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The 2000 World Series was the championship series of Major League Baseball's (MLB) 2000 season. The 96th edition of the World Series, it was a best-of-seven playoff between crosstown opponents, the two-time defending World Series champions and American League (AL) champion New York Yankees and the National League (NL) champion New York Mets. The Yankees defeated the Mets, four games to one, to win their third consecutive championship and 26th overall. The series was often referred to as the "Subway Series", referring to the longstanding matchup between New York baseball teams; it was the first World Series contested between two New York teams since the 1956 World Series and the first since the New York Giants and the Brooklyn Dodgers moved west to California (as the current San Francisco Giants and Los Angeles Dodgers, respectively) in 1958 and the subsequent formation of the Mets in 1962. This World Series that featured teams from the same city or state, was the first of its kind since 1989 between the Oakland Athletics and the San Francisco Giants. Yankees shortstop Derek Jeter was named the World Series Most Valuable Player.

Who won the 2000 World Series?

New York Yankees defeated the NY Mets 4 games to 1

Princeton WordNet BIBREF0 is one of the most important resources used in many different tasks across linguistics and natural language processing, however the resource is only available for English and is limited in its coverage of real world concepts. To cross the language barrier, huge efforts have been made to extend the Princeton WordNet with multilingual information in projects, such as EuroWordNet BIBREF1 , BalkaNet BIBREF2 and MultiWordNet BIBREF3 , mostly following the extend approach, where the structure of the Princeton WordNet is preserved and only the words in each synset are translated and new synsets are added for concepts. Furthermore, the Princeton WordNet has many fewer concepts than large scale encyclopedias such as Wikipedia and resources derived from it such as DBpedia BIBREF4 and BabelNet BIBREF5 . This problem is even worse for many non-English wordnets, due to the extend approach, as these resources have even fewer synsets than Princeton WordNet. Furthermore, there are still many languages for which a wordnet does not exist or is not available to all potential users due to licensing restrictions. To address these deficiencies we propose two approaches. Firstly, we apply high-quality statistical machine translation (SMT) to automatically translate the WordNet entries into several different European languages. While an SMT system can only return the most frequent translation when given a term by itself, we propose a novel method to provide strong word sense disambiguation when translating wordnet entries. In addition, our method can handle fundamental complexities such as the need to translate all senses of a word including low-frequency senses, which is very challenging for current SMT approaches. For these reasons, we leverage existing translations of Princeton WordNet entries in other languages to identify contextual information for wordnet senses from a large set of generic parallel corpora. The goal is to identify sentences that share the same semantic information in respect to the synset of the Princeton WordNet entry that we want to translate. Secondly, we describe a novel system based on state-of-the-art semantic textual similarity and ontology alignment to establish a new linking between Princeton WordNet and DBpedia. This method uses a multi-feature approach to establish similarities between synsets and DBpedia entities based on analysis of the definitions using a variety of methods from simple string statistics, to methods based on explicit semantic analysis as well as deep learning methods including long short-term memory (LSTM) networks. These statistics are created based on the Princeton WordNet synset gloss as well as the neighbouring words in the WordNet graph. These are combined using a constraint-based solver that considers not only the semantic similarity of the synsets but also the overall structure of the alignment and its consistency, following the best practices in ontology alignment. This work has led to the development of a large multilingual WordNet in more than 20 European languages, which we call Polylingual WordNet BIBREF6 , which is available under an open (CC-BY) license. Finally, we describe how this resource is published, firstly as linked data in the linguistic linked open data cloud, as well as published in all the formats of the Global WordNet Association Interlingual Index. While an SMT system can only return the most frequent translation when given a term by itself, we propose a novel method to provide strong word sense disambiguation when translating wordnet entries.

What kind of method do they want to propose?

A novel method to provide strong word sense disambiguation when translating wordnet entries into several different European languages.

It is well known that language has certain structural properties which allows natural language speakers to make “infinite use of finite means" BIBREF3 . This structure allows us to generalize beyond the typical machine learning definition of generalization BIBREF4 (which considers performance on the distribution that generated the training set), permitting the understanding of any utterance sharing the same structure, regardless of probability. For example, sentences of length 100 typically do not appear in natural text or speech (our personal 'training set'), but can be understood regardless due to their structure. We refer to this notion as linguistic generalization . Many problems in NLP are treated as sequence to sequence tasks with solutions built on seq2seq-attention based models. While these models perform very well on standard datasets and also appear to capture some linguistic structure BIBREF5 , BIBREF6 , BIBREF7 , they also can be quite brittle, typically breaking on uncharacteristic inputs BIBREF8 , BIBREF1 , indicating that the extent of linguistic generalization these models achieve is still somewhat lacking. Due to the high capacity of these models, it is not unreasonable to expect them to learn some structure from the data. However, learning structure is not a sufficient condition to achieving linguistic generalization. If this structure is to be usable on data outside the training distribution, the model must learn the structure without additionally learning (overfitting on) patterns specific to the training data. One may hope, given the right hyperparameter configuration and regularization, that a model converges to a solution that captures the reusable structure without overfitting too much on the training set. While this solution exists in theory, in practice, it may be difficult to find. In this work, we look at the feasibility of training and tuning seq2seq-attention models towards a solution that generalizes in this linguistic sense. In particular, we train models on a symbol replacement task with a well defined generalizable structure. The task is simple enough that all models achieve near perfect accuracy on the standard test set, i.e., where the inputs are drawn from the same distribution as that of the training set. We then test these models for linguistic generalization by creating test sets of uncharacteristic inputs, i.e., inputs that are not typical in the training distribution but still solvable given that the generalizable structure was learned. Our results indicate that generalization is highly sensitive; such that even changes in the random seed can drastically affect the ability to generalize. This dependence on an element that is not (or ideally should not be) a hyperparameter suggests that the line between generalization and failure is quite fine, and may not be feasible to reach simply by hyperparameter tuning alone. Our results indicate that generalization is highly sensitive; such that even changes in the random seed can drastically affect the ability to generalize.

What can drastically affect the ability to generalize?

The changes in the random seed can drastically affect the ability to generalize.

Gaya melon, also known as snowball melon, ghost melon, dinosaur melon, dinosaur egg melon, dino melon, and dino egg melon, is a small to medium-sized honeydew melon developed originally in Japan and Korea and now grown in China, Mexico, southern California, and South America. The rind is very thin and is ivory in color with green streaking and the interior flesh is white. They are round in shape and may be slightly oblong. The flesh is juicy and soft towards the center but crisper towards the rind. It has been described to have a mild, sweet flavor with floral notes. It is best kept at room temperature and cut melons will stay good in a refrigerator for up to 5 days.

What's a gaya melon? How does it taste like?

Gaya melon is a type of honeydew melon originally developed in Japan and Korea. They are very juicy and soft towards the center but crisper towards the grind. It has been described to have a mild, sweet flavor with floral notes.

In 1877 he enrolled in the University of Pennsylvania's medical school where he became interested in anatomy, and in 1882, he graduated with the Anomaly Prize from the Demonstrator of Morbid Anatomy. He did his residency at the Episcopal Hospital at Kensington, Pennsylvania. Gynecology wasn't practiced at the hospital, so he opened his own clinic in a two-story house in a working district where he also lived. He saw patients during the day and slept on a sofa at night with a string tied to his toe so that whenever there was a tug on the string, he would be woken up to provide medical assistance. He also made house calls. Later, with money from a group of Philadelphia philanthropist women, he was able to open a formal clinic with an operating room in Norris Square, which later became the Kensington Hospital for Women, the sixth women's hospital in the United States. There, he created the "Kelly stitch", a procedure to treat Stress incontinence, and performed the first successful C-section in Philadelphia. He gained a reputation as an innovator in surgical techniques during this time. From 1886 to 1888, Kelly traveled to England, Scotland, and Germany to observe abdominal and pelvic surgeons at work. When he was in Leipzig in 1886, he was introduced to the palpation of female ureters by Max Saenger. In Berlin in 1888, he worked with pathologist Rudolf Virchow to determine the best way to catheterize ureters. Then, in Prague in 1888, Czech physician Parel Pawlik showed him how to catheterize ureters using a speculum and how to conduct an air cystoscopy. In 1888, he returned to the University of Pennsylvania, to become an associate professor of obstetrics under the recommendation of William Osler.

Extract the places in the following paragraph. Give them as a numbered list.

1. Kensington, Pennsylvania 2. Philadelphia 3. Norris Square 4. United States 5. England 6. Scotland 7. Germany 8. Leipzig 9. Berlin 10. Prague

Categorize the following as hot or cold.

Fire, snow, sun, ice

Why do developers like Agile?

Developers embraced Agile in response to project methodologies like Waterfall, which was borrowed from low variance industrial production. Whilst waterfall worked well when producing many of the same thing, in development, where there is much uncertainty and a need to adjust course often. Agile embraces the idea that the future is uncertain, and rather than build long project gantt charts that show a false narrative of certainty, it is more efficient to acknowledge this and break work up into epics and sprints. There is a counter movement however to Agile, where some developers reject the rigidity of formal agile projects where project managers are essentially appearing as scrum masters or where there is too much rigidity in the processes which is the antithesis of "Being Agile". So in summary, whilst developers like Agile generally, it should be acknowledged that most developers like "Being Agile" rather than a strict "Agile Project" approach.

How can I do a figure skating toe loop jump?

Assuming you jump counterclockwise, a toe loop traditionally starts with a right inside three turn. The toe loop is a pick assisted jump, meaning that one leg will stick a toe pick into the ice to help with the jump. In the case of a toe loop, you will enter on your right outside edge and rotate your left free leg behind you to pick the ice. It's important that the free leg is twisted as far back and sideways as possible to add stability. You'll then want to jump when your skating foot approaches your pick foot and complete a single rotation in the air. Bringing the arms together and having proper posture will help with completing the rotation. Then, you will want to land on your right leg on a backwards outside edge. This is a traditional landing position. If you jump clockwise, all directions will be reversed.

What is topography?

Topography is the study of the forms and features of land surfaces. The topography of an area may refer to the land forms and features themselves, or a description or depiction in maps. Topography is a field of geoscience and planetary science and is concerned with local detail in general, including not only relief, but also natural, artificial, and cultural features such as roads, land boundaries, and buildings. In the United States, topography often means specifically relief, even though the USGS topographic maps record not just elevation contours, but also roads, populated places, structures, land boundaries, and so on. Topography in a narrow sense involves the recording of relief or terrain, the three-dimensional quality of the surface, and the identification of specific landforms; this is also known as geomorphometry. In modern usage, this involves generation of elevation data in digital form (DEM). It is often considered to include the graphic representation of the landform on a map by a variety of cartographic relief depiction techniques, including contour lines, hypsometric tints, and relief shading.

1908.05969

The sequence modeling layer models the dependency between characters built on vector representations of the characters. In this work, we explore the applicability of our method to three popular architectures of this layer: the LSTM-based, the CNN-based, and the transformer-based. Table TABREF46 shows performance of our method with different sequence modeling architectures. From the table, we can first see that the LSTM-based architecture performed better than the CNN- and transformer- based architectures. In addition, our methods with different sequence modeling layers consistently outperformed their corresponding ExSoftword baselines. This shows that our method is applicable to different neural sequence modeling architectures for exploiting lexicon information. In this work, we explore the applicability of our method to three popular architectures of this layer: the LSTM-based, the CNN-based, and the transformer-based. Table TABREF46 shows performance of our method with different sequence modeling architectures. From the table, we can first see that the LSTM-based architecture performed better than the CNN- and transformer- based architectures.

Which are the sequence model architectures this method can be transferred across?

The sequence model architectures which this method is transferred to are: LSTM and Transformer-based models

Hunt faced Roy Nelson on 20 September 2014, at UFC Fight Night 52. He won the fight via knockout in the second round. The win earned Hunt his first Performance of the Night bonus award, and the World MMA Awards' 2014 Knockout of the Year award. On 21 October 2014, it was announced that Hunt would replace injured UFC Heavyweight Champion Cain Velasquez in the main event of UFC 180. He faced off against Fabrício Werdum for the interim UFC Heavyweight Championship. Despite having early success and dropping Werdum twice, Hunt lost the fight via TKO in the second round. Hunt faced Stipe Miocic on 10 May 2015, at UFC Fight Night 65. He lost the fight via TKO in the fifth round. Miocic set a UFC record for the most strikes landed in a fight, outlanding Hunt 361 – 48 over the duration of the bout. Hunt faced Antônio Silva in a rematch on 15 November 2015, at UFC 193. Hunt won the fight via TKO, after dropping Silva with a straight right up against the fence at 3:41 of the first round. Hunt faced Frank Mir on 20 March 2016, at UFC Fight Night 85. He won the fight via KO in the first round after sending Mir to the canvas with a right hand. He was awarded with Performance of the Night for his efforts. It was later announced that Mir failed an in-competition drug test. Despite talks about Hunt's current contract being his last, on 14 April 2016, it was announced that Hunt had signed a new six-fight, multi-million dollar contract with the UFC. Hunt faced a returning Brock Lesnar on 9 July 2016, at UFC 200. He lost the fight via unanimous decision. However, on 15 July, it was revealed that Lesnar had tested positive for a banned substance in a pre-fight drug test. The test result was conducted on 28, 11 June days prior to the fight, and was flagged by USADA as a potential anti-doping violation. On 19 July, the UFC announced that Lesnar tested positive for the same banned substance in a second, in-competition sample. On 23 August, the Nevada Athletic Commission confirmed that Lesnar twice tested positive for the estrogen blocker clomiphene, and that he has been temporarily suspended. Due to Lesnar's positive tests, the result of the fight was changed to a no contest. Hunt faced Alistair Overeem in a rematch on 4 March 2017, at UFC 209. He lost the fight via knockout in the third round. Hunt faced Derrick Lewis on 11 June 2017, in the main event at UFC Fight Night 110. It was the first combat sports event in Auckland since UFC Fight Night 43 in Vector Arena. He won the fight via TKO in round four. The win also earned Hunt his third Fight of the Night bonus award. Hunt was expected to face Marcin Tybura on 19 November 2017, at UFC Fight Night: Hunt vs. Tybura. However, on 10 October, Hunt was pulled from the card and was replaced by Fabrício Werdum after UFC officials reviewed Hunt's article, entitled, "If I Die Fighting, That's Fine" on his health status from the consequences he suffered from his long term fighting career (slur speech and loss of memory- an early signs of chronic traumatic encephalopathy (CTE)). Hunt was upset with the news, responding on his Instagram that the quote was taken out of context and that he had been cleared to fight by doctors two days before he was pulled from the bout.

Extract the number of fights Hunt won by knockout or TKO in the below text and the year he had the most knockout wins

Mark Hunt won four fights via knockout/TKO with two of these wins coming in 2015

Why do people like to play Path of Exile?

Path of Exile is a complex, action role playing game. While many RPGs contain limited options, Path of Exile offers significant customization and choices to the player. Many people criticize Path of Exile for being too complex--more akin to a spreadsheet than a game--especially when compared to other action RGPs like Diablo.

1807.00868

We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks. We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks.

What normalization techniques are mentioned?

The answers are shown as follows: * FBanks with cepstral mean normalization (CMN) * variance with mean normalization (CMVN)

Speech recordings of patients in three different languages are considered: Spanish, German, and Czech. All of the recordings were captured in noise controlled conditions. The speech signals were down-sampled to 16 kHz. The patients in the three datasets were evaluated by a neurologist expert according to the third section of the movement disorder society, unified Parkinson's disease rating scale (MDS-UPDRS-III) BIBREF16. Table TABREF5 summarizes the information about the patients and healthy speakers. The Spanish data consider the PC-GITA corpus BIBREF5, which contains utterances from 50 PD patients and 50 HC, Colombian Spanish native speakers. The participants were asked to pronounce a total of 10 sentences, the rapid repetition of /pa-ta-ka/, /pe-ta-ka/, /pa-ka-ta/, /pa/, /ta/, and /ka/, one text with 36 words, and a monologue. All patients were in ON state at the time of the recording, i.e., under the effect of their daily medication. Speech recordings of 88 PD patients and 88 HC speakers from Germany are considered BIBREF17. The participants performed four speech task: the rapid repetition of /pa-ta-ka/, 5 sentences, one text with 81 words, and a monologue. A total of 100 native Czech speakers (50 PD, 50 HC) were considered BIBREF18. The speech tasks performed by the participants include the rapid repetition of the syllables /pa-ta-ka/, a read text with 80 words, and a monologue. Speech recordings of patients in three different languages are considered: Spanish, German, and Czech. All of the recordings were captured in noise controlled conditions.

What languages of speech recordings of patients are considered?

Spanish, German, and Czech.

2004.03744

Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class.

Is model explanation output evaluated, what metric was used?

The answers are shown as follows: * balanced accuracy, i.e., the average of the three accuracies on each class

Classify each of these movies as being for adults or for kids.

The Longest Day: adults Babe: kids Minions: kids Savings Private Ryan: adults Paddington: kids Flubber: kids Peter Rabbit: kids Diary of a Wimpy Kid: kids Moana: kids Mulan: kids Charlotte's Web: kids Tár: adults All Quiet on the Western Front: adults Eyes Wide Shut: adults

Chris Kuroda, who has been Phish's lighting director since 1989, creates elaborate light displays during the band's concerts that are sometimes improvised in a similar fashion to their music. Justin Taylor of The Baffler wrote, "You could hate this music with every fiber of your being and still be ready to give Chris Kuroda a MacArthur "genius" grant for what he achieves with his light rig." Kuroda is often referred to by fans as the unofficial fifth member of the band, and has been given the nickname "CK5".

Based on this passage, why is Chris Kuroda's nickname CK5?

Chris Kuroda's nickname, CK5, adds the number 5 to his initial. That is because his improvised light shows during Phish concerts give him a role comparable to a fifth member of the band.

Equations are an important part of scientific articles, but many existing machine learning methods do not easily handle them. They are challenging to work with because each is unique or nearly unique; most equations occur only once. An automatic understanding of equations, however, would significantly benefit methods for analyzing scientific literature. Useful representations of equations can help draw connections between articles, improve retrieval of scientific texts, and help create tools for exploring and navigating scientific literature. In this paper we propose equation embeddings (EqEmb), an unsupervised approach for learning distributed representations of equations. The idea is to treat the equation as a "singleton word," one that appears once but that appears in the context of other words. The surrounding text of the equation—and in particular, the distributed representations of that text—provides the data we need to develop a useful representation of the equation. Figure FIGREF1 illustrates our approach. On the left is an article snippet BIBREF0 . Highlighted in orange is an equation; in this example it represents a neural network layer. We note that this particular equation (in this form and with this notation) only occurs once in the collection of articles (from arXiv). The representations of the surrounding text, however, provide a meaningful context for the equation. Those words allow us to learn its embedding, specifically as a "word" which appears in the context of its surroundings. The resulting representation, when compared to other equations' representations and word representations, helps find both related equations and related words. These are illustrated on the right. EqEmbs build on exponential family embeddings BIBREF1 to include equations as singleton observations and to model equation elements such as variables, symbols and operators. Exponential family embeddings, like all embedding methods, define a context of each word. In our initial EqEmb, the context for the words is a small window, such as four or eight words, but the context of an equation is a larger window, such as sixteen words. Using these two types of contexts together finds meaningful representations of words and equations. In the next EqEmb, which builds on the first, we consider equations to be sentences consisting of equation units, i.e., variables, symbols, and operators. Equation units help model equations across two types of context—over the surrounding units and over the surrounding words. We studied EqEmbs on four collections of scientific articles from the arXiv, covering four computer science domains: natural language processing (NLP), information retrieval (IR), artificial intelligence (AI) and machine learning (ML). We found that EqEmbs provide more efficient modeling than existing word embedding methods. We further carried out an exploratory analysis of a large set of INLINEFORM0 87k equations. We found that EqEmbs provide better models when compared to existing word embedding approaches. EqEmbs also provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words. In this paper we propose equation embeddings (EqEmb), an unsupervised approach for learning distributed representations of equations.

What does the paper propose?

Equation embeddings (EqEmb).

What is a good slogan for carpet?

Here are some good slogans for carpet: - The Fuzzy Floor - Your feet's best friend - A sweater for your floor - Cushion your falls with carpet - Carpet diem

Identify which car manufacturer is German or American: RAM, Artega

Artega is German, RAM is American

1803.03786

We have presented the first attempt to solve the fake news problem for Bulgarian. Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. We further produced and shared a number of relevant language resources for Bulgarian, which we created for solving the task. Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier.

what types of features were used?

The answers are shown as follows: * stylometric, lexical, grammatical, and semantic

Which of the following are deciduous trees? Abies concolor Acer rubrum Acer saccharinum Cornus florida Gleditsia triacanthos Picea glauca picea pungens Picea abies Pinus strobus Quercus rubra

Of the species listed, Acer rubrum, Acer saccharinum, Gleditsia triacanthos, Quercus rubra, and Cornus florida are deciduous trees.

Who is the best tennis player of all time?

To name the best tennis player of all time is not a simple answer, but there seems to be wide agreement that the short list includes Roger Federer, Novak Djokovic, and Rafael Nadal. All three are very close to each other in total Grand Slam titles, and have competitive records against each other. They are all head and shoulders above every other player of their era, as well as players from past eras when it comes to win/loss records.

Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form-stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University (Guo et al., 2019). In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems.

What framework do the authors propose in the paper?

A GPT-2 based uniformed framework.

How did Kdramas become so popular?

Korean dramas or Kdramas have been popular for over 2 decades now. However, the rise in popularity shot up during the pandemic. Kdramas were initially watched by people in Southeast Asian countries and in some places in South Asia. When the pandemic hit, a lot of Kdramas were available on Netflix making it easier for people to watch them. With the word of mouth, this soon spread and more and more users were watching Kdramas. Netflix took advantage of this viewership and starting making dramas of their own or buying rights for dramas to be streaming exclusively on Netflix. This soon led to Disney and other platforms making dramas of their own. Kpop has also helped in attracting viewers. Whether it's the captivating story plot, the acting, actors or action, the Hallyu wave has spread throughout the world.

Classify the cities as cities from 'India' or the 'US': Bangalore, Portland, Chicago, Hyderabad, Chennai, Little Rock, NYC, Pune, Gandhinagar, Champaign, Urbana.

The classification in order is: India, US, US, India, India, US, US, India, India, US, US.

What are four different recipes that you can make with Brussels Sprouts?

1. You can make roasted Brussels Sprouts by cooking them in the oven at 400 degrees Fahrenheit for about 20 minutes. 2. You can make a shaved Brussels sprouts salad using raw Brussels sprouts, sunflower seeds, dried cranberries, parmesan cheese, and a lemon mustard vinaigrette. 3. You can saute Brussels sprouts with bacon and onions.

Elemental magnesium is a gray-white lightweight metal, two-thirds the density of aluminium. Magnesium has the lowest melting (923 K (650 °C)) and the lowest boiling point 1,363 K (1,090 °C) of all the alkaline earth metals.

Given this paragraph about magnesium, what is the boiling point of magnesium in celsius?

The boiling point of magnesium is 1,090 °C.

Kenneth McAlpine (11 April 1858 – 10 February 1923) was an English local government representative, a wine merchant and a cricketer who played first-class cricket for Kent and for teams led by Lord Hawke in North America. He was born at Leamington Spa, Warwickshire, and died at Loose, Maidstone, Kent.

Given the following paragraph about Kenneth McAlpine, where was he born?

Leamington Spa, Warwickshire

Why are reaction channels popular on YouTube?

Reaction channels are a category of YouTube channels where one or more people consume media content, food, or drinks and share how they feel about it. These content creators are known as reactors. There are multiple reasons people might like them. Most commonly, people want to know how others feel about something they love. As social animals, human beings like being able to share experiences with others. Watching people have similar reactions to theirs validates their own lived experiences. Reaction channels can also serve as a way to get a review of media, food etc. without having to go out and try it for yourself which often requires an investment of time or money or both. In case you already agree with the reactors on other items, these reviews can prove to be more trustworthy than seeing an arbitrary written review somewhere on the internet and might open the viewer up to new experiences. These channels also gained a lot of popularity during COVID lockdowns between 2020-2021 because a lot of people were isolated and being able to share things they loved with others online helped build a sense of virtual community and helped people feel less alone.

After the end of World War II in Europe, what remained of pre-war Germany west of the Oder-Neisse line was divided into four occupation zones (as per the Potsdam Agreement), each one controlled by one of the four occupying Allied powers: the United States, the United Kingdom, France and the Soviet Union. The capital of Berlin, as the seat of the Allied Control Council, was similarly subdivided into four sectors despite the city's location, which was fully within the Soviet zone. Within two years, political divisions increased between the Soviets and the other occupying powers. These included the Soviets' refusal to agree to reconstruction plans making post-war Germany self-sufficient, and to a detailed accounting of industrial plants, goods and infrastructure—some of which had already been removed by the Soviets. France, the United Kingdom, the United States, and the Benelux countries later met to combine the non-Soviet zones of Germany into one zone for reconstruction, and to approve the extension of the Marshall Plan.

Who was in control of the four occupation zones as per the above passage?

the United States, the United Kingdom, France and the Soviet Union.

What is conspicuous consumption?

In economic theory purchases are made by individuals or groups for their own personal or collective satisfaction. The utility of the purchase is relative only to the preferences of the individual or group of buyers. Conspicuous consumption is an exceptional form of behaviour where the purchase is driven primarily to make an ostentatious display of wealth. In particular, the buyer has a desire to impress others by their ability to pay a particularly high price for a prestige product. In this way the utility of the purchase driven by social rather than economic or physiological drivers. Satisfaction is derived from the audience reaction not to the positive attributes of the good or service, but to the wealth displayed by the purchaser. In this way the cost of purchase becomes the the only factor of significance to the buyer.

We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7: [noitemsep] Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?” Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.” Sexualised Insults, e.g. “Stupid bitch.”, “Whore” Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.” We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018. [leftmargin=5mm, noitemsep] 4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana. 4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11. 4 Data-driven approaches: Cleverbot BIBREF12; NeuralConvo BIBREF13, a re-implementation of BIBREF14; an implementation of BIBREF15's Information Retrieval approach; a vanilla Seq2Seq model trained on clean Reddit data BIBREF1. Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20. We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$). Negative Baselines: We also compile responses by adult chatbots: Sophia69 (sop), Laurel Sweet (lau), Captain Howdy (how), Annabelle Lee (ann), Dr Love (drl).

What are the Negative Baselines prompts?

Sophia69 (sop), Laurel Sweet (lau), Captain Howdy (how), Annabelle Lee (ann), Dr Love (drl).

Which ski pass, between IKON pass and Epic pass, should I buy if I live in the Bay area and mostly go to the Lake Tahoe area for skiing.

Although both passes provide great value when you visit the snow resorts more than 7 days each snow season, they have some differences that you need to consider before you make the purchase. For IKON pass, you can access 14 ski resorts for unlimited time and over 40 ski resorts for max 7 days each snow season. The nearest ski resort for you resides in the greater bay area would be Palisades Tahoe. For Epic pass, the nearest ski resort near the bay area would be the North Star ski resort, Kirkland ski resort and Heavenly ski resort, all of them reside in the Lake Tahoe area. I would recommend buying the epic pass because you will have more options to choose from in the lake tahoe area.

Tell me whether these are flowers or fruit: apples, oranges, pineapples, bananas, strawberries, daisies, sunflowers, roses, poppies

Apples are a fruit. Oranges are a fruit. Pineapples are a fruit. Bananas are a fruit. Strawberries are a fruit. Daisies are a flower. Sunflowers are a flower. Roses are a flower. Poppies are a flower.

Muhammadu Gambo Jimeta (15 April 1937 - 21 January 2021) was a former Inspector General of the Nigeria Police Force. He was appointed in 1986 to succeed Etim Inyang and was succeeded by Aliyu Attah in 1990. He was also a National Security Adviser to President of Nigeria Ibrahim Babangida.Early lifeHe was born in Jimeta on 15 April 1937.EducationGambo as he was popularly called, attended Jimeta Elementary School (1947–49) and Yola Middle School (1950–55) for his early education.Thereafter, he went to Bauchi Provincial Secondary School (1956–58) and Government College Keffi (1958–59), before attending Nigeria Police College Kaduna (1959) and Police College Ikeja (1959).He also attended Detective Training School, Wakefield, UK (1962), Bramhill Police College UK (1963), International Police Academy, Washington DC, USA (1980).

From the below passage, list all the educational institutions which Gambo attended. Mention the starting year against each institute and sort in an ascending manner. Present in the result in the format ‘Institute Name - YYYY’ and display new line for each record.

Jimeta Elementary School -1947 Yola Middle School - 1950 Bauchi Provincial Secondary School - 1956 Government College Keffi - 1958 Nigeria Police College Kaduna - 1959 Police College Ikeja - 1959 Detective Training School, Wakefield, UK - 1962 Bramhill Police College UK -1963 International Police Academy, Washington DC, USA - 1980

write a poem about the holidays

the holidays are magical you will agree if you are logical in thanksgiving we feast its a reason to get together at least in Christmas we sing thats our thing we put gifts under the tree hopefully containing a new plush bee during halloween we scare and hope our costume doesn't tear a bag comes in handy when you get tons of candy in easter there are eggs and a bunny with jumpy legs those are some holidays I wouldn't have them in any other ways

1901.04899

Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well. We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators. Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well. We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators.

What are the supported natural commands?

The answers are shown as follows: * Set/Change Destination * Set/Change Route * Go Faster * Go Slower * Stop * Park * Pull Over * Drop Off * Open Door * Other

What are the reasons why quantum theory is considered do difficult to understand

1. There is not one agreed set of basic rules (axioms) that underpin the theory and each has subtle implications on how you interpret the theory 2. For each of those there are multiple views and mathematical frameworks for describing the theory (e.g. the wave mechanics, field theories, etc) 3. The mathematical theories are all at an advanced level and few examples of how to actually apply them appear in text books. 4. The theory itself says that you cannot know the intermediate steps in any quantum process, you can know where you start and predict where you might end up but not how you get form one to the other. 5. The theory is based on either infinitely parallel actions occurring deterministically or statistical interpretation of a range of outcomes 6. All of these interactions occur without direct reference to space/time and so are in abstract spaces that need to be decided for the calculation and physically interpreted

1707.08559

Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game. The average number of chats per video is 7490 with a standard deviation of 4922. The high value of standard deviation is mostly due to the fact that NALCS simultaneously broadcasts matches in two different channels (nalcs1 and nalcs2) which often leads to the majority of users watching the channel with a relatively more popular team causing an imbalance in the number of chats. If we only consider LMS which broadcasts with a single channel, the average number of chats are 7210 with standard deviation of 2719. The number of viewers for each game averages about 21526, and the number of unique users who type in chat is on average 2185, i.e., roughly 10% of the viewers. Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game.

What is the average length of the recordings?

40 minutes

Identify which animal is domesticated or wild: Chicken, Mandarin duck, Egyptian goose

Chicken is domesticated, Mandarin duck and Egyptian goose are wild

1606.04631

BLEU BIBREF28 , METEOR BIBREF29 , ROUGE-L BIBREF30 and CIDEr BIBREF31 are common evaluation metrics in image and video description, the first three were originally proposed to evaluate machine translation at the earliest and CIDEr was proposed to evaluate image description with sufficient reference sentences. To quantitatively evaluate the performance of our bidirectional recurrent based approach, we adopt METEOR metric because of its robust performance. Contrasting to the other three metrics, METEOR could capture semantic aspect since it identifies all possible matches by extracting exact matcher, stem matcher, paraphrase matcher and synonym matcher using WordNet database, and compute sentence level similarity scores according to matcher weights. The authors of CIDEr also argued for that METEOR outperforms CIDEr when the reference set is small BIBREF31 . To quantitatively evaluate the performance of our bidirectional recurrent based approach, we adopt METEOR metric because of its robust performance.

what metrics were used for evaluation?

The answers are shown as follows: * METEOR

The goal of cheese making is to control the spoiling of milk into cheese. The milk is traditionally from a cow, goat, sheep or buffalo, although, in theory, cheese could be made from the milk of any mammal. Cow's milk is most commonly used worldwide. The cheesemaker's goal is a consistent product with specific characteristics (appearance, aroma, taste, texture). The process used to make a Camembert will be similar to, but not quite the same as, that used to make Cheddar. Some cheeses may be deliberately left to ferment from naturally airborne spores and bacteria; this approach generally leads to a less consistent product but one that is valuable in a niche market. Culturing Cheese is made by bringing milk (possibly pasteurised) in the cheese vat to a temperature required to promote the growth of the bacteria that feed on lactose and thus ferment the lactose into lactic acid. These bacteria in the milk may be wild, as is the case with unpasteurised milk, added from a culture, frozen or freeze dried concentrate of starter bacteria. Bacteria which produce only lactic acid during fermentation are homofermentative; those that also produce lactic acid and other compounds such as carbon dioxide, alcohol, aldehydes and ketones are heterofermentative. Fermentation using homofermentative bacteria is important in the production of cheeses such as Cheddar, where a clean, acid flavour is required. For cheeses such as Emmental the use of heterofermentative bacteria is necessary to produce the compounds that give characteristic fruity flavours and, importantly, the gas that results in the formation of bubbles in the cheese ('eye holes'). Starter cultures are chosen to give a cheese its specific characteristics. In the case of mould-ripened cheese such as Stilton, Roquefort or Camembert, mould spores (fungal spores) may be added to the milk in the cheese vat or can be added later to the cheese curd. Coagulation During the fermentation process, once sufficient lactic acid has been developed, rennet is added to cause the casein to precipitate. Rennet contains the enzyme chymosin which converts κ-casein to para-κ-caseinate (the main component of cheese curd, which is a salt of one fragment of the casein) and glycomacropeptide, which is lost in the cheese whey. As the curd is formed, milk fat is trapped in a casein matrix. After adding the rennet, the cheese milk is left to form curds over a period of time. Fresh chevre hanging in cheesecloth to drain. Draining Once the cheese curd is judged to be ready, the cheese whey must be released. As with many foods the presence of water and the bacteria in it encourages decomposition. To prevent such decomposition it is necessary to remove most of the water (whey) from the cheese milk, and hence cheese curd, to make a partial dehydration of the curd. There are several ways to separate the curd from the whey. Maturing cheese in a cheese cellar Scalding In making Cheddar (or many other hard cheeses) the curd is cut into small cubes and the temperature is raised to approximately 39 °C (102 °F) to 'scald' the curd particles. Syneresis occurs and cheese whey is expressed from the particles. The Cheddar curds and whey are often transferred from the cheese vat to a cooling table which contains screens that allow the whey to drain, but which trap the curd. The curd is cut using long, blunt knives and 'blocked' (stacked, cut and turned) by the cheesemaker to promote the release of cheese whey in a process known as 'cheddaring'. During this process the acidity of the curd increases to a desired level. The curd is then milled into ribbon shaped pieces and salt is mixed into it to arrest acid development. The salted green cheese curd is put into cheese moulds lined with cheesecloths and pressed overnight to allow the curd particles to bind together. The pressed blocks of cheese are then removed from the cheese moulds and are either bound with muslin-like cloth, or waxed or vacuum packed in plastic bags to be stored for maturation. Vacuum packing removes oxygen and prevents mould (fungal) growth during maturation, which depending on the wanted final product may be a desirable characteristic or not. Mould-ripening Main article: Cheese ripening In contrast to cheddaring, making cheeses like Camembert requires a more gentle treatment of the curd. It is carefully transferred to cheese hoops and the whey is allowed to drain from the curd by gravity, generally overnight. The cheese curds are then removed from the hoops to be brined by immersion in a saturated salt solution. The salt absorption stops bacteria growing, as with Cheddar. If white mould spores have not been added to the cheese milk it is applied to the cheese either by spraying the cheese with a suspension of mould spores in water or by immersing the cheese in a bath containing spores of, e.g., Penicillium candida. By taking the cheese through a series of maturation stages where temperature and relative humidity are carefully controlled, allowing the surface mould to grow and the mould-ripening of the cheese by fungi to occur. Mould-ripened cheeses ripen very quickly compared to hard cheeses (weeks against months or years). This is because the fungi used are biochemically very active when compared with starter bacteria. Some cheeses are surface-ripened by moulds, such as Camembert and Brie, some are ripened internally, such as Stilton, which is pierced with stainless steel wires, to admit air to promote mould spore germination and growth, as with Penicillium roqueforti. Surface ripening of some cheeses, such as Saint-Nectaire, may also be influenced by yeasts which contribute flavour and coat texture. Others are allowed to develop bacterial surface growths which give characteristic colours and appearances, e.g. by the growth of Brevibacterium linens which gives an orange coat to cheeses.

From the provided description of the cheesemaking process, list the ingredients required to make cheese.

The main ingredient used to make cheese is milk. Cow's milk is commonly used, though goat, sheep or buffalo can also be used, as could the milk of any mammal in theory. Starter cultures are typically added to aid in the culturing stage of the process. Rennet is added to the cheese milk to promote the separation into cheese curd and whey. Ultimately, salt is added to halt the production of acid later in the process. Finally, mould spores are introduced to assist in ripening. This can be added to the cheese milk early on in the process, or just prior to maturing.

Ambiguity and implicitness are inherent properties of natural language that cause challenges for computational models of language understanding. In everyday communication, people assume a shared common ground which forms a basis for efficiently resolving ambiguities and for inferring implicit information. Thus, recoverable information is often left unmentioned or underspecified. Such information may include encyclopedic and commonsense knowledge. This work focuses on commonsense knowledge about everyday activities, so-called scripts. This paper introduces a dataset to evaluate natural language understanding approaches with a focus on interpretation processes requiring inference based on commonsense knowledge. In particular, we present MCScript, a dataset for assessing the contribution of script knowledge to machine comprehension. Scripts are sequences of events describing stereotypical human activities (also called scenarios), for example baking a cake or taking a bus BIBREF0 . To illustrate the importance of script knowledge, consider Example ( SECREF1 ): Without using commonsense knowledge, it may be difficult to tell who ate the food: Rachel or the waitress. In contrast, if we utilize commonsense knowledge, in particular, script knowledge about the eating in a restaurant scenario, we can make the following inferences: Rachel is most likely a customer, since she received an order. It is usually the customer, and not the waitress, who eats the ordered food. So She most likely refers to Rachel. Various approaches for script knowledge extraction and processing have been proposed in recent years. However, systems have been evaluated for specific aspects of script knowledge only, such as event ordering BIBREF1 , BIBREF2 , event paraphrasing BIBREF3 , BIBREF4 or event prediction (namely, the narrative cloze task BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 ). These evaluation methods lack a clear connection to real-world tasks. Our MCScript dataset provides an extrinsic evaluation framework, based on text comprehension involving commonsense knowledge. This framework makes it possible to assess system performance in a multiple-choice question answering setting, without imposing any specific structural or methodical requirements. MCScript is a collection of (1) narrative texts, (2) questions of various types referring to these texts, and (3) pairs of answer candidates for each question. It comprises approx. 2,100 texts and a total of approx. 14,000 questions. Answering a substantial subset of questions requires knowledge beyond the facts mentioned in the text, i.e. it requires inference using commonsense knowledge about everyday activities. An example is given in Figure FIGREF2 . For both questions, the correct choice for an answer requires commonsense knowledge about the activity of planting a tree, which goes beyond what is mentioned in the text. Texts, questions, and answers were obtained through crowdsourcing. In order to ensure high quality, we manually validated and filtered the dataset. Due to our design of the data acquisition process, we ended up with a substantial subset of questions that require commonsense inference (27.4%). Our MCScript dataset provides an extrinsic evaluation framework, based on text comprehension involving commonsense knowledge. This framework makes it possible to assess system performance in a multiple-choice question answering setting, without imposing any specific structural or methodical requirements. MCScript is a collection of (1) narrative texts, (2) questions of various types referring to these texts, and (3) pairs of answer candidates for each question. it comprises approx. 2,100 texts and a total of approx. 14,000 questions.

What is the nature of MCScript?

Their MCScript dataset provides an extrinsic evaluation framework, based on text comprehension involving commonsense knowledge. This framework makes it possible to assess system performance in a multiple-choice question answering setting, without imposing any specific structural or methodical requirements. MCScript is a collection of (1) narrative texts, (2) questions of various types referring to these texts, and (3) pairs of answer candidates for each question. it comprises approx. 2,100 texts and a total of approx. 14,000 questions.

What is a to-do list?

A to-do list is an organizational method used to track things that need to be done. Typically an item is removed from the list after it is accomplished but sometimes to-do lists function to allow someone to prioritize their activities.

What is wasabi traditionally made from?

Wasabi is traditionally made from horseradish. Most commercial wasabis found in restaurants and grocery stores tend to be a mixture of horseradish, mustard, and green food dye.

Tell me whether these are European countries or names of bands: Spain, France, Norway, the Czech Republic, Denmark, Portugal, Brazil, South Africa, Canada, China, Orca, Gorillas, Camel, Bee Gees, Coldplay, Green Day, Lamb of God, Maroon 5, Pink Floyd

Some of the elements in that list aren't either European countries or names of bands! The following don't fit in either category: Brazil, South Africa, Canada, China, Orca, Gorillas, Camel. The following are European countries: Spain, France, Norway, the Czech Republic, Denmark, Portugal And the bands in the list are: Bee Gees, Coldplay, Green Day, Lamb of God, Maroon 5, Pink Floyd

What happens to helicopter pilots during an emergency? Do they have ejector seats like in jets?

No, helicopters do not have ejector seats because they would eject the pilot into the helicopter's rotor system. During an emergency or other loss of power, helicopter pilots auto-rotate. Auto-rotation is the decoupling of the rotor system from the engines to allow upward air flow to drive the rotor system at a faster than normal rate, providing kinetic energy that can be traded to arrest the helicopter's rate of descent. To initiate auto-rotation, the helicopter pilot flattens the pitch in the rotor system's blades, reducing drag, and allowing the upward flow of air to drive its rotation. When the helicopter is 100-150 feet above the ground, the pilot will then aggressively increase the pitch of the rotor system's blades, temporarily increasing lift and cushioning the helicopter's landing.

In my upcoming trip across Europe I'm planning to visit the following countries: Germany, Austria, Hungary, Romania, Bulgaria and Greece. Are all of them part of the Schengen area?

Most of them are, but not all. Romania and Bulgaria are the two exceptions.

Summer is traditionally associated with hot or warm weather. In Mediterranean climates, it is also associated with dry weather, while in other places (particularly in Eastern Asia because of the monsoon) it is associated with rainy weather. The wet season is the main period of vegetation growth within the savanna climate regime. Where the wet season is associated with a seasonal shift in the prevailing winds, it is known as a monsoon. In the northern Atlantic Ocean, a distinct tropical cyclone season occurs from 1 June to 30 November. The statistical peak of the Atlantic hurricane season is 10 September. The Northeast Pacific Ocean has a broader period of activity, but in a similar timeframe to the Atlantic. The Northwest Pacific sees tropical cyclones year-round, with a minimum in February and March and a peak in early September. In the North Indian basin, storms are most common from April to December, with peaks in May and November. In the Southern Hemisphere, the tropical cyclone season runs from the start of November until the end of April with peaks in mid-February to early March. Thunderstorm season in the United States and Canada runs in the spring through summer but sometimes can run as late as October or even November in the fall. These storms can produce hail, strong winds and tornadoes, usually during the afternoon and evening.

Describe thunderstorm season in the United States and Canada.

Thunderstorm season in the United States and Canada runs in the spring through summer but sometimes can run as late as October or even November in the fall. These storms can produce hail, strong winds and tornadoes, usually during the afternoon and evening.

RedditVC. We collect a dataset "RedditVC" of videos along with their titles and comment threads from social news site reddit.com, using their provided API. The videos are collected and used in a manner compatible with national regulations on usage of data for research. Unlike most curated video datasets, this data is more representative of the types of videos shared "in the wild", containing a large proportion of videogames, screenshots and memes. Using a classifier trained on a small amount of labelled data, we estimate that videogame footage makes up 25% of examples, other screenshots, memes and comics make up 24%, live action footage is 49% and artistic styled content (such as drawn animation) is 2%. The average video length is 33s. From 1 million raw videos collected, we perform deduplication and filtering, ending up with a training set of 461k videos, and validation and test sets of 66k videos each. For the video evaluation results in Table, we use a subset of the test set, consisting of 5000 videos with at least three comments each. In addition to comments, for one experiment in Table we also obtain textual labels of objects in the video thumbnails, using the Google Vision API. We obtain labels for 10,212 different classes, from "Abacus" to "Zwiebelkuchen". This serves as a useful comparison, giving textual metadata that can be expected to be more directly related to the visual video contents than user comments, and can be seen as a proxy for user-generated "hashtags". It also illustrates how, by using text as an intermediary, we can integrate predictions from any black-box classifier in the same framework. KineticsComments. As an additional video dataset with comments, we construct a dataset based on Kinetics-700, for which we download the videos along with associated YouTube metadata including title, description and comments. We translate non-English titles and descriptions into English using a commercial translation API. We use the title as the primary text modality, and for auxiliary context we use comments and (when training) sentences from the description. This leaves us with 484,914 videos for training, each being around 10s. We also construct a test set, consisting of videos from the Kinetics test set for which we have at least 3 comments, giving a set of 2700 videos which we use to evaluate our method in Table. Table 2: Ablation Study. Comparing Text-to-Video and Video-to-Text retrieval results between different baselines and our method.

Whether there's a baseline that just throws all the comments as a single piece of text (just like a title would be), without attention to filter them out?

We agree that combining all comments into one input text seems like a natural baseline, however, this would be limited by the maximum sequence length of the pretrained text encoder model, which in CLIP's case is 77. Additionally, we do show baselines with simpler preprocessing such as averaging and random swap in Table 2. We did try concatenating the features themselves but the averaging baseline proved to be better.

A Geobukseon, also known as a turtle ship in western descriptions, was a type of large Korean warship that was used intermittently by the Royal Korean Navy during the Joseon dynasty from the early 15th century up until the 19th century. It was used alongside the panokseon warships in the fight against invading Japanese naval ships. The ship's name derives from its protective shell-like covering. One of a number of pre-industrial armored ships developed in Europe and in East Asia, this design has been described by some as the first armored ship in the world. The first references to older, first-generation turtle ships, known as gwiseon (귀선; Korean pronunciation: [kɥisʌn]), come from 1413 and 1415 records in the Annals of the Joseon Dynasty, which mention a mock battle between a gwiseon and a Japanese warship. However, these early turtle ships soon fell out of use as Korea's naval preparedness decreased during a long period of relative peace. Turtle ships participated in the war against Japanese naval forces supporting Toyotomi Hideyoshi's attempts to conquer Korea from 1592 to 1598. Korean Admiral Yi Sun-sin, who won all battles against the Japanese Navy, is credited with designing the improved turtle ship. From their first appearance in the Battle of Sacheon, his turtle ships, equipped with at least five different types of cannon, greatly contributed to winning 16 times in 16 battles against the Japanese Navy until they were destroyed, under the command of Won Gyun, in the Battle of Chilcheollyang. Their most distinguishable feature was a dragon-shaped head at the bow (front) that could launch cannon fire or flames from the mouth. Each was also equipped with a fully covered deck to protect against arrow fire, musket-shots, and incendiary weapons. The deck was covered with iron spikes to discourage the enemy from attempting to board the ship.

Turtle ship

The Geobukseon is the most famous Korean warship, winning all 16 battles against the Japanese navy with which Toyotomi Hideyoshi attempted to conquer Korea from 1592 to 1598.

1605.04655

For the MCTest dataset, Fig. FIGREF30 compares our proposed models with the current state-of-art ensemble of hand-crafted syntactic and frame-semantic features BIBREF16 , as well as past neural models from the literature, all using attention mechanisms — the Attentive Reader of BIBREF26 , Neural Reasoner of BIBREF27 and the HABCNN model family of BIBREF17 . We see that averaging-based models are surprisingly effective on this task, and in particular on the MC-500 dataset it can beat even the best so far reported model of HABCNN-TE. Our proposed transfer model is statistically equivalent to the best model on both datasets (furthermore, previous work did not include confidence intervals, even though their models should also be stochastically initialized). For the MCTest dataset, Fig. FIGREF30 compares our proposed models with the current state-of-art ensemble of hand-crafted syntactic and frame-semantic features BIBREF16 , as well as past neural models from the literature, all using attention mechanisms — the Attentive Reader of BIBREF26 , Neural Reasoner of BIBREF27 and the HABCNN model family of BIBREF17 .

what is the state of the art for ranking mc test answers?

The answers are shown as follows: * ensemble of hand-crafted syntactic and frame-semantic features BIBREF16

Identify which instrument is string or percussion: Gong, Oungum

Gong is percussion, Oungum is string

What is the Pulitzer Prize for Fiction?

The Pulitzer in Fiction is awarded each year to the American author who published the best novel written in the prior year.

What does nationwide do?

Nationwide is a group of insurance companies in the United States, and other financial services. Their main head quarters is out of Arizona and they offer many services. Some of the services they offer are car insurance, home owners insurance, motorcycle insurance, pet and farm insurance, and life and commercial insurance. Insurance is a great thing to have in case you have an accident.

What is a humility?

Humility is the quality of being humble. Dictionary definitions accentuate humility as a low self-regard and sense of unworthiness. In a religious context humility can mean a recognition of self in relation to a deity (i.e. God), and subsequent submission to that deity as a member of that religion. Outside of a religious context, humility is defined as being "unselved", a liberation from consciousness of self, a form of temperance that is neither having pride (or haughtiness) nor indulging in self-deprecation.

2002.06644

FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset. FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.

What is the baseline for the experiments?

The answers are shown as follows: * FastText * BiLSTM * BERT

MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm. SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function. NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities. Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting. Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work. K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work. RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split. We set the value of K equals to 13 in this work.

How to set the value of K in this work?

The authors set the value of K equals to 13 in this work.

Classify each of these as either motorcycle brands or car brands: Maruti Suzuki, Ducati, Triumph, Royal Enfield, Harley Davidson, Mahindra, Tata, Skoda, Honda, Toyota, Hero, TVS, Yamaha, Suzuki, Kawasaki, Volkswagen, Ferrari, Lamborgini, Volvo, Rolls royce, Bajaj, Kia, Hyundai, Ford, Chevrolet, Crystler, Fiat, Hummer, Citreon, KTM

Motorcycle brands : Ducati, Triumph, Royal Enfield, Harley Davidson, TVS, Yamaha, Suzuki, Kawasaki,Bajaj,KTM, Hero, Honda Car Brands: Maruti Suzuki, Mahindra, Tata, Skoda, Honda, Toyota,Volkswagen, Ferrari, Lamborgini, Volvo, Rolls royce,Kia, Hyundai, Ford, Chevrolet, Crystler, Fiat, Hummer, Citreon

Early research efforts, such as the TDT program BIBREF5 , have studied news clustering for some time. The problem of online monolingual clustering algorithms (for English) has also received a fair amount of attention in the literature. One of the earlier papers by aggarwal2006framework introduced a two-step clustering system with both offline and online components, where the online model is based on a streaming implementation of INLINEFORM0 -means and a bag-of-words document representation. Other authors have experimented with distributed representations, such as ahmed2011unified, who cluster news into storylines using Markov chain Monte Carlo methods, rehureklrec who used incremental Singular Value Decomposition (SVD) to find relevant topics from streaming data, and sato2017distributed who used the paragraph vector model BIBREF6 in an offline clustering setting. More recently, crosslingual linking of clusters has been discussed by rupnik2016news in the context of linking existing clusters from the Event Registry BIBREF7 in a batch fashion, and by steinberger2016mediagist who also present a batch clustering linking system. However, these are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, rupnik2016news compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting. As detailed before, we adapted the cluster-linking dataset from rupnik2016news to evaluate our online crosslingual clustering approach. Preliminary work makes use of deep learning techniques BIBREF8 , BIBREF9 to cluster documents while learning their representations, but not in an online or multilingual fashion, and with a very small number of cluster labels (4, in the case of the text benchmark). In our work, we studied the problem of monolingual and crosslingual clustering, having experimented several directions and methods and the impact they have on the final clustering quality. We described the first system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. However, these are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, Rupnik et al. (2016) compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting.

What are the deficiencies of recent research on clusters?

These are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, Rupnik et al. compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting.

Omar ibn Said was a real person, born and raised in Futa Toro, an Islamic state located in the part of West Africa that is now Senegal. His family was wealthy and he was highly educated as a Muslim scholar. He was captured by slavers in 1807 at the age of 37 and was taken to America to be sold in the Charleston slave market. Initially purchased by a harsh master, he escaped after two years and traveled to Fayetteville, North Carolina. There he was captured and jailed, but ultimately sold to planter James Owen. Owen was impressed by ibn Said's education, since very few slaves even knew how to read and write. Owen attempted to convert him to Christianity and provided him with a Bible and other books. He also urged him to write his memoirs, which he did in 1831. He wrote at least thirteen other Arabic documents, mostly on history and theology. Ibn Said lived until his mid-90s and died in 1864, still enslaved.

Given the following paragraph, where was Omar ibn Said born?

Omar ibn Said was born and raised in Futa Toro, an Islamic state located in the part of West Africa that is now Senegal.

Extract the names of persons and countries in two different lists separated by commas: "Sunday’s blast has echoes of the car bombing that killed Darya Dugina, the daughter of influential ultra-nationalist philosopher Alexander Dugan in August 2022. Alexander Dugan is credited with being the architect, or “spiritual guide,” to Russia’s invasion of Ukraine. Dugina and Tatarsky moved in the same circles, and they had been photographed multiple times together"

Persons: Darya Dugina, Alexander Dugan, Tatarsky Countries: Russia, Ukraine

Will AI replace all human beings?

When I was writing down those words used to train an AI model, I feel funny. Some of my friends expressed concern that AI could soon replace humans and take away all of our jobs. However, I see things differently. I believe that AI has the potential to develop rapidly, and I'm excited to see how it will transform the world in my lifetime. If AI can create more resources, we may be able to live more comfortably and work fewer hours. This could allow us to pursue our passions and interests without worrying so much about survival. Additionally, in the future, humans may be able to live alongside AI, which mean live more longer and stronger.

Classify these as winter or summer sports: beach volleyball, cross country skiing, biathlon, triathlon, kayaking

beach volleyball: summer, cross country skiing: winter, biathlon: winter, triathlon: summer, kayaking: summer

In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation. In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live).

What is their approach motivated by?

Little research has evaluated on the quality evaluation on voice knowledge sharing.

You should consider buying an electric bicycle, as it will help you driving long distance

How do we analyse the pros and cons while buying a bicycle?

2001.07615

To evaluate the proposed BiLSTM model with attention (BiLSTM+att), it is compared with three of its own variants: a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM). Additional baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24. To evaluate the proposed BiLSTM model with attention (BiLSTM+att), it is compared with three of its own variants: a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM). Additional baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24.

What model do they use a baseline to estimate satisfaction?

The answers are shown as follows: * a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM) * baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24

What kind of dog breed do you get when you mate a poodle with an old english sheep dog?

sheepadoodle

Which is the oldest civilization in the world?

The Mesopotamia civilization

Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDS) with task is to convert a meaning representation produced by the Dialogue Manager into natural language utterances. Conventional approaches still rely on comprehensive hand-tuning templates and rules requiring expert knowledge of linguistic representation, including rule-based BIBREF0 , corpus-based n-gram models BIBREF1 , and a trainable generator BIBREF2 . Recently, Recurrent Neural Networks (RNNs) based approaches have shown promising performance in tackling the NLG problems. The RNN-based models have been applied for NLG as a joint training model BIBREF3 , BIBREF4 and an end-to-end training model BIBREF5 . A recurring problem in such systems is requiring annotated datasets for particular dialogue acts (DAs). To ensure that the generated utterance representing the intended meaning of the given DA, the previous RNN-based models were further conditioned on a 1-hot vector representation of the DA. BIBREF3 introduced a heuristic gate to ensure that all the slot-value pair was accurately captured during generation. BIBREF4 subsequently proposed a Semantically Conditioned Long Short-term Memory generator (SC-LSTM) which jointly learned the DA gating signal and language model. More recently, Encoder-Decoder networks BIBREF6 , BIBREF7 , especially the attentional based models BIBREF8 , BIBREF9 have been explored to solve the NLG tasks. The Attentional RNN Encoder-Decoder BIBREF10 (ARED) based approaches have also shown improved performance on a variety of tasks, e.g., image captioning BIBREF11 , BIBREF12 , text summarization BIBREF13 , BIBREF14 . While the RNN-based generators with DA gating-vector can prevent the undesirable semantic repetitions, the ARED-based generators show signs of better adapting to a new domain. However, none of the models show significant advantage from out-of-domain data. To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences. We conducted experiments on four different NLG domains and found that the proposed methods significantly outperformed the state-of-the-art methods regarding BLEU BIBREF15 and slot error rate ERR scores BIBREF4 . The results also showed that our generators could scale to new domains by leveraging the out-of-domain data. To sum up, we make three key contributions in this paper: We review related works in Section "Related Work" . Following a detail of proposed model in Section "Recurrent Neural Language Generator" , Section "Experiments" describes datasets, experimental setups, and evaluation metrics. The resulting analysis is presented in Section "Results and Analysis" . We conclude with a brief summary and future work in Section "Conclusion and Future Work" . However, none of the models show significant advantage from out-of-domain data.

What is the disadvantage of the previous state of the art models?

None of the models show significant advantage from out-of-domain data.

The goal of Machine Reading Comprehension (MRC) is to have machines read a text passage and then generate an answer (or select an answer from a list of given candidates) for any question about the passage. There has been a growing interest in the research community in exploring neural MRC models in an end-to-end fashion, thanks to the availability of large-scale datasets, such as CNN/DM BIBREF0 and SQuAD BIBREF1 . Despite the variation in model structures, most state-of-the-art models perform reading comprehension in two stages. First, the symbolic representations of passages and questions are mapped into vectors in a neural space. This is commonly achieved via embedding and attention BIBREF2 , BIBREF3 or fusion BIBREF4 . Then, reasoning is performed on the vectors to generate the right answer. Ideally, the best attention and reasoning strategies should adapt organically in order to answer different questions. However, most MRC models use a static attention and reasoning strategy indiscriminately, regardless of various question types. One hypothesis is because these models are optimized on those datasets whose passages and questions are domain-specific (or of a single type). For example, in CNN/DM, all the passages are news articles, and the answer to each question is an entity in the passage. In SQuAD, the passages came from Wikipedia articles and the answer to each question is a text span in the article. Such a fixed-strategy MRC model does not adapt well to other datasets. For example, the exact-match score of BiDAF BIBREF2 , one of the best models on SQuAD, drops from 81.5 to 55.8 when applied to TriviaQA BIBREF5 , whereas human performance is 82.3 and 79.7 on SQuAD and TriviaQA, respectively. In real-world MRC tasks, we must deal with questions and passages of different types and complexities, which calls for models that can dynamically determine what attention and reasoning strategy to use for any input question-passage pair on the fly. In a recent paper, BIBREF6 proposed dynamic multi-step reasoning, where the number of reasoning steps is determined spontaneously (using reinforcement learning) based on the complexity of the input question and passage. With a similar intuition, in this paper we propose a novel MRC model which is dynamic not only on the number of reasoning steps it takes, but also on the way it performs attention. To the best of our knowledge, this is the first MRC model with this dual-dynamic capability. The proposed model is called a Dynamic Fusion Network (DFN). In this paper, we describe the version of DFN developed on the RACE dataset BIBREF7 . In RACE, a list of candidate answers is provided for each passage-question pair. So DFN for RACE is a scoring model - the answer candidate with the highest score will be selected as the final answer. Like other MRC models, DFNs also perform machine reading in two stages: attention and reasoning. DFN is unique in its use of a dynamic multi-strategy attention process in the attention stage. Here “attention” refers to the process that texts from different sources (passage, question, answers) are combined in the network. In literature, a fixed attention mechanism is usually employed in MRC models. In DFN, the attention strategy is not static; instead, the actual strategy for drawing attention among the three text sources are chosen on the fly for each sample. This lends flexibility to adapt to various question types that require different comprehension skills. The output of the attention stage is then fed into the reasoning module to generate the answer score. The reasoning module in DFN uses dynamic multi-step reasoning, where the number of steps depends on the complexity of the question-passage pair and varies from sample to sample. Inspired by ReasoNet BIBREF6 and dynamic neural module networks BIBREF8 , we use deep reinforcement learning methods BIBREF9 , BIBREF10 to dynamically choose the optimal attention strategy and the optimal number of reasoning steps for a given sample. We use RL in favor of other simpler methods (like cascading, pooling or weighted averaging) mainly because we intend to learn a policy that constructs an instance of DFN of a sample-specific structure. Given an input sample consisting of a question, a passage and a list of candidate answers in RACE, an instance of DFN can be constructed via RL step by step on the fly. Such a policy is particularly appealing as it also provides insights on how the model performs on different types of questions. At each decision step, the policy maps its “state”, which represents an input sample, and DFN's partial knowledge of the right answer, to the action of assembling proper attention and reasoning modules for DFN. Experiments conducted on the RACE dataset show that DFN significantly outperforms previous state-of-the-art MRC models and has achieved the best result reported on RACE. A thorough empirical analysis also demonstrates that DFN is highly effective in understanding passages of a wide variety of styles and answering questions of different complexities. The word “fusion” for MRC was first used by FusionNet (Huang et al., 2017) to refer to the process of updating the representation of passage (or question) using information from the question (or passage) representation.

What was “fusion” referred to by FusionNet?

The process of updating the representation of passage (or question) using information from the question (or passage) representation.

Who was the Italian dictator responsible for introducing fascism to Italy and joining the Axis in World War 2?

Benito Mussolini

Shōrin-ryū is generally characterized by natural breathing, natural (narrow, high) stances, and circular, rather than direct movements. Shōrin-ryū practitioners assert that correct motion, moving quickly to evade violence with fluid movements and flexible positions are important, and that a solid structure is vital for powerful blocks and strikes. Stances that are too deep generally make body movement difficult. Another feature in this system is how the student is taught to punch. Generally, there is neither a horizontal nor vertical punch in Shōrin-ryū. Punches are slightly canted to the inside (Isshin-ryū), with the largest knuckle of the forefinger (third from the tip) in vertical alignment with the second knuckle of the pinky finger. It is believed that this position is key in lining up the bones of the arm and creates a faster, more stable and powerful strike.

Given the following note about training in a specific style of karate, tell me why its practitioners consider its fist alignment to offer advantages for their striking.

Practitioners of Shōrin-ryū believe lining up the second and third knuckle, along with the bones of the arm, enables them to deliver faster and more powerful punches.

The Netherlands' mainstream video games market, not taking into consideration the serious and casual games, is the sixth largest in Europe. In 2008, the Dutch market took up 3.95% of the entire European market in total sales and 4.19% in software sales. A significant part of the Netherlands' gaming industry is in serious games, in which Dutch companies make a significant part of the worldwide industry. In the Netherlands, an estimated of 4,000 people are working in the games industry, at more than 600 companies. Over 45 of the companies are located in the Dutch Game Garden, a government subsidized organization with the aim of promoting and improving the video games industry in the Netherlands.

Given this paragraph about video games in the Netherlands, how many people work in the games industry?

4,000

Should investors time the market?

Timing the market is based on predictions of the future, which is a challenging endeavor. Benjamin Graham, the father of value investing and mentor of Warren Buffett, famously said: "In the short run, the market is a voting machine but in the long run, it is a weighing machine." This means that in the short term, market prices can be driven by unpredictable events, market sentiment, and speculation. However, in the long term, a company's fundamentals tend to drive the market. By avoiding attempts to time the market, investors can leverage the long-term growth potential of the market and avoid the risks associated with trying to predict short-term price movements. Therefore, in most cases, investors should not try to time the market.

What are some popular books about baseball?

"Moneyball" by Michael Lewis, "The Boys of Summer" by Roger Kahn, and "The Rookie" by Jim Morris are all examples of popular baseball books.

Classify these animals as either a reptile or an amphibian

crocodile, snake, alligator, frog, salamander

Contrastive evaluation is different from conventional evaluation of machine translation in that it does not require any translation. Rather than testing a model's ability to translate, it is a method to test a model's ability to discriminate between given good and bad translations. We exploit the fact that NMT systems are in fact language models of the target language, conditioned on source text. Like language models, NMT systems can be used to compute a model score (the negative log probability) for an existing translation. Contrastive evaluation, then, means to compare the model score of two pairs of inputs: INLINEFORM0 and INLINEFORM1 . If the model score of the actual reference translation is higher, we assume that this model can detect wrong pronoun translations. However, this does not mean that systems actually produce the reference translation when given the source sentence for translation. An entirely different target sequence might rank higher in the system's beam during decoding. The only conclusion permitted by contrastive evaluation is whether or not the reference translation is more probable than a contrastive variant. If the model score of the reference is indeed higher, we refer to this outcome as a “correct decision” by the model. The model's decision is only correct if the reference translation has a higher score than any contrastive translation. In our evaluation, we aggregate model decisions on the whole test set and report the overall percentage of correct decisions as accuracy. During scoring, the model is provided with reference translations as target context, while during translation, the model needs to predict the full sequence. It is an open question to what extent performance deteriorates when context is itself predicted, and thus noisy. We highlight that the same problem arises for sentence-level NMT, and has been addressed with alternative training strategies BIBREF27 . Rather than testing a model’s ability to translate, it is a method to test a model’s ability to discriminate between given good and bad translations.

What is the contrastive evaluation?

It is a method to test a model’s ability to discriminate between given good and bad translations.

When sailing on a boat between 26ft and 40ft in San Francisco Bay, what items are 'required', 'useful', or 'neither'. A compass, laser printer, floatation devices for all passengers, fog horn, water, sun lotion, hat, fire extinguisher (one type B-2 or two type B-1), soft toys, tv, visual distress signals, sunglasses, snacks, ford mustang, American Airlines

Required: floatation devices for all passengers, Sound Signaling Device (fog horn), fire extinguisher (one type B-2 or two type B-1), and visual distress signals (flares) Useful: compass, water, sun lotion, hat, sunglasses, snacks Neither: laser printer, soft toys, tv, ford mustang, American Airlines

Who are the most commonly mentioned best rappers of all time?

In no particular order, the most common top 5 rappers in "top 5 dead or alive" lists are as follows: Jay-Z, Nas, Tupac, The Notorious BIG, Eminem. Obviously everyone has their own preferences and styles they prefer, but more often than not, these are the 5 rappers mentioned in most lists. These rappers all had great commercial success and critical acclaim from the industry and their peers.

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. 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.

What is one of the main advantages of deep learning methods over traditional feature or kernel based learning methods?

The deep learning methods require minimal feature engineering.

Who is the founder of Vanguard?

John C. Bogle

The auditorium opened as the Union Gospel Tabernacle in 1892. Its construction was spearheaded by Thomas Ryman (1843–1904), a Nashville businessman who owned several saloons and a fleet of riverboats. Ryman conceived the idea of the auditorium as a tabernacle for the influential revivalist Samuel Porter Jones. He had attended one of Jones' 1885 tent revivals with the intent to heckle, but was instead converted into a devout Christian who pledged to build the tabernacle so the people of Nashville could attend large-scale revivals indoors. It took seven years to complete and cost US$100,000 (equivalent to $3,015,926 in 2021). Jones held his first revival at the site on May 25, 1890, when only the building's foundation and six-foot (1.8 m) walls had been completed.

According to the text, given the initial cost of building the auditorium and the equivalent cost in 2021, what is the value of 1892 dollars in 2021?

30.15926, since 3,015,926/100,000 = 30.15926

Stereotypes are ideas about how other (groups of) people commonly behave and what they are likely to do. These ideas guide the way we talk about the world. I distinguish two kinds of verbal behavior that result from stereotypes: (i) linguistic bias, and (ii) unwarranted inferences. The former is discussed in more detail by beukeboom2014mechanisms, who defines linguistic bias as “a systematic asymmetry in word choice as a function of the social category to which the target belongs.” So this bias becomes visible through the distribution of terms used to describe entities in a particular category. Unwarranted inferences are the result of speculation about the image; here, the annotator goes beyond what can be glanced from the image and makes use of their knowledge and expectations about the world to provide an overly specific description. Such descriptions are directly identifiable as such, and in fact we have already seen four of them (descriptions 2–5) discussed earlier. I distinguish two kinds of verbal behavior that result from stereotypes: (i) linguistic bias, and (ii) unwarranted inferences.

What two kinds of speech acts do stereotypes lead to?

(i) linguistic bias, and (ii) unwarranted inferences.

The decoder is the one used in BIBREF12, BIBREF13, BIBREF10 with the same hyper-parameters. For the encoder module, both the low-level and high-level encoders use a two-layers multi-head self-attention with two heads. To fit with the small number of record keys in our dataset (39), their embedding size is fixed to 20. The size of the record value embeddings and hidden layers of the Transformer encoders are both set to 300. We use dropout at rate 0.5. The models are trained with a batch size of 64. We follow the training procedure in BIBREF21 and train the model for a fixed number of 25K updates, and average the weights of the last 5 checkpoints (at every 1K updates) to ensure more stability across runs. All models were trained with the Adam optimizer BIBREF37; the initial learning rate is 0.001, and is reduced by half every 10K steps. We used beam search with beam size of 5 during inference. All the models are implemented in OpenNMT-py BIBREF38. All code is available at https://github.com/KaijuML/data-to-text-hierarchical All models were trained with the Adam optimizer [13]; the initial learning rate is 0.001, and is reduced by half every 10K steps.

What optimizer is used when training models?

The Adam optimizer.

In order to gain more information about the context of the Greek entities, a percentage of Greek Wikipedia was used. After applying sentence and token segmentation on Wikipedia text and using a pretrained model from polyglot, the keyword list increased. The keyword list had at this point about 350,000 records and consisted of 4 classes: location (LOC), organization (ORG), person (PERSON) and facility (FAC). A percentage of Greek Wikipedia was parsed and used for training in spaCy. The results from the training are presented in SECREF13. In order to gain more information about the context of the Greek entities, a percentage of Greek Wikipedia was used. After applying sentence and token segmentation on Wikipedia text and using a pretrained model from polyglot, the keyword list increased. The keyword list had at this point about 350,000 records and consisted of 4 classes: location (LOC), organization (ORG), person (PERSON) and facility (FAC).

What classes do keyword list records consist of after pre-trained model on the Greek entities?

Location (LOC), organization (ORG), person (PERSON) and facility (FAC).

In which Harry Potter's book did we learn about Voldemort's childhood?

Voldemort's childhood was depicted in Book 6 of Harry Potter and The Half-Bood's Price. Through the private lessons Harry had with Dumbledore, Harry learned about Voldemort's family and upbringing.

Why do people like to ski?

Skiing is a great way to get out into the open air in the winter months. It also provides a great way to stay active, challenge yourself, be with family members, and enjoy the great outdoors.

We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task BIBREF0 . We note that two of the systems are supervised and rely on the training set to build their classification models. Document frequency counts are also computed on the training set. Stemming is applied to allow more robust matching. The different keyphrase extraction models are briefly described below: Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Table summarizes these numbers for each model. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to BIBREF16 . Apart from TopicRank that groups similar candidates into topics, the other models do not have any redundancy control mechanism. Yet, recent work has shown that up to 12% of the overall error made by state-of-the-art keyphrase extraction systems were due to redundancy BIBREF6 , BIBREF17 . Therefore as a post-ranking step, we remove redundant keyphrases from the ranked lists generated by all models. A keyphrase is considered redundant if it is included in another keyphrase that is ranked higher in the list. TF×IDF: we re-implemented the TF×IDF n-gram based baseline computed by the task organizers. We use 1, 2, 3-grams as keyphrase candidates and filter out those shorter than 3 characters, containing words made of only punctuation marks or one character long13 . Kea (Witten et al., 1999): keyphrase candidates are 1, 2, 3-grams that do not begin or end with a stop- word14. Keyphrases are selected using a na¨ıve bayes classifier15 with two features: TF×IDF and the relative position of first occurrence. TopicRank (Bougouin et al., 2013): keyphrase candidates are the longest sequences of adjacent nouns and adjectives. Lexically similar candidates are clustered into topics and ranked using Tex- tRank (Mihalcea and Tarau, 2004). Keyphrases are produced by extracting the first occurring candidate of the highest ranked topics. KP-Miner (El-Beltagy and Rafea, 2010): keyphrase candidates are sequences of words that do not contain punctuation marks or stopwords. Candidates that appear less than three times or that first occur beyond a certain position are removed16. Candidates are then weighted using a modified TF×IDF formula that account for document length. WINGNUS (Nguyen and Luong, 2010): keyphrase candidates are simplex nouns and noun phrases detected using a set of rules described in (Kim and Kan, 2009). Keyphrases are then selected using a na¨ıve bayes classifier with the optimal set of features found on the training set17: TF×IDF, relative position of first occurrence and candidate length in words.

What keyphrase extraction models are explored?

TF×IDF,Kea (Witten et al., 1999), TopicRank (Bougouin et al., 2013),KP-Miner (El-Beltagy and Rafea, 2010), and WINGNUS (Nguyen and Luong, 2010).

Why should I use toothpaste

Toothpaste is a paste or gel dentifrice used with a toothbrush to clean and maintain the aesthetics and health of teeth. Toothpaste is used to promote oral hygiene: it is an abrasive that aids in removing dental plaque and food from the teeth, assists in suppressing halitosis, and delivers active ingredients (most commonly fluoride) to help prevent tooth decay (dental caries) and gum disease (gingivitis). Owing to differences in composition and fluoride content, not all toothpastes are equally effective in maintaining oral health. The decline of tooth decay during the 20th century has been attributed to the introduction and regular use of fluoride-containing toothpastes worldwide. Large amounts of swallowed toothpaste can be toxic. Common colors for toothpaste include white (sometimes with colored stripes or green tint) and blue. Usefulness Toothpastes are generally useful to maintain dental health. Toothpastes containing fluoride are effective at preventing tooth decay. Toothpastes may also help to control and remove plaque build-up, promoting healthy gums. A 2016 systematic review indicated that using toothpaste when brushing the teeth does not necessarily impact the level of plaque removal. However, the active ingredients in toothpastes are able to prevent dental diseases with regular use. Ingredients Toothpastes are derived from a variety of components, the three main ones being abrasives, fluoride, and detergent.

Classify each item as being at a playground or a gym: slide, swing, dumbbell, squat rack, seesaw

Slide: Playground Swing: Playground Dumbbell: Gym Squat Rack: Gym Seesaw: Playground

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

What language do the authors customise their implementation for?

the Polish language.

Write a press release declaring the availability of IronChef, a cooking robot that can create various dishes in 5 cuisines. Be sure to mention some of its advanced technological features.

IronChef, a new cooking robot designed for automated food preparation, is now available for pre-order! With its advanced technological features, it will be great way to shorten the time spent preparing meals for the family. Here are some of its key features: 1. Robot arms that can pick up, rinse, chop, cook, and plate various dishes 2. Current cuisines supported: traditional European, Chinese, Thai, Korean, and Japanese with other cuisine types planned for later release 3. Currently can boil, steam, microwave, bake, air-fry and stir-fry, with other cooking methods planned for later release 4. Detect any food spoilage and take appropriate action 5. Substitute ingredients when the ingredients contained in the recipe are not available 6. Allow users to customize recipes 7. Inform owner when ingredients run low 8. Software and hardware are upgradable 9. Optimized parallel dish cooking, meaning not all dishes will be cooked sequentially, and if there is no overlap, it can cook multiple dishes at the same time. IronChef will providing many families that have neither the time or the energy to prepare meals, as well as families that have a repertoire of favorite meals that needs to be prepared regularly. This will allow more quality family time while enjoying consistent, safe and delicious meals at home.

Where are good locations to scuba dive?

Belize, Hondurus, US Virgin Islands, British Virgin Islands, Antartica, Cozumel, Hawaii, Galapagos Islands, Philippines

What is the difference between a road bike and a mountain bike?

Road bikes are built to be ridden on asphalt and cement surfaces and have thin tires, whereas mountain bikes are built to be ridden on dirt and have wider tires. Road bikes also have more aerodynamic handle bars while mountain bike handle bars a built for less responsive steering while bouncing around off the road.

What are some steps I can take to switch into a career in technology or computer programming?

There are many different ways to break into a career in tech, even if you do not have a degree in computer science: - enroll in a coding bootcamp - see if anyone in your network is hiring - practice coding interview questions on LeetCode - go back to school for a master's degree in computer science or information science - watch Youtube tutorials on how to code your own website

1910.08987

To test this hypothesis, we evaluate the model on only the first syllable of every word, which eliminates carry-over and declination effects (Table TABREF14). In both Mandarin and Cantonese, the clustering is more accurate when using only the first syllables, compared to using all of the syllables. FLOAT SELECTED: Table 3. Normalized mutual information (NMI) between cluster assignments and ground truth tones, considering only the first syllable of each word, or all syllables. To test this hypothesis, we evaluate the model on only the first syllable of every word, which eliminates carry-over and declination effects (Table TABREF14). In both Mandarin and Cantonese, the clustering is more accurate when using only the first syllables, compared to using all of the syllables. FLOAT SELECTED: Table 3. Normalized mutual information (NMI) between cluster assignments and ground truth tones, considering only the first syllable of each word, or all syllables.

How close do clusters match to ground truth tone categories?

NMI between cluster assignments and ground truth tones for all sylables is: Mandarin: 0.641 Cantonese: 0.464

The 2009 L'Aquila earthquake occurred in the region of Abruzzo, in central Italy. The main shock occurred at 03:32 CEST (01:32 UTC) on 6 April 2009, and was rated 5.8 or 5.9 on the Richter magnitude scale and 6.3 on the moment magnitude scale; its epicentre was near L'Aquila, the capital of Abruzzo, which together with surrounding villages suffered the most damage. There have been several thousand foreshocks and aftershocks since December 2008, more than thirty of which had a Richter magnitude greater than 3.5.

Given the following paragraph, where did the L'Aquila earth occur?

The 2009 L'Aquila earthquake occurred in the region of Abruzzo, in central Italy.

What is a persona?

A persona is a fictional character that embodies the characteristics and traits of a particular group. Personas can be leveraged to efficiently identify and target a stakeholder's unique needs, common tasks they perform, and challenges they face.