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which band sold the most albums of all time?

The Beatles. Perhaps unsurprisingly, British rock band The Beatles are top of the list for best-selling artists worldwide, with 183 million units certified sales. The Beatles were an English rock band, formed in Liverpool in 1960, that comprised John Lennon, Paul McCartney, George Harrison and Ringo Starr. They are regarded as the most influential band of all time and were integral to the development of 1960s counterculture and popular music's recognition as an art form.

Which national park is hardest to visit?

Its hard to say which national park is the most difficult to visit. There are numerous locations across multiple geographies including Guam, American Samoa, Northern Mariana Islands, Puerto Rico, The U.S Virgin Islands and the United States itself. Many national parks don't receive a lot of visitors because of their remote locations. Alaska contains many national parks that are hard to visit including: Kobuk Valley National Park, Katmai National Park and Preserve, And Gates of the Arctic. Gates of the Arctic National Park for example is the least visited overall park because its extremely remote and can only be reached by plane or on foot. Other National Parks like Isle Royale in Michigan or Dry Tortugas National Park in Florida are also hard to visit as they can only be reached by boat or seaplane and contain rugged terrain and sensitive reefs.

Do the new shift rules in Major League Baseball make the game more exciting?

Prior to this season, Major League Baseball(MLB) allowed infielders to shift based on who was at bat for the opposing team. The shift simply means that the defending infield could load a certain side of the infield. For example, the shortstop could move to where the 2nd baseman normally is positioned and the 2nd baseman might play in shallow right field. The 3rd baseman would occupy the left side of the infield. The theory behind this shift strategy is based on advanced analytics used in the game of baseball. Using a spray chart to determine where a hitter normally puts a ball in play and with what type of pitch thrown, the defending team would shift to cover more of the playing field and therefore increase the chance the defending team would record an out based on this strategy. It was increasingly used over the last 3-4 years and some argue that employing the shift decreased the number of hits and runs scored during a game. The downstream effect of this made for a lower scoring affair and a less exciting game for a fan base that is increasingly younger and looking for more excitement during baseball games. One argument for allowing the shift is that hitters, being professionals, would adjust and try to beat the shift by hitting a ball away from the shift or hitting to the "opposite field." Hitters answer to beating the shift was to increase their launch angle and play for the home run. While the home run was an important run scoring mechanism, MLB decided to outlaw the shift from defensive strategy for the 2023 season. Hitters refusal to adjust to the shift led to this decision by MLB. The concern was games were not action packed enough and ultimately MLB would continue to lose popularity by its younger viewers, opting for other sports like Basketball, Football, or Hockey to spend their money on. No longer can you sift defensive players. The rules for this season are there must be 2 defenders on each side of 2nd base and their feet must be on the infield dirt as the pitch is being delivered. The hope is more hits will be produced, more runs scored, and an increase in fan approval created. The early opinion after the first week of the MLB season is that the games are proving to be faster paced with more runs and all around action created.

Tell me if these drinks are alcoholic or not: Coca-Cola, Mountain Dew, Budweiser, Rum, Vodka, Tomato Juice, Orange Juice, Sprite, Tequila, Margarita

Budweiser, Rum, Vodka, Tequila and Margarita are alcoholic beverages

What is the weather like in Seattle?

Seattle weather is gray and rainy for the majority of the year. This temperate climate contributes to lush green spaces and landscapes in and around the city. It tends to be sunny and incredibly beautiful during summer, especially from July through September. For many residents, this makes living in Seattle for the rest of the year worth it! Unfortunately, wildfire smoke has been a significant issue in recent years during the summer months. It rarely snows in Seattle, but when it does, the city tends to shut down due to a lack of infrastructure to handle snow and ice across the hilly geography.

In this experiment we compare the performance of the proposed decoder adaptation methods in § 4.1 and § 4.2 on simulated channel variations. Figures 12, 13, and 14 compare the performance of the two methods on different source/target channels using 20, 40, and 60 samples per symbol for adaptation. We observe that in most cases, the MAP SE method outperforms the affine transformation based method. This could be due to the fact that MAP SE has an optimal transformation from the channel output y to the most probable input x, which better compensates for changes in the channel distribution.

Under “observations and takeaways” the authors state that “Between the two adaptation methods, MAP SE performs marginally better than the Affine method.” Which one of the two would the author recommend? Are they equivalent in terms of complexity?

In the revised paper, we include a direct comparison of the performance of the two methods in Appendix D.4 (Figs. 12, 13, and 14). The improvement in BLER of the MAP SE is clear from these results. From the discussion in Appendix C.4, both the methods have a comparable computational complexity, but the affine transformation based method has a higher complexity by a factor of k (the number of components). Therefore, I would recommend the MAP SE method.

1910.07181

To measure the effect of adding Bertram to BERT on downstream tasks, we apply the procedure described in Section SECREF4 to a commonly used textual entailment dataset as well as two text classification datasets: MNLI BIBREF21, AG's News BIBREF22 and DBPedia BIBREF23. For all three datasets, we use BERT$_\text{base}$ as a baseline model and create the substitution dictionary $S$ using the synonym relation of WordNet BIBREF20 and the pattern library BIBREF34 to make sure that all synonyms have consistent parts of speech. As an additional source of word substitutions, we make use of the misspellings dataset of BIBREF25, which is based on query logs of a search engine. To prevent misspellings from dominating the resulting dataset, we only assign misspelling-based substitutes to randomly selected 10% of the words contained in each sentence. Motivated by the results on WNLaMPro-medium, we consider every word that occurs less than 100 times in the WWC and our BooksCorpus replica combined as being rare. Some examples of entries in the resulting datasets can be seen in Table TABREF35. To measure the effect of adding Bertram to BERT on downstream tasks, we apply the procedure described in Section SECREF4 to a commonly used textual entailment dataset as well as two text classification datasets: MNLI BIBREF21, AG's News BIBREF22 and DBPedia BIBREF23.

What are three downstream task datasets?

The answers are shown as follows: * MNLI BIBREF21 * AG's News BIBREF22 * DBPedia BIBREF23

Who was the first country to win the cricket world cup in their home soil

India was the first country to win Cricket World Cup on home soil in 2011.The Indian cricket team are two times World Champions they also won the 1983 Cricket World Cup beating then world champion West Indies.

Tell me which of the following circumstances warrant calling 911: fridge runs out of food, micro kitchen runs out of slacks, someone gets hit by a car, a child is drowning in a public pool, your saving account runs out of money, someone carries assault weapons into campus.

Given the listed circumstances, you should call 911 immediately when someone gets hit by a car, when a child is drowning in a public pool, or you see someone carrying assault weapons on campus.

Crashmo, known as Fallblox in Europe and Australia and as Hiku Otsu in Japan, is a puzzle video game developed by Intelligent Systems and published by Nintendo for the Nintendo 3DS via the Nintendo eShop. The game is a sequel to Pushmo and was released in Japan on October 31, 2012, in the PAL regions on November 15, 2012, and in North America on November 22, 2012.

Using given text as a reference, write some details about game Crashmo

1. Crashmo is a puzzle video game created by Intelligent Systems and released by Nintendo for the Nintendo 3DS via the Nintendo eShop. It is also known as Fallblox in Europe, Australia, and Japan, and as Hiku Otsu in North America. 2. The Pushmo sequel was made available in North America on November 22, 2012, the PAL countries on November 15, and Japan on October 31, 2012.

Classify each of the following as "heavy" or "light": elephant, feather, hippo, paper, whale

Elephant: Heavy Feather: Light Hippo: Heavy Paper: Light Whale: Heavy

What is the best hotel in Sakleshpur

Rosetta By Ferns. The food and Hospitality is outstanding.

Classify each of the following as a state or city: Portland, Austin, San Antonio, Alaska, Anchorage, Maine, Vermont

Portland: City Austin: City San Antonio: City Alaska: State Anchorage: City Maine: State Vermont: State

Bully (alternatively titled Bully: The Musical) is a 2017 musical featurette written and directed by Aaron Alon and produced by Thunderclap Productions. Bully is a dramatic musical about Sam Bradley, a young man who kills himself after being repeatedly bullied in school because others suspect he is gay. The story follows Sam in the days leading up to his death and the lives of those around him in the days following his suicide. Bully premiered in the United States on September 24, 2017, at Alamo Drafthouse – Mason Park in Houston, Texas. The film had its film festival premiere in the 2018 Rainier Independent Film Festival in Ashford, Washington, on May 20, 2018.

Given a reference text about Bully, tell me when it premiered and what it was about.

Bully premiered on September 24, 2017 and was about a young man who commits suicide after being bullied in school because others thought he was gay.

A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks. Properties Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design. Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to "memorize" a great quantity of facts during training. Hallucinations Main article: Hallucination (artificial intelligence) In artificial intelligence in general, and in large language models in particular, a "hallucination" is a confident response that does not seem to be justified by the model's training data. Emergent abilities On a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities. Unpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called "emergent abilities". Researchers note that such abilities "cannot be predicted simply by extrapolating the performance of smaller models". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs. Architecture and training Large language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or "filling in the blanks", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus. The earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens. LLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million. A 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters). Application to downstream tasks Between 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via "prompting" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions. Fine-tuning Main article: Fine-tuning (machine learning) Fine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be "frozen", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen). Prompting See also: Prompt engineering and Few-shot learning (natural language processing) In the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In "few-shot prompting", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows: Review: This movie stinks. Sentiment: negative Review: This movie is fantastic! Sentiment: If the model outputs "positive", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be "The sentiment associated with the movie review 'This movie is fantastic!' is". Few-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering. Instruction tuning Instruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt "Write an essay about the main themes of Hamlet." might provide a completion such as "A late penalty of 10% per day will be applied to submissions received after March 17." In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, "self-instruct", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples). https://en.wikipedia.org/wiki/Large_language_model

Given these paragraphs about Large language models, what are some examples of emergent abilities?

Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.

King Field (alternately, Kingfield) is a neighborhood in the Southwest community in Minneapolis, Minnesota. Its boundaries are 36th Street to the north, Interstate 35W to the east, 46th Street to the south, and Lyndale Avenue to the west. King Field, within the King Field neighborhood is a park named after Martin Luther King Jr. This was farm country in the 2nd half of the 19th century. Transportation was by horse and buggy. Fewer than 20 farms had been established by 1874. C.C. Garvey owned a dairy farm near 44th Street and Grand Ave. George Bichnell farmed 18 acres (73,000 m2) of land from Lyndale to Pleasant, 42nd to 43rd Streets. Hiram Van Nest farmed 28 acres (110,000 m2) of land from 40th to 42nd, Pleasant to Lyndale. The Farmsworth farm occupied 57 acres (230,000 m2) south of 47th Street and East of Nicollet.

How many acres did Hiram farm?

28 acres

How can I learn how to play volleyball?

Learning to play volleyball requires a combination of skills which include hand-eye coordination, speed and proper body positioning. Traditional indoor volleyball is played with 6 players on each side of the net and the goal is to score by hitting the ball on the ground on the opposing teams defined court dimensions or through the other team making an error in handling the ball. Beach volleyball is played on sand and typically played with 2 players against an opposing 2 players. In order to excel in volleyball you must first master the process of what is called pass, set and hit. Each team is allowed 3 touches of the volleyball before it crosses over the net to the other team. If you can coordinate with your teammates to run more advanced plays, you have a higher likelihood to score. You must learn the skills of passing, setting and hitting in order to do this well. Players of all ages can enjoy volleyball and it makes for a fun indoor/outdoor activity. Playing at a high level in volleyball offers a great cardiovascular and muscle strengthening activity.

Transportation Research Part D: Transport and Environment is a peer-reviewed, international scientific journal which publishes work relating to land, sea, and air transportation systems and their impact on environmental systems. It was established in 1996 and is published by Elsevier. The editors-in-chief are Robert Noland (Rutgers University) and Jason Cao (University of Minnesota, Twin Cities).

Extract the names of the editors of Transportation Research Part D. Separate them with a comma.

Robert Noland, Jason Cao

What is a viral video?

Viral video is one that is popular and generate a lot of views which is appealing for advertisers

Give me a list of hobbies

Pickleball, soccer, writing, reading, cooking, running, biking

Why do i see a dark section near the CD Drive ring, when looked from a tilted angle?

Those darker regions closer to the ring lock on CD Drive are an indication of how much of the CD Drive space is written with data. CD Drive has sectors and the rpm of the CD drive Optical Reader will read these sectors and show it into the file system of the operating system you are using. The dark section does not necessarily show how much is available space but can be an indicator of if the CD drive is empty

Fossil Fighters is a role-playing video game series published by Nintendo for the Nintendo DS and Nintendo 3DS consoles. Its gameplay revolves around digging up and extracting dinosaur fossils, reviving them into supernatural "vivosaurs" and engaging in battle.

What is fossil fighter gameplay like?

Fossil fighters is a third-person, single-player game on the Nintendo DS focused around finding, cleaning, and reviving dinosaur fossils on Vivosaur Island. Once dinosaurs (called vivosaurs in the game) are revived, they can be used to battle other fossil fighters. The goal of the game is to reach the highest fossil fighter license level, find better and better fossils, defeat the BB Bandits who are determined to cause trouble around the island, and uncover various mysteries surrounding the island.

Write a dialogue describing a phone call between a parent named June Smith and her daughter's doctors office making an appointment for a sports physical. The daughter is named May, she plays soccer, and the doctor's schedule is very busy at the start of the school year.

Dr's Office: Hello, this is Dr. Marshall's office, how can I help you? June: Hi, this is June Smith, and I'm calling to make an appointment for my daughter May. Dr's Office: Okay, what type of appointment is this? June: She needs a sports physical for her school soccer team Dr's Office: Sounds good, Dr. Marshall has an appointment available in two weeks on Friday at 10 am June: Is there anyway to see the doctor before then? Dr's Office: Unfortunately, with school starting the schedule is very packed, so that is the first available appointment. June: Okay, I'll take the appointment on the Friday then. See you then! Dr's Office: Sounds good, I'll put you in the schedule. Is there anything else I can help with? June: Nope, that's all. Have a nice day!

why pluto is not a planet?

The IAU (International Astronomical Union) terms a an object in a solar system as planet if it meets following 3 criteria: 1) The object must orbit around the Sun. 2) The object must have sufficient mass to assume hydrostatic equilibrium (a nearly round shape). 3) The object have cleared the neighborhood around its orbit. Pluto meets the first 2 criteria but fails to meet the third. Hence, Pluto has been categorized as a dwarf planet.

“Laughter is the best Medicine” is a saying which is popular with most of the people. Humor is a form of communication that bridges the gap between various languages, cultures, ages and demographics. That's why humorous content with funny and witty hashtags are so much popular on social media. It is a very powerful tool to connect with the audience. Automatic Humor Recognition is the task of determining whether a text contains some level of humorous content or not. First conference on Computational humor was organized in 1996, since then many research have been done in this field. kao2016computational does pun detection in one-liners and dehumor detects humor in Yelp reviews. Because of the complex and interesting aspects involved in detecting humor in texts, it is one of the challenging research field in Natural Language Processing BIBREF3 . Identifying humor in a sentence sometimes require a great amount of external knowledge to completely understand it. There are many types of humor, namely anecdotes, fantasy, insult, irony, jokes, quote, self deprecation etc BIBREF4 , BIBREF5 . Most of the times there are different meanings hidden inside a sentence which is grasped differently by individuals, making the task of humor identification difficult, which is why the development of a generalized algorithm to classify different type of humor is a challenging task. Majority of the researches on social media texts is focused on English. A study by schroeder2010half shows that, a high percentage of these texts are in non-English languages. fischer2011language gives some interesting information about the languages used on Twitter based on the geographical locations. With a huge amount of such user generated data available on social media, there is a need to develop technologies for non-English languages. In multilingual regions like South Asia, majority of the social media users speak more than two languages. In India, Hindi is the most spoken language (spoken by 41% of the population) and English is the official language of the country. Twitter has around 23.2 million monthly active users in India. Native speakers of Hindi often put English words in the sentences and transliterate the whole sentence to Latin script while posting on social media, thereby making the task of automatic text classification a very challenging problem. Linguists came up with a term for any type of language mixing, known as `code-mixing' or `code-switching' BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . Both the terms are used interchangeably, but there is a slight difference between the two terms. Code-mixing refers to the insertion of words, phrases, and morphemes of one language into a statement or an expression of another language, whereas transliteration of every word in a sentence to another script ( here Devanagari to Latin) is coined code-switching BIBREF10 . The first tweet in Figure 1 is an example of code-mixing and second is an example of code-switching. In this paper, we use code-mixing to denote both cases. In this paper, we present a freely available corpus containing code-mixed tweets in Hindi and English language with tweets written in Latin script. Tweets are manually classified into humorous and non-humorous classes. Moreover, each token in the tweets is also given a language tag which determines the source or origin language of the token (English or Hindi). The paper is divided in sections as follows, we start by describing the corpus and the annotation scheme in Section 2. Section 3 summarizes our supervised classification system which includes pre-processing of the tweets in the dataset and the feature extraction followed by the method used to identify humor in tweets. In the next subsection, we describe the classification model and the results of the experiments conducted using character and word level features. In the last section, we conclude the paper followed by future work and references. Moreover, each token in the tweets is also given a language tag which determines the source or origin language of the token (English or Hindi).

How to identify Hindi tweets in the corpus?

Language tags are given to identify the source or origin language.

1810.00663

The baseline approach is based on BIBREF20 . It divides the task of interpreting commands for behavioral navigation into two steps: path generation, and path verification. For path generation, this baseline uses a standard sequence-to-sequence model augmented with an attention mechanism, similar to BIBREF23 , BIBREF6 . For path verification, the baseline uses depth-first search to find a route in the graph that matches the sequence of predicted behaviors. If no route matches perfectly, the baseline changes up to three behaviors in the predicted sequence to try to turn it into a valid path. The baseline approach is based on BIBREF20 . It divides the task of interpreting commands for behavioral navigation into two steps: path generation, and path verification. For path generation, this baseline uses a standard sequence-to-sequence model augmented with an attention mechanism, similar to BIBREF23 , BIBREF6 . For path verification, the baseline uses depth-first search to find a route in the graph that matches the sequence of predicted behaviors. If no route matches perfectly, the baseline changes up to three behaviors in the predicted sequence to try to turn it into a valid path.

What baselines did they compare their model with?

the baseline where path generation uses a standard sequence-to-sequence model augmented with attention mechanism and path verification uses depth-first search

Question classification typically makes use of a combination of syntactic, semantic, surface, and embedding methods. Syntactic patterns BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 and syntactic dependencies BIBREF3 have been shown to improve performance, while syntactically or semantically important words are often expanding using Wordnet hypernyms or Unified Medical Language System categories (for the medical domain) to help mitigate sparsity BIBREF22 , BIBREF23 , BIBREF24 . Keyword identification helps identify specific terms useful for classification BIBREF25 , BIBREF3 , BIBREF26 . Similarly, named entity recognizers BIBREF6 , BIBREF27 or lists of semantically related words BIBREF6 , BIBREF24 can also be used to establish broad topics or entity categories and mitigate sparsity, as can word embeddings BIBREF28 , BIBREF29 . Here, we empirically demonstrate many of these existing methods do not transfer to the science domain. The highest performing question classification systems tend to make use of customized rule-based pattern matching BIBREF30 , BIBREF7 , or a combination of rule-based and machine learning approaches BIBREF19 , at the expense of increased model construction time. A recent emphasis on learned methods has shown a large set of CNN BIBREF29 and LSTM BIBREF8 variants achieve similar accuracy on TREC question classification, with these models exhibiting at best small gains over simple term frequency models. These recent developments echo the observations of Roberts et al. BIBREF3 , who showed that existing methods beyond term frequency models failed to generalize to medical domain questions. Here we show that strong performance across multiple datasets is possible using a single learned model. Due to the cost involved in their construction, question classification datasets and classification taxonomies tend to be small, which can create methodological challenges. Roberts et al. BIBREF3 generated the next-largest dataset from TREC, containing 2,936 consumer health questions classified into 13 question categories. More recently, Wasim et al. BIBREF4 generated a small corpus of 780 biomedical domain questions organized into 88 categories. In the education domain, Godea et al. BIBREF5 collected a set of 1,155 classroom questions and organized these into 16 categories. To enable a detailed study of science domain question classification, here we construct a large-scale challenge dataset that exceeds the size and classification specificity of other datasets, in many cases by nearly an order of magnitude. Here, we empirically demonstrate many of these existing methods do not transfer to the science domain.

Are there any limitations in the existing methods?

Yes, many of them do not transfer to the science domain.

Which of the following countries are monarchies? United Kingdom, the Netherlands, Germany, Luxembourg, United States, Russia, Poland, Denmark, Norway, Sweden, Finland, Spain

The monarchies in that list are the United Kingdom, the Netherlands, Luxembourg, Denmark, Norway, Sweden and Spain

Given these two sets of instructions, will you end up at the same end point if you started at the same beginning point? First instructions: 1) Take 2 steps forward 2) Turn left 3) Take 3 steps forward 4) Turn left 5) take 2 steps forward. Second instructions: 1) Turn left 2) Take 6 steps forward 3) Take 1 step backward 4) Take 2 steps backward.

Yes, you will end up at the same point for both sets of instructions. In the first set of instructions, you will go forward, to the left, and then when turn again you will be facing opposite your starting orientation. Therefore, stepping forward will take you closer to your starting point. Finally you end 3 steps to the left of your starting point. For the second set of instructions, you turn left and go forward 6 then backwards 3 altogether, ending 3 steps to the left of the starting point.

1809.09795

We test our proposed approach for binary classification on either sarcasm or irony, on seven benchmark datasets retrieved from different media sources. Below we describe each dataset, please see Table TABREF1 below for a summary. Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol. Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 . The dataset was manually annotated using binary labels. We also use the dataset by BIBREF4 , which is manually annotated for sarcasm. Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag. Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 . We test our proposed approach for binary classification on either sarcasm or irony, on seven benchmark datasets retrieved from different media sources. Below we describe each dataset, please see Table TABREF1 below for a summary. Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol. Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18 . The dataset was manually annotated using binary labels. We also use the dataset by BIBREF4 , which is manually annotated for sarcasm. Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag. Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 .

What are the three different sources of data?

The answers are shown as follows: * Twitter * Reddit * Online Dialogues

Although internet users accept unilateral contracts such as terms of service on a regular basis, it is well known that these users rarely read them. Nonetheless, these are binding contractual agreements. A recent study suggests that up to 98% of users do not fully read the terms of service before accepting them BIBREF0 . Additionally, they find that two of the top three factors users reported for not reading these documents were that they are perceived as too long (`information overload') and too complicated (`difficult to understand'). This can be seen in Table TABREF3 , where a section of the terms of service for a popular phone app includes a 78-word paragraph that can be distilled down to a 19-word summary. The European Union's BIBREF1 , the United States' BIBREF2 , and New York State's BIBREF3 show that many levels of government have recognized the need to make legal information more accessible to non-legal communities. Additionally, due to recent social movements demanding accessible and transparent policies on the use of personal data on the internet BIBREF4 , multiple online communities have formed that are dedicated to manually annotating various unilateral contracts. We propose the task of the automatic summarization of legal documents in plain English for a non-legal audience. We hope that such a technological advancement would enable a greater number of people to enter into everyday contracts with a better understanding of what they are agreeing to. Automatic summarization is often used to reduce information overload, especially in the news domain BIBREF5 . Summarization has been largely missing in the legal genre, with notable exceptions of judicial judgments BIBREF6 , BIBREF7 and case reports BIBREF8 , as well as information extraction on patents BIBREF9 , BIBREF10 . While some companies have conducted proprietary research in the summarization of contracts, this information sits behind a large pay-wall and is geared toward law professionals rather than the general public. In an attempt to motivate advancement in this area, we have collected 446 sets of contract sections and corresponding reference summaries which can be used as a test set for such a task. We have compiled these sets from two websites dedicated to explaining complicated legal documents in plain English. Rather than attempt to summarize an entire document, these sources summarize each document at the section level. In this way, the reader can reference the more detailed text if need be. The summaries in this dataset are reviewed for quality by the first author, who has 3 years of professional contract drafting experience. The dataset we propose contains 446 sets of parallel text. We show the level of abstraction through the number of novel words in the reference summaries, which is significantly higher than the abstractive single-document summaries created for the shared tasks of the Document Understanding Conference (DUC) in 2002 BIBREF11 , a standard dataset used for single document news summarization. Additionally, we utilize several common readability metrics to show that there is an average of a 6 year reading level difference between the original documents and the reference summaries in our legal dataset. In initial experimentation using this dataset, we employ popular unsupervised extractive summarization models such as TextRank BIBREF12 and Greedy KL BIBREF13 , as well as lead baselines. We show that such methods do not perform well on this dataset when compared to the same methods on DUC 2002. These results highlight the fact that this is a very challenging task. As there is not currently a dataset in this domain large enough for supervised methods, we suggest the use of methods developed for simplification and/or style transfer. In this paper, we begin by discussing how this task relates to the current state of text summarization and similar tasks in Section SECREF2 . We then introduce the novel dataset and provide details on the level of abstraction, compression, and readability in Section SECREF3 . Next, we provide results and analysis on the performance of extractive summarization baselines on our data in Section SECREF5 . Finally, we discuss the potential for unsupervised systems in this genre in Section SECREF6 . In an attempt to motivate advancement in this area, we have collected 446 sets of contract sections and corresponding reference summaries which can be used as a test set for such a task.

How many sets of contract sections and corresponding reference summaries are the test set?

446 sets.

How to evaluate an equation with multiple operations?

Use PEMDAS (Parantheses, Exponents, Multiplication, Division, Addition and Substraction) as the order in which calculations are performed

Name 10 colors

Fuschia, magenta, orange, green, blue, red, yellow, chartreuse, aquamarine, sienna

Hockey Club Davos is a professional Swiss ice hockey club based in Davos, Switzerland. Davos plays in the National League (NL). It is generally referred to by its abbreviation "HCD" by the fans. The team is usually a strong force in the league and often bolster their roster with Swiss national team players and players who once played for National Hockey League teams. HC Davos also has hosted the annual Spengler Cup tournament, an invitational competition, since 1923. The team had the lowest attendance of the National League for the 2016–17 season, averaging 4,792 spectators over their 25 regular season home games. History HC Davos was founded in 1921. In 1923, Davos presented the first Spengler Cup, one of the oldest tournaments in sports still being played. HC Davos was led by a forward line known as "The ni-storm" (German: Der ni-sturm) from 1933 to 1950, which included Bibi Torriani, along with brothers Hans Cattini and Ferdinand Cattini. The line was named for the last syllable (-ni) of players' surnames. The ni-storm was regarded as the top line of HC Davos and the Switzerland men's national ice hockey team from 1933 to 1950. During this time, the club won 18 Swiss championships, and six Spengler Cups. The team was relegated into 1. Liga in 1990, one of the lowest points in its history. After a new beginning, the club moved into the Swiss League, and two years later returned to the NL. Davos has been among the best teams in the league since, with recent league championships in 2002, 2005, 2007, 2009, 2011, and 2015.[citation needed]

Does Davos have a good hockey team

Davos indeed has an excellent hockey team and a record of most Swiss national titles. Hockey Club Davos is a professional Swiss ice hockey club based in Davos, Switzerland. Davos plays in the National League (NL). It is generally referred to by its abbreviation "HCD" by the fans. The team is usually a strong force in the league and often bolster their roster with Swiss national team players and players who once played for National Hockey League teams. HC Davos also has hosted the annual Spengler Cup tournament, an invitational competition, since 1923. The team had the lowest attendance of the National League for the 2016–17 season, averaging 4,792 spectators over their 25 regular season home games. History HC Davos was founded in 1921. In 1923, Davos presented the first Spengler Cup, one of the oldest tournaments in sports still being played. HC Davos was led by a forward line known as "The ni-storm" (German: Der ni-sturm) from 1933 to 1950, which included Bibi Torriani, along with brothers Hans Cattini and Ferdinand Cattini. The line was named for the last syllable (-ni) of players' surnames. The ni-storm was regarded as the top line of HC Davos and the Switzerland men's national ice hockey team from 1933 to 1950. During this time, the club won 18 Swiss championships, and six Spengler Cups. The team was relegated into 1. Liga in 1990, one of the lowest points in its history. After a new beginning, the club moved into the Swiss League, and two years later returned to the NL. Davos has been among the best teams in the league since, with recent league championships in 2002, 2005, 2007, 2009, 2011, and 2015.

Give me a bulleted list of brands that make golf clubs

- Taylormade - Titleist - Callaway - Ping - Cobra - PXG

What are the primary causes of inflation?

One of the primary causes of inflation are when there is an overall increase in the amount of money people are being paid, therefore it drives up the price of everything else.

Identify which instrument is string or woodwind: Ukulele, Oboe

Ukulele is string, Oboe is woodwind.

The Tough Guy Contest was an elimination style mixed martial arts tournament billed as “Anything Goes” and “Organized, Legalized, Streetfighting.” The inaugural field of 64 fighters was broken into light and heavyweight divisions. Bill Viola wrote the first codified set of mixed martial arts rules in 1979. Fighters were required to use open fingered padded gloves and permitted to use any combination of martial arts skills, including but not limited to boxing, wrestling, grappling, karate, judo, and jiu-jitsu techniques. Bouts were 3 two minute rounds judged using a 10-point-must system. Fighters could win by knockout, technical knockout, submission or decision. Fighters were broken into weight classes, required to wear Olympic style headgear, and subject to a doctor’s approval to fight.

How does someone win the Tough Guy Contest?

Fighters win the Tough Guy Contest by winning every round of an elimination style tournament in their weight class. Fighters may use any combination of mixed martial arts skills to win each round by knockout, technical knockout, submission or decision based on a 10-point-must system.

At sufficiently low temperatures, electrons near the Fermi surface become unstable against the formation of Cooper pairs. Cooper showed such binding will occur in the presence of an attractive potential, no matter how weak. In conventional superconductors, an attraction is generally attributed to an electron-lattice interaction. The BCS theory, however, requires only that the potential be attractive, regardless of its origin. In the BCS framework, superconductivity is a macroscopic effect which results from the condensation of Cooper pairs. These have some bosonic properties, and bosons, at sufficiently low temperature, can form a large Bose–Einstein condensate. Superconductivity was simultaneously explained by Nikolay Bogolyubov, by means of the Bogoliubov transformations.

Summarize the mechanism for conventional superconductors.

Cooper Pairs - consisting of two lightly attracted electrons - still carry charge, but now act more like bosons than fermions. Unlike fermions, bosons can all behave the exact same. Cooper Pairs all "acting the same" is the mechanism of conventional superconductivity.

Rabindranath Tagore FRAS (/rəˈbɪndrənɑːt tæˈɡɔːr/ (listen); pronounced [rɔˈbindɾɔnatʰ ˈʈʰakuɾ]; 7 May 1861 – 7 August 1941) was a Bengali polymath who worked as a poet, writer, playwright, composer, philosopher, social reformer and painter. He reshaped Bengali literature and music as well as Indian art with Contextual Modernism in the late 19th and early 20th centuries. Author of the "profoundly sensitive, fresh and beautiful" poetry of Gitanjali, he became in 1913 the first non-European and the first lyricist to win the Nobel Prize in Literature. Tagore's poetic songs were viewed as spiritual and mercurial; however, his "elegant prose and magical poetry" remain largely unknown outside Bengal. He was a fellow of the Royal Asiatic Society. Referred to as "the Bard of Bengal", Tagore was known by sobriquets: Gurudeb, Kobiguru, Biswokobi.

From the passage provided, extract the sobriquets Tagore was known by. Separate them with a comma.

Gurudeb, Kobiguru, Biswokobi.

Give me some ideas on how to make money while traveling the world.

Freelancing online Language tuition Teach other people your skills Make things to sell Offer your services at hostels Sell your photos Street performances Seasonal work

How did Abraham Lincoln die?

Abraham Lincoln was assassinated during a play in Ford's Theater while serving as President

Bill Rasmussen came up with the concept of ESPN in May 1978, after he was fired from his job with the World Hockey Association's New England Whalers. Bill and his son Scott (who had also been let go by the Whalers) first rented office space in Plainville, Connecticut. However, the plan to base ESPN there was put on hold because of a local ordinance prohibiting buildings from bearing rooftop satellite dishes. Available land to build their own facility on was quickly found in Bristol, Connecticut (where the channel remains headquartered to this day), with funding to buy the property provided by Getty Oil, which purchased 85% of the company from Bill Rasmussen on February 22, 1979, in an attempt to diversify the company's holdings. This helped the credibility of the fledgling company; however, there were still many doubters about the viability of their sports channel concept. Another event that helped build ESPN's credibility was securing an advertising agreement with Anheuser-Busch in the spring of 1979; the company invested $1 million to be the "exclusive beer advertised on the network."

Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}

• Bill Rasmussen • Scott Rasmussen

Datasets. We perform our experiments with two predetermined event categories: cyber security (CyberAttack) and death of politicians (PoliticianDeath). These event categories are chosen as they are representative of important event types that are of interest to many governments and companies. The need to create our own dataset was motivated by the lack of public datasets for event detection on microposts. The few available datasets do not suit our requirements. For example, the publicly available Events-2012 Twitter dataset BIBREF20 contains generic event descriptions such as Politics, Sports, Culture etc. Our work targets more specific event categories BIBREF21. Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively. Unlabeled microposts are collected by using the keyword `hack' for CyberAttack, while for PoliticianDeath, we use a set of keywords related to `politician' and `death' (such as `bureaucrat', `dead' etc.) For each dataset, we randomly select 500 tweets from the unlabeled subset and manually label them for evaluation. Table TABREF25 shows key statistics from our two datasets. Comparison Methods. To demonstrate the generality of our approach on different event detection models, we consider Logistic Regression (LR) BIBREF1 and Multilayer Perceptron (MLP) BIBREF2 as the target models. As the goal of our experiments is to demonstrate the effectiveness of our approach as a new model training technique, we use these widely used models. Also, we note that in our case other neural network models with more complex network architectures for event detection, such as the bi-directional LSTM BIBREF17, turn out to be less effective than a simple feedforward network. For both LR and MLP, we evaluate our proposed human-AI loop approach for keyword discovery and expectation estimation by comparing against the weakly supervised learning method proposed by BIBREF1 (BIBREF1) and BIBREF17 (BIBREF17) where only one initial keyword is used with an expectation estimated by an individual expert. Parameter Settings. We empirically set optimal parameters based on a held-out validation set that contains 20% of the test data. These include the hyperparamters of the target model, those of our proposed probabilistic model, and the parameters used for training the target model. We explore MLP with 1, 2 and 3 hidden layers and apply a grid search in 32, 64, 128, 256, 512 for the dimension of the embeddings and that of the hidden layers. For the coefficient of expectation regularization, we follow BIBREF6 (BIBREF6) and set it to $\lambda =10 \times $ #labeled examples. For model training, we use the Adam BIBREF22 optimization algorithm for both models. Evaluation. Following BIBREF1 (BIBREF1) and BIBREF3 (BIBREF3), we use accuracy and area under the precision-recall curve (AUC) metrics to measure the performance of our proposed approach. We note that due to the imbalance in our datasets (20% positive microposts in CyberAttack and 27% in PoliticianDeath), accuracy is dominated by negative examples; AUC, in comparison, better characterizes the discriminative power of the model. Crowdsourcing. We chose Level 3 workers on the Figure-Eight crowdsourcing platform for our experiments. The inter-annotator agreement in micropost classification is taken into account through the EM algorithm. For keyword discovery, we filter keywords based on the frequency of the keyword being selected by the crowd. In terms of cost-effectiveness, our approach is motivated from the fact that crowdsourced data annotation can be expensive, and is thus designed with minimal crowd involvement. For each iteration, we selected 50 tweets for keyword discovery and 50 tweets for micropost classification per keyword. For a dataset with 80k tweets (e.g., CyberAttack), our approach only requires to manually inspect 800 tweets (for 8 keywords), which is only 1% of the entire dataset. To demonstrate the generality of our approach on different event detection models, we consider Logistic Regression (LR) (Ritter et al. 2015) and Multilayer Perceptron (MLP) (Chambers, Fry, and McMasters 2018) as the target models.

What are the target models?

LR and MLP.

What is the most famous landmark in Cologne?

The most famous landmark in Cologne is the Cologne Cathedral. The construction of the cathedral started in 1248 and was finished in 1880. The cathedral is famous for its gothic architecture. It is located in the center of the city of Cologne. More than 20.000 tourists visit the cathedral every day.

What are some of the major food allergens in the USA?

Some of the major food allergens in the USA as identified by the Food and Drug Administration (FDA) include: Wheat, peanuts, tree nuts, soy, shellfish, fish, eggs, milk, and sesame.

When did money originate?

1200 B.C.

The term "unplugged" has come to refer to music that would usually be played on electrified instruments (such as an electric guitar or synthesizer) but is rendered instead on instruments that can be played without electricity, for example acoustic guitar or traditional piano, although a microphone is still used. In most cases, the bass (or bass guitar) is amplified, and a Hammond organ is sometimes used.

What is unplugged?

Unplugged is a style of playing music. It is the performance of music that is intended to be played on instruments that use electricity, using instruments that do not use electricity. This allows you to touch the essential part of the original music and performance. In contrast to standing and swinging in a large stadium, performing in a small music hall allows the performers and audience to enjoy playing close together.

Li "Oliveira" Peinan (born June 28, 2000) is a Chinese Terran player who is currently playing for DKZ Gaming. He is formerly known as "TIME".

Who is TIME

ollowing his performance at GPL 2015 Season 3 Premier League, he was invited to become a member of X-Team at the beginning of 2016. He became famous in NEO Star League 2016 International, where he beat MarineLorD 2-0 through his strongest matchup (TvT). In the 3rd place playoff of WESG 2017 - Asia-Pacific Qualifier, he beat Maru 3-2 in an official offline tournament. After One More was disbanded, Oliveira joined Clan SCBoy for 4 months before leaving. After being teamless for another 4 months, he joined Newbee in the Summer of 2018. In 2018 WCS Montreal, Oliveira reached a new milestone in his SC2 career. He never expected himself to go that deep into the tournament. Oliveira defeated Has 3:2, who was the runner-up of 2018 WCS Valencia, and he advanced to the quarterfinals. In Ro8, he surprised most people by sweeping the series against HeRoMaRinE, one of the best EU Terran players, 3:0. Before him, the Chinese audience had sorely missed one of their own players in the semifinals, the last two being MacSed & Jim in 2013. Even though Reynor ended Oliveira's journey, he had a bright future in the coming years. On April 2019, despite losing to MaSa in 2019 WCS Winter AM, Oliveira saw his 4th trip to the Top 8 of a premier tournament. Just one month after 2019 WCS Winter AM, Oliveira made his breakthrough and stood in front of Serral in the quarterfinals of 2019 WCS Spring. Even though he felt anxious while facing the world's best player, Oliveira still managed to grab one map after an unsuccessful Nydus timing attack from Serral. He said that he had seen the gap between him and the best player and what he could improve in the next tournament after that game. On July 2019, Oliveira was defeated by ShoWTimE, the best German Protoss, 1:3 in the quarterfinals of 2019 WCS Summer. Later, he was successfully qualified for Assembly Summer 2019 after defeating Scarlett for the first time. He got revenge against ShoWTimE in Group Stage 2 with two decisive SCV pulls. He then faced Serral in the quarterfinals for the second time and showed his improvement after 3 months' practice. No one expected that Oliveira would almost take down Serral in this BO5, but he narrowly lost 2:3. He even led 2:1 in the series but Serral pulled off a comeback after a vigorous effort and defeated Oliveira with 3:2. During the winner's interview, the reigning WCS Champion said that there was a moment he thought he might have lost the game. Oliveira showed his excellent control just like what ByuN did 3 years ago, and earned himself many fans worldwide. On Aug 15, 2019 GSL vs. the World, he dominated one of the best Zerg players from Korea, soO. There was no doubt that Oliveira was now one of the best Terran players in the world. After defeating MaNa 3:1 and Astrea 3:0 in 2019 WCS Fall, Oliveira became the first Mainland Chinese player to qualify for 2019 WCS Global Finals in Anaheim. It had taken 8 years for Chinese fans to see a local player competing in the Global Finals for the first time. No matter the result, Oliveira had initiated a new era for the Chinese StarCraft 2 scene. Winning IEM Katowice 2023 In one of the biggest upsets in Starcraft II history, Oliveira defeated Maru 4:1 in the Grand Final of IEM Katowice 2023 to become the 2023 World Champion. After narrowly escaping his group with a 2-3 overall record with only beating Terrans, he beat 2022 IEM Katowice Semifinalist HeRoMaRinE, 2021 IEM Katowice Champion Reynor and 2022 Season 2 GSL Code S Champion herO to reach the finals. His opponent in that match, 5-time GSL Code S Champion Maru, was the 94% favourite according to Aligulac and won the first map in a convincing fashion. However, Oliveira responded with a series of impressive victories to take the series 4:1 and lift the trophy.

What should I do if my motherboard won't post?

Remove any recently installed components. Reset CMOS. If that doesn't fix the problem, check all cables are plugged in completely. If that does not help, reseat the graphics card, memory, and CPU one by one.

In Biomedical Information Extraction, Relation Extraction involves finding related entities of many different kinds. Some of these include protein-protein interactions, disease-gene relations and drug-drug interactions. Due to the explosion of available biomedical literature, it is impossible for one person to extract relevant relations from published material. Automatic extraction of relations assists in the process of database creation, by suggesting potentially related entities with links to the source article. For example, a database of drug-drug interactions is important for clinicians who administer multiple drugs simultaneously to their patients- it is imperative to know if one drug will have an adverse effect on the other. A variety of methods have been developed for relation extractions, and are often inspired by Relation Extraction in NLP tasks. These include rule-based approaches, hand-crafted patterns, feature-based and kernel machine learning methods, and more recently deep learning architectures. Relation Extraction systems over Biomedical Corpora are often affected by noisy extraction of entities, due to ambiguities in names of proteins, genes, drugs etc. BIBREF12 was one of the first large scale Information Extraction efforts to study the feasibility of extraction of protein-protein interactions (such as “protein A activates protein B") from Biomedical text. Using 8 hand-crafted regular expressions over a fixed vocabulary, the authors were able to achieve a recall of 30% for interactions present in The Dictionary of Interacting Proteins (DIP) from abstracts in Medline. The method did not differentiate between the type of relation. The reasons for the low recall were the inconsistency in protein nomenclature, information not present in the abstract, and due to specificity of the hand-crafted patterns. On a small subset of extracted relations, they found that about 60% were true interactions between proteins not present in DIP. BIBREF13 combine sentence level relation extraction for protein interactions with corpus level statistics. Similar to BIBREF12 , they do not consider the type of interaction between proteins- only whether they interact in the general sense of the word. They also do not differentiate between genes and their protein products (which may share the same name). They use Pointwise Mutual Information (PMI) for corpus level statistics to determine whether a pair of proteins occur together by chance or because they interact. They combine this with a confidence aggregator that takes the maximum of the confidence of the extractor over all extractions for the same protein-pair. The extraction uses a subsequence kernel based on BIBREF14 . The integrated model, that combines PMI with aggregate confidence, gives the best performance. Kernel methods have widely been studied for Relation Extraction in Biomedical Literature. Common kernels used usually exploit linguistic information by utilising kernels based on the dependency tree BIBREF15 , BIBREF16 , BIBREF17 . BIBREF18 look at the extraction of diseases and their relevant genes. They use a dictionary from six public databases to annotate genes and diseases in Medline abstracts. In their work, the authors note that when both genes and diseases are correctly identified, they are related in 94% of the cases. The problem then reduces to filtering incorrect matches using the dictionary, which occurs due to false positives resulting from ambiguities in the names as well as ambiguities in abbreviations. To this end, they train a Max-Ent based NER classifier for the task, and get a 26% gain in precision over the unfiltered baseline, with a slight hit in recall. They use POS tags, expanded forms of abbreviations, indicators for Greek letters as well as suffixes and prefixes commonly used in biomedical terms. BIBREF19 adopt a supervised feature-based approach for the extraction of drug-drug interaction (DDI) for the DDI-2013 dataset BIBREF20 . They partition the data in subsets depending on the syntactic features, and train a different model for each. They use lexical, syntactic and verb based features on top of shallow parse features, in addition to a hand-crafted list of trigger words to define their features. An SVM classifier is then trained on the feature vectors, with a positive label if the drug pair interacts, and negative otherwise. Their method beats other systems on the DDI-2013 dataset. Some other feature-based approaches are described in BIBREF21 , BIBREF22 . Distant supervision methods have also been applied to relation extraction over biomedical corpora. In BIBREF23 , 10,000 neuroscience articles are distantly supervised using information from UMLS Semantic Network to classify brain-gene relations into geneExpression and otherRelation. They use lexical (bag of words, contextual) features as well as syntactic (dependency parse features). They make the “at-least one” assumption, i.e. at least one of the sentences extracted for a given entity-pair contains the relation in database. They model it as a multi-instance learning problem and adopt a graphical model similar to BIBREF24 . They test using manually annotated examples. They note that the F-score achieved are much lesser than that achieved in the general domain in BIBREF24 , and attribute to generally poorer performance of NER tools in the biomedical domain, as well as less training examples. BIBREF25 explore distant supervision methods for protein-protein interaction extraction. More recently, deep learning methods have been applied to relation extraction in the biomedical domain. One of the main advantages of such methods over traditional feature or kernel based learning methods is that they require minimal feature engineering. In BIBREF26 , skip-gram vectors BIBREF27 are trained over 5.6Gb of unlabelled text. They use these vectors to extract protein-protein interactions by converting them into features for entities, context and the entire sentence. Using an SVM for classification, their method is able to outperform many kernel and feature based methods over a variety of datasets. BIBREF28 follow a similar method by using word vectors trained on PubMed articles. They use it for the task of relation extraction from clinical text for entities that include problem, treatment and medical test. For a given sentence, given labelled entities, they predict the type of relation exhibited (or None) by the entity pair. These types include “treatment caused medical problem”, “test conducted to investigate medical problem”, “medical problem indicates medical problems”, etc. They use a Convolutional Neural Network (CNN) followed by feedforward neural network architecture for prediction. In addition to pre-trained word vectors as features, for each token they also add features for POS tags, distance from both the entities in the sentence, as well BIO tags for the entities. Their model performs better than a feature based SVM baseline that they train themselves. The BioNLP'16 Shared Tasks has also introduced some Relation Extraction tasks, in particular the BB3-event subtask that involves predicting whether a “lives-in” relation holds for a Bacteria in a location. Some of the top performing models for this task are deep learning models. BIBREF29 train word embeddings with six billions words of scientific texts from PubMed. They then consider the shortest dependency path between the two entities (Bacteria and location). For each token in the path, they use word embedding features, POS type embeddings and dependency type embeddings. They train a unidirectional LSTM BIBREF30 over the dependency path, that achieves an F-Score of 52.1% on the test set. BIBREF31 improve the performance by making modifications to the above model. Instead of using the shortest dependency path, they modify the parse tree based on some pruning strategies. They also add feature embeddings for each token to represent the distance from the entities in the shortest path. They then train a Bidirectional LSTM on the path, and obtain an F-Score of 57.1%. The recent success of deep learning models in Biomedical Relation Extraction that require minimal feature engineering is promising. This also suggests new avenues of research in the field. An approach as in BIBREF32 can be used to combine multi-instance learning and distant supervision with a neural architecture. To this end, they train a Max-Ent based NER classifier for the task, and get a 26% gain in precision over the unfiltered baseline, with a slight hit in recall.

What is the performance of the Max-Ent-based NER classifier?

It gets a 26% gain in precision over the unfiltered baseline, with a slight hit in recall.

Make me a list of some countries I could visit if I wanted to take a beach vacation. Give me options in multiple timezones

Some different options you could visit include Greece, Mexico, Tahiti, Spain, Brazil and Costa Rica

What is a GIS?

A GIS is a tool for making and using spatial information. Among the many definitions of GIS, we choose: A GIS is a computer-based system to aid in the collection, maintenance, storage, analysis, output, and distribution of spatial information. When used wisely, GIS can help us livehealthier, wealthier, and safer lives. Each GIS user may decide what features and attributes are important. For example, forests are good for us. They may protect water supplies, yield wood, harbor wildlife,and provide space to recreate.We are concerned about the level of harvest, the adjacent land use, pollution from nearby industries, or where forests burn. Informed management requires knowledge of all these related factors and, perhaps above all, the spatial arrangement of these factors. Buffer strips near rivers may protect water supplies,clearings may prevent the spread of fire, and polluters upwind may harm our forests. A GIS helps us analyze these spatial interactions, and is also particularly useful at displaying spatial data and analysis. A GIS is often the only way to solve spatially-related problems.

Due to weather conditions, peanuts are usually planted in spring. The peanut comes from a yellow flower that bends over and penetrates the soil after blooming and wilting, and the peanut starts to grow in the soil. Peanuts are harvested from late August to October, while the weather is clear. This weather allows for dry soil so that when picked, the soil does not stick to the stems and pods. The peanuts are then removed from vines and transported to a peanut shelling machine for mechanical drying. After cropping, the peanuts are delivered to warehouses for cleaning, where they are stored unshelled in silos.

Given this paragraph about planting and harvesting for peanut butter, when are peanuts typically harvested?

Peanuts are typically harvested from late August to October

Classify each as a ocean, sea, or lake: Pacific, Mediterranean, Erie, Atlantic, Dead Sea, Black, Michigan

Pacific - ocean Mediterranean - sea Erie - lake Atlantic - ocean Dead Sea - lake Black - sea Michigan - lake

These islands can be divided into three recognizable groups located on the Azores Plateau: The Eastern Group (Grupo Oriental) of São Miguel, Santa Maria and Formigas Islets The Central Group (Grupo Central) of Terceira, Graciosa, São Jorge, Pico and Faial The Western Group (Grupo Ocidental) of Flores and Corvo.

Extract the names of the islands located on the Eastern Group of the Azores plateau from the text. Separate them with a comma.

Flores and Corvo

We evaluated ARAML on three datasets: COCO image caption dataset BIBREF28 , EMNLP2017 WMT dataset and WeiboDial single-turn dialogue dataset BIBREF29 . COCO and EMNLP2017 WMT are the common benchmarks with no input to evaluate the performance of discrete GANs, and we followed the existing works to preprocess these datasets BIBREF12 , BIBREF11 . WeiboDial, as a dialogue dataset, was applied to test the performance of our model with input trigger. We simply removed post-response pairs containing low-frequency words and randomly selected a subset for our training/test set. The statistics of three datasets are presented in Table TABREF28 . We evaluated ARAML on three datasets: COCO image caption dataset (Chen et al., 2015), EMNLP2017 WMT dataset1 and WeiboDial single-turn dialogue dataset (Qian et al., 2018).

What are the datasets in this paper?

COCO image caption dataset (Chen et al., 2015), EMNLP2017 WMT dataset1 and WeiboDial single-turn dialogue dataset (Qian et al., 2018).

Where is Jasienica?

Jasienica [jaɕeˈnit͡sa] is a village in the administrative district of Gmina Łoniów, within Sandomierz County, Świętokrzyskie Voivodeship, in south-central Poland. It lies approximately 3 kilometres (2 mi) south-east of Łoniów, 21 km (13 mi) south-west of Sandomierz, and 76 km (47 mi) south-east of the regional capital Kielce.

1706.07179

Recently, BIBREF17 proposed a dynamic memory based neural network for implicitly modeling the state of entities present in the text for question answering. However, this model lacks any module for relational reasoning. In response, we propose RelNet, which extends memory-augmented neural networks with a relational memory to reason about relationships between multiple entities present within the text. Our end-to-end method reads text, and writes to both memory slots and edges between them. Intuitively, the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector. The only supervision signal for our method comes from answering questions on the text. Our end-to-end method reads text, and writes to both memory slots and edges between them. Intuitively, the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector.

How is knowledge retrieved in the memory?

The answers are shown as follows: * the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector.

Yahalom is the author of numerous books and articles on Hebrew poetry, from Byzantine Palestine (5th century CE) to the early modern period. Much of his work explores the connections between Hebrew poetry and the literature of the other groups living in the same time and place, for example, the link between piyyut and Syriac and Greek Christian hymns; between Hebrew poetry from Spain and the emerging romances in European languages; and between early modern mystical Hebrew poetry in the Ottoman Empire and Turkish songs. He also has written extensively about the bilingual (Hebrew and Arabic) culture of Judah Al-Harizi.

Given this paragraph about Yahalom and his works, tell me when he was born?

Based off of this paragraph, it is clear that Yahalom wrote books and articles "from Byzantine Palestine (5th century CE) to the early modern period". Therefore, it is likely that Yahalom was born in the early modern period or sometime after the early modern period. The exact date of Yahalom's birth cannot be determined given the provided information.

Manchester United have won a record 20 League titles, 12 FA Cups, six League Cups, and a record 21 FA Community Shields. They have won the European Cup/UEFA Champions League three times, and the UEFA Europa League, the UEFA Cup Winners' Cup, the UEFA Super Cup, the Intercontinental Cup and the FIFA Club World Cup once each. In 1968, under the management of Matt Busby, 10 years after eight of the club's players were killed in the Munich air disaster, they became the first English club to win the European Cup. Sir Alex Ferguson is the club's longest-serving and most successful manager, winning 38 trophies, including 13 league titles, five FA Cups, and two Champions League titles between 1986 and 2013. In the 1998–99 season, under Ferguson, the club became the first in the history of English football to achieve the European treble of the Premier League, FA Cup, and UEFA Champions League. In winning the UEFA Europa League under José Mourinho in 2016–17, they became one of five clubs to have won the original three main UEFA club competitions (the Champions League, Europa League and Cup Winners' Cup).

Who was Manchester United's most successful manager?

Manchester United's most successful manager is Sir Alex Ferguson who won 38 trophies including 13 league titles

A red envelope or red packet (simplified Chinese: 红包; traditional Chinese: 紅包; pinyin: hóngbāo; Pe̍h-ōe-jī: âng-pau) is a monetary gift given during holidays or for special occasions such as a wedding, a graduation, or the birth of a baby.

What is a Chinese red envalope?

A red envelope is usually for celebrations like weddings, birthdays, or graduations. Receiving a red pouch is a sign of good luck and to ward off evil spirits. Usually it contains money and people give them during the holidays. People usually put in even amount of money including 9 because it is a homophonous to the word long. 4 should never be given or using 4 in any of the digits because the pronunciation of 4 is similar to death.

Dartmouth Workshop 1956: the birth of AI The Dartmouth Workshop of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it". The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research. At the workshop Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field. The 1956 Dartmouth workshop was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI. The term "Artificial Intelligence" was chosen by McCarthy to avoid associations with cybernetics and connections with the influential cyberneticist Norbert Wiener.

When and where was the field of AI officially born?

The field of AI was officially born in 1956 during a workshop organised at Dartmouth College

Eupithecia spinibarbata is a moth in the family Geometridae. It is found in India (Darjeeling). The wingspan is about 19 mm. The forewings are pale whitish grey.

What is the Eupithecia spinibarbata

The Eupithecia spinibarbata is a moth in the family Geometridae. It is found in India (Darjeeling). The wingspan is about 19 mm. The forewings are pale whitish grey.

Write a grocery list of typical items a person might get from the grocery store.

The items a person might need from the grocery store will vary on the dietary needs of the person, as well as the amount of people in the household the person is shopping for, and if they're shopping for a special event or just for regular daily meals. With that in mind, here are some typical grocery shopping items that a person might get from a typical grocery shopping trip: milk, bread, eggs, coffee, olive oil, rice, chicken, steak, apples, oranges, salad mix, soda, sugar, flour, salt.

Learning semantic representations (embeddings) of different entities, such as textual, commercial, and physical, has been a recent and active area of research. Such representations can facilitate applications that rely on a notion of similarity, for example recommendation systems and ranking algorithms in e-commerce. In natural language processing, word2vec BIBREF0 learns vector representations of words from large quantities of text, where each word is mapped to a $d$-dimensional vector such that semantically similar words have geometrically closer vectors. This is achieved by predicting either the context words appearing in a window around a given target word (skip-gram model), or the target word given the context (CBOW model). The main assumption is that words appearing frequently in similar contexts share statistical properties (the distributional hypothesis). Crucially, word2vec models, like many other word embedding models, preserve sequential information encoded in text so as to leverage word co-occurrence statistics. The skip-gram model has been adapted to other domains in order to learn dense representations of items other than words. For example, product embeddings in e-commerce BIBREF1 or vacation rental embeddings in the hospitality domain BIBREF2 can be learned by treating purchase histories or user click sequences as sentences and applying a word2vec approach. Most of the prior work on item embedding exploit the co-occurrence of items in a sequence as the main signal for learning the representation. One disadvantage of this approach is that it fails to incorporate rich structured information associated with the embedded items. For example, in the travel domain, where we seek to embed hotels and other travel-related entities, it could be helpful to encode explicit information such as user ratings, star ratings, hotel amenities, and location in addition to implicit information encoded in the click-stream. In this work, we propose an algorithm for learning hotel embeddings that combines sequential user click information in a word2vec approach with additional structured information about hotels. We propose a neural architecture that adopts and extends the skip-gram model to accommodate arbitrary relevant information of embedded items, including but not limited to geographic information, ratings, and item attributes. In experimental results, we show that enhancing the neural network to jointly encode click and supplemental structured information outperforms a skip-gram model that encodes the click information alone. The proposed architecture also naturally handles the cold-start problem for hotels with little or no historical clicks. Specifically, we can infer an embedding for these properties by leveraging their supplemental structured metadata. Compared to previous work on item embeddings, the novel contributions of this paper are as follows: We propose a novel framework for fusing multiple sources of information about an item (such as user click sequences and item-specific information) to learn item embeddings via self-supervised learning. We generate an interpretable embedding which can be decomposed into sub-embeddings for clicks, location, ratings, and attributes, and employed either as separate component embeddings or a single, unified embedding. It is also dynamic, meaning it is easy to reflect future changes in attributes such as star-rating or addition of amenities in the embedding vectors without retraining. We address the cold-start problem by including hotel metadata which are independent of user click-stream interactions and available for all hotels. This helps us to better impute embeddings for sparse items/hotels. We show significant gains over previous work based on click-embedding in several experimental studies. The structure of the remainder of this paper is as follows. Section 2 gives an overview of some of the recent works on neural embedding. Section 3 provides details of the proposed framework, including the neural network architecture, training methodology, and how the cold-start problem is addressed. In Section 4, we present experimental results on several different tasks and a comparison with previous state-of-the-art work. Section 5 concludes the paper. In this work, we propose an algorithm for learning hotel embeddings that combines sequential user click information in a word2vec approach with additional structured information about hotels.

What does the algorithm for learning hotel embeddings combine?

It combines sequential user click information in a word2vec approach with additional structured information about hotels.

Which of these are ski mountains and which are deserts: Killington, Stowe, Sahara, Kalahari, Palisades, Gobi, Northstar, and Kirkwood.

The deserts include: Sahara, Kalahari, and Gobi. The ski resorts are: Killington, Stowe, Palisades, Northstar, and Kirkwood.

How many players are in a cricket team?

A cricket team is formally comprised of eleven players

What is the difference between a bike and an electric bike

An electric bike is very similar to a traditional bike with a few additional components. They have a similar style of wheels, handlebars, and frame style as a regular bike. In addition, they have an electric motor, battery, and controller for the user. The battery powers the motors which offer assistance while pedaling. An e-bike does not change the pedaling action, it just makes it feel simpler.

In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes. They are used in many disciplines, including robotics, automatic control, economics and manufacturing. The name of MDPs comes from the Russian mathematician Andrey Markov as they are an extension of Markov chains.

According to the below, what is the Markov decision process?

A Markov decision process (MDP) is a mathematical framework for modeling decision making in cases where outcomes are not entirely under the control of decision makers. MDPs are used in a variety of disciplines, such as robotics and economics.

Define judicial review and explain John Marshall’s role in establishing judicial review.

Judicial review is a power the courts have to declare that a federal or state law violates the Constitution thereby nullifying the law making it enforceable. Just before President Adams left office he appointed new justices of the peace for Washington D.C., Jefferson, the next President refused to deliver the commissions. This led to the case Marbury vs Madison. The basis of the case was that the Judiciary Act of 1789 allowed for the appointed to request a court order to grant their appointments with original jurisdiction given to the Supreme Court. John Marshall saw that siding with Jefferson would make it seem like the Supreme Court was his lackey. If they sided with Adams, there was no way to make Jefferson enforce the ruling. Both decisions would reduce the Supreme Court's legitimacy. John Marshall side-stepped this problem by striking down the Judiciary Act on the grounds that it was unconstitutional. This was the establishment of judicial review, a Supreme Court power that was only hinted at in the Constitution.

Why is healthcare so bad in US?

Let's talk what is bad mean? Bad here probably refers to the cost of drugs and how the insurance industry operates. It also refers to not everyone having universal health care. Ask yourself what is the goal of the pharma and insurance industry. Dig deep and you will find that it is not driven to improve the health of the citizen and society but make money at the cost of people lives. Someone rightfully said US pharma industry is only interested in diseases of the rich like heart and diabetes and not of the poor. We have seen changes in the auto industry, software industry and it is time we bring change in healthcare industry where it is affordable and everyone has right to good healthcare. Lobbying and exclusive control (yes, they invest billions) needs change.

Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios. However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 . In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 . We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows: To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling and domain adaptation.

What method does this paper propose?

A novel multistage fine-tuning method for NMT.

Building effective machine learning models for text requires data and different resources such as pre-trained word embeddings and reusable lexicons. Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages. In this paper, we studied the research question: Can we build reusable sentiment analysis models that can be utilized for making inferences in different languages without requiring separate models and resources for each language? To that end, we built a recurrent neural network model in the language that had largest data available. We took a general-to-specific model building strategy where the larger corpus that had reviews from different domains was first used to train the RNN model and a smaller single-domain corpus of sentiment reviews was used to specialize the model on the given domain. During scoring time, we used corpora for the given domain in different languages and translated them to English to be able to classify sentiments with the trained model. Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline. In this paper we made the sentiment analysis model specific to a single domain. For future work, we would like to investigate the effectiveness of our model on different review domains including hotel reviews and on different problems such as detecting stance. Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.

What did the experiment show?

Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.

A generative artificial intelligence or generative AI is a type of AI system capable of generating text, images, or other media in response to prompts. Generative AI systems use generative models such as large language models to statistically sample new data based on the training data set that was used to create them.

According to the paragraph below, what is Generative AI?

Generative Artificial Intelligence (AI) refers to AI systems that respond to user prompts to generate text, images and other media.

1811.01001

Training and testing are done in alternating steps: In each epoch, for training, we first present to an LSTM network 1000 samples in a given language, which are generated according to a certain discrete probability distribution supported on a closed finite interval. We then freeze all the weights in our model, exhaustively enumerate all the sequences in the language by their lengths, and determine the first $k$ shortest sequences whose outputs the model produces inaccurately. We remark, for the sake of clarity, that our test design is slightly different from the traditional testing approaches used by BIBREF10 , BIBREF9 , BIBREF12 , since we do not consider the shortest sequence in a language whose output was incorrectly predicted by the model, or the largest accepted test set, or the accuracy of the model on a fixed test set. It has been shown by BIBREF9 that LSTMs can learn $a^n b^n$ and $a^n b^n c^n$ with 1 and 2 hidden units, respectively. Similarly, BIBREF24 demonstrated that a simple RNN architecture containing a single hidden unit with carefully tuned parameters can develop a canonical linear counting mechanism to recognize the simple context-free language $a^n b^n$ , for $n \le 250$ . We wanted to explore whether the stability of the networks would improve with an increase in capacity of the LSTM model. We, therefore, varied the number of hidden units in our LSTM models as follows. We experimented with 1, 2, 3, and 36 hidden units for $a^n b^n$ ; 2, 3, 4, and 36 hidden units for $a^n b^n c^n$ ; and 3, 4, 5, and 36 hidden units for $a^n b^n c^n d^n$ . The 36 hidden unit case represents an over-parameterized network with more than enough theoretical capacity to recognize all these languages. Following the traditional approach adopted by BIBREF7 , BIBREF12 , BIBREF9 and many other studies, we train our neural network as follows. At each time step, we present one input character to our model and then ask it to predict the set of next possible characters, based on the current character and the prior hidden states. Given a vocabulary $\mathcal {V}^{(i)}$ of size $d$ , we use a one-hot representation to encode the input values; therefore, all the input vectors are $d$ -dimensional binary vectors. The output values are $(d+1)$ -dimensional though, since they may further contain the termination symbol $\dashv $ , in addition to the symbols in $\mathcal {V}^{(i)}$ . The output values are not always one-hot encoded, because there can be multiple possibilities for the next character in the sequence, therefore we instead use a $k$ -hot representation to encode the output values. Our objective is to minimize the mean-squared error (MSE) of the sequence predictions. During testing, we use an output threshold criterion of $0.5$ for the sigmoid output layer to indicate which characters were predicted by the model. We then turn this prediction task into a classification task by accepting a sample if our model predicts all of its output values correctly and rejecting it otherwise. Training and testing are done in alternating steps: In each epoch, for training, we first present to an LSTM network 1000 samples in a given language, which are generated according to a certain discrete probability distribution supported on a closed finite interval. We then freeze all the weights in our model, exhaustively enumerate all the sequences in the language by their lengths, and determine the first $k$ shortest sequences whose outputs the model produces inaccurately. experimented with 1, 2, 3, and 36 hidden units for $a^n b^n$ ; 2, 3, 4, and 36 hidden units for $a^n b^n c^n$ ; and 3, 4, 5, and 36 hidden units for $a^n b^n c^n d^n$ . Following the traditional approach adopted by BIBREF7 , BIBREF12 , BIBREF9 and many other studies, we train our neural network as follows. At each time step, we present one input character to our model and then ask it to predict the set of next possible characters, based on the current character and the prior hidden states. Given a vocabulary $\mathcal {V}^{(i)}$ of size $d$ , we use a one-hot representation to encode the input values; therefore, all the input vectors are $d$ -dimensional binary vectors. The output values are $(d+1)$ -dimensional though, since they may further contain the termination symbol $\dashv $ , in addition to the symbols in $\mathcal {V}^{(i)}$ . The output values are not always one-hot encoded, because there can be multiple possibilities for the next character in the sequence, therefore we instead use a $k$ -hot representation to encode the output values. Our objective is to minimize the mean-squared error (MSE) of the sequence predictions.

What training settings did they try?

The answers are shown as follows: * Training and testing are done in alternating steps: In each epoch, for training, we first present to an LSTM network 1000 samples in a given language, which are generated according to a certain discrete probability distribution supported on a closed finite interval. We then freeze all the weights in our model, exhaustively enumerate all the sequences in the language by their lengths, and determine the first $k$ shortest sequences whose outputs the model produces inaccurately. * experimented with 1, 2, 3, and 36 hidden units for $a^n b^n$ ; 2, 3, 4, and 36 hidden units for $a^n b^n c^n$ ; and 3, 4, 5, and 36 hidden units for $a^n b^n c^n d^n$ . * Following the traditional approach adopted by BIBREF7 , BIBREF12 , BIBREF9 and many other studies, we train our neural network as follows. At each time step, we present one input character to our model and then ask it to predict the set of next possible characters, based on the current character and the prior hidden states. Given a vocabulary $\mathcal {V}^{(i)}$ of size $d$ , we use a one-hot representation to encode the input values; therefore, all the input vectors are $d$ -dimensional binary vectors. The output values are $(d+1)$ -dimensional though, since they may further contain the termination symbol $\dashv $ , in addition to the symbols in $\mathcal {V}^{(i)}$ . The output values are not always one-hot encoded, because there can be multiple possibilities for the next character in the sequence, therefore we instead use a $k$ -hot representation to encode the output values. Our objective is to minimize the mean-squared error (MSE) of the sequence predictions.

Habiganj Bazar–Shaistaganj–Balla line During the colonial British rule, train services were started by rail at Habiganj Mahukuma in Sylhet district of the then (Undivided British-India) Assam province. In 1928, the British government built the Habiganj Bazar-Shaistaganj-Balla line as railway line and built infrastructure. The railway line was opened by the Assam Bengal Railway by the then British government from Habiganj district headquarters town to Balla border via Shaistaganj junction, about 45 or 52 kilometers long railway line. Of these, the Shaistaganj-Habiganj (15 or 16 km) railway line was inaugurated in 1928 and the Shaistaganj-Balla (30 or 36 km) railway line was inaugurated in 1929. Coal-engined trains used to run between eight stations at Habiganj Bazar, Habiganj Court, Shaistaganj Junction, Shakir Mohammad, Chunarughat, Amuroad, Assampara and Balla bordering Tripura. Of these, Chunarughat, Amur road and Assampara stations were of great importance. Tea produced in 22 tea gardens from those three stations was transported by rail. At that time, this railway was the only means of exporting tea leaves of 13 gardens of Chunarughat upazila of Habiganj at a low cost and importing related items including garden rations. There are a total of 4 stations on the Shaistaganj-Habiganj railway line (excluding Shaistaganj Junction), namely: Habiganj Bazar, Habiganj Court, Dhuliakhal and Paikpara. The Shaistaganj-Balla railway line has a total of 7 stations (excluding Shaistaganj Junction), namely: Barkula, Shakir Muhammed, Sutang Bazar, Chunarughat, Amu Road, Assampara and Balla. After the independence of Bangladesh, the importance of the Balla train increased further. For this reason, the railway authorities built two more stations named Sutang Bazar and Barkula, known as remote areas. At that time, the role of the train in bringing back refugees from India was commendable. At that time, a diesel engine was added to the ballar train. The train used to travel twice a day from Habiganj to the border station Balla. After the end of the refugee transportation phase, the smugglers took over the train in Balla. Later, the train of Balla became a train of smugglers. At first, the passengers protested about this, but later the passengers got the opportunity to travel without a ticket. In such a situation, the running train suffered losses. The railway authorities suspended the renovation work of the railway line.

Please give short summary of Habiaganj Bazar railway station based on below

Habiaganj Bazar railway opened in 1928 in Sylhet district. The Habiaganj railway line operated from Habiganj district headquarters town to the Balla border via Shaistaganj junction. Coal-engined trains used to run between eight stations: Habiganj Bazar, Habiganj Court, Shaistaganj Junction, Shakir Mohammad, Chunarughat, Amuroad, Assampara, and Balla bordering Tripura. At that time, this railway was the only means of exporting tea leaves from the 13 gardens in Chunarughat upazila of Habiganj at a low cost. After the independence of Bangladesh, the importance of the Balla train increased. The train's role at the time was to bring refugees back from India with an added diesel engine. The train used to travel twice a day from Habiganj to the border station at Balla. After the end of the refugee transportation phase, the train of BALAS became the train of smugglers. Initially, passengers protested, but the train suffered losses as they could travel without a ticket. Because of losses, train authorities suspended the renovation work.

The Flex Computer System was developed by Michael Foster and Ian Currie of Royal Signals and Radar Establishment in Malvern, England, during the late 1970s and 1980s. It used a tagged storage scheme to implement a capability architecture, and was designed for the safe and efficient implementation of strongly typed procedures.

Given this reference text about the Flex Computer System, why was it created?

It was designed for the safe and efficient implementation of strongly typed procedures.

Tell me whether these are dogs or cats: bulldogs, poodles, huskies, collies, shepherds

Dogs

A. M. Shastri estimates the reigns of the Sharabhapuriya rulers as follows: based on Sharabha's identification with the Sharabharaja mentioned in the 510 CE Eran inscription, he assigns Sharabha's reign to 475–500 CE (assuming a 25-year reign). Similarly, he assigns 25 years to the next two kings. Since Jayaraja's latest inscription is dated to 9th regnal year, Shastri assumes a 10-year reign for Jayaraja, and some subsequent kings. Sharabha (Śarabha), c. 475–500 CE Narendra, c. 500–525 CE Prasanna, c. 525–550 CE Jayarāja, c. 550–560 CE Sudevarāja, c 560–570 CE Manamatra alias Durgarāja, c. 570–580 CE Sudevarāja, c. 570–580 CE Pravarāja, c. 580–590 CE Alternative dates have been proposed by other historians. For example, D. C. Sircar estimated Sharabha's reign as 465–480 CE, while V. V. Mirashi estimated it as 460–480 CE.

Who was the ruling member of the Sharabhapuriya dynasty in 575 CE?

Based on that paragraph, the ruler during 575 CE was Sudevarāja, who ruled from c. 570–580 CE. Note that this may be an estimate, since all of the time periods listed have a "c." (short for "circa"), which indicates some uncertainty about the time ranges listed.

List five titles by Robert Jordan.

The Great Hunt, Towers of Midnight, Lord of Chaos, A Crown of Swords, Crossroads of Twilight

1901.03438

We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations. We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations. We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations.

Which models are best for learning long-distance movement?

The answers are shown as follows: * the transformer models

What is the difference between men's and women's lacrosse

Men's lacrosse is a contact sport and the players wear helmets, shoulder pads, chest protection and elbow pads. They also have a position of defense called Long Pole where they can defend from far away. Women's lacrosse does not allow contact between players and the only required equipment is a mouth guard and goggles. Women's lacrosse relies on the shooting space rule to protect players from potential injury instead of requiring them to wear additional equipment. Another difference is the field dimensions. The women’s field is 120 yards long and 70 yards wide, compared to the men’s field, which is slightly smaller at 110 yards long and 60 yards wide. Lastly, men play with 9 field players whereas women play with 11.

Knowledge sharing platforms such as Quora and Zhihu emerge as very convenient tools for acquiring knowledge. These question and answer (Q&A) platforms are newly emerged communities about knowledge acquisition, experience sharing and social networks services (SNS). Unlike many other Q&A platforms, Zhihu platform resembles a social network community. Users can follow other people, post ideas, up-vote or down-vote answers, and write their own answers. Zhihu allows users to keep track of specific fields by following related topics, such as “Education”, “Movie”, “Technology” and “Music”. Once a Zhihu user starts to follow a specific topic or a person, the related updates are automatically pushed to the user's feed timeline. Although these platforms have exploded in popularity, they face some potential problems. The key problem is that as the number of users grows, a large volume of low-quality questions and answers emerge and overwhelm users, which make users hard to find relevant and helpful information. Zhihu Live is a real-time voice-answering product on the Zhihu platform, which enables the speakers to share knowledge, experience, and opinions on a subject. The audience can ask questions and get answers from the speakers as well. It allows communication with the speakers easily and efficiently through the Internet. Zhihu Live provides an extremely useful reward mechanism (like up-votes, following growth and economic returns), to encourage high-quality content providers to generate high-level information on Zhihu platform. However, due to the lack of efficient filter mechanism and evaluation schemes, many users suffer from lots of low-quality contents, which affects the service negatively. Recently, studies on social Q&A platforms and knowledge sharing are rising and have achieved many promising results. Shah et al. BIBREF0 propose a data-driven approach with logistic regression and carefully designed hand-crafted features to predict the answer quality on Yahoo! Answers. Wang et al. BIBREF1 illustrate that heterogeneity in the user and question graphs are important contributors to the quality of Quora's knowledge base. Paul et al. BIBREF2 explore reputation mechanism in quora through detailed data analysis, their experiments indicate that social voting helps users identify and promote good content but is prone to preferential attachment. Patil et al. BIBREF3 propose a method to detect experts on Quora by their activity, quality of answers, linguistic characteristics and temporal behaviors, and achieves 97% accuracy and 0.987 AUC. Rughinis et al. BIBREF4 indicate that there are different regimes of engagement at the intersection of the technological infrastructure and users' participation in Quora. All of these works are mainly focused on answer ranking and answer quality evaluation. But there is little research achievement about quality evaluation in voice-answering areas. In this work, we present a data-driven approach for quality evaluation about Zhihu Live, by consuming the dataset we collected to gather knowledge and insightful conclusion. The proposed data-driven approach includes data collection, storage, preprocessing, data analysis, and predictive analysis via machine learning. The architecture of our data-driven method is shown in Fig. FIGREF3 . The records are crawled from Zhihu Live official website and stored in MongoDB. Data preprocessing methods include cleaning and data normalization to make the dataset satisfy our target problem. Descriptive data analysis and predictive analysis are also conducted for deeper analysis about this dataset. The main contributions of this paper are as follows: (1) We release a public benchmark dataset which contains 7242 records and 286,938 text comments about Zhihu Live. Detailed analysis about the dataset is also discussed in this paper. This dataset could help researchers verify their ideas in related fields. (2) By analyzing this dataset, we gain several insightful conclusion about Zhihu Live. (3) We also propose a multi-branched neural network (MTNet) to evaluate Zhihu Lives' scores. The superiority of our proposed model is demonstrated by comparing performance with other mainstream regressors. The rest of this paper is organized as follows: Section 2 describes detailed procedures of ZhihuLive-DB collection, and descriptive analysis. Section 3 illustrates our proposed MTNet. In section 4, we give a detailed description of experiments, and the last section discusses the conclusion of this paper and future work. Patil et al. [8] propose a method to detect experts on Quora by their activity, quality of answers, linguistic characteristics and temporal behaviors, and achieves 97% accuracy and 0.987 AUC.

What is the accuracy of the method proposed by Patil et al.?

97%.

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Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. On the Mboshi side, we consider alphabetic representation with no tonal information. On the French side,we simply consider the default segmentation into words. Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17.

Which language family does Mboshi belong to?

The answers are shown as follows: * Bantu

Table TABREF5 shows the results of various methods on the hate speech detection task. Part A shows results for baseline methods. Parts B and C focus on the proposed methods where part B contains methods using neural networks only, while part C uses average of word embeddings learned by DNNs as features for GBDTs. We experimented with multiple classifiers but report results mostly for GBDTs only, due to lack of space. As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method. Among part B methods, CNN performed better than LSTM which was better than FastText. Surprisingly, initialization with random embeddings is slightly better than initialization with GloVe embeddings when used along with GBDT. Finally, part C methods are better than part B methods. The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier. Combinations of CNN, LSTM, FastText embeddings as features for GBDTs did not lead to better results. Also note that the standard deviation for all these methods varies from 0.01 to 0.025. To verify the task-specific nature of the embeddings, we show top few similar words for a few chosen words in Table TABREF7 using the original GloVe embeddings and also embeddings learned using DNNs. The similar words obtained using deep neural network learned embeddings clearly show the “hatred” towards the target words, which is in general not visible at all in similar words obtained using GloVe. The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier.

According to their experiment, what kind of method combination achieves the best result?

The best method is “LSTM + Random Embedding + GBDT”.

Propose a text about young boy that wanted to fly his kite over the skyscrapers. Make this a very positive story with a twist

Bob is a fun a loving kid that spends lots of his free time outdoors. Whenever there is a good wind he takes his kite, a gift from his dad, and goes out to play and fly. One day his dad was going to a very big city to talk with some of his partners. Bob got immediately very excited. It was his dream to take his kite and fly it from one of the very big and tall buildings. His dad made this all happen. Together with his son they went on the roof and played with the kite.

1909.00578

FLOAT SELECTED: Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories. FLOAT SELECTED: Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories.

What linguistic quality aspects are addressed?

Grammaticality, non-redundancy, referential clarity, focus, structure & coherence

What are the common personality traits of cats?

Cats are all individuals, they each may have different personality traits. Some common personality traits that humans typically ascribe to cats include loving, standoffish, active, aloof, playful, and compassionate.

What is the largest of the North American Great Lakes?

Lake Superior is the largest of the Great Lakes. The water volume from the other four Great Lakes (Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario) could all fit within Lake Superior.

What did Stevie Ray Vaughan name his most beloved guitar?

Jimbo.

Robert Mitchum (1917–1997) was an American actor who appeared in over 110 films and television series over the course of his career. He is ranked 23rd on the American Film Institute's list of the 50 greatest American screen legends of all time. His first credited named role was as Quinn in the 1943 western Border Patrol. That same year he appeared in the films Follow the Band, Beyond the Last Frontier, Cry 'Havoc' and Gung Ho! as well as several Hopalong Cassidy films including Colt Comrades, Bar 20, False Colors, and Riders of the Deadline. In 1944, he starred in the western Nevada as Jim "Nevada" Lacy, and a year later in the film West of the Pecos as Pecos Smith. During the 1940s, he was also cast in the film noirs Undercurrent (1946), Crossfire (1947), Out of the Past (1947) and The Big Steal (1949). Mitchum was nominated for the Academy Award for Best Supporting Actor for his role as a world-weary soldier in the 1945 film The Story of G.I. Joe, which received critical acclaim and was a commercial success.

Who is Robert Mitchum?

Robert Mitchum was a prolific American actor, appearing in over 110 films and television shows. He was an Academy Award nominee and his primary film genre was Western.

Old French (franceis, françois, romanz; Modern French: ancien français) was the language spoken in most of the northern half of France from approximately the 8th to the 14th centuries. Rather than a unified language, Old French was a linkage of Romance dialects, mutually intelligible yet diverse, spoken in the northern half of France. These dialects came to be collectively known as the langue d'oïl, contrasting with the langue d'oc in the south of France. The mid-14th century witnessed the emergence of Middle French, the language of the French Renaissance in the Île de France region; this dialect was a predecessor to Modern French. Other dialects of Old French evolved themselves into modern forms (Poitevin-Saintongeais, Gallo, Norman, Picard, Walloon, etc.), each with its own linguistic features and history.

Where was Old French language spoken?

Northern half of France in the 8th to 14th centuries.

What is RSU?

Restricted stock units (RSU), also known as restricted securities, is stock of a company that is not fully transferable (from the stock-issuing company to the person receiving the stock award) until certain conditions (restrictions) have been met. Upon satisfaction of those conditions, the stock is no longer restricted, and becomes transferable to the person holding the award. Restricted stock is often used as a form of employee compensation, in which case it typically becomes transferable ("vests") upon the satisfaction of certain conditions, such as continued employment for a period of time or the achievement of particular product-development milestones, earnings per share goals or other financial targets. Restricted stock is a popular alternative to stock options, particularly for executives, due to favorable accounting rules and income tax treatment.

Write a paragraph to convince someone to visit Porto Portugal.

Porto Portugal is an amazing travel destination that is easily accessible by train from the capital of Lisbon. Porto is coastal town with great beaches and is the primary distributer of port wine that is grown in vineyards from the Douro Valley. In Porto you can explore the many distributors that age the port wine and take a trip on the Douro River in one of the boats that used to be used to transport port barrels from the vineyards to Porto. You can also take a day trip by bus to the Douro Valley and visit the many vineyards and sample the port wine.

List five titles by J.K. Rowling.

Harry Potter and the Sorcerer's Stone, The Ickabog, Fantastic Beasts and Where to Find Them, The Cuckoo's Calling, Harry Potter and the Order of the Phoenix

In geology, what are the main classification for rocks?

There are three general classes of rocks: Igneous, Sedimentary, and Metamorphic.

How do I find the best interior decorator and best price?

There are many ways to find the best interior decorator. You can use tools like Angie's List, Yelp, or Google to first search for an interior decorator. After you find someone evaluate their qualifications based on other projects they have completed. If you like their portfolio ask them about pricing and share your requirements for your interior design project requirements. After an agreement is made the interior project can be executed successfully between both parties for the best cost and best choice for interior designer.

Identify which instrument is string or percussion: Ranat ek, Sasando

Sasando is string, Ranat ek is percussion.

Currently the cutting edge of fighter design, fifth-generation fighters are characterized by being designed from the start to operate in a network-centric combat environment, and to feature extremely low, all-aspect, multi-spectral signatures employing advanced materials and shaping techniques. They have multifunction AESA radars with high-bandwidth, low-probability of intercept (LPI) data transmission capabilities. The infra-red search and track sensors incorporated for air-to-air combat as well as for air-to-ground weapons delivery in the 4.5th generation fighters are now fused in with other sensors for Situational Awareness IRST or SAIRST, which constantly tracks all targets of interest around the aircraft so the pilot need not guess when he glances. These sensors, along with advanced avionics, glass cockpits, helmet-mounted sights (not currently on F-22), and improved secure, jamming-resistant LPI datalinks are highly integrated to provide multi-platform, multi-sensor data fusion for vastly improved situational awareness while easing the pilot's workload. Avionics suites rely on extensive use of very high-speed integrated circuit (VHSIC) technology, common modules, and high-speed data buses. Overall, the integration of all these elements is claimed to provide fifth-generation fighters with a "first-look, first-shot, first-kill capability".

Summarize the following paragraph about modern fighter jets

Fifth-generation fighter jets take advantage of variety of technological advancements, new materials and design principals to give pilots comprehensive, early visibility of all potential threats while minimizing their own detectability.