (1) vary by period of time; (2) vary due to confounding factors such as academic age and areas of research. We explore these next. Q. How has the citation gap across genders changed over the years? A. Figure FIGREF61 (left side) shows the citation statistics across four time periods. Discussion: Observe that female first authors have always been a minority in the history of ACL; however, on average, their papers from the early years (1965 to 1989) received a markedly higher number of citations than those of male first authors from the same period. We can see from the graph that this changed in the 1990s where male first-author papers obtained markedly more citations on average. The citation gap reduced considerably in the 2000s, and the 2010–2016 period saw a further slight reduction in the citation gap. It is also interesting to note that the gender-unknown category has almost bridged the gap with the males in this most recent time period. Further, the proportion of the gender-unknown authors has increased over the years—arguably, an indication of better representations of authors from around the world in recent years. (Nonetheless, as indicated in Part I, there is still plenty to be done to promote greater inclusion of authors from Africa and South America.) Q. How have citations varied by gender and academic age? Are women less cited because of a greater proportion of new-to-NLP female first authors than new-to-NLP male first authors? A. Figure FIGREF61 (right side) shows citation statistics broken down by gender and academic age. (This figure is similar to the academic age graph seen earlier, except that it shows separate average and median lines for female, male, and unknown gender first authors.) Discussion: The graphs show that female first authors consistently receive fewer citations than male authors for the first fifteen years. The trend is inverted with a small citation gap in the 15th to 34th years period. Q. Is the citation gap common across the vast majority of areas of research within NLP? Is the gap simply because more women work in areas that receive low numbers of citations (regardless of gender)? A. Figure FIGREF64 shows the most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers. (This figure is similar to the areas of research graph seen earlier, except that it shows separate citation statistics for the genders.) Note that the figure includes rows for only those bigram and gender pairs with at least 30 AA’ papers (published between 1965 and 2016). Thus for some of the bigrams certain gender entries are not shown. Discussion: Numbers for an additional 32 areas are available online. Observe that in only about 12% (7 of the top 59) of the most cited areas of research, women received higher average citations than men. These include: sentiment analysis, information extraction, document summarization, spoken dialogue, cross lingual (research), dialogue, systems, language generation. (Of course, note that some of the 59 areas, as estimated using title term bigrams, are overlapping. Also, we did not include large scale in the list above because the difference in averages is very small and it is not really an area of research.) Thus, the citation gap is common across a majority of the high-citations areas within NLP. ### Conclusions This work examined the ACL Anthology to identify broad trends in productivity, focus, and impact. We examined several questions such as: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? Particular attention was paid to the demographics and inclusiveness of the NLP community. Notably, we showed that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also showed that, on average, female first authors are cited less than male first authors, even when controlling for academic age. We hope that recording citation and participation gaps across demographic groups will encourage our university, industry, and government research labs to be more inclusive and fair. Several additional aspects of the AA will be explored in future work (see the bottom of the blog posts). Acknowledgments This work was possible due to the helpful discussion and encouragement from a number of awesome people, including: Dan Jurafsky, Tara Small, Michael Strube, Cyril Goutte, Eric Joanis, Matt Post, Patrick Littell, Torsten Zesch, Ellen Riloff, Norm Vinson, Iryna Gurevych, Rebecca Knowles, Isar Nejadgholi, and Peter Turney. Also, a big thanks to the ACL Anthology team for creating and maintaining a wonderful resource. Figure 1 The number of AA papers published in each of the years from 1965 to 2018. Figure 2 The number of authors of AA papers from 1965 to 2018. Figure 3 Number of AA papers by type. Figure 4 The number of main conference papers for various venues and paper types (workshop papers, demos, etc.). Figure 5 Female first author (FFA) percentage over the years. Figure 6 FFA percentages for papers that have the word discourse in the title. Figure 7 FFA percentages for papers that have the word neural in the title. Figure 8 Lists of terms with the highest and lowest FFA percentages, respectively. Figure 9 Graphs showing average academic age, median academic age, and percentage of first-time publishers in AA over time. Figure 10 The distribution of authors in academic age bins for papers published 2011–2018. Figure 11 A treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green. Figure 12 The most common unigrams and bigrams in the titles of AA papers published 1980–2019. Figure 13 The most frequent unigrams and bigrams in the titles of papers published 2016 Jan to 2019 June (time of data collection). Figure 14 The timeline graph for parsing. Figure 15 The timelines for three bigrams statistical machine, neural machine, and machine translation. Figure 16 A timeline graph where each year has a bar with height corresponding to the number of citations received by papers published in that year. The bar has colored fragments corresponding to each of the papers and the height of a fragment (paper) is proportional to the number of citations it has received. Figure 17 The most cited papers in AA’. Figure 18 The most cited AA’ papers in the 2010s. Figure 19 Left-side graph: Total number of citations received by AAâĂŹ papers in various 5-year time spans. Right-side graph 2: Average citations per paper from various time spans. Figure 20 Average citations by paper type when considering papers published 1965âĂŞ2016. Figure 21 Average citations by paper type when considering papers published 2010–2016. Figure 22 Median citations by paper type when considering papers published 1965–2016 Figure 23 Median citations by paper type when considering papers published 2010–2016. Figure 24 Average and median citations for long and short papers. Figure 25 Average citations for papers published 1965–2016 (left side) and 2010–2016 (right side), grouped by venue and paper type. Figure 26 The percentage of AAâĂŹ papers in various citation bins. Figure 27 The citation bin percentages for individual venues and paper types. Figure 28 The top 50 title bigrams ordered by decreasing number of total citations. Figure 29 The lists of top 25 bigrams ordered by average citations. Figure 30 Aggregate citation statistics by academic age. Figure 31 Average citations received by female and male first authors. Figure 32 Citation gap across genders for papers: (a) published in different time spans, (b) by academic age. Figure 33 The most cited areas of research along with citation statistics split by gender of the first authors of corresponding papers.	44,896 articles
Which of these is not true about Harrel Critten? A. He was in cahoots with the captain all along B. He is a member of the crew C. He was hired by the same people as the reporter was D. He is killed in order to protect the secret of the Red Mask	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 is killed in order to protect the secret of the Red Mask
How do plants factor into the cloning argument? A. They show that the idea of cloning is a possible one because some plants undergo a similar process. B. Plant cloning is unnatural and a human-made process. C. They are another example of how humans have influenced reproduction before. D. They are another example of it happening in nature, and being normal in our day-to-day lives.	Human Clones: Why Not? If you can clone a sheep, you can almost certainly clone a human being. Some of the most powerful people in the world have felt compelled to act against this threat. President Clinton swiftly imposed a ban on federal funding for human-cloning research. Bills are in the works in both houses of Congress to outlaw human cloning--a step urged on all governments by the pope himself. Cloning humans is taken to be either 1) a fundamentally evil thing that must be stopped or, at the very least, 2) a complex ethical issue that needs legislation and regulation. But what, exactly, is so bad about it? Start by asking whether human beings have a right to reproduce. I say "yes." I have no moral right to tell other people they shouldn't be able to have children, and I don't see that Bill Clinton has that right either. When Clinton says, "Let us resist the temptation to copy ourselves," it comes from a man not known for resisting other temptations of the flesh. And for a politician, making noise about cloning is pretty close to a fleshly temptation itself. It's an easy way to show sound-bite leadership on an issue that everybody is talking about, without much risk of bitter consequences. After all, how much federally funded research was stopped by this ban? Probably almost none, because Clinton has maintained Ronald Reagan's policy of minimizing federal grants for research in human reproduction. Besides, most researchers thought cloning humans was impossible--so, for the moment, there's unlikely to be a grant-request backlog. There is nothing like banning the nonexistent to show true leadership. The pope, unlike the president, is known for resisting temptation. He also openly claims the authority to decide how people reproduce. I respect the pope's freedom to lead his religion, and his followers' freedom to follow his dictate. But calling for secular governments to implement a ban, thus extending his power beyond those he can persuade, shows rather explicitly that the pope does not respect the freedom of others. The basic religious doctrine he follows was set down some two millennia ago. Sheep feature prominently in the Bible, but cloning does not. So the pope's views on cloning are 1 st century rules applied using 15 th century religious thinking to a 21 st century issue. If humans have a right to reproduce, what right does society have to limit the means? Essentially all reproduction is done these days with medical help--at delivery, and often before. Truly natural human reproduction would mean 50 percent infant mortality and make pregnancy-related death the No. 1 killer of adult women. True, some forms of medical help are more invasive than others. With in vitro fertilization, the sperm and egg are combined in the lab and surgically implanted in the womb. Less than two decades ago, a similar concern was raised over the ethical issues involved in "test-tube babies." To date, nearly 30,000 such babies have been born in the United States alone. Many would-be parents have been made happy. Who has been harmed? The cloning procedure is similar to IVF. The only difference is that the DNA of sperm and egg would be replaced by DNA from an adult cell. What law or principle--secular, humanist, or religious--says that one combination of genetic material in a flask is OK, but another is not? No matter how closely you study the 1 st century texts, I don't think you'll find the answer. Even if people have the right to do it, is cloning a good idea? Suppose that every prospective parent in the world stopped having children naturally, and instead produced clones of themselves. What would the world be like in another 20 or 30 years? The answer is: much like today. Cloning would only copy the genetic aspects of people who are already here. Hating a world of clones is hating the current populace. Never before was Pogo so right: We have met the enemy, and he is us ! Adifferent scare scenario is a world filled with copies of famous people only. We'll treat celebrity DNA like designer clothes, hankering for Michael Jordan's genes the way we covet his Nike sneakers today. But even celebrity infatuation has its limits. People are not more taken with celebrities than they are with themselves. Besides, such a trend would correct itself in a generation or two, because celebrity is closely linked to rarity. The world seems amused by one Howard Stern, but give us a hundred or a million of them, and they'll seem a lot less endearing. Clones already exist. About one in every 1,000 births results in a pair of babies with the same DNA. We know them as identical twins. Scientific studies on such twins--reared together or apart--show that they share many characteristics. Just how many they share is a contentious topic in human biology. But genetic determinism is largely irrelevant to the cloning issue. Despite how many or how few individual characteristics twins--or other clones--have in common, they are different people in the most fundamental sense . They have their own identities, their own thoughts, and their own rights. Should you be confused on this point, just ask a twin. Suppose that Unsolved Mysteries called you with news of a long-lost identical twin. Would that suddenly make you less of a person, less of an individual? It is hard to see how. So, why would a clone be different? Your clone would be raised in a different era by different people--like the lost identical twin, only younger than you. A person's basic humanity is not governed by how he or she came into this world, or whether somebody else happens to have the same DNA. Twins aren't the only clones in everyday life. Think about seedless grapes or navel oranges--if there are no seeds, where did they come from? It's the plant equivalent of virgin birth--which is to say that they are all clones, propagated by cutting a shoot and planting it. Wine is almost entirely a cloned product. The grapes used for wine have seeds, but they've been cloned from shoots for more than a hundred years in the case of many vineyards. The same is true for many flowers. Go to a garden store, and you'll find products with delightful names like "Olivia's Cloning Compound," a mix of hormones to dunk on the cut end of a shoot to help it take root. One recurring image in anti-cloning propaganda is of some evil dictator raising an army of cloned warriors. Excuse me, but who is going to raise such an army ("raise" in the sense used by parents)? Clones start out life as babies . Armies are far easier to raise the old fashioned way--by recruiting or drafting naive young adults. Dulce et decorum est pro patria mori has worked well enough to send countless young men to their deaths through the ages. Why mess with success? Remember that cloning is not the same as genetic engineering. We don't get to make superman--we have to find him first. Maybe we could clone the superwarrior from Congressional Medal of Honor winners. Their bravery might--or might not--be genetically determined. But, suppose that it is. You might end up with such a brave battalion of heroes that when a grenade lands in their midst, there is a competition to see who gets to jump on it to save the others. Admirable perhaps, but not necessarily the way to win a war. And what about the supply sergeants? The army has a lot more of them than heroes. You could try to breed an expert for every job, including the petty bureaucrats, but what's the point? There's not exactly a shortage of them. What if Saddam Hussein clones were to rule Iraq for another thousand years? Sounds bad, but Saddam's natural son Uday is reputed to make his father seem saintly by comparison. We have no more to fear from a clone of Saddam, or of Hitler, than we do from their natural-born kin--which is to say, we don't have much to fear: Dictators' kids rarely pose a problem. Stalin's daughter retired to Arizona, and Kim Jong Il of North Korea is laughable as Great Leader, Version 2.0. The notion of an 80-year-old man cloning himself to cheat death is quaint, but it is unrealistic. First, the baby wouldn't really be him. Second, is the old duffer really up to changing diapers? A persistent octogenarian might convince a younger couple to have his clone and raise it, but that is not much different from fathering a child via a surrogate mother. Fear of clones is just another form of racism. We all agree it is wrong to discriminate against people based on a set of genetic characteristics known as "race." Calls for a ban on cloning amount to discrimination against people based on another genetic trait--the fact that somebody already has an identical DNA sequence. The most extreme form of discrimination is genocide--seeking to eliminate that which is different. In this case, the genocide is pre-emptive--clones are so scary that we must eliminate them before they exist with a ban on their creation. What is so special about natural reproduction anyway? Cloning is the only predictable way to reproduce, because it creates the identical twin of a known adult. Sexual reproduction is a crap shoot by comparison--some random mix of mom and dad. In evolutionary theory, this combination is thought to help stir the gene pool, so to speak. However, evolution for humans is essentially over, because we use medical science to control the death rate. Whatever the temptations of cloning, the process of natural reproduction will always remain a lot more fun. An expensive and uncomfortable lab procedure will never offer any real competition for sex. The people most likely to clone will be those in special circumstances--infertile couples who must endure IVF anyway, for example. Even there, many will mix genetics to mimic nature. Another special case is where one member of a couple has a severe genetic disease. They might choose a clone of the healthy parent, rather than burden their child with a joint heritage that could be fatal. The most upsetting possibility in human cloning isn't superwarriors or dictators. It's that rich people with big egos will clone themselves. The common practice of giving a boy the same name as his father or choosing a family name for a child of either sex reflects our hunger for vicarious immortality. Clones may resonate with this instinct and cause some people to reproduce this way. So what? Rich and egotistic folks do all sorts of annoying things, and the law is hardly the means with which to try and stop them. The "deep ethical issues" about cloning mainly boil down to jealousy. Economic jealousy is bad enough, and it is a factor here, but the thing that truly drives people crazy is sexual jealousy. Eons of evolution through sexual selection have made the average man or woman insanely jealous of any interloper who gains a reproductive advantage--say by diddling your spouse. Cloning is less personal than cuckoldry, but it strikes a similar chord: Someone has got the reproductive edge on you. Once the fuss has died down and further animal research has paved the way, direct human cloning will be one more option among many specialized medical interventions in human reproduction, affecting only a tiny fraction of the population. Research into this area could bring far wider benefits. Clinton's knee-jerk policy changes nothing in the short run, but it is ultimately a giant step backward. In using an adult cell to create a clone, the "cellular clock" that determines the difference between an embryo and adult was somehow reset. Work in this area might help elucidate the process by which aging occurs and yield a way to reset the clocks in some of our own cells, allowing us to regenerate. Selfishly speaking, that would be more exciting to me than cloning, because it would help me . That's a lot more directly useful than letting me sire an identical twin 40 years my junior. To some, the scientist laboring away to unlock the mysteries of life is a source of evil, never to be trusted. To others, including me, the scientist is the ray of light, illuminating the processes that make the universe work and making us better through that knowledge. Various arguments can be advanced toward either view, but one key statistic is squarely on my side. The vast majority of people, including those who rail against science, owe their very lives to previous medical discoveries. They embody the fruits of science. Don't let the forces of darkness, ignorance, and fear turn us back from research. Instead, let us raise--and yes, even clone--new generations of hapless ingrates, who can whine and rail against the discoveries of the next age.	D. They are another example of it happening in nature, and being normal in our day-to-day lives.
What makes the protagonists become less concerned about being trapped by the beasts? A. They realized that the beasts were not actually interested in hurting them, so they were able to calmly leave their hiding spot. B. They realized that the beasts were too big to fit into the space they were in, so they could camp out in that spot indefinitely. C. They realized the beasts were not actual beasts, but were meant to seem real. D. They realized that the beasts die when their photo is taken, and they had captured many of the beasts on camera.	The Monster Maker By RAY BRADBURY "Get Gunther," the official orders read. It was to laugh! For Click and Irish were marooned on the pirate's asteroid—their only weapons a single gun and a news-reel camera. [Transcriber's Note: This etext was produced from Planet Stories Spring 1944. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] Suddenly, it was there. There wasn't time to blink or speak or get scared. Click Hathaway's camera was loaded and he stood there listening to it rack-spin film between his fingers, and he knew he was getting a damned sweet picture of everything that was happening. The picture of Marnagan hunched huge over the control-console, wrenching levers, jamming studs with freckled fists. And out in the dark of the fore-part there was space and a star-sprinkling and this meteor coming like blazing fury. Click Hathaway felt the ship move under him like a sensitive animal's skin. And then the meteor hit. It made a spiked fist and knocked the rear-jets flat, and the ship spun like a cosmic merry-go-round. There was plenty of noise. Too damned much. Hathaway only knew he was picked up and hurled against a lever-bank, and that Marnagan wasn't long in following, swearing loud words. Click remembered hanging on to his camera and gritting to keep holding it. What a sweet shot that had been of the meteor! A sweeter one still of Marnagan beating hell out of the controls and keeping his words to himself until just now. It got quiet. It got so quiet you could almost hear the asteroids rushing up, cold, blue and hard. You could hear your heart kicking a tom-tom between your sick stomach and your empty lungs. Stars, asteroids revolved. Click grabbed Marnagan because he was the nearest thing, and held on. You came hunting for a space-raider and you ended up cradled in a slab-sized Irishman's arms, diving at a hunk of metal death. What a fade-out! "Irish!" he heard himself say. "Is this IT?" "Is this what ?" yelled Marnagan inside his helmet. "Is this where the Big Producer yells CUT!?" Marnagan fumed. "I'll die when I'm damned good and ready. And when I'm ready I'll inform you and you can picture me profile for Cosmic Films!" They both waited, thrust against the shipside and held by a hand of gravity; listening to each other's breathing hard in the earphones. The ship struck, once. Bouncing, it struck again. It turned end over and stopped. Hathaway felt himself grabbed; he and Marnagan rattled around—human dice in a croupier's cup. The shell of the ship burst, air and energy flung out. Hathaway screamed the air out of his lungs, but his brain was thinking quick crazy, unimportant things. The best scenes in life never reach film, or an audience. Like this one, dammit! Like this one! His brain spun, racketing like the instantaneous, flicking motions of his camera. Silence came and engulfed all the noise, ate it up and swallowed it. Hathaway shook his head, instinctively grabbed at the camera locked to his mid-belt. There was nothing but stars, twisted wreckage, cold that pierced through his vac-suit, and silence. He wriggled out of the wreckage into that silence. He didn't know what he was doing until he found the camera in his fingers as if it had grown there when he was born. He stood there, thinking "Well, I'll at least have a few good scenes on film. I'll—" A hunk of metal teetered, fell with a crash. Marnagan elevated seven feet of bellowing manhood from the wreck. "Hold it!" cracked Hathaway's high voice. Marnagan froze. The camera whirred. "Low angle shot; Interplanetary Patrolman emerges unscathed from asteroid crackup. Swell stuff. I'll get a raise for this!" "From the toe of me boot!" snarled Marnagan brusquely. Oxen shoulders flexed inside his vac-suit. "I might've died in there, and you nursin' that film-contraption!" Hathaway felt funny inside, suddenly. "I never thought of that. Marnagan die? I just took it for granted you'd come through. You always have. Funny, but you don't think about dying. You try not to." Hathaway stared at his gloved hand, but the gloving was so thick and heavy he couldn't tell if it was shaking. Muscles in his bony face went down, pale. "Where are we?" "A million miles from nobody." They stood in the middle of a pocked, time-eroded meteor plain that stretched off, dipping down into silent indigo and a rash of stars. Overhead, the sun poised; black and stars all around it, making it look sick. "If we walk in opposite directions, Click Hathaway, we'd be shaking hands the other side of this rock in two hours." Marnagan shook his mop of dusty red hair. "And I promised the boys at Luna Base this time I'd capture that Gunther lad!" His voice stopped and the silence spoke. Hathaway felt his heart pumping slow, hot pumps of blood. "I checked my oxygen, Irish. Sixty minutes of breathing left." The silence punctuated that sentence, too. Upon the sharp meteoric rocks Hathaway saw the tangled insides of the radio, the food supply mashed and scattered. They were lucky to have escaped. Or was suffocation a better death...? Sixty minutes. They stood and looked at one another. "Damn that meteor!" said Marnagan, hotly. Hathaway got hold of an idea; remembering something. He said it out: "Somebody tossed that meteor, Irish. I took a picture of it, looked it right in the eye when it rolled at us, and it was poker-hot. Space-meteors are never hot and glowing. If it's proof you want, I've got it here, on film." Marnagan winced his freckled square of face. "It's not proof we need now, Click. Oxygen. And then food . And then some way back to Earth." Hathaway went on saying his thoughts: "This is Gunther's work. He's here somewhere, probably laughing his guts out at the job he did us. Oh, God, this would make great news-release stuff if we ever get back to Earth. I.P.'s Irish Marnagan, temporarily indisposed by a pirate whose dirty face has never been seen, Gunther by name, finally wins through to a triumphant finish. Photographed on the spot, in color, by yours truly, Click Hathaway. Cosmic Films, please notice." They started walking, fast, over the pocked, rubbled plain toward a bony ridge of metal. They kept their eyes wide and awake. There wasn't much to see, but it was better than standing still, waiting. Marnagan said, "We're working on margin, and we got nothin' to sweat with except your suspicions about this not being an accident. We got fifty minutes to prove you're right. After that—right or wrong—you'll be Cosmic Films prettiest unmoving, unbreathin' genius. But talk all you like, Click. It's times like this when we all need words, any words, on our tongues. You got your camera and your scoop. Talk about it. As for me—" he twisted his glossy red face. "Keeping alive is me hobby. And this sort of two-bit death I did not order." Click nodded. "Gunther knows how you'd hate dying this way, Irish. It's irony clean through. That's probably why he planned the meteor and the crash this way." Marnagan said nothing, but his thick lips went down at the corners, far down, and the green eyes blazed. They stopped, together. "Oops!" Click said. "Hey!" Marnagan blinked. "Did you feel that ?" Hathaway's body felt feathery, light as a whisper, boneless and limbless, suddenly. "Irish! We lost weight, coming over that ridge!" They ran back. "Let's try it again." They tried it. They scowled at each other. The same thing happened. "Gravity should not act this way, Click." "Are you telling me? It's man-made. Better than that—it's Gunther! No wonder we fell so fast—we were dragged down by a super-gravity set-up! Gunther'd do anything to—did I say anything ?" Hathaway leaped backward in reaction. His eyes widened and his hand came up, jabbing. Over a hill-ridge swarmed a brew of unbelievable horrors. Progeny from Frankenstein's ARK. Immense crimson beasts with numerous legs and gnashing mandibles, brown-black creatures, some tubular and fat, others like thin white poisonous whips slashing along in the air. Fangs caught starlight white on them. Hathaway yelled and ran, Marnagan at his heels, lumbering. Sweat broke cold on his body. The immense things rolled, slithered and squirmed after him. A blast of light. Marnagan, firing his proton-gun. Then, in Click's ears, the Irishman's incredulous bellow. The gun didn't hurt the creatures at all. "Irish!" Hathaway flung himself over the ridge, slid down an incline toward the mouth a small cave. "This way, fella!" Hathaway made it first, Marnagan bellowing just behind him. "They're too big; they can't get us in here!" Click's voice gasped it out, as Marnagan squeezed his two-hundred-fifty pounds beside him. Instinctively, Hathaway added, "Asteroid monsters! My camera! What a scene!" "Damn your damn camera!" yelled Marnagan. "They might come in!" "Use your gun." "They got impervious hides. No use. Gahh! And that was a pretty chase, eh, Click?" "Yeah. Sure. You enjoyed it, every moment of it." "I did that." Irish grinned, showing white uneven teeth. "Now, what will we be doing with these uninvited guests at our door?" "Let me think—" "Lots of time, little man. Forty more minutes of air, to be exact." They sat, staring at the monsters for about a minute. Hathaway felt funny about something; didn't know what. Something about these monsters and Gunther and— "Which one will you be having?" asked Irish, casually. "A red one or a blue one?" Hathaway laughed nervously. "A pink one with yellow ruffles—Good God, now you've got me doing it. Joking in the face of death." "Me father taught me; keep laughing and you'll have Irish luck." That didn't please the photographer. "I'm an Anglo-Swede," he pointed out. Marnagan shifted uneasily. "Here, now. You're doing nothing but sitting, looking like a little boy locked in a bedroom closet, so take me a profile shot of the beasties and myself." Hathaway petted his camera reluctantly. "What in hell's the use? All this swell film shot. Nobody'll ever see it." "Then," retorted Marnagan, "we'll develop it for our own benefit; while waitin' for the U.S. Cavalry to come riding over the hill to our rescue!" Hathaway snorted. "U.S. Cavalry." Marnagan raised his proton-gun dramatically. "Snap me this pose," he said. "I paid your salary to trot along, photographing, we hoped, my capture of Gunther, now the least you can do is record peace negotiations betwixt me and these pixies." Marnagan wasn't fooling anybody. Hathaway knew the superficial palaver for nothing but a covering over the fast, furious thinking running around in that red-cropped skull. Hathaway played the palaver, too, but his mind was whirring faster than his camera as he spun a picture of Marnagan standing there with a useless gun pointed at the animals. Montage. Marnagan sitting, chatting at the monsters. Marnagan smiling for the camera. Marnagan in profile. Marnagan looking grim, without much effort, for the camera. And then, a closeup of the thrashing death wall that holed them in. Click took them all, those shots, not saying anything. Nobody fooled nobody with this act. Death was near and they had sweaty faces, dry mouths and frozen guts. When Click finished filming, Irish sat down to save oxygen, and used it up arguing about Gunther. Click came back at him: "Gunther drew us down here, sure as Ceres! That gravity change we felt back on that ridge, Irish; that proves it. Gunther's short on men. So, what's he do; he builds an asteroid-base, and drags ships down. Space war isn't perfect yet, guns don't prime true in space, trajectory is lousy over long distances. So what's the best weapon, which dispenses with losing valuable, rare ships and a small bunch of men? Super-gravity and a couple of well-tossed meteors. Saves all around. It's a good front, this damned iron pebble. From it, Gunther strikes unseen; ships simply crash, that's all. A subtle hand, with all aces." Marnagan rumbled. "Where is the dirty son, then!" "He didn't have to appear, Irish. He sent—them." Hathaway nodded at the beasts. "People crashing here die from air-lack, no food, or from wounds caused at the crackup. If they survive all that—the animals tend to them. It all looks like Nature was responsible. See how subtle his attack is? Looks like accidental death instead of murder, if the Patrol happens to land and finds us. No reason for undue investigation, then." "I don't see no Base around." Click shrugged. "Still doubt it? Okay. Look." He tapped his camera and a spool popped out onto his gloved palm. Holding it up, he stripped it out to its full twenty inch length, held it to the light while it developed, smiling. It was one of his best inventions. Self-developing film. The first light struck film-surface, destroyed one chemical, leaving imprints; the second exposure simply hardened, secured the impressions. Quick stuff. Inserting the film-tongue into a micro-viewer in the camera's base, Click handed the whole thing over. "Look." Marnagan put the viewer up against the helmet glass, squinted. "Ah, Click. Now, now. This is one lousy film you invented." "Huh?" "It's a strange process'll develop my picture and ignore the asteroid monsters complete." "What!" Hathaway grabbed the camera, gasped, squinted, and gasped again: Pictures in montage; Marnagan sitting down, chatting conversationally with nothing ; Marnagan shooting his gun at nothing ; Marnagan pretending to be happy in front of nothing . Then, closeup—of—NOTHING! The monsters had failed to image the film. Marnagan was there, his hair like a red banner, his freckled face with the blue eyes bright in it. Maybe— Hathaway said it, loud: "Irish! Irish! I think I see a way out of this mess! Here—" He elucidated it over and over again to the Patrolman. About the film, the beasts, and how the film couldn't be wrong. If the film said the monsters weren't there, they weren't there. "Yeah," said Marnagan. "But step outside this cave—" "If my theory is correct I'll do it, unafraid," said Click. Marnagan scowled. "You sure them beasts don't radiate ultra-violet or infra-red or something that won't come out on film?" "Nuts! Any color we see, the camera sees. We've been fooled." "Hey, where you going?" Marnagan blocked Hathaway as the smaller man tried pushing past him. "Get out of the way," said Hathaway. Marnagan put his big fists on his hips. "If anyone is going anywhere, it'll be me does the going." "I can't let you do that, Irish." "Why not?" "You'd be going on my say-so." "Ain't your say-so good enough for me?" "Yes. Sure. Of course. I guess—" "If you say them animals ain't there, that's all I need. Now, stand aside, you film-developing flea, and let an Irishman settle their bones." He took an unnecessary hitch in trousers that didn't exist except under an inch of porous metal plate. "Your express purpose on this voyage, Hathaway, is taking films to be used by the Patrol later for teaching Junior Patrolmen how to act in tough spots. First-hand education. Poke another spool of film in that contraption and give me profile a scan. This is lesson number seven: Daniel Walks Into The Lion's Den." "Irish, I—" "Shut up and load up." Hathaway nervously loaded the film-slot, raised it. "Ready, Click?" "I—I guess so," said Hathaway. "And remember, think it hard, Irish. Think it hard. There aren't any animals—" "Keep me in focus, lad." "All the way, Irish." "What do they say...? Oh, yeah. Action. Lights. Camera!" Marnagan held his gun out in front of him and still smiling took one, two, three, four steps out into the outside world. The monsters were waiting for him at the fifth step. Marnagan kept walking. Right out into the middle of them.... That was the sweetest shot Hathaway ever took. Marnagan and the monsters! Only now it was only Marnagan. No more monsters. Marnagan smiled a smile broader than his shoulders. "Hey, Click, look at me! I'm in one piece. Why, hell, the damned things turned tail and ran away!" "Ran, hell!" cried Hathaway, rushing out, his face flushed and animated. "They just plain vanished. They were only imaginative figments!" "And to think we let them hole us in that way, Click Hathaway, you coward!" "Smile when you say that, Irish." "Sure, and ain't I always smilin'? Ah, Click boy, are them tears in your sweet grey eyes?" "Damn," swore the photographer, embarrassedly. "Why don't they put window-wipers in these helmets?" "I'll take it up with the Board, lad." "Forget it. I was so blamed glad to see your homely carcass in one hunk, I couldn't help—Look, now, about Gunther. Those animals are part of his set-up. Explorers who land here inadvertently, are chased back into their ships, forced to take off. Tourists and the like. Nothing suspicious about animals. And if the tourists don't leave, the animals kill them." "Shaw, now. Those animals can't kill." "Think not, Mr. Marnagan? As long as we believed in them they could have frightened us to death, forced us, maybe, to commit suicide. If that isn't being dangerous—" The Irishman whistled. "But, we've got to move , Irish. We've got twenty minutes of oxygen. In that time we've got to trace those monsters to their source, Gunther's Base, fight our way in, and get fresh oxy-cannisters." Click attached his camera to his mid-belt. "Gunther probably thinks we're dead by now. Everyone else's been fooled by his playmates; they never had a chance to disbelieve them." "If it hadn't been for you taking them pictures, Click—" "Coupled with your damned stubborn attitude about the accident—" Click stopped and felt his insides turning to water. He shook his head and felt a film slip down over his eyes. He spread his legs out to steady himself, and swayed. "I—I don't think my oxygen is as full as yours. This excitement had me double-breathing and I feel sick." Marnagan's homely face grimaced in sympathy. "Hold tight, Click. The guy that invented these fish-bowls didn't provide for a sick stomach." "Hold tight, hell, let's move. We've got to find where those animals came from! And the only way to do that is to get the animals to come back!" "Come back? How?" "They're waiting, just outside the aura of our thoughts, and if we believe in them again, they'll return." Marnagan didn't like it. "Won't—won't they kill us—if they come—if we believe in 'em?" Hathaway shook a head that was tons heavy and weary. "Not if we believe in them to a certain point . Psychologically they can both be seen and felt. We only want to see them coming at us again." " Do we, now?" "With twenty minutes left, maybe less—" "All right, Click, let's bring 'em back. How do we do it?" Hathaway fought against the mist in his eyes. "Just think—I will see the monsters again. I will see them again and I will not feel them. Think it over and over." Marnagan's hulk stirred uneasily. "And—what if I forget to remember all that? What if I get excited...?" Hathaway didn't answer. But his eyes told the story by just looking at Irish. Marnagan cursed. "All right, lad. Let's have at it!" The monsters returned. A soundless deluge of them, pouring over the rubbled horizon, swarming in malevolent anticipation about the two men. "This way, Irish. They come from this way! There's a focal point, a sending station for these telepathic brutes. Come on!" Hathaway sludged into the pressing tide of color, mouths, contorted faces, silvery fat bodies misting as he plowed through them. Marnagan was making good progress ahead of Hathaway. But he stopped and raised his gun and made quick moves with it. "Click! This one here! It's real!" He fell back and something struck him down. His immense frame slammed against rock, noiselessly. Hathaway darted forward, flung his body over Marnagan's, covered the helmet glass with his hands, shouting: "Marnagan! Get a grip, dammit! It's not real—don't let it force into your mind! It's not real, I tell you!" "Click—" Marnagan's face was a bitter, tortured movement behind glass. "Click—" He was fighting hard. "I—I—sure now. Sure—" He smiled. "It—it's only a shanty fake!" "Keep saying it, Irish. Keep it up." Marnagan's thick lips opened. "It's only a fake," he said. And then, irritated, "Get the hell off me, Hathaway. Let me up to my feet!" Hathaway got up, shakily. The air in his helmet smelled stale, and little bubbles danced in his eyes. "Irish, you forget the monsters. Let me handle them, I know how. They might fool you again, you might forget." Marnagan showed his teeth. "Gah! Let a flea have all the fun? And besides, Click, I like to look at them. They're pretty." The outpour of animals came from a low lying mound a mile farther on. Evidently the telepathic source lay there. They approached it warily. "We'll be taking our chances on guard," hissed Irish. "I'll go ahead, draw their attention, maybe get captured. Then, you show up with your gun...." "I haven't got one." "We'll chance it, then. You stick here until I see what's ahead. They probably got scanners out. Let them see me—" And before Hathaway could object, Marnagan walked off. He walked about five hundred yards, bent down, applied his fingers to something, heaved up, and there was a door opening in the rock. His voice came back across the distance, into Click's earphones. "A door, an air-lock, Click. A tunnel leading down inside!" Then, Marnagan dropped into the tunnel, disappearing. Click heard the thud of his feet hitting the metal flooring. Click sucked in his breath, hard and fast. "All right, put 'em up!" a new harsh voice cried over a different radio. One of Gunther's guards. Three shots sizzled out, and Marnagan bellowed. The strange harsh voice said, "That's better. Don't try and pick that gun up now. Oh, so it's you. I thought Gunther had finished you off. How'd you get past the animals?" Click started running. He switched off his sending audio, kept his receiving on. Marnagan, weaponless. One guard. Click gasped. Things were getting dark. Had to have air. Air. Air. He ran and kept running and listening to Marnagan's lying voice: "I tied them pink elephants of Gunther's in neat alphabetical bundles and stacked them up to dry, ya louse!" Marnagan said. "But, damn you, they killed my partner before he had a chance!" The guard laughed. The air-lock door was still wide open when Click reached it, his head swimming darkly, his lungs crammed with pain-fire and hell-rockets. He let himself down in, quiet and soft. He didn't have a weapon. He didn't have a weapon. Oh, damn, damn! A tunnel curved, ending in light, and two men silhouetted in that yellow glare. Marnagan, backed against a wall, his helmet cracked, air hissing slowly out of it, his face turning blue. And the guard, a proton gun extended stiffly before him, also in a vac-suit. The guard had his profile toward Hathaway, his lips twisting: "I think I'll let you stand right there and die," he said quietly. "That what Gunther wanted, anway. A nice sordid death." Hathaway took three strides, his hands out in front of him. "Don't move!" he snapped. "I've got a weapon stronger than yours. One twitch and I'll blast you and the whole damned wall out from behind you! Freeze!" The guard whirled. He widened his sharp eyes, and reluctantly, dropped his gun to the floor. "Get his gun, Irish." Marnagan made as if to move, crumpled clumsily forward. Hathaway ran in, snatched up the gun, smirked at the guard. "Thanks for posing," he said. "That shot will go down in film history for candid acting." "What!" "Ah: ah! Keep your place. I've got a real gun now. Where's the door leading into the Base?" The guard moved his head sullenly over his left shoulder. Click was afraid he would show his weak dizziness. He needed air. "Okay. Drag Marnagan with you, open the door and we'll have air. Double time! Double!" Ten minutes later, Marnagan and Hathaway, fresh tanks of oxygen on their backs, Marnagan in a fresh bulger and helmet, trussed the guard, hid him in a huge trash receptacle. "Where he belongs," observed Irish tersely. They found themselves in a complete inner world; an asteroid nothing more than a honey-comb fortress sliding through the void unchallenged. Perfect front for a raider who had little equipment and was short-handed of men. Gunther simply waited for specific cargo ships to rocket by, pulled them or knocked them down and swarmed over them for cargo. The animals served simply to insure against suspicion and the swarms of tourists that filled the void these days. Small fry weren't wanted. They were scared off. The telepathic sending station for the animals was a great bank of intricate, glittering machine, through which strips of colored film with images slid into slots and machine mouths that translated them into thought-emanations. A damned neat piece of genius. "So here we are, still not much better off than we were," growled Irish. "We haven't a ship or a space-radio, and more guards'll turn up any moment. You think we could refocus this doohingey, project the monsters inside the asteroid to fool the pirates themselves?" "What good would that do?" Hathaway gnawed his lip. "They wouldn't fool the engineers who created them, you nut." Marnagan exhaled disgustedly. "Ah, if only the U.S. Cavalry would come riding over the hill—" "Irish!" Hathaway snapped that, his face lighting up. "Irish. The U.S. Cavalry it is!" His eyes darted over the machines. "Here. Help me. We'll stage everything on the most colossal raid of the century." Marnagan winced. "You breathing oxygen or whiskey?" "There's only one stipulation I make, Irish. I want a complete picture of Marnagan capturing Raider's Base. I want a picture of Gunther's face when you do it. Snap it, now, we've got rush work to do. How good an actor are you?" "That's a silly question." "You only have to do three things. Walk with your gun out in front of you, firing. That's number one. Number two is to clutch at your heart and fall down dead. Number three is to clutch at your side, fall down and twitch on the ground. Is that clear?" "Clear as the Coal Sack Nebula...." An hour later Hathaway trudged down a passageway that led out into a sort of city street inside the asteroid. There were about six streets, lined with cube houses in yellow metal, ending near Hathaway in a wide, green-lawned Plaza. Hathaway, weaponless, idly carrying his camera in one hand, walked across the Plaza as if he owned it. He was heading for a building that was pretentious enough to be Gunther's quarters. He got halfway there when he felt a gun in his back. He didn't resist. They took him straight ahead to his destination and pushed him into a room where Gunther sat. Hathaway looked at him. "So you're Gunther?" he said, calmly. The pirate was incredibly old, his bulging forehead stood out over sunken, questioningly dark eyes, and his scrawny body was lost in folds of metal-link cloth. He glanced up from a paper-file, surprised. Before he could speak, Hathaway said: "Everything's over with, Mr. Gunther. The Patrol is in the city now and we're capturing your Base. Don't try to fight. We've a thousand men against your eighty-five." Gunther sat there, blinking at Hathaway, not moving. His thin hands twitched in his lap. "You are bluffing," he said, finally, with a firm directness. "A ship hasn't landed here for an hour. Your ship was the last. Two people were on it. The last I saw of them they were being pursued to the death by the Beasts. One of you escaped, it seemed." "Both. The other guy went after the Patrol." "Impossible!" "I can't respect your opinion, Mr. Gunther." A shouting rose from the Plaza. About fifty of Gunther's men, lounging on carved benches during their time-off, stirred to their feet and started yelling. Gunther turned slowly to the huge window in one side of his office. He stared, hard. The Patrol was coming! Across the Plaza, marching quietly and decisively, came the Patrol. Five hundred Patrolmen in one long, incredible line, carrying paralysis guns with them in their tight hands. Gunther babbled like a child, his voice a shrill dagger in the air. "Get out there, you men! Throw them back! We're outnumbered!" Guns flared. But the Patrol came on. Gunther's men didn't run, Hathaway had to credit them on that. They took it, standing. Hathaway chuckled inside, deep. What a sweet, sweet shot this was. His camera whirred, clicked and whirred again. Nobody stopped him from filming it. Everything was too wild, hot and angry. Gunther was throwing a fit, still seated at his desk, unable to move because of his fragile, bony legs and their atrophied state. Some of the Patrol were killed. Hathaway chuckled again as he saw three of the Patrolmen clutch at their hearts, crumple, lie on the ground and twitch. God, what photography! Gunther raged, and swept a small pistol from his linked corselet. He fired wildly until Hathaway hit him over the head with a paper-weight. Then Hathaway took a picture of Gunther slumped at his desk, the chaos taking place immediately outside his window. The pirates broke and fled, those that were left. A mere handful. And out of the chaos came Marnagan's voice, "Here!"	C. They realized the beasts were not actual beasts, but were meant to seem real.
How big was the dataset presented?	### Introduction On-line eSports events provide a new setting for observing large-scale social interaction focused on a visual story that evolves over time—a video game. While watching sporting competitions has been a major source of entertainment for millennia, and is a significant part of today's culture, eSports brings this to a new level on several fronts. One is the global reach, the same games are played around the world and across cultures by speakers of several languages. Another is the scale of on-line text-based discourse during matches that is public and amendable to analysis. One of the most popular games, League of Legends, drew 43 million views for the 2016 world series final matches (broadcast in 18 languages) and a peak concurrent viewership of 14.7 million. Finally, players interact through what they see on screen while fans (and researchers) can see exactly the same views. This paper builds on the wealth of interaction around eSports to develop predictive models for match video highlights based on the audience's online chat discourse as well as the visual recordings of matches themselves. ESports journalists and fans create highlight videos of important moments in matches. Using these as ground truth, we explore automatic prediction of highlights via multimodal CNN+RNN models for multiple languages. Appealingly this task is natural, as the community already produces the ground truth and is global, allowing multilingual multimodal grounding. Highlight prediction is about capturing the exciting moments in a specific video (a game match in this case), and depends on the context, the state of play, and the players. This task of predicting the exciting moments is hence different from summarizing the entire match into a story summary. Hence, highlight prediction can benefit from the available real-time text commentary from fans, which is valuable in exposing more abstract background context, that may not be accessible with computer vision techniques that can easily identify some aspects of the state of play. As an example, computer vision may not understand why Michael Jordan's dunk is a highlight over that of another player, but concurrent fan commentary might reveal this. We collect our dataset from Twitch.tv, one of the live-streaming platforms that integrates comments (see Fig. FIGREF2 ), and the largest live-streaming platform for video games. We record matches of the game League of Legends (LOL), one of the largest eSports game in two subsets, 1) the spring season of the North American League of Legends Championship Series (NALCS), and 2) the League of Legends Master Series (LMS) hosted in Taiwan/Macau/HongKong, with chat comments in English and traditional Chinese respectively. We use the community created highlights to label each frame of a match as highlight or not. In addition to our new dataset, we present several experiments with multilingual character-based models, deep-learning based vision models either per-frame or tied together with a video-sequence LSTM-RNN, and combinations of language and vision models. Our results indicate that while surprisingly the visual models generally outperform language-based models, we can still build reasonably useful language models that help disambiguate difficult cases for vision models, and that combining the two sources is the most effective model (across multiple languages). ### Related Work We briefly discuss a small sample of the related work on language and vision datasets, summarization, and highlight prediction. There has been a surge of vision and language datasets focusing on captions over the last few years, BIBREF0 , BIBREF1 , BIBREF2 , followed by efforts to focus on more specific parts of images BIBREF3 , or referring expressions BIBREF4 , or on the broader context BIBREF5 . For video, similar efforts have collected descriptions BIBREF6 , while others use existing descriptive video service (DVS) sources BIBREF7 , BIBREF8 . Beyond descriptions, other datasets use questions to relate images and language BIBREF9 , BIBREF10 . This approach is extended to movies in MovieQA. The related problem of visually summarizing videos (as opposed to finding the highlights) has produced datasets of holiday and sports events with multiple users making summary videos BIBREF11 and multiple users selecting summary key-frames BIBREF12 from short videos. For language-based summarization, Extractive models BIBREF13 , BIBREF14 generate summaries by selecting important sentences and then assembling these, while Abstractive models BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 generate/rewrite the summaries from scratch. Closer to our setting, there has been work on highlight prediction in football (soccer) and basketball based on audio of broadcasts BIBREF19 BIBREF20 where commentators may have an outsized impact or visual features BIBREF21 . In the spirit of our study, there has been work looking at tweets during sporting events BIBREF22 , but the tweets are not as immediate or as well aligned with the games as the eSports comments. More closely related to our work, yahooesports collects videos for Heroes of the Storm, League of Legends, and Dota2 on online broadcasting websites of around 327 hours total. They also provide highlight labeling annotated by four annotators. Our method, on the other hand, has a similar scale of data, but we use existing highlights, and we also employ textual audience chat commentary, thus providing a new resource and task for Language and Vision research. In summary, we present the first language-vision dataset for video highlighting that contains audience reactions in chat format, in multiple languages. The community produced ground truth provides labels for each frame and can be used for supervised learning. The language side of this new dataset presents interesting challenges related to real-world Internet-style slang. ### Data Collection Our dataset covers 218 videos from NALCS and 103 from LMS for a total of 321 videos from week 1 to week 9 in 2017 spring series from each tournament. Each week there are 10 matches for NALCS and 6 matches for LMS. Matches are best of 3, so consist of two games or three games. The first and third games are used for training. The second games in the first 4 weeks are used as validation and the remainder of second games are used as test. Table TABREF3 lists the numbers of videos in train, validation, and test subsets. Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game. The average number of chats per video is 7490 with a standard deviation of 4922. The high value of standard deviation is mostly due to the fact that NALCS simultaneously broadcasts matches in two different channels (nalcs1 and nalcs2) which often leads to the majority of users watching the channel with a relatively more popular team causing an imbalance in the number of chats. If we only consider LMS which broadcasts with a single channel, the average number of chats are 7210 with standard deviation of 2719. The number of viewers for each game averages about 21526, and the number of unique users who type in chat is on average 2185, i.e., roughly 10% of the viewers. ### Model In this section, we explain the proposed models and components. We first describe the notation and definition of the problem, plus the evaluation metric used. Next, we explain our vision model V-CNN-LSTM and language model L-Char-LSTM. Finally, we describe the joint multimodal model INLINEFORM0 -LSTM. ### Conclusion We presented a new dataset and multimodal methods for highlight prediction, based on visual cues and textual audience chat reactions in multiple languages. We hope our new dataset can encourage further multilingual, multimodal research. ### Acknowledgments We thank Tamara Berg, Phil Ammirato, and the reviewers for their helpful suggestions, and we acknowledge support from NSF 1533771. Figure 1: Pictures of Broadcasting platforms:(a) Twitch: League of Legends Tournament Broadcasting, (b) Youtube: News Channel, (c)Facebook: Personal live sharing Figure 2: Highlight Labeling: (a) The feature representation of each frame is calculated by averaging each color channel in each subregion. (b) After template matching, the top bar shows the maximum of similarity matching of each frame in the highlight and the bottom bar is the labeling result of the video. Table 1: Dataset statistics (number of videos). Figure 3: Network architecture of proposed models. Table 2: Ablation Study: Effects of various models. C:Chat, V:Video, UF: % of frames Used in highlight clips as positive training examples; P: Precision, R: Recall, F: F-score. Table 3: Test Results on the NALCS (English) and LMS (Traditional Chinese) datasets.	321 videos
What baseline and classification systems are used in experiments?	### Introduction In recent years we have witnessed a great surge in activity in the area of computational argument analysis (e.g. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 ), and the emergence of dedicated venues such as the ACL Argument Mining workshop series starting in 2014 BIBREF4 . Argumentative relation classification is a sub-task of argument analysis that aims to determine relations between argumentative units A and B, for example, A supports B; A attacks B. Consider the following argumentative units (1) and (2), given the topic (0) “Marijuana should be legalized”: This example is modeled in Figure FIGREF3 . It is clear that (1) has a negative stance towards the topic and (2) has a positive stance towards the topic. Moreover, we can say that (2) attacks (1). In discourse, such a relation is often made explicit through discourse markers: (1). However, (2); On the one hand (1), on the other (2); (1), although (2); Admittedly, (2); etc. In the absence of such markers we must determine this relation by assessing the semantics of the individual argumentative units, including (often implicit) world knowledge about how they are related to each other. In this work, we show that argumentative relation classifiers – when provided with textual context surrounding an argumentative unit's span – are very prone to neglect the actual textual content of the EAU span. Instead they heavily rely on contextual markers, such as conjunctions or adverbials, as a basis for prediction. We argue that a system's capacity of predicting the correct relation based on the argumentative units' content is important in many circumstances, e.g., when an argumentative debate crosses document boundaries. For example, the prohibition of marijuana debate extends across populations and countries – argumentative units for this debate can be recovered from thousands of documents scattered across the world wide web. As a consequence, argumentative relation classification systems should not be (immensely) dependent on contextual clues – in the discussed cross-document setting these clues may even be misleading for such a system, since source and target arguments can be embedded in different textual contexts (e.g., when (1) and (2) stem from different documents it is easy to imagine a textual context where (2) is not introduced by however but instead by an `inverse' form such as e.g. moreover). ### Related Work It is well-known that the rhetorical and argumentative structure of texts bear great similarities. For example, BIBREF5 , BIBREF6 , BIBREF0 observe that elementary discourse units (EDUs) in RST BIBREF7 share great similarity with elementary argumentative units (EAUs) in argumentation analysis. BIBREF8 experiment with a modified version of the Microtext corpus BIBREF9 , which is an extensively annotated albeit small corpus. Similar to us, they separate argumentative units from discursive contextual markers. While BIBREF8 conduct a human evaluation to investigate the separation of Logos and Pathos aspects of arguments, our work investigates how (de-)contextualization of argumentative units affects automatic argumentative relation classification models. ### Argumentative Relation Prediction: Models and Features In this section, we describe different formulations of the argumentative relation classification task and describe features used by our replicated model. In order to test our hypotheses, we propose to group all features into three distinct types. ### Models Now, we introduce a classification of three different prediction models used in the argumentative relation prediction literature. We will inspect all of them and show that all can suffer from severe issues when focusing (too much) on the context. The model INLINEFORM0 adopts a discourse parsing view on argumentative relation prediction and predicts one outgoing edge for an argumentative unit (one-outgoing edge). Model INLINEFORM1 assumes a connected graph with argumentative units and is tasked with predicting edge labels for unit tuples (labeling relations in a graph). Finally, a model INLINEFORM2 is given two (possibly) unrelated argumentative units and is tasked with predicting connections as well as edge labels (joint edge prediction and labeling). BIBREF13 divide the task into relation prediction INLINEFORM0 and relation class assignment INLINEFORM1 : DISPLAYFORM0 DISPLAYFORM0 which the authors describe as argumentative relation identification ( INLINEFORM0 ) and stance detection ( INLINEFORM1 ). In their experiments, INLINEFORM2 , i.e., no distinction is made between features that access only the argument content (EAU span) or only the EAU's embedding context, and some features also consider both (e.g., discourse features). This model adopts a parsing view on argumentative relation classification: every unit is allowed to have only one type of outgoing relation (this follows trivially from the fact that INLINEFORM3 has only one input). Applying such a model to argumentative attack and support relations might impose unrealistic constraints on the resulting argumentation graph: A given premise might in fact attack or support several other premises. The approach may suffice for the case of student argumentative essays, where EAUs are well-framed in a discourse structure, but seems overly restrictive for many other scenarios. Another way of framing the task, is to learn a function DISPLAYFORM0 Here, an argumentative unit is allowed to be in a attack or support relation to multiple other EAUs. Yet, both INLINEFORM0 and INLINEFORM1 assume that inputs are already linked and only the class of the link is unknown. Thus, we might also model the task in a three-class classification setting to learn a more general function that performs relation prediction and classification jointly (see also, e.g., BIBREF10 ): DISPLAYFORM0 The model described by Eq. EQREF22 is the most general one: not only does it assume a graph view on argumentative units and their relations (as does Eq. EQREF20 ); in model formulation (Eq. EQREF22 ), an argumentative unit can have no or multiple support or attack relations. It naturally allows for cases where an argumentative unit INLINEFORM0 (supports INLINEFORM1 INLINEFORM2 attacks INLINEFORM3 INLINEFORM4 is-unrelated-to INLINEFORM5 ). Given a set of EAUs mined from different documents, this model enables us to construct a full-fledged argumentation graph. ### Feature implementation Our feature implementation follows the feature descriptions for Stance recognition and link identification in BIBREF13 . These features and variations of them have been used successfully in several successive works (cf. BIBREF1 , BIBREF16 , BIBREF15 ). For any model the features are indexed by INLINEFORM0 . We create a function INLINEFORM1 which maps from feature indices to feature types. In other words, INLINEFORM2 tells us, for any given feature, whether it is content-based ( INLINEFORM3 ), content-ignorant ( INLINEFORM4 ) or full access ( INLINEFORM5 ). The features for, e.g., the joint prediction model INLINEFORM6 of type INLINEFORM7 ( INLINEFORM8 ) can then simply be described as INLINEFORM9 . Recall that features computed on the basis of the EAU span are content-based ( INLINEFORM10 ), features from the EAU-surrounding text are content-ignorant ( INLINEFORM11 ) and features computed from both are denoted by full-access ( INLINEFORM12 ). Details on the extraction of features are provided below. These consist of boolean values indicating whether a certain word appears in the argumentative source or target EAU or both (and separately, their contexts). More precisely, for any classification instance we extract uni-grams from within the span of the EAU (if INLINEFORM0 ) or solely from the sentence-context surrounding the EAUs (if INLINEFORM1 ). Words which occur in both bags are only visible in the full-access setup INLINEFORM2 and are modeled as binary indicators. Such features consist of syntactic production rules extracted from constituency trees – they are modelled analogously to the lexical features as a bag of production rules. To make a clear division between features derived from the EAU embedding context and features derived from within the EAU span, we divide the constituency tree in two parts, as is illustrated in Figure FIGREF26 . If the EAU is embedded in a covering sentence, we cut the syntax tree at the corresponding edge ( in Figure FIGREF26 ). In this example, the content-ignorant ( INLINEFORM0 ) bag-of-word production rule representation includes the rules INLINEFORM1 and INLINEFORM2 . Analogously to the lexical features, the production rules are modeled as binary indicator features. These features describe shallow statistics such as the ratio of argumentative unit tokens compared to sentence tokens or the position of the argumentative unit in the paragraph. We set these features to zero for the content representation of the argumentative unit and replicate those features that allow us to treat the argumentative unit as a black-box. For example, in the content-based ( INLINEFORM0 ) system that has access only to the EAU, we can compute the #tokens in the EAU, but not the #tokens in EAU divided by #tokens in the sentence. The latter feature is only accessible in the full access system variants. Hence, in the content-based ( INLINEFORM1 ) system most of these statistics are set to zero since they cannot be computed by considering only the EAU span. For the content-based representation we retrieve only discourse relations that are confined within the span of the argumentative unit. In the very frequent case that discourse features cross the boundaries of embedding context and EAU span, we only take them into account for INLINEFORM0 . We use the element-wise sum of 300-dimensional pre-trained GloVe vectors BIBREF24 corresponding to the words within the EAU span ( INLINEFORM0 ) and the words of the EAU-surrounding context ( INLINEFORM1 ). Additionally, we compute the element-wise subtraction of the source EAU vector from the target EAU vector, with the aim of modelling directions in distributional space, similarly to BIBREF25 . Words with no corresponding pre-trained word vector and empty sequences (e.g., no preceding context available) are treated as a zero-vector. Tree-based sentiment annotations are sentiment scores assigned to nodes in constituency parse trees BIBREF26 . We represent these scores by a one-hot vector of dimension 5 (5 is very positive, 1 is very negative). We determine the contextual ( INLINEFORM0 ) sentiment by looking at the highest possible node of the context which does not contain the EAU (ADVP in Figure FIGREF26 ). The sentiment for an EAU span ( INLINEFORM1 ) is assigned to the highest possible node covering the EAU span which does not contain the context sub-tree (S in Figure FIGREF26 ). The full-access ( INLINEFORM2 ) score is assigned to the lowest possible node which covers both the EAU span and its surrounding context (S' in Figure FIGREF26 ). Next to the sentiment scores for the selected tree nodes and analogously to the word embeddings, we also calculate the element-wise subtraction of the one-hot sentiment source vectors from the one-hot sentiment target vectors. This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors. ### Results Our first step towards our main experiments is to replicate the competitive argumentative relation classifier of BIBREF13 , BIBREF1 . Hence, for comparison purposes, we first formulate the task exactly as it was done in this prior work, using the model formulation in Eq. EQREF17 , which determines the type of outgoing edge from a source (i.e., tree-like view). The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features. The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1. Intriguingly, the content-ignorant models ( INLINEFORM6 ) always perform significantly better than the models which only have access to the EAUs' content ( INLINEFORM7 , INLINEFORM8 ). In the most general task formulation ( INLINEFORM9 ), we observe that INLINEFORM10 even significantly outperforms the model which has maximum access (seeing both EAU spans and surrounding contexts: INLINEFORM11 ). At first glance, the results of the purely EAU focused systems ( INLINEFORM0 ) are disappointing, since they fall far behind their competitors. On the other hand, their F1 scores are not devastatingly bad. The strong most-frequent-class baseline is significantly outperformed by the content-based ( INLINEFORM1 ) system, across all three prediction settings. In summary our findings are as follows: (i) models which see the EAU span (content-based, INLINEFORM0 ) are significantly outperformed by models that have no access to the span itself (content-ignorant, INLINEFORM1 ) across all settings; (ii) in two of three prediction settings ( INLINEFORM2 and INLINEFORM3 ), the model which only has access to the context even outperforms the model that has access to all information in the input. The fact that using features derived exclusively from the EAU embedding context ( INLINEFORM4 ) can lead to better results than using a full feature-system ( INLINEFORM5 ) suggests that some information from the EAU can even be harmful. Why this is the case, we cannot answer exactly. A plausible cause might be related to the smaller dimension of the feature space, which makes the SVM less likely to overfit. Still, this finding comes as a surprise and calls for further investigation in future work. A system for argumentative relation classification can be applied in one of two settings: single-document or cross-document, as illustrated in Figure FIGREF42 : in the first case (top), a system is tasked to classify EAUs that appear linearly in one document – here contextual clues can often highlight the relationship between two units. This is the setting we have been considering up to now. However, in the second scenario (bottom), we have moved away from the closed single-document setting and ask the system to classify two EAUs extracted from different document contexts. This setting applies, for instance, when we are mining arguments from multiple sources. In both cases, however, a system that relies more on contextual clues than on the content expressed in the EAUs is problematic: in the single-document setting, such a system will rely on discourse indicators – whether or not they are justified by content – and can thus easily be fooled. In the cross-document setting, discourse-based indicators – being inherently defined with respect to their internal document context – do not have a defined rhetorical function with respect to EAUs in a separate document and thus a system that has learned to rely on such markers within a single-document setting can be seriously misled. We believe that the cross-document setting should be an important goal in argumentation analysis, since it generalizes better to many debates of interest, where EAUs can be found scattered across thousands of documents. For example, for the topic of legalizing marijuana, EAUs may be mined from millions of documents and thus their relations may naturally extend across document boundaries. If a system learns to over-proportionally attend to the EAUs' surrounding contexts it is prone to making many errors. In what follows we are simulating the effects that an overly context-sensitive classifier could have in a cross-document setting, by modifying our experimental setting, and study the effects on the different model types: In one setup – we call it randomized-context – we systematically distort the context of our testing instances by exchanging the context in a randomized manner; in the other setting – called no-context, we are deleting the context around the ADUs to be classified. Randomized-context simulates an open world debate where argumentative units may occur in different contexts, sometimes with discourse markers indicating an opposite class. In other words, in this setting we want to examine effects when porting a context-sensitive system to a multi-document setting. For example, as seen in Figure FIGREF42 , the context of an argumentative unit may change from “However” to “Moreover” – which can happen naturally in open debates. The results are displayed in Figure FIGREF43 . In the standard setting (Figure FIGREF43 ), the models that have access to the context besides the content ( INLINEFORM0 ) and the models that are only allowed to access the context ( INLINEFORM1 ), always perform better than the content-based models ( INLINEFORM2 ) (bars above zero). However, when we randomly flip contexts of the test instances (Figure FIGREF43 ), or suppress them entirely (Figure FIGREF43 ), the opposite picture emerges: the content-based models always outperform the other models. For some classes (support, INLINEFORM3 ) the difference can exceed 50 F1 percentage points. These two studies, where testing examples are varied regarding their context (randomized-context or no-context) simulates what can be expected if we apply our systems for relation class assignment to EAUs stemming from heterogeneous sources. While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they perform worse than the content-based model. We calculate the ANOVA classification F scores of the features with respect to our three task formulations INLINEFORM0 and INLINEFORM1 . The F percentiles of features extracted from the EAU surrounding text ( INLINEFORM2 ) and features extracted from the EAU span ( INLINEFORM3 ), are displayed in Figure FIGREF50 . It clearly stands out that features obtained from the EAU surrounding context ( INLINEFORM0 ) are assigned much higher scores compared to features stemming from the EAU span ( INLINEFORM1 ). This holds true for all three task formulations and provides further evidence that models – when given the option – put a strong focus on contextual clues while neglecting the information provided by the EAU span itself. ### Discussion While competitive systems for argumentative relation classification are considered to be robust, our experiments have shown that despite confidence-inspiring scores on unseen testing data, such systems can easily be fooled – they can deliver strong performance scores although the classifier does not have access to the content of the EAUs. In this respect, we have provided evidence that there is a danger in case models focus too much on rhetorical indicators, in detriment of the context. Thus, the following question arises: How can we prevent argumentation models from modeling arguments or argumentative units and their relations in overly naïve ways? A simple and intuitive way is to dissect EAUs from their surrounding document context. Models trained on data that is restricted to the EAUs' content will be forced to focus on the content of EAUs. We believe that this will enhance the robustness of such models and allows them to generalize to cross-document argument relation classification. The corpus of student essays makes such transformations straightforward: only the EAUs were annotated (e.g., “However, INLINEFORM0 A INLINEFORM1 ”). If annotations extend over the EAUs (e.g., only full sentences are annotated, “ INLINEFORM2 However, A INLINEFORM3 ”), such transformations could be performed automatically after a discourse parsing step. When inspecting the student essays corpus, we further observed that an EAU mining step should involve coreference resolution to better capture relations between EAUs that involve anaphors (e.g., “Exercising makes you feel better” and “It INLINEFORM4 increases endorphin levels”). Thus, in order to conduct real-world end-to-end argumentation relation mining for a given topic, we envision a system that addresses three steps: (i) mining of EAUs and (ii) replacement of pronouns in EAUs with referenced entities (e.g., INLINEFORM0 ). Finally (iii), given the cross product of mined EAUs we can apply a model of type INLINEFORM1 to construct a full-fledged argumentation graph, possibly spanning multiple documents. We have shown that in order to properly perform step (iii), we need stronger models that are able to better model EAU contents. Hence, we encourage the argumentation community to test their systems on a decontextualized version of the student essays, including the proposed – and possibly further extended – testing setups, to challenge the semantic representation and reasoning capacities of argument analysis models. This will lead to more realistic performance estimates and increased robustness of systems when addressing desirable multi-document tasks. ### Conclusion We have shown that systems which put too much focus on discourse information may be easily fooled – an issue which has severe implications when systems are applied to cross-document argumentative relation classification tasks. The strong reliance on contextual clues is also problematic in single-document contexts, where systems can run a risk of assigning relation labels relying on contextual and rhetorical effects – instead of focusing on content. Hence, we propose that researchers test their argumentative relation classification systems on two alternative versions of the StudentEssay data that reflect different access levels. (i) EAU-span only, where systems only see the EAU spans and (ii) context-only, where systems can only see the EAU-surrounding context. These complementary settings will (i) challenge the semantic capacities of a system, and (ii) unveil the extent to which a system is focusing on the discourse context when making decisions. We will offer our testing environments to the research community through a platform that provides datasets and scripts and a table to trace the results of content-based systems. ### Acknowledgments This work has been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) under grant no. GRK 1994/1 and by the Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling”, supported by the Leibniz Association under grant no. SAS-2015-IDS-LWC and by the Ministry of Science, Research, and Art of Baden-Württemberg. Figure 1: A graph representation of a topic (node w/ dashed line), two argumentative premise units (nodes w/ solid line), premise-topic relations (positive or negative) and premise-premise relations (here: attacks). Figure 2: Production rule extraction from constituency parse for two different argumentative units. Table 1: Data set statistics. Table 2: Baseline system replication results. Table 3: Argumentative relation classification models h, f, g with different access to content and context; models of type CI (content-ignorant) have no access to the EAU span. †: significantly better than mfs baseline (p < 0.005); ‡ significantly better than content-based (p < 0.005). Figure 3: Single-document (top) vs. cross-document (bottom) argumentative relation classification. Black edge: gold label; purple edge: predicted label. Figure 4: Randomized-context test set: models are applied to testing instances with randomly flipped contexts. No-context test set: models can only access the EAU span of a testing instance. A bar below/above zero means that a system that can access context (content-ignorant CI or full-access FA) is worse/better than the content-based baseline CB that only has access to the EAU span (its performance is not affected by modified context, cf. Tab. 3). Figure 5: ANOVA F score percentiles for contentbased vs. content-ignorant features in the training data. A higher feature score suggests greater predictive capacity.	BIBREF13, majority baseline
How does the volplas' culture differ from traditional human Western culture? A. Volplas have a superior morality. B. Volplas have larger families. C. Volplas care for their children. D. Volplas can be promiscuous.	Volpla By WYMAN GUIN Illustrated by DICK FRANCIS [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.] The only kind of gag worth pulling, I always maintained, was a cosmic one—till I learned the Cosmos has a really nasty sense of humor! There were three of them. Dozens of limp little mutants that would have sent an academic zoologist into hysterics lay there in the metabolic accelerator. But there were three of them . My heart took a great bound. I heard my daughter's running feet in the animal rooms and her rollerskates banging at her side. I closed the accelerator and walked across to the laboratory door. She twisted the knob violently, trying to hit a combination that would work. I unlocked the door, held it against her pushing and slipped out so that, for all her peering, she could see nothing. I looked down on her tolerantly. "Can't adjust your skates?" I asked again. "Daddy, I've tried and tried and I just can't turn this old key tight enough." I continued to look down on her. "Well, Dad-dee, I can't!" "Tightly enough." "What?" "You can't turn this old key tightly enough." "That's what I say -yud." "All right, wench. Sit on this chair." I got down and shoved one saddle shoe into a skate. It fitted perfectly. I strapped her ankle and pretended to use the key to tighten the clamp. Volplas at last. Three of them. Yet I had always been so sure I could create them that I had been calling them volplas for ten years. No, twelve. I glanced across the animal room to where old Nijinsky thrust his graying head from a cage. I had called them volplas since the day old Nijinsky's elongated arms and his cousin's lateral skin folds had given me the idea of a flying mutant. When Nijinsky saw me looking at him, he started a little tarantella about his cage. I smiled with nostalgia when the fifth fingers of his hands, four times as long as the others, uncurled as he spun about the cage. I turned to the fitting of my daughter's other skate. "Daddy?" "Yes?" "Mother says you are eccentric. Is that true?" "I'll speak to her about it." "Don't you know ?" "Do you understand the word?" "No." I lifted her out of the chair and stood her on her skates. "Tell your mother that I retaliate. I say she is beautiful." She skated awkwardly between the rows of cages from which mutants with brown fur and blue fur, too much and too little fur, enormously long and ridiculously short arms, stared at her with simian, canine or rodent faces. At the door to the outside, she turned perilously and waved. Again in the laboratory, I entered the metabolic accelerator and withdrew the intravenous needles from my first volplas. I carried their limp little forms out to a mattress in the lab, two girls and a boy. The accelerator had forced them almost to adulthood in less than a month. It would be several hours before they would begin to move, to learn to feed and play, perhaps to learn to fly. Meanwhile, it was clear that here was no war of dominant mutations. Modulating alleles had smoothed the freakish into a beautiful pattern. These were no monsters blasted by the dosage of radiation into crippled structures. They were lovely, perfect little creatures. My wife tried the door, too, but more subtly, as if casually touching the knob while calling. "Lunch, dear." "Be right there." She peeked too, as she had for fifteen years, but I blocked her view when I slipped out. "Come on, you old hermit. I have a buffet on the terrace." "Our daughter says I'm eccentric. Wonder how the devil she found out." "From me, of course." "But you love me just the same." "I adore you." She stretched on tiptoe and put her arms over my shoulders and kissed me. My wife did indeed have a delicious-looking buffet ready on the terrace. The maid was just setting down a warmer filled with hot hamburgers. I gave the maid a pinch and said, "Hello, baby." My wife looked at me with a puzzled smile. "What on Earth's got into you?" The maid beat it into the house. I flipped a hamburger and a slice of onion onto a plate and picked up the ketchup and said, "I've reached the dangerous age." "Oh, good heavens!" I dowsed ketchup over the hamburger, threw the onion on and closed it. I opened a bottle of beer and guzzled from it, blew out my breath and looked across the rolling hills and oak woods of our ranch to where the Pacific shimmered. I thought, "All this and three volplas, too." I wiped the back of my hand across my mouth and said aloud, "Yes, sir, the dangerous age. And, lady, I'm going to have fun." My wife sighed patiently. I walked over and put the arm that held the beer bottle around her shoulder and chucked her chin up with my other hand. The golden sun danced in her blue eyes. I watched that light in her beautiful eyes and said, "But you're the only one I'm dangerous about." I kissed her until I heard rollerskates coming across the terrace from one direction and a horse galloping toward the terrace from the other direction. "You have lovely lips," I whispered. "Thanks. Yours deserve the Good Housekeeping Seal of Approval, too." Our son reared the new palomino I had just bought him for his fourteenth birthday and yelled down, "Unhand that maiden, Burrhead, or I'll give you lead poisoning." I laughed and picked up my plate and sat down in a chair. My wife brought me a bowl of salad and I munched the hamburger and watched the boy unsaddle the horse and slap it away to the pasture. I thought, "By God, wouldn't he have a fit if he knew what I have back there in that lab! Wouldn't they all!" The boy carried the saddle up onto the terrace and dropped it. "Mom, I'd like a swim before I eat." He started undressing. "You look as though a little water might help," she agreed, sitting down next to me with her plate. The girl was yanking off her skates. "And I want one." "All right. But go in the house and put on your swim suit." "Oh, Mother . Why?" "Because, dear, I said so." The boy had already raced across the terrace and jack-knifed into the pool. The cool sound of the dive sent the girl scurrying for her suit. I looked at my wife. "What's the idea?" "She's going to be a young woman soon." "Is that any reason for wearing clothes? Look at him. He's a young man sooner than already." "Well, if you feel that way about it, they'll both have to start wearing clothes." I gulped the last of my hamburger and washed it down with the beer. "This place is going to hell," I complained. "The old man isn't allowed to pinch the maid and the kids can't go naked." I leaned toward her and smacked her cheek. "But the food and the old woman are still the best." "Say, what goes with you? You've been grinning like a happy ape ever since you came out of the lab." "I told you—" "Oh, not that again! You were dangerous at any age." I stood up and put my plate aside and bent over her. "Just the same, I'm going to have a new kind of fun." She reached up and grabbed my ear. She narrowed her eyes and put a mock grimness on her lips. "It's a joke," I assured her. "I'm going to play a tremendous joke on the whole world. I've only had the feeling once before in a small way, but I've always...." She twisted my ear and narrowed her eyes even more. "Like?" "Well, when my old man was pumping his first fortune out of some oil wells in Oklahoma, we lived down there. Outside this little town, I found a litter of flat stones that had young black-snakes under each slab. I filled a pail with them and took them into town and dumped them on the walk in front of the movie just as Theda Bara's matinee let out. The best part was that no one had seen me do it. They just couldn't understand how so many snakes got there. I learned how great it can be to stand around quietly and watch people encounter the surprise that you have prepared for them." She let go of my ear. "Is that the kind of fun you're going to have?" "Yep." She shook her head. "Did I say you are eccentric ?" I grinned. "Forgive me if I eat and run, dear. Something in the lab can't wait." The fact was that I had something more in the lab than I had bargained for. I had aimed only at a gliding mammal a little more efficient than the Dusky Glider of Australia, a marsupial. Even in the basically mutating colony, there had been a decidedly simian appearance in recent years, a long shift from the garbage-dump rats I had started with. But my first volplas were shockingly humanoid. They were also much faster than had been their predecessors in organizing their nervous activity after the slumbrous explosion of growth in the metabolic accelerator. When I returned to the lab, they were already moving about on the mattress and the male was trying to stand. He was a little the larger and stood twenty-eight inches high. Except for the face, chest and belly, they were covered with a soft, almost golden down. Where it was bare of this golden fur, the skin was pink. On their heads and across the shoulders of the male stood a shock of fur as soft as chinchilla. The faces were appealingly humanoid, except that the eyes were large and nocturnal. The cranium was in the same proportion to the body as it is in the human. When the male spread his arms, the span was forty-eight inches. I held his arms out and tried to tease the spars open. They were not new. The spars had been common to the basic colony for years and were the result of serial mutations effecting those greatly elongated fifth fingers that had first appeared in Nijinsky. No longer jointed like a finger, the spar turned backward sharply and ran alongside the wrist almost to the elbow. Powerful wrist muscles could snap it outward and forward. Suddenly, as I teased the male volpla, this happened. The spars added nine inches on each side to his span. As they swept out and forward, the lateral skin that had, till now, hung in resting folds was tightened in a golden plane that stretched from the tip of the spar to his waist and continued four inches wide down his legs to where it anchored at the little toe. This was by far the most impressive plane that had appeared till now. It was a true gliding plane, perhaps even a soaring one. I felt a thrill run along my back. By four o'clock that afternoon, I was feeding them solid food and, with the spars closed, they were holding little cups and drinking water from them in a most humanlike way. They were active, curious, playful and decidedly amorous. Their humanoid qualities were increasingly apparent. There was a lumbar curvature and buttocks. The shoulder girdle and pectoral muscles were heavy and out of proportion, of course, yet the females had only one pair of breasts. The chin and jaw were humanlike instead of simian and the dental equipment was appropriate to this structure. What this portended was brought home to me with a shock. I was kneeling on the mattress, cuffing and roughing the male as one might a puppy dog, when one of the females playfully climbed up my back. I reached around and brought her over my shoulder and sat her down. I stroked the soft fur on her head and said, "Hello, pretty one. Hello." The male watched me, grinning. He said, "'Ello, 'ello." As I walked into the kitchen, giddy with this enormous joke, my wife said, "Guy and Em are flying up for dinner. That rocket of Guy's they launched in the desert yesterday was a success. It pulled Guy up to Cloud Nine and he wants to celebrate." I danced a little jig the way old Nijinsky might do it. "Oh, great! Oh, wonderful! Good old Guy! Everybody's a success. It's great. It's wonderful. Success on success!" I danced into the kitchen table and tipped over a basket of green corn. The maid promptly left the kitchen for some other place. My wife just stared at me. "Have you been drinking the lab alcohol?" "I've been drinking the nectar of the gods. My Hera, you're properly married to Zeus. I've my own little Greeks descended from Icarus." She pretended a hopeless sag of her pretty shoulders. "Wouldn't you just settle for a worldly martini?" "I will, yes. But first a divine kiss." I sipped at my martini and lounged in a terrace chair watching the golden evening slant across the beautiful hills of our ranch. I dreamed. I would invent a euphonious set of words to match the Basic English vocabulary and teach it to them as their language. They would have their own crafts and live in small tree houses. I would teach them legends: that they had come from the stars, that they had subsequently watched the first red men and then the first white men enter these hills. When they were able to take care of themselves, I would turn them loose. There would be volpla colonies all up and down the Coast before anyone suspected. One day, somebody would see a volpla. The newspapers would laugh. Then someone authoritative would find a colony and observe them. He would conclude, "I am convinced that they have a language and speak it intelligently." The government would issue denials. Reporters would "expose the truth" and ask, "Where have these aliens come from?" The government would reluctantly admit the facts. Linguists would observe at close quarters and learn the simple volpla language. Then would come the legends. Volpla wisdom would become a cult—and of all forms of comedy, cults, I think, are the funniest. "Darling, are you listening to me?" my wife asked with impatient patience. "What? Sure. Certainly." "You didn't hear a word. You just sit there and grin into space." She got up and poured me another martini. "Here, maybe this will sober you up." I pointed. "That's probably Guy and Em." A 'copter sidled over the ridge, then came just above the oak woods toward us. Guy set it gently on the landing square and we walked down to meet them. I helped Em out and hugged her. Guy jumped out, asking, "Do you have your TV set on?" "No," I answered. "Should I?" "It's almost time for the broadcast. I was afraid we would miss it." "What broadcast?" "From the rocket." "Rocket?" "For heaven's sake, darling," my wife complained, "I told you about Guy's rocket being a success. The papers are full of it. So are the broadcasts." As we stepped up on the terrace, she turned to Guy and Em. "He's out of contact today. Thinks he's Zeus." I asked our son to wheel a TV set out onto the terrace while I made martinis for our friends. Then we sat down and drank the cocktails and the kids had fruit juice and we watched the broadcast Guy had tuned in. Some joker from Cal Tech was explaining diagrams of a multi-stage rocket. After a bit, I got up and said, "I have something out in the lab I want to check on." "Hey, wait a minute," Guy objected. "They're about to show the shots of the launching." My wife gave me a look; you know the kind. I sat down. Then I got up and poured myself another martini and freshened Em's up, too. I sat down again. The scene had changed to a desert launching site. There was old Guy himself explaining that when he pressed the button before him, the hatch on the third stage of the great rocket in the background would close and, five minutes later, the ship would fire itself. Guy, on the screen, pushed the button, and I heard Guy, beside me, give a sort of little sigh. We watched the hatch slowly close. "You look real good," I said. "A regular Space Ranger. What are you shooting at?" "Darling, will you please—be— quiet ?" "Yeah, Dad. Can it, will you? You're always gagging around." On the screen, Guy's big dead-earnest face was explaining more about the project and suddenly I realized that this was an instrument-bearing rocket they hoped to land on the Moon. It would broadcast from there. Well, now—say, that would be something! I began to feel a little ashamed of the way I had been acting and I reached out and slapped old Guy on the shoulder. For just a moment, I thought of telling him about my volplas. But only for a moment. A ball of flame appeared at the base of the rocket. Miraculously, the massive tower lifted, seemed for a moment merely to stand there on a flaming pillar, then was gone. The screen returned to a studio, where an announcer explained that the film just shown had been taken day before yesterday. Since then, the rocket's third stage was known to have landed successfully at the south shore of Mare Serenitatis. He indicated the location on a large lunar map behind him. "From this position, the telemeter known as Rocket Charlie will be broadcasting scientific data for several months. But now, ladies and gentlemen, we will clear the air for Rocket Charlie's only general broadcast. Stand by for Rocket Charlie." A chronometer appeared on the screen and, for several seconds, there was silence. I heard my boy whisper, "Uncle Guy, this is the biggest!" My wife said, "Em, I think I'll just faint." Suddenly there was a lunar landscape on the screen, looking just as it's always been pictured. A mechanical voice cut in. "This is Rocket Charlie saying, 'Hello, Earth,' from my position in Mare Serenitatis. First I will pan the Menelaus Mountains for fifteen seconds. Then I will focus my camera on Earth for five seconds." The camera began to move and the mountains marched by, stark and awesomely wild. Toward the end of the movement, the shadow of the upright third stage appeared in the foreground. Abruptly the camera made a giddy swing, focused a moment, and we were looking at Earth. At that time, there was no Moon over California. It was Africa and Europe we were looking at. "This is Rocket Charlie saying, 'Good-by, Earth.'" Well, when that screen went dead, there was pandemonium around our terrace. Big old Guy was so happy, he was wiping tears from his eyes. The women were kissing him and hugging him. Everybody was yelling at once. I used the metabolic accelerator to cut the volplas' gestation down to one week. Then I used it to bring the infants to maturity in one month. I had luck right off. Quite by accident, the majority of the early infants were females, which sped things up considerably. By the next spring, I had a colony of over a hundred volplas and I shut down the accelerator. From now on, they could have babies in their own way. I had devised the language for them, using Basic English as my model, and during the months while every female was busy in the metabolic accelerator, I taught the language to the males. They spoke it softly in high voices and the eight hundred words didn't seem to tax their little skulls a bit. My wife and the kids went down to Santa Barbara for a week and I took the opportunity to slip the oldest of the males and his two females out of the lab. I put them in the jeep beside me and drove to a secluded little valley about a mile back in the ranch. They were all three wide-eyed at the world and jabbered continuously. They kept me busy relating their words for "tree," "rock," "sky" to the objects. They had a little trouble with "sky." Until I had them out in the open country, it had been impossible to appreciate fully what lovely little creatures they were. They blended perfectly with the California landscape. Occasionally, when they raised their arms, the spars would open and spread those glorious planes. Almost two hours went by before the male made it into the air. His playful curiosity about the world had been abandoned momentarily and he was chasing one of the girls. As usual, she was anxious to be caught and stopped abruptly at the bottom of a little knoll. He probably meant to dive for her. But when he spread his arms, the spars snapped out and those golden planes sheared into the air. He sailed over her in a stunning sweep. Then he rose up and up until he hung in the breeze for a long moment, thirty feet above the ground. He turned a plaintive face back to me, dipped worriedly and skimmed straight for a thorn bush. He banked instinctively, whirled toward us in a golden flash and crashed with a bounce to the grass. The two girls reached him before I did and stroked and fussed over him so that I could not get near. Suddenly he laughed with a shrill little whoop. After that, it was a carnival. They learned quickly and brilliantly. They were not fliers; they were gliders and soarers. Before long, they took agilely to the trees and launched themselves in beautiful glides for hundreds of feet, banking, turning and spiraling to a gentle halt. I laughed out loud with anticipation. Wait till the first pair of these was brought before a sheriff! Wait till reporters from the Chronicle motored out into the hills to witness this! Of course, the volplas didn't want to return to the lab. There was a tiny stream through there and at one point it formed a sizable pool. They got into this and splashed their long arms about and they scrubbed each other. Then they got out and lay on their backs with the planes stretched to dry. I watched them affectionately and wondered about the advisability of leaving them out here. Well, it had to be done sometime. Nothing I could tell them about surviving would help them as much as a little actual surviving. I called the male over to me. He came and squatted, conference fashion, the elbows resting on the ground, the wrists crossed at his chest. He spoke first. "Before the red men came, did we live here?" "You lived in places like this all along these mountains. Now there are very few of you left. Since you have been staying at my place, you naturally have forgotten the ways of living outdoors." "We can learn again. We want to stay here." His little face was so solemn and thoughtful that I reached out and stroked the fur on his head reassuringly. We both heard the whir of wings overhead. Two mourning doves flew across the stream and landed in an oak on the opposite hillside. I pointed. "There's your food, if you can kill it." He looked at me. "How?" "I don't think you can get at them in the tree. You'll have to soar up above and catch one of them on the wing when they fly away. Think you can get up that high?" He looked around slowly at the breeze playing in the branches and dancing along the hillside grass. It was as if he had been flying a thousand years and was bringing antique wisdom to bear. "I can get up there. I can stay for a while. How long will they be in the tree?" "Chances are they won't stay long. Keep your eye on the tree in case they leave while you are climbing." He ran to a nearby oak and clambered aloft. Presently he launched himself, streaked down-valley a way and caught a warm updraft on a hillside. In no time, he was up about two hundred feet. He began criss-crossing the ridge, working his way back to us. The two girls were watching him intently. They came over to me wonderingly, stopping now and then to watch him. When they were standing beside me, they said nothing. They shaded their eyes with tiny hands and watched him as he passed directly above us at about two hundred and fifty feet. One of the girls, with her eyes fast on his soaring planes, reached out and grasped my sleeve tightly. He flashed high above the stream and hung behind the crest of the hill where the doves rested. I heard their mourning from the oak tree. It occurred to me they would not leave that safety while the hawklike silhouette of the volpla marred the sky so near. I took the girl's hand from my sleeve and spoke to her, pointing as I did so. "He is going to catch a bird. The bird is in that tree. You can make the bird fly so that he can catch it. Look here." I got up and found a stick. "Can you do this?" I threw the stick up into a tree near us. Then I found her a stick. She threw it better than I had expected. "Good, pretty one. Now run across the stream and up to that tree and throw a stick into it." She climbed skillfully into the tree beside us and launched herself across the stream. She swooped up the opposite hillside and landed neatly in the tree where the doves rested. The birds came out of the tree, climbing hard with their graceful strokes. I looked back, as did the girl remaining beside me. The soaring volpla half closed his planes and started dropping. He became a golden flash across the sky. The doves abruptly gave up their hard climbing and fell away with swiftly beating wings. I saw one of the male volpla's planes open a little. He veered giddily in the new direction and again dropped like a molten arrow. The doves separated and began to zigzag down the valley. The volpla did something I would not have anticipated—he opened his planes and shot lower than the bird he was after, then swept up and intercepted the bird's crossward flight. I saw the planes close momentarily. Then they opened again and the bird plummeted to a hillside. The volpla landed gently atop the hill and stood looking back at us. The volpla beside me danced up and down shrieking in a language all her own. The girl who had raised the birds from the tree volplaned back to us, yammering like a bluejay. It was a hero's welcome. He had to walk back, of course—he had no way to carry such a load in flight. The girls glided out to meet him. Their lavish affection held him up for a time, but eventually he strutted in like every human hunter. They were raptly curious about the bird. They poked at it, marveled at its feathers and danced about it in an embryonic rite of the hunt. But presently the male turned to me. "We eat this?" I laughed and took his tiny, four-fingered hand. In a sandy spot beneath a great tree that overhung the creek, I built a small fire for them. This was another marvel, but first I wanted to teach them how to clean the bird. I showed them how to spit it and turn it over their fire. Later, I shared a small piece of the meat in their feast. They were gleeful and greasily amorous during the meal. When I had to leave, it was dark. I warned them to stand watches, keep the fire burning low and take to the tree above if anything approached. The male walked a little away with me when I left the fire. I said again, "Promise me you won't leave here until we've made you ready for it." "We like it here. We will stay. Tomorrow you bring more of us?" "Yes. I will bring many more of you, if you promise to keep them all here in this woods until they're ready to leave." "I promise." He looked up at the night sky and, in the firelight, I saw his wonder. "You say we came from there?" "The old ones of your kind told me so. Didn't they tell you?" "I can't remember any old ones. You tell me." "The old ones told me you came long before the red men in a ship from the stars." Standing there in the dark, I had to grin, visioning the Sunday supplements that would be written in about a year, maybe even less. He looked into the sky for a long time. "Those little lights are the stars?" "That's right." "Which star?" I glanced about and presently pointed over a tree. "From Venus." Then I realized I had blundered by passing him an English name. "In your language, Pohtah." He looked at the planet a long time and murmured, "Venus. Pohtah." That next week, I transported all of the volplas out to the oak woods. There were a hundred and seven men, women and children. With no design on my part, they tended to segregate into groups consisting of four to eight couples together with the current children of the women. Within these groups, the adults were promiscuous, but apparently not outside the group. The group thus had the appearance of a super-family and the males indulged and cared for all the children without reference to actual parenthood. By the end of the week, these super-families were scattered over about four square miles of the ranch. They had found a new delicacy, sparrows, and hunted them easily as they roosted at night. I had taught the volplas to use the fire drill and they were already utilizing the local grasses, vines and brush to build marvelously contrived tree houses in which the young, and sometimes the adults, slept through midday and midnight. The afternoon my family returned home, I had a crew of workmen out tearing down the animal rooms and lab building. The caretakers had anesthetized all the experimental mutants, and the metabolic accelerator and other lab equipment was being dismantled. I wanted nothing around that might connect the sudden appearance of the volplas with my property. It was already apparent that it would take the volplas only a few more weeks to learn their means of survival and develop an embryonic culture of their own. Then they could leave my ranch and the fun would be on. My wife got out of the car and looked around at the workmen hurrying about the disemboweled buildings and she said, "What on Earth is going on here?" "I've finished my work and we no longer need the buildings. I'm going to write a paper about my results." My wife looked at me appraisingly and shook her head. "I thought you meant it. But you really ought to. It would be your first." My son asked, "What happened to the animals?" "Turned them over to the university for further study," I lied. "Well," he said to her, "you can't say our pop isn't a man of decision." Twenty-four hours later, there wasn't a sign of animal experimentation on the ranch. Except, of course, that the woods were full of volplas. At night, I could hear them faintly when I sat out on the terrace. As they passed through the dark overhead, they chattered and laughed and sometimes moaned in winged love. One night a flight of them soared slowly across the face of the full Moon, but I was the only one who noticed.	B. Volplas have larger families.
How does Sattell hope to get rid of Pop? A. Luring him down into the Big Crack and killing him B. Hiring an assassin from a neighboring planet C. Blowing up the shack near the edge of the Big Crack D. Escaping on board a secondhand lunar tour vessel	SCRIMSHAW The old man just wanted to get back his memory—and the methods he used were gently hellish, from the viewpoint of the others.... BY MURRAY LEINSTER Illustrated by Freas Pop Young was the one known man who could stand life on the surface of the Moon's far side, and, therefore, he occupied the shack on the Big Crack's edge, above the mining colony there. Some people said that no normal man could do it, and mentioned the scar of a ghastly head-wound to explain his ability. One man partly guessed the secret, but only partly. His name was Sattell and he had reason not to talk. Pop Young alone knew the whole truth, and he kept his mouth shut, too. It wasn't anybody else's business. The shack and the job he filled were located in the medieval notion of the physical appearance of hell. By day the environment was heat and torment. By night—lunar night, of course, and lunar day—it was frigidity and horror. Once in two weeks Earth-time a rocketship came around the horizon from Lunar City with stores for the colony deep underground. Pop received the stores and took care of them. He handed over the product of the mine, to be forwarded to Earth. The rocket went away again. Come nightfall Pop lowered the supplies down the long cable into the Big Crack to the colony far down inside, and freshened up the landing field marks with magnesium marking-powder if a rocket-blast had blurred them. That was fundamentally all he had to do. But without him the mine down in the Crack would have had to shut down. The Crack, of course, was that gaping rocky fault which stretches nine hundred miles, jaggedly, over the side of the Moon that Earth never sees. There is one stretch where it is a yawning gulf a full half-mile wide and unguessably deep. Where Pop Young's shack stood it was only a hundred yards, but the colony was a full mile down, in one wall. There is nothing like it on Earth, of course. When it was first found, scientists descended into it to examine the exposed rock-strata and learn the history of the Moon before its craters were made. But they found more than history. They found the reason for the colony and the rocket landing field and the shack. The reason for Pop was something else. The shack stood a hundred feet from the Big Crack's edge. It looked like a dust-heap thirty feet high, and it was. The outside was surface moondust, piled over a tiny dome to be insulation against the cold of night and shadow and the furnace heat of day. Pop lived in it all alone, and in his spare time he worked industriously at recovering some missing portions of his life that Sattell had managed to take away from him. He thought often of Sattell, down in the colony underground. There were galleries and tunnels and living-quarters down there. There were air-tight bulkheads for safety, and a hydroponic garden to keep the air fresh, and all sorts of things to make life possible for men under if not on the Moon. But it wasn't fun, even underground. In the Moon's slight gravity, a man is really adjusted to existence when he has a well-developed case of agoraphobia. With such an aid, a man can get into a tiny, coffinlike cubbyhole, and feel solidity above and below and around him, and happily tell himself that it feels delicious. Sometimes it does. But Sattell couldn't comfort himself so easily. He knew about Pop, up on the surface. He'd shipped out, whimpering, to the Moon to get far away from Pop, and Pop was just about a mile overhead and there was no way to get around him. It was difficult to get away from the mine, anyhow. It doesn't take too long for the low gravity to tear a man's nerves to shreds. He has to develop kinks in his head to survive. And those kinks— The first men to leave the colony had to be knocked cold and shipped out unconscious. They'd been underground—and in low gravity—long enough to be utterly unable to face the idea of open spaces. Even now there were some who had to be carried, but there were some tougher ones who were able to walk to the rocketship if Pop put a tarpaulin over their heads so they didn't have to see the sky. In any case Pop was essential, either for carrying or guidance. Sattell got the shakes when he thought of Pop, and Pop rather probably knew it. Of course, by the time he took the job tending the shack, he was pretty certain about Sattell. The facts spoke for themselves. Pop had come back to consciousness in a hospital with a great wound in his head and no memory of anything that had happened before that moment. It was not that his identity was in question. When he was stronger, the doctors told him who he was, and as gently as possible what had happened to his wife and children. They'd been murdered after he was seemingly killed defending them. But he didn't remember a thing. Not then. It was something of a blessing. But when he was physically recovered he set about trying to pick up the threads of the life he could no longer remember. He met Sattell quite by accident. Sattell looked familiar. Pop eagerly tried to ask him questions. And Sattell turned gray and frantically denied that he'd ever seen Pop before. All of which happened back on Earth and a long time ago. It seemed to Pop that the sight of Sattell had brought back some vague and cloudy memories. They were not sharp, though, and he hunted up Sattell again to find out if he was right. And Sattell went into panic when he returned. Nowadays, by the Big Crack, Pop wasn't so insistent on seeing Sattell, but he was deeply concerned with the recovery of the memories that Sattell helped bring back. Pop was a highly conscientious man. He took good care of his job. There was a warning-bell in the shack, and when a rocketship from Lunar City got above the horizon and could send a tight beam, the gong clanged loudly, and Pop got into a vacuum-suit and went out the air lock. He usually reached the moondozer about the time the ship began to brake for landing, and he watched it come in. He saw the silver needle in the sky fighting momentum above a line of jagged crater-walls. It slowed, and slowed, and curved down as it drew nearer. The pilot killed all forward motion just above the field and came steadily and smoothly down to land between the silvery triangles that marked the landing place. Instantly the rockets cut off, drums of fuel and air and food came out of the cargo-hatch and Pop swept forward with the dozer. It was a miniature tractor with a gigantic scoop in front. He pushed a great mound of talc-fine dust before him to cover up the cargo. It was necessary. With freight costing what it did, fuel and air and food came frozen solid, in containers barely thicker than foil. While they stayed at space-shadow temperature, the foil would hold anything. And a cover of insulating moondust with vacuum between the grains kept even air frozen solid, though in sunlight. At such times Pop hardly thought of Sattell. He knew he had plenty of time for that. He'd started to follow Sattell knowing what had happened to his wife and children, but it was hearsay only. He had no memory of them at all. But Sattell stirred the lost memories. At first Pop followed absorbedly from city to city, to recover the years that had been wiped out by an axe-blow. He did recover a good deal. When Sattell fled to another continent, Pop followed because he had some distinct memories of his wife—and the way he'd felt about her—and some fugitive mental images of his children. When Sattell frenziedly tried to deny knowledge of the murder in Tangier, Pop had come to remember both his children and some of the happiness of his married life. Even when Sattell—whimpering—signed up for Lunar City, Pop tracked him. By that time he was quite sure that Sattell was the man who'd killed his family. If so, Sattell had profited by less than two days' pay for wiping out everything that Pop possessed. But Pop wanted it back. He couldn't prove Sattell's guilt. There was no evidence. In any case, he didn't really want Sattell to die. If he did, there'd be no way to recover more lost memories. Sometimes, in the shack on the far side of the Moon, Pop Young had odd fancies about Sattell. There was the mine, for example. In each two Earth-weeks of working, the mine-colony nearly filled up a three-gallon cannister with greasy-seeming white crystals shaped like two pyramids base to base. The filled cannister would weigh a hundred pounds on Earth. Here it weighed eighteen. But on Earth its contents would be computed in carats, and a hundred pounds was worth millions. Yet here on the Moon Pop kept a waiting cannister on a shelf in his tiny dome, behind the air-apparatus. It rattled if he shook it, and it was worth no more than so many pebbles. But sometimes Pop wondered if Sattell ever thought of the value of the mine's production. If he would kill a woman and two children and think he'd killed a man for no more than a hundred dollars, what enormity would he commit for a three-gallon quantity of uncut diamonds? But he did not dwell on such speculation. The sun rose very, very slowly in what by convention was called the east. It took nearly two hours to urge its disk above the horizon, and it burned terribly in emptiness for fourteen times twenty-four hours before sunset. Then there was night, and for three hundred and thirty-six consecutive hours there were only stars overhead and the sky was a hole so terrible that a man who looked up into it—what with the nagging sensation of one-sixth gravity—tended to lose all confidence in the stability of things. Most men immediately found it hysterically necessary to seize hold of something solid to keep from falling upward. But nothing felt solid. Everything fell, too. Wherefore most men tended to scream. But not Pop. He'd come to the Moon in the first place because Sattell was here. Near Sattell, he found memories of times when he was a young man with a young wife who loved him extravagantly. Then pictures of his children came out of emptiness and grew sharp and clear. He found that he loved them very dearly. And when he was near Sattell he literally recovered them—in the sense that he came to know new things about them and had new memories of them every day. He hadn't yet remembered the crime which lost them to him. Until he did—and the fact possessed a certain grisly humor—Pop didn't even hate Sattell. He simply wanted to be near him because it enabled him to recover new and vivid parts of his youth that had been lost. Otherwise, he was wholly matter-of-fact—certainly so for the far side of the Moon. He was a rather fussy housekeeper. The shack above the Big Crack's rim was as tidy as any lighthouse or fur-trapper's cabin. He tended his air-apparatus with a fine precision. It was perfectly simple. In the shadow of the shack he had an unfailing source of extreme low temperature. Air from the shack flowed into a shadow-chilled pipe. Moisture condensed out of it here, and CO 2 froze solidly out of it there, and on beyond it collected as restless, transparent liquid air. At the same time, liquid air from another tank evaporated to maintain the proper air pressure in the shack. Every so often Pop tapped the pipe where the moisture froze, and lumps of water ice clattered out to be returned to the humidifier. Less often he took out the CO 2 snow, and measured it, and dumped an equivalent quantity of pale-blue liquid oxygen into the liquid air that had been purified by cold. The oxygen dissolved. Then the apparatus reversed itself and supplied fresh air from the now-enriched fluid, while the depleted other tank began to fill up with cold-purified liquid air. Outside the shack, jagged stony pinnacles reared in the starlight, and craters complained of the bombardment from space that had made them. But, outside, nothing ever happened. Inside, it was quite different. Working on his memories, one day Pop made a little sketch. It helped a great deal. He grew deeply interested. Writing-material was scarce, but he spent most of the time between two particular rocket-landings getting down on paper exactly how a child had looked while sleeping, some fifteen years before. He remembered with astonishment that the child had really looked exactly like that! Later he began a sketch of his partly-remembered wife. In time—he had plenty—it became a really truthful likeness. The sun rose, and baked the abomination of desolation which was the moonscape. Pop Young meticulously touched up the glittering triangles which were landing guides for the Lunar City ships. They glittered from the thinnest conceivable layer of magnesium marking-powder. He checked over the moondozer. He tended the air apparatus. He did everything that his job and survival required. Ungrudgingly. Then he made more sketches. The images to be drawn came back more clearly when he thought of Sattell, so by keeping Sattell in mind he recovered the memory of a chair that had been in his forgotten home. Then he drew his wife sitting in it, reading. It felt very good to see her again. And he speculated about whether Sattell ever thought of millions of dollars' worth of new-mined diamonds knocking about unguarded in the shack, and he suddenly recollected clearly the way one of his children had looked while playing with her doll. He made a quick sketch to keep from forgetting that. There was no purpose in the sketching, save that he'd lost all his young manhood through a senseless crime. He wanted his youth back. He was recovering it bit by bit. The occupation made it absurdly easy to live on the surface of the far side of the Moon, whether anybody else could do it or not. Sattell had no such device for adjusting to the lunar state of things. Living on the Moon was bad enough anyhow, then, but living one mile underground from Pop Young was much worse. Sattell clearly remembered the crime Pop Young hadn't yet recalled. He considered that Pop had made no overt attempt to revenge himself because he planned some retaliation so horrible and lingering that it was worth waiting for. He came to hate Pop with an insane ferocity. And fear. In his mind the need to escape became an obsession on top of the other psychotic states normal to a Moon-colonist. But he was helpless. He couldn't leave. There was Pop. He couldn't kill Pop. He had no chance—and he was afraid. The one absurd, irrelevant thing he could do was write letters back to Earth. He did that. He wrote with the desperate, impassioned, frantic blend of persuasion and information and genius-like invention of a prisoner in a high-security prison, trying to induce someone to help him escape. He had friends, of a sort, but for a long time his letters produced nothing. The Moon swung in vast circles about the Earth, and the Earth swung sedately about the Sun. The other planets danced their saraband. The rest of humanity went about its own affairs with fascinated attention. But then an event occurred which bore directly upon Pop Young and Sattell and Pop Young's missing years. Somebody back on Earth promoted a luxury passenger-line of spaceships to ply between Earth and Moon. It looked like a perfect set-up. Three spacecraft capable of the journey came into being with attendant reams of publicity. They promised a thrill and a new distinction for the rich. Guided tours to Lunar! The most expensive and most thrilling trip in history! One hundred thousand dollars for a twelve-day cruise through space, with views of the Moon's far side and trips through Lunar City and a landing in Aristarchus, plus sound-tapes of the journey and fame hitherto reserved for honest explorers! It didn't seem to have anything to do with Pop or with Sattell. But it did. There were just two passenger tours. The first was fully booked. But the passengers who paid so highly, expected to be pleasantly thrilled and shielded from all reasons for alarm. And they couldn't be. Something happens when a self-centered and complacent individual unsuspectingly looks out of a spaceship port and sees the cosmos unshielded by mists or clouds or other aids to blindness against reality. It is shattering. A millionaire cut his throat when he saw Earth dwindled to a mere blue-green ball in vastness. He could not endure his own smallness in the face of immensity. Not one passenger disembarked even for Lunar City. Most of them cowered in their chairs, hiding their eyes. They were the simple cases of hysteria. But the richest girl on Earth, who'd had five husbands and believed that nothing could move her—she went into catatonic withdrawal and neither saw nor heard nor moved. Two other passengers sobbed in improvised strait jackets. The first shipload started home. Fast. The second luxury liner took off with only four passengers and turned back before reaching the Moon. Space-pilots could take the strain of space-flight because they had work to do. Workers for the lunar mines could make the trip under heavy sedation. But it was too early in the development of space-travel for pleasure-passengers. They weren't prepared for the more humbling facts of life. Pop heard of the quaint commercial enterprise through the micro-tapes put off at the shack for the men down in the mine. Sattell probably learned of it the same way. Pop didn't even think of it again. It seemed to have nothing to do with him. But Sattell undoubtedly dealt with it fully in his desperate writings back to Earth. Pop matter-of-factly tended the shack and the landing field and the stores for the Big Crack mine. Between-times he made more drawings in pursuit of his own private objective. Quite accidentally, he developed a certain talent professional artists might have approved. But he was not trying to communicate, but to discover. Drawing—especially with his mind on Sattell—he found fresh incidents popping up in his recollection. Times when he was happy. One day he remembered the puppy his children had owned and loved. He drew it painstakingly—and it was his again. Thereafter he could remember it any time he chose. He did actually recover a completely vanished past. He envisioned a way to increase that recovery. But there was a marked shortage of artists' materials on the Moon. All freight had to be hauled from Earth, on a voyage equal to rather more than a thousand times around the equator of the Earth. Artists' supplies were not often included. Pop didn't even ask. He began to explore the area outside the shack for possible material no one would think of sending from Earth. He collected stones of various sorts, but when warmed up in the shack they were useless. He found no strictly lunar material which would serve for modeling or carving portraits in the ground. He found minerals which could be pulverized and used as pigments, but nothing suitable for this new adventure in the recovery of lost youth. He even considered blasting, to aid his search. He could. Down in the mine, blasting was done by soaking carbon black—from CO 2 —in liquid oxygen, and then firing it with a spark. It exploded splendidly. And its fumes were merely more CO 2 which an air-apparatus handled easily. He didn't do any blasting. He didn't find any signs of the sort of mineral he required. Marble would have been perfect, but there is no marble on the Moon. Naturally! Yet Pop continued to search absorbedly for material with which to capture memory. Sattell still seemed necessary, but— Early one lunar morning he was a good two miles from his shack when he saw rocket-fumes in the sky. It was most unlikely. He wasn't looking for anything of the sort, but out of the corner of his eye he observed that something moved. Which was impossible. He turned his head, and there were rocket-fumes coming over the horizon, not in the direction of Lunar City. Which was more impossible still. He stared. A tiny silver rocket to the westward poured out monstrous masses of vapor. It decelerated swiftly. It curved downward. The rockets checked for an instant, and flamed again more violently, and checked once more. This was not an expert approach. It was a faulty one. Curving surface-ward in a sharply changing parabola, the pilot over-corrected and had to wait to gather down-speed, and then over-corrected again. It was an altogether clumsy landing. The ship was not even perfectly vertical when it settled not quite in the landing-area marked by silvery triangles. One of its tail-fins crumpled slightly. It tilted a little when fully landed. Then nothing happened. Pop made his way toward it in the skittering, skating gait one uses in one-sixth gravity. When he was within half a mile, an air-lock door opened in the ship's side. But nothing came out of the lock. No space-suited figure. No cargo came drifting down with the singular deliberation of falling objects on the Moon. It was just barely past lunar sunrise on the far side of the Moon. Incredibly long and utterly black shadows stretched across the plain, and half the rocketship was dazzling white and half was blacker than blackness itself. The sun still hung low indeed in the black, star-speckled sky. Pop waded through moondust, raising a trail of slowly settling powder. He knew only that the ship didn't come from Lunar City, but from Earth. He couldn't imagine why. He did not even wildly connect it with what—say—Sattell might have written with desperate plausibility about greasy-seeming white crystals out of the mine, knocking about Pop Young's shack in cannisters containing a hundred Earth-pounds weight of richness. Pop reached the rocketship. He approached the big tail-fins. On one of them there were welded ladder-rungs going up to the opened air-lock door. He climbed. The air-lock was perfectly normal when he reached it. There was a glass port in the inner door, and he saw eyes looking through it at him. He pulled the outer door shut and felt the whining vibration of admitted air. His vacuum suit went slack about him. The inner door began to open, and Pop reached up and gave his helmet the practiced twisting jerk which removed it. Then he blinked. There was a red-headed man in the opened door. He grinned savagely at Pop. He held a very nasty hand-weapon trained on Pop's middle. "Don't come in!" he said mockingly. "And I don't give a damn about how you are. This isn't social. It's business!" Pop simply gaped. He couldn't quite take it in. "This," snapped the red-headed man abruptly, "is a stickup!" Pop's eyes went through the inner lock-door. He saw that the interior of the ship was stripped and bare. But a spiral stairway descended from some upper compartment. It had a handrail of pure, transparent, water-clear plastic. The walls were bare insulation, but that trace of luxury remained. Pop gazed at the plastic, fascinated. The red-headed man leaned forward, snarling. He slashed Pop across the face with the barrel of his weapon. It drew blood. It was wanton, savage brutality. "Pay attention!" snarled the red-headed man. "A stickup, I said! Get it? You go get that can of stuff from the mine! The diamonds! Bring them here! Understand?" Pop said numbly: "What the hell?" The red-headed man hit him again. He was nerve-racked, and, therefore, he wanted to hurt. "Move!" he rasped. "I want the diamonds you've got for the ship from Lunar City! Bring 'em!" Pop licked blood from his lips and the man with the weapon raged at him. "Then phone down to the mine! Tell Sattell I'm here and he can come on up! Tell him to bring any more diamonds they've dug up since the stuff you've got!" He leaned forward. His face was only inches from Pop Young's. It was seamed and hard-bitten and nerve-racked. But any man would be quivering if he wasn't used to space or the feel of one-sixth gravity on the Moon. He panted: "And get it straight! You try any tricks and we take off! We swing over your shack! The rocket-blast smashes it! We burn you down! Then we swing over the cable down to the mine and the rocket-flame melts it! You die and everybody in the mine besides! No tricks! We didn't come here for nothing!" He twitched all over. Then he struck cruelly again at Pop Young's face. He seemed filled with fury, at least partly hysterical. It was the tension that space-travel—then, at its beginning—produced. It was meaningless savagery due to terror. But, of course, Pop was helpless to resent it. There were no weapons on the Moon and the mention of Sattell's name showed the uselessness of bluff. He'd pictured the complete set-up by the edge of the Big Crack. Pop could do nothing. The red-headed man checked himself, panting. He drew back and slammed the inner lock-door. There was the sound of pumping. Pop put his helmet back on and sealed it. The outer door opened. Outrushing air tugged at Pop. After a second or two he went out and climbed down the welded-on ladder-bars to the ground. He headed back toward his shack. Somehow, the mention of Sattell had made his mind work better. It always did. He began painstakingly to put things together. The red-headed man knew the routine here in every detail. He knew Sattell. That part was simple. Sattell had planned this multi-million-dollar coup, as a man in prison might plan his break. The stripped interior of the ship identified it. It was one of the unsuccessful luxury-liners sold for scrap. Or perhaps it was stolen for the journey here. Sattell's associates had had to steal or somehow get the fuel, and somehow find a pilot. But there were diamonds worth at least five million dollars waiting for them, and the whole job might not have called for more than two men—with Sattell as a third. According to the economics of crime, it was feasible. Anyhow it was being done. Pop reached the dust-heap which was his shack and went in the air lock. Inside, he went to the vision-phone and called the mine-colony down in the Crack. He gave the message he'd been told to pass on. Sattell to come up, with what diamonds had been dug since the regular cannister was sent up for the Lunar City ship that would be due presently. Otherwise the ship on the landing strip would destroy shack and Pop and the colony together. "I'd guess," said Pop painstakingly, "that Sattell figured it out. He's probably got some sort of gun to keep you from holding him down there. But he won't know his friends are here—not right this minute he won't." A shaking voice asked questions from the vision-phone. "No," said Pop, "they'll do it anyhow. If we were able to tell about 'em, they'd be chased. But if I'm dead and the shacks smashed and the cable burnt through, they'll be back on Earth long before a new cable's been got and let down to you. So they'll do all they can no matter what I do." He added, "I wouldn't tell Sattell a thing about it, if I were you. It'll save trouble. Just let him keep on waiting for this to happen. It'll save you trouble." Another shaky question. "Me?" asked Pop. "Oh, I'm going to raise what hell I can. There's some stuff in that ship I want." He switched off the phone. He went over to his air apparatus. He took down the cannister of diamonds which were worth five millions or more back on Earth. He found a bucket. He dumped the diamonds casually into it. They floated downward with great deliberation and surged from side to side like a liquid when they stopped. One-sixth gravity. Pop regarded his drawings meditatively. A sketch of his wife as he now remembered her. It was very good to remember. A drawing of his two children, playing together. He looked forward to remembering much more about them. He grinned. "That stair-rail," he said in deep satisfaction. "That'll do it!" He tore bed linen from his bunk and worked on the emptied cannister. It was a double container with a thermware interior lining. Even on Earth newly-mined diamonds sometimes fly to pieces from internal stress. On the Moon, it was not desirable that diamonds be exposed to repeated violent changes of temperature. So a thermware-lined cannister kept them at mine-temperature once they were warmed to touchability. Pop packed the cotton cloth in the container. He hurried a little, because the men in the rocket were shaky and might not practice patience. He took a small emergency-lamp from his spare spacesuit. He carefully cracked its bulb, exposing the filament within. He put the lamp on top of the cotton and sprinkled magnesium marking-powder over everything. Then he went to the air-apparatus and took out a flask of the liquid oxygen used to keep his breathing-air in balance. He poured the frigid, pale-blue stuff into the cotton. He saturated it. All the inside of the shack was foggy when he finished. Then he pushed the cannister-top down. He breathed a sigh of relief when it was in place. He'd arranged for it to break a frozen-brittle switch as it descended. When it came off, the switch would light the lamp with its bare filament. There was powdered magnesium in contact with it and liquid oxygen all about. He went out of the shack by the air lock. On the way, thinking about Sattell, he suddenly recovered a completely new memory. On their first wedding anniversary, so long ago, he and his wife had gone out to dinner to celebrate. He remembered how she looked: the almost-smug joy they shared that they would be together for always, with one complete year for proof. Pop reflected hungrily that it was something else to be made permanent and inspected from time to time. But he wanted more than a drawing of this! He wanted to make the memory permanent and to extend it— If it had not been for his vacuum suit and the cannister he carried, Pop would have rubbed his hands. Tall, jagged crater-walls rose from the lunar plain. Monstrous, extended inky shadows stretched enormous distances, utterly black. The sun, like a glowing octopod, floated low at the edge of things and seemed to hate all creation. Pop reached the rocket. He climbed the welded ladder-rungs to the air lock. He closed the door. Air whined. His suit sagged against his body. He took off his helmet. When the red-headed man opened the inner door, the hand-weapon shook and trembled. Pop said calmly: "Now I've got to go handle the hoist, if Sattell's coming up from the mine. If I don't do it, he don't come up." The red-headed man snarled. But his eyes were on the cannister whose contents should weigh a hundred pounds on Earth. "Any tricks," he rasped, "and you know what happens!" "Yeah," said Pop. He stolidly put his helmet back on. But his eyes went past the red-headed man to the stair that wound down, inside the ship, from some compartment above. The stair-rail was pure, clear, water-white plastic, not less than three inches thick. There was a lot of it! The inner door closed. Pop opened the outer. Air rushed out. He climbed painstakingly down to the ground. He started back toward the shack. There was the most luridly bright of all possible flashes. There was no sound, of course. But something flamed very brightly, and the ground thumped under Pop Young's vacuum boots. He turned. The rocketship was still in the act of flying apart. It had been a splendid explosion. Of course cotton sheeting in liquid oxygen is not quite as good an explosive as carbon-black, which they used down in the mine. Even with magnesium powder to start the flame when a bare light-filament ignited it, the cannister-bomb hadn't equaled—say—T.N.T. But the ship had fuel on board for the trip back to Earth. And it blew, too. It would be minutes before all the fragments of the ship returned to the Moon's surface. On the Moon, things fall slowly. Pop didn't wait. He searched hopefully. Once a mass of steel plating fell only yards from him, but it did not interrupt his search. When he went into the shack, he grinned to himself. The call-light of the vision-phone flickered wildly. When he took off his helmet the bell clanged incessantly. He answered. A shaking voice from the mining-colony panted: "We felt a shock! What happened? What do we do?" "Don't do a thing," advised Pop. "It's all right. I blew up the ship and everything's all right. I wouldn't even mention it to Sattell if I were you." He grinned happily down at a section of plastic stair-rail he'd found not too far from where the ship exploded. When the man down in the mine cut off, Pop got out of his vacuum suit in a hurry. He placed the plastic zestfully on the table where he'd been restricted to drawing pictures of his wife and children in order to recover memories of them. He began to plan, gloatingly, the thing he would carve out of a four-inch section of the plastic. When it was carved, he'd paint it. While he worked, he'd think of Sattell, because that was the way to get back the missing portions of his life—the parts Sattell had managed to get away from him. He'd get back more than ever, now! He didn't wonder what he'd do if he ever remembered the crime Sattell had committed. He felt, somehow, that he wouldn't get that back until he'd recovered all the rest. Gloating, it was amusing to remember what people used to call such art-works as he planned, when carved by other lonely men in other faraway places. They called those sculptures scrimshaw. But they were a lot more than that! THE END Transcriber's Note: This etext was produced from Astounding Science Fiction September 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	D. Escaping on board a secondhand lunar tour vessel
What is the Geiger counter and how exactly is it used in the present chapters? A. A Geiger counter is used to measure radioactivity. Mr. Taylor uses it at Cedar Point. B. A Geiger counter is used to measure radioactivity. Mr. Taylor uses it to measure the radiation present in the hills behind his college. C. A Geiger counter is used to measure radioactivity. Eddie uses it to prospect the hills behind the college. D. A Geiger counter is used to measure radioactivity. Eddie uses it to prospect Cedar Point.	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.	C. A Geiger counter is used to measure radioactivity. Eddie uses it to prospect the hills behind the college.
Which type of person is likely to receive the most brutal treatment in the legal system, compared to the other response options? A. masculine faces B. sharp-featured faces C. overfamiliar faces D. suspicious faces	Face value When the BBC broadcast the recent documentary by Louis Theroux that looked back at the time he spent in the company of Jimmy Savile, there was disbelief across social media that no one had stepped in to stop Savile from committing his crimes. Some blamed the BBC, some blamed those in Savile's immediate circle, but others blamed a simple error of human judgment. "He literally couldn't look more like a paedophile," read one post – one of many to state a supposedly incontrovertible truth: that Savile's criminal tendencies could have been detected from the shape of his features, his eyes, his hair. Moreover, this has nothing to do with the benefit of hindsight and should have been picked up at the time. His looks, they suggested, were a moral indicator, with a wealth of compelling visual evidence to support the claim. We know that paedophiles, murderers and other violent criminals come in many shapes and sizes. If we knew nothing about their criminal history, some of their photos might even appear attractive. But the idea that someone's features betray their character is something rooted deep within us; it's the reason why certain photos perform well on dating apps, or why trustworthy-looking politicians might rack up votes. But how wrong are our hunches of perceived criminality? A recent paper, published by Xiaolin Wu and Xi Zhang of Shanghai's Jiao Tong University, claims to be the first to use machine learning and neural networks to attempt a fully automated inference of criminality from facial images, removing prejudice from the equation and testing the validity of our gut feelings. "What facial features influence the average Joe's impulsive and yet consensual judgments on social attributes?" they ask. Through a study of 1,856 images ("controlled for race, gender, age and facial expression") they claim to have established the validity of "automated, face-induced inference on criminality, despite the historical controversy surrounding this line of enquiry." In other words, they believe that they've found a relationship between looking like a criminal and actually being one. It's a claim that's been made many times over the years. Physiognomy, the 'science' of judging people by their appearance, was first theorised by the ancient Greeks in around the 5th century BC. Aristotle's pronouncement that "it is possible to infer character from features" led to a number of works relating to 'Physiognomica', a word derived from physis (nature), nomos (law) and (or) gnomon (judge or interpreter). All of Greek society, it was claimed, could benefit from this skill: it could assist with choosing an employee, a slave or a spouse, while its inherent vagueness made it intriguing to philosophers and useful for scientists who bent the theories to support their own beliefs. It became a recognised science in the Islamic world, and was used and taught in Europe throughout late antiquity and the early Middle Ages, despite nagging doubts among thinkers and physicians of the day. In the early 16th century, Leonardo da Vinci claimed not to "concern myself with false physiognomy, because these chimeras have no scientific foundation." Theories of physiognomy, however, would persist beyond the Renaissance. In 1586, Italian scholar Giambattista della Porta published a book, De humana physiognomonia libri IIII, which established him as the 'father of Physiognomy'. Della Porta's thinking was based on the 'doctrine of signatures'; the idea that the appearance of plants and animals offers clues to their nature. For example, as one writer of the time suggested, walnuts are good for curing headaches because they're shaped a bit like a human head. The theories in della Porta's book were supported by dozens of detailed illustrations which, by comparing human faces to those of animals, suggested that they must surely share similar character traits. In the 17th century, Swiss poet Johann Caspar Lavater took della Porta's methodology and ran with it, commissioning artists to illustrate his popular Essays On Physiognomy – which, to the chagrin of his contemporary, the writer Hannah More, sold for "fifteen guineas a set… while in vain we boast that philosophy [has] broken down all the strongholds of prejudice, ignorance, and superstition." Lavater's work was criticised for being ridden with bias (black faces rarely emerged well from his analyses) but he was right in one respect: "Whether they are or are not sensible of it," he wrote, "all men are daily influenced by physiognomy." Many studies have been done into our psychological response to faces, and it's clear that a so-called halo effect will inevitably work its magic. "Attractive people are regarded as better at everything," says Professor Peter Hancock, lecturer in Psychology at Stirling University. "And we can't shake that off because there's some truth to it. Good genes produce intelligent people, attractive faces, fit bodies, and we imagine that they're going to be good at everything else, too. We don't have good insight into our own behaviour. We tend to think we understand what we're doing, but we don't." Hancock describes attending a conference where one speaker showed a series of black faces and white faces to students (who were mostly white) and asked them what they thought the experiment was about. "They knew that he was trying to assess whether they would rate the black ones as more criminal," says Hancock. "But then they did!" We attribute social characteristics based on opinions we already hold about certain kinds of faces: whether they look unusual in some way, whether they resemble a partner, a family member or even ourselves, or perhaps have some other cultural association. Physiognomy ultimately stems from what Alexander Todorov, professor of psychology at Princeton University, calls an 'overgeneralisation hypothesis'. "People," he wrote, "use easily accessible facial information (eg an expression such as a smile, cues to gender and ethnic group) to make social attributions congruent with this information (eg a nice person)." In a social media age, the pictures we choose to represent ourselves online are a form of self-presentation driven by those social attributions and the knowledge that our pictures are being judged. Experiments at Princeton found that we take less than one tenth of a second to form an opinion of strangers from their pictures, and those opinions tend to stand firm even if we're exposed to those pictures for a longer period of time. That tendency to judge instantly gives rise to a number of selfie tropes that are deemed to elicit positive responses, particularly when it comes to photos on dating profiles: certain angles, particular expressions, minute adjustments of eyebrows and lips that might appear to be about narcissism and vanity, but are more about a fear of being incorrectly assessed. After all, false suppositions based on people's faces are hugely influential within society, and in extreme cases they can have a huge impact on people's lives. When retired teacher Christopher Jefferies was held by police in connection with the murder of Joanna Yeates in Bristol back in 2010, more than half a dozen newspapers gave his unusual appearance particular scrutiny and made assumptions accordingly, which in turn influenced public opinion. This culminated in substantial damages for defamation, two convictions for contempt of court and a painful ordeal for Jefferies, who was entirely innocent. This kind of deep-seated bias looms large throughout physiognomic works of the 19th and 20th centuries, from absurdities such as Vaught's Practical Character Reader of 1902 (handy if you want to find out what a "deceitful chin" looks like) to more inherently troubling volumes such as Cesare Lombroso's Criminal Man. After performing a number of autopsies on criminals, the Italian physician claimed to have discovered a number of common characteristics, and it's worth listing them if only to establish the supposed criminality of pretty much everyone you know: Unusually short or tall height; small head, but large face; fleshy lips, but thin upper lip; protuberances on head and around ear; wrinkles on forehead and face; large sinus cavities or bumpy face; tattoos; receding hairline; large incisors; bushy eyebrows, tending to meet across nose; large eye sockets but deep-set eyes; beaked or flat nose; strong jaw line; small and sloping forehead; small or weak chin; thin neck; sloping shoulders but large chest; large, protruding ears; long arms; high cheek bones; pointy or snubbed fingers or toes. In a woeful misreading of Darwinian theory, Lombroso unwittingly founded the field of anthropological criminology, and more specifically the idea of the born criminal: a hereditary quality that posed a danger to society and must be rooted out. His theories became discredited during the 20th century, but the kind of bias displayed by Lombroso can still be found in legal systems across the world; studies show that people with stereotypically 'untrustworthy' faces tend to receive harsher treatment than those who don't. There's evidently some consensus over people's attitudes toward certain faces, but it doesn't follow that the consensus is correct. The only attributes that we're reasonably good at detecting, according to research done at the University of Michigan in the 1960s and later tested at the University of Stirling in 2007, are extroversion and conscientiousness. For other traits there's insufficient evidence that our hunches are correct, with anomalies explained by our evolved aversion to 'ugliness', established links between broader faces and powerful physiques, or cultural associations with certain demographics which are reinforced with nagging regularity by newspapers, books, television and film. Data-driven studies, based upon huge quantities of facial data, would seem to offer the final word on this. Since 2005, computational models have used various techniques to test for links between social attributes and facial features, resulting in suggestions that our faces can betray, for example, political leanings, sexual orientation and criminality. One BBC Future article from 2015 even describes the 'discipline' of physiognomy as 'gaining credibility'. But Todorov details many problems with these studies, pointing out the challenging nature of doing such experiments with sufficient rigour – not least because different images of the same people can prompt wildly differing results. The aforementioned study at Shanghai's Jiao Tong University, with its enthusiastic, data-driven analyses of such questions as "What features of a human face betray its owner's propensity for crimes?" prompted a wave of press coverage. The vision outlined in these articles is of an unethical dystopia where neural networks can assess our faces and establish a likely score for criminality – but Todorov is scathing about this paper, too. "The main problem is the sampling of the images," he says. "There is not enough information about the [nature of] the images of the people who were convicted. Second, clearly, there are huge differences between the two samples [of convicts and non-convicts] [in terms of] education and socio-economic status." In other words, your appearance is affected by the kind of life you've led, so the classifiers within the computer program are simply distinguishing between different demographics rather than detecting a propensity for criminal behaviour. Todorov is also wary of these classifiers misidentifying more 'innocent' people than identifying actual criminals, and accuracy is a concern shared by Peter Hancock. "Networks don't assess faces in the same way that we do," he says. "One of our systems, which is a deep network, has a recognition engine which generates an ordered list of how similar various faces are. And sometimes you get good matches – but other times you look at them and say, well, it's the wrong race! To humans they look completely different. And that underlines the fact that the networks are working in a different sort of way, and actually you don't really know how they're working. They're the ultimate black box." This isn't to say that the use of big data, and particularly the use of composite imagery (digitally blending together certain types of faces) doesn't give us useful information and fascinating correlations. "You can, for example, take a given face and use computer software to make it look more or less trustworthy," says Hancock. "I remember a colleague playing with this and he made a less trustworthy version of George W Bush – and how shifty did he look! I'm surprised that they're not using these techniques in political advertising, because you couldn't tell that anything had been done [to the picture], but when you look at it you think 'I wouldn't trust him'." The revitalisation of the theory of physiognomy by the Shanghai students is, according to Todorov, deeply problematic on a theoretical level. "Are we back to Lombroso's theory," he asks, "that criminals were anomalous creatures, evolutionary degenerates? How does one become criminal, and what role do various life forces play into this? There are people making claims that you just need to look at the face to predict personality and behaviour, but many of these people have not given much thought to their underlying assumptions." While it's true that we judge books by their covers, covers are more than just faces; we piece together all kinds of cues from people to form our impressions of them. Jimmy Savile's appearance was unusual by any standards, but we absorbed a great deal of information about him over the years that will have influenced our opinions – not least from the original Louis Theroux programme from 2000 that was reexamined in that recent BBC documentary. Savile's vague resemblance to the Child Catcher from the film Chitty Chitty Bang Bang is convenient but ultimately misleading, and the way it reinforces the idea of what a paedophile might 'look like' is unfortunate; not least because it helps to sustain a low-level belief in the 'science' of physiognomy, despite its tendency to crumble under the slightest cross examination. This article was originally published on TheLong+Short. Read the original article.	D. suspicious faces
Why are ratings provided in count of curled toes? A. Because the writer and his wife are playing footsie. B. Because this image is evocative of sexual animalistic desires. C. Because this image evokes tension, gratification, and release. D. Because the writer and his wife are having physical therapy.	More Bang for the Buck A friend of mine offers a theory about why Bill Clinton's poll numbers stayed so high throughout the Lewinsky scandal: The news made it possible for serious-minded people to spend lots of time--at the office and over lunch--talking about semen stains, vaginal insertions, and blow jobs. And the people were grateful. That's probably because they're not getting all that much themselves. A recent University of Chicago survey of 10,000 adults found that Americans are having considerably less sex than was generally thought. Only one American in 20 has sex three times a week. One in five didn't score at all last year. If that's true, many of us could use a little sexual self-improvement. Not me, of course. I have been happily married for 26 years, since the age of 21. Deb and I have what seems to us to be a perfectly fine amorous life, yet everywhere I turn the culture tells me--almost mocks me-- you can do better! What would happen to our sex life then, if Deb (who participated in this story because she loves me and because she has tenure) and I tried for the first time to make something happen to it? And so it was that we found ourselves for the first time ever in a sex-toy store, A Touch of Romance, located near our home in Los Angeles, across the street from a Macy's. The idea behind shops like these is to make obtaining the materials of sexual experimentation as ordinary as purchasing plumbing supplies or housewares. Which sort of works--the only sexual thrill I got from the visit was knowing that Microsoft just bought a cock ring. Choosing it wasn't easy. Most of them came in presized sets of three. I couldn't figure out which would fit right and intuited that try-ons weren't an option. So I opted instead for an adjustable circumference version, a little strip of vinyl with snaps for $11.95. Man, what a rip-off! Unless it works. It doesn't. Back home, I derived a certain depraved buzz in cinching the device on, but that was soon eclipsed. The thing works on the Roach Motel principle--your blood gets in but it can't get out. But then I got to thinking: Under battlefield conditions it doesn't get out anyway. And while I should have been paying more attention to other things, this led to thinking about the old joke with the punch line "... and right ball go POW." My wife hadn't noticed any difference at all. Overall rating, on a scale of 1 to 10: 2 toes curled. A woman I know says women's magazines are the best places in America to find sex tips. She's right--go ahead, just try to find a sewing pattern in Redbook . You're much more likely to land on "Try phone sex, dirty notes, porn videos, fantasy games and sex in new places. ... Try lingerie and no underwear. ... Try talking dirty and silk scarves. Try anything at all," or articles such as "Eight New Games for the Foreplay Challenged." An article in the April Cosmopolitan , "The Six Best Sex Positions," seemed more promising than the Redbook playbook. Each position was accompanied by a succinct write-up and a stick-figure diagram. The position we settled on was "The Butterfly," which we had to read three times to comprehend. The man stands, the woman remains supine on a bed or counter-top with her feet up on his shoulders. The whole idea is to produce a pelvic tilt for better access to the G spot. Instead, we experienced an uncomfortable pretzel feeling that stick figures must be immune to. And in general, Cosmopolitan 's exotic sex positions require the sort of body placement you can't remember in the moment of passion and even if you could, for proper alignment, you still might need mood-killing accessories such as a plumb line and a laser pen. Rating: 3 toes curled. Next we tried those "Better Sex" instructional videos advertised in the New York Times Book Review. I ordered Better Sexual Techniques , Advanced Sexual Techniques , Making Sex Fun , and Advanced Oral Sex Techniques (priced about $11.95 each, not including shipping and handling). My wife couldn't bear to watch them; I persevered but must admit it was a chore. The oral-sex tape starts with "well-known sex therapist" Diana Wiley, in her poofy hair and broad-shouldered blue power suit, looking like she was about to explain how the sales force could increase its third-quarter productivity. Instead she runs through all the euphemisms for oral sex and then the video cuts to XXX action with gratuitous commentary. Wiley's overexplanation of everything two people can do to each other with their mouths raises this question: Do you really need a five-minute video segment on whether or not to swallow? In the great tradition of hotel and travel ads, the guys tend to be markedly less attractive than the women. No way he'd be with her if this wasn't an instructional sex video! The inanity of the experts and the dubious casting make these films about as erotic as ... well, as the New York Times . You could learn more from any randomly selected porn video. Rating: 0 toes curled. Another approach is food. The notion that certain foods, such as oysters or rhino horn, are aphrodisiacs has been pretty much discounted. But it's plausible to think that cooking a meal together and then dining on it, just the two of you, could be erotic. Especially if (like me) your schedule frequently forces you to eat alone and you often find yourself standing in front of the microwave, screaming, "Come on, goddammit!" Intercourses , by Martha Hopkins and Randall Lockridge ($24.95, Terrace Publishing, 1997), preaches that for every time of day and every phase of a relationship there is a type of eating experience that will heighten sexual response. (There's also a chart showing which foods are good for eating off which body parts.) Deb and I blocked off a whole Saturday afternoon and evening for the Intercourses experiment, settling on rosemary-scented lamb over pasta (Page 87) followed by frozen coffee almond dessert (Page 31). According to the book, rosemary is sexy because of its fragrance (used in many perfumes) and because of its texture, which, so the text assured, tickles nerve endings. The dessert was mostly coffee, rum, and Kahlua, which has worked before. We shopped for the food together and cooked together, drinking wine and beer along the way. At one point while I was working on the dessert, I asked my wife how long to beat the heavy cream mixture. "Till it's stiff--it's an aphrodisiac," she said. Preparation took less than an hour, and everything came out perfectly. Eating at our dining room table for the first time ever without guests, we were having fun by candlelight. But the mood was romantic, not erotic. Overall rating: 4 toes curled. That's when we went for the Viagra ($212.50 for 10 doses, which includes a "consultation" fee). The drug was prescribed by a doctor, whom I've never met, and ordered from a pharmacy in Miami Beach, Fla., where I've never been. I completed the transaction via the Internet after filling out a cover-their-ass questionnaire in three minutes. We each decided to take one pill, clinked our glasses, and gulped. And then what? It felt awkward sitting in our bedroom, knowing that it could take up to an hour for Viagra to "work." I suggested that we play strip poker, something I'd never done. Deb had never even played poker, so I had to explain the rules. I won in about six hands, auspiciously I thought, with three aces. But we still weren't really in the mood yet. So then I got out the other purchase I'd made at A Touch of Romance--"Dirty Dice" ($4.95). One of the two pink cubes is marked with these words instead of dots: "lips," "above waist," "ear," "breast," "below waist," and "?". The other cube is labeled "kiss," "squeeze," "lick," "blow," "suck," and "eat." We took turns throwing the dice, but the activities generated seemed forced and arbitrary. Finally, as they say at NASA, there was word from the pad that the launch sequence was initiating. It was pretty much like all other sex, except for a slight lightheadedness. Deb said she noticed a remote tingling sensation. On the plus side, there was no priapism and neither of us experienced disruption of our color vision nor a fatal heart attack, which was nice. Overall rating: 5 toes curled. St. Augustine held lust to be a fitting punishment for man's disobedience to God: the body's disobeying of the mind, the will, the spirit, and even of itself. (The paradigm of this for him is the unbidden hard-on.) Jean-Paul Sartre discovered something similar, although celebrating it rather than deploring it: Essential to the erotic is the body's defiance of design and control. (The paradigm of this for him is the jiggle.) Sartre's view yields a sort of sexual Heisenberg principle: There is an inherent tension between physically abandoning yourself to another on the one hand and sexual planning on the other. The more of the one, the less of the other. And this, I discovered, is the chief obstacle to sexual self-help. Getting an erection is sexy. Making one is not. As my wife said about Viagra, "You start to have a new feeling and then you realize where it came from and then you don't have it so much. ... Anything that makes you think about it like that is just creepy." This is not to say there isn't a way out of this conflict between desire and design. With homage to our potent POTUS, there is, I think, a Third Way that's neither sexual complacency nor standard self-help. If the intrusion of consciousness is the problem, then maybe the answer is to block it out. Sure, you could do this the old-fashioned way: with alcohol and drugs. But then you have all the traditional drawbacks, including diminished physical attractiveness and degraded sexual performance. So how about this instead? Go for all the sexual self-help you can, but do it covertly . Watch a sex video (or porn flick) if you want--but by yourself, and then try to share what you learned without sharing how you learned it. Don't tell your partner you took Viagra. Or give each other standing permission to slip it into the odd after-dinner drink, saying nothing. (Of course, when you do it you'll still know, but having an unselfconsciously turned-on partner is a real compensation for that, and next time, your partner can surprise you. And yes, this requires trust. But why would you be having sex with someone you don't trust?) My main conclusion is that contrary to our blabby culture, the key to a better sex life is less communication.	C. Because this image evokes tension, gratification, and release.
How do they evaluate generated stories?	### Introduction Collaborative human-machine story-writing has had a recent resurgence of attention from the research community BIBREF0 , BIBREF1 . It represents a frontier for AI research; as a research community we have developed convincing NLP systems for some generative tasks like machine translation, but lag behind in creative areas like open-domain storytelling. Collaborative open-domain storytelling incorporates human interactivity for one of two aims: to improve human creativity via the aid of a machine, or to improve machine quality via the aid of a human. Previously existing approaches treat the former aim, and have shown that storytelling systems are not yet developed enough to help human writers. We attempt the latter, with the goal of investigating at what stage human collaboration is most helpful. gordon2009sayanything use an information retrieval based system to write by alternating turns between a human and their system. clark2018mil use a similar turn-taking approach to interactivity, but employ a neural model for generation and allow the user to edit the generated sentence before accepting it. They find that users prefer a full-sentence collaborative setup (vs. shorter fragments) but are mixed with regard to the system-driven approach to interaction. roemmele2017eval experiment with a user-driven setup, where the machine doesn't generate until the user requests it to, and then the user can edit or delete at will. They leverage user-acceptance or rejection of suggestions as a tool for understanding the characteristics of a helpful generation. All of these systems involve the user in the story-writing process, but lack user involvement in the story-planning process, and so they lean on the user's ability to knit a coherent overall story together out of locally related sentences. They also do not allow a user to control the novelty or “unexpectedness” of the generations, which clark2018mil find to be a weakness. Nor do they enable iteration; a user cannot revise earlier sentences and have the system update later generations. We develop a system that allows a user to interact in all of these ways that were limitations in previous systems; it enables involvement in planning, editing, iterative revising, and control of novelty. We conduct experiments to understand which types of interaction are most effective for improving stories and for making users satisfied and engaged. We have two main interfaces that enable human interaction with the computer. There is cross-model interaction, where the machine does all the composition work, and displays three different versions of a story written by three distinct models for a human to compare. The user guides generation by providing a topic for story-writing and by tweaking decoding parameters to control novelty, or diversity. The second interface is intra-model interaction, where a human can select the model to interact with (potentially after having chosen it via cross-model), and can collaborate at all stages to jointly create better stories. The full range of interactions available to a user is: select a model, provide a topic, change diversity of content, collaborate on the planning for the story, and collaborate on the story sentences. It is entirely user-driven, as the users control how much is their own work and how much is the machine's at every stage. It supports revision; a user can modify an earlier part of a written story or of the story plan at any point, and observe how this affects later generations. ### System Overview Figure FIGREF3 shows a diagram of the interaction system. The dotted arrows represent optional user interactions. requires the user to enter a topic, such as “the not so haunted house”, and can optionally vary the diversity used in the Storyline Planner or the Story Writer. Diversity numbers correspond directly to softmax temperatures, which we restrict to a reasonable range, determined empirically. The settings are sent to the Storyline Planner module, which generates a storyline for the story in the form of a sequence of phrases as per the method of yao2018plan. Everything is then sent to the Story Writer, which will return three stories. enables advanced interactions with one story system of the user's choice. The Storyline Planner returns either one storyline phrase or many, and composes the final storyline out of the combination of phrases the system generated, the user has written, and edits the user has made. These are sent to the Story Writer, which returns either a single sentence or a full story as per user's request. The process is flexible and iterative. The user can choose how much or little content they want to provide, edit, or re-generate, and they can return to any step at any time until they decide they are done. To enable interactive flexibility, the system must handle open-domain user input. User input is lower-cased and tokenized to match the model training data via spaCy. Model output is naively detokenized via Moses BIBREF2 based on feedback from users that this was more natural. User input OOV handling is done via WordNet BIBREF3 by recursively searching for hypernyms and hyponyms (in that order) until either an in-vocabulary word is found or until a maximum distance from the initial word is reached. We additionally experimented with using cosine similarity to GloVe vectors BIBREF4 , but found that to be slower and not qualitatively better for this domain. ### Web Interface Figure FIGREF10 shows screenshots for both the cross-model and intra-model modes of interaction. Figure FIGREF10 shows that the cross-model mode makes clear the differences between different model generations for the same topic. Figure FIGREF10 shows the variety of interactions a user can take in intra-model interaction, and is annotated with an example-in-action. User inserted text is underlined in blue, generated text that has been removed by the user is in grey strike-through. The refresh symbol marks areas that the user re-generated to get a different sentence (presumably after being unhappy with the first result). As can be seen in this example, minor user involvement can result in a significantly better story. ### Model Design All models for both the Storyline Planner and Story Writer modules are conditional language models implemented with LSTMs based on merity2018regularizing. These are 3-stacked LSTMs that include weight-dropping, weight-tying, variable length back propagation with learning rate adjustment, and Averaged Stochastic Gradient Descent (ASGD). They are trained on the ROC dataset BIBREF5 , which after lowercasing and tokenization has a vocabulary of 38k. Storyline Phrases are extracted as in yao2018plan via the RAKE algorithm BIBREF6 which results in a slightly smaller Storyline vocabulary of 31k. The Storyline Planner does decoding via sampling to encourage creative exploration. The Story Writer has an option to use one or all three systems, all of which decode via beamsearch and are detailed below. The Title-to-Story system is a baseline, which generates directly from topic. The Plan-and-Write system adopts the static model in yao2018plan to use the storyline to supervise story-writing. Plan-and-Revise is a new system that combines the strengths of yao2018plan and holtzman2018learning. It supplements the Plan-and-Write model by training two discriminators on the ROC data and using them to re-rank the LSTM generations to prefer increased creativity and relevance. Thus the decoding objective of this system becomes INLINEFORM0 where INLINEFORM1 is the conditional language model probability of the LSTM, INLINEFORM2 is the discriminator scoring function, and INLINEFORM3 is the learned weight of that discriminator. At each timestep all live beam hypotheses are scored and re-ranked. Discriminator weights are learnt by minimizing Mean Squared Error on the difference between the scores of gold standard and generated story sentences. ### Experiments We experiment with six types of interaction: five variations created by restricting different capabilities of our system, and a sixth turn-taking baseline that mimics the interaction of the previous work BIBREF1 , BIBREF7 . We choose our experiments to address the research questions: What type of interaction is most engaging? Which type results in the best stories? Can a human tasked with correcting for certain weaknesses of a model successfully do so? The variations on interactions that we tested are: We expand experiment 5 to answer the question of whether a human-in-the-loop interactive system can address specific shortcomings of generated stories. We identify three types of weaknesses common to generation systems – Creativity, Relevance, and Causal & Temporal Coherence, and conduct experiments where the human is instructed to focus on improving specifically one of them. The targeted human improvement areas intentionally match the Plan-and-Revise discriminators, so that, if successful, the "human discriminator" data can assist in training the machine discriminators. All experiments (save experiment 2, which lets the user pick between models) use the Plan-and-Revise system. ### Details We recruit 30 Mechanical Turk workers per experiment (270 unique workers total) to complete story writing tasks with the system. We constrain them to ten minutes of work (five for writing and five for a survey) and provide them with a fixed topic to control this factor across experiments. They co-create a story and complete a questionnaire which asks them to self-report on their engagement, satisfaction, and perception of story quality. For the additional focused error-correction experiments, we instruct Turkers to try to improve the machine-generated stories with regard to the given aspect, under the same time constraints. As an incentive, they are given a small bonus if they are later judged to have succeeded. We then ask a separate set of Turkers to rate the stories for overall quality and the three improvement areas. All ratings are on a five-point scale. We collect two ratings per story, and throw out ratings that disagree by more than 2 points. A total of 11% of ratings were thrown out, leaving four metrics across 241 stories for analysis. ### Conclusions and Future Work We have shown that all levels of human-computer collaboration improve story quality across all metrics, compared to a baseline computer-only story generation system. We have also shown that flexible interaction, which allows the user to return to edit earlier text, improves the specific metrics of creativity and causal-temporal coherence above previous rigid turn-taking approaches. We find that, as well as improving story quality, more interaction makes users more engaged and likely to use the system again. Users tasked with collaborating to improve a specific story quality were able to do so, as judged by independent readers. As the demo system has successfully used an ensemble of collaborative discriminators to improve the same qualities that untrained human users were able to improve even further, this suggests promising future research into human-collaborative stories as training data for new discriminators. It could be used both to strengthen existing discriminators and to develop novel ones, since discriminators are extensible to arbitrarily many story aspects. ### Acknowledgments We thank the anonymous reviewers for their feedback, as well as the members of the PLUS lab for their thoughts and iterative testing. This work is supported by Contract W911NF-15- 1-0543 with the US Defense Advanced Research Projects Agency (DARPA). ### Demo Video The three-minute video demonstrating the interaction capabilities of the system can be viewed at https://youtu.be/-hGd2399dnA. (Same video as linked in the paper footnote). ### Decoding Default diversity (Softmax Temperature) for Storyline Planner is 0.5, for Story Writer it is None (as beamsearch is used an thus can have but does not require a temperature). Beam size for all Story Writer models is 5. Additionally, Storyline Phrases are constrained to be unique (unless a user duplicates them), and Beamsearch is not normalized by length (both choices determined empirically). ### Training We follow the parameters used in yao2018plan and merity2018regularizing. ### Mechanical Turk Materials Following are examples of the materials used in doing Mechanical Turk User Studies. Figure FIGREF37 is an example of the All + Creative focused experiment for story-writing. The instructions per experiment differ across all, but the template is the same. Figure FIGREF38 is the survey for ranking stories across various metrics. This remains constant save that story order was shuffled every time to control for any effects of the order a story was read in. Figure 1: Diagram of human-computer interaction mediated by the the demo system. The dotted arrows represent optional interactions that the user can take. Depending on the set-up, the user may choose to interact with one or all story models. Figure 2: Screenshots of the demo user interface Table 1: User self-reported scores, from 1-5. E: Entertainment value, Q: Quality of Story, S: Satisfaction with Story. Note that the final column Use Again is based on converting “no” to 0, “conditional” to 1, and “yes” to 2. Table 2: Results for all experiments, from 1-5. Best scores per metric are bolded, scores not significantly different (α = 0.1, per Wilcoxon Signed-Rank Test) are starred. C-T stands for Causal-Temporal Coherence, the + experiments are the extensions where the user focuses on improving a particular quality. Table 3: Training parameters for models used in demo. Table 4: Questionnaire for user self-reporting, range 1 to 5 (1 low). Figure 3: Template & Instructions for Writing Stories in the All + Creative experiment.	separate set of Turkers to rate the stories for overall quality and the three improvement areas
In 2022 Q2, which of JPM's business segments had the highest net income?	Evidence 0: Segment results managed basis The following tables summarize the Firms results by segment for the periods indicated. Three months ended June 30, Consumer & Community Banking Corporate & Investment Bank Commercial Banking (in millions, except ratios) 2022 2021 Change 2022 2021 Change 2022 2021 Change Total net revenue $ 12,614 $ 12,760 (1) % $ 11,947 $ 13,214 (10) % $ 2,683 $ 2,483 8 % Total noninterest expense 7,723 7,062 9 6,745 6,523 3 1,156 981 18 Pre-provision profit/(loss) 4,891 5,698 (14) 5,202 6,691 (22) 1,527 1,502 2 Provision for credit losses 761 (1,868) NM 59 (79) NM 209 (377) NM Net income/(loss) 3,100 5,645 (a) (45) 3,725 5,020 (a) (26) 994 1,422 (a) (30) Return on equity (ROE) 24 % 44 % 14 % 23 % 15 % 23 % Three months ended June 30, Asset & Wealth Management Corporate Total (in millions, except ratios) 2022 2021 Change 2022 2021 Change 2022 2021 Change Total net revenue $ 4,306 $ 4,107 5 % $ 80 $ (1,169) NM $ 31,630 $ 31,395 1 % Total noninterest expense 2,919 2,586 13 206 515 (60) 18,749 17,667 6 Pre-provision profit/(loss) 1,387 1,521 (9) (126) (1,684) 93 12,881 13,728 (6) Provision for credit losses 44 (10) NM 28 49 (43) 1,101 (2,285) NM Net income/(loss) 1,004 1,156 (a) (13) (174) (1,295) (a) 87 8,649 11,948 (28) ROE 23 % 32 % NM NM 13 % 18 %	Corporate & Investment Bank. Its net income was $3725 million.
Which neural network architectures are employed?	### Introduction Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered BIBREF1 . Since Chinese EHRs are recorded without explicit word delimiters (e.g., “UTF8gkai糖尿病酮症酸中毒” (diabetic ketoacidosis)), Chinese word segmentation (CWS) is a prerequisite for processing EHRs. Currently, state-of-the-art CWS methods usually require large amounts of manually-labeled data to reach their full potential. However, there are many challenges inherent in labeling EHRs. First, EHRs have many medical terminologies, such as “UTF8gkai高血压性心脏病” (hypertensive heart disease) and “UTF8gkai罗氏芬” (Rocephin), so only annotators with medical backgrounds can be qualified to label EHRs. Second, EHRs may involve personal privacies of patients. Therefore, they cannot be openly published on a large scale for labeling. The above two problems lead to the high annotation cost and insufficient training corpus in the research of CWS in medical text. CWS was usually formulated as a sequence labeling task BIBREF2 , which can be solved by supervised learning approaches, such as hidden markov model (HMM) BIBREF3 and conditional random field (CRF) BIBREF4 . However, these methods rely heavily on handcrafted features. To relieve the efforts of feature engineering, neural network-based methods are beginning to thrive BIBREF5 , BIBREF6 , BIBREF7 . However, due to insufficient annotated training data, conventional models for CWS trained on open corpus often suffer from significant performance degradation when transferred to a domain-specific text. Moreover, the task in medical domain is rarely dabbled, and only one related work on transfer learning is found in recent literatures BIBREF8 . However, researches related to transfer learning mostly remain in general domains, causing a major problem that a considerable amount of manually annotated data is required, when introducing the models into specific domains. One of the solutions for this obstacle is to use active learning, where only a small scale of samples are selected and labeled in an active manner. Active learning methods are favored by the researchers in many natural language processing (NLP) tasks, such as text classification BIBREF9 and named entity recognition (NER) BIBREF10 . However, only a handful of works are conducted on CWS BIBREF2 , and few focuses on medical domain tasks. Given the aforementioned challenges and current researches, we propose a word segmentation method based on active learning. To model the segmentation history, we incorporate a sampling strategy consisting of word score, link score and sequence score, which effectively evaluates the segmentation decisions. Specifically, we combine information branch and gated neural network to determine if the segment is a legal word, i.e., word score. Meanwhile, we use the hidden layer output of the long short-term memory (LSTM) BIBREF11 to find out how the word is linked to its surroundings, i.e., link score. The final decision on the selection of labeling samples is made by calculating the average of word and link scores on the whole segmented sentence, i.e., sequence score. Besides, to capture coherence over characters, we additionally add K-means clustering features to the input of CRF-based word segmenter. To sum up, the main contributions of our work are summarized as follows: The rest of this paper is organized as follows. Section SECREF2 briefly reviews the related work on CWS and active learning. Section SECREF3 presents an active learning method for CWS. We experimentally evaluate our proposed method in Section SECREF4 . Finally, Section SECREF5 concludes the paper and envisions on future work. ### Chinese Word Segmentation In past decades, researches on CWS have a long history and various methods have been proposed BIBREF13 , BIBREF14 , BIBREF15 , which is an important task for Chinese NLP BIBREF7 . These methods are mainly focus on two categories: supervised learning and deep learning BIBREF2 . Supervised Learning Methods. Initially, supervised learning methods were widely-used in CWS. Xue BIBREF13 employed a maximum entropy tagger to automatically assign Chinese characters. Zhao et al. BIBREF16 used a conditional random field for tag decoding and considered both feature template selection and tag set selection. However, these methods greatly rely on manual feature engineering BIBREF17 , while handcrafted features are difficult to design, and the size of these features is usually very large BIBREF6 . Deep Learning Methods. Recently, neural networks have been applied in CWS tasks. To name a few, Zheng et al. BIBREF14 used deep layers of neural networks to learn feature representations of characters. Chen et al. BIBREF6 adopted LSTM to capture the previous important information. Chen et al. BIBREF18 proposed a gated recursive neural network (GRNN), which contains reset and update gates to incorporate the complicated combinations of characters. Jiang and Tang BIBREF19 proposed a sequence-to-sequence transformer model to avoid overfitting and capture character information at the distant site of a sentence. Yang et al. BIBREF20 investigated subword information for CWS and integrated subword embeddings into a Lattice LSTM (LaLSTM) network. However, general word segmentation models do not work well in specific field due to lack of annotated training data. Currently, a handful of domain-specific CWS approaches have been studied, but they focused on decentralized domains. In the metallurgical field, Shao et al. BIBREF15 proposed a domain-specific CWS method based on Bi-LSTM model. In the medical field, Xing et al. BIBREF8 proposed an adaptive multi-task transfer learning framework to fully leverage domain-invariant knowledge from high resource domain to medical domain. Meanwhile, transfer learning still greatly focuses on the corpus in general domain. When it comes to the specific domain, large amounts of manually-annotated data is necessary. Active learning can solve this problem to a certain extent. However, due to the challenges faced by performing active learning on CWS, only a few studies have been conducted. On judgements, Yan et al. BIBREF21 adopted the local annotation strategy, which selects substrings around the informative characters in active learning. However, their method still stays at the statistical level. Unlike the above method, we propose an active learning approach for CWS in medical text, which combines information entropy with neural network to effectively reduce annotation cost. ### Active Learning Active learning BIBREF22 mainly aims to ease the data collection process by automatically deciding which instances should be labeled by annotators to train a model as quickly and effectively as possible BIBREF23 . The sampling strategy plays a key role in active learning. In the past decade, the rapid development of active learning has resulted in various sampling strategies, such as uncertainty sampling BIBREF24 , query-by-committee BIBREF25 and information gain BIBREF26 . Currently, the most mainstream sampling strategy is uncertainty sampling. It focuses its selection on samples closest to the decision boundary of the classifier and then chooses these samples for annotators to relabel BIBREF27 . The formal definition of uncertainty sampling is to select a sample INLINEFORM0 that maximizes the entropy INLINEFORM1 over the probability of predicted classes: DISPLAYFORM0 where INLINEFORM0 is a multi-dimensional feature vector, INLINEFORM1 is its binary label, and INLINEFORM2 is the predicted probability, through which a classifier trained on training sets can map features to labels. However, in some complicated tasks, such as CWS and NER, only considering the uncertainty of classifier is obviously not enough. ### Active Learning for Chinese Word Segmentation Active learning methods can generally be described into two parts: a learning engine and a selection engine BIBREF28 . The learning engine is essentially a classifier, which is mainly used for training of classification problems. The selection engine is based on the sampling strategy, which chooses samples that need to be relabeled by annotators from unlabeled data. Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the accuracy of the classifier. In this paper, a CRF-based segmenter and a scoring model are employed as learning engine and selection engine, respectively. Fig. FIGREF7 and Algorithm SECREF3 demonstrate the procedure of CWS based on active learning. First, we train a CRF-based segmenter by train set. Then, the segmenter is employed to annotate the unlabeled set roughly. Subsequently, information entropy based scoring model picks INLINEFORM0 -lowest ranking samples for annotators to relabel. Meanwhile, the train sets and unlabeled sets are updated. Finally, we re-train the segmenter. The above steps iterate until the desired accuracy is achieved or the number of iterations has reached a predefined threshold. [!ht] Active Learning for Chinese Word Segmentation labeled data INLINEFORM1 , unlabeled data INLINEFORM2 , the number of iterations INLINEFORM3 , the number of samples selected per iteration INLINEFORM4 , partitioning function INLINEFORM5 , size INLINEFORM6 a word segmentation model INLINEFORM7 with the smallest test set loss INLINEFORM8 Initialize: INLINEFORM9 train a word segmenter INLINEFORM0 estimate the test set loss INLINEFORM0 label INLINEFORM0 by INLINEFORM1 INLINEFORM0 to INLINEFORM1 INLINEFORM2 compute INLINEFORM3 by branch information entropy based scoring model select INLINEFORM0 -lowest ranking samples INLINEFORM1 relabel INLINEFORM0 by annotators form a new labeled dataset INLINEFORM0 form a new unlabeled dataset INLINEFORM0 train a word segmenter INLINEFORM0 estimate the new test loss INLINEFORM0 compute the loss reduction INLINEFORM0 INLINEFORM0 INLINEFORM1 INLINEFORM0 INLINEFORM0 INLINEFORM1 with the smallest test set loss INLINEFORM2 INLINEFORM3 ### CRF-based Word Segmenter CWS can be formalized as a sequence labeling problem with character position tags, which are (`B', `M', `E', `S'). So, we convert the labeled data into the `BMES' format, in which each character in the sequence is assigned into a label as follows one by one: B=beginning of a word, M=middle of a word, E=end of a word and S=single word. In this paper, we use CRF as a training model for CWS task. Given the observed sequence, CRF has a single exponential model for the joint probability of the entire sequence of labels, while maximum entropy markov model (MEMM) BIBREF29 uses per-state exponential models for the conditional probabilities of next states BIBREF4 . Therefore, it can solve the label bias problem effectively. Compared with neural networks, it has less dependency on the corpus size. First, we pre-process EHRs at the character-level, separating each character of raw EHRs. For instance, given a sentence INLINEFORM0 , where INLINEFORM1 represents the INLINEFORM2 -th character, the separated form is INLINEFORM3 . Then, we employ Word2Vec BIBREF30 to train pre-processed EHRs to get character embeddings. To capture interactions between adjacent characters, K-means clustering algorithm BIBREF31 is utilized to feature the coherence over characters. In general, K-means divides INLINEFORM4 EHR characters into INLINEFORM5 groups of clusters and the similarity of EHR characters in the same cluster is higher. With each iteration, K-means can classify EHR characters into the nearest cluster based on distance to the mean vector. Then, recalculating and adjusting the mean vectors of these clusters until the mean vector converges. K-means features explicitly show the difference between two adjacent characters and even multiple characters. Finally, we additionally add K-means clustering features to the input of CRF-based segmenter. The segmenter makes positional tagging decisions over individual characters. For example, a Chinese segmented sentence UTF8gkai“病人/长期/于/我院/肾病科/住院/治疗/。/" (The patient was hospitalized for a long time in the nephrology department of our hospital.) is labeled as `BEBESBEBMEBEBES'. ### Information Entropy Based Scoring Model To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model. We use gated neural network and information entropy to capture the likelihood of the segment being a legal word. The architecture of word score model is depicted in Fig. FIGREF12 . Gated Combination Neural Network (GCNN) To effectively learn word representations through character embeddings, we use GCNN BIBREF32 . The architecture of GCNN is demonstrated in Fig. FIGREF13 , which includes update gate and reset gate. The gated mechanism not only captures the characteristics of the characters themselves, but also utilizes the interaction between the characters. There are two types of gates in this network structure: reset gates and update gates. These two gated vectors determine the final output of the gated recurrent neural network, where the update gate helps the model determine what to be passed, and the reset gate primarily helps the model decide what to be cleared. In particular, the word embedding of a word with INLINEFORM0 characters can be computed as: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are update gates for new combination vector INLINEFORM2 and the i-th character INLINEFORM3 respectively, the combination vector INLINEFORM4 is formalized as: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are reset gates for characters. Left and Right Branch Information Entropy In general, each string in a sentence may be a word. However, compared with a string which is not a word, the string of a word is significantly more independent. The branch information entropy is usually used to judge whether each character in a string is tightly linked through the statistical characteristics of the string, which reflects the likelihood of a string being a word. The left and right branch information entropy can be formalized as follows: DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 denotes the INLINEFORM1 -th candidate word, INLINEFORM2 denotes the character set, INLINEFORM3 denotes the probability that character INLINEFORM4 is on the left of word INLINEFORM5 and INLINEFORM6 denotes the probability that character INLINEFORM7 is on the right of word INLINEFORM8 . INLINEFORM9 and INLINEFORM10 respectively represent the left and right branch information entropy of the candidate word INLINEFORM11 . If the left and right branch information entropy of a candidate word is relatively high, the probability that the candidate word can be combined with the surrounded characters to form a word is low, thus the candidate word is likely to be a legal word. To judge whether the candidate words in a segmented sentence are legal words, we compute the left and right entropy of each candidate word, then take average as the measurement standard: DISPLAYFORM0 We represent a segmented sentence with INLINEFORM0 candidate words as [ INLINEFORM1 , INLINEFORM2 ,..., INLINEFORM3 ], so the INLINEFORM4 ( INLINEFORM5 ) of the INLINEFORM6 -th candidate word is computed by its average entropy: DISPLAYFORM0 In this paper, we use LSTM to capture the coherence between words in a segmented sentence. This neural network is mainly an optimization for traditional RNN. RNN is widely used to deal with time-series prediction problems. The result of its current hidden layer is determined by the input of the current layer and the output of the previous hidden layer BIBREF33 . Therefore, RNN can remember historical results. However, traditional RNN has problems of vanishing gradient and exploding gradient when training long sequences BIBREF34 . By adding a gated mechanism to RNN, LSTM effectively solves these problems, which motivates us to get the link score with LSTM. Formally, the LSTM unit performs the following operations at time step INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 are the inputs of LSTM, all INLINEFORM3 and INLINEFORM4 are a set of parameter matrices to be trained, and INLINEFORM5 is a set of bias parameter matrices to be trained. INLINEFORM6 and INLINEFORM7 operation respectively represent matrix element-wise multiplication and sigmoid function. In the LSTM unit, there are two hidden layers ( INLINEFORM8 , INLINEFORM9 ), where INLINEFORM10 is the internal memory cell for dealing with vanishing gradient, while INLINEFORM11 is the main output of the LSTM unit for complex operations in subsequent layers. We denotes INLINEFORM0 as the word embedding of time step INLINEFORM1 , a prediction INLINEFORM2 of next word embedding INLINEFORM3 can be computed by hidden layer INLINEFORM4 : DISPLAYFORM0 Therefore, link score of next word embedding INLINEFORM0 can be computed as: DISPLAYFORM0 Due to the structure of LSTM, vector INLINEFORM0 contains important information of entire segmentation decisions. In this way, the link score gets the result of the sequence-level word segmentation, not just word-level. Intuitively, we can compute the score of a segmented sequence by summing up word scores and link scores. However, we find that a sequence with more candidate words tends to have higher sequence scores. Therefore, to alleviate the impact of the number of candidate words on sequence scores, we calculate final scores as follows: DISPLAYFORM0 where INLINEFORM0 denotes the INLINEFORM1 -th segmented sequence with INLINEFORM2 candidate words, and INLINEFORM3 represents the INLINEFORM4 -th candidate words in the segmented sequence. When training the model, we seek to minimize the sequence score of the corrected segmented sentence and the predicted segmented sentence. DISPLAYFORM0 where INLINEFORM0 is the loss function. ### Datasets We collect 204 EHRs with cardiovascular diseases from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and each contains 27 types of records. We choose 4 different types with a total of 3868 records from them, which are first course reports, medical records, chief ward round records and discharge records. The detailed information of EHRs are listed in Table TABREF32 . We split our datasets as follows. First, we randomly select 3200 records from 3868 records as unlabeled set. Then, we manually annotate remaining 668 records as labeled set, which contains 1170 sentences. Finally, we divide labeled set into train set and test set with the ratio of 7:3 randomly. Statistics of datasets are listed in Table TABREF33 . ### Parameter Settings To determine suitable parameters, we divide training set into two sets, the first 80% sentences as training set and the rest 20% sentences as validation set. Character embedding dimensions and K-means clusters are two main parameters in the CRF-based word segmenter. In this paper, we choose character-based CRF without any features as baseline. First, we use Word2Vec to train character embeddings with dimensions of [`50', `100', `150', `200', `300', `400'] respectively, thus we obtain 6 different dimensional character embeddings. Second, these six types of character embeddings are used as the input to K-means algorithm with the number of clusters [`50', `100', `200', `300', `400', `500', `600'] respectively to capture the corresponding features of character embeddings. Then, we add K-means clustering features to baseline for training. As can be seen from Fig. FIGREF36 , when the character embedding dimension INLINEFORM0 = 150 and the number of clusters INLINEFORM1 = 400, CRF-based word segmenter performs best, so these two parameters are used in subsequent experiments. Hyper-parameters of neural network have a great impact on the performance. The hyper-parameters we choose are listed in Table TABREF38 . The dimension of character embeddings is set as same as the parameter used in CRF-based word segmenter and the number of hidden units is also set to be the same as it. Maximum word length is ralated to the number of parameters in GCNN unit. Since there are many long medical terminologies in EHRs, we set the maximum word length as 6. In addition, dropout is an effective way to prevent neural networks from overfitting BIBREF35 . To avoid overfitting, we drop the input layer of the scoring model with the rate of 20%. ### Experimental Results Our work experimentally compares two mainstream CWS tools (LTP and Jieba) on training and testing sets. These two tools are widely used and recognized due to their high INLINEFORM0 -score of word segmentation in general fields. However, in specific fields, there are many terminologies and uncommon words, which lead to the unsatisfactory performance of segmentation results. To solve the problem of word segmentation in specific fields, these two tools provide a custom dictionary for users. In the experiments, we also conduct a comparative experiment on whether external domain dictionary has an effect on the experimental results. We manually construct the dictionary when labeling EHRs. From the results in Table TABREF41 , we find that Jieba benefits a lot from the external dictionary. However, the Recall of LTP decreases when joining the domain dictionary. Generally speaking, since these two tools are trained by general domain corpus, the results are not ideal enough to cater to the needs of subsequent NLP of EHRs when applied to specific fields. To investigate the effectiveness of K-means features in CRF-based segmenter, we also compare K-means with 3 different clustering features, including MeanShift BIBREF36 , SpectralClustering BIBREF37 and DBSCAN BIBREF38 on training and testing sets. From the results in Table TABREF43 , by adding additional clustering features in CRF-based segmenter, there is a significant improvement of INLINEFORM0 -score, which indicates that clustering features can effectively capture the semantic coherence between characters. Among these clustering features, K-means performs best, so we utlize K-means results as additional features for CRF-based segmenter. In this experiment, since uncertainty sampling is the most popular strategy in real applications for its simpleness and effectiveness BIBREF27 , we compare our proposed strategy with uncertainty sampling in active learning. We conduct our experiments as follows. First, we employ CRF-based segmenter to annotate the unlabeled set. Then, sampling strategy in active learning selects a part of samples for annotators to relabel. Finally, the relabeled samples are added to train set for segmenter to re-train. Our proposed scoring strategy selects samples according to the sequence scores of the segmented sentences, while uncertainty sampling suggests relabeling samples that are closest to the segmenter’s decision boundary. Generally, two main parameters in active learning are the numbers of iterations and samples selected per iteration. To fairly investigate the influence of two parameters, we compare our proposed strategy with uncertainty sampling on the same parameter. We find that though the number of iterations is large enough, it has a limited impact on the performance of segmenter. Therefore, we choose 30 as the number of iterations, which is a good trade-off between speed and performance. As for the number of samples selected per iteration, there are 6078 sentences in unlabeled set, considering the high cost of relabeling, we set four sizes of samples selected per iteration, which are 2%, 5%, 8% and 11%. The experimental results of two sampling strategies with 30 iterations on four different proportions of relabeled data are shown in Fig. FIGREF45 , where x-axis represents the number of iterations and y-axis denotes the INLINEFORM0 -score of the segmenter. Scoring strategy shows consistent improvements over uncertainty sampling in the early iterations, indicating that scoring strategy is more capable of selecting representative samples. Furthermore, we also investigate the relations between the best INLINEFORM0 -score and corresponding number of iteration on two sampling strategies, which is depicted in Fig. FIGREF46 . It is observed that in our proposed scoring model, with the proportion of relabeled data increasing, the iteration number of reaching the optimal word segmentation result is decreasing, but the INLINEFORM0 -score of CRF-based word segmenter is also gradually decreasing. When the proportion is 2%, the segmenter reaches the highest INLINEFORM1 -score: 90.62%. Obviously, our proposed strategy outperforms uncertainty sampling by a large margin. Our proposed method needs only 2% relabeled samples to obtain INLINEFORM2 -score of 90.62%, while uncertainty sampling requires 8% samples to reach its best INLINEFORM3 -score of 88.98%, which indicates that with our proposed method, we only need to manually relabel a small number of samples to achieve a desired segmentation result. ### Conclusion and Future Work To relieve the efforts of EHRs annotation, we propose an effective word segmentation method based on active learning, in which the sampling strategy is a scoring model combining information entropy with neural network. Compared with the mainstream uncertainty sampling, our strategy selects samples from statistical perspective and deep learning level. In addition, to capture coherence between characters, we add K-means clustering features to CRF-based word segmenter. Based on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, we evaluate our method on CWS task. Compared with uncertainty sampling, our method requires 6% less relabeled samples to achieve better performance, which proves that our method can save the cost of manual annotation to a certain extent. In future, we plan to employ other widely-used deep neural networks, such as convolutional neural network and attention mechanism, in the research of EHRs segmentation. Then, we believe that our method can be applied to other tasks as well, so we will fully investigate the application of our method in other tasks, such as NER and relation extraction. ### Acknowledgment The authors would like to appreciate any suggestions or comments from the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (No. 61772201) and the National Key R&D Program of China for “Precision medical research" (No. 2018YFC0910550). Fig. 1. The diagram of active learning for the Chinese word segmentation. Fig. 2. The architecture of the information entropy based scoring model, where ‘/’ represents candidate word separator, xi represents the one-hot encoding of the i-th character, cj represents the j-th character embedding learned by Word2Vec, wm represents the distributed representation of the mth candidate word and pn represents the prediction of the (n+1)-th candidate word. Fig. 3. The architecture of word score, where ‘/’ represents candidate word separator, ci represents the i-th character embedding, wj represents the j-th candidate word embedding and ScoreWord(wk) represents the word score of the k-th candidate word. Fig. 4. The architecture of GCNN. TABLE I DETAILED INFORMATION OF EHRS TABLE III HYPER-PARAMETER SETTING. TABLE IV EXPERIMENTAL RESULTS WITH DIFFERENT WORD SEGMENTATION TOOLS. Fig. 5. The relation between F1-score and K-means class with different character embedding dimensions. TABLE II STATISTICS OF DATASETS TABLE V COMPARISON WITH DIFFERENT CLUSTERING FEATURES. Fig. 7. The relations between the best F1-score and corresponding iteration on two sampling strategies with different relabeled sample sizes. Fig. 6. The results of two sampling strategies with different relabeled sample sizes.	gated neural network
What likely caused the most problems? A. the toxic gases B. the high temperatures C. vehicle trouble D. incorrect mapping	Brightside Crossing by Alan E. Nourse JAMES BARON was not pleased to hear that he had had a visitor when he reached the Red Lion that evening. He had no stomach for mysteries, vast or trifling, and there were pressing things to think about at this time. Yet the doorman had flagged him as he came in from the street: “A thousand pardons, Mr. Baron. The gentleman—he would leave no name. He said you’d want to see him. He will be back by eight.” Now Baron drummed his fingers on the table top, staring about the quiet lounge. Street trade was discouraged at the Red Lion, gently but persuasively; the patrons were few in number. Across to the right was a group that Baron knew vaguely—Andean climbers, or at least two of them were. Over near the door he recognized old Balmer, who had mapped the first passage to the core of Vulcan Crater on Venus. Baron returned his smile with a nod. Then he settled back and waited impatiently for the intruder who demanded his time without justifying it. Presently a small, grizzled man crossed the room and sat down at Baron’s table. He was short and wiry. His face held no key to his age—he might have been thirty or a thousand—but he looked weary and immensely ugly. His cheeks and forehead were twisted and brown, with scars that were still healing. The stranger said, “I’m glad you waited. I’ve heard you’re planning to attempt the Brightside.” Baron stared at the man for a moment. “I see you can read telecasts,” he said coldly. “The news was correct. We are going to make a Brightside Crossing.” “At perihelion?” “Of course. When else?” The grizzled man searched Baron’s face for a moment without expression. Then he said slowly, “No, I’m afraid you’re not going to make the Crossing.” “Say, who are you, if you don’t mind?” Baron demanded. “The name is Claney,” said the stranger. There was a silence. Then: “Claney? Peter Claney?” “That’s right.” Baron’s eyes were wide with excitement, all trace of anger gone. “Great balls of fire, man— where have you been hiding? We’ve been trying to contact you for months!” “I know. I was hoping you’d quit looking and chuck the whole idea.” “Quit looking!” Baron bent forward over the table. “My friend, we’d given up hope, but we’ve never quit looking. Here, have a drink. There’s so much you can tell us.” His fingers were trembling. Peter Claney shook his head. “I can’t tell you anything you want to hear.” “But you’ve got to. You’re the only man on Earth who’s attempted a Brightside Crossing and lived through it! And the story you cleared for the news—it was nothing. We need details . Where did your equipment fall down? Where did you miscalculate? What were the trouble spots?” Baron jabbed a finger at Claney’s face. “That, for instance—epithelioma? Why? What was wrong with your glass? Your filters? We’ve got to know those things. If you can tell us, we can make it across where your attempt failed—” “You want to know why we failed?” asked Claney. “Of course we want to know. We have to know.” “It’s simple. We failed because it can’t be done. We couldn’t do it and neither can you. No human beings will ever cross the Brightside alive, not if they try for centuries.” “Nonsense,” Baron declared. “We will.” Claney shrugged. “I was there. I know what I’m saying. You can blame the equipment or the men—there were flaws in both quarters—but we just didn’t know what we were fighting. It was the planet that whipped us, that and the Sun . They’ll whip you, too, if you try it.” “Never,” said Baron. “Let me tell you,” Peter Claney said. I’d been interested in the Brightside for almost as long as I can remember (Claney said). I guess I was about ten when Wyatt and Carpenter made the last attempt—that was in 2082, I think. I followed the news stories like a tri-V serial and then I was heartbroken when they just disappeared. I know now that they were a pair of idiots, starting off without proper equipment, with practically no knowledge of surface conditions, without any charts—they couldn’t have made a hundred miles—but I didn’t know that then and it was a terrible tragedy. After that, I followed Sanderson’s work in the Twilight Lab up there and began to get Brightside into my blood, sure as death. But it was Mikuta’s idea to attempt a Crossing. Did you ever know Tom Mikuta? I don’t suppose you did. No, not Japanese—Polish-American. He was a major in the Interplanetary Service for some years and hung onto the title after he gave up his commission. He was with Armstrong on Mars during his Service days, did a good deal of the original mapping and surveying for the Colony there. I first met him on Venus; we spent five years together up there doing some of the nastiest exploring since the Matto Grasso. Then he made the attempt on Vulcan Crater that paved the way for Balmer a few years later. I’d always liked the Major—he was big and quiet and cool, the sort of guy who always had things figured a little further ahead than anyone else and always knew what to do in a tight place. Too many men in this game are all nerve and luck, with no judgment. The Major had both. He also had the kind of personality that could take a crew of wild men and make them work like a well-oiled machine across a thousand miles of Venus jungle. I liked him and I trusted him. He contacted me in New York and he was very casual at first. We spent an evening here at the Red Lion, talking about old times; he told me about the Vulcan business, and how he’d been out to see Sanderson and the Twilight Lab on Mercury, and how he preferred a hot trek to a cold one any day of the year—and then he wanted to know what I’d been doing since Venus and what my plans were. “No particular plans,” I told him. “Why?” He looked me over. “How much do you weigh, Peter?” I told him one-thirty-five. “That much!” he said. “Well, there can’t be much fat on you, at any rate. How do you take heat?” “You should know,” I said. “Venus was no icebox.” “No, I mean real heat.” Then I began to get it. “You’re planning a trip.” “That’s right. A hot trip.” He grinned at me. “Might be dangerous, too.” “What trip?” “Brightside of Mercury,” the Major said. I whistled cautiously. “At aphelion?” He threw his head back. “Why try a Crossing at aphelion? What have you done then? Four thousand miles of butcherous heat, just to have some joker come along, use your data and drum you out of the glory by crossing at perihelion forty-four days later? No, thanks. I want the Brightside without any nonsense about it.” He leaned across me eagerly. “I want to make a Crossing at perihelion and I want to cross on the surface. If a man can do that, he’s got Mercury. Until then, nobody’s got Mercury. I want Mercury—but I’ll need help getting it.” I’d thought of it a thousand times and never dared consider it. Nobody had, since Wyatt and Carpenter disappeared. Mercury turns on its axis in the same time that it wheels around the Sun, which means that the Brightside is always facing in. That makes the Brightside of Mercury at perihelion the hottest place in the Solar System, with one single exception: the surface of the Sun itself. It would be a hellish trek. Only a few men had ever learned just how hellish and they never came back to tell about it. It was a real hell’s Crossing, but someday, I thought, somebody would cross it. I wanted to be along. The Twilight Lab, near the northern pole of Mercury, was the obvious jumping-off place. The setup there wasn’t very extensive—a rocket landing, the labs and quarters for Sanderson’s crew sunk deep into the crust, and the tower that housed the Solar ’scope that Sanderson had built up there ten years before. Twilight Lab wasn’t particularly interested in the Brightside, of course—the Sun was Sanderson’s baby and he’d picked Mercury as the closest chunk of rock to the Sun that could hold his observatory. He’d chosen a good location, too. On Mercury, the Brightside temperature hits 770° F. at perihelion and the Darkside runs pretty constant at -410° F. No permanent installation with a human crew could survive at either extreme. But with Mercury’s wobble, the twilight zone between Brightside and Darkside offers something closer to survival temperatures. Sanderson built the Lab up near the pole, where the zone is about five miles wide, so the temperature only varies 50 to 60 degrees with the libration. The Solar ’scope could take that much change and they’d get good clear observation of the Sun for about seventy out of the eighty-eight days it takes the planet to wheel around. The Major was counting on Sanderson knowing something about Mercury as well as the Sun when we camped at the Lab to make final preparations. Sanderson did. He thought we’d lost our minds and he said so, but he gave us all the help he could. He spent a week briefing Jack Stone, the third member of our party, who had arrived with the supplies and equipment a few days earlier. Poor Jack met us at the rocket landing almost bawling, Sanderson had given him such a gloomy picture of what Brightside was like. Stone was a youngster—hardly twenty-five, I’d say—but he’d been with the Major at Vulcan and had begged to join this trek. I had a funny feeling that Jack really didn’t care for exploring too much, but he thought Mikuta was God, followed him around like a puppy. It didn’t matter to me as long as he knew what he was getting in for. You don’t go asking people in this game why they do it—they’re liable to get awfully uneasy and none of them can ever give you an answer that makes sense. Anyway, Stone had borrowed three men from the Lab, and had the supplies and equipment all lined up when we got there, ready to check and test. We dug right in. With plenty of funds—tri-V money and some government cash the Major had talked his way around—our equipment was new and good. Mikuta had done the designing and testing himself, with a big assist from Sanderson. We had four Bugs, three of them the light pillow-tire models, with special lead-cooled cut-in engines when the heat set in, and one heavy-duty tractor model for pulling the sledges. The Major went over them like a kid at the circus. Then he said, “Have you heard anything from McIvers?” “Who’s he?” Stone wanted to know. “He’ll be joining us. He’s a good man—got quite a name for climbing, back home.” The Major turned to me. “You’ve probably heard of him.” I’d heard plenty of stories about Ted McIvers and I wasn’t too happy to hear that he was joining us. “Kind of a daredevil, isn’t he?” “Maybe. He’s lucky and skillful. Where do you draw the line? We’ll need plenty of both.” “Have you ever worked with him?” I asked. “No. Are you worried?” “Not exactly. But Brightside is no place to count on luck.” The Major laughed. “I don’t think we need to worry about McIvers. We understood each other when I talked up the trip to him and we’re going to need each other too much to do any fooling around.” He turned back to the supply list. “Meanwhile, let’s get this stuff listed and packed. We’ll need to cut weight sharply and our time is short. Sanderson says we should leave in three days.” Two days later, McIvers hadn’t arrived. The Major didn’t say much about it. Stone was getting edgy and so was I. We spent the second day studying charts of the Brightside, such as they were. The best available were pretty poor, taken from so far out that the detail dissolved into blurs on blow-up. They showed the biggest ranges of peaks and craters and faults, and that was all. Still, we could use them to plan a broad outline of our course. “This range here,” the Major said as we crowded around the board, “is largely inactive, according to Sanderson. But these to the south and west could be active. Seismograph tracings suggest a lot of activity in that region, getting worse down toward the equator—not only volcanic, but sub-surface shifting.” Stone nodded. “Sanderson told me there was probably constant surface activity.” The Major shrugged. “Well, it’s treacherous, there’s no doubt of it. But the only way to avoid it is to travel over the Pole, which would lose us days and offer us no guarantee of less activity to the west. Now we might avoid some if we could find a pass through this range and cut sharp east—” It seemed that the more we considered the problem, the further we got from a solution. We knew there were active volcanoes on the Brightside—even on the Darkside, though surface activity there was pretty much slowed down and localized. But there were problems of atmosphere on Brightside, as well. There was an atmosphere and a constant atmospheric flow from Brightside to Darkside. Not much—the lighter gases had reached escape velocity and disappeared from Brightside millennia ago—but there was CO 2 , and nitrogen, and traces of other heavier gases. There was also an abundance of sulfur vapor, as well as carbon disulfide and sulfur dioxide. The atmospheric tide moved toward the Darkside, where it condensed, carrying enough volcanic ash with it for Sanderson to estimate the depth and nature of the surface upheavals on Brightside from his samplings. The trick was to find a passage that avoided those upheavals as far as possible. But in the final analysis, we were barely scraping the surface. The only way we would find out what was happening where was to be there. Finally, on the third day, McIvers blew in on a freight rocket from Venus. He’d missed the ship that the Major and I had taken by a few hours, and had conned his way to Venus in hopes of getting a hop from there. He didn’t seem too upset about it, as though this were his usual way of doing things and he couldn’t see why everyone should get so excited. He was a tall, rangy man with long, wavy hair prematurely gray, and the sort of eyes that looked like a climber’s—half-closed, sleepy, almost indolent, but capable of abrupt alertness. And he never stood still; he was always moving, always doing something with his hands, or talking, or pacing about. Evidently the Major decided not to press the issue of his arrival. There was still work to do, and an hour later we were running the final tests on the pressure suits. That evening, Stone and McIvers were thick as thieves, and everything was set for an early departure after we got some rest. “And that,” said Baron, finishing his drink and signaling the waiter for another pair, “was your first big mistake.” Peter Claney raised his eyebrows. “McIvers?” “Of course.” Claney shrugged, glanced at the small quiet tables around them. “There are lots of bizarre personalities around a place like this, and some of the best wouldn’t seem to be the most reliable at first glance. Anyway, personality problems weren’t our big problem right then. Equipment worried us first and route next.” Baron nodded in agreement. “What kind of suits did you have?” “The best insulating suits ever made,” said Claney. “Each one had an inner lining of a fiberglass modification, to avoid the clumsiness of asbestos, and carried the refrigerating unit and oxygen storage which we recharged from the sledges every eight hours. Outer layer carried a monomolecular chrome reflecting surface that made us glitter like Christmas trees. And we had a half-inch dead-air space under positive pressure between the two layers. Warning thermocouples, of course—at 770 degrees, it wouldn’t take much time to fry us to cinders if the suits failed somewhere.” “How about the Bugs?” “They were insulated, too, but we weren’t counting on them too much for protection.” “You weren’t!” Baron exclaimed. “Why not?” “We’d be in and out of them too much. They gave us mobility and storage, but we knew we’d have to do a lot of forward work on foot.” Claney smiled bitterly. “Which meant that we had an inch of fiberglass and a half-inch of dead air between us and a surface temperature where lead flowed like water and zinc was almost at melting point and the pools of sulfur in the shadows were boiling like oatmeal over a campfire.” Baron licked his lips. His fingers stroked the cool, wet glass as he set it down on the tablecloth. “Go on,” he said tautly. “You started on schedule?” “Oh, yes,” said Claney, “we started on schedule, all right. We just didn’t quite end on schedule, that was all. But I’m getting to that.” He settled back in his chair and continued. We jumped off from Twilight on a course due southeast with thirty days to make it to the Center of Brightside. If we could cross an average of seventy miles a day, we could hit Center exactly at perihelion, the point of Mercury’s closest approach to the Sun—which made Center the hottest part of the planet at the hottest it ever gets. The Sun was already huge and yellow over the horizon when we started, twice the size it appears on Earth. Every day that Sun would grow bigger and whiter, and every day the surface would get hotter. But once we reached Center, the job was only half done—we would still have to travel another two thousand miles to the opposite twilight zone. Sanderson was to meet us on the other side in the Laboratory’s scout ship, approximately sixty days from the time we jumped off. That was the plan, in outline. It was up to us to cross those seventy miles a day, no matter how hot it became, no matter what terrain we had to cross. Detours would be dangerous and time-consuming. Delays could cost us our lives. We all knew that. The Major briefed us on details an hour before we left. “Peter, you’ll take the lead Bug, the small one we stripped down for you. Stone and I will flank you on either side, giving you a hundred-yard lead. McIvers, you’ll have the job of dragging the sledges, so we’ll have to direct your course pretty closely. Peter’s job is to pick the passage at any given point. If there’s any doubt of safe passage, we’ll all explore ahead on foot before we risk the Bugs. Got that?” McIvers and Stone exchanged glances. McIvers said: “Jack and I were planning to change around. We figured he could take the sledges. That would give me a little more mobility.” The Major looked up sharply at Stone. “Do you buy that, Jack?” Stone shrugged. “I don’t mind. Mac wanted—” McIvers made an impatient gesture with his hands. “It doesn’t matter. I just feel better when I’m on the move. Does it make any difference?” “I guess it doesn’t,” said the Major. “Then you’ll flank Peter along with me. Right?” “Sure, sure.” McIvers pulled at his lower lip. “Who’s going to do the advance scouting?” “It sounds like I am,” I cut in. “We want to keep the lead Bug light as possible.” Mikuta nodded. “That’s right. Peter’s Bug is stripped down to the frame and wheels.” McIvers shook his head. “No, I mean the advance work. You need somebody out ahead—four or five miles, at least—to pick up the big flaws and active surface changes, don’t you?” He stared at the Major. “I mean, how can we tell what sort of a hole we may be moving into, unless we have a scout up ahead?” “That’s what we have the charts for,” the Major said sharply. “Charts! I’m talking about detail work. We don’t need to worry about the major topography. It’s the little faults you can’t see on the pictures that can kill us.” He tossed the charts down excitedly. “Look, let me take a Bug out ahead and work reconnaissance, keep five, maybe ten miles ahead of the column. I can stay on good solid ground, of course, but scan the area closely and radio back to Peter where to avoid the flaws. Then—” “No dice,” the Major broke in. “But why not? We could save ourselves days!” “I don’t care what we could save. We stay together. When we get to the Center, I want live men along with me. That means we stay within easy sight of each other at all times. Any climber knows that everybody is safer in a party than one man alone—any time, any place.” McIvers stared at him, his cheeks an angry red. Finally he gave a sullen nod. “Okay. If you say so.” “Well, I say so and I mean it. I don’t want any fancy stuff. We’re going to hit Center together, and finish the Crossing together. Got that?” McIvers nodded. Mikuta then looked at Stone and me and we nodded, too. “All right,” he said slowly. “Now that we’ve got it straight, let’s go.” It was hot. If I forget everything else about that trek, I’ll never forget that huge yellow Sun glaring down, without a break, hotter and hotter with every mile. We knew that the first few days would be the easiest and we were rested and fresh when we started down the long ragged gorge southeast of the Twilight Lab. I moved out first; back over my shoulder, I could see the Major and McIvers crawling out behind me, their pillow tires taking the rugged floor of the gorge smoothly. Behind them, Stone dragged the sledges. Even at only 30 per cent Earth gravity they were a strain on the big tractor, until the ski-blades bit into the fluffy volcanic ash blanketing the valley. We even had a path to follow for the first twenty miles. I kept my eyes pasted to the big polaroid binocs, picking out the track the early research teams had made out into the edge of Brightside. But in a couple of hours we rumbled past Sanderson’s little outpost observatory and the tracks stopped. We were in virgin territory and already the Sun was beginning to bite. We didn’t feel the heat so much those first days out. We saw it. The refrig units kept our skins at a nice comfortable seventy-five degrees Fahrenheit inside our suits, but our eyes watched that glaring Sun and the baked yellow rocks going past, and some nerve pathways got twisted up, somehow. We poured sweat as if we were in a superheated furnace. We drove eight hours and slept five. When a sleep period came due, we pulled the Bugs together into a square, threw up a light aluminum sun-shield and lay out in the dust and rocks. The sun-shield cut the temperature down sixty or seventy degrees, for whatever help that was. And then we ate from the forward sledge—sucking through tubes—protein, carbohydrates, bulk gelatin, vitamins. The Major measured water out with an iron hand, because we’d have drunk ourselves into nephritis in a week otherwise. We were constantly, unceasingly thirsty. Ask the physiologists and psychiatrists why—they can give you have a dozen interesting reasons—but all we knew, or cared about, was that it happened to be so. We didn’t sleep the first few stops, as a consequence. Our eyes burned in spite of the filters and we had roaring headaches, but we couldn’t sleep them off. We sat around looking at each other. Then McIvers would say how good a beer would taste, and off we’d go. We’d have murdered our grandmothers for one ice-cold bottle of beer. After a few driving periods, I began to get my bearings at the wheel. We were moving down into desolation that made Earth’s old Death Valley look like a Japanese rose garden. Huge sun-baked cracks opened up in the floor of the gorge, with black cliffs jutting up on either side; the air was filled with a barely visible yellowish mist of sulfur and sulfurous gases. It was a hot, barren hole, no place for any man to go, but the challenge was so powerful you could almost feel it. No one had ever crossed this land before and escaped. Those who had tried it had been cruelly punished, but the land was still there, so it had to be crossed. Not the easy way. It had to be crossed the hardest way possible: overland, through anything the land could throw up to us, at the most difficult time possible. Yet we knew that even the land might have been conquered before, except for that Sun. We’d fought absolute cold before and won. We’d never fought heat like this and won. The only worse heat in the Solar System was the surface of the Sun itself. Brightside was worth trying for. We would get it or it would get us. That was the bargain. I learned a lot about Mercury those first few driving periods. The gorge petered out after a hundred miles and we moved onto the slope of a range of ragged craters that ran south and east. This range had shown no activity since the first landing on Mercury forty years before, but beyond it there were active cones. Yellow fumes rose from the craters constantly; their sides were shrouded with heavy ash. We couldn’t detect a wind, but we knew there was a hot, sulfurous breeze sweeping in great continental tides across the face of the planet. Not enough for erosion, though. The craters rose up out of jagged gorges, huge towering spears of rock and rubble. Below were the vast yellow flatlands, smoking and hissing from the gases beneath the crust. Over everything was gray dust—silicates and salts, pumice and limestone and granite ash, filling crevices and declivities—offering a soft, treacherous surface for the Bug’s pillow tires. I learned to read the ground, to tell a covered fault by the sag of the dust; I learned to spot a passable crack, and tell it from an impassable cut. Time after time the Bugs ground to a halt while we explored a passage on foot, tied together with light copper cable, digging, advancing, digging some more until we were sure the surface would carry the machines. It was cruel work; we slept in exhaustion. But it went smoothly, at first. Too smoothly, it seemed to me, and the others seemed to think so, too. McIvers’ restlessness was beginning to grate on our nerves. He talked too much, while we were resting or while we were driving; wisecracks, witticisms, unfunny jokes that wore thin with repetition. He took to making side trips from the route now and then, never far, but a little further each time. Jack Stone reacted quite the opposite; he grew quieter with each stop, more reserved and apprehensive. I didn’t like it, but I figured that it would pass off after a while. I was apprehensive enough myself; I just managed to hide it better. And every mile the Sun got bigger and whiter and higher in the sky and hotter. Without our ultra-violet screens and glare filters we would have been blinded; as it was our eyes ached constantly and the skin on our faces itched and tingled at the end of an eight-hour trek. But it took one of those side trips of McIvers’ to deliver the penultimate blow to our already fraying nerves. He had driven down a side-branch of a long canyon running off west of our route and was almost out of sight in a cloud of ash when we heard a sharp cry through our earphones. I wheeled my Bug around with my heart in my throat and spotted him through the binocs, waving frantically from the top of his machine. The Major and I took off, lumbering down the gulch after him as fast as the Bugs could go, with a thousand horrible pictures racing through our minds.... We found him standing stock-still, pointing down the gorge and, for once, he didn’t have anything to say. It was the wreck of a Bug; an old-fashioned half-track model of the sort that hadn’t been in use for years. It was wedged tight in a cut in the rock, an axle broken, its casing split wide open up the middle, half-buried in a rock slide. A dozen feet away were two insulated suits with white bones gleaming through the fiberglass helmets. This was as far as Wyatt and Carpenter had gotten on their Brightside Crossing. On the fifth driving period out, the terrain began to change. It looked the same, but every now and then it felt different. On two occasions I felt my wheels spin, with a howl of protest from my engine. Then, quite suddenly, the Bug gave a lurch; I gunned my motor and nothing happened. I could see the dull gray stuff seeping up around the hubs, thick and tenacious, splattering around in steaming gobs as the wheels spun. I knew what had happened the moment the wheels gave and, a few minutes later, they chained me to the tractor and dragged me back out of the mire. It looked for all the world like thick gray mud, but it was a pit of molten lead, steaming under a soft layer of concealing ash. I picked my way more cautiously then. We were getting into an area of recent surface activity; the surface was really treacherous. I caught myself wishing that the Major had okayed McIvers’ scheme for an advanced scout; more dangerous for the individual, maybe, but I was driving blind now and I didn’t like it. One error in judgment could sink us all, but I wasn’t thinking much about the others. I was worried about me , plenty worried. I kept thinking, better McIvers should go than me. It wasn’t healthy thinking and I knew it, but I couldn’t get the thought out of my mind. It was a grueling eight hours and we slept poorly. Back in the Bug again, we moved still more slowly—edging out on a broad flat plateau, dodging a network of gaping surface cracks—winding back and forth in an effort to keep the machines on solid rock. I couldn’t see far ahead, because of the yellow haze rising from the cracks, so I was almost on top of it when I saw a sharp cut ahead where the surface dropped six feet beyond a deep crack. I let out a shout to halt the others; then I edged my Bug forward, peering at the cleft. It was deep and wide. I moved fifty yards to the left, then back to the right. There was only one place that looked like a possible crossing; a long, narrow ledge of gray stuff that lay down across a section of the fault like a ramp. Even as I watched it, I could feel the surface crust under the Bug trembling and saw the ledge shift over a few feet.	B. the high temperatures
Where can a person go to be with friendly faces in Tangier? A. The Place de France B. The Boulevard Pasteur C. The Cafe de Paris D. The Grand Socco	One can't be too cautious about the people one meets in Tangier. They're all weirdies of one kind or another. Me? Oh, I'm A Stranger Here Myself By MACK REYNOLDS The Place de France is the town's hub. It marks the end of Boulevard Pasteur, the main drag of the westernized part of the city, and the beginning of Rue de la Liberté, which leads down to the Grand Socco and the medina. In a three-minute walk from the Place de France you can go from an ultra-modern, California-like resort to the Baghdad of Harun al-Rashid. It's quite a town, Tangier. King-size sidewalk cafes occupy three of the strategic corners on the Place de France. The Cafe de Paris serves the best draft beer in town, gets all the better custom, and has three shoeshine boys attached to the establishment. You can sit of a sunny morning and read the Paris edition of the New York Herald Tribune while getting your shoes done up like mirrors for thirty Moroccan francs which comes to about five cents at current exchange. You can sit there, after the paper's read, sip your expresso and watch the people go by. Tangier is possibly the most cosmopolitan city in the world. In native costume you'll see Berber and Rif, Arab and Blue Man, and occasionally a Senegalese from further south. In European dress you'll see Japs and Chinese, Hindus and Turks, Levantines and Filipinos, North Americans and South Americans, and, of course, even Europeans—from both sides of the Curtain. In Tangier you'll find some of the world's poorest and some of the richest. The poorest will try to sell you anything from a shoeshine to their not very lily-white bodies, and the richest will avoid your eyes, afraid you might try to sell them something. In spite of recent changes, the town still has its unique qualities. As a result of them the permanent population includes smugglers and black-marketeers, fugitives from justice and international con men, espionage and counter-espionage agents, homosexuals, nymphomaniacs, alcoholics, drug addicts, displaced persons, ex-royalty, and subversives of every flavor. Local law limits the activities of few of these. Like I said, it's quite a town. I looked up from my Herald Tribune and said, "Hello, Paul. Anything new cooking?" He sank into the chair opposite me and looked around for the waiter. The tables were all crowded and since mine was a face he recognized, he assumed he was welcome to intrude. It was more or less standard procedure at the Cafe de Paris. It wasn't a place to go if you wanted to be alone. Paul said, "How are you, Rupert? Haven't seen you for donkey's years." The waiter came along and Paul ordered a glass of beer. Paul was an easy-going, sallow-faced little man. I vaguely remembered somebody saying he was from Liverpool and in exports. "What's in the newspaper?" he said, disinterestedly. "Pogo and Albert are going to fight a duel," I told him, "and Lil Abner is becoming a rock'n'roll singer." He grunted. "Oh," I said, "the intellectual type." I scanned the front page. "The Russkies have put up another manned satellite." "They have, eh? How big?" "Several times bigger than anything we Americans have." The beer came and looked good, so I ordered a glass too. Paul said, "What ever happened to those poxy flying saucers?" "What flying saucers?" A French girl went by with a poodle so finely clipped as to look as though it'd been shaven. The girl was in the latest from Paris. Every pore in place. We both looked after her. "You know, what everybody was seeing a few years ago. It's too bad one of these bloody manned satellites wasn't up then. Maybe they would've seen one." "That's an idea," I said. We didn't say anything else for a while and I began to wonder if I could go back to my paper without rubbing him the wrong way. I didn't know Paul very well, but, for that matter, it's comparatively seldom you ever get to know anybody very well in Tangier. Largely, cards are played close to the chest. My beer came and a plate of tapas for us both. Tapas at the Cafe de Paris are apt to be potato salad, a few anchovies, olives, and possibly some cheese. Free lunch, they used to call it in the States. Just to say something, I said, "Where do you think they came from?" And when he looked blank, I added, "The Flying Saucers." He grinned. "From Mars or Venus, or someplace." "Ummmm," I said. "Too bad none of them ever crashed, or landed on the Yale football field and said Take me to your cheerleader , or something." Paul yawned and said, "That was always the trouble with those crackpot blokes' explanations of them. If they were aliens from space, then why not show themselves?" I ate one of the potato chips. It'd been cooked in rancid olive oil. I said, "Oh, there are various answers to that one. We could probably sit around here and think of two or three that made sense." Paul was mildly interested. "Like what?" "Well, hell, suppose for instance there's this big Galactic League of civilized planets. But it's restricted, see. You're not eligible for membership until you, well, say until you've developed space flight. Then you're invited into the club. Meanwhile, they send secret missions down from time to time to keep an eye on your progress." Paul grinned at me. "I see you read the same poxy stuff I do." A Moorish girl went by dressed in a neatly tailored gray jellaba, European style high-heeled shoes, and a pinkish silk veil so transparent that you could see she wore lipstick. Very provocative, dark eyes can be over a veil. We both looked after her. I said, "Or, here's another one. Suppose you have a very advanced civilization on, say, Mars." "Not Mars. No air, and too bloody dry to support life." "Don't interrupt, please," I said with mock severity. "This is a very old civilization and as the planet began to lose its water and air, it withdrew underground. Uses hydroponics and so forth, husbands its water and air. Isn't that what we'd do, in a few million years, if Earth lost its water and air?" "I suppose so," he said. "Anyway, what about them?" "Well, they observe how man is going through a scientific boom, an industrial boom, a population boom. A boom, period. Any day now he's going to have practical space ships. Meanwhile, he's also got the H-Bomb and the way he beats the drums on both sides of the Curtain, he's not against using it, if he could get away with it." Paul said, "I got it. So they're scared and are keeping an eye on us. That's an old one. I've read that a dozen times, dished up different." I shifted my shoulders. "Well, it's one possibility." "I got a better one. How's this. There's this alien life form that's way ahead of us. Their civilization is so old that they don't have any records of when it began and how it was in the early days. They've gone beyond things like wars and depressions and revolutions, and greed for power or any of these things giving us a bad time here on Earth. They're all like scholars, get it? And some of them are pretty jolly well taken by Earth, especially the way we are right now, with all the problems, get it? Things developing so fast we don't know where we're going or how we're going to get there." I finished my beer and clapped my hands for Mouley. "How do you mean, where we're going ?" "Well, take half the countries in the world today. They're trying to industrialize, modernize, catch up with the advanced countries. Look at Egypt, and Israel, and India and China, and Yugoslavia and Brazil, and all the rest. Trying to drag themselves up to the level of the advanced countries, and all using different methods of doing it. But look at the so-called advanced countries. Up to their bottoms in problems. Juvenile delinquents, climbing crime and suicide rates, the loony-bins full of the balmy, unemployed, threat of war, spending all their money on armaments instead of things like schools. All the bloody mess of it. Why, a man from Mars would be fascinated, like." Mouley came shuffling up in his babouche slippers and we both ordered another schooner of beer. Paul said seriously, "You know, there's only one big snag in this sort of talk. I've sorted the whole thing out before, and you always come up against this brick wall. Where are they, these observers, or scholars, or spies or whatever they are? Sooner or later we'd nab one of them. You know, Scotland Yard, or the F.B.I., or Russia's secret police, or the French Sûreté, or Interpol. This world is so deep in police, counter-espionage outfits and security agents that an alien would slip up in time, no matter how much he'd been trained. Sooner or later, he'd slip up, and they'd nab him." I shook my head. "Not necessarily. The first time I ever considered this possibility, it seemed to me that such an alien would base himself in London or New York. Somewhere where he could use the libraries for research, get the daily newspapers and the magazines. Be right in the center of things. But now I don't think so. I think he'd be right here in Tangier." "Why Tangier?" "It's the one town in the world where anything goes. Nobody gives a damn about you or your affairs. For instance, I've known you a year or more now, and I haven't the slightest idea of how you make your living." "That's right," Paul admitted. "In this town you seldom even ask a man where's he's from. He can be British, a White Russian, a Basque or a Sikh and nobody could care less. Where are you from, Rupert?" "California," I told him. "No, you're not," he grinned. I was taken aback. "What do you mean?" "I felt your mind probe back a few minutes ago when I was talking about Scotland Yard or the F.B.I. possibly flushing an alien. Telepathy is a sense not trained by the humanoids. If they had it, your job—and mine—would be considerably more difficult. Let's face it, in spite of these human bodies we're disguised in, neither of us is humanoid. Where are you really from, Rupert?" "Aldebaran," I said. "How about you?" "Deneb," he told me, shaking. We had a laugh and ordered another beer. "What're you doing here on Earth?" I asked him. "Researching for one of our meat trusts. We're protein eaters. Humanoid flesh is considered quite a delicacy. How about you?" "Scouting the place for thrill tourists. My job is to go around to these backward cultures and help stir up inter-tribal, or international, conflicts—all according to how advanced they are. Then our tourists come in—well shielded, of course—and get their kicks watching it." Paul frowned. "That sort of practice could spoil an awful lot of good meat." THE END Transcriber's Note: This etext was produced from Amazing Stories December 1960. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	C. The Cafe de Paris
Which word best describes the writers of 2, 7, and 17? A. optimistic B. enthusiastic C. sorrowful D. sarcastic	Eleven-Twelfths of 1999 In Review When Chatterbox invited readers to nominate events, significant deaths, good and bad movies, etc., for 1999--a year likely to get little attention in the coming weeks, as news organizations choose instead to review the entire century or millennium--the response was overwhelming. Chatterbox had promised to publish his official "1999 In Review" item before Thanksgiving, but some distant memory of a scruple persuaded him to wait till November was over. Nothing ever happens in December. OK, that's not quite true. Hordes of protesters in Seattle are making the World Trade Organization's meeting there a much more exciting TV story than anyone expected it to be. Reader Dan Crist (who finds Chatterbox's habit of referring to himself in the third person "rather annoying and less than professional") points out that Japan bombed Pearl Harbor in Dec. 1941. Also, Chatterbox (moonlighting as "Today's Papers" columnist) observed not quite one year ago that the House of Representatives cast its second presidential-impeachment vote in U.S. history on Dec. 19, 1998. (That same news-filled day, the U.S. ended an air war against Iraq and Bob Livingston said he'd decided not to become House speaker after all.) Two months after the impeachment vote, the Senate failed to convict the president--a highly significant event of 1999 that, for some bizarre reason, slipped Chatterbox's mind until several indignant readers wrote in to remind him of it. By now, it should be clear that Chatterbox isn't much good at year-in-review journalism. Fortunately, Chatterbox's readers are very good at it. He will now turn this survey over to them. ( Disclaimer: Although Chatterbox previously stated that he wouldn't include opinions he disagreed with, that standard proved too confining. Where Chatterbox has solid information or opinions to the contrary, he occasionally interjects below. Obviously stupid or unnecessarily sour reader comments were discarded, but if you don't find your nominee below it doesn't necessarily mean that it was obviously stupid or unnecessarily sour. ) Here are 20 important things that happened in 1999: 1. Most Hated Celebrity--Ever? The New York Times reported on Nov. 10, 1999, that a new record had been set in the latest Times /CBS poll: [Its] highest negative rating ever scored by a person in the news. The honor went to Reform Party candidate Donald Trump, who managed to make an unfavorable impression upon some 70 percent of those polled. The paper noted that this achievement far eclipsed the last comparably negative rating--the 55 percent score attained by Linda Tripp. Presumably this came as no surprise to Mr. Trump, who, upon announcing the formation of a presidential exploratory committee on Oct. 7, 1999, had cited polls with "amazing results"--a remark that was widely misinterpreted at the time. -- Jodie Allen of U.S. News & World Report (and frequent Slate contributor) 2. Most Foolishly Ignored Parts of the World in 1999 The dog that did bark but no one noticed--the political turmoil in the three great South Asian nations of India, Pakistan, and Indonesia, which now are well on the way to passing the three northern Asian nations of China, Japan, and Russia in population (Indonesia is fourth, Pakistan just passed Japan to seventh, India will soon pass China to first). But Americans are still fixated on northern Asia--Clinton says he must deal with China, because "you can't ignore a billion people with nuclear weapons," but his own policy toward India shows that you sure can! --Jim Chapin 3. Worst/Best Films of 1999 Here's my nominee for worst movie of the year (complete category should be: "Worst Movie of the Year That Assumedly Adult Male Reviewers Slathered Over"): There's Something About Mary --a pathetically sophomoric, penis-obsessed mess that wouldn't even appeal to Larry Flynt! -- Felicia, Menlo Park, Cal. Chatterbox replies: You've got the wrong year. That was 1998 . [Chatterbox didn't have the heart to add that he thought There's Something About Mary was pretty funny, especially the joke about "the franks or the beans."] Felicia replies: Oops ... well then, the best of '99 was The Red Violin --lyrical, magical, musical, wonderful! [Chatterbox hasn't seen it.] 4. Most Shameless (and Unsuccessful) Attempt To Have It Both Ways in 1999 : Sen. Arlen Specter, citing Scottish law, finds Clinton "not proven" on the impeachment charges. --Andrew Solovay 5. Rest in Peace in 1999: Stanley Kubrick (multiple sources) John Kennedy Jr. (multiple sources) Susan Strasberg (anonymous tipster; Strasberg played Anne Frank in the original production of the Broadway adaptation, which some people think wasn't Jewish enough) Mel Torme (Steve Reiness) Mrs. Whozit [ Chatterbox interjects : her name was Anne Sheafe Miller], the first person ever to be saved by penicillin (Blair Bolles) 6. 1999: The Road Not Taken What an extraordinary year! A right-wing conspiracy topples the president, and the governor of Texas reveals himself in a series of debates to be a natural leader with an innate gift for connecting with his audience, a sure sign of his electoral success next year. A new Thomas Harris book brilliantly takes us deeper into the mind of a serial killer; a new Star Wars movie redefines the very nature of entertainment; a new Stanley Kubrick film changes the whole national dialogue about sex and marriage; a new TV series from the creator of SportsNight --oh, I can't even bring myself to bash that piece of do-gooder twaddle. If only McDonald's had come out with three more boldly adult-flavored hamburgers, it would have been a perfect year for dud megaevents--all leading up of course to Y2K, the limpest milestone in human history. --Mike Gebert 7 . Children Behaving Badly in 1999 Don't forget Woodstock 1999 --the concert of "peace and love" that ended in a literal blaze of glory when in an hours-long tribute to the original Woodstock, the mob started ripping down vendor booths and anything else that would burn and piling it onto the bonfires scattered about the scene. [ Chatterbox interjects: Didn't people get assaulted and raped, too?] I'm getting all sentimental just thinking about it. You also left out all the shooting rampages . Several were done in the name of God or love supposedly. They were all committed by "quiet, shy" people who "mostly kept to" themselves. I've started to hang around only loud, obnoxious people. --Susan Hoechstetter 8. A Lunatic Rhapsody for the New York Yankees The Yankees can actually be referred to as the glue that held the century together. Of course, as the 1999 World Series champions, they are a significant "story of the year." However, this one singular achievement must be considered in a broader context. 1999 represented the team's 25th championship of the century. This beats, by one, the most championships any one team won during the century. The Montreal Canadiens have won 23 Stanley Cups. However, the Yankees, an American team, playing in the "City of the Century" (so called by me to reflect the amazing growth and transformation of one city during this period), who play the "National Pastime," are truly an amazing story. The team's first championship occurred in 1921; therefore, they have won 25 of the last 78 years, nearly one in three. This level of sustained excellence is not matched in sports or in any other aspect of society. The 1999 win is possibly the most unique. With free-agency, expansion, and three levels of playoffs, it is much harder to win today than in past years. In fact, by winning three of the last four championships, they are the first team to accomplish this feat during the eras of free-agency and of divisional play. The Sultan of Swat, the Iron Man, the Yankee Clipper, the Mick, and Yogi--these strong, masculine names are synonymous with the team, the sport, and American history. They went hand in hand with two world wars, Superman, and America's superpower status. The 1999 squad does not feature "a name." This team, with its myriad of human-interest stories, its international roster, and no star, is representative of '90s man, male sensitivity, Pax American interests, and the new political paradigm. --Jim Landau from North Potomac, Md. (formerly of the Bronx) 9. A Big Shot Calls for Decriminalizing Drug Use in 1999 New Mexico Gov. Gary Johnson came out for ending drug prohibition. Though this by itself has no immediate effect, it makes it respectable, for the first time, for political leaders to discuss the subject, and thereby brings closer the day when the vast majority of crimes will no longer be committed, when billions of dollars will be freed to help the inner city instead of to ruin black people's lives, and when we will stop, as in Samuel Butler's Erewhon , imprisoning people for the crime of being sick. --Henry Cohen Chatterbox interjects: Didn't Baltimore Mayor Kurt Schmoke do the same thing 11 years ago? 10. Don't Worry in 1999 The Dalai Lama proclaimed that most important thing in the world is to be happy. --Margaret Taylor 11. The Athletic Bra Seen 'Round the World in 1999 Public interest and media attention to the women's World Cup in soccer. --Tom Horton 12. Another Overlooked Foreign-Policy Event in 1999 Presidential primary elections for the first time ever in Mexico. --Tom Horton 13. Policing the World Is Shown To Work in 1999 I nominate as the most under-reported story of the year (and the last few years) the continuing alarmist predictions by foreign-policy and military experts about peacekeeping efforts, which are then proved wrong and immediately forgotten. This year, the obvious one is Kosovo, but the year is also ending with East Timor, where the Aussies and their allies successfully stopped the slaughter with no casualties. These followed Haiti, Bosnia, and Rwanda as places where the West delayed sending in troops because of alarmist predictions. --Jerry Skurnik 14. Barbara Walters Did This One on Her Year-End Special, But It's Still Good Don't forget, Susan Lucci finally won an Emmy . --anonymous tipster 15. Annals of Justice in 1999 Matthew Shepard: the despicable defense . -- anonymous tipster 16. Get Me a New Century, Quick A sitting president was accused of rape. --Ananda Gupta Chatterbox interjects: Yes, but the evidence was shaky--something the Wall Street Journal 's editorial page, which broke the story, was not very forthcoming about. As Jack Shafer wrote in this column, Ronald Reagan, after he left office, was also accused of having once committed rape. The evidence there was shaky, too. 17. The Most Important Thing of All That Happened in 1999 In 1999, more than half of U.S. homes had a PC, for the first time (i.e., home-PC penetration passed 50 percent). Of course, most of these PCs crashed all the time, but it's still a significant development. By the way, Internet hookups in homes are still well below 50 percent. --Walt Mossberg, "Personal Technology" columnist for the Wall Street Journal (and occasional rock-music historian for this column) 18. All Dolled Up and Nowhere To Go in 1999 General Pinochet --Jodie Maurer 19. Senate Endorses Nuclear Proliferation in 1999 The Senate rejected the Comprehensive Test Ban Treaty , thereby decapitating nuclear-arms control and sending Iraq, Iran, and North Korea the message that the United States won't raise a big stink if they try to join India and Pakistan. The president woke up to this possibility at about the moment it was realized, and started lobbying for passage of the treaty a day after it became too late. --Josh Pollack 20. Unremarked Natural Disaster in 1999 The Indian Supercyclone is the biggest, this century at least. --Samir Raiyani Photographs of: Donald Trump by Peter Morgan/Reuters; Natalie Portman by Keith Hamshere/Lucasfilm Ltd./Reuters; New York Yankees players by Gary Hershorn/Reuters; KLA member by Hazir Reka/Reuters.	D. sarcastic
What is the image Hatcher’s team sees on the viewing consul? A. A human female B. Hatcher’s specimen C. The Jordell Bank D. A human male	THE FIVE HELLS OF ORION BY FREDERICK POHL Out in the great gas cloud of the Orion Nebula McCray found an ally—and a foe! [Transcriber's Note: This etext was produced from Worlds of If Science Fiction, January 1963. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] His name was Herrell McCray and he was scared. As best he could tell, he was in a sort of room no bigger than a prison cell. Perhaps it was a prison cell. Whatever it was, he had no business in it; for five minutes before he had been spaceborne, on the Long Jump from Earth to the thriving colonies circling Betelgeuse Nine. McCray was ship's navigator, plotting course corrections—not that there were any, ever; but the reason there were none was that the check-sightings were made every hour of the long flight. He had read off the azimuth angles from the computer sights, automatically locked on their beacon stars, and found them correct; then out of long habit confirmed the locking mechanism visually. It was only a personal quaintness; he had done it a thousand times. And while he was looking at Betelgeuse, Rigel and Saiph ... it happened. The room was totally dark, and it seemed to be furnished with a collection of hard, sharp, sticky and knobby objects of various shapes and a number of inconvenient sizes. McCray tripped over something that rocked under his feet and fell against something that clattered hollowly. He picked himself up, braced against something that smelled dangerously of halogen compounds, and scratched his shoulder, right through his space-tunic, against something that vibrated as he touched it. McCray had no idea where he was, and no way to find out. Not only was he in darkness, but in utter silence as well. No. Not quite utter silence. Somewhere, just at the threshold of his senses, there was something like a voice. He could not quite hear it, but it was there. He sat as still as he could, listening; it remained elusive. Probably it was only an illusion. But the room itself was hard fact. McCray swore violently and out loud. It was crazy and impossible. There simply was no way for him to get from a warm, bright navigator's cubicle on Starship Jodrell Bank to this damned, dark, dismal hole of a place where everything was out to hurt him and nothing explained what was going on. He cried aloud in exasperation: "If I could only see !" He tripped and fell against something that was soft, slimy and, like baker's dough, not at all resilient. A flickering halo of pinkish light appeared. He sat up, startled. He was looking at something that resembled a suit of medieval armor. It was, he saw in a moment, not armor but a spacesuit. But what was the light? And what were these other things in the room? Wherever he looked, the light danced along with his eyes. It was like having tunnel vision or wearing blinders. He could see what he was looking at, but he could see nothing else. And the things he could see made no sense. A spacesuit, yes; he knew that he could construct a logical explanation for that with no trouble—maybe a subspace meteorite striking the Jodrell Bank , an explosion, himself knocked out, brought here in a suit ... well, it was an explanation with more holes than fabric, like a fisherman's net, but at least it was rational. How to explain a set of Gibbon's Decline and Fall of the Roman Empire? A space-ax? Or the old-fashioned child's rocking-chair, the chemistry set—or, most of all, the scrap of gaily printed fabric that, when he picked it up, turned out to be a girl's scanty bathing suit? It was slightly reassuring, McCray thought, to find that most of the objects were more or less familiar. Even the child's chair—why, he'd had one more or less like that himself, long before he was old enough to go to school. But what were they doing here? Not everything he saw was familiar. The walls of the room itself were strange. They were not metal or plaster or knotty pine; they were not papered, painted or overlaid with stucco. They seemed to be made of some sort of hard organic compound, perhaps a sort of plastic or processed cellulose. It was hard to tell colors in the pinkish light. But they seemed to have none. They were "neutral"—the color of aged driftwood or unbleached cloth. Three of the walls were that way, and the floor and ceiling. The fourth wall was something else. Areas in it had the appearance of gratings; from them issued the pungent, distasteful halogen odor. They might be ventilators, he thought; but if so the air they brought in was worse than what he already had. McCray was beginning to feel more confident. It was astonishing how a little light made an impossible situation bearable, how quickly his courage flowed back when he could see again. He stood still, thinking. Item, a short time ago—subjectively it seemed to be minutes—he had been aboard the Jodrell Bank with nothing more on his mind than completing his check-sighting and meeting one of the female passengers for coffee. Item, apart from being shaken up and—he admitted it—scared damn near witless, he did not seem to be hurt. Item, wherever he was now, it became, not so much what had happened to him, but what had happened to the ship? He allowed that thought to seep into his mind. Suppose there had been an accident to the Jodrell Bank . He could, of course, be dead. All this could be the fantasies of a cooling brain. McCray grinned into the pink-lit darkness. The thought had somehow refreshed him, like icewater between rounds, and with a clearing head he remembered what a spacesuit was good for. It held a radio. He pressed the unsealing tabs, slipped his hand into the vacant chest of the suit and pulled out the hand mike. "This is Herrell McCray," he said, "calling the Jodrell Bank ." No response. He frowned. "This is Herrell McCray, calling Jodrell Bank . "Herrell McCray, calling anybody, come in, please." But there was no answer. Thoughtfully he replaced the microphone. This was ultrawave radio, something more than a million times faster than light, with a range measured, at least, in hundreds of light-years. If there was no answer, he was a good long way from anywhere. Of course, the thing might not be operating. He reached for the microphone again— He cried aloud. The pinkish lights went out. He was in the dark again, worse dark than before. For before the light had gone, McCray had seen what had escaped his eyes before. The suit and the microphone were clear enough in the pinkish glimmer; but the hand—his own hand, cupped to hold the microphone—he had not seen at all. Nor his arm. Nor, in one fleeting moment of study, his chest. McCray could not see any part of his own body at all. II Someone else could. Someone was watching Herrell McCray, with the clinical fascination of a biochemist observing the wigglings of paramecia in a new antibiotic—and with the prayerful emotions of a starving, shipwrecked, sailor, watching the inward bobbing drift of a wave-born cask that may contain food. Suppose you call him "Hatcher" (and suppose you call it a "him.") Hatcher was not exactly male, because his race had no true males; but it did have females and he was certainly not that. Hatcher did not in any way look like a human being, but they had features in common. If Hatcher and McCray had somehow managed to strike up an acquaintance, they might have got along very well. Hatcher, like McCray, was an adventurous soul, young, able, well-learned in the technical sciences of his culture. Both enjoyed games—McCray baseball, poker and three-dimensional chess; Hatcher a number of sports which defy human description. Both held positions of some importance—considering their ages—in the affairs of their respective worlds. Physically they were nothing alike. Hatcher was a three-foot, hard-shelled sphere of jelly. He had "arms" and "legs," but they were not organically attached to "himself." They were snakelike things which obeyed the orders of his brain as well as your mind can make your toes curl; but they did not touch him directly. Indeed, they worked as well a yard or a quarter-mile away as they did when, rarely, they rested in the crevices they had been formed from in his "skin." At greater distances they worked less well, for reasons irrelevant to the Law of Inverse Squares. Hatcher's principal task at this moment was to run the "probe team" which had McCray under observation, and he was more than a little excited. His members, disposed about the room where he had sent them on various errands, quivered and shook a little; yet they were the calmest limbs in the room; the members of the other team workers were in a state of violent commotion. The probe team had had a shock. "Paranormal powers," muttered Hatcher's second in command, and the others mumbled agreement. Hatcher ordered silence, studying the specimen from Earth. After a long moment he turned his senses from the Earthman. "Incredible—but it's true enough," he said. "I'd better report. Watch him," he added, but that was surely unnecessary. Their job was to watch McCray, and they would do their job; and even more, not one of them could have looked away to save his life from the spectacle of a creature as odd and, from their point of view, hideously alien as Herrell McCray. Hatcher hurried through the halls of the great buried structure in which he worked, toward the place where the supervising council of all probes would be in permanent session. They admitted him at once. Hatcher identified himself and gave a quick, concise report: "The subject recovered consciousness a short time ago and began to inspect his enclosure. His method of doing so was to put his own members in physical contact with the various objects in the enclosure. After observing him do this for a time we concluded he might be unable to see and so we illuminated his field of vision for him. "This appeared to work well for a time. He seemed relatively undisturbed. However, he then reverted to physical-contact, manipulating certain appurtenances of an artificial skin we had provided for him. "He then began to vibrate the atmosphere by means of resonating organs in his breathing passage. "Simultaneously, the object he was holding, attached to the artificial skin, was discovered to be generating paranormal forces." The supervising council rocked with excitement. "You're sure?" demanded one of the councilmen. "Yes, sir. The staff is preparing a technical description of the forces now, but I can say that they are electromagnetic vibrations modulating a carrier wave of very high speed, and in turn modulated by the vibrations of the atmosphere caused by the subject's own breathing." "Fantastic," breathed the councillor, in a tone of dawning hope. "How about communicating with him, Hatcher? Any progress?" "Well ... not much, sir. He suddenly panicked. We don't know why; but we thought we'd better pull back and let him recover for a while." The council conferred among itself for a moment, Hatcher waiting. It was not really a waste of time for him; with the organs he had left in the probe-team room, he was in fairly close touch with what was going on—knew that McCray was once again fumbling among the objects in the dark, knew that the team-members had tried illuminating the room for him briefly and again produced the rising panic. Still, Hatcher fretted. He wanted to get back. "Stop fidgeting," commanded the council leader abruptly. "Hatcher, you are to establish communication at once." "But, sir...." Hatcher swung closer, his thick skin quivering slightly; he would have gestured if he had brought members with him to gesture with. "We've done everything we dare. We've made the place homey for him—" actually, what he said was more like, we've warmed the biophysical nuances of his enclosure —"and tried to guess his needs; and we're frightening him half to death. We can't go faster. This creature is in no way similar to us, you know. He relies on paranormal forces—heat, light, kinetic energy—for his life. His chemistry is not ours, his processes of thought are not ours, his entire organism is closer to the inanimate rocks of a sea-bottom than to ourselves." "Understood, Hatcher. In your first report you stated these creatures were intelligent." "Yes, sir. But not in our way." "But in a way, and you must learn that way. I know." One lobster-claw shaped member drifted close to the councillor's body and raised itself in an admonitory gesture. "You want time. But we don't have time, Hatcher. Yours is not the only probe team working. The Central Masses team has just turned in a most alarming report." "Have they secured a subject?" Hatcher demanded jealously. The councillor paused. "Worse than that, Hatcher. I am afraid their subjects have secured one of them. One of them is missing." There was a moment's silence. Frozen, Hatcher could only wait. The council room was like a tableau in a museum until the councillor spoke again, each council member poised over his locus-point, his members drifting about him. Finally the councillor said, "I speak for all of us, I think. If the Old Ones have seized one of our probers our time margin is considerably narrowed. Indeed, we may not have any time at all. You must do everything you can to establish communication with your subject." "But the danger to the specimen—" Hatcher protested automatically. "—is no greater," said the councillor, "than the danger to every one of us if we do not find allies now ." Hatcher returned to his laboratory gloomily. It was just like the council to put the screws on; they had a reputation for demanding results at any cost—even at the cost of destroying the only thing you had that would make results possible. Hatcher did not like the idea of endangering the Earthman. It cannot be said that he was emotionally involved; it was not pity or sympathy that caused him to regret the dangers in moving too fast toward communication. Not even Hatcher had quite got over the revolting physical differences between the Earthman and his own people. But Hatcher did not want him destroyed. It had been difficult enough getting him here. Hatcher checked through the members that he had left with the rest of his team and discovered that there were no immediate emergencies, so he took time to eat. In Hatcher's race this was accomplished in ways not entirely pleasant to Earthmen. A slit in the lower hemisphere of his body opened, like a purse, emitting a thin, pussy, fetid fluid which Hatcher caught and poured into a disposal trough at the side of the eating room. He then stuffed the slit with pulpy vegetation the texture of kelp; it closed, and his body was supplied with nourishment for another day. He returned quickly to the room. His second in command was busy, but one of the other team workers reported—nothing new—and asked about Hatcher's appearance before the council. Hatcher passed the question off. He considered telling his staff about the disappearance of the Central Masses team member, but decided against it. He had not been told it was secret. On the other hand, he had not been told it was not. Something of this importance was not lightly to be gossiped about. For endless generations the threat of the Old Ones had hung over his race, those queer, almost mythical beings from the Central Masses of the galaxy. One brush with them, in ages past, had almost destroyed Hatcher's people. Only by running and hiding, bearing one of their planets with them and abandoning it—with its population—as a decoy, had they arrived at all. Now they had detected mapping parties of the Old Ones dangerously near the spiral arm of the galaxy in which their planet was located, they had begun the Probe Teams to find some way of combating them, or of fleeing again. But it seemed that the Probe Teams themselves might be betraying their existence to their enemies— "Hatcher!" The call was urgent; he hurried to see what it was about. It was his second in command, very excited. "What is it?" Hatcher demanded. "Wait...." Hatcher was patient; he knew his assistant well. Obviously something was about to happen. He took the moment to call his members back to him for feeding; they dodged back to their niches on his skin, fitted themselves into their vestigial slots, poured back their wastes into his own circulation and ingested what they needed from the meal he had just taken.... "Now!" cried the assistant. "Look!" At what passed among Hatcher's people for a viewing console an image was forming. Actually it was the assistant himself who formed it, not a cathode trace or projected shadow; but it showed what it was meant to show. Hatcher was startled. "Another one! And—is it a different species? Or merely a different sex?" "Study the probe for yourself," the assistant invited. Hatcher studied him frostily; his patience was not, after all, endless. "No matter," he said at last. "Bring the other one in." And then, in a completely different mood, "We may need him badly. We may be in the process of killing our first one now." "Killing him, Hatcher?" Hatcher rose and shook himself, his mindless members floating away like puppies dislodged from suck. "Council's orders," he said. "We've got to go into Stage Two of the project at once." III Before Stage Two began, or before Herrell McCray realized it had begun, he had an inspiration. The dark was absolute, but he remembered where the spacesuit had been and groped his way to it and, yes, it had what all spacesuits had to have. It had a light. He found the toggle that turned it on and pressed it. Light. White, flaring, Earthly light, that showed everything—even himself. "God bless," he said, almost beside himself with joy. Whatever that pinkish, dancing halo had been, it had thrown him into a panic; now that he could see his own hand again, he could blame the weird effects on some strange property of the light. At the moment he heard the click that was the beginning of Stage Two. He switched off the light and stood for a moment, listening. For a second he thought he heard the far-off voice, quiet, calm and almost hopeless, that he had sensed hours before; but then that was gone. Something else was gone. Some faint mechanical sound that had hardly registered at the time, but was not missing. And there was, perhaps, a nice new sound that had not been there before; a very faint, an almost inaudible elfin hiss. McCray switched the light on and looked around. There seemed to be no change. And yet, surely, it was warmer in here. He could see no difference; but perhaps, he thought, he could smell one. The unpleasant halogen odor from the grating was surely stronger now. He stood there, perplexed. A tinny little voice from the helmet of the space suit said sharply, amazement in its tone, "McCray, is that you? Where the devil are you calling from?" He forgot smell, sound and temperature and leaped for the suit. "This is Herrell McCray," he cried. "I'm in a room of some sort, apparently on a planet of approximate Earth mass. I don't know—" "McCray!" cried the tiny voice in his ear. "Where are you? This is Jodrell Bank calling. Answer, please!" "I am answering, damn it," he roared. "What took you so long?" "Herrell McCray," droned the tiny voice in his ear, "Herrell McCray, Herrell McCray, this is Jodrell Bank responding to your message, acknowledge please. Herrell McCray, Herrell McCray...." It kept on, and on. McCray took a deep breath and thought. Something was wrong. Either they didn't hear him, which meant the radio wasn't transmitting, or—no. That was not it; they had heard him, because they were responding. But it seemed to take them so long.... Abruptly his face went white. Took them so long! He cast back in his mind, questing for a fact, unable to face its implications. When was it he called them? Two hours ago? Three? Did that mean—did it possibly mean—that there was a lag of an hour or two each way? Did it, for example, mean that at the speed of his suit's pararadio, millions of times faster than light, it took hours to get a message to the ship and back? And if so ... where in the name of heaven was he? Herrell McCray was a navigator, which is to say, a man who has learned to trust the evidence of mathematics and instrument readings beyond the guesses of his "common sense." When Jodrell Bank , hurtling faster than light in its voyage between stars, made its regular position check, common sense was a liar. Light bore false witness. The line of sight was trustworthy directly forward and directly after—sometimes not even then—and it took computers, sensing their data through instruments, to comprehend a star bearing and convert three fixes into a position. If the evidence of his radio contradicted common sense, common sense was wrong. Perhaps it was impossible to believe what the radio's message implied; but it was not necessary to "believe," only to act. McCray thumbed down the transmitter button and gave a concise report of his situation and his guesses. "I don't know how I got here. I don't know how long I've been gone, since I was unconscious for a time. However, if the transmission lag is a reliable indication—" he swallowed and went on—"I'd estimate I am something more than five hundred light-years away from you at this moment. That's all I have to say, except for one more word: Help." He grinned sourly and released the button. The message was on its way, and it would be hours before he could have a reply. Therefore he had to consider what to do next. He mopped his brow. With the droning, repetitious call from the ship finally quiet, the room was quiet again. And warm. Very warm, he thought tardily; and more than that. The halogen stench was strong in his nostrils again. Hurriedly McCray scrambled into the suit. By the time he was sealed down he was coughing from the bottom of his lungs, deep, tearing rasps that pained him, uncontrollable. Chlorine or fluorine, one of them was in the air he had been breathing. He could not guess where it had come from; but it was ripping his lungs out. He flushed the interior of the suit out with a reckless disregard for the wastage of his air reserve, holding his breath as much as he could, daring only shallow gasps that made him retch and gag. After a long time he could breathe, though his eyes were spilling tears. He could see the fumes in the room now. The heat was building up. Automatically—now that he had put it on and so started its servo-circuits operating—the suit was cooling him. This was a deep-space suit, regulation garb when going outside the pressure hull of an FTL ship. It was good up to at least five hundred degrees in thin air, perhaps three or four hundred in dense. In thin air or in space it was the elastic joints and couplings that depolymerized when the heat grew too great; in dense air, with conduction pouring energy in faster than the cooling coils could suck it out and hurl it away, it was the refrigerating equipment that broke down. McCray had no way of knowing just how hot it was going to get. Nor, for that matter, had the suit been designed to operate in a corrosive medium. All in all it was time for him to do something. Among the debris on the floor, he remembered, was a five-foot space-ax, tungsten-steel blade and springy aluminum shaft. McCray caught it up and headed for the door. It felt good in his gauntlets, a rewarding weight; any weapon straightens the back of the man who holds it, and McCray was grateful for this one. With something concrete to do he could postpone questioning. Never mind why he had been brought here; never mind how. Never mind what he would, or could, do next; all those questions could recede into the background of his mind while he swung the ax and battered his way out of this poisoned oven. Crash-clang! The double jolt ran up the shaft of the ax, through his gauntlets and into his arm; but he was making progress, he could see the plastic—or whatever it was—of the door. It was chipping out. Not easily, very reluctantly; but flaking out in chips that left a white powdery residue. At this rate, he thought grimly, he would be an hour getting through it. Did he have an hour? But it did not take an hour. One blow was luckier than the rest; it must have snapped the lock mechanism. The door shook and slid ajar. McCray got the thin of the blade into the crack and pried it wide. He was in another room, maybe a hall, large and bare. McCray put the broad of his back against the broken door and pressed it as nearly closed as he could; it might not keep the gas and heat out, but it would retard them. The room was again unlighted—at least to McCray's eyes. There was not even that pink pseudo-light that had baffled him; here was nothing but the beam of his suit lamp. What it showed was cryptic. There were evidences of use: shelves, boxy contraptions that might have been cupboards, crude level surfaces attached to the walls that might have been workbenches. Yet they were queerly contrived, for it was not possible to guess from them much about the creatures who used them. Some were near the floor, some at waist height, some even suspended from the ceiling itself. A man would need a ladder to work at these benches and McCray, staring, thought briefly of many-armed blind giants or shapeless huge intelligent amoebae, and felt the skin prickle at the back of his neck. He tapped half-heartedly at one of the closed cupboards, and was not surprised when it proved as refractory as the door. Undoubtedly he could batter it open, but it was not likely that much would be left of its contents when he was through; and there was the question of time. But his attention was diverted by a gleam from one of the benches. Metallic parts lay heaped in a pile. He poked at them with a stiff-fingered gauntlet; they were oddly familiar. They were, he thought, very much like the parts of a bullet-gun. In fact, they were. He could recognize barrel, chamber, trigger, even a couple of cartridges, neatly opened and the grains of powder stacked beside them. It was an older, clumsier model than the kind he had seen in survival locker, on the Jodrell Bank —and abruptly wished he were carrying now—but it was a pistol. Another trophy, like the strange assortment in the other room? He could not guess. But the others had been more familiar; they all have come from his own ship. He was prepared to swear that nothing like this antique had been aboard. The drone began again in his ear, as it had at five-minute intervals all along: "Herrell McCray, Herrell McCray, Herrell McCray, this is Jodrell Bank calling Herrell McCray...." And louder, blaring, then fading to normal volume as the AVC circuits toned the signal down, another voice. A woman's voice, crying out in panic and fear: " Jodrell Bank! Where are you? Help!" IV Hatcher's second in command said: "He has got through the first survival test. In fact, he broke his way out! What next?" "Wait!" Hatcher ordered sharply. He was watching the new specimen and a troublesome thought had occurred to him. The new one was female and seemed to be in pain; but it was not the pain that disturbed Hatcher, it was something far more immediate to his interests. "I think," he said slowly, "that they are in contact." His assistant vibrated startlement. "I know," Hatcher said, "but watch. Do you see? He is going straight toward her." Hatcher, who was not human, did not possess truly human emotions; but he did feel amazement when he was amazed, and fear when there was cause to be afraid. These specimens, obtained with so much difficulty, needed so badly, were his responsibility. He knew the issues involved much better than any of his helpers. They could only be surprised at the queer antics of the aliens with attached limbs and strange powers. Hatcher knew that this was not a freak show, but a matter of life and death. He said, musing: "This new one, I cannot communicate with her, but I get—almost—a whisper, now and then. The first one, the male, nothing. But this female is perhaps not quite mute." "Then shall we abandon him and work with her, forgetting the first one?" Hatcher hesitated. "No," he said at last. "The male is responding well. Remember that when last this experiment was done every subject died; he is alive at least. But I am wondering. We can't quite communicate with the female—" "But?" "But I'm not sure that others can't." The woman's voice was at such close range that McCray's suit radio made a useful RDF set. He located her direction easily enough, shielding the tiny built-in antenna with the tungsten-steel blade of the ax, while she begged him to hurry. Her voice was heavily accented, with some words in a language he did not recognize. She seemed to be in shock. McCray was hardly surprised at that; he had been close enough to shock himself. He tried to reassure her as he searched for a way out of the hall, but in the middle of a word her voice stopped. He hesitated, hefting the ax, glancing back at the way he had come. There had to be a way out, even if it meant chopping through a wall. When he turned around again there was a door. It was oddly shaped and unlike the door he had hewn through, but clearly a door all the same, and it was open. McCray regarded it grimly. He went back in his memory with meticulous care. Had he not looked at, this very spot a matter of moments before? He had. And had there been an open door then? There had not. There hadn't been even a shadowy outline of the three-sided, uneven opening that stood there now. Still, it led in the proper direction. McCray added one more inexplicable fact to his file and walked through. He was in another hall—or tunnel—rising quite steeply to the right. By his reckoning it was the proper direction. He labored up it, sweating under the weight of the suit, and found another open door, this one round, and behind it— Yes, there was the woman whose voice he had heard. It was a woman, all right. The voice had been so strained that he hadn't been positive. Even now, short black hair might not have proved it, and she was lying face down but the waist and hips were a woman's, even though she wore a bulky, quilted suit of coveralls. He knelt beside her and gently turned her face. She was unconscious. Broad, dark face, with no make-up; she was apparently in her late thirties. She appeared to be Chinese. She breathed, a little raggedly but without visible discomfort; her face was relaxed as though she were sleeping. She did not rouse as he moved her. He realized she was breathing the air of the room they were in. His instant first thought was that she was in danger of asphyxiation;	A. A human female
Murray and Herrstein believe that _____ is not important to an individual’s success. A. Education B. IQ C. Parents' status D. Ability	The Bell Curve Flattened Charles Murray is a publicity genius, and the publication of his and Richard Herrnstein's book, The Bell Curve: Intelligence and Class Structure in American Life , in the fall of 1994 was his masterpiece. Virtually all ambitious trade hardcover books are preceded by an edition of 100 to 200 flimsy "galley proofs." These are sent out to people who might generate buzz for the book: blurbists, bookers for television talk shows, editors, and--most important--book critics. There is an ethos of letting the chips fall where they may about the sending out of galleys: Now the book will begin to receive uncontrolled reaction. (For example, back in 1991, Murray somehow got hold of the galleys of my own last book, and wrote me heatedly denying that he was working on a book about black genetic intellectual inferiority, as I had asserted. I left the passage in, but softened it.) The Bell Curve was not circulated in galleys before publication. The effect was, first, to increase the allure of the book (There must be something really hot in there!), and second, to ensure that no one inclined to be skeptical would be able to weigh in at the moment of publication. The people who had galley proofs were handpicked by Murray and his publisher. The ordinary routine of neutral reviewers having a month or two to go over the book with care did not occur. Another handpicked group was flown to Washington at the expense of the American Enterprise Institute and given a weekend-long personal briefing on the book's contents by Murray himself (Herrnstein had died very recently), just before publication. The result was what you'd expect: The first wave of publicity was either credulous or angry, but short on evidence, because nobody had had time to digest and evaluate the book carefully. The Bell Curve isn't a typical work of trade nonfiction. It is gotten up as a work of original scholarly research. Most works containing fresh regression analysis and historical argument from primary sources would be published in academic quarterlies that send manuscripts out for elaborate, lengthy evaluation before deciding whether to publish them. Herrnstein and Murray didn't do this, so it wasn't until a full year or more after The Bell Curve was published that the leading experts on its subject had a chance to go through the underlying data with care. Therefore, as time went on, the knowledgeability of the Bell Curve discussion grew, but the attention paid to that discussion inevitably shrank. The debate on publication day was conducted in the mass media by people with no independent ability to assess the book. Over the next few months, intellectuals took some pretty good shots at it in smaller publications like the New Republic and the New York Review of Books . It wasn't until late 1995 that the most damaging criticism of The Bell Curve began to appear, in tiny academic journals. What follows is a brief summary of that last body of work. The Bell Curve , it turns out, is full of mistakes ranging from sloppy reasoning to mis-citations of sources to outright mathematical errors. Unsurprisingly, all the mistakes are in the direction of supporting the authors' thesis. First, a quick prÃ©cis of The Bell Curve . IQ tests, according to Murray and Herrnstein, measure an essential human quality, general intelligence. During the second half of the 20 th century, this quality has risen to supreme importance, because society has become increasingly complex. The intelligent have therefore gone through an "invisible migration," from points of origin all over the class system to a concentration at the top of business, government, and the professions. They are likely to become ever more dominant and prosperous. The unintelligent are falling further and further behind. Because intelligence is substantially inherited, nothing is likely to reverse this process. Blacks are overrepresented among the unintelligent. Any efforts government might make to improve the economic opportunities of poor people, especially poor black people, are likely to fail, because their poverty is so much the result of inherited low intelligence. About the best that can be done for these people is an effort to create a world of simple, decent, honorable toil for them. Herrnstein and Murray begin by telling us that the liberal position on IQ--namely, "Intelligence is a bankrupt concept"--has been discredited, and that "a scholarly consensus has been reached" around their position. This consensus is "beyond significant technical dispute." Thus, by the end of their introduction, they have arranged matters so that if intelligence has any meaning at all, the idiotic liberals stand discredited; and meanwhile, extremely broad claims for intelligence have the cover of "consensus." The notion that IQ tests are completely useless never prevailed in liberal academia to nearly the extent that Herrnstein and Murray say. A more accurate rendering of the liberal position would be that rather than a single "general intelligence," there are a handful of crucial--and separate--mental abilities; that none of these abilities is important enough to obviate the role of family background and education; and that native ability (and economic success independent of native ability) can be enhanced by improving education, training, and public health. The Bell Curve refers in passing to some of these points, but on the whole it sets up a cartoon-left position as its (easy) target. Meanwhile, the psychometricians who dominate the footnotes of The Bell Curve are John Hunter, Arthur Jensen, Malcolm Ree, and Frank Schmidt. These men are well known within the field as representing its right wing, not a mainstream consensus. The next problem with The Bell Curve 's thesis is in the idea of the rise to dominance of the cognitive elite. To the book's initial audience of Ivy Leaguers, this idea seemed valid on its face. Everybody knows that the best universities, law firms, hospitals, investment banks, and the State Department used to be run by preppies whose main virtue was fortunate birth, and are now open to one and all on the basis of merit. But the larger premise--that intelligent people used to be scattered throughout the class structure, and are now concentrated at the top--is almost impossible to prove, simply because the mass administration of mental tests is such a recent phenomenon. High scorers on mental tests do "bunch up" (as Herrnstein and Murray put it) in elite-university student bodies. But this is tautological: Any group selected on the basis of scores on mental tests will be composed disproportionately of people who score high on mental tests. Proving The Bell Curve 's thesis would require proving that success increasingly correlates with IQ in areas of life where mental tests are not the explicit gatekeepers. To see how The Bell Curve tries and fails to get around these inherent problems, see and . Having conditioned its audience to view IQ as all-important, The Bell Curve then manipulates statistics in a way that makes IQ look bigger, and everything else smaller, in determining Americans' life-chances. The basic tool of statistical social science in general, and of The Bell Curve in particular, is regression analysis, a technique used to assign weights to various factors (called "independent variables") in determining a final outcome (called the "dependent variable"). The original statistical work in The Bell Curve consists of regression analyses on a database called the National Longitudinal Study of Youth. The authors claim to demonstrate that high IQ is more predictive of economic success than any other factor, and that low IQ is more predictive of poverty and social breakdown. Virtually all the early commentators on The Bell Curve were unable to assess the merits of the regression analysis. "I am not a scientist. I know nothing about psychometrics," wrote Leon Wieseltier (who was otherwise quite critical) in a typical disclaimer. But by now the statistics have been gone over by professionals, who have come up with different results. The key points of their critique of The Bell Curve are as follows: What Herrnstein and Murray used to measure IQ is actually a measure of education as well as intelligence. All the people tracked in the National Longitudinal Study of Youth took the Armed Forces Qualifying Test, which Herrnstein and Murray treat as a good measure of intelligence. Because the material covered in the test includes subjects like trigonometry, many academic critics of The Bell Curve have objected to its use as a measure only of IQ and not at all of academic achievement. Herrnstein and Murray concede in the footnotes that scores tend to rise with the subjects' education--but they seriously underestimate the magnitude of this rise, as shows. And they resist the obvious inference that the test scores are measuring something other than intelligence. Most of The Bell Curve 's analysis is devoted to proving that IQ has more predictive power than parental "socio-economic status." But Herrnstein and Murray's method of figuring socioeconomic status seems designed to low-ball its influence, as explains. Herrnstein and Murray begin their discussion of the National Longitudinal Study of Youth data by announcing that they aren't going to analyze the effect of education, because education is too much a result of IQ. It's not an independent variable. (Of course, according to their theory, socioeconomic status is also a result of IQ, but somehow, that doesn't stop them.) Therefore, what you'd most want to know from a policy standpoint--how much education can increase opportunity--isn't dealt with in the book, except in two obscure footnotes. Both would seem to support the liberal, pro-education position that Herrnstein and Murray say is futile. One footnote shows education increasing IQ year by year. The other shows a higher correlation between college degree and family income than between IQ and family income. One of The Bell Curve 's theoretical linchpins is the high heritability of IQ. Herrnstein and Murray, sounding like the souls of caution, write that "half a century of work, now amounting to hundreds of empirical and theoretical studies, permits a broad conclusion that the genetic component of IQ is unlikely to be smaller than 40 per cent or higher than 80 per cent. ... For purposes of this discussion, we will adopt a middling estimate of 60 per cent heritability." This now looks seriously overstated. Michael Daniels, Bernie Devlin, and Kathryn Roeder of Carnegie Mellon University took the same studies on which Herrnstein and Murray based their estimate, and subjected them to a computer meta-analysis ("a powerful method of statistical analysis"-- The Bell Curve ). Their paper, which has not yet been published, says: "In brief, studies of IQ, and our reanalyses of them, suggest a narrow-sense heritability of 34 per cent and a broad-sense heritability of 46 per cent. [The difference between broad and narrow is too technical to explain in this limited space.] This is a far cry from Herrnstein and Murray's maximum value of 80 per cent or their middling value of 60 per cent. Consequently, Herrnstein and Murray give the impression that IQ is highly 'heritable,' but it is not." If the purpose of the whole exercise is to figure out what our social policies should be, then, "Which is more predictive, IQ or socioeconomic status?" isn't the essential question anyway. Making it the essential question avoids the issue of whether IQ is really so massively predictive that it drowns out everything else. (Herrnstein and Murray mostly leave the evidence for this, their central contention, to footnotes. The figures they offer are far from dispositive.) The chapter of The Bell Curve on policies that might be able to overcome the fate of a low IQ focuses mainly on whether early-childhood programs like Head Start (most of which aren't run with raising IQ as their primary goal) can raise IQ significantly over the long term, and sorrowfully concludes that they can't. What the book doesn't discuss is whether public schools--by far the biggest government social program--can raise IQ, or earnings after you control for IQ. As James Heckman of the University of Chicago wrote in the Journal of Political Economy , " Evidence of a genetic component to skills has no bearing on the efficacy of any social policy. ... The relevant issue is the cost effectiveness of the intervention." (As an example of where the kind of analysis Herrnstein and Murray didn't do can lead, a new study by Jay Girotto and Paul Peterson of Harvard shows that students who raise their grades and take harder courses can increase their IQ scores by an average of eight points during the first three years of high school.) At the beginning of The Bell Curve , Herrnstein and Murray declare that "the concept of intelligence has taken on a much higher place in the pantheon of human virtues than it deserves." And they claim that their view of IQ tests is "squarely in the middle of the scientific road." They end by expressing the hope that we can "be a society that makes good on the fundamental promise of the American tradition: the opportunity for everyone, not just the lucky ones, to live a satisfying life." Throughout, Herrnstein and Murray consistently present themselves as fair- (or even liberal-) minded technicians who have, with great caution, followed the evidence where it leads--which, unfortunately, is to a few unassailable if unpleasant scientific truths that it is their reluctant duty to report. In fact, The Bell Curve is a relentless brief for the conservative position in psychometrics and social policy. For all its talk of reflecting a consensus, the sources it draws upon are heavily skewed to the right. Herrnstein and Murray used quasi-nutty studies that support their position (as Charles Lane demonstrated in the New York Review of Books ), and ignore mainstream studies that contradict it (as Richard Nisbett showed in the New Republic ). The data in The Bell Curve are consistently massaged to produce conservative conclusions; not once is a finding that contradicts the main thesis reported in the text. ( shows how Herrnstein and Murray have made the convergence in black-white IQ scores, which they claim to find "encouraging," look smaller than it actually is.) The Bell Curve 's air of strict scientism doesn't preclude the use of lightly sourced or unsourced assertions, such as the statement that the median IQ of all black Africans is 75, or that "intermarriage among people in the top few percentiles of intelligence may be increasing far more rapidly than suspected" (no footnote). Though they piously claim not to be doing so, Herrnstein and Murray leave readers with the distinct impression that IQ is the cause of economic success and failure, and that genetic difference explains the black-white IQ gap. In the most famous passage in The Republic , Plato describes an underground cave where people are held prisoner in chains, unable to see anything but the shadows cast by figures passing outside; they mistake the shadows for reality. The Republic is probably the first place in history where an idea like that of Murray and Herrnstein's cognitive elite appears. Plato believed that through education, people could leave the cave and be able to see the truth instead of the shadows, thus fitting themselves to become the wise rulers of society. But he was quick to insert a cautionary note: Those who have left the cave might be tempted to think they can see perfectly clearly, while actually they would be "dazzled by excess of light." The image applies to The Bell Curve : Presented as an exact representation of reality, in opposition to the shadows of political correctness, it actually reflects the blinkered vision of one part of the American elite. It constantly tells these people that they are naturally superior, and offers lurid descriptions of aspects of national life that they know about only by rumor. Readers who accept The Bell Curve as tough-minded and realistic, and who assume that all criticism of it is ignorant and ideologically motivated, are not as far removed from Plato's cave as they might think. : Dumb College Students : Smart Rich People : Education and IQ : Socioeconomic Status : Black-White Convergence	A. Education
How do they evaluate generated text quality?	### Introduction Variational Autoencoder (VAE) BIBREF1 is a powerful method for learning representations of high-dimensional data. However, recent attempts of applying VAEs to text modelling are still far less successful compared to its application to image and speech BIBREF2, BIBREF3, BIBREF4. When applying VAEs for text modelling, recurrent neural networks (RNNs) are commonly used as the architecture for both encoder and decoder BIBREF0, BIBREF5, BIBREF6. While such a VAE-RNN based architecture allows encoding and generating sentences (in the decoding phase) with variable-length effectively, it is also vulnerable to an issue known as latent variable collapse (or KL loss vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Various efforts have been made to alleviate the latent variable collapse issue. BIBREF0 uses KL annealing, where a variable weight is added to the KL term in the cost function at training time. BIBREF7 discovered that there is a trade-off between the contextual capacity of the decoder and effective use of encoding information, and developed a dilated CNN as decoder which can vary the amount of conditioning context. They also introduced a loss clipping strategy in order to make the model more robust. BIBREF5 addressed the problem by replacing the standard normal distribution for the prior with the von Mises-Fisher (vMF) distribution. With vMF, the KL loss only depends on the concentration parameter which is fixed during training and testing, and hence results in a constant KL loss. In a more recent work, BIBREF6 avoided latent variable collapse by including skip connections in the generative model, where the skip connections enforce strong links between the latent variables and the likelihood function. Although the aforementioned works show effectiveness in addressing the latent variable collapse issue to some extent, they either require carefully engineering to balance the weight between the reconstruction loss and KL loss BIBREF0, BIBREF8, or resort to designing more sophisticated model structures BIBREF7, BIBREF5, BIBREF6. In this paper, we present a simple architecture called holistic regularisation VAE (HR-VAE), which can effectively avoid latent variable collapse. In contrast to existing VAE-RNN models for text modelling which merely impose a standard normal distribution prior on the last hidden state of the RNN encoder, our HR-VAE model imposes regularisation for all hidden states of the RNN encoder. Another advantage of our model is that it is generic and can be applied to any existing VAE-RNN-based architectures. We evaluate our model against several strong baselines which apply VAE for text modelling BIBREF0, BIBREF7, BIBREF5. We conducted experiments based on two public benchmark datasets, namely, the Penn Treebank dataset BIBREF9 and the end-to-end (E2E) text generation dataset BIBREF10. Experimental results show that our HR-VAE model not only can effectively mitigate the latent variable collapse issue with a stable training process, but also can give better predictive performance than the baselines, as evidenced by both quantitative (e.g., negative log likelihood and perplexity) and qualitative evaluation. The code for our model is available online. ### Methodology ::: Background of VAE A variational autoencoder (VAE) is a deep generative model, which combines variational inference with deep learning. The VAE modifies the conventional autoencoder architecture by replacing the deterministic latent representation $\mathbf {z}$ of an input $\mathbf {x}$ with a posterior distribution $P(\mathbf {z}\|\mathbf {x})$, and imposing a prior distribution on the posterior, such that the model allows sampling from any point of the latent space and yet able to generate novel and plausible output. The prior is typically chosen to be standard normal distributions, i.e., $P(\mathbf {z}) = \mathcal {N}(\mathbf {0},\mathbf {1})$, such that the KL divergence between posterior and prior can be computed in closed form BIBREF1. To train a VAE, we need to optimise the marginal likelihood $P_{\theta }(\mathbf {x})=\int {P(\mathbf {z})P_{\theta }(\mathbf {x}\|\mathbf {z})d\mathbf {z}}$, where the log likelihood can take following form: Here $Q_{\phi }(\mathbf {z}\|\mathbf {x})$ is the variational approximation for the true posterior $P_{\theta }(\mathbf {z}\|\mathbf {x})$. Specifically, $Q_{\phi }(\mathbf {z}\|\mathbf {x})$ can be regarded as an encoder (a.k.a. the recognition model) and $P_{\theta }(\mathbf {x}\|\mathbf {z})$ the decoder (a.k.a. the generative model). Both encoder and decoder are implemented via neural networks. As proved in BIBREF1, optimising the marginal log likelihood is essentially equivalent to maximising $\mathcal {L}(\theta ,\phi ;\mathbf {x})$, i.e., the evidence lower bound (ELBO), which consists of two terms. The first term is the expected reconstruction error indicating how well the model can reconstruct data given a latent variable. The the second term is the KL divergence of the approximate posterior from prior, i.e., a regularisation pushing the learned posterior to be as close to the prior as possible. ### Methodology ::: Variational Autoendoder with Holistic Regularisation In this section, we discuss the technical details of the proposed holistic regularisation VAE (HR-VAE) model, a general architecture which can effectively mitigate the KL vanishing phenomenon. Our model design is motivated by one noticeable defect shared by the VAE-RNN based models in previous works BIBREF0, BIBREF7, BIBREF5, BIBREF6. That is, all these models, as shown in Figure FIGREF2, only impose a standard normal distribution prior on the last hidden state of the RNN encoder, which potentially leads to learning a suboptimal representation of the latent variable and results in model vulnerable to KL loss vanishing. Our hypothesis is that to learn a good representation of data and a good generative model, it is crucial to impose the standard normal prior on all the hidden states of the RNN-based encoder (see Figure FIGREF2), which allows a better regularisation of the model learning process. We implement the HR-VAE model using a two-layer LSTM for both the encoder and decoder. However, one should note that our architecture can be readily applied to other types of RNN such as GRU. For each time stamp $t$ (see Figure FIGREF2), we concatenate the hidden state $\mathbf {h}_t$ and the cell state $\mathbf {c}_t$ of the encoder. The concatenation (i.e., $[\mathbf {h}_t;\mathbf {c}_t]$) is then fed into two linear transformation layers for estimating $\mu _t$ and $\sigma ^2_t$, which are parameters of a normal distribution corresponding to the concatenation of $\mathbf {h}_t$ and $\mathbf {c}_t$. Let $Q_{\phi _t}(\mathbf {z}_t \| \mathbf {x})=\mathcal {N}(\mathbf {z}_t\|\mu _t,\sigma ^2_t)$, we wish $Q_{\phi _t}(\mathbf {z}_t \| \mathbf {x})$ to be close to a prior $P(\mathbf {z}_t)$, which is a standard Gaussian. Finally, the KL divergence between these two multivariate Gaussian distributions (i.e., $Q_{\phi _t}$ and $P(\mathbf {z}_t)$) will contribute to the overall KL loss of the ELBO. By taking the average of the KL loss at each time stamp $t$, the resulting ELBO takes the following form As can be seen in Eq. DISPLAY_FORM10, our solution to the KL collapse issue does not require any engineering for balancing the weight between the reconstruction term and KL loss as commonly the case in existing works BIBREF0, BIBREF8. The weight between these two terms of our model is simply $1:1$. ### Experimental Setup ::: Datasets We evaluate our model on two public datasets, namely, Penn Treebank (PTB) BIBREF9 and the end-to-end (E2E) text generation corpus BIBREF10, which have been used in a number of previous works for text generation BIBREF0, BIBREF5, BIBREF11, BIBREF12. PTB consists of more than 40,000 sentences from Wall Street Journal articles whereas the E2E dataset contains over 50,000 sentences of restaurant reviews. The statistics of these two datasets are summarised in Table TABREF11. ### Experimental Setup ::: Implementation Details For the PTB dataset, we used the train-test split following BIBREF0, BIBREF5. For the E2E dataset, we used the train-test split from the original dataset BIBREF10 and indexed the words with a frequency higher than 3. We represent input data with 512-dimensional word2vec embeddings BIBREF13. We set the dimension of the hidden layers of both encoder and decoder to 256. The Adam optimiser BIBREF14 was used for training with an initial learning rate of 0.0001. Each utterance in a mini-batch was padded to the maximum length for that batch, and the maximum batch-size allowed is 128. ### Experimental Setup ::: Baselines We compare our HR-VAE model with three strong baselines using VAE for text modelling: VAE-LSTM-base: A variational autoencoder model which uses LSTM for both encoder and decoder. KL annealing is used to tackled the latent variable collapse issue BIBREF0; VAE-CNN: A variational autoencoder model with a LSTM encoder and a dilated CNN decoder BIBREF7; vMF-VAE: A variational autoencoder model using LSTM for both encoder and decoder where the prior distribution is the von Mises-Fisher (vMF) distribution rather than a Gaussian distribution BIBREF5. ### Experimental Results We evaluate our HR-VAE model in two experimental settings, following the setup of BIBREF0, BIBREF5. In the standard setting, the input to the decoder at each time stamp is the concatenation of latent variable $\mathbf {z}$ and the ground truth word of the previous time stamp. Under this setting, the decoder will be more powerful because it uses the ground truth word as input, resulting in little information of the training data captured by latent variable $\mathbf {z}$. The inputless setting, in contrast, does not use the previous ground truth word as input for the decoder. In other words, the decoder needs to predict the entire sequence with only the help of the given latent variable $\mathbf {z}$. In this way, a high-quality representation abstracting the information of the input sentence is much needed for the decoder, and hence enforcing $\mathbf {z}$ to learn the required information. Overall performance. Table TABREF13 shows the language modelling results of our approach and the baselines. We report negative log likelihood (NLL), KL loss, and perplexity (PPL) on the test set. As expected, all the models have a higher KL loss in the inputless setting than the standard setting, as $\mathbf {z}$ is required to encode more information about the input data for reconstruction. In terms of overall performance, our model outperforms all the baselines in both datasets (i.e., PTB and E2E). For instance, when comparing with the strongest baseline vMF-VAE in the standard setting, our model reduces NLL from 96 to 79 and PPL from 98 to 43 in PTB, respectively. In the inputless setting, our performance gain is even higher, i.e., NLL reduced from 117 to 85 and PPL from 262 to 54. A similar pattern can be observed for the E2E dataset. These observations suggest that our approach can learn a better generative model for data. Loss analysis. To conduct a more thorough evaluation, we further investigate model behaviours in terms of both reconstruction loss and KL loss, as shown in Figure FIGREF14. These plots were obtained based on the E2E training set using the inputless setting. We can see that the KL loss of VAE-LSTM-base, which uses Sigmoid annealing BIBREF0, collapses to zero, leading to a poor generative performance as indicated by the high reconstruction loss. The KL loss for both VAE-CNN and vMF-VAE are nonzero, where the former mitigates the KL collapse issue with a KL loss clipping strategy and the latter by replacing the standard normal distribution for the prior with the vMF distribution (i.e., with the vMF distribution, the KL loss only depends on a fixed concentration parameter, and hence results in a constant KL loss). Although both VAE-CNN and vMF-VAE outperform VAE-LSTM-base by a large margin in terms of reconstruction loss as shown in Figure FIGREF14, one should also notice that these two models actually overfit the training data, as their performance on the test set is much worse (cf. Table TABREF13). In contrast to the baselines which mitigate the KL collapse issue by carefully engineering the weight between the reconstruction loss and KL loss or choosing a different choice of prior, we provide a simple and elegant solution through holistic KL regularisation, which can effectively mitigate the KL collapse issue and achieve a better reconstruction error in both training and testing. Sentence reconstruction. Lastly, we show some sentence examples reconstructed by vMF-VAE (i.e., the best baseline) and our model in the inputless setting using sentences from the E2E test set as input. As shown in Table TABREF15, the sentences generated by vMF-VAE contain repeated words in quite a few cases, such as `city city area' and `blue spice spice'. In addition, vMF-VAE also tends to generate unnecessary or unrelated words at the end of sentences, making the generated sentences ungrammatical. The sentences reconstructed by our model, in contrast, are more grammatical and more similar to the corresponding ground truth sentences than vMF-VAE. ### Conclusion In this paper, we present a simple and generic architecture called holistic regularisation VAE (HR-VAE), which can effectively avoid latent variable collapse. In contrast to existing VAE-RNN models which merely impose a standard normal distribution prior on the last hidden state of the RNN encoder, our HR-VAE model imposes regularisation on all the hidden states, allowing a better regularisation of the model learning process. Empirical results show that our model can effectively mitigate the latent variable collapse issue while giving a better predictive performance than the baselines. ### Acknowledgment This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P011829/1). Figure 1: (a) The typical architecture of RNN-based VAE; (b) the proposed HR-VAE architecture. Table 1: The statistics of the PTB and E2E datasets. Table 2: Language modelling results on the PTB and E2E datasets. † indicates the results which are reported from the prior publications. KL loss is shown in the parenthesis. Figure 2: Training curves of reconstruction loss and KL loss of (a) VAE-LSTM-base, (b) VAE-CNN, (c) vMFVAE, and (d) our model, based on the E2E training set using the inputless setting. Table 3: Example input sentences from the E2E test dataset (top); sentences reconstructed by vMF-VAE (middle); sentences reconstructed by our model (bottom).	Loss analysis. To conduct a more thorough evaluation, we further investigate model behaviours in terms of both reconstruction loss and KL loss, as shown in Figure FIGREF14. These plots were obtained based on the E2E training set using the inputless setting.
Which complementary currency didn't work out? A. The Stroud Pound B. The Totnes Pound C. The Liverpool Pound D. The Brixton Pound	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. The Stroud Pound
What is one description of a putative right to individual self-expression? A. The right to orthodox self-expression B. The right to hate but not to be hated C. The right to engage in debate unencumbered by speech laws D. The right of the donkey to drool	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.	D. The right of the donkey to drool
What did the dead man compare the Spacemen to in disgust? A. Bees B. Garbage C. Maggots D. Flies	A Coffin for Jacob By EDWARD W. LUDWIG Illustrated by EMSH [Transcriber's Note: This etext was produced from Galaxy Science Fiction May 1956. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] With never a moment to rest, the pursuit through space felt like a game of hounds and hares ... or was it follow the leader? Ben Curtis eased his pale, gaunt body through the open doorway of the Blast Inn, the dead man following silently behind him. His fear-borne gaze traveled into the dimly illumined Venusian gin mill. The place was like an evil caldron steaming with a brew whose ingredients had been culled from the back corners of three planets. Most of the big room lay obscured behind a shimmering veil of tobacco smoke and the sweet, heavy fumes of Martian Devil's Egg. Here and there, Ben saw moving figures. He could not tell if they were Earthmen, Martians or Venusians. Someone tugged at his greasy coat. He jumped, thinking absurdly that it was the dead man's hand. " Coma esta, senor? " a small voice piped. " Speken die Deutsch? Desirez-vous d'amour? Da? Nyet? " Ben looked down. The speaker was an eager-eyed Martian boy of about ten. He was like a red-skinned marionette with pipestem arms and legs, clad in a torn skivvy shirt and faded blue dungarees. "I'm American," Ben muttered. "Ah, buena ! I speak English tres fine, senor . I have Martian friend, she tres pretty and tres fat. She weigh almost eighty pounds, monsieur . I take you to her, si ?" Ben shook his head. He thought, I don't want your Martian wench. I don't want your opium or your Devil's Egg or your Venusian kali. But if you had a drug that'd bring a dead man to life, I'd buy and pay with my soul. "It is deal, monsieur ? Five dollars or twenty keelis for visit Martian friend. Maybe you like House of Dreams. For House of Dreams—" "I'm not buying." The dirty-faced kid shrugged. "Then I show you to good table,— tres bien . I do not charge you, senor ." The boy grabbed his hand. Because Ben could think of no reason for resisting, he followed. They plunged into shifting layers of smoke and through the drone of alcohol-cracked voices. They passed the bar with its line of lean-featured, slit-eyed Earthmen—merchant spacemen. They wormed down a narrow aisle flanked by booths carved from Venusian marble that jutted up into the semi-darkness like fog-blanketed tombstones. Several times, Ben glimpsed the bulky figures of CO 2 -breathing Venusians, the first he'd ever seen. They were smoky gray, scaly, naked giants, toads in human shape. They stood solitary and motionless, aloof, their green-lidded eyes unblinking. They certainly didn't look like telepaths, as Ben had heard they were, but the thought sent a fresh rivulet of fear down his spine. Once he spied a white-uniformed officer of Hoover City's Security Police. The man was striding down an aisle, idly tapping his neuro-club against the stone booths. Keep walking , Ben told himself. You look the same as anyone else here. Keep walking. Look straight ahead. The officer passed. Ben breathed easier. "Here we are, monsieur ," piped the Martian boy. "A tres fine table. Close in the shadows." Ben winced. How did this kid know he wanted to sit in the shadows? Frowning, he sat down—he and the dead man. He listened to the lonely rhythms of the four-piece Martian orchestra. The Martians were fragile, doll-like creatures with heads too large for their spindly bodies. Their long fingers played upon the strings of their cirillas or crawled over the holes of their flutes like spider legs. Their tune was sad. Even when they played an Earth tune, it still seemed a song of old Mars, charged with echoes of lost voices and forgotten grandeur. For an instant, Ben's mind rose above the haunting vision of the dead man. He thought, What are they doing here, these Martians? Here, in a smoke-filled room under a metalite dome on a dust-covered world? Couldn't they have played their music on Mars? Or had they, like me, felt the challenge of new worlds? He sobered. It didn't matter. He ordered a whiskey from a Chinese waiter. He wet his lips but did not drink. His gaze wandered over the faces of the Inn's other occupants. You've got to find him , he thought. You've got to find the man with the red beard. It's the only way you can escape the dead man. The dead man was real. His name was Cobb. He was stout and flabby and about forty and he hated spacemen. His body was buried now—probably in the silent gray wastes outside Luna City. But he'd become a kind of invisible Siamese twin, as much a part of Ben as sight in his eyes. Sometimes the image would be shuffling drunkenly beside him, its lips spitting whiskey-slurred curses. Again, its face would be a pop-eyed mask of surprise as Ben's fist thudded into its jaw. More often, the face would be frozen in the whiteness of death. The large eyes would stare. Blood would trickle from a corner of the gaping mouth. You can forget a living man. You can defeat him or submit to him or ignore him, and the matter is over and done. You can't escape from a memory that has burned into your mind. It had begun a week ago in Luna City. The flight from White Sands had been successful. Ben, quietly and moderately, wanted to celebrate. He stopped alone in a rocketfront bar for a beer. The man named Cobb plopped his portly and unsteady posterior on the stool next to him. "Spacemen," he muttered, "are getting like flies. Everywhere, all you see's spacemen." He was a neatly dressed civilian. Ben smiled. "If it weren't for spacemen, you wouldn't be here." "The name's Cobb." The man hiccoughed. "Spacemen in their white monkey suits. They think they're little tin gods. Betcha you think you're a little tin god." He downed a shot of whiskey. Ben stiffened. He was twenty-four and dressed in the white, crimson-braided uniform of the Odyssey's junior astrogation officer. He was three months out of the Academy at White Sands and the shining uniform was like a key to all the mysteries of the Universe. He'd sought long for that key. At the age of five—perhaps in order to dull the memory of his parents' death in a recent strato-jet crash—he'd spent hours watching the night sky for streaking flame-tails of Moon rockets. At ten, he'd ground his first telescope. At fourteen, he'd converted an abandoned shed on the government boarding-school grounds to a retreat which housed his collection of astronomy and rocketry books. At sixteen, he'd spent every weekend holiday hitchhiking from Boys Town No. 5 in the Catskills to Long Island Spaceport. There, among the grizzled veterans of the old Moon Patrol, he'd found friends who understood his dream and who later recommended his appointment to the U. S. Academy for the Conquest of Space. And a month ago, he'd signed aboard the Odyssey —the first ship, it was rumored, equipped to venture as far as the asteroids and perhaps beyond. Cobb was persistent: "Damn fools shoulda known enough to stay on Earth. What the hell good is it, jumpin' from planet to planet?" The guy's drunk , Ben thought. He took his drink and moved three stools down the bar. Cobb followed. "You don't like the truth, eh, kid? You don't like people to call you a sucker." Ben rose and started to leave the bar, but Cobb grabbed his arm and held him there. "Thas what you are—a sucker. You're young now. Wait ten years. You'll be dyin' of radiation rot or a meteor'll get you. Wait and see, sucker!" Until this instant, Ben had suppressed his anger. Now, suddenly and without warning, it welled up into savage fury. His fist struck the man on the chin. Cobb's eyes gaped in shocked horror. He spun backward. His head cracked sickeningly on the edge of the bar. The sound was like a punctuation mark signaling the end of life. He sank to the floor, eyes glassy, blood tricking down his jaw. Ben knew that he was dead. Then, for a single absurd second, Ben was seized with terror—just as, a moment before, he'd been overwhelmed with anger. He ran. For some twenty minutes, he raced through a dizzying, nightmare world of dark rocketfront alleys and shouting voices and pursuing feet. At last, abruptly, he realized that he was alone and in silence. He saw that he was still on the rocketfront, but in the Tycho-ward side of the city. He huddled in a dark corner of a loading platform and lit a cigarette. A thousand stars—a thousand motionless balls of silver fire—shone above him through Luna City's transparent dome. He was sorry he'd hit Cobb, of course. He was not sorry he'd run. Escaping at least gave him a power of choice, of decision. You can do two things , he thought. You can give yourself up, and that's what a good officer would do. That would eliminate the escape charge. You'd get off with voluntary manslaughter. Under interplanetary law, that would mean ten years in prison and a dishonorable discharge. And then you'd be free. But you'd be through with rockets and space. They don't want new men over thirty-four for officers on rockets or even for third-class jet-men on beat-up freighters—they don't want convicted killers. You'd get the rest of the thrill of conquering space through video and by peeking through electric fences of spaceports. Or— There were old wives' tales of a group of renegade spacemen who operated from the Solar System's frontiers. The spacemen weren't outlaws. They were misfits, rejectees from the clearing houses on Earth. And whereas no legally recognized ship had ventured past Mars, the souped-up renegade rigs had supposedly hit the asteroids. Their headquarters was Venus. Their leader—a subject of popular and fantastic conjecture in the men's audiozines—was rumored to be a red-bearded giant. So , Ben reflected, you can take a beer-and-pretzels tale seriously. You can hide for a couple of days, get rid of your uniform, change your name. You can wait for a chance to get to Venus. To hell with your duty. You can try to stay in space, even if you exile yourself from Earth. After all, was it right for a single second, a single insignificant second, to destroy a man's life and his dream? He was lucky. He found a tramp freighter whose skipper was on his last flight before retirement. Discipline was lax, investigation of new personnel even more so. Ben Curtis made it to Venus. There was just one flaw in his decision. He hadn't realized that the memory of the dead man's face would haunt him, torment him, follow him as constantly as breath flowed into his lungs. But might not the rumble of atomic engines drown the murmuring dead voice? Might not the vision of alien worlds and infinite spaceways obscure the dead face? So now he sat searching for a perhaps nonexistent red-bearded giant, and hoping and doubting and fearing, all at once. "You look for someone, senor ?" He jumped. "Oh. You still here?" " Oui. " The Martian kid grinned, his mouth full of purple teeth. "I keep you company on your first night in Hoover City, n'est-ce-pas ?" "This isn't my first night here," Ben lied. "I've been around a while." "You are spacemen?" Ben threw a fifty-cent credit piece on the table. "Here. Take off, will you?" Spiderlike fingers swept down upon the coin. " Ich danke, senor. You know why city is called Hoover City?" Ben didn't answer. "They say it is because after women come, they want first thing a thousand vacuum cleaners for dust. What is vacuum cleaner, monsieur ?" Ben raised his hand as if to strike the boy. " Ai-yee , I go. You keep listen to good Martian music." The toothpick of a body melted into the semi-darkness. Minutes passed. There were two more whiskeys. A ceaseless parade of faces broke through the smoky veil that enclosed him—reddish balloon faces, scaly reptilian faces, white-skinned, slit-eyed faces, and occasionally a white, rouged, powdered face. But nowhere was there a face with a red beard. A sense of hopelessness gripped Ben Curtis. Hoover City was but one of a dozen cities of Venus. Each had twenty dives such as this. He needed help. But his picture must have been 'scoped to Venusian visiscreens. A reward must have been offered for his capture. Whom could he trust? The Martian kid, perhaps? Far down the darkened aisle nearest him, his eyes caught a flash of white. He tensed. Like the uniform of a Security Policeman, he thought. His gaze shifted to another aisle and another hint of whiteness. And then he saw another and another and another. Each whiteness became brighter and closer, like shrinking spokes of a wheel with Ben as their focal point. You idiot! The damned Martian kid! You should have known! Light showered the room in a dazzling explosion. Ben, half blinded, realized that a broad circle of unshaded globes in the ceiling had been turned on. The light washed away the room's strangeness and its air of brooding wickedness, revealing drab concrete walls and a debris-strewn floor. Eyes blinked and squinted. There were swift, frightened movements and a chorus of angry murmurs. The patrons of the Blast Inn were like tatter-clad occupants of a house whose walls have been ripped away. Ben Curtis twisted his lean body erect. His chair tumbled backward, falling. The white-clad men charged, neuro-clubs upraised. A woman screamed. The music ceased. The Martian orchestra slunk with feline stealth to a rear exit. Only the giant Venusians remained undisturbed. They stood unmoving, their staring eyes shifting lazily in Ben's direction. "Curtis!" one of the policemen yelled. "You're covered! Hold it!" Ben whirled away from the advancing police, made for the exit into which the musicians had disappeared. A hissing sound traveled past his left ear, a sound like compressed air escaping from a container. A dime-sized section of the concrete wall ahead of him crumbled. He stumbled forward. They were using deadly neuro-pistols now, not the mildly stunning neuro-clubs. Another hiss passed his cheek. He was about twelve feet from the exit. Another second , his brain screamed. Just another second— Or would the exits be guarded? He heard the hiss. It hit directly in the small of his back. There was no pain, just a slight pricking sensation, like the shallow jab of a needle. He froze as if yanked to a stop by a noose. His body seemed to be growing, swelling into balloon proportions. He knew that the tiny needle had imbedded itself deep in his flesh, knew that the paralyzing mortocain was spreading like icy fire into every fiber and muscle of his body. He staggered like a man of stone moving in slow motion. He'd have fifteen—maybe twenty—seconds before complete lethargy of mind and body overpowered him. In the dark world beyond his fading consciousness, he heard a voice yell, "Turn on the damn lights!" Then a pressure and a coldness were on his left hand. He realized that someone had seized it. A soft feminine voice spoke to him. "You're wounded? They hit you?" "Yes." His thick lips wouldn't let go of the word. "You want to escape—even now?" "Yes." "You may die if you don't give yourself up." "No, no." He tried to stumble toward the exit. "All right then. Not that way. Here, this way." Heavy footsteps thudded toward them. A few yards away, a flashlight flicked on. Hands were guiding him. He was aware of being pushed and pulled. A door closed behind him. The glare of the flashlight faded from his vision—if he still had vision. "You're sure?" the voice persisted. "I'm sure," Ben managed to say. "I have no antidote. You may die." His mind fought to comprehend. With the anti-paralysis injection, massage and rest, a man could recover from the effects of mortocain within half a day. Without treatment, the paralysis could spread to heart and lungs. It could become a paralysis of death. An effective weapon: the slightest wound compelled the average criminal to surrender at once. "Anti ... anti ..." The words were as heavy as blobs of mercury forced from his throat. "No ... I'm sure ... sure." He didn't hear the answer or anything else. Ben Curtis had no precise sensation of awakening. Return to consciousness was an intangible evolution from a world of black nothingness to a dream-like state of awareness. He felt the pressure of hands on his naked arms and shoulders, hands that massaged, manipulated, fought to restore circulation and sensitivity. He knew they were strong hands. Their strength seemed to transfer itself to his own body. For a long time, he tried to open his eyes. His lids felt welded shut. But after a while, they opened. His world of darkness gave way to a translucent cloak of mist. A round, featureless shape hovered constantly above him—a face, he supposed. He tried to talk. Although his lips moved slightly, the only sound was a deep, staccato grunting. But he heard someone say, "Don't try to talk." It was the same gentle voice he'd heard in the Blast Inn. "Don't talk. Just lie still and rest. Everything'll be all right." Everything all right , he thought dimly. There were long periods of lethargy when he was aware of nothing. There were periods of light and of darkness. Gradually he grew aware of things. He realized that the soft rubber mouth of a spaceman's oxygen mask was clamped over his nose. He felt the heat of electric blankets swathed about his body. Occasionally a tube would be in his mouth and he would taste liquid food and feel a pleasant warmth in his stomach. Always, it seemed, the face was above him, floating in the obscuring mist. Always, it seemed, the soft voice was echoing in his ears: "Swallow this now. That's it. You must have food." Or, "Close your eyes. Don't strain. It won't be long. You're getting better." Better , he'd think. Getting better.... At last, after one of the periods of lethargy, his eyes opened. The mist brightened, then dissolved. He beheld the cracked, unpainted ceiling of a small room, its colorless walls broken with a single, round window. He saw the footboard of his aluminite bed and the outlines of his feet beneath a faded blanket. Finally he saw the face and figure that stood at his side. "You are better?" the kind voice asked. The face was that of a girl probably somewhere between twenty-five and thirty. Her features, devoid of makeup, had an unhealthy-looking pallor, as if she hadn't used a sunlamp for many weeks. Yet, at the same time, her firm slim body suggested a solidity and a strength. Her straight brown hair was combed backward, tight upon her scalp, and drawn together in a knot at the nape of her neck. "I—I am better," he murmured. His words were still slow and thick. "I am going to live?" "You will live." He thought for a moment. "How long have I been here?" "Nine days." "You took care of me?" He noted the deep, dark circles beneath her sleep-robbed eyes. She nodded. "You're the one who carried me when I was shot?" "Yes." "Why?" Suddenly he began to cough. Breath came hard. She held the oxygen mask in readiness. He shook his head, not wanting it. "Why?" he asked again. "It would be a long story. Perhaps I'll tell you tomorrow." A new thought, cloaked in sudden fear, entered his murky consciousness. "Tell me, will—will I be well again? Will I be able to walk?" He lay back then, panting, exhausted. "You have nothing to worry about," the girl said softly. Her cool hand touched his hot forehead. "Rest. We'll talk later." His eyes closed and breath came easier. He slept. When he next awoke, his gaze turned first to the window. There was light outside, but he had no way of knowing if this was morning, noon or afternoon—or on what planet. He saw no white-domed buildings of Hoover City, no formal lines of green-treed parks, no streams of buzzing gyro-cars. There was only a translucent and infinite whiteness. It was as if the window were set on the edge of the Universe overlooking a solemn, silent and matterless void. The girl entered the room. "Hi," she said, smiling. The dark half-moons under her eyes were less prominent. Her face was relaxed. She increased the pressure in his rubberex pillows and helped him rise to a sitting position. "Where are we?" he asked. "Venus." "We're not in Hoover City?" "No." He looked at her, wondering. "You won't tell me?" "Not yet. Later, perhaps." "Then how did you get me here? How did we escape from the Inn?" She shrugged. "We have friends who can be bribed. A hiding place in the city, the use of a small desert-taxi, a pass to leave the city—these can be had for a price." "You'll tell me your name?" "Maggie." "Why did you save me?" Her eyes twinkled mischievously. "Because you're a good astrogator." His own eyes widened. "How did you know that?" She sat on a plain chair beside his bed. "I know everything about you, Lieutenant Curtis." "How did you learn my name? I destroyed all my papers—" "I know that you're twenty-four. Born July 10, 1971. Orphaned at four, you attended Boys Town in the Catskills till you were 19. You graduated from the Academy at White Sands last June with a major in Astrogation. Your rating for the five-year period was 3.8—the second highest in a class of fifty-seven. Your only low mark in the five years was a 3.2 in History of Martian Civilization. Want me to go on?" Fascinated, Ben nodded. "You were accepted as junior astrogation officer aboard the Odyssey . You did well on your flight from Roswell to Luna City. In a barroom fight in Luna City, you struck and killed a man named Arthur Cobb, a pre-fab salesman. You've been charged with second degree murder and escape. A reward of 5,000 credits has been offered for your capture. You came to Hoover City in the hope of finding a renegade group of spacemen who operate beyond Mars. You were looking for them in the Blast Inn." He gaped incredulously, struggling to rise from his pillows. "I—don't get it." "There are ways of finding out what we want to know. As I told you, we have many friends." He fell back into his pillows, breathing hard. She rose quickly. "I'm sorry," she said. "I shouldn't have told you yet. I felt so happy because you're alive. Rest now. We'll talk again soon." "Maggie, you—you said I'd live. You didn't say I'd be able to walk again." She lowered her gaze. "I hope you'll be able to." "But you don't think I will, do you?" "I don't know. We'll try walking tomorrow. Don't think about it now. Rest." He tried to relax, but his mind was a vortex of conjecture. "Just one more question," he almost whispered. "Yes?" "The man I killed—did he have a wife?" She hesitated. He thought, Damn it, of all the questions, why did I ask that? Finally she said, "He had a wife." "Children?" "Two. I don't know their ages." She left the room. He sank into the softness of his bed. As he turned over on his side, his gaze fell upon an object on a bureau in a far corner of the room. He sat straight up, his chest heaving. The object was a tri-dimensional photo of a rock-faced man in a merchant spaceman's uniform. He was a giant of a man with a neatly trimmed red beard ! Ben stared at the photo for a long time. At length, he slipped into restless sleep. Images of faces and echoes of words spun through his brain. The dead man returned to him. Bloodied lips cursed at him. Glassy eyes accused him. Somewhere were two lost children crying in the night. And towering above him was a red-bearded man whose great hands reached down and beckoned to him. Ben crawled through the night on hands and knees, his legs numb and useless. The crying of the children was a chilling wail in his ears. His head rose and turned to the red-bearded man. His pleading voice screamed out to him in a thick, harsh cackle. Yet even as he screamed, the giant disappeared, to be replaced by white-booted feet stomping relentlessly toward him. He awoke still screaming.... A night without darkness passed. Ben lay waiting for Maggie's return, a question already formed in his mind. She came and at once he asked, "Who is the man with the red beard?" She smiled. "I was right then when I gave you that thumbnail biog. You were looking for him, weren't you?" "Who is he?" She sat on the chair beside him. "My husband," she said softly. He began to understand. "And your husband needs an astrogator? That's why you saved me?" "We need all the good men we can get." "Where is he?" She cocked her head in mock suspicion. "Somewhere between Mercury and Pluto. He's building a new base for us—and a home for me. When his ship returns, I'll be going to him." "Why aren't you with him now?" "He said unexplored space is no place for a woman. So I've been studying criminal reports and photos from the Interplanetary Bureau of Investigation and trying to find recruits like yourself. You know how we operate?" He told her the tales he'd heard. She nodded. "There are quite a few of us now—about a thousand—and a dozen ships. Our base used to be here on Venus, down toward the Pole. The dome we're in now was designed and built by us a few years ago after we got pushed off Mars. We lost a few men in the construction, but with almost every advance in space, someone dies." "Venus is getting too civilized. We're moving out and this dome is only a temporary base when we have cases like yours. The new base—I might as well tell you it's going to be an asteroid. I won't say which one." "Don't get the idea that we're outlaws. Sure, about half our group is wanted by the Bureau, but we make honest livings. We're just people like yourself and Jacob." "Jacob? Your husband?" She laughed. "Makes you think of a Biblical character, doesn't it? Jacob's anything but that. And just plain 'Jake' reminds one of a grizzled old uranium prospector and he isn't like that, either." She lit a cigarette. "Anyway, the wanted ones stay out beyond the frontiers. Jacob and those like him can never return to Earth—not even to Hoover City—except dead. The others are physical or psycho rejects who couldn't get clearance if they went back to Earth. They know nothing but rocketing and won't give up. They bring in our ships to frontier ports like Hoover City to unload cargo and take on supplies." "Don't the authorities object?" "Not very strongly. The I. B. I. has too many problems right here to search the whole System for a few two-bit crooks. Besides, we carry cargoes of almost pure uranium and tungsten and all the stuff that's scarce on Earth and Mars and Venus. Nobody really cares whether it comes from the asteroids or Hades. If we want to risk our lives mining it, that's our business." She pursed her lips. "But if they guessed how strong we are or that we have friends planted in the I. B. I.—well, things might be different. There probably would be a crackdown." Ben scowled. "What happens if there is a crackdown? And what will you do when Space Corps ships officially reach the asteroids? They can't ignore you then." "Then we move on. We dream up new gimmicks for our crates and take them to Jupiter, Saturn, Uranus, Neptune, Pluto. In time, maybe, we'll be pushed out of the System itself. Maybe it won't be the white-suited boys who'll make that first hop to the stars. It could be us, you know—if we live long enough. But that Asteroid Belt is murder. You can't follow the text-book rules of astrogation out there. You make up your own." Ben stiffened. "And that's why you want me for an astrogator." Maggie rose, her eyes wistful. "If you want to come—and if you get well." She looked at him strangely. "Suppose—" He fought to find the right words. "Suppose I got well and decided not to join Jacob. What would happen to me? Would you let me go?" Her thin face was criss-crossed by emotion—alarm, then bewilderment, then fear. "I don't know. That would be up to Jacob." He lay biting his lip, staring at the photo of Jacob. She touched his hand and it seemed that sadness now dominated the flurry of emotion that had coursed through her. "The only thing that matters, really," she murmured, "is your walking again. We'll try this afternoon. Okay?" "Okay," he said. When she left, his eyes were still turned toward Jacob's photo. He was like two people, he thought. Half of him was an officer of the Space Corps. Perhaps one single starry-eyed boy out of ten thousand was lucky enough to reach that goal. He remembered a little picture book his mother had given him when she was alive. Under the bright pictures of spacemen were the captions: "A Space Officer Is Honest" "A Space Officer Is Loyal." "A Space Officer Is Dutiful." Honesty, loyalty, duty. Trite words, but without those concepts, mankind would never have broken away from the planet that held it prisoner for half a million years. Without them, Everson, after three failures and a hundred men dead, would never have landed on the Moon twenty-seven years ago.	D. Flies
Based on indicators in the passage, what can be inferred as the time setting of the story? A. The present, based on the character use of credit cards. B. The past, based on the dialogue used by characters. C. The future, based on the advanced technology D. The present, due to the government restrictions on space exploration.	SPACEMAN ON A SPREE BY MACK REYNOLDS Illustrated by Nodel [Transcriber's Note: This etext was produced from Worlds of Tomorrow June 1963 Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.] What's more important—Man's conquest of space, or one spaceman's life? I They gave him a gold watch. It was meant to be symbolical, of course. In the old tradition. It was in the way of an antique, being one of the timepieces made generations past in the Alpine area of Eur-Asia. Its quaintness lay in the fact that it was wound, not electronically by power-radio, but by the actual physical movements of the bearer, a free swinging rotor keeping the mainspring at a constant tension. They also had a banquet for him, complete with speeches by such bigwigs of the Department of Space Exploration as Academician Lofting Gubelin and Doctor Hans Girard-Perregaux. There was also somebody from the government who spoke, but he was one of those who were pseudo-elected and didn't know much about the field of space travel nor the significance of Seymour Pond's retirement. Si didn't bother to remember his name. He only wondered vaguely why the cloddy had turned up at all. In common with recipients of gold watches of a score of generations before him, Si Pond would have preferred something a bit more tangible in the way of reward, such as a few shares of Variable Basic to add to his portfolio. But that, he supposed, was asking too much. The fact of the matter was, Si knew that his retiring had set them back. They hadn't figured he had enough shares of Basic to see him through decently. Well, possibly he didn't, given their standards. But Space Pilot Seymour Pond didn't have their standards. He'd had plenty of time to think it over. It was better to retire on a limited crediting, on a confoundedly limited crediting, than to take the two or three more trips in hopes of attaining a higher standard. He'd had plenty of time to figure it out, there alone in space on the Moon run, there on the Venus or Mars runs. There on the long, long haul to the Jupiter satellites, fearfully checking the symptoms of space cafard, the madness compounded of claustrophobia, monotony, boredom and free fall. Plenty of time. Time to decide that a one room mini-auto-apartment, complete with an autochair and built-in autobar, and with one wall a teevee screen, was all he needed to find contentment for a mighty long time. Possibly somebody like Doc Girard-Perregaux might be horrified at the idea of living in a mini-auto-apartment ... not realizing that to a pilot it was roomy beyond belief compared to the conning tower of a space craft. No. Even as Si listened to their speeches, accepted the watch and made a halting little talk of his own, he was grinning inwardly. There wasn't anything they could do. He had them now. He had enough Basic to keep him comfortably, by his standards, for the rest of his life. He was never going to subject himself to space cafard again. Just thinking about it, now, set the tic to going at the side of his mouth. They could count down and blast off, for all he gave a damn. The gold watch idea had been that of Lofting Gubelin, which was typical, he being in the way of a living anachronism himself. In fact, Academician Gubelin was possibly the only living man on North America who still wore spectacles. His explanation was that a phobia against having his eyes touched prohibited either surgery to remould his eyeballs and cure his myopia, or contact lenses. That was only an alibi so far as his closest associate, Hans Girard-Perregaux, was concerned. Doctor Girard-Perregaux was convinced Gubelin would have even worn facial hair, had he but a touch more courage. Gubelin longed for yesteryear, a seldom found phenomenon under the Ultrawelfare State. Slumped in an autochair in the escape room of his Floridian home, Lofting Gubelin scowled at his friend. He said, acidly, "Any more bright schemes, Hans? I presume you now acknowledge that appealing to the cloddy's patriotism, sentiment and desire for public acclaim have miserably failed." Girard-Perregaux said easily, "I wouldn't call Seymour Pond a cloddy. In his position, I am afraid I would do the same thing he has." "That's nonsense, Hans. Zoroaster! Either you or I would gladly take Pond's place were we capable of performing the duties for which he has been trained. There aren't two men on North America—there aren't two men in the world!—who better realize the urgency of continuing our delving into space." Gubelin snapped his fingers. "Like that, either of us would give our lives to prevent man from completely abandoning the road to his destiny." His friend said drily, "Either of us could have volunteered for pilot training forty years ago, Lofting. We didn't." "At that time there wasn't such a blistering percentage of funkers throughout this whole blistering Ultrawelfare State! Who could foresee that eventually our whole program would face ending due to lack of courageous young men willing to take chances, willing to face adventure, willing to react to the stimulus of danger in the manner our ancestors did?" Girard-Perregaux grunted his sarcasm and dialed a glass of iced tea and tequila. He said, "Nevertheless, both you and I conform with the present generation in finding it far more pleasant to follow one's way of life in the comfort of one's home than to be confronted with the unpleasantness of facing nature's dangers in more adventurous pastimes." Gubelin, half angry at his friend's argument, leaned forward to snap rebuttal, but the other was wagging a finger at him negatively. "Face reality, Lofting. Don't require or expect from Seymour Pond more than is to be found there. He is an average young man. Born in our Ultrawelfare State, he was guaranteed his fundamental womb-to-tomb security by being issued that minimum number of Basic shares in our society that allows him an income sufficient to secure the food, clothing, shelter, medical care and education to sustain a low level of subsistence. Percentages were against his ever being drafted into industry. Automation being what it is, only a fraction of the population is ever called up. But Pond was. His industrial aptitude dossier revealed him a possible candidate for space pilot, and it was you yourself who talked him into taking the training ... pointing out the more pragmatic advantages such as complete retirement after but six trips, added shares of Basic so that he could enjoy a more comfortable life than most and the fame that would accrue to him as one of the very few who still participate in travel to the planets. Very well. He was sold. Took his training, which, of course, required long years of drudgery to him. Then, performing his duties quite competently, he made his six trips. He is now legally eligible for retirement. He was drafted into the working force reserves, served his time, and is now free from toil for the balance of his life. Why should he listen to our pleas for a few more trips?" "But has he no spirit of adventure? Has he no feeling for...." Girard-Perregaux was wagging his finger again, a gesture that, seemingly mild though it was, had an astonishing ability to break off the conversation of one who debated with the easy-seeming, quiet spoken man. He said, "No, he hasn't. Few there are who have, nowadays. Man has always paid lip service to adventure, hardships and excitement, but in actuality his instincts, like those of any other animal, lead him to the least dangerous path. Today we've reached the point where no one need face danger—ever. There are few who don't take advantage of the fact. Including you and me, Lofting, and including Seymour Pond." His friend and colleague changed subjects abruptly, impatiently. "Let's leave this blistering jabber about Pond's motivation and get to the point. The man is the only trained space pilot in the world. It will take months, possibly more than a year, to bring another novitiate pilot to the point where he can safely be trusted to take our next explorer craft out. Appropriations for our expeditions have been increasingly hard to come by—even though in our minds, Hans, we are near important breakthroughs, breakthroughs which might possibly so spark the race that a new dream to push man out to the stars will take hold of us. If it is admitted that our organization has degenerated to the point that we haven't a single pilot, then it might well be that the Economic Planning Board, and especially those cloddies on Appropriations, will terminate the whole Department of Space Exploration." "So...." Girard-Perregaux said gently. "So some way we've got to bring Seymour Pond out of his retirement!" "Now we are getting to matters." Girard-Perregaux nodded his agreement. Looking over the rim of his glass, his eyes narrowed in thought as his face took on an expression of Machiavellianism. "And do not the ends justify the means?" Gubelin blinked at him. The other chuckled. "The trouble with you, Lofting, is that you have failed to bring history to bear on our problem. Haven't you ever read of the sailor and his way of life?" "Sailor? What in the name of the living Zoroaster has the sailor got to do with it?" "You must realize, my dear Lofting, that our Si Pond is nothing more than a latter-day sailor, with many of the problems and view-points, tendencies and weaknesses of the voyager of the past. Have you never heard of the seaman who dreamed of returning to the village of his birth and buying a chicken farm or some such? All the long months at sea—and sometimes the tramp freighters or whaling craft would be out for years at a stretch before returning to home port—he would talk of his retirement and his dream. And then? Then in port, it would be one short drink with the boys, before taking his accumulated pay and heading home. The one short drink would lead to another. And morning would find him, drunk, rolled, tattooed and possibly sleeping it off in jail. So back to sea he'd have to go." Gubelin grunted bitterly. "Unfortunately, our present-day sailor can't be separated from his money quite so easily. If he could, I'd personally be willing to lure him down some dark alley, knock him over the head and roll him myself. Just to bring him back to his job again." He brought his wallet from his pocket, and flicked it open to his universal credit card. "The ultimate means of exchange," he grunted. "Nobody can spend your money, but you, yourself. Nobody can steal it, nobody can, ah, con you out of it. Just how do you expect to sever our present-day sailor and his accumulated nest egg?" The other chuckled again. "It is simply a matter of finding more modern methods, my dear chap." II Si Pond was a great believer in the institution of the spree. Any excuse would do. Back when he had finished basic education at the age of twenty-five and was registered for the labor draft, there hadn't been a chance in a hundred that he'd have the bad luck to have his name pulled. But when it had been, Si had celebrated. When he had been informed that his physical and mental qualifications were such that he was eligible for the most dangerous occupation in the Ultrawelfare State and had been pressured into taking training for space pilot, he had celebrated once again. Twenty-two others had taken the training with him, and only he and Rod Cameroon had passed the finals. On this occasion, he and Rod had celebrated together. It had been quite a party. Two weeks later, Rod had burned on a faulty take-off on what should have been a routine Moon run. Each time Si returned from one of his own runs, he celebrated. A spree, a bust, a bat, a wing-ding, a night on the town. A commemoration of dangers met and passed. Now it was all over. At the age of thirty he was retired. Law prevented him from ever being called up for contributing to the country's labor needs again. And he most certainly wasn't going to volunteer. He had taken his schooling much as had his contemporaries. There wasn't any particular reason for trying to excell. You didn't want to get the reputation for being a wise guy, or a cloddy either. Just one of the fellas. You could do the same in life whether you really studied or not. You had your Inalienable Basic stock, didn't you? What else did you need? It had come as a surprise when he'd been drafted for the labor force. In the early days of the Ultrawelfare State, they had made a mistake in adapting to the automation of the second industrial revolution. They had attempted to give everyone work by reducing the number of working hours in the day, and the number of working days in the week. It finally became ludicrous when employees of industry were working but two days a week, two hours a day. In fact, it got chaotic. It became obvious that it was more practical to have one worker putting in thirty-five hours a week and getting to know his job well, than it was to have a score of employees, each working a few hours a week and none of them ever really becoming efficient. The only fair thing was to let the technologically unemployed remain unemployed, with their Inalienable Basic stock as the equivalent of unemployment insurance, while the few workers still needed put in a reasonable number of hours a day, a reasonable number of weeks a year and a reasonable number of years in a life time. When new employees were needed, a draft lottery was held. All persons registered in the labor force participated. If you were drawn, you must need serve. The dissatisfaction those chosen might feel at their poor luck was offset by the fact that they were granted additional Variable Basic shares, according to the tasks they fulfilled. Such shares could be added to their portfolios, the dividends becoming part of their current credit balance, or could be sold for a lump sum on the market. Yes, but now it was all over. He had his own little place, his own vacuum-tube vehicle and twice the amount of shares of Basic that most of his fellow citizens could boast. Si Pond had it made. A spree was obviously called for. He was going to do this one right. This was the big one. He'd accumulated a lot of dollars these past few months and he intended to blow them, or at least a sizeable number of them. His credit card was burning a hole in his pocket, as the expression went. However, he wasn't going to rush into things. This had to be done correctly. Too many a spree was played by ear. You started off with a few drinks, fell in with some second rate mopsy and usually wound up in a third rate groggery where you spent just as much as though you'd been in the classiest joint in town. Came morning and you had nothing to show for all the dollars that had been spent but a rum-head. Thus, Si was vaguely aware, it had always been down through the centuries since the Phoenecian sailor, back from his year-long trip to the tin mines of Cornwall, blew his hard earned share of the voyage's profits in a matter of days in the wine shops of Tyre. Nobody gets quite so little for his money as that loneliest of all workers, he who must leave his home for distant lands, returning only periodically and usually with the salary of lengthy, weary periods of time to be spent hurriedly in an attempt to achieve the pleasure and happiness so long denied him. Si was going to do it differently this time. Nothing but the best. Wine, women, song, food, entertainment. The works. But nothing but the best. To start off, he dressed with great care in the honorable retirement-rank suit he had so recently purchased. His space pin he attached carefully to the lapel. That was a good beginning, he decided. A bit of prestige didn't hurt you when you went out on the town. In the Ultrawelfare State hardly one person in a hundred actually ever performed anything of value to society. The efforts of most weren't needed. Those few who did contribute were awarded honors, decorations, titles. Attired satisfactorily, Si double-checked to see that his credit card was in his pocket. As an after-thought, he went over to the auto-apartment's teevee-phone, flicked it on, held the card to the screen and said, "Balance check, please." In a moment, the teevee-phone's robot voice reported, "Ten shares of Inalienable Basic. Twelve shares of Variable Basic, current value, four thousand, two hundred and thirty-three dollars and sixty-two cents apiece. Current cash credit, one thousand and eighty-four dollars." The screen went dead. One thousand and eighty-four dollars. That was plenty. He could safely spend as much as half of it, if the spree got as lively as he hoped it would. His monthly dividends were due in another week or so, and he wouldn't have to worry about current expenses. Yes, indeedy, Si Pond was as solvent as he had ever been in his thirty years. He opened the small, closet-like door which housed his vacuum-tube two-seater, and wedged himself into the small vehicle. He brought down the canopy, dropped the pressurizer and considered the dial. Only one place really made sense. The big city. He considered for a moment, decided against the boroughs of Baltimore and Boston, and selected Manhattan instead. He had the resources. He might as well do it up brown. He dialed Manhattan and felt the sinking sensation that presaged his car's dropping to tube level. While it was being taken up by the robot controls, being shuttled here and there preparatory to the shot to his destination, he dialed the vehicle's teevee-phone for information on the hotels of the island of the Hudson. He selected a swank hostelry he'd read about and seen on the teevee casts of society and celebrity gossip reporters, and dialed it on the car's destination dial. "Nothing too good for ex-Space Pilot Si Pond," he said aloud. The car hesitated for a moment, that brief hesitation before the shot, and Si took the involuntary breath from which only heroes could refrain. He sank back slowly into the seat. Moments passed, and the direction of the pressure was reversed. Manhattan. The shuttling began again, and one or two more traversing sub-shots. Finally, the dash threw a green light and Si opened the canopy and stepped into his hotel room. A voice said gently, "If the quarters are satisfactory, please present your credit card within ten minutes." Si took his time. Not that he really needed it. It was by far the most swank suite he had ever seen. One wall was a window of whatever size the guest might desire and Si touched the control that dilated it to the full. His view opened in such wise that he could see both the Empire State Building Museum and the Hudson. Beyond the river stretched the all but endless city which was Greater Metropolis. He didn't take the time to flick on the menu, next to the auto-dining table, nor to check the endless potables on the autobar list. All that, he well knew, would be superlative. Besides, he didn't plan to dine or do much drinking in his suite. He made a mock leer. Not unless he managed to acquire some feminine companionship, that was. He looked briefly into the swimming pool and bath, then flopped himself happily onto the bed. It wasn't up to the degree of softness he presently desired, and he dialed the thing to the ultimate in that direction so that with a laugh he sank almost out of sight into the mattress. He came back to his feet, gave his suit a quick patting so that it fell into press and, taking his credit card from his pocket, put it against the teevee-phone screen and pressed the hotel button so that registration could be completed. For a moment he stood in the center of the floor, in thought. Take it easy, Si Pond, take it all easy, this time. No throwing his dollars around in second-class groggeries, no eating in automated luncheterias. This time, be it the only time in his life, he was going to frolic in the grand manner. No cloddy was Si Pond. He decided a drink was in order to help him plan his strategy. A drink at the hotel's famous Kudos Room where celebrities were reputed to be a dime a dozen. He left the suite and stepped into one of the elevators. He said, "Kudos Room." The auto-elevator murmured politely, "Yes, sir, the Kudos Room." At the door to the famous rendezvous of the swankiest set, Si paused a moment and looked about. He'd never been in a place like this, either. However, he stifled his first instinct to wonder about what this was going to do to his current credit balance with an inner grin and made his way to the bar. There was actually a bartender. Si Pond suppressed his astonishment and said, offhand, attempting an air of easy sophistication, "Slivovitz Sour." "Yes, sir." The drinks in the Kudos Room might be concocted by hand, but Si noticed they had the routine teevee screens built into the bar for payment. He put his credit card on the screen immediately before him when the drink came, and had to quell his desire to dial for a balance check, so as to be able to figure out what the Sour had cost him. Well, this was something like it. This was the sort of thing he'd dreamed about, out there in the great alone, seated in the confining conning tower of his space craft. He sipped at the drink, finding it up to his highest expectations, and then swiveled slightly on his stool to take a look at the others present. To his disappointment, there were no recognizable celebrities. None that he placed, at least—top teevee stars, top politicians of the Ultrawelfare State or Sports personalities. He turned back to his drink and noticed, for the first time, the girl who occupied the stool two down from him. Si Pond blinked. He blinked and then swallowed. " Zo-ro-as-ter ," he breathed. She was done in the latest style from Shanghai, even to the point of having cosmetically duplicated the Mongolian fold at the corners of her eyes. Every pore, but every pore, was in place. She sat with the easy grace of the Orient, so seldom found in the West. His stare couldn't be ignored. She looked at him coldly, turned to the bartender and murmured, "A Far Out Cooler, please, Fredric." Then deliberately added, "I thought the Kudos Room was supposed to be exclusive." There was nothing the bartender could say to that, and he went about building the drink. Si cleared his throat. "Hey," he said, "how about letting this one be on me?" Her eyebrows, which had been plucked and penciled to carry out her Oriental motif, rose. "Really!" she said, drawing it out. The bartender said hurriedly, "I beg your pardon, sir...." The girl, her voice suddenly subtly changed, said, "Why, isn't that a space pin?" Si, disconcerted by the sudden reversal, said, "Yeah ... sure." "Good Heavens, you're a spaceman?" "Sure." He pointed at the lapel pin. "You can't wear one unless you been on at least a Moon run." She was obviously both taken back and impressed. "Why," she said, "you're Seymour Pond, the pilot. I tuned in on the banquet they gave you." Si, carrying his glass, moved over to the stool next to her. "Call me Si," he said. "Everybody calls me Si." She said, "I'm Natalie. Natalie Paskov. Just Natalie. Imagine meeting Seymour Pond. Just sitting down next to him at a bar. Just like that." "Si," Si said, gratified. Holy Zoroaster, he'd never seen anything like this rarified pulchritude. Maybe on teevee, of course, one of the current sex symbols, but never in person. "Call me Si," he said again. "I been called Si so long, I don't even know who somebody's talking to if they say Seymour." "I cried when they gave you that antique watch," she said, her tone such that it was obvious she hadn't quite adjusted as yet to having met him. Si Pond was surprised. "Cried?" he said. "Well, why? I was kind of bored with the whole thing. But old Doc Gubelin, I used to work under him in the Space Exploration department, he was hot for it." " Academician Gubelin?" she said. "You just call him Doc ?" Si was expansive. "Why, sure. In the Space Department we don't have much time for formality. Everybody's just Si, and Doc, and Jim. Like that. But how come you cried?" She looked down into the drink the bartender had placed before her, as though avoiding his face. "I ... I suppose it was that speech Doctor Girard-Perregaux made. There you stood, so fine and straight in your space-pilot uniform, the veteran of six exploration runs to the planets...." "Well," Si said modestly, "two of my runs were only to the Moon." "... and he said all those things about man's conquest of space. And the dream of the stars which man has held so long. And then the fact that you were the last of the space pilots. The last man in the whole world trained to pilot a space craft. And here you were, retiring." Si grunted. "Yeah. That's all part of the Doc's scheme to get me to take on another three runs. They're afraid the whole department'll be dropped by the Appropriations Committee on this here Economic Planning Board. Even if they can find some other patsy to train for the job, it'd take maybe a year before you could even send him on a Moon hop. So old man Gubelin, and Girard-Perregaux too, they're both trying to pressure me into more trips. Otherwise they got a Space Exploration Department, with all the expense and all, but nobody to pilot their ships. It's kind of funny, in a way. You know what one of those spaceships costs?" "Funny?" she said. "Why, I don't think it's funny at all." Si said, "Look, how about another drink?" Natalie Paskov said, "Oh, I'd love to have a drink with you, Mr...." "Si," Si said. He motioned to the bartender with a circular twist of the hand indicating their need for two more of the same. "How come you know so much about it? You don't meet many people who are interested in space any more. In fact, most people are almost contemptuous, like. Think it's kind of a big boondoggle deal to help use up a lot of materials and all and keep the economy going." Natalie said earnestly, "Why, I've been a space fan all my life. I've read all about it. Have always known the names of all the space pilots and everything about them, ever since I was a child. I suppose you'd say I have the dream that Doctor Girard-Perregaux spoke about." Si chuckled. "A real buff, eh? You know, it's kind of funny. I was never much interested in it. And I got a darn sight less interested after my first run and I found out what space cafard was." She frowned. "I don't believe I know much about that." Sitting in the Kudos Room with the most beautiful girl to whom he had ever talked, Si could be nonchalant about the subject. "Old Gubelin keeps that angle mostly hushed up and out of the magazine and newspaper articles. Says there's enough adverse publicity about space exploration already. But at this stage of the game when the whole ship's crammed tight with this automatic scientific apparatus and all, there's precious little room in the conning tower and you're the only man aboard. The Doc says later on when ships are bigger and there's a whole flock of people aboard, there won't be any such thing as space cafard, but...." Of a sudden the right side of Si Pond's mouth began to tic and he hurriedly took up his drink and knocked it back.	C. The future, based on the advanced technology
Which corpora do they use?	### Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative model INLINEFORM0 from which artificial data examples can be sampled, and a discriminative model INLINEFORM1 which classifies real data examples and artificial ones from INLINEFORM2 . By training INLINEFORM3 to maximize its generation power, and training INLINEFORM4 to minimize the generation power of INLINEFORM5 , so that ideally there will be no difference between the true and artificial examples, a minimax problem can be established. The GAN model has been shown to closely replicate a number of image data sets, such as MNIST, Toronto Face Database (TFD), CIFAR-10, SVHN, and ImageNet (Goodfellow et al., 2014; Salimans et al. 2016). The GAN model has been extended to text data in a number of ways. For instance, Zhang et al. (2016) applied a long-short term memory (Hochreiter and Schmidhuber, 1997) generator and approximated discretization to generate text data. Moreover, Li et al. (2017) applied the GAN model to generate dialogues, i.e. pairs of questions and answers. Meanwhile, the GAN model can also be applied to generate bag-of-words embeddings of text data, which focus more on key terms in a text document rather than the original document itself. Glover (2016) provided such a model with the energy-based GAN (Zhao et al., 2017). To the best of our knowledge, there has been no literature on applying the GAN model to multiple corpora of text data. Multi-class GANs (Liu and Tuzel, 2016; Mirza and Osindero, 2014) have been proposed, but a class in multi-class classification is not the same as multiple corpora. Because knowing the underlying corpus membership of each text document can provide better information on how the text documents are organized, and documents from the same corpus are expected to share similar topics or key words, considering the membership information can benefit the training of a text model from a supervised perspective. We consider two problems associated with training multi-corpus text data: (1) Given a separate set of word embeddings from each corpus, such as the word2vec embeddings (Mikolov et al., 2013), how to obtain a better set of cross-corpus word embeddings from them? (2) How to incorporate the generation of document embeddings from different corpora in a single GAN model? For the first problem, we train a GAN model which discriminates documents represented by different word embeddings, and train the cross-corpus word embedding so that it is similar to each existing word embedding per corpus. For the second problem, we train a GAN model which considers both cross-corpus and per-corpus “topics” in the generator, and applies a discriminator which considers each original and artificial document corpus. We also show that with sufficient training, the distribution of the artificial document embeddings is equivalent to the original ones. Our work has the following contributions: (1) we extend GANs to multiple corpora of text data, (2) we provide applications of GANs to finetune word embeddings and to create robust document embeddings, and (3) we establish theoretical convergence results of the multi-class GAN model. Section 2 reviews existing GAN models related to this paper. Section 3 describes the GAN models on training cross-corpus word embeddings and generating document embeddings for each corpora, and explains the associated algorithms. Section 4 presents the results of the two models on text data sets, and transfers them to supervised learning. Section 5 summarizes the results and concludes the paper. ### Literature Review In a GAN model, we assume that the data examples INLINEFORM0 are drawn from a distribution INLINEFORM1 , and the artificial data examples INLINEFORM2 are transformed from the noise distribution INLINEFORM3 . The binary classifier INLINEFORM4 outputs the probability of a data example (or an artificial one) being an original one. We consider the following minimax problem DISPLAYFORM0 With sufficient training, it is shown in Goodfellow et al. (2014) that the distribution of artificial data examples INLINEFORM0 is eventually equivalent to the data distribution INLINEFORM1 , i.e. INLINEFORM2 . Because the probabilistic structure of a GAN can be unstable to train, the Wasserstein GAN (Arjovsky et al., 2017) is proposed which applies a 1-Lipschitz function as a discriminator. In a Wasserstein GAN, we consider the following minimax problem DISPLAYFORM0 These GANs are for the general purpose of learning the data distribution in an unsupervised way and creating perturbed data examples resembling the original ones. We note that in many circumstances, data sets are obtained with supervised labels or categories, which can add explanatory power to unsupervised models such as the GAN. We summarize such GANs because a corpus can be potentially treated as a class. The main difference is that classes are purely for the task of classification while we are interested in embeddings that can be used for any supervised or unsupervised task. For instance, the CoGAN (Liu and Tuzel, 2016) considers pairs of data examples from different categories as follows INLINEFORM0 where the weights of the first few layers of INLINEFORM0 and INLINEFORM1 (i.e. close to INLINEFORM2 ) are tied. Mirza and Osindero (2014) proposed the conditional GAN where the generator INLINEFORM3 and the discriminator INLINEFORM4 depend on the class label INLINEFORM5 . While these GANs generate samples resembling different classes, other variations of GANs apply the class labels for semi-supervised learning. For instance, Salimans et al. (2016) proposed the following objective DISPLAYFORM0 where INLINEFORM0 has INLINEFORM1 classes plus the INLINEFORM2 -th artificial class. Similar models can be found in Odena (2016), the CatGAN in Springenberg (2016), and the LSGAN in Mao et al. (2017). However, all these models consider only images and do not produce word or document embeddings, therefore being different from our models. For generating real text, Zhang et al. (2016) proposed textGAN in which the generator has the following form, DISPLAYFORM0 where INLINEFORM0 is the noise vector, INLINEFORM1 is the generated sentence, INLINEFORM2 are the words, and INLINEFORM3 . A uni-dimensional convolutional neural network (Collobert et al, 2011; Kim, 2014) is applied as the discriminator. Also, a weighted softmax function is applied to make the argmax function differentiable. With textGAN, sentences such as “we show the efficacy of our new solvers, making it up to identify the optimal random vector...” can be generated. Similar models can also be found in Wang et al. (2016), Press et al. (2017), and Rajeswar et al. (2017). The focus of our work is to summarize information from longer documents, so we apply document embeddings such as the tf-idf to represent the documents rather than to generate real text. For generating bag-of-words embeddings of text, Glover (2016) proposed the following model DISPLAYFORM0 and INLINEFORM0 is the mean squared error of a de-noising autoencoder, and INLINEFORM1 is the one-hot word embedding of a document. Our models are different from this model because we consider tf-idf document embeddings for multiple text corpora in the deGAN model (Section 3.2), and weGAN (Section 3.1) can be applied to produce word embeddings. Also, we focus on robustness based on several corpora, while Glover (2016) assumed a single corpus. For extracting word embeddings given text data, Mikolov et al. (2013) proposed the word2vec model, for which there are two variations: the continuous bag-of-words (cBoW) model (Mikolov et al., 2013b), where the neighboring words are used to predict the appearance of each word; the skip-gram model, where each neighboring word is used individually for prediction. In GloVe (Pennington et al., 2013), a bilinear regression model is trained on the log of the word co-occurrence matrix. In these models, the weights associated with each word are used as the embedding. For obtaining document embeddings, the para2vec model (Le and Mikolov, 2014) adds per-paragraph vectors to train word2vec-type models, so that the vectors can be used as embeddings for each paragraph. A simpler approach by taking the average of the embeddings of each word in a document and output the document embedding is exhibited in Socher et al. (2013). ### Models and Algorithms Suppose we have a number of different corpora INLINEFORM0 , which for example can be based on different categories or sentiments of text documents. We suppose that INLINEFORM1 , INLINEFORM2 , where each INLINEFORM3 represents a document. The words in all corpora are collected in a dictionary, and indexed from 1 to INLINEFORM4 . We name the GAN model to train cross-corpus word embeddings as “weGAN,” where “we” stands for “word embeddings,” and the GAN model to generate document embeddings for multiple corpora as “deGAN,” where “de” stands for “document embeddings.” ### weGAN: Training cross-corpus word embeddings We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature vectors. We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . Note that INLINEFORM7 are given but INLINEFORM8 is trained. Here we consider INLINEFORM9 as the generator, and the goal of the discriminator is to distinguish documents represented by the original embeddings INLINEFORM10 and the same documents represented by the new embeddings INLINEFORM11 . Next we describe how the documents are represented by a set of embeddings INLINEFORM0 and INLINEFORM1 . For each document INLINEFORM2 , we define its document embedding with INLINEFORM3 as follows, DISPLAYFORM0 where INLINEFORM0 can be any mapping. Similarly, we define the document embedding of INLINEFORM1 with INLINEFORM2 as follows, with INLINEFORM3 trainable DISPLAYFORM0 In a typical example, word embeddings would be based on word2vec or GLoVe. Function INLINEFORM0 can be based on tf-idf, i.e. INLINEFORM1 where INLINEFORM2 is the word embedding of the INLINEFORM3 -th word in the INLINEFORM4 -th corpus INLINEFORM5 and INLINEFORM6 is the tf-idf representation of the INLINEFORM7 -th document INLINEFORM8 in the INLINEFORM9 -th corpus INLINEFORM10 . To train the GAN model, we consider the following minimax problem DISPLAYFORM0 where INLINEFORM0 is a discriminator of whether a document is original or artificial. Here INLINEFORM1 is the label of document INLINEFORM2 with respect to classifier INLINEFORM3 , and INLINEFORM4 is a unit vector with only the INLINEFORM5 -th component being one and all other components being zeros. Note that INLINEFORM6 is equivalent to INLINEFORM7 , but we use the former notation due to its brevity. The intuition of problem (8) is explained as follows. First we consider a discriminator INLINEFORM0 which is a feedforward neural network (FFNN) with binary outcomes, and classifies the document embeddings INLINEFORM1 against the original document embeddings INLINEFORM2 . Discriminator INLINEFORM3 minimizes this classification error, i.e. it maximizes the log-likelihood of INLINEFORM4 having label 0 and INLINEFORM5 having label 1. This corresponds to DISPLAYFORM0 For the generator INLINEFORM0 , we wish to minimize (8) against INLINEFORM1 so that we can apply the minimax strategy, and the combined word embeddings INLINEFORM2 would resemble each set of word embeddings INLINEFORM3 . Meanwhile, we also consider classifier INLINEFORM4 with INLINEFORM5 outcomes, and associates INLINEFORM6 with label INLINEFORM7 , so that the generator INLINEFORM8 can learn from the document labeling in a semi-supervised way. If the classifier INLINEFORM0 outputs a INLINEFORM1 -dimensional softmax probability vector, we minimize the following against INLINEFORM2 , which corresponds to (8) given INLINEFORM3 and INLINEFORM4 : DISPLAYFORM0 For the classifier INLINEFORM0 , we also minimize its negative log-likelihood DISPLAYFORM0 Assembling (9-11) together, we retrieve the original minimax problem (8). We train the discriminator and the classifier, INLINEFORM0 , and the combined embeddings INLINEFORM1 according to (9-11) iteratively for a fixed number of epochs with the stochastic gradient descent algorithm, until the discrimination and classification errors become stable. The algorithm for weGAN is summarized in Algorithm 1, and Figure 1 illustrates the weGAN model. Algorithm 1. Train INLINEFORM0 based on INLINEFORM1 from all corpora INLINEFORM2 . Randomly initialize the weights and biases of the classifier INLINEFORM3 and discriminator INLINEFORM4 . Until maximum number of iterations reached Update INLINEFORM5 and INLINEFORM6 according to (9) and (11) given a mini-batch INLINEFORM7 of training examples INLINEFORM8 . Update INLINEFORM9 according to (10) given a mini-batch INLINEFORM10 of training examples INLINEFORM11 . Output INLINEFORM12 as the cross-corpus word embeddings. ### deGAN: Generating document embeddings for multi-corpus text data In this section, our goal is to generate document embeddings which would resemble real document embeddings in each corpus INLINEFORM0 , INLINEFORM1 . We construct INLINEFORM2 generators, INLINEFORM3 so that INLINEFORM4 generate artificial examples in corpus INLINEFORM5 . As in Section 3.1, there is a certain document embedding such as tf-idf, bag-of-words, or para2vec. Let INLINEFORM6 . We initialize a noise vector INLINEFORM7 , where INLINEFORM8 , and INLINEFORM9 is any noise distribution. For a generator INLINEFORM0 represented by its parameters, we first map the noise vector INLINEFORM1 to the hidden layer, which represents different topics. We consider two hidden vectors, INLINEFORM2 for general topics and INLINEFORM3 for specific topics per corpus, DISPLAYFORM0 Here INLINEFORM0 represents a nonlinear activation function. In this model, the bias term can be ignored in order to prevent the “mode collapse” problem of the generator. Having the hidden vectors, we then map them to the generated document embedding with another activation function INLINEFORM1 , DISPLAYFORM0 To summarize, we may represent the process from noise to the document embedding as follows, DISPLAYFORM0 Given the generated document embeddings INLINEFORM0 , we consider the following minimax problem to train the generator INLINEFORM1 and the discriminator INLINEFORM2 : INLINEFORM3 INLINEFORM4 Here we assume that any document embedding INLINEFORM0 in corpus INLINEFORM1 is a sample with respect to the probability density INLINEFORM2 . Note that when INLINEFORM3 , the discriminator part of our model is equivalent to the original GAN model. To explain (15), first we consider the discriminator INLINEFORM0 . Because there are multiple corpora of text documents, here we consider INLINEFORM1 categories as output of INLINEFORM2 , from which categories INLINEFORM3 represent the original corpora INLINEFORM4 , and categories INLINEFORM5 represent the generated document embeddings (e.g. bag-of-words) from INLINEFORM6 . Assume the discriminator INLINEFORM7 , a feedforward neural network, outputs the distribution of a text document being in each category. We maximize the log-likelihood of each document being in the correct category against INLINEFORM8 DISPLAYFORM0 Such a classifier does not only classifies text documents into different categories, but also considers INLINEFORM0 “fake” categories from the generators. When training the generators INLINEFORM1 , we minimize the following which makes a comparison between the INLINEFORM2 -th and INLINEFORM3 -th categories DISPLAYFORM0 The intuition of (17) is that for each generated document embedding INLINEFORM0 , we need to decrease INLINEFORM1 , which is the probability of the generated embedding being correctly classified, and increase INLINEFORM2 , which is the probability of the generated embedding being classified into the target corpus INLINEFORM3 . The ratio in (17) reflects these two properties. We iteratively train (16) and (17) until the classification error of INLINEFORM0 becomes stable. The algorithm for deGAN is summarized in Algorithm 2, and Figure 2 illustrates the deGAN model.. Algorithm 2. Randomly initialize the weights of INLINEFORM0 . Initialize the discriminator INLINEFORM1 with the weights of the first layer (which takes document embeddings as the input) initialized by word embeddings, and other parameters randomly initialized. Until maximum number of iterations reached Update INLINEFORM2 according to (16) given a mini-batch of training examples INLINEFORM3 and samples from noise INLINEFORM4 . Update INLINEFORM5 according to (17) given a mini-batch of training examples INLINEFORM6 and samples form noise INLINEFORM7 . Output INLINEFORM8 as generators of document embeddings and INLINEFORM9 as a corpus classifier. We next show that from (15), the distributions of the document embeddings from the optimal INLINEFORM0 are equal to the data distributions of INLINEFORM1 , which is a generalization of Goodfellow et al. (2014) to the multi-corpus scenario. Proposition 1. Let us assume that the random variables INLINEFORM0 are continuous with probability density INLINEFORM1 which have bounded support INLINEFORM2 ; INLINEFORM3 is a continuous random variable with bounded support and activations INLINEFORM4 and INLINEFORM5 are continuous; and that INLINEFORM6 are solutions to (15). Then INLINEFORM7 , the probability density of the document embeddings from INLINEFORM8 , INLINEFORM9 , are equal to INLINEFORM10 . Proof. Since INLINEFORM0 is bounded, all of the integrals exhibited next are well-defined and finite. Since INLINEFORM1 , INLINEFORM2 , and INLINEFORM3 are continuous, it follows that for any parameters, INLINEFORM4 is a continuous random variable with probability density INLINEFORM5 with finite support. From the first line of (15), INLINEFORM0 This problem reduces to INLINEFORM0 subject to INLINEFORM1 , the solution of which is INLINEFORM2 , INLINEFORM3 . Therefore, the solution to (18) is DISPLAYFORM0 We then obtain from the second line of (15) that INLINEFORM0 From non-negativity of the Kullback-Leibler divergence, we conclude that INLINEFORM0 ### Experiments In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all characters to lower case, stemmed the documents, and ran the word2vec model on each corpora to obtain word embeddings with a size of 300. In all subsequent models, we only consider the most frequent INLINEFORM0 words across all corpora in a data set. The document embedding in weGAN is the tf-idf weighted word embedding transformed by the INLINEFORM0 activation, i.e. DISPLAYFORM0 For deGAN, we use INLINEFORM0 -normalized tf-idf as the document embedding because it is easier to interpret than the transformed embedding in (20). For weGAN, the cross-corpus word embeddings are initialized with the word2vec model trained from all documents. For training our models, we apply a learning rate which increases linearly from INLINEFORM0 to INLINEFORM1 and train the models for 100 epochs with a batch size of 50 per corpus. The classifier INLINEFORM2 has a single hidden layer with 50 hidden nodes, and the discriminator with a single hidden layer INLINEFORM3 has 10 hidden nodes. All these parameters have been optimized. For the labels INLINEFORM4 in (8), we apply corpus membership of each document. For the noise distribution INLINEFORM0 for deGAN, we apply the uniform distribution INLINEFORM1 . In (14) for deGAN, INLINEFORM2 and INLINEFORM3 so that the model outputs document embedding vectors which are comparable to INLINEFORM4 -normalized tf-idf vectors for each document. For the discriminator INLINEFORM5 of deGAN, we apply the word2vec embeddings based on all corpora to initialize its first layer, followed by another hidden layer of 50 nodes. For the discriminator INLINEFORM6 , we apply a learning rate of INLINEFORM7 , and for the generator INLINEFORM8 , we apply a learning rate of INLINEFORM9 , because the initial training phase of deGAN can be unstable. We also apply a batch size of 50 per corpus. For the softmax layers of deGAN, we initialize them with the log of the topic-word matrix in latent Dirichlet allocation (LDA) (Blei et al., 2003) in order to provide intuitive estimates. For weGAN, we consider two metrics for comparing the embeddings trained from weGAN and those trained from all documents: (1) applying the document embeddings to cluster the documents into INLINEFORM0 clusters with the K-means algorithm, and calculating the Rand index (RI) (Rand, 1971) against the original corpus membership; (2) finetuning the classifier INLINEFORM1 and comparing the classification error against an FFNN of the same structure initialized with word2vec (w2v). For deGAN, we compare the performance of finetuning the discriminator of deGAN for document classification, and the performance of the same FFNN. Each supervised model is trained for 500 epochs and the validation data set is used to choose the best epoch. ### The CNN data set In the CNN data set, we collected all news links on www.cnn.com in the GDELT 1.0 Event Database from April 1st, 2013 to July 7, 2017. We then collected the news articles from the links, and kept those belonging to the three largest categories: “politics,” “world,” and “US.” We then divided these documents into INLINEFORM0 training documents, from which INLINEFORM1 validation documents are held out, and INLINEFORM2 testing documents. We hypothesize that because weGAN takes into account document labels in a semi-supervised way, the embeddings trained from weGAN can better incorporate the labeling information and therefore, produce document embeddings which are better separated. The results are shown in Table 1 and averaged over 5 randomized runs. Performing the Welch's t-test, both changes after weGAN training are statistically significant at a INLINEFORM0 significance level. Because the Rand index captures matching accuracy, we observe from the Table 1 that weGAN tends to improve both metrics. Meanwhile, we also wish to observe the spatial structure of the trained embeddings, which can be explored by the synonyms of each word measured by the cosine similarity. On average, the top 10 synonyms of each word differ by INLINEFORM0 word after weGAN training, and INLINEFORM1 of all words have different top 10 synonyms after training. Therefore, weGAN tends to provide small adjustments rather than structural changes. Table 2 lists the 10 most similar terms of three terms, “Obama,” “Trump,” and “U.S.,” before and after weGAN training, ordered by cosine similarity. We observe from Table 2 that for “Obama,” ”Trump” and “Tillerson” are more similar after weGAN training, which means that the structure of the weGAN embeddings can be more up-to-date. For “Trump,” we observe that “Clinton” is not among the synonyms before, but is after, which shows that the synonyms after are more relevant. For “U.S.,” we observe that after training, “American” replaces “British” in the list of synonyms, which is also more relevant. We next discuss deGAN. In Table 3, we compare the performance of finetuning the discriminator of deGAN for document classification, and the performance of the FFNN initialized with word2vec. The change is also statistically significant at the INLINEFORM0 level. From Table 3, we observe that deGAN improves the accuracy of supervised learning. To compare the generated samples from deGAN with the original bag-of-words, we randomly select one record in each original and artificial corpus. The records are represented by the most frequent words sorted by frequency in descending order where the stop words are removed. The bag-of-words embeddings are shown in Table 4. From Table 4, we observe that the bag-of-words embeddings of the original documents tend to contain more name entities, while those of the artificial deGAN documents tend to be more general. There are many additional examples not shown here with observed artificial bag-of-words embeddings having many name entities such as “Turkey,” “ISIS,” etc. from generated documents, e.g. “Syria eventually ISIS U.S. details jet aircraft October video extremist...” We also perform dimensional reduction using t-SNE (van der Maaten and Hinton, 2008), and plot 100 random samples from each original or artificial category. The original samples are shown in red and the generated ones are shown in blue in Figure 3. We do not further distinguish the categories because there is no clear distinction between the three original corpora, “politics,” “world,” and “US.” The results are shown in Figure 3. We observe that the original and artificial examples are generally mixed together and not well separable, which means that the artificial examples are similar to the original ones. However, we also observe that the artificial samples tend to be more centered and have no outliers (represented by the outermost red oval). ### The TIME data set In the TIME data set, we collected all news links on time.com in the GDELT 1.0 Event Database from April 1st, 2013 to July 7, 2017. We then collected the news articles from the links, and kept those belonging to the five largest categories: “Entertainment,” “Ideas,” “Politics,” “US,” and “World.” We divided these documents into INLINEFORM0 training documents, from which INLINEFORM1 validation documents are held out, and INLINEFORM2 testing documents. Table 5 compares the clustering results of word2vec and weGAN, and the classification accuracy of an FFNN initialized with word2vec, finetuned weGAN, and finetuned deGAN. The results in Table 5 are the counterparts of Table 1 and Table 3 for the TIME data set. The differences are also significant at the INLINEFORM0 level. From Table 5, we observe that both GAN models yield improved performance of supervised learning. For weGAN, on an average, the top 10 synonyms of each word differ by INLINEFORM0 word after weGAN training, and INLINEFORM1 of all words have different top 10 synonyms after training. We also compare the synonyms of the same common words, “Obama,” “Trump,” and “U.S.,” which are listed in Table 6. In the TIME data set, for “Obama,” “Reagan” is ranked slightly higher as an American president. For “Trump,” “Bush” and “Sanders” are ranked higher as American presidents or candidates. For “U.S.,” we note that “Pentagon” is ranked higher after weGAN training, which we think is also reasonable because the term is closely related to the U.S. government. For deGAN, we also compare the original and artificial samples in terms of the highest probability words. Table 7 shows one record for each category. From Table 7, we observe that the produced bag-of-words are generally alike, and the words in the same sample are related to each other to some extent. We also perform dimensional reduction using t-SNE for 100 examples per corpus and plot them in Figure 4. We observe that the points are generated mixed but deGAN cannot reproduce the outliers. ### The 20 Newsgroups data set The 20 Newsgroups data set is a collection of news documents with 20 categories. To reduce the number of categories so that the GAN models are more compact and have more samples per corpus, we grouped the documents into 6 super-categories: “religion,” “computer,” “cars,” “sport,” “science,” and “politics” (“misc” is ignored because of its noisiness). We considered each super-category as a different corpora. We then divided these documents into INLINEFORM0 training documents, from which INLINEFORM1 validation documents are held out, and INLINEFORM2 testing documents. We train weGAN and deGAN in the the beginning of Section 4, except that we use a learning rate of INLINEFORM3 for the discriminator in deGAN to stabilize the cost function. Table 8 compares the clustering results of word2vec and weGAN, and the classification accuracy of the FFNN initialized with word2vec, finetuned weGAN, and finetuned deGAN. All comparisons are statistically significant at the INLINEFORM4 level. The other results are similar to the previous two data sets and are thereby omitted here. ### The Reuters-21578 data set The Reuters-21578 data set is a collection of newswire articles. Because the data set is highly skewed, we considered the eight categories with more than 100 training documents: “earn,” “acq,” “crude,” “trade,” “money-fx,” “interest,” “money-supply,” and “ship.” We then divided these documents into INLINEFORM0 training documents, from which 692 validation documents are held out, and INLINEFORM1 testing documents. We train weGAN and deGAN in the same way as in the 20 Newsgroups data set. Table 9 compares the clustering results of word2vec and weGAN, and the classification accuracy of the FFNN initialized with word2vec, finetuned weGAN, and finetuned deGAN. All comparisons are statistically significant at the INLINEFORM2 level except the Rand index. The other results are similar to the CNN and TIME data sets and are thereby omitted here. ### Conclusion In this paper, we have demonstrated the application of the GAN model on text data with multiple corpora. We have shown that the GAN model is not only able to generate images, but also able to refine word embeddings and generate document embeddings. Such models can better learn the inner structure of multi-corpus text data, and also benefit supervised learning. The improvements in supervised learning are not large but statistically significant. The weGAN model outperforms deGAN in terms of supervised learning for 3 out of 4 data sets, and is thereby recommended. The synonyms from weGAN also tend to be more relevant than the original word2vec model. The t-SNE plots show that our generated document embeddings are similarly distributed as the original ones. ### Reference M. Arjovsky, S. Chintala, and L. Bottou. (2017). Wasserstein GAN. arXiv:1701.07875. D. Blei, A. Ng, and M. Jordan. 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Figure 1: Model structure of weGAN. Figure 2: Model structure of deGAN. Table 1: A comparison between word2vec and weGAN in terms of the Rand index and the classification accuracy for the CNN data set. Table 3: A comparison between word2vec and deGAN in terms of the accuracy for the CNN data set. Figure 3: 2-d representation of original (red) and artificial (blue) examples in the CNN data set. Table 4: Bag-of-words representations of original and artificial text in the CNN data set. Table 5: A comparison between word2vec, weGAN, and deGAN in terms of the Rand index and the classification accuracy for the TIME data set. Table 6: Synonyms of “Obama,” “Trump,” and “U.S.” before and after weGAN training for the TIME data set. Table 7: Bag-of-words representations of original and artificial text in the TIME data set. Figure 4: 2-d representation of original (red) and artificial (blue) examples in the TIME data set. Table 8: A comparison between word2vec, weGAN, and deGAN in terms of the Rand index and the classification accuracy for the 20 Newsgroups data set. Table 9: A comparison between word2vec, weGAN, and deGAN in terms of the Rand index and the classification accuracy for the Reuters-21578 data set.	CNN, TIME, 20 Newsgroups, and Reuters-21578
What are least important components identified in the the training of VQA models?	### Introduction Recent research advances in Computer Vision (CV) and Natural Language Processing (NLP) introduced several tasks that are quite challenging to be solved, the so-called AI-complete problems. Most of those tasks require systems that understand information from multiple sources, i.e., semantics from visual and textual data, in order to provide some kind of reasoning. For instance, image captioning BIBREF0, BIBREF1, BIBREF2 presents itself as a hard task to solve, though it is actually challenging to quantitatively evaluate models on that task, and that recent studies BIBREF3 have raised questions on its AI-completeness. The Visual Question Answering (VQA) BIBREF3 task was introduced as an attempt to solve that issue: to be an actual AI-complete problem whose performance is easy to evaluate. It requires a system that receives as input an image and a free-form, open-ended, natural-language question to produce a natural-language answer as the output BIBREF3. It is a multidisciplinary topic that is gaining popularity by encompassing CV and NLP into a single architecture, what is usually regarded as a multimodal model BIBREF4, BIBREF5, BIBREF6. There are many real-world applications for models trained for Visual Question Answering, such as automatic surveillance video queries BIBREF7 and visually-impaired aiding BIBREF8, BIBREF9. Models trained for VQA are required to understand the semantics from images while finding relationships with the asked question. Therefore, those models must present a deep understanding of the image to properly perform inference and produce a reasonable answer to the visual question BIBREF10. In addition, it is much easier to evaluate this task since there is a finite set of possible answers for each image-question pair. Traditionally, VQA approaches comprise three major steps: (i) representation learning of the image and the question; (ii) projection of a single multimodal representation through fusion and attention modules that are capable of leveraging both visual and textual information; and (iii) the generation of the natural language answer to the question at hand. This task often requires sophisticated models that are able to understand a question expressed in text, identify relevant elements of the image, and evaluate how these two inputs correlate. Given the current interest of the scientific community in VQA, many recent advances try to improve individual components such as the image encoder, the question representation, or the fusion and attention strategies to better leverage both information sources. With so many approaches currently being introduced at the same time, it becomes unclear the real contribution and importance of each component within the proposed models. Thus, the main goal of this work is to understand the impact of each component on a proposed baseline architecture, which draws inspiration from the pioneer VQA model BIBREF3 (Fig. FIGREF1). Each component within that architecture is then systematically tested, allowing us to understand its impact on the system's final performance through a thorough set of experiments and ablation analysis. More specifically, we observe the impact of: (i) pre-trained word embeddings BIBREF11, BIBREF12, recurrent BIBREF13 and transformer-based sentence encoders BIBREF14 as question representation strategies; (ii) distinct convolutional neural networks used for visual feature extraction BIBREF15, BIBREF16, BIBREF17; and (iii) standard fusion strategies, as well as the importance of two main attention mechanisms BIBREF18, BIBREF19. We notice that even using a relatively simple baseline architecture, our best models are competitive to the (maybe overly-complex) state-of-the-art models BIBREF20, BIBREF21. Given the experimental nature of this work, we have trained over 130 neural network models, accounting for more than 600 GPU processing hours. We expect our findings to be useful as guidelines for training novel VQA models, and that they serve as a basis for the development of future architectures that seek to maximize predictive performance. ### Related Work The task of VAQ has gained attention since Antol et al. BIBREF3 presented a large-scale dataset with open-ended questions. Many of the developed VQA models employ a very similar architecture BIBREF3, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27: they represent images with features from pre-trained convolutional neural networks; they use word embeddings or recurrent neural networks to represent questions and/or answers; and they combine those features in a classification model over possible answers. Despite their wide adoption, RNN-based models suffer from their limited representation power BIBREF28, BIBREF29, BIBREF30, BIBREF31. Some recent approaches have investigated the application of the Transformer model BIBREF32 to tasks that incorporate visual and textual knowledge, as image captioning BIBREF28. Attention-based methods are also being continuously investigated since they enable reasoning by focusing on relevant objects or regions in original input features. They allow models to pay attention on important parts of visual or textual inputs at each step of a task. Visual attention models focus on small regions within an image to extract important features. A number of methods have adopted visual attention to benefit visual question answering BIBREF27, BIBREF33, BIBREF34. Recently, dynamic memory networks BIBREF27 integrate an attention mechanism with a memory module, and multimodal bilinear pooling BIBREF22, BIBREF20, BIBREF35 is exploited to expressively combine multimodal features and predict attention over the image. These methods commonly employ visual attention to find critical regions, but textual attention has been rarely incorporated into VQA systems. While all the aforementioned approaches have exploited those kind of mechanisms, in this paper we study the impact of such choices specifically for the task of VQA, and create a simple yet effective model. Burns et al. BIBREF36 conducted experiments comparing different word embeddings, language models, and embedding augmentation steps on five multimodal tasks: image-sentence retrieval, image captioning, visual question answering, phrase grounding, and text-to-clip retrieval. While their work focuses on textual experiments, our experiments cover both visual and textual elements, as well as the combination of these representations in form of fusion and attention mechanisms. To the best of our knowledge, this is the first paper that provides a comprehensive analysis on the impact of each major component within a VQA architecture. ### Impact of VQA Components In this section we first introduce the baseline approach, with default image and text encoders, alongside a pre-defined fusion strategy. That base approach is inspired by the pioneer of Antol et al. on VQA BIBREF3. To understand the importance of each component, we update the base architecture according to each component we are investigating. In our baseline model we replace the VGG network from BIBREF19 by a Faster RCNN pre-trained in the Visual Genome dataset BIBREF37. The default text encoding is given by the last hidden-state of a Bidirectional LSTM network, instead of the concatenation of the last hidden-state and memory cell used in the original work. Fig. FIGREF1 illustrates the proposed baseline architecture, which is subdivided into three major segments: independent feature extraction from (1) images and (2) questions, as well as (3) the fusion mechanism responsible to learn cross-modal features. The default text encoder (denoted by the pink rectangle in Fig. FIGREF1) employed in this work comprises a randomly initialized word-embedding module that takes a tokenized question and returns a continuum vector for each token. Those vectors are used to feed an LSTM network. The last hidden-state is used as the question encoding, which is projected with a linear layer into a $d$-dimensional space so it can be fused along to the visual features. As the default option for the LSTM network, we use a single layer with 2048 hidden units. Given that this text encoding approach is fully trainable, we hereby name it Learnable Word Embedding (LWE). For the question encoding, we explore pre-trained and randomly initialized word-embeddings in various settings, including Word2Vec (W2V) BIBREF12 and GloVe BIBREF11. We also explore the use of hidden-states of Skip-Thoughts Vector BIBREF13 and BERT BIBREF14 as replacements for word-embeddings and sentence encoding approaches. Regarding the visual feature extraction (depicted as the green rectangle in Fig. FIGREF1), we decided to use the pre-computed features proposed in BIBREF19. Such an architecture employs a ResNet-152 with a Faster-RCNN BIBREF15 fine-tuned on the Visual Genome dataset. We opted for this approach due to the fact that using pre-computed features is far more computationally efficient, allowing us to train several models with distinct configurations. Moreover, several recent approaches BIBREF20, BIBREF21, BIBREF38 employ that same strategy as well, making it easier to provide fair comparison to the state-of-the-art approaches. In this study we perform experiments with two additional networks widely used for the task at hand, namely VGG-16 BIBREF16 and ReSNet-101 BIBREF17. Given the multimodal nature of the problem we are dealing with, it is quite challenging to train proper image and question encoders so as to capture relevant semantic information from both of them. Nevertheless, another essential aspect of the architecture is the component that merges them altogether, allowing for the model to generate answers based on both information sources BIBREF39. The process of multimodal fusion consists itself in a research area with many approaches being recently proposed BIBREF20, BIBREF40, BIBREF22, BIBREF41. The fusion module receives the extracted image and query features, and provides multimodal features that theoretically present information that allows the system to answer to the visual question. There are many fusion strategies that can either assume quite simple forms, such as vector multiplication or concatenation, or be really complex, involving multilayered neural networks, tensor decomposition, and bi-linear pooling, just to name a few. Following BIBREF3, we adopt the element-wise vector multiplication (also referred as Hadamard product) as the default fusion strategy. This approach requires the feature representations to be fused to have the same dimensionality. Therefore, we project them using a fully-connected layer to reduce their dimension from 2048 to 1024. After being fused together, the multimodal features are finally passed through a fully-connected layer that provides scores (logits) further converted into probabilities via a softmax function ($S$). We want to maximize the probability $P(Y=y\|X=x,Q=q)$ of the correct answer $y$ given the image $X$ and the provided question $Q$. Our models are trained to choose within a set comprised by the 3000 most frequent answers extracted from both training and validation sets of the VQA v2.0 dataset BIBREF42. ### Experimental Setup ::: Dataset For conducting this study we decided to use the VQA v2.0 dataset BIBREF42. It is one of the largest and most frequently used datasets for training and evaluation of models in this task, being the official dataset used in yearly challenges hosted by mainstream computer vision venues . This dataset enhances the original one BIBREF3 by alleviating bias problems within the data and increasing the original number of instances. VQA v2.0 contains over $200,000$ images from MSCOCO BIBREF43, over 1 million questions and $\approx 11$ million answers. In addition, it has at least two questions per image, which prevents the model from answering the question without considering the input image. We follow VQA v2.0 standards and adopt the official provided splits allowing for fair comparison with other approaches. The splits we use are Validation, Test-Dev, Test-Standard. In this work, results of the ablation experiments are reported on the Validation set, which is the default option used for this kind of experiment. In some experiments we also report the training set accuracy to verify evidence of overfitting due to excessive model complexity. Training data has a total of $443,757$ questions labeled with 4 million answers, while the Test-Dev has a total of $214,354$ questions. Note that the validation size is about 4-fold larger than ImageNet's, which contains about $50,000$ samples. Therefore, one must keep in mind that even small performance gaps might indicate quite significant results improvement. For instance, 1% accuracy gains depict $\approx 2,000$ additional instances being correctly classified. We submit the predictions of our best models to the online evaluation servers BIBREF44 so as to obtain results for the Test-Standard split, allowing for a fair comparison to state-of-the-art approaches. ### Experimental Setup ::: Evaluation Metric Free and open-ended questions result in a diverse set of possible answers BIBREF3. For some questions, a simple yes or no answer may be sufficient. Other questions, however, may require more complex answers. In addition, it is worth noticing that multiple answers may be considered correct, such as gray and light gray. Therefore, VQA v2.0 provides ten ground-truth answers for each question. These answers were collected from ten different randomly-chosen humans. The evaluation metric used to measure model performance in the open-ended Visual Question Answering task is a particular kind of accuracy. For each question in the input dataset, the model's most likely response is compared to the ten possible answers provided by humans in the dataset associated with that question BIBREF3, and evaluated according to Equation DISPLAY_FORM7. In this approach, the prediction is considered totally correct only if at least 3 out of 10 people provided that same answer. ### Experimental Setup ::: Hyper-parameters As in BIBREF20 we train our models in a classification-based manner, in which we minimize the cross-entropy loss calculated with an image-question-answer triplet sampled from the training set. We optimize the parameters of all VQA models using Adamax BIBREF45 optimizer with a base learning rate of $7 \times 10^{-4}$, with exception of BERT BIBREF14 in which we apply a 10-fold reduction as suggested in the original paper. We used a learning rate warm-up schedule in which we halve the base learning rate and linearly increase it until the fourth epoch where it reaches twice its base value. It remains the same until the tenth epoch, where we start applying a 25% decay every two epochs. Gradients are calculated using batch sizes of 64 instances, and we train all models for 20 epochs. ### Experimental Analysis In this section we show the experimental analysis for each component in the baseline VQA model. We also provide a summary of our findings regarding the impact of each part. Finally, we train a model with all the components that provide top results and compare it against state-of-the-art approaches. ### Experimental Analysis ::: Text Encoder In our first experiment, we analyze the impact of different embeddings for the textual representation of the questions. To this end, we evaluate: (i) the impact of word-embeddings (pre-trained, or trained from scratch); and (ii) the role of the temporal encoding function, i.e., distinct RNN types, as well as pre-trained sentence encoders (e.g., Skip-Thoughts, BERT). The word-embedding strategies we evaluate are Learnable Word Embedding (randomly initialized and trained from scratch), Word2Vec BIBREF12, and GloVe BIBREF11. We also use word-level representations from widely used sentence embeddings strategies, namely Skip-Thoughts BIBREF13 and BERT BIBREF14. To do so, we use the hidden-states from the Skip-thoughts GRU network, while for BERT we use the activations of the last layer as word-level information. Those vectors feed an RNN that encodes the temporal sequence into a single global vector. Different types of RNNs are also investigated for encoding textual representation, including LSTM BIBREF46, Bidirectional LSTM BIBREF47, GRU BIBREF48, and Bidirectional GRU. For bidirectional architectures we concatenate both forward and backward hidden-states so as to aggregate information from both directions. Those approaches are also compared to a linear strategy, where we use a fully-connected layer followed by a global average pooling on the temporal dimension. The linear strategy discards any order information so we can demonstrate the role of the recurrent network as a temporal encoder to improve model performance. Figure FIGREF5 shows the performance variation of different types of word-embeddings, recurrent networks, initialization strategies, and the effect of fine-tuning the textual encoder. Clearly, the linear layer is outperformed by any type of recurrent layer. When using Skip-Thoughts the difference reaches $2.22\%$, which accounts for almost $5,000$ instances that the linear model mistakenly labeled. The only case in which the linear approach performed well is when trained with BERT. That is expected since Transformer-based architectures employ several attention layers that present the advantage of achieving the total receptive field size in all layers. While doing so, BERT also encodes temporal information with special positional vectors that allow for learning temporal relations. Hence, it is easier for the model to encode order information within word-level vectors without using recurrent layers. For the Skip-Thoughts vector model, considering that its original architecture is based on GRUs, we evaluate both the randomly initialized and the pre-trained GRU of the original model, described as [GRU] and [GRU (skip)], respectively. We noticed that both options present virtually the same performance. In fact, GRU trained from scratch performed $0.13\%$ better than its pre-trained version. Analyzing the results obtained with pre-trained word embeddings, it is clear that GloVe obtained consistently better results than the Word2Vec counterpart. We believe that GloVe vectors perform better given that they capture not only local context statistics as in Word2Vec, but they also incorporate global statistics such as co-occurrence of words. One can also observe that the use of different RNNs models inflicts minor effects on the results. It might be more advisable to use GRU networks since they halve the number of trainable parameters when compared to the LSTMs, albeit being faster and consistently presenting top results. Note also that the best results for Skip-Thoughts, Word2Vec, and GloVe were all quite similar, without any major variation regarding accuracy. The best overall result is achieved when using BERT to extract the textual features. BERT versions using either the linear layer or the RNNs outperformed all other pre-trained embeddings and sentence encoders. In addition, the overall training accuracy for BERT models is not so high compared to all other approaches. That might be an indication that BERT models are less prone to overfit training data, and therefore present better generalization ability. Results make it clear that when using BERT, one must fine-tune it for achieving top performance. Figure FIGREF5 shows that it is possible to achieve a $3\%$ to $4\%$ accuracy improvement when updating BERT weights with $1/10$ of the base learning rate. Moreover, Figure FIGREF6 shows that the use of a pre-training strategy is helpful, once Skip-thoughts and BERT outperform trainable word-embeddings in most of the evaluated settings. Is also make clear that using a single-layered RNNs provide best results, and are far more efficient in terms of parameters. ### Experimental Analysis ::: Image Encoder Experiments in this section analyze the visual feature extraction layers. The baseline uses the Faster-RCNN BIBREF15 network, and we will also experiment with other pre-trained neural networks to encode image information so we can observe their impact on predictive performance. Additionally to Faster-RCNN, we experiment with two widely used networks for VQA, namely ResNet-101 BIBREF17 and VGG-16 BIBREF16. Table TABREF11 illustrates the result of this experiment. Intuitively, visual features provide a larger impact on model's performance. The accuracy difference between the best and the worst performing approaches is $\approx 5\%$. That difference accounts for roughly $10,000$ validation set instances. VGG-16 visual features presented the worst accuracy, but that was expected since it is the oldest network used in this study. In addition, it is only sixteen layers deep, and it has been shown that the depth of the network is quite important to hierarchically encode complex structures. Moreover, VGG-16 architecture encodes all the information in a 4096 dimensional vector that is extracted after the second fully-connected layer at the end. That vector encodes little to none spatial information, which makes it almost impossible for the network to answer questions on the spatial positioning of objects. ResNet-101 obtained intermediate results. It is a much deeper network than VGG-16 and it achieves much better results on ImageNet, which shows the difference of the the learning capacity of both networks. ResNet-101 provides information encoded in 2048 dimensional vectors, extracted from the global average pooling layer, which also summarizes spatial information into a fixed-sized representation. The best result as a visual feature extractor was achieved by the Faster-RCNN fine-tuned on the Visual Genome dataset. Such a network employs a ResNet-152 as backbone for training an RPN-based object detector. In addition, given that it was fine-tuned on the Visual Genome dataset, it allows for the training of robust models suited for general feature extraction. Hence, differently from the previous ResNet and VGG approaches, the Faster-RCNN approach is trained to detect objects, and therefore one can use it to extract features from the most relevant image regions. Each region is encoded as a 2048 dimensional vector. They contain rich information regarding regions and objects, since object detectors often operate over high-dimensional images, instead of resized ones (e.g., $256 \times 256$) as in typical classification networks. Hence, even after applying global pooling over regions, the network still has access to spatial information because of the pre-extracted regions of interest from each image. ### Experimental Analysis ::: Fusion strategy In order to analyze the impact that the different fusion methods have on the network performance, three simple fusion mechanisms were analyzed: element-wise multiplication, concatenation, and summation of the textual and visual features. The choice of the fusion component is essential in VQA architectures, since its output generates multi-modal features used for answering the given visual question. The resulting multi-modal vector is projected into a 3000-dimensional label space, which provides a probability distribution over each possible answer to the question at hand BIBREF39. Table presents the experimental results with the fusion strategies. The best result is obtained using the element-wise multiplication. Such an approach functions as a filtering strategy that is able to scale down the importance of irrelevant dimensions from the visual-question feature vectors. In other words, vector dimensions with high cross-modal affinity will have their magnitudes increased, differently from the uncorrelated ones that will have their values reduced. Summation does provide the worst results overall, closely followed by the concatenation operator. Moreover, among all the fusion strategies used in this study, multiplication seems to ease the training process as it presents a much higher training set accuracy ($\approx 11\% $ improvement) as well. ### Experimental Analysis ::: Attention Mechanism Finally, we analyze the impact of different attention mechanisms, such as Top-Down Attention BIBREF19 and Co-Attention BIBREF18. These mechanisms are used to provide distinct image representations according to the asked questions. Attention allows the model to focus on the most relevant visual information required to generate proper answers to the given questions. Hence, it is possible to generate several distinct representations of the same image, which also has a data augmentation effect. ### Experimental Analysis ::: Attention Mechanism ::: Top-Down Attention Top-down attention, as the name suggests, uses global features from questions to weight local visual information. The global textual features $\mathbf {q} \in \mathbb {R}^{2048}$ are selected from the last internal state of the RNN, and the image features $V \in \mathbb {R}^{k \times 2048}$ are extracted from the Faster-RCNN, where $k$ represents the number of regions extracted from the image. In the present work we used $k=36$. The question features are linearly projected so as to reduce its dimension to 512, which is the size used in the original paper BIBREF19. Image features are concatenated with the textual features, generating a matrix $C$ of dimensions $k \times 2560$. Features resulting from that concatenation are first non-linearly projected with a trainable weight matrix $W_1^{2560 \times 512}$ generating a novel multimodal representation for each image region: Therefore, such a layer learns image-question relations, generating $k \times 512 $ features that are transformed by an activation function $\phi $. Often, $\phi $ is ReLU BIBREF49, Tanh BIBREF50, or Gated Tanh BIBREF51. The latter employs both the logistic Sigmoid and Tanh, in a gating scheme $\sigma (x) \times \textsc {tanh}(x)$. A second fully-connected layer is employed to summarize the 512-dimensional vectors into $h$ values per region ($k \times h$). It is usual to use a small value for $h$ such as $\lbrace 1, 2\rbrace $. The role of $h$ is to allow the model to produce distinct attention maps, which is useful for understanding complex sentences that require distinct viewpoints. Values produced by this layer are normalized with a softmax function applied on the columns of the matrix, as follows. It generates an attention mask $A^{k \times h}$ used to weight image regions, producing the image vector $\hat{\mathbf {v}}$, as shown in Equation DISPLAY_FORM17. Note that when $h>1$, the dimensionality of the visual features increases $h$-fold. Hence, $\hat{\mathbf {v}}^{h \times 2048}$, which we reshape to be a $(2048\times h)\times 1$ vector, constitutes the final question-aware image representation. ### Experimental Analysis ::: Attention Mechanism ::: Co-Attention Unlike the Top-Down attention mechanism, Co-Attention is based on the computation of local similarities between all questions words and image regions. It expects two inputs: an image feature matrix $V^{k \times 2048}$, such that each image feature vector encodes an image region out of $k$; and a set of word-level features $Q^{n \times 2048}$. Both $V$ and $Q$ are normalized to have unit $L_2$ norm, so their multiplication $VQ^T$ results in the cosine similarity matrix used as guidance for generating the filtered image features. A context feature matrix $C^{k \times 2048}$ is given by: Finally, $C$ is normalized with a $\textsc {softmax}$ function, and the $k$ regions are summed so as to generate a 1024-sized vector $\hat{\mathbf {v}}$ to represent relevant visual features $V$ based on question $Q$: Table depicts the results obtained by adding the attention mechanisms to the baseline model. For these experiments we used only element-wise multiplication as fusion strategy, given that it presented the best performance in our previous experiments. We observe that attention is a crucial mechanism for VQA, leading to an $\approx 6\%$ accuracy improvement. The best performing attention approach was Top-Down attention with ReLU activation, followed closely by Co-Attention. We noticed that when using Gated Tanh within Top-Down attention, results degraded 2%. In addition, experiments show that $L_2$ normalization is quite important in Co-Attention, providing an improvement of almost $6\%$. ### Findings Summary The experiments presented in Section SECREF9 have shown that the best text encoder approach is fine-tuning a pre-trained BERT model with a GRU network trained from scratch. In Section SECREF10 we performed experiments for analyzing the impact of pre-trained networks to extract visual features, among them Faster-RCNN, ResNet-101, and VGG-16. The best result was using a Faster-RCNN, reaching a $3\%$ improvement in the overall accuracy. We analyzed different ways to perform multimodal feature fusion in Section SECREF12. In this sense, the fusion mechanism that obtained the best result was the element-wise product. It provides $\approx 3\%$ higher overall accuracy when compared to the other fusion approaches. Finally, in Section SECREF13 we have studied two main attention mechanisms and their variations. They aim to provide question-aware image representation by attending to the most important spatial features. The top performing mechanism is the Top-Down attention with the ReLU activation function, which provided an $\approx 6\%$ overall accuracy improvement when compared to the base architecture. ### Comparison to state-of-the-art methods After evaluating individually each component in a typical VQA architecture, our goal in this section is to compare the approach that combines the best performing components into a single model with the current state-of-the-art in VQA. Our comparison involves the following VQA models: Deeper-lstm-q BIBREF3, MCB BIBREF22, ReasonNet BIBREF52, Tips&Tricks BIBREF53, and the recent block BIBREF20. Tables TABREF21 and show that our best architecture outperforms all competitors but block, in both Test-Standard (Table TABREF21) and Test-Dev sets (Table ). Despite block presenting a marginal advantage in accuracy, we have shown in this paper that by carefully analyzing each individual component we are capable of generating a method, without any bells and whistles, that is on par with much more complex methods. For instance, block and MCB require 18M and 32M parameters respectively for the fusion scheme alone, while our fusion approach is parameter-free. Moreover, our model performs far better than BIBREF22, BIBREF52, and BIBREF53, which are also arguably much more complex methods. ### Conclusion In this study we observed the actual impact of several components within VQA models. We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Our experiments have demonstrated that even simple fusion strategies can achieve performance on par with the state-of-the-art. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy. For future work, we expect to expand this study in two main ways: (i) cover additional datasets, such as Visual Genome BIBREF37; and (ii) study in an exhaustive fashion how distinct components interact with each other, instead of observing their impact alone on the classification performance. ### Acknowledgment This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior – Brasil (CAPES) – Finance Code 001. We also would like to thank FAPERGS for funding this research. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the graphics cards used for this research. Fig. 1. Baseline architecture proposed for the experimental setup. Fig. 2. Overall validation accuracy improvement (∆) over the baseline architecture. Models denoted with * present fixed word-embedding representations, i.e., they are not updated via back-propagation. Fig. 3. Overall accuracy vs. number of parameters trade-off analysis. Circled markers denote two-layered RNNs. Number of parameters increases due to the number of hidden units H within the RNN. In this experiment we vary H ∈ {128, 256, 512, 1024, 2048}. TABLE III EXPERIMENT USING DIFFERENT ATTENTION MECHANISMS. TABLE IV COMPARISON OF THE MODELS ON VQA2 TEST-STANDARD SET. THE MODELS WERE TRAINED ON THE UNION OF VQA 2.0 TRAINVAL SPLIT AND VISUALGENOME [38] TRAIN SPLIT. All IS THE OVERALL OPENENDED ACCURACY (HIGHER IS BETTER). Yes/No, Numbers, AND Others ARE SUBSETS THAT CORRESPOND TO ANSWERS TYPES. * SCORES REPORTED FROM [21]. TABLE V COMPARISON OF THE MODELS ON VQA2 TEST-DEV SET. All IS THE OVERALL OPENENDED ACCURACY (HIGHER IS BETTER). Yes/No, Numbers, AND Others ARE SUBSETS THAT CORRESPOND TO ANSWERS TYPES. * SCORES REPORTED FROM [21].	some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant
When did the Hanseatic League begin? A. The 1200s B. The 1500s C. The 1400s D. The 1300s	What cities in the post-Brexit era could learn from a 14th-century trading bloc As you fly from the country now known as Germany to Britain, the coastal geography of northern European cities gently unfurls. You can see where the sea smacks into them, or where yawning estuaries unfold like funnels between green and brown city and choppy blue water. You can track the snaking rivers and canals that form unrepentant umbilical connections to the settlements set a little further inland. By their nature cities along coasts and rivers developed so they could be open to trade with each other. From the middle of the 13th century, and for some 300 years after, many settlements dotted along this route formed the prosperous Hanseatic League, a European trading confederation of market towns, before the rise of the nation state led to its dissolution. The Hanseatic League is not well known, and today it lives on most prominently in the name of the German national airline Lufthansa, literally the 'Hansa of the skies', whose planes you can look out of – and down towards the Hanseatic cities – on the short journeys between mainland Europe and Britain. The letters HH on the number plates of cars in Hamburg stand for Hansestadt Hamburg: another proud little memory of this hidden history. In the traumatised atmosphere of post-Brexit Britain, it is worth remembering the Hanseatic League. It could point us towards new relationships between progressive city dwellers in a world that otherwise seems to be putting the brakes on modernity. Despite some of Britain's Leave voters longing to inhabit a fantastical realm immune to foreign influence, the reality is patently very different to that. In the late 1300s, Chaucer wrote about characters travelling to Jerusalem, and others who came from Europe; and it was at exactly this point that the Hanseatic League slowly started to coalesce, eventually influencing our isles. The League is most easily understood as a loose federation of cities that acted together in self-interest to promote trade. The Hanseatic cities developed their own legal system, and their armies came to one another's aid. Merchants who wanted to buy and sell and travel were taking the lead at a time when nation states were not fit for purpose: in the case of England or Denmark, leadership was too centralised and authoritarian, while in German-speaking lands a nation had yet to be formed. We think of nations today as elemental almost, immovable. Yet look at any city of Mitteleuropa and you'll see the many different names it has had as borders and regimes have shifted with the sands of time. Nations come and go. Cities endure. "It is often said that great cities survived great empires," says Cristina Ampatzidou, editor-in-chief of the Rotterdam-based online publishing platform Amateur Cities. "So it is not unrealistic to think of cities as discrete entities that compete and collaborate with each other, independently from the states to which they belong." The cities involved in the Hanseatic League are found along the Baltic and North Sea coasts, and slightly inland too. The League stretched from Novgorod in the east – in what is now Russia – to London in the west. Tallinn, Riga, Gdańsk, Visby, Berlin, Cologne, Antwerp, Stockholm, Bergen, Kiel, Rostock, Dinant, Bruges, Turku, Groningen, Hanover, Wroclaw, Kaliningrad: all were involved at different stages in the Hanse's history, which ran on into the 1500s. The League covered lands that today find themselves a part of the modern nations of Finland, Sweden, Poland, the Netherlands, Belgium, France, Norway, Lithuania, Estonia and Latvia. It was a huge – and hugely ambitious – undertaking in the days when communications consisted of ink and paper and the only viable method of travel was by ship. Wood, fur, wool, silver, herring, cod and salt were the main items traded. But what was also exchanged was knowledge. In some ways it was an exercise in what we today call 'soft diplomacy'. There was no maniacal ruler overseeing things – merchants met and talked. They raised armies and waged war against kings who threatened their businesses and their freedoms and their peace. There was a kind of proto-democracy at work. Professor Rainer Postel, of the Bundeswehr Universität (Germany's equivalent of Sandhurst military academy), has described the Hanse as "a community of interests without power politics". As David Abulafia, Professor of Mediterranean History at Cambridge points out, "The lack of an elaborate superstructure was one of the things that made the Hanse work. Having said that, one should recognise that Lübeck in particular dominated the League for long periods." Lübeck was where the merchants most often met; and where renewed recent interest in the Hanse eventually led to Angela Merkel cutting the ribbon at the brand new European Hansemuseum in the city last year. Germany today – multicultural, economically and culturally motoring, free and fair – seems like the ideal model for a modern European nation state. And part of that success lies in the gravitas the country has given to its Hanseatic history. For Germany is not a top-down country with one city unhealthily dominating as with France and Britain (regional economic inequalities have plagued Britain since the painful de-industrialisation of the 1980s, especially in the north). Germany respects federalism and its cities exist on a much more even keel. The way that Cologne, Munich, Frankfurt, Dusseldorf and Stuttgart all bring varied economic and cultural character to the party is pure Hanse. The former Hanseatic cities of Hamburg, Berlin and Bremen have city state status within Germany, putting them on the same level as a whole region or 'land' like Bavaria or Brandenburg. So how about a new Hanseatic League? I ask Benjamin Barber, senior fellow at New York's Fordham University. "I believe you will find there is a new Hanse," he says, "that constituted itself about 10 or 11 years ago – including many of the original Hanseatic League cities." Barber is founder of the Global Parliament of Mayors, which he describes as a kind of Hanse of all cities, not just European ports, which will give cities a global urban voice and a common platform for action. The parliament convenes for its inaugural session in The Hague in September. "Cities both exist within nations and transcend nations. Their power lies not just in the extent of de jure autonomy ceded or granted by 'higher' levels of government," says Bruce Katz, centennial scholar at the Washington DC thinktank the Brookings Institution. "Rather, cities have de facto power, the result of larger market and demographic forces and environmental imperatives that value proximity, density, connectivity and quality. Smart nations will see themselves as partners to their cities, setting strong platforms for urban prosperity and devolving powers, where appropriate, to give cities the flexibility to perform… Dumb nations will continue to dictate from above, stifling market activity and urban potential." But could we go further? Could cities like London declare independence from the UK? London's economy is larger than that of Scotland and Wales combined. "States will not vanish or surrender their waning sovereignty," says Benjamin Barber. "But cities will meet across frontiers and work together to solve problems. The objective is not an independent London or New York, but interdependent cities collaborating globally. And that is happening." London's voters largely wanted to remain a part of the EU and to maintain the city's status as an entrepôt. There is clearly a widening chasm between urban and rural life at the heart of many nations. Visualisations of Austria's recent presidential election showed the issue clearly: the country's cities voted for the Green candidate Alexander Van der Bellen, while the the rural districts went for right-wing nationalist Norbert Hofer (whose legal challenge to the close result has resulted in a rerun being announced for October). And in the USA in November, it's likely that Trump voters will also come from rural areas and Clinton voters from the cities. City dwellers are finding ever more in common with the world's other city dwellers than with their countrymen 50 miles down the road. Back in Britain, one of history's little oddities pops up on the east coast. Boston in Lincolnshire and King's Lynn in Norfolk were both forward-looking Hanseatic League towns that traded with far-flung ports and hosted foreign merchants. King's Lynn contains the only extantHanse House left in Britain (London's was knocked down to build Cannon Street Station in the 1800s). Yet in the EU referendum these two areas polled among the highest Leave votes of anywhere in the country. "Things change," says LSE's Professor Tony Travers. "[King's Lynn] used to be very highly connected, but the economy moved on and left those trading ports like it in a different situation." Take, for example, the pivot towards the New World, with which trade made more sense from the west-coast ports like Bristol and Liverpool. While these boomed between the 1600s and 1800s, the Hanseatic ports declined and then died out. "One of the things that's interesting about the [referendum] decision is that it begs all sorts of questions about the future of the UK and its relationship with Europe; and of London and Scotland and their relationship with the rest of Europe. When the EU began as the EEC in the mid-20th century some saw it as a modern day Hanse. Now the EU seems to be waning, perhaps its successor will have to ape the Hanse even more." For all its complex beauty, life can ultimately be reduced to a series of binary options: yes or no, stick or twist, in or out, innovation or stagnation, modernity or mythology. The referendum result was disappointing for many progressive observers because it felt like a step backwards. Despite being primarily about trade monopolies and money making, the Hanse was, in its way, an early stab at stepping forwards: it encompassed internationalism, rational thought, free trade, loose democratic institutions and, most crucially of all, movement. The future, for many observers, can only be understood in terms of the free movement of people, capital, goods and ideas. It is this necessary movement, and its possible curtailment, that could be the spark that leads to cities like London to seek independence and parity with other world cities – rather than with the rural hinterlands of Britain. Of course, cities seceding from their nation states would provide huge headaches for countries whose biggest economic driver had been removed – as well as likely deepening ideological differences between city and rural dwellers. Moreover, cities need the food the countryside provides. Yet for all the potential pitfalls, city states can thrive. Look at Singapore, Hong Kong, or de facto city states like Dubai and Abu Dhabi. One of the most telling characteristics about these four – all of course former British imperial enclaves – is that they are utterly outward looking. To return to the sky analogy, it's the airlines of each of these (Singapore Airlines, Cathay Pacific, Emirates and Etihad) that open up each respective city to the world in the way that the machinery of the Hanse did on the Baltic Sea 600 years ago. And it's the unions each city makes with other places that also look thoroughly Hanseatic in character. A model for modern city states, then. But is it one that we want? "The Hanseatic League was not always accepted by local citizens," says Cristina Ampatzidou, "because the privileges granted to the Hanse merchants were forcing local traders out of competition and many cities took steps to eliminate them. The reasons the countryside is turning to the right [globally] are not independent from cities turning increasingly into speculation machines for the profit of a happy few. It is basically these systemic contradictions that must be addressed before we resort to more isolationist ideas that would intensify the urban-rural political divide. The bottom line is not whether a contemporary Hanse-esque federation is possible, it probably is; but whether it is actually desirable." This article was originally published on TheLong+Short. Read the original article.	D. The 1300s
Which effect of Russell's decision to kill Dunbar was likely most surprising to Russell? A. The fact that nobody agreed on which sun was the correct one B. The decrease in chatter in the communication system C. The way the Dunbar died without much drama D. He sabotaged himself by ensuring his loneliness	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. He sabotaged himself by ensuring his loneliness
What hypernymy tasks do they study?	### Introduction Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , pattern-based methods have been one of the most influential approaches to this problem. Their key idea is to exploit certain lexico-syntactic patterns to detect is-a relations in text. For instance, patterns like “ INLINEFORM0 such as INLINEFORM1 ”, or “ INLINEFORM2 and other INLINEFORM3 ” often indicate hypernymy relations of the form INLINEFORM4 is-a INLINEFORM5 . Such patterns may be predefined, or they may be learned automatically BIBREF1 , BIBREF2 . However, a well-known problem of Hearst-like patterns is their extreme sparsity: words must co-occur in exactly the right configuration, or else no relation can be detected. To alleviate the sparsity issue, the focus in hypernymy detection has recently shifted to distributional representations, wherein words are represented as vectors based on their distribution across large corpora. Such methods offer rich representations of lexical meaning, alleviating the sparsity problem, but require specialized similarity measures to distinguish different lexical relationships. The most successful measures to date are generally inspired by the Distributional Inclusion Hypothesis (DIH) BIBREF3 , which states roughly that contexts in which a narrow term INLINEFORM0 may appear (“cat”) should be a subset of the contexts in which a broader term INLINEFORM1 (“animal”) may appear. Intuitively, the DIH states that we should be able to replace any occurrence of “cat” with “animal” and still have a valid utterance. An important insight from work on distributional methods is that the definition of context is often critical to the success of a system BIBREF4 . Some distributional representations, like positional or dependency-based contexts, may even capture crude Hearst pattern-like features BIBREF5 , BIBREF6 . While both approaches for hypernym detection rely on co-occurrences within certain contexts, they differ in their context selection strategy: pattern-based methods use predefined manually-curated patterns to generate high-precision extractions while DIH methods rely on unconstrained word co-occurrences in large corpora. Here, we revisit the idea of using pattern-based methods for hypernym detection. We evaluate several pattern-based models on modern, large corpora and compare them to methods based on the DIH. We find that simple pattern-based methods consistently outperform specialized DIH methods on several difficult hypernymy tasks, including detection, direction prediction, and graded entailment ranking. Moreover, we find that taking low-rank embeddings of pattern-based models substantially improves performance by remedying the sparsity issue. Overall, our results show that Hearst patterns provide high-quality and robust predictions on large corpora by capturing important contextual constraints, which are not yet modeled in distributional methods. ### Models In the following, we discuss pattern-based and distributional methods to detect hypernymy relations. We explicitly consider only relatively simple pattern-based approaches that allow us to directly compare their performance to DIH-based methods. ### Pattern-based Hypernym Detection First, let INLINEFORM0 denote the set of hypernymy relations that have been extracted via Hearst patterns from a text corpus INLINEFORM1 . Furthermore let INLINEFORM2 denote the count of how often INLINEFORM3 has been extracted and let INLINEFORM4 denote the total number extractions. In the first, most direct application of Hearst patterns, we then simply use the counts INLINEFORM5 or, equivalently, the extraction probability DISPLAYFORM0 to predict hypernymy relations from INLINEFORM0 . However, simple extraction probabilities as in eq:prob are skewed by the occurrence probabilities of their constituent words. For instance, it is more likely that we extract (France, country) over (France, republic), just because the word country is more likely to occur than republic. This skew in word distributions is well-known for natural language and also translates to Hearst patterns (see also fig:dist). For this reason, we also consider predicting hypernymy relations based on the Pointwise Mutual Information of Hearst patterns: First, let INLINEFORM0 and INLINEFORM1 denote the probability that INLINEFORM2 occurs as a hyponym and hypernym, respectively. We then define the Positive Pointwise Mutual Information for INLINEFORM3 as DISPLAYFORM0 While eq:pmi can correct for different word occurrence probabilities, it cannot handle missing data. However, sparsity is one of the main issues when using Hearst patterns, as a necessarily incomplete set of extraction rules will lead inevitably to missing extractions. For this purpose, we also study low-rank embeddings of the PPMI matrix, which allow us to make predictions for unseen pairs. In particular, let INLINEFORM0 denote the number of unique terms in INLINEFORM1 . Furthermore, let INLINEFORM2 be the PPMI matrix with entries INLINEFORM3 and let INLINEFORM4 be its Singular Value Decomposition (SVD). We can then predict hypernymy relations based on the truncated SVD of INLINEFORM5 via DISPLAYFORM0 where INLINEFORM0 , INLINEFORM1 denote the INLINEFORM2 -th and INLINEFORM3 -th row of INLINEFORM4 and INLINEFORM5 , respectively, and where INLINEFORM6 is the diagonal matrix of truncated singular values (in which all but the INLINEFORM7 largest singular values are set to zero). eq:spmi can be interpreted as a smoothed version of the observed PPMI matrix. Due to the truncation of singular values, eq:spmi computes a low-rank embedding of INLINEFORM0 where similar words (in terms of their Hearst patterns) have similar representations. Since eq:spmi is defined for all pairs INLINEFORM1 , it allows us to make hypernymy predictions based on the similarity of words. We also consider factorizing a matrix that is constructed from occurrence probabilities as in eq:prob, denoted by INLINEFORM2 . This approach is then closely related to the method of BIBREF7 , which has been proposed to improve precision and recall for hypernymy detection from Hearst patterns. ### Distributional Hypernym Detection Most unsupervised distributional approaches for hypernymy detection are based on variants of the Distributional Inclusion Hypothesis BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF4 . Here, we compare to two methods with strong empirical results. As with most DIH measures, they are only defined for large, sparse, positively-valued distributional spaces. First, we consider WeedsPrec BIBREF8 which captures the features of INLINEFORM0 which are included in the set of a broader term's features, INLINEFORM1 : DISPLAYFORM0 Second, we consider invCL BIBREF11 which introduces a notion of distributional exclusion by also measuring the degree to which the broader term contains contexts not used by the narrower term. In particular, let INLINEFORM0 denote the degree of inclusion of INLINEFORM0 in INLINEFORM1 as proposed by BIBREF12 . To measure both the inclusion of INLINEFORM2 in INLINEFORM3 and the non-inclusion of INLINEFORM4 in INLINEFORM5 , invCL is then defined as INLINEFORM6 Although most unsupervised distributional approaches are based on the DIH, we also consider the distributional SLQS model based on on an alternative informativeness hypothesis BIBREF10 , BIBREF4 . Intuitively, the SLQS model presupposes that general words appear mostly in uninformative contexts, as measured by entropy. Specifically, SLQS depends on the median entropy of a term's top INLINEFORM0 contexts, defined as INLINEFORM1 where INLINEFORM0 is the Shannon entropy of context INLINEFORM1 across all terms, and INLINEFORM2 is chosen in hyperparameter selection. Finally, SLQS is defined using the ratio between the two terms: INLINEFORM3 Since the SLQS model only compares the relative generality of two terms, but does not make judgment about the terms' relatedness, we report SLQS-cos, which multiplies the SLQS measure by cosine similarity of INLINEFORM0 and INLINEFORM1 BIBREF10 . For completeness, we also include cosine similarity as a baseline in our evaluation. ### Evaluation To evaluate the relative performance of pattern-based and distributional models, we apply them to several challenging hypernymy tasks. ### Tasks Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. For this task, we evaluate all models on five benchmark datasets: First, we employ the noun-noun subset of bless, which contains hypernymy annotations for 200 concrete, mostly unambiguous nouns. Negative pairs contain a mixture of co-hyponymy, meronymy, and random pairs. This version contains 14,542 total pairs with 1,337 positive examples. Second, we evaluate on leds BIBREF13 , which consists of 2,770 noun pairs balanced between positive hypernymy examples, and randomly shuffled negative pairs. We also consider eval BIBREF14 , containing 7,378 pairs in a mixture of hypernymy, synonymy, antonymy, meronymy, and adjectival relations. eval is notable for its absence of random pairs. The largest dataset is shwartz BIBREF2 , which was collected from a mixture of WordNet, DBPedia, and other resources. We limit ourselves to a 52,578 pair subset excluding multiword expressions. Finally, we evaluate on wbless BIBREF15 , a 1,668 pair subset of bless, with negative pairs being selected from co-hyponymy, random, and hyponymy relations. Previous work has used different metrics for evaluating on BLESS BIBREF11 , BIBREF5 , BIBREF6 . We chose to evaluate the global ranking using Average Precision. This allowed us to use the same metric on all detection benchmarks, and is consistent with evaluations in BIBREF4 . Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. For this task, we evaluate all models on three datasets described by BIBREF16 : On bless, the task is to predict the direction for all 1337 positive pairs in the dataset. Pairs are only counted correct if the hypernymy direction scores higher than the reverse direction, i.e. INLINEFORM0 . We reserve 10% of the data for validation, and test on the remaining 90%. On wbless, we follow prior work BIBREF17 , BIBREF18 and perform 1000 random iterations in which 2% of the data is used as a validation set to learn a classification threshold, and test on the remainder of the data. We report average accuracy across all iterations. Finally, we evaluate on bibless BIBREF16 , a variant of wbless with hypernymy and hyponymy pairs explicitly annotated for their direction. Since this task requires three-way classification (hypernymy, hyponymy, and other), we perform two-stage classification. First, a threshold is tuned using 2% of the data, identifying whether a pair exhibits hypernymy in either direction. Second, the relative comparison of scores determines which direction is predicted. As with wbless, we report the average accuracy over 1000 iterations. Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds. For this task, we follow prior work BIBREF19 , BIBREF18 and use the noun part of hyperlex BIBREF20 , consisting of 2,163 noun pairs which are annotated to what degree INLINEFORM0 is-a INLINEFORM1 holds on a scale of INLINEFORM2 . For all models, we report Spearman's rank correlation INLINEFORM3 . We handle out-of-vocabulary (OOV) words by assigning the median of the scores (computed across the training set) to pairs with OOV words. ### Experimental Setup Pattern-based models: We extract Hearst patterns from the concatenation of Gigaword and Wikipedia, and prepare our corpus by tokenizing, lemmatizing, and POS tagging using CoreNLP 3.8.0. The full set of Hearst patterns is provided in Table TABREF8 . Our selected patterns match prototypical Hearst patterns, like “animals such as cats,” but also include broader patterns like “New Year is the most important holiday.” Leading and following noun phrases are allowed to match limited modifiers (compound nouns, adjectives, etc.), in which case we also generate a hit for the head of the noun phrase. During postprocessing, we remove pairs which were not extracted by at least two distinct patterns. We also remove any pair INLINEFORM0 if INLINEFORM1 . The final corpus contains roughly 4.5M matched pairs, 431K unique pairs, and 243K unique terms. For SVD-based models, we select the rank from INLINEFORM2 {5, 10, 15, 20, 25, 50, 100, 150, 200, 250, 300, 500, 1000} on the validation set. The other pattern-based models do not have any hyperparameters. Distributional models: For the distributional baselines, we employ the large, sparse distributional space of BIBREF4 , which is computed from UkWaC and Wikipedia, and is known to have strong performance on several of the detection tasks. The corpus was POS tagged and dependency parsed. Distributional contexts were constructed from adjacent words in dependency parses BIBREF21 , BIBREF22 . Targets and contexts which appeared fewer than 100 times in the corpus were filtered, and the resulting co-occurrence matrix was PPMI transformed. The resulting space contains representations for 218K words over 732K context dimensions. For the SLQS model, we selected the number of contexts INLINEFORM0 from the same set of options as the SVD rank in pattern-based models. ### Results Table TABREF13 shows the results from all three experimental settings. In nearly all cases, we find that pattern-based approaches substantially outperform all three distributional models. Particularly strong improvements can be observed on bless (0.76 average precision vs 0.19) and wbless (0.96 vs. 0.69) for the detection tasks and on all directionality tasks. For directionality prediction on bless, the SVD models surpass even the state-of-the-art supervised model of BIBREF18 . Moreover, both SVD models perform generally better than their sparse counterparts on all tasks and datasets except on hyperlex. We performed a posthoc analysis of the validation sets comparing the ppmi and spmi models, and found that the truncated SVD improved recall via its matrix completion properties. We also found that the spmi model downweighted many high-scoring outlier pairs composed of rare terms. When comparing the INLINEFORM0 and ppmi models to distributional models, we observe mixed results. The shwartz dataset is difficult for sparse models due to its very long tail of low frequency words that are hard to cover using Hearst patterns. On eval, Hearst-pattern based methods get penalized by OOV words, due to the large number of verbs and adjectives in the dataset, which are not captured by our patterns. However, in 7 of the 9 datasets, at least one of the sparse models outperforms all distributional measures, showing that Hearst patterns can provide strong performance on large corpora. ### Conclusion We studied the relative performance of Hearst pattern-based methods and DIH-based methods for hypernym detection. Our results show that the pattern-based methods substantially outperform DIH-based methods on several challenging benchmarks. We find that embedding methods alleviate sparsity concerns of pattern-based approaches and substantially improve coverage. We conclude that Hearst patterns provide important contexts for the detection of hypernymy relations that are not yet captured in DIH models. Our code is available at https://github.com/facebookresearch/hypernymysuite. ### Acknowledgments We would like to thank the anonymous reviewers for their helpful suggestions. We also thank Vered Shwartz, Enrico Santus, and Dominik Schlechtweg for providing us with their distributional spaces and baseline implementations. Figure 1: Frequency distribution of words appearing in Hearst patterns. Table 1: Hearst patterns used in this study. Patterns are lemmatized, but listed as inflected for clarity. Table 2: Experimental results comparing distributional and pattern-based methods in all settings.	Detection, Direction, Graded Entailment
What dataset is used?	### Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blending with Punjabi, Gujarati, Marathi and Hindi. Sometimes, though rarely, Hinglish is used to refer to Hindi written in English script and mixing with English words or phrases. This makes analyzing the language very interesting. Its rampant usage in social media like Twitter, Facebook, Online blogs and reviews has also led to its usage in delivering hate and abuses in similar platforms. We aim to find such content in the social media focusing on the tweets. Hypothetically, if we can classify such tweets, we might be able to detect them and isolate them for further analysis before it reaches public. This will a great application of AI to the social cause and thus is motivating. An example of a simple, non offensive message written in Hinglish could be: "Why do you waste your time with <redacted content>. Aapna ghar sambhalta nahi(<redacted content>). Chale dusro ko basane..!!" The second part of the above sentence is written in Hindi while the first part is in English. Second part calls for an action to a person to bring order to his/her home before trying to settle others. ### Introduction ::: Modeling challenges From the modeling perspective there are couple of challenges introduced by the language and the labelled dataset. Generally, Hinglish follows largely fuzzy set of rules which evolves and is dependent upon the users preference. It doesn't have any formal definitions and thus the rules of usage are ambiguous. Thus, when used by different users the text produced may differ. Overall the challenges posed by this problem are: Geographical variation: Depending upon the geography of origination, the content may be be highly influenced by the underlying region. Language and phonetics variation: Based on a census in 2001, India has 122 major languages and 1599 other languages. The use of Hindi and English in a code switched setting is highly influenced by these language. No grammar rules: Hinglish has no fixed set of grammar rules. The rules are inspired from both Hindi and English and when mixed with slur and slang produce large variation. Spelling variation: There is no agreement on the spellings of the words which are mixed with English. For example to express love, a code mixed spelling, specially when used social platforms might be pyaar, pyar or pyr. Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. Prior work addresses this concern by using Transfer Learning on an architecture learnt on about 14,500 messages with an accuracy of 83.90. We addressed this concern using data augmentation techniques applied on text data. ### Related Work ::: Transfer learning based approaches Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%. The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work. ### Related Work ::: Hybrid models In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture. ### Dataset and Features We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below: ### Dataset and Features ::: Challenges The obtained data set had many challenges and thus a data preparation task was employed to clean the data and make it ready for the deep learning pipeline. The challenges and processes that were applied are stated below: Messy text messages: The tweets had urls, punctuations, username mentions, hastags, emoticons, numbers and lots of special characters. These were all cleaned up in a preprocessing cycle to clean the data. Stop words: Stop words corpus obtained from NLTK was used to eliminate most unproductive words which provide little information about individual tweets. Transliteration: Followed by above two processes, we translated Hinglish tweets into English words using a two phase process Transliteration: In phase I, we used translation API's provided by Google translation services and exposed via a SDK, to transliteration the Hinglish messages to English messages. Translation: After transliteration, words that were specific to Hinglish were translated to English using an Hinglish-English dictionary. By doing this we converted the Hinglish message to and assortment of isolated words being presented in the message in a sequence that can also be represented using word to vector representation. Data augmentation: Given the data set was very small with a high degree of imbalance in the labelled messages for three different classes, we employed a data augmentation technique to boost the learning of the deep network. Following techniques from the paper by Jason et al. was utilized in this setting that really helped during the training phase.Thsi techniques wasnt used in previous studies. The techniques were: Synonym Replacement (SR):Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random. Random Insertion (RI):Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times. Random Swap (RS):Randomly choose two words in the sentence and swap their positions. Do this n times. Random Deletion (RD):For each word in the sentence, randomly remove it with probability p. Word Representation: We used word embedding representations by Glove for creating word embedding layers and to obtain the word sequence vector representations of the processed tweets. The pre-trained embedding dimension were one of the hyperparamaters for model. Further more, we introduced another bit flag hyperparameter that determined if to freeze these learnt embedding. Train-test split: The labelled dataset that was available for this task was very limited in number of examples and thus as noted above few data augmentation techniques were applied to boost the learning of the network. Before applying augmentation, a train-test split of 78%-22% was done from the original, cleansed data set. Thus, 700 tweets/messages were held out for testing. All model evaluation were done in on the test set that got generated by this process. The results presented in this report are based on the performance of the model on the test set. The training set of 2489 messages were however sent to an offline pipeline for augmenting the data. The resulting training dataset was thus 7934 messages. the final distribution of messages for training and test was thus below: ### Model Architecture We tested the performance of various model architectures by running our experiment over 100 times on a CPU based compute which later as migrated to GPU based compute to overcome the slow learning progress. Our universal metric for minimizing was the validation loss and we employed various operational techniques for optimizing on the learning process. These processes and its implementation details will be discussed later but they were learning rate decay, early stopping, model checkpointing and reducing learning rate on plateau. ### Model Architecture ::: Loss function For the loss function we chose categorical cross entropy loss in finding the most optimal weights/parameters of the model. Formally this loss function for the model is defined as below: The double sum is over the number of observations and the categories respectively. While the model probability is the probability that the observation i belongs to category c. ### Model Architecture ::: Models Among the model architectures we experimented with and without data augmentation were: Fully Connected dense networks: Model hyperparameters were inspired from the previous work done by Vo et al and Mathur et al. This was also used as a baseline model but we did not get appreciable performance on such architecture due to FC networks not being able to capture local and long term dependencies. Convolution based architectures: Architecture and hyperparameter choices were chosen from the past study Deon on the subject. We were able to boost the performance as compared to only FC based network but we noticed better performance from architectures that are suitable to sequences such as text messages or any timeseries data. Sequence models: We used SimpleRNN, LSTM, GRU, Bidirectional LSTM model architecture to capture long term dependencies of the messages in determining the class the message or the tweet belonged to. Based on all the experiments we conducted below model had best performance related to metrics - Recall rate, F1 score and Overall accuracy. ### Model Architecture ::: Hyper parameters Choice of model parameters were in the above models were inspired from previous work done but then were tuned to the best performance of the Test dataset. Following parameters were considered for tuning. Learning rate: Based on grid search the best performance was achieved when learning rate was set to 0.01. This value was arrived by a grid search on lr parameter. Number of Bidirectional LSTM units: A set of 32, 64, 128 hidden activation units were considered for tuning the model. 128 was a choice made by Vo et al in modeling for Vietnamese language but with our experiments and with a small dataset to avoid overfitting to train dataset, a smaller unit sizes were considered. Embedding dimension: 50, 100 and 200 dimension word representation from Glove word embedding were considered and the best results were obtained with 100d representation, consistent with choices made in the previous work. Transfer learning on Embedding; Another bit flag for training the embedding on the train data or freezing the embedding from Glove was used. It was determined that set of pre-trained weights from Glove was best when it was fine tuned with Hinglish data. It provides evidence that a separate word or sentence level embedding when learnt for Hinglish text analysis will be very useful. Number of dense FC layers. Maximum length of the sequence to be considered: The max length of tweets/message in the dataset was 1265 while average was 116. We determined that choosing 200 resulted in the best performance. ### Results During our experimentation, it was evident that this is a hard problem especially detecting the hate speech, text in a code- mixed language. The best recall rate of 77 % for hate speech was obtained by a Bidirectional LSTM with 32 units with a recurrent drop out rate of 0.2. Precision wise GRU type of RNN sequence model faired better than other kinds for hate speech detection. On the other hand for detecting offensive and non offensive tweets, fairly satisfactory results were obtained. For offensive tweets, 92 % precision was and recall rate of 88% was obtained with GRU versus BiLSTM based models. Comparatively, Recall of 85 % and precision of 76 % was obtained by again GRU and BiLSTM based models as shown and marked in the results. ### Conclusion and Future work The results of the experiments are encouraging on detective offensive vs non offensive tweets and messages written in Hinglish in social media. The utilization of data augmentation technique in this classification task was one of the vital contributions which led us to surpass results obtained by previous state of the art Hybrid CNN-LSTM based models. However, the results of the model for predicting hateful tweets on the contrary brings forth some shortcomings of the model. The biggest shortcoming on the model based on error analysis indicates less than generalized examples presented by the dataset. We also note that the embedding learnt from the Hinglish data set may be lacking and require extensive training to have competent word representations of Hinglish text. Given this learning's, we identify that creating word embeddings on much larger Hinglish corpora may have significant results. We also hypothesize that considering alternate methods than translation and transliteration may prove beneficial. ### References [1] Mathur, Puneet and Sawhney, Ramit and Ayyar, Meghna and Shah, Rajiv, Did you offend me? classification of offensive tweets in hinglish language, Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) [2] Mathur, Puneet and Shah, Rajiv and Sawhney, Ramit and Mahata, Debanjan Detecting offensive tweets in hindi-english code-switched language Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media [3] Vo, Quan-Hoang and Nguyen, Huy-Tien and Le, Bac and Nguyen, Minh-Le Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE) [4] Hochreiter, Sepp and Schmidhuber, Jürgen Long short-term memory Neural computation 1997 [5] Sinha, R Mahesh K and Thakur, Anil Multi-channel LSTM-CNN model for Vietnamese sentiment analysis 2017 9th international conference on knowledge and systems engineering (KSE) [6] Pennington, Jeffrey and Socher, Richard and Manning, Christopher Glove: Global vectors for word representation Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) [7] Zhang, Lei and Wang, Shuai and Liu, Bing Deep learning for sentiment analysis: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery [8] Caruana, Rich and Lawrence, Steve and Giles, C Lee Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping Advances in neural information processing systems [9] Beale, Mark Hudson and Hagan, Martin T and Demuth, Howard B Neural network toolbox user’s guide The MathWorks Incs [10] Chollet, François and others Keras: The python deep learning library Astrophysics Source Code Library [11] Wei, Jason and Zou, Kai EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) Table 1: Annotated Data set Table 2: Examples in the dataset Table 3: Train-test split Figure 1: Deep learning network used for the modeling Figure 2: Results of various experiments	HEOT
Why did Kalvin continue researching on his own at home? A. He wanted to be sure it was safe. B. He needed to work extra hours to meet the deadline. C. He wanted to patent the Super-Opener idea for himself. D. He wanted to better understand the technology and create a solution.	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.	D. He wanted to better understand the technology and create a solution.
What is the Ki67 labeling index percentage noted in Mrs. Linda Mayer's left breast carcinoma diagnosis? Choose the correct answer from the following options: A. 12% B. 15% C. 17% D. 2% E. Not specified.	### Patient Report 0 Dear colleague, We report to you about Mrs. Linda Mayer, born on 01/12/1948, who presented to our outpatient clinic on 07/13/19. Diagnoses: - BIRADS IV, recommended biopsy during breast diagnostics. - Left breast carcinoma: iT1b; iN0; MX; ER: 12/12; PR: 2/12; Her-2: neg; Ki67: 15%. Other Diagnoses: - Status post apoplexy - Status post cataract surgery - Status post right hip total hip replacement (THR) - Pemphigus vulgaris under azathioprine therapy - Osteoporosis - Obesity with a BMI of 35 - Undergoing immunosuppressive therapy with prednisolone Family History: - Sister deceased at age 39 from breast cancer. - Mother and grandmother (maternal and paternal) were diagnosed with breast cancer. Medical History: The CT thorax report indicates the presence of inflammatory foci, warranting further follow-up. The relevant data was documented and presented during the tumor conference. Subsequently, a telephone conversation was conducted with the patient to discuss the next steps. Tumor board decision from 07/13/2019: Imaging: 1) MRI examination detected a unifocal lesion on the left external aspect, measuring approximately 2.4 cm in size. 2) CT scan (thorax/abdomen 07/12/2019) revealed a previously known liver lesion, likely a hemangioma. No evidence of metastases was identified. Nonspecific, small foci were observed in the lungs, likely indicative of post-inflammatory changes. Recommendations: 1. If no metastasis (M0): Fast-track BRCA testing is recommended. 2. If BRCA testing returns negative: Proceed with a selective excision of the left breast after ultrasound-guided fine needle marking and sentinel lymph node biopsy on the left side. Additionally, perform Endopredict analysis on the surgical specimen. Current Medication: Medication Dosage Route Frequency ------------------------------- ------------ ----------- --------------- Aspirin 100mg Oral 1-0-0 Simvastatin (Zocor) 40mg Oral 0-1-0 Haloperidol (Haldol) 100mg Oral ½-0-½ Zopiclone (Imovane) 7.5mg Oral 0-0-1 Trazodone (Desyrel) 100mg Oral 0-0-½- Calcium Supplement (Caltrate) 500mg Oral 1-0-1 Nystatin (Bio-Statin) As advised Oral 1-1-1-1 Pantoprazole (Protonix) 40mg Oral 1-0-0 Prednisolone (Prelone) 40mg Oral As advised Tramadol/Naloxone (Ultram) 50/4mg Oral 1-0-1 Acyclovir (Zovirax) 800mg Oral 1-1-1 Mammography and Tomosynthesis from 07/8/2019: [Findings]{.underline}: During the inspection and palpation, no significant findings were noted on either side. Some areas with higher mammographic density were observed, which slightly limited the assessment. However, during the initial examination, a small architectural irregularity was identified on the outer left side. This irregularity appeared as a small, roundish compression measuring approximately 6mm and was visible only in the medio-lateral oblique image, with a nipple distance of 8cm. Apart from this discovery, there were no other suspicious focal findings on either side. No clustered or irregular microcalcifications were detected. Additionally, a long-term, unchanged observation noted some asymmetry with denser breast tissue present on both sides, particularly on the outer aspects. Sonographic evaluation posed challenges due to the mixed echogenic glandular tissue. As a possible corresponding feature to the questionable architectural irregularity on the outer left side, a blurred, echo-poor area with a vertical alignment measuring about 7x5mm was identified. Importantly, no other suspicious focal findings were observed, and there was no evidence of enlarged lymph nodes in the axilla on both sides. [Assessment]{.underline}: The observed finding on the left side presents an uncertain nature, categorized as BIRADS IVb. In contrast, the finding on the right side appears benign, categorized as BIRADS II. To gain a more conclusive understanding of the left-sided finding, we recommend a histological assessment through a sonographically guided high-speed punch biopsy. An appointment has been scheduled with the patient to proceed with this biopsy and obtain a definitive diagnosis.Formularbeginn FormularendeCurrent Recommendations:\ A fast-track decision will be made regarding tumor genetics, and the patient will be notified of the appointment via telephone. The patient should bring the pathology blocks from Fairview Clinic on the day of blood collection for genetic testing, along with a referral for an Endopredict test. A multidisciplinary team meeting will be convened after the Endopredict test and genetic testing results are available. If there is persistence or worsening of symptoms, we strongly advise the patient to seek immediate re-evaluation. Additionally, outside of regular office hours, the patient can seek assistance at the emergency care unit in case of emergency. MRI from 07/11/2019: [Technique:]{.underline} Breast MRI (3T scanner) with dedicated mammary surface coil: [Findings:]{.underline} The overall contrast enhancement was observed bilaterally to evaluate the Grade II findings. There was low to moderate small-spotted contrast enhancement with slightly limited assessability. The contrast dynamics revealed a patchy, confluent, blurred, and elongated contrast enhancement, corresponding to the primary lesion, which measured approximately 2.4 cm on the lower left exterior. Single spicules were noted, and the lesion appeared hypointense in T1w imaging. No suspicious focal findings with contrast enhancement were detected on the right side. Small axillary lymph nodes were observed on the left side, but they did not appear suspicious based on MR morphology. Additionally, there were no suspicious lymph nodes on the right side. [Assessment:]{.underline} An unifocal primary lesion measuring approximately 2.4 cm in diameter was identified on the lower left exterior. It exhibited patchy confluent enhancement and architectural disturbance, with single spicules. No evidence of suspicious lymph nodes was found. The left side is categorized as BIRADS 6, indicating a high suspicion of malignancy, while the right side is categorized as BIRADS 2, indicating a benign finding. ### Patient Report 1 Dear colleague, We are writing to provide you with an update on the medical condition of Mrs. Linda Mayer, born on 01/12/1948, who attended our outpatient clinic on 08/02/2019. Diagnoses: - Vacuum-assisted biopsy-confirmed ductal carcinoma in situ (DCIS) of the right breast (17mm) - Histological grade G3, estrogen receptor (ER) and progesterone receptor (PR) negative. - Postmenopausal for the past eight years. - Previous surgical history includes an appendectomy. - Allergies: Hay fever Current Presentation: The patient sought consultation following a confirmed diagnosis of DCIS (Ductal Carcinoma In Situ) in the right breast, which was determined through a vacuum-assisted biopsy. Physical Examination: Upon physical examination, there is evidence of a post-intervention hematoma located in the upper right quadrant of the right breast. However, the clip from the biopsy is not clearly visible. A sonographic examination of the right axilla reveals no abnormalities. Current Recommendations: - Imaging studies have been conducted. - A case presentation is scheduled for our mammary conference tomorrow. - Subsequently, planning for surgery will commence, including the evaluation of sentinel lymph nodes following a right mastectomy and axillary lymph node dissection. ### Patient Report 2 Dear colleague, We are writing to provide an update regarding Mrs. Linda Mayer, born on 01/12/1948, who received outpatient care at our facility on 08/29/2019. Diagnoses: - Vacuum-assisted biopsy-confirmed ductal carcinoma in situ (DCIS) of the right breast, measuring 17mm in size, classified as Grade 3, and testing negative for estrogen receptors (ER) and progesterone receptors (PR). - Mrs. Mayer has been postmenopausal for eight years. - Notable allergy: Hay fever Tumor Board Decision: Mammography imaging revealed a clip associated with a focal finding in the right breast adjacent to calcifications. [Recommendation]{.underline}: Proceed with sentinel lymph node evaluation after right mastectomy, including clip localization on the right side. Current Presentation: During the patient\'s recent outpatient visit, an extensive pre-operative consultation was conducted. This discussion covered the indications for the surgery, details of the surgical process, potential alternative options, as well as general and specific risks associated with the procedure. These risks included the possibility of an aesthetically suboptimal outcome and the chance of encountering an R1 situation. The patient did not have any further questions and provided written consent for the procedure. Physical Examination: Both breasts appear normal upon inspection and palpation. The right axilla shows no abnormalities. Medical History: Mrs. Linda Mayer presented to our clinic with a vacuum biopsy-confirmed DCIS of the right breast for therapeutic intervention. The decision for surgery was reached following a comprehensive review by our interdisciplinary breast board. After an extensive discussion of the procedure\'s scope, associated risks, and alternative options, the patient provided informed consent for the proposed surgery. Preoperative Procedure: Sonographic and mammographic fine needle marking of the remaining findings and the clip in the right breast. Surgical Report: Team time-out conducted with colleagues of anesthesia. Patient positioned in the supine position. Surgical site disinfection and sterile draping. Marking of the incision site. A semicircular incision was made laterally on the right breast. Visualization and dissection along the marking wire towards the marked finding. Excision of the marked findings, with a safety margin of approximately 1-2 cm. The excised specimen measured approximately 4 x 5 x 3 cm. Markings using standard protocol (green thread cranially, blue thread ventrally). The excised specimen was sent for preparation radiography. Hemostasis was meticulously ensured. Insertion of a 10Ch Blake drain into the segmental cavity, followed by suturing. Verification of a blood-dry wound cavity. Preparation radiography included the marked area and the marking wires. The excised material was transferred to our pathology colleagues for histological examination. Subdermal and intracutaneous sutures with Monocryl 3/0 in a continuous manner. Application of Steristrips and dressing. Instruments, swabs, and cloths were accounted for per the nurse\'s checklist. The patient was correctly positioned throughout the operation. The anesthesiologic course was without significant problems. A thorax compression bandage was applied in the operating room as a preventive measure against bleeding. Postoperative Procedure: Pain management, thrombosis prophylaxis, application of a pressure dressing, drainage under suction. Examinations: Digital Mammography performed on 08/29/2019 [Clinical indication]{.underline}: DCIS right [Question]{.underline}: Please send specimen + Mx-FNM Findings: Sonographically guided wire marking of the maximum microcalcification group measuring about 12 mm. Local hematoma cavity and inset clip marking directly cranial to the finding. Stitch direction from lateral to medial. The wire is positioned with the tip caudal to the clip in close proximity to the microcalcification. Additional marking of the focal localization on the skin. Documentation of the wire course in two planes. - Telephone discussion of findings with the surgeon. - Preparation radiography and preparation sonography are recommended. - Marking wire and suspicious focal findings centrally included in the preparation. - Intraoperative report of findings has been conveyed to the surgeon. Current Recommendations: - Scheduled for inpatient admission on ward 22 tomorrow. - Right breast mastectomy with sentinel lymph node evaluation. ### Patient Report 3 Dear colleague, We are writing to update you on the clinical course of Mrs. Linda Mayer, born on 01/12/1948, who was under our inpatient care from 08/30/2019 to 09/12/2019. Diagnosis: Vacuum-assisted biopsy confirmed Ductal Carcinoma In Situ (DCIS) in the right breast, measuring 17mm, Grade 3, ER/PR negative. Tumor Board Decision (07/13/2019): [Imaging:]{.underline} Clip identified in focal lesion in the right breast, adjacent to calcifications. [Recommendation]{.underline}: Spin Echo following fine-needle localization with mammography-guided control of the clip in the right breast. [Subsequent Recommendation (08/27/2019):]{.underline} Radiation therapy to the right breast. Regular follow-up is advised. Medical History: Ms. Linda Mayer presented to our facility on 08/30/2019 for the aforementioned surgical procedure. After a comprehensive discussion regarding the surgical plan, potential risks, and possible complications, the patient consented to proceed. The surgery was executed without complications on 09/01/2019. The postoperative course was unremarkable, allowing for Ms. Mayer\'s discharge on 09/12/2019 in stable condition and with no signs of wound irritation. Histopathological Findings (09/01/2019): The resected segment from the right breast showed a maximum necrotic zone of 1.6 cm with foreign body reaction, chronic resorptive inflammation, fibrosis, and residual hemorrhage. These findings primarily correspond to the pre-biopsy site. Surrounding this were areas of DCIS with solid and cribriform growth patterns and comedonecrosis, WHO Grade 3, Nuclear Grade 3, with a reconstructed extent of 3.5 cm. Resection margins were as follows: ventral 0.15 cm, caudal 0.2 cm, dorsal 0.4 cm, with remaining margins exceeding 0.5 cm. TNM Classification (8th Edition, 2017): pTis (DCIS), R0, G3. Additional immunohistochemical studies are underway to determine hormone receptor status; a supplementary report will follow. Postoperative Plan: The patient was educated on standard postoperative care and the importance of immediate re-evaluation for any persistent or worsening symptoms. Radiation therapy to the right breast is planned, along with regular follow-up appointments. Should you have any questions or require further clarification, we are readily available. For urgent concerns outside of regular office hours, emergency care is available at the Emergency Department. Internal Histopathological Findings Report Clinical Data: DCIS in the right breast (17 mm), Grade 3, ER/PR negative. Macroscopic Examination: The resected mammary segment from the right breast, marked with dual threads and containing a fine-needle marker inserted ventro-laterally, measures 4.5 x 5.5 x 3 cm (HxWxD) and weighs 35 grams. The specimen was sectioned from medial to lateral into 14 lamellae. The cut surface predominantly shows yellowish, lobulated mammary parenchyma with sparse striated whitish glandular components. A DCIS-suspected area, up to 2.1 cm in size, is evident caudally and centro-ventrally (from lamellae 4-10), displaying both reddish-hemorrhagic and whitish-nodular indurations. Minimal distances from the suspicious area to the resection margins are as follows: cranial 2 cm, caudal 0.2 cm, dorsal 0.2 cm, ventral 0.1 cm, medial 1.6 cm, lateral 2.5 cm. The suspect area was completely embedded. Ink markings: green/cranial, yellow/caudal, blue/ventral, black/dorsal. Microscopic Examination: Histological sections of the mammary parenchyma reveal fibro-lipomatous stroma and glandular lobules with a two-layered epithelial lining. In lamellae 3-6 and 11, solid and cribriform epithelial proliferations are evident. Cells are cuboidal with variably enlarged, predominantly moderately pleomorphic, round to oval nuclei. Comedo-like necroses are occasionally observed in secondary lumina. Microscopic distances to the deposition margins are consistent with the macroscopic findings. The surrounding stroma in lamellae 6-9 shows extensive geographic adipose tissue necrosis, multinucleated foreign body-type giant cells, foamy cell macrophages, collagen fiber proliferation, and fresh hemorrhages. Supplemental Immunohistochemical Findings (09/04/2019): Microscopy: In the meantime, the material was further processed as announced. Here, the previously described intraductal epithelial growths, each with negative staining reaction for the estrogen and progesterone receptor (with regular external and internal control reaction). Critical Findings: Resected mammary segment with paracentral, max. 1.6 cm necrotic zone with foreign body reaction, chronic resorptive. Chronic resorptive inflammation, fibrosis, and hemorrhage remnants (primarily corresponding to the pre-biopsy site), and surrounding portions of ductal carcinoma in situ. Ductal carcinoma in situ, solid and rib-shaped growth type with comedonecrosis, WHO grade 3, nuclear grade 3. The resection was locally complete with the following Safety margins: ventral 0.15 cm, caudal 0.2 cm, dorsal 0.4 cm, and the remaining sedimentation margins more than 0.5 cm. TNM classification (8th edition 2017): pTis (DCIS), R0, G3. [Hormone receptor status:]{.underline} - Estrogen receptor: negative (0%). - Progesterone receptor: negative (0%). ### Patient Report 4 Dear colleague, We are writing to provide an update regarding Mrs. Linda Mayer, born on 01/12/1948, who received outpatient treatment on 27/09/2019. Diagnoses: Left breast carcinoma; iT1c; iN0; MX; ER:12/12; PR:2/12; Her-2: neg; Ki67:15%, BRCA 2 mutation. Other Diagnoses: - Hailey-Hailey disease - currently regressing under prednisolone. - History of apoplexy in 2016 with no residuals - Depressive episodes - Right hip total hip replacement - History of left adnexectomy in 1980 due to extrauterine pregnancy - Tubal sterilization in 1988. - Uterine curettage (Abrasio) in 2004 - Hysterectomy in 2005 Allergies: Hay fever Imaging: - CT revealed a cystic lesion in the liver, not suspicious for metastasis. Granulomatous, post-inflammatory changes in the lung. - An MRI of the left breast showed a unifocal lesion on the outer left side with a 2.4 cm extension. Histology: Gene score of 6.5, indicating a high-risk profile (pT2 or pN1) if BRCA negative. Recommendation: If BRCA negative, SE left mamma after ultrasound-FNM with correlation in Mx and SLNB on the left. Current Presentation: Mrs. Linda Mayer presented for pre-operative evaluation for left mastectomy. BRCA testing confirmed a BRCA2 mutation, warranting bilateral subcutaneous mastectomy and SLNB on the left. Reconstruction with implants and mesh is planned, along with a breast lift as requested by the patient. Macroscopy: Left Subcutaneous Mastectomy (Blue/Ventral, Green/Cranial): - Specimen Size: 17 x 15 x 6 cm (Height x Width x Depth), Weight: 410 g - Description: Dual filament-labeled subcutaneous mastectomy specimen - Specimen Workup: 27 lamellae from lateral to medial - Tumor-Suspect Area (Lamellae 17-21): Max. 1.6 cm, white dermal, partly blurred - Margins from Tumor Area: Ventral 0.1 cm, Caudal 1 cm, Dorsal 1.2 cm, Cranial \> 5 cm, Lateral \> 5 cm, Medial \> 2 cm - Remaining Mammary Parenchyma: Predominantly yellowish lipomatous with focal nodular appearance - Ink Markings: Cranial/Green, Caudal/Yellow, Ventral/Blue, Dorsal/Black - A: Lamella 17 - Covers dorsal and caudal - B: Lamella 18 - Covers ventral - C: Lamella 19 - Covers ventral - D: Blade 21 - Covers ventral - E: Lamella 20 - Reference cranial - F: Lamella 16 - Immediately laterally following mammary parenchyma - G: Blade 22 - Reference immediately medial following mammary tissue - H: Lamella 12 - Central section - I: Lamella 8 - Documented section top/outside - J: Lamella 3 - Vestigial section below/outside - K: Lamella 21 - White-nodular imposing area - L: Lamella 8 - Further section below/outside with nodular area - M: Lateral border lamella perpendicularly - N: Medial border lamella perpendicular (Exemplary) Second Sentinel Lymph Node on the Left: - Specimen: Maximum of 6 cm of fat tissue resectate with 1 to 2 cm of lymph nodes and smaller nodular indurations. - A, B: One lymph node each divided - C: Further nodular indurations Palpable Lymph Nodes Level I: - Specimen: One max. 4.5 cm large fat resectate with nodular indurations up to 1.5 cm in size - A: One nodular induration divided - B: Further nodular indurated portions Right Subcutaneous Mastectomy: - Specimen: Double thread-labeled 450 g subcutaneous mastectomy specimen - Assumed Suture Markings: Blue (Ventral) and Green (Cranial) - Dorsal Fascia Intact - [Specimen Preparation:]{.underline} 16 lamellae from medial to lateral - Predominantly yellowish lobulated with streaky, beige, impinging strands of tissue - Isolated hemorrhages in the parenchyma - Ink Markings: Green = Cranial, Yellow = Caudal, Blue = Ventral, Black = Dorsal <!-- --> - A: Medial border lamella perpendicular (Exemplary) - B: Lamella 5 with reference ventrally (below inside) - C: Lamella 8 with reference ventrally (below inside) - D: Lamella 6 with ventral and dorsal reference (upper inside) - E: Blade 8 with ventral and dorsal cover (top inside) - F: Blade 11 with cover dorsal and caudal (bottom outside) - G: Blade 13 with dorsal cover (bottom outside) - H: Blade 10 with ventral and dorsal cover (top outside) - I: Lamella 14 with reference cranial and dorsal and bleeding in (upper outer) - J: Lateral border lamella perpendicular (Exemplary) Microscopy: 1\) In the tumor-suspicious area, a blurred large fibrosis zone with star-shaped extensions is visible. Intercalated are single-cell and stranded epithelial cells with a high nuclear-cytoplasmic ratio. The nuclei are monomorphic with finely dispersed chromatin, at most, very isolated mitoses. Adjacent distended glandular ducts with a discohesive cell proliferate with the same cytomorphology. Sporadically, preexistent glandular ducts are sheared disc-like by the infiltrative tumor cells. Samples from the nodular area of lamella 21 show areas of cell-poor hyaline sclerosis with partly ectatically dilated glandular ducts. 2\) Second lymph node with partial infiltrates of the neoplasia described above. The cells here are relatively densely packed. Somewhat increased mitoses. In the lymph nodes, iron deposition is also in the sinus histiocytes. 3\) Lymph nodes with partly sparse iron deposition. No epithelial foreign infiltrates. 4\) Regular mammary gland parenchyma. No tumor infiltrates. Part of the glandular ducts are slightly cystically dilated. Preliminary Critical Findings Report: Left breast carcinoma measuring max 1.6 cm diagnosed as moderately differentiated invasive lobular carcinoma, B.R.E. score 6 (3+2+1, G2). Presence of tumor-associated and peritumoral lobular carcinoma in situ. Resection status indicates locally complete excision of both invasive and non-invasive carcinoma; minimal margins as follows: ventral \<0.1 cm, caudal 0.2 cm, dorsal 0.8 cm, remaining margins ≥0.5 cm. Nodal status reveals max 0.25 cm metastasis in 1/5 nodes, 0/2 additional nodes, without extracapsular spread. Right mammary gland from subcutaneous mastectomy shows tumor-free parenchyma. TNM classification (8th ed. 2017): pT1c, pTis (LCIS), pN1a, G2, L0, V0, Pn0, R0. Investigations to determine tumor biology were initiated. Addendum follows. Supplementary findings on 10/07/2019 Editing: immunohistochemistry: Estrogen receptor, Progesterone receptor, Her2neu, MIB-1 (block 1D). Critical Findings Report: Breast carcinoma on the left with a 1.6 cm invasive lobular carcinoma, moderately differentiated, with a B.R.E. score of 6 (3+2+1, G2). Additionally, tumor-associated and peritumoral lobular carcinoma in situ are noted. Resection status confirms locally complete excision of both invasive and non-invasive carcinomas; minimal resection margins are ventral \<0.1 cm, caudal (LCIS) 0.2 cm, dorsal 0.8 cm, and all other margins ≥0.5 cm. Nodal assessment reveals a single metastasis with a maximum dimension of 0.25 cm among 7 lymph nodes, specifically found in 1/5 nodes, with no additional metastasis in 0/2 nodes and no extracapsular extension. Contralateral right mammary gland from subcutaneous mastectomy is tumor-free. Tumor biology of the invasive carcinoma demonstrates strong positive estrogen receptor expression in 100% of tumor cells, strong positive progesterone receptor expression in 1% of tumor cells, negative HER2/neu status (Score 1+), and a Ki67 (MIB-1) proliferation index of 25%. TNM classification (8th Edition 2017): pT1c, pTis (LCIS), pN1a (1/7 ECE-, sn), G2, L0, V0, Pn0, R0. Surgery Report (Vac Change + Irrigation): Indication for VAC change. After a detailed explanation of the procedure, its risks, and alternatives, the patient agrees to the proposed procedure. The course of surgery: Proper positioning in a supine position. Removal of the VAC sponge. A foul odor appears from the wound cavity. Careful disinfection of the surgical area. Sterile draping. Detailed inspection of the wound conditions. Wound debridement with removal of fibrin coatings and freshening of the wound. Resection of necrotic material in places with sharp spoon. Followed by extensive Irrigation of the entire wound bed and wound edges using 1 l Polyhexanide solution. Renewed VAC sponge application according to standard. Postoperative procedure: Pain medication, thrombosis prophylaxis, continuation of antibiotic therapy. In the case of abundant Staphylococcus aureus and isolated Pseudomosas in the smear and still clinical suspected infection, extension of antibiotic treatment to Meropenem. Surgery Report: Implant Placement Type of Surgery: Implant placement and wound closure. Report: After infection and VAC therapy, clean smears and planning of reinsertion. Informed consent. Intraoperative consults: Anesthesia. Course of Surgery: Team time out. Removal VAC sponge. Disinfection and covering. Irrigation of the wound cavity with Serasept. Blust irrigation. Fixation cranially and laterally with 4 fixation sutures with Vircryl 2-0. Choice of trial implant. Temporary insertion. Control in sitting and lying positions. Choice of the implant. Repeated disinfection. Change of gloves. Insertion of the implant into the pocket. Careful hemostasis. Insertion of a Blake drain into the wound cavity. Suturing of the drainage. Subcutaneous sutures with Monocryl 3-0. Type of Surgery: Prophylactic open Laparoscopy, extensive adhesiolysis Type of Anesthesia: ITN Report: Patient presented for prophylactic right adnexectomy in the course of hysterectomy and left adnexectomy due to genetic burden. Intraoperatively, secondary wound closure was to be performed in the case of a right mammary wound weeping more than one year postoperatively. The patient agreed to the planned procedure in writing after receiving detailed information about the extent, the risks, and the alternatives. Course of the Operation: Team time out with anesthesia colleagues. Flat lithotomy positioning, disinfection, and sterile draping. Placement of permanent transurethral catheter. Subumbilical incision and dissection onto the fascia. Opening of the fascia and suturing of the same. Exposure of the peritoneum and opening of the same. Insertion of the 10-mm optic trocar. Insertion of three additional trocars into the lower abdomen (left and center right, each 5mm; right 10mm). The following situation is seen: when the camera is inserted from the umbilical region, an extensive adhesion is seen. Only by changing the camera to the right lower bay is extensive adhesiolysis possible. The omentum is fused with the peritoneum and the serosa of the uterus. Upper abdomen as far as visible inconspicuous. After hysterectomy and adnexectomy on the left side, adnexa on the right side atrophic and inconspicuous. The peritoneum is smooth as far as can be seen. Visualization of the right adnexa and the suspensory ligament of ovary. Coagulation of the suspensory ligament of ovary ligament after visualization of the ureter on the same side. Stepwise dissection of the adnexa from the pelvic wall. Recovery via endobag. Hemostasis. Inspection of the situs. Removal of instrumentation under vision and draining of pneumoperitoneum. Closure of the abdominal fascia at the umbilicus and right lower abdomen. Suturing of the skin with Monocryl 3/0. Compression bandage at each trocar insertion site. Inspection of the right mamma. In the area of the surgical scar laterally/externally, 2-3 small epithelium-lined pore-like openings are visible; here, on pressure, discharge of rather viscous/sebaceous, non-odorous, or purulent fluid. No dehiscence is visible, suspected. fistula ducts to the implant cavity. After consultation with the mamma surgeon, a two-stage procedure was planned for the treatment of the fistula tracts. Correct positioning and inconspicuous anesthesiological course. Instrumentation, swabs, and cloths complete according to the operating room nurse. Postoperative procedures include analgesia, mobilization, thrombosis prophylaxis, and waiting for histology. Internal Histopathological Report [Clinical information/question]{.underline}: Fistula formation mammary right. Dignity? [Macroscopy]{.underline}: Skin spindle from scar mammary right: fix. a 2.4 cm long, stranded skin-subcutaneous excidate. Lamellation and complete embedding. [Processing]{.underline}: 1 block, HE [Microscopy]{.underline}: Histologic skin/subcutaneous cross-sections with overlay by a multilayered keratinizing squamous epithelium. The dermis with few inset regular skin adnexal structures, sparse to moderately dense mononuclear-dominated inflammatory infiltrates, and proliferation of cell-poor, fiber-rich collagenous connective tissue. Critical Findings Report: Skin spindle on scar mamma right: skin/subcutaneous resectate with fibrosis and chronic inflammation. To ensure that all findings are recorded, the material will be further processed. A follow-up report will follow. [Microscopy]{.underline}: In the meantime, the material was further processed as announced. The van Gieson stain showed extensive proliferation of collagenous and, in some places elastic fibers. Also in the additional immunohistochemical staining against no evidence of atypical epithelial infiltrates. Lab results upon Discharge: Parameter Results Reference Range -------------------------------- ------------- --------------------- Sodium 141 mEq/L 132-146 mEq/L Potassium 4.2 mEq/L 3.4-4.5 mEq/L Creatinine 0.82 mg/dL 0.50-0.90 mg/dL Estimated GFR (eGFR CKD-EPI) \>90 \- Total Bilirubin 0.21 mg/dL \< 1.20 mg/dL Albumin 4.09 g/dL 3.5-5.2 g/dL CRP 7.8 mg/L \< 5.0 mg/L Haptoglobin 108 mg/dL 30-200 mg/dL Ferritin 24 µg/L 13-140 µg/L ALT 24 U/L \< 31 U/L AST 37 U/L \< 35 U/L Gamma-GT 27 U/L 5-36 U/L Lactate Dehydrogenase 244 U/L 135-214 U/L 25-OH-Vitamin D3 91.7 nmol/L 50.0-150.0 nmol/L Hemoglobin 11.1 g/dL 12.0-15.6 g/dL Hematocrit 40.0% 35.5-45.5% Red Blood Cells 3.5 M/uL 3.9-5.2 M/uL White Blood Cells 2.41 K/uL 3.90-10.50 K/uL Platelets 142 K/uL 150-370 K/uL MCV 73.0 fL 80.0-99.0 fL MCH 23.9 pg 27.0-33.5 pg MCHC 32.7 g/dL 31.5-36.0 g/dL MPV 10.7 fL 7.0-12.0 fL RDW-CV 14.8% 11.5-15.0% Absolute Neutrophils 1.27 K/uL 1.50-7.70 K/uL Absolute Immature Granulocytes 0.000 K/uL \< 0.050 K/uL Absolute Lymphocytes 0.67 K/uL 1.10-4.50 K/uL Absolute Monocytes 0.34 K/uL 0.10-0.90 K/uL Absolute Eosinophils 0.09 K/uL 0.02-0.50 K/uL Absolute Basophils 0.04 K/uL 0.00-0.20 K/uL Free Hemoglobin 5.00 mg/dL \< 20.00 mg/dL ### Patient Report 5 Dear colleague, We would like to provide an update on Mrs. Linda Mayer, born on 01/12/1948, who received inpatient care at our facility from 01/01/2021 to 01/14/2021. Diagnosis: Hailey-Hailey disease. - Upon admission, the patient was under treatment with Acitretin 25mg. Other Diagnoses: - History of apoplexy in 2016 with no residuals - Depressive episodes - Right hip total hip replacement - History of left adnexectomy in 1980 die to extrauterine pregnancy - Tubal sterilization in 1988. - Uterine curettage in 2004 - Hysterectomy in 2005 Medical History: Mrs. Linda Mayer was referred to our hospital for the management of Hailey-Hailey disease after assessment in our outpatient clinic. She reported a worsening of painful skin erosions on her neck and inner thighs over a span of approximately 3 weeks. Itchiness was not reported. Prior attempts at treatment, including the topical use of Fucicort, Prednisolone with Octenidine, and Polidocanol gel, had provided limited relief. She denied any other physical complaints, dyspnea, B symptoms, infections, or irregularities in stool and micturition. Her history revealed the initial onset of Hailey-Hailey disease, initially presenting as itching followed by skin erosions, which subsequently healed with scarring. The diagnosis was established at the Fairview Clinic. Previous therapeutic interventions included systemic cortisone shock therapy, as-needed application of Fucicort ointment, and axillary laser therapy. Family History: - Father: Hailey-Hailey Disease (M. Hailey-Hailey) - Mother and Sister: Breast carcinoma Psychosocial History: Socially, Ms. Linda Mayer is described as a retiree, having previously worked as a nurse. Physical Examination on Admission: Height: 16 cm, Body Weight: 80.0 kg, BMI: 29.7 Physical Examination Findings: Generally stable condition with increased nutritional status. Her consciousness was unremarkable, and cranial mobility was free. Ocular mobility was regular, with prompt pupillary reflexes to accommodation and light. She exhibited a normal heart rate, and cardiac and pulmonary examinations were unremarkable. No heart murmurs were detected. Renal bed and spine were not palpable. Further internal and orienting neurological examinations revealed no pathological findings. Skin Findings on Admission: Sharp erosions, approximately 10x10 cm in size, with a livid-erythematous base, partly crusty, were observed on the neck and proximal inner thighs. In the axillary regions on both sides, there were marginal, livid-erythematous, well-demarcated plaques interspersed with scarring strands, more pronounced on the right side. Skin type II. Mucous membranes appeared normal. Dermographism was noted to be ruber. Medication Dosage Frequency ------------------------------ ------------ ------------------------------- Prednisolone (Deltasone) 5 mg 1.5-0-0-0-0-0 Aspirin (Bayer) 100 mg 0-1-0-0-0-0 Simvastatin (Zocor) 40 mg 0-0-0-0-1 Pantoprazole (Protonix) 45.1 mg 1-0-0-0-0 Acitretin (Soriatane) 25 mg 1-0-0-0-0 Tetrabenazine (Xenazine) 111 mg 0.25-0.25-0.25-0.25-0.25-0.25 Letrozole (Femara) 2.5 mg 0-0-1-0 Risedronate Sodium (Actonel) 35 mg 1-0-0-0-0 Acetaminophen (Tylenol) 500 mg 0-1-0-1 Naloxone (Narcan) 8.8 mg 1-0-1-0 Eszopiclone (Lunesta) 7.5 mg 0-0-1-0 Other Findings: MRSA Smears: - Nasal Smear: Normal flora, no MRSA. - Throat Swab: Normal flora, no MRSA. - Non-lesional Skin Smear: Normal flora. - Lesional Skin Swab: Abundant Pseudomonas aeruginosa, abundant Klebsiella oxytoca, and abundant Serratia sp., sensitive to piperacillin-tazobactam. Therapy and Progression: Mrs. Linda Mayer was admitted on 01/01/2021 as an inpatient for a refractory exacerbation of previously diagnosed Hailey-Hailey disease. On admission, both bacteriological and mycological smears were conducted, which indicated abundant levels of Pseudomonas aeruginosa, Klebsiella oxytoca, and Serratia sp. Lab tests showed a CRP level of 2.83 mg/dL and a leukocyte count of 8.8 G/L. Initial topical therapy consisted of Zinc oxide ointment, Clotrimazole paste, and Triamcinolone Acetonide shake lotion. Treatment was modified on 01/04/2021 to include Clotrimazole (Lotrimin) paste in the mornings and methylprednisolone emulsion in the evenings. Starting on 01/08, eosin aqueous solution was introduced for application on the thighs, serving antiseptic and drying purposes. A hydrophilic prednicarbate cream at 0.25% concentration, combined with octenidine at 0.1%, was applied to the neck and thighs twice daily, also starting on 01/08. For showering, octenidine-based wash lotion was utilized. Additionally, Mrs. Linda Mayer received an emulsifying ointment as part of her treatment. ### Patient Report 6 Dear colleague, We are providing an update on our patient Mrs. Linda Mayer, born on 01/12/1948, who presented to our outpatient clinic on 09/22/2021. Diagnoses: M. Hailey-Hailey Medical History: - Diagnosis of M. Hailey-Hailey at the Fairview Clinic <!-- --> - Treatment involved systemic steroid shock therapy, laser therapy, and the initiation of Acitretin in October 2021, with no observed improvement. <!-- --> - A dermabrasion procedure was scheduled on 03/18/2021, during a previous inpatient admission. - Acitretin 25mg has been administered daily, with favorable outcomes noted when using Triamcinolone/Triclosan or Prednisolone + Octenidine. - A history of mastectomy with Vacuum-Assisted Closure (VAC) has resulted in breast erosion. Skin Findings: - Erythematous and partially mottled lesions have been identified in the axillary and inguinal regions, with some scarring observed in the axillary area. - On 04/28/2021, somewhat erosive plaques were noted in the inguinal regions. - As of 05/05/2021 discrete erosions are currently present on both forearms. Current Recommendations: - Inpatient admission is scheduled for September 2021. - The prescribed treatment plan includes topical prednicarbate (Dermatop) 0.25% with Octenidine 0.1%, per NRF 11.145, in a 50g container, to be applied once daily for 1-2 weeks. - Hydrocortisone 5% in a suitable base, 200g, is to be applied daily. - The regimen also includes prednicarbate (Dermatop) combined with Octenidine. - Acitretin will be continued temporarily. - A follow-up appointment in the outpatient clinic is scheduled for three months from now. - Discontinuation of Acitretin. - It is recommended to avoid the use of compresses on the erosions to prevent constant trauma. - Topical therapy with petrolatum-based wound ointment and sterile compresses.	15%
How much better performing is the proposed method over the baselines?	### Introduction Recent NLP studies have thrived on distributional hypothesis. More recently, there have been efforts in applying the intuition to larger semantic units, such as sentences, or documents. However, approaches based on distributional semantics are limited by the grounding problem BIBREF0 , which calls for techniques to ground certain conceptual knowledge in perceptual information. Both NLP and vision communities have proposed various multi-modal learning methods to bridge the gap between language and vision. However, how general sentence representations can be benefited from visual grounding has not been fully explored yet. Very recently, BIBREF1 proposed a multi-modal encoder-decoder framework that, given an image caption, jointly predicts another caption and the features of associated image. The work showed promising results for further improving general sentence representations by grounding them visually. However, according to the model, visual association only occurs at the final hidden state of the encoder, potentially limiting the effect of visual grounding. Attention mechanism helps neural networks to focus on specific input features relevant to output. In the case of visually grounded multi-modal framework, applying such attention mechanism could help the encoder to identify visually significant words or phrases. We hypothesize that a language-attentive multi-modal framework has an intuitive basis on how humans mentally visualize certain concepts in sentences during language comprehension. In this paper, we propose an enhanced multi-modal encoder-decoder model, in which the encoder attends to the input sentence and the decoders predict image features and the target sentence. We train the model on images and respective captions from COCO5K dataset BIBREF2 . We augment the state-of-the-art sentence representations with those produced by our model and conduct a series of experiments on transfer tasks to test the quality of sentence representations. Through detailed analysis, we confirm our hypothesis that self-attention help our model produce more feature-rich visually grounded sentence representations. ### Related Work Sentence Representations. Since the inception of word embeddings BIBREF3 , extensive work have emerged for larger semantic units, such as sentences and paragraphs. These works range from deep neural models BIBREF4 to log-bilinear models BIBREF5 , BIBREF6 . A recent work proposed using supervised learning of a specific task as a leverage to obtain general sentence representation BIBREF7 . Joint Learning of Language and Vision. Convergence between computer vision and NLP researches have increasingly become common. Image captioning BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 and image synthesis BIBREF12 are two common tasks. There have been significant studies focusing on improving word embeddings BIBREF13 , BIBREF14 , phrase embeddings BIBREF15 , sentence embeddings BIBREF1 , BIBREF16 , language models BIBREF17 through multi-modal learning of vision and language. Among all studies, BIBREF1 is the first to apply skip-gram-like intuition (predicting multiple modalities from langauge) to joint learning of language and vision in the perspective of general sentence representations. Attention Mechanism in Multi-Modal Semantics. Attention mechanism was first introduced in BIBREF18 for neural machine translation. Similar intuitions have been applied to various NLP BIBREF19 , BIBREF20 , BIBREF21 and vision tasks BIBREF8 . BIBREF8 applied attention mechanism to images to bind specific visual features to language. Recently, self-attention mechanism BIBREF21 has been proposed for situations where there are no extra source of information to “guide the extraction of sentence embedding”. In this work, we propose a novel sentence encoder for the multi-modal encoder-decoder framework that leverages the self-attention mechanism. To the best of our knowledge, such attempt is the first among studies on joint learning of language and vision. ### Proposed Method Given a data sample INLINEFORM0 , where INLINEFORM1 is the source caption, INLINEFORM2 is the target caption, and INLINEFORM3 is the hidden representation of the image, our goal is to predict INLINEFORM4 and INLINEFORM5 with INLINEFORM6 , and the hidden representation in the middle serves as the general sentence representation. ### Visually Grounded Encoder-Decoder Framework We base our model on the encoder-decoder framework introduced in BIBREF1 . A bidirectional Long Short-Term Memory (LSTM) BIBREF22 encodes an input sentence and produces a sentence representation for the input. A pair of LSTM cells encodes the input sequence in both directions and produce two final hidden states: INLINEFORM0 and INLINEFORM1 . The hidden representation of the entire sequence is produced by selecting maximum elements between the two hidden states: INLINEFORM2 . The decoder calculates the probability of a target word INLINEFORM0 at each time step INLINEFORM1 , conditional to the sentence representation INLINEFORM2 and all target words before INLINEFORM3 . INLINEFORM4 . The objective of the basic encoder-decoder model is thus the negative log-likelihood of the target sentence given all model parameters: INLINEFORM0 . ### Visual Grounding Given the source caption representation INLINEFORM0 and the relevant image representation INLINEFORM1 , we associate the two representations by projecting INLINEFORM2 into image feature space. We train the model to rank the similarity between predicted image features INLINEFORM3 and the target image features INLINEFORM4 higher than other pairs, which is achieved by ranking loss functions. Although margin ranking loss has been the dominant choice for training cross-modal feature matching BIBREF17 , BIBREF1 , BIBREF23 , we find that log-exp-sum pairwise ranking BIBREF24 yields better results in terms of evaluation performance and efficiency. Thus, the objective for ranking DISPLAYFORM0 where INLINEFORM0 is the set of negative examples and INLINEFORM1 is cosine similarity. ### Visual Grounding with Self-Attention Let INLINEFORM0 be the encoder hidden state at timestep INLINEFORM1 concatenated from two opposite directional LSTMs ( INLINEFORM2 is the dimensionality of sentence representations). Let INLINEFORM3 be the hidden state matrix where INLINEFORM4 -th column of INLINEFORM5 is INLINEFORM6 . The self-attention mechanism aims to learn attention weight INLINEFORM7 , i.e. how much attention must be paid to hidden state INLINEFORM8 , based on all hidden states INLINEFORM9 . Since there could be multiple ways to attend depending on desired features, we allow multiple attention vectors to be learned. Attention matrix INLINEFORM10 is a stack of INLINEFORM11 attention vectors, obtained through attention layers: INLINEFORM12 . INLINEFORM13 and INLINEFORM14 are attention parameters and INLINEFORM15 is a hyperparameter. The context matrix INLINEFORM16 is obtained by INLINEFORM17 . Finally, we compress the context matrix into a fixed size representation INLINEFORM18 by max-pooling all context vectors: INLINEFORM19 . Attended representation INLINEFORM20 and encoder-decoder representation INLINEFORM21 are concatenated into the final self-attentive sentence representation INLINEFORM22 . This hybrid representation replaces INLINEFORM23 and is used to predict image features (Section SECREF2 ) and target caption (Section SECREF1 ). ### Learning Objectives Following the experimental design of BIBREF1 , we conduct experiments on three different learning objectives: Cap2All, Cap2Cap, Cap2Img. Under Cap2All, the model is trained to predict both the target caption and the associated image: INLINEFORM0 . Under Cap2Cap, the model is trained to predict only the target caption ( INLINEFORM1 ) and, under Cap2Img, only the associated image ( INLINEFORM2 ). ### Implementation Details Word embeddings INLINEFORM0 are initialized with GloVe BIBREF25 . The hidden dimension of each encoder and decoder LSTM cell ( INLINEFORM1 ) is 1024. We use Adam optimizer BIBREF26 and clip the gradients to between -5 and 5. Number of layers, dropout, and non-linearity for image feature prediction layers are 4, 0.3 and ReLU BIBREF27 respectively. Dimensionality of hidden attention layers ( INLINEFORM3 ) is 350 and number of attentions ( INLINEFORM4 ) is 30. We employ orthogonal initialization BIBREF28 for recurrent weights and xavier initialization BIBREF29 for all others. For the datasets, we use Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10 . Image features are prepared by extracting hidden representations at the final layer of ResNet-101 BIBREF30 . We evaluate sentence representation quality using SentEval BIBREF7 , BIBREF1 scripts. Mini-batch size is 128 and negative samples are prepared from remaining data samples in the same mini-batch. ### Evaluation Adhering to the experimental settings of BIBREF1 , we concatenate sentence representations produced from our model with those obtained from the state-of-the-art unsupervised learning model (Layer Normalized Skip-Thoughts, ST-LN) BIBREF31 . We evaluate the quality of sentence representations produced from different variants of our encoders on well-known transfer tasks: movie review sentiment (MR) BIBREF32 , customer reviews (CR) BIBREF33 , subjectivity (SUBJ) BIBREF34 , opinion polarity (MPQA) BIBREF35 , paraphrase identification (MSRP) BIBREF36 , binary sentiment classification (SST) BIBREF37 , SICK entailment and SICK relatedness BIBREF38 . ### Results Results are shown in Table TABREF11 . Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks. Considering that models with self-attention employ smaller LSTM cells (1024) than those without (2048) (Section SECREF6 ), the performance improvements are significant. Results on COCO5K image and caption retrieval tasks (not included in the paper due to limited space) show comparable performances to other more specialized methods BIBREF10 , BIBREF39 . ### Attention Mechanism at Work In order to study the effects of incorporating self-attention mechanism in joint prediction of image and language features, we examine attention vectors for selected samples from MS-COCO dataset and compare them to associated images (Figure FIGREF13 ). For example, given the sentence “man in black shirt is playing guitar”, our model identifies words that have association with strong visual imagery, such as “man”, “black” and “guitar”. Given the second sentence, our model learned to attend to visually significant words such as “cat” and “bowl”. These findings show that visually grounding self-attended sentence representations helps to expose word-level visual features onto sentence representations BIBREF1 . ### Conclusion and Future Work In this paper, we proposed a novel encoder that exploits self-attention mechanism. We trained the model using MS-COCO dataset and evaluated sentence representations produced by our model (combined with universal sentence representations) on several transfer tasks. Results show that the self-attention mechanism not only improves the qualities of general sentence representations but also guides the encoder to emphasize certain visually associable words, which helps to make visual features more prominent in the sentence representations. As future work, we intend to explore cross-modal attention mechanism to further intertwine language and visual information for the purpose of improving sentence representation quality. Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †. Figure 1: Activated attention weights on two samples from MS-COCO dataset. Vertical axis shows attention vectors learned by our model (compressed due to space limit). Note how the sentence encoder learned to identify words with strong visual associations.	original models were better in some tasks (CR, MPQA, MRPC), utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks
Which of these was not an effect of giving the police force half-powered staters? A. It caused some issues while the police force got trigger-happy, adding to the paranoia B. It made the ship's environment safer now that the police were armed C. The passenger police force felt they had some power D. It was what allowed the Red Mask to finally acquire a weapon	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.	B. It made the ship's environment safer now that the police were armed
What was the height of Mr. Miller as indicated in one of the reports? Choose the correct answer from the following options: A. 169 cm B. 160 cm C. 180 cm D. 175 cm E. 170 cm	### Patient Report 0 Dear colleague, Patient: Miller, John, born 04/07/1961 We report to you about our common patient, Mr. John Miller, who is in our inpatient treatment since 07/30/2019. Diagnoses: \- Suspected right cerebral glioblastoma (first diagnosis) \- Symptoms: Aphasia, passive confusion Patient history: Mr. Miller was admitted as an emergency. He was on the phone with a friend when he suddenly began to exhibit speech difficulties and struggled to find the right words. Consequently, his friend called 911. Upon the ambulance\'s arrival, Mr. Miller was disoriented and exhibited aggressive behavior. There was evidence of a torn door. He had blood on his right forearm and around his mouth, but there were no indications of a tongue bite or urinary incontinence. Upon admission, Mr. Miller was coherent and showed no speech issues. He attributed a mild weakness in his right arm to pre-existing pain in the upper arm. An immediate CT scan revealed a mass suggestive of a glioblastoma in the right cerebral hemisphere, leading to a neurosurgical consultation. Given the possibility of an epileptic seizure, Mr. Miller was hospitalized and started on Levetiracetam. He is currently unaware of his regular medications but takes antihypertensives and diabetes medications, among others. His friend and brother have been notified and are ensuring that a detailed medication list is provided. After a brief stay in ward ABC, Mr. Miller was transferred to the neurosurgery team for further evaluation and treatment. We appreciate the prompt transfer and are available for any further inquiries. Planned Procedures: -Schedule EEG -Clarify routine medications Surgery Report Diagnosis: Suspected HGG (high-grade glioma) of the right hemisphere Procedure: Microsurgical navigation-guided resection of the tumor with intraoperative neuromonitoring (stable MEPs) and intraoperative MRI using 5-ALA. Pathology samples taken (Preliminary: HGG; Final to be confirmed). Resection was followed by duragen placement, watertight dural closure, and multilayer wound closure, with skin sutures. Time: -Start: 11:12 am -Finish: 3:54 pm -Duration: 4 hours 42 minutes Assessment: Mr. Miller presented with a seizure characterized by speech disturbance and disorientation. Imaging revealed a significant right hemispheral mass, likely representing a high-grade brain tumor. The need for surgical resection was determined following discussions at our interdisciplinary tumor board. After being informed about the procedure, alternative treatments, the operation\'s urgency, benefits, and potential risks, Mr. Miller provided written consent following ample time for consideration and the opportunity to ask further questions. Procedure Details: The patient was positioned supine with his head secured in a Nova clamp. Navigation data were read, followed by skin preparation, and the surgical field was sterilized and draped. An arch-shaped incision was made, followed by hemostasis, deep tissue dissection, placement of a burr hole, and the creation of a large bone flap over the lesion. The bone flap was then elevated. Multiple washings were performed, followed by dural opening under microscopic visualization. A corticotomy was carried out with bipolar forceps, CUSA, and suction to progressively reduce the tumor, utilizing 5-ALA fluorescence and continuous neurophysiological monitoring. An intraoperative MRI showed residual tumor, prompting further resection. Hemostasis was achieved, and the wound was closed using tabotamp, followed by duragen placement and dural suturing. The bone flap was refixed using Dogbone plates. The wound area was irrigated extensively once more, followed by subcutaneous and skin sutures. Date: 10/01/2019 Clinical Indication:Suspected recurrence of GBM in the right hemisphere Requested Imaging:cMRI with or without contrast + DTI. Findings:Imaging Modality (GE 3.0T): 3D FLAIR, DTI, SWI, T2\* perfusion, 3D T1 with and without contrast, 3D T2, subtraction.Following resection of a right hemispheric glioblastoma in 08/19, and compared to the last two scans (external: 07/19, internal: 08/19), there is a notable expansion of the prior detected flair hyperintense regions. These now span from the right parietal-subcortical area across the right basal ganglia to the right temporo-occipital/right temporal pole. Specifically, at the dorsocranial edge of the resection cavity, the hyperintense regions appear to have grown since 08/19. These coincide with hyperperfused regions in the T2\* perfusion. Linear SWI signal changes are suggestive of mild post-surgical bleeding. No significant postoperative hemorrhage or territorial ischemia is detected. A normal venous sinus drainage is observed. Right temporal horn appears congested, possibly due to CSF trapping. Assessment: Following the resection of the right hemispheric glioblastoma in 08/19: -Markedly progressive flair edema and evolving barrier disturbance. Regions especially towards the dorsal side of the resection cavity show this alongside associated hyperperfusion. With the recent PET imaging from 10/19, there is an indication of progressive disease as per RANO criteria. -Long-term progressive congestion of the right temporal horn, likely CSF trapping. Surgery ReportDiagnosis:Tumor recurrence after resection of a glioblastoma (IDH wild type, WHO CNS grade 4) of the right hemisphere in 08/2019. The patient underwent combined radiochemotherapy at the local clinical center. Procedure:Navigated, microsurgical resection supported by 5-ALA, with stable MEPs through the previous right temporal access. iMRI conducted between 11:10 and 11:50. Used Duragen/TachoSil, dog bones for closure, followed by layered wound closure and skin sutures. Timing: -Incision: 09:23 am on 10/12/19 -Suture: 12:32 pm on 10/12/19 Assessment:The patient had previously undergone surgery for a glioblastoma and a recurrence was detected on imaging. The tumor board had already deliberated on the surgery. The patient was informed about the surgical procedure, particularly about conducting an extensive resection caudally without impacting function post-mapping. The patient consented, understanding the potential for longer progression-free survival. Procedure Details:The patient was placed in a supine position with the head turned to the side and fixed using the Noras clamp. The right shoulder was padded. The surgical area was prepared by trimming hair around the previous scar, followed by sterilization and draping. After a team time-out, prophylactic antibiotics were administered. The previous scar was reopened and old plates were removed. The microscope was then swung into position and the dura was opened. Navigation proceeded beneath the labbé vein, with tumor resection as guided by ALA fluorescence. Post-tumor removal, extensive hemostasis was achieved using absorbent cotton and TABOTAMP. Intraoperative MRI confirmed a complete resection of the tumor. The dura was sealed using DuraGen, ensuring a watertight closure. The bone flap was reinserted, followed by subcutaneous suturing, skin suturing, and sterile dressing of the wound. ### Patient Report 1 Dear colleague, We write to update you regarding our shared patient, Mr. John Miller, born on 07/04/1961, who visited us on 12/02/2019. Diagnosis:Glioblastoma recurrence, IDH 1 wild type. Tumor Location:Right hemisphere including temporal regions. Clinical History & Treatment: -07/2019: Mr. Miller experienced speech disturbances and confusion. -08/01/2019: Brain PET-MRI revealed a suspected malignancy in the right hemisphere, including temporal areas. -08/11/2019: Glioblastoma was resected at our facility. -08-09/2019: He underwent adjuvant radiochemotherapy (43.4 at 2.7 Gy with a boost of 52.4 Gy at 3 Gy) and Temodal treatment at the local clinical center. -10/01/2019: cMRI with suspected recurrence. -10/12/2019: A recurrent resection was performed at our facility. -11/02/2019: Postoperative brain MRI showed no suspected tumor remnants. Recent Evaluation (12/01/2019):Mr. Miller visited our facility with his brother. Our assessment, based on CTCAE criteria, indicated that he is in a fair but stable general and nutritional health (KPS 70-80%, weight undisclosed, height 175 cm). Neurological and general evaluations revealed a degree of aphasia, mainly with word-finding difficulty, and short-term memory impairment. However, he remains fully oriented and independent in daily life. Additional Observations: His surgical wound has healed well. Pre-existing Conditions: Arterial hypertension, Diabetes Mellitus Type II Allergies: None Current Medications: Antihypertensive drugs and insulin. Postoperatively, Mr. Miller remains in good health. A recent brain MRI noted that the suspected recurring GBM lesion near the superior border of the surgical site was entirely resected. Furthermore, CT scans at the anterior medial and lateral edges suggest that a complete resection was most likely achieved. Given the presumed complete resection of the glioblastoma recurrence, we have recommended Mr. Miller for a neuro-oncology review and a follow-up with PET-MRI in three months. Next Steps: -He has a scheduled appointment in neuro-oncology on 01/23/2020 at 10:00 AM. -A follow-up PET-MRI is set for 01/29/2020 at 12:45 PM. We have advised Mr. Miller to fast for 4 hours prior and to bring a referral from his primary care physician, along with a recent creatinine test result. -A review of these findings will be held in our outpatient department on 01/30/2020 at 2:00 PM. Thank you for your continued care and collaboration. Please do not hesitate to reach out for any additional information. Warm regards, ### Patient Report 2 Dear colleague, Regarding our mutual patient, Mr. John Miller, born 04/07/1961: Diagnosis: Glioblastoma recurrence, IDH 1 wild type Tumor Location: Right hemisphere/temporal. Medical History: 07/2019: Onset of speech arrest and confusion. 08/2019: PET brain MRI indicated a suspected malignant mass in the right hemisphere 08/11/2019: Glioblastoma resection performed in our neurosurgery department. 08-09/2019: He underwent adjuvant radiochemotherapy (43.4 at 2.7 Gy with a boost of 52.4 Gy at 3 Gy) and Temodal treatment at the local clinical center. 10/12/2019: A recurrent resection was performed at our facility. 11/02/2019: Postoperative brain MRI showed no suspected tumor remnants. Mr. Miller came in on 12/01/2019 with his brother. Clinical examination findings are as follows: -General health: Stable with reduced vitality. -Nutritional status: Stable (KPS 70-80%, weight in kg, height 169 cm). -No evident motor, sensory, visual, or cranial nerve deficits. -Neurocognitive deficits: Short-term memory issues. -Aphasia: Grade II (mainly word-finding disorders). The patient is fully oriented and independent in daily life. -No evidence of recurrence in PET-MRI. Next imaging scheduled in 03/2020. Past Medical Conditions: -Hypertension -Type II Diabetes Mellitus Allergies: None. Medications: -Antihypertensive medications -Insulin Best Regards, ### Patient Report 3 Dear colleague, Updating you on our mutual patient, Mr. John Miller, born 04/07/1961: Diagnosis: Recurrent Glioblastoma, IDH 1 wild type (ICD-10: 71.8). Molecular Pathology: No p.R132H mutation in IDH. No combined 1p/19q loss. Suspected CDKN2A/B deletion. Medical History: No pain or B symptoms. Intermittent dizziness and headaches since the last check-up. Neurological Findings: Patient is alert and oriented. Weight: 80 kg (total loss: 15 kg), Height: 175 cm. Karnofsky Performance Score: 80%. Motor function and sensory assessments were unremarkable. Allergies: None. Medications: Lisinopril 10mg once daily in the morning Bisoprolol 2.5mg once daily at bedtime Januvia 50mg twice daily Allopurinol 100mg once daily in the morning Ezetimibe 10mg once daily at bedtime Levetiracetam 1000mg once daily in the morning Insulin as per regimen Secondary Diagnoses: Hypertension Type II Diabetes Mellitus Medical Course: Details from 07/2019 through 03/2020 provided, including surgeries, radiochemotherapies, and diagnostics. In summary, Mr. Miller\'s glioblastoma diagnosis in 07/2019 led to various treatments, including radiochemotherapy and surgeries. His recent PET/MRI on 01/2020 indicates potential recurrent areas. Best regards, Patient: John Miller DOB: 04/07/1961 Admission Date: 04/11/2020 Discharge Date: 04/18/2020 Admission Diagnosis: Recurrent tumor in the hippocampal region and along the prior resection cavity. History of glioblastoma (IDH wild type, WHO CNS grade 4) in the right hemisphere, resected on 08/2019. History of combined radiochemotherapy with Temodar (Temozolomide) from August to September 2019 at the local clinical center. Subsequent first re-resection in 10/2019. Presenting Complaint: Mr. Miller presented to the neurosurgical outpatient department accompanied by his wife. Recent imaging indicated a potential recurrence of the glioblastoma. The neuro-oncological board on 04/12/2020 recommended a re-resection. Physical eon Admission: Alert, oriented x4, cooperative. Non-fluent aphasia. Cranial nerves intact. No sensory or motor deficits in the extremities. Surgical scar clean and dry. No signs of neurogenic bladder or rectal dysfunction. KPSS 70%. Medications on Admission: -Lisinopril 10mg daily -Bisoprolol 2.5mg nightly -Januvia 50mg twice daily -Allopurinol 100mg daily -Atorvastatin 40mg nightly -Ezetimibe 10mg nightly -Levetiracetam 1000mg twice daily -Actraphane insulin as prescribed Surgical Intervention (04/12/2020): Navigated microsurgical resection of tumor spots assisted with 5-ALA. Stable MEPs were maintained. An intraoperative MRI (iMRI) was utilized. Post-resection, the surgical area was managed using Tabotamp, Duragen, TachoSil, and dog-bone plates, concluding with layered wound closure. Postoperative Course: Uncomplicated recovery. Post-op MRI showed no residual tumor. Surgical site remained clean, dry, and showed no signs of infection or irritation. Discharge Diagnosis: Recurrence of known glioblastoma, WHO CNS grade 4. Interdisciplinary Neuro-oncological Tumor Board Recommendation (04/20/2020): Molecular tumor board review. Offer reinitiation of Temozolomide chemotherapy. Physical Examination on Discharge: Similar to admission, with suture in place and wound site in good condition. KPSS 70%. Medications on Discharge: -Allopurinol 100mg daily (morning) -Atorvastatin 40mg nightly (evening) -Bisoprolol 2.5mg nightly (evening) -Ezetimibe 10mg nightly (evening) -Sitagliptin (Januvia) 50mg twice daily (morning and evening) -Levetiracetam (Keppra) 1000mg twice daily (morning and evening) -Lisinopril 10mg daily (morning) -Acetaminophen 500mg as needed for pain or fever -Actraphane insulin as prescribed Surgery Report Diagnosis: Tumor recurrence in the right hippocampal region and along the resection cavity post glioblastoma resection on 8/11/2019. Previous treatments include radiochemotherapy at our clinic. Local therapy center (from August to September 2019) and re-resection on 10/12/2019. Surgery Type: Re-opening of the temporal region with navigation, microsurgery using 5-ALA assistance, iMRI, and re-resection, among other procedures. Procedure Details: Start: 11:50 pm on 04/12/2020 End: 4:00 pm on 04/12/2020 Duration: 4 hours 10 minutes. Assessment: Evidence of recurrent glioblastoma areas warranted another biopsy and resection. After informing Mr. Miller about the procedure\'s risks and benefits, he provided written consent. Operation: Details on the positioning, pre-operative preparations, resection, and post-operative procedures are provided. Best regards, MEDICAL HISTORY: Mr. Miller underwent surgery because of tumor recurrence in the right hemisphere last November to treat a right temporal glioblastoma. He presented to our private outpatient clinic due to a wound complication, specifically a wound dehiscence measuring about 1 cm. On closer examination, pus was noted. Despite being symptom-free otherwise, a consultation with Dr. Doe was scheduled. After discussion, it was decided to clean the wound, trim the deteriorated wound edges, and clean the bone flap with an antibiotic solution before reinserting it. The patient was thoroughly educated about the nature and risks of the procedure and gave consent. OPERATION: The patient was placed in a supine position. The hair surrounding the surgical site was trimmed, followed by skin disinfection and sterile draping. The bicoronal skin incision was reopened. Deteriorated wound edges were excised and the wound was extensively cleaned with irrigation. The bone flap was removed and immersed in a Refobacin solution. The epidural pannus tissue was removed. The dura was completely sutured. Multiple samples were collected both subgaleally and epidurally. A sponge was applied, followed by the reinsertion of the bone flap using a dog bone miniplate fixation and local application of vancomycin powder. A subgaleal drain was placed, and the skin was closed using Donati continuous sutures. A sterile staple dressing was applied, and the patient was transferred to recovery. CLINICAL NOTES: Epidural pannus suggestive of infection. Past surgical history includes glioblastoma removal in 8/11/2019 and subsequent surgeries because of recurrence, the last of these in 04/12/2020. Nature and type of growth? MACROSCOPIC EXAMINATION: Fixed tissue samples measuring 1.2 x 1.0 x 0.4 cm were entirely embedded. STAINING: 1 block, Hematoxylin & Eosin (HE), Periodic Acid Schiff (PAS). MICROSCOPIC EXAMINATION: Histology shows entirely necrotic tissue and some bony fragments. No microorganisms were detected in the PAS stain. FINDINGS: Fully necrotic tissue. No signs of inflammation or malignancy in the available samples. OPERATIVE REPORT: Diagnosis: Wound healing disruption high on the forehead, following a resection of recurrent gioblastoma in the right hemisphere in 04/12/2020. Procedure: Wound revision, thorough wound cleaning, reinsertion of the autologous bone flap. Time of incision: 3:30 PM, 06/01/2020 Time of suture completion: 4:35, 06/01/2020 Duration: 65 minutes PATIENT HISTORY: The patient had two prior surgeries with our team. The most recent was a revision due to a wound healing complication. The patient was informed about the procedure\'s nature, extent, risks, and potential outcomes, and was given ample opportunity to ask questions. After thoughtful consideration, written consent was obtained. OPERATION: The patient was positioned supine with the head in a neutral position. The surgical area was sterilized and draped. Antibiotic prophylaxis was administered, and a timeout procedure was conducted. The old wound was reopened and slightly extended by about 1 cm in both directions. The bone flap was removed, inspected, and cleaned. It was then reinserted after refreshing the bone edges. The wound edges were refreshed, and the wound was irrigated again before closure. SUMMARY: Successful wound revision without complications for a wound healing complication post-glioblastoma surgery. CLINICAL NOTES: Complication in wound healing after glioblastoma removal and radiation therapy. Possible inflammation? Evaluate for pus. Nature and type of growth? MACROSCOPIC EXAMINATION: Subcutaneous tissue samples, 1.2 x 1.0 x 0.4 cm, were completely embedded after being cut into two. Epidural tissue samples, 5.6 x 5.3 x 1.0 cm, were partially embedded. STAINING: 2 blocks, Hematoxylin & Eosin (HE), Periodic Acid Schiff (PAS). MICROSCOPIC EXAMINATION: Histology displays connective tissue surrounded by a pronounced inflammatory infiltrate, comprised of neutrophils, lymphocytes, and numerous eosinophils. Additionally, budding capillaries were seen. No specific findings in the PAS stain. Histologically, connective tissue infiltrated by predominantly lymphocytic inflammation was observed. Eosinophils and abundant necrotic tissue were also seen. Additionally, polarizable material was noted, occasionally engulfed by multinucleated giant cells. Hemorrhagic signs were indicated by hemosiderin deposits. No specific findings in the PAS stain. FINDINGS: 1 & 2. Soft tissue displays acute phlegmonous inflammation and chronic granulating inflammation. Brief report (07/15/2020): Diagnosis: superficial wound healing disorder and symptomatic, simple focal epileptic seizure dated 06/01/2020. Wound healing disorder right parietal at the site of previous right-side glioblastoma, last revision 04/12/2020. -Single right body focal seizure 04/20/2020, single generalized seizure 05/12/2020 -Previous resection on 10/12/2019 -Wound healing disorder with subsequent wound revision (06/2020) -Last cMRI on 05/03/2020: no recurrence observed. Secondary diagnoses: Hypothyroidism Surgery type: injection of Ropivacaine, smear for microbiology, readaptation of three small wound dehiscence, tobacco bag suture, overlay polyhexanide gel, plaster application Instructions: Return next Tuesday for wound check. Sutures to remain in place for 10 days. Check microbiology results on Thursday. Clinically, no signs of infection observed. Surgical report: Diagnosis: superficial wound healing disorder and symptomatic, simple focal epileptic seizure dated 04/20/2020. ASSESSMENT: The patient was presented at the emergency unit after observing a small wound dehiscence after the aforementioned surgery for a wound healing disorder. The treating surgeon recommended a second local wound revision attempting to readapt the wound with a minor surgery. The patient provided written consent for the procedure. The intervention was conducted with standard coagulation parameters. SURGERY: Sterile preparation and draping of the surgical area. Initial injection of Ropivacaine. This was followed by swab collection for microbiology. The wound edges were excised and the wound dehiscence was readapted using a tobacco bag suture. Afterward, polyhexanide gel was applied, followed by a sterile plaster dressing. Surgery report: Diagnosis: significant scalp wound healing disorder in the prior surgical access area post-resection and irradiation of a glioblastoma multiforme. Operating time: 49 minutes ### Patient Report 4 Dear colleague, we report on Mr. Miller, John, born 04/07/1961, who was in our inpatient treatment from 09/12/2020 to 10/07/2020. Discharge diagnosis: Recurrence of the pre-described glioblastoma right insular, WHO CNS grade 4 (IDH wild type, MGMT methylated). Physical examination on admission: Patient awake, fully oriented, cooperative. Non-fluent aphasia. Speech clear and fluent. Latent left hemisymptomatic with strength grades 4+/5, stance and gait unsteady. Cranial nerve status regular. Scar conditions non-irritant except for frontal superficial erosion at frontal wound pole of pterional approach. Karnofsky 70%. Medication on Admission: Levothyroxine sodium 50 μg/1 pc (Synthroid® 50 micrograms, tablets) 1-0-0-0 Lorazepam 0.5 mg/1 pc (Ativan® 0.5 mg, tablets) 1-1-1-1 Lacosamide 100 mg/1 pc (Vimpat® 100 mg film-coated tablets) 1-0-1-0 Imaging: cMRI +/- contrast agent dated 09/15/2020: There is a contrast enhancing formation on the right temporo-mesial with approach to the insular cistern with suspected tumor recurrence. PET dated 09/10/2020 (external): Significant tracer multinodulation with active areas in the islet region is seen. Surgery of 09/18/2020: Reopening of existing skin incision and extension of craniotomy cranially, microsurgical navigated tumor resection right insular (IONM: MEP waste lower extremity with incomplete recovery) CUSA; extensive hemostasis, intraoperative MRI, sutures, reimplantation of bone flap with multilayer wound closure. Skin suture. Histopathological report: Recurrence of pre-described glioblastoma, WHO CNS grade 4 (IDH wild type, MGMT methylated). Course: The patient initially presented postoperatively with left hemiplegia in the sense of SMA. This regressed significantly during the inpatient stay. Postoperative imaging revealed a regular resection finding. In case of a possible adjustment disorder, the patient was treated with sertraline and lorazepam. The wound was dry and non-irritant during the inpatient stay. The patient received regular physiotherapeutic exercise. The patient\'s case was discussed in our neuro-oncological tumor board on 09/29/2020, where the decision was made for adjuvant definitive radiochemotherapy. An inpatient transfer was offered by the colleagues of radiotherapy. Procedure: We transfer Mr. Miller today in good clinical general condition to your further treatment and thank you for the kind takeover. We ask for regular wound controls as well as regular ECG controls to exclude a QTc-time prolongation under sertraline. Furthermore, the medication with lorazepam should be further phased out in the course of time. In case of acute neurological deterioration, a re-presentation in our neurosurgical outpatient clinic or surgical emergency room is possible at any time. Clinical examination findings at discharge: Patient awake, fully oriented, cooperative. Speech clear and fluent. Cranial nerve status without pathological findings. Hemiparesis left-sided strength grade 4/5, right-sided no sensorimotor deficit. Stance and gait unsteady. Non-fluent aphasia. Wound dry, without irritation. Karnofsky 70%. Medication at Discharge: Levothyroxine sodium 50 μg (Synthroid® 50 micrograms, tablets) 1-0-0-0 Lorazepam 0.5 mg (Ativan® 0.5 mg, tablets) as needed Lacosamide 100 mg (Vimpat® 100 mg film-coated tablets) 1-0-1-0 Acetaminophen 500 mg (Tylenol® 500 mg tablets) 1-1-1-1 Sertraline 50 mg (Zoloft® 50 mg film-coated tablets) by regimen Magnetic Resonance Imaging (MRI) Report Date of Examination: 02/02/2021 Clinical Indication: Multifocal glioblastoma WHO grade IV, IDH wild type, MGMT methylated. Clinical Query: Hemiparesis on the right side. Is there a structural correlate? Tumor progression? Previous Imaging: Multiple prior studies. The most recent contrasted MRI was on 09/15/2020. Findings: Imaging Device: GE 3T; Protocol: 3D FLAIR, 3D T1 Mprage with and without contrast, SWI, DWI, 3D T2, axial T2\, perfusion, DTI. 1. Report: * Known multimodal pretreated GBM since 2019, recently post-surgical resection for tumor progression in the frontotemporal region on 09/18/2020. Also noted is a post-surgical resection of additional foci in the right insular region. The resection cavity in the right frontal, insular, and temporal regions appears unchanged in size and configuration. Residual blood products are noted within. There are increased areas of contrast enhancement compared to the immediate post-operative images. There is a minor growth in a nodular enhancement posterior to the right middle cerebral artery. Adjacent to this, there\'s a new nodular enhancement, which could be a postoperative reactive change or a new tumor lesion. Ongoing diffusion abnormalities are observed in the right caput nuclei caudatus, putamen, and globus pallidus, especially pronounced in posterior sections. Persistent FLAIR-hyperintense peritumoral edema in the right hemisphere remains unchanged. The midline shift is approximately 9mm to the left, which remains unchanged. Post-operative swelling and fluid accumulation are noted at the surgical entry point. The bone flap is in place. The width of both the internal and external CSF spaces remains constant, with no evidence of obstruction. The orbital contents are symmetrical. Paranasal sinuses and mastoid air cells are aerated appropriately. Impression: Residual tumor segments along the right middle cerebral artery showing growth. Adjacent to it, a new nodular area of contrast enhancement suggests either a postoperative change or a new tumor lesion. Previously identified ischemic changes in the right caput nuclei caudatus, putamen, and globus pallidus. Persistent brain edema with a leftward midline shift of approximately 9mm remains unchanged. ### Patient Report 5 Dear colleague, Herewith we report on our common patient Mr. John Miller, born 04/07/1961, who was at our clinic between 02/04/2021 to 04/22/2021. -Recurrent manifestation of a glioblastoma -Stage: WHO CNS grade 4 Histology: Recurrence of the pre-described glioblastoma, WHO CNS grade 4. Molecular pathological findings: IDH status: no p.R132H mutation (immunohistochemical). ATRX: preservation of nuclear expression (immunohistochemical). p53: technically not evaluable (immunohistochemical). 1p/19q status: no combined loss (850k methylation analysis). CDKN2A/B: Deleted (850k methylation analysis). MGMT promoter: Methylated (850k methylation analysis). Tumor localization: Islet/frontal right Secondary diagnoses: Symptomatic epilepsy Hypothyroidism Nausea Leukopenia I° (CTCAE) Anemia II° (CTCAE) Previous course / therapies: 08/2019: PET brain MRI indicated a suspected malignant mass in the right hemisphere 08/11/2019: Glioblastoma resection performed in our neurosurgery department. 08-09/2019: He underwent adjuvant radiochemotherapy (43.4 at 2.7 Gy with a boost of 52.4 Gy at 3 Gy) and Temodal treatment at the local clinical center. 10/12/2019: A recurrent resection was performed at our facility. 11/02/2019: Postoperative brain MRI showed no suspected tumor remnants. 03/2020: Suspected recurrence 04/2020: Revision surgery 06-07/2020 Wound revisions and flap plasty for atrophic wound healing disorder 02/2021: Suspected recurrence with new FLAIR-positive tumor manifestation insular on the right side. Stereotactic biopsy with evidence of glioblastoma. Pathology: Renewed manifestation of a glioblastoma. Recommended radiochemotherapy. According to the interdisciplinary neuro-oncology board of 01/26/2021, we gave the indication for adjuvant radiochemotherapy for the recurrence of glioblastoma. Radiochemotherapy: Technique: 1\) Percutaneous intensity-modulated radiotherapy was administered to the former recurrence tumor region in the frontal/insular right after CT- and MRI-guided radiation planning with 6 MV-photons in helical tomotherapy technique with a single dose of 2 Gy up to a total dose of 60 Gy with 5 fractions per week. Daily position controls by CT. 2\) Subsequently, local dose saturation of the macroscopic tumor remnant was performed. Insular right stereotactic ablative radiosurgery at the gamma knee to saturate the macroscopic GammaKnife (Cobalt-60: 1.17 MeV and 1.33 MeV photons) in mask fixation after CT- and MRI-guided radiotherapy planning under image-guided setting (ConeBeam-CT) with a dose of 6 Gy in 2 Gy single dose to the 68% isodose up to a total cumulative dose of 66 Gy. Chemotherapy: Concurrent chemotherapy with 75mg/m²KOF temozolomide daily (120 mg daily). Absolute dose: 5000 mg. Treatment Period: Radiotherapy 03/09/2021 -- 04/21/2021 Chemotherapy 03/09/2021 -- 04/21/2021 Course under therapy: We took over Mr. Miller on 03/06/2021 in reduced general and slightly reduced nutritional condition (Kanofsky index: 70 %, BMI: 18.5 kg/m²) from the Clinic for Neurosurgery for adjuvant re-radiochemotherapy on our radiooncology ward. At the time of admission, the patient had arm right hemiparesis (strength grades arm: 2/5, leg: 3/5). The patient was ambulatory with assistance. Cranial nerve status was unremarkable. Headache, nausea or dizziness were denied. On 09/03/2021, combined re-radiochemotherapy was initiated. During the course of therapy with temozolomide, mild nausea occurred, which was treated with ondansetron 4mg as needed and dimenhydrinate Sustained-release tablets 150 mg as needed. Under this treatment the symptoms clearly regressed. Mild constipation was treated. Laboratory tests revealed mild leukopenia I° and anemia II° (CTCAE). Otherwise, the re-radiochemotherapy was very well tolerated overall. Mr. Miller received physiotherapeutic exercise and psychotherapy during the entire physiotherapeutic training and psycho-oncological support. Under the physiotherapeutic treatment, his motor skills improved significantly. At the end of the therapy, the patient was also mobile outside the house without any aids. On 04/22/2021 we discharged Mr. Miller to the outpatient care by his family doctor. 04/11/2021: MR brain post contrast After renewed radiochemotherapy for a glioblastoma recurrence, a residual suspicious barrier disturbance adjacent to the adjacent to the right cerebral artery. Stable nodular contrast enhancement Postoperative/reactive changes as described above. MR perfusion sequences show residual, contrast-absorbing tumor portions along the right A. cerebri media. Lateral to this, new nodular contrast-absorbing lesion, possibly postoperative reactive change. Previously known ischemia at the right caput nuclei caudatii, putamen, and globus pallidus. Unchanged medullary edema with midline shift to the left by approximately 9mm. Last lab: MCHC 29.4 g/dL (32 - 36) 04/20/2021 MCH 25 pg (27 - 32) 04/20/2021 Leukocytes 3.32 G/l (4.0 - 9.0) 04/20/2021 Hematocrit 31.6 % (37 - 43) 04/20/2021 Hemoglobin 9.3 g/dL (12 - 16) 04/20/2021 Erythrocytes 3.7 T/l (4.1 - 5.4) 04/20/2021 Uric acid 2.2 mg/dl (2.5 - 5.5) 04/20/2021 Further Procedure: Further skin care and behavior regarding side effects were explained to the patient in detail. A first radiooncological control appointment was scheduled for 06/05/2021 at 12:00 AM in our outpatient clinic. Prior to this, on 06/02/2021 at 10:30 AM, an imaging exam (brain MRI) is scheduled. A further neuro-oncological connection is planned close to home via the treating oncologist. After radiation therapy, we recommend annual ophthalmological check-ups and annual endocrinologic. with testing of the hypothalamic-pituitary hormone axes. ### Patient Report 6 Dear colleague, This is a report on our mutual patient, Mr. John Miller, born 04/07/1961: Diagnosis: Recurrent manifestation of glioblastoma WHO CNS grade 4 Tumor localization: Right isle/frontal Secondary diagnoses: Symptomatic epilepsy Hypothyroidism Previous Treatments / Therapies Resection and revisions Adjuvant radiochemotherapy Two wound revisions and flap plasty for atrophic wound healing disorder New FLAIR-positive tumor manifestation insular on the right side (02/2021) Stereotactic biopsy with histopathology with evidence of glioblastoma Tumor Board: Recommended new radiochemotherapy up to a total dose of 60 Gy à 2 Gy single dose. Subsequent local dose saturation of the macroscopic tumor remnant as radiosurgery on GammaKnife with a dose of 6 Gy à 2 Gy single dose (\@68% isodose). 03-4/2021 Repeat stereotactic RTx in the area of the basal ganglia and the resection cavity right frontal on the Gamma Knife with 46 Gy à 2 Gy; 3 doses of Bevacizumab 7.5 mg/kg i.v. Summary: On 07/03/2023, we conducted an initial telephone follow-up with the patient, Mr. Miller, for a radio-oncology consultation. Presently, Mr. Miller is undergoing rehabilitation, from which he feels he is deriving substantial benefits. His recent radiotherapy was well-received without any complications. Since the onset of his symptoms, there have been no new developments. Symptoms related to intracranial pressure or new neurological deficits were denied. Fortunately, while on anticonvulsant therapy with Lacosamide, Mr. Miller experienced no epileptic seizures. His skin condition is normal. However, Mr. Miller did mention some cognitive challenges that minimally impact his daily activities, alongside feelings of fatigue and grade I CTCAE symptoms. The cMRI scan from 06/02/2021 revealed a notable reduction in the barrier disturbance of the right-sided basal ganglia. This was accompanied by small, mildly hyperperfused residual findings near the third ventricle. Moreover, the pinpoint contrast enhancement in the left parietal lobe appeared unchanged, suggesting it is a scarring reaction. In collaboration with the neurooncology team, Mr. Miller has discussed starting chemotherapy. The next imaging assessment is scheduled for mid-September. We have also scheduled another radio-oncologic follow-up with Mr. Miller for September 28th, per his preference, via telephone. For patient safety, Mr. Miller is prohibited from operating private or commercial vehicles for 3 months post-intracerebral radiotherapy. This duration may extend if there are existing or progressing brain conditions. Following radiotherapy, we are mandated by the Radiation Protection Act to facilitate regular checks. Hence, we encourage enrollment in the aftercare calendar, prompt reporting of any significant findings, and attendance of scheduled follow-ups. Alongside these, regular oncological check-ups by specialist practitioners are mandatory. Mr. Miller has been duly informed of all these requirements.	175 cm
What does Johnny Ashlew best represent? A. Slichow's greatest fear B. Kolin's ego speaking its truth C. Subtle omniscience D. Freedom from conformity	By H. B. Fyfe THE TALKATIVE TREE Dang vines! Beats all how some plants have no manners—but what do you expect, when they used to be men! All things considered—the obscure star, the undetermined damage to the stellar drive and the way the small planet's murky atmosphere defied precision scanners—the pilot made a reasonably good landing. Despite sour feelings for the space service of Haurtoz, steward Peter Kolin had to admit that casualties might have been far worse. Chief Steward Slichow led his little command, less two third-class ration keepers thought to have been trapped in the lower hold, to a point two hundred meters from the steaming hull of the Peace State . He lined them up as if on parade. Kolin made himself inconspicuous. "Since the crew will be on emergency watches repairing the damage," announced the Chief in clipped, aggressive tones, "I have volunteered my section for preliminary scouting, as is suitable. It may be useful to discover temporary sources in this area of natural foods." Volunteered HIS section! thought Kolin rebelliously. Like the Supreme Director of Haurtoz! Being conscripted into this idiotic space fleet that never fights is bad enough without a tin god on jets like Slichow! Prudently, he did not express this resentment overtly. His well-schooled features revealed no trace of the idea—or of any other idea. The Planetary State of Haurtoz had been organized some fifteen light-years from old Earth, but many of the home world's less kindly techniques had been employed. Lack of complete loyalty to the state was likely to result in a siege of treatment that left the subject suitably "re-personalized." Kolin had heard of instances wherein mere unenthusiastic posture had betrayed intentions to harbor treasonable thoughts. "You will scout in five details of three persons each," Chief Slichow said. "Every hour, each detail will send one person in to report, and he will be replaced by one of the five I shall keep here to issue rations." Kolin permitted himself to wonder when anyone might get some rest, but assumed a mildly willing look. (Too eager an attitude could arouse suspicion of disguising an improper viewpoint.) The maintenance of a proper viewpoint was a necessity if the Planetary State were to survive the hostile plots of Earth and the latter's decadent colonies. That, at least, was the official line. Kolin found himself in a group with Jak Ammet, a third cook, and Eva Yrtok, powdered foods storekeeper. Since the crew would be eating packaged rations during repairs, Yrtok could be spared to command a scout detail. Each scout was issued a rocket pistol and a plastic water tube. Chief Slichow emphasized that the keepers of rations could hardly, in an emergency, give even the appearance of favoring themselves in regard to food. They would go without. Kolin maintained a standard expression as the Chief's sharp stare measured them. Yrtok, a dark, lean-faced girl, led the way with a quiet monosyllable. She carried the small radio they would be permitted to use for messages of utmost urgency. Ammet followed, and Kolin brought up the rear. To reach their assigned sector, they had to climb a forbidding ridge of rock within half a kilometer. Only a sparse creeper grew along their way, its elongated leaves shimmering with bronze-green reflections against a stony surface; but when they topped the ridge a thick forest was in sight. Yrtok and Ammet paused momentarily before descending. Kolin shared their sense of isolation. They would be out of sight of authority and responsible for their own actions. It was a strange sensation. They marched down into the valley at a brisk pace, becoming more aware of the clouds and atmospheric haze. Distant objects seemed blurred by the mist, taking on a somber, brooding grayness. For all Kolin could tell, he and the others were isolated in a world bounded by the rocky ridge behind them and a semi-circle of damp trees and bushes several hundred meters away. He suspected that the hills rising mistily ahead were part of a continuous slope, but could not be sure. Yrtok led the way along the most nearly level ground. Low creepers became more plentiful, interspersed with scrubby thickets of tangled, spike-armored bushes. Occasionally, small flying things flickered among the foliage. Once, a shrub puffed out an enormous cloud of tiny spores. "Be a job to find anything edible here," grunted Ammet, and Kolin agreed. Finally, after a longer hike than he had anticipated, they approached the edge of the deceptively distant forest. Yrtok paused to examine some purple berries glistening dangerously on a low shrub. Kolin regarded the trees with misgiving. "Looks as tough to get through as a tropical jungle," he remarked. "I think the stuff puts out shoots that grow back into the ground to root as they spread," said the woman. "Maybe we can find a way through." In two or three minutes, they reached the abrupt border of the odd-looking trees. Except for one thick trunked giant, all of them were about the same height. They craned their necks to estimate the altitude of the monster, but the top was hidden by the wide spread of branches. The depths behind it looked dark and impenetrable. "We'd better explore along the edge," decided Yrtok. "Ammet, now is the time to go back and tell the Chief which way we're— Ammet! " Kolin looked over his shoulder. Fifty meters away, Ammet sat beside the bush with the purple berries, utterly relaxed. "He must have tasted some!" exclaimed Kolin. "I'll see how he is." He ran back to the cook and shook him by the shoulder. Ammet's head lolled loosely to one side. His rather heavy features were vacant, lending him a doped appearance. Kolin straightened up and beckoned to Yrtok. For some reason, he had trouble attracting her attention. Then he noticed that she was kneeling. "Hope she didn't eat some stupid thing too!" he grumbled, trotting back. As he reached her, whatever Yrtok was examining came to life and scooted into the underbrush with a flash of greenish fur. All Kolin saw was that it had several legs too many. He pulled Yrtok to her feet. She pawed at him weakly, eyes as vacant as Ammet's. When he let go in sudden horror, she folded gently to the ground. She lay comfortably on her side, twitching one hand as if to brush something away. When she began to smile dreamily, Kolin backed away. The corners of his mouth felt oddly stiff; they had involuntarily drawn back to expose his clenched teeth. He glanced warily about, but nothing appeared to threaten him. "It's time to end this scout," he told himself. "It's dangerous. One good look and I'm jetting off! What I need is an easy tree to climb." He considered the massive giant. Soaring thirty or forty meters into the thin fog and dwarfing other growth, it seemed the most promising choice. At first, Kolin saw no way, but then the network of vines clinging to the rugged trunk suggested a route. He tried his weight gingerly, then began to climb. "I should have brought Yrtok's radio," he muttered. "Oh, well, I can take it when I come down, if she hasn't snapped out of her spell by then. Funny … I wonder if that green thing bit her." Footholds were plentiful among the interlaced lianas. Kolin progressed rapidly. When he reached the first thick limbs, twice head height, he felt safer. Later, at what he hoped was the halfway mark, he hooked one knee over a branch and paused to wipe sweat from his eyes. Peering down, he discovered the ground to be obscured by foliage. "I should have checked from down there to see how open the top is," he mused. "I wonder how the view will be from up there?" "Depends on what you're looking for, Sonny!" something remarked in a soughing wheeze. Kolin, slipping, grabbed desperately for the branch. His fingers clutched a handful of twigs and leaves, which just barely supported him until he regained a grip with the other hand. The branch quivered resentfully under him. "Careful, there!" whooshed the eerie voice. "It took me all summer to grow those!" Kolin could feel the skin crawling along his backbone. "Who are you?" he gasped. The answering sigh of laughter gave him a distinct chill despite its suggestion of amiability. "Name's Johnny Ashlew. Kinda thought you'd start with what I am. Didn't figure you'd ever seen a man grown into a tree before." Kolin looked about, seeing little but leaves and fog. "I have to climb down," he told himself in a reasonable tone. "It's bad enough that the other two passed out without me going space happy too." "What's your hurry?" demanded the voice. "I can talk to you just as easy all the way down, you know. Airholes in my bark—I'm not like an Earth tree." Kolin examined the bark of the crotch in which he sat. It did seem to have assorted holes and hollows in its rough surface. "I never saw an Earth tree," he admitted. "We came from Haurtoz." "Where's that? Oh, never mind—some little planet. I don't bother with them all, since I came here and found out I could be anything I wanted." "What do you mean, anything you wanted?" asked Kolin, testing the firmness of a vertical vine. "Just what I said," continued the voice, sounding closer in his ear as his cheek brushed the ridged bark of the tree trunk. "And, if I do have to remind you, it would be nicer if you said 'Mr. Ashlew,' considering my age." "Your age? How old—?" "Can't really count it in Earth years any more. Lost track. I always figured bein' a tree was a nice, peaceful life; and when I remembered how long some of them live, that settled it. Sonny, this world ain't all it looks like." "It isn't, Mr. Ashlew?" asked Kolin, twisting about in an effort to see what the higher branches might hide. "Nope. Most everything here is run by the Life—that is, by the thing that first grew big enough to do some thinking, and set its roots down all over until it had control. That's the outskirts of it down below." "The other trees? That jungle?" "It's more'n a jungle, Sonny. When I landed here, along with the others from the Arcturan Spark , the planet looked pretty empty to me, just like it must have to—Watch it, there, Boy! If I didn't twist that branch over in time, you'd be bouncing off my roots right now!" "Th-thanks!" grunted Kolin, hanging on grimly. "Doggone vine!" commented the windy whisper. " He ain't one of my crowd. Landed years later in a ship from some star towards the center of the galaxy. You should have seen his looks before the Life got in touch with his mind and set up a mental field to help him change form. He looks twice as good as a vine!" "He's very handy," agreed Kolin politely. He groped for a foothold. "Well … matter of fact, I can't get through to him much, even with the Life's mental field helping. Guess he started living with a different way of thinking. It burns me. I thought of being a tree, and then he came along to take advantage of it!" Kolin braced himself securely to stretch tiring muscles. "Maybe I'd better stay a while," he muttered. "I don't know where I am." "You're about fifty feet up," the sighing voice informed him. "You ought to let me tell you how the Life helps you change form. You don't have to be a tree." "No?" " Uh -uh! Some of the boys that landed with me wanted to get around and see things. Lots changed to animals or birds. One even stayed a man—on the outside anyway. Most of them have to change as the bodies wear out, which I don't, and some made bad mistakes tryin' to be things they saw on other planets." "I wouldn't want to do that, Mr. Ashlew." "There's just one thing. The Life don't like taking chances on word about this place gettin' around. It sorta believes in peace and quiet. You might not get back to your ship in any form that could tell tales." "Listen!" Kolin blurted out. "I wasn't so much enjoying being what I was that getting back matters to me!" "Don't like your home planet, whatever the name was?" "Haurtoz. It's a rotten place. A Planetary State! You have to think and even look the way that's standard thirty hours a day, asleep or awake. You get scared to sleep for fear you might dream treason and they'd find out somehow." "Whooeee! Heard about them places. Must be tough just to live." Suddenly, Kolin found himself telling the tree about life on Haurtoz, and of the officially announced threats to the Planetary State's planned expansion. He dwelt upon the desperation of having no place to hide in case of trouble with the authorities. A multiple system of such worlds was agonizing to imagine. Somehow, the oddity of talking to a tree wore off. Kolin heard opinions spouting out which he had prudently kept bottled up for years. The more he talked and stormed and complained, the more relaxed he felt. "If there was ever a fellow ready for this planet," decided the tree named Ashlew, "you're it, Sonny! Hang on there while I signal the Life by root!" Kolin sensed a lack of direct attention. The rustle about him was natural, caused by an ordinary breeze. He noticed his hands shaking. "Don't know what got into me, talking that way to a tree," he muttered. "If Yrtok snapped out of it and heard, I'm as good as re-personalized right now." As he brooded upon the sorry choice of arousing a search by hiding where he was or going back to bluff things out, the tree spoke. "Maybe you're all set, Sonny. The Life has been thinkin' of learning about other worlds. If you can think of a safe form to jet off in, you might make yourself a deal. How'd you like to stay here?" "I don't know," said Kolin. "The penalty for desertion—" "Whoosh! Who'd find you? You could be a bird, a tree, even a cloud." Silenced but doubting, Kolin permitted himself to try the dream on for size. He considered what form might most easily escape the notice of search parties and still be tough enough to live a long time without renewal. Another factor slipped into his musings: mere hope of escape was unsatisfying after the outburst that had defined his fuming hatred for Haurtoz. I'd better watch myself! he thought. Don't drop diamonds to grab at stars! "What I wish I could do is not just get away but get even for the way they make us live … the whole damn set-up. They could just as easy make peace with the Earth colonies. You know why they don't?" "Why?" wheezed Ashlew. "They're scared that without talk of war, and scouting for Earth fleets that never come, people would have time to think about the way they have to live and who's running things in the Planetary State. Then the gravy train would get blown up—and I mean blown up!" The tree was silent for a moment. Kolin felt the branches stir meditatively. Then Ashlew offered a suggestion. "I could tell the Life your side of it," he hissed. "Once in with us, you can always make thinking connections, no matter how far away. Maybe you could make a deal to kill two birds with one stone, as they used to say on Earth…." Chief Steward Slichow paced up and down beside the ration crate turned up to serve him as a field desk. He scowled in turn, impartially, at his watch and at the weary stewards of his headquarters detail. The latter stumbled about, stacking and distributing small packets of emergency rations. The line of crewmen released temporarily from repair work was transient as to individuals but immutable as to length. Slichow muttered something profane about disregard of orders as he glared at the rocky ridges surrounding the landing place. He was so intent upon planning greetings with which to favor the tardy scouting parties that he failed to notice the loose cloud drifting over the ridge. It was tenuous, almost a haze. Close examination would have revealed it to be made up of myriads of tiny spores. They resembled those cast forth by one of the bushes Kolin's party had passed. Along the edges, the haze faded raggedly into thin air, but the units evidently formed a cohesive body. They drifted together, approaching the men as if taking intelligent advantage of the breeze. One of Chief Slichow's staggering flunkies, stealing a few seconds of relaxation on the pretext of dumping an armful of light plastic packing, wandered into the haze. He froze. After a few heartbeats, he dropped the trash and stared at ship and men as if he had never seen either. A hail from his master moved him. "Coming, Chief!" he called but, returning at a moderate pace, he murmured, "My name is Frazer. I'm a second assistant steward. I'll think as Unit One." Throughout the cloud of spores, the mind formerly known as Peter Kolin congratulated itself upon its choice of form. Nearer to the original shape of the Life than Ashlew got , he thought. He paused to consider the state of the tree named Ashlew, half immortal but rooted to one spot, unable to float on a breeze or through space itself on the pressure of light. Especially, it was unable to insinuate any part of itself into the control center of another form of life, as a second spore was taking charge of the body of Chief Slichow at that very instant. There are not enough men , thought Kolin. Some of me must drift through the airlock. In space, I can spread through the air system to the command group. Repairs to the Peace State and the return to Haurtoz passed like weeks to some of the crew but like brief moments in infinity to other units. At last, the ship parted the air above Headquarters City and landed. The unit known as Captain Theodor Kessel hesitated before descending the ramp. He surveyed the field, the city and the waiting team of inspecting officers. "Could hardly be better, could it?" he chuckled to the companion unit called Security Officer Tarth. "Hardly, sir. All ready for the liberation of Haurtoz." "Reformation of the Planetary State," mused the captain, smiling dreamily as he grasped the handrail. "And then—formation of the Planetary Mind!" END Transcriber's Note: This e-text was produced from Worlds of If January 1962 . Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed.	D. Freedom from conformity
Which groups does Fiss claim his book is advocating for? A. women, gays, victims of war crimes , the poor, and people who are critical of market capitalism B. women, gays, victims of racial-hate speech, the rich, and people who are critical of market capitalism. C. women, gays, victims of racial-hate speech, the poor, and those who are critical of market capitalism D. women, gays, victims of racial-hate speech, the poor, and people who are critical of communism.	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.	C. women, gays, victims of racial-hate speech, the poor, and those who are critical of market capitalism
What are the nine types?	### Introduction As the reliance on social media as a source of news increases and the reliability of sources is increasingly debated, it is important to understand how users react to various sources of news. Most studies that investigate misinformation spread in social media focus on individual events and the role of the network structure in the spread BIBREF0 , BIBREF1 , BIBREF2 or detection of false information BIBREF3 . These studies have found that the size and shape of misinformation cascades within a social network depends heavily on the initial reactions of the users. Other work has focused on the language of misinformation in social media BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 to detect types of deceptive news. As an alternative to studying newsworthy events one at a time BIBREF10 , the current work applies linguistically-infused models to predict user reactions to deceptive and trusted news sources. Our analysis reveals differences in reaction types and speed across two social media platforms — Twitter and Reddit. The first metric we report is the reaction type. Recent studies have found that 59% of bitly-URLs on Twitter are shared without ever being read BIBREF11 , and 73% of Reddit posts were voted on without reading the linked article BIBREF12 . Instead, users tend to rely on the commentary added to retweets or the comments section of Reddit-posts for information on the content and its credibility. Faced with this reality, we ask: what kind of reactions do users find when they browse sources of varying credibility? Discourse acts, or speech acts, can be used to identify the use of language within a conversation, e.g., agreement, question, or answer. Recent work by Zhang et al. zhang2017characterizing classified Reddit comments by their primary discourse act (e.g., question, agreement, humor), and further analyzed patterns from these discussions. The second metric we report is reaction speed. A study by Jin et al. jin2013epidemiological found that trusted news stories spread faster than misinformation or rumor; Zeng et al. zeng2016rumors found that tweets which deny rumors had shorter delays than tweets of support. Our second goal is to determine if these trends are maintained for various types of news sources on Twitter and Reddit. Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources. ### Reaction Type Classification In this section, we describe our approach to classify user reactions into one of eight types of discourse: agreement, answer, appreciation, disagreement, elaboration, humor, negative reaction, or question, or as none of the given labels, which we call “other”, using linguistically-infused neural network models. ### Reddit Data We use a manually annotated Reddit dataset from Zhang et al. zhang2017characterizing to train our reaction classification model. Annotations from 25 crowd-workers labelled the primary discourse act for 101,525 comments within 9,131 comment threads on Reddit. The Reddit IDs, but not the text content of the comments themselves, were released with the annotations. So we collected the content of Reddit posts and comments from a public archive of Reddit posts and comments. Some content was deleted prior to archival, so the dataset shown in Table TABREF3 is a subset of the original content. Despite the inability to capture all of the original dataset, Table TABREF3 shows a similar distribution between our dataset and the original. ### Model We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent. ### Reaction Type Classification Results As shown in Figure FIGREF7 , our linguistically-infused neural network model that relies solely on the content of the reaction and its parent has comparable performance to the more-complex CRF model by Zhang et al. zhang2017characterizing, which relies on content as well as additional metadata like the author, thread (e.g., the size of the the thread, the number of branches), structure (e.g., the position within the thread), and community (i.e., the subreddit in which the comment is posted). ### Measuring Reactions to Trusted and Deceptive News Sources In this section, we present key results of our analysis of how often and how quickly users react to content from sources of varying credibility using the reaction types predicted by our linguistically-infused neural network model. ### Twitter and Reddit News Data We focus on trusted news sources that provide factual information with no intent to deceive and deceptive news sources. Deceptive sources are ranked by their intent to deceive as follows: clickbait (attention-grabbing, misleading, or vague headlines to attract an audience), conspiracy theory (uncorroborated or unreliable information to explain events or circumstances), propaganda (intentionally misleading information to advance a social or political agenda), and disinformation (fabricated or factually incorrect information meant to intentionally deceive readers). Trusted, clickbait, conspiracy, and propaganda sources were previously compiled by Volkova et al. volkova2017separating through a combination of crowd-sourcing and public resources. Trusted news sources with Twitter-verified accounts were manually labeled and clickbait, conspiracy, and propaganda news sources were collected from several public resources that annotate suspicious news accounts. We collected news sources identified as spreading disinformation by the European Union's East Strategic Communications Task Force from euvsdisinfo.eu. In total, there were 467 news sources: 251 trusted and 216 deceptive. We collected reaction data for two popular platforms, Reddit and Twitter, using public APIs over the 13 month period from January 2016 through January 2017. For our Reddit dataset, we collected all Reddit posts submitted during the 13 month period that linked to domains associated with one of our labelled news sources. Then we collected all comments that directly responded to those posts. For our Twitter dataset, we collected all tweets posted in the 13 month period that explicitly @mentioned or directly retweeted content from a source and then assigned a label to each tweet based on the class of the source @mentioned or retweeted. A breakdown of each dataset by source type is shown in Table TABREF10 . Figure FIGREF11 illustrates the distribution of deceptive news sources and reactions across the four sub-categories of deceptive news sources. In our analysis, we consider the set of all deceptive sources and the set excluding the most extreme (disinformation). ### Methodology We use the linguistically-infused neural network model from Figure FIGREF5 to label the reaction type of each tweet or comment. Using these labels, we examine how often response types occur when users react to each type of news source. For clarity, we report the five most frequently occurring reaction types (expressed in at least 5% of reactions within each source type) and compare the distributions of reaction types for each type of news source. To examine whether users react to content from trusted sources differently than from deceptive sources, we measure the reaction delay, which we define as the time elapsed between the moment the link or content was posted/tweeted and the moment that the reaction comment or tweet occurred. We report the cumulative distribution functions (CDFs) for each source type and use Mann Whitney U (MWU) tests to compare whether users respond with a given reaction type with significantly different delays to news sources of different levels of credibility. ### Results and Discussion For both Twitter and Reddit datasets, we found that the primary reaction types were answer, appreciation, elaboration, question, or “other” (no label was predicted). Figure FIGREF13 illustrates the distribution of reaction types among Reddit comments (top plot) or tweets (bottom plot) responding to each type of source, as a percentage of all comments/tweets reacting to sources of the given type (i.e., trusted, all deceptive, and deceptive excluding disinformation sources). For Twitter, we report clear differences in user reactions to trusted vs. deceptive sources. Deceptive (including disinformation) sources have a much higher rate of appreciation reactions and a lower rate of elaboration responses, compared to trusted news sources. Differences are still significant ( INLINEFORM0 ) but the trends reverse if we do not include disinformation sources. We also see an increase in the rate of question-reactions compared to trusted news sources if we exclude disinformation sources. For Reddit, there appears to be a very similar distribution across reaction types for trusted and deceptive sources. However, MWU tests still found that the differences between trusted and deceptive news sources were statistically significant ( INLINEFORM0 ) — regardless of whether we include or exclude disinformation sources. Posts that link to deceptive sources have higher rates of question, appreciation, and answering reactions, while posts that link to trusted sources have higher rates of elaboration, agreement, and disagreement. Next, we compared the speed with which users reacted to posts of sources of varying credibility. Our original hypothesis was that users react to posts of trusted sources faster than posts of deceptive sources. The CDFs for each source type and platform (solid and dashed lines represent Reddit and Twitter respectively) are shown in Figure FIGREF14 . We observe that the lifetime of direct reactions to news sources on Twitter is often more extended than for sources on Reddit. One exception is answer reactions which almost always occur within the first hour after the Twitter new source originally posted the tweet being answered. This may be due to the different ways that users consume content on the two platforms. Users follow accounts on Twitter, whereas on Reddit users “follow” topics through their subscriptions to various subreddits. Users can view the news feeds of individual sources on Twitter and view all of the sources' posts. Reddit, on the other hand, is not designed to highlight individual users or news sources; instead new posts (regardless of the source) are viewed based on their hotness score within each subreddit. In addition, we observe that reactions to posts linked to trusted sources are less heavily concentrated within the first 12 to 15 hours of the post's lifetime on Reddit. The opposite is found on Twitter. Twitter sources may have a larger range of reaction delays, but they are also more heavily concentrated in the lower end of that range ( INLINEFORM0 ). ### Related Work As we noted above, most studies that examine misinformation spread focus on individual events such as natural disasters BIBREF17 , political elections BIBREF18 , or crises BIBREF19 and examine the response to the event on social media. A recent study by Vosoughi et al. vosoughi2018spread found that news stories that were fact-checked and found to be false spread faster and to more people than news items found to be true. In contrast, our methodology considers immediate reactions to news sources of varying credibility, so we can determine whether certain reactions or reactions to trusted or deceptive news sources evoke more or faster responses from social media users. ### Conclusion In the current work, we have presented a content-based model that classifies user reactions into one of nine types, such as answer, elaboration, and question, etc., and a large-scale analysis of Twitter posts and Reddit comments in response to content from news sources of varying credibility. Our analysis of user reactions to trusted and deceptive sources on Twitter and Reddit shows significant differences in the distribution of reaction types for trusted versus deceptive news. However, due to differences in the user interface, algorithmic design, or user-base, we find that Twitter users react to trusted and deceptive sources very differently than Reddit users. For instance, Twitter users questioned disinformation sources less often and more slowly than they did trusted news sources; Twitter users also expressed appreciation towards disinformation sources more often and faster than towards trusted sources. Results from Reddit show similar, but far less pronounced, reaction results. Future work may focus on analysis of reaction behavior from automated (i.e., 'bot'), individual, or organization accounts; on additional social media platforms and languages; or between more fine-grained categories of news source credibility. ### Acknowledgments The research described in this paper is based on Twitter and Reddit data collected by the University of Notre Dame using public APIs. The research was supported by the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy. This research is also supported by the Defense Advanced Research Projects Agency (DARPA), contract W911NF-17-C-0094. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government. Figure 1: Architecture of neural network model used to predict reaction types. Table 1: Summary of the training data we recovered compared to the data collected by Zhang et al. (2017) reported as distributions of comments across reaction types. Figure 2: Comparison of our model’s performance, measured using F1 score, trained only on content features, with the performance reported by Zhang et al. (2017) trained on content, author, thread, structure, and community features. Table 2: Summary of Twitter and Reddit datasets used to measure the speed and types of reactions to Trusted and Deceptive news sources excluding (no disinformation) or including (All) the most extreme of the deceptive sources — those identified as spreading disinformation. Figure 3: Distributions of Deceptive news sources and reactions to those sources (Reddit comments or tweets, respectively) for the Reddit and Twitter datasets across the four subcategories of deceptive news sources. Figure 4: Distributions of five most frequently occurring reaction types within comments on Reddit and tweets on Twitter for each news source type (MWU p < 0.01). Figure 5: CDF plots of the volumes of reactions by reaction delays for the frequently occurring reactions (i.e., , reactions that occur in at least 5% of comments) for each source-type, using a step size of one hour. The CDF for Elaboration-reactions to Deceptive (no disinformation) Twitter news sources is occluded by the CDF for Deceptive Twitter news sources. This figure is best viewed in color.	agreement, answer, appreciation, disagreement, elaboration, humor, negative reaction, question, other
How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk?	### Introduction Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train their model using real restaurant reviews from yelp.com BIBREF2 . Once trained, the model is used to generate reviews character-by-character. Due to the generation methodology, it cannot be easily targeted for a specific context (meaningful side information). Consequently, the review generation process may stray off-topic. For instance, when generating a review for a Japanese restaurant in Las Vegas, the review generation process may include references to an Italian restaurant in Baltimore. The authors of BIBREF0 apply a post-processing step (customization), which replaces food-related words with more suitable ones (sampled from the targeted restaurant). The word replacement strategy has drawbacks: it can miss certain words and replace others independent of their surrounding words, which may alert savvy readers. As an example: when we applied the customization technique described in BIBREF0 to a review for a Japanese restaurant it changed the snippet garlic knots for breakfast with garlic knots for sushi). We propose a methodology based on neural machine translation (NMT) that improves the generation process by defining a context for the each generated fake review. Our context is a clear-text sequence of: the review rating, restaurant name, city, state and food tags (e.g. Japanese, Italian). We show that our technique generates review that stay on topic. We can instantiate our basic technique into several variants. We vet them on Amazon Mechanical Turk and find that native English speakers are very poor at recognizing our fake generated reviews. For one variant, the participants' performance is close to random: the class-averaged F-score of detection is INLINEFORM0 (whereas random would be INLINEFORM1 given the 1:6 imbalance in the test). Via a user study with experienced, highly educated participants, we compare this variant (which we will henceforth refer to as NMT-Fake* reviews) with fake reviews generated using the char-LSTM-based technique from BIBREF0 . We demonstrate that NMT-Fake* reviews constitute a new category of fake reviews that cannot be detected by classifiers trained only using previously known categories of fake reviews BIBREF0 , BIBREF3 , BIBREF4 . Therefore, NMT-Fake* reviews may go undetected in existing online review sites. To meet this challenge, we develop an effective classifier that detects NMT-Fake* reviews effectively (97% F-score). Our main contributions are: ### Background Fake reviews User-generated content BIBREF5 is an integral part of the contemporary user experience on the web. Sites like tripadvisor.com, yelp.com and Google Play use user-written reviews to provide rich information that helps other users choose where to spend money and time. User reviews are used for rating services or products, and for providing qualitative opinions. User reviews and ratings may be used to rank services in recommendations. Ratings have an affect on the outwards appearance. Already 8 years ago, researchers estimated that a one-star rating increase affects the business revenue by 5 – 9% on yelp.com BIBREF6 . Due to monetary impact of user-generated content, some businesses have relied on so-called crowd-turfing agents BIBREF7 that promise to deliver positive ratings written by workers to a customer in exchange for a monetary compensation. Crowd-turfing ethics are complicated. For example, Amazon community guidelines prohibit buying content relating to promotions, but the act of writing fabricated content is not considered illegal, nor is matching workers to customers BIBREF8 . Year 2015, approximately 20% of online reviews on yelp.com were suspected of being fake BIBREF9 . Nowadays, user-generated review sites like yelp.com use filters and fraudulent review detection techniques. These factors have resulted in an increase in the requirements of crowd-turfed reviews provided to review sites, which in turn has led to an increase in the cost of high-quality review. Due to the cost increase, researchers hypothesize the existence of neural network-generated fake reviews. These neural-network-based fake reviews are statistically different from human-written fake reviews, and are not caught by classifiers trained on these BIBREF0 . Detecting fake reviews can either be done on an individual level or as a system-wide detection tool (i.e. regulation). Detecting fake online content on a personal level requires knowledge and skills in critical reading. In 2017, the National Literacy Trust assessed that young people in the UK do not have the skillset to differentiate fake news from real news BIBREF10 . For example, 20% of children that use online news sites in age group 12-15 believe that all information on news sites are true. Neural Networks Neural networks are function compositions that map input data through INLINEFORM0 subsequent layers: DISPLAYFORM0 where the functions INLINEFORM0 are typically non-linear and chosen by experts partly for known good performance on datasets and partly for simplicity of computational evaluation. Language models (LMs) BIBREF11 are generative probability distributions that assign probabilities to sequences of tokens ( INLINEFORM1 ): DISPLAYFORM0 such that the language model can be used to predict how likely a specific token at time step INLINEFORM0 is, based on the INLINEFORM1 previous tokens. Tokens are typically either words or characters. For decades, deep neural networks were thought to be computationally too difficult to train. However, advances in optimization, hardware and the availability of frameworks have shown otherwise BIBREF1 , BIBREF12 . Neural language models (NLMs) have been one of the promising application areas. NLMs are typically various forms of recurrent neural networks (RNNs), which pass through the data sequentially and maintain a memory representation of the past tokens with a hidden context vector. There are many RNN architectures that focus on different ways of updating and maintaining context vectors: Long Short-Term Memory units (LSTM) and Gated Recurrent Units (GRUs) are perhaps most popular. Neural LMs have been used for free-form text generation. In certain application areas, the quality has been high enough to sometimes fool human readers BIBREF0 . Encoder-decoder (seq2seq) models BIBREF13 are architectures of stacked RNNs, which have the ability to generate output sequences based on input sequences. The encoder network reads in a sequence of tokens, and passes it to a decoder network (a LM). In contrast to simpler NLMs, encoder-decoder networks have the ability to use additional context for generating text, which enables more accurate generation of text. Encoder-decoder models are integral in Neural Machine Translation (NMT) BIBREF14 , where the task is to translate a source text from one language to another language. NMT models additionally use beam search strategies to heuristically search the set of possible translations. Training datasets are parallel corpora; large sets of paired sentences in the source and target languages. The application of NMT techniques for online machine translation has significantly improved the quality of translations, bringing it closer to human performance BIBREF15 . Neural machine translation models are efficient at mapping one expression to another (one-to-one mapping). Researchers have evaluated these models for conversation generation BIBREF16 , with mixed results. Some researchers attribute poor performance to the use of the negative log likelihood cost function during training, which emphasizes generation of high-confidence phrases rather than diverse phrases BIBREF17 . The results are often generic text, which lacks variation. Li et al. have suggested various augmentations to this, among others suppressing typical responses in the decoder language model to promote response diversity BIBREF17 . ### System Model We discuss the attack model, our generative machine learning method and controlling the generative process in this section. ### Attack Model Wang et al. BIBREF7 described a model of crowd-turfing attacks consisting of three entities: customers who desire to have fake reviews for a particular target (e.g. their restaurant) on a particular platform (e.g. Yelp), agents who offer fake review services to customers, and workers who are orchestrated by the agent to compose and post fake reviews. Automated crowd-turfing attacks (ACA) replace workers by a generative model. This has several benefits including better economy and scalability (human workers are more expensive and slower) and reduced detectability (agent can better control the rate at which fake reviews are generated and posted). We assume that the agent has access to public reviews on the review platform, by which it can train its generative model. We also assume that it is easy for the agent to create a large number of accounts on the review platform so that account-based detection or rate-limiting techniques are ineffective against fake reviews. The quality of the generative model plays a crucial role in the attack. Yao et al. BIBREF0 propose the use of a character-based LSTM as base for generative model. LSTMs are not conditioned to generate reviews for a specific target BIBREF1 , and may mix-up concepts from different contexts during free-form generation. Mixing contextually separate words is one of the key criteria that humans use to identify fake reviews. These may result in violations of known indicators for fake content BIBREF18 . For example, the review content may not match prior expectations nor the information need that the reader has. We improve the attack model by considering a more capable generative model that produces more appropriate reviews: a neural machine translation (NMT) model. ### Generative Model We propose the use of NMT models for fake review generation. The method has several benefits: 1) the ability to learn how to associate context (keywords) to reviews, 2) fast training time, and 3) a high-degree of customization during production time, e.g. introduction of specific waiter or food items names into reviews. NMT models are constructions of stacked recurrent neural networks (RNNs). They include an encoder network and a decoder network, which are jointly optimized to produce a translation of one sequence to another. The encoder rolls over the input data in sequence and produces one INLINEFORM0 -dimensional context vector representation for the sentence. The decoder then generates output sequences based on the embedding vector and an attention module, which is taught to associate output words with certain input words. The generation typically continues until a specific EOS (end of sentence) token is encountered. The review length can be controlled in many ways, e.g. by setting the probability of generating the EOS token to zero until the required length is reached. NMT models often also include a beam search BIBREF14 , which generates several hypotheses and chooses the best ones amongst them. In our work, we use the greedy beam search technique. We forgo the use of additional beam searches as we found that the quality of the output was already adequate and the translation phase time consumption increases linearly for each beam used. We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 –5 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context. The Yelp Challenge dataset includes metadata about restaurants, including their names, food tags, cities and states these restaurants are located in. For each restaurant review, we fetch this metadata and use it as our input context in the NMT model. The corresponding restaurant review is similarly set as the target sentence. This method produced 2.9 million pairs of sentences in our parallel corpus. We show one example of the parallel training corpus in Example 1 below: 5 Public House Las Vegas NV Gastropubs Restaurants > Excellent food and service . Pricey , but well worth it . I would recommend the bone marrow and sampler platter for appetizers . \end{verbatim} \noindent The order {\textbf{[rating name city state tags]}} is kept constant. Training the model conditions it to associate certain sequences of words in the input sentence with others in the output. \subsubsection{Training Settings} We train our NMT model on a commodity PC with a i7-4790k CPU (4.00GHz), with 32GB RAM and one NVidia GeForce GTX 980 GPU. Our system can process approximately 1,300 \textendash 1,500 source tokens/s and approximately 5,730 \textendash 5,830 output tokens/s. Training one epoch takes in average 72 minutes. The model is trained for 8 epochs, i.e. over night. We call fake review generated by this model \emph{NMT-Fake reviews}. We only need to train one model to produce reviews of different ratings. We use the training settings: adam optimizer \cite{kingma2014adam} with the suggested learning rate 0.001 \cite{klein2017opennmt}. For most parts, parameters are at their default values. Notably, the maximum sentence length of input and output is 50 tokens by default. We leverage the framework openNMT-py \cite{klein2017opennmt} to teach the our NMT model. We list used openNMT-py commands in Appendix Table~\ref{table:openNMT-py_commands}. \begin{figure}[t] \begin{center} \begin{tabular}{ \| l \| } \hline Example 2. Greedy NMT \\ Great food, \underline{great} service, \underline{great} \textit{\textit{beer selection}}. I had the \textit{Gastropubs burger} and it \\ was delicious. The \underline{\textit{beer selection}} was also \underline{great}. \\ \\ Example 3. NMT-Fake* \\ I love this restaurant. Great food, great service. It's \textit{a little pricy} but worth\\ it for the \textit{quality} of the \textit{beer} and atmosphere you can see in \textit{Vegas} \\ \hline \end{tabular} \label{table:output_comparison} \end{center} \caption{Na\"{i}ve text generation with NMT vs. generation using our NTM model. Repetitive patterns are \underline{underlined}. Contextual words are \emph{italicized}. Both examples here are generated based on the context given in Example~1.} \label{fig:comparison} \end{figure} \subsection{Controlling generation of fake reviews} \label{sec:generating} Greedy NMT beam searches are practical in many NMT cases. However, the results are simply repetitive, when naively applied to fake review generation (See Example~2 in Figure~\ref{fig:comparison}). The NMT model produces many \emph{high-confidence} word predictions, which are repetitive and obviously fake. We calculated that in fact, 43\% of the generated sentences started with the phrase ``Great food''. The lack of diversity in greedy use of NMTs for text generation is clear. \begin{algorithm}[!b] \KwData{Desired review context $C_\mathrm{input}$ (given as cleartext), NMT model} \KwResult{Generated review $out$ for input context $C_\mathrm{input}$} set $b=0.3$, $\lambda=-5$, $\alpha=\frac{2}{3}$, $p_\mathrm{typo}$, $p_\mathrm{spell}$ \\ $\log p \leftarrow \text{NMT.decode(NMT.encode(}C_\mathrm{input}\text{))}$ \\ out $\leftarrow$ [~] \\ $i \leftarrow 0$ \\ $\log p \leftarrow \text{Augment}(\log p$, $b$, $\lambda$, $1$, $[~]$, 0)~~~~~~~~~~~~~~~ \|~random penalty~\\ \While{$i=0$ or $o_i$ not EOS}{ $\log \Tilde{p} \leftarrow \text{Augment}(\log p$, $b$, $\lambda$, $\alpha$, $o_i$, $i$)~~~~~~~~~~~ \|~start \& memory penalty~\\ $o_i \leftarrow$ \text{NMT.beam}($\log \Tilde{p}$, out) \\ out.append($o_i$) \\ $i \leftarrow i+1$ }\text{return}~$\text{Obfuscate}$(out,~$p_\mathrm{typo}$,~$p_\mathrm{spell}$) \caption{Generation of NMT-Fake* reviews.} \label{alg:base} \end{algorithm} In this work, we describe how we succeeded in creating more diverse and less repetitive generated reviews, such as Example 3 in Figure~\ref{fig:comparison}. We outline pseudocode for our methodology of generating fake reviews in Algorithm~\ref{alg:base}. There are several parameters in our algorithm. The details of the algorithm will be shown later. We modify the openNMT-py translation phase by changing log-probabilities before passing them to the beam search. We notice that reviews generated with openNMT-py contain almost no language errors. As an optional post-processing step, we obfuscate reviews by introducing natural typos/misspellings randomly. In the next sections, we describe how we succeeded in generating more natural sentences from our NMT model, i.e. generating reviews like Example~3 instead of reviews like Example~2. \subsubsection{Variation in word content} Example 2 in Figure~\ref{fig:comparison} repeats commonly occurring words given for a specific context (e.g. \textit{great, food, service, beer, selection, burger} for Example~1). Generic review generation can be avoided by decreasing probabilities (log-likelihoods \cite{murphy2012machine}) of the generators LM, the decoder. We constrain the generation of sentences by randomly \emph{imposing penalties to words}. We tried several forms of added randomness, and found that adding constant penalties to a \emph{random subset} of the target words resulted in the most natural sentence flow. We call these penalties \emph{Bernoulli penalties}, since the random variables are chosen as either 1 or 0 (on or off). \paragraph{Bernoulli penalties to language model} To avoid generic sentences components, we augment the default language model $p(\cdot)$ of the decoder by \begin{equation} \log \Tilde{p}(t_k) = \log p(t_k \| t_i, \dots, t_1) + \lambda q, \end{equation} where $q \in R^{V}$ is a vector of Bernoulli-distributed random values that obtain values $1$ with probability $b$ and value $0$ with probability $1-b_i$, and $\lambda < 0$. Parameter $b$ controls how much of the vocabulary is forgotten and $\lambda$ is a soft penalty of including ``forgotten'' words in a review. $\lambda q_k$ emphasizes sentence forming with non-penalized words. The randomness is reset at the start of generating a new review. Using Bernoulli penalties in the language model, we can ``forget'' a certain proportion of words and essentially ``force'' the creation of less typical sentences. We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\lambda$, with a user study in Section~\ref{sec:varying}. \paragraph{Start penalty} We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, great service''). Inspired by \cite{li2016diversity}, we add a random start penalty $\lambda s^\mathrm{i}$, to our language model, which decreases monotonically for each generated token. We set $\alpha \leftarrow 0.66$ as it's effect decreases by 90\% every 5 words generated. \paragraph{Penalty for reusing words} Bernoulli penalties do not prevent excessive use of certain words in a sentence (such as \textit{great} in Example~2). To avoid excessive reuse of words, we included a memory penalty for previously used words in each translation. Concretely, we add the penalty $\lambda$ to each word that has been generated by the greedy search. \subsubsection{Improving sentence coherence} \label{sec:grammar} We visually analyzed reviews after applying these penalties to our NMT model. While the models were clearly diverse, they were \emph{incoherent}: the introduction of random penalties had degraded the grammaticality of the sentences. Amongst others, the use of punctuation was erratic, and pronouns were used semantically wrongly (e.g. \emph{he}, \emph{she} might be replaced, as could ``and''/``but''). To improve the authenticity of our reviews, we added several \emph{grammar-based rules}. English language has several classes of words which are important for the natural flow of sentences. We built a list of common pronouns (e.g. I, them, our), conjunctions (e.g. and, thus, if), punctuation (e.g. ,/.,..), and apply only half memory penalties for these words. We found that this change made the reviews more coherent. The pseudocode for this and the previous step is shown in Algorithm~\ref{alg:aug}. The combined effect of grammar-based rules and LM augmentation is visible in Example~3, Figure~\ref{fig:comparison}. \begin{algorithm}[!t] \KwData{Initial log LM $\log p$, Bernoulli probability $b$, soft-penalty $\lambda$, monotonic factor $\alpha$, last generated token $o_i$, grammar rules set $G$} \KwResult{Augmented log LM $\log \Tilde{p}$} \begin{algorithmic}[1] \Procedure {Augment}{$\log p$, $b$, $\lambda$, $\alpha$, $o_i$, $i$}{ \\ generate $P_{\mathrm{1:N}} \leftarrow Bernoulli(b)$~~~~~~~~~~~~~~~\|~$\text{One value} \in \{0,1\}~\text{per token}$~ \\ $I \leftarrow P>0$ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\|~Select positive indices~\\ $\log \Tilde{p} \leftarrow$ $\text{Discount}$($\log p$, $I$, $\lambda \cdot \alpha^i$,$G$) ~~~~~~ \|~start penalty~\\ $\log \Tilde{p} \leftarrow$ $\text{Discount}$($\log \Tilde{p}$, $[o_i]$, $\lambda$,$G$) ~~~~~~~~~ \|~memory penalty~\\ \textbf{return}~$\log \Tilde{p}$ } \EndProcedure \\ \Procedure {Discount}{$\log p$, $I$, $\lambda$, $G$}{ \State{\For{$i \in I$}{ \eIf{$o_i \in G$}{ $\log p_{i} \leftarrow \log p_{i} + \lambda/2$ }{ $\log p_{i} \leftarrow \log p_{i} + \lambda$} }\textbf{return}~$\log p$ \EndProcedure }} \end{algorithmic} \caption{Pseudocode for augmenting language model. } \label{alg:aug} \end{algorithm} \subsubsection{Human-like errors} \label{sec:obfuscation} We notice that our NMT model produces reviews without grammar mistakes. This is unlike real human writers, whose sentences contain two types of language mistakes 1) \emph{typos} that are caused by mistakes in the human motoric input, and 2) \emph{common spelling mistakes}. We scraped a list of common English language spelling mistakes from Oxford dictionary\footnote{\url{https://en.oxforddictionaries.com/spelling/common-misspellings}} and created 80 rules for randomly \emph{re-introducing spelling mistakes}. Similarly, typos are randomly reintroduced based on the weighted edit distance\footnote{\url{https://pypi.python.org/pypi/weighted-levenshtein/0.1}}, such that typos resulting in real English words with small perturbations are emphasized. We use autocorrection tools\footnote{\url{https://pypi.python.org/pypi/autocorrect/0.1.0}} for finding these words. We call these augmentations \emph{obfuscations}, since they aim to confound the reader to think a human has written them. We omit the pseudocode description for brevity. \subsection{Experiment: Varying generation parameters in our NMT model} \label{sec:varying} Parameters $b$ and $\lambda$ control different aspects in fake reviews. We show six different examples of generated fake reviews in Table~\ref{table:categories}. Here, the largest differences occur with increasing values of $b$: visibly, the restaurant reviews become more extreme. This occurs because a large portion of vocabulary is ``forgotten''. Reviews with $b \geq 0.7$ contain more rare word combinations, e.g. ``!!!!!'' as punctuation, and they occasionally break grammaticality (''experience was awesome''). Reviews with lower $b$ are more generic: they contain safe word combinations like ``Great place, good service'' that occur in many reviews. Parameter $\lambda$'s is more subtle: it affects how random review starts are and to a degree, the discontinuation between statements within the review. We conducted an Amazon Mechanical Turk (MTurk) survey in order to determine what kind of NMT-Fake reviews are convincing to native English speakers. We describe the survey and results in the next section. \begin{table}[!b] \caption{Six different parametrizations of our NMT reviews and one example for each. The context is ``5 P~.~F~.~Chang ' s Scottsdale AZ'' in all examples.} \begin{center} \begin{tabular}{ \| l \| l \| } \hline $(b, \lambda)$ & Example review for context \\ \hline \hline $(0.3, -3)$ & I love this location! Great service, great food and the best drinks in Scottsdale. \\ & The staff is very friendly and always remembers u when we come in\\\hline $(0.3, -5)$ & Love love the food here! I always go for lunch. They have a great menu and \\ & they make it fresh to order. Great place, good service and nice staff\\\hline $(0.5, -4)$ & I love their chicken lettuce wraps and fried rice!! The service is good, they are\\ & always so polite. They have great happy hour specials and they have a lot\\ & of options.\\\hline $(0.7, -3)$ & Great place to go with friends! They always make sure your dining \\ & experience was awesome.\\ \hline $(0.7, -5)$ & Still haven't ordered an entree before but today we tried them once..\\ & both of us love this restaurant....\\\hline $(0.9, -4)$ & AMAZING!!!!! Food was awesome with excellent service. Loved the lettuce \\ & wraps. Great drinks and wine! Can't wait to go back so soon!!\\ \hline \end{tabular} \label{table:categories} \end{center} \end{table} \subsubsection{MTurk study} \label{sec:amt} We created 20 jobs, each with 100 questions, and requested master workers in MTurk to complete the jobs. We randomly generated each survey for the participants. Each review had a 50\% chance to be real or fake. The fake ones further were chosen among six (6) categories of fake reviews (Table~\ref{table:categories}). The restaurant and the city was given as contextual information to the participants. Our aim was to use this survey to understand how well English-speakers react to different parametrizations of NMT-Fake reviews. Table~\ref{table:amt_pop} in Appendix summarizes the statistics for respondents in the survey. All participants were native English speakers from America. The base rate (50\%) was revealed to the participants prior to the study. We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \emph{F-score of only 56\%}, with 53\% F-score for fake review detection and 59\% F-score for real review detection. The results are very close to \emph{random detection}, where precision, recall and F-score would each be 50\%. Results are recorded in Table~\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random. \begin{table}[t] \caption{Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants).} \begin{center} \begin{tabular}{ \| c \| c \|c \|c \| c \| } \hline \multicolumn{5}{\|c\|}{Classification report} \\ \hline Review Type & Precision & Recall & F-score & Support \\ \hline \hline Human & 55\% & 63\% & 59\% & 994\\ NMT-Fake & 57\% & 50\% & 53\% & 1006 \\ \hline \end{tabular} \label{table:MTurk_super} \end{center} \end{table} We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \lambda=-5)$, where true positive rate was $40.4\%$, while the true negative rate of the real class was $62.7\%$. The precision were $16\%$ and $86\%$, respectively. The class-averaged F-score is $47.6\%$, which is close to random. Detailed classification reports are shown in Table~\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \emph{our NMT-Fake reviews pose a significant threat to review systems}, since \emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper. \section{Evaluation} \graphicspath{ {figures/}} We evaluate our fake reviews by first comparing them statistically to previously proposed types of fake reviews, and proceed with a user study with experienced participants. We demonstrate the statistical difference to existing fake review types \cite{yao2017automated,mukherjee2013yelp,rayana2015collective} by training classifiers to detect previous types and investigate classification performance. \subsection{Replication of state-of-the-art model: LSTM} \label{sec:repl} Yao et al. \cite{yao2017automated} presented the current state-of-the-art generative model for fake reviews. The model is trained over the Yelp Challenge dataset using a two-layer character-based LSTM model. We requested the authors of \cite{yao2017automated} for access to their LSTM model or a fake review dataset generated by their model. Unfortunately they were not able to share either of these with us. We therefore replicated their model as closely as we could, based on their paper and e-mail correspondence\footnote{We are committed to sharing our code with bonafide researchers for the sake of reproducibility.}. We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post-processed the reviews using the customization methodology described in \cite{yao2017automated} and email correspondence. We call fake reviews generated by this model LSTM-Fake reviews. \subsection{Similarity to existing fake reviews} \label{sec:automated} We now want to understand how NMT-Fake* reviews compare to a) LSTM fake reviews and b) human-generated fake reviews. We do this by comparing the statistical similarity between these classes. For `a' (Figure~\ref{fig:lstm}), we use the Yelp Challenge dataset. We trained a classifier using 5,000 random reviews from the Yelp Challenge dataset (``human'') and 5,000 fake reviews generated by LSTM-Fake. Yao et al. \cite{yao2017automated} found that character features are essential in identifying LSTM-Fake reviews. Consequently, we use character features (n-grams up to 3). For `b' (Figure~\ref{fig:shill}),we the ``Yelp Shills'' dataset (combination of YelpZip \cite{mukherjee2013yelp}, YelpNYC \cite{mukherjee2013yelp}, YelpChi \cite{rayana2015collective}). This dataset labels entries that are identified as fraudulent by Yelp's filtering mechanism (''shill reviews'')\footnote{Note that shill reviews are probably generated by human shills \cite{zhao2017news}.}. The rest are treated as genuine reviews from human users (''genuine''). We use 100,000 reviews from each category to train a classifier. We use features from the commercial psychometric tool LIWC2015 \cite{pennebaker2015development} to generated features. In both cases, we use AdaBoost (with 200 shallow decision trees) for training. For testing each classifier, we use a held out test set of 1,000 reviews from both classes in each case. In addition, we test 1,000 NMT-Fake* reviews. Figures~\ref{fig:lstm} and~\ref{fig:shill} show the results. The classification threshold of 50\% is marked with a dashed line. \begin{figure} \begin{subfigure}[b]{0.5\columnwidth} \includegraphics[width=\columnwidth]{figures/lstm.png} \caption{Human--LSTM reviews.} \label{fig:lstm} \end{subfigure} \begin{subfigure}[b]{0.5\columnwidth} \includegraphics[width=\columnwidth]{figures/distribution_shill.png} \caption{Genuine--Shill reviews.} \label{fig:shill} \end{subfigure} \caption{ Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (\emph{genuine} and \emph{shill}) reviews. Figure~\ref{fig:lstm} shows that a classifier trained to distinguish ``human'' vs. LSTM-Fake cannot distinguish ``human'' vs NMT-Fake* reviews. Figure~\ref{fig:shill} shows NMT-Fake* reviews are more similar to \emph{genuine} reviews than \emph{shill} reviews. } \label{fig:statistical_similarity} \end{figure} We can see that our new generated reviews do not share strong attributes with previous known categories of fake reviews. If anything, our fake reviews are more similar to genuine reviews than previous fake reviews. We thus conjecture that our NMT-Fake* fake reviews present a category of fake reviews that may go undetected on online review sites. \subsection{Comparative user study} \label{sec:comparison} We wanted to evaluate the effectiveness of fake reviews againsttech-savvy users who understand and know to expect machine-generated fake reviews. We conducted a user study with 20 participants, all with computer science education and at least one university degree. Participant demographics are shown in Table~\ref{table:amt_pop} in the Appendix. Each participant first attended a training session where they were asked to label reviews (fake and genuine) and could later compare them to the correct answers -- we call these participants \emph{experienced participants}. No personal data was collected during the user study. Each person was given two randomly selected sets of 30 of reviews (a total of 60 reviews per person) with reviews containing 10 \textendash 50 words each. Each set contained 26 (87\%) real reviews from Yelp and 4 (13\%) machine-generated reviews, numbers chosen based on suspicious review prevalence on Yelp~\cite{mukherjee2013yelp,rayana2015collective}. One set contained machine-generated reviews from one of the two models (NMT ($b=0.3, \lambda=-5$) or LSTM), and the other set reviews from the other in randomized order. The number of fake reviews was revealed to each participant in the study description. Each participant was requested to mark four (4) reviews as fake. Each review targeted a real restaurant. A screenshot of that restaurant's Yelp page was shown to each participant prior to the study. Each participant evaluated reviews for one specific, randomly selected, restaurant. An example of the first page of the user study is shown in Figure~\ref{fig:screenshot} in Appendix. \begin{figure}[!ht] \centering \includegraphics[width=.7\columnwidth]{detection2.png} \caption{Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are $0.8\pm0.7$ for NMT-Fake* and $2.5\pm1.0$ for LSTM-Fake. $n=20$. A sample of random detection is shown as comparison.} \label{fig:aalto} \end{figure} Figure~\ref{fig:aalto} shows the distribution of detected reviews of both types. A hypothetical random detector is shown for comparison. NMT-Fake* reviews are significantly more difficult to detect for our experienced participants. In average, detection rate (recall) is $20\%$ for NMT-Fake* reviews, compared to $61\%$ for LSTM-based reviews. The precision (and F-score) is the same as the recall in our study, since participants labeled 4 fakes in each set of 30 reviews \cite{murphy2012machine}. The distribution of the detection across participants is shown in Figure~\ref{fig:aalto}. \emph{The difference is statistically significant with confidence level $99\%$} (Welch's t-test). We compared the detection rate of NMT-Fake* reviews to a random detector, and find that \emph{our participants detection rate of NMT-Fake* reviews is not statistically different from random predictions with 95\% confidence level} (Welch's t-test). \section{Defenses} \label{sec:detection} We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\ref{table:features_adaboost} (Appendix). We used word-level features based on spaCy-tokenization \cite{honnibal-johnson:2015:EMNLP} and constructed n-gram representation of POS-tags and dependency tree tags. We added readability features from NLTK~\cite{bird2004nltk}. \begin{figure}[ht] \centering \includegraphics[width=.7\columnwidth]{obf_score_fair_2.png} \caption{ Adaboost-based classification of NMT-Fake and human-written reviews. Effect of varying $b$ and $\lambda$ in fake review generation. The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97\%).} \label{fig:adaboost_matrix_b_lambda} \end{figure} Figure~\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \lambda=-5$) are detected with an excellent 97\% F-score. The most important features for the classification were counts for frequently occurring words in fake reviews (such as punctuation, pronouns, articles) as well as the readability feature ``Automated Readability Index''. We thus conclude that while NMT-Fake reviews are difficult to detect for humans, they can be well detected with the right tools. \section{Related Work} Kumar and Shah~\cite{kumar2018false} survey and categorize false information research. Automatically generated fake reviews are a form of \emph{opinion-based false information}, where the creator of the review may influence reader's opinions or decisions. Yao et al. \cite{yao2017automated} presented their study on machine-generated fake reviews. Contrary to us, they investigated character-level language models, without specifying a specific context before generation. We leverage existing NMT tools to encode a specific context to the restaurant before generating reviews. Supporting our study, Everett et al~\cite{Everett2016Automated} found that security researchers were less likely to be fooled by Markov chain-generated Reddit comments compared to ordinary Internet users. Diversification of NMT model outputs has been studied in \cite{li2016diversity}. The authors proposed the use of a penalty to commonly occurring sentences (\emph{n-grams}) in order to emphasize maximum mutual information-based generation. The authors investigated the use of NMT models in chatbot systems. We found that unigram penalties to random tokens (Algorithm~\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses. \section {Discussion and Future Work} \paragraph{What makes NMT-Fake* reviews difficult to detect?} First, NMT models allow the encoding of a relevant context for each review, which narrows down the possible choices of words that the model has to choose from. Our NMT model had a perplexity of approximately $25$, while the model of \cite{yao2017automated} had a perplexity of approximately $90$ \footnote{Personal communication with the authors}. Second, the beam search in NMT models narrows down choices to natural-looking sentences. Third, we observed that the NMT model produced \emph{better structure} in the generated sentences (i.e. a more coherent story). \paragraph{Cost of generating reviews} With our setup, generating one review took less than one second. The cost of generation stems mainly from the overnight training. Assuming an electricity cost of 16 cents / kWh (California) and 8 hours of training, training the NMT model requires approximately 1.30 USD. This is a 90\% reduction in time compared to the state-of-the-art \cite{yao2017automated}. Furthermore, it is possible to generate both positive and negative reviews with the same model. \paragraph{Ease of customization} We experimented with inserting specific words into the text by increasing their log likelihoods in the beam search. We noticed that the success depended on the prevalence of the word in the training set. For example, adding a +5 to \emph{Mike} in the log-likelihood resulted in approximately 10\% prevalence of this word in the reviews. An attacker can therefore easily insert specific keywords to reviews, which can increase evasion probability. \paragraph{Ease of testing} Our diversification scheme is applicable during \emph{generation phase}, and does not affect the training setup of the network in any way. Once the NMT model is obtained, it is easy to obtain several different variants of NMT-Fake reviews by varying parameters $b$ and $\lambda$. \paragraph{Languages} The generation methodology is not per-se language-dependent. The requirement for successful generation is that sufficiently much data exists in the targeted language. However, our language model modifications require some knowledge of that target language's grammar to produce high-quality reviews. \paragraph{Generalizability of detection techniques} Currently, fake reviews are not universally detectable. Our results highlight that it is difficult to claim detection performance on unseen types of fake reviews (Section~\ref{sec:automated}). We see this an open problem that deserves more attention in fake reviews research. \paragraph{Generalizability to other types of datasets} Our technique can be applied to any dataset, as long as there is sufficient training data for the NMT model. We used approximately 2.9 million reviews for this work. \section{Conclusion} In this paper, we showed that neural machine translation models can be used to generate fake reviews that are very effective in deceiving even experienced, tech-savvy users. This supports anecdotal evidence \cite{national2017commission}. Our technique is more effective than state-of-the-art \cite{yao2017automated}. We conclude that machine-aided fake review detection is necessary since human users are ineffective in identifying fake reviews. We also showed that detectors trained using one type of fake reviews are not effective in identifying other types of fake reviews. Robust detection of fake reviews is thus still an open problem. \section{Acknowledgments} We thank Tommi Gr\"{o}ndahl for assistance in planning user studies and the participants of the user study for their time and feedback. We also thank Luiza Sayfullina for comments that improved the manuscript. 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SAC '16, ACM (2016) \end{thebibliography} \section{Appendix} We present basic demographics of our MTurk study and the comparative study with experienced users in Table~\ref{table:amt_pop}. \begin{table} \caption{User study statistics.} \begin{center} \begin{tabular}{ \| l \| c \| c \| } \hline Quality & Mechanical Turk users & Experienced users\\ \hline Native English Speaker & Yes (20) & Yes (1) No (19) \\ Fluent in English & Yes (20) & Yes (20) \\ Age & 21-40 (17) 41-60 (3) & 21-25 (8) 26-30 (7) 31-35 (4) 41-45 (1)\\ Gender & Male (14) Female (6) & Male (17) Female (3)\\ Highest Education & High School (10) Bachelor (10) & Bachelor (9) Master (6) Ph.D. (5) \\ \hline \end{tabular} \label{table:amt_pop} \end{center} \end{table} Table~\ref{table:openNMT-py_commands} shows a listing of the openNMT-py commands we used to create our NMT model and to generate fake reviews. \begin{table}[t] \caption{Listing of used openNMT-py commands.} \begin{center} \begin{tabular}{ \| l \| l \| } \hline Phase & Bash command \\ \hline Preprocessing & \begin{lstlisting}[language=bash] python preprocess.py -train_src context-train.txt -train_tgt reviews-train.txt -valid_src context-val.txt -valid_tgt reviews-val.txt -save_data model -lower -tgt_words_min_frequency 10 \end{lstlisting} \\ & \\ Training & \begin{lstlisting}[language=bash] python train.py -data model -save_model model -epochs 8 -gpuid 0 -learning_rate_decay 0.5 -optim adam -learning_rate 0.001 -start_decay_at 3\end{lstlisting} \\ & \\ Generation & \begin{lstlisting}[language=bash] python translate.py -model model_acc_35.54_ppl_25.68_e8.pt -src context-tst.txt -output pred-e8.txt -replace_unk -verbose -max_length 50 -gpu 0 \end{lstlisting} \\ \hline \end{tabular} \label{table:openNMT-py_commands} \end{center} \end{table} Table~\ref{table:MTurk_sub} shows the classification performance of Amazon Mechanical Turkers, separated across different categories of NMT-Fake reviews. The category with best performance ($b=0.3, \lambda=-5$) is denoted as NMT-Fake. \begin{table}[b] \caption{MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are $p_\mathrm{human} = 86\%$ and $p_\mathrm{machine} = 14\%$, with $r_\mathrm{human} = r_\mathrm{machine} = 50\%$. Class-averaged F-scores for random predictions are $42\%$.} \begin{center} \begin{tabular}{ \| c \|\| c \|c \|c \| c \| } \hline $(b=0.3, \lambda = -3)$ & Precision & Recall & F-score & Support \\ \hline Human & 89\% & 63\% & 73\% & 994\\ NMT-Fake & 15\% & 45\% & 22\% & 146 \\ \hline \hline $(b=0.3, \lambda = -5)$ & Precision & Recall & F-score & Support \\ \hline Human & 86\% & 63\% & 73\% & 994\\ NMT-Fake & 16\% & 40\% & 23\% & 171 \\ \hline \hline $(b=0.5, \lambda = -4)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 21\% & 55\% & 30\% & 181 \\ \hline \hline $(b=0.7, \lambda = -3)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 19\% & 50\% & 27\% & 170 \\ \hline \hline $(b=0.7, \lambda = -5)$ & Precision & Recall & F-score & Support \\ \hline Human & 89\% & 63\% & 74\% & 994\\ NMT-Fake & 21\% & 57\% & 31\% & 174 \\ \hline \hline $(b=0.9, \lambda = -4)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 18\% & 50\% & 27\% & 164 \\ \hline \end{tabular} \label{table:MTurk_sub} \end{center} \end{table} Figure~\ref{fig:screenshot} shows screenshots of the first two pages of our user study with experienced participants. \begin{figure}[ht] \centering \includegraphics[width=1.\columnwidth]{figures/screenshot_7-3.png} \caption{ Screenshots of the first two pages in the user study. Example 1 is a NMT-Fake* review, the rest are human-written. } \label{fig:screenshot} \end{figure} Table~\ref{table:features_adaboost} shows the features used to detect NMT-Fake reviews using the AdaBoost classifier. \begin{table} \caption{Features used in NMT-Fake review detector.} \begin{center} \begin{tabular}{ \| l \| c \| } \hline Feature type & Number of features \\ \hline \hline Readability features & 13 \\ \hline Unique POS tags & $~20$ \\ \hline Word unigrams & 22,831 \\ \hline 1/2/3/4-grams of simple part-of-speech tags & 54,240 \\ \hline 1/2/3-grams of detailed part-of-speech tags & 112,944 \\ \hline 1/2/3-grams of syntactic dependency tags & 93,195 \\ \hline \end{tabular} \label{table:features_adaboost} \end{center} \end{table} \end{document} Fig. 1: Näıve text generation with NMT vs. generation using our NTM model. Repetitive patterns are underlined. Contextual words are italicized. Both examples here are generated based on the context given in Example 1. Table 1: Six different parametrizations of our NMT reviews and one example for each. The context is “5 P . F . Chang ’ s Scottsdale AZ” in all examples. Table 2: Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants). Fig. 2: Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (genuine and shill) reviews. Figure 2a shows that a classifier trained to distinguish “human” vs. LSTM-Fake cannot distinguish “human” vs NMT-Fake* reviews. Figure 2b shows NMT-Fake* reviews are more similar to genuine reviews than shill reviews. Fig. 3: Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are 0.8±0.7 for NMT-Fake* and 2.5±1.0 for LSTM-Fake. n = 20. A sample of random detection is shown as comparison. Fig. 4: Adaboost-based classification of NMT-Fake and human-written reviews. Effect of varying b and λ in fake review generation. The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97%). Table 3: User study statistics. Table 4: Listing of used openNMT-py commands. Table 5: MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are phuman = 86% and pmachine = 14%, with rhuman = rmachine = 50%. Class-averaged F-scores for random predictions are 42%. Fig. 5: Screenshots of the first two pages in the user study. Example 1 is a NMTFake* review, the rest are human-written. Table 6: Features used in NMT-Fake review detector.	1,006 fake reviews and 994 real reviews
Which best describes the narrator's attitude towards his work? A. He is frustrated that nobody ever recognizes his progress B. He wishes he were in a different country interacting with his own people C. He is proud to be contributing to broad scientific questions D. He is disappointed he has to work inside a lab but enjoys research	Every writer must seek his own Flowery Kingdom in imagination's wide demesne, and if that search can begin and end on Earth his problem has been greatly simplified. In post-war Japan Walt Sheldon has found not only serenity, but complete freedom to write undisturbed about the things he treasures most. A one-time Air Force officer, he has turned to fantasy in his lighter moments, to bring us such brightly sparkling little gems as this. houlihan's equation by ... Walt Sheldon The tiny spaceship had been built for a journey to a star. But its small, mischievous pilots had a rendezvous with destiny—on Earth. I must admit that at first I wasn't sure I was hearing those noises. It was in a park near the nuclear propulsion center—a cool, green spot, with the leaves all telling each other to hush, be quiet, and the soft breeze stirring them up again. I had known precisely such a secluded little green sanctuary just over the hill from Mr. Riordan's farm when I was a boy. Now it was a place I came to when I had a problem to thrash out. That morning I had been trying to work out an equation to give the coefficient of discharge for the matter in combustion. You may call it gas, if you wish, for we treated it like gas at the center for convenience—as it came from the rocket tubes in our engine. Without this coefficient to give us control, we would have lacked a workable equation when we set about putting the first moon rocket around those extraordinary engines of ours, which were still in the undeveloped blueprint stage. I see I shall have to explain this, although I had hoped to get right along with my story. When you start from scratch, matter discharged from any orifice has a velocity directly proportional to the square root of the pressure-head driving it. But when you actually put things together, contractions or expansions in the gas, surface roughness and other factors make the velocity a bit smaller. At the terrible discharge speed of nuclear explosion—which is what the drive amounts to despite the fact that it is simply water in which nuclear salts have been previously dissolved—this small factor makes quite a difference. I had to figure everything into it—diameter of the nozzle, sharpness of the edge, the velocity of approach to the point of discharge, atomic weight and structure— Oh, there is so much of this that if you're not a nuclear engineer yourself it's certain to weary you. Perhaps you had better take my word for it that without this equation—correctly stated, mind you—mankind would be well advised not to make a first trip to the moon. And all this talk of coefficients and equations sits strangely, you might say, upon the tongue of a man named Kevin Francis Houlihan. But I am, after all, a scientist. If I had not been a specialist in my field I would hardly have found myself engaged in vital research at the center. Anyway, I heard these little noises in the park. They sounded like small working sounds, blending in eerily mysterious fashion with a chorus of small voices. I thought at first it might be children at play, but then at the time I was a bit absent-minded. I tiptoed to the edge of the trees, not wanting to deprive any small scalawags of their pleasure, and peered out between the branches. And what do you suppose I saw? Not children, but a group of little people, hard at work. There was a leader, an older one with a crank face. He was beating the air with his arms and piping: "Over here, now! All right, bring those electrical connections over here—and see you're not slow as treacle about it!" There were perhaps fifty of the little people. I was more than startled by it, too. I had not seen little people in—oh, close to thirty years. I had seen them first as a boy of eight, and then, very briefly again, on my tenth birthday. And I had become convinced they could never be seen here in America. I had never seen them so busy, either. They were building something in the middle of the glade. It was long and shiny and upright and a little over five feet in height. "Come along now, people!" said this crotchety one, looking straight at me. "Stop starin' and get to work! You'll not be needin' to mind that man standin' there! You know he can't see nor hear us!" Oh, it was good to hear the rich old tongue again. I smiled, and the foreman of the leprechauns—if that's what he was—saw me smile and became stiff and alert for a moment, as though suspecting that perhaps I actually could see him. Then he shrugged and turned away, clearly deeming such a thing impossible. I said, "Just a minute, friend, and I'll beg your pardon. It so happens I can see you." He whirled to face me again, staring open-mouthed. Then he said, "What? What's that, now?" "I can see you," I said. "Ohhh!" he said and put his palms to his cheekbones. "Saints be with us! He's a believer! Run everybody—run for your lives!" And they all began running, in as many directions as there were little souls. They began to scurry behind the trees and bushes, and a sloping embankment nearby. "No, wait!" I said. "Don't go away! I'll not be hurting you!" They continued to scurry. I knew what it was they feared. "I don't intend catching one of you!" I said. "Come back, you daft little creatures!" But the glade was silent, and they had all disappeared. They thought I wanted their crock of gold, of course. I'd be entitled to it if I could catch one and keep him. Or so the legends affirmed, though I've wondered often about the truth of them. But I was after no gold. I only wanted to hear the music of an Irish tongue. I was lonely here in America, even if I had latched on to a fine job of work for almost shamefully generous pay. You see, in a place as full of science as the nuclear propulsion center there is not much time for the old things. I very much wanted to talk to the little people. I walked over to the center of the glade where the curious shiny object was standing. It was as smooth as glass and shaped like a huge cigar. There were a pair of triangular fins down at the bottom, and stubby wings amidships. Of course it was a spaceship, or a miniature replica of one. I looked at it more closely. Everything seemed almost miraculously complete and workable. I shook my head in wonder, then stepped back from the spaceship and looked about the glade. I knew they were all hiding nearby, watching me apprehensively. I lifted my head to them. "Listen to me now, little people!" I called out. "My name's Houlihan of the Roscommon Houlihans. I am descended from King Niall himself—or so at least my father used to say! Come on out now, and pass the time o' day!" Then I waited, but they didn't answer. The little people always had been shy. Yet without reaching a decision in so many words I knew suddenly that I had to talk to them. I'd come to the glen to work out a knotty problem, and I was up against a blank wall. Simply because I was so lonely that my mind had become clogged. I knew that if I could just once hear the old tongue again, and talk about the old things, I might be able to think the problem through to a satisfactory conclusion. So I stepped back to the tiny spaceship, and this time I struck it a resounding blow with my fist. "Hear me now, little people! If you don't show yourselves and come out and talk to me, I'll wreck this spaceship from stem to stern!" I heard only the leaves rustling softly. "Do you understand? I'll give you until I count three to make an appearance! One!" The glade remained deathly silent. "Two!" I thought I heard a stirring somewhere, as if a small, brittle twig had snapped in the underbrush. " Three! " And with that the little people suddenly appeared. The leader—he seemed more wizened and bent than before—approached me slowly and warily as I stood there. The others all followed at a safe distance. I smiled to reassure them and then waved my arm in a friendly gesture of greeting. "Good morning," I said. "Good morning," the foreman said with some caution. "My name is Keech." "And mine's Houlihan, as I've told you. Are you convinced now that I have no intention of doing you any injury?" "Mr. Houlihan," said Keech, drawing a kind of peppered dignity up about himself, "in such matters I am never fully convinced. After living for many centuries I am all too acutely aware of the perversity of human nature." "Yes," I said. "Well, as you will quickly see, all I want to do is talk." I nodded as I spoke, and sat down cross-legged upon the grass. "Any Irishman wants to talk, Mr. Houlihan." "And often that's all he wants," I said. "Sit down with me now, and stop staring as if I were a snake returned to the Island." He shook his head and remained standing. "Have your say, Mr. Houlihan. And afterward we'll appreciate it if you'll go away and leave us to our work." "Well, now, your work," I said, and glanced at the spaceship. "That's exactly what's got me curious." The others had edged in a bit now and were standing in a circle, intently staring at me. I took out my pipe. "Why," I asked, "would a group of little people be building a spaceship here in America—out in this lonely place?" Keech stared back without much expression, and said, "I've been wondering how you guessed it was a spaceship. I was surprised enough when you told me you could see us but not overwhelmingly so. I've run into believers before who could see the little people. It happens every so often, though not as frequently as it did a century ago. But knowing a spaceship at first glance! Well, I must confess that does astonish me." "And why wouldn't I know a spaceship when I see one?" I said. "It just so happens I'm a doctor of science." "A doctor of science, now," said Keech. "Invited by the American government to work on the first moon rocket here at the nuclear propulsion center. Since it's no secret I can advise you of it." "A scientist, is it," said Keech. "Well, now, that's very interesting." "I'll make no apologies for it," I said. "Oh, there's no need for apology," said Keech. "Though in truth we prefer poets to scientists. But it has just now crossed my mind, Mr. Houlihan that you, being a scientist, might be of help to us." "How?" I asked. "Well, I might try starting at the beginning," he replied. "You might," I said. "A man usually does." Keech took out his own pipe—a clay dudeen—and looked hopeful. I gave him a pinch of tobacco from my pouch. "Well, now," he said, "first of all you're no doubt surprised to find us here in America." "I am surprised from time to time to find myself here," I said. "But continue." "We had to come here," said Keech, "to learn how to make a spaceship." "A spaceship, now," I said, unconsciously adopting some of the old manner. "Leprechauns are not really mechanically inclined," said Keech. "Their major passions are music and laughter and mischief, as anyone knows." "Myself included," I agreed. "Then why do you need a spaceship?" "Well, if I may use an old expression, we've had a feelin' lately that we're not long for this world. Or let me put it this way. We feel the world isn't long for itself." I scratched my cheek. "How would a man unravel a statement such as that?" "It's very simple. With all the super weapons you mortals have developed, there's the distinct possibility you might be blowin' us all up in the process of destroying yourselves." "There is that possibility," I said. "Well, then, as I say," said Keech, "the little people have decided to leave the planet in a spaceship. Which we're buildin' here and now. We've spied upon you and learned how to do it. Well—almost how to do it. We haven't learned yet how to control the power—" "Hold on, now," I said. "Leaving the planet, you say. And where would you be going?" "There's another committee working on that. 'Tis not our concern. I was inclined to suggest the constellation Orion, which sounds as though it has a good Irish name, but I was hooted down. Be that as it may, my own job was to go into your nuclear center, learn how to make the ship, and proceed with its construction. Naturally, we didn't understand all of your high-flyin' science, but some of our people are pretty clever at gettin' up replicas of things." "You mean you've been spying on us at the center all this time? Do you know, we often had the feeling we were being watched, but we thought it was by the Russians. There's one thing which puzzles me, though. If you've been constantly around us—and I'm still able to see the little people—why did I never see you before?" "It may be we never crossed your path. It may be you can only see us when you're thinkin' of us, and of course truly believin' in us. I don't know—'tis a thing of the mind, and not important at the moment. What's important is for us to get our first ship to workin' properly and then we'll be on our way." "You're determined to go." "Truly we are, Mr. Houlihan. Now—to business. Just during these last few minutes a certain matter has crossed my mind. That's why I'm wastin' all this time with you, sir. You say you are a scientist." "A nuclear engineer." "Well, then, it may be that you can help us—now that you know we're here." "Help you?" "The power control, Mr. Houlihan. As I understand it, 'tis necessary to know at any instant exactly how much thrust is bein' delivered through the little holes in back. And on paper it looks simple enough—the square of somethin' or other. I've got the figures jotted in a book when I need 'em. But when you get to doin' it it doesn't come out exactly as it does on paper." "You're referring to the necessity for a coefficient of discharge." "Whatever it might be named," said Keech, shrugging. "'Tis the one thing we lack. I suppose eventually you people will be gettin' around to it. But meanwhile we need it right now, if we're to make our ship move." "And you want me to help you with this?" "That is exactly what crossed my mind." I nodded and looked grave and kneaded my chin for a moment softly. "Well, now, Keech," I said finally, "why should I help you?" "Ha!" said Keech, grinning, but not with humor, "the avarice of humans! I knew it! Well, Mr. Houlihan, I'll give you reason enough. The pot o' gold, Mr. Houlihan!" "The one at the end of the rainbow?" "It's not at the end of the rainbow. That's a grandmother's tale. Nor is it actually in an earthen crock. But there's gold, all right, enough to make you rich for the rest of your life. And I'll make you a proposition." "Go ahead." "We'll not be needin' gold where we're goin'. It's yours if you show us how to make our ship work." "Well, now, that's quite an offer," I said. Keech had the goodness to be quiet while I sat and thought for a while. My pipe had gone out and I lit it again. I finally said, "Let's have a look at your ship's drive and see what we can see." "You accept the proposition then?" "Let's have a look," I said, and that was all. Well, we had a look, and then several looks, and before the morning was out we had half the spaceship apart, and were deep in argument about the whole project. It was a most fascinating session. I had often wished for a true working model at the center, but no allowance had been inserted in the budget for it. Keech brought me paper and pencil and I talked with the aid of diagrams, as engineers are wont to do. Although the pencils were small and I had to hold them between thumb and forefinger, as you would a needle, I was able to make many sensible observations and even a few innovations. I came back again the next day—and every day for the following two weeks. It rained several times, but Keech and his people made a canopy of boughs and leaves and I was comfortable enough. Every once in a while someone from the town or the center itself would pass by, and stop to watch me. But of course they wouldn't see the leprechauns or anything the leprechauns had made, not being believers. I would halt work, pass the time of day, and then, in subtle fashion, send the intruder on his way. Keech and the little people just stood by and grinned all the while. At the end of sixteen days I had the entire problem all but whipped. It is not difficult to understand why. The working model and the fact that the small people with their quick eyes and clever fingers could spot all sorts of minute shortcomings was a great help. And I was hearing the old tongue and talking of the old things every day, and truly that went far to take the clutter out of my mind. I was no longer so lonely that I couldn't think properly. On the sixteenth day I covered a piece of paper with tiny mathematical symbols and handed it to Keech. "Here is your equation," I said. "It will enable you to know your thrust at any given moment, under any circumstances, in or out of gravity, and under all conditions of friction and combustion." "Thank you, Mr. Houlihan," said Keech. All his people had gathered in a loose circle, as though attending a rite. They were all looking at me quietly. "Mr. Houlihan," said Keech, "you will not be forgotten by the leprechauns. If we ever meet again, upon another world perchance, you'll find our friendship always eager and ready." "Thank you," I said. "And now, Mr. Houlihan," said Keech, "I'll see that a quantity of gold is delivered to your rooms tonight, and so keep my part of the bargain." "I'll not be needing the gold," I said. Keech's eyebrows popped upward. "What's this now?" "I'll not be needing it," I repeated. "I don't feel it would be right to take it for a service of this sort." "Well," said Keech in surprise, and in some awe, too, "well, now, musha Lord help us! 'Tis the first time I ever heard such a speech from a mortal." He turned to his people. "We'll have three cheers now, do you hear, for Mr. Houlihan—friend of the little people as long as he shall live!" And they cheered. And little tears crept into the corners of some of their turned-up eyes. We shook hands, all of us, and I left. I walked through the park, and back to the nuclear propulsion center. It was another cool, green morning with the leaves making only soft noises as the breezes came along. It smelled exactly like a wood I had known in Roscommon. And I lit my pipe and smoked it slowly and chuckled to myself at how I had gotten the best of the little people. Surely it was not every mortal who could accomplish that. I had given them the wrong equation, of course. They would never get their spaceship to work now, and later, if they tried to spy out the right information I would take special measures to prevent it, for I had the advantage of being able to see them. As for our own rocket ship, it should be well on its way by next St. Patrick's Day. For I had indeed determined the true coefficient of discharge, which I never could have done so quickly without those sessions in the glade with Keech and his working model. It would go down in scientific literature now, I suppose, as Houlihan's Equation, and that was honor and glory enough for me. I could do without Keech's pot of gold, though it would have been pleasant to be truly rich for a change. There was no sense in cheating him out of the gold to boot, for leprechauns are most clever in matters of this sort and he would have had it back soon enough—or else made it a burden in some way. Indeed, I had done a piece of work greatly to my advantage, and also to the advantage of humankind, and when a man can do the first and include the second as a fortunate byproduct it is a most happy accident. For if I had shown the little people how to make a spaceship they would have left our world. And this world, as long as it lasts—what would it be in that event? I ask you now, wouldn't we be even more likely to blow ourselves to Kingdom Come without the little people here for us to believe in every now and then? Transcriber's Note: This etext was produced from Fantastic Universe September 1955. Extensive research did not uncover any evidence that the U.S. copyright on this publication was renewed. Minor spelling and typographical errors have been corrected without note.	C. He is proud to be contributing to broad scientific questions
How much was the Real change in Sales for AMCOR in FY 2023 vs FY 2022, if we exclude the impact of FX movement, passthrough costs and one-off items?	Evidence 0: Three Months Ended June 30 Twelve Months Ended June 30 ($ million) Flexibles Rigid Packaging Total Flexibles Rigid Packaging Total Net sales fiscal year 2023 2,777 897 3,673 11,154 3,540 14,694 Net sales fiscal year 2022 2,967 942 3,909 11,151 3,393 14,544 Reported Growth % (6) (5) (6) 4 1 FX % 1 (1) (4) (1) (3) Constant Currency Growth % (7) (4) (6) 4 5 4 Raw Material Pass Through % 1 1 5 8 5 Items affecting comparability % (3) (2) (2) (1) Comparable Constant Currency Growth % (5) (4) (5) 1 (3) Volume % (7) (6) (7) (3) (4) (3) Price/Mix % 2 2 2 4 1 3	The Real Growth was flat in FY 2023 vs FY 2022.
Why is it suggested that you should not tell your partner when you take Viagra? A. Viagra is expensive and you don't want the conversation about money to distract from intimacy B. You don't want to be embarassed when they find out you need help getting aroused C. Viagra isn't something you need to be honest about with your partner D. Telling them takes some of the mystery out of the situation and is less fun	More Bang for the Buck A friend of mine offers a theory about why Bill Clinton's poll numbers stayed so high throughout the Lewinsky scandal: The news made it possible for serious-minded people to spend lots of time--at the office and over lunch--talking about semen stains, vaginal insertions, and blow jobs. And the people were grateful. That's probably because they're not getting all that much themselves. A recent University of Chicago survey of 10,000 adults found that Americans are having considerably less sex than was generally thought. Only one American in 20 has sex three times a week. One in five didn't score at all last year. If that's true, many of us could use a little sexual self-improvement. Not me, of course. I have been happily married for 26 years, since the age of 21. Deb and I have what seems to us to be a perfectly fine amorous life, yet everywhere I turn the culture tells me--almost mocks me-- you can do better! What would happen to our sex life then, if Deb (who participated in this story because she loves me and because she has tenure) and I tried for the first time to make something happen to it? And so it was that we found ourselves for the first time ever in a sex-toy store, A Touch of Romance, located near our home in Los Angeles, across the street from a Macy's. The idea behind shops like these is to make obtaining the materials of sexual experimentation as ordinary as purchasing plumbing supplies or housewares. Which sort of works--the only sexual thrill I got from the visit was knowing that Microsoft just bought a cock ring. Choosing it wasn't easy. Most of them came in presized sets of three. I couldn't figure out which would fit right and intuited that try-ons weren't an option. So I opted instead for an adjustable circumference version, a little strip of vinyl with snaps for $11.95. Man, what a rip-off! Unless it works. It doesn't. Back home, I derived a certain depraved buzz in cinching the device on, but that was soon eclipsed. The thing works on the Roach Motel principle--your blood gets in but it can't get out. But then I got to thinking: Under battlefield conditions it doesn't get out anyway. And while I should have been paying more attention to other things, this led to thinking about the old joke with the punch line "... and right ball go POW." My wife hadn't noticed any difference at all. Overall rating, on a scale of 1 to 10: 2 toes curled. A woman I know says women's magazines are the best places in America to find sex tips. She's right--go ahead, just try to find a sewing pattern in Redbook . You're much more likely to land on "Try phone sex, dirty notes, porn videos, fantasy games and sex in new places. ... Try lingerie and no underwear. ... Try talking dirty and silk scarves. Try anything at all," or articles such as "Eight New Games for the Foreplay Challenged." An article in the April Cosmopolitan , "The Six Best Sex Positions," seemed more promising than the Redbook playbook. Each position was accompanied by a succinct write-up and a stick-figure diagram. The position we settled on was "The Butterfly," which we had to read three times to comprehend. The man stands, the woman remains supine on a bed or counter-top with her feet up on his shoulders. The whole idea is to produce a pelvic tilt for better access to the G spot. Instead, we experienced an uncomfortable pretzel feeling that stick figures must be immune to. And in general, Cosmopolitan 's exotic sex positions require the sort of body placement you can't remember in the moment of passion and even if you could, for proper alignment, you still might need mood-killing accessories such as a plumb line and a laser pen. Rating: 3 toes curled. Next we tried those "Better Sex" instructional videos advertised in the New York Times Book Review. I ordered Better Sexual Techniques , Advanced Sexual Techniques , Making Sex Fun , and Advanced Oral Sex Techniques (priced about $11.95 each, not including shipping and handling). My wife couldn't bear to watch them; I persevered but must admit it was a chore. The oral-sex tape starts with "well-known sex therapist" Diana Wiley, in her poofy hair and broad-shouldered blue power suit, looking like she was about to explain how the sales force could increase its third-quarter productivity. Instead she runs through all the euphemisms for oral sex and then the video cuts to XXX action with gratuitous commentary. Wiley's overexplanation of everything two people can do to each other with their mouths raises this question: Do you really need a five-minute video segment on whether or not to swallow? In the great tradition of hotel and travel ads, the guys tend to be markedly less attractive than the women. No way he'd be with her if this wasn't an instructional sex video! The inanity of the experts and the dubious casting make these films about as erotic as ... well, as the New York Times . You could learn more from any randomly selected porn video. Rating: 0 toes curled. Another approach is food. The notion that certain foods, such as oysters or rhino horn, are aphrodisiacs has been pretty much discounted. But it's plausible to think that cooking a meal together and then dining on it, just the two of you, could be erotic. Especially if (like me) your schedule frequently forces you to eat alone and you often find yourself standing in front of the microwave, screaming, "Come on, goddammit!" Intercourses , by Martha Hopkins and Randall Lockridge ($24.95, Terrace Publishing, 1997), preaches that for every time of day and every phase of a relationship there is a type of eating experience that will heighten sexual response. (There's also a chart showing which foods are good for eating off which body parts.) Deb and I blocked off a whole Saturday afternoon and evening for the Intercourses experiment, settling on rosemary-scented lamb over pasta (Page 87) followed by frozen coffee almond dessert (Page 31). According to the book, rosemary is sexy because of its fragrance (used in many perfumes) and because of its texture, which, so the text assured, tickles nerve endings. The dessert was mostly coffee, rum, and Kahlua, which has worked before. We shopped for the food together and cooked together, drinking wine and beer along the way. At one point while I was working on the dessert, I asked my wife how long to beat the heavy cream mixture. "Till it's stiff--it's an aphrodisiac," she said. Preparation took less than an hour, and everything came out perfectly. Eating at our dining room table for the first time ever without guests, we were having fun by candlelight. But the mood was romantic, not erotic. Overall rating: 4 toes curled. That's when we went for the Viagra ($212.50 for 10 doses, which includes a "consultation" fee). The drug was prescribed by a doctor, whom I've never met, and ordered from a pharmacy in Miami Beach, Fla., where I've never been. I completed the transaction via the Internet after filling out a cover-their-ass questionnaire in three minutes. We each decided to take one pill, clinked our glasses, and gulped. And then what? It felt awkward sitting in our bedroom, knowing that it could take up to an hour for Viagra to "work." I suggested that we play strip poker, something I'd never done. Deb had never even played poker, so I had to explain the rules. I won in about six hands, auspiciously I thought, with three aces. But we still weren't really in the mood yet. So then I got out the other purchase I'd made at A Touch of Romance--"Dirty Dice" ($4.95). One of the two pink cubes is marked with these words instead of dots: "lips," "above waist," "ear," "breast," "below waist," and "?". The other cube is labeled "kiss," "squeeze," "lick," "blow," "suck," and "eat." We took turns throwing the dice, but the activities generated seemed forced and arbitrary. Finally, as they say at NASA, there was word from the pad that the launch sequence was initiating. It was pretty much like all other sex, except for a slight lightheadedness. Deb said she noticed a remote tingling sensation. On the plus side, there was no priapism and neither of us experienced disruption of our color vision nor a fatal heart attack, which was nice. Overall rating: 5 toes curled. St. Augustine held lust to be a fitting punishment for man's disobedience to God: the body's disobeying of the mind, the will, the spirit, and even of itself. (The paradigm of this for him is the unbidden hard-on.) Jean-Paul Sartre discovered something similar, although celebrating it rather than deploring it: Essential to the erotic is the body's defiance of design and control. (The paradigm of this for him is the jiggle.) Sartre's view yields a sort of sexual Heisenberg principle: There is an inherent tension between physically abandoning yourself to another on the one hand and sexual planning on the other. The more of the one, the less of the other. And this, I discovered, is the chief obstacle to sexual self-help. Getting an erection is sexy. Making one is not. As my wife said about Viagra, "You start to have a new feeling and then you realize where it came from and then you don't have it so much. ... Anything that makes you think about it like that is just creepy." This is not to say there isn't a way out of this conflict between desire and design. With homage to our potent POTUS, there is, I think, a Third Way that's neither sexual complacency nor standard self-help. If the intrusion of consciousness is the problem, then maybe the answer is to block it out. Sure, you could do this the old-fashioned way: with alcohol and drugs. But then you have all the traditional drawbacks, including diminished physical attractiveness and degraded sexual performance. So how about this instead? Go for all the sexual self-help you can, but do it covertly . Watch a sex video (or porn flick) if you want--but by yourself, and then try to share what you learned without sharing how you learned it. Don't tell your partner you took Viagra. Or give each other standing permission to slip it into the odd after-dinner drink, saying nothing. (Of course, when you do it you'll still know, but having an unselfconsciously turned-on partner is a real compensation for that, and next time, your partner can surprise you. And yes, this requires trust. But why would you be having sex with someone you don't trust?) My main conclusion is that contrary to our blabby culture, the key to a better sex life is less communication.	D. Telling them takes some of the mystery out of the situation and is less fun
What shared task does this system achieve SOTA in?	### Introduction Language identification is a crucial first step in textual data processing and is considered feasible over formal texts BIBREF0 . The task is harder for social media (e.g. Twitter) where text is less formal, noisier and can be written in wide range of languages. We focus on identifying similar languages, where surface-level content alone may not be sufficient. Our approach combines a content model with evidence propagated over the social network of the authors. For example, a user well-connected to users posting in a language is more likely to post in that language. Our system scores 76.63%, 1.4% higher than the top submission to the tweetLID workshop. ### Background Traditional language identification compares a document with a language fingerprint built from n-gram bag-of-words (character or word level). Tweets carry additional metadata useful for identifying language, such as geolocation BIBREF1 , username BIBREF2 , BIBREF1 and urls mentioned in the tweet BIBREF2 . Other methods expand beyond the tweet itself to use a histogram of previously predicted languages, those of users @-mentioned and lexical content of other tweets in a discussion BIBREF1 . Discriminating between similar languages was the focus of the VarDial workshop BIBREF3 , and most submissions used content analysis. These methods make limited use of the social context in which the authors are tweeting – our research question is “Can we identify the language of a tweet using the social graph of the tweeter?”. Label propagation approaches BIBREF4 are powerful techniques for semi-supervised learning where the domain can naturally be described using an undirected graph. Each node contains a probability distribution over labels, which may be empty for unlabelled nodes, and these labels are propagated over the graph in an iterative fashion. Modified Adsorption (mad) BIBREF5 , is an extension that allows more control of the random walk through the graph. Applications of lp and mad are varied, including video recommendation BIBREF6 and sentiment analysis over Twitter BIBREF7 . ### Method Our method predicts the language INLINEFORM0 for a tweet INLINEFORM1 by combining scores from a content model and a graph model that takes social context into account, as per Equation EQREF2 : DISPLAYFORM0 Where INLINEFORM0 are the content model parameters, INLINEFORM1 the social model parameters. ### Content model Our content model is a 1 vs. all INLINEFORM0 regularised logistic regression model with character 2- to 5-grams features, not spanning over word boundaries. The scores for a tweet are normalised to obtain a probability distribution. ### Social model We use a graph to model the social media context, relating tweets to one another, authors to tweets and other authors. Figure FIGREF7 shows the graph, composed of three types of nodes: tweets (T), users (U) and the “world” (W). Edges are created between nodes and weighted as follows: T-T the unigram cosine similarity between tweets, T-U weighted 100 between a tweet and its author, U-U weighted 1 between two users in a “follows” relationship and U-W weighted 0.001 to ensure a connected graph for the mad algorithm. We create the graph using all data, and training set tweets have an initial language label distribution. A naïve approach to building the tweet-tweet subgraph requires O( INLINEFORM0 ) comparisons, measuring the similarity of each tweet with all others. Instead, we performed INLINEFORM1 -nearest-neighbour classification on all tweets, represented as a bag of unigrams, and compared each tweet and the top- INLINEFORM2 neighbours. We use Junto (mad) BIBREF5 to propagate labels from labelled to unlabelled nodes. Upon convergence, we renormalise label scores for initially unlabelled nodes to find the value of INLINEFORM4 . ### Evaluation The tweetLID workshop shared task requires systems to identify the language of tweets written in Spanish (es), Portuguese (pt), Catalan (ca), English (en), Galician (gl) and Basque (eu). Some language pairs are similar (es and ca; pt and gl) and this poses a challenge to systems that rely on content features alone. We use the supplied evaluation corpus, which has been manually labelled with six languages and evenly split into training and test collections. We use the official evaluation script and report precision, recall and F-score, macro-averaged across languages. This handles ambiguous tweets by permitting systems to return any of the annotated languages. Table TABREF10 shows that using the content model alone is more effective for languages that are distinct in our set of languages (i.e. English and Basque). For similar languages, adding the social model helps discriminate them (i.e. Spanish, Portuguese, Catalan and Galician), particularly those where a less-resourced language is similar to a more popular one. Using the social graph almost doubles the F-score for undecided (und) languages, either not in the set above or hard-to-identify, from 18.85% to 34.95%. Macro-averaged, our system scores 76.63%, higher than the best score in the competition: 75.2%. ### Conclusion Our approach uses social information to help identify the language of tweets. This shows state-of-the-art performance, especially when discriminating between similar languages. A by-product of our approach is that users are assigned a language distribution, which may be useful for other tasks. Table 1: Experimental results. ♦/♠ are similar pairs. Figure 1: Graph topology. Rectangular nodes are tweets, circular nodes are users and the diamond represents the world. Some tweet nodes are labelled with an initial distribution over language labels and others are unlabelled.	tweetLID workshop shared task
Why had Ann Hartley written the first letter to Hartshorne-Logan? A. To disregard her complaint about the package not being received. B. To complain about incorrect items being sent. C. To complain about the package not being received. D. To request a refund for the package being damaged.	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."	C. To complain about the package not being received.
What is their model?	### Introduction Named Entity Recognition is a major natural language processing task that recognizes the proper labels such as LOC (Location), PER (Person), ORG (Organization), etc. Like words or phrase, being a sort of language constituent, named entities also benefit from better representation for better processing. Continuous word representations, known as word embeddings, well capture semantic and syntactic regularities of words BIBREF0 and perform well in monolingual NE recognition BIBREF1 , BIBREF2 . Word embeddings also exhibit isomorphism structure across languages BIBREF3 . On account of these characteristics above, we attempt to utilize word embeddings to improve NE recognition for resource-poor languages with the help of richer ones. The state-of-the-art cross-lingual NE recognition methods are mainly based on annotation projection methods according to parallel corpora, translations BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 and Wikipedia methods BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . Most annotated corpus based NE recognition tasks can benefit a great deal from a known NE dictionary, as NEs are those words which carry common sense knowledge quite differ from the rest ones in any language vocabulary. This work will focus on the NE recognition from plain text instead of corpus based NE recognition. For a purpose of learning from limited annotated linguistic resources, our preliminary discovery shows that it is possible to build a geometric space projection between embedding spaces to help cross-lingual NE recognition. Our study contains two main steps: First, we explore the NE distribution in monolingual case. Next, we learn a hypersphere mapping between embedding spaces of languages with minimal supervision. Despite the simplicity of our model, we make the following contributions. First, for word embeddings generated by different dimensions and objective functions, all common NE types (PER, LOC, ORG) tend to be densely distributed in a hypersphere, which gives a better solution to characterize the general NE distribution rather than existing closed dictionary definition for NE. Second, with the help of the hypersphere mapping, it is possible to capture the NE distribution of resource-poor languages with only a small amount of annotated data. Third, our method is highly friendly to unregistered NEs, as the distance to each hypersphere center is the only factor needed to determine their NE categories. Finally, by adding hypersphere features we can significantly improve the performance of off-the-shelf named entity recognition (NER) systems. ### Word Embeddings Seok BIBREF2 proposed that similar words are more likely to occupy close spatial positions, since their word embeddings carries syntactical and semantical informative clues. For an intuitive understanding, they listed the nearest neighbors of words included in the PER and ORG tags under cosine similarity metric. To empirically verify this observation and explore the performance of this property in Euclidean space , we list Top-5 nearest neighbors under Euclidean distance metric in Table 1 and illustrate a standard t-SNE BIBREF12 2- $D$ projection of the embeddings of three entity types with a sample of 500 words for each type. Nearest neighbors are calculated by comparing the Euclidean distance between the embedding of each word (such as Fohnsdorf, Belgian, and Ltd.) and the embeddings of all other words in the vocabulary. We pre-train word embeddings using the continuous skip-gram model BIBREF13 with the tool, and obtain multi-word and single-word phrases with a maximum length of 8, and a minimum word frequency cutoff of 3. The examples in Table 1 and visualization in Figure 1 demonstrate that the above observation suits well under Euclidean distance metric for NE recognition either for monolingual or multilingual situations. ### Model Encouraged by the verification of nearest neighbors of NEs still being NEs, we attempt to build a model which can represent this property with least parameters. Namely, given an NE dictionary on a monolingual, we build a model to describe the distribution of the word embeddings of these entities, then we can easily use these parameters as a decoder for any word to directly determine whether it belongs to a certain type of entity. In this section, we first introduce the open modeling from embedding distribution in monolingual cases, and then put forward the mapping of the distribution model between languages, and then use the mapping to build named entity dataset for resource-poor languages. Finally, we use the proposed named entity model to improve the performance of state-of-the-art NE recognition systems. ### Open Monolingual NE Modeling As illustrated is Figure 1, the embedding distribution of NEs is aggregated, and there exists a certain boundary between different types of NEs. We construct an open representation for each type of NEs – hypersphere, the NE type of any entity can be easily judged by checking whether it is inside a hypersphere, which makes a difference from the defining way of any limited and insufficient NE dictionary. The hypersphere can be expressed as follows: $$E( X, O) \le r$$ (Eq. 9) where E represents the adopted Euclidean distance, X is referred to any point in the hypersphere, $ O $ and $ r $ are the center vector and radius. For each entity type, we attempt to construct a hypersphere which encompass as many congeneric NEs as possible, and as few as possible inhomogeneous NEs, we use $F_1$ score as a trade-off between these two concerns. We carefully tune the center and radius of the hypersphere to maximize its $F_1$ score: we first fix the center as the average of all NE embeddings from known NE dictionaries, and search the best radius in $[minDist, maxDist]$ , where $minDist/maxDist$ refers to the distance between the center and its nearest/farthest neighbors; Then, we kick NEs which are far from the center with the distance threshold $q$ (much larger than the radius) to generate a new center; Finally, we tune the threshold $q$ and repeat the above steps to find the most suitable center and radius. The mathematical intuition for using a hypersphere can be interpreted in a manner similar to support vector machine (SVM) BIBREF14 , which uses the kernel to obtain the optimal margin in very high dimensional spaces through linear hyperplane separation in Descartes coordination. We transfer the idea to the separation of NE distributions. The only difference is about boundary shape, what we need is a closed surface instead of an open hyperplane, and hypersphere is such a smooth, closed boundary (with least parameters as well) in polar coordinates as counterpart of hyperplane in Descartes coordinates. Using the least principle to model the mathematical objective also follows the Occam razor principle. Figure 1 also reveals that the distribution of PER NEs is compact, while ORG NE distribution is relatively sparse. Syntactically, PER NEs are more stable in terms of position and length in sentences compared to ORG NEs, so that they have a more accurate embedding representation with strong strong syntax and semantics, making the corresponding word embeddings closer to central region of the hypersphere. ### Embedding Distribution Mapping As the isomorphism characteristic exists between languages BIBREF3 , BIBREF15 , we can apply the distributional modeling for every languages in the same way. For a target language without an NE dictionary, its NE distribution can be obtained from a source language with known NE distributions by learning the transforming function between these two languages. We construct the transformation matrix $W$ via a set of parallel word pairs (the set will be referred to seed pairs hereafter) and their word embeddings $\lbrace X^{(i)}, Z^{(i)}\rbrace _{i=1}^m$ BIBREF3 , $\lbrace X^{(i)}\rbrace _{i=1}^m$ , $\lbrace Z^{(i)}\rbrace _{i=1}^m$ are the source and target word embeddings respectively. $W$ can be learned by solving the matrix equation $XW = Z$ . Then, given the source center vector ${ O_1}$ , the mapping center vector ${O_2}$ can be expressed as: $${ O_2} = W^T{O_1}$$ (Eq. 11) Actually, the isomorphism (mapping) between embedding spaces is the type of affine isomorphism by furthermore considering embedding in continuous space. The invariant characteristics of relative position BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 in affine transformation is applied to correct transformation matrix errors caused by limited amount of parallel word pairs (the set will be referred to seed pairs hereafter). As shown in Figure 2, the ratio of the line segments keep constant when the distance is linearly enlarged or shortened. Recall that point $Q$ is an affine combination of two other noncoincident points $Q_1$ and $Q_2$ on the line: $Q = (1-t)Q_1 + tQ_2 $ . We apply the affine mapping $f$ and get: $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$ Obviously, the constant ratio $t$ is not affected by the affine transformation $f$ . That is, $Q$ has the same relative distances between it and $Q_1$ and $Q_2$ during the process of transformation. Based on the above characteristic, for any point $X^{(i)}$ in the source space and its mapping point $Z^{(i)}$ , $X^{(i)}$ and $f(Q) = f((1-t)Q_1 + tQ_2) = (1-t)f(Q_1) + tf(Q_2)$0 cut off radiuses with the same ratio, namely, the ratio of the distance of these two points to their centers and their radiuses remains unchanged. $$\frac{E( O_1, X^{(i)})}{r_1} = \frac{E( O_2, Z^{(i)})}{r_2}$$ (Eq. 15) where $E$ represents the adopted Euclidean distance, ${O_1, O_2, r_1, r_2}$ are the centers and radii of hyperspheres. We convert the equation and learn the optimized mapping center ${O_2}$ and ratio $K$ via the seed pairs: $${K = \frac{r_2}{r_1} = \frac{E( O_2, Z^{(i)})}{E( O_1, X^{(i)})}}$$ (Eq. 16) $$\begin{aligned} E( O_2, Z^{(i)}) &= K * E( O_1, X^{(i)}) \quad r_2 &= K * r_1 \\ \end{aligned}$$ (Eq. 17) Given the seed pairs $\lbrace X^{(i)}, Z^{(i)}\rbrace _{i=1}^m$ , the initialized center $O_2$ in Equation (3), the center $ O_1 $ and radius $ r_1 $ of the hypersphere in source language space, we may work out the optimized ratio $K$ , the mapping center $ O_2 $ and radius $ r_2 $ in target language space by solving the linear equation group (5). ### Hypersphere features for NE Recognition The Euclidean distance between word and hypersphere centers can be pre-computed as its NE likelihood, which may provide informative clues for NE recognition. We only consider three entity types in our experiment, and the Euclidean distance which is represented as a 3- $D$ vector and referred to HS vector hereafter) is added to four representative off-the-shelf NER systems to verify its effectiveness. We feed HS vector into different layers of the neural network: (1) input layer $[x_k; c_k; HS]$ ; (2) output layer of LSTM $[h_k; HS]$ , where $x_k$ , $w_k$ and $h_k$ represent word embeddings, char embeddings and the output of LSTM, respectively. All of these models are based on classical BiLSTM-CRF architecture BIBREF20 , except that BIBREF21 replaces CRF layer with softmax. These four baseline systems are introduced as follows. BIBREF22 concatenates ELMo with word embeddings as the input of LSTM to enhance word representations as it carries both syntactic and semantic information. BIBREF21 uses distant supervision for NER task and propose a new Tie or Break tagging scheme, where entity spans and entity types are encoded into two folds. They first build a binary classifier to distinguish Break from Tie, and then learn the entity types according to their occurrence and frequency in NE dictionary. The authors conduct their experiments on biomedical datasets rather than standard benchmark, so we extract the NEs in training data as the domain-specific dictionary. This work creates a promising prospect for using dictionary to replace the role of training data. BIBREF23 takes advantage of the power of the 120 entity types from annotated data in Wikipedia. Cosine similarity between the word embedding and the embedding of each entity type is concatenated as the 120- $D$ feature vector (which is called LS vector in their paper) and then fed into the input layer of LSTM. Lexical feature has been shown a key factor to NE recognition. BIBREF24 passes sentences as sequences of characters into a character-level language model to produce a novel type of word embedding, contextual string embeddings, where one word may have different embeddings as the embeddings are computed both on the characters of a word and its surrounding context. Such embeddings are then fed into the input layer of LSTM. ### Experiment In this section, we evaluate the hypersphere model based on the three models introduced above: open monolingual NE modeling, embedding distribution mapping and refinement NE recognition. ### Setup In this experiment, we adopt pre-trained word embeddings from Wikipedia corpus. Our preliminary experiments will be conducted on English and Chinese. For the former, we use NLTK toolkit and LANGID toolkit to perform the pre-processing. For the latter, we first use OpenCC to simplify characters, and then use THULAC to perform word segmentation. In order to make the experimental results more accurate and credible, we manually annotate two large enough Chinese and English NE dictionaries for training and test. Table 2 lists the statistics of Wikipedia corpus and the annotated data. Our dictionary contains many multi-word NEs in LOC and ORG types as accounted in the second column for each language in Table 2, while we only include single-word PER NEs in our dictionary, since the English first name and last name are separated, and Chinese word segmentation cuts most of the PER entities together. We pre-train quality multi-word and single-word embeddings and aim to maximize the coverage of the NEs in the dictionary. The pre-trained word embeddings cover 82.3% / 82.51% of LOC NEs and 70.2% / 63.61% of ORG NEs in English and Chinese, respectively. For other multi-word NEs, we simply calculate the average vector of each word embedding as their representations. ### Monolingual Embedding Distribution The NE distribution is closely correlated to the dimension of the embedding space, we train the word embeddings from 2- $D$ to 300- $D$ and search for the most suitable dimension for each NE type. For each dimension, we carefully tune the center and radius of the hypersphere using the method introduced in section 3.1 for maximize $F_1$ score, and select the dimension with maximize $F_1$ score. The most suitable dimension for ORG, PER, LOC are 16- ${D}$ /16- ${D}$ /24- ${D}$ (these dimensions will be used as parameters in the following experiments), respectively . We discover that in low-dimensional space, the distributions of NEs are better. In high dimensions, the curse of dimension could be the main reason to limit the performance. Table 3 lists the final maximum $F_1$ score of three NE types. The results of the three types of NE are almost 50%, and PER type performs best. The main factor may be that PER NEs are represented as single-word in our dictionary, and word embeddings can better represents their meanings. The result also states that better representations for multi-word NEs which are not covered by the dictionary instead of the average of each word may help bring better results. Besides, the incompleteness of NE dictionaries and noises during pre-processing may cause a decrease on the performance. Overall, hypersphere model has shown been effectively used as the open modeling for NEs. ### Hypersphere Mapping The following preparations were made for the mapping: $(i)$ A large enough NE dictionary in source (resource-rich) corpus; $(ii)$ A small amount of annotated seed pairs. We use $s$ to represent the number of seed pairs and $d$ to represent the number of unknown variables. With seed pair size $s < d$ , the matrix can be solved with much loose constraints, and $F_1$ score remarkably increases with more seed pairs. Once $s > d$ , the linear equation group will be always determined by strong enough constraints, which leads to a stable solution. Based on the characteristics, we only take two dozen of seed pairs on each type in following experiments. We combine human translation and online translation together for double verification for this small set of seed pairs. In this part, we utilize English and Chinese as the corpus of known NEs in turn, and predict the NE distribution of the other language. Evaluation In order to quantitatively represent the mapping effect, we present a new evaluation method to judge the hypersphere mapping between English and Chinese: $$\begin{aligned} P = \frac{V_i}{V_m} \quad R = \frac{V_i}{V_t} \quad F_1 = \frac{2 * P * R}{P + R} \end{aligned}$$ (Eq. 29) where ${V_t, V_m, V_i}$ represent the volumes of the target, mapping and intersection hyperspheres. Due to the difficulty of calculating the volume of hyperspheres in high dimensions, we adopt Monte Carlo methods to simulate the volume BIBREF25 . we generate a great quantity of points in the embedding spaces, and take the amount of the points falling in each hypersphere as its volume. Mapping between English and Chinese Table 4 shows the comparisons of cross-lingual named entity extraction performance. We use the unsupervised method proposed in BIBREF26 to generate cross-lingual embeddings. $k$ -NN and SVM are the same as monolingual cases in Table 3 except for the training set. $k$ -NN $_{150}$ and SVM $_{150}$ use 20% of the NEs in source language and 150 NEs (50 LOC, PER and ORG) in target language for training, while $k$ -NN $_{2500}$ and SVM $_{2500}$ use 20% of the NEs in source language and 2500 NEs (1000 LOC and PER, 500 ORG) in target language. $k$ -NN and SVM depend much on the annotated training set, requiring more than $1K$ training samples to provide a performance as our model offers. Due to the instability of ORG type in length, taking the average of each word embedding may disobey the syntactic and semantic regularities of ORG NEs, thereby undermines the multilingual isomorphism characteristics, which causes the inferior performance of our model on this type of NEs. This suggests that build better representations NEs for multi-word NEs may contribute to a better performance in our model. Mapping to truly Low-resource Language We build named entity dataset for a truly resource-poor language, Indonesian, and manually examine the nearest words to the hypersphere center for 'gold-standard' evaluation. We take English as the source language, the settings of the dimension $D$ and the number of seed pairs $s$ are the same as the above experiments between Chinese and English. From the results listed in Table 5, we can see that even the precision of the top-100 NEs are 0.350 $F_1$ /0.440 $F_1$ /0.310 $F_1$ , respectively, which proves the this distribution can indeed serves as a candidate NE dictionary for Indonesian. [9] The authors of BIBREF24 publish an updated results (92.98) on CoNLL-2003 dataset in https://github.com/zalandoresearch/flair/issues/206 on their 0.3.2 version, and this is the best result at our most try. [10] This is the reported state-of-the-art result in their github. [11]We use the same parameters as the authors release in https://github.com/zalandoresearch/flair/issues/173 and obtain the result of 89.45 on ONTONOTES 5.0 dataset. ### Off-the-shelf NE Recognition Systems To evaluate the influence of our hypersphere feature for off-the-shelf NER systems, we perform the NE recognition on two standard NER benchmark datasets, CoNLL2003 and ONTONOTES 5.0. Our results in Table 6 and Table 7 demonstrate the power of hypersphere features, which contribute to nearly all of the three types of entities as shown in Table 6, except for a slight drop in the PER type of BIBREF22 on a strong baseline. HS features stably enhance all strong state-of-the-art baselines, BIBREF22 , BIBREF21 and BIBREF23 by 0.33/0.72/0.23 $F_1$ point and 0.13/0.3/0.1 $F_1$ point on both benchmark datasets, CoNLL-2003 and ONTONOTES 5.0. We show that our HS feature is also comparable with previous much more complicated LS feature, and our model surpasses their baseline (without LS feature) by 0.58/0.78 $F_1$ point with only HS features. We establish a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0, while matching state-of-the-art performance with a $F_1$ score of 92.95 on CoNLL-2003 dataset. ### Related Work In recent years, word embeddings have also been used as a feature to enhance the NE recognition, with the revealing of linguistic features in morphological, syntactic and semantic perspective. BIBREF1 clustered the word embeddings and combined multiple cluster granularities to improve the NE recognition performance. Our work likewise use word embeddings to help NE recognition, we make use of the characteristic that syntactically and semantically s are more likely to be neighbors in embedding spaces and construct a hypersphere model to encompass NEs. Cross-lingual knowledge transfer is a highly promising work for resource-poor languages, annotation projection and representation projection are widely used in NE recognition BIBREF27 , BIBREF5 , BIBREF4 , BIBREF28 , BIBREF29 , BIBREF30 . These works put forward inconvenient requirements for parallel or comparable corpora, a large amount of annotated or translation data or bilingual lexicon. Different from any existing work to the best of our knowledge, this is the first work that merely uses isomorphic mappings in low-dimensional embedding spaces to recognize NEs, and we introduce a mathematically simple model to describe NE embedding distribution from visualization results, showing it works for both monolingual and cross-lingual situations. ### Conclusion Named entities being an open set which keeps expanding are difficult to represent through a closed NE dictionary. This work mitigates significant defects in previous closed NE definitions and proposes a new open definition for NEs by modeling their embedding distributions with least parameters. We visualize NE distributions in monolingual case and perform an effective isomorphism spaces mapping in cross-lingual case. According to our work, we demonstrate that common named entity types (PER, LOC, ORG) tend to be densely distributed in a hypersphere and it is possible to build a mapping between the NE distributions in embedding spaces to help cross-lingual NE recognition. Experimental results show that the distribution of named entities via mapping can be used as a good enough replacement for the original distribution. Then the discovery is used to build an NE dictionary for Indonesian being a truly low-resource language, which also gives satisfactory precision. Finally, our simple hypersphere features being the representation of NE likelihood can be used for enhancing off-the-shelf NER systems by concatenating with word embeddings and the output of BiLSTM in the input layer and encode layer, respectively, and we achieve a new state-of-the-art $F_1$ score of 89.75 on ONTONOTES 5.0 benchmark. In this work, we also give a better solution for unregistered NEs. For any newly emerged NE together with its embedding, in case we obtain the hypersphere of each named entity, the corresponding named entity category can be determined by calculating the distance between its word embedding and the center of each hypersphere. Table 1: Top-5 Nearest Neighbors. Figure 1: Graphical representation of the distribution of the NEs in zh (left) and en (right). Big Xs indicate the center of each entity type, while circles refer to words. Language code: zh-Chinese, en-English, same for all the figures and tables hereafter. Figure 2: Affine mappings preserve relative ratios. Table 2: Statistics of Wikipedia corpus and annotated data (the digit in parentheses indicates the proportion of the single-word NEs). Table 3: Maximum F1 scores for NE types. Table 4: Comparisons of NE extraction performance with cross-lingual embeddings. Table 5: Manually examine the precision on Top-100 nearest words to the hypersphere center. Table 6: F1 scores on CoNLL-2003 and ONTONOTES 5.0 datasets. HS represents hypersphere features. The title reported indicates the results reported from the original corresponding paper, while our run indicates the results from our re-implementation or re-run the code provided by the authors. ERR in the brackets is the relative error rate reduction of our models compared to the respective baselines. Table 7: Comparisons with state-of-the-art systems on CoNLL-2003 dataset (Peters et al., 2018; Ghaddar and Langlais, 2018) for each entity type.	cross-lingual NE recognition
Which method do they use for word segmentation?	### Introduction Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on the basis of the information obtained from their sensors, e.g., laser sensors, microphones, and cameras, and recognize a variety of objects, places, and situations in an ambient environment. Above all, we consider it important for the robot to learn the names that humans associate with places in the environment and the spatial areas corresponding to these names; i.e., the robot has to be able to understand words related to places. Therefore, it is important to deal with considerable uncertainty, such as the robot's movement errors, sensor noise, and speech recognition errors. Several studies on language acquisition by robots have assumed that robots have no prior lexical knowledge. These studies differ from speech recognition studies based on a large vocabulary and natural language processing studies based on lexical, syntactic, and semantic knowledge BIBREF0 , BIBREF1 . Studies on language acquisition by robots also constitute a constructive approach to the human developmental process and the emergence of symbols. The objectives of this study were to build a robot that learns words related to places and efficiently utilizes this learned vocabulary in self-localization. Lexical acquisition related to places is expected to enable a robot to improve its spatial cognition. A schematic representation depicting the target task of this study is shown in Fig. FIGREF3 . This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes. The robot then performs self-localization while moving around in the environment, as shown in Fig. FIGREF3 (a). An utterer speaks a sentence including the name of the place to the robot, as shown in Fig. FIGREF3 (b). For the purposes of this study, we need to consider the problems of self-localization and lexical acquisition simultaneously. When a robot learns novel words from utterances, it is difficult to determine segmentation boundaries and the identity of different phoneme sequences from the speech recognition results, which can lead to errors. First, let us consider the case of the lexical acquisition of an isolated word. For example, if a robot obtains the speech recognition results “aporu”, “epou”, and “aqpuru” (incorrect phoneme recognition of apple), it is difficult for the robot to determine whether they denote the same referent without prior knowledge. Second, let us consider a case of the lexical acquisition of the utterance of a sentence. For example, a robot obtains a speech recognition result, such as “thisizanaporu.” The robot has to necessarily segment a sentence into individual words, e.g., “this”, “iz”, “an”, and “aporu”. In addition, it is necessary for the robot to recognize words referring to the same referent, e.g., the fruit apple, from among the many segmented results that contain errors. In case of Fig. FIGREF3 (c), there is some possibility of learning names including phoneme errors, e.g., “afroqtabutibe,” because the robot does not have any lexical knowledge. On the other hand, when a robot performs online probabilistic self-localization, we assume that the robot uses sensor data and control data, e.g., values obtained using a range sensor and odometry. If the position of the robot on the global map is unclear, the difficulties associated with the identification of the self-position by only using local sensor information become problematic. In the case of global localization using local information, e.g., a range sensor, the problem that the hypothesis of self-position is present in multiple remote locations, frequently occurs, as shown in Fig. FIGREF3 (d). In order to solve the abovementioned problems, in this study, we adopted the following approach. An utterance is recognized as not a single phoneme sequence but a set of candidates of multiple phonemes. We attempt to suppress the variability in the speech recognition results by performing word discovery taking into account the multiple candidates of speech recognition. In addition, the names of places are learned by associating with words and positions. The lexical acquisition is complemented by using certain particular spatial information; i.e., this information is obtained by hearing utterances including the same word in the same place many times. Furthermore, in this study, we attempt to address the problem of the uncertainty of self-localization by improving the self-position errors by using a recognized utterance including the name of the current place and the acquired spatial concepts, as shown in Fig. FIGREF3 (e). In this paper, we propose nonparametric Bayesian spatial concept acquisition method (SpCoA) on basis of unsupervised word segmentation and a nonparametric Bayesian generative model that integrates self-localization and a clustering in both words and places. The main contributions of this paper are as follows: The remainder of this paper is organized as follows: In Section SECREF2 , previous studies on language acquisition and lexical acquisition relevant to our study are described. In Section SECREF3 , the proposed method SpCoA is presented. In Sections SECREF4 and SECREF5 , we discuss the effectiveness of SpCoA in the simulation and in the real environment. Section SECREF6 concludes this paper. ### Lexical acquisition Most studies on lexical acquisition typically focus on lexicons about objects BIBREF0 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . Many of these studies have not be able to address the lexical acquisition of words other than those related to objects, e.g., words about places. Roy et al. proposed a computational model that enables a robot to learn the names of objects from an object image and spontaneous infant-directed speech BIBREF0 . Their results showed that the model performed speech segmentation, word discovery, and visual categorization. Iwahashi et al. reported that a robot properly understands the situation and acquires the relationship of object behaviors and sentences BIBREF2 , BIBREF3 , BIBREF4 . Qu & Chai focused on the conjunction between speech and eye gaze and the use of domain knowledge in lexical acquisition BIBREF6 , BIBREF7 . They proposed an unsupervised learning method that automatically acquires novel words for an interactive system. Qu & Chai's method based on the IBM translation model BIBREF11 estimates the word-entity association probability. Nakamura et al. proposed a method to learn object concepts and word meanings from multimodal information and verbal information BIBREF9 . The method proposed in BIBREF9 is a categorization method based on multimodal latent Dirichlet allocation (MLDA) that enables the acquisition of object concepts from multimodal information, such as visual, auditory, and haptic information BIBREF12 . Araki et al. addressed the development of a method combining unsupervised word segmentation from uttered sentences by a nested Pitman-Yor language model (NPYLM) BIBREF13 and the learning of object concepts by MLDA BIBREF10 . However, the disadvantage of using NPYLM was that phoneme sequences with errors did not result in appropriate word segmentation. These studies did not address the lexical acquisition of the space and place that can also tolerate the uncertainty of phoneme recognition. However, for the introduction of robots into the human living environment, robots need to acquire a lexicon related to not only objects but also places. Our study focuses on the lexical acquisition related to places. Robots can adaptively learn the names of places in various human living environments by using SpCoA. We consider that the acquired names of places can be useful for various tasks, e.g., tasks with a movement of robots by the speech instruction. ### Simultaneous learning of places and vocabulary The following studies have addressed lexical acquisition related to places. However, these studies could not utilize the learned language knowledge in other estimations such as the self-localization of a robot. Taguchi et al. proposed a method for the unsupervised learning of phoneme sequences and relationships between words and objects from various user utterances without any prior linguistic knowledge other than an acoustic model of phonemes BIBREF1 , BIBREF14 . Further, they proposed a method for the simultaneous categorization of self-position coordinates and lexical learning BIBREF15 . These experimental results showed that it was possible to learn the name of a place from utterances in some cases and to output words corresponding to places in a location that was not used for learning. Milford et al. proposed RatSLAM inspired by the biological knowledge of a pose cell of the hippocampus of rodents BIBREF16 . Milford et al. proposed a method that enables a robot to acquire spatial concepts by using RatSLAM BIBREF17 . Further, Lingodroids, mobile robots that learn a language through robot-to-robot communication, have been studied BIBREF18 , BIBREF19 , BIBREF20 . Here, a robot communicated the name of a place to other robots at various locations. Experimental results showed that two robots acquired the lexicon of places that they had in common. In BIBREF20 , the researchers showed that it was possible to learn temporal concepts in a manner analogous to the acquisition of spatial concepts. These studies reported that the robots created their own vocabulary. However, these studies did not consider the acquisition of a lexicon by human-to-robot speech interactions. Welke et al. proposed a method that acquires spatial representation by the integration of the representation of the continuous state space on the sensorimotor level and the discrete symbolic entities used in high-level reasoning BIBREF21 . This method estimates the probable spatial domain and word from the given objects by using the spatial lexical knowledge extracted from Google Corpus and the position information of the object. Their study is different from ours because their study did not consider lexicon learning from human speech. In the case of global localization, the hypothesis of self-position often remains in multiple remote places. In this case, there is some possibility of performing an incorrect estimation and increasing the estimation error. This problem exists during teaching tasks and self-localization after the lexical acquisition. The abovementioned studies could not deal with this problem. In this paper, we have proposed a method that enables a robot to perform more accurate self-localization by reducing the estimation error of the teaching time by using a smoothing method in the teaching task and by utilizing words acquired through the lexical acquisition. The strengths of this study are that learning of spatial concept and self-localization represented as one generative model and robots are able to utilize acquired lexicon to self-localization autonomously. ### Spatial Concept Acquisition We propose nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates a nonparametric morphological analyzer for the lattice BIBREF22 , i.e., latticelm, a spatial clustering method, and Monte Carlo localization (MCL) BIBREF23 . ### Generative model In our study, we define a position as a specific coordinate or a local point in the environment, and the position distribution as the spatial area of the environment. Further, we define a spatial concept as the names of places and the position distributions corresponding to these names. The model that was developed for spatial concept acquisition is a probabilistic generative model that integrates a self-localization with the simultaneous clustering of places and words. Fig. FIGREF13 shows the graphical model for spatial concept acquisition. Table TABREF14 shows each variable of the graphical model. The number of words in a sentence at time INLINEFORM0 is denoted as INLINEFORM1 . The generative model of the proposed method is defined as equation ( EQREF11 -). DISPLAYFORM0 Then, the probability distribution for equation () can be defined as follows: DISPLAYFORM0 The prior distribution configured by using the stick breaking process (SBP) BIBREF24 is denoted as INLINEFORM0 , the multinomial distribution as INLINEFORM1 , the Dirichlet distribution as INLINEFORM2 , the inverse–Wishart distribution as INLINEFORM3 , and the multivariate Gaussian (normal) distribution as INLINEFORM4 . The motion model and the sensor model of self-localization are denoted as INLINEFORM5 and INLINEFORM6 in equations () and (), respectively. This model can learn an appropriate number of spatial concepts, depending on the data, by using a nonparametric Bayesian approach. We use the SBP, which is one of the methods based on the Dirichlet process. In particular, this model can consider a theoretically infinite number of spatial concepts INLINEFORM0 and position distributions INLINEFORM1 . SBP computations are difficult because they generate an infinite number of parameters. In this study, we approximate a number of parameters by setting sufficiently large values, i.e., a weak-limit approximation BIBREF25 . It is possible to correlate a name with multiple places, e.g., “staircase” is in two different places, and a place with multiple names, e.g., “toilet” and “restroom” refer to the same place. Spatial concepts are represented by a word distribution of the names of the place INLINEFORM0 and several position distributions ( INLINEFORM1 , INLINEFORM2 ) indicated by a multinomial distribution INLINEFORM3 . In other words, this model is capable of relating the mixture of Gaussian distributions to a multinomial distribution of the names of places. It should be noted that the arrows connecting INLINEFORM4 to the surrounding nodes of the proposed graphical model differ from those of ordinal Gaussian mixture model (GMM). We assume that words obtained by the robot do not change its position, but that the position of the robot affects the distribution of words. Therefore, the proposed generative process assumes that the index of position distribution INLINEFORM5 , i.e., the category of the place, is generated from the position of the robot INLINEFORM6 . This change can be naturally introduced without any troubles by introducing equation ( EQREF12 ). ### Overview of the proposed method SpCoA We assume that a robot performs self-localization by using control data and sensor data at all times. The procedure for the learning of spatial concepts is as follows: An utterer teaches a robot the names of places, as shown in Fig. FIGREF3 (b). Every time the robot arrives at a place that was a designated learning target, the utterer says a sentence, including the name of the current place. The robot performs speech recognition from the uttered speech signal data. Thus, the speech recognition system includes a word dictionary of only Japanese syllables. The speech recognition results are obtained in a lattice format. Word segmentation is performed by using the lattices of the speech recognition results. The robot learns spatial concepts from words obtained by word segmentation and robot positions obtained by self-localization for all teaching times. The details of the learning are given in SECREF23 . The procedure for self-localization utilizing spatial concepts is as follows: The words of the learned spatial concepts are registered to the word dictionary of the speech recognition system. When a robot obtains a speech signal, speech recognition is performed. Then, a word sequence as the 1-best speech recognition result is obtained. The robot modifies the self-localization from words obtained by speech recognition and the position likelihood obtained by spatial concepts. The details of self-localization are provided in SECREF35 . The proposed method can learn words related to places from the utterances of sentences. We use an unsupervised word segmentation method latticelm that can directly segment words from the lattices of the speech recognition results of the uttered sentences BIBREF22 . The lattice can represent to a compact the set of more promising hypotheses of a speech recognition result, such as N-best, in a directed graph format. Unsupervised word segmentation using the lattices of syllable recognition is expected to be able to reduce the variability and errors in phonemes as compared to NPYLM BIBREF13 , i.e., word segmentation using the 1-best speech recognition results. The self-localization method adopts MCL BIBREF23 , a method that is generally used as the localization of mobile robots for simultaneous localization and mapping (SLAM) BIBREF26 . We assume that a robot generates an environment map by using MCL-based SLAM such as FastSLAM BIBREF27 , BIBREF28 in advance, and then, performs localization by using the generated map. Then, the environment map of both an occupancy grid map and a landmark map is acceptable. ### Learning of spatial concept Spatial concepts are learned from multiple teaching data, control data, and sensor data. The teaching data are a set of uttered sentences for all teaching times. Segmented words of an uttered sentence are converted into a bag-of-words (BoW) representation as a vector of the occurrence counts of words INLINEFORM0 . The set of the teaching times is denoted as INLINEFORM1 , and the number of teaching data items is denoted as INLINEFORM2 . The model parameters are denoted as INLINEFORM3 . The initial values of the model parameters can be set arbitrarily in accordance with a condition. Further, the sampling values of the model parameters from the following joint posterior distribution are obtained by performing Gibbs sampling. DISPLAYFORM0 where the hyperparameters of the model are denoted as INLINEFORM0 . The algorithm of the learning of spatial concepts is shown in Algorithm SECREF23 . The conditional posterior distribution of each element used for performing Gibbs sampling can be expressed as follows: An index INLINEFORM0 of the position distribution is sampled for each data INLINEFORM1 from a posterior distribution as follows: DISPLAYFORM0 An index INLINEFORM0 of the spatial concepts is sampled for each data item INLINEFORM1 from a posterior distribution as follows: DISPLAYFORM0 where INLINEFORM0 denotes a vector of the occurrence counts of words in the sentence at time INLINEFORM1 . A posterior distribution representing word probabilities of the name of place INLINEFORM2 is calculated as follows: DISPLAYFORM0 where variables with the subscript INLINEFORM0 denote the set of all teaching times. A word probability of the name of place INLINEFORM1 is sampled for each INLINEFORM2 as follows: DISPLAYFORM0 where INLINEFORM0 represents the posterior parameter and INLINEFORM1 denotes the BoW representation of all sentences of INLINEFORM2 in INLINEFORM3 . A posterior distribution representing the position distribution INLINEFORM4 is calculated as follows: DISPLAYFORM0 A position distribution INLINEFORM0 , INLINEFORM1 is sampled for each INLINEFORM2 as follows: DISPLAYFORM0 where INLINEFORM0 denotes the Gaussian–inverse–Wishart distribution; INLINEFORM1 , and INLINEFORM2 represent the posterior parameters; and INLINEFORM3 indicates the set of the teaching positions of INLINEFORM4 in INLINEFORM5 . A topic probability distribution INLINEFORM6 of spatial concepts is sampled as follows: DISPLAYFORM0 A posterior distribution representing the mixed weights INLINEFORM0 of the position distributions is calculated as follows: DISPLAYFORM0 A mixed weight INLINEFORM0 of the position distributions is sampled for each INLINEFORM1 as follows: DISPLAYFORM0 where INLINEFORM0 denotes a vector counting all the indices of the Gaussian distribution of INLINEFORM1 in INLINEFORM2 . Self-positions INLINEFORM0 are sampled by using a Monte Carlo fixed-lag smoother BIBREF29 in the learning phase. The smoother can estimate self-position INLINEFORM1 and not INLINEFORM2 , i.e., a sequential estimation from the given data INLINEFORM3 until time INLINEFORM4 , but it can estimate INLINEFORM5 , i.e., an estimation from the given data INLINEFORM6 until time INLINEFORM7 later than INLINEFORM8 INLINEFORM9 . In general, the smoothing method can provide a more accurate estimation than the MCL of online estimation. In contrast, if the self-position of a robot INLINEFORM10 is sampled like direct assignment sampling for each time INLINEFORM11 , the sampling of INLINEFORM12 is divided in the case with the teaching time INLINEFORM13 and another time INLINEFORM14 as follows: DISPLAYFORM0 [tb] Learning of spatial concepts [1] INLINEFORM0 , INLINEFORM1 Localization and speech recognition INLINEFORM2 to INLINEFORM3 INLINEFORM4 BIBREF29 the speech signal is observed INLINEFORM5 add INLINEFORM6 to INLINEFORM7 Registering the lattice add INLINEFORM8 to INLINEFORM9 Registering the teaching time Word segmentation using lattices INLINEFORM10 BIBREF22 Gibbs sampling Initialize parameters INLINEFORM11 , INLINEFORM12 , INLINEFORM13 INLINEFORM14 to INLINEFORM15 INLINEFORM16 ( EQREF25 ) INLINEFORM17 ( EQREF26 ) INLINEFORM18 ( EQREF28 ) INLINEFORM19 ( EQREF30 ) INLINEFORM20 ( EQREF31 ) INLINEFORM21 ( EQREF33 ) INLINEFORM22 to INLINEFORM23 INLINEFORM24 ( EQREF34 ) INLINEFORM25 ### Self-localization of after learning spatial concepts A robot that acquires spatial concepts can leverage spatial concepts to self-localization. The estimated model parameters INLINEFORM0 and a speech recognition sentence INLINEFORM1 at time INLINEFORM2 are given to the condition part of the probability formula of MCL as follows: DISPLAYFORM0 When the robot hears the name of a place spoken by the utterer, in addition to the likelihood of the sensor model of MCL, the likelihood of INLINEFORM0 with respect to a speech recognition sentence is calculated as follows: DISPLAYFORM0 The algorithm of self-localization utilizing spatial concepts is shown in Algorithm SECREF35 . The set of particles is denoted as INLINEFORM0 , the temporary set that stores the pairs of the particle INLINEFORM1 and the weight INLINEFORM2 , i.e., INLINEFORM3 , is denoted as INLINEFORM4 . The number of particles is INLINEFORM5 . The function INLINEFORM6 is a function that moves each particle from its previous state INLINEFORM7 to its current state INLINEFORM8 by using control data. The function INLINEFORM9 calculates the likelihood of each particle INLINEFORM10 using sensor data INLINEFORM11 . These functions are normally used in MCL. For further details, please refer to BIBREF26 . In this case, a speech recognition sentence INLINEFORM12 is obtained by the speech recognition system using a word dictionary containing all the learned words. [tb] Self-localization utilizing spatial concepts [1] INLINEFORM13 INLINEFORM14 INLINEFORM15 INLINEFORM16 to INLINEFORM17 INLINEFORM18 () INLINEFORM19 () the speech signal is observed INLINEFORM20 add INLINEFORM21 to INLINEFORM22 INLINEFORM23 to INLINEFORM24 draw INLINEFORM25 with probability INLINEFORM26 add INLINEFORM27 to INLINEFORM28 INLINEFORM29 ### Experiment I In this experiment, we validate the evidence of the proposed method (SpCoA) in an environment simulated on the simulator platform SIGVerse BIBREF30 , which enables the simulation of social interactions. The speech recognition is performed using the Japanese continuous speech recognition system Julius BIBREF31 , BIBREF32 . The set of 43 Japanese phonemes defined by Acoustical Society of Japan (ASJ)'s speech database committee is adopted by Julius BIBREF31 . The representation of these phonemes is also adopted in this study. The Julius system uses a word dictionary containing 115 Japanese syllables. The microphone attached on the robot is SHURE's PG27-USB. Further, an unsupervised morphological analyzer, a latticelm 0.4, is implemented BIBREF22 . In the experiment, we compare the following three types of word segmentation methods. A set of syllable sequences is given to the graphical model of SpCoA by each method. This set is used for the learning of spatial concepts as recognized uttered sentences INLINEFORM0 . The remainder of this section is organized as follows: In Section SECREF43 , the conditions and results of learning spatial concepts are described. The experiments performed using the learned spatial concepts are described in Section SECREF49 to SECREF64 . In Section SECREF49 , we evaluate the accuracy of the phoneme recognition and word segmentation for uttered sentences. In Section SECREF56 , we evaluate the clustering accuracy of the estimation results of index INLINEFORM0 of spatial concepts for each teaching utterance. In Section SECREF60 , we evaluate the accuracy of the acquisition of names of places. In Section SECREF64 , we show that spatial concepts can be utilized for effective self-localization. ### Learning of spatial concepts We conduct this experiment of spatial concept acquisition in the environment prepared on SIGVerse. The experimental environment is shown in Fig. FIGREF45 . A mobile robot can move by performing forward, backward, right rotation, or left rotation movements on a two-dimensional plane. In this experiment, the robot can use an approximately correct map of the considered environment. The robot has a range sensor in front and performs self-localization on the basis of an occupancy grid map. The initial particles are defined by the true initial position of the robot. The number of particles is INLINEFORM0 . The lag value of the Monte Carlo fixed-lag smoothing is fixed at 100. The other parameters of this experiment are as follows: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 , INLINEFORM7 , and INLINEFORM8 . The number of iterations used for Gibbs sampling is 100. This experiment does not include the direct assignment sampling of INLINEFORM9 in equation ( EQREF34 ), i.e., lines 22–24 of Algorithm SECREF23 are omitted, because we consider that the self-position can be obtained with sufficiently good accuracy by using the Monte Carlo smoothing. Eight places are selected as the learning targets, and eight types of place names are considered. Each uttered place name is shown in Fig. FIGREF45 . These utterances include the same name in different places, i.e., “teeburunoatari” (which means near the table in English), and different names in the same place, i.e., “kiqchiN” and “daidokoro” (which mean a kitchen in English). The other teaching names are “geNkaN” (which means an entrance or a doorway in English); “terebimae” (which means the front of the TV in English); “gomibako” (which means a trash box in English); “hoNdana” (which means a bookshelf in English); and “sofaamae” (which means the front of the sofa in English). The teaching utterances, including the 10 types of phrases, are spoken for a total of 90 times. The phrases in each uttered sentence are listed in Table TABREF46 . The learning results of spatial concepts obtained by using the proposed method are presented here. Fig. FIGREF47 shows the position distributions learned in the experimental environment. Fig. FIGREF47 (top) shows the word distributions of the names of places for each spatial concept, and Fig. FIGREF47 (bottom) shows the multinomial distributions of the indices of the position distributions. Consequently, the proposed method can learn the names of places corresponding to each place of the learning target. In the spatial concept of index INLINEFORM0 , the highest probability of words was “sofamae”, and the highest probability of the indices of the position distribution was INLINEFORM1 ; therefore, the name of a place “sofamae” was learned to correspond to the position distribution of INLINEFORM2 . In the spatial concept of index INLINEFORM3 , “kiqchi” and “daidokoro” were learned to correspond to the position distribution of INLINEFORM4 . Therefore, this result shows that multiple names can be learned for the same place. In the spatial concept of index INLINEFORM5 , “te” and “durunoatari” (one word in a normal situation) were learned to correspond to the position distributions of INLINEFORM6 and INLINEFORM7 . Therefore, this result shows that the same name can be learned for multiple places. ### Phoneme recognition accuracy of uttered sentences We compared the performance of three types of word segmentation methods for all the considered uttered sentences. It was difficult to weigh the ambiguous syllable recognition and the unsupervised word segmentation separately. Therefore, this experiment considered the positions of a delimiter as a single letter. We calculated the matching rate of a phoneme string of a recognition result of each uttered sentence and the correct phoneme string of the teaching data that was suitably segmented into Japanese morphemes using MeCab, which is an off-the-shelf Japanese morphological analyzer that is widely used for natural language processing. The matching rate of the phoneme string was calculated by using the phoneme accuracy rate (PAR) as follows: DISPLAYFORM0 The numerator of equation ( EQREF52 ) is calculated by using the Levenshtein distance between the correct phoneme string and the recognition phoneme string. INLINEFORM0 denotes the number of substitutions; INLINEFORM1 , the number of deletions; and INLINEFORM2 , the number of insertions. INLINEFORM3 represents the number of phonemes of the correct phoneme string. Table TABREF54 shows the results of PAR. Table TABREF55 presents examples of the word segmentation results of the three considered methods. We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format. ### Estimation accuracy of spatial concepts We compared the matching rate with the estimation results of index INLINEFORM0 of the spatial concepts of each teaching utterance and the classification results of the correct answer given by humans. The evaluation of this experiment used the adjusted Rand index (ARI) BIBREF33 . ARI is a measure of the degree of similarity between two clustering results. Further, we compared the proposed method with a method of word clustering without location information for the investigation of the effect of lexical acquisition using location information. In particular, a method of word clustering without location information used the Dirichlet process mixture (DPM) of the unigram model of an SBP representation. The parameters corresponding to those of the proposed method were the same as the parameters of the proposed method and were estimated using Gibbs sampling. Fig. FIGREF59 shows the results of the average of the ARI values of 10 trials of learning by Gibbs sampling. Here, we found that the proposed method showed the best score. These results and the results reported in Section SECREF49 suggest that learning by uttered sentences obtained by better phoneme recognition and better word segmentation produces a good result for the acquisition of spatial concepts. Furthermore, in a comparison of two clustering methods, we found that SpCoA was considerably better than DPM, a word clustering method without location information, irrespective of the word segmentation method used. The experimental results showed that it is possible to improve the estimation accuracy of spatial concepts and vocabulary by performing word clustering that considered location information. ### Accuracy of acquired phoneme sequences representing the names of places We evaluated whether the names of places were properly learned for the considered teaching places. This experiment assumes a request for the best phoneme sequence INLINEFORM0 representing the self-position INLINEFORM1 for a robot. The robot moves close to each teaching place. The probability of a word INLINEFORM2 when the self-position INLINEFORM3 of the robot is given, INLINEFORM4 , can be obtained by using equation ( EQREF37 ). The word having the best probability was selected. We compared the PAR with the correct phoneme sequence and a selected name of the place. Because “kiqchiN” and “daidokoro” were taught for the same place, the word whose PAR was the higher score was adopted. Fig. FIGREF63 shows the results of PAR for the word considered the name of a place. SpCoA (latticelm), the proposed method using the results of unsupervised word segmentation on the basis of the speech recognition results in the lattice format, showed the best PAR score. In the 1-best and BoS methods, a part syllable sequence of the name of a place was more minutely segmented as shown in Table TABREF55 . Therefore, the robot could not learn the name of the teaching place as a coherent phoneme sequence. In contrast, the robot could learn the names of teaching places more accurately by using the proposed method. ### Self-localization that utilizes acquired spatial concepts In this experiment, we validate that the robot can make efficient use of the acquired spatial concepts. We compare the estimation accuracy of localization for the proposed method (SpCoA MCL) and the conventional MCL. When a robot comes to the learning target, the utterer speaks out the sentence containing the name of the place once again for the robot. The moving trajectory of the robot and the uttered positions are the same in all the trials. In particular, the uttered sentence is “kokowa dayo”. When learning a task, this phrase is not used. The number of particles is INLINEFORM0 , and the initial particles are uniformly distributed in the considered environment. The robot performs a control operation for each time step. The estimation error in the localization is evaluated as follows: While running localization, we record the estimation error (equation ( EQREF66 )) on the INLINEFORM0 plane of the floor for each time step. DISPLAYFORM0 where INLINEFORM0 denote the true position coordinates of the robot as obtained from the simulator, and INLINEFORM1 , INLINEFORM2 represent the weighted mean values of localization coordinates. The normalized weight INLINEFORM3 is obtained from the sensor model in MCL as a likelihood. In the utterance time, this likelihood is multiplied by the value calculated using equation ( EQREF37 ). INLINEFORM4 , INLINEFORM5 denote the INLINEFORM6 -coordinate and the INLINEFORM7 -coordinate of index INLINEFORM8 of each particle at time INLINEFORM9 . After running the localization, we calculated the average of INLINEFORM10 . Further, we compared the estimation accuracy rate (EAR) of the global localization. In each trial, we calculated the proportion of time step in which the estimation error was less than 50 cm. Fig. FIGREF68 shows the results of the estimation error and the EAR for 10 trials of each method. All trials of SpCoA MCL (latticelm) and almost all trials of the method using 1-best NPYLM and BoS showed relatively small estimation errors. Results of the second trial of 1-best NPYLM and the fifth trial of BoS showed higher estimation errors. In these trials, many particles converged to other places instead of the place where the robot was, based on utterance information. Nevertheless, compared with those of the conventional MCL, the results obtained using spatial concepts showed an obvious improvement in the estimation accuracy. Consequently, spatial concepts acquired by using the proposed method proved to be very helpful in improving the localization accuracy. ### Experiment II In this experiment, the effectiveness of the proposed method was tested by using an autonomous mobile robot TurtleBot 2 in a real environment. Fig. FIGREF70 shows TurtleBot 2 used in the experiments. Mapping and self-localization are performed by the robot operating system (ROS). The speech recognition system, the microphone, and the unsupervised morphological analyzer were the same as those described in Section SECREF4 . ### Learning of spatial concepts in the real environment We conducted an experiment of the spatial concept acquisition in a real environment of an entire floor of a building. In this experiment, self-localization was performed using a map generated by SLAM. The initial particles are defined by the true initial position of the robot. The generated map in the real environment and the names of teaching places are shown in Fig. FIGREF73 . The number of teaching places was 19, and the number of teaching names was 16. The teaching utterances were performed for a total of 100 times. Fig. FIGREF75 shows the position distributions learned on the map. Table TABREF76 shows the five best elements of the multinomial distributions of the name of place INLINEFORM0 and the multinomial distributions of the indices of the position distribution INLINEFORM1 for each index of spatial concept INLINEFORM2 . Thus, we found that the proposed method can learn the names of places corresponding to the considered teaching places in the real environment. For example, in the spatial concept of index INLINEFORM0 , “torire” was learned to correspond to a position distribution of INLINEFORM1 . Similarly, “kidanokeN” corresponded to INLINEFORM2 in INLINEFORM3 , and “kaigihitsu” was corresponded to INLINEFORM4 in INLINEFORM5 . In the spatial concept of index INLINEFORM6 , a part of the syllable sequences was minutely segmented as “sohatsuke”, “N”, and “tani”, “guchi”. In this case, the robot was taught two types of names. These words were learned to correspond to the same position distribution of INLINEFORM7 . In INLINEFORM8 , “gomibako” showed a high probability, and it corresponded to three distributions of the position of INLINEFORM9 . The position distribution of INLINEFORM10 had the fourth highest probability in the spatial concept INLINEFORM11 . Therefore, “raqkukeN,” which had the fifth highest probability in the spatial concept INLINEFORM12 (and was expected to relate to the spatial concept INLINEFORM13 ), can be estimated as the word drawn from spatial concept INLINEFORM14 . However, in practice, this situation did not cause any severe problems because the spatial concept of the index INLINEFORM15 had the highest probabilities for the word “rapukeN” and the position distribution INLINEFORM16 than INLINEFORM17 . In the probabilistic model, the relative probability and the integrative information are important. When the robot listened to an utterance related to “raqkukeN,” it could make use of the spatial concept of index INLINEFORM18 for self-localization with a high probability, and appropriately updated its estimated self-location. We expected that the spatial concept of index INLINEFORM19 was learned as two separate spatial concepts. However, “watarirooka” and “kaidaNmae” were learned as the same spatial concept. Therefore, the multinomial distribution INLINEFORM20 showed a higher probability for the indices of the position distribution corresponding to the teaching places of both “watarirooka” and “kaidaNmae”. The proposed method adopts a nonparametric Bayesian method in which it is possible to form spatial concepts that allow many-to-many correspondences between names and places. In contrast, this can create ambiguity that classifies originally different spatial concepts into one spatial concept as a side effect. There is a possibility that the ambiguity of concepts such as INLINEFORM0 will have a negative effect on self-localization, even though the self-localization performance was (overall) clearly increased by employing the proposed method. The solution of this problem will be considered in future work. In terms of the PAR of uttered sentences, the evaluation value from the evaluation method used in Section SECREF49 is 0.83; this value is comparable to the result in Section SECREF49 . However, in terms of the PAR of the name of the place, the evaluation value from the evaluation method used in Section SECREF60 is 0.35, which is lower than that in Section SECREF60 . We consider that the increase in uncertainty in the real environment and the increase in the number of teaching words reduced the performance. We expect that this problem could be improved using further experience related to places, e.g., if the number of utterances per place is increased, and additional sensory information is provided. ### Modification of localization by the acquired spatial concepts In this experiment, we verified the modification results of self-localization by using spatial concepts in global self-localization. This experiment used the learning results of spatial concepts presented in Section SECREF71 . The experimental procedures are shown below. The initial particles were uniformly distributed on the entire floor. The robot begins to move from a little distance away to the target place. When the robot reached the target place, the utterer spoke the sentence containing the name of the place for the robot. Upon obtaining the speech information, the robot modifies the self-localization on the basis of the acquired spatial concepts. The number of particles was the same as that mentioned in Section SECREF71 . Fig. FIGREF80 shows the results of the self-localization before (the top part of the figure) and after (the bottom part of the figure) the utterance for three places. The particle states are denoted by red arrows. The moving trajectory of the robot is indicated by a green dotted arrow. Figs. FIGREF80 (a), (b), and (c) show the results for the names of places “toire”, “souhatsukeN”, and “gomibako”. Further, three spatial concepts, i.e., those at INLINEFORM0 , were learned as “gomibako”. In this experiment, the utterer uttered to the robot when the robot came close to the place of INLINEFORM1 . In all the examples shown in the top part of the figure, the particles were dispersed in several places. In contrast, the number of particles near the true position of the robot showed an almost accurate increase in all the examples shown in the bottom part of the figure. Thus, we can conclude that the proposed method can modify self-localization by using spatial concepts. ### Conclusion and Future Work In this paper, we discussed the spatial concept acquisition, lexical acquisition related to places, and self-localization using acquired spatial concepts. We proposed nonparametric Bayesian spatial concept acquisition method SpCoA that integrates latticelm BIBREF22 , a spatial clustering method, and MCL. We conducted experiments for evaluating the performance of SpCoA in a simulation and a real environment. SpCoA showed good results in all the experiments. In experiments of the learning of spatial concepts, the robot could form spatial concepts for the places of the learning targets from human continuous speech signals in both the room of the simulation environment and the entire floor of the real environment. Further, the unsupervised word segmentation method latticelm could reduce the variability and errors in the recognition of phonemes in all the utterances. SpCoA achieved more accurate lexical acquisition by performing word segmentation using the lattices of the speech recognition results. In the self-localization experiments, the robot could effectively utilize the acquired spatial concepts for recognizing self-position and reducing the estimation errors in self-localization. As a method that further improves the performance of the lexical acquisition, a mutual learning method was proposed by Nakamura et al. on the basis of the integration of the learning of object concepts with a language model BIBREF34 , BIBREF35 . Following a similar approach, Heymann et al. proposed a method that alternately and repeatedly updates phoneme recognition results and the language model by using unsupervised word segmentation BIBREF36 . As a result, they achieved robust lexical acquisition. In our study, we can expect to improve the accuracy of lexical acquisition for spatial concepts by estimating both the spatial concepts and the language model. Furthermore, as a future work, we consider it necessary for robots to learn spatial concepts online and to recognize whether the uttered word indicates the current place or destination. Furthermore, developing a method that simultaneously acquires spatial concepts and builds a map is one of our future objectives. We believe that the spatial concepts will have a positive effect on the mapping. We also intend to examine a method that associates the image and the landscape with spatial concepts and a method that estimates both spatial concepts and object concepts. [] Akira Taniguchi received his BE degree from Ritsumeikan University in 2013 and his ME degree from the Graduate School of Information Science and Engineering, Ritsumeikan University, in 2015. He is currently working toward his PhD degree at the Emergent System Lab, Ritsumeikan University, Japan. His research interests include language acquisition, concept acquisition, and symbol emergence in robotics. [] Tadahiro Taniguchi received the ME and PhD degrees from Kyoto University in 2003 and 2006, respectively. From April 2005 to March 2006, he was a Japan Society for the Promotion of Science (JSPS) research fellow (DC2) in the Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University. From April 2006 to March 2007, he was a JSPS research fellow (PD) in the same department. From April 2007 to March 2008, he was a JSPS research fellow in the Department of Systems Science, Graduate School of Informatics, Kyoto University. From April 2008 to March 2010, he was an assistant professor at the Department of Human and Computer Intelligence, Ritsumeikan University. Since April 2010, he has been an associate professor in the same department. He is currently engaged in research on machine learning, emergent systems, and semiotics. [] Tetsunari Inamura received the BE, MS and PhD degrees from the University of Tokyo, in 1995, 1997 and 2000, respectively. He was a Researcher of the CREST program, Japanese Science and Technology Cooperation, from 2000 to 2003, and then joined the Department of Mechano-Informatics, School of Information Science and Technology, University of Tokyo as a Lecturer, from 2003 to 2006. He is now an Associate Professor in the Principles of Informatics Research Division, National Institute of Informatics, and an Associate Professor in the Department of Informatics, School of Multidisciplinary Sciences, Graduate University for Advanced Studies (SOKENDAI). His research interests include imitation learning and symbol emergence on humanoid robots, development of interactive robots through virtual reality and so on. Fig. 1. Schematic representation of the target task: (a) Learning targets are three places near the objects. (b) When an utterer and a robot came in front of the TV, the utterer spoke “Here is the front of TV.” The same holds true for the other places. (c) The robot performs word discovery from the uttered sentences and learns the related spatial concepts. Words are related to each place. (d) The robot is in front of TV actually. However, the hypothesis of the self-position of the robot is uncertain. Then, the robot asks a neighbor what the current place is. (e) By utilizing spatial concepts and an uttered sentence, the robot can narrow down the hypothesis of self-position. Fig. 2. Graphical model of the proposed method SpCoA TABLE I EACH ELEMENT OF THE GRAPHICAL MODEL Fig. 3. Environment to be used for learning and localization on SIGVerse: This is a pseudo-room in the simulated real world. There is a robot in the center of the room. The size of the room is 500 cm × 1,000 cm, and the size of the robot is 50 cm × 50 cm. Fig. 4. Learning result of the position distribution: A point group of each color to represent each position distribution is drawn on an map of the considered environment. The colors of the point groups are determined randomly. Each balloon shows the index number for each position distribution. TABLE II VARIOUS PHRASES OF EACH JAPANESE SENTENCE: “” IS USED AS A PLACEHOLDER FOR THE NAME OF EACH PLACE. EXAMPLES OF THESE PHRASES ARE “ is here.”, “This place is .”, “This place’s name is .”, AND “Came to .” IN ENGLISH. Fig. 5. Learning result of the multinomial distributions of the names of places W (top); multinomial distributions of the index of the position distribution φl (bottom): All the words obtained during the experiment are shown. TABLE III COMPARISON OF THE PHONEME ACCURACY RATES OF UTTERED SENTENCES FOR DIFFERENT WORD SEGMENTATION METHODS TABLE IV EXAMPLES OF WORD SEGMENTATION RESULTS OF UTTERED SENTENCES. “\|” DENOTES A WORD SEGMENT POINT. Fig. 6. Comparison of the accuracy rates of the estimation results of spatial concepts Fig. 7. PAR scores for the word considered the name of a place Fig. 10. Teaching places and the names of places shown on the generated map. The teaching places included places having two names each and multiple places having the same names. Fig. 8. Results of estimation errors and EARs of self-localization Fig. 9. Autonomous mobile robot TurtleBot 2: The robot is based on Yujin Robot Kobuki and Microsoft Kinect for its use as a range sensor. Fig. 11. Learning result of each position distribution: A point group of each color denoting each position distribution was drawn on the map. The colors of the point groups were determined randomly. Further, each index number is denoted as it = k. TABLE V LEARNING RESULT OF HIGH-PROBABILITY WORDS AND INDICES OF THE POSITION DISTRIBUTION FOR EACH SPATIAL CONCEPT Fig. 12. States of particles: before the teaching utterance (top); after the teaching utterance (bottom). The uttered sentence is “kokowa dayo,” (which means “Here is .”) “**” is the name of the place.	unsupervised word segmentation method latticelm
From the passage, are we able to infer that the author is for or against cloning and why? A. Against, because he says humans have no right to reproduce themselves B. Against, because he fears the cloned warriors C. For, because he says that humans have the right to reproduce how they see fit. D. For, because he hopes for the cloned warriors	Human Clones: Why Not? If you can clone a sheep, you can almost certainly clone a human being. Some of the most powerful people in the world have felt compelled to act against this threat. President Clinton swiftly imposed a ban on federal funding for human-cloning research. Bills are in the works in both houses of Congress to outlaw human cloning--a step urged on all governments by the pope himself. Cloning humans is taken to be either 1) a fundamentally evil thing that must be stopped or, at the very least, 2) a complex ethical issue that needs legislation and regulation. But what, exactly, is so bad about it? Start by asking whether human beings have a right to reproduce. I say "yes." I have no moral right to tell other people they shouldn't be able to have children, and I don't see that Bill Clinton has that right either. When Clinton says, "Let us resist the temptation to copy ourselves," it comes from a man not known for resisting other temptations of the flesh. And for a politician, making noise about cloning is pretty close to a fleshly temptation itself. It's an easy way to show sound-bite leadership on an issue that everybody is talking about, without much risk of bitter consequences. After all, how much federally funded research was stopped by this ban? Probably almost none, because Clinton has maintained Ronald Reagan's policy of minimizing federal grants for research in human reproduction. Besides, most researchers thought cloning humans was impossible--so, for the moment, there's unlikely to be a grant-request backlog. There is nothing like banning the nonexistent to show true leadership. The pope, unlike the president, is known for resisting temptation. He also openly claims the authority to decide how people reproduce. I respect the pope's freedom to lead his religion, and his followers' freedom to follow his dictate. But calling for secular governments to implement a ban, thus extending his power beyond those he can persuade, shows rather explicitly that the pope does not respect the freedom of others. The basic religious doctrine he follows was set down some two millennia ago. Sheep feature prominently in the Bible, but cloning does not. So the pope's views on cloning are 1 st century rules applied using 15 th century religious thinking to a 21 st century issue. If humans have a right to reproduce, what right does society have to limit the means? Essentially all reproduction is done these days with medical help--at delivery, and often before. Truly natural human reproduction would mean 50 percent infant mortality and make pregnancy-related death the No. 1 killer of adult women. True, some forms of medical help are more invasive than others. With in vitro fertilization, the sperm and egg are combined in the lab and surgically implanted in the womb. Less than two decades ago, a similar concern was raised over the ethical issues involved in "test-tube babies." To date, nearly 30,000 such babies have been born in the United States alone. Many would-be parents have been made happy. Who has been harmed? The cloning procedure is similar to IVF. The only difference is that the DNA of sperm and egg would be replaced by DNA from an adult cell. What law or principle--secular, humanist, or religious--says that one combination of genetic material in a flask is OK, but another is not? No matter how closely you study the 1 st century texts, I don't think you'll find the answer. Even if people have the right to do it, is cloning a good idea? Suppose that every prospective parent in the world stopped having children naturally, and instead produced clones of themselves. What would the world be like in another 20 or 30 years? The answer is: much like today. Cloning would only copy the genetic aspects of people who are already here. Hating a world of clones is hating the current populace. Never before was Pogo so right: We have met the enemy, and he is us ! Adifferent scare scenario is a world filled with copies of famous people only. We'll treat celebrity DNA like designer clothes, hankering for Michael Jordan's genes the way we covet his Nike sneakers today. But even celebrity infatuation has its limits. People are not more taken with celebrities than they are with themselves. Besides, such a trend would correct itself in a generation or two, because celebrity is closely linked to rarity. The world seems amused by one Howard Stern, but give us a hundred or a million of them, and they'll seem a lot less endearing. Clones already exist. About one in every 1,000 births results in a pair of babies with the same DNA. We know them as identical twins. Scientific studies on such twins--reared together or apart--show that they share many characteristics. Just how many they share is a contentious topic in human biology. But genetic determinism is largely irrelevant to the cloning issue. Despite how many or how few individual characteristics twins--or other clones--have in common, they are different people in the most fundamental sense . They have their own identities, their own thoughts, and their own rights. Should you be confused on this point, just ask a twin. Suppose that Unsolved Mysteries called you with news of a long-lost identical twin. Would that suddenly make you less of a person, less of an individual? It is hard to see how. So, why would a clone be different? Your clone would be raised in a different era by different people--like the lost identical twin, only younger than you. A person's basic humanity is not governed by how he or she came into this world, or whether somebody else happens to have the same DNA. Twins aren't the only clones in everyday life. Think about seedless grapes or navel oranges--if there are no seeds, where did they come from? It's the plant equivalent of virgin birth--which is to say that they are all clones, propagated by cutting a shoot and planting it. Wine is almost entirely a cloned product. The grapes used for wine have seeds, but they've been cloned from shoots for more than a hundred years in the case of many vineyards. The same is true for many flowers. Go to a garden store, and you'll find products with delightful names like "Olivia's Cloning Compound," a mix of hormones to dunk on the cut end of a shoot to help it take root. One recurring image in anti-cloning propaganda is of some evil dictator raising an army of cloned warriors. Excuse me, but who is going to raise such an army ("raise" in the sense used by parents)? Clones start out life as babies . Armies are far easier to raise the old fashioned way--by recruiting or drafting naive young adults. Dulce et decorum est pro patria mori has worked well enough to send countless young men to their deaths through the ages. Why mess with success? Remember that cloning is not the same as genetic engineering. We don't get to make superman--we have to find him first. Maybe we could clone the superwarrior from Congressional Medal of Honor winners. Their bravery might--or might not--be genetically determined. But, suppose that it is. You might end up with such a brave battalion of heroes that when a grenade lands in their midst, there is a competition to see who gets to jump on it to save the others. Admirable perhaps, but not necessarily the way to win a war. And what about the supply sergeants? The army has a lot more of them than heroes. You could try to breed an expert for every job, including the petty bureaucrats, but what's the point? There's not exactly a shortage of them. What if Saddam Hussein clones were to rule Iraq for another thousand years? Sounds bad, but Saddam's natural son Uday is reputed to make his father seem saintly by comparison. We have no more to fear from a clone of Saddam, or of Hitler, than we do from their natural-born kin--which is to say, we don't have much to fear: Dictators' kids rarely pose a problem. Stalin's daughter retired to Arizona, and Kim Jong Il of North Korea is laughable as Great Leader, Version 2.0. The notion of an 80-year-old man cloning himself to cheat death is quaint, but it is unrealistic. First, the baby wouldn't really be him. Second, is the old duffer really up to changing diapers? A persistent octogenarian might convince a younger couple to have his clone and raise it, but that is not much different from fathering a child via a surrogate mother. Fear of clones is just another form of racism. We all agree it is wrong to discriminate against people based on a set of genetic characteristics known as "race." Calls for a ban on cloning amount to discrimination against people based on another genetic trait--the fact that somebody already has an identical DNA sequence. The most extreme form of discrimination is genocide--seeking to eliminate that which is different. In this case, the genocide is pre-emptive--clones are so scary that we must eliminate them before they exist with a ban on their creation. What is so special about natural reproduction anyway? Cloning is the only predictable way to reproduce, because it creates the identical twin of a known adult. Sexual reproduction is a crap shoot by comparison--some random mix of mom and dad. In evolutionary theory, this combination is thought to help stir the gene pool, so to speak. However, evolution for humans is essentially over, because we use medical science to control the death rate. Whatever the temptations of cloning, the process of natural reproduction will always remain a lot more fun. An expensive and uncomfortable lab procedure will never offer any real competition for sex. The people most likely to clone will be those in special circumstances--infertile couples who must endure IVF anyway, for example. Even there, many will mix genetics to mimic nature. Another special case is where one member of a couple has a severe genetic disease. They might choose a clone of the healthy parent, rather than burden their child with a joint heritage that could be fatal. The most upsetting possibility in human cloning isn't superwarriors or dictators. It's that rich people with big egos will clone themselves. The common practice of giving a boy the same name as his father or choosing a family name for a child of either sex reflects our hunger for vicarious immortality. Clones may resonate with this instinct and cause some people to reproduce this way. So what? Rich and egotistic folks do all sorts of annoying things, and the law is hardly the means with which to try and stop them. The "deep ethical issues" about cloning mainly boil down to jealousy. Economic jealousy is bad enough, and it is a factor here, but the thing that truly drives people crazy is sexual jealousy. Eons of evolution through sexual selection have made the average man or woman insanely jealous of any interloper who gains a reproductive advantage--say by diddling your spouse. Cloning is less personal than cuckoldry, but it strikes a similar chord: Someone has got the reproductive edge on you. Once the fuss has died down and further animal research has paved the way, direct human cloning will be one more option among many specialized medical interventions in human reproduction, affecting only a tiny fraction of the population. Research into this area could bring far wider benefits. Clinton's knee-jerk policy changes nothing in the short run, but it is ultimately a giant step backward. In using an adult cell to create a clone, the "cellular clock" that determines the difference between an embryo and adult was somehow reset. Work in this area might help elucidate the process by which aging occurs and yield a way to reset the clocks in some of our own cells, allowing us to regenerate. Selfishly speaking, that would be more exciting to me than cloning, because it would help me . That's a lot more directly useful than letting me sire an identical twin 40 years my junior. To some, the scientist laboring away to unlock the mysteries of life is a source of evil, never to be trusted. To others, including me, the scientist is the ray of light, illuminating the processes that make the universe work and making us better through that knowledge. Various arguments can be advanced toward either view, but one key statistic is squarely on my side. The vast majority of people, including those who rail against science, owe their very lives to previous medical discoveries. They embody the fruits of science. Don't let the forces of darkness, ignorance, and fear turn us back from research. Instead, let us raise--and yes, even clone--new generations of hapless ingrates, who can whine and rail against the discoveries of the next age.	C. For, because he says that humans have the right to reproduce how they see fit.

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