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There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions. The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification. For each language, we define a train, development and test corpus.

What corpus did the authors define for each language?

The authors define a train, development and test corpus for each language.

What are the different ways to score in American football?

The most common way to score in American football is a touchdown, which is worth 6 points. After each touchdown, you have the option of scoring 1 or 2 points during the "extra point". Many teams elect to kick field goals when they can't get close enough to score a touchdown. Field goals are worth 3 points each. Defensive players can also score a safety, which is worth 2 points.

Cytokines are a broad and loose category of small proteins (~5–25 kDa) important in cell signaling. Cytokines are peptides and cannot cross the lipid bilayer of cells to enter the cytoplasm. Cytokines have been shown to be involved in autocrine, paracrine and endocrine signaling as immunomodulating agents.Cytokines include chemokines, interferons, interleukins, lymphokines, and tumour necrosis factors, but generally not hormones or growth factors (despite some overlap in the terminology). Cytokines are produced by a broad range of cells, including immune cells like macrophages, B lymphocytes, T lymphocytes and mast cells, as well as endothelial cells, fibroblasts, and various stromal cells; a given cytokine may be produced by more than one type of cell. They act through cell surface receptors and are especially important in the immune system; cytokines modulate the balance between humoral and cell-based immune responses, and they regulate the maturation, growth, and responsiveness of particular cell populations. Some cytokines enhance or inhibit the action of other cytokines in complex ways. They are different from hormones, which are also important cell signaling molecules. Hormones circulate in higher concentrations, and tend to be made by specific kinds of cells. Cytokines are important in health and disease, specifically in host immune responses to infection, inflammation, trauma, sepsis, cancer, and reproduction.The word comes from the ancient Greek language: cyto, from Greek κύτος, kytos, 'cavity, cell' + kines, from Greek κίνησις, kinēsis, 'movement'.

From the passage identify the areas where cytokines can help the host immune responses. Display the results in a comma separated format.

infection, inflammation, trauma, sepsis, cancer, reproduction

What is the largest state by land size in the United States of America?

The largest state by land size in the United States of America is Alaska.

What are some tools that every homeowner should have?

There is a tool designed for almost every challenge a homeowner will face, but purchasing every tool could prove to be cost prohibitive, especially if they are highly specialized for tasks that may occur infrequently. It's best to have a general set of tools that have a wide variety of uses, including a hammer, a set of screwdrivers both flat and philips, a tape measure, a level, a stud finder, and a utility knife. A battery powered drill and impact driver can also save time, and make mounting projects easier.

1908.04917

Based on the above considerations, in this paper, we present CSSMCM, a sentence-level Chinese Mandarin lip reading network, which contains three sub-networks. Same as BIBREF5 , in the first sub-network, pinyin sequence is predicted from the video. Different from BIBREF5 , which predicts pinyin characters from video, pinyin is taken as a whole in CSSMCM, also known as syllables. As we know, Mandarin Chinese is a syllable-based language and syllables are their logical unit of pronunciation. Compared with pinyin characters, syllables are a longer linguistic unit, and can reduce the difficulty of syllable choices in the decoder by sequence-to-sequence attention-based models BIBREF6 . Chen et al. BIBREF7 find that there might be a relationship between the production of lexical tones and the visible movements of the neck, head, and mouth. Motivated by this observation, in the second sub-network, both video and pinyin sequence is used as input to predict tone. Then in the third sub-network, video, pinyin, and tone sequence work together to predict the Chinese character sequence. At last, three sub-networks are jointly finetuned to improve overall performance. Same as BIBREF5 , in the first sub-network, pinyin sequence is predicted from the video. Different from BIBREF5 , which predicts pinyin characters from video, pinyin is taken as a whole in CSSMCM, also known as syllables. As we know, Mandarin Chinese is a syllable-based language and syllables are their logical unit of pronunciation.

What syntactic structure is used to model tones?

The answers are shown as follows: * syllables

Few companies produce planes on a large scale. However, the production of a plane for one company is a process that actually involves dozens, or even hundreds, of other companies and plants, that produce the parts that go into the plane. For example, one company can be responsible for the production of the landing gear, while another one is responsible for the radar. The production of such parts is not limited to the same city or country; in the case of large plane manufacturing companies, such parts can come from all over the world. The parts are sent to the main plant of the plane company, where the production line is located. In the case of large planes, production lines dedicated to the assembly of certain parts of the plane can exist, especially the wings and the fuselage

Are there companies that build a plane end to end?

A plane is typically not created by a single entity, but instead is a collaboration between numerous parts manufacturers and assemblers.

What is "Scandoval?"

"Scandoval" is a term coined to describe the interpersonal relationship drama between multiple cast members on the reality show Vanderpump Rules (VPR). It combines the word "scandal" with the surname of the main perpetrator, Tom Sandoval. Tom Sandoval, a sometime bartender and longtime cast member on VPR, had an affair on his girlfriend of 8 years, fellow bartender and cast member Ariana Maddox, with Raquel Leviss. Leviss is a VPR cast member, former beauty queen, and most notably, one of Ariana's closest friends. The scandal shocked cast members and VPR fans alike, primarily due to the unforeseen nature of the affair, as well as Tom and Raquel's lack of remorse for their behavior. The drama became so nationally amplified that even celebrities - from model Chrissy Teigen to SNL's Aidy Bryant - and fashion brands - including bag brand Beis - made public comments regarding it.

Telugu cinema, also known as Tollywood, is the segment of Indian cinema dedicated to the production of motion pictures in the Telugu language, widely spoken in the states of Andhra Pradesh and Telangana. Telugu cinema is based in Film Nagar, Hyderabad. As of 2021, Telugu cinema is the largest film industry in India in terms of box-office. Telugu films sold 23.3 crore (233 million) tickets in 2022, the highest among various film industries in India.

What is tollywood?

Tollywood is a nickname for movies made by Telugu cinema in India.

United States attorneys are officials of the U.S. Department of Justice who serve as the chief federal law enforcement officers in each of the 94 U.S. federal judicial districts. Each U.S. attorney serves as the United States' chief federal criminal prosecutor in their judicial district and represents the U.S. federal government in civil litigation in federal and state court within their geographic jurisdiction. U.S. attorneys must be nominated by the President and confirmed by the Senate, after which they serve four-year terms.

Provide me some details about United States attorneys with the given text as a reference.

1. In each of the 94 federal court districts in the United States, United States attorneys are representatives of the U.S. Department of Justice who act as the top federal law enforcement officials. 2. Each U.S. attorney represents the U.S. federal government in civil action in federal and state court within their geographic jurisdiction and acts as the country's top federal criminal prosecutor in their judicial district. 3. U.S. attorneys serve four-year terms after being appointed by the President and being confirmed by the Senate.

why do dogs like humans?

As humans we might not understand why dogs are such big fans of us. It could be that from a dogs perspective, humans seem to have an unlimited source of food at their disposal. Dogs may have developed their love for humans just because of all the free food they get from them. Since wolves evolved into dogs because of their symbiotic relationship with humans, one could argue that the domestication of wolves via selective breeding led to the reason most dogs like humans. Dogs also like humans because humans play with them and scratch them behind the ear. It could even be that dogs like humans because of a humans opposable thump, as humans could not throw a ball without them.

1702.06777

After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords. Importantly, we emphasize that both distance measures (cosine similarity and Jensen-Shanon) give rise to the same result, with little discrepancies on the numerical values that are not significant. The presence of two Twitter superdialects (urban and rural) has been recently suggested BIBREF10 based on a machine learning approach. Here, we arrive at the same conclusion but with a totally distinct model and corpus. The advantage of our proposal is that it may serve as a useful tool for dialectometric purposes. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc.

Do the authors mention any possible confounds in their study?

Yes.

Tessa Thompson is an American actress of film and television. Thompson gained favorable notices for her early film performances in the comedy-drama Dear White People, Selma (both in 2014). She gained mainstream attention for her roles in franchise films playing Bianca Taylor in the sport dramas Creed (2015) and Creed II (2018), and for her starring role as Valkyrie in the Marvel Cinematic Universe superhero films Thor: Ragnarok (2017), Avengers: Endgame (2019), and Thor: Love and Thunder (2022), as well as her leading role in the science fiction franchise film Men in Black: International (2019). She also has received critical praise for her roles in independent films such as Sorry to Bother You (2018), Little Woods (2018), Annihilation (2018), the romantic drama Sylvie's Love (2020), and the period film Passing (2021), the latter of which earned her a British Academy Film Award nomination.

Please provide a bulleted list of the movies Tessa Thompson performed in from this passage.

Tessa Thompson performed in the following films: Dear White People Selma Creed Creed II Thor: Ragnarok Avengers: Endgame Thor: Love and Thunder Men In Black: International Sorry To Bother You Little Woods Annihilation Sylvie’s Love Passing

Asia is the largest continent on Earth. It covers 9% of the Earth's total surface area (or 30% of its land area), and has the longest coastline, at 62,800 kilometres (39,022 mi). Asia is generally defined as comprising the eastern four-fifths of Eurasia. It is located to the east of the Suez Canal and the Ural Mountains, and south of the Caucasus Mountains (or the Kuma–Manych Depression) and the Caspian and Black Seas. It is bounded on the east by the Pacific Ocean, on the south by the Indian Ocean and on the north by the Arctic Ocean. Asia is subdivided into 49 countries, five of them (Georgia, Azerbaijan, Russia, Kazakhstan and Turkey) are transcontinental countries lying partly in Europe. Geographically, Russia is partly in Asia, but is considered a European nation, both culturally and politically.

Given a reference text that provides the geographical description of continent Asia, how many Asian countries have a part of their land in Europe as well?

5

What's the best way to potty train a puppy?

You just adopted a puppy, congratulations! There are many methods to successfully house-train or potty-train puppies. First, it's important to remain patient and diligent around maintaining a strict schedule for feeding, rest, play and potty times. This should start the moment your puppy arrives as this is going to set them and you up for success. Second, when accidents happen, which will occur for the first few weeks, remember to be patient and kind to your puppy. This will develop a deep sense of trust in you as the authority figure in the home. Generally, puppies need to be let outside to go potty outside within a secure area after waking, playtime and after every meal. It's important not to confuse potty time with playtime and that your puppy is placed safely in a crate or gated area if they aren't being watched closely. How do you know when your puppy needs to go potty? Most puppies will begin sniffing the ground when they need to go potty so if you witness this happening indoors, quickly usher your puppy outside or to a training pad. It's important to provide your puppy a lot of praise for a job well done. For some puppies, verbal praise from their family member while providing them with a small treat will help your puppy make the connection that going potty in the appropriate area is a very good thing for them and for you.

The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each. It is commonly used to train and evaluate neural network models that generate image descriptions (e.g. BIBREF2 ). An untested assumption behind the dataset is that the descriptions are based on the images, and nothing else. Here are the authors (about the Flickr8K dataset, a subset of Flickr30K): “By asking people to describe the people, objects, scenes and activities that are shown in a picture without giving them any further information about the context in which the picture was taken, we were able to obtain conceptual descriptions that focus only on the information that can be obtained from the image alone.” BIBREF1 What this assumption overlooks is the amount of interpretation or recontextualization carried out by the annotators. Let us take a concrete example. Figure FIGREF1 shows an image from the Flickr30K dataset. This image comes with the five descriptions below. All but the first one contain information that cannot come from the image alone. Relevant parts are highlighted in bold: We need to understand that the descriptions in the Flickr30K dataset are subjective descriptions of events. This can be a good thing: the descriptions tell us what are the salient parts of each image to the average human annotator. So the two humans in Figure FIGREF1 are relevant, but the two soap dispensers are not. But subjectivity can also result in stereotypical descriptions, in this case suggesting that the male is more likely to be the manager, and the female is more likely to be the subordinate. rashtchian2010collecting do note that some descriptions are speculative in nature, which they say hurts the accuracy and the consistency of the descriptions. But the problem is not with the lack of consistency here. Quite the contrary: the problem is that stereotypes may be pervasive enough for the data to be consistently biased. And so language models trained on this data may propagate harmful stereotypes, such as the idea that women are less suited for leadership positions. This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices. I will build on earlier work on linguistic bias in general BIBREF3 , providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences. Finally, I will discuss several methods to analyze the data in order to detect biases. This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices.

What is the purpose of this article?

This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices.

The ability to continuously learn and accumulate knowledge throughout a lifetime and reuse it effectively to adapt to a new problem quickly is a hallmark of general intelligence. State-of-the-art machine learning models work well on a single dataset given enough training examples, but they often fail to isolate and reuse previously acquired knowledge when the data distribution shifts (e.g., when presented with a new dataset)—a phenomenon known as catastrophic forgetting BIBREF0 , BIBREF1 . The three main approaches to address catastrophic forgetting are based on: (i) augmenting the loss function that is being minimized during training with extra terms (e.g., a regularization term, an optimization constraint) to prevent model parameters learned on a new dataset from significantly deviating from parameters learned on previously seen datasets BIBREF2 , BIBREF3 , BIBREF4 , (ii) adding extra learning phases such as a knowledge distillation phase, an experience replay BIBREF5 , BIBREF6 , and (iii) augmenting the model with an episodic memory module BIBREF7 . Recent methods have shown that these approaches can be combined—e.g., by defining optimization constraints using samples from the episodic memory BIBREF8 , BIBREF9 . In language learning, progress in unsupervised pretraining BIBREF10 , BIBREF11 , BIBREF12 has driven advances in many language understanding tasks BIBREF13 , BIBREF14 . However, these models have been shown to require a lot of in-domain training examples, rapidly overfit to particular datasets, and are prone to catastrophic forgetting BIBREF15 , making them unsuitable as a model of general linguistic intelligence. In this paper, we investigate the role of episodic memory for learning a model of language in a lifelong setup. We propose to use such a component for sparse experience replay and local adaptation to allow the model to continually learn from examples drawn from different data distributions. In experience replay, we randomly select examples from memory to retrain on. Our model only performs experience replay very sparsely to consolidate newly acquired knowledge with existing knowledge in the memory into the model. We show that a 1% experience replay to learning new examples ratio is sufficient. Such a process bears some similarity to memory consolidation in human learning BIBREF16 . In local adaptation, we follow Memory-based Parameter Adaptation BIBREF7 and use examples retrieved from memory to update model parameters used to make a prediction of a particular test example. Our setup is different than a typical lifelong learning setup. We assume that the model only makes one pass over the training examples, similar to BIBREF9 . However, we also assume neither our training nor test examples have dataset identifying information (e.g., a dataset identity, a dataset descriptor). Our experiments focus on lifelong language learning on two tasks—text classification and question answering. BIBREF17 show that many language processing tasks (e.g., classification, summarization, natural language inference, etc.) can be formulated as a question answering problem. We argue that our lifelong language learning setup—where a model is presented with question-answer examples without an explicit identifier about which dataset (distribution) the examples come from—is a more realistic setup to learn a general linguistic intelligence model. Our main contributions in this paper are: We assume that all our training examples in the series come from multiple datasets of the same task (e.g., a text classification task, a question answering task), and each dataset comes one after the other.

What is their assumption?

All the training examples in the series come from multiple datasets of the same task (e.g., a text classification task, a question answering task, etc.) and each dataset comes one after the other.

2001.05970

In this study, we limit the sample size to the followers identified as English speakers in the U.S. News Top 200 National Universities. We utilize the Jefferson-Henrique script, a web scraper designed for Twitter to retrieve a total of over 300,000 #MeToo tweets from October 15th, when Alyssa Milano posted the inceptive #MeToo tweet, to November 15th of 2017 to cover a period of a month when the trend was on the rise and attracting mass concerns. Since the lists of the followers of the studied colleges might overlap and many Twitter users tend to reiterate other's tweets, simply putting all the data collected together could create a major redundancy problem. We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users. We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users.

How many followers did they analyze?

The answers are shown as follows: * 51,104

When did the first World war start?

July 28, 1914

What is a knowledge base?

A knowledge base is a set of articles composed of words and sentences that explains concepts of what are known but perhaps hard to understand. A kb can be used to help streamline and making training more readily accessible. A kb is also a place where exchange of ideas occurs through asking interactive questions on published articles. An example of a widely use kb is stackoverflow.com

This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/ Entity linking (EL), mapping entity mentions in texts to a given knowledge base (KB), serves as a fundamental role in many fields, such as question answering BIBREF0 , semantic search BIBREF1 , and information extraction BIBREF2 , BIBREF3 . However, this task is non-trivial because entity mentions are usually ambiguous. As shown in Figure FIGREF1 , the mention England refers to three entities in KB, and an entity linking system should be capable of identifying the correct entity as England cricket team rather than England and England national football team. Entity linking is typically broken down into two main phases: (i) candidate generation obtains a set of referent entities in KB for each mention, and (ii) named entity disambiguation selects the possible candidate entity by solving a ranking problem. The key challenge lies in the ranking model that computes the relevance between candidates and the corresponding mentions based on the information both in texts and KBs BIBREF4 . In terms of the features used for ranking, we classify existing EL models into two groups: local models to resolve mentions independently relying on textual context information from the surrounding words BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , and global (collective) models, which are the main focus of this paper, that encourage the target entities of all mentions in a document to be topically coherent BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 . Global models usually build an entity graph based on KBs to capture coherent entities for all identified mentions in a document, where the nodes are entities, and edges denote their relations. The graph provides highly discriminative semantic signals (e.g., entity relatedness) that are unavailable to local model BIBREF15 . For example (Figure FIGREF1 ), an EL model seemly cannot find sufficient disambiguation clues for the mention England from its surrounding words, unless it utilizes the coherence information of consistent topic “cricket" among adjacent mentions England, Hussain, and Essex. Although the global model has achieved significant improvements, its limitation is threefold: To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability. In such models, words/entities are represented by low dimensional vectors in a continuous space, and features for mention as well as candidate entities are automatically learned from data BIBREF4 . However, existing NN-based methods for EL are either local models BIBREF16 , BIBREF17 or merely use word/entity embeddings for feature extraction and rely on another modules for collective disambiguation, which thus cannot fully utilize the power of NN models for collective EL BIBREF18 , BIBREF19 , BIBREF20 . The second drawback of the global approach has been alleviated through approximate optimization techniques, such as PageRank/random walks BIBREF21 , graph pruning BIBREF22 , ranking SVMs BIBREF23 , or loopy belief propagation (LBP) BIBREF18 , BIBREF24 . However, these methods are not differentiable and thus difficult to be integrated into neural network models (the solution for the first limitation). To overcome the third issue of inadequate training data, BIBREF17 has explored a massive amount of hyperlinks in Wikipedia, but these potential annotations for EL contain much noise, which may distract a naive disambiguation model BIBREF6 . In this paper, we propose a novel Neural Collective Entity Linking model (NCEL), which performs global EL combining deep neural networks with Graph Convolutional Network (GCN) BIBREF25 , BIBREF26 that allows flexible encoding of entity graphs. It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion. Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise. On the other hand, we apply GCNs to improve discriminative signals of candidate entities by exploiting the rich structure underlying the correct entities. To alleviate the global computations, we propose to convolute on the subgraph of adjacent mentions. Thus, the overall coherence shall be achieved in a chain-like way via a sliding window over the document. To the best of our knowledge, this is the first effort to develop a unified model for neural collective entity linking. In experiments, we first verify the efficiency of NCEL via theoretically comparing its time complexity with other collective alternatives. Afterwards, we train our neural model using collected Wikipedia hyperlinks instead of dataset-specific annotations, and perform evaluations on five public available benchmarks. The results show that NCEL consistently outperforms various baselines with a favorable generalization ability. Finally, we further present the performance on a challenging dataset WW BIBREF19 as well as qualitative results, investigating the effectiveness of each key module. Although the global model has achieved significant improvements, its limitation is threefold: 1.The global approach suffers from the data sparsity issue of unseen words/entities, and the failure to induce underlying discriminative features for EL. 2.The joint inference mechanism in the global approach leads to expensive computations, especially when the entity graph may contain hundreds of nodes in case of long documents. 3.The annotated EL training data is usually expensive to obtain or only available in narrow domains, which results in possible overfitting issue or domain bias.

What are the limitations of global model ?

Although the global model has achieved significant improvements, its limitation is threefold: 1.The global approach suffers from the data sparsity issue of unseen words/entities, and the failure to induce underlying discriminative features for EL. 2.The joint inference mechanism in the global approach leads to expensive computations, especially when the entity graph may contain hundreds of nodes in case of long documents.3.The annotated EL training data is usually expensive to obtain or only available in narrow domains, which results in possible overfitting issue or domain bias.

What does the 57 on Heinz ketchup bottles mean?

The 57 on a Heinz ketchup bottle represents the number of varieties of pickles the company once had.

What was the cause of MH370 flight's disappearance?

There are many conspiracy theories surrounding the disposition of the flight, ranging from a military downing, aircraft hijacking, terrorist attack, to a pilot suicide over his deteriorating personal life, but all of these suggestions have not been proven substantially. We will really never know, and will just have to leave this an unsolved mystery.

Write a short news story about a dog who is elected to the Wisconsin state supreme court. Avoid discussing the dog's political party by name.

Rex, the newest member of the Wisconsin State Supreme Court, was an unlikely candidate and an unlikely winner of the recent election, but the people of Wisconsin have spoken. Rex's win over conservative former state Supreme Court Justice Dan Kelly could have implications for an important spaying case and could ultimately impact redistricting in the state, with fire hydrants, trees, and car tires marking the likely boundaries of new districts.

Determine the company which produces the following automotive brands: Chevrolet, GMC, Dodge, Lexus, Buick, Cadillac, Pontiac, Acura, Lincoln, and Jeep.

Chevrolet, GMC, Cadillac, and Buick are all brands of General Motors. Pontiac was also a brand of General Motors, but it was phased out in 2010. Lincoln, Jeep, and Dodge are Stellantis brands. Lexus and Acura is the luxury brands of Toyota and Honda respectively.

In the series A Song of Ice and Fire, who is the founder of House Longwaters?

The founder of Longwaters is only known as a knight and son of Ser Jon Waters. He is only known as Longwaters.

Marvel Comics is an American comic book publisher and the flagship property of Marvel Entertainment, a division of The Walt Disney Company since September 1, 2009. Evolving from Timely Comics in 1939, Magazine Management/Atlas Comics in 1951 and its predecessor, Marvel Mystery Comics, the Marvel Comics title/name/brand was first used in June 1961. Marvel was started in 1939 by Martin Goodman as Timely Comics, and by 1951 had generally become known as Atlas Comics. The Marvel era began in June 1961 with the launch of The Fantastic Four and other superhero titles created by Stan Lee, Jack Kirby, Steve Ditko and many others. The Marvel brand, which had been used over the years and decades, was solidified as the company's primary brand. Marvel counts among its characters such well-known superheroes as Spider-Man, Iron Man, Captain America, Thor, Doctor Strange, Hulk, Daredevil, Wolverine, Black Panther and Captain Marvel, as well as popular superhero teams such as the Avengers, X-Men, Fantastic Four, and Guardians of the Galaxy. Its stable of well-known supervillains includes the likes of Doctor Doom, Magneto, Ultron, Thanos, Kang the Conqueror, Green Goblin, Galactus, Loki, and Kingpin. Most of Marvel's fictional characters operate in a single reality known as the Marvel Universe, with most locations mirroring real-life places; many major characters are based in New York City, New York, United States. Additionally, Marvel has published several licensed properties from other companies. This includes Star Wars comics twice from 1977 to 1986 and again since 2015.

Name some of the superhero teams of Marvel Comics as per the passage.

Avengers, X-Men, Fantastic Four, and Guardians of the Galaxy.

The Masters Tournament (usually referred to as simply The Masters, or the U.S. Masters outside North America) is one of the four major championships in professional golf. Scheduled for the first full week of April, the Masters is the first major of the year, and unlike the others, it is always held at the same location, Augusta National Golf Club, a private course in the city of Augusta, Georgia, in the southeastern United States. The Masters was started by amateur champion Bobby Jones and investment banker Clifford Roberts. After his grand slam in 1930, Jones acquired the former plant nursery and co-designed Augusta National with course architect Alister MacKenzie. First played 89 years ago in 1934, the tournament is an official money event on the PGA Tour, the European Tour, and the Japan Golf Tour. The field of players is smaller than those of the other major championships because it is an invitational event, held by the Augusta National Golf Club. The tournament has a number of traditions. Since 1949, a green jacket has been awarded to the champion, who must return it to the clubhouse one year after his victory, although it remains his personal property and is stored with other champions' jackets in a specially designated cloakroom. In most instances, only a first-time and currently reigning champion may remove his jacket from the club grounds. A golfer who wins the event multiple times uses the same green jacket awarded upon his initial win (unless they need to be re-fitted with a new jacket). The Champions Dinner, inaugurated by Ben Hogan in 1952, is held on the Tuesday before each tournament, and is open only to past champions and certain board members of the Augusta National Golf Club. Beginning in 1963, legendary golfers, usually past champions, have hit an honorary tee shot on the morning of the first round to commence play. These have included Fred McLeod, Jock Hutchinson, Gene Sarazen, Sam Snead, Byron Nelson, Arnold Palmer, Jack Nicklaus, Gary Player, Lee Elder, and Tom Watson. Since 1960, a semi-social contest at the par-3 course has been played on Wednesday, the day before the first round.

What is the Masters Tournament?

The Masters Tournament is played in Augusta, Georgia. It is four major championships in professional golf. It is usually played the first week of April. It is known for the Green Jacket that is awarded to the winner of the tournament. The Masters Tournament is an invitation only event so therefore it is usually smaller than a typical PGA event. Augusta National Golf Club is a private invitation only club.

What is the concept of a good life according to Socrates and Aristotle, and what are the components that contribute to it? Is a good life innate or learned, and can anyone achieve it?

One of the most tantalizing and theorized about topics is that of the meaning of life and how to live a “good” one. It is human nature to explain the unknown and create explanations for the way the world works; throughout history philosophers have come to their own conclusions on why some lives are good and others are not. Two key figures that lend insight in these respects are Socrates in Plato’s The Apology, and Aristotle in Nicomachean Ethics. A seemingly simple concept quickly devolves into a confusing and complex web of components that only together in the right proportions lend each man or woman a full good life. In order to discuss the way in which we are to lead a good life we must first discuss the differing factors that contribute to the definition of good in the eyes of Socrates and Aristotle. What are the components of the concept of good? Is it innate or learned? Can it be purchased? Is it temporary or is it eternal? Does it apply to one’s self or a community as a whole? Does one conduct oneself to be good? How Is a good life given by the gods or earned by humans? And although there is no definitive answer to any of these questions, in both author’s texts living a good life comes down to the choices you make and how they compare to the definition of good you subscribe to. Socrates and Aristotle use words such as virtue, courage, happiness, and intelligence to describe their versions of the path you take to reach a good life, and all are important, but the main driver of a good life is one’s ability to find the proper equilibrium between Dahlke 1 each of their characteristics that lend towards the development of a commonly defined good life. Theorizing on how to live a good life leads to the necessity of defining what a good life is and the components that make it up. Aristotle theorized in Nicomachean Ethics that the idea of virtue, or excellence, can be an overarching theme for the components that make up the highest “good”, the good that is an endpoint or product encompassing the entire life before it. And the development of virtue lies within a midpoint between too much and too little of any emotion, action or characteristic. ... it is in emotions and actions that excess, deficiency and the median are found. Thus we can experience fear confidence, desire, anger, pity and generally any kind of pleasure and pain either too much or too little, and in either case not properly. But to experience all this at the right time, towards the right objects, towards the right people, for the right reason, and in the right manner — that is the median and the best course, the course that is a mark of virtue. (Aristotle. Bk. 2, pg. 43) And still, even with the most precise definition Aristotle or Socrates gives there is a great amount of ambiguity. While some characteristics or actions are classified as inherently good or bad Aristotle theorizes that simply over exercising the usage of one or two characteristics doesn’t mean you are a good person living a good life. But rather, he states that there is a median usage point between the extrema of never and always that contributes to virtue, and then a good life. Where that point is is still unknown, partly due to the infinite situational differences one person can have versus another person Dahlke 2 during their lifetime and partly due to the complexity and magnitude of the variables that contribute to the person as a whole. However, Aristotle goes into a few overarching descriptors of his definition of a virtuous man or woman to help define the median between the two extrema. For example, the median for pleasure and pain is self control, between fear and confidence is courage, and righteous falls between envy and spite.1 These examples shine a light on how to find the middle between two opposing qualities, but do not instruct on how we are to lead a good life. While definitively defining the idea of good is complex the framework of thought to use when faced with daily decisions Aristotle provides allows for the discussion on how to live a good life. Creating a structured plan for how to live a good life brings about the question can anyone live a good life? Furthermore, are the segments that complete virtue innate or are they learned? Aristotle delves into two schools of thought, the first being that there are some characteristics that born with can impede or halt one’s quest for moral virtue, and secondly, that one develops these traits over time and through ritual habit. The latter, he describes in analogy, “Similarly, we become just by the practice of just actions, self-controlled by exercising self-control, and courageous by performing acts of courage.” (Aristotle. Bk. 2, pg. 34) In other words, one becomes virtuous by continually being virtuous. Therefore, one finds oneself in a good life when one habitually lives within the middle of all opposing qualities. While not everyone in a society will live a good life, everyone within a society can live a good life. At this point it is important to 1 Examples given in Book II Ch. 7 - Nicomachean Ethics Dahlke 3 refer back to the definition of what a good life is, it does not mean success, fame or wealth, but rather practicing the combination of elements that create virtue. While some qualities like virtue are relatively undisputed as good, wealth is a heavily debated part of a good life. Both Aristotle and Socrates agree that wealth is not an indicator of a good life but their thought differs when it comes to the part money plays in the development of such a life. While Aristotle believes wealth is a necessity for aspects of a virtuous life, Socrates believes that wealth is only a byproduct of a good life. Aristotle does concede to saying that, “wealth is not the good which we are trying to find, for it is only useful, i.e., it is a means to something else.” (Aristotle. Bk. 1, pg. 9) Otherwise saying that while wealth is not the end goal and is meaningless when thinking of one’s worth, it is helpful in the creation of a good life. On the other hand, Socrates writes of wealth as something that does not create excellence and at times can even inhibit it’s development. In The Apology Socrates says to the jury, “Wealth does not bring about excellence, but excellence brings about wealth and all other public and private blessings for men.” (Plato. 30b) While this quote is taken out of the context of a defensive argument, it still holds Socrates’ beliefs on wealth. He, an impoverished man, does not find the same value in money as sophists like Aristotle, and while defending his choice to not charge a fee to the pupils that followed his philosophical thought like other sophists did, he said that he is here at the service to god. Both theories on wealth’s role in a good life are important to consider. Does wealth lead to corruption and poverty to a more sinful life? Wealth, similarly to fear, needs a median. As Aristotle describes fear, too little is being reckless, while too much is being cowardly. In wealth the same thought Dahlke 4 can be applied, too little creates extreme poverty which distracts from living a virtuous life and too much can create different distractions that lead to the same result. Another contesting theory is that of the length in which “goodness” lasts. When defining a good life, it is important to think about when you can use good as a descriptor. If goodness in reference to one’s life lasts forever then the term is permanent and can be applied throughout one's life. If on the contrary, goodness comes and goes throughout life the term can only be used temporarily or in posterity noting one’s life as a whole. Socrates, after the announcement of his execution states, “a good man cannot be harmed either in life or death, and that his affairs are not neglected by the gods.” (Plato. 41 c-d) Lending the idea that a good man or woman living a good life is continuous and permanent, throughout life and death. However, Aristotle argues that since a good life is a sum of smaller goods, to use the term to describe someone's life, it must come at the end of their life, looking at it as a whole. After describing smaller goals and endpoints he goes further and says, “The highest good, on the other hand, must be something final” (Aristotle. Bk. 1 pg. 14) Stating that while there are different branches of a good life, virtuous behavior is not a constant and therefore you cannot give a description to their life as a whole until it’s end. Socrates’ arguments on the length of goodness are rather naive, seeing as people change over time, only with the entire information of their lives can one make a claim as to whether or not they had a good life. In summary, the two different views on how we are to live a good life together provide insight and theory into the subject but neither provides a complete instruction on doing so. Socrates, as written down by Plato, describes his own life which he Dahlke 5 considers to be a virtuous one and Aristotle provides a philosophical theory behind how to live in a virtuous way with the end goal of happiness or in other words a good life. The closest we can get to understanding how to live a good life is by understanding what makes up a good life, and even this is subjective. One person’s views on what a virtuous life means can differ greatly from the next depending on where they lived, their religious beliefs, or social status. These variances result in many different guides to and definitions of a good life. Within all the different viewpoints, a concept introduced in Nicomachean Ethics that can be widely accepted is that within someone’s life there is a median point between each of their emotions, actions and characteristics that create virtue and lead towards a good life.

Summarize the following paragraph about Microsoft in 2 sentences: Microsoft Corporation is an American multinational technology corporation headquartered in Redmond, Washington. Microsoft's best-known software products are the Windows line of operating systems, the Microsoft Office suite, and the Internet Explorer and Edge web browsers. Its flagship hardware products are the Xbox video game consoles and the Microsoft Surface lineup of touchscreen personal computers. Microsoft ranked No. 14 in the 2022 Fortune 500 rankings of the largest United States corporations by total revenue; it was the world's largest software maker by revenue as of 2022. It is considered as one of the Big Five American information technology companies, alongside Alphabet (parent company of Google), Amazon, Apple, and Meta (formerly Facebook). Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. It rose to dominate the personal computer operating system market with MS-DOS in the mid-1980s, followed by Windows. The company's 1986 initial public offering (IPO), and subsequent rise in its share price, created three billionaires and an estimated 12,000 millionaires among Microsoft employees. Since the 1990s, it has increasingly diversified from the operating system market and has made a number of corporate acquisitions, their largest being the acquisition of LinkedIn for $26.2 billion in December 2016, followed by their acquisition of Skype Technologies for $8.5 billion in May 2011. As of 2015, Microsoft is market-dominant in the IBM PC compatible operating system market and the office software suite market, although it has lost the majority of the overall operating system market to Android. The company also produces a wide range of other consumer and enterprise software for desktops, laptops, tabs, gadgets, and servers, including Internet search (with Bing), the digital services market (through MSN), mixed reality (HoloLens), cloud computing (Azure), and software development (Visual Studio).

Microsoft is an American software company that is well known for its Windows operating system, Microsoft Office suite, and Cloud Computing (Azure), etc amongst many others. It was founded by Bill Gates and worth over a trillion dollars.

Medical providers across the United States are required to document clinical visits in the Electronic Health Records. This need for documentation takes up a disproportionate amount of their time and attention, resulting in provider burnout BIBREF0, BIBREF1. One study found that full-time primary care physicians spent about 4.5 hours of an 11-hour workday interacting with the clinical documentation systems, yet were still unable to finish their documentation and had to spend an additional 1.4 hours after normal clinical hours BIBREF2. Speech and natural language processing are now sufficiently mature that there is considerable interest, both in academia and industry, to investigate how these technologies can be exploited to simplify the task of documentation, and to allow providers to dedicate more time to patients. While domain-specific automatic speech recognition (ASR) systems that allow providers to dictate notes have been around for a while, recent work has begun to address the challenges associated with generating clinical notes directly from speech recordings. This includes inducing topic structure from conversation data, extracting relevant information, and clinical summary generation BIBREF3. In one recent work, authors outlined an end-to-end system; however, the details were scant without empirical evaluations of their building blocks BIBREF4. One of the simplistic approaches uses a hand crafted finite state machine based grammar to locate clinical entities in the ASR transcripts and map them to canonical clinical terms BIBREF5. This seems to perform well in a narrowly scoped task. A more ambitious approach mapped ASR transcripts to clinical notes by adopting a machine translation approach BIBREF6. However this performed poorly. To address the difficulty in accessing clinical data, researchers have experimented with synthetic data to develop a system for documenting nurse-initiated telephone conversations for congestive heart failure patients who are undergoing telemonitoring after they have been discharged from the hospital BIBREF7. In their task, a question-answer based model achieved an F-score of 0.80. This naturally raises the question of how well state-of-art techniques will perform in helping the broader population of clinicians such as primary care providers. One might expect that the task of extracting clinical concepts from audio faces challenges similar to the domain of unstructured clinical texts. In that domain, one of the earliest public-domain tasks is the i2b2 relations challenge, defined on a small corpus of written discharge summaries consisting of 394 reports for training, 477 for test, and 877 for evaluation BIBREF8. Given the small amount of training data, not surprisingly, a disproportionately large number of teams fielded rule-based systems. Conditional random field-based (CRF) systems BIBREF9 however did better even with the limited amount of training data BIBREF10. Other i2b2/n2c2 challenges focused on coreference resolution BIBREF11, temporal relation extraction BIBREF12, drug event extraction BIBREF13 on medical records, and extracting family history BIBREF14. Even though the text was largely unstructured, they benefited from punctuation and capitalization, section headings and other cues in written domain which are unavailable in audio to the same extent. With the goal of creating an automated medical scribe, we broke down the task into modular components, including ASR and speaker diarization which are described elsewhere BIBREF15. In this work, we investigate the task of extracting relevant clinical concepts from transcripts. Our key contributions include: (i) defining three tasks – the Medications Task, the Symptoms Task, and the Conditions Task along with principles employed in developing the annotation guidelines for them (Section SECREF2); (ii) measuring the label quality using inter-labeler agreements and refining the quality iteratively (Section SECREF3), (iii) evaluating the performance of the state-of-the-art models on these tasks (Section SECREF4), and (iv) a comprehensive analysis of the performance of the models including manual error categorization (Section SECREF5). The corpus we have created in this work is based on private, proprietary data that cannot be publicly shared. Instead, we are sharing the learnings from our experience that might be useful for the wider community as well as the detailed labeling guidelines as supplementary material in the extended version of this paper on arxiv.org. The corpus we have created in this arXiv:2003.11531v1 [cs.CL] 12 Mar 2020 work is based on private, proprietary data that cannot be publicly shared.

Is their dataset an open one?

No.

You is an American psychological thriller television series based on the books by Caroline Kepnes, developed by Greg Berlanti and Sera Gamble, and produced by Berlanti Productions, Alloy Entertainment, and A+E Studios in association with Warner Horizon Television, now Warner Bros. Television. The first season, which is based on the novel You, premiered on Lifetime in September 2018, and follows Joe Goldberg, a bookstore manager and serial killer who falls in love and develops an extreme obsession. The season stars Penn Badgley, Elizabeth Lail, Luca Padovan, Zach Cherry, and Shay Mitchell. Lifetime announced in July 2018 that You had been renewed for a second season, based on Kepnes' follow-up novel Hidden Bodies. The series later moved to Netflix and the second season was released in December 2019. The season follows Joe as he moves to Los Angeles and falls in love with local heiress Love Quinn. For the second season, Ambyr Childers was upgraded to a series regular, joining newly cast Victoria Pedretti, James Scully, Jenna Ortega, and Carmela Zumbado. In January 2020, the series was renewed for a third season by Netflix, which was released on October 15, 2021. In the third season, Saffron Burrows was upgraded to a series regular, joining newly cast Travis Van Winkle, Shalita Grant, Tati Gabrielle, and Dylan Arnold. In October 2021, ahead of the third-season premiere, the series was renewed for a fourth season, which was released over two parts on February 9 and March 9, 2023. The season also stars Charlotte Ritchie, Tilly Keeper, Amy-Leigh Hickman, Ed Speleers and Lukas Gage. In March 2023, the series was renewed for a fifth and final season.

List the years in which the various seasons of You broadcasted as per the given passage. Use the format ‘season #no : Year’ in a bulleted list

Season 1 : 2018 Season 2 : 2019 Season 3 : 2021 Season 4 : 2023

Which is a species of fish? Paradise fish or Heaven

Paradise fish

Who is the St Louis Cardinals?

The St Louis Cardinals is a major league baseball team that play's in the national league central division.

Who was the Colossus of Rhodes a likeness of?

The Colossus of Rhodes may have been viewed differently by different audiences. The statue had an inscription identifying it as an image of Helios, the god of the sun and local deity to Rhodes. In the wider Ancient Greek world Helios would likely be identified more readily as Apollo. The statue may have been made in the likeness of Alexander the Great's official image. This would have served to represent a bond between Rhodes and Alexandria with Alexander's image serving as the founder of the Egyptian city. Support for this idea comes from the fact that the Colossus was made to celebrate Rhodes' victory over a siege. This victory was largely facilitated by Ptolemy from Alexandria, and Alexandria is also a likely source for the funds of the ambitious project. The artist Chares of Lindos would also have been well selected to achieve this double representation. He was local to the island of Rhodes (Lindos is another city on the same island) and would have been familiar with images of Helios. He was also the student of Lysippos who was the exclusive personal sculptor to Alexander the Great during his lifetime, and so Chares would have also been uniquely knowledgeable about the image of Alexander. Some but not all scholars argue for the Colossus to be understood as an image of Alexander posing as Helios that would be seen by the people of Rhodes as their local deity but seen by the people of Alexandria as their founder. The Statue of Liberty built by France and gifted to the USA was partially modeled after the Colossus and is similarly possible to read as a celebration of American independence and also a reference to one the three values Liberté, Égalité and Fraternité central to French identity.

What city has Kogoshima as its airport

Tokyo

Who is the all time leading passer in the NFL?

As of 2023, Tom Brady is NFL's all-time regular season career passing yards leader with 89,214 . He also holds the playoff record with 13,400 yards for a combined total of 102,614. Tom Brady also holds the all time record for number of Passing Touchdowns with 649.

Is the earth flat?

No, the Earth is round, just like all other planets. The gravitational pull of its mass forces it into a spherical shape. The gravity force pulls the material toward the center of the planet. The flat earth theory is scientifically disproven. The earth is not entirely round, though, because the centrifugal force of its spin causes the earth to bulge slightly around the equator.

The United States Navy Strike Fighter Tactics Instructor program (SFTI program), more popularly known as Top Gun (stylized as TOPGUN), is an American military program that teaches fighter and strike tactics and techniques to selected naval aviators and naval flight officers, who return to their operating units as surrogate instructors.

Extract what is Top Gun

The United States Navy Fighter Weapons School was established on 3 March 1969, at Naval Air Station Miramar, California. The unit's purpose was to train fighter air crews in all aspects of fighter weapons systems and the art of aerial combat. It serves to build a nucleus of eminently knowledgeable fighter crews to construct, guide, and enhance weapons training cycles and subsequent aircrew performance. This select group acts as the F-4 community’s most operationally orientated weapons specialists. Top Gun's efforts are dedicated to the Navy’s professional fighter crews, past, present and future.

What were the horcruxes in the Harry Potter book series?

In the Harry Potter book series, a horcrux is defined as an object that contains a shard of someones soul. The main villain of the story, Voldemort, performed a violent ceremony to split his soul a total of seven times (although one was accidental). The horcruxes he made were Tom Riddle's diary, Marvolo Gaunt's ring, Slytherin's locket, Hufflepuff's cup, Ravenclaw's diadem, Nagini (the snake), and Harry Potter.

What is a quark?

A quark are elementary particles that make up a class of matter called hadrons, the most common of which are protons and neutrons. According to the Standard Model of elementary particle physics there are six types of quarks which come in pairs or families; Up/Down, Strange/Charm, and Bottom/Top.

Finally, we consider appropriateness per system. Following related work by BIBREF21, BIBREF24, we use Trueskill BIBREF25 to cluster systems into equivalently rated groups according to their partial relative rankings. The results in Table TABREF36 show that the highest rated systen is Alley, a purpose build bot for online language learning. Alley produces “polite refusal” (2b) - the top ranked strategy - 31% of the time. Comparatively, commercial systems politely refuse only between 17% (Cortana) and 2% (Alexa). Most of the time commercial systems tend to “play along” (3a), joke (3b) or don't know how to answer (1e) which tend to receive lower ratings, see Figure FIGREF38. Rule-based systems most often politely refuse to answer (2b), but also use medium ranked strategies, such as deflect (2c) or chastise (2d). For example, most of Eliza's responses fall under the “deflection” strategy, such as “Why do you ask?”. Data-driven systems rank low in general. Neuralconvo and Cleverbot are the only ones that ever politely refuse and we attribute their improved ratings to this. In turn, the “clean” seq2seq often produces responses which can be interpreted as flirtatious (44%), and ranks similarly to Annabelle Lee and Laurel Sweet, the only adult bots that politely refuses ( 16% of the time). Ritter:2010:UMT:1857999.1858019's IR approach is rated similarly to Capt Howdy and both produce a majority of retaliatory (2e) responses - 38% and 58% respectively - followed by flirtatious responses. Finally, Dr Love and Sophia69 produce almost exclusively flirtatious responses which are consistently ranked low by users. Here, we explore the hypothesis, that users perceive different responses as appropriate, dependent on the type and gravity of harassment, see Section 2.

What hypothesis do the authors explore?

The authors explore the hypothesis that users perceive different responses as appropriate, dependent on the type and gravity of harassment.

Which of these colors are primary colors: Red, Yellow, Blue, Orange, Green, Violet.

There are three primary colors which are Red, Yellow, and Blue. The three secondary colors are Orange, Green, and Violet.

Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required. LID is further also an important step in harvesting scarce language resources. Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data. Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries. Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research. However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation. Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa. These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages. This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long. The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks. Section SECREF2 reviews existing works on the topic and summarises the remaining research problems. Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results. This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long.

What task is the classifier designed for？

It is designed for LID task of short pieces of text of 15-20 characters long.

Name five surf spots on the North Shore of Oahu in Hawaii

Pipeline Sunset Beach Velzyland Log Cabins Chun's Reef

How can you make a science fair volcano?

A simple way to create a volcano for a science fair is to use the reaction of baking soda and vinegar colored with red food dye. When combined, these two chemicals will fizz and bubble in a safe but visually appealing reaction.

A hedge fund is a pooled investment fund that trades in relatively liquid assets and is able to make extensive use of more complex trading, portfolio-construction, and risk management techniques in an attempt to improve performance, such as short selling, leverage, and derivatives. Financial regulators generally restrict hedge fund marketing to institutional investors, high net worth individuals, and accredited investors.

What is hedge fund?

A hedge fund is a limited partnership of private investors whose money is managed by professional fund managers who use a wide range of strategies, including leveraging or trading of non-traditional assets, to earn above-average investment returns. Hedge fund investment is often considered a risky alternative investment choice and usually requires a high minimum investment or net worth, often targeting wealthy clients.

Give me a list of beginner friendly Python open source projects to contribute.

1. Ansible: IT automation platform for provisioning various resources 2. Jupyter notebook: Interactive notebook popular in data engineering and machine learning world. 3. Pandas: Flexible and powerful data analysis library beloved by data engineers, scientists and analysts. 4. Scrapy: Web crawling and scraping framework written in python. 5. scikit-learn: This library is power packed with implementation of various machine learning algorithms.

Give me six story ideas for writing a children's book.

1. A dog gets lost and needs to find its owner. 2. A child is exploring a field and discovers lots of animals hiding in it. 3. A child and their doll go through a day, waking up, eating breakfast, going outside, taking a nap, reading a book, eating dinner, taking a bath, and going to sleep. 4. A giraffe astronaut flies to Mars and discovers friendly Martians who help the giraffe to fix his spaceship and return home to Earth. 5. Ten different animals must work together to pass a letter from a remote village to an apartment in a city. 6. The book can explain the concept of gravity in simple, funny ways which entertain both parents and children.

What are some famous Belgian singers?

Famous Belgian singers include Jacques Brel, Stromae and Helmut Lotti

1910.04006

These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. In our paper we also showed that this automatic pipeline achieves reasonably strong F-scores, with an overall performance of 0.828 F1 for the topic extraction component and 0.5 F1 on the clinical sentiment component. These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.

How do they extract topics?

The answers are shown as follows: * automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15

Who were the original Fantastic Four in the comics?

In the original 1961 Fantastic Four Marvel comic book, the Fantastic Four lineup consisted of Reed Richards (Mr. Fantastic), Sue Storm (the Invisible Girl, later the Invisible Woman), Johnny Storm (the Human Torch), and Ben Grimm (the Thing).

What is Pickleball?

Pickleball is an indoor or outdoor racket/paddle sport where two players (singles), or four players (doubles), hit a perforated hollow plastic ball over a 36-inch-high (0.91 m) net using solid-faced paddles. Opponents on either side of the net hit the ball back and forth until one side commits a rule infraction. Pickleball was invented in 1965 as a children's backyard game on Bainbridge Island, Washington, US. In 2022, pickleball was adopted as the official state sport of Washington.

Walmart is the world's largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in October 2022. It is also the largest private employer in the world with 2.2 million employees. It is a publicly traded family-owned business, as the company is controlled by the Walton family. Sam Walton's heirs own over 50 percent of Walmart through both their holding company Walton Enterprises and their individual holdings. Walmart was the largest United States grocery retailer in 2019, and 65 percent of Walmart's US$510.329 billion sales came from U.S. operations.

What is the largest employer in the world?

Walmart

2002.09758

Table shows how unsupervised decompositions affect QA. Our RoBERTa baseline performs quite well on HotpotQA (77.0 F1), despite processing each paragraph separately, which prohibits inter-paragraph reasoning. The result is in line with prior work which found that a version of our baseline QA model using BERT BIBREF26 does well on HotpotQA by exploiting single-hop reasoning shortcuts BIBREF21. We achieve significant gains over our strong baseline by leveraging decompositions from our best decomposition model, trained with USeq2Seq on FastText pseudo-decompositions; we find a 3.1 F1 gain on the original dev set, 11 F1 gain on the multi-hop dev set, and 10 F1 gain on the out-of-domain dev set. Unsupervised decompositions even match the performance of using (within our pipeline) supervised and heuristic decompositions from DecompRC (i.e., 80.1 vs. 79.8 F1 on the original dev set). We fine-tune a pre-trained model to take a question and several paragraphs and predicts the answer, similar to the single-hop QA model from BIBREF21. The model computes a separate forward pass on each paragraph (with the question). For each paragraph, the model learns to predict the answer span if the paragraph contains the answer and to predict “no answer” otherwise. We treat yes and no predictions as spans within the passage (prepended to each paragraph), as in BIBREF22 on HotpotQA. During inference, for the final softmax, we consider all paragraphs as a single chunk. Similar to BIBREF23, we subtract a paragraph's “no answer” logit from the logits of all spans in that paragraph, to reduce or increase span probabilities accordingly. In other words, we compute the probability $p(s_p)$ of each span $s_p$ in a paragraph $p \in \lbrace 1, \dots , P \rbrace $ using the predicted span logit $l(s_p)$ and “no answer” paragraph logit $n(p)$ as follows: We use $\textsc {RoBERTa}_{\textsc {LARGE}}$ BIBREF24 as our pre-trained initialization. Later, we also experiment with using the $\textsc {BERT}_{\textsc {BASE}}$ ensemble from BIBREF3. Our RoBERTa baseline performs quite well on HotpotQA (77.0 F1), despite processing each paragraph separately, which prohibits inter-paragraph reasoning. We fine-tune a pre-trained model to take a question and several paragraphs and predicts the answer, similar to the single-hop QA model from BIBREF21. We use $\textsc {RoBERTa}_{\textsc {LARGE}}$ BIBREF24 as our pre-trained initialization.

What is the strong baseline that this work outperforms?

The answers are shown as follows: * RoBERTa baseline

1911.03514

The discourse dependency structure of each multi-party dialogue can be regarded as a graph. To learn better graph representation of multi-party dialogues, we adopt the dialogues with 8-15 utterances and 3-7 speakers. To simplify the task, we filter the dialogues with long sentences (more than 20 words). Finally, we obtain 52,053 dialogues and 460,358 utterances. To learn better graph representation of multi-party dialogues, we adopt the dialogues with 8-15 utterances and 3-7 speakers. To simplify the task, we filter the dialogues with long sentences (more than 20 words). Finally, we obtain 52,053 dialogues and 460,358 utterances.

How large is the proposed dataset?

The answers are shown as follows: * we obtain 52,053 dialogues and 460,358 utterances

Can you give me a brief history of Data Storage?

In 1725, Basile Bouchon invented punch cards, which communicate information in a machine language. The punched holes communicated a sequence of instructions and acted as on/off switches for devices such as self-playing pianos or looms. About a century later, Charles Babbage invented the first mechanical calculator, the Difference Engine using punch card instructions. Herman Hollerith continued developing this idea and made the Analytical Engine a reality by using punch holes to be a sequence of instructions and also store the instruction data on the device. In 1890, he developed a punch card data processing system for the US Census and went on to form the Tabulating Machine Company in 1896, later renamed International Business Machines Corporation (IBM). By the 1950s punch cards were being used regularly by the US government and industry. Even today, punch cards are still used in standardized tests and voting ballots. Initially invented by Fritz Pfleumer in 1928, magnetic storage gradually replaced punch cards in the 1960s. By 1965, Mohawk Data Sciences Corporation sold a key-to-tape magnetic tape device that was specifically marketed to replace the punch card. Cassette tapes started to become commonly used for most computers in the 70s and 80s. Fast forward to the 90s, affordable personal computers and magnetic disk storage replaced almost all punch card devices.

Classify the following breeds as either cats or dogs: Dalmatian, Abyssinian, Aegean, American Bobtail, Bombay, Saint Bernard, Burmese, Chartreux, Golden Retriever, Welsh Hound, Shiba Inu

Cats: Abyssinian, Aegean, American Bobtail, Bombay, Burmese, Chartreux Dogs: Dalmatian, Saint Bernard, Golden Retriever, Welsh Hound, Shiba Inu

Gemini (♊︎) (/ˈdʒɛmɪnaɪ/ JEM-in-eye, Greek: Δίδυμοι, romanized: Dídymoi, Latin for "twins") is the third astrological sign in the zodiac. Under the tropical zodiac, the sun transits this sign between about May 21 to June 21. Gemini is represented by the twins, Castor and Pollux, known as the Dioscuri in Greek mythology. It is a positive, mutable sign.

For the Zodiac sign Gemini, Share some important details from given text.

1. In Zodiac gemini is the third astrological sign. 2. The sun transits Gemini between about May 21 to June 21. 3. Gemini is represented by the twins, Castor and Pollux, known as the Dioscuri in Greek mythology. 4. Gemini is a positive and mutable sign.

Write a leave letter to your teacher.

To, The Teacher Sir/Madam, This is to inform you that I will be going out of station for 3 days from <date> to <date>, to attend my sister's wedding celebrations. I request you to kindly consider this and grant me leave. Yours faithfully, XYZ

How to start a side hustle

Participate in paid online surveys Get paid to test apps and websites Transcribe videos, phone calls, and other recordings Become a rideshare driver Deliver groceries Rent out your home to vacationers Deliver food Become an affiliate marketer Tutor online Walk dogs and pet-sit Start a blog on Substack Deliver packages Sell your photography Start a podcast Design and sell your own t-shirts Wash cars Start a social media page and build income through sponsorships Give tours of your neighborhood Create and sell your own handmade goods Mow lawns and do other landscaping tasks Start a dropshipping business Create a clothing line Start a YouTube channel Sell digital products

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. More congregation of data to single database so a single query engine can be used to present data in an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long-running analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Organize and disambiguate repetitive data.

What are the benefits of a data warehouse?

A data warehouse provides multiple benefits including but not limited to: the integration of data from multiple sources in a single database and data model, maintaining data history, integrating data from multiple sources and providing access to such data across the enterprise, and presenting the information consistently and in a way that makes sense to business users so that they can do decision making.

Susanna Gossweiler (1740–1793), was a Swiss educator. She was appointed the first principal of the girls' school Töchterschule in Zürich in 1774, and regarded a pioneer of women's education in Switzerland.

From the passage provided, extract when Susanna started as a principal.

Susanna started as a principal of Töchterschule in 1774.

2003.05995

The solution we propose in this paper tries to minimise these costs by increasing the pool of Wizards to anyone wanting to collaborate in the data collection, by providing them the necessary guidance to generate the desired dialogue behaviour. This is a valuable solution for collecting dialogues in domains where specific expertise is required and the cost of training capable Wizards is high. We required fine-grained control over the Wizard interface so as to be able to generate more directed dialogues for specialised domains, such as emergency response for offshore facilities. By providing the Wizard with several dialogue options (aside from free text), we guided the conversation and could introduce actions that change an internal system state. This proposes several advantages: A guided dialogue allows for set procedures to be learned and reduces the amount of data needed for a machine learning model for dialogue management to converge. Providing several dialogue options to the Wizard increases the pace of the interaction and allows them to understand and navigate more complex scenarios. Dialogue structure: we introduced structured dialogues through a Finite State Machine (FSM) that controls the current dialogue state and offers multiple suitable and relevant state transitions (actions) to the Wizard depending on the point in the interaction, the state of the world and the history. A graph of dialogue states, transitions and utterances is loaded when the system is initialised, and each chat room has its own dialogue state, which changes through actions. The dialogue structure for the Emergency Assistant (the Wizard) followed a dialogue flow previously used for the original lab-based Wizard-of-Oz study BIBREF4 but which was slightly modified and simplified for this crowdsourced data collection. In addition to the transitions that the FSM provides, there are other fixed dialogue options always available such as “Hold on, 2 seconds”, “Okay” or “Sorry, can you repeat that?” as a shortcut for commonly used dialogue acts, as well as the option to type a message freely. The dialogue has several paths to reach the same states with varying levels of Operator control or engagement that enriched the heterogeneity of conversations. The Emergency Assistant dialogue options show various speaking styles, with a more assertive tone (“I am sending Husky 1 to east tower”) or others with more collaborative connotations (“Which robot do you want to send?” or “Husky 1 is available to send to east tower”). Refer to BIBREF4 for more details. Furthermore, neither participants were restricted in the number of messages that they could send and we did not require a balanced number of turns between them. However, there were several dialogue transitions that required an answer or authorisation from the Operator, so the FSM would lock the dialogue state until the condition was met. As mentioned earlier, the commands to control the robots are also transitions of the FSM, so they were not always available. By providing the Wizard with several dialogue options (aside from free text), we guided the conversation and could introduce actions that change an internal system state. This proposes several advantages: A guided dialogue allows for set procedures to be learned and reduces the amount of data needed for a machine learning model for dialogue management to converge. Providing several dialogue options to the Wizard increases the pace of the interaction and allows them to understand and navigate more complex scenarios. Dialogue structure: we introduced structured dialogues through a Finite State Machine (FSM) that controls the current dialogue state and offers multiple suitable and relevant state transitions (actions) to the Wizard depending on the point in the interaction, the state of the world and the history. A graph of dialogue states, transitions and utterances is loaded when the system is initialised, and each chat room has its own dialogue state, which changes through actions. In addition to the transitions that the FSM provides, there are other fixed dialogue options always available such as “Hold on, 2 seconds”, “Okay” or “Sorry, can you repeat that?” as a shortcut for commonly used dialogue acts, as well as the option to type a message freely. The dialogue has several paths to reach the same states with varying levels of Operator control or engagement that enriched the heterogeneity of conversations.

What is meant by semiguided dialogue, what part of dialogue is guided?

The answers are shown as follows: * The Wizard can select one of several predefined messages to send, or type their own message if needed. Free text messages do not change the dialogue state in the FSM, so it is important to minimise their use by providing enough dialogue options to the Wizard.

What are some sought-after boutique electric guitar builders?

A few famous ones include Tom Anderson, John Suhr, Paul Reed Smith, James Tyler, Kiesel, Tuttle and etc... Most of them have exceptionally high build quality and craftsmanship, and the differences are mostly tonal, cosmetic and playability.

When was world war 2?

World War 2 started in September 1939. France and Britain declared war on Germany. They did this after Hitler decided to invade Poland. World War 2 would last for 6 years, and no other war in history would take as many lives as World War 2 did. In December 1941 the United States would get involved in the second world war after Japan decided to bomb Pearl Harbor.

Write one paragraph on the relevance of AI/ML in the field of Data Engineering

AI/ML can be used for multiple aspects of Data Engineering including but not limited to data pipeline automation, automated anomaly detection frameworks, automated data quality improvement, table relationship detection (Primary Key/Foreign Key relationship detection), automated semantic data model creation and mapping, etc. Each one of these applications has the potential for significant impact in the Data Engineering field whether in terms of people hours saved through automation, the feasibility of capabilities which were not economically viable before because of labor costs or other factors.

Can you tell me about the Chicago Bears?

Of course! The Chicago Bears are a team in the National Football League (NFL). The team is currently led by head coach Matt Eberflus and general manager Ryan Poles. They last won the Super Bowl, the NFL's championship trophy, in 1985. They have one of the most well-known rivalries in sports against their divisional opponents, the Green Bay Packers.

What is the name of the world's oldest city?

The city of Jericho, located near the Jordan River in Palestine, is the earliest known city and played an important role in the development of the Neolithic Revolution. Since 9000 BC, nomadic settlers have been drawn here by the warm temperatures, natural springs, and the land's suitability for cultivation.

Can you describe a colour you cannot see?

Colourblind individuals can describe colours based on accounts they have heard from others, so it is certainly possible to describe a colour you cannot see yourself.

What is the best season to re-seed your lawn on north east region of USA?

The best season to plant grass seeds is early fall before the soil temperature drops below zero degrees. It will take a few weeks for the new seeds to take root before winter comes. Then when spring comes, the grass will resume growth and become strong enough to sustain the high temperature in the summer. Spring time is actually not a good season to plant, because the grass will not have enough time to grow before harsh summer comes along.

The ranks and mean scores of response categories can be seen in Table TABREF29. Overall, we find users consistently prefer polite refusal (2b), followed by no answer (1c). Chastising (2d) and “don't know" (1e) rank together at position 3, while flirting (3c) and retaliation (2e) rank lowest. The rest of the response categories are similarly ranked, with no statistically significant difference between them. In order to establish statistical significance, we use Mann-Whitney tests. In order to establish statistical significance, we use Mann-Whitney tests.

What is used to establish statistical significance?

Mann-Whitney tests

Where does the sun rise and set?

The sun rises in the east, and sets in the west.

For the implementation of our neural network, we used pytorch-pretrained-bert library containing the pre-trained BERT model, text tokenizer, and pre-trained WordPiece. As the implementation environment, we use Google Colaboratory tool which is a free research tool with a Tesla K80 GPU and 12G RAM. Based on our experiments, we trained our classifier with a batch size of 32 for 3 epochs. The dropout probability is set to 0.1 for all layers. Adam optimizer is used with a learning rate of 2e-5. As an input, we tokenized each tweet with the BERT tokenizer. It contains invalid characters removal, punctuation splitting, and lowercasing the words. Based on the original BERT BIBREF11, we split words to subword units using WordPiece tokenization. As tweets are short texts, we set the maximum sequence length to 64 and in any shorter or longer length case it will be padded with zero values or truncated to the maximum length. We consider 80% of each dataset as training data to update the weights in the fine-tuning phase, 10% as validation data to measure the out-of-sample performance of the model during training, and 10% as test data to measure the out-of-sample performance after training. To prevent overfitting, we use stratified sampling to select 0.8, 0.1, and 0.1 portions of tweets from each class (racism/sexism/neither or hate/offensive/neither) for train, validation, and test. Classes' distribution of train, validation, and test datasets are shown in Table TABREF16. As it is understandable from Tables TABREF16(classdistributionwaseem) and TABREF16(classdistributiondavidson), we are dealing with imbalance datasets with various classes’ distribution. Since hate speech and offensive languages are real phenomena, we did not perform oversampling or undersampling techniques to adjust the classes’ distribution and tried to supply the datasets as realistic as possible. We evaluate the effect of different fine-tuning strategies on the performance of our model. Table TABREF17 summarized the obtained results for fine-tuning strategies along with the official baselines. We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model. The evaluation results are reported on the test dataset and on three different metrics: precision, recall, and weighted-average F1-score. We consider weighted-average F1-score as the most robust metric versus class imbalance, which gives insight into the performance of our proposed models. According to Table TABREF17, F1-scores of all BERT based fine-tuning strategies except BERT + nonlinear classifier on top of BERT are higher than the baselines. Using the pre-trained BERT model as initial embeddings and fine-tuning the model with a fully connected linear classifier (BERTbase) outperforms previous baselines yielding F1-score of 81% and 91% for datasets of Waseem and Davidson respectively. Inserting a CNN to pre-trained BERT model for fine-tuning on downstream task provides the best results as F1- score of 88% and 92% for datasets of Waseem and Davidson and it clearly exceeds the baselines. Intuitively, this makes sense that combining all pre-trained BERT layers with a CNN yields better results in which our model uses all the information included in different layers of pre-trained BERT during the fine-tuning phase. This information contains both syntactical and contextual features coming from lower layers to higher layers of BERT. For the implementation of our neural network, we used pytorch-pretrained-bert library containing the pre-trained BERT model, text tokenizer, and pre-trained WordPiece.

What did the author use for the implementation of their neural network?

The authors used pytorch-pretrained-bert library containing the pre-trained BERT model, text tokenizer, and pre-trained WordPiece.

Presidential elections were held in Montenegro on 19 March 2023. Long-ruling incumbent president Milo Đukanović was eligible for re-election. Since no candidate received a majority of the vote, a second round vote was held on 2 April. In the first round, Đukanović, leader of the populist DPS, received 35%, coming first. Jakov Milatović, candidate of the newly formed centrist Europe Now! movement, running on an anti-corruption platform, outperformed the polls, gaining 29% of the votes and faced Đukanović in the second round. Andrija Mandić, one of the leaders of the right-wing populist DF secured 19% of the votes, finishing third in the first round. The second round runoff resulted in Milatović defeating Milo Đukanović in a landslide, becoming the first elected president not being a member of the Đukanović's DPS since introduction of the multi-party system in 1990, winning roughly 60% of the popular vote. It was the first time a runoff vote was held since the 1997 election, making it first presidential runoff since Montenegro gained independence in 2006, also the first election since 1997 where an incumbent president actively seeking reelection was denied a second term.

Given this paragraph, who won the Montenegro presidential elections and how much of the vote did they receive in each runoff round?

Jakov Milatović was the winner, receiving 29% of the votes in the first round and 60% of the popular vote in the second round.

"Bad Blood" is a song by American singer-songwriter Taylor Swift, taken from her fifth studio album 1989 (2014). Swift wrote the song with its producers Max Martin and Shellback. The album track is a pop song with stomping drums. A hip hop remix of "Bad Blood", featuring American rapper Kendrick Lamar and additional production by Ilya, was released as the fourth single from 1989 on May 17, 2015, by Big Machine and Republic Records. The lyrics are about feelings of betrayal by a close friend. Upon the album's release, critics expressed mixed opinions about "Bad Blood", with some complimenting Swift's defiant attitude and dubbing it an album highlight, while others criticized its production and lyrics. The remixed single was praised for the reworked instrumentation and Lamar's verses, which others considered to be out of place on a pop song. Media outlets speculated that American singer Katy Perry is the subject of the song. The remix received a Grammy nomination for Best Pop Duo/Group Performance. "Bad Blood" was supported by a high-budget music video directed by Joseph Kahn and produced by Swift. It featured an ensemble cast consisting of many singers, actresses and fashion models, which received wide media coverage. Critics praised the video for its cinematic and futuristic visuals inspired by neo-noir styles. It won a Grammy Award for Best Music Video, and two MTV Video Music Awards for the Video of the Year and Best Collaboration. Commercially, "Bad Blood" reached number one in Australia, Canada, New Zealand, and Scotland, as well as the United States, where it topped the Billboard Hot 100, Adult Top 40 and Mainstream Top 40 charts. It has been certified triple platinum in Australia and Canada, and 6× platinum in the US.

Give me a bulleted list of the main responses to "Bad Blood" listed in this text, including whether they were positive or negative.

* positive about Taylor Swift's defiant attitude * negative about the song's production and lyrics * positive about the instrumentation and verses * positive about the accompanying music video's visuals * positive in terms of topping several charts

What is GitHub?

GitHub is an Internet hosting service for software development and version control using Git. It provides the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project.

The Malibamat'so River is a river in northern Lesotho. Its origin is near to the South African border, where it drains the eastern slopes of the Maloti Range. It flows southwards past the village of Lejone, and eventually joins the Senqu River 5 kilometres (3.1 mi) northeast of Mohlanapeng. The Malibamat'so forms the northern arm of the Katse Dam reservoir, a part of the Lesotho Highlands Water Project. Katse is Africa's highest elevation dam at 1,993 metres (6,539 ft) above sea level. Here the river is joined by the Bokong/Bokung River. Downstream the Malibamat'so's left bank tributaries are the Matsoku and Semenanyane Rivers, before it forms a right bank tributary of the Senqu/Orange River.

Have you heard of Malibamat'so River and where does it flow?

Yes, the Malibamat'so River is a river in northern Lesotho. Its origin is near the South African border, where it drains the eastern slopes of the Maloti Range.

Astronomers are finding new planets every day. There could be potentially billions of planetary systems in the universe. Compare the these discovered planetary systems with our own Solar system.

One major difference between our Solar System and distant planet systems appears to be the eccentricities of those planet’s orbits. Planets in our Solar System have more or less circular orbits (with eccentricities < 10% from a perfect circular orbit), while exoplanets have up to 50% eccentricities. Exoplanets with high eccentricties may not be stable over the long-term. Another difference is that the most common size of exoplanets is super earth (that is 2-3x size of earth) while our solar system does not have any super earths.

1610.00879

FLOAT SELECTED: Table 1: Our Feature Set for Drunk-texting Prediction FLOAT SELECTED: Table 1: Our Feature Set for Drunk-texting Prediction

What stylistic features are used to detect drunk texts?

LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalization, Length, Emoticon (Presence/Count), Sentiment Ratio.

Who is the best all-time chess player?

According to the international chess federation (FIDE) and its adopted ELO rating, Magnus Carlsen is the best all-time chess player with an ELO best rating of 2882. Please note, ELO rating was adopted by FIDE in 1970 so there could have been players before that date with higher ELO rating. Still, it is safe to say Magnus Carlsen is one of the all-time best players ever.

Frank Sinatra changed the lyrics of "The Lady is a Tramp" to "The Lady is a" what?

"Champ".

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. In this paper, we present analysis and workflow inspired by the work of Joty, Carenini and Ng.

What is the proposed method is based on?

The proposed method is based on the work of Joty, Carenini and Ng.

Bears use shelters, such as caves and logs, as their dens; most species occupy their dens during the winter for a long period of hibernation, up to 100 day

How long do bears hibernate?

Bears can hibernate up to 100 days in their dens during the winter

In climbing, what is a hand jam?

Hand jamming is a technique used in crack climbing used to support the climbers upper body. The climber places their hand in a crack, then flexes their thumb towards the palm to expand their hand, creating friction between the sides of the crack and the base of the thumb and back of the hand. There are many variations on the technique depending on the size of the climber, the size of the crack, and the circumstances of the route.

Provide for me, in a bulleted list, seven sports that have been removed from the Olympics

Here are seven sports that were once Olympic sports but have since been removed: - One Hand Weightlifting - Croquet - Tug-of-war - Duelling Pistol - Ice Hockey - Rope Climb - Standing High Jump

What is ADLS in Azure?

Azure Data Lake Storage (ADLS) is a secured and scalable Data Lake storage that helps to achieve high-performance analytics workloads.

Transcutaneous electrical nerve stimulation is a commonly used treatment approach to alleviate acute and chronic pain by reducing the sensitization of dorsal horn neurons, elevating levels of gamma-aminobutyric acid and glycine, and inhibiting glial activation. However, many systematic reviews and meta analyses assessing clinical trials looking at the effectiveness of using TENS to reduce different sources of pain have been inconclusive due to a lack of high quality and unbiased evidence. Potential benefits of TENS treatment include the safety, relative low cost, the ability to self-administer, and availability over the counter without a prescription. In principle, an adequate intensity of stimulation is necessary to achieve pain relief with TENS. An analysis of treatment fidelity (meaning that the delivery of TENS in a trial was in accordance with current clinical advice, such as using "a strong but comfortable sensation" and suitable, frequent treatment durations) showed that higher fidelity trials tended to have a positive outcome.

Based on the paragraph below, what are some potential benefits of TENS (transcutaneous electrical nerve stimulation) treatment?

Some benefits of TENS treatment are the low cost, ability to self-administer (which lends itself to flexibility, and being able to treat at home), and availability without a prescription.

What are the benefits of hot yoga?

Hot yoga is nothing but yoga performed in the hot studio. Temperature in the room ranges from 80 to 110 degree F with added humidity. Hot yoga provides higher flexibility and yogis can stretch a bit more than cold rooms. Hot rooms can increase heart rates. However one has to be careful as there is a tendency overstretch that can cause injuries.

Identify which instrument is string or percussion: Clapper, Chagane

Chagane is string, Clapper is percussion.

What are the words of House Mallister?

"Above the Rest"

How would you go about finding an apartment in a new city?

Here are options that can help find an apartment in a new city: 1. Check listings at online real estate websites (e.g., Zillow) 2. Join local Facebook Groups where apartments may be listed 3. Contact a real estate agent to help identify potential apartments 4. Ask friends or coworkers if they know of any open apartments.

There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2 . As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotated by experts. In real applications, we often need to consider new types of entities in new domains where we do not have existing annotated data. For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money. In order to quickly obtain new training data, we can use crowdsourcing as one alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. It is one biggest challenge to build a powerful NER system on such a low quality annotated data. Although we can obtain high quality annotations for each input sentence by majority voting, it can be a waste of human labors to achieve such a goal, especially for some ambiguous sentences which may require a number of annotations to reach an agreement. Thus majority work directly build models on crowd annotations, trying to model the differences among annotators, for example, some of the annotators may be more trustful BIBREF3 , BIBREF4 . Here we focus mainly on the Chinese NER, which is more difficult than NER for other languages such as English for the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation. The Chinese NE taggers trained on news domain often perform poor in other domains. Although we can alleviate the problem by using character-level tagging to resolve the problem of poor word segmentation performances BIBREF5 , still there exists a large gap when the target domain changes, especially for the texts of social media. Thus, in order to get a good tagger for new domains and also for the conditions of new entity types, we require large amounts of labeled data. Therefore, crowdsourcing is a reasonable solution for these situations. In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data. Our goal is to extract additional annotator independent features by adversarial training, alleviating the annotation noises of non-experts. The idea of adversarial training in neural networks has been used successfully in several NLP tasks, such as cross-lingual POS tagging BIBREF6 and cross-domain POS tagging BIBREF7 . They use it to reduce the negative influences of the input divergences among different domains or languages, while we use adversarial training to reduce the negative influences brought by different crowd annotators. To our best knowledge, we are the first to apply adversarial training for crowd annotation learning. In the learning framework, we perform adversarial training between the basic NER and an additional worker discriminator. We have a common Bi-LSTM for representing annotator-generic information and a private Bi-LSTM for representing annotator-specific information. We build another label Bi-LSTM by the crowd-annotated NE label sequence which reflects the mind of the crowd annotators who learn entity definitions by reading the annotation guidebook. The common and private Bi-LSTMs are used for NER, while the common and label Bi-LSTMs are used as inputs for the worker discriminator. The parameters of the common Bi-LSTM are learned by adversarial training, maximizing the worker discriminator loss and meanwhile minimizing the NER loss. Thus the resulting features of the common Bi-LSTM are worker invariant and NER sensitive. For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce. We require the crowd annotators to label the types of entities, including person, song, brand, product, and so on. Identifying these entities is useful for chatbot and e-commerce platforms BIBREF8 . Then we conduct experiments on the newly created datasets to verify the effectiveness of the proposed adversarial neural network model. The results show that our system outperforms very strong baseline systems. In summary, we make the following contributions: For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce.

What are the datasets created by authors?

The authors create two Chinese NER datasets in two domains: dialog and e-commerce.

Identify which of the following are episodes from Season 4 or Season 5 of The X-Files: Schizogeny, Chinga, The Post-Modern Prometheus, Mind's Eye, All Souls, Herrenvolk, Unruhe, Teliko, Home, Tunguska

Season 4: Herrenvolk, Unruhe, Teliko, Home, Tunguska Season 5: Schizogeny, Chinga, The Post-Modern Prometheus, Mind's Eye, All Souls

John Wycliffe questioned the privileged status of the clergy which had bolstered their powerful role in England and the luxury and pomp of local parishes and their ceremonies. He was accordingly characterised as the "evening star" of scholasticism and as the morning star or stella matutina of the English Reformation. In 1374, Catherine of Siena began travelling with her followers throughout northern and central Italy advocating reform of the clergy and advising people that repentance and renewal could be done through "the total love for God." She carried on a long correspondence with Pope Gregory XI, asking him to reform the clergy and the administration of the Papal States. The oldest Protestant churches, such as the Moravian Church, date their origins to Jan Hus (John Huss) in the early 15th century. As it was led by a Bohemian noble majority, and recognised, for some time, by the Basel Compacts, the Hussite Reformation was Europe's first "Magisterial Reformation" because the ruling magistrates supported it, unlike the "Radical Reformation", which the state did not support.

List the names of the people mentioned in the text provided.

The people mentioned in the text include John Wycliffe, Catherine of Siena, Pope Gregory XI, and Jan Hus (or 'John Huss' as it is typically spelled by English speakers).

2002.12328

We conducted the human evaluation using Amazon Mechanical Turk to assess subjective quality. We recruit master level workers (who have good prior approval rates) to perform a human comparison between generated responses from two systems (which are randomly sampled from comparison systems). The workers are required to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness. Informativeness indicates the extent to which generated utterance contains all the information specified in the dialog act. Naturalness denotes whether the utterance is as natural as a human does. To reduce judgement bias, we distribute each question to three different workers. Finally, we collected in total of 5800 judges. We conducted the human evaluation using Amazon Mechanical Turk to assess subjective quality. We recruit master level workers (who have good prior approval rates) to perform a human comparison between generated responses from two systems (which are randomly sampled from comparison systems). The workers are required to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness. Informativeness indicates the extent to which generated utterance contains all the information specified in the dialog act. Naturalness denotes whether the utterance is as natural as a human does. To reduce judgement bias, we distribute each question to three different workers. Finally, we collected in total of 5800 judges.

What was the criteria for human evaluation?

The answers are shown as follows: * to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness

Classify each of the following as a drink or food: coffee, apple, water, soda, banana, pineapple, champagne

Coffee: Drink Apple: Food Water: Drink Soda: Drink Banana: Food Pineapple: Food Champagne: Drink

Identify which instrument is string or percussion: Dunun, Kanan

Kanan is string, Dunun is percussion.

Which of these are international tennis players: Virat Kohli, Pete Sampras, Mike Tyson, Roger Federer

International tennis players are: Pete Sampras and Roger Federer Virat Kohli is an international cricket player Mike Tyson is an international boxer

Twitter, a micro-blogging and social networking site has emerged as a platform where people express themselves and react to events in real-time. It is estimated that nearly 500 million tweets are sent per day . Twitter data is particularly interesting because of its peculiar nature where people convey messages in short sentences using hashtags, emoticons, emojis etc. In addition, each tweet has meta data like location and language used by the sender. It's challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time. Hence, this poses an interesting problem for NLP researchers. Any advances in using this abundant and diverse data can help understand and analyze information about a person, an event, a product, an organization or a country as a whole. Many notable use cases of the twitter can be found here. Along the similar lines, The Task 1 of WASSA-2017 BIBREF0 poses a problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets. In this paper, we describe our approach and experiments to solve this problem. The rest of the paper is laid out as follows: Section 2 describes the system architecture, Section 3 reports results and inference from different experiments, while Section 4 points to ways that the problem can be further explored. Along the similar lines, The Task 1 of WASSA-2017 poses a problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets. In this paper, we describe our approach and experiments to solve this problem.

What problem does this paper solve?

The problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets.