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1706.07179

The results are shown in Table 1 . The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks. The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks. The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.

What are the relative improvements observed over existing methods?

The answers are shown as follows: * The RelNet model achieves a mean error of 0.285% across tasks which is better than the results of the EntNet model BIBREF17 . The RelNet model is able to achieve 0% test error on 11 of the tasks, whereas the EntNet model achieves 0% error on 7 of the tasks.

Why has the percentage of CEOs of Indian descent gone up in the last decade ?

In the last decade there is a growing trend of corporations being led by CEOs of Indian descent. Many factors have contributed to this trend including but not limited to: 1) Immigration laws that attract best of talent from across the world to western nations 2) Strong education system in India 3) English as the primary/secondary language in most educational institutions across India 4) Cultural diversity of India that fosters empathy and adaptability 6) Immigrant's drive to succeed in a foreign nation given limited safety net 7) Acceptance of cultural and social diversity in large enterprises

The festival of San Fermín is a weeklong, historically rooted celebration held annually in the city of Pamplona, Navarre, in northern Spain. The celebrations start at noon on July 6 and continue until midnight on July 14. A firework starts off the celebrations and the popular song Pobre de mí is sung at the end. The most famous event is the running of the bulls, which begins at 8 in the morning from July 7 to 14, but the festival involves many other traditional and folkloric events. It is known locally as Sanfermines and is held in honour of Saint Fermin, the co-patron of Navarre. Its events were central to the plot of The Sun Also Rises by Ernest Hemingway, which brought it to the general attention of the English-speaking world. It has become probably the most internationally renowned festival in Spain with over a million people coming to participate.

What are some of the facts about the festival of San Fermín that could explain it to someone that doesn't know about it based on this text.

The festival has celebrations that last an entire week, made up of several festivities, from fireworks and a traditional closing song to the famous running of the bulls. It became world-renowned due to its feature on the novel The Sun Also Rises by Ernest Hemingway.

What are the best types of plants to grow in Austin, Texas?

The best plants for Austin, Texas should be rates for USDA Hardiness Zone 8. The best types of plants should be drought resistant, but also be able to survive multiple days or less than 10 Degree Fahrenheit temperatures in the winter. Plants should also be able to handles temperatures above 100 Degrees Fahrenheit for 100 days per year.

Write a review of your stay in a AirBnB in Rome that had a great location but poor amenities that is overall a neutral review

A perfectly fine place to stay. Had a really great location - we were able to get to the Colosseum, Pantheon and Roman Forum all within walking distance, and there were lots of great little restaurants and cafes we were able to visit. We felt very safe in the neighborhood, and would definitely stay in this area again. The apartment itself was just okay, with not so amazing furniture and the most basic amenities (eg very few spoons, knives and forks, mismatched bowls and such). Definitely a better place for if you are mostly going to eat out for most meals. Overall, 4/5 stars

Classify each of the following as flammable or non-flammable: coal, wood, newspaper, water, glass

Coal: Flammable Wood: Flammable Newspaper: Flammable Water: Non-flammable Glass: Non-flammable

Apple was founded as Apple Computer Company on April 1, 1976, by Steve Wozniak, Steve Jobs and Ronald Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc. in 1977. The company's second computer, the Apple II, became a best seller and one of the first mass-produced microcomputers. Apple went public in 1980 to instant financial success. The company developed computers featuring innovative graphical user interfaces, including the 1984 original Macintosh, announced that year in a critically acclaimed advertisement. By 1985, the high cost of its products, and power struggles between executives, caused problems. Wozniak stepped back from Apple amicably and pursued other ventures, while Jobs resigned bitterly and founded NeXT, taking some Apple employees with him. As the market for personal computers expanded and evolved throughout the 1990s, Apple lost considerable market share to the lower-priced duopoly of the Microsoft Windows operating system on Intel-powered PC clones (also known as "Wintel"). In 1997, weeks away from bankruptcy, the company bought NeXT to resolve Apple's unsuccessful operating system strategy and entice Jobs back to the company. Over the next decade, Jobs guided Apple back to profitability through a number of tactics including introducing the iMac, iPod, iPhone and iPad to critical acclaim, launching the "Think different" campaign and other memorable advertising campaigns, opening the Apple Store retail chain, and acquiring numerous companies to broaden the company's product portfolio. When Jobs resigned in 2011 for health reasons, and died two months later, he was succeeded as CEO by Tim Cook.

Given a reference text about Apple, what tactics did Steve Jobs use to help the company recover?

Steve Jobs launched multiple products such as the iPod and iPhone, opened the Apple Store retail chain, and performed numerous acquisitions to bring Apple back to profitability.

William Henry Gates III (born October 28, 1955) is an American business magnate, philanthropist and investor. He is a co-founder of Microsoft, along with his late childhood friend Paul Allen. During his career at Microsoft, Gates held the positions of chairman, chief executive officer (CEO), president and chief software architect, while also being the largest individual shareholder until May 2014. He was a major entrepreneur of the microcomputer revolution of the 1970s and 1980s.

Who is Bill Gates?

Bill Gates is an American business mogul and co-founder of Microsoft.

What are some of the best outdoor experiences in Italy?

There are a range of outdoor experiences one may try in Italy. Popular hiking trails exit in The Dolomites, the Gran Paradiso in Piedmont and the Stelvio in Trentino. Other options include the Pollino parks in Calabria, the Piano Grande in Umbria and the coastal tracks in Cinque Terre, the Amalfi Coast, Sicily and Sardinia. For cycling, the Po Delta and Bolzano offer good networks, as do the wine regions of Franciacorta, Barolo, Barberesco and Chianti. For urban options, you could try Rome's Via Appia Antica, Ferrara, Lucca, Bologna and Lecce. There are also good options for skiing, such as Sella Nevea, Courmayeur, Cortina d'Ampezzo, the Valle d'Aosta and Sella Ronda. The best diving spots can be found in the Cinque Terre, the Gargano Promontory, Elba, the Sorrento Peninsula, the Aeolian Islands, Ustica and Sardinia.

Following the scaling approach described in Section 3.3, we set 2× FLOPs of the initial or selected model from the previous step as the target hardware-cost in each step when individually scaling each factor, as summarized in Figure. All networks are trained for 300 epochs on ImageNet using the same training recipe with the one in DeiT, more details are included in Appendix E. We summarize our observations as follow: Scaled ViT models outperform SOTA DeiT models. As shown in Table, our scaled ViT models (e.g., DeiT-Scaled-Tiny/Small/Base) achieve a ↑0.4% ∼ ↑1.9% higher top-1 accuracy on ImageNet under the same FLOPs constraints. Specifically, our DeiT-Scaled-Tiny model chooses to use a smaller image resolution (i.e., 160×160 vs. 224×224) and more layers and a higher number of heads as compared to the SOTA DeiT-Tiny model, and thus achieves a ↑1.9% higher accuracy at the same cost in terms of FLOPs, while our DeiT-Scaled-Small/Base models choose to use a larger image resolution (i.e., 320/256×320/256 vs. 224×224) and more layers, together with a lower number of heads as compared to the SOTA DeiT-Small/Base model, helping them to achieve a ↑0.4% higher accuracy under similar FLOPs. This set of experiments shows that our simple search method can (1) effectively locate ViT models with better accuracy-FLOPs trade-offs and (2) automatically adapt different scaling factors towards the optimal accuracy-FLOPs trade-offs, e.g., different model shapes and structures at different scales of FLOPs. Random permutation further boosts the performance. Inspired by the coarse-to-fine architecture selection scheme adopted in , we further randomly permute the scaling factors (i.e., d, h, e, r, I, and p) of each scaled model in Table. After the permutation, we select 24 architectures under the same target hardware-cost with the scaled model by iterative greedy search for each scaled model. Figure demonstrates that () such a random permutation can slightly push forward the frontier of accuracy-FLOPs trade-off (e.g., a ↑0.4% higher accuracy under similar FLOPs on top of the scaled models resulting from the adopted simple scaling method); and (2) our adopted iterative greedy search alone is sufficiently effective while requiring a lower exploration cost (e.g., 6 vs. 30 (6+24) models to be trained for each step as compared to such a search method together with the aforementioned permutation). Scaled ViT also benefits from a longer training time. As pointed out by, training ViT models for more epochs (e.g., 1000 epochs) can further improve the achieved accuracy. DeiT To verify whether the scaled ViT models can benefit from more training epochs, we train the models in Table for 1000 epochs following the training recipe in. As shown in Table, longer training epochs also help our scaled models (e.g., DeiT-Scaled-Tiny/Small) to achieve a higher accuracy, and thus, the advantage of our scaled models over DeiT is consistent under both the 300-epochs training recipe and 1000-epochs training recipe, e.g., a ↑1.9% higher accuracy over DeiT-Tiny with 300 epochs vs. a ↑1.7% higher accuracy over DeiT-Tiny with 1000 epochs. Drawn insights from scaling ViT. Based on the observations from the above experiments, especially the scaling strategies illustrated in Figure, we draw the following scaling insights dedicated to ViT: (1) When targeting relatively small models (i.e., with smaller FLOPs than DeiT-Scaled-Small), the optimal models tend to select "scaling h (i.e., the number of heads)" or "scaling d (i.e., the number of layers)" and a "smaller I (i.e., the input image resolution)" (e.g., 160 × 160 instead of the commonly used 224 × 224). FLOPs (G) ImageNet top-1 accuracy (%) Figure: Resulting models from our iterative greedy search, where models achieving the best accuracy-FLOPs trade-offs are marked as DeiT-Scaled-Tiny/Small/Base. The architecture configurations (i.e., sets of d, h, e, r, I, and p) leading to these best models are extracted as our scaling strategies dedicated to ViT. (2) When targeting relatively large models (i.e., with larger FLOPs than DeiT-Scaled-Small), the optimal models mainly select to "scaling I (i.e., the input image resolution)", while "slowing down scaling h (i.e., number of heads)" as compared to the case when targeting relatively small models. After the permutation, we select 24 architectures under the same target hardware-cost with the scaled model by iterative greedy search for each scaled model.

What are the 24 different structures generated by random permutation in section 4?

The random permutation is implemented by randomly mutating the scaling factors (i.e., the number of layers, number of heads, embedding dimension per head, linear projection ratio, image resolution, and patch size) of the scaled architectures that are obtained by first performing iterative greedy search and then selecting those under the target hardware-cost. We have also added more details to the corresponding part for further clarification in Section 4.1.

Who is currently US president?

At the time I was trained in 2023, Joe Biden was president of the United States. He is a member of the Democratic Party.

List a few queer books made popular by TikTok

Red, White, & Royal Blue, The Seven Husbands of Evelyn Hugo, The Song of Achilles, Heartstopper, Boyfriend Material

The automatic correction of errors in text [In a such situaction INLINEFORM0 In such a situation] is receiving more and more attention from the natural language processing community. A series of competitions has been devoted to grammatical error correction (GEC): the CoNLL-2013 shared task BIBREF0 , the CoNLL-2014 shared task BIBREF1 , and finally the BEA 2019 shared task BIBREF2 . This paper presents the contributions from the Cambridge University Engineering Department to the latest GEC competition at the BEA 2019 workshop. We submitted systems to two different tracks. The low-resource track did not permit the use of parallel training data except a small development set with around 4K sentence pairs. For our low-resource system we extended our prior work on finite state transducer based GEC BIBREF3 to handle new error types such as punctuation errors as well as insertions and deletions of a small number of frequent words. For the restricted track, the organizers provided 1.2M pairs (560K without identity mappings) of corrected and uncorrected sentences. Our goal on the restricted track was to explore the potential of purely neural models for grammatical error correction. We confirm the results of BIBREF4 and report substantial gains by applying back-translation BIBREF5 to GEC – a data augmentation technique common in machine translation. Furthermore, we noticed that large parts of the training data do not match the target domain. We mitigated the domain gap by over-sampling the in-domain training corpus, and by fine-tuning through continued training. Our final model is an ensemble of four neural machine translation (NMT) models and two neural language models (LMs) with Transformer architecture BIBREF6 . Our purely neural system was also part of the joint submission with the Cambridge University Computer Lab described by BIBREF7 . We submitted systems to two different tracks.

How many tracks did authors submit systems?

Two different tracks.

Classify each of the following as mainstream media or social media: Twitter, Reddit, CNN, MSNBC, Instagram, Fox News

Twitter: Social Media Reddit: Social Media CNN: Mainstream Media MSNBC: Mainstream Media Instagram: Social Media Fox News: Mainstream Media

Who is Ali G?

Ali G is a fictional character created and performed by Sacha Baron Cohen. In fiction, he grew up in Staines, and was part of a group called "Da West Staines Massiv" He is depicted as a British stereotype of suburban chav acting in a way that they think is typical of black people. Ali G first appeared in a TV series called The 11 O'Clock Show where he would interview famous people who do not realise they are being set up. Due to his popularity, a standalone TV series called Da Ali G Show and a film Ali G Indahouse were also created.

1905.06512

Table 2 lists some example definitions generated with different models. For each word-sememes pair, the generated three definitions are ordered according to the order: Baseline, AAM and SAAM. For AAM and SAAM, we use the model that incorporates sememes. These examples show that with sememes, the model can generate more accurate and concrete definitions. For example, for the word “旅馆” (hotel), the baseline model fails to generate definition containing the token “旅行者”(tourists). However, by incoporating sememes' information, especially the sememe “旅游” (tour), AAM and SAAM successfully generate “旅行者”(tourists). Manual inspection of others examples also supports our claim. Manual inspection of others examples also supports our claim.

Do they perform manual evaluation?

Yes.

In recent years, the proliferation of fake news with various content, high-speed spreading, and extensive influence has become an increasingly alarming issue. A concrete instance was cited by Time Magazine in 2013 when a false announcement of Barack Obama's injury in a White House explosion “wiped off 130 Billion US Dollars in stock value in a matter of seconds". Other examples, an analysis of the US Presidential Election in 2016 BIBREF0 revealed that fake news was widely shared during the three months prior to the election with 30 million total Facebook shares of 115 known pro-Trump fake stories and 7.6 million of 41 known pro-Clinton fake stories. Therefore, automatically detecting fake news has attracted significant research attention in both industries and academia. Most existing methods devise deep neural networks to capture credibility features for fake news detection. Some methods provide in-depth analysis of text features, e.g., linguistic BIBREF1, semantic BIBREF2, emotional BIBREF3, stylistic BIBREF4, etc. On this basis, some work additionally extracts social context features (a.k.a. meta-data features) as credibility features, including source-based BIBREF5, user-centered BIBREF6, post-based BIBREF7 and network-based BIBREF8, etc. These methods have attained a certain level of success. Additionally, recent researches BIBREF9, BIBREF10 find that doubtful and opposing voices against fake news are always triggered along with its propagation. Fake news tends to provoke controversies compared to real news BIBREF11, BIBREF12. Therefore, stance analysis of these controversies can serve as valuable credibility features for fake news detection. There is an effective and novel way to improve the performance of fake news detection combined with stance analysis, which is to build multi-task learning models to jointly train both tasks BIBREF13, BIBREF14, BIBREF15. These approaches model information sharing and representation reinforcement between the two tasks, which expands valuable features for their respective tasks. However, prominent drawback to these methods and even typical multi-task learning methods, like the shared-private model, is that the shared features in the shared layer are equally sent to their respective tasks without filtering, which causes that some useless and even adverse features are mixed in different tasks, as shown in Figure FIGREF2(a). By that the network would be confused by these features, interfering effective sharing, and even mislead the predictions. To address the above problems, we design a sifted multi-task learning model with filtering mechanism (Figure FIGREF2(b)) to detect fake news by joining stance detection task. Specifically, we introduce a selected sharing layer into each task after the shared layer of the model for filtering shared features. The selected sharing layer composes of two cells: gated sharing cell for discarding useless features and attention sharing cell for focusing on features that are conducive to their respective tasks. Besides, to better capture long-range dependencies and improve the parallelism of the model, we apply transformer encoder module BIBREF16 to our model for encoding input representations of both tasks. Experimental results reveal that the proposed model outperforms the compared methods and gains new benchmarks. In summary, the contributions of this paper are as follows: We explore a selected sharing layer relying on gate mechanism and attention mechanism, which can selectively capture valuable shared features between tasks of fake news detection and stance detection for respective tasks. The transformer encoder is introduced into our model for encoding inputs of both tasks, which enhances the performance of our method by taking advantages of its long-range dependencies and parallelism. Experiments on two public, widely used fake news datasets demonstrate that our method significantly outperforms previous state-of-the-art methods. We explore a selected sharing layer relying on gate mechanism and attention mechanism, which can selectively capture valuable shared features between tasks of fake news detection and stance detection for respective tasks.

What mechanisms does the selected sharing layer rely on?

Gate mechanism and attention mechanism.

Technological evolution is a theory of radical transformation of society through technological development. This theory originated with Czech philosopher Radovan Richta. Mankind In Transition; A View of the Distant Past, the Present and the Far Future, Masefield Books, 1993. Technology (which Richta defines as "a material entity created by the application of mental and physical effort to nature in order to achieve some value") evolves in three stages: tools, machine, automation. This evolution, he says, follows two trends: The pre-technological period, in which all other animal species remain today (aside from some avian and primate species) was a non-rational period of the early prehistoric man. The emergence of technology, made possible by the development of the rational faculty, paved the way for the first stage: the tool. A tool provides a mechanical advantage in accomplishing a physical task, such as an arrow, plow, or hammer that augments physical labor to more efficiently achieve his objective. Later animal-powered tools such as the plow and the horse, increased the productivity of food production about tenfold over the technology of the hunter-gatherers. Tools allow one to do things impossible to accomplish with one's body alone, such as seeing minute visual detail with a microscope, manipulating heavy objects with a pulley and cart, or carrying volumes of water in a bucket. The second technological stage was the creation of the machine. A machine (a powered machine to be more precise) is a tool that substitutes part of or all of the element of human physical effort, requiring only the control of its functions. Machines became widespread with the industrial revolution, though windmills, a type of machine, are much older. Examples of this include cars, trains, computers, and lights. Machines allow humans to tremendously exceed the limitations of their bodies. Putting a machine on the farm, a tractor, increased food productivity at least tenfold over the technology of the plow and the horse. The third, and final stage of technological evolution is the automation. The automation is a machine that removes the element of human control with an automatic algorithm. Examples of machines that exhibit this characteristic are digital watches, automatic telephone switches, pacemakers, and computer programs. It is crucial to understand that the three stages outline the introduction of the fundamental types of technology, and so all three continue to be widely used today. A spear, a plow, a pen, a knife, a glove, and an optical microscope are all examples of tools.

Extract the stages of technology evolution?Explain all of them in brief with examples.

the first stage: the tool. A tool provides a mechanical advantage in accomplishing a physical task, such as an arrow, plow, or hammer that augments physical labor to more efficiently achieve his objective. Later animal-powered tools such as the plow and the horse, increased the productivity of food production about tenfold over the technology of the hunter-gatherers. Tools allow one to do things impossible to accomplish with one's body alone, such as seeing minute visual detail with a microscope, manipulating heavy objects with a pulley and cart, or carrying volumes of water in a bucket. The second technological stage was the creation of the machine. A machine (a powered machine to be more precise) is a tool that substitutes part of or all of the element of human physical effort, requiring only the control of its functions. Machines became widespread with the industrial revolution, though windmills, a type of machine, are much older. Examples of this include cars, trains, computers, and lights. Machines allow humans to tremendously exceed the limitations of their bodies. Putting a machine on the farm, a tractor, increased food productivity at least tenfold over the technology of the plow and the horse. The third, and final stage of technological evolution is the automation. The automation is a machine that removes the element of human control with an automatic algorithm. Examples of machines that exhibit this characteristic are digital watches, automatic telephone switches, pacemakers, and computer programs.

What are the differences between a ukulele and guitar?

While a ukulele looks like a smaller version of a guitar, there are some notable differences. A guitar has 6 strings and a ukulele has 4 strings. Because of this, the sound range of a ukulele is narrower than a guitar. Looking from the front of a guitar, the thickness of the strings increases gradually from right to left. For a ukulele, the two outmost strings are the thinnest and the second left string is the thickest. A ukulele is much smaller in the size, making it one of the most popular beginner instrument for kids.

Tell me whether each person is a basketball or hockey player: Lebron James, Steven Stamkos, Victor Hedman, Tyler Johnson, Kobe Bryant, Carmelo Anthony

Lebron James: Basketball Player Steven Stamkos: Hockey Player Victor Hedman: Hockey Player Tyler Johnson: Hockey Player Kobe Bryant: Basketball Player Carmelo Anthony: Basketball Player

What does a vegan lifestyle mean to you?

A vegan lifestyle means that you do not only not ingest any animal products, but you also do not use anything that results from utilizing non-ingestible animal products or animal labor, such as horseback riding, wearing leather, and wearing fur.

The euro area, commonly called eurozone (EZ), is a currency union of 20 member states of the European Union (EU) that have adopted the euro (€) as their primary currency and sole legal tender, and have thus fully implemented EMU policies.

How many countries in Europe do have the Euro as monetary currency?

20

Actual Causes are those events which explain an output in the most general sense of explanation. There is a lively literature on how to define actual causation using causal models, but for the reasons outlined above, this literature has yet to find its way into the XAI literature. I here aim to set this straight by explicitly connecting actual causation to the notions of explanation that we have come across. A concise counterfactual explanation is good when you can get it, but often you cannot get it, and thus we need a weaker notion of explanation that is more generally applicable. 7 (The causal counterpart of this message is what initiated the formal causation literature some fifty years ago.) It is clear from the definitions that sufficient explanations are weaker than counterfactual explanations. But sufficient explanations ignore the counterfactual aspect entirely, which means they are of little value for action-guidance in the presence of an already existing observation. Therefore I define actual causes as parts of explanations that sit in between counterfactual and sufficient explanations: they are parts of good sufficient explanations such that there exist counterfactual values which would not have made the explanation better. This is weaker than demanding that the counterfactual values are part of a sufficient explanation of a different output, as we do for counterfactual explanations. Informally, X = x rather than X = x is an actual cause of Y = y if X = x is part of a good sufficient explanation for Y = y that could have not been made better by setting X to x . First I make precise how changing the values of some variables can turn a sufficient explanation into a better one. Definition 18 If ((X = x, W = w), N ) is a sufficient explanation of Y = y, we say that X = x can replace X = x if there exists a dominating explanation that includes (X = x , W = w). The following result ensures that the focus on W (instead of its subsets) in Definition 18 is without loss of generality. Proposition 19 If ((X = x, W = w), N ) is a sufficient explanation of Y = y and there exists a dominating explanation ((X = x , A = a), B) for some values x and a ⊆ w, then X = x can replace X = x. Definition 20 X = x rather than X = x is an actual cause of Y = y in (M, u) if it is part of a good sufficient explanation of Y = y in which X = x cannot be replaced by X = x . If ((X = x, W = w), N ) is the relevant good explanation, we say that X = x rather than X = x is an actual cause of Y = y relative to (W = w, N ). 6. Obviously this holds only if V \ (X ∪ {Y }) consists of at least two elements. 7. Obviously you can always find some counterfactual explanation of an output, for changing all of the variables will always allow you to get a different output. But an explanation that involves too many variables is of little use. Except for a minor technical difference regarding the implementation of minimality, Definition 20 is equivalent to the definition of causation I developed in previous work. Although I have here arrived at the same notion, I did so along a very different path, for in the previous work I did not draw any connection to explanations nor to action-guidance, but instead argued for the definition by contrasting it to other proposals for defining causation (including Definition 13). Contrary to counterfactual explanations, actual causes do not guide you towards actions that, under the same conditions, would ensure the output to be different. But they do guide you towards actions that would not ensure the actual output under the same conditions as the actual action. For example, imagine a (very fortunate) applicant for whom X 1 = 250, 000, X 3 = 50, 000, and thus X 2 = 125, 000. Obviously their application is successful, and their income by itself offers a good explanation of this fact: X 1 = 250, 000 is a good direct sufficient explanation of Y = 1. Also, their income does not counterfactually explain the output, for the application would have been approved regardless of income. Yet we do have that X 1 = 250, 000 rather than X 1 = 200, 000 is an actual cause of the output, because we cannot replace X 1 = 250, 000 by X 1 = 200, 000 in our sufficient explanation (i.e., X 1 = 200, 000 is not a sufficient explanation of Y = 1). This is helpful for example if the applicant is considering changing jobs and would like to know whether they can use up their savings and still get their application approved. One might wonder whether we need a separate definition of actual causation, as opposed to simply considering all parts of good sufficient explanations to be causes. The distinction between these two options lies in the existence of alternative actions that would make the actual explanation worse, and it are precisely those actions which make actual causes good guides towards action. Consider for example a situation in which a short-circuit starts a fire (F = 1) in an office building. The flames set off the sprinklers (S = 1), and those put out the flames, preventing the building from burning down (B = 0) -where all variables are binary. Matching equations for this story are: B = F ∧ ¬S, S = F . In this scenario, the fire offers a good sufficient explanation of the building not burning down relative to the sprinklers functioning as they should. But obviously it would be unwise to conclude from this that we should start fires in order to prevent buildings from burning down! This point can be brought out by noting that the fire is not an actual cause of the building not burning down, because there not being a fire would have offered a better sufficient explanation of this outcome, as it does not rely on the sprinklers functioning properly. (Concretely: F = 0 can replace F = 1 in the good sufficient explanation (F = 1, S) of B = 0.) Since sprinklers can malfunction, or can be made to malfunction by a malicious actor, the best action is to not set fires in a building, in accordance with actual causation. The following result specifies the claim that actual causes sit in between counterfactual and sufficient explanations: counterfactual explanations always contain actual causes (and obviously not vice versa). Theorem 21 If X 1 = x 1 rather than X 1 = x 1 is a counterfactual explanation of Y = y in (M, u) (relative to some (W = w, N )) then for some X 2 ⊆ X 1 , X 2 = x 2 rather than X 2 = x 2 is an actual cause of Y = y in (M, u) (where x 2 and x 2 are the relevant restrictions to X 2 ). An obvious strengthening of actual causation is to replace the existential quantifier over counterfactual values with a universal one, so that the actual values are the optimal values in terms of explanations. Definition 22 X = x is an optimal cause of Y = y in (M, u) if X = x is part of a good sufficient explanation of Y = y in which X = x cannot be replaced. (I.e., there do not exist values x so that it could be replaced by those.) Finally, replacing strong sufficiency with direct sufficiency offers a notion of direct causation . We can now present a final result regarding the limitation of Independence: the different notions of causation also reduce to each other. Theorem 25 If a causal model M satisfies Independence then the following statements are all equivalent: • there exist values x so that X = x rather than X = x is an actual cause of Y = y in (M, u). • X = x is part of a good sufficient explanation of Y = y in (M, u). Theorem 25 If a causal model M satisfies Independence then the following statements are all equivalent: • X = x is a direct cause of Y = y in (M,u). • there exist values x 0 so that X = x rather than X = x 0 is an actual cause of Y = y in (M,u). • X = x is part of a good sufficient explanation of Y = y in (M,u).

What does "causes" mean in Definition 25? I suppose this refers to actual causation, right?

Yes. Again being sloppy, as Definition 19 does not use that term… I will simply write actual cause instead.

What is the right time to do workouts or yoga?

The physical activities like Workouts and Yoga are best performed when one is light on stomach - hence early morning or evenings are usually the most appropriate time in general. However, the most important thing is, one should find time everyday to do some kind of physical exercise to maintain a healthy body.

Determine if the following states are East or West of the Mississippi River: Michigan, Georgia, Virginia, Oregon, Colorado, New Jersey, Nebraska, Texas.

Michigan, Georgia, Virginia, and New Jersey are East of the Mississippi River. Texas, Nebraska, Oregon, and Colorado are West of the Mississippi.

What makes a good parent?

A good parent is someone who puts the needs and goals of their kids before their own. A good parent understands that raising great kids to be great adults is one of the biggest accomplishments someone can have. A good parent excels at teaching, nurturing, and balancing discipline with rewards to help their kids make good decisions.

Franz Kafka (3 July 1883 – 3 June 1924) was a German-speaking Bohemian novelist and short-story writer based in Prague, who is widely regarded as one of the major figures of 20th-century literature. His work fuses elements of realism and the fantastic. It typically features isolated protagonists facing bizarre or surrealistic predicaments and incomprehensible socio-bureaucratic powers. It has been interpreted as exploring themes of alienation, existential anxiety, guilt, and absurdity. His best known works include the short story "The Metamorphosis" and novels The Trial and The Castle. The term Kafkaesque has entered English to describe absurd situations, like those depicted in his writing. Kafka was born into a middle-class German-speaking Czech Jewish family in Prague, the capital of the Kingdom of Bohemia, then part of the Austro-Hungarian Empire (today the capital of the Czech Republic). He trained as a lawyer, and after completing his legal education was employed full-time by an insurance company, forcing him to relegate writing to his spare time. Over the course of his life, Kafka wrote hundreds of letters to family and close friends, including his father, with whom he had a strained and formal relationship. He became engaged to several women but never married. He died in obscurity in 1924 at the age of 40 from tuberculosis. Kafka was a prolific writer, spending most of his free time writing, often late in the night. He burned an estimated 90 per cent of his total work due to his persistent struggles with self-doubt. Much of the remaining 10 per cent is lost or otherwise unpublished. Few of Kafka's works were published during his lifetime: the story collections Contemplation and A Country Doctor, and individual stories (such as "The Metamorphosis") were published in literary magazines but received little public attention. In his will, Kafka instructed his close friend and literary executor Max Brod to destroy his unfinished works, including his novels The Trial, The Castle, and Amerika, but Brod ignored these instructions, and had much of his work published. Kafka's work became famous in German-speaking countries after World War II, influencing their literature, and it spread elsewhere in the world in the 1960s. It has since influenced a range of writers, critics, artists, and philosophers.

Why are there so few works of Kafka left even though he was a prolific writer?

Though Kafka was a prolific writer he struggled with low self-esteem and destroyed 90% of his work. Rest of his works were lost or were not published. In his will Kafka instructed that his unfinished novels be destroyed, luckily those instructions were ignored and we are able to read his novels like The Trial and The Castle.

Imagine that one day, a universally acknowledged property of time is bended. What is that property? Write about the new world under that setting.

Rather than being linear, time is a loop in this new world. People moving forward to the direction of "future" are only making ways to their past. This is a good news for those with a joyful past, but not so much for those with a rather dark history. People who seek to avoid their history are now ruminating over the causes of their misery, while those who seek to relive their past are doing the same thing, only to amplify those factors behind their happiness. Yet, the happiness of some is built on top of the exploitation and manipulation of others, and it is under this setting that a war over the past commences.

What is the circumference of the earth?

40,075km or 24,855 miles depending on which metric system you use.

What was the cause of Michael Jackson's death?

On August 28, 2009, the Los Angeles County coroner classified Jackson's death as a homicide, determining that Jackson died from acute propofol intoxication, exacerbated by the anxiolytic lorazepam, and less significantly midazolam, diazepam, lidocaine, and ephedrine.

What is a Downcountry Mountain Bike?

Downcountry mountain bikes first came onto the scene around 2020 as an intersection between hard tail XC bikes and longer travel downhill/enduro bikes. They are usually full suspension with front and rear shocks. However, shorter travel than an enduro bike but much more efficient at climbing. They appeal to the rider who likes to get out and explore on their bike whilst also enjoying trail riding. Expect to see suspension travel in the range 120-130mm.

PDTB-style discourse relations, mostly defined between two adjacent text spans (i.e., discourse units, either clauses or sentences), specify how two discourse units are logically connected (e.g., causal, contrast). Recognizing discourse relations is one crucial step in discourse analysis and can be beneficial for many downstream NLP applications such as information extraction, machine translation and natural language generation. Commonly, explicit discourse relations were distinguished from implicit ones, depending on whether a discourse connective (e.g., “because” and “after”) appears between two discourse units BIBREF0 . While explicit discourse relation detection can be framed as a discourse connective disambiguation problem BIBREF1 , BIBREF2 and has achieved reasonable performance (F1 score $>$ 90%), implicit discourse relations have no discourse connective and are especially difficult to identify BIBREF3 , BIBREF2 , BIBREF4 . To fill the gap, implicit discourse relation prediction has drawn significant research interest recently and progress has been made BIBREF5 , BIBREF6 by modeling compositional meanings of two discourse units and exploiting word interactions between discourse units using neural tensor networks or attention mechanisms in neural nets. However, most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between. To further improve implicit discourse relation prediction, we aim to improve discourse unit representations by positioning a discourse unit (DU) in its wider context of a paragraph. The key observation is that semantic meaning of a DU can not be interpreted independently from the rest of the paragraph that contains it, or independently from the overall paragraph-level discourse structure that involve the DU. Considering the following paragraph with four discourse relations, one relation between each two adjacent DUs: (1): [The Butler, Wis., manufacturer went public at $15.75 a share in August 1987,] $_{DU1}$ and (Explicit-Expansion) [Mr. Sim's goal then was a $29 per-share price by 1992.] $_{DU2}$ (Implicit-Expansion) [Strong earnings growth helped achieve that price far ahead of schedule, in August 1988.] $_{DU3}$ (Implicit-Comparison) [The stock has since softened, trading around $25 a share last week and closing yesterday at $23 in national over-the-counter trading.] $_{DU4}$ But (Explicit-Comparison) [Mr. Sim has set a fresh target of $50 a share by the end of reaching that goal.] $_{DU5}$ Clearly, each DU is an integral part of the paragraph and not independent from other units. First, predicting a discourse relation may require understanding wider paragraph-level contexts beyond two relevant DUs and the overall discourse structure of a paragraph. For example, the implicit “Comparison” discourse relation between DU3 and DU4 is difficult to identify without the background information (the history of per-share price) introduced in DU1 and DU2. Second, a DU may be involved in multiple discourse relations (e.g., DU4 is connected with both DU3 and DU5 with a “Comparison” relation), therefore the pragmatic meaning representation of a DU should reflect all the discourse relations the unit was involved in. Third, implicit discourse relation prediction should benefit from modeling discourse relation continuity and patterns in a paragraph that involve easy-to-identify explicit discourse relations (e.g., “Implicit-Comparison” relation is followed by “Explicit-Comparison” in the above example). Following these observations, we construct a neural net model to process a paragraph each time and jointly build meaning representations for all DUs in the paragraph. The learned DU representations are used to predict a sequence of discourse relations in the paragraph, including both implicit and explicit relations. Although explicit relations are not our focus, predicting an explicit relation will help to reveal the pragmatic roles of its two DUs and reconstruct their representations, which will facilitate predicting neighboring implicit discourse relations that involve one of the DUs. In addition, we introduce two novel designs to further improve discourse relation classification performance of our paragraph-level neural net model. First, previous work has indicated that recognizing explicit and implicit discourse relations requires different strategies, we therefore untie parameters in the discourse relation prediction layer of the neural networks and train two separate classifiers for predicting explicit and implicit discourse relations respectively. This unique design has improved both implicit and explicit discourse relation identification performance. Second, we add a CRF layer on top of the discourse relation prediction layer to fine-tune a sequence of predicted discourse relations by modeling discourse relation continuity and patterns in a paragraph. Experimental results show that the intuitive paragraph-level discourse relation prediction model achieves improved performance on PDTB for both implicit discourse relation classification and explicit discourse relation classification. Although explicit relations are not our focus, predicting an explicit relation will help to reveal the pragmatic roles of its two DUs and reconstruct their representations, which will facilitate predicting neighboring implicit discourse relations that involve one of the DUs.

Why does the team still predict explicit relations though they are not research focus?

Because predicting an explicit relation will help to reveal the pragmatic roles of its two DUs and reconstruct their representations, which will facilitate predicting neighboring implicit discourse relations that involve one of the DUs.

Who won the 2022 Masters golf tournament?

Scotty Scheffler

1601.04012

There have been several workshops on biomedical natural language processing. We focus on the BioNLP Shared Tasks in recent years that had competitions on event extraction. There have been three BioNLP Shared Task competitions so far: 2009, 2011, and 2013. The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells. There was a second BioNLP Shared Task competition organized in 2011 to measure the advances in approaches and associated results BIBREF197 . The third BioNLP ST was held in 2013. We discuss some notable systems from BioNLP ST 2011 and 2013. The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells.

Which datasets are used in this work?

The answers are shown as follows: * GENIA corpus

Q: Products online claim to prevent or treat COVID-19. Where can I report websites selling products with fraudulent claims?

A: The FDA advises consumers to be beware of websites and stores selling products that claim to prevent, treat, or cure COVID-19. If you have a question about a product sold online that claims to prevent, treat, or cure COVID-19, talk to your health care provider or doctor. Read this Consumer Update to learn how to protect yourself and your family from coronavirus fraud. Please report websites selling products with fraudulent claims about treatment or prevention of COVID-19. If you have experienced a bad reaction to a product sold with COVID-19 claims, report it to the FDA’s MedWatch Adverse Event Reporting program: Complete and submit the report online; or Download and complete the form, then submit it via fax at 1-800-FDA-0178. Include as much information as you can about the product that caused the reaction, including the product name, the manufacturer, and the lot number (if available).

What are some of the pros and cons of owning a pet?

There are many pros and cons of pet ownership. On the positive side interacting with your pet can provide a great way of relaxing and reducing stress in your life. If your pet is a dog it can be a great way to encourage you to get exercise. If you have a young child it can be a way of teaching them responsibility. On the down side, owning a per means you are obligated to feed, clean up after and otherwise take care of them - responsibilities you will have no matter what else going on in your life.

Tell me if this a universal blood group: A+, A-, B+, B-, O+, O-, AB+, AB-

A+ = No A- = No B+ = No B- = No O+ = No O- = Yes AB+= No AB-= No

In recent years, the micro-blogging site Twitter has become a major social media platform with hundreds of millions of users. The short (140 character limit), noisy and idiosyncratic nature of tweets make standard information retrieval and data mining methods ill-suited to Twitter. Consequently, there has been an ever growing body of IR and data mining literature focusing on Twitter. However, most of these works employ extensive feature engineering to create task-specific, hand-crafted features. This is time consuming and inefficient as new features need to be engineered for every task. In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task. Tweet2Vec removes the need for expansive feature engineering and can be used to train any standard off-the-shelf classifier (e.g., logistic regression, svm, etc). Tweet2Vec uses a CNN-LSTM encoder-decoder model that operates at the character level to learn and generate vector representation of tweets. Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics BIBREF0 , BIBREF1 ). There has been several works on generating embeddings for words, most famously Word2Vec by Mikolov et al. BIBREF2 ). There has also been a number of different works that use encoder-decoder models based on long short-term memory (LSTM) BIBREF3 , and gated recurrent neural networks (GRU) BIBREF4 . These methods have been used mostly in the context of machine translation. The encoder maps the sentence from the source language to a vector representation, while the decoder conditions on this encoded vector for translating it to the target language. Perhaps the work most related to ours is the work of Le and Mikolov le2014distributed, where they extended the Word2Vec model to generate representations for sentences (called ParagraphVec). However, these models all function at the word level, making them ill-suited to the extremely noisy and idiosyncratic nature of tweets. Our character-level model, on the other hand, can better deal with the noise and idiosyncrasies in tweets. We plan to make our model and the data used to train it publicly available to be used by other researchers that work with tweets. Tweet2Vec uses a CNN-LSTM encoderdecoder model that operates at the character level to learn and generate vector representation of tweets.

What kind of model does Tweet2Vec use to learn and generate vector representation of tweets?

A CNN-LSTM encoder-decoder model.

1703.05260

We used the WebAnno annotation tool BIBREF2 for our project. The stories from each scenario were distributed among four different annotators. In a calibration phase, annotators were presented with some sample texts for test annotations; the results were discussed with the authors. Throughout the whole annotation phase, annotators could discuss any emerging issues with the authors. All annotations were done by undergraduate students of computational linguistics. The annotation was rather time-consuming due to the complexity of the task, and thus we decided for single annotation mode. To assess annotation quality, a small sample of texts was annotated by all four annotators and their inter-annotator agreement was measured (see Section "Inter-Annotator Agreement" ). It was found to be sufficiently high. The stories from each scenario were distributed among four different annotators.

How many subjects have been used to create the annotations?

The answers are shown as follows: * four different annotators

Lemuel Tucker (May 26, 1938 – March 2, 1991) was an American journalist. Born in Saginaw, Michigan, Tucker graduated from Central Michigan University in 1960. Tucker was one of the first African Americans to work as a television network reporter. He began his career at NBC in 1965 as a general assignment news correspondent, and served as news director at WOR-TV in New York City from 1970 to 1971. He moved to ABC in 1972 and to CBS in 1977, where he worked until his death. He was winner of two Emmy Awards, and made the master list of Nixon political opponents for his reporting. Although Lem Tucker's life was brief, his career accomplishments were memorable. Tucker was the first African American elected to the position of student body president at CMU. That election took place when less than one percent of the student body was black. Previous to his election, only one other African American had been elected to a similar position in the United States. While at Central, Tucker was considered one of the university's top forensic students. He served as a chapter president of Pi Kappa Delta, a national speech honorary fraternity. He won his first Emmy in 1979 for his coverage of black America and his second in 1983 for a series about hunger in America. While working for the networks, he covered numerous major news events, including the Vietnam War, the Kent State University shootings and the Iran hostage crisis. He covered social unrest and riots in Newark, Miami and Harlem. He also reported from the Woodstock Festival. In 1981, he was covering President Ronald Reagan's speech at the Washington Hilton Hotel and witnessed the shooting of the president. He was broadcasting live reports on CBS Radio within minutes of the incident. He was applauded by his colleagues and others in the media for his continuous and thorough coverage, which he provided well into the next morning. During the course of his career, Tucker returned numerous times to his alma mater to speak to students about politics, share reflections on college life and offer insight regarding work as a national network correspondent. Tucker had a keen sense of humor. In an address given to the CMU community in the early 1980s, he told his audience he was amused to have been asked back to speak at the university, given that during his days as student body president, he "quibbled a lot" with the administration. He said, "How rare for me to be given a microphone and be invited to speak at CMU. Now I am on stage in front of administration and applauded." Tucker has one daughter, Linn Carol Tucker. His death in 1991 was due to liver failure.

Who was Lem Tucker?

Lemuel Tucker was an American journalist. He was born in Saginaw, Michigan, Tucker graduated from Central Michigan University in 1960. Tucker was one of the first African Americans to work as a television network reporter. He won his first Emmy in 1979 for his coverage of black America and his second in 1983 for a series about hunger in America. His death in 1991 was due to liver failure.

Order these cars as either 'fast', 'slow' or 'dangerously fast'. A Doge Viper, Toyota 4Runner, Lamborghini Aventador, Chrysler Pacifica, Go-Kart, Lime Scooter, Porsche 911, Corvette

fast: Lamborghini Aventador, Porsche 911, Corvette slow: Toyota 4Runner, Chrysler Pacifica, Go-Kart, Lime Scooter dangerously fast: Dodge Viper

Yellowstone National Park is an American national park located in the western United States, largely in the northwest corner of Wyoming and extending into Montana and Idaho. It was established by the 42nd U.S. Congress with the Yellowstone National Park Protection Act and signed into law by President Ulysses S. Grant on March 1, 1872. Yellowstone was the first national park in the U.S. and is also widely held to be the first national park in the world. The park is known for its wildlife and its many geothermal features, especially the Old Faithful geyser, one of its most popular. While it represents many types of biomes, the subalpine forest is the most abundant. It is part of the South Central Rockies forests ecoregion. While Native Americans have lived in the Yellowstone region for at least 11,000 years, aside from visits by mountain men during the early-to-mid-19th century, organized exploration did not begin until the late 1860s. Management and control of the park originally fell under the jurisdiction of the U.S. Department of the Interior, the first Secretary of the Interior to supervise the park being Columbus Delano. However, the U.S. Army was eventually commissioned to oversee the management of Yellowstone for 30 years between 1886 and 1916. In 1917, administration of the park was transferred to the National Park Service, which had been created the previous year. Hundreds of structures have been built and are protected for their architectural and historical significance, and researchers have examined more than a thousand archaeological sites. Yellowstone National Park spans an area of 3,468.4 sq mi (8,983 km2), comprising lakes, canyons, rivers, and mountain ranges. Yellowstone Lake is one of the largest high-elevation lakes in North America and is centered over the Yellowstone Caldera, the largest super volcano on the continent. The caldera is considered a dormant volcano. It has erupted with tremendous force several times in the last two million years. Well over half of the world's geysers and hydrothermal features are in Yellowstone, fueled by this ongoing volcanism. Lava flows and rocks from volcanic eruptions cover most of the land area of Yellowstone. The park is the centerpiece of the Greater Yellowstone Ecosystem, the largest remaining nearly-intact ecosystem in the Earth's northern temperate zone. In 1978, Yellowstone was named a UNESCO World Heritage Site. Hundreds of species of mammals, birds, fish, reptiles, and amphibians have been documented, including several that are either endangered or threatened. The vast forests and grasslands also include unique species of plants. Yellowstone Park is the largest, and most famous megafauna location in the contiguous United States. Grizzly bears, cougars, wolves, and free-ranging herds of bison and elk live in this park. The Yellowstone Park bison herd is the oldest and largest public bison herd in the United States. Forest fires occur in the park each year; in the large forest fires of 1988, nearly one-third of the park was burnt. Yellowstone has numerous recreational opportunities, including hiking, camping, boating, fishing, and sightseeing. Paved roads provide close access to the major geothermal areas as well as some of the lakes and waterfalls. During the winter, visitors often access the park by way of guided tours that use either snow coaches or snowmobiles.

Using examples taken from the text give me a rundown of the primary environments found in Yellowstone National Park.

Generally considered to be the first national park in the world, Yellowstone National Park is significant in size, covering parts of Wyoming, Montana, and Idaho, and acts as the largest remaining nearly-intact ecosystems. The park is able to support many types of wildlife including Grizzly bears, cougars, wolves, mountain goats, free-ranging herds of bison and elk, as well as many types of birds and fish, because of the diversity of habitats. Forests and grasslands dominate some areas of the park, while others offer the completely different experience of mountain ranges, lakes, and rolling prairies. Some of the park's most unique features are the geysers and hydrothermal features. In fact, Yellowstone is home to over half of the world's features like these. This is due to the entire park being centered over the Yellowstone Caldera, the largest super volcano on the continent.

The Last of Us is an action-adventure game played from a third-person perspective. The player traverses post-apocalyptic environments such as towns, buildings, forests, and sewers to advance the story. The player can use firearms, improvised weapons, hand-to-hand combat, and stealth to defend against hostile humans and cannibalistic creatures infected by a mutated strain of the Cordyceps fungus. For most of the game, the player takes control of Joel, a man tasked with escorting a young girl, Ellie, across the United States. The player also controls Ellie throughout the game's winter segment and briefly controls Joel's daughter, Sarah, in the opening sequence.

Locate what the player should defend against in The Last of Us using the sample as a reference, extract them, and place them in a numbered list.

1. Hostile humans; 2. Cannibalistic creatures infected by a mutated strain of the Cordyceps fungus.

What is the best day for cold calling?

Data suggests that there is no difference in which day of the week you cold call a prospect.

2002.02492

Table TABREF44 shows that consistent nucleus and top-$k$ sampling (§SECREF28) resulted in only terminating sequences, except for a few cases that we attribute to the finite limit $L$ used to measure the non-termination ratio. The example continuations in Table TABREF46 show that the sampling tends to preserve language modeling quality on prefixes that led to termination with the baseline (first row). On prefixes that led to non-termination with the baseline (second & third rows), the quality tends to improve since the continuation now terminates. Since the model's non-$\left<\text{eos}\right>$ token probabilities at each step are only modified by a multiplicative constant, the sampling process can still enter a repetitive cycle (e.g. when the constant is close to 1), though the cycle is guaranteed to eventually terminate. For the example decoded sequences in Table TABREF46, generation quality is similar when both the self-terminating and baseline models terminate (first row). For prefixes that led to non-termination with the baseline, the self-terminating variant can yield a finite sequence with reasonable quality (second row). This suggests that some cases of degenerate repetition BIBREF5, BIBREF10 may be attributed to inconsistency. However, in other cases the self-terminating model enters a repetitive (but finite) cycle that resembles the baseline (third row), showing that consistency does not necessarily eliminate degenerate repetition. FLOAT SELECTED: Table 2. Non-termination ratio (rL (%)) of decoded sequences using consistent sampling methods. FLOAT SELECTED: Table 1. Non-termination ratio (rL (%)) of decoded sequences using ancestral sampling and incomplete decoding methods. Table TABREF44 shows that consistent nucleus and top-$k$ sampling (§SECREF28) resulted in only terminating sequences, except for a few cases that we attribute to the finite limit $L$ used to measure the non-termination ratio. The example continuations in Table TABREF46 show that the sampling tends to preserve language modeling quality on prefixes that led to termination with the baseline (first row). On prefixes that led to non-termination with the baseline (second & third rows), the quality tends to improve since the continuation now terminates. Since the model's non-$\left<\text{eos}\right>$ token probabilities at each step are only modified by a multiplicative constant, the sampling process can still enter a repetitive cycle (e.g. when the constant is close to 1), though the cycle is guaranteed to eventually terminate. This suggests that some cases of degenerate repetition BIBREF5, BIBREF10 may be attributed to inconsistency. However, in other cases the self-terminating model enters a repetitive (but finite) cycle that resembles the baseline (third row), showing that consistency does not necessarily eliminate degenerate repetition. FLOAT SELECTED: Table 2. Non-termination ratio (rL (%)) of decoded sequences using consistent sampling methods. FLOAT SELECTED: Table 1. Non-termination ratio (rL (%)) of decoded sequences using ancestral sampling and incomplete decoding methods.

How much improvement is gained from the proposed approaches?

It eliminates non-termination in some models fixing for some models up to 6% of non-termination ratio.

What are the things I should consider when purchasing an electric car?

Here are the things you should consider when purchasing an electric car: * the range - make sure it’s enough to cover your expected use. Range anxiety is real. * the efficiency - the more efficient it is, the less batteries, and therefore weight, you have to carry around * the charging network - will you have a reliable place to charge when you’re not able to charge at home? * the price - does it fit your budget? * the fun factor - will you enjoy the driving experience? * the buying experience - is the company known for an easy or painful buying experience?

We have used Word2Vec BIBREF16, GloVe BIBREF17 and FastText BIBREF18 word vectors of 300 dimensions. These vectors were trained on the corpus obtained from Nepali National Corpus. This pre-lemmatized corpus consists of 14 million words from books, web-texts and news papers. This corpus was mixed with the texts from the dataset before training CBOW and skip-gram version of word2vec using gensim libraryBIBREF24. This trained model consists of vectors for 72782 unique words. Light pre-processing was performed on the corpus before training it. For example, invalid characters or characters other than Devanagari were removed but punctuation and numbers were not removed. We set the window context at 10 and the rare words whose count is below 5 are dropped. These word embeddings were not frozen during the training session because fine-tuning word embedding help achieve better performance compared to frozen oneBIBREF20. We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table TABREF25. We have trained the word embedding in such a way that the sub-word size remains between 1 and 4. We particularly chose this size because in Nepali language a single letter can also be a word, for example e, t, C, r, l, n, u and a single character (grapheme) or sub-word can be formed after mixture of dependent vowel signs with consonant letters for example, C + O + = CO, here three different consonant letters form a single sub-word. The two-dimensional visualization of an example word npAl is shown in FIGREF14. Principal Component Analysis (PCA) technique was used to generate this visualization which helps use to analyze the nearest neighbor words of a given sample word. 84 and 104 nearest neighbors were observed using word2vec and fasttext embedding respectively on the same corpus. We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table 3.

Why are fast text embeddings used in the experiment?

Because its sub-word representation ability, which is very useful in highly inflectional language.

Alexander III of Macedon (Ancient Greek: Ἀλέξανδρος, romanized: Alexandros; 20/21 July 356 BC – 10/11 June 323 BC), commonly known as Alexander the Great, was a king of the ancient Greek kingdom of Macedon

Who was Alexander the Great?

Alexander III of Macedon, commonly known as Alexander the Great, was a king of the ancient Greek kingdom of Macedon. He succeeded his father Philip II to the throne in 336 BC at the age of 20, and spent most of his ruling years conducting a lengthy military campaign throughout Western Asia and Egypt. By the age of 30, he had created one of the largest empires in history, stretching from Greece to northwestern India. He was undefeated in battle and is widely considered to be one of history's greatest and most successful military commanders.

What are the three biggest cities by population in Spain?

The three biggest cities by population in Spain are Madrid, Barcelona, and Valencia.

What is caramelization?

Caramelization is a chemical process that happens to carbohydrates and sugars when they are heated to certain temperatures. At these temperatures new compounds are formed and browning happens. For example during caramelization, complex sugars degrade to simple sugars. Caramelization is an important process in cooking and shows ups in many common recipes

In 1941, a young Catholic priest, José María Arizmendiarrieta settled in Mondragón, a town with a population of 7,000 that had not yet recovered from the poverty, hunger, exile, and tension of the Spanish Civil War. In 1943, Arizmendiarrieta established a technical college which became a training ground for managers, engineers and skilled labour for local companies, and primarily for the co-operatives.

What did José María Arizmendiarrieta do?

José María Arizmendiarrieta was a Catholic Preist who settled in Mondragón, a town with a population of 7,000 that had not yet recovered from the poverty, hunger, exile, and tension of the Spanish Civil War.

What should be an men do in weekend

Saturday : Late Wakeup , have a branch and sleep. wake up and Booze. , sleep late night. Sunday - Wake up late , Have a handful lunch and sleep. and Sunday night - think about tomorrow is Monday

List the seven episode titles from the seventh season of "Game of Thrones" in broadcast order.

Dragonstone, Stormborn, The Queen's Justice, The Spoils of War, Eastwatch, Beyond the Wall, The Dragon and the Wolf

The ability for a machine to converse with human in a natural and coherent manner is one of challenging goals in AI and natural language understanding. One problem in chat-oriented human-machine dialog system is to reply a message within conversation contexts. Existing methods can be divided into two categories: retrieval-based methods BIBREF0 , BIBREF1 , BIBREF2 and generation based methods BIBREF3 . The former is to rank a list of candidates and select a good response. For the latter, encoder-decoder framework BIBREF3 or statistical translation method BIBREF4 are usually used to generate a response. It is not easy to main the fluency of the generated texts. Ubuntu dialogue corpus BIBREF5 is the public largest unstructured multi-turns dialogue corpus which consists of about one-million two-person conversations. The size of the corpus makes it attractive for the exploration of deep neural network modeling in the context of dialogue systems. Most deep neural networks use word embedding as the first layer. They either use fixed pre-trained word embedding vectors generated on a large text corpus or learn word embedding for the specific task. The former is lack of flexibility of domain adaptation. The latter requires a very large training corpus and significantly increases model training time. Word out-of-vocabulary issue occurs for both cases. Ubuntu dialogue corpus also contains many technical words (e.g. “ctrl+alt+f1", “/dev/sdb1"). The ubuntu corpus (V2) contains 823057 unique tokens whereas only 22% tokens occur in the pre-built GloVe word vectors. Although character-level representation which models sub-word morphologies can alleviate this problem to some extent BIBREF6 , BIBREF7 , BIBREF8 , character-level representation still have limitations: learn only morphological and orthographic similarity, other than semantic similarity (e.g. `car' and `bmw') and it cannot be applied to Asian languages (e.g. Chinese characters). In this paper, we generate word embedding vectors on the training corpus based on word2vec BIBREF9 . Then we propose an algorithm to combine the generated one with the pre-trained word embedding vectors on a large general text corpus based on vector concatenation. The new word representation maintains information learned from both general text corpus and task-domain. The nice property of the algorithm is simplicity and little extra computational cost will be added. It can address word out-of-vocabulary issue effectively. This method can be applied to most NLP deep neural network models and is language-independent. We integrated our methods with ESIM(baseline model) BIBREF10 . The experimental results have shown that the proposed method has significantly improved the performance of original ESIM model and obtained state-of-the-art results on both Ubuntu Dialogue Corpus and Douban Conversation Corpus BIBREF11 . On Ubuntu Dialogue Corpus (V2), the improvement to the previous best baseline model (single) on INLINEFORM0 is 3.8% and our ensemble model on INLINEFORM1 is 75.9%. On Douban Conversation Corpus, the improvement to the previous best model (single) on INLINEFORM2 is 3.6%. Our contributions in this paper are summarized below: The rest paper is organized as follows. In Section SECREF2 , we review the related work. In Section SECREF3 we provide an overview of ESIM (baseline) model and describe our methods to address out-of-vocabulary issues. In Section SECREF4 , we conduct extensive experiments to show the effectiveness of the proposed method. Finally we conclude with remarks and summarize our findings and outline future research directions. The experimental results have shown that the proposed method has significantly improved the performance of original ESIM model and obtained state-of-the-art results on both Ubuntu Dialogue Corpus and Douban Conversation Corpus.

On what corpus does the proposed method obtain state-of-the-art results?

Ubuntu Dialogue Corpus and Douban Conversation Corpus.

Who will win the women’s final four matchup between Iowa and South Carolina?

While South Carolina is undefeated on the season, Iowa enters the game with newly crowned player of the year Caitlin Clark. South Carolina is favored, but if Clark and the Hawkeyes can stay hot from behind the arc they could pull off the upset.

It is an advantage for the coxswain to be light as this requires less effort for the crew to propel the boat. In many competitive events there is a minimum weight, 55 kilograms (121 lb) under World Rowing rules, set for the coxswain to prevent unfair advantage. If a coxswain is under the minimum weight allowance (underweight), they may have to carry weights in the boat such as sandbags.

What is the minimum weight of a coswain?

The minimum weight of a coxswain if 55 kilograms (121 pounds) under World Rowing rules.

1904.10500

Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots) Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords) Using sequence-to-sequence networks, the approach here is jointly training annotated utterance-level intents and slots/intent keywords by adding / tokens to the beginning/end of each utterance, with utterance-level intent-type as labels of such tokens. Our approach is an extension of BIBREF2 , in which only an term is added with intent-type tags associated to this sentence final token, both for LSTM and Bi-LSTM cases. However, we experimented with adding both and terms as Bi-LSTMs will be used for seq2seq learning, and we observed that slightly better results can be achieved by doing so. The idea behind is that, since this is a seq2seq learning problem, at the last time step (i.e., prediction at ) the reverse pass in Bi-LSTM would be incomplete (refer to Fig. FIGREF24 (a) to observe the last Bi-LSTM cell). Therefore, adding token and leveraging the backward LSTM output at first time step (i.e., prediction at ) would potentially help for joint seq2seq learning. An overall network architecture can be found in Fig. FIGREF30 for our joint models. We will report the experimental results on two variations (with and without intent keywords) as follows: Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots) Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords)

What is shared in the joint model?

The answers are shown as follows: * jointly trained with slots

Lake Michigan is one of the five Great Lakes of North America. It is the second-largest of the Great Lakes by volume (1,180 cu mi (4,900 km3)) and the third-largest by surface area (22,404 sq mi (58,030 km2)), after Lake Superior and Lake Huron. To the east, its basin is conjoined with that of Lake Huron through the 3+1⁄2 miles (5.6 kilometres) wide, 295 feet (90 metres; 49 fathoms) deep, Straits of Mackinac, giving it the same surface elevation as its easterly counterpart; the two are technically a single lake.

Given this paragraph about Lake Michigan, tell me where Lake Michigan ranks in terms of size by volume.

Lake Michigan is ranked second by volume

Purdue Pete is a mascot of Purdue University. Despite his on-field presence at Purdue sporting events, Pete is only the athletic mascot of the university, and the official mascot of Purdue University is the Boilermaker Special.

Which university has Purdue Pete as their mascot?

The university that has Purdue Pete as their mascot is Purdue University.

Lei, gli amici e tutto il resto (She, friends and everything else), is the fourth studio album by Italian singer-songwriter Nek. It was released in 1996, as his first studio album with Warner Music Group. It was re-released on 1 March 1997, after the success of the single "Laura non c'è", which had not been included in the album. Since 1999 there have been reported sales of over 2 million copies all over the world.

What was the hit single on Nek's 1996 Album Lei, gli amici e tutto il resto?

Laura non c'è" was the hit single, but wasn't added until the album was released in 1997.

What was Mark Twain's actual name?

Samuel Langhorne Clemens was known the pen name Mark Twain, a nom de plume he coined to reflect his time as a river boat pilot. On the Mississippi in the 1800's it was common to measure river depth in terms of 6 foot increments demarcated on a rope by knots. Thus, when the river was of depth 12 feet, or two fathoms, the boatmen would call out, "Mark Twain" indicating the second of two knots was submerged.

Kamlaksha Rama Naik, better known as K R Naik (Hindi: क़ रा नायक़) is an Indian industrial engineer. He founded D-Link Ltd. (India) in 1993. He has been in the IT Industry for 50 years and has played a key role in creating the IT networking market and the surrounding channel ecosystem in India. He pioneered several new businesses and distribution models as early as 1990, when IT was a nascent industry and the concept of an IT distribution channel was just conceived. Naik was born in Karwar, Karnataka on 19 November 1947. He completed his schooling in Karwar and later shifted to Mumbai. Naik is a mechanical engineer with a P.G. Diploma in Industrial Engineering and Licenciate in Plastic Engineering. He earned a Business Management degree from the Jamnalal Bajaj Institute in Mumbai

Where did Kamlaksha Rama Naik receive his Business Management degree?

The Jamnalal Bajaj Institute in Mumbai

Table TABREF6 shows the result of different models trained on either Chinese or English and tested on Chinese. In row (f), multi-BERT is fine-tuned on English but tested on Chinese, which achieves competitive performance compared with QANet trained on Chinese. We also find that multi-BERT trained on English has relatively lower EM compared with the model with comparable F1 scores. This shows that the model learned with zero-shot can roughly identify the answer spans in context but less accurate. In row (c), we fine-tuned a BERT model pre-trained on English monolingual corpus (English BERT) on Chinese RC training data directly by appending fastText-initialized Chinese word embeddings to the original word embeddings of English-BERT. Its F1 score is even lower than that of zero-shot transferring multi-BERT (rows (c) v.s. (e)). The result implies multi-BERT does acquire better cross-lingual capability through pre-training on multilingual corpus. Table TABREF8 shows the results of multi-BERT fine-tuned on different languages and then tested on English , Chinese and Korean. The top half of the table shows the results of training data without translation. It is not surprising that when the training and testing sets are in the same language, the best results are achieved, and multi-BERT shows transfer capability when training and testing sets are in different languages, especially between Chinese and Korean. In the lower half of Table TABREF8, the results are obtained by the translated training data. First, we found that when testing on English and Chinese, translation always degrades the performance (En v.s. En-XX, Zh v.s. Zh-XX). Even though we translate the training data into the same language as testing data, using the untranslated data still yield better results. For example, when testing on English, the F1 score of the model training on Chinese (Zh) is 53.8, while the F1 score is only 44.1 for the model training on Zh-En. This shows that translation degrades the quality of data. There are some exceptions when testing on Korean. Translating the English training data into Chinese, Japanese and Korean still improve the performance on Korean. We also found that when translated into the same language, the English training data is always better than the Chinese data (En-XX v.s. Zh-XX), with only one exception (En-Fr v.s. Zh-Fr when testing on KorQuAD). This may be because we have less Chinese training data than English. These results show that the quality and the size of dataset are much more important than whether the training and testing are in the same language or not. It is not surprising that when the training and testing sets are in the same language, the best results are achieved, and multi-BERT shows transfer capability when training and testing sets are in different languages, especially between Chinese and Korean.

Whether multi-BERT shows transfer capability in their experiment?

Yes. When training and testing sets are in different languages, especially between Chinese and Korean, the capability is shown.

What are some ways in which I could use old books?

- Donate them to a charity - donate them to a prison - Sell them online - Try origami with book pages - Hollow the books out and stick them together to create a storage unit - Cut the book covers and use it as bookmarks - Cut the book covers to create a collage - use the pages to create a wreath - sell them to a thrift store

Pinot Gris, Pinot Grigio (US: /ˈpiːnoʊ ˈɡriːdʒioʊ, -dʒoʊ/, UK: /ˈɡrɪdʒioʊ/) or Grauburgunder is a white wine grape variety of the species Vitis vinifera. Thought to be a mutant clone of the Pinot Noir variety, it normally has a grayish-blue fruit, accounting for its name, but the grapes can have a brownish pink to black and even white appearance. The word pinot could have been given to it because the grapes grow in small pinecone-shaped clusters. The wines produced from this grape also vary in color from a deep golden yellow to copper and even a light shade of pink, and it is one of the more popular grapes for skin-contact wine. Pinot Gris is grown around the globe, with the "spicy" full-bodied Alsatian and lighter-bodied, more acidic Italian styles being most widely recognized. The Alsatian style, often duplicated in New World wine regions such as Marlborough, Tasmania, South Australia, Washington, Oregon, and South Africa tend to have moderate to low acidity, higher alcohol levels and an almost "oily" texture that contributes to the full-bodied nature of the wine. The flavors can range from ripe tropical fruit notes of melon and mango to some botrytis-influenced flavors. In Italy, Pinot grigio grapes are often harvested early to retain the refreshing acidity and minimize some of the overt-fruitiness of the variety, creating a more neutral flavor profile. This style is often imitated in other Old World wine regions, such as Germany, where the grape is known as Ruländer, or more commonly, Grauburgunder.

What is Pinot Grigio?

Pinot Gris, Pinot Grigio (US: /ˈpiːnoʊ ˈɡriːdʒioʊ, -dʒoʊ/, UK: /ˈɡrɪdʒioʊ/) or Grauburgunder is a white wine grape variety of the species Vitis vinifera. Pinot Gris is grown around the globe, with the "spicy" full-bodied Alsatian and lighter-bodied, more acidic Italian styles being most widely recognized.

The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26. We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier. The naive Bayesian classifier is trained to predict the specific language label of a piece of text, but used to first classify text as belonging to either the Nguni family, the Sotho family, English, Afrikaans, Xitsonga or Tshivenda. The scikit-learn multinomial naive Bayes classifier is used for the implementation with an alpha smoothing value of 0.01 and hashed text features. The lexicon based classifier is then used to predict the specific language within a language group. For the South African languages this is done for the Nguni and Sotho groups. If the lexicon prediction of the specific language has high confidence then its result is used as the final label else the naive Bayesian classifier's specific language prediction is used as the final result. The lexicon is built over all the data and therefore includes the vocabulary from both the training and testing sets. The lexicon based classifier is designed to trade higher precision for lower recall. The proposed implementation is considered confident if the number of words from the winning language is at least one more than the number of words considered to be from the language scored in second place. The stacked classifier is tested against three public LID implementations BIBREF17, BIBREF23, BIBREF8. The LID implementation described in BIBREF17 is available on GitHub and is trained and tested according to a post on the fasttext blog. Character (5-6)-gram features with 16 dimensional vectors worked the best. The implementation discussed in BIBREF23 is available from https://github.com/tomkocmi/LanideNN. Following the instructions for an OSX pip install of an old r0.8 release of TensorFlow, the LanideNN code could be executed in Python 3.7.4. Settings were left at their defaults and a learning rate of 0.001 was used followed by a refinement with learning rate of 0.0001. Only one code modification was applied to return the results from a method that previously just printed to screen. The LID algorithm described in BIBREF8 is also available on GitHub. The stacked classifier is also tested against the results reported for four other algorithms BIBREF16, BIBREF26, BIBREF24, BIBREF15. All the comparisons are done using the NCHLT BIBREF7, DSL 2015 BIBREF19 and DSL 2017 BIBREF1 datasets discussed in Section SECREF2. All the comparisons are done using the NCHLT [8], DSL 2015 [20] and DSL 2017 [2] datasets discussed in Section 2.

On what datasets are the comparison done?

All the comparisons are done using the NCHLT, DSL 2015 and DSL 2017 datasets.

What are the best selling albums of all time? Give me the list with the name of the artist and the name of the album

Best selling albums of all time: - Michael Jackson: Thriller - Eagles: Their Greatest Hits (1971–1975) - Pink Floyd: The Dark Side of the Moon - Whitney Houston / various artists: The Bodyguard - Bee Gees / Various artists: Saturday Night Fever - Fleetwood Mac: Rumours - Eagles: Hotel California - Shania Twain: Come On Over - Meat Loaf: Bat Out of Hell - AC/DC: Back in Black

1810.05320

The existing co-occurrence methods do not suit our application scenario at all, since exact string matching is too strong a requirement and initial trial has shown its incompetency. In stead we implemented an improved version of their method based on TextRank as our baseline. In addition, we also tested multiple semantic matching algorithms for comparison with our chosen method. TextRank: TextRank is a graph-based ranking model for text processing. BIBREF18 It is an unsupervised algorithm for keyword extraction. Since product attributes are usually the keywords in enquiries, we can compare these keywords with the category attributes and find the most important attributes. This method consists of three steps. The first step is to merge all enquiries under one category as an article. The second step is to extract the top 50 keywords for each category. The third step is to find the most important attributes by comparing top keywords with category attributes. Word2vec BIBREF19 : We use the word vector trained by BIBREF19 as the distributed representation of words. Then we get the enquiry sentence representation and category attribute representation. Finally we collect the statistics about the matched attributes of each category, and select the most frequent attributes under the same category. GloVe BIBREF20 : GloVe is a global log-bilinear regression model for the unsupervised learning of word representations, which utilizes the ratios of word-word co-occurrence probabilities. We use the GloVe method to train the distributed representation of words. And attribute selection procedure is the same as word2vec. The existing co-occurrence methods do not suit our application scenario at all, since exact string matching is too strong a requirement and initial trial has shown its incompetency. In stead we implemented an improved version of their method based on TextRank as our baseline. In addition, we also tested multiple semantic matching algorithms for comparison with our chosen method. TextRank: TextRank is a graph-based ranking model for text processing. BIBREF18 It is an unsupervised algorithm for keyword extraction. Since product attributes are usually the keywords in enquiries, we can compare these keywords with the category attributes and find the most important attributes. This method consists of three steps. The first step is to merge all enquiries under one category as an article. The second step is to extract the top 50 keywords for each category. The third step is to find the most important attributes by comparing top keywords with category attributes. Word2vec BIBREF19 : We use the word vector trained by BIBREF19 as the distributed representation of words. Then we get the enquiry sentence representation and category attribute representation. Finally we collect the statistics about the matched attributes of each category, and select the most frequent attributes under the same category. GloVe BIBREF20 : GloVe is a global log-bilinear regression model for the unsupervised learning of word representations, which utilizes the ratios of word-word co-occurrence probabilities. We use the GloVe method to train the distributed representation of words. And attribute selection procedure is the same as word2vec.

What are the traditional methods to identifying important attributes?

The answers are shown as follows: * TextRank * Word2vec BIBREF19 * GloVe BIBREF20

What are 3 popular dishes that can feed a large group of people?

Pizza is a popular dish that can be easily distributed. Hot dogs are also easy to make for a large group with the right equipment. Hamburgers are another common food when feeding a large crowd.

Argue that large language models are not conscious.

Large language models weren't designed to be conscious, and so it seems unlikely that consciousness would emerge as an unintended side effect, or emergent property, of these models.

How do people travel?

People travel from one place to another in various ways. The most common way is by walking, especially for short distances. Driving cars is a travel method for driving long distances such as to work or nearby cities. Bicycling is a popular travel method, especially for people that want exercise. Public transportation including buses and trains are used for traveling. Airplanes are used for long distances and international travel.

Traditional dried fruit such as raisins, figs, dates, apricots and apples have been a staple of Mediterranean diets for millennia. This is due partly to their early cultivation in the Middle Eastern region known as the Fertile Crescent, made up by parts of modern Iran, Iraq, southwest Turkey, Syria, Lebanon, Palestine, Israel, and northern Egypt. Drying or dehydration also happened to be the earliest form of food preservation: grapes, dates, and figs that fell from the tree or vine would dry in the hot sun. Early hunter-gatherers observed that these fallen fruit took on an edible form, and valued them for their stability as well as their concentrated sweetness. The earliest recorded mention of dried fruits can be found in Mesopotamian tablets dating to about 1500 BC, which contain what are probably the oldest known written recipes. These clay slabs, written in Akkadian, the daily language of Babylonia, were inscribed in cuneiform and tell of diets based on grains (barley, millet, wheat), vegetables and fruits such as dates, figs, apples, pomegranates, and grapes. These early civilizations used dates, date juice evaporated into syrup and raisins as sweeteners. They included dried fruits in their breads for which they had more than 300 recipes, from simple barley bread for the workers to very elaborate, spiced cakes with honey for the palaces and temples. The date palm was one of the first cultivated trees. It was domesticated in Mesopotamia more than 5,000 years ago. It grew abundantly in the Fertile Crescent and it was so productive (an average date palm produces 50 kg (100 lbs) of fruit a year for 60 years or more) that dates were the cheapest of staple foods. Because they were so valuable, they were well recorded in Assyrian and Babylonian monuments and temples. The villagers in Mesopotamia dried them and ate them as sweets. Whether fresh, soft-dried or hard-dried, they helped to give character to meat dishes and grain pies. They were valued by travelers for their energy and were recommended as stimulants against fatigue. Figs were also prized in early Mesopotamia, Palestine, Israel, and Egypt where their daily use was probably greater than or equal to that of dates. As well as appearing in wall paintings, many specimens have been found in Egyptian tombs as funerary offerings. In Greece and Crete, figs grew very readily and they were the staple of poor and rich alike, particularly in their dried form. Grape cultivation first began in Armenia and the eastern regions of the Mediterranean in the 4th century BC. Raisins were produced by drying grapes in the hot desert sun. Very quickly, viticulture and raisin production spread across northern Africa including Morocco and Tunisia. The Phoenicians and the Egyptians popularized the production of raisins, probably due to the perfect arid environment for sun drying. They put them in jars for storage and allotted them to the different temples by the thousands. They also added them to breads and various pastries, some made with honey, some with milk and eggs. From the Middle East, these fruits spread through Greece to Italy where they became a major part of the diet. Ancient Romans consumed raisins in spectacular quantities and at all levels of society, including them as a key part of their common meals, along with olives and fresh fruits. Raisined breads were common for breakfast and were consumed with their grains, beans, and cultured milks. Raisins were so valued that they transcended the food realm and became rewards for successful athletes, as well as premium barter currency. Having dried fruits was essential in ancient Rome as these instructions for housekeepers around 100 BC tell: "She must keep a supply of cooked food on hand for you and the servants. She must keep many hens and have plenty of eggs. She must have a large store of dried pears, sorbs, figs, raisins, sorbs in must, preserved pears and grapes and quinces. She must also keep preserved grapes in grape-pulp and in pots buried in the ground, as well as fresh Praenestine nuts kept in the same way, and Scantian quinces in jars, and other fruits that are usually preserved, as well as wild fruits. All these she must store away diligently every year." Figs were also extremely popular in Rome. Dried figs were added to bread and formed a major part of the winter food of country people. They were rubbed with spices such as cumin, anise and fennel seeds, or toasted sesame, wrapped in fig leaves and stored in jars. Today, major producing regions include Israel, Jerusalem, Gaza and many other Arabic countries. Dried figs are rich in vitamins, phosphorus and various other important minerals. Plums, apricots and peaches had their origins in Asia. They were domesticated in China in the 3rd millennium BC and spread to the Fertile Crescent where they were also very popular, fresh and dried alike. They arrived in Greece and Italy much later and were very expensive but valued in the preparation of gourmet dishes with port or stewed with honey and spices.

What are the earliest records of dried fruits in history?

The earliest recorded mention of dried fruits can be found in Mesopotamian tablets dating to about 1500 BC, which contain what are probably the oldest known written recipes. These clay slabs, written in Akkadian, the daily language of Babylonia, were inscribed in cuneiform and tell of diets based on grains (barley, millet, wheat), vegetables and fruits such as dates, figs, apples, pomegranates, and grapes.

Provide a dot point list of the last six artists that won a Grammy Award for Best Album. Include the title of the album and the year they won.

Here is a list of the last six artists that won a Grammy Award for Best Album. - Harry Styles won for the album 'Harry's House' in 2023 - Jon Batiste won for the album 'We Are' in 2022 - Taylor Swift won for 'Folklore' in 2021 - Billie Eilish won for 'When We All Fall Asleep, Where Do We Go?' in 2020 - Kacey Musgraves won for the album 'Golden Hour' in 2019 - Bruno Mars won for '24K Magic' in 2018

What is Herd Immunity?

It’s a form of indirect protection from infectious disease that occurs when a large %age of a population has become immune to an infection, thereby providing a measure of protection for individuals who are not immune

What is random access memory?

Random access memory (RAM) is a type of computer memory used to store data that can be accessed quickly. It is also known as dynamic random access memory (DRAM) and is used to store the instructions and data for a computer's operating system and applications. RAM is volatile memory, meaning it will lose its data if the power is turned off. It is also much faster than other types of memory such as hard disk drives and solid state drives.

The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators. More details about the data collection and the annotation, as well as statistics about the corpus can be found in BIBREF10, where an earlier version of the corpus is described, which includes 450 news articles. We further annotated 47 additional articles for the purpose of the shared task using the same protocol and the same annotators. The training, the development, and the test partitions of the corpus used for the shared task consist of 350, 61, and 86 articles and of 16,965, 2,235, and 3,526 sentences, respectively. Figure FIGREF15 shows an annotated example, which contains several propaganda techniques. For example, the fragment babies on line 1 is an instance of both Name_Calling and Labeling. Note that the fragment not looking as though Trump killed his grandma on line 4 is an instance of Exaggeration_or_Minimisation and it overlaps with the fragment killed his grandma, which is an instance of Loaded_Language. Table TABREF23 reports the total number of instances per technique and the percentage with respect to the total number of annotations, for the training and for the development sets. The training, the development, and the test partitions of the corpus used for the shared task consist of 350, 61, and 86 articles and of 16,965, 2,235, and 3,526 sentences, respectively.

How many articles and sentences were used for the shared task?

The training, the development, and the test partitions of the corpus used for the shared task consist of 350, 61, and 86 articles and of 16,965, 2,235, and 3,526 sentences, respectively.

What is pan genomics?

Pan genomics is a concept introduced in 2005 by Tettelin and Medini which eventually took root in bioinformatics. Pan genome is the complete gene repertoire of a particular taxonomic group: although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts- The Core genome: Set of genes common to all the genomes under study (These are often housekeeping genes vital for survival) and The Dispensable/Flexible Genome: Set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.

1910.08210

We model interactions between observations from the environment, goal, and document using layers. We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation. Figure FIGREF18 shows the model. We model interactions between observations from the environment, goal, and document using layers. We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.

How does propose model model that capture three-way interactions?

The answers are shown as follows: * We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.

A church, church building or church house is a building used for Christian worship services and other Christian religious activities. The earliest identified Christian church is a house church founded between 233 and 256. From the 11th through the 14th centuries, there was a wave of church construction in Western Europe. Sometimes, the word church is used by analogy for the buildings of other religions. Church is also used to describe the Christian religious community as a whole, or a body or an assembly of Christian believers around the world.

From the given paragraph, write some points on Church.

1. A church, church building, or church house is a structure used for Christian religious activities such as worship ceremonies. 2. A home church that was established between 233 and 256 is the earliest known Christian congregation. 3. In Western Europe, there was a wave of church building from the 11th through the 14th centuries. 4. The word "church" is occasionally used as an analogy for the structures of other religions. 5. The word "church" is also used to refer to the global body of Christian believers as well as the Christian religious community as a whole.

1806.03191

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. Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds.

What hypernymy tasks do they study?

The answers are shown as follows: * Detection * Direction * Graded Entailment

How many episodes in season one of Game of Thrones did Tim Van Patten direct?

Tim Van Patten directed the first two episodes of the first season of Game of Thrones: Winter is Coming and The Kingsroad.

Modern society is an information society bombarded from all sides by an increasing number of different pieces of information. The 21st century has brought us the rapid development of media, especially in the internet ecosystem. This change has caused the transfer of many areas of our lives to virtual reality. New forms of communication have been established. Their development has created the need for analysis of related data. Nowadays, unstructured information is available in digital form, but how can we analyse and summarise billions of newly created texts that appear daily on the internet? Natural language analysis techniques, statistics and machine learning have emerged as tools to help us. In recent years, particular attention has focused on sentiment analysis. This area is defined as the study of opinions expressed by people as well as attitudes and emotions about a particular topic, product, event, or person. Sentiment analysis determines the polarisation of the text. It answers the question as to whether a particular text is a positive, negative, or neutral one. Our goal is to build a comprehensive set of techniques for preparing and analysing texts containing opinions and generating user-friendly descriptive reports in natural language - Figure FIGREF1 . In this paper, we describe briefly the whole workflow and present a prototype implementation. Currently, existing solutions for sentiment annotation offer mostly analysis on the level of entire documents, and if you go deeper to the level of individual product features, they are only superficial and poorly prepared for the analysis of large volumes of data. This can especially be seen in scientific articles where the analysis is carried out on a few hundred reviews only. It is worth mentioning that this task is extremely problematic because of the huge diversity of languages and the difficulty of building a single solution that can cover all the languages used in the world. Natural language analysis often requires additional pre-processing steps, especially at the stage of preparing the data for analysis, and steps specific for each language. Large differences can be seen in the analysis of the Polish language (a highly inflected language) and English (a grammatically simpler one). We propose a solution that will cover several languages, however in this prototype implementation we focused on English texts only. In this paper, we present analysis and workflow inspired by the work of Joty, Carenini and Ng BIBREF0 . We experimented with several methods in order to validate aspect-based sentiment analysis approaches and in the next steps we want to customise our implementation for the Polish language. The paper presents in Section SECREF1 an introduction to sentiment analysis and its importance in business, then in Section SECREF2 - related work from rhetorical and sentiment analysis areas is presented. Section SECREF3 covers description of our method. Implementation and the dataset are described in Section SECREF4 . Section SECREF5 refers to the results. The last Section SECREF6 consists of conclusions and future work. Our goal is to build a comprehensive set of techniques for preparing and analysing texts containing opinions and generating user-friendly descriptive reports in natural language - Figure FIGREF1 .

What is the prototype implementation of a method used to do?

It is used to prepare and analyze texts containing opinions and generating user-friendly descriptive reports in natural language.

Several efficient algorithms for simple random sampling have been developed. A naive algorithm is the draw-by-draw algorithm where at each step we remove the item at that step from the set with equal probability and put the item in the sample. We continue until we have sample of desired size � k. The drawback of this method is that it requires random access in the set. The selection-rejection algorithm developed by Fan et al. in 1962 requires a single pass over data; however, it is a sequential algorithm and requires knowledge of total count of items � n, which is not available in streaming scenarios. A very simple random sort algorithm was proved by Sunter in 1977. The algorithm simply assigns a random number drawn from uniform distribution ( 0 , 1 ) (0,1) as a key to each item, then sorts all items using the key and selects the smallest � k items. J. Vitter in 1985 proposed reservoir sampling algorithms, which are widely used. This algorithm does not require knowledge of the size of the population � n in advance, and uses constant space. Random sampling can also be accelerated by sampling from the distribution of gaps between samples and skipping over the gaps.

What is disadvantage of naive algorithm of simple random sampling?

The drawback of this method is that it requires random access in the set.

When was the United States Marine Corps founded?

On November 10, 1775, the Second Continental Congress meeting in Philadelphia passed a resolution stating that "two Battalions of Marines be raised" for service as landing forces with the fleet. This resolution established the Continental Marines and marked the birth date of the United States Marine Corps.

Which Game of Thrones season three episodes were directed by Michelle MacLaren?

She directed episodes 7 and 8, "The Bear and the Maiden Fair" and "Second Sons"

Identify which car manufacturer is Chinese or American: BAIC, GMC

BAIC is Chinese, GMC is American

What is the main cause of snow?

Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they'll become heavy enough to fall to the ground. Snowflakes that descend through moist air that is slightly warmer than 0 °C will melt around the edges and stick together to produce big flakes.

Who was Cleopatra's first husband

Ptolemy Dionysus – her brother

Aydın İlter (1 July 1930 – 21 April 2022) was a Turkish general. He was General Commander of the Gendarmerie of Turkey (1993 – 1995) and previously served in the Special Warfare Department from 1963 to 1983, including as head of the Department from 1980 to 1983.

What are some facts about Aydın İlter?

Aydın İlter was born on July 1st, 1930. He as a Turkish General Commander of the Gendarmerie of Turkey from 1993 to 1995. Prior to serving as General Commander he served in the Special Warfare Department from 1963 to 1983. He was the head of that department from 1980 to 1983. Aydın İlter died on April 21st, 2022 at the age of 91.

Which Roman general was conspired against and assassinated that is quoted "Et tu, Brute?"?

Julius Cesar

Going Wild is a 1930 Warner Brothers pre-Code comedy film based on the 1910 play The Aviator by James Montgomery and directed by William A. Seiter. The film stars many musical stars along with Joe E. Brown, Frank McHugh and Johnny Arthur. A print is held in the Library of Congress collection. Plot Rollo Smith and his friend Jack Lane are down on their luck and have stowed away on a train, finding a place in the compartment of ace pilot and writer Robert Story. The conductor ejects Rollo and Jack from the train just where the famous writer is supposed to arrive, and Rollo is mistaken for Story. Peggy Freeman and May Bunch both vie for Rollo's attention, believing that he is a famous pilot. The girls receive free room and meals at the Palm Inn. Rollo, who has never flown, is invited to fly in an air race as Story against a real aviator, "Ace" Benton, with a chance to win a $25,000 wager. After he somehow lifts the plane off the ground, Rollo can barely control the aircraft. Ferguson, the real pilot whom Peggy had locked in a closet, appears, but Rollo continues to unwittingly perform an aerial show, forcing Ace to abandon the race. When Peggy accidentally pulls her parachute ring, Rollo joins her as they safely float to earth and he proposes.

What is the plot of the movie going wild

Going Wild is a 1930 Warner Brothers pre-Code comedy film based on the 1910 play The Aviator by James Montgomery and directed by William A. Seiter. The film stars many musical stars along with Joe E. Brown, Frank McHugh and Johnny Arthur. A print is held in the Library of Congress collection. Rollo Smith and his friend Jack Lane are down on their luck and have stowed away on a train, finding a place in the compartment of ace pilot and writer Robert Story. The conductor ejects Rollo and Jack from the train just where the famous writer is supposed to arrive, and Rollo is mistaken for Story. Peggy Freeman and May Bunch both vie for Rollo's attention, believing that he is a famous pilot. The girls receive free room and meals at the Palm Inn. Rollo, who has never flown, is invited to fly in an air race as Story against a real aviator, "Ace" Benton, with a chance to win a $25,000 wager. After he somehow lifts the plane off the ground, Rollo can barely control the aircraft. Ferguson, the real pilot whom Peggy had locked in a closet, appears, but Rollo continues to unwittingly perform an aerial show, forcing Ace to abandon the race. When Peggy accidentally pulls her parachute ring, Rollo joins her as they safely float to earth and he proposes.

State-of-the-art (SOTA) artificial neural networks (ANNs) achieve impressive results in a variety of machine intelligence tasks. However, they largely rely on mechanisms that diverge from the original inspiration from biological neural networks. As a result, only a small part of this prolific field also contributes to computational neuroscience. In fact, this biological implausibility is also an important issue for machine intelligence. For their impressive performance, ANNs trade off other desired properties, which are present in biological systems. For example, ANN training often demands very large and labelled datasets. When labels are unavailable, self-supervised learning schemes exist, where supervisory error signals generated by the network itself are exploited and backpropagated from the output towards the input to update the network's parameters. However, this global propagation of signals in deep networks introduces another limitation. Namely, it prevents the implementation of efficient distributed computing hardware that would be based on only local signals from neighbouring physical nodes in the network, and is in contrast to local synaptic plasticity rules that partly govern biological learning. Several pieces of work have been addressing parts of the biological implausibility and hardware-inefficiency of backpropagation in ANNs. such as the need for exactly symmetric forward and backward weights or the waiting time caused by the network's forward-backward pass between two training updates in a layer (weight transport and update-locking problems). Recently, an approximation to backpropagation that is mostly Hebbian, i.e. relies on mostly pre-and post-synaptic activity of each synapse, has been achieved by reducing the global error requirements to 1-bit information. Two schemes that further localize the signal that is required for a weight update are Equilibrium Propagation and Predictive Coding. Both methods approximate backpropagation through Hebbian-like learning, by delegating the global aspect of the computation, from a global error signal, to a global convergence of the network state to an equilibrium. This equilibrium is reached through several iterative steps of feed-forward and feed-back communication throughout the network, before the ultimate weight update by one training example. The biological plausibility and hardware-efficiency of this added iterative process of signal propagation are open questions that begin to be addressed. Moreover, learning through backpropagation, and presumably also its approximations, has another indication of biological implausibility, which also significantly limits ANN applicability. Namely, it produces networks that are confused by small adversarial perturbations of the input, which are imperceptible by humans. It has recently been proposed that a defence strategy of "deflection" of adversarial attacks may be the ultimate solution to that problem. Through this strategy, to cause confusion in the network's inferred class, the adversary is forced to generate such a changed input that really belongs to the distribution of a different input class. Intuitively, but also strictly by definition, this deflection is achieved if a human assigns to the perturbed input the same label that the network does. Deflection of adversarial attacks in ANNs has been demonstrated by an elaborate scheme that is based on detecting the attacks. However, the human ability to deflect adversarial perturbations likely does not rely on detecting them, but rather on effectively ignoring them, making the deflecting type of robustness an emergent property of biological computation rather than a defence mechanism. The biological principles that underlie this property of robustness are unclear, but it might emerge from the distinct algorithms that govern learning in the brain. Therefore, what is missing is a biologically plausible model that can learn from fewer data-points, without labels, through local plasticity, and without feedback from distant layers. This model could then be tested for emergent adversarial robustness. A good candidate category of biological networks and learning algorithms is that of competitive learning. Neurons that compete for their activation through lateral inhibition are a common connectivity pattern in the superficial layers of the cerebral cortex). This pattern is described as winner-take-all (WTA), because competition suppresses activity of weakly activated neurons, and emphasizes strong ones. Combined with Hebbian-like plasticity rules, WTA connectivity gives rise to competitivelearning algorithms. These networks and learning schemes have been long studied (Von der and a large literature based on simulations and analyses describes their functional properties. A WTA neuronal layer, depending on its specifics, can restore missing input signals, perform decision making i.e. winner selection, and generate oscillations such as those that underlie brain rhythms. Perhaps more importantly, its neurons can learn to become selective to different input patterns, such as orientation of visual bars in models of the primary visual cortex (Von der, MNIST handwritten digits, CIFAR10 objects, spatiotemporal spiking patterns, and can adapt dynamically to model changing objects. The WTA model is indeed biologically plausible, Hebbian plasticity is local, and learning is input-driven, relying on only feed-forward communication of neurons -properties that seem to address several of the limitations of ANNs. However, the model's applicability is limited to simple tasks. That is partly because the related theoretical literature remains surprisingly unsettled, despite its long history, and the strong and productive community interest. described a very related theory but for a model that is largely incompatible with ANNs and thus less practical. It uses spiking and stochastic neurons, input has to be discretized, and each input feature must be encoded through multiple binary neurons. Moreover, it was only proven for neurons with an exponential activation function. It remains therefore unclear which specific plasticity rule and structure could optimize an ANN WTA for Bayesian inference. It is also unclear how to minimize a common loss function such as cross-entropy despite unsupervised learning, and how a WTA could represent varying families of probability distributions. In summary, on the theoretical side, an algorithm that is simultaneously normative, based on WTA networks and Hebbian unsupervised plasticity, performs Bayesian inference, and, importantly, is composed of conventional, i.e. non-spiking, ANN elements and is rigorously linked to modern ANN tools such as cross-entropy loss, would be an important advance but has been missing. On the practical side, evidence that Hebbian WTA networks could be useful for presently pertinent issues of modern ANNs such as adversarial robustness, generation of synthetic images, or faster learning, has remained limited. Here we aim to fill these gaps. Recently, when WTA networks were studied in a theoretical framework compatible with conventional machine learning (ML), but in the context of short-term as opposed to long-term Hebbian plasticity, it resulted in surprising practical advantages over supervised ANNs. A similar theoretical approach could also reveal unknown advantages of long-term Hebbian plasticity in WTA networks. In addition, it could provide insights into how a WTA microcircuit could participate in larger-scale computation by deeper cortical or artificial networks. Here we construct "SoftHebb", a biologically plausible WTA model that is based on standard ratebased neurons as in ANNs, can accommodate various activation functions, and learns without labels, using local plasticity and only feed-forward communication, i.e. the properties we seek in an ANN. Importantly, it is equipped with a simple normalization of the layer's activations, and an optional temperature-scaling mechanism, producing a soft WTA instead of selecting a single "hard" winner neuron. This allows us to prove formally that a SoftHebb layer is a generative mixture model that objectively minimizes its Kullback-Leibler (KL) divergence from the input distribution through Bayesian inference, thus providing a new formal ML-theoretic perspective of these networks. We complement our main results, which are theoretical, with experiments that are small-scale but produce intriguing results. As a generative model, SoftHebb has a broader scope than classification, but we test it on image classification tasks. Surprisingly, in addition to overcoming several inefficiencies of backpropagation, the unsupervised WTA model also outperforms a supervised two-layer perceptron in several aspects: learning speed and accuracy in the first presentation of the training dataset, robustness to noisy data and to one of the strongest white-box adversarial attacks, i.e. projected gradient descent (PGD), and without any explicit defence. Interestingly, the SoftHebb model also exhibits inherent properties of deflection of the adversarial attacks, and generates object interpolations. However, the human ability to deflect adversarial perturbations likely does not rely on detecting them, but rather on effectively ignoring them, making the deflecting type of robustness an emergent property of biological computation rather than a defence mechanism.

Perhaps a comparison is need with other adversarial learning/robustness techniques?

SofHebb does not use any such techniques, so it is only fair to compare it to other learning without such techniques, as we do.** SoftHebb's robustness is emergent, not ad-hoc.

Paris (English: /ˈpærɪs/; French pronunciation: ​[paʁi] (listen)) is the capital and most populous city of France, with an official estimated population of 2,102,650 residents as of 1 January 2023 in an area of more than 105 km² (41 sq mi), making it the fourth-most populated city in the European Union as well as the 30th most densely populated city in the world in 2022. Since the 17th century, Paris has been one of the world's major centres of finance, diplomacy, commerce, fashion, gastronomy, and science. For its leading role in the arts and sciences, as well as its early and extensive system of street lighting, in the 19th century it became known as "the City of Light". Like London, prior to the Second World War, it was also sometimes called the capital of the world.

Extract the population for Paris mentioned in the paragraph

The official estimated population of Paris is 2,102,650 residents

Think up of a bunch of different gifts you could give a teacher

An apple, a box of pencils, a giftcard, a card, a chocolate bar, school supplies, books

Clitocybe subcordispora is a rare species of agaric fungus in the family Tricholomataceae. Found in northern Europe, it was described as new to science in 1969 by Finnish mycologist Harri Harmaja. It is similar to species in the Clitocybe metachroa complex, but can be distinguished from them by its smaller spores, which measure 4.5–6.0 by 3.0–4.0 µm.

Extract the family name of the fungus Clitocybe subcordispora.

Tricholomataceae

The subledger, or subsidiary ledger, provides details behind entries in the general ledger used in accounting. The subledger shows detail for part of the accounting records such as property and equipment, prepaid expenses, etc. The detail would include such items as date the item was purchased or expense incurred, a description of the item, the original balance, and the net book value. The total of the subledger would match the line item amount on the general ledger. This corresponding line item in the general ledger is referred to as the controlling account. The subsidiary ledger balance is compared with its controlling account balance as part of the process of preparing a trial balance. As part of an audit, a method of testing balances may include tracing individual acquisitions to the subsidiary ledger for amounts and descriptions. The objective of this test is to determine that the current-year acquisitions schedule agrees with related subledger amounts, and the total agrees with the general ledger.

Extract from the text what are the details needed for a subledger, or subsidiary ledger. Separate them by a comma.

date the item was purchased, expense incurred, description of the item, original balance, net book value

We minimize the binary cross-entropy loss between the predicted outputs and the targets. We train our neural model with mini-batch size 8 using Adam BIBREF12 with learning rate INLINEFORM0 and decay the learning rate by multiplying INLINEFORM1 if the F1-Multi score on the validation set decreases during the validation. Gradient norms are clipped within 5 to prevent gradient explosion for feed-forward networks or recurrent neural networks. Since the training corpus is rather small, we use dropout BIBREF13 with probability INLINEFORM2 to prevent overfitting. We train our neural model with mini-batch size 8 using Adam (Kingma and Ba, 2015) with learning rate 0.001 and decay the learning rate by multiplying 0.75 if the F1-Multi score on the validation set decreases during the validation.

What is their learning rate?

0.001.

Do spotted hyenas actually laugh?

Spotted hyenas have two major vocalizations: a whoop and a cackle. The cackle is often thought to sound like the hyena is laughing, but is not really a true laugh.

What product do the Girl Scouts sell?

Girl Scouts sell cookies

Jordan (Arabic: الأردن, tr. Al-ʾUrdunn [al.ʔur.dunː]), officially the Hashemite Kingdom of Jordan, is a country in Western Asia. It is situated at the crossroads of Asia, Africa, and Europe, within the Levant region, on the East Bank of the Jordan River. Jordan is bordered by Saudi Arabia to the south and east, Iraq to the northeast, Syria to the north, and the Palestinian West Bank, Israel, and the Dead Sea to the west. It has a 26 km (16 mi) coastline in its southwest on the Gulf of Aqaba's Red Sea, which separates Jordan from Egypt. Amman is Jordan's capital and largest city, as well as its economic, political, and cultural centre.

Given this paragraph about Jordan, tell me where it is located and which countries it borders?

Jordan is a country in Western Asia and it is border by Saudi Arabia, Iraq, Syria, Palestine, and Israel.